Repository: zvtvz/zvt Branch: master Commit: f971f00c2181 Files: 547 Total size: 2.0 MB Directory structure: gitextract_pjklxszz/ ├── .coveragerc ├── .github/ │ └── workflows/ │ ├── build.yaml │ └── package.yaml ├── .gitignore ├── .pre-commit-config.yaml ├── .readthedocs.yaml ├── LICENSE ├── MANIFEST.in ├── README-cn.md ├── README.md ├── api-tests/ │ ├── create_stock_pool_info.http │ ├── create_stock_pools.http │ ├── del_stock_pool.http │ ├── event/ │ │ ├── create_stock_topic.http │ │ ├── get_stock_event.http │ │ ├── get_stock_news_analysis.http │ │ ├── get_tag_suggestions_stats.http │ │ ├── ignore_stock_news.http │ │ ├── query_stock_topic.http │ │ └── update_stock_topic.http │ ├── factor/ │ │ ├── get_factors.http │ │ └── query_factor_result.http │ ├── get_main_tags_in_stock_pool.http │ ├── get_stock_pool_info.http │ ├── get_stock_pools.http │ ├── tag/ │ │ ├── batch_set_stock_tags.http │ │ ├── build_main_tag_industry_relation.http │ │ ├── build_main_tag_sub_tag_relation.http │ │ ├── build_stock_tags.http │ │ ├── change_main_tag.http │ │ ├── create_hidden_tag_info.http │ │ ├── create_main_tag_info.http │ │ ├── create_sub_tag_info.http │ │ ├── del_hidden_tag.http │ │ ├── del_main_tag.http │ │ ├── del_sub_tag.http │ │ ├── get_hidden_tag_info.http │ │ ├── get_industry_info.http │ │ ├── get_main_tag_industry_relation.http │ │ ├── get_main_tag_info.http │ │ ├── get_main_tag_sub_tag_relation.http │ │ ├── get_stock_tag_options.http │ │ ├── get_sub_tag_info.http │ │ ├── query_simple_stock_tags.http │ │ ├── query_stock_tag_stats.http │ │ ├── query_stock_tags.http │ │ └── set_stock_tags.http │ ├── test.http │ └── trading/ │ ├── build_query_stock_quote_setting.http │ ├── build_trading_plan.http │ ├── get_current_trading_plan.http │ ├── get_future_trading_plan.http │ ├── get_query_stock_quote_setting.http │ ├── get_quote_stats.http │ ├── query_kdata.http │ ├── query_stock_quotes.http │ ├── query_tag_quotes.http │ ├── query_trading_plan.http │ └── query_ts.http ├── build.sh ├── code_of_conduct.md ├── docs/ │ ├── Makefile │ ├── make.bat │ ├── source/ │ │ ├── _templates/ │ │ │ ├── custom-class-template.rst │ │ │ ├── custom-module-template.rst │ │ │ └── sidebarlogo.html │ │ ├── api/ │ │ │ └── index.rst │ │ ├── conf.py │ │ ├── contributor.rst │ │ ├── core_concepts.rst │ │ ├── data/ │ │ │ ├── adding_new_entity.rst │ │ │ ├── data_concepts.rst │ │ │ ├── extending_data.rst │ │ │ ├── index.rst │ │ │ ├── record_and_query.rst │ │ │ └── trading_anything.rst │ │ ├── drawer/ │ │ │ ├── drawer_concepts.rst │ │ │ └── index.rst │ │ ├── factor/ │ │ │ ├── extending_factor.rst │ │ │ ├── factor_concepts.rst │ │ │ └── index.rst │ │ ├── index.rst │ │ ├── install.rst │ │ ├── intro.rst │ │ ├── ml/ │ │ │ └── index.rst │ │ ├── trader/ │ │ │ ├── index.rst │ │ │ └── trader_concepts.rst │ │ └── usage.rst │ └── storage_config.md ├── examples/ │ ├── README.MD │ ├── __init__.py │ ├── data_runner/ │ │ ├── __init__.py │ │ ├── actor_runner.py │ │ ├── finance_runner.py │ │ ├── index_runner.py │ │ ├── joinquant_fund_runner.py │ │ ├── joinquant_kdata_runner.py │ │ ├── kdata_runner.py │ │ ├── sina_data_runner.py │ │ └── trading_runner.py │ ├── factors/ │ │ ├── __init__.py │ │ ├── boll_factor.py │ │ ├── fundamental_selector.py │ │ └── tech_factor.py │ ├── hot.json │ ├── intent/ │ │ ├── __init__.py │ │ └── intent.py │ ├── main_line_hidden_tags.json │ ├── main_line_tags.json │ ├── migration.py │ ├── ml/ │ │ ├── __init__.py │ │ └── sgd.py │ ├── query_snippet.py │ ├── report_utils.py │ ├── reports/ │ │ ├── __init__.py │ │ ├── report_bull.py │ │ ├── report_core_compay.py │ │ ├── report_tops.py │ │ ├── report_vol_up.py │ │ └── subscriber_emails.json │ ├── requirements.txt │ ├── research/ │ │ ├── __init__.py │ │ ├── dragon_and_tiger.py │ │ ├── top_dragon_tiger.py │ │ └── top_tags.py │ ├── result.json │ ├── stock_tags.json │ ├── tag_utils.py │ ├── trader/ │ │ ├── __init__.py │ │ ├── dragon_and_tiger_trader.py │ │ ├── follow_ii_trader.py │ │ ├── keep_run_trader.py │ │ ├── ma_trader.py │ │ ├── macd_day_trader.py │ │ └── macd_week_and_day_trader.py │ ├── utils.py │ └── z.sh ├── init_env.sh ├── pyproject.toml ├── requirements/ │ ├── dev.txt │ └── docs.txt ├── requirements.txt ├── setup.py ├── sql/ │ ├── change_indices.sql │ ├── change_stock_index.sql │ ├── reduce_size.sql │ └── samples.sql ├── src/ │ └── zvt/ │ ├── __init__.py │ ├── api/ │ │ ├── __init__.py │ │ ├── intent.py │ │ ├── kdata.py │ │ ├── portfolio.py │ │ ├── selector.py │ │ ├── stats.py │ │ └── utils.py │ ├── autocode/ │ │ ├── __init__.py │ │ ├── generator.py │ │ └── templates/ │ │ ├── .coveragerc.template │ │ ├── .gitignore.template │ │ ├── .travis.yml.template │ │ ├── LICENSE.template │ │ ├── MANIFEST.in.template │ │ ├── README-en.md.template │ │ ├── README.md.template │ │ ├── __init__.py │ │ ├── fill_project.py.template │ │ ├── kdata_common.py.template │ │ ├── kdata_recorder.py.template │ │ ├── meta.py.template │ │ ├── meta_recorder.py.template │ │ ├── pkg_init.py.template │ │ ├── requirements.txt.template │ │ ├── setup.py.template │ │ └── test_pass.py.template │ ├── broker/ │ │ ├── __init__.py │ │ └── qmt/ │ │ ├── __init__.py │ │ ├── context.py │ │ ├── errors.py │ │ ├── qmt_account.py │ │ └── qmt_quote.py │ ├── common/ │ │ ├── __init__.py │ │ ├── query_models.py │ │ └── trading_models.py │ ├── config.json │ ├── consts.py │ ├── contract/ │ │ ├── __init__.py │ │ ├── api.py │ │ ├── base_service.py │ │ ├── context.py │ │ ├── data_type.py │ │ ├── drawer.py │ │ ├── factor.py │ │ ├── model.py │ │ ├── normal_data.py │ │ ├── reader.py │ │ ├── recorder.py │ │ ├── register.py │ │ ├── route_registry.py │ │ ├── schema.py │ │ ├── storage.py │ │ ├── utils.py │ │ └── zvt_info.py │ ├── domain/ │ │ ├── __init__.py │ │ ├── actor/ │ │ │ ├── __init__.py │ │ │ ├── actor_meta.py │ │ │ └── stock_actor.py │ │ ├── emotion/ │ │ │ ├── __init__.py │ │ │ └── emotion.py │ │ ├── fundamental/ │ │ │ ├── __init__.py │ │ │ ├── dividend_financing.py │ │ │ ├── finance.py │ │ │ ├── trading.py │ │ │ └── valuation.py │ │ ├── macro/ │ │ │ ├── __init__.py │ │ │ ├── macro.py │ │ │ └── monetary.py │ │ ├── meta/ │ │ │ ├── __init__.py │ │ │ ├── block_meta.py │ │ │ ├── blockus_meta.py │ │ │ ├── cbond_meta.py │ │ │ ├── country_meta.py │ │ │ ├── currency_meta.py │ │ │ ├── etf_meta.py │ │ │ ├── fund_meta.py │ │ │ ├── future_meta.py │ │ │ ├── index_meta.py │ │ │ ├── indexhk_meta.py │ │ │ ├── indexus_meta.py │ │ │ ├── stock_meta.py │ │ │ ├── stockhk_meta.py │ │ │ └── stockus_meta.py │ │ ├── misc/ │ │ │ ├── __init__.py │ │ │ ├── holder.py │ │ │ ├── money_flow.py │ │ │ ├── overall.py │ │ │ └── stock_news.py │ │ └── quotes/ │ │ ├── __init__.py │ │ ├── block/ │ │ │ ├── __init__.py │ │ │ ├── block_1d_kdata.py │ │ │ ├── block_1mon_kdata.py │ │ │ └── block_1wk_kdata.py │ │ ├── currency/ │ │ │ ├── __init__.py │ │ │ └── currency_1d_kdata.py │ │ ├── etf/ │ │ │ ├── __init__.py │ │ │ └── etf_1d_kdata.py │ │ ├── future/ │ │ │ ├── __init__.py │ │ │ └── future_1d_kdata.py │ │ ├── index/ │ │ │ ├── __init__.py │ │ │ ├── index_1d_kdata.py │ │ │ ├── index_1m_kdata.py │ │ │ └── index_1wk_kdata.py │ │ ├── indexhk/ │ │ │ ├── __init__.py │ │ │ └── indexhk_1d_kdata.py │ │ ├── indexus/ │ │ │ ├── __init__.py │ │ │ └── indexus_1d_kdata.py │ │ ├── stock/ │ │ │ ├── __init__.py │ │ │ ├── stock_15m_hfq_kdata.py │ │ │ ├── stock_15m_kdata.py │ │ │ ├── stock_1d_hfq_kdata.py │ │ │ ├── stock_1d_kdata.py │ │ │ ├── stock_1h_hfq_kdata.py │ │ │ ├── stock_1h_kdata.py │ │ │ ├── stock_1m_hfq_kdata.py │ │ │ ├── stock_1m_kdata.py │ │ │ ├── stock_1mon_hfq_kdata.py │ │ │ ├── stock_1mon_kdata.py │ │ │ ├── stock_1wk_hfq_kdata.py │ │ │ ├── stock_1wk_kdata.py │ │ │ ├── stock_30m_hfq_kdata.py │ │ │ ├── stock_30m_kdata.py │ │ │ ├── stock_4h_hfq_kdata.py │ │ │ ├── stock_4h_kdata.py │ │ │ ├── stock_5m_hfq_kdata.py │ │ │ ├── stock_5m_kdata.py │ │ │ ├── stock_quote.py │ │ │ └── stock_quote_log.py │ │ ├── stockhk/ │ │ │ ├── __init__.py │ │ │ ├── stockhk_1d_hfq_kdata.py │ │ │ ├── stockhk_1d_kdata.py │ │ │ └── stockhk_quote.py │ │ ├── stockus/ │ │ │ ├── __init__.py │ │ │ ├── stockus_1d_hfq_kdata.py │ │ │ ├── stockus_1d_kdata.py │ │ │ └── stockus_quote.py │ │ └── trade_day.py │ ├── factors/ │ │ ├── __init__.py │ │ ├── algorithm.py │ │ ├── factor_models.py │ │ ├── factor_service.py │ │ ├── fundamental/ │ │ │ ├── __init__.py │ │ │ └── finance_factor.py │ │ ├── ma/ │ │ │ ├── __init__.py │ │ │ ├── domain/ │ │ │ │ ├── __init__.py │ │ │ │ ├── common.py │ │ │ │ ├── stock_1d_ma_factor.py │ │ │ │ └── stock_1d_ma_stats_factor.py │ │ │ ├── ma_factor.py │ │ │ ├── ma_stats_factor.py │ │ │ └── top_bottom_factor.py │ │ ├── macd/ │ │ │ ├── __init__.py │ │ │ └── macd_factor.py │ │ ├── shape.py │ │ ├── target_selector.py │ │ ├── technical_factor.py │ │ ├── top_stocks.py │ │ ├── transformers.py │ │ └── zen/ │ │ ├── __init__.py │ │ ├── base_factor.py │ │ ├── domain/ │ │ │ ├── __init__.py │ │ │ ├── common.py │ │ │ ├── index_1d_zen_factor.py │ │ │ ├── stock_1d_zen_factor.py │ │ │ └── stock_1wk_zen_factor.py │ │ └── zen_factor.py │ ├── fill_project.py │ ├── informer/ │ │ ├── __init__.py │ │ ├── inform_utils.py │ │ └── informer.py │ ├── main.py │ ├── misc/ │ │ ├── __init__.py │ │ ├── constants.py │ │ ├── misc_models.py │ │ ├── misc_service.py │ │ └── zhdate.py │ ├── ml/ │ │ ├── __init__.py │ │ ├── lables.py │ │ └── ml.py │ ├── plugin.py │ ├── recorders/ │ │ ├── __init__.py │ │ ├── consts.py │ │ ├── eastmoney/ │ │ │ ├── __init__.py │ │ │ ├── common.py │ │ │ ├── dividend_financing/ │ │ │ │ ├── __init__.py │ │ │ │ ├── eastmoney_dividend_detail_recorder.py │ │ │ │ ├── eastmoney_dividend_financing_recorder.py │ │ │ │ ├── eastmoney_rights_issue_detail_recorder.py │ │ │ │ └── eastmoney_spo_detail_recorder.py │ │ │ ├── finance/ │ │ │ │ ├── __init__.py │ │ │ │ ├── base_china_stock_finance_recorder.py │ │ │ │ ├── eastmoney_balance_sheet_recorder.py │ │ │ │ ├── eastmoney_cash_flow_recorder.py │ │ │ │ ├── eastmoney_finance_factor_recorder.py │ │ │ │ └── eastmoney_income_statement_recorder.py │ │ │ ├── holder/ │ │ │ │ ├── __init__.py │ │ │ │ ├── eastmoney_stock_actor_recorder.py │ │ │ │ ├── eastmoney_top_ten_holder_recorder.py │ │ │ │ └── eastmoney_top_ten_tradable_holder_recorder.py │ │ │ ├── meta/ │ │ │ │ ├── __init__.py │ │ │ │ ├── eastmoney_block_meta_recorder.py │ │ │ │ └── eastmoney_stock_meta_recorder.py │ │ │ └── trading/ │ │ │ ├── __init__.py │ │ │ ├── eastmoney_holder_trading_recorder.py │ │ │ └── eastmoney_manager_trading_recorder.py │ │ ├── em/ │ │ │ ├── __init__.py │ │ │ ├── actor/ │ │ │ │ ├── __init__.py │ │ │ │ ├── em_stock_actor_summary_recorder.py │ │ │ │ ├── em_stock_ii_recorder.py │ │ │ │ ├── em_stock_top_ten_free_recorder.py │ │ │ │ └── em_stock_top_ten_recorder.py │ │ │ ├── em_api.py │ │ │ ├── macro/ │ │ │ │ ├── __init__.py │ │ │ │ └── em_treasury_yield_recorder.py │ │ │ ├── meta/ │ │ │ │ ├── __init__.py │ │ │ │ ├── em_block_meta_recorder.py │ │ │ │ ├── em_cbond_meta_recorder.py │ │ │ │ ├── em_currency_meta_recorder.py │ │ │ │ ├── em_future_meta_recorder.py │ │ │ │ ├── em_index_meta_recorder.py │ │ │ │ ├── em_indexhk_meta_recorder.py │ │ │ │ ├── em_indexus_meta_recorder.py │ │ │ │ ├── em_stock_meta_recorder.py │ │ │ │ ├── em_stockhk_meta_recorder.py │ │ │ │ └── em_stockus_meta_recorder.py │ │ │ ├── misc/ │ │ │ │ ├── __init__.py │ │ │ │ └── em_stock_news_recorder.py │ │ │ ├── quotes/ │ │ │ │ ├── __init__.py │ │ │ │ └── em_kdata_recorder.py │ │ │ └── trading/ │ │ │ ├── __init__.py │ │ │ └── em_dragon_and_tiger_recorder.py │ │ ├── exchange/ │ │ │ ├── __init__.py │ │ │ ├── api/ │ │ │ │ ├── __init__.py │ │ │ │ ├── cn_index_api.py │ │ │ │ ├── cn_index_stock_api.py │ │ │ │ ├── cs_index_api.py │ │ │ │ └── cs_index_stock_api.py │ │ │ ├── exchange_etf_meta_recorder.py │ │ │ ├── exchange_index_recorder.py │ │ │ ├── exchange_index_stock_recorder.py │ │ │ ├── exchange_stock_meta_recorder.py │ │ │ └── exchange_stock_summary_recorder.py │ │ ├── joinquant/ │ │ │ ├── __init__.py │ │ │ ├── common.py │ │ │ ├── fundamental/ │ │ │ │ ├── __init__.py │ │ │ │ ├── jq_etf_valuation_recorder.py │ │ │ │ ├── jq_margin_trading_recorder.py │ │ │ │ └── jq_stock_valuation_recorder.py │ │ │ ├── meta/ │ │ │ │ ├── __init__.py │ │ │ │ ├── jq_fund_meta_recorder.py │ │ │ │ ├── jq_stock_meta_recorder.py │ │ │ │ └── jq_trade_day_recorder.py │ │ │ ├── misc/ │ │ │ │ ├── __init__.py │ │ │ │ ├── jq_hk_holder_recorder.py │ │ │ │ ├── jq_index_money_flow_recorder.py │ │ │ │ └── jq_stock_money_flow_recorder.py │ │ │ ├── overall/ │ │ │ │ ├── __init__.py │ │ │ │ ├── jq_cross_market_recorder.py │ │ │ │ ├── jq_margin_trading_recorder.py │ │ │ │ └── jq_stock_summary_recorder.py │ │ │ └── quotes/ │ │ │ ├── __init__.py │ │ │ ├── jq_index_kdata_recorder.py │ │ │ └── jq_stock_kdata_recorder.py │ │ ├── jqka/ │ │ │ ├── __init__.py │ │ │ ├── emotion/ │ │ │ │ ├── JqkaEmotionRecorder.py │ │ │ │ └── __init__.py │ │ │ └── jqka_api.py │ │ ├── qmt/ │ │ │ ├── __init__.py │ │ │ ├── index/ │ │ │ │ ├── __init__.py │ │ │ │ └── qmt_index_recorder.py │ │ │ ├── meta/ │ │ │ │ ├── __init__.py │ │ │ │ └── qmt_stock_meta_recorder.py │ │ │ └── quotes/ │ │ │ ├── __init__.py │ │ │ └── qmt_kdata_recorder.py │ │ ├── sina/ │ │ │ ├── __init__.py │ │ │ ├── meta/ │ │ │ │ ├── __init__.py │ │ │ │ └── sina_block_recorder.py │ │ │ ├── money_flow/ │ │ │ │ ├── __init__.py │ │ │ │ ├── sina_block_money_flow_recorder.py │ │ │ │ └── sina_stock_money_flow_recorder.py │ │ │ └── quotes/ │ │ │ ├── __init__.py │ │ │ ├── sina_etf_kdata_recorder.py │ │ │ └── sina_index_kdata_recorder.py │ │ └── wb/ │ │ ├── __init__.py │ │ ├── wb_api.py │ │ ├── wb_country_recorder.py │ │ └── wb_economy_recorder.py │ ├── resources/ │ │ ├── concept_main_tag_mapping.json │ │ ├── hk_industry_main_tag_mapping.json │ │ ├── industry_main_tag_mapping.json │ │ ├── log_conf.yaml │ │ ├── missed_concept.json │ │ ├── missed_industry.json │ │ └── us_industry_main_tag_mapping.json │ ├── rest/ │ │ ├── __init__.py │ │ ├── data.py │ │ ├── factor.py │ │ ├── misc.py │ │ ├── trading.py │ │ └── work.py │ ├── samples/ │ │ ├── __init__.py │ │ └── stock_traders.py │ ├── sched/ │ │ ├── __init__.py │ │ └── sched.py │ ├── tag/ │ │ ├── __init__.py │ │ ├── ai_suggestion.py │ │ ├── common.py │ │ ├── tag_models.py │ │ ├── tag_schemas.py │ │ ├── tag_service.py │ │ ├── tag_stats.py │ │ ├── tag_utils.py │ │ └── tagger.py │ ├── tasks/ │ │ ├── init_tag_system.py │ │ ├── qmt_data_runner.py │ │ ├── qmt_tick_runner.py │ │ ├── stock_pool_runner.py │ │ ├── today_shoot_runner.py │ │ └── today_top_runner.py │ ├── trader/ │ │ ├── __init__.py │ │ ├── errors.py │ │ ├── sim_account.py │ │ ├── trader.py │ │ ├── trader_info_api.py │ │ ├── trader_models.py │ │ └── trader_schemas.py │ ├── trading/ │ │ ├── __init__.py │ │ ├── common.py │ │ ├── trading_models.py │ │ ├── trading_schemas.py │ │ └── trading_service.py │ ├── ui/ │ │ ├── __init__.py │ │ ├── apps/ │ │ │ └── factor_app.py │ │ ├── assets/ │ │ │ ├── __init__.py │ │ │ ├── base.css │ │ │ └── custom.css │ │ └── components/ │ │ ├── __init__.py │ │ └── dcc_components.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── decorator.py │ │ ├── file_utils.py │ │ ├── git_utils.py │ │ ├── model_utils.py │ │ ├── pd_utils.py │ │ ├── recorder_utils.py │ │ ├── str_utils.py │ │ ├── time_utils.py │ │ ├── utils.py │ │ └── zip_utils.py │ └── zvt_server.py └── tests/ ├── __init__.py ├── api/ │ ├── __init__.py │ ├── test_common.py │ ├── test_dividend_financing.py │ ├── test_finance.py │ ├── test_holder.py │ ├── test_intent.py │ ├── test_joinquant_quotes.py │ ├── test_kdata.py │ ├── test_technical.py │ └── test_trading.py ├── context.py ├── contract/ │ ├── __init__.py │ ├── test_add_provider_plugin.py │ ├── test_entity.py │ ├── test_reader.py │ └── test_schema.py ├── factors/ │ ├── __init__.py │ ├── test_algorithm.py │ ├── test_factor_select_targets.py │ ├── test_factors.py │ ├── test_technical_factor.py │ └── test_transformers.py ├── ml/ │ ├── __init__.py │ └── test_sgd.py ├── recorders/ │ ├── __init__.py │ ├── common/ │ │ ├── __init__.py │ │ └── test_china_stock_list_recorder.py │ ├── eastmoney/ │ │ ├── __init__.py │ │ ├── test_dividend_financing_recorder.py │ │ ├── test_finance.py │ │ ├── test_holder_recorder.py │ │ ├── test_meta_recorder.py │ │ └── test_trading_recorder.py │ ├── em/ │ │ ├── __init__.py │ │ ├── test_em_api.py │ │ └── test_kdata_recorder.py │ ├── joinquant/ │ │ ├── __init__.py │ │ ├── test_quote_recorder.py │ │ └── test_stock_money_flow.py │ └── sina/ │ ├── __init__.py │ └── test_money_flow_recorder.py ├── test_generator.py ├── trader/ │ ├── __init__.py │ └── test_trader.py └── utils/ ├── __init__.py ├── test_pd_utils.py ├── test_time_utils.py └── test_utils.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .coveragerc ================================================ [run] omit = src/zvt/recorders/* src/zvt/autocode/* src/zvt/samples/* *__init__* ================================================ FILE: .github/workflows/build.yaml ================================================ name: build on: [push] jobs: build: runs-on: ubuntu-latest strategy: matrix: python-version: [3.12] steps: - uses: actions/checkout@v2 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v2 with: python-version: ${{ matrix.python-version }} - name: Cache pip uses: actions/cache@v4 with: # This path is specific to Ubuntu path: ~/.cache/pip # Look to see if there is a cache hit for the corresponding requirements file key: ${{ runner.os }}-pip-${{ hashFiles('requirements.txt') }} restore-keys: | ${{ runner.os }}-pip- ${{ runner.os }}- - name: Install dependencies run: | python -m pip install --upgrade pip if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - name: Test with pytest run: | pip install pytest pip install pytest-cov pytest ./tests --cov-config=.coveragerc --cov-report=xml --cov=./src/zvt --ignore=tests/recorders/ --ignore=tests/domain/ - name: Codecov uses: codecov/codecov-action@v2 with: verbose: true ================================================ FILE: .github/workflows/package.yaml ================================================ name: package on: release: types: [published] jobs: deploy: runs-on: ubuntu-latest steps: - uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.12' - name: Install dependencies run: | python -m pip install --upgrade pip pip install build - name: Build package run: python -m build - name: Publish package uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29 with: user: __token__ password: ${{ secrets.PYPI }} ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so #idea .idea .vscode # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ docs/source/api/_autosummary/ # PyBuilder target/ # IPython Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv venv/ ENV/ /ve/ # Spyder project settings .spyderproject # Rope project settings .ropeproject /zvt-home /zvt-test-home # Local/VIP extensions (not part of open source) zvt_vip/ *.tar.gz .pytest_cache/ # LibreOffice locks .~lock.*# node_modules/ *running.ipynb .DS_Store a.json ================================================ FILE: .pre-commit-config.yaml ================================================ ci: autoupdate_schedule: monthly repos: - repo: https://github.com/psf/black rev: 24.10.0 hooks: - id: black # - repo: https://github.com/PyCQA/flake8 # rev: 4.0.1 # hooks: # - id: flake8 # additional_dependencies: # - flake8-bugbear # - flake8-implicit-str-concat # - repo: https://github.com/pre-commit/pre-commit-hooks # rev: v4.0.1 # hooks: # - id: fix-byte-order-marker # - id: trailing-whitespace # - id: end-of-file-fixer ================================================ FILE: .readthedocs.yaml ================================================ version: 2 build: os: "ubuntu-20.04" tools: python: "3.12" python: install: - requirements: requirements/docs.txt - method: pip path: . sphinx: builder: dirhtml fail_on_warning: false configuration: docs/source/conf.py #formats: # - pdf ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2023-2026 zvtvz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 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IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: MANIFEST.in ================================================ include *.md include *.txt include LICENSE recursive-include src/zvt/* * ================================================ FILE: README-cn.md ================================================ [![github](https://img.shields.io/github/stars/zvtvz/zvt.svg)](https://github.com/zvtvz/zvt) [![image](https://img.shields.io/pypi/v/zvt.svg)](https://pypi.org/project/zvt/) [![image](https://img.shields.io/pypi/l/zvt.svg)](https://pypi.org/project/zvt/) [![image](https://img.shields.io/pypi/pyversions/zvt.svg)](https://pypi.org/project/zvt/) [![build](https://github.com/zvtvz/zvt/actions/workflows/build.yaml/badge.svg)](https://github.com/zvtvz/zvt/actions/workflows/build.yml) [![package](https://github.com/zvtvz/zvt/actions/workflows/package.yaml/badge.svg)](https://github.com/zvtvz/zvt/actions/workflows/package.yaml) [![Documentation Status](https://readthedocs.org/projects/zvt/badge/?version=latest)](https://zvt.readthedocs.io/en/latest/?badge=latest) [![codecov.io](https://codecov.io/github/zvtvz/zvt/coverage.svg?branch=master)](https://codecov.io/github/zvtvz/zvt) [![Downloads](https://pepy.tech/badge/zvt/month)](https://pepy.tech/project/zvt) **缘起** [炒股的三大原理](https://mp.weixin.qq.com/s/FoFR63wFSQIE_AyFubkZ6Q) **声明** 本项目目前不保证任何向后兼容性，请谨慎升级。 随着作者思想的变化，一些以前觉得重要的东西可能也变得不重要，从而可能不会进行维护。 而一些新的东西的加入对你是否有用，需要自己去评估。 **Read this in other languages: [English](README-cn.md).** **详细文档:[https://zvt.readthedocs.io/en/latest/](https://zvt.readthedocs.io/en/latest/)** ## 市场模型 ZVT 将市场抽象为如下的模型:

* TradableEntity (交易标的) * ActorEntity (市场参与者) * EntityEvent (交易标的 和 市场参与者 发生的事件) ## 快速开始 ### 安装 ``` python3 -m pip install -U zvt ``` ### 使用展示 #### 主界面 #### Dash & Plotly UI > 适用于回测和研究，不太适用于实时行情和用户交互 安装完成后，在命令行下输入 zvt ```shell zvt ``` 打开 [http://127.0.0.1:8050/](http://127.0.0.1:8050/) > 这里展示的例子依赖后面的下载历史数据，数据更新请参考后面文档

> 系统的核心概念是可视化的，界面的名称与其一一对应，因此也是统一可扩展的。 > 你可以在你喜欢的ide里编写和运行策略，然后运行界面查看其相关的标的，因子，信号和净值展示。 #### 前后端分离的UI > 更灵活和可扩展，更适合于处理实时行情和用户交互，结合ZVT的动态tag系统，提供了一种量化结合主观的交易方式 - 初始化tag系统 运行以下脚本: https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/init_tag_system.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/stock_pool_runner.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_data_runner.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_tick_runner.py - 安装 uvicorn ```shell pip install uvicorn ``` - 运行 zvt server 安装完成后，在命令行下输入 zvt_server ```shell zvt_server ``` 或者从代码运行: https://github.com/zvtvz/zvt/blob/master/src/zvt/zvt_server.py - api 文档 open [http://127.0.0.1:8090/docs](http://127.0.0.1:8090/docs) - 部署前端 前端代码: https://github.com/zvtvz/zvt_ui 修改前端环境文件: https://github.com/zvtvz/zvt_ui/blob/main/.env 设置 {your server IP}, 即zvt_server服务的地址 ```text NEXT_PUBLIC_SERVER = {your server IP} ``` 然后参考前端的readme启动前端服务 打开 [http://127.0.0.1:3000/trade](http://127.0.0.1:3000/trade)

#### 见证奇迹的时刻 ``` >>> from zvt.domain import Stock, Stock1dHfqKdata >>> from zvt.ml import MaStockMLMachine >>> Stock.record_data(provider="em") >>> entity_ids = ["stock_sz_000001", "stock_sz_000338", "stock_sh_601318"] >>> Stock1dHfqKdata.record_data(provider="em", entity_ids=entity_ids, sleeping_time=1) >>> machine = MaStockMLMachine(entity_ids=["stock_sz_000001"], data_provider="em") >>> machine.train() >>> machine.predict() >>> machine.draw_result(entity_id="stock_sz_000001") ```

> 以上几行代码实现了：数据的抓取，持久化，增量更新，机器学习，预测，展示结果。 > 熟悉系统的核心概念后，可以应用到市场中的任何标的。 ### 核心概念 ``` >>> from zvt.domain import * ``` ### TradableEntity (交易标的) #### A股交易标的 ``` >>> Stock.record_data() >>> df = Stock.query_data(index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code 000001 stock_sz_000001 stock_sz_000001 1991-04-03 stock sz 000001 平安银行 1991-04-03 None 000002 stock_sz_000002 stock_sz_000002 1991-01-29 stock sz 000002 万 科Ａ 1991-01-29 None 000004 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None 000005 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None 000006 stock_sz_000006 stock_sz_000006 1992-04-27 stock sz 000006 深振业Ａ 1992-04-27 None ... ... ... ... ... ... ... ... ... ... 605507 stock_sh_605507 stock_sh_605507 2021-08-02 stock sh 605507 国邦医药 2021-08-02 None 605577 stock_sh_605577 stock_sh_605577 2021-08-24 stock sh 605577 龙版传媒 2021-08-24 None 605580 stock_sh_605580 stock_sh_605580 2021-08-19 stock sh 605580 恒盛能源 2021-08-19 None 605588 stock_sh_605588 stock_sh_605588 2021-08-12 stock sh 605588 冠石科技 2021-08-12 None 605589 stock_sh_605589 stock_sh_605589 2021-08-10 stock sh 605589 圣泉集团 2021-08-10 None [4136 rows x 9 columns] ``` #### 美股交易标的 ``` >>> Stockus.record_data() >>> df = Stockus.query_data(index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code A stockus_nyse_A stockus_nyse_A NaT stockus nyse A 安捷伦 None None AA stockus_nyse_AA stockus_nyse_AA NaT stockus nyse AA 美国铝业 None None AAC stockus_nyse_AAC stockus_nyse_AAC NaT stockus nyse AAC Ares Acquisition Corp-A None None AACG stockus_nasdaq_AACG stockus_nasdaq_AACG NaT stockus nasdaq AACG ATA Creativity Global ADR None None AACG stockus_nyse_AACG stockus_nyse_AACG NaT stockus nyse AACG ATA Creativity Global ADR None None ... ... ... ... ... ... ... ... ... ... ZWRK stockus_nasdaq_ZWRK stockus_nasdaq_ZWRK NaT stockus nasdaq ZWRK Z-Work Acquisition Corp-A None None ZY stockus_nasdaq_ZY stockus_nasdaq_ZY NaT stockus nasdaq ZY Zymergen Inc None None ZYME stockus_nyse_ZYME stockus_nyse_ZYME NaT stockus nyse ZYME Zymeworks Inc None None ZYNE stockus_nasdaq_ZYNE stockus_nasdaq_ZYNE NaT stockus nasdaq ZYNE Zynerba Pharmaceuticals Inc None None ZYXI stockus_nasdaq_ZYXI stockus_nasdaq_ZYXI NaT stockus nasdaq ZYXI Zynex Inc None None [5826 rows x 9 columns] >>> Stockus.query_data(code='AAPL') id entity_id timestamp entity_type exchange code name list_date end_date 0 stockus_nasdaq_AAPL stockus_nasdaq_AAPL None stockus nasdaq AAPL 苹果 None None ``` #### 港股交易标的 ``` >>> Stockhk.record_data() >>> df = Stockhk.query_data(index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code 00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None 00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None 00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None 00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None 00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None ... ... ... ... ... ... ... ... ... ... 09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None 09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None 09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None 09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None 80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None [2597 rows x 9 columns] >>> df[df.code=='00700'] id entity_id timestamp entity_type exchange code name list_date end_date 2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None ``` #### 还有更多 ``` >>> from zvt.contract import * >>> zvt_context.tradable_schema_map {'stockus': zvt.domain.meta.stockus_meta.Stockus, 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk, 'index': zvt.domain.meta.index_meta.Index, 'etf': zvt.domain.meta.etf_meta.Etf, 'stock': zvt.domain.meta.stock_meta.Stock, 'block': zvt.domain.meta.block_meta.Block, 'fund': zvt.domain.meta.fund_meta.Fund} ``` 其中key为交易标的的类型，value为其schema，系统为schema提供了统一的 **记录(record_data)** 和 **查询(query_data)** 方法。 ``` >>> Index.record_data() >>> df=Index.query_data(filters=[Index.category=='scope',Index.exchange='sh']) >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date publisher category base_point 0 index_sh_000001 index_sh_000001 1990-12-19 index sh 000001 上证指数 1991-07-15 None csindex scope 100.00 1 index_sh_000002 index_sh_000002 1990-12-19 index sh 000002 Ａ股指数 1992-02-21 None csindex scope 100.00 2 index_sh_000003 index_sh_000003 1992-02-21 index sh 000003 B股指数 1992-08-17 None csindex scope 100.00 3 index_sh_000010 index_sh_000010 2002-06-28 index sh 000010 上证180 2002-07-01 None csindex scope 3299.06 4 index_sh_000016 index_sh_000016 2003-12-31 index sh 000016 上证50 2004-01-02 None csindex scope 1000.00 .. ... ... ... ... ... ... ... ... ... ... ... ... 25 index_sh_000020 index_sh_000020 2007-12-28 index sh 000020 中型综指 2008-05-12 None csindex scope 1000.00 26 index_sh_000090 index_sh_000090 2009-12-31 index sh 000090 上证流通 2010-12-02 None csindex scope 1000.00 27 index_sh_930903 index_sh_930903 2012-12-31 index sh 930903 中证Ａ股 2016-10-18 None csindex scope 1000.00 28 index_sh_000688 index_sh_000688 2019-12-31 index sh 000688 科创50 2020-07-23 None csindex scope 1000.00 29 index_sh_931643 index_sh_931643 2019-12-31 index sh 931643 科创创业50 2021-06-01 None csindex scope 1000.00 [30 rows x 12 columns] ``` ### EntityEvent (交易标的 发生的事件) 有了交易标的，才有交易标的 发生的事。 #### 行情数据 交易标的 **行情schema** 遵从如下的规则: ``` {entity_shema}{level}{adjust_type}Kdata ``` * entity_schema 就是前面说的TradableEntity，比如Stock,Stockus等。 * level ``` >>> for level in IntervalLevel: print(level.value) ``` * adjust type ``` >>> for adjust_type in AdjustType: print(adjust_type.value) ``` > 注意: 为了兼容历史数据，前复权是个例外，{adjust_type}不填 前复权 ``` >>> Stock1dKdata.record_data(code='000338', provider='em') >>> df = Stock1dKdata.query_data(code='000338', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 2.33 2.00 2.40 1.87 207375.0 1.365189e+09 3.2472 0.1182 1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 2.11 1.94 2.20 1.87 86299.0 5.563198e+08 -0.0300 0.0492 2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 1.90 1.81 1.94 1.66 93823.0 5.782065e+08 -0.0670 0.0535 3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 1.78 1.85 1.98 1.75 47720.0 2.999226e+08 0.0221 0.0272 4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 1.81 1.73 1.81 1.66 39273.0 2.373126e+08 -0.0649 0.0224 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 19.39 20.30 20.30 19.25 1688497.0 3.370241e+09 0.0601 0.0398 3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 20.30 20.09 20.31 19.78 1187601.0 2.377957e+09 -0.0103 0.0280 3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 20.20 20.07 20.63 19.70 1143985.0 2.295195e+09 -0.0010 0.0270 3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 19.98 19.68 19.98 19.15 1218697.0 2.383841e+09 -0.0194 0.0287 3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 19.71 19.85 19.97 19.24 1023545.0 2.012006e+09 0.0086 0.0241 [3431 rows x 15 columns] >>> Stockus1dKdata.record_data(code='AAPL', provider='em') >>> df = Stockus1dKdata.query_data(code='AAPL', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stockus_nasdaq_AAPL_1984-09-07 stockus_nasdaq_AAPL 1984-09-07 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2981600.0 0.000000e+00 0.0000 0.0002 1 stockus_nasdaq_AAPL_1984-09-10 stockus_nasdaq_AAPL 1984-09-10 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2346400.0 0.000000e+00 0.0000 0.0001 2 stockus_nasdaq_AAPL_1984-09-11 stockus_nasdaq_AAPL 1984-09-11 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 5444000.0 0.000000e+00 0.0018 0.0003 3 stockus_nasdaq_AAPL_1984-09-12 stockus_nasdaq_AAPL 1984-09-12 None AAPL 苹果 1d -5.58 -5.59 -5.58 -5.59 4773600.0 0.000000e+00 -0.0018 0.0003 4 stockus_nasdaq_AAPL_1984-09-13 stockus_nasdaq_AAPL 1984-09-13 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 7429600.0 0.000000e+00 0.0018 0.0004 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 8765 stockus_nasdaq_AAPL_2021-08-27 stockus_nasdaq_AAPL 2021-08-27 None AAPL 苹果 1d 147.48 148.60 148.75 146.83 55802388.0 8.265452e+09 0.0072 0.0034 8766 stockus_nasdaq_AAPL_2021-08-30 stockus_nasdaq_AAPL 2021-08-30 None AAPL 苹果 1d 149.00 153.12 153.49 148.61 90956723.0 1.383762e+10 0.0304 0.0055 8767 stockus_nasdaq_AAPL_2021-08-31 stockus_nasdaq_AAPL 2021-08-31 None AAPL 苹果 1d 152.66 151.83 152.80 151.29 86453117.0 1.314255e+10 -0.0084 0.0052 8768 stockus_nasdaq_AAPL_2021-09-01 stockus_nasdaq_AAPL 2021-09-01 None AAPL 苹果 1d 152.83 152.51 154.98 152.34 80313711.0 1.235321e+10 0.0045 0.0049 8769 stockus_nasdaq_AAPL_2021-09-02 stockus_nasdaq_AAPL 2021-09-02 None AAPL 苹果 1d 153.87 153.65 154.72 152.40 71171317.0 1.093251e+10 0.0075 0.0043 [8770 rows x 15 columns] ``` 后复权 ``` >>> Stock1dHfqKdata.record_data(code='000338', provider='em') >>> df = Stock1dHfqKdata.query_data(code='000338', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 70.00 64.93 71.00 62.88 207375.0 1.365189e+09 2.1720 0.1182 1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 66.60 64.00 68.00 62.88 86299.0 5.563198e+08 -0.0143 0.0492 2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 63.32 62.00 63.88 59.60 93823.0 5.782065e+08 -0.0313 0.0535 3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 61.50 62.49 64.48 61.01 47720.0 2.999226e+08 0.0079 0.0272 4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 61.90 60.65 61.90 59.70 39273.0 2.373126e+08 -0.0294 0.0224 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 331.97 345.95 345.95 329.82 1688497.0 3.370241e+09 0.0540 0.0398 3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 345.95 342.72 346.10 337.96 1187601.0 2.377957e+09 -0.0093 0.0280 3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 344.41 342.41 351.02 336.73 1143985.0 2.295195e+09 -0.0009 0.0270 3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 341.03 336.42 341.03 328.28 1218697.0 2.383841e+09 -0.0175 0.0287 3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 336.88 339.03 340.88 329.67 1023545.0 2.012006e+09 0.0078 0.0241 [3431 rows x 15 columns] ``` #### 财务因子 ``` >>> FinanceFactor.record_data(code='000338') >>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp') basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp timestamp 2002-12-31 NaN 1.962000e+07 2.471000e+06 NaN NaN NaN NaN 0.2068 0.1259 2002-12-31 2003-12-31 1.27 3.574000e+09 2.739000e+08 181.2022 109.8778 0.7729 0.1783 0.2551 0.0766 2003-12-31 2004-12-31 1.75 6.188000e+09 5.369000e+08 0.7313 0.9598 0.3245 0.1474 0.2489 0.0868 2004-12-31 2005-12-31 0.93 5.283000e+09 3.065000e+08 -0.1463 -0.4291 0.1327 0.0603 0.2252 0.0583 2005-12-31 2006-03-31 0.33 1.859000e+09 1.079000e+08 NaN NaN NaN NaN NaN 0.0598 2006-03-31 ... ... ... ... ... ... ... ... ... ... ... 2020-08-28 0.59 9.449000e+10 4.680000e+09 0.0400 -0.1148 0.0983 0.0229 0.1958 0.0603 2020-08-28 2020-10-31 0.90 1.474000e+11 7.106000e+09 0.1632 0.0067 0.1502 0.0347 0.1949 0.0590 2020-10-31 2021-03-31 1.16 1.975000e+11 9.207000e+09 0.1327 0.0112 0.1919 0.0444 0.1931 0.0571 2021-03-31 2021-04-30 0.42 6.547000e+10 3.344000e+09 0.6788 0.6197 0.0622 0.0158 0.1916 0.0667 2021-04-30 2021-08-31 0.80 1.264000e+11 6.432000e+09 0.3375 0.3742 0.1125 0.0287 0.1884 0.0653 2021-08-31 [66 rows x 10 columns] ``` #### 财务三张表 ``` #资产负债表 >>> BalanceSheet.record_data(code='000338') #利润表 >>> IncomeStatement.record_data(code='000338') #现金流量表 >>> CashFlowStatement.record_data(code='000338') ``` #### 还有更多 ``` >>> zvt_context.schemas [zvt.domain.dividend_financing.DividendFinancing, zvt.domain.dividend_financing.DividendDetail, zvt.domain.dividend_financing.SpoDetail...] ``` zvt_context.schemas为系统支持的schema,schema即表结构，即数据，其字段含义的查看方式如下： * help 输入schema.按tab提示其包含的字段，或者.help() ``` >>> FinanceFactor.help() ``` * 源码 [domain](https://github.com/zvtvz/zvt/tree/master/src/zvt/domain)里的文件为schema的定义，查看相应字段的注释即可。 通过以上的例子，你应该掌握了统一的记录数据的方法： > Schema.record_data(provider='your provider',codes='the codes') 注意可选参数provider，其代表数据提供商，一个schema可以有多个provider，这是系统稳定的基石。 查看**已实现**的provider ``` >>> Stock.provider_map_recorder {'joinquant': zvt.recorders.joinquant.meta.jq_stock_meta_recorder.JqChinaStockRecorder, 'exchange': zvt.recorders.exchange.exchange_stock_meta_recorder.ExchangeStockMetaRecorder, 'em': zvt.recorders.em.meta.em_stock_meta_recorder.EMStockRecorder, 'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder.EastmoneyChinaStockListRecorder} ``` 你可以使用任意一个provider来获取数据，默认使用第一个。 再举个例子，股票板块数据获取： ``` >>> Block.provider_map_recorder {'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_block_meta_recorder.EastmoneyChinaBlockRecorder, 'sina': zvt.recorders.sina.meta.sina_block_recorder.SinaBlockRecorder} >>> Block.record_data(provider='sina') Block registered recorders:{'eastmoney': , 'sina': } 2020-03-04 23:56:48,931 INFO MainThread finish record sina blocks:industry 2020-03-04 23:56:49,450 INFO MainThread finish record sina blocks:concept ``` 再多了解一点record_data： * 参数code[单个]，codes[多个]代表需要抓取的股票代码 * 不传入code,codes则是全市场抓取 * 该方法会把数据存储到本地并只做增量更新 定时任务的方式更新可参考[定时更新](https://github.com/zvtvz/zvt/blob/master/examples/data_runner) #### 全市场选股 查询数据使用的是query_data方法，把全市场的数据记录下来后，就可以在本地快速查询需要的数据了。 一个例子：2018年年报 roe>8% 营收增长>8% 的前20个股 ``` >>> df=FinanceFactor.query_data(filters=[FinanceFactor.roe>0.08,FinanceFactor.report_period=='year',FinanceFactor.op_income_growth_yoy>0.08],start_timestamp='2019-01-01',order=FinanceFactor.roe.desc(),limit=20,columns=["code"]+FinanceFactor.important_cols(),index='code') code basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp code 000048 000048 2.7350 4.919000e+09 1.101000e+09 0.4311 1.5168 0.7035 0.1988 0.5243 0.2355 2020-04-30 000912 000912 0.3500 4.405000e+09 3.516000e+08 0.1796 1.2363 4.7847 0.0539 0.2175 0.0795 2019-03-20 002207 002207 0.2200 3.021000e+08 5.189000e+07 0.1600 1.1526 1.1175 0.1182 0.1565 0.1718 2020-04-27 002234 002234 5.3300 3.276000e+09 1.610000e+09 0.8023 3.2295 0.8361 0.5469 0.5968 0.4913 2020-04-21 002458 002458 3.7900 3.584000e+09 2.176000e+09 1.4326 4.9973 0.8318 0.6754 0.6537 0.6080 2020-02-20 ... ... ... ... ... ... ... ... ... ... ... ... 600701 600701 -3.6858 7.830000e+08 -3.814000e+09 1.3579 -0.0325 1.9498 -0.7012 0.4173 -4.9293 2020-04-29 600747 600747 -1.5600 3.467000e+08 -2.290000e+09 2.1489 -0.4633 3.1922 -1.5886 0.0378 -6.6093 2020-06-30 600793 600793 1.6568 1.293000e+09 1.745000e+08 0.1164 0.8868 0.7490 0.0486 0.1622 0.1350 2019-04-30 600870 600870 0.0087 3.096000e+07 4.554000e+06 0.7773 1.3702 0.7458 0.0724 0.2688 0.1675 2019-03-30 688169 688169 15.6600 4.205000e+09 7.829000e+08 0.3781 1.5452 0.7172 0.4832 0.3612 0.1862 2020-04-28 [20 rows x 11 columns] ``` 以上，你应该会回答如下的三个问题了： * 有什么数据? * 如何记录数据? * 如何查询数据? 更高级的用法以及扩展数据，可以参考详细文档里的数据部分。 ### 写个策略 有了 **交易标的** 和 **交易标的发生的事**，就可以写策略了。 所谓策略回测，无非就是，重复以下过程： #### 在某时间点，找到符合条件的标的，对其进行买卖，看其表现。 系统支持两种模式: * solo (随意的) 在 某个时间 根据发生的事件 计算条件 并买卖 * formal (正式的) 系统设计的二维索引多标的计算模型 #### 一个很随便的人(solo) 嗯，这个策略真的很随便，就像我们大部分时间做的那样。 > 报表出来的时，我看一下报表，机构加仓超过5%我就买入，机构减仓超过50%我就卖出。 代码如下: ``` # -*- coding: utf-8 -*- import pandas as pd from zvt.api import get_recent_report_date from zvt.contract import ActorType, AdjustType from zvt.domain import StockActorSummary, Stock1dKdata from zvt.trader import StockTrader from zvt.utils import pd_is_not_null, is_same_date, to_pd_timestamp class FollowIITrader(StockTrader): finish_date = None def on_time(self, timestamp: pd.Timestamp): recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp)) if self.finish_date and is_same_date(recent_report_date, self.finish_date): return filters = [StockActorSummary.actor_type == ActorType.raised_fund.value, StockActorSummary.report_date == recent_report_date] if self.entity_ids: filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)] df = StockActorSummary.query_data(filters=filters) if pd_is_not_null(df): self.logger.info(f'{df}') self.finish_date = recent_report_date long_df = df[df['change_ratio'] > 0.05] short_df = df[df['change_ratio'] < -0.5] try: self.trade_the_targets(due_timestamp=timestamp, happen_timestamp=timestamp, long_selected=set(long_df['entity_id'].to_list()), short_selected=set(short_df['entity_id'].to_list())) except Exception as e: self.logger.error(e) if __name__ == '__main__': entity_id = 'stock_sh_600519' Stock1dKdata.record_data(entity_id=entity_id, provider='em') StockActorSummary.record_data(entity_id=entity_id, provider='em') FollowIITrader(start_timestamp='2002-01-01', end_timestamp='2021-01-01', entity_ids=[entity_id], provider='em', adjust_type=AdjustType.qfq, profit_threshold=None).run() ``` 所以，写一个策略其实还是很简单的嘛。 你可以发挥想象力，社保重仓买买买，外资重仓买买买，董事长跟小姨子跑了卖卖卖...... 然后，刷新一下[http://127.0.0.1:8050/](http://127.0.0.1:8050/)，看你运行策略的performance 更多可参考[策略例子](https://github.com/zvtvz/zvt/tree/master/examples/trader) #### 严肃一点(formal) 简单的计算可以通过query_data来完成，这里说的是系统设计的二维索引多标的计算模型。 下面以技术因子为例对**计算流程**进行说明: ``` In [7]: from zvt.factors import * In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer(count_live_dead=True)) ``` ### data_df data_df为factor的原始数据，即通过query_data从数据库读取到的数据,为一个**二维索引**DataFrame ``` In [11]: factor.data_df Out[11]: level high id entity_id open low timestamp close entity_id timestamp stock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54 ... ... ... ... ... ... ... ... ... stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02 [208 rows x 8 columns] ``` ### factor_df factor_df为transformer对data_df进行计算后得到的数据，设计因子即对[transformer](https://github.com/zvtvz/zvt/blob/master/src/zvt/contract/factor.py#L34)进行扩展，例子中用的是MacdTransformer()。 ``` In [12]: factor.factor_df Out[12]: level high id entity_id open low timestamp close diff dea macd entity_id timestamp stock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94 NaN NaN NaN 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42 NaN NaN NaN 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31 NaN NaN NaN 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03 NaN NaN NaN 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54 NaN NaN NaN ... ... ... ... ... ... ... ... ... ... ... ... stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81 -0.121336 -0.145444 0.048215 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73 -0.133829 -0.143121 0.018583 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59 -0.153260 -0.145149 -0.016223 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65 -0.161951 -0.148509 -0.026884 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02 -0.137399 -0.146287 0.017776 [208 rows x 11 columns] ``` ### result_df result_df为可用于选股器的**二维索引**DataFrame，通过对data_df或factor_df计算来实现。 该例子在计算macd之后，利用factor_df,黄白线在0轴上为True,否则为False，[具体代码](https://github.com/zvtvz/zvt/blob/master/src/zvt/factors/technical_factor.py#L56) ``` In [14]: factor.result_df Out[14]: score entity_id timestamp stock_sh_601318 2019-01-02 False 2019-01-03 False 2019-01-04 False 2019-01-07 False 2019-01-08 False ... ... stock_sz_000338 2019-06-03 False 2019-06-04 False 2019-06-05 False 2019-06-06 False 2019-06-10 False [208 rows x 1 columns] ``` result_df的格式如下：

filter_result 为 True 或 False, score_result 取值为 0 到 1。 结合选股器和回测，整个流程如下：

## 环境设置（可选） ``` >>> from zvt import * >>> zvt_env {'zvt_home': '/Users/foolcage/zvt-home', 'data_path': '/Users/foolcage/zvt-home/data', 'tmp_path': '/Users/foolcage/zvt-home/tmp', 'ui_path': '/Users/foolcage/zvt-home/ui', 'log_path': '/Users/foolcage/zvt-home/logs'} >>> zvt_config ``` * jq_username 聚宽数据用户名 * jq_password 聚宽数据密码 * smtp_host 邮件服务器host * smtp_port 邮件服务器端口 * email_username smtp邮箱账户 * email_password smtp邮箱密码 * wechat_app_id * wechat_app_secrect ``` >>> init_config(current_config=zvt_config, jq_username='xxx', jq_password='yyy') ``` > 通用的配置方式为: init_config(current_config=zvt_config, **kv) ### 历史数据 ZVT支持数据增量更新，用户之间可以共享历史数据，这样可以节省很多时间。 #### 数据源 > 新UI实时行情的计算基于QMT数据源，需要开通的同学可联系作者。 项目数据支持多provider，在数据schema一致性的基础上，可根据需要进行选择和扩展，目前支持新浪，东财，交易所等免费数据。 #### 数据的设计上是让provider来适配schema,而不是反过来，这样即使某provider不可用了，换一个即可，不会影响整个系统的使用。 但免费数据的缺点是显而易见的:不稳定，爬取清洗数据耗时耗力，维护代价巨大，且随时可能不可用。 个人建议：如果只是学习研究，可以使用免费数据；如果是真正有意投身量化，还是选一家可靠的数据提供商。 > 项目中大部分的免费数据目前都是比较稳定的，且做过严格测试，特别是东财的数据，可放心使用 > 添加其他数据提供商， 请参考[数据扩展教程](https://zvtvz.github.io/zvt/#/data_extending) ## 开发 ### clone代码 ``` git clone https://github.com/zvtvz/zvt.git ``` 设置项目的virtual env(python>=3.6),安装依赖 ``` pip3 install -r requirements.txt pip3 install pytest ``` ### 测试案例 pycharm导入工程(推荐,你也可以使用其他ide)，然后pytest跑测试案例

大部分功能使用都可以从tests里面参考 ## 贡献 期待能有更多的开发者参与到 zvt 的开发中来，我会保证尽快 Reivew PR 并且及时回复。但提交 PR 请确保 先看一下[1分钟代码规范](https://github.com/zvtvz/zvt/blob/master/code_of_conduct.md) 1. 通过所有单元测试，如若是新功能，请为其新增单元测试 2. 遵守开发规范 3. 如若需要，请更新相对应的文档 也非常欢迎开发者能为 zvt 提供更多的示例，共同来完善文档。 ## 请作者喝杯咖啡 如果你觉得项目对你有帮助,可以请作者喝杯咖啡 Alipay　　　　　 Wechat ## 联系方式 目前只接受开户朋友的进群申请，微信号 foolcage Wechat ------ 微信公众号: Wechat 知乎专栏: https://zhuanlan.zhihu.com/automoney ## Thanks

jetbrains

================================================ FILE: README.md ================================================ [![github](https://img.shields.io/github/stars/zvtvz/zvt.svg)](https://github.com/zvtvz/zvt) [![image](https://img.shields.io/pypi/v/zvt.svg)](https://pypi.org/project/zvt/) [![image](https://img.shields.io/pypi/l/zvt.svg)](https://pypi.org/project/zvt/) [![image](https://img.shields.io/pypi/pyversions/zvt.svg)](https://pypi.org/project/zvt/) [![build](https://github.com/zvtvz/zvt/actions/workflows/build.yaml/badge.svg)](https://github.com/zvtvz/zvt/actions/workflows/build.yml) [![package](https://github.com/zvtvz/zvt/actions/workflows/package.yaml/badge.svg)](https://github.com/zvtvz/zvt/actions/workflows/package.yaml) [![Documentation Status](https://readthedocs.org/projects/zvt/badge/?version=latest)](https://zvt.readthedocs.io/en/latest/?badge=latest) [![codecov.io](https://codecov.io/github/zvtvz/zvt/coverage.svg?branch=master)](https://codecov.io/github/zvtvz/zvt) [![Downloads](https://pepy.tech/badge/zvt/month)](https://pepy.tech/project/zvt) **The origin of ZVT** [The Three Major Principles of Stock Trading](https://mp.weixin.qq.com/s/FoFR63wFSQIE_AyFubkZ6Q) **Declaration** This project does not currently guarantee any backward compatibility, so please upgrade with caution. As the author's thoughts evolve, some things that were once considered important may become less so, and thus may not be maintained. Whether the addition of some new elements will be useful to you needs to be assessed by yourself. **Read this in other languages: [中文](README-cn.md).** **Read the docs:[https://zvt.readthedocs.io/en/latest/](https://zvt.readthedocs.io/en/latest/)** ### Install ``` python3 -m pip install -U zvt ``` ### Main ui #### Dash & Plotly UI > It's good for backtest and research, but it is not applicable for real-time market data and user interaction. After the installation is complete, enter zvt on the command line ```shell zvt ``` open [http://127.0.0.1:8050/](http://127.0.0.1:8050/) > The example shown here relies on data, factor, trader, please read [docs](https://zvt.readthedocs.io/en/latest/)

> The core concept of the system is visual, and the name of the interface corresponds to it one-to-one, so it is also uniform and extensible. > You can write and run the strategy in your favorite ide, and then view its related targets, factor, signal and performance on the UI. #### Rest api and standalone UI > It is more flexible and more scalable, more suitable for handling real-time market data and user interaction. > Combined with the dynamic tag system provided by ZVT, it offers a trading approach that combines AI with human intervention. - Init tag system run following scripts: https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/init_tag_system.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/stock_pool_runner.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_data_runner.py https://github.com/zvtvz/zvt/blob/master/src/zvt/tasks/qmt_tick_runner.py - Install uvicorn ```shell pip install uvicorn ``` - Run zvt server After the installation is complete, enter zvt_server on the command line ```shell zvt_server ``` Or run it from source code: https://github.com/zvtvz/zvt/blob/master/src/zvt/zvt_server.py - Check the api docs open [http://127.0.0.1:8090/docs](http://127.0.0.1:8090/docs) - Deploy the front end service Front end source code: https://github.com/zvtvz/zvt_ui Change the env file: https://github.com/zvtvz/zvt_ui/blob/main/.env Set {your server IP} to zvt_server IP ```text NEXT_PUBLIC_SERVER = {your server IP} ``` Then refer to the frontend's README to start the frontend service. open [http://127.0.0.1:3000/trade](http://127.0.0.1:3000/trade)

### Behold, the power of zvt: ``` >>> from zvt.domain import Stock, Stock1dHfqKdata >>> from zvt.ml import MaStockMLMachine >>> Stock.record_data(provider="em") >>> entity_ids = ["stock_sz_000001", "stock_sz_000338", "stock_sh_601318"] >>> Stock1dHfqKdata.record_data(provider="em", entity_ids=entity_ids, sleeping_time=1) >>> machine = MaStockMLMachine(entity_ids=["stock_sz_000001"], data_provider="em") >>> machine.train() >>> machine.predict() >>> machine.draw_result(entity_id="stock_sz_000001") ```

> The few lines of code above has done: data capture, persistence, incremental update, machine learning, prediction, and display results. > Once you are familiar with the core concepts of the system, you can apply it to any target in the market. ### Data #### China stock ``` >>> from zvt.domain import * >>> Stock.record_data(provider="em") >>> df = Stock.query_data(provider="em", index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code 000001 stock_sz_000001 stock_sz_000001 1991-04-03 stock sz 000001 平安银行 1991-04-03 None 000002 stock_sz_000002 stock_sz_000002 1991-01-29 stock sz 000002 万 科Ａ 1991-01-29 None 000004 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None 000005 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None 000006 stock_sz_000006 stock_sz_000006 1992-04-27 stock sz 000006 深振业Ａ 1992-04-27 None ... ... ... ... ... ... ... ... ... ... 605507 stock_sh_605507 stock_sh_605507 2021-08-02 stock sh 605507 国邦医药 2021-08-02 None 605577 stock_sh_605577 stock_sh_605577 2021-08-24 stock sh 605577 龙版传媒 2021-08-24 None 605580 stock_sh_605580 stock_sh_605580 2021-08-19 stock sh 605580 恒盛能源 2021-08-19 None 605588 stock_sh_605588 stock_sh_605588 2021-08-12 stock sh 605588 冠石科技 2021-08-12 None 605589 stock_sh_605589 stock_sh_605589 2021-08-10 stock sh 605589 圣泉集团 2021-08-10 None [4136 rows x 9 columns] ``` #### USA stock ``` >>> Stockus.record_data() >>> df = Stockus.query_data(index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code A stockus_nyse_A stockus_nyse_A NaT stockus nyse A 安捷伦 None None AA stockus_nyse_AA stockus_nyse_AA NaT stockus nyse AA 美国铝业 None None AAC stockus_nyse_AAC stockus_nyse_AAC NaT stockus nyse AAC Ares Acquisition Corp-A None None AACG stockus_nasdaq_AACG stockus_nasdaq_AACG NaT stockus nasdaq AACG ATA Creativity Global ADR None None AACG stockus_nyse_AACG stockus_nyse_AACG NaT stockus nyse AACG ATA Creativity Global ADR None None ... ... ... ... ... ... ... ... ... ... ZWRK stockus_nasdaq_ZWRK stockus_nasdaq_ZWRK NaT stockus nasdaq ZWRK Z-Work Acquisition Corp-A None None ZY stockus_nasdaq_ZY stockus_nasdaq_ZY NaT stockus nasdaq ZY Zymergen Inc None None ZYME stockus_nyse_ZYME stockus_nyse_ZYME NaT stockus nyse ZYME Zymeworks Inc None None ZYNE stockus_nasdaq_ZYNE stockus_nasdaq_ZYNE NaT stockus nasdaq ZYNE Zynerba Pharmaceuticals Inc None None ZYXI stockus_nasdaq_ZYXI stockus_nasdaq_ZYXI NaT stockus nasdaq ZYXI Zynex Inc None None [5826 rows x 9 columns] >>> Stockus.query_data(code='AAPL') id entity_id timestamp entity_type exchange code name list_date end_date 0 stockus_nasdaq_AAPL stockus_nasdaq_AAPL None stockus nasdaq AAPL 苹果 None None ``` #### Hong Kong stock ``` >>> Stockhk.record_data() >>> df = Stockhk.query_data(index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code 00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None 00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None 00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None 00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None 00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None ... ... ... ... ... ... ... ... ... ... 09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None 09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None 09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None 09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None 80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None [2597 rows x 9 columns] >>> df[df.code=='00700'] id entity_id timestamp entity_type exchange code name list_date end_date 2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None ``` #### And more ``` >>> from zvt.contract import * >>> zvt_context.tradable_schema_map {'stockus': zvt.domain.meta.stockus_meta.Stockus, 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk, 'index': zvt.domain.meta.index_meta.Index, 'etf': zvt.domain.meta.etf_meta.Etf, 'stock': zvt.domain.meta.stock_meta.Stock, 'block': zvt.domain.meta.block_meta.Block, 'fund': zvt.domain.meta.fund_meta.Fund} ``` The key is tradable entity type, and the value is the schema. The system provides unified **record (record_data)** and **query (query_data)** methods for the schema. ``` >>> Index.record_data() >>> df=Index.query_data(filters=[Index.category=='scope',Index.exchange='sh']) >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date publisher category base_point 0 index_sh_000001 index_sh_000001 1990-12-19 index sh 000001 上证指数 1991-07-15 None csindex scope 100.00 1 index_sh_000002 index_sh_000002 1990-12-19 index sh 000002 Ａ股指数 1992-02-21 None csindex scope 100.00 2 index_sh_000003 index_sh_000003 1992-02-21 index sh 000003 B股指数 1992-08-17 None csindex scope 100.00 3 index_sh_000010 index_sh_000010 2002-06-28 index sh 000010 上证180 2002-07-01 None csindex scope 3299.06 4 index_sh_000016 index_sh_000016 2003-12-31 index sh 000016 上证50 2004-01-02 None csindex scope 1000.00 .. ... ... ... ... ... ... ... ... ... ... ... ... 25 index_sh_000020 index_sh_000020 2007-12-28 index sh 000020 中型综指 2008-05-12 None csindex scope 1000.00 26 index_sh_000090 index_sh_000090 2009-12-31 index sh 000090 上证流通 2010-12-02 None csindex scope 1000.00 27 index_sh_930903 index_sh_930903 2012-12-31 index sh 930903 中证Ａ股 2016-10-18 None csindex scope 1000.00 28 index_sh_000688 index_sh_000688 2019-12-31 index sh 000688 科创50 2020-07-23 None csindex scope 1000.00 29 index_sh_931643 index_sh_931643 2019-12-31 index sh 931643 科创创业50 2021-06-01 None csindex scope 1000.00 [30 rows x 12 columns] ``` ### EntityEvent We have tradable entity and then events about them. #### Market quotes the TradableEntity quote schema follows the following rules: ``` {entity_shema}{level}{adjust_type}Kdata ``` * entity_schema TradableEntity class，e.g., Stock,Stockus. * level ``` >>> for level in IntervalLevel: print(level.value) ``` * adjust type ``` >>> for adjust_type in AdjustType: print(adjust_type.value) ``` > Note: In order to be compatible with historical data, the pre-reset is an exception, {adjust_type} is left empty qfq ``` >>> Stock1dKdata.record_data(code='000338', provider='em') >>> df = Stock1dKdata.query_data(code='000338', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 2.33 2.00 2.40 1.87 207375.0 1.365189e+09 3.2472 0.1182 1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 2.11 1.94 2.20 1.87 86299.0 5.563198e+08 -0.0300 0.0492 2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 1.90 1.81 1.94 1.66 93823.0 5.782065e+08 -0.0670 0.0535 3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 1.78 1.85 1.98 1.75 47720.0 2.999226e+08 0.0221 0.0272 4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 1.81 1.73 1.81 1.66 39273.0 2.373126e+08 -0.0649 0.0224 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 19.39 20.30 20.30 19.25 1688497.0 3.370241e+09 0.0601 0.0398 3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 20.30 20.09 20.31 19.78 1187601.0 2.377957e+09 -0.0103 0.0280 3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 20.20 20.07 20.63 19.70 1143985.0 2.295195e+09 -0.0010 0.0270 3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 19.98 19.68 19.98 19.15 1218697.0 2.383841e+09 -0.0194 0.0287 3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 19.71 19.85 19.97 19.24 1023545.0 2.012006e+09 0.0086 0.0241 [3431 rows x 15 columns] >>> Stockus1dKdata.record_data(code='AAPL', provider='em') >>> df = Stockus1dKdata.query_data(code='AAPL', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stockus_nasdaq_AAPL_1984-09-07 stockus_nasdaq_AAPL 1984-09-07 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2981600.0 0.000000e+00 0.0000 0.0002 1 stockus_nasdaq_AAPL_1984-09-10 stockus_nasdaq_AAPL 1984-09-10 None AAPL 苹果 1d -5.59 -5.59 -5.58 -5.59 2346400.0 0.000000e+00 0.0000 0.0001 2 stockus_nasdaq_AAPL_1984-09-11 stockus_nasdaq_AAPL 1984-09-11 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 5444000.0 0.000000e+00 0.0018 0.0003 3 stockus_nasdaq_AAPL_1984-09-12 stockus_nasdaq_AAPL 1984-09-12 None AAPL 苹果 1d -5.58 -5.59 -5.58 -5.59 4773600.0 0.000000e+00 -0.0018 0.0003 4 stockus_nasdaq_AAPL_1984-09-13 stockus_nasdaq_AAPL 1984-09-13 None AAPL 苹果 1d -5.58 -5.58 -5.58 -5.58 7429600.0 0.000000e+00 0.0018 0.0004 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 8765 stockus_nasdaq_AAPL_2021-08-27 stockus_nasdaq_AAPL 2021-08-27 None AAPL 苹果 1d 147.48 148.60 148.75 146.83 55802388.0 8.265452e+09 0.0072 0.0034 8766 stockus_nasdaq_AAPL_2021-08-30 stockus_nasdaq_AAPL 2021-08-30 None AAPL 苹果 1d 149.00 153.12 153.49 148.61 90956723.0 1.383762e+10 0.0304 0.0055 8767 stockus_nasdaq_AAPL_2021-08-31 stockus_nasdaq_AAPL 2021-08-31 None AAPL 苹果 1d 152.66 151.83 152.80 151.29 86453117.0 1.314255e+10 -0.0084 0.0052 8768 stockus_nasdaq_AAPL_2021-09-01 stockus_nasdaq_AAPL 2021-09-01 None AAPL 苹果 1d 152.83 152.51 154.98 152.34 80313711.0 1.235321e+10 0.0045 0.0049 8769 stockus_nasdaq_AAPL_2021-09-02 stockus_nasdaq_AAPL 2021-09-02 None AAPL 苹果 1d 153.87 153.65 154.72 152.40 71171317.0 1.093251e+10 0.0075 0.0043 [8770 rows x 15 columns] ``` hfq ``` >>> Stock1dHfqKdata.record_data(code='000338', provider='em') >>> df = Stock1dHfqKdata.query_data(code='000338', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 70.00 64.93 71.00 62.88 207375.0 1.365189e+09 2.1720 0.1182 1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 66.60 64.00 68.00 62.88 86299.0 5.563198e+08 -0.0143 0.0492 2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 63.32 62.00 63.88 59.60 93823.0 5.782065e+08 -0.0313 0.0535 3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 61.50 62.49 64.48 61.01 47720.0 2.999226e+08 0.0079 0.0272 4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 61.90 60.65 61.90 59.70 39273.0 2.373126e+08 -0.0294 0.0224 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 331.97 345.95 345.95 329.82 1688497.0 3.370241e+09 0.0540 0.0398 3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 345.95 342.72 346.10 337.96 1187601.0 2.377957e+09 -0.0093 0.0280 3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 344.41 342.41 351.02 336.73 1143985.0 2.295195e+09 -0.0009 0.0270 3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 341.03 336.42 341.03 328.28 1218697.0 2.383841e+09 -0.0175 0.0287 3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 336.88 339.03 340.88 329.67 1023545.0 2.012006e+09 0.0078 0.0241 [3431 rows x 15 columns] ``` #### Finance factor ``` >>> FinanceFactor.record_data(code='000338') >>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp') basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp timestamp 2002-12-31 NaN 1.962000e+07 2.471000e+06 NaN NaN NaN NaN 0.2068 0.1259 2002-12-31 2003-12-31 1.27 3.574000e+09 2.739000e+08 181.2022 109.8778 0.7729 0.1783 0.2551 0.0766 2003-12-31 2004-12-31 1.75 6.188000e+09 5.369000e+08 0.7313 0.9598 0.3245 0.1474 0.2489 0.0868 2004-12-31 2005-12-31 0.93 5.283000e+09 3.065000e+08 -0.1463 -0.4291 0.1327 0.0603 0.2252 0.0583 2005-12-31 2006-03-31 0.33 1.859000e+09 1.079000e+08 NaN NaN NaN NaN NaN 0.0598 2006-03-31 ... ... ... ... ... ... ... ... ... ... ... 2020-08-28 0.59 9.449000e+10 4.680000e+09 0.0400 -0.1148 0.0983 0.0229 0.1958 0.0603 2020-08-28 2020-10-31 0.90 1.474000e+11 7.106000e+09 0.1632 0.0067 0.1502 0.0347 0.1949 0.0590 2020-10-31 2021-03-31 1.16 1.975000e+11 9.207000e+09 0.1327 0.0112 0.1919 0.0444 0.1931 0.0571 2021-03-31 2021-04-30 0.42 6.547000e+10 3.344000e+09 0.6788 0.6197 0.0622 0.0158 0.1916 0.0667 2021-04-30 2021-08-31 0.80 1.264000e+11 6.432000e+09 0.3375 0.3742 0.1125 0.0287 0.1884 0.0653 2021-08-31 [66 rows x 10 columns] ``` #### Three financial tables ``` >>> BalanceSheet.record_data(code='000338') >>> IncomeStatement.record_data(code='000338') >>> CashFlowStatement.record_data(code='000338') ``` #### And more ``` >>> zvt_context.schemas [zvt.domain.dividend_financing.DividendFinancing, zvt.domain.dividend_financing.DividendDetail, zvt.domain.dividend_financing.SpoDetail...] ``` All schemas is registered in zvt_context.schemas, **schema** is table, data structure. The fields and meaning could be checked in following ways: * help type the schema. and press tab to show its fields or .help() ``` >>> FinanceFactor.help() ``` * source code Schemas defined in [domain](https://github.com/zvtvz/zvt/tree/master/src/zvt/domain) From above examples, you should know the unified way of recording data: > Schema.record_data(provider='your provider',codes='the codes') Note the optional parameter provider, which represents the data provider. A schema can have multiple providers, which is the cornerstone of system stability. Check the provider has been implemented: ``` >>> Stock.provider_map_recorder {'joinquant': zvt.recorders.joinquant.meta.jq_stock_meta_recorder.JqChinaStockRecorder, 'exchange': zvt.recorders.exchange.exchange_stock_meta_recorder.ExchangeStockMetaRecorder, 'em': zvt.recorders.em.meta.em_stock_meta_recorder.EMStockRecorder, 'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder.EastmoneyChinaStockListRecorder} ``` You can use any provider to get the data, the first one is used by default. One more example, the stock sector data recording: ``` >>> Block.provider_map_recorder {'eastmoney': zvt.recorders.eastmoney.meta.eastmoney_block_meta_recorder.EastmoneyChinaBlockRecorder, 'sina': zvt.recorders.sina.meta.sina_block_recorder.SinaBlockRecorder} >>> Block.record_data(provider='sina') Block registered recorders:{'eastmoney': , 'sina': } 2020-03-04 23:56:48,931 INFO MainThread finish record sina blocks:industry 2020-03-04 23:56:49,450 INFO MainThread finish record sina blocks:concept ``` Learn more about record_data * The parameter code[single], codes[multiple] represent the stock codes to be recorded * Recording the whole market if not set code, codes * This method will store the data locally and only do incremental updates Refer to the scheduling recoding way[data runner](https://github.com/zvtvz/zvt/blob/master/examples/data_runner) #### Market-wide stock selection After recording the data of the whole market, you can quickly query the required data locally. An example: the top 20 stocks with roe>8% and revenue growth>8% in the 2018 annual report ``` >>> df=FinanceFactor.query_data(filters=[FinanceFactor.roe>0.08,FinanceFactor.report_period=='year',FinanceFactor.op_income_growth_yoy>0.08],start_timestamp='2019-01-01',order=FinanceFactor.roe.desc(),limit=20,columns=["code"]+FinanceFactor.important_cols(),index='code') code basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp code 000048 000048 2.7350 4.919000e+09 1.101000e+09 0.4311 1.5168 0.7035 0.1988 0.5243 0.2355 2020-04-30 000912 000912 0.3500 4.405000e+09 3.516000e+08 0.1796 1.2363 4.7847 0.0539 0.2175 0.0795 2019-03-20 002207 002207 0.2200 3.021000e+08 5.189000e+07 0.1600 1.1526 1.1175 0.1182 0.1565 0.1718 2020-04-27 002234 002234 5.3300 3.276000e+09 1.610000e+09 0.8023 3.2295 0.8361 0.5469 0.5968 0.4913 2020-04-21 002458 002458 3.7900 3.584000e+09 2.176000e+09 1.4326 4.9973 0.8318 0.6754 0.6537 0.6080 2020-02-20 ... ... ... ... ... ... ... ... ... ... ... ... 600701 600701 -3.6858 7.830000e+08 -3.814000e+09 1.3579 -0.0325 1.9498 -0.7012 0.4173 -4.9293 2020-04-29 600747 600747 -1.5600 3.467000e+08 -2.290000e+09 2.1489 -0.4633 3.1922 -1.5886 0.0378 -6.6093 2020-06-30 600793 600793 1.6568 1.293000e+09 1.745000e+08 0.1164 0.8868 0.7490 0.0486 0.1622 0.1350 2019-04-30 600870 600870 0.0087 3.096000e+07 4.554000e+06 0.7773 1.3702 0.7458 0.0724 0.2688 0.1675 2019-03-30 688169 688169 15.6600 4.205000e+09 7.829000e+08 0.3781 1.5452 0.7172 0.4832 0.3612 0.1862 2020-04-28 [20 rows x 11 columns] ``` So, you should be able to answer the following three questions now: * What data is there? * How to record data? * How to query data? For more advanced usage and extended data, please refer to the data section in the detailed document. ### Write strategy Now we could write strategy basing on TradableEntity and EntityEvent. The so-called strategy backtesting is nothing but repeating the following process： #### At a certain time, find the targets which matching conditions, buy and sell them, and see the performance. Two modes to write strategy: * solo (free style) At a certain time, calculate conditions according to the events, buy and sell * formal The calculation model of the two-dimensional index and multi-entity #### a too simple,sometimes naive person (solo) Well, this strategy is really too simple,sometimes naive, as we do most of the time. > When the report comes out, I look at the report. > If the institution increases its position by more than 5%, I will buy it, and if the institution reduces its position by more than 50%, I will sell it. Show you the code: ``` # -*- coding: utf-8 -*- import pandas as pd from zvt.api import get_recent_report_date from zvt.contract import ActorType, AdjustType from zvt.domain import StockActorSummary, Stock1dKdata from zvt.trader import StockTrader from zvt.utils import pd_is_not_null, is_same_date, to_pd_timestamp class FollowIITrader(StockTrader): finish_date = None def on_time(self, timestamp: pd.Timestamp): recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp)) if self.finish_date and is_same_date(recent_report_date, self.finish_date): return filters = [StockActorSummary.actor_type == ActorType.raised_fund.value, StockActorSummary.report_date == recent_report_date] if self.entity_ids: filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)] df = StockActorSummary.query_data(filters=filters) if pd_is_not_null(df): self.logger.info(f'{df}') self.finish_date = recent_report_date long_df = df[df['change_ratio'] > 0.05] short_df = df[df['change_ratio'] < -0.5] try: self.trade_the_targets(due_timestamp=timestamp, happen_timestamp=timestamp, long_selected=set(long_df['entity_id'].to_list()), short_selected=set(short_df['entity_id'].to_list())) except Exception as e: self.logger.error(e) if __name__ == '__main__': entity_id = 'stock_sh_600519' Stock1dKdata.record_data(entity_id=entity_id, provider='em') StockActorSummary.record_data(entity_id=entity_id, provider='em') FollowIITrader(start_timestamp='2002-01-01', end_timestamp='2021-01-01', entity_ids=[entity_id], provider='em', adjust_type=AdjustType.qfq, profit_threshold=None).run() ``` So, writing a strategy is not that complicated. Just use your imagination, find the relation of the price and the events. Then refresh [http://127.0.0.1:8050/](http://127.0.0.1:8050/)，check the performance of your strategy. More examples is in [Strategy example](https://github.com/zvtvz/zvt/tree/master/examples/trader) #### Be serious (formal) Simple calculation can be done through query_data. Now it's time to introduce the two-dimensional index multi-entity calculation model. Takes technical factors as an example to illustrate the **calculation process**: ``` In [7]: from zvt.factors import * In [8]: factor = BullFactor(codes=['000338','601318'],start_timestamp='2019-01-01',end_timestamp='2019-06-10', transformer=MacdTransformer(count_live_dead=True)) ``` ### data_df **two-dimensional index** DataFrame read from the schema by query_data. ``` In [11]: factor.data_df Out[11]: level high id entity_id open low timestamp close entity_id timestamp stock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54 ... ... ... ... ... ... ... ... ... stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02 [208 rows x 8 columns] ``` ### factor_df **two-dimensional index** DataFrame which calculating using data_df by [transformer](https://github.com/zvtvz/zvt/blob/master/src/zvt/contract/factor.py#L34) e.g., MacdTransformer. ``` In [12]: factor.factor_df Out[12]: level high id entity_id open low timestamp close diff dea macd entity_id timestamp stock_sh_601318 2019-01-02 1d 54.91 stock_sh_601318_2019-01-02 stock_sh_601318 54.78 53.70 2019-01-02 53.94 NaN NaN NaN 2019-01-03 1d 55.06 stock_sh_601318_2019-01-03 stock_sh_601318 53.91 53.82 2019-01-03 54.42 NaN NaN NaN 2019-01-04 1d 55.71 stock_sh_601318_2019-01-04 stock_sh_601318 54.03 53.98 2019-01-04 55.31 NaN NaN NaN 2019-01-07 1d 55.88 stock_sh_601318_2019-01-07 stock_sh_601318 55.80 54.64 2019-01-07 55.03 NaN NaN NaN 2019-01-08 1d 54.83 stock_sh_601318_2019-01-08 stock_sh_601318 54.79 53.96 2019-01-08 54.54 NaN NaN NaN ... ... ... ... ... ... ... ... ... ... ... ... stock_sz_000338 2019-06-03 1d 11.04 stock_sz_000338_2019-06-03 stock_sz_000338 10.93 10.74 2019-06-03 10.81 -0.121336 -0.145444 0.048215 2019-06-04 1d 10.85 stock_sz_000338_2019-06-04 stock_sz_000338 10.84 10.57 2019-06-04 10.73 -0.133829 -0.143121 0.018583 2019-06-05 1d 10.92 stock_sz_000338_2019-06-05 stock_sz_000338 10.87 10.59 2019-06-05 10.59 -0.153260 -0.145149 -0.016223 2019-06-06 1d 10.71 stock_sz_000338_2019-06-06 stock_sz_000338 10.59 10.49 2019-06-06 10.65 -0.161951 -0.148509 -0.026884 2019-06-10 1d 11.05 stock_sz_000338_2019-06-10 stock_sz_000338 10.73 10.71 2019-06-10 11.02 -0.137399 -0.146287 0.017776 [208 rows x 11 columns] ``` ### result_df **two-dimensional index** DataFrame which calculating using factor_df or(and) data_df. It's used by TargetSelector. e.g.,[macd](https://github.com/zvtvz/zvt/blob/master/src/zvt/factors/technical_factor.py#L56) ``` In [14]: factor.result_df Out[14]: filter_result entity_id timestamp stock_sh_601318 2019-01-02 False 2019-01-03 False 2019-01-04 False 2019-01-07 False 2019-01-08 False ... ... stock_sz_000338 2019-06-03 False 2019-06-04 False 2019-06-05 False 2019-06-06 False 2019-06-10 False [208 rows x 1 columns] ``` The format of result_df is as follows:

filter_result is True or False, score_result is from 0 to 1 Combining the stock picker and backtesting, the whole process is as follows:

## Env settings（optional） ``` >>> from zvt import * >>> zvt_env {'zvt_home': '/Users/foolcage/zvt-home', 'data_path': '/Users/foolcage/zvt-home/data', 'tmp_path': '/Users/foolcage/zvt-home/tmp', 'ui_path': '/Users/foolcage/zvt-home/ui', 'log_path': '/Users/foolcage/zvt-home/logs'} >>> zvt_config ``` * jq_username 聚宽数据用户名 * jq_password 聚宽数据密码 * smtp_host 邮件服务器host * smtp_port 邮件服务器端口 * email_username smtp邮箱账户 * email_password smtp邮箱密码 * wechat_app_id * wechat_app_secrect ``` >>> init_config(current_config=zvt_config, jq_username='xxx', jq_password='yyy') ``` > config others this way: init_config(current_config=zvt_config, **kv) ### History data ZVT supports incremental data updates, and sharing historical data among users is encouraged for time-saving efficiency #### Data providers > The new UI's real-time quotes are based on the QMT data source. To obtain access, please contact the author. the data could be updated from different provider, this make the system stable. > add other providers， [Data extension tutorial](https://zvtvz.github.io/zvt/#/data_extending) ## Development ### Clone ``` git clone https://github.com/zvtvz/zvt.git ``` set up virtual env(python>=3.8),install requirements ``` pip3 install -r requirements.txt pip3 install pytest ``` ### Tests ```shell pytest ./tests --ignore=tests/recorders/ ```

Most of the features can be referenced from the tests ## Contribution [code of conduct](https://github.com/zvtvz/zvt/blob/master/code_of_conduct.md) 1. Pass all unit tests, if it is a new feature, please add a new unit test for it 2. Compliance with development specifications 3. If necessary, please update the corresponding document Developers are also very welcome to provide more examples for zvt, and work together to improve the documentation. ## Buy me a coffee Alipay　　　　　 Wechat ## Contact wechat:foolcage Wechat ------ wechat subscription: Wechat zhihu: https://zhuanlan.zhihu.com/automoney ## Thanks

jetbrains

================================================ FILE: api-tests/create_stock_pool_info.http ================================================ POST http://127.0.0.1:8090/api/work/create_stock_pool_info accept: application/json Content-Type: application/json { "stock_pool_name": "核心资产", "stock_pool_type": "custom" } ================================================ FILE: api-tests/create_stock_pools.http ================================================ POST http://127.0.0.1:8090/api/work/create_stock_pools accept: application/json Content-Type: application/json { "stock_pool_name": "核心资产", "entity_ids": [ "stock_sh_600519" ] } ================================================ FILE: api-tests/del_stock_pool.http ================================================ DELETE http://127.0.0.1:8090/api/work/delete_stock_pool?stock_pool_name=今日异动 accept: application/json Content-Type: application/json ================================================ FILE: api-tests/event/create_stock_topic.http ================================================ POST http://127.0.0.1:8090/api/event/create_stock_topic accept: application/json Content-Type: application/json { "name": "特斯拉FSD入华", "desc": "Tesla Al在社交媒体平台“X”上发帖称，特斯拉计划明年第一季度在中国和欧洲推出被其称为“全自动驾驶”(Full Self Driving)的高级驾驶辅助系统，目前正在等待监管部门的批准。", "created_timestamp": "2024-09-05 09:00:00", "trigger_date": "2024-09-05", "due_date": "2025-01-01", "main_tag": "车路云", "sub_tag_list": [ "自动驾驶", "车路云" ] } ================================================ FILE: api-tests/event/get_stock_event.http ================================================ GET http://127.0.0.1:8090/api/event/get_stock_event?entity_id=stock_sz_000034 accept: application/json ================================================ FILE: api-tests/event/get_stock_news_analysis.http ================================================ GET http://127.0.0.1:8090/api/event/get_stock_news_analysis accept: application/json ================================================ FILE: api-tests/event/get_tag_suggestions_stats.http ================================================ GET http://127.0.0.1:8090/api/event/get_tag_suggestions_stats accept: application/json ================================================ FILE: api-tests/event/ignore_stock_news.http ================================================ POST http://127.0.0.1:8090/api/event/ignore_stock_news accept: application/json Content-Type: application/json { "news_id": "stock_sz_000034_2024-07-17 16:08:17" } ================================================ FILE: api-tests/event/query_stock_topic.http ================================================ POST http://127.0.0.1:8090/api/event/query_stock_topic accept: application/json Content-Type: application/json { "limit": 20 } ================================================ FILE: api-tests/event/update_stock_topic.http ================================================ POST http://127.0.0.1:8090/api/event/update_stock_topic accept: application/json Content-Type: application/json { "id": "admin_特斯拉FSD入华", "desc": "Tesla Al在社交媒体平台“X”上发帖称，特斯拉计划明年第一季度在中国和欧洲推出被其称为“全自动驾驶”(Full Self Driving)的高级驾驶辅助系统，目前正在等待监管部门的批准。", "created_timestamp": "2024-09-05 09:00:00", "trigger_date": "2024-09-05", "due_date": "2025-01-01", "main_tag": "车路云", "sub_tag_list": [ "自动驾驶", "车路云" ] } ================================================ FILE: api-tests/factor/get_factors.http ================================================ GET http://127.0.0.1:8090/api/factor/get_factors accept: application/json ================================================ FILE: api-tests/factor/query_factor_result.http ================================================ POST http://127.0.0.1:8090/api/factor/query_factor_result accept: application/json Content-Type: application/json { "entity_ids": [ "stock_sz_300133" ], "factor_name": "LiveOrDeadFactor" } ================================================ FILE: api-tests/get_main_tags_in_stock_pool.http ================================================ GET http://127.0.0.1:8090/api/work/get_main_tags_in_stock_pool?stock_pool_name=大局 accept: application/json ================================================ FILE: api-tests/get_stock_pool_info.http ================================================ GET http://127.0.0.1:8090/api/work/get_stock_pool_info accept: application/json ================================================ FILE: api-tests/get_stock_pools.http ================================================ GET http://127.0.0.1:8090/api/work/get_stock_pools?stock_pool_name=main_line accept: application/json ================================================ FILE: api-tests/tag/batch_set_stock_tags.http ================================================ POST http://127.0.0.1:8090/api/work/batch_set_stock_tags accept: application/json Content-Type: application/json { "tag": "脑机接口", "tag_type": "sub_tag", "entity_ids": [ "stock_sh_600775", "stock_sz_002173", "stock_sz_301293", "stock_sz_300753", "stock_sz_300430", "stock_sz_002243" ], "tag_reason": "脑机接口消息刺激" } ================================================ FILE: api-tests/tag/build_main_tag_industry_relation.http ================================================ POST http://127.0.0.1:8090/api/work/build_main_tag_industry_relation accept: application/json Content-Type: application/json { "main_tag": "商业航天", "industry_list": [ "航天航空" ] } ================================================ FILE: api-tests/tag/build_main_tag_sub_tag_relation.http ================================================ POST http://127.0.0.1:8090/api/work/build_main_tag_sub_tag_relation accept: application/json Content-Type: application/json { "main_tag": "商业航天", "sub_tag_list": [ "航天概念", "天基互联", "北斗导航", "通用航空" ], "activate": true } ================================================ FILE: api-tests/tag/build_stock_tags.http ================================================ POST http://127.0.0.1:8090/api/work/build_stock_tags accept: application/json Content-Type: application/json [ { "entity_id": "stock_sz_002085", "name": "万丰奥威", "main_tag": "低空经济", "main_tag_reason": "2023年12月27日回复称,公司钻石eDA40纯电动飞机已成功首飞;eVTOL项目已联动海外钻石技术开发团队,在绿色、智能、垂直起降等方面的设计体现未来领域应用场景。", "sub_tag": "低空经济", "sub_tag_reason": "2023年12月27日回复称,公司钻石eDA40纯电动飞机已成功首飞;eVTOL项目已联动海外钻石技术开发团队,在绿色、智能、垂直起降等方面的设计体现未来领域应用场景。", "active_hidden_tags": null } ] ================================================ FILE: api-tests/tag/change_main_tag.http ================================================ POST http://127.0.0.1:8090/api/work/change_main_tag accept: application/json Content-Type: application/json { "current_main_tag": "医疗器械", "new_main_tag": "医药" } ================================================ FILE: api-tests/tag/create_hidden_tag_info.http ================================================ POST http://127.0.0.1:8090/api/work/create_hidden_tag_info accept: application/json Content-Type: application/json { "tag": "中字头", "tag_reason": "央企，国资委控股" } ================================================ FILE: api-tests/tag/create_main_tag_info.http ================================================ POST http://127.0.0.1:8090/api/work/create_main_tag_info accept: application/json Content-Type: application/json { "tag": "稳定币", "tag_reason": "数字货币的一种，通常与法定货币挂钩，旨在减少价格波动。" } ================================================ FILE: api-tests/tag/create_sub_tag_info.http ================================================ POST http://127.0.0.1:8090/api/work/create_sub_tag_info accept: application/json Content-Type: application/json { "tag": "低空经济", "tag_reason": "低空经济是飞行器和各种产业形态的融合，例如\"无人机+配送\"、\"直升机或evto载人飞行\"、\"无人机+应急救援\"、\"无人机+工业场景巡检\"等" } ================================================ FILE: api-tests/tag/del_hidden_tag.http ================================================ DELETE http://127.0.0.1:8090/api/work/delete_hidden_tag?tag=妖 accept: application/json ================================================ FILE: api-tests/tag/del_main_tag.http ================================================ DELETE http://127.0.0.1:8090/api/work/delete_main_tag?tag=国企 accept: application/json ================================================ FILE: api-tests/tag/del_sub_tag.http ================================================ DELETE http://127.0.0.1:8090/api/work/delete_sub_tag?tag=天然气 accept: application/json ================================================ FILE: api-tests/tag/get_hidden_tag_info.http ================================================ GET http://127.0.0.1:8090/api/work/get_hidden_tag_info accept: application/json ================================================ FILE: api-tests/tag/get_industry_info.http ================================================ GET http://127.0.0.1:8090/api/work/get_industry_info accept: application/json ================================================ FILE: api-tests/tag/get_main_tag_industry_relation.http ================================================ GET http://127.0.0.1:8090/api/work/get_main_tag_industry_relation?main_tag=AI accept: application/json ================================================ FILE: api-tests/tag/get_main_tag_info.http ================================================ GET http://127.0.0.1:8090/api/work/get_main_tag_info accept: application/json ================================================ FILE: api-tests/tag/get_main_tag_sub_tag_relation.http ================================================ GET http://127.0.0.1:8090/api/work/get_main_tag_sub_tag_relation?main_tag=军工 accept: application/json ================================================ FILE: api-tests/tag/get_stock_tag_options.http ================================================ GET http://127.0.0.1:8090/api/work/get_stock_tag_options?entity_id=stock_sh_600733 accept: application/json ================================================ FILE: api-tests/tag/get_sub_tag_info.http ================================================ GET http://127.0.0.1:8090/api/work/get_sub_tag_info accept: application/json ================================================ FILE: api-tests/tag/query_simple_stock_tags.http ================================================ POST http://127.0.0.1:8090/api/work/query_simple_stock_tags accept: application/json Content-Type: application/json { "entity_ids": [ "stock_sz_002085", "stock_sz_000099", "stock_sz_002130" ] } ================================================ FILE: api-tests/tag/query_stock_tag_stats.http ================================================ POST http://127.0.0.1:8090/api/work/query_stock_tag_stats accept: application/json Content-Type: application/json { "stock_pool_name": "main_line" } ================================================ FILE: api-tests/tag/query_stock_tags.http ================================================ POST http://127.0.0.1:8090/api/work/query_stock_tags accept: application/json Content-Type: application/json { "entity_ids": [ "stock_sz_000099", "stock_sz_002085", "stock_sz_001696" ] } ================================================ FILE: api-tests/tag/set_stock_tags.http ================================================ POST http://127.0.0.1:8090/api/work/set_stock_tags accept: application/json Content-Type: application/json { "entity_id": "stock_sz_001366", "name": "播恩集团", "main_tag": "医药", "main_tag_reason": "合成生物概念", "main_tags": { "农业": "来自行业:农牧饲渔" }, "sub_tag": "生物医药", "sub_tag_reason": "合成生物概念", "sub_tags": null, "active_hidden_tags": null } ================================================ FILE: api-tests/test.http ================================================ ================================================ FILE: api-tests/trading/build_query_stock_quote_setting.http ================================================ POST http://127.0.0.1:8090/api/trading/build_query_stock_quote_setting accept: application/json Content-Type: application/json { "stock_pool_name": "main_line", "main_tags": ["低空经济","新能源"] } ================================================ FILE: api-tests/trading/build_trading_plan.http ================================================ POST http://127.0.0.1:8090/api/trading/build_trading_plan accept: application/json Content-Type: application/json { "stock_id": "stock_sz_300133", "trading_date": "2024-04-23", "expected_open_pct": 0.02, "buy_price": 6.9, "sell_price": null, "trading_reason": "主线", "trading_signal_type": "open_long" } ================================================ FILE: api-tests/trading/get_current_trading_plan.http ================================================ GET http://127.0.0.1:8090/api/trading/get_current_trading_plan accept: application/json ================================================ FILE: api-tests/trading/get_future_trading_plan.http ================================================ GET http://127.0.0.1:8090/api/trading/get_future_trading_plan accept: application/json ================================================ FILE: api-tests/trading/get_query_stock_quote_setting.http ================================================ GET http://127.0.0.1:8090/api/trading/get_query_stock_quote_setting accept: application/json ================================================ FILE: api-tests/trading/get_quote_stats.http ================================================ GET http://127.0.0.1:8090/api/trading/get_quote_stats accept: application/json ================================================ FILE: api-tests/trading/query_kdata.http ================================================ POST http://127.0.0.1:8090/api/trading/query_kdata accept: application/json Content-Type: application/json { "data_provider": "em", "entity_ids": [ "stock_sz_002085", "stock_sz_300133" ], "adjust_type": "hfq" } ================================================ FILE: api-tests/trading/query_stock_quotes.http ================================================ POST http://127.0.0.1:8090/api/trading/query_stock_quotes accept: application/json Content-Type: application/json { "main_tag": "低空经济", "stock_pool_name": "vol_up", "limit": 500, "order_by_field": "change_pct" } ================================================ FILE: api-tests/trading/query_tag_quotes.http ================================================ POST http://127.0.0.1:8090/api/trading/query_tag_quotes accept: application/json Content-Type: application/json { "main_tags": [ "低空经济", "半导体", "化工", "消费电子" ], "stock_pool_name": "main_line" } ================================================ FILE: api-tests/trading/query_trading_plan.http ================================================ POST http://127.0.0.1:8090/api/trading/query_trading_plan accept: application/json Content-Type: application/json { "time_range": { "relative_time_range": { "interval": -30, "time_unit": "day" } } } ================================================ FILE: api-tests/trading/query_ts.http ================================================ POST http://127.0.0.1:8090/api/trading/query_ts accept: application/json Content-Type: application/json { "entity_ids": [ "stock_sz_002085", "stock_sz_300133" ] } ================================================ FILE: build.sh ================================================ python3 setup.py sdist bdist_wheel ================================================ FILE: code_of_conduct.md ================================================ ## 1. 命名 * snake_case适用于 * variable(变量) * package(包) * module(模块) * function(函数) * method(方法) * CamelCase适用于 * class(类) ## 2. 对外接口 各module对外暴露的接口应显式声明于__all__,例子: [contract模块](https://github.com/zvtvz/zvt/blob/master/zvt/contract/__init__.py)以此结尾: ``` __all__ = ['IntervalLevel', 'Mixin', 'NormalMixin', 'EntityMixin', 'NormalEntityMixin', 'zvt_context'] ``` ## 3. 引入接口 不要使用 from zvt.module import *,你要什么就import什么，不要污染。 ## 4. 没了 No api is the best api. Code as comment. ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.https://www.sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: docs/source/_templates/custom-class-template.rst ================================================ {{ objname | escape | underline}} .. currentmodule:: {{ module }} .. autoclass:: {{ objname }} :members: :show-inheritance: :inherited-members: {% block methods %} .. automethod:: __init__ {% endblock %} ================================================ FILE: docs/source/_templates/custom-module-template.rst ================================================ {{ fullname | escape | underline}} .. automodule:: {{ fullname }} {% block attributes %} {% if attributes %} .. rubric:: {{ _('Module Attributes') }} .. autosummary:: :toctree: {% for item in attributes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block functions %} {% if functions %} .. rubric:: {{ _('Functions') }} .. autosummary:: :toctree: {% for item in functions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block classes %} {% if classes %} .. rubric:: {{ _('Classes') }} .. autosummary:: :toctree: :template: custom-class-template.rst {% for item in classes %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block exceptions %} {% if exceptions %} .. rubric:: {{ _('Exceptions') }} .. autosummary:: :toctree: {% for item in exceptions %} {{ item }} {%- endfor %} {% endif %} {% endblock %} {% block modules %} {% if modules %} .. rubric:: Modules .. autosummary:: :toctree: :template: custom-module-template.rst :recursive: {% for item in modules %} {{ item }} {%- endfor %} {% endif %} {% endblock %} ================================================ FILE: docs/source/_templates/sidebarlogo.html ================================================

================================================ FILE: docs/source/api/index.rst ================================================ API === .. autosummary:: :toctree: _autosummary :template: custom-module-template.rst :recursive: zvt zvt.api zvt.autocode zvt.consts zvt.common zvt.contract zvt.domain zvt.factors zvt.fill_project zvt.informer zvt.misc zvt.ml zvt.plugin zvt.recorders zvt.rest zvt.samples zvt.tag zvt.trader zvt.trading zvt.ui zvt.utils ================================================ FILE: docs/source/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. # import os import sys sys.path.insert(0, os.path.abspath("../../src")) # -- Project information ----------------------------------------------------- project = "zvt" copyright = "2026, foolcage" author = "foolcage" # The full version, including alpha/beta/rc tags release = "0.13.5" # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosummary", "IPython.sphinxext.ipython_directive", "IPython.sphinxext.ipython_console_highlighting", ] # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [] # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # html_theme = "alabaster" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] html_sidebars = { "**": ["sidebarlogo.html", "globaltoc.html", "sourcelink.html", "searchbox.html", "localtoc.html", "relations.html"] } autosummary_generate = True autodoc_default_options = { "member-order": "bysource", } ================================================ FILE: docs/source/contributor.rst ================================================ ==================== Contributor ==================== Thanks, my friends. * 隋鹏飞 * `Tiger `_ * 蓝小球 * 青玄 * \*崇 * \*阳 * 张凯 * Rubicon_陈小虾 * `看门大爷 `_ * 芝华 * 雨多田光 * 叶金荣 * 冷冷 * Landy * Sunny * Ccc国毅 * Jim Tong * tom * 小七 * 小强 * Cascara Latte- 刘可烁 ================================================ FILE: docs/source/core_concepts.rst ================================================ ==================== Core concepts ==================== Entity the existence described by itself, classification of existential concepts. EntityEvent the event happened on the entity. TradableEntity the Entity could be traded, e.g, Stock, Future. ActorEntity the Entity acting in market, e.g, Fund, Individual, Government. IntervalLevel repeated fixed time interval, e.g, 5m, 1d. Schema the data structure with fields, one schema could have multiple storage with different Providers. Kdata(Quote) the candlestick data with OHLC. Provider the data provider. Storage the sql database, e.g, sqlite, mysql. Recorder class for recording data for Schema. Factor data describing market.It's computed from from Schema, and save as new Schema if need. TargetSelector the class select targets according to Factor. Trader the backtest engine using TargetSelector, MLMachine or free style. Tagger classify TradableEntity by different dimensions, could be used as ml category feature. MLMachine the ml engine. TradingSignal the signal contains information about how to trade. Drawer the class for draw charts. Intent what do you want to do, e.g. compare, distribute, composite. Normal data the pandas dataframe with multiple index which level 0 is entity_id and level 1 is timestamp: =============== ========== ===== ===== ===== ===== entity_id timestamp col1 col2 col3 col4 =============== ========== ===== ===== ===== ===== stock_sz_000338 2020-05-05 1.2 0.5 0.3 a ... 2020-05-06 1.0 0.7 0.2 b stock_sz_000778 2020-05-05 1.2 0.5 0.3 a ... 2020-05-06 1.0 0.7 0.2 b =============== ========== ===== ===== ===== ===== ================================================ FILE: docs/source/data/adding_new_entity.rst ================================================ .. _adding_new_entity: ================= Adding new entity ================= It's human nature to like the new and hate the old. Adding new TradableEntity is easy in zvt. Adding new entity is nothing than a specific case of :ref:`Adding data `. Let's show the key steps below which add :class:`~.zvt.domain.meta.future_meta.Future`. Define entity Schema -------------------------- :: # -*- coding: utf-8 -*- from sqlalchemy.ext.declarative import declarative_base from zvt.contract.register import register_schema, register_entity from zvt.contract.schema import TradableEntity FutureMetaBase = declarative_base() @register_entity(entity_type="future") class Future(FutureMetaBase, TradableEntity): __tablename__ = "future" register_schema(providers=["em"], db_name="future_meta", schema_base=FutureMetaBase) Implement recorder for the entity --------------------------------- :: from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Future from zvt.recorders.em import em_api class EMFutureRecorder(Recorder): provider = "em" data_schema = Future def run(self): df = em_api.get_tradable_list(entity_type="future") self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) Define OHLC schema(kdata) for the entity ---------------------------------------- zvt provide a standard way to generate OHLC schema for the tradable entity. All `OHLC schemas `_ is generated by `fill project script `_. e.g generate Future OHLC schema. :: gen_kdata_schema( pkg="zvt", providers=["em"], entity_type="future", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True, ) The OHLC schema definition principle is: **one level one file** So we would define a common OHLC schema for each entity type in `quotes module `_. e.g. Future common OHLC schema :: class FutureKdataCommon(KdataCommon): #: 持仓量 interest = Column(Float) #: 结算价 settlement = Column(Float) #: 涨跌幅(按收盘价) # change_pct = Column(Float) #: 涨跌幅(按结算价) change_pct1 = Column(Float) And we could relate the common kdata schema with the recorder and route level to specific schema automatically. Implement recorder for OHLC schema(kdata) ----------------------------------------- Check `em quotes recorder `_ for the details. :: class EMFutureKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Future data_schema = FutureKdataCommon Use them in zvt way ------------------- Fetch the entity list: :: >>> from zvt.domain import Future >>> Future.record_data() >>> df = Future.query_data() >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date 0 future_cffex_IC future_cffex_IC NaT future cffex IC 中证当月连续 NaT None 1 future_cffex_IF future_cffex_IF NaT future cffex IF 沪深当月连续 NaT None 2 future_cffex_IH future_cffex_IH NaT future cffex IH 上证当月连续 NaT None 3 future_cffex_T future_cffex_T NaT future cffex T 十债当季连续 NaT None 4 future_cffex_TF future_cffex_TF NaT future cffex TF 五债当季连续 NaT None .. ... ... ... ... ... ... ... ... ... 65 future_ine_LU future_ine_LU 2020-06-22 future ine LU 低硫燃油主力 2020-06-22 None 66 future_czce_PF future_czce_PF 2020-10-12 future czce PF 短纤主力 2020-10-12 None 67 future_ine_BC future_ine_BC 2020-11-19 future ine BC 国际铜主力 2020-11-19 None 68 future_dce_LH future_dce_LH 2021-01-08 future dce LH 生猪主力 2021-01-08 None 69 future_czce_PK future_czce_PK 2021-02-01 future czce PK 花生主力 2021-02-01 None [70 rows x 9 columns] Fetch the quotes: :: >>> from zvt.domain import Future1dKdata >>> Future1dKdata.record_data(code="CU") >>> df = Future1dKdata.query_data(code="CU") >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate interest settlement change_pct1 0 future_shfe_CU_1996-04-03 future_shfe_CU 1996-04-03 em CU 沪铜主力 1d 22930.0 22840.0 23000.0 22840.0 353.0 0.000000e+00 0.0000 0.0 None None None 1 future_shfe_CU_1996-04-04 future_shfe_CU 1996-04-04 em CU 沪铜主力 1d 22700.0 22750.0 22820.0 22650.0 251.0 0.000000e+00 -0.0039 0.0 None None None 2 future_shfe_CU_1996-04-05 future_shfe_CU 1996-04-05 em CU 沪铜主力 1d 22520.0 22780.0 22820.0 22500.0 298.0 0.000000e+00 0.0013 0.0 None None None 3 future_shfe_CU_1996-04-08 future_shfe_CU 1996-04-08 em CU 沪铜主力 1d 22660.0 22650.0 22680.0 22600.0 98.0 0.000000e+00 -0.0057 0.0 None None None 4 future_shfe_CU_1996-04-09 future_shfe_CU 1996-04-09 em CU 沪铜主力 1d 22830.0 22810.0 22860.0 22810.0 56.0 0.000000e+00 0.0071 0.0 None None None ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 6343 future_shfe_CU_2022-04-21 future_shfe_CU 2022-04-21 em CU 沪铜主力 1d 74140.0 74480.0 74750.0 74140.0 48008.0 1.787678e+10 -0.0004 0.0 None None None 6344 future_shfe_CU_2022-04-22 future_shfe_CU 2022-04-22 em CU 沪铜主力 1d 74800.0 75010.0 75200.0 74690.0 58874.0 2.205633e+10 0.0073 0.0 None None None 6345 future_shfe_CU_2022-04-25 future_shfe_CU 2022-04-25 em CU 沪铜主力 1d 74900.0 73660.0 75190.0 73660.0 107455.0 3.989090e+10 -0.0168 0.0 None None None 6346 future_shfe_CU_2022-04-26 future_shfe_CU 2022-04-26 em CU 沪铜主力 1d 73170.0 73260.0 73750.0 72500.0 113019.0 4.130931e+10 -0.0132 0.0 None None None 6347 future_shfe_CU_2022-04-27 future_shfe_CU 2022-04-27 em CU 沪铜主力 1d 72990.0 73100.0 73560.0 72910.0 61563.0 2.254089e+10 0.0000 0.0 None None None [6348 rows x 18 columns] ================================================ FILE: docs/source/data/data_concepts.rst ================================================ ============= Data concepts ============= .. _data.tradable_entity: TradableEntity ------------------------------ :class:`~.zvt.contract.schema.TradableEntity` is anything could be traded, it could be :class:`~.zvt.domain.meta.stock_meta.Stock`, :class:`~.zvt.domain.meta.etf_meta.Etf`, :class:`~.zvt.domain.meta.index_meta.Index`, future, cryptocurrency, or even a **sports match**. Let's start with the real world tradable entities: China stock and USA stock ——— the world's most involved trading targets. record and query stock: :: >>> from zvt.domain import Stock >>> Stock.record_data() >>> df = Stock.query_data() >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date 0 stock_sh_600651 stock_sh_600651 1986-09-26 stock sh 600651 *ST飞乐 1986-09-26 None 1 stock_sz_000004 stock_sz_000004 1990-12-01 stock sz 000004 国华网安 1990-12-01 None 2 stock_sz_000005 stock_sz_000005 1990-12-10 stock sz 000005 世纪星源 1990-12-10 None 3 stock_sh_600601 stock_sh_600601 1990-12-19 stock sh 600601 方正科技 1990-12-19 None 4 stock_sh_600602 stock_sh_600602 1990-12-19 stock sh 600602 云赛智联 1990-12-19 None ... ... ... ... ... ... ... ... ... ... 4615 stock_sh_603176 stock_sh_603176 2021-12-31 stock sh 603176 汇通集团 2021-12-31 None 4616 stock_sh_600941 stock_sh_600941 2022-01-05 stock sh 600941 中国移动 2022-01-05 None 4617 stock_sh_688262 stock_sh_688262 2022-01-06 stock sh 688262 国芯科技 2022-01-06 None 4618 stock_sh_688176 stock_sh_688176 2022-01-07 stock sh 688176 亚虹医药 2022-01-07 None 4619 stock_sz_301159 stock_sz_301159 2022-01-07 stock sz 301159 N三维 2022-01-07 None [4620 rows x 9 columns] record and query stockus: :: >>> from zvt.domain import Stockus >>> Stockus.record_data() >>> df = Stockus.query_data() >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date 0 stockus_nasdaq_CMII stockus_nasdaq_CMII None stockus nasdaq CMII CM Life Sciences II Inc-A None None 1 stockus_nasdaq_CBLI stockus_nasdaq_CBLI None stockus nasdaq CBLI Cytocom Inc None None 2 stockus_nyse_XPOw stockus_nyse_XPOw None stockus nyse XPOw XPO Logistics Inc WI None None 3 stockus_nyse_WRI stockus_nyse_WRI None stockus nyse WRI 魏因加滕房地产投资 None None 4 stockus_nyse_PROS stockus_nyse_PROS None stockus nyse PROS ProSight Global Inc None None ... ... ... ... ... ... ... ... ... ... 6345 stockus_nyse_FATH stockus_nyse_FATH None stockus nyse FATH Fathom Digital Manufacturing Co None None 6346 stockus_nyse_LOCL stockus_nyse_LOCL None stockus nyse LOCL Local Bounti Corp None None 6347 stockus_nyse_VLD stockus_nyse_VLD None stockus nyse VLD Velo3D Inc None None 6348 stockus_nyse_AHI stockus_nyse_AHI None stockus nyse AHI Advanced Human Imaging Ltd ADR None None 6349 stockus_nyse_HLGN stockus_nyse_HLGN None stockus nyse HLGN Heliogen Inc None None | what about other tradable entities? | Show current registered tradable entity type and its schema: :: >>> from zvt.contract import zvt_context >>> zvt_context.tradable_schema_map {'stockus': zvt.domain.meta.stockus_meta.Stockus, 'stockhk': zvt.domain.meta.stockhk_meta.Stockhk, 'index': zvt.domain.meta.index_meta.Index, 'etf': zvt.domain.meta.etf_meta.Etf, 'stock': zvt.domain.meta.stock_meta.Stock, 'block': zvt.domain.meta.block_meta.Block, 'fund': zvt.domain.meta.fund_meta.Fund} The key is **entity_type** and the value is its :ref:`Schema`. From intuition, stockhk should be stock of hongkong: :: >>> from zvt.domain import Stockhk >>> Stockhk.record_data() >>> df = Stockhk.query_data(index='code') >>> print(df) id entity_id timestamp entity_type exchange code name list_date end_date code 00001 stockhk_hk_00001 stockhk_hk_00001 NaT stockhk hk 00001 长和 None None 00002 stockhk_hk_00002 stockhk_hk_00002 NaT stockhk hk 00002 中电控股 None None 00003 stockhk_hk_00003 stockhk_hk_00003 NaT stockhk hk 00003 香港中华煤气 None None 00004 stockhk_hk_00004 stockhk_hk_00004 NaT stockhk hk 00004 九龙仓集团 None None 00005 stockhk_hk_00005 stockhk_hk_00005 NaT stockhk hk 00005 汇丰控股 None None ... ... ... ... ... ... ... ... ... ... 09996 stockhk_hk_09996 stockhk_hk_09996 NaT stockhk hk 09996 沛嘉医疗-B None None 09997 stockhk_hk_09997 stockhk_hk_09997 NaT stockhk hk 09997 康基医疗 None None 09998 stockhk_hk_09998 stockhk_hk_09998 NaT stockhk hk 09998 光荣控股 None None 09999 stockhk_hk_09999 stockhk_hk_09999 NaT stockhk hk 09999 网易-S None None 80737 stockhk_hk_80737 stockhk_hk_80737 NaT stockhk hk 80737 湾区发展-R None None [2597 rows x 9 columns] >>> df[df.code=='00700'] id entity_id timestamp entity_type exchange code name list_date end_date 2112 stockhk_hk_00700 stockhk_hk_00700 None stockhk hk 00700 腾讯控股 None None From intuition, other tradable entities could be added to the system and used in the same way. Just follow :ref:`Add tradable entity ` .. _data.actor_entity: ActorEntity ------------------------------ :class:`~.zvt.contract.schema.ActorEntity` is the beings acting in the market, it could be government, company, fund or individual. :: >>> from zvt.domain import StockInstitutionalInvestorHolder >>> entity_ids = ["stock_sz_000338", "stock_sz_000001"] >>> StockInstitutionalInvestorHolder.record_data(entity_ids=entity_ids) >>> df = StockInstitutionalInvestorHolder.query_data(entity_ids=entity_ids) >>> print(df) id entity_id timestamp code name actor_id actor_type actor_code actor_name report_period report_date holding_numbers holding_ratio holding_values 0 stock_sz_000001_1998-06-30_raised_fund_cn_184688 stock_sz_000001 1998-06-30 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 half_year 1998-06-30 1.896697e+06 0.001771 3.269906e+07 1 stock_sz_000001_1998-09-30_raised_fund_cn_184688 stock_sz_000001 1998-09-30 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 season3 1998-09-30 2.634093e+06 0.002460 4.151331e+07 2 stock_sz_000001_1998-12-31_raised_fund_cn_184688 stock_sz_000001 1998-12-31 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 year 1998-12-31 2.673900e+06 0.002497 3.992133e+07 3 stock_sz_000001_1999-03-31_raised_fund_cn_184688 stock_sz_000001 1999-03-31 000001 平安银行 raised_fund_cn_184688 raised_fund 184688 基金开元 season1 1999-03-31 2.378977e+06 0.002221 3.256820e+07 4 stock_sz_000001_1999-06-30_raised_fund_cn_500005 stock_sz_000001 1999-06-30 000001 平安银行 raised_fund_cn_500005 raised_fund 500005 基金汉盛 half_year 1999-06-30 4.989611e+06 0.004659 1.386613e+08 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 22463 stock_sz_000338_2021-09-30_broker_cn_71067063 stock_sz_000338 2021-09-30 000338 潍柴动力 broker_cn_71067063 broker 71067063 东方红信和添安4号 season3 2021-09-30 5.000000e+04 0.000012 8.580000e+05 22464 stock_sz_000338_2021-09-30_corporation_cn_1003... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10030838 corporation 10030838 潍柴控股集团有限公司 season3 2021-09-30 1.422551e+09 0.018071 2.441097e+10 22465 stock_sz_000338_2021-09-30_corporation_cn_1067... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10671586 corporation 10671586 香港中央结算有限公司 season3 2021-09-30 4.992710e+08 0.117713 8.567490e+09 22466 stock_sz_000338_2021-09-30_corporation_cn_1019... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10196008 corporation 10196008 中国证券金融股份有限公司 season3 2021-09-30 1.636089e+08 0.038574 2.807529e+09 22467 stock_sz_000338_2021-09-30_corporation_cn_1008... stock_sz_000338 2021-09-30 000338 潍柴动力 corporation_cn_10086358 corporation 10086358 奥地利IVM技术咨询维也纳有限公司 season3 2021-09-30 1.139387e+08 0.026863 1.955188e+09 [22468 rows x 14 columns] .. note:: A good actor should know the good or bad actors in history, more importantly, the mind behind them. .. _data.schema: Schema ------------------------------ Data structure describing :class:`~.zvt.contract.schema.TradableEntity`, :class:`~.zvt.contract.schema.ActorEntity` or events happen on them. Physically it's table with columns in sql database. One schema could have multiple storage with different providers. .. _data.schema_usage: From specific to general, all zvt schema usage is in the same way. * from zvt.domain import {Schema} * {Schema}.record_data * {Schema}.query_data Explore :py:mod:`~.zvt.domain` for pre defined schemas. And check :ref:`record_data & query_data details ` :: >>> from zvt.domain import * >>> entity_ids = ["stock_sz_000338", "stock_sz_000001"] >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider="em") >>> df = Stock1dHfqKdata.query_data(entity_ids=entity_ids, provider="em") >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000001_1991-04-03 stock_sz_000001 1991-04-03 em 000001 平安银行 1d 49.00 49.00 49.00 49.00 1.0 5.000000e+03 0.2250 0.0000 1 stock_sz_000001_1991-04-04 stock_sz_000001 1991-04-04 em 000001 平安银行 1d 48.76 48.76 48.76 48.76 3.0 1.500000e+04 -0.0049 0.0000 2 stock_sz_000001_1991-04-05 stock_sz_000001 1991-04-05 em 000001 平安银行 1d 48.52 48.52 48.52 48.52 2.0 1.000000e+04 -0.0049 0.0000 3 stock_sz_000001_1991-04-06 stock_sz_000001 1991-04-06 em 000001 平安银行 1d 48.28 48.28 48.28 48.28 7.0 3.400000e+04 -0.0049 0.0000 4 stock_sz_000001_1991-04-08 stock_sz_000001 1991-04-08 em 000001 平安银行 1d 48.04 48.04 48.04 48.04 2.0 1.000000e+04 -0.0050 0.0000 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 10859 stock_sz_000338_2022-01-10 stock_sz_000338 2022-01-10 em 000338 潍柴动力 1d 314.38 314.38 320.37 312.69 956271.0 1.735153e+09 0.0149 0.0190 10860 stock_sz_000001_2022-01-11 stock_sz_000001 2022-01-11 em 000001 平安银行 1d 2974.07 2998.45 3019.58 2954.57 1581999.0 2.752485e+09 0.0121 0.0082 10861 stock_sz_000338_2022-01-11 stock_sz_000338 2022-01-11 em 000338 潍柴动力 1d 312.69 307.01 314.23 306.70 812187.0 1.444640e+09 -0.0234 0.0161 10862 stock_sz_000001_2022-01-12 stock_sz_000001 2022-01-12 em 000001 平安银行 1d 2998.45 2931.82 3004.95 2915.56 1502164.0 2.561266e+09 -0.0222 0.0077 10863 stock_sz_000338_2022-01-12 stock_sz_000338 2022-01-12 em 000338 潍柴动力 1d 307.01 305.78 309.62 302.86 882165.0 1.542044e+09 -0.0040 0.0175 [10864 rows x 15 columns] The data of the schema is recorded in local database by default and could be updated incrementally. Find them in this way: :: {Schema}.get_storages() e.g. :: >>> Stock1dHfqKdata.get_storages() [Engine(sqlite:////Users/foolcage/zvt-home/data/joinquant_stock_1d_hfq_kdata.db?check_same_thread=False), Engine(sqlite:////Users/foolcage/zvt-home/data/em_stock_1d_hfq_kdata.db?check_same_thread=False)] IntervalLevel ------------------------------ :class:`~.zvt.contract.IntervalLevel` is repeated fixed time interval, e.g, 5m, 1d. It's used in OHLC data for describing time window. :: >>> from zvt.contract import * >>> for level in IntervalLevel: >>> print(level.value) tick 1m 5m 15m 30m 1h 4h 1d 1wk 1mon Kdata(Quote, OHLC) ------------------------------ the candlestick data with OHLC. the :class:`~.zvt.contract.schema.TradableEntity` quote schema name follows below rules: :: {entity_shema}{level}{adjust_type}Kdata * entity_schema TradableEntity class，e.g. Stock,Stockus. * level IntervalLevel value, e.g. 1d,1wk. * adjust type pre adjusted(qfq), post adjusted(hfq), or not adjusted(bfq). :: >>> for adjust_type in AdjustType: >>> print(adjust_type.value) .. note:: In order to be compatible with historical data, the qfq is an exception, {adjust_type} is left empty The pre defined kdata schema could be found in :py:mod:`~.zvt.domain.quotes`, it's separated by entity_schema, level, and adjust type. e.g. Stock1dHfqKdata means China Stock daily hfq quotes. :: >>> from zvt.domain import Stock1dHfqKdata >>> Stock1dHfqKdata.record_data(code='000338', provider='em') >>> df = Stock1dHfqKdata.query_data(code='000338', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000338_2007-04-30 stock_sz_000338 2007-04-30 None 000338 潍柴动力 1d 70.00 64.93 71.00 62.88 207375.0 1.365189e+09 2.1720 0.1182 1 stock_sz_000338_2007-05-08 stock_sz_000338 2007-05-08 None 000338 潍柴动力 1d 66.60 64.00 68.00 62.88 86299.0 5.563198e+08 -0.0143 0.0492 2 stock_sz_000338_2007-05-09 stock_sz_000338 2007-05-09 None 000338 潍柴动力 1d 63.32 62.00 63.88 59.60 93823.0 5.782065e+08 -0.0313 0.0535 3 stock_sz_000338_2007-05-10 stock_sz_000338 2007-05-10 None 000338 潍柴动力 1d 61.50 62.49 64.48 61.01 47720.0 2.999226e+08 0.0079 0.0272 4 stock_sz_000338_2007-05-11 stock_sz_000338 2007-05-11 None 000338 潍柴动力 1d 61.90 60.65 61.90 59.70 39273.0 2.373126e+08 -0.0294 0.0224 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3426 stock_sz_000338_2021-08-27 stock_sz_000338 2021-08-27 None 000338 潍柴动力 1d 331.97 345.95 345.95 329.82 1688497.0 3.370241e+09 0.0540 0.0398 3427 stock_sz_000338_2021-08-30 stock_sz_000338 2021-08-30 None 000338 潍柴动力 1d 345.95 342.72 346.10 337.96 1187601.0 2.377957e+09 -0.0093 0.0280 3428 stock_sz_000338_2021-08-31 stock_sz_000338 2021-08-31 None 000338 潍柴动力 1d 344.41 342.41 351.02 336.73 1143985.0 2.295195e+09 -0.0009 0.0270 3429 stock_sz_000338_2021-09-01 stock_sz_000338 2021-09-01 None 000338 潍柴动力 1d 341.03 336.42 341.03 328.28 1218697.0 2.383841e+09 -0.0175 0.0287 3430 stock_sz_000338_2021-09-02 stock_sz_000338 2021-09-02 None 000338 潍柴动力 1d 336.88 339.03 340.88 329.67 1023545.0 2.012006e+09 0.0078 0.0241 [3431 rows x 15 columns] e.g. Stock30mHfqKdata means China Stock 30 minutes hfq quotes. :: >>> from zvt.domain import Stock30mHfqKdata >>> Stock30mHfqKdata.record_data(code='000338', provider='em') >>> df = Stock30mHfqKdata.query_data(code='000338', provider='em') >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate 0 stock_sz_000338_2022-01-07T10:00:00.000 stock_sz_000338 2022-01-07 10:00:00 em 000338 潍柴动力 30m 312.23 313.77 317.30 310.70 288036.0 521397671.0 0.0049 0.0057 1 stock_sz_000338_2022-01-07T10:30:00.000 stock_sz_000338 2022-01-07 10:30:00 em 000338 潍柴动力 30m 313.92 313.77 315.15 312.54 111887.0 201667653.0 0.0000 0.0022 2 stock_sz_000338_2022-01-07T11:00:00.000 stock_sz_000338 2022-01-07 11:00:00 em 000338 潍柴动力 30m 313.77 314.07 314.69 313.00 80072.0 144303962.0 0.0010 0.0016 3 stock_sz_000338_2022-01-07T11:30:00.000 stock_sz_000338 2022-01-07 11:30:00 em 000338 潍柴动力 30m 314.23 316.23 316.84 313.61 160797.0 291742498.0 0.0069 0.0032 4 stock_sz_000338_2022-01-07T13:30:00.000 stock_sz_000338 2022-01-07 13:30:00 em 000338 潍柴动力 30m 316.23 314.07 316.99 314.07 115775.0 210236422.0 -0.0068 0.0023 .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 251 stock_sz_000338_2022-02-28T11:30:00.000 stock_sz_000338 2022-02-28 11:30:00 em 000338 潍柴动力 30m 268.15 268.30 268.61 267.99 34581.0 52053276.0 0.0006 0.0007 252 stock_sz_000338_2022-02-28T13:30:00.000 stock_sz_000338 2022-02-28 13:30:00 em 000338 潍柴动力 30m 268.46 268.46 268.61 268.15 38019.0 57268380.0 0.0006 0.0008 253 stock_sz_000338_2022-02-28T14:00:00.000 stock_sz_000338 2022-02-28 14:00:00 em 000338 潍柴动力 30m 268.46 269.22 269.53 268.30 41713.0 62994140.0 0.0028 0.0008 254 stock_sz_000338_2022-02-28T14:30:00.000 stock_sz_000338 2022-02-28 14:30:00 em 000338 潍柴动力 30m 269.22 269.22 269.53 268.61 40815.0 61676966.0 0.0000 0.0008 255 stock_sz_000338_2022-02-28T15:00:00.000 stock_sz_000338 2022-02-28 15:00:00 em 000338 潍柴动力 30m 269.07 269.84 269.84 268.76 60190.0 91032952.0 0.0023 0.0012 [256 rows x 15 columns] FinanceFactor ------------------------------ The usage is same as other entity events. :: >>> from zvt.domain import FinanceFactor >>> FinanceFactor.record_data(code='000338') >>> FinanceFactor.query_data(code='000338',columns=FinanceFactor.important_cols(),index='timestamp') basic_eps total_op_income net_profit op_income_growth_yoy net_profit_growth_yoy roe rota gross_profit_margin net_margin timestamp timestamp 2002-12-31 NaN 1.962000e+07 2.471000e+06 NaN NaN NaN NaN 0.2068 0.1259 2002-12-31 2003-12-31 1.27 3.574000e+09 2.739000e+08 181.2022 109.8778 0.7729 0.1783 0.2551 0.0766 2003-12-31 2004-12-31 1.75 6.188000e+09 5.369000e+08 0.7313 0.9598 0.3245 0.1474 0.2489 0.0868 2004-12-31 2005-12-31 0.93 5.283000e+09 3.065000e+08 -0.1463 -0.4291 0.1327 0.0603 0.2252 0.0583 2005-12-31 2006-03-31 0.33 1.859000e+09 1.079000e+08 NaN NaN NaN NaN NaN 0.0598 2006-03-31 ... ... ... ... ... ... ... ... ... ... ... 2020-08-28 0.59 9.449000e+10 4.680000e+09 0.0400 -0.1148 0.0983 0.0229 0.1958 0.0603 2020-08-28 2020-10-31 0.90 1.474000e+11 7.106000e+09 0.1632 0.0067 0.1502 0.0347 0.1949 0.0590 2020-10-31 2021-03-31 1.16 1.975000e+11 9.207000e+09 0.1327 0.0112 0.1919 0.0444 0.1931 0.0571 2021-03-31 2021-04-30 0.42 6.547000e+10 3.344000e+09 0.6788 0.6197 0.0622 0.0158 0.1916 0.0667 2021-04-30 2021-08-31 0.80 1.264000e+11 6.432000e+09 0.3375 0.3742 0.1125 0.0287 0.1884 0.0653 2021-08-31 [66 rows x 10 columns] Three financial tables :: >>> BalanceSheet.record_data(code='000338') >>> IncomeStatement.record_data(code='000338') >>> CashFlowStatement.record_data(code='000338') .. note:: Just remember, all :ref:`schema usage ` is in the same way. ================================================ FILE: docs/source/data/extending_data.rst ================================================ .. _extending_data: ============== Extend data ============== Data structure -------------------------- zvt use sql database(default sqlite, and easy extend to others) to store data and provide uniform api to record and query data.(Thanks to the great `Sqlalchemy `_) As we know, :ref:`TradableEntity` and :ref:`ActorEntity` is everything about the market, data is the events happen on them. The Schema of the data would always in following structure: .. image:: ../_static/data_structure.png It shows how we define the Entity in the market: * entity_type tradable entity type, e.g. stock, future, bond and so on. * exchange the exchange of entity * code the code of the entity Naturally the id of the entity is defined: :: {entity_type}_{exchange}_{code} And the common fields: * entity_id entity id * timestamp happen time * id the id of the record, most of time, it's: :: {entity_id}_{timestamp} Data definition -------------------------- zvt data is defined by three concepts: * Database The rule is one module define one db. * Schema The Schema defined in the module belongs to the Database defined inside. * Provider The Database could have different providers Many pre supported data is in `zvt.domain modules `_, e.g. `Finance data `_, you could refer them when you want to extend data. .. _extending_data.add_data: Key steps to add data -------------------------- Let's show the key steps to add data. 1. Intent ~~~~~~~~~~~~~~~~~~~~ I want to add some data about the news on Stock. 2. Find data source ~~~~~~~~~~~~~~~~~~~~ The data source could be whatever you can see in the web ——— just write some web crawlers to get them, e.g, most of `pre-implemented recorders `_. And you could use data from commercial data provider in the same way, e.g. `joinquant recorders `_. Let's show the crawler way here: Open `eastmoney wap `_ and Find `news api `_ The news item result: :: { "Art_ShowTime": "2022-02-09 14:12:30", "Art_Image": "", "Art_MediaName": "每日经济新闻", "Art_Code": "202202092271136373", "Art_Title": "首创证券维持索菲亚买入评级 公司简评报告：多重因素影响短期压制业绩 看好2022年利润修复", "Art_SortStart": "1644387150036373", "Art_VideoCount": 0, "Art_OriginUrl": "http://finance.eastmoney.com/news/1354,202202092271136373.html", "Art_Url": "http://finance.eastmoney.com/a/202202092271136373.html" } 3. Define Schema ~~~~~~~~~~~~~~~~~~~~ No matter what the format of the data outside zvt is, we use zvt simple and uniform contract to define them. :: >>> from sqlalchemy import Column, String >>> from sqlalchemy.orm import declarative_base >>> from zvt.contract import Mixin >>> from zvt.contract.register import register_schema >>> NewsBase = declarative_base() >>> class StockNews(NewsBase, Mixin): >>> __tablename__ = "stock_news" >>> #: news title >>> news_title = Column(String) >>> >>> register_schema(providers=["em"], db_name="stock_news", schema_base=NewsBase, entity_type="stock") Check the defined db :: >>> StockNews.get_storages() [Engine(sqlite:////Users/foolcage/zvt-home/data/em_stock_news.db?check_same_thread=False)] As you see, the db file name format is: :: {provider}_{db_name} If you have another provider, e.g. sina, just register it too: :: >>> register_schema(providers=["em", "sina"], db_name="stock_news", schema_base=NewsBase, entity_type="stock") And you could find another db file: :: >>> StockNews.get_storages() [Engine(sqlite:////Users/foolcage/zvt-home/data/sina_stock_news.db?check_same_thread=False)] The advantage of this mechanism is: * schema is the way you want * provider could be switched seamlessly 4. Implement recorder ~~~~~~~~~~~~~~~~~~~~~ Let's implement a recorder for StockNews. :: import pandas as pd from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Stock from zvt.domain.misc.stock_news import StockNews from zvt.recorders.em import em_api class EMStockNewsRecorder(FixedCycleDataRecorder): original_page_url = "https://wap.eastmoney.com/quote/stock/0.002572.html" url = "https://np-listapi.eastmoney.com/comm/wap/getListInfo?cb=callback&client=wap&type=1&mTypeAndCode=0.002572&pageSize=200&pageIndex={}&callback=jQuery1830017478247906740352_1644568731256&_=1644568879493" # the entity class you record for entity_schema = Stock # connect schema with recorder data_schema = StockNews # entity data provider entity_provider = "em" # data schema provider provider = "em" def record(self, entity, start, end, size, timestamps): news = em_api.get_news(entity_id=entity.id) df = pd.DataFrame.from_records(news) self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) After that, you could use the data in zvt way: :: >>> StockNews.record_data(provider="em", entity_id="stock_sz_002572") >>> df = StockNews.query_data(entity_id="stock_sz_002572") >>> print(df) id entity_id timestamp news_title 0 stock_sz_002572_2019-05-20 19:50:42 stock_sz_002572 2019-05-20 19:50:42 索菲亚（002572.SZ）：股价回撤超65% 是低吸机会吗? 1 stock_sz_002572_2019-05-24 22:39:47 stock_sz_002572 2019-05-24 22:39:47 索菲亚拟发行不超过5亿元超短期融资券 2 stock_sz_002572_2019-05-24 22:48:54 stock_sz_002572 2019-05-24 22:48:54 索菲亚(002572.SZ)拟终止发行不超10亿元的可转换公司债券 3 stock_sz_002572_2019-06-04 14:06:42 stock_sz_002572 2019-06-04 14:06:42 索菲亚(002572.SZ)截至5月底已累计回购2.02亿元的股份 4 stock_sz_002572_2019-06-13 06:24:44 stock_sz_002572 2019-06-13 06:24:44 索菲亚功臣王飚能否扶起汉森中国 .. ... ... ... ... 367 stock_sz_002572_2022-02-07 14:04:40 stock_sz_002572 2022-02-07 14:04:40 开源证券维持索菲亚买入评级 近期获6份券商研报关注 目标均价涨幅59.82% 368 stock_sz_002572_2022-02-07 15:32:46 stock_sz_002572 2022-02-07 15:32:46 【调研快报】索菲亚接待机构投资者调研 369 stock_sz_002572_2022-02-08 16:50:37 stock_sz_002572 2022-02-08 16:50:37 索菲亚：公司承接了容东片区安置房项目 370 stock_sz_002572_2022-02-08 21:59:00 stock_sz_002572 2022-02-08 21:59:00 9亿坏账拖累净利下降九成 家具巨头索菲亚“甩包袱”起跑 腰斩的股价能否趁势抬头？ 371 stock_sz_002572_2022-02-09 14:12:30 stock_sz_002572 2022-02-09 14:12:30 首创证券维持索菲亚买入评级 公司简评报告：多重因素影响短期压制业绩 看好2022年利润修复 [372 rows x 4 columns] .. _extending_data.tradable_entity: Add new TradableEntity -------------------------- It's human nature to like the new and hate the old. Adding new TradableEntity is easy in zvt. And from a higher perspective, trading is everywhere. you make trading everytime when you make the decision. So you could treat Country as TradableEntity and make trading when making decision where to live or invest. Let's show the key steps to add new TradableEntity. 1. Define entity Schema ~~~~~~~~~~~~~~~~~~~~~~~ :: # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract.schema import TradableEntity from zvt.contract.register import register_schema, register_entity CountryMetaBase = declarative_base() @register_entity(entity_type="country") class Country(CountryMetaBase, TradableEntity): __tablename__ = "country" #: 区域 #: region region = Column(String(length=128)) #: 首都 #: capital city capital_city = Column(String(length=128)) #: 收入水平 #: income level income_level = Column(String(length=64)) #: 贷款类型 #: lending type lending_type = Column(String(length=64)) #: 经度 #: longitude longitude = Column(Float) #: 纬度 #: latitude latitude = Column(Float) register_schema(providers=["wb"], db_name="country_meta", schema_base=CountryMetaBase) entity_type, exchange and code define the entity, for country, it's in following way: :: entity_type = "country" exchange = "galaxy" code = "iso code" e.g. country_galaxy_CN = China, country_galaxy_US = United States of America 2. Implement recorder for the entity ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ :: from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.country_meta import Country from zvt.recorders.wb import wb_api class WBCountryRecorder(Recorder): provider = "wb" data_schema = Country def run(self): df = wb_api.get_countries() df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) 3. Define schema for the entity ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ e.g treasury yield of the country :: # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema CurrencyBase = declarative_base() class TreasuryYield(CurrencyBase, Mixin): __tablename__ = "treasury_yield" code = Column(String(length=32)) # 2年期 yield_2 = Column(Float) # 5年期 yield_5 = Column(Float) # 10年期 yield_10 = Column(Float) # 30年期 yield_30 = Column(Float) register_schema(providers=["em"], db_name="currency", schema_base=CurrencyBase) And the `recorder `_ for the schema 4. Use them in zvt way ~~~~~~~~~~~~~~~~~~~~~~ Find the rich country: :: >>> from zvt.domain import Country >>> Country.record_data() >>> df = Country.query_data() >>> df[df['income_level']=='High income'] id entity_id timestamp entity_type exchange code name list_date end_date region capital_city income_level lending_type longitude latitude 0 country_galaxy_AW country_galaxy_AW None country galaxy AW Aruba None None Latin America & Caribbean Oranjestad High income Not classified -70.016700 12.516700 7 country_galaxy_AD country_galaxy_AD None country galaxy AD Andorra None None Europe & Central Asia Andorra la Vella High income Not classified 1.521800 42.507500 9 country_galaxy_AE country_galaxy_AE None country galaxy AE United Arab Emirates None None Middle East & North Africa Abu Dhabi High income Not classified 54.370500 24.476400 13 country_galaxy_AG country_galaxy_AG None country galaxy AG Antigua and Barbuda None None Latin America & Caribbean Saint John's High income IBRD -61.845600 17.117500 14 country_galaxy_AU country_galaxy_AU None country galaxy AU Australia None None East Asia & Pacific Canberra High income Not classified 149.129000 -35.282000 .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 277 country_galaxy_TW country_galaxy_TW None country galaxy TW Taiwan, China None None East Asia & Pacific High income Not classified NaN NaN 282 country_galaxy_UY country_galaxy_UY None country galaxy UY Uruguay None None Latin America & Caribbean Montevideo High income IBRD -56.067500 -34.894100 283 country_galaxy_US country_galaxy_US None country galaxy US United States None None North America Washington D.C. High income Not classified -77.032000 38.889500 287 country_galaxy_VG country_galaxy_VG None country galaxy VG British Virgin Islands None None Latin America & Caribbean Road Town High income Not classified -64.623056 18.431389 288 country_galaxy_VI country_galaxy_VI None country galaxy VI Virgin Islands (U.S.) None None Latin America & Caribbean Charlotte Amalie High income Not classified -64.896300 18.335800 [80 rows x 15 columns] Compare treasury yields of different maturities: :: >>> from zvt.domain import TreasuryYield >>> from zvt.api.intent import compare >>> TreasuryYield.record_data() >>> compare(codes=["US"], schema=TreasuryYield, columns=["yield_2", "yield_10", "yield_30"]) .. image:: ../_static/compare_yields.png ================================================ FILE: docs/source/data/index.rst ================================================ ========== Data ========== | Without data, quant becomes a castle in the air. | So, what exactly is data? | zvt makes a concise and unified abstraction of data: In the world of zvt, there are two kinds of entities, one is :ref:`tradable entity `, the other is :ref:`actor entity `. Data is the events happened on them. .. image:: ../_static/view.png .. note:: Philosophically, entity is the existence described by itself, classification of existential concepts. .. toctree:: :maxdepth: 2 data_concepts record_and_query extending_data adding_new_entity trading_anything ================================================ FILE: docs/source/data/record_and_query.rst ================================================ .. _record_and_query: ============== Record & query ============== Dualism -------------------------- In and out, write and read, and so record and query. Record data -------------------------- Query data -------------------------- ================================================ FILE: docs/source/data/trading_anything.rst ================================================ .. trading_anything: ======================= Capital without country ======================= From a higher perspective, trading is everywhere. you make trading everytime when you make the decision. So you could treat Country as TradableEntity and make trading when making decision where to live or invest. It's nothing than a specific case of :ref:`Adding new entity `. Let's show the key steps below. Define entity Schema -------------------------- :: # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract.schema import TradableEntity from zvt.contract.register import register_schema, register_entity CountryMetaBase = declarative_base() @register_entity(entity_type="country") class Country(CountryMetaBase, TradableEntity): __tablename__ = "country" #: 区域 #: region region = Column(String(length=128)) #: 首都 #: capital city capital_city = Column(String(length=128)) #: 收入水平 #: income level income_level = Column(String(length=64)) #: 贷款类型 #: lending type lending_type = Column(String(length=64)) #: 经度 #: longitude longitude = Column(Float) #: 纬度 #: latitude latitude = Column(Float) register_schema(providers=["wb"], db_name="country_meta", schema_base=CountryMetaBase) entity_type, exchange and code define the entity, for country, it's in following way: :: entity_type = "country" exchange = "galaxy" code = "iso code" e.g. country_galaxy_CN = China, country_galaxy_US = United States of America Implement recorder for the entity --------------------------------- :: from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.country_meta import Country from zvt.recorders.wb import wb_api class WBCountryRecorder(Recorder): provider = "wb" data_schema = Country def run(self): df = wb_api.get_countries() df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) Define schema for the entity ---------------------------- e.g treasury yield of the country :: # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema MonetaryBase = declarative_base() class TreasuryYield(MonetaryBase, Mixin): __tablename__ = "treasury_yield" code = Column(String(length=32)) # 2年期 yield_2 = Column(Float) # 5年期 yield_5 = Column(Float) # 10年期 yield_10 = Column(Float) # 30年期 yield_30 = Column(Float) register_schema(providers=["em"], db_name="monetary", schema_base=MonetaryBase) # the __all__ is generated __all__ = ["TreasuryYield"] And the `recorder `_ for the schema Use them in zvt way ------------------- Find the rich country: :: >>> from zvt.domain import Country >>> Country.record_data() >>> df = Country.query_data() >>> df[df['income_level']=='High income'] id entity_id timestamp entity_type exchange code name list_date end_date region capital_city income_level lending_type longitude latitude 0 country_galaxy_AW country_galaxy_AW None country galaxy AW Aruba None None Latin America & Caribbean Oranjestad High income Not classified -70.016700 12.516700 7 country_galaxy_AD country_galaxy_AD None country galaxy AD Andorra None None Europe & Central Asia Andorra la Vella High income Not classified 1.521800 42.507500 9 country_galaxy_AE country_galaxy_AE None country galaxy AE United Arab Emirates None None Middle East & North Africa Abu Dhabi High income Not classified 54.370500 24.476400 13 country_galaxy_AG country_galaxy_AG None country galaxy AG Antigua and Barbuda None None Latin America & Caribbean Saint John's High income IBRD -61.845600 17.117500 14 country_galaxy_AU country_galaxy_AU None country galaxy AU Australia None None East Asia & Pacific Canberra High income Not classified 149.129000 -35.282000 .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 277 country_galaxy_TW country_galaxy_TW None country galaxy TW Taiwan, China None None East Asia & Pacific High income Not classified NaN NaN 282 country_galaxy_UY country_galaxy_UY None country galaxy UY Uruguay None None Latin America & Caribbean Montevideo High income IBRD -56.067500 -34.894100 283 country_galaxy_US country_galaxy_US None country galaxy US United States None None North America Washington D.C. High income Not classified -77.032000 38.889500 287 country_galaxy_VG country_galaxy_VG None country galaxy VG British Virgin Islands None None Latin America & Caribbean Road Town High income Not classified -64.623056 18.431389 288 country_galaxy_VI country_galaxy_VI None country galaxy VI Virgin Islands (U.S.) None None Latin America & Caribbean Charlotte Amalie High income Not classified -64.896300 18.335800 [80 rows x 15 columns] Compare treasury yields of different maturities: :: >>> from zvt.domain import TreasuryYield >>> from zvt.api.intent import compare >>> TreasuryYield.record_data() >>> compare(codes=["US"], schema=TreasuryYield, columns=["yield_2", "yield_10", "yield_30"]) .. image:: ../_static/compare_yields.png ================================================ FILE: docs/source/drawer/drawer_concepts.rst ================================================ =============== Drawer concepts =============== Intent ------------------------------ There are many kinds of charts, but the intent is much less. zvt classify intent as compare, distribute and composite. We could analyze the entity itself or look at it in the whole market by comparing with others. :ref:`NormalData` uses different structure to express different intents. Compare ------------------------------ Based on zvt's world view and data model, you could compare anything you care about. Think about that you want to compare S&P 500 index with Shanghai index. It's easy and comfortable with no pain. :: >>> from zvt.api.intent import compare >>> from zvt.domain import Indexus1dKdata, Index, Indexus, Index1dKdata >>> Index.record_data(provider="em") >>> Indexus.record_data(provider="em") >>> Index1dKdata.record_data(entity_id="index_sh_000001", provider="em") >>> Indexus1dKdata.record_data(entity_id="indexus_us_SPX", provider="em") >>> compare(entity_ids=["index_sh_000001", "indexus_us_SPX"], start_timestamp="2000-01-01", scale_value=100) .. image:: ../_static/compare_cn_us.png Try some other style: :: >>> compare(entity_ids=["index_sh_000001", "indexus_us_SPX"], start_timestamp="2000-01-01", schema_map_columns={Index1dKdata:["close"],Indexus1dKdata:["close"]}) ================================================ FILE: docs/source/drawer/index.rst ================================================ ========== Drawer ========== | A picture is worth a thousand words. | So, what exactly is behind the picture? | it's drawer in zvt: Drawer is who can draw the picture with NormalData by Intent. .. toctree:: :maxdepth: 2 drawer_concepts ================================================ FILE: docs/source/factor/extending_factor.rst ================================================ .. _factor.extending_factor: ================ Extending Factor ================ Rethink NormalData -------------------------- Normal data format is as below: .. image:: ../_static/normal_data.png | Why use this format? | The reason is that human is comfortable for two-dimensional space and high-dimensional space could be reduced to it. Obviously, It's complete and consistent. You could **calculate oneself in time intervals** or **calculate with others in specific time or intervals**. And it's easy to analyze the data with charts by Intent. Factor data structure -------------------------- The factor computing flow is as below: .. image:: ../_static/factor_flow.png * data_df NormalData df read from the schema. * factor_df NormalData df computed by data_df using use :class:`~.zvt.contract.factor.Transformer`, :class:`~.zvt.contract.factor.Accumulator` or your custom logic. * result_df NormalData df containing columns **filter_result** and(or) **score_result** which calculated using factor_df or(and) data_df. Filter_result is True or False, score_result is from 0 to 1. You could use TargetSelector to select targets at specific time when filter_result is True and(or) score_result >=0.8 by default or do more precise control by setting other arguments. Let's take BullFactor to illustrate the calculation process: :: >>> from zvt.factors.macd import BullFactor >>> from zvt.domain import Stock1dHfqKdata >>> entity_ids = ["stock_sh_601318", "stock_sz_000338"] >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider="em") >>> factor = BullFactor(entity_ids=entity_ids, provider="em", start_timestamp='2019-01-01', end_timestamp='2019-06-10') check the dfs: :: >>> factor.data_df >>> factor.factor_df >>> factor.result_df .. _factor.write_transformer: Write transformer -------------------------- Transformer works as bellow: .. image:: ../_static/transformer.png What's in your mind is the NormalData format, and then practice the skills to manipulate it. You could use other ta lib with it easily, e.g Bollinger Bands using `TA lib `_ install ta at first: :: pip install --upgrade ta write boll transformer and factor: :: from typing import Optional, List import pandas as pd from ta.volatility import BollingerBands from zvt.contract.factor import * from zvt.factors import TechnicalFactor class BollTransformer(Transformer): def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame: indicator_bb = BollingerBands(close=df["close"], window=20, window_dev=2) # Add Bollinger Bands features df["bb_bbm"] = indicator_bb.bollinger_mavg() df["bb_bbh"] = indicator_bb.bollinger_hband() df["bb_bbl"] = indicator_bb.bollinger_lband() # Add Bollinger Band high indicator df["bb_bbhi"] = indicator_bb.bollinger_hband_indicator() # Add Bollinger Band low indicator df["bb_bbli"] = indicator_bb.bollinger_lband_indicator() # Add Width Size Bollinger Bands df["bb_bbw"] = indicator_bb.bollinger_wband() # Add Percentage Bollinger Bands df["bb_bbp"] = indicator_bb.bollinger_pband() return df class BollFactor(TechnicalFactor): transformer = BollTransformer() def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: # set the factor to show return [self.factor_df[["bb_bbm", "bb_bbh", "bb_bbl"]]] def compute_result(self): # set filter_result, which bb_bbli=1.0 buy and bb_bbhi=1.0 sell super().compute_result() self.result_df = (self.factor_df["bb_bbli"] - self.factor_df["bb_bbhi"]).to_frame(name="filter_result") self.result_df[self.result_df == 0] = None self.result_df[self.result_df == 1] = True self.result_df[self.result_df == -1] = False Let's show it: :: >>> from zvt.domain import Stock1dHfqKdata >>> provider = "em" >>> entity_ids = ["stock_sz_000338", "stock_sh_601318"] >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=provider,) >>> factor = BollFactor( >>> entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp="2019-01-01" >>> ) >>> factor.draw(show=True) .. image:: ../_static/boll_factor.png Most of ta lib support calculate single target by default, so we implement transform_one of the Transformer. If you want to calculate many targets at the same time you could implement transform directly and it would be faster. And Transformer is stateless, so it's easy to reuse in different factor if need. Factor with IntervalLevel -------------------------- After you write the transformer and construct the factor, it's easy to use in different levels. Let's use BollFactor in IntervalLevel 30m: :: >>> from zvt.domain import Stock30mHfqKdata >>> provider = "em" >>> entity_ids = ["stock_sz_000338", "stock_sh_601318"] >>> Stock30mHfqKdata.record_data(entity_ids=entity_ids, provider=provider) >>> factor = BollFactor( entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp="2021-01-01" ) >>> factor.draw(show=True) Stream computing -------------------------- The data is coming continuously and the factor using the data need computing continuously too. It's simple and straightforward: * {Schema}.record_data in one process * {Factor}.move_on which call {Schema}.query_data in another process .. image:: ../_static/stream.png We keep the simple enough philosophy: single process and thread. Enjoy programming and make everything clear. Factor persistence -------------------------- Getting data and computing factor continuously is cool. But...If It took a long time to calculate the factor and crashed.How would you feel? .. image:: ../_static/bear.gif :align: center Select the targets -------------------------- You could select the targets according result_df of the factor by yourself. Or use TargetSelector do it for you. .. image:: ../_static/factor_result.png Write accumulator -------------------------- .. image:: ../_static/accumulator.png ================================================ FILE: docs/source/factor/factor_concepts.rst ================================================ .. _factor.factor_concepts: =============== Factor concepts =============== .. _factor.normal_data: Normal data ------------------------------ :class:`~.zvt.contract.normal_data.NormalData` is the data containing pandas dataframe with multiple index which level 0 named entity_id and level 1 named timestamp: =============== ========== ===== ===== ===== ===== entity_id timestamp col1 col2 col3 col4 =============== ========== ===== ===== ===== ===== stock_sz_000338 2020-05-05 1.2 0.5 0.3 a ... 2020-05-06 1.0 0.7 0.2 b stock_sz_000778 2020-05-05 1.2 0.5 0.3 a ... 2020-05-06 1.0 0.7 0.2 b =============== ========== ===== ===== ===== ===== This data structure is used heavily in zvt computing, you should be familiar with it. `Pandas multiple index guide `_ is a good start. Query data returning normal data in this way: :: {Schema}.query_data(index=["entity_id, timestamp"]) e.g. :: >>> from zvt.domain import * >>> entity_ids = ["stock_sz_000338", "stock_sz_000001"] >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider="em") >>> df = Stock1dHfqKdata.query_data(entity_ids=entity_ids, provider="em", index=["entity_id", "timestamp"]) >>> print(df) id entity_id timestamp provider code name level open close high low volume turnover change_pct turnover_rate entity_id timestamp stock_sz_000001 1991-04-03 stock_sz_000001_1991-04-03 stock_sz_000001 1991-04-03 em 000001 平安银行 1d 49.00 49.00 49.00 49.00 1.0 5.000000e+03 0.2250 0.0000 1991-04-04 stock_sz_000001_1991-04-04 stock_sz_000001 1991-04-04 em 000001 平安银行 1d 48.76 48.76 48.76 48.76 3.0 1.500000e+04 -0.0049 0.0000 1991-04-05 stock_sz_000001_1991-04-05 stock_sz_000001 1991-04-05 em 000001 平安银行 1d 48.52 48.52 48.52 48.52 2.0 1.000000e+04 -0.0049 0.0000 1991-04-06 stock_sz_000001_1991-04-06 stock_sz_000001 1991-04-06 em 000001 平安银行 1d 48.28 48.28 48.28 48.28 7.0 3.400000e+04 -0.0049 0.0000 1991-04-08 stock_sz_000001_1991-04-08 stock_sz_000001 1991-04-08 em 000001 平安银行 1d 48.04 48.04 48.04 48.04 2.0 1.000000e+04 -0.0050 0.0000 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... stock_sz_000338 2022-01-17 stock_sz_000338_2022-01-17 stock_sz_000338 2022-01-17 em 000338 潍柴动力 1d 296.26 297.64 298.71 293.49 504866.0 8.546921e+08 0.0026 0.0100 2022-01-18 stock_sz_000338_2022-01-18 stock_sz_000338 2022-01-18 em 000338 潍柴动力 1d 298.10 300.87 302.71 296.10 622455.0 1.064735e+09 0.0109 0.0124 2022-01-19 stock_sz_000338_2022-01-19 stock_sz_000338 2022-01-19 em 000338 潍柴动力 1d 299.64 299.48 304.24 298.56 610096.0 1.049195e+09 -0.0046 0.0121 2022-01-20 stock_sz_000338_2022-01-20 stock_sz_000338 2022-01-20 em 000338 潍柴动力 1d 298.10 294.87 299.18 290.11 812949.0 1.361764e+09 -0.0154 0.0161 2022-01-21 stock_sz_000338_2022-01-21 stock_sz_000338 2022-01-21 em 000338 潍柴动力 1d 292.72 287.04 293.34 284.58 754156.0 1.234360e+09 -0.0266 0.0150 [10878 rows x 15 columns] .. _factor.factor: Factor ------------------------------ :class:`~.zvt.contract.factor.Factor` is a computing facility to build *factor* according your mind ——— algorithm. It reads data from schema, use :class:`~.zvt.contract.factor.Transformer`, :class:`~.zvt.contract.factor.Accumulator` or your custom logic to compute, and save the result to new schema if need. It also provides a standard way to evaluate the targets which could be used by :class:`~.zvt.factors.target_selector.TargetSelector` and :class:`~.zvt.trader.trader.Trader` for backtesting or real trading. Transformer ------------------------------ Computing factor which depends on input data only. Here is an example: :class:`~.zvt.factors.algorithm.MaTransformer` Accumulator ------------------------------ Computing factor which depends on input data and previous result of the factor. Here is an example: :class:`~.zvt.factors.ma.ma_stats_factor.MaStatsAccumulator.` Let's have a look by example: :: >>> from zvt.factors import GoldCrossFactor >>> from zvt.domain import Stock1dHfqKdata >>> entity_ids = ["stock_sz_000338"] >>> Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider="em") >>> factor = GoldCrossFactor(entity_ids=entity_ids, provider="em", start_timestamp="2018-01-01") >>> print(factor.factor_df) >>> print(factor.result_df) >>> factor.draw(show=True) level turnover high id open low entity_id timestamp close turnover_rate volume diff dea macd live bull live_count entity_id timestamp stock_sz_000338 2018-01-02 1d 8.325588e+08 145.97 stock_sz_000338_2018-01-02 141.21 141.06 stock_sz_000338 2018-01-02 145.67 0.0225 972471.0 NaN NaN NaN -1 False -1 2018-01-03 1d 7.530370e+08 147.66 stock_sz_000338_2018-01-03 146.13 144.29 stock_sz_000338 2018-01-03 144.44 0.0202 870225.0 NaN NaN NaN -1 False -2 2018-01-04 1d 4.917067e+08 145.51 stock_sz_000338_2018-01-04 144.75 143.67 stock_sz_000338 2018-01-04 145.21 0.0133 574335.0 NaN NaN NaN -1 False -3 2018-01-05 1d 5.282211e+08 146.59 stock_sz_000338_2018-01-05 146.44 143.21 stock_sz_000338 2018-01-05 143.21 0.0143 616244.0 NaN NaN NaN -1 False -4 2018-01-08 1d 1.255871e+09 150.43 stock_sz_000338_2018-01-08 143.82 143.82 stock_sz_000338 2018-01-08 150.12 0.0331 1426567.0 NaN NaN NaN -1 False -5 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 2022-01-17 1d 8.546921e+08 298.71 stock_sz_000338_2022-01-17 296.26 293.49 stock_sz_000338 2022-01-17 297.64 0.0100 504866.0 -1.386687 1.781615 -6.336603 -1 False -12 2022-01-18 1d 1.064735e+09 302.71 stock_sz_000338_2022-01-18 298.10 296.10 stock_sz_000338 2022-01-18 300.87 0.0124 622455.0 -1.694421 1.086407 -5.561657 -1 False -13 2022-01-19 1d 1.049195e+09 304.24 stock_sz_000338_2022-01-19 299.64 298.56 stock_sz_000338 2022-01-19 299.48 0.0121 610096.0 -2.027097 0.463707 -4.981607 -1 False -14 2022-01-20 1d 1.361764e+09 299.18 stock_sz_000338_2022-01-20 298.10 290.11 stock_sz_000338 2022-01-20 294.87 0.0161 812949.0 -2.632389 -0.155513 -4.953753 -1 False -15 2022-01-21 1d 1.234360e+09 293.34 stock_sz_000338_2022-01-21 292.72 284.58 stock_sz_000338 2022-01-21 287.04 0.0150 754156.0 -3.701237 -0.864657 -5.673159 -1 False -16 [987 rows x 17 columns] filter_result entity_id timestamp stock_sz_000338 2018-01-02 False 2018-01-03 False 2018-01-04 False 2018-01-05 False 2018-01-08 False ... ... 2022-01-17 False 2022-01-18 False 2022-01-19 False 2022-01-20 False 2022-01-21 False [987 rows x 1 columns] .. image:: ../_static/factor_draw.png Follow :ref:`Extending factor ` to do the funny part. TargetSelector ------------------------------ The class select targets according to Factors. You could calculate factors in the whole market and use selector to choose the targets. :: from zvt.contract import IntervalLevel from zvt.factors.target_selector import TargetSelector from zvt.factors.ma.ma_factor import CrossMaFactor entity_ids = ["stock_sz_000338"] entity_type = "stock" start_timestamp = "2018-01-01" end_timestamp = "2019-06-30" my_selector = TargetSelector( entity_ids=entity_ids, entity_schema=entity_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp ) # add the factors my_selector.add_factor( CrossMaFactor( entity_provider="em", provider="em", entity_ids=entity_ids, start_timestamp=start_timestamp, end_timestamp=end_timestamp, computing_window=10, windows=[5, 10], need_persist=False, level=IntervalLevel.LEVEL_1DAY, adjust_type="hfq", ) ) my_selector.run() print(my_selector.open_long_df) print(my_selector.open_short_df) my_selector.get_open_long_targets("2019-06-27") If not set entity_ids arguments, the selected targets would be in whole market. And it provides get_open_long_targets function to select targets on the timestamp. For multiple targets backtesting, this pre computed factor would be very fast. ================================================ FILE: docs/source/factor/index.rst ================================================ ========== Factor ========== | Without factor, the heart of quant is empty. | So, what exactly is factor? | zvt makes a concise and unified abstraction of factor: In the world of zvt, factor is data describing market. It's computed from from Schema, and save as new Schema if need. .. toctree:: :maxdepth: 2 factor_concepts extending_factor ================================================ FILE: docs/source/index.rst ================================================ Welcome to zvt ============== Contents -------- .. toctree:: :maxdepth: 2 install intro data/index factor/index trader/index ml/index drawer/index contributor api/index Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` ================================================ FILE: docs/source/install.rst ================================================ .. _install: Installation of zvt ======================== This part of the documentation covers the installation of zvt. The first step to using any software package is getting it properly installed. Python version support ---------------------- Officially Python 3.7, and 3.8. $ python -m pip install -U zvt -------------------------------- To install zvt, simply run this simple command in your terminal of choice:: $ python -m pip install -U zvt Get the Source Code ------------------- zvt is actively developed on GitHub, where the code is `always available `_. You can clone the public repository:: $ git clone git://github.com/zvtvz/zvt.git Once you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily:: $ cd zvt $ python -m pip install . ================================================ FILE: docs/source/intro.rst ================================================ ========== Intro ========== This is a short introduction to zvt, you would learn the basic usage and glance a global view here. Rethink market and programming ------------------------------ For practical trading, complex algorithm is fragile, complex algorithm building on complex facility is more fragile, complex algorithm building on complex facility by complex team is more and more fragile. zvt want to provide a simple facility for building straightforward algorithm, it should be: * **use the most basic programming concepts** * **concise abstraction of the market** * **correctness is obvious** Core concepts building zvt ------------------------------ | Technologies come and technologies go, but market insight is forever. | Your world is built by core concepts inside you, so it's you. | zvt world is built by core concepts inside market, so it's zvt. | We would show how core concepts building zvt in four aspects： .. toctree:: :maxdepth: 2 data/index factor/index trader/index ml/index ================================================ FILE: docs/source/ml/index.rst ================================================ ==================== Machine learning ==================== | In today's world, you're not fashion without machine learning. | So, what could zvt do with ML? | Being a fashion-feeling person, zvt love ML in this way: zvt provide a simple facility to define X and y. you could adapt any sophisticated ML library to zvt. Tagger ------------------------------ :class:`~.zvt.tag.tag.Tagger` is a computing facility for classifying TradableEntity by different dimensions as tag which could be used as ml category feature. People are divided into groups, things are grouped together. .. image:: ../_static/tag.png The most important part for trading is which category is suitable to trade now. ML could help you find which category perform better in different cases ——— deterministic factor. The tag could be **priori** or calculated from characteristics of the entity ——— some factor. MLMachine ------------------------------ the ml engine. ================================================ FILE: docs/source/trader/index.rst ================================================ ========== Trader ========== | What we want in the end is to trade and make money. | So, what exactly is Trader? Trader is a simple facility helping you backtesting and generating trading signals using Factor, TargetSelector, MLMachine or you custom free style algorithm. .. toctree:: :maxdepth: 2 trader_concepts extending_trader ================================================ FILE: docs/source/trader/trader_concepts.rst ================================================ ==================== Trader ==================== Trader ------------------------------ the backtest engine using TargetSelector, MLMachine or free style. TradingSignal ------------------------------ the signal contains information about how to trade. ================================================ FILE: docs/source/usage.rst ================================================ ==================== Usage ==================== Here show some use cases of zvt. Data ------------------------------ Factor ------------------------------ Backtesting ------------------------------ Machine learning ------------------------------ Draw by intent ------------------------------ Informer ------------------------------ ================================================ FILE: docs/storage_config.md ================================================ # Storage 配置说明 (Phase 2) 在 `config.json`（或 `zvt_home/config.json`）中可添加 `storage` 配置块，用于自定义存储路径和路由。 ## 配置结构 ```json { "storage": { "base_path": null, "path_template": null, "storage_routes": {}, "schema_providers": {} } } ``` ### 字段说明 | 字段 | 类型 | 说明 | |------|------|------| | `base_path` | string \| null | 数据根路径。`null` 时使用 `zvt_env["data_path"]`（即 `zvt_home/data`） | | `path_template` | string \| null | 路径模板。占位符：`{base_path}`, `{provider}`, `{db_name}`, `{storage_id}`。`null` 时使用默认规则 | | `storage_routes` | object | 路由覆盖。键格式 `"provider\|db_name"`，值为自定义 `storage_id` | | `schema_providers` | object | 无 Recorder 的 schema 的 provider 映射。键为 `db_name`，值为 `["provider"]` 数组。用于 fallback 或 internal schema | ### 默认行为（不配置时） - `storage_id = "{provider}_{db_name}"`（如 `em_stock_meta`） - 路径：`{data_path}/{provider}/{provider}_{db_name}.db` ### 示例 **自定义 base_path：** ```json { "storage": { "base_path": "/custom/data/path" } } ``` **自定义 path_template（扁平目录）：** ```json { "storage": { "path_template": "{base_path}/{storage_id}.db" } } ``` **自定义 storage_routes（某 provider 使用不同 storage_id）：** ```json { "storage": { "storage_routes": { "qmt|stock_1d_kdata": "qmt_realtime_stock_1d" } } } ``` ## Internal Schema（内部 schema） 业务逻辑类数据（如 `zvt_info`、`trader_info`、`stock_tags`）与外部数据源无关，通过 `register_schema(..., internal=True)` 标记为 internal。Internal schema 只做存储路由，不参与 provider 切换。其 provider（如 `zvt`）在 `schema_providers` 中配置，用于确定存储路径。 ================================================ FILE: examples/README.MD ================================================ The examples are updating with master source code. ================================================ FILE: examples/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/data_runner/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/data_runner/actor_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.domain import ( StockInstitutionalInvestorHolder, StockTopTenFreeHolder, StockActorSummary, ) from zvt.utils.recorder_utils import run_data_recorder logger = logging.getLogger(__name__) sched = BackgroundScheduler() @sched.scheduled_job("cron", hour=1, minute=00, day_of_week=2) def record_actor_data(data_provider="em", entity_provider="em"): run_data_recorder( domain=StockInstitutionalInvestorHolder, data_provider=data_provider, entity_provider=entity_provider, day_data=True, ) run_data_recorder( domain=StockTopTenFreeHolder, data_provider=data_provider, entity_provider=entity_provider, day_data=True ) run_data_recorder( domain=StockActorSummary, data_provider=data_provider, entity_provider=entity_provider, day_data=True ) if __name__ == "__main__": init_log("actor_runner.log") record_actor_data() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/finance_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.domain import ( Stock, StockDetail, FinanceFactor, BalanceSheet, IncomeStatement, CashFlowStatement, ) from zvt.utils.recorder_utils import run_data_recorder logger = logging.getLogger(__name__) sched = BackgroundScheduler() @sched.scheduled_job("cron", hour=1, minute=00, day_of_week=5) def record_actor_data(data_provider="eastmoney", entity_provider="eastmoney"): run_data_recorder(domain=Stock, data_provider=data_provider) run_data_recorder(domain=StockDetail, data_provider=data_provider) run_data_recorder(domain=FinanceFactor, data_provider=data_provider, entity_provider=entity_provider, day_data=True) run_data_recorder(domain=BalanceSheet, data_provider=data_provider, entity_provider=entity_provider, day_data=True) run_data_recorder( domain=IncomeStatement, data_provider=data_provider, entity_provider=entity_provider, day_data=True ) run_data_recorder( domain=CashFlowStatement, data_provider=data_provider, entity_provider=entity_provider, day_data=True ) if __name__ == "__main__": init_log("finance_runner.log") record_actor_data() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/index_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.consts import IMPORTANT_INDEX from zvt.domain import Index, Index1dKdata, IndexStock from zvt.utils.recorder_utils import run_data_recorder logger = logging.getLogger(__name__) sched = BackgroundScheduler() # 自行更改定定时运行时间 @sched.scheduled_job("cron", hour=1, minute=00, day_of_week=5) def record_index(): run_data_recorder(domain=Index, data_provider="exchange") # 默认只抓取 国证1000 国证2000 国证成长 国证价值 的组成个股 index_ids = ["index_sz_399311", "index_sz_399303", "index_sz_399370", "index_sz_399371"] run_data_recorder(domain=IndexStock, data_provider="exchange", entity_provider="exchange", entity_ids=index_ids) @sched.scheduled_job("cron", hour=16, minute=20) def record_index_kdata(): run_data_recorder(domain=Index, data_provider="em") run_data_recorder( domain=Index1dKdata, data_provider="em", entity_provider="em", codes=IMPORTANT_INDEX, day_data=True ) if __name__ == "__main__": init_log("index_runner.log") record_index() record_index_kdata() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/joinquant_fund_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.domain import Fund, FundStock, StockValuation from zvt.utils.recorder_utils import run_data_recorder logger = logging.getLogger(__name__) sched = BackgroundScheduler() # 周6抓取 @sched.scheduled_job("cron", hour=10, minute=00, day_of_week=5) def record_fund_data(data_provider="joinquant", entity_provider="joinquant"): # 基金 run_data_recorder(domain=Fund, data_provider=data_provider, sleeping_time=0) # 基金持仓 run_data_recorder(domain=FundStock, data_provider=data_provider, entity_provider=entity_provider, sleeping_time=0) # 个股估值 run_data_recorder( domain=StockValuation, data_provider=data_provider, entity_provider=entity_provider, sleeping_time=0, day_data=True, ) if __name__ == "__main__": init_log("joinquant_fund_runner.log") record_fund_data() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/joinquant_kdata_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt.domain import StockTradeDay logger = logging.getLogger(__name__) sched = BackgroundScheduler() # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt.utils.recorder_utils import run_data_recorder from zvt import init_log from zvt.domain import Stock, Stock1dHfqKdata logger = logging.getLogger(__name__) sched = BackgroundScheduler() @sched.scheduled_job("cron", hour=15, minute=30) def record_stock_data(data_provider="joinquant", entity_provider="joinquant"): # A股标的 run_data_recorder(domain=Stock, data_provider=data_provider, force_update=False) # 交易日 run_data_recorder(domain=StockTradeDay, data_provider=data_provider) # A股后复权行情 run_data_recorder( domain=Stock1dHfqKdata, data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=0, ) if __name__ == "__main__": init_log("joinquant_kdata_runner.log") record_stock_data() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/kdata_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from examples.report_utils import inform from examples.utils import get_hot_topics from zvt import init_log, zvt_config from zvt.api.selector import get_entity_ids_by_filter from zvt.domain import ( Stock, Stock1dHfqKdata, Stockhk, Stockhk1dHfqKdata, Block, Block1dKdata, BlockCategory, Index, Index1dKdata, StockNews, LimitUpInfo, ) from zvt.informer import EmailInformer from zvt.utils.time_utils import current_date from zvt.utils.recorder_utils import run_data_recorder logger = logging.getLogger(__name__) sched = BackgroundScheduler() @sched.scheduled_job("cron", hour=16, minute=30, day_of_week="mon-fri") def record_stock_news(data_provider="em"): normal_stock_ids = get_entity_ids_by_filter( provider="em", ignore_delist=True, ignore_st=False, ignore_new_stock=False ) run_data_recorder( entity_ids=normal_stock_ids, day_data=True, domain=StockNews, data_provider=data_provider, force_update=False, sleeping_time=2, ) def report_limit_up(): latest_data = LimitUpInfo.query_data(order=LimitUpInfo.timestamp.desc(), limit=1, return_type="domain") timestamp = latest_data[0].timestamp df = LimitUpInfo.query_data(start_timestamp=timestamp, end_timestamp=timestamp, columns=["code", "name", "reason"]) df["reason"] = df["reason"].str.split("+") print(df) EmailInformer().send_message(zvt_config["email_username"], f"{timestamp} 热门报告", f"{df}") def report_hot_topics(): topics_long = get_hot_topics(days_ago=20) topics_short = get_hot_topics(days_ago=5) set1 = set(topics_long.keys()) set2 = set(topics_short.keys()) same = set1 & set2 print(same) old_topics = set1 - set2 print(old_topics) new_topics = set2 - set1 print(new_topics) msg = f""" 一直热门:{same} ---:{old_topics} +++:{new_topics} 长期统计:{topics_long} 短期统计:{topics_short} """ print(msg) EmailInformer().send_message(zvt_config["email_username"], f"{current_date()} 热门报告", msg) @sched.scheduled_job("cron", hour=15, minute=30, day_of_week="mon-fri") def record_stock_data(data_provider="em", entity_provider="em", sleeping_time=0): email_action = EmailInformer() # 涨停数据 run_data_recorder(domain=LimitUpInfo, data_provider=None, force_update=False) report_limit_up() # A股指数 run_data_recorder(domain=Index, data_provider=data_provider, force_update=False) # A股指数行情 run_data_recorder( domain=Index1dKdata, data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=sleeping_time, ) # 板块(概念，行业) run_data_recorder(domain=Block, entity_provider=entity_provider, data_provider=entity_provider, force_update=False) # 板块行情(概念，行业) run_data_recorder( domain=Block1dKdata, entity_provider=entity_provider, data_provider=entity_provider, day_data=True, sleeping_time=sleeping_time, ) # run_data_recorder( # domain=BlockStock, # entity_provider=entity_provider, # data_provider=entity_provider, # sleeping_time=sleeping_time, # ) # 报告新概念和行业 df = Block.query_data( filters=[Block.category == BlockCategory.concept.value], order=Block.list_date.desc(), index="entity_id", limit=7, ) inform( action=email_action, entity_ids=df.index.tolist(), target_date=current_date(), title="report 新概念", entity_provider=entity_provider, entity_type="block", em_group=None, em_group_over_write=False, ) # A股标的 run_data_recorder(domain=Stock, data_provider=data_provider, force_update=False) # A股后复权行情 normal_stock_ids = get_entity_ids_by_filter( provider="em", ignore_delist=True, ignore_st=False, ignore_new_stock=False ) run_data_recorder( entity_ids=normal_stock_ids, domain=Stock1dHfqKdata, data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=sleeping_time, return_unfinished=True, ) @sched.scheduled_job("cron", hour=16, minute=30, day_of_week="mon-fri") def record_stockhk_data(data_provider="em", entity_provider="em", sleeping_time=2): # 港股标的 run_data_recorder(domain=Stockhk, data_provider=data_provider, force_update=False) # 港股后复权行情 df = Stockhk.query_data(filters=[Stockhk.south == True], index="entity_id") run_data_recorder( domain=Stockhk1dHfqKdata, entity_ids=df.index.tolist(), data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=sleeping_time, ) if __name__ == "__main__": init_log("kdata_runner.log") record_stock_data() record_stockhk_data() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/sina_data_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.domain import * from zvt.utils.recorder_utils import run_data_recorder logger = logging.getLogger(__name__) sched = BackgroundScheduler() @sched.scheduled_job("cron", hour=15, minute=30, day_of_week=3) def record_block(): run_data_recorder(domain=Block, data_provider="sina") run_data_recorder(domain=Block, data_provider="sina", entity_provider="sina") @sched.scheduled_job("cron", hour=15, minute=30) def record_money_flow(): run_data_recorder(domain=BlockMoneyFlow, data_provider="sina", entity_provider="sina", day_data=True) if __name__ == "__main__": init_log("sina_data_runner.log") record_block() record_money_flow() sched.start() sched._thread.join() ================================================ FILE: examples/data_runner/trading_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from sqlalchemy import or_, and_ from examples.report_utils import inform from zvt import init_log from zvt.api.kdata import get_latest_kdata_date from zvt.api.selector import get_big_players from zvt.domain import ( DragonAndTiger, Stock1dHfqKdata, ) from zvt.informer import EmailInformer from zvt.utils.recorder_utils import run_data_recorder from zvt.utils.time_utils import date_time_by_interval, current_date, to_pd_timestamp logger = logging.getLogger(__name__) sched = BackgroundScheduler() @sched.scheduled_job("cron", hour=18, minute=00, day_of_week="mon-fri") def record_dragon_tiger(data_provider="em", entity_provider="em", sleeping_time=2): # 龙虎榜数据 run_data_recorder( domain=DragonAndTiger, data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=sleeping_time, ) email_action = EmailInformer() # recent year start_timestamp = date_time_by_interval(current_date(), -400) # 最近一年牛x的营业部 players = get_big_players(start_timestamp=start_timestamp) # 最近30天有牛x的营业部上榜的个股 recent_date = date_time_by_interval(current_date(), -30) selected = [] for player in players: filters = [ or_( and_(DragonAndTiger.dep1 == player, DragonAndTiger.dep1_rate >= 5), and_(DragonAndTiger.dep2 == player, DragonAndTiger.dep2_rate >= 5), and_(DragonAndTiger.dep3 == player, DragonAndTiger.dep3_rate >= 5), and_(DragonAndTiger.dep4 == player, DragonAndTiger.dep4_rate >= 5), and_(DragonAndTiger.dep5 == player, DragonAndTiger.dep5_rate >= 5), ) ] df = DragonAndTiger.query_data( start_timestamp=recent_date, filters=filters, columns=[DragonAndTiger.timestamp, DragonAndTiger.entity_id, DragonAndTiger.code, DragonAndTiger.name], index="entity_id", ) selected = selected + df.index.tolist() if selected: selected = list(set(selected)) target_date = get_latest_kdata_date(provider=data_provider, entity_type="stock", adjust_type="hfq") df = Stock1dHfqKdata.query_data( provider=data_provider, entity_ids=selected, filters=[ Stock1dHfqKdata.turnover_rate > 0.02, Stock1dHfqKdata.timestamp == to_pd_timestamp(target_date), Stock1dHfqKdata.turnover > 300000000, ], index=["entity_id"], ) inform( action=email_action, entity_ids=df.index.tolist(), target_date=current_date(), title="report 龙虎榜", entity_provider=entity_provider, entity_type="stock", em_group="重要指数", em_group_over_write=False, ) if __name__ == "__main__": init_log("trading_runner.log") record_dragon_tiger() sched.start() sched._thread.join() ================================================ FILE: examples/factors/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/factors/boll_factor.py ================================================ # -*- coding: utf-8 -*- from typing import Optional, List import pandas as pd from ta.volatility import BollingerBands from zvt.contract.factor import Transformer from zvt.factors.technical_factor import TechnicalFactor class BollTransformer(Transformer): def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame: indicator_bb = BollingerBands(close=df["close"], window=20, window_dev=2) # Add Bollinger Bands features df["bb_bbm"] = indicator_bb.bollinger_mavg() df["bb_bbh"] = indicator_bb.bollinger_hband() df["bb_bbl"] = indicator_bb.bollinger_lband() # Add Bollinger Band high indicator df["bb_bbhi"] = indicator_bb.bollinger_hband_indicator() # Add Bollinger Band low indicator df["bb_bbli"] = indicator_bb.bollinger_lband_indicator() # Add Width Size Bollinger Bands df["bb_bbw"] = indicator_bb.bollinger_wband() # Add Percentage Bollinger Bands df["bb_bbp"] = indicator_bb.bollinger_pband() return df class BollFactor(TechnicalFactor): transformer = BollTransformer() def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: return [self.factor_df[["bb_bbm", "bb_bbh", "bb_bbl"]]] def compute_result(self): super().compute_result() self.result_df = (self.factor_df["bb_bbli"] - self.factor_df["bb_bbhi"]).to_frame(name="filter_result") self.result_df[self.result_df == 0] = None self.result_df[self.result_df == 1] = True self.result_df[self.result_df == -1] = False if __name__ == "__main__": from zvt.domain import Stock1dHfqKdata provider = "em" entity_ids = ["stock_sz_000338", "stock_sh_601318"] Stock1dHfqKdata.record_data(entity_ids=entity_ids, provider=provider) factor = BollFactor( entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp="2019-01-01" ) factor.draw(show=True) from zvt.domain import Stock30mHfqKdata provider = "em" entity_ids = ["stock_sz_000338", "stock_sh_601318"] Stock30mHfqKdata.record_data(entity_ids=entity_ids, provider=provider) factor = BollFactor( entity_ids=entity_ids, provider=provider, entity_provider=provider, start_timestamp="2021-01-01" ) factor.draw(show=True) ================================================ FILE: examples/factors/fundamental_selector.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import BalanceSheet from zvt.factors.fundamental.finance_factor import GoodCompanyFactor from zvt.factors.target_selector import TargetSelector class FundamentalSelector(TargetSelector): def init_factors(self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, level): # 核心资产=(高ROE 高现金流 高股息 低应收 低资本开支 低财务杠杆 有增长) # 高roe 高现金流 低财务杠杆 有增长 factor1 = GoodCompanyFactor( entity_ids=entity_ids, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="eastmoney", ) self.factors.append(factor1) # 高股息 低应收 factor2 = GoodCompanyFactor( data_schema=BalanceSheet, entity_ids=entity_ids, codes=codes, columns=[BalanceSheet.accounts_receivable], filters=[BalanceSheet.accounts_receivable <= 0.3 * BalanceSheet.total_current_assets], start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="eastmoney", col_period_threshold=None, ) self.factors.append(factor2) if __name__ == "__main__": selector: TargetSelector = FundamentalSelector(start_timestamp="2015-01-01", end_timestamp="2019-06-30") selector.run() print(selector.get_targets("2019-06-30")) ================================================ FILE: examples/factors/tech_factor.py ================================================ # -*- coding: utf-8 -*- from typing import Type, List, Union import pandas as pd from zvt.contract import AdjustType, TradableEntity, IntervalLevel from zvt.contract.factor import Transformer, Accumulator from zvt.domain import Stock from zvt.factors.macd.macd_factor import MacdFactor from zvt.factors.transformers import CrossMaTransformer class BullAndUpFactor(MacdFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, turnover_threshold=400000000, turnover_rate_threshold=0.02, ) -> None: self.turnover_threshold = turnover_threshold self.turnover_rate_threshold = turnover_rate_threshold super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) def compute_result(self): super().compute_result() t = CrossMaTransformer(windows=[5, 120, 250]) self.factor_df = t.transform(self.factor_df) s = (self.factor_df["turnover"] > self.turnover_threshold) & ( self.factor_df["turnover_rate"] > self.turnover_rate_threshold ) self.result_df = (self.factor_df["filter_result"] & self.factor_df["bull"] & s).to_frame(name="filter_result") ================================================ FILE: examples/hot.json ================================================ { "减肥药": [ "减肥药" ], "房地产": [ "房地产", "新型城镇化", "棚改", "建材" ], "新型工业化": [ "新型工业化", "工业母机" ], "华为": [ "华为", "mate60 pro,mate", "星闪", "问界", "麒麟", "昇腾", "鸿蒙" ], "新能源": [ "新能源", "锂电,锂电池", "钠离子电池", "光伏", "太阳能", "储能", "TOPCON电池", "风电", "核电" ], "新能车": [ "新能车,新能源汽车", "整车,汽车整车", "汽车零部件,汽车零件", "无人驾驶", "压铸一体化,一体化压铸" ], "人工智能": [ "人工智能,AI", "GPT,CHATGPT", "算力" ], "机器人": [ "机器人", "减速器", "伺服,伺服系统", "控制系统", "电机" ], "核心资产": [ "核心资产", "消费,白酒,食品,饮料", "白马", "沪深300", "基金重仓", "上证50" ], "一带一路": [ "一带一路", "人民币国际化", "跨境支付" ] } ================================================ FILE: examples/intent/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/intent/intent.py ================================================ # -*- coding: utf-8 -*- from zvt.api.intent import compare from zvt.domain import Indexus1dKdata, Index, Indexus, Index1dKdata, Currency1dKdata from zvt.domain import TreasuryYield def china_vs_us_stock(): # 上证，道琼斯指数 Index.record_data() Indexus.record_data() Index1dKdata.record_data(entity_id="index_sh_000001") Indexus1dKdata.record_data(entity_id="indexus_us_SPX") compare(entity_ids=["index_sh_000001", "indexus_us_SPX"], start_timestamp="2000-01-01", scale_value=100) def us_yield_and_stock(): # 美债收益率，道琼斯指数 entity_ids = ["country_galaxy_US", "indexus_us_SPX"] compare( entity_ids=entity_ids, start_timestamp="1990-01-01", scale_value=None, schema_map_columns={TreasuryYield: ["yield_2", "yield_5"], Indexus1dKdata: ["close"]}, ) def commodity_and_stock(): # 江西铜业，沪铜 entity_ids = ["stock_sh_600362", "future_shfe_CU"] compare( entity_ids=entity_ids, start_timestamp="2005-01-01", scale_value=100, ) def compare_metal(): # 沪铜,沪铝,螺纹钢 entity_ids = ["future_shfe_CU", "future_shfe_AL", "future_shfe_RB"] compare( entity_ids=entity_ids, start_timestamp="2009-04-01", scale_value=100, ) def compare_udi_and_stock(): # 美股指数 # Indexus.record_data() entity_ids = ["indexus_us_NDX", "indexus_us_SPX", "indexus_us_UDI"] # Indexus1dKdata.record_data(entity_ids=entity_ids, sleeping_time=0) compare( entity_ids=entity_ids, start_timestamp="2015-01-01", scale_value=100, schema_map_columns={Indexus1dKdata: ["close"]}, ) def compare_cny_and_stock(): Currency1dKdata.record_data(entity_id="currency_forex_USDCNYC") entity_ids = ["index_sh_000001", "currency_forex_USDCNYC"] compare( entity_ids=entity_ids, start_timestamp="2005-01-01", scale_value=100, schema_map_columns={Currency1dKdata: ["close"], Index1dKdata: ["close"]}, ) if __name__ == "__main__": # compare_kline() # us_yield_and_stock() # commodity_and_stock() # compare_metal() # compare_udi_and_stock() compare_cny_and_stock() ================================================ FILE: examples/main_line_hidden_tags.json ================================================ [ "次新股" ] ================================================ FILE: examples/main_line_tags.json ================================================ [ "智能机器", "半导体", "AI" ] ================================================ FILE: examples/migration.py ================================================ # -*- coding: utf-8 -*- from datetime import datetime from typing import Dict from pydantic import BaseModel, ConfigDict from sqlalchemy import Column, String, JSON from sqlalchemy.orm import declarative_base from zvt.contract.api import get_db_session from zvt.contract.register import register_schema from zvt.contract.schema import Mixin ZvtInfoBase = declarative_base() class User(Mixin, ZvtInfoBase): __tablename__ = "user" added_col = Column(String) json_col = Column(JSON) class UserModel(BaseModel): model_config = ConfigDict(from_attributes=True) id: str entity_id: str timestamp: datetime added_col: str json_col: Dict register_schema(providers=["zvt"], db_name="test", schema_base=ZvtInfoBase) if __name__ == "__main__": user_model = UserModel( id="user_cn_jack_2020-01-01", entity_id="user_cn_jack", timestamp="2020-01-01", added_col="test", json_col={"a": 1}, ) session = get_db_session(provider="zvt", data_schema=User) user = session.query(User).filter(User.id == "user_cn_jack_2020-01-01").first() print(UserModel.validate(user)) ================================================ FILE: examples/ml/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/ml/sgd.py ================================================ # -*- coding: utf-8 -*- from sklearn.linear_model import SGDClassifier, SGDRegressor from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from zvt.ml import MaStockMLMachine def sgd_classification(): machine = MaStockMLMachine(data_provider="em", entity_ids=["stock_sz_000001"], label_method="behavior_cls") clf = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000, tol=1e-3)) machine.train(model=clf) machine.predict() machine.draw_result(entity_id="stock_sz_000001") def sgd_regressor(): machine = MaStockMLMachine(data_provider="em", entity_ids=["stock_sz_000001"], label_method="raw") reg = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3)) machine.train(model=reg) machine.predict() machine.draw_result(entity_id="stock_sz_000001") if __name__ == "__main__": sgd_classification() sgd_regressor() ================================================ FILE: examples/query_snippet.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import func from zvt.api.selector import get_entity_ids_by_filter from zvt.contract import Exchange from zvt.domain import Stock, BlockStock from zvt.recorders.em import em_api from zvt.tag.tag_schemas import StockTags def query_json(): df = StockTags.query_data( filters=[func.json_extract(StockTags.sub_tags, '$."低空经济"') != None], columns=[StockTags.sub_tags] ) print(df) def get_stocks_has_tag(): df = StockTags.query_data(filters=[StockTags.latest.is_(True)], columns=[StockTags.entity_id]) return df["entity_id"].tolist() def get_stocks_without_tag(): entity_ids = get_entity_ids_by_filter(provider="em", ignore_delist=True, ignore_st=True, ignore_new_stock=False) stocks_has_tag = get_stocks_has_tag() return list(set(entity_ids) - set(stocks_has_tag)) def get_all_delist_stocks(): stocks = [] df1 = em_api.get_tradable_list(entity_type="stock", exchange=Exchange.sh) stocks = stocks + df1["entity_id"].tolist() df2 = em_api.get_tradable_list(entity_type="stock", exchange=Exchange.sz) stocks = stocks + df2["entity_id"].tolist() df3 = em_api.get_tradable_list(entity_type="stock", exchange=Exchange.bj) stocks = stocks + df3["entity_id"].tolist() return stocks def get_block_stocks(name="低空经济"): df = BlockStock.query_data(provider="em", filters=[BlockStock.name == name], columns=[BlockStock.stock_id]) return df["stock_id"].tolist() def get_sub_tag_stocks(tag="低空经济"): df = StockTags.query_data( provider="zvt", filters=[func.json_extract(StockTags.sub_tags, f'$."{tag}"') != None], columns=[StockTags.entity_id], ) return df["entity_id"].tolist() if __name__ == "__main__": # a = get_block_stocks() # b = get_sub_tag_stocks() # print(set(a) - set(b)) print(Stock.query_data(provider="em", return_type="dict")) ================================================ FILE: examples/report_utils.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import Type from examples.tag_utils import group_stocks_by_tag, get_main_line_tags, get_main_line_hidden_tags from examples.utils import msg_group_stocks_by_topic from zvt import zvt_config from zvt.api.kdata import get_latest_kdata_date, get_kdata_schema, default_adjust_type from zvt.api.selector import get_limit_up_stocks from zvt.api.stats import get_top_performance_entities_by_periods, get_top_volume_entities, TopType from zvt.contract import IntervalLevel from zvt.contract.api import get_entities, get_entity_schema from zvt.contract.factor import Factor, TargetType from zvt.domain import StockNews from zvt.informer import EmailInformer from zvt.informer.inform_utils import add_to_eastmoney from zvt.utils.time_utils import date_time_by_interval logger = logging.getLogger("__name__") def inform( action: EmailInformer, entity_ids, target_date, title, entity_provider, entity_type, em_group, em_group_over_write, em_group_over_write_tag=False, group_by_topic=False, group_by_tag=True, special_hidden_tag="北交所", ): msg = "no targets" if entity_ids: entities = get_entities( provider=entity_provider, entity_type=entity_type, entity_ids=entity_ids, return_type="domain" ) entities = [entity for entity in entities if entity.entity_id in entity_ids] print(len(entities)) print(len(entity_ids)) assert len(entities) == len(entity_ids) if group_by_topic and (entity_type == "stock"): StockNews.record_data( entity_ids=entity_ids, provider="em", force_update=False, sleeping_time=0.05, day_data=True, ) msg = msg_group_stocks_by_topic(entities=entities, threshold=1, days_ago=60) logger.info(msg) action.send_message(zvt_config["email_username"], f"{target_date} {title}", msg) if group_by_tag and (entity_type == "stock"): main_line_hidden_tags = get_main_line_hidden_tags() sorted_entities = group_stocks_by_tag( entities=entities, hidden_tags=main_line_hidden_tags + [special_hidden_tag] ) msg = "" main_line = [] others = [] special = [] main_line_tags = get_main_line_tags() for index, (tag, stocks) in enumerate(sorted_entities): msg = msg + f"^^^^^^ {tag}[{len(stocks)}/{len(entities)}] ^^^^^^\n" msg = msg + "\n".join([f"{stock.name}({stock.code})" for stock in stocks]) + "\n" if tag == special_hidden_tag: special = special + stocks elif (not main_line_tags) and (tag != "未知") and (index < 3): main_line = main_line + stocks elif main_line_tags and (tag in main_line_tags): main_line = main_line + stocks elif main_line_hidden_tags and (tag in main_line_hidden_tags): main_line = main_line + stocks else: others = others + stocks # 主线 if main_line: codes = [entity.code for entity in main_line] add_to_eastmoney(codes=codes, entity_type=entity_type, group="主线", over_write=em_group_over_write_tag) # 其他 if others: codes = [entity.code for entity in others] if not em_group: em_group = "其他" add_to_eastmoney(codes=codes, entity_type=entity_type, group=em_group, over_write=em_group_over_write) # 特别处理 if special: codes = [entity.code for entity in special] add_to_eastmoney( codes=codes, entity_type=entity_type, group=special_hidden_tag, over_write=em_group_over_write_tag ) else: if em_group: try: codes = [entity.code for entity in entities] add_to_eastmoney( codes=codes, entity_type=entity_type, group=em_group, over_write=em_group_over_write ) except Exception as e: action.send_message( zvt_config["email_username"], f"{target_date} {title} error", f"{target_date} {title} error: {e}", ) infos = [f"{entity.name}({entity.code})" for entity in entities] msg = "\n".join(infos) + "\n" logger.info(msg) action.send_message(zvt_config["email_username"], f"{target_date} {title}", msg) def report_targets( factor_cls: Type[Factor], entity_provider, data_provider, title, entity_type="stock", informer: EmailInformer = None, em_group=None, em_group_over_write=True, em_group_over_write_tag=False, filter_by_volume=True, adjust_type=None, start_timestamp="2019-01-01", **factor_kv, ): logger.info( f"entity_provider: {entity_provider}, data_provider: {data_provider}, entity_type: {entity_type}, start_timestamp: {start_timestamp}" ) error_count = 0 while error_count <= 10: try: if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) target_date = get_latest_kdata_date( provider=data_provider, entity_type=entity_type, adjust_type=adjust_type ) logger.info(f"target_date :{target_date}") current_entity_pool = None if filter_by_volume: # 成交量 vol_df = get_top_volume_entities( entity_type=entity_type, start_timestamp=date_time_by_interval(target_date, -30), end_timestamp=target_date, adjust_type=adjust_type, pct=0.4, data_provider=data_provider, ) current_entity_pool = vol_df.index.tolist() logger.info(f"current_entity_pool({len(current_entity_pool)}): {current_entity_pool}") kdata_schema = get_kdata_schema(entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type) filters = [] if "turnover_threshold" in factor_kv: filters = filters + [kdata_schema.turnover >= factor_kv.get("turnover_threshold")] if "turnover_rate_threshold" in factor_kv: filters = filters + [kdata_schema.turnover_rate >= factor_kv.get("turnover_rate_threshold")] if filters: filters = filters + [kdata_schema.timestamp == target_date] kdata_df = kdata_schema.query_data( provider=data_provider, filters=filters, columns=["entity_id", "timestamp"], index="entity_id" ) if current_entity_pool: current_entity_pool = set(current_entity_pool) & set(kdata_df.index.tolist()) else: current_entity_pool = kdata_df.index.tolist() if "entity_ids" in factor_kv: if current_entity_pool: current_entity_pool = set(current_entity_pool) & set(factor_kv.pop("entity_ids")) else: current_entity_pool = set(factor_kv.pop("entity_ids")) # add the factor entity_schema = get_entity_schema(entity_type=entity_type) tech_factor = factor_cls( entity_schema=entity_schema, entity_provider=entity_provider, provider=data_provider, entity_ids=current_entity_pool, start_timestamp=start_timestamp, end_timestamp=target_date, adjust_type=adjust_type, **factor_kv, ) long_stocks = tech_factor.get_targets(timestamp=target_date, target_type=TargetType.positive) inform( informer, entity_ids=long_stocks, target_date=target_date, title=f"{entity_type} {title}({len(long_stocks)})", entity_provider=entity_provider, entity_type=entity_type, em_group=em_group, em_group_over_write=em_group_over_write, em_group_over_write_tag=em_group_over_write_tag, ) break except Exception as e: logger.exception("report error:{}".format(e)) time.sleep(60 * 3) error_count = error_count + 1 if error_count == 10: informer.send_message( zvt_config["email_username"], f"report {entity_type}{factor_cls.__name__} error", f"report {entity_type}{factor_cls.__name__} error: {e}", ) def report_top_entities( entity_provider, data_provider, periods=None, ignore_new_stock=True, ignore_st=True, entity_ids=None, entity_type="stock", adjust_type=None, top_count=30, turnover_threshold=100000000, turnover_rate_threshold=0.02, informer: EmailInformer = None, title="最强", em_group=None, em_group_over_write=True, em_group_over_write_tag=False, return_type=TopType.positive, include_limit_up=False, ): error_count = 0 if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) while error_count <= 10: try: target_date = get_latest_kdata_date( provider=data_provider, entity_type=entity_type, adjust_type=adjust_type ) selected, real_period = get_top_performance_entities_by_periods( entity_provider=entity_provider, data_provider=data_provider, periods=periods, ignore_new_stock=ignore_new_stock, ignore_st=ignore_st, entity_ids=entity_ids, entity_type=entity_type, adjust_type=adjust_type, top_count=top_count, turnover_threshold=turnover_threshold, turnover_rate_threshold=turnover_rate_threshold, return_type=return_type, ) if include_limit_up and (entity_type == "stock"): limit_up_stocks = get_limit_up_stocks(timestamp=target_date) if limit_up_stocks: selected = list(set(selected + limit_up_stocks)) inform( informer, entity_ids=selected, target_date=target_date, title=f"{entity_type} {title}({len(selected)})", entity_provider=entity_provider, entity_type=entity_type, em_group=em_group, em_group_over_write=em_group_over_write, em_group_over_write_tag=em_group_over_write_tag, ) return real_period except Exception as e: logger.exception("report error:{}".format(e)) time.sleep(30) error_count = error_count + 1 if __name__ == "__main__": report_top_entities( entity_type="block", entity_provider="em", data_provider="em", top_count=10, periods=[365, 750], ignore_new_stock=False, ignore_st=False, adjust_type=None, turnover_threshold=50000000, turnover_rate_threshold=0.005, em_group=None, em_group_over_write=False, return_type=TopType.negative, ) # the __all__ is generated __all__ = ["report_targets", "report_top_entities"] ================================================ FILE: examples/reports/__init__.py ================================================ # -*- coding: utf-8 -*- import datetime import json import os from typing import List from sqlalchemy import or_ from zvt.api.utils import float_to_pct_str from zvt.contract import ActorType from zvt.domain import FinanceFactor, BalanceSheet, IncomeStatement, Stock, StockActorSummary from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp, now_date_time_str def get_subscriber_emails(): emails_file = os.path.abspath(os.path.join(os.path.dirname(__file__), "subscriber_emails.json")) with open(emails_file) as f: return json.load(f) def risky_company(the_date=to_pd_timestamp(now_date_time_str()), income_yoy=-0.1, profit_yoy=-0.1, entity_ids=None): codes = [] start_timestamp = to_pd_timestamp(the_date) - datetime.timedelta(130) # 营收降，利润降,流动比率低，速动比率低 finance_filter = or_( FinanceFactor.op_income_growth_yoy < income_yoy, FinanceFactor.net_profit_growth_yoy <= profit_yoy, FinanceFactor.current_ratio < 0.7, FinanceFactor.quick_ratio < 0.5, ) df = FinanceFactor.query_data( entity_ids=entity_ids, start_timestamp=start_timestamp, filters=[finance_filter], columns=["code"] ) if pd_is_not_null(df): codes = codes + df.code.tolist() # 高应收，高存货，高商誉 balance_filter = ( BalanceSheet.accounts_receivable + BalanceSheet.inventories + BalanceSheet.goodwill ) > BalanceSheet.total_equity df = BalanceSheet.query_data( entity_ids=entity_ids, start_timestamp=start_timestamp, filters=[balance_filter], columns=["code"] ) if pd_is_not_null(df): codes = codes + df.code.tolist() # 应收>利润*1/2 df1 = BalanceSheet.query_data( entity_ids=entity_ids, start_timestamp=start_timestamp, columns=[BalanceSheet.code, BalanceSheet.accounts_receivable], ) if pd_is_not_null(df1): df1.drop_duplicates(subset="code", keep="last", inplace=True) df1 = df1.set_index("code", drop=True).sort_index() df2 = IncomeStatement.query_data( entity_ids=entity_ids, start_timestamp=start_timestamp, columns=[IncomeStatement.code, IncomeStatement.net_profit], ) if pd_is_not_null(df2): df2.drop_duplicates(subset="code", keep="last", inplace=True) df2 = df2.set_index("code", drop=True).sort_index() if pd_is_not_null(df1) and pd_is_not_null(df2): codes = codes + df1[df1.accounts_receivable > df2.net_profit / 2].index.tolist() return list(set(codes)) def stocks_with_info(stocks: List[Stock]): infos = [] for stock in stocks: info = f"{stock.name}({stock.code})" summary: List[StockActorSummary] = StockActorSummary.query_data( entity_id=stock.entity_id, order=StockActorSummary.timestamp.desc(), filters=[StockActorSummary.actor_type == ActorType.raised_fund.value], limit=1, return_type="domain", ) if summary: info = ( info + f"([{summary[0].timestamp}]共{summary[0].actor_count}家基金持股占比:{float_to_pct_str(summary[0].holding_ratio)}, 变化: {float_to_pct_str(summary[0].change_ratio)})" ) summary: List[StockActorSummary] = StockActorSummary.query_data( entity_id=stock.entity_id, order=StockActorSummary.timestamp.desc(), filters=[StockActorSummary.actor_type == ActorType.qfii.value], limit=1, return_type="domain", ) if summary: info = ( info + f"([{summary[0].timestamp}]共{summary[0].actor_count}家qfii持股占比:{float_to_pct_str(summary[0].holding_ratio)}, 变化: {float_to_pct_str(summary[0].change_ratio)})" ) infos.append(info) return infos if __name__ == "__main__": print(get_subscriber_emails()) ================================================ FILE: examples/reports/report_bull.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from examples.factors.tech_factor import BullAndUpFactor from examples.report_utils import report_targets from zvt import init_log from zvt.api.kdata import get_latest_kdata_date from zvt.api.selector import get_middle_and_big_stock from zvt.contract import AdjustType from zvt.informer import EmailInformer logger = logging.getLogger(__name__) sched = BackgroundScheduler() email_informer = EmailInformer() @sched.scheduled_job("cron", hour=18, minute=0, day_of_week="mon-fri") def report_bull(): target_date = get_latest_kdata_date(entity_type="stock", adjust_type=AdjustType.hfq, provider="em") entity_ids = get_middle_and_big_stock(timestamp=target_date) report_targets( factor_cls=BullAndUpFactor, entity_provider="em", data_provider="em", title="bull股票", entity_type="stock", informer=email_informer, em_group="bull股票", em_group_over_write=False, filter_by_volume=False, adjust_type=AdjustType.hfq, start_timestamp="2019-01-01", turnover_threshold=300000000, turnover_rate_threshold=0.02, entity_ids=entity_ids, ) report_targets( factor_cls=BullAndUpFactor, entity_provider="em", data_provider="em", title="bull板块", entity_type="block", informer=email_informer, em_group="bull股票", em_group_over_write=False, filter_by_volume=False, adjust_type=AdjustType.qfq, start_timestamp="2019-01-01", turnover_threshold=10000000000, turnover_rate_threshold=0.02, ) if __name__ == "__main__": init_log("report_bull.log") report_bull() sched.start() sched._thread.join() ================================================ FILE: examples/reports/report_core_compay.py ================================================ # -*- coding: utf-8 -*- import logging import time from apscheduler.schedulers.background import BackgroundScheduler from examples.factors.fundamental_selector import FundamentalSelector from examples.reports import get_subscriber_emails, stocks_with_info from zvt import init_log, zvt_config from zvt.contract.api import get_entities from zvt.domain import Stock from zvt.factors.target_selector import TargetSelector from zvt.informer.inform_utils import add_to_eastmoney from zvt.informer.informer import EmailInformer from zvt.utils.time_utils import now_pd_timestamp, to_date_time_str logger = logging.getLogger(__name__) sched = BackgroundScheduler() # 基本面选股 每周一次即可 基本无变化 @sched.scheduled_job("cron", hour=16, minute=0, day_of_week="6") def report_core_company(): while True: error_count = 0 email_action = EmailInformer() try: # StockTradeDay.record_data(provider='joinquant') # Stock.record_data(provider='joinquant') # FinanceFactor.record_data(provider='eastmoney') # BalanceSheet.record_data(provider='eastmoney') target_date = to_date_time_str(now_pd_timestamp()) my_selector: TargetSelector = FundamentalSelector(start_timestamp="2016-01-01", end_timestamp=target_date) my_selector.run() long_targets = my_selector.get_open_long_targets(timestamp=target_date) if long_targets: stocks = get_entities( provider="joinquant", entity_schema=Stock, entity_ids=long_targets, return_type="domain" ) # add them to eastmoney try: codes = [stock.code for stock in stocks] add_to_eastmoney(codes=codes, entity_type="stock", group="core") except Exception as e: email_action.send_message( zvt_config["email_username"], f"report_core_company error", "report_core_company error:{}".format(e), ) infos = stocks_with_info(stocks) msg = "\n".join(infos) else: msg = "no targets" logger.info(msg) email_action.send_message(get_subscriber_emails(), f"{to_date_time_str(target_date)} 核心资产选股结果", msg) break except Exception as e: logger.exception("report_core_company error:{}".format(e)) time.sleep(60 * 3) error_count = error_count + 1 if error_count == 10: email_action.send_message( zvt_config["email_username"], f"report_core_company error", "report_core_company error:{}".format(e) ) if __name__ == "__main__": init_log("report_core_company.log") report_core_company() sched.start() sched._thread.join() ================================================ FILE: examples/reports/report_tops.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from examples.report_utils import report_top_entities, inform from zvt import init_log from zvt.api.stats import TopType, get_latest_kdata_date from zvt.contract import AdjustType from zvt.domain import Block, BlockCategory from zvt.factors.top_stocks import get_top_stocks from zvt.informer import EmailInformer logger = logging.getLogger(__name__) sched = BackgroundScheduler() email_informer = EmailInformer() @sched.scheduled_job("cron", hour=17, minute=0, day_of_week="mon-fri") def report_top_stocks(): # compute_top_stocks() provider = "em" entity_type = "stock" target_date = get_latest_kdata_date(provider=provider, entity_type=entity_type, adjust_type=AdjustType.hfq) selected = get_top_stocks(target_date=target_date, return_type="short") inform( email_informer, entity_ids=selected, target_date=target_date, title=f"stock 短期最强({len(selected)})", entity_provider=provider, entity_type=entity_type, em_group="短期最强", em_group_over_write=True, em_group_over_write_tag=True, ) selected = get_top_stocks(target_date=target_date, return_type="long") inform( email_informer, entity_ids=selected, target_date=target_date, title=f"stock 中期最强({len(selected)})", entity_provider=provider, entity_type=entity_type, em_group="中期最强", em_group_over_write=True, em_group_over_write_tag=False, ) # report_top_entities( # entity_type="stock", # entity_provider="em", # data_provider="em", # periods=[365, 750], # ignore_new_stock=False, # ignore_st=True, # adjust_type=None, # top_count=25, # turnover_threshold=100000000, # turnover_rate_threshold=0.01, # informer=email_informer, # em_group="谁有我惨", # em_group_over_write=True, # return_type=TopType.negative, # ) @sched.scheduled_job("cron", hour=17, minute=30, day_of_week="mon-fri") def report_top_blocks(): df = Block.query_data(filters=[Block.category == BlockCategory.industry.value], index="entity_id") entity_ids = df.index.tolist() report_top_entities( entity_type="block", entity_provider="em", data_provider="em", periods=[*range(2, 30)], ignore_new_stock=False, ignore_st=False, adjust_type=None, top_count=10, turnover_threshold=0, turnover_rate_threshold=0, informer=email_informer, em_group="最强行业", title="最强行业", em_group_over_write=True, return_type=TopType.positive, entity_ids=entity_ids, ) df = Block.query_data(filters=[Block.category == BlockCategory.concept.value], index="entity_id") df = df[~df.name.str.contains("昨日")] entity_ids = df.index.tolist() report_top_entities( entity_type="block", entity_provider="em", data_provider="em", periods=[*range(2, 30)], ignore_new_stock=False, ignore_st=False, adjust_type=None, top_count=10, turnover_threshold=0, turnover_rate_threshold=0, informer=email_informer, em_group="最强概念", title="最强概念", em_group_over_write=True, return_type=TopType.positive, entity_ids=entity_ids, ) @sched.scheduled_job("cron", hour=17, minute=30, day_of_week="mon-fri") def report_top_stockhks(): report_top_entities( entity_type="stockhk", entity_provider="em", data_provider="em", top_count=10, periods=[*range(1, 15)], ignore_new_stock=False, ignore_st=False, adjust_type=None, turnover_threshold=30000000, turnover_rate_threshold=0.01, informer=email_informer, em_group="短期最强", title="短期最强", em_group_over_write=False, return_type=TopType.positive, ) report_top_entities( entity_type="stockhk", entity_provider="em", data_provider="em", top_count=10, periods=[30, 50], ignore_new_stock=True, ignore_st=False, adjust_type=None, turnover_threshold=30000000, turnover_rate_threshold=0.01, informer=email_informer, em_group="中期最强", title="中期最强", em_group_over_write=False, return_type=TopType.positive, ) # report_top_entities( # entity_type="stockhk", # entity_provider="em", # data_provider="em", # top_count=20, # periods=[365, 750], # ignore_new_stock=True, # ignore_st=False, # adjust_type=None, # turnover_threshold=50000000, # turnover_rate_threshold=0.005, # informer=email_informer, # em_group="谁有我惨", # em_group_over_write=False, # return_type=TopType.negative, # ) if __name__ == "__main__": init_log("report_tops.log") report_top_stocks() # report_top_blocks() report_top_stockhks() sched.start() sched._thread.join() ================================================ FILE: examples/reports/report_vol_up.py ================================================ # -*- coding: utf-8 -*- import logging from zvt.factors.ma import VolumeUpMaFactor from apscheduler.schedulers.background import BackgroundScheduler from examples.report_utils import report_targets, inform from zvt import init_log from zvt.api.kdata import get_latest_kdata_date from zvt.contract import AdjustType from zvt.factors.top_stocks import get_top_stocks from zvt.informer import EmailInformer logger = logging.getLogger(__name__) sched = BackgroundScheduler() email_informer = EmailInformer() @sched.scheduled_job("cron", hour=17, minute=0, day_of_week="mon-fri") def report_vol_up_stocks(): provider = "em" entity_type = "stock" target_date = get_latest_kdata_date(provider=provider, entity_type=entity_type, adjust_type=AdjustType.hfq) selected = get_top_stocks(target_date=target_date, return_type="small_vol_up") inform( email_informer, entity_ids=selected, target_date=target_date, title=f"stock 放量突破(半)年线小市值股票({len(selected)})", entity_provider=provider, entity_type=entity_type, em_group="年线股票", em_group_over_write=True, em_group_over_write_tag=False, ) selected = get_top_stocks(target_date=target_date, return_type="big_vol_up") inform( email_informer, entity_ids=selected, target_date=target_date, title=f"stock 放量突破(半)年线大市值股票({len(selected)})", entity_provider=provider, entity_type=entity_type, em_group="年线股票", em_group_over_write=False, em_group_over_write_tag=False, ) @sched.scheduled_job("cron", hour=17, minute=30, day_of_week="mon-fri") def report_vol_up_stockhks(): report_targets( factor_cls=VolumeUpMaFactor, entity_provider="em", data_provider="em", informer=email_informer, em_group="年线股票", title="放量突破(半)年线港股", entity_type="stockhk", em_group_over_write=False, filter_by_volume=False, adjust_type=AdjustType.hfq, start_timestamp="2021-01-01", # factor args windows=[120, 250], over_mode="or", up_intervals=60, turnover_threshold=100000000, turnover_rate_threshold=0.01, ) if __name__ == "__main__": init_log("report_vol_up.log") report_vol_up_stocks() report_vol_up_stockhks() sched.start() sched._thread.join() ================================================ FILE: examples/reports/subscriber_emails.json ================================================ [ "test@qq.com", "test1@qq.com" ] ================================================ FILE: examples/requirements.txt ================================================ zvt >= 0.10.1 apscheduler >= 3.4.0 tabulate>=0.8.8 ta ================================================ FILE: examples/research/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/research/dragon_and_tiger.py ================================================ # -*- coding: utf-8 -*- from zvt.api.selector import get_big_players, get_player_success_rate from zvt.domain import DragonAndTiger from zvt.utils.time_utils import date_time_by_interval, current_date if __name__ == "__main__": provider = "em" DragonAndTiger.record_data(provider=provider) end_timestamp = date_time_by_interval(current_date(), -60) # recent year start_timestamp = date_time_by_interval(end_timestamp, -400) print(f"{start_timestamp} to {end_timestamp}") players = get_big_players(start_timestamp=start_timestamp, end_timestamp=end_timestamp) print(players) df = get_player_success_rate( start_timestamp=start_timestamp, end_timestamp=end_timestamp, intervals=[3, 5, 10], players=players ) print(df) ================================================ FILE: examples/research/top_dragon_tiger.py ================================================ # -*- coding: utf-8 -*- from typing import Optional, Type, List, Union import pandas as pd from zvt.api.selector import get_players from zvt.api.stats import get_top_performance_by_month from zvt.contract import TradableEntity, IntervalLevel, AdjustType from zvt.contract.factor import Transformer, Accumulator from zvt.domain import Stock from zvt.factors.technical_factor import TechnicalFactor from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import pre_month_start_date, date_time_by_interval def top_dragon_and_tiger(data_provider="em", start_timestamp="2021-01-01", end_timestamp="2022-01-01"): dfs = [] for start_date, end_date, df in get_top_performance_by_month( start_timestamp=start_timestamp, end_timestamp=end_timestamp, list_days=250, data_provider=data_provider ): pre_month_start = pre_month_start_date(start_date) for entity_id in df.index[:30]: players = get_players( entity_id=entity_id, start_timestamp=date_time_by_interval(start_date, 15), end_timestamp=end_timestamp, provider=data_provider, direction="in", ) print(players) dfs.append(players) player_df = pd.concat(dfs, sort=True) return player_df.sort_index(level=[0, 1]) class DragonTigerFactor(TechnicalFactor): def __init__( self, entity_id: str, entity_schema: Type[TradableEntity] = Stock, provider: str = "em", entity_provider: str = "em", exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, ) -> None: super().__init__( entity_schema, provider, entity_provider, [entity_id], exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) self.player_df = get_players( entity_id=entity_id, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="em", direction="in", ) def drawer_annotation_df(self) -> Optional[pd.DataFrame]: def order_type_flag(df): return "
".join(df.tolist()) if pd_is_not_null(self.player_df): annotation_df = self.player_df.copy() annotation_df["value"] = self.factor_df.loc[annotation_df.index]["close"] annotation_df["flag"] = annotation_df[["dep1", "dep2", "dep3", "dep4", "dep5"]].apply( lambda x: order_type_flag(x), axis=1 ) annotation_df["color"] = "#ff7f0e" return annotation_df if __name__ == "__main__": top_dragon_and_tiger() # Stock1dHfqKdata.record_data(entity_id="stock_sz_002561", provider="em") # f = DragonTigerFactor(entity_id="stock_sz_002561", provider="em") # f.draw(show=True) ================================================ FILE: examples/research/top_tags.py ================================================ # -*- coding: utf-8 -*- from zvt.api.stats import get_top_performance_by_month from zvt.domain import Stock1dHfqKdata from zvt.utils.time_utils import date_time_by_interval, month_end_date, is_same_date # 每月涨幅前30，市值90%分布在100亿以下 # 重复上榜的有1/4左右 # 连续两个月上榜的1/10左右 def top_tags(data_provider="em", start_timestamp="2020-01-01", end_timestamp="2021-01-01"): records = [] for _, timestamp, df in get_top_performance_by_month( start_timestamp=start_timestamp, end_timestamp=end_timestamp, list_days=250, data_provider=data_provider ): for entity_id in df.index[:30]: query_timestamp = timestamp while True: kdata = Stock1dHfqKdata.query_data( provider=data_provider, entity_id=entity_id, start_timestamp=query_timestamp, order=Stock1dHfqKdata.timestamp.asc(), limit=1, return_type="domain", ) if not kdata or kdata[0].turnover_rate == 0: if is_same_date(query_timestamp, month_end_date(query_timestamp)): break query_timestamp = date_time_by_interval(query_timestamp) continue cap = kdata[0].turnover / kdata[0].turnover_rate break records.append( {"entity_id": entity_id, "timestamp": timestamp, "cap": cap, "score": df.loc[entity_id, "score"]} ) return records if __name__ == "__main__": print(top_tags()) ================================================ FILE: examples/result.json ================================================ {} ================================================ FILE: examples/stock_tags.json ================================================ [ { "code": "000972", "name": "中基健康", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "002269", "name": "美邦服饰", "tag": "大消费", "reason": "" }, { "code": "002762", "name": "金发拉比", "tag": "大消费", "reason": "" }, { "code": "603170", "name": "宝立食品", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600257", "name": "大湖股份", "tag": "大消费", "reason": "" }, { "code": "002803", "name": "吉宏股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "002186", "name": "全 聚 德", "tag": "大消费", "reason": "" }, { "code": "600365", "name": "ST通葡", "tag": "大消费", "reason": "" }, { "code": "600252", "name": "中恒集团", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "002419", "name": "天虹股份", "tag": "大消费", "reason": "" }, { "code": "600771", "name": "广誉远", "tag": "医药", "hidden_tag": "央企", "reason": "" }, { "code": "600655", "name": "豫园股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "000501", "name": "武商集团", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600332", "name": "白云山", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "002183", "name": "怡 亚 通", "tag": "大消费", "reason": "" }, { "code": "002374", "name": "中锐股份", "tag": "大消费", "reason": "" }, { "code": "002342", "name": "巨力索具", "tag": "大消费", "reason": "" }, { "code": "600382", "name": "广东明珠", "tag": "大消费", "reason": "" }, { "code": "600696", "name": "岩石股份", "tag": "大消费", "reason": "" }, { "code": "603919", "name": "金徽酒", "tag": "大消费", "reason": "" }, { "code": "603779", "name": "威龙股份", "tag": "大消费", "reason": "" }, { "code": "603777", "name": "来伊份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600059", "name": "古越龙山", "tag": "大消费", "reason": "" }, { "code": "000860", "name": "顺鑫农业", "tag": "大消费", "reason": "" }, { "code": "600467", "name": "好当家", "tag": "大消费", "reason": "" }, { "code": "603299", "name": "苏盐井神", "tag": "大消费", "reason": "" }, { "code": "000995", "name": "*ST皇台", "tag": "大消费", "reason": "" }, { "code": "002646", "name": "天佑德酒", "tag": "大消费", "reason": "" }, { "code": "000596", "name": "古井贡酒", "tag": "大消费", "reason": "" }, { "code": "600559", "name": "老白干酒", "tag": "大消费", "reason": "" }, { "code": "600702", "name": "舍得酒业", "tag": "大消费", "reason": "" }, { "code": "601579", "name": "会稽山", "tag": "大消费", "reason": "" }, { "code": "600153", "name": "建发股份", "tag": "房地产", "reason": "" }, { "code": "603589", "name": "口子窖", "tag": "大消费", "reason": "" }, { "code": "600809", "name": "山西汾酒", "tag": "大消费", "reason": "" }, { "code": "600197", "name": "伊力特", "tag": "大消费", "reason": "" }, { "code": "600238", "name": "海南椰岛", "tag": "大消费", "reason": "" }, { "code": "000568", "name": "泸州老窖", "tag": "大消费", "reason": "" }, { "code": "603198", "name": "迎驾贡酒", "tag": "大消费", "reason": "" }, { "code": "600199", "name": "金种子酒", "tag": "大消费", "reason": "" }, { "code": "000930", "name": "中粮科技", "tag": "大消费", "reason": "", "hidden_tag": "中字头" }, { "code": "600779", "name": "水井坊", "tag": "大消费", "reason": "" }, { "code": "600519", "name": "贵州茅台", "tag": "大消费", "reason": "" }, { "code": "600381", "name": "青海春天", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "603369", "name": "今世缘", "tag": "大消费", "reason": "" }, { "code": "000858", "name": "五 粮 液", "tag": "大消费", "reason": "" }, { "code": "000799", "name": "酒鬼酒", "tag": "大消费", "reason": "", "hidden_tag": "央企" }, { "code": "002304", "name": "洋河股份", "tag": "大消费", "reason": "" }, { "code": "600679", "name": "上海凤凰", "tag": "大消费", "reason": "", "hidden_tag": "央企" }, { "code": "603983", "name": "丸美股份", "tag": "大消费", "reason": "" }, { "code": "301187", "name": "欧圣电气", "tag": "大消费", "reason": "" }, { "code": "600493", "name": "凤竹纺织", "tag": "大消费", "reason": "" }, { "code": "605136", "name": "丽人丽妆", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600448", "name": "华纺股份", "tag": "大消费", "reason": "" }, { "code": "600439", "name": "瑞贝卡", "tag": "大消费", "reason": "" }, { "code": "000521", "name": "长虹美菱", "tag": "大消费", "reason": "" }, { "code": "603499", "name": "翔港科技", "tag": "大消费", "reason": "" }, { "code": "301193", "name": "家联科技", "tag": "大消费", "reason": "" }, { "code": "600935", "name": "华塑股份", "tag": "大消费", "reason": "" }, { "code": "301376", "name": "致欧科技", "tag": "大消费", "reason": "", "hidden_tag": "次新股" }, { "code": "603863", "name": "松炀资源", "tag": "大消费", "reason": "" }, { "code": "002615", "name": "哈尔斯", "tag": "大消费", "reason": "" }, { "code": "000850", "name": "华茂股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "001209", "name": "洪兴股份", "tag": "大消费", "reason": "" }, { "code": "001216", "name": "华瓷股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "001368", "name": "通达创智", "tag": "大消费", "reason": "" }, { "code": "001238", "name": "浙江正特", "tag": "大消费", "reason": "", "hidden_tag": "次新股" }, { "code": "000025", "name": "特力A", "tag": "大消费", "reason": "" }, { "code": "603661", "name": "恒林股份", "tag": "大消费", "reason": "" }, { "code": "002059", "name": "云南旅游", "tag": "大消费", "reason": "", "hidden_tag": "央企" }, { "code": "001387", "name": "雪祺电气", "tag": "大消费", "reason": "", "hidden_tag": "次新股" }, { "code": "603600", "name": "永艺股份", "tag": "大消费", "reason": "" }, { "code": "600156", "name": "华升股份", "tag": "大消费", "reason": "" }, { "code": "601113", "name": "华鼎股份", "tag": "大消费", "reason": "" }, { "code": "002731", "name": "萃华珠宝", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600400", "name": "红豆股份", "tag": "大消费", "reason": "" }, { "code": "605180", "name": "华生科技", "tag": "大消费", "reason": "" }, { "code": "000592", "name": "平潭发展", "tag": "大消费", "reason": "" }, { "code": "002357", "name": "富临运业", "tag": "大消费", "reason": "" }, { "code": "603958", "name": "哈森股份", "tag": "大消费", "reason": "" }, { "code": "002969", "name": "嘉美包装", "tag": "大消费", "reason": "" }, { "code": "000428", "name": "华天酒店", "tag": "大消费", "reason": "" }, { "code": "600882", "name": "妙可蓝多", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600697", "name": "欧亚集团", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "300740", "name": "水羊股份", "tag": "大消费", "reason": "" }, { "code": "000888", "name": "峨眉山Ａ", "tag": "大消费", "reason": "" }, { "code": "002356", "name": "赫美集团", "tag": "大消费", "reason": "" }, { "code": "000610", "name": "西安旅游", "tag": "大消费", "reason": "" }, { "code": "605188", "name": "国光连锁", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "000861", "name": "海印股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "603199", "name": "九华旅游", "tag": "大消费", "reason": "" }, { "code": "600706", "name": "曲江文旅", "tag": "大消费", "reason": "" }, { "code": "600824", "name": "益民集团", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "002820", "name": "桂发祥", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "603719", "name": "良品铺子", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "003000", "name": "劲仔食品", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600828", "name": "茂业商业", "tag": "大消费", "reason": "" }, { "code": "002561", "name": "徐家汇", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600280", "name": "中央商场", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "000679", "name": "大连友谊", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "300783", "name": "三只松鼠", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "000017", "name": "深中华A", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "000523", "name": "红棉股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "603518", "name": "锦泓集团", "tag": "大消费", "reason": "" }, { "code": "603877", "name": "太平鸟", "tag": "大消费", "reason": "" }, { "code": "601086", "name": "国芳集团", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "603231", "name": "索宝蛋白", "tag": "大消费", "reason": "", "hidden_tag": "次新股" }, { "code": "002719", "name": "麦趣尔", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "000558", "name": "莱茵体育", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600628", "name": "新世界", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "600630", "name": "龙头股份", "tag": "大消费", "reason": "" }, { "code": "600250", "name": "南京商旅", "tag": "大消费", "reason": "" }, { "code": "605080", "name": "浙江自然", "tag": "大消费", "reason": "" }, { "code": "603099", "name": "长白山", "tag": "大消费", "reason": "" }, { "code": "002033", "name": "丽江股份", "tag": "大消费", "reason": "" }, { "code": "600593", "name": "大连圣亚", "tag": "大消费", "reason": "" }, { "code": "002780", "name": "三夫户外", "tag": "大消费", "reason": "" }, { "code": "000978", "name": "桂林旅游", "tag": "大消费", "reason": "" }, { "code": "688363", "name": "华熙生物", "tag": "大消费", "reason": "" }, { "code": "002612", "name": "朗姿股份", "tag": "大消费", "reason": "" }, { "code": "300896", "name": "爱美客", "tag": "大消费", "reason": "" }, { "code": "000715", "name": "中兴商业", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "603536", "name": "惠发食品", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "002495", "name": "佳隆股份", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "601566", "name": "九牧王", "tag": "大消费", "reason": "" }, { "code": "603711", "name": "香飘飘", "tag": "大消费", "reason": "", "hidden_tag": null }, { "code": "688577", "name": "浙海德曼", "tag": "智能机器", "reason": "", "hidden_tag": "次新股" }, { "code": "003025", "name": "思进智能", "tag": "智能机器", "reason": "" }, { "code": "603289", "name": "泰瑞机器", "tag": "智能机器", "reason": "" }, { "code": "002708", "name": "光洋股份", "tag": "智能机器", "reason": "" }, { "code": "300276", "name": "三丰智能", "tag": "智能机器", "reason": "", "hidden_tag": null }, { "code": "603211", "name": "晋拓股份", "tag": "智能机器", "reason": "" }, { "code": "603095", "name": "越剑智能", "tag": "智能机器", "reason": "" }, { "code": "603015", "name": "弘讯科技", "tag": "智能机器", "reason": "", "hidden_tag": null }, { "code": 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"tag": "医药", "reason": "" }, { "code": "603216", "name": "梦天家居", "tag": "房地产", "reason": "" }, { "code": "301177", "name": "迪阿股份", "tag": "大消费", "reason": "" }, { "code": "301138", "name": "华研精机", "tag": "智能机器", "reason": "" }, { "code": "873132", "name": "泰鹏智能", "tag": "大消费", "reason": "", "hidden_tag": "北交所" }, { "code": "603071", "name": "物产环能", "tag": "大消费", "reason": "" }, { "code": "301101", "name": "明月镜片", "tag": "大消费", "reason": "" }, { "code": "301100", "name": "风光股份", "tag": "化工", "reason": "" }, { "code": "301096", "name": "百诚医药", "tag": "医药", "reason": "" }, { "code": "301211", "name": "亨迪药业", "tag": "医药", "reason": "" }, { "code": "301113", "name": "雅艺科技", "tag": "房地产", "reason": "" }, { "code": "870357", "name": "雅葆轩", "tag": "消费电子", "reason": "", "hidden_tag": "北交所" }, { "code": "600927", "name": "永安期货", "tag": "金融", "reason": "" }, { "code": "301186", "name": "超达装备", "tag": "汽车", "reason": "" }, { "code": "301190", "name": "善水科技", "tag": "化工", "reason": "" }, { "code": "001296", "name": "长江材料", "tag": "化工", "reason": "" }, { "code": "688210", "name": "统联精密", "tag": "消费电子", "reason": "" }, { "code": "688206", "name": "概伦电子", "tag": "AI", "reason": "" }, { "code": "301189", "name": "奥尼电子", "tag": "AI", "reason": "" }, { "code": "301166", "name": "优宁维", "tag": "专业服务", "reason": "" }, { "code": "301127", "name": "天源环保", "tag": "公用", "reason": "" }, { "code": "688227", "name": "品高股份", "tag": "AI", "reason": "" }, { "code": "603176", "name": "汇通集团", "tag": "房地产", "reason": "" }, { "code": "301159", "name": "三维天地", "tag": "AI", "reason": "" }, { "code": "873001", "name": "纬达光电", "tag": "VR", "reason": "", "hidden_tag": "北交所" }, { "code": "001234", "name": "泰慕士", "tag": "大消费", "reason": "" }, { "code": "301196", "name": "唯科科技", "tag": "化工", "reason": "" }, { "code": "301136", "name": "招标股份", "tag": "房地产", "reason": "" }, { "code": "301117", "name": "佳缘科技", "tag": "AI", "reason": "" }, { "code": "301158", "name": "德石股份", "tag": "智能机器", "reason": "" }, { "code": "301201", "name": "诚达药业", "tag": "医药", "reason": "" }, { "code": "603150", "name": "万朗磁塑", "tag": "化工", "reason": "" }, { "code": "688223", "name": "晶科能源", "tag": "赛道", "reason": "" }, { "code": "688171", "name": "纬德信息", "tag": "AI", "reason": "" }, { "code": "301228", "name": "实朴检测", "tag": "专业服务", "reason": "" }, { "code": "301235", "name": "华康医疗", "tag": "医药", "reason": "" }, { "code": "301106", "name": "骏成科技", "tag": "VR", "reason": "" }, { "code": "688225", "name": "亚信安全", "tag": "AI", "reason": "" }, { "code": "688283", "name": "坤恒顺维", "tag": "智能机器", "reason": "" }, { "code": "001313", "name": "粤海饲料", "tag": "农业", "reason": "" }, { "code": "688267", "name": "中触媒", "tag": "化工", "reason": "" }, { "code": "603215", "name": "比依股份", "tag": "大消费", "reason": "" }, { "code": "873593", "name": "鼎智科技", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "301181", "name": "标榜股份", "tag": "汽车", "reason": "" }, { "code": "873665", "name": "科强股份", "tag": "化工", "reason": "", "hidden_tag": "北交所" }, { "code": "301229", "name": "纽泰格", "tag": "汽车", "reason": "" }, { "code": "301130", "name": "西点药业", "tag": "医药", "reason": "" }, { "code": "301200", "name": "大族数控", "tag": "智能机器", "reason": "" }, { "code": "001266", "name": "宏英智能", "tag": "智能机器", "reason": "" }, { "code": "688281", "name": "华秦科技", "tag": "化工", "reason": "" }, { "code": "301218", "name": "华是科技", "tag": "AI", "reason": "" }, { "code": "301222", "name": "浙江恒威", "tag": "赛道", "reason": "" }, { "code": "603070", "name": "万控智造", "tag": "电力", "reason": "" }, { "code": "301110", "name": "青木股份", "tag": "AI", "reason": "" }, { "code": "688115", "name": "思林杰", "tag": "智能机器", "reason": "" }, { "code": "301131", "name": "聚赛龙", "tag": "化工", "reason": "" }, { "code": "688175", "name": "高凌信息", "tag": "AI", "reason": "" }, { "code": "603261", "name": "立航科技", "tag": "军工", "reason": "" }, { "code": "688207", "name": "格灵深瞳", "tag": "AI", "reason": "" }, { "code": "688282", "name": "理工导航", "tag": "AI", "reason": "" }, { "code": "688150", "name": "莱特光电", "tag": "VR", "reason": "" }, { "code": "301256", "name": "华融化学", "tag": "化工", "reason": "" }, { "code": "301103", "name": "何氏眼科", "tag": "医药", "reason": "" }, { "code": "688238", "name": "和元生物", "tag": "医药", "reason": "" }, { "code": "301237", "name": "和顺科技", "tag": "化工", "reason": "" }, { "code": "603209", "name": "兴通股份", "tag": "公用", "reason": "" }, { "code": "301226", "name": "祥明智能", "tag": "智能机器", "reason": "" }, { "code": "301258", "name": "富士莱", "tag": "医药", "reason": "" }, { "code": "301216", "name": "万凯新材", "tag": "化工", "reason": "" }, { "code": "301263", "name": "泰恩康", "tag": "医药", "reason": "" }, { "code": "301268", "name": "铭利达", "tag": "智能机器", "reason": "" }, { "code": "688337", "name": "普源精电", "tag": "智能机器", "reason": "" }, { "code": "301151", "name": "冠龙节能", "tag": "智能机器", "reason": "" }, { "code": "301279", "name": "金道科技", "tag": "智能机器", "reason": "" }, { "code": "301109", "name": "军信股份", "tag": "公用", "reason": "" }, { "code": "688125", "name": "安达智能", "tag": "智能机器", "reason": "" }, { "code": "301212", "name": "联盛化学", "tag": "化工", "reason": "" }, { "code": "301163", "name": "宏德股份", "tag": "智能机器", "reason": "" }, { "code": "301120", "name": "新特电气", "tag": "电力", "reason": "" }, { "code": "688326", "name": "经纬恒润-W", "tag": "汽车", "reason": "" }, { "code": "301248", "name": "杰创智能", "tag": "AI", "reason": "" }, { "code": "301148", "name": "嘉戎技术", "tag": "公用", "reason": "" }, { "code": "301288", "name": "清研环境", "tag": "公用", "reason": "" }, { "code": "301259", "name": "艾布鲁", "tag": "公用", "reason": "" }, { "code": "603191", "name": "望变电气", "tag": "电力", "reason": "" }, { "code": "688320", "name": "禾川科技", "tag": "智能机器", "reason": "" }, { "code": "301162", "name": "国能日新", "tag": "AI", "reason": "" }, { "code": "688170", "name": "德龙激光", "tag": "智能机器", "reason": "" }, { "code": "001228", "name": "永泰运", "tag": "统一大市场", "reason": "" }, { "code": "001319", "name": "铭科精技", "tag": "汽车", "reason": "" }, { "code": "301257", "name": "普蕊斯", "tag": "专业服务", "reason": "" }, { "code": "301153", "name": "中科江南", "tag": "AI", "reason": "" }, { "code": "603272", "name": "联翔股份", "tag": "房地产", "reason": "" }, { "code": "301183", "name": "东田微", "tag": "VR", "reason": "" }, { "code": "301107", "name": "瑜欣电子", "tag": "智能机器", "reason": "" }, { "code": "688287", "name": "观典防务", "tag": "军工", "reason": "" }, { "code": "301191", "name": "菲菱科思", "tag": "AI", "reason": "" }, { "code": "873690", "name": "捷众科技", "tag": "汽车", "reason": "", "hidden_tag": "北交所" }, { "code": "301160", "name": "翔楼新材", "tag": "汽车", "reason": "" }, { "code": "688251", "name": "井松智能", "tag": "智能机器", "reason": "" }, { "code": "838837", "name": "华原股份", "tag": "汽车", "reason": "", "hidden_tag": "北交所" }, { "code": "301125", "name": "腾亚精工", "tag": "智能机器", "reason": "" }, { "code": "688348", "name": "昱能科技", "tag": "赛道", "reason": "" }, { "code": "301286", "name": "侨源股份", "tag": "化工", "reason": "" }, { "code": "301156", "name": "美农生物", "tag": "农业", "reason": "" }, { "code": "001226", "name": "拓山重工", "tag": "智能机器", "reason": "" }, { "code": "301220", "name": "亚香股份", "tag": "化工", "reason": "" }, { "code": "688349", "name": "三一重能", "tag": "赛道", "reason": "" }, { "code": "301289", "name": "国缆检测", "tag": "专业服务", "reason": "" }, { "code": "001323", "name": "慕思股份", "tag": "大消费", "reason": "" }, { "code": "001316", "name": "润贝航科", "tag": "大消费", "reason": "" }, { "code": "601089", "name": "福元医药", "tag": "医药", "reason": "" }, { "code": "001268", "name": "联合精密", "tag": "智能机器", "reason": "" }, { "code": "873833", "name": "美心翼申", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "688237", "name": "超卓航科", "tag": "军工", "reason": "" }, { "code": "301239", "name": "普瑞眼科", "tag": "医药", "reason": "" }, { "code": "301233", "name": "盛帮股份", "tag": "化工", "reason": "" }, { "code": "688400", "name": "凌云光", "tag": "智能机器", "reason": "" }, { "code": "688322", "name": "奥比中光-UW", "tag": "消费电子", "reason": "" }, { "code": "301208", "name": "中亦科技", "tag": "AI", "reason": "" }, { "code": "301139", "name": "元道通信", "tag": "AI", "reason": "" }, { "code": "688053", "name": "思科瑞", "tag": "消费电子", "reason": "" }, { "code": "301312", "name": "智立方", "tag": "智能机器", "reason": "" }, { "code": "001230", "name": "劲旅环境", "tag": "公用", "reason": "" }, { "code": "001336", "name": "楚环科技", "tag": "智能机器", "reason": "" }, { "code": "301306", "name": "西测测试", "tag": "半导体", "reason": "" }, { "code": "301269", "name": "华大九天", "tag": "AI", "reason": "" }, { "code": "603201", "name": "常润股份", "tag": "汽车", "reason": "" }, { "code": "301195", "name": "北路智控", "tag": "AI", "reason": "" }, { "code": "688371", "name": "菲沃泰", "tag": "消费电子", "reason": "" }, { "code": "301333", "name": "诺思格", "tag": "医药", "reason": "" }, { "code": "301197", "name": "工大科雅", "tag": "AI", "reason": "" }, { "code": "688205", "name": "德科立", "tag": "VR", "reason": "" }, { "code": "301132", "name": "满坤科技", "tag": "消费电子", "reason": "" }, { "code": "301192", "name": "泰祥股份", "tag": "汽车", "reason": "" }, { "code": "301336", "name": "趣睡科技", "tag": "大消费", "reason": "" }, { "code": "836547", "name": "无锡晶海", "tag": "医药", "reason": "", "hidden_tag": "北交所" }, { "code": "873570", "name": "坤博精工", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "834950", "name": "迅安科技", "tag": "消费电子", "reason": "", "hidden_tag": "北交所" }, { "code": "836699", "name": "海达尔", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "870726", "name": "鸿智科技", "tag": "大消费", "reason": "", "hidden_tag": "北交所" }, { "code": "872953", "name": "国子软件", "tag": "AI", "reason": "", "hidden_tag": "北交所" }, { "code": "873726", "name": "卓兆点胶", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "873693", "name": "阿为特", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "873679", "name": "前进科技", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "873703", "name": "广厦环能", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "871263", "name": "莱赛激光", "tag": "智能机器", "reason": "", "hidden_tag": "北交所" }, { "code": "873806", "name": "云星宇", "tag": "AI", "reason": "", "hidden_tag": "北交所" }, { "code": "603082", "name": "C北自", "tag": "智能机器", "reason": "", "hidden_tag": "次新股" }, { "code": "301502", "name": "N华阳智", "tag": "智能机器", "reason": "", "hidden_tag": "次新股" }, { "code": "688709", "name": "C华微", "tag": "半导体", "reason": "" }, { "code": "688584", "name": "C合晶", "tag": "半导体", "reason": "" }, { "code": "301589", "name": "C诺瓦", "tag": "AI", "reason": "" } ] ================================================ FILE: examples/tag_utils.py ================================================ # -*- coding: utf-8 -*- import json import os from collections import Counter from zvt.api.utils import china_stock_code_to_id, get_china_exchange from zvt.domain import BlockStock, Block, Stock, LimitUpInfo def get_limit_up_reasons(entity_id): info = LimitUpInfo.query_data( entity_id=entity_id, order=LimitUpInfo.timestamp.desc(), limit=1, return_type="domain" ) topics = [] if info and info[0].reason: topics = topics + info[0].reason.split("+") return topics def get_concept(code): with open(os.path.join(os.path.dirname(__file__), "concept.json")) as f: concept_map = json.load(f) concepts = [item for sublist in concept_map.values() for item in sublist] df = BlockStock.query_data(filters=[BlockStock.stock_code == code, BlockStock.name.in_(concepts)]) return df["name"].tolist() def industry_to_tag(industry): if industry in ["风电设备", "电池", "光伏设备", "能源金属", "电源设备"]: return "赛道" if industry in ["半导体", "电子化学品"]: return "半导体" if industry in ["医疗服务", "中药", "化学制药", "生物制品", "医药商业"]: return "医药" if industry in ["医疗器械"]: return "医疗器械" if industry in ["教育"]: return "教育" if industry in ["贸易行业", "家用轻工", "造纸印刷", "酿酒行业", "珠宝首饰", "美容护理", "食品饮料", "旅游酒店", "商业百货", "纺织服装", "家电行业"]: return "大消费" if industry in ["小金属", "贵金属", "有色金属", "煤炭行业"]: return "资源" if industry in ["消费电子", "电子元件"]: return "消费电子" if industry in ["汽车零部件", "汽车服务", "汽车整车"]: return "汽车" if industry in ["电机", "通用设备", "专用设备", "仪器仪表"]: return "智能机器" if industry in ["电网设备", "电力行业"]: return "电力" if industry in ["光学光电子"]: return "VR" if industry in ["房地产开发", "房地产服务", "工程建设", "水泥建材", "装修装饰", "装修建材", "工程咨询服务", "钢铁行业", "工程机械"]: return "房地产" if industry in ["非金属材料", "包装材料", "化学制品", "化肥行业", "化学原料", "化纤行业", "塑料制品", "玻璃玻纤", "橡胶制品"]: return "化工" if industry in ["交运设备", "船舶制造", "航运港口", "公用事业", "燃气", "航空机场", "环保行业", "石油行业", "铁路公路", "采掘行业"]: return "公用" if industry in ["证券", "保险", "银行", "多元金融"]: return "金融" if industry in ["互联网服务", "软件开发", "计算机设备", "游戏", "通信服务", "通信设备"]: return "AI" if industry in ["文化传媒"]: return "传媒" if industry in ["农牧饲渔", "农药兽药"]: return "农业" if industry in ["物流行业"]: return "统一大市场" if industry in ["航天航空", "船舶制造"]: return "军工" if industry in ["专业服务"]: return "专业服务" def build_default_tags(codes, provider="em"): df_block = Block.query_data(provider=provider, filters=[Block.category == "industry"]) industry_codes = df_block["code"].tolist() tags = [] for code in codes: block_stocks = BlockStock.query_data( provider=provider, filters=[BlockStock.code.in_(industry_codes), BlockStock.stock_code == code], return_type="domain", ) if block_stocks: block_stock = block_stocks[0] tags.append( { "code": block_stock.stock_code, "name": block_stock.stock_name, "tag": industry_to_tag(block_stock.name), "reason": "", } ) else: print(f"no industry for {code}") return tags def get_main_line_tags(): with open(os.path.join(os.path.dirname(__file__), "main_line_tags.json")) as f: return json.load(f) def get_main_line_hidden_tags(): with open(os.path.join(os.path.dirname(__file__), "main_line_hidden_tags.json")) as f: return json.load(f) def replace_tags(old_tag="仪器仪表"): with open(os.path.join(os.path.dirname(__file__), "stock_tags.json")) as f: stock_tags = json.load(f) for stock_tag in stock_tags: if stock_tag["tag"] == old_tag: df = BlockStock.query_data(filters=[BlockStock.stock_code == stock_tag["code"]]) blocks = df["name"].tolist() for block in blocks: tag = industry_to_tag(industry=block) if tag: stock_tag["tag"] = tag break with open("result.json", "w") as json_file: json.dump(stock_tags, json_file, indent=2, ensure_ascii=False) def check_tags(): with open(os.path.join(os.path.dirname(__file__), "stock_tags.json")) as f: stock_tags = json.load(f) tags = set() hidden_tags = set() stocks = [] final_tags = [] for stock_tag in stock_tags: stock_code = stock_tag["code"] if not stock_code.isdigit() or (len(stock_code) != 6): print(stock_code) tags.add(stock_tag["tag"]) hidden_tags.add(stock_tag.get("hidden_tag")) if stock_code in stocks: print(stock_tag) else: final_tags.append(stock_tag) stocks.append(stock_code) # with open("result.json", "w") as json_file: # json.dump(final_tags, json_file, indent=2, ensure_ascii=False) print(tags) print(hidden_tags) print(len(stocks)) count = Counter(stocks) duplicates = [item for item, frequency in count.items() if frequency > 1] print(duplicates) def get_hidden_code(code): exchange = get_china_exchange(code=code) if exchange == "bj": return "北交所" def get_core_tag(codes): # 从stock_tags.json读取 other_codes = [] with open(os.path.join(os.path.dirname(__file__), "stock_tags.json")) as f: stock_tags = json.load(f) code_tag_hidden_tag_list = [ ( stock_tag["code"], stock_tag["tag"], stock_tag.get("hidden_tag") if stock_tag.get("hidden_tag") else get_hidden_code(stock_tag["code"]), ) for stock_tag in stock_tags if stock_tag["code"] in codes ] other_codes = [code for code in codes if code not in [item[0] for item in code_tag_hidden_tag_list]] for code in other_codes: tags = get_limit_up_reasons(entity_id=china_stock_code_to_id(code=code)) if tags: code_tag_hidden_tag_list.append((code, tags[0], None)) else: code_tag_hidden_tag_list.append((code, "未知", get_hidden_code(code))) return code_tag_hidden_tag_list def group_stocks_by_tag(entities, hidden_tags=None): code_entities_map = {entity.code: entity for entity in entities} tag_stocks = {} code_tag_hidden_tag_list = get_core_tag([entity.code for entity in entities]) for code, tag, hidden_tag in code_tag_hidden_tag_list: if hidden_tags and (hidden_tag in hidden_tags): tag_stocks.setdefault(hidden_tag, []) tag_stocks.get(hidden_tag).append(code_entities_map.get(code)) if (tag != hidden_tag) or (not hidden_tags): tag_stocks.setdefault(tag, []) tag_stocks.get(tag).append(code_entities_map.get(code)) sorted_entities = sorted(tag_stocks.items(), key=lambda x: len(x[1]), reverse=True) return sorted_entities def build_stock_tags_by_block(block_name, tag, hidden_tag): block_stocks = BlockStock.query_data(filters=[BlockStock.name == block_name], return_type="domain") datas = [ { "code": block_stock.stock_code, "name": block_stock.stock_name, "tag": tag, "hidden_tag": hidden_tag, "reason": "", } for block_stock in block_stocks ] # Specify the file path where you want to save the JSON data file_path = f"{tag}.json" # Write JSON data to the file with open(file_path, "w") as json_file: json.dump(datas, json_file, indent=2, ensure_ascii=False) def merge_tags(current_tags, added_tags, force_update=False): code_tags_map = {item["code"]: item for item in current_tags} # Merge for added_tag in added_tags: code_from_added = added_tag["code"] if code_from_added not in code_tags_map: current_tags.append(added_tag) else: # update hidden_tag from added_tag if force_update or (not code_tags_map[code_from_added].get("hidden_tag")): code_tags_map[code_from_added]["hidden_tag"] = added_tag["hidden_tag"] return current_tags def merge_tags_file(current_tags_file, added_tags_file, result_file, force_update=False): # current_tags_file读取 with open(os.path.join(os.path.dirname(__file__), current_tags_file)) as f: current_tags = json.load(f) # added_tags_file读取 with open(os.path.join(os.path.dirname(__file__), added_tags_file)) as f: added_tags = json.load(f) current_tags = merge_tags(current_tags, added_tags, force_update) with open(result_file, "w") as json_file: json.dump(current_tags, json_file, indent=2, ensure_ascii=False) def complete_tags(): with open(os.path.join(os.path.dirname(__file__), "stock_tags.json")) as f: stock_tags = json.load(f) current_codes = [stock_tag["code"] for stock_tag in stock_tags] df = Stock.query_data( provider="em", filters=[ Stock.code.not_in(current_codes), Stock.name.not_like("%退%"), ], ) codes = df["code"].tolist() print(len(codes)) added_tags = build_default_tags(codes=codes, provider="em") with open("result.json", "w") as json_file: json.dump(stock_tags + added_tags, json_file, indent=2, ensure_ascii=False) def refresh_hidden_tags(): with open(os.path.join(os.path.dirname(__file__), "stock_tags.json")) as f: stock_tags = json.load(f) for stock_tag in stock_tags: if not stock_tag.get("hidden_tag"): exchange = get_china_exchange(code=stock_tag["code"]) if exchange == "bj": stock_tag["hidden_tag"] = "北交所" with open("result.json", "w") as json_file: json.dump(stock_tags, json_file, indent=2, ensure_ascii=False) if __name__ == "__main__": # build_stock_tags(block_name="化工原料", tag="化工", hidden_tag=None) # merge_tags(tags_file="stock_tags.json", hidden_tags_file="化工.json", result_file="result.json", force_update=False) # replace_tags(old_tag="仪器仪表") # check_tags() # complete_tags() # refresh_hidden_tags() print(get_concept(code="688787")) ================================================ FILE: examples/trader/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: examples/trader/dragon_and_tiger_trader.py ================================================ # -*- coding: utf-8 -*- from typing import List, Union import pandas as pd from zvt.contract import IntervalLevel from zvt.contract.factor import Factor, Transformer, Accumulator from zvt.domain import Stock, DragonAndTiger from zvt.trader import StockTrader class DragonTigerFactor(Factor): def __init__( self, provider: str = "em", entity_provider: str = "em", entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = [DragonAndTiger.dep1 == "机构专用"], order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, ) -> None: super().__init__( DragonAndTiger, Stock, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, ) def compute_result(self): self.factor_df["filter_result"] = True super().compute_result() class MyTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): return [ DragonTigerFactor( entity_ids=entity_ids, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) ] if __name__ == "__main__": trader = MyTrader(start_timestamp="2020-01-01", end_timestamp="2022-05-01") trader.run() ================================================ FILE: examples/trader/follow_ii_trader.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.api.utils import get_recent_report_date from zvt.contract import ActorType, AdjustType from zvt.domain import StockActorSummary, Stock1dKdata, Stock from zvt.trader import StockTrader from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import is_same_date, to_pd_timestamp class FollowIITrader(StockTrader): finish_date = None def on_time(self, timestamp: pd.Timestamp): recent_report_date = to_pd_timestamp(get_recent_report_date(timestamp)) if self.finish_date and is_same_date(recent_report_date, self.finish_date): return filters = [ StockActorSummary.actor_type == ActorType.raised_fund.value, StockActorSummary.report_date == recent_report_date, ] if self.entity_ids: filters = filters + [StockActorSummary.entity_id.in_(self.entity_ids)] df = StockActorSummary.query_data(filters=filters) if pd_is_not_null(df): self.logger.info(f"{df}") self.finish_date = recent_report_date long_df = df[df["change_ratio"] > 0.05] short_df = df[df["change_ratio"] < -0.5] try: long_targets = set(long_df["entity_id"].to_list()) short_targets = set(short_df["entity_id"].to_list()) if long_targets: self.buy(timestamp=timestamp, entity_ids=long_targets) if short_targets: self.sell(timestamp=timestamp, entity_ids=short_targets) except Exception as e: self.logger.error(e) if __name__ == "__main__": code = "600519" Stock.record_data(provider="em") Stock1dKdata.record_data(code=code, provider="em") StockActorSummary.record_data(code=code, provider="em") FollowIITrader( start_timestamp="2002-01-01", end_timestamp="2021-01-01", codes=[code], provider="em", adjust_type=AdjustType.qfq, profit_threshold=None, ).run() ================================================ FILE: examples/trader/keep_run_trader.py ================================================ # -*- coding: utf-8 -*- import logging from zvt.api.stats import get_top_fund_holding_stocks from zvt.api.stats import get_top_volume_entities from zvt.contract import IntervalLevel from zvt.factors.macd.macd_factor import BullFactor from zvt.trader import StockTrader from zvt.trader.trader_info_api import clear_trader from zvt.utils.time_utils import split_time_interval, date_time_by_interval logger = logging.getLogger(__name__) class MultipleLevelTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): start_timestamp = date_time_by_interval(start_timestamp, -50) return [ BullFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=date_time_by_interval(start_timestamp, -200), end_timestamp=end_timestamp, provider="joinquant", level=IntervalLevel.LEVEL_1WEEK, ), GoldCrossFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="joinquant", level=IntervalLevel.LEVEL_1DAY, ), ] if __name__ == "__main__": start = "2019-01-01" end = "2021-01-01" trader_name = "keep_run_trader" clear_trader(trader_name=trader_name) for time_interval in split_time_interval(start=start, end=end, interval=40): start_timestamp = time_interval[0] end_timestamp = time_interval[-1] # 成交量 vol_df = get_top_volume_entities( entity_type="stock", start_timestamp=date_time_by_interval(start_timestamp, -50), end_timestamp=start_timestamp, pct=0.3, ) # 机构重仓 ii_df = get_top_fund_holding_stocks(timestamp=start_timestamp, pct=0.3, by="trading") current_entity_pool = list(set(vol_df.index.tolist()) & set(ii_df.index.tolist())) logger.info(f"current_entity_pool({len(current_entity_pool)}):{current_entity_pool}") trader = MultipleLevelTrader( start_timestamp=start_timestamp, end_timestamp=end_timestamp, entity_ids=current_entity_pool, trader_name=trader_name, keep_history=True, draw_result=False, rich_mode=False, ) trader.run() ================================================ FILE: examples/trader/ma_trader.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel from zvt.factors.ma.ma_factor import CrossMaFactor from zvt.factors.macd.macd_factor import BullFactor from zvt.trader.trader import StockTrader class MyMaTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): return [ CrossMaFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, windows=[5, 10], need_persist=False, ) ] class MyBullTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): return [ BullFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, adjust_type="hfq", ) ] if __name__ == "__main__": # single stock with cross ma factor MyBullTrader( codes=["000338"], level=IntervalLevel.LEVEL_1DAY, start_timestamp="2019-01-01", end_timestamp="2019-06-30", trader_name="000338_ma_trader", ).run() # single stock with bull factor # MyBullTrader(codes=['000338'], level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01', # end_timestamp='2019-06-30', trader_name='000338_bull_trader').run() # multiple stocks with cross ma factor # MyMaTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01', # end_timestamp='2019-06-30', trader_name='sample_stocks_ma_trader').run() # multiple stocks with bull factor # MyBullTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01', # end_timestamp='2019-06-30', trader_name='sample_stocks_bull_trader').run() ================================================ FILE: examples/trader/macd_day_trader.py ================================================ # -*- coding: utf-8 -*- from typing import List, Tuple import pandas as pd from zvt.contract import IntervalLevel from zvt.contract.factor import Factor from zvt.factors.macd.macd_factor import GoldCrossFactor from zvt.trader import TradingSignal from zvt.trader.trader import StockTrader # 依赖数据 # data_schema: Stock1dHfqKdata # provider: joinquant from zvt.utils.time_utils import date_time_by_interval class MacdDayTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): # 日线策略 start_timestamp = date_time_by_interval(start_timestamp, -50) return [ GoldCrossFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="joinquant", level=IntervalLevel.LEVEL_1DAY, ) ] def on_profit_control(self): # 覆盖该函数做止盈 止损 return super().on_profit_control() def on_time(self, timestamp: pd.Timestamp): # 对selectors选出的标的做进一步处理，或者不使用selector完全自己根据时间和数据生成交易信号 super().on_time(timestamp) def on_trading_signals(self, trading_signals: List[TradingSignal]): # 批量处理交易信号，比如连接交易接口，发邮件，微信推送等 super().on_trading_signals(trading_signals) def on_trading_open(self, timestamp): # 开盘自定义逻辑 super().on_trading_open(timestamp) def on_trading_close(self, timestamp): # 收盘自定义逻辑 super().on_trading_close(timestamp) def on_trading_finish(self, timestamp): # 策略退出自定义逻辑 super().on_trading_finish(timestamp) def on_trading_error(self, timestamp, error): # 出错处理 super().on_trading_error(timestamp, error) def long_position_control(self): # 多头仓位管理 return super().long_position_control() def short_position_control(self): # 空头仓位管理 return super().short_position_control() def on_factor_targets_filtered( self, timestamp, level, factor: Factor, long_targets: List[str], short_targets: List[str] ) -> Tuple[List[str], List[str]]: # 过滤某级别选出的 标的 return super().on_factor_targets_filtered(timestamp, level, factor, long_targets, short_targets) if __name__ == "__main__": trader = MacdDayTrader(start_timestamp="2019-01-01", end_timestamp="2020-01-01") trader.run() # f = VolFactor(start_timestamp='2020-01-01', end_timestamp='2020-04-01') # print(f.result_df) ================================================ FILE: examples/trader/macd_week_and_day_trader.py ================================================ # -*- coding: utf-8 -*- from typing import List, Tuple from zvt.contract import IntervalLevel from zvt.factors.macd.macd_factor import GoldCrossFactor from zvt.trader.trader import StockTrader # 依赖数据 # dataschema: Stock1dHfqKdata Stock1wkHfqKdata # provider: joinquant class MultipleLevelTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): # 同时使用周线和日线策略 return [ GoldCrossFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="joinquant", level=IntervalLevel.LEVEL_1WEEK, ), GoldCrossFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider="joinquant", level=IntervalLevel.LEVEL_1DAY, ), ] def on_targets_selected_from_levels(self, timestamp) -> Tuple[List[str], List[str]]: # 过滤多级别做 多/空 的标的 return super().on_targets_selected_from_levels(timestamp) if __name__ == "__main__": trader = MultipleLevelTrader(start_timestamp="2019-01-01", end_timestamp="2020-01-01") trader.run() ================================================ FILE: examples/utils.py ================================================ # -*- coding: utf-8 -*- import json import logging import os import pandas as pd from zvt.domain import StockNews, Stock, LimitUpInfo from zvt.utils.time_utils import date_time_by_interval, now_pd_timestamp logger = logging.getLogger(__name__) def get_hot_words_config(): with open(os.path.join(os.path.dirname(__file__), "hot.json")) as f: return json.load(f) def count_hot_words(text: str): text = text.upper() hot_words_config = get_hot_words_config() word_stats = {} topic_stats = {} for topic in hot_words_config: topic_count = 0 for word in hot_words_config[topic]: word_stats[word] = text.lower().count(word) topic_count = topic_count + word_stats[word] topic_stats[topic] = topic_count return topic_stats, word_stats def hot_stats(data: pd.Series): pass def group_stocks_by_topic( keyword=None, entities=None, hot_words_config=None, start_timestamp=None, days_ago=60, threshold=3 ): """ :param keyword: :param entities: :param hot_words_config: hot words config为二重结构，即 主题:[分支1，分支2,...]的形式 比如一个有效的item：{"华为":["华为", "mate pro", "星闪", "问界"]} :param start_timestamp: :param days_ago: :param threshold: :return: """ if not start_timestamp: start_timestamp = date_time_by_interval(now_pd_timestamp(), -days_ago) stock_map = {} entity_ids = None if entities: entity_ids = [entity.entity_id for entity in entities] else: entities = Stock.query_data(provider="em", return_type="domain") for entity in entities: stock_map[entity.entity_id] = {"code": entity.code, "name": entity.name} filters = None if keyword: filters = [StockNews.news_title.contains(keyword)] df = StockNews.query_data(start_timestamp=start_timestamp, entity_ids=entity_ids, filters=filters) df = df.groupby("entity_id")["news_title"].apply(",".join).reset_index() if not hot_words_config: hot_words_config = get_hot_words_config() hot_stocks_map = {} topic_count = {} word_count = {} for _, row in df[["entity_id", "news_title"]].iterrows(): entity_id = row["entity_id"] text = row["news_title"] is_hot = False for topic in hot_words_config: topic_count.setdefault(topic, 0) for words in hot_words_config[topic]: hot_stocks_map.setdefault(words, []) word_count.setdefault(words, 0) count = 0 for word in words.split(","): count = text.lower().count(word) + count if count >= threshold: word_count[words] = word_count[words] + 1 topic_count[topic] = topic_count[topic] + 1 hot_stocks_map[words].append( (f"{stock_map[entity_id]['code']}({stock_map[entity_id]['name']})", count) ) is_hot = True if not is_hot: hot_stocks_map.setdefault("其他", []) hot_stocks_map["其他"].append((f"{stock_map[entity_id]['code']}({stock_map[entity_id]['name']})", 0)) sorted_topics = sorted(topic_count.items(), key=lambda item: item[1], reverse=True) sorted_words = sorted(word_count.items(), key=lambda item: item[1], reverse=True) result = [] for topic, count in sorted_topics: topic_words = hot_words_config[topic] topic_words_stocks = [ (f"{words}({count})", sorted(hot_stocks_map[words], key=lambda item: item[1], reverse=True)) for (words, count) in sorted_words if words in topic_words ] result.append((f"{topic}({count})", topic_words_stocks)) result.append(("其他", [("其他", hot_stocks_map.get("其他", ""))])) return result def msg_group_stocks_by_topic( keyword=None, entities=None, hot_words_config=None, start_timestamp=None, days_ago=60, threshold=3 ): group_info = group_stocks_by_topic( keyword=keyword, entities=entities, hot_words_config=hot_words_config, start_timestamp=start_timestamp, days_ago=days_ago, threshold=threshold, ) msg = "" for group in group_info: topic = group[0] msg = msg + f"^^^^^^ {topic} ^^^^^^\n" for topic_word, stocks_count in group[1]: msg = msg + f"{topic_word}\n" stocks = [f"{stock_count[0]} {stock_count[1]}" for stock_count in stocks_count] msg = msg + "\n".join(stocks) + "\n" return msg def get_hot_topics(start_timestamp=None, days_ago=20, limit=15): if not start_timestamp: start_timestamp = date_time_by_interval(now_pd_timestamp(), -days_ago) df = LimitUpInfo.query_data(start_timestamp=start_timestamp, columns=["reason"]) df["reason"] = df["reason"].str.split("+") result = df["reason"].tolist() result = [item for sublist in result for item in sublist] result = pd.Series(result) result = result.value_counts() result = result[:limit].to_dict() return result if __name__ == "__main__": # ids = get_top_performance_entities_by_periods(entity_provider="em", data_provider="em") # # entities = get_entities(provider="em", entity_type="stock", entity_ids=ids, return_type="domain") # # print(msg_group_stocks_by_topic(entities=entities, threshold=1)) get_hot_topics(days_ago=10) ================================================ FILE: examples/z.sh ================================================ nohup python examples/data_runner/kdata_runner.py >/dev/null 2>&1 & nohup python examples/reports/report_tops.py >/dev/null 2>&1 & nohup python examples/reports/report_vol_up.py >/dev/null 2>&1 & ================================================ FILE: init_env.sh ================================================ #!/bin/bash -e platform='unknown' unamestr=`uname` if [[ "$unamestr" == 'Linux' ]]; then platform='linux' elif [[ "$unamestr" == 'Darwin' ]]; then platform='mac' fi install_cmd='sudo apt-get install' if [[ "$platform" == 'mac' ]]; then install_cmd='brew install' fi BASEDIR=`dirname $0` if ! which python > /dev/null; then echo -e "Python3 not found! Install? (y/n) \c" read if [ "$REPLY" = "y" ]; then $install_cmd install python3 fi fi pip_opt='' #pip_opt='-i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn' if ! which virtualenv > /dev/null; then echo -e "virtualenv not found! Install? (y/n) \c" read if [ "$REPLY" = "y" ]; then pip3 install virtualenv $pip_opt fi fi if [ ! -d "$BASEDIR/ve" ]; then virtualenv -p python3 $BASEDIR/ve --no-download echo "Virtualenv created." fi source $BASEDIR/ve/bin/activate cd $BASEDIR export PYTHONPATH=$PYTHONPATH:./src if [ ! -f "$BASEDIR/ve/updated" -o $BASEDIR/requirements.txt -nt $BASEDIR/ve/updated ]; then pip install -r $BASEDIR/requirements.txt $pip_opt touch $BASEDIR/ve/updated echo "Requirements installed." fi pip install ipython jupyterlab $pip_opt echo "env ok" ================================================ FILE: pyproject.toml ================================================ [tool.black] line-length = 120 ================================================ FILE: requirements/dev.txt ================================================ # dev tools pytest == 6.2.0 pre-commit == 2.15.0 rst2pdf ================================================ FILE: requirements/docs.txt ================================================ sphinx==8.1.3 sphinx_autodoc_typehints==3.0.1 rst2pdf==0.103.1 ipython==8.29.0 sphinx_rtd_theme==3.0.2 readthedocs-sphinx-search==0.3.2 ================================================ FILE: requirements.txt ================================================ requests==2.32.0 SQLAlchemy==2.0.36 pandas==2.2.3 pydantic==2.6.4 arrow==1.3.0 openpyxl==3.1.1 demjson3==3.0.6 plotly==5.13.0 dash==2.18.2 jqdatapy==0.1.8 dash-bootstrap-components==1.3.1 dash_daq==0.5.0 scikit-learn==1.5.2 fastapi==0.110.0 fastapi-pagination==0.12.23 apscheduler==3.10.4 eastmoneypy==0.2.0 orjson==3.10.3 numpy==2.1.3 ================================================ FILE: setup.py ================================================ #!/usr/bin/env python # To use a consistent encoding from codecs import open from os import path # Always prefer setuptools over distutils from setuptools import setup, find_packages here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, "README.md"), encoding="utf-8") as f: long_description = f.read() setup( name="zvt", version="0.13.5", description="unified,modular quant framework for human beings ", long_description=long_description, url="https://github.com/zvtvz/zvt", author="foolcage", author_email="5533061@qq.com", classifiers=[ # Optional "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Customer Service", "Intended Audience :: Education", "Intended Audience :: Financial and Insurance Industry", "Topic :: Software Development :: Build Tools", "Topic :: Office/Business :: Financial :: Investment", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", ], keywords="quant stock finance fintech big-data zvt technical-analysis trading-platform pandas fundamental-analysis", package_dir={"": "src"}, packages=find_packages(where="src"), python_requires=">=3.9, <4", include_package_data=True, install_requires=[ "requests==2.32.0", "SQLAlchemy==2.0.36", "pandas==2.2.3", "pydantic==2.6.4", "arrow==1.3.0", "openpyxl==3.1.1", "demjson3==3.0.6", "plotly==5.13.0", "dash==2.18.2", "jqdatapy==0.1.8", "dash-bootstrap-components==1.3.1", "dash_daq==0.5.0", "scikit-learn==1.5.2", "fastapi==0.110.0", "fastapi-pagination==0.12.23", "apscheduler==3.10.4", "eastmoneypy==0.2.0", "orjson==3.10.3", "numpy==2.1.3", ], project_urls={ # Optional "Bug Reports": "https://github.com/zvtvz/zvt/issues", "Funding": "https://www.foolcage.com/zvt", "Say Thanks!": "https://saythanks.io/to/foolcage", "Source": "https://github.com/zvtvz/zvt", }, long_description_content_type="text/markdown", entry_points={ "console_scripts": [ "zvt = zvt.main:main", "zvt_server = zvt.zvt_server:main", "zvt_export = zvt.plugin:export", ], }, license_file="LICENSE", ) ================================================ FILE: sql/change_indices.sql ================================================ -- 由于创建的table有不少使用了enum,导致有类似的约束 -- constraint provider -- check (provider IN ('eastmoney', 'sina', 'netease')) -- 当需要在原来的数据文件上做扩展时,可以去掉约束并做数据迁移 create table indices1 ( id VARCHAR(128) not null, provider VARCHAR(9) not null, timestamp DATETIME, exchange VARCHAR(32), type VARCHAR(5), code VARCHAR(32), name VARCHAR(32), is_delisted BOOLEAN, category VARCHAR(8), primary key (id, provider), check (is_delisted IN (0, 1)) ); INSERT INTO indices1 SELECT * FROM index; DROP TABLE index; ALTER TABLE indices1 RENAME TO indices; update income_statement set report_period='season3' where report_period='seanson3' update finance_factor set report_period='season3' where report_period='seanson3' update balance_sheet set report_period='season3' where report_period='seanson3' update cash_flow_statement set report_period='season3' where report_period='seanson3' ================================================ FILE: sql/change_stock_index.sql ================================================ alter table index_stock add report_period VARCHAR(32); alter table index_stock add report_date DATETIME; alter table index_stock add proportion FLOAT; alter table index_stock add shares FLOAT; alter table index_stock add market_cap FLOAT; ================================================ FILE: sql/reduce_size.sql ================================================ -- k线数据去除索引，方便传输原始数据，重跑zvt会重建 -- 再压缩一下，大小为原来的1/10 drop index stock_1d_hfq_kdata_entity_id_index; drop index stock_1d_hfq_kdata_code_index; drop index stock_1d_hfq_kdata_timestamp_index; VACUUM; ================================================ FILE: sql/samples.sql ================================================ select * from stock_valuation where CAST(strftime('%s', timestamp) AS integer) ==CAST(strftime('%s', '2009-12-14') AS integer) ; ================================================ FILE: src/zvt/__init__.py ================================================ # -*- coding: utf-8 -*- import importlib import json import logging import os import pkgutil import pprint import shutil from logging.handlers import RotatingFileHandler from typing import List import pandas as pd import pkg_resources from pkg_resources import get_distribution, DistributionNotFound from zvt.consts import DATA_SAMPLE_ZIP_PATH, ZVT_TEST_HOME, ZVT_HOME, ZVT_TEST_DATA_PATH, ZVT_TEST_ZIP_DATA_PATH try: dist_name = __name__ __version__ = get_distribution(dist_name).version except DistributionNotFound: __version__ = "unknown" finally: del get_distribution, DistributionNotFound logger = logging.getLogger(__name__) def init_log(file_name="zvt.log", log_dir=None, simple_formatter=True): if not log_dir: log_dir = zvt_env["log_path"] root_logger = logging.getLogger() # reset the handlers root_logger.handlers = [] root_logger.setLevel(logging.INFO) file_name = os.path.join(log_dir, file_name) file_log_handler = RotatingFileHandler(file_name, maxBytes=524288000, backupCount=10) file_log_handler.setLevel(logging.INFO) console_log_handler = logging.StreamHandler() console_log_handler.setLevel(logging.INFO) # create formatter and add it to the handlers if simple_formatter: formatter = logging.Formatter("%(asctime)s %(levelname)s %(threadName)s %(message)s") else: formatter = logging.Formatter( "%(asctime)s %(levelname)s %(threadName)s %(name)s:%(filename)s:%(lineno)s %(funcName)s %(message)s" ) file_log_handler.setFormatter(formatter) console_log_handler.setFormatter(formatter) # add the handlers to the logger root_logger.addHandler(file_log_handler) root_logger.addHandler(console_log_handler) os.environ.setdefault("SQLALCHEMY_WARN_20", "1") pd.set_option("expand_frame_repr", False) pd.set_option("mode.chained_assignment", "raise") pd.set_option("display.max_rows", None) pd.set_option("display.max_columns", None) zvt_env = {} # load default config with open(pkg_resources.resource_filename("zvt", "config.json")) as f: zvt_config = json.load(f) _plugins = {} def init_env(zvt_home: str, **kwargs) -> dict: """ init env :param zvt_home: home path for zvt """ data_path = os.path.join(zvt_home, "data") resource_path = os.path.join(zvt_home, "resources") tmp_path = os.path.join(zvt_home, "tmp") if not os.path.exists(data_path): os.makedirs(data_path) if not os.path.exists(resource_path): os.makedirs(resource_path) if not os.path.exists(tmp_path): os.makedirs(tmp_path) zvt_env["zvt_home"] = zvt_home zvt_env["data_path"] = data_path zvt_env["resource_path"] = resource_path zvt_env["tmp_path"] = tmp_path # path for storing ui results zvt_env["ui_path"] = os.path.join(zvt_home, "ui") if not os.path.exists(zvt_env["ui_path"]): os.makedirs(zvt_env["ui_path"]) # path for storing logs zvt_env["log_path"] = os.path.join(zvt_home, "logs") if not os.path.exists(zvt_env["log_path"]): os.makedirs(zvt_env["log_path"]) init_log() pprint.pprint(zvt_env) init_resources(resource_path=resource_path) # init config init_config(current_config=zvt_config, **kwargs) # init plugin # init_plugins() return zvt_env def init_resources(resource_path, force_overwrite=True): package_name = "zvt" package_dir = pkg_resources.resource_filename(package_name, "resources") from zvt.utils.file_utils import list_all_files files: List[str] = list_all_files(package_dir, ext=None) for source_file in files: dst_file = os.path.join(resource_path, source_file[len(package_dir) + 1 :]) if force_overwrite or not os.path.exists(dst_file): shutil.copyfile(source_file, dst_file) def init_config(pkg_name: str = None, current_config: dict = None, **kwargs) -> dict: """ init config """ # create default config.json if not exist if pkg_name: config_file = f"{pkg_name}_config.json" else: pkg_name = "zvt" config_file = "config.json" logger.info(f"init config for {pkg_name}, current_config:{current_config}") config_path = os.path.join(zvt_env["zvt_home"], config_file) if not os.path.exists(config_path): try: sample_config = pkg_resources.resource_filename(pkg_name, "config.json") if os.path.exists(sample_config): shutil.copyfile(sample_config, config_path) except Exception as e: logger.warning(f"could not load config.json from package {pkg_name}") if os.path.exists(config_path): with open(config_path) as f: config_json = json.load(f) for k in config_json: current_config[k] = config_json[k] # set and save the config for k in kwargs: current_config[k] = kwargs[k] with open(config_path, "w+") as outfile: json.dump(current_config, outfile) pprint.pprint(current_config) logger.info(f"current_config:{current_config}") return current_config def init_plugins(): for finder, name, ispkg in pkgutil.iter_modules(): if name.startswith("zvt_"): try: _plugins[name] = importlib.import_module(name) except Exception as e: logger.warning(f"failed to load plugin {name}", e) logger.info(f"loaded plugins:{_plugins}") def old_db_to_provider_dir(data_path): files = os.listdir(data_path) for file in files: if file.endswith(".db"): # Split the file name to extract the provider provider = file.split("_")[0] # Define the destination directory destination_dir = os.path.join(data_path, provider) # Create the destination directory if it doesn't exist if not os.path.exists(destination_dir): os.makedirs(destination_dir) # Define the source and destination paths source_path = os.path.join(data_path, file) destination_path = os.path.join(destination_dir, file) # Move the file to the destination directory if not os.path.exists(destination_path): shutil.move(source_path, destination_path) logger.info(f"Moved {file} to {destination_dir}") if os.getenv("TESTING_ZVT"): init_env(zvt_home=ZVT_TEST_HOME) # init the sample data if need same = False if os.path.exists(ZVT_TEST_ZIP_DATA_PATH): import filecmp same = filecmp.cmp(ZVT_TEST_ZIP_DATA_PATH, DATA_SAMPLE_ZIP_PATH) if not same: from zvt.contract import * from zvt.utils.zip_utils import unzip shutil.copyfile(DATA_SAMPLE_ZIP_PATH, ZVT_TEST_ZIP_DATA_PATH) unzip(ZVT_TEST_ZIP_DATA_PATH, ZVT_TEST_DATA_PATH) else: init_env(zvt_home=ZVT_HOME) old_db_to_provider_dir(zvt_env["data_path"]) # register to meta import zvt.contract as zvt_contract import zvt.recorders as zvt_recorders import zvt.factors as zvt_factors import platform if platform.system() == "Windows": try: import zvt.recorders.qmt as qmt_recorder except Exception as e: logger.error("QMT not work", e) else: logger.warning("QMT need run in Windows!") __all__ = ["zvt_env", "zvt_config", "init_log", "init_env", "init_config", "__version__"] ================================================ FILE: src/zvt/api/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/api/intent.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.api.kdata import get_kdata_schema from zvt.contract.api import decode_entity_id from zvt.contract.drawer import Drawer, ChartType from zvt.utils.time_utils import to_pd_timestamp def compare( entity_ids=None, codes=None, schema=None, columns=None, schema_map_columns: dict = None, chart_type: ChartType = ChartType.line, start_timestamp=None, scale_value: int = None, ): """ compare indicators(columns) of entities :param entity_ids: :param codes: :param schema: :param columns: :param schema_map_columns: key represents schema, value represents columns :param chart_type: "line", "area", "scatter", default "line" :param start_timestamp: " :param scale_value: compare with same value which scaled to scale_value """ dfs = [] # default compare kdata if schema_map_columns is None and schema is None: entity_type_map_ids = _group_entity_ids(entity_ids=entity_ids) for entity_type in entity_type_map_ids: schema = get_kdata_schema(entity_type=entity_type) df = schema.query_data(entity_ids=entity_type_map_ids.get(entity_type), start_timestamp=start_timestamp) dfs.append(df) all_df = pd.concat(dfs) drawer = Drawer(main_df=all_df, sub_df_list=[all_df[["entity_id", "timestamp", "turnover"]].copy()]) drawer.draw_kline(show=True, scale_value=scale_value) else: if schema_map_columns: for schema in schema_map_columns: columns = ["entity_id", "timestamp"] + schema_map_columns.get(schema) df = schema.query_data( entity_ids=entity_ids, codes=codes, columns=columns, start_timestamp=start_timestamp ) dfs.append(df) elif schema: columns = ["entity_id", "timestamp"] + columns df = schema.query_data(entity_ids=entity_ids, codes=codes, columns=columns, start_timestamp=start_timestamp) dfs.append(df) all_df = pd.concat(dfs) drawer = Drawer(main_df=all_df) drawer.draw(main_chart=chart_type, show=True, scale_value=scale_value) def compare_df(df: pd.DataFrame, chart_type: ChartType = ChartType.line): """ compare indicators(columns) of entities in df :param df: normal df :param chart_type: """ drawer = Drawer(main_df=df) drawer.draw(main_chart=chart_type, show=True) def distribute(data_schema, columns, entity_ids=None, codes=None, histnorm="percent", nbinsx=20, filters=None): """ distribute indicators(columns) of entities :param data_schema: :param columns: :param entity_ids: :param codes: :param histnorm: "percent", "probability", default "percent" :param nbinsx: :param filters: """ columns = ["entity_id", "timestamp"] + columns df = data_schema.query_data(entity_ids=entity_ids, codes=codes, columns=columns, filters=filters) if not entity_ids or codes: df["entity_id"] = "entity_x_distribute" distribute_df(df=df, histnorm=histnorm, nbinsx=nbinsx) def distribute_df(df, histnorm="percent", nbinsx=20): """ distribute indicators(columns) of entities in df :param df: normal df :param histnorm: "percent", "probability", default "percent" :param nbinsx: """ drawer = Drawer(main_df=df) drawer.draw_histogram(show=True, histnorm=histnorm, nbinsx=nbinsx) def composite(entity_id, data_schema, columns, filters=None): """ composite indicators(columns) of entity :param entity_id: :param data_schema: :param columns: :param filters: """ columns = ["entity_id", "timestamp"] + columns df = data_schema.query_data(entity_id=entity_id, columns=columns, filters=filters) composite_df(df=df) def composite_df(df): """ composite indicators(columns) of entity in df :param df: """ drawer = Drawer(main_df=df) drawer.draw_pie(show=True) def composite_all(data_schema, column, timestamp, provider=None, entity_ids=None, filters=None): if type(column) is not str: column = column.name if filters: filters.append([data_schema.timestamp == to_pd_timestamp(timestamp)]) else: filters = [data_schema.timestamp == to_pd_timestamp(timestamp)] df = data_schema.query_data( provider=provider, entity_ids=entity_ids, columns=["entity_id", "timestamp", column], filters=filters, index="entity_id", ) entity_type, exchange, _ = decode_entity_id(df["entity_id"].iloc[0]) pie_df = pd.DataFrame(columns=df.index, data=[df[column].tolist()]) pie_df["entity_id"] = f"{entity_type}_{exchange}_{column}" pie_df["timestamp"] = timestamp drawer = Drawer(main_df=pie_df) drawer.draw_pie(show=True) def _group_entity_ids(entity_ids): entity_type_map_ids = {} for entity_id in entity_ids: entity_type, _, _ = decode_entity_id(entity_id) ids: List = entity_type_map_ids.setdefault(entity_type, []) ids.append(entity_id) return entity_type_map_ids if __name__ == "__main__": # from zvt.domain import Index1wkKdata # from zvt.api.intent import compare # # Index1wkKdata.record_data(provider="em", codes=["399370", "399371"]) # df1 = Index1wkKdata.query_data(code="399371", index="timestamp") # df2 = Index1wkKdata.query_data(code="399370", index="timestamp") # se = df1["close"] / (df2["close"]) # # compare(se) from zvt.domain import CashFlowStatement # # composite( # entity_id="stock_sz_000338", # data_schema=CashFlowStatement, # columns=[ # CashFlowStatement.net_op_cash_flows, # CashFlowStatement.net_investing_cash_flows, # CashFlowStatement.net_financing_cash_flows, # ], # filters=[ # CashFlowStatement.report_period == "year", # CashFlowStatement.report_date == to_pd_timestamp("2015-12-31"), # ], # ) df = CashFlowStatement.query_data( entity_id="stock_sz_000338", columns=[ CashFlowStatement.net_op_cash_flows, CashFlowStatement.net_investing_cash_flows, CashFlowStatement.net_financing_cash_flows, ], filters=[ CashFlowStatement.report_period == "year", CashFlowStatement.report_date == to_pd_timestamp("2015-12-31"), ], index="timestamp", ) composite_df(df=df) # the __all__ is generated __all__ = ["compare", "compare_df", "distribute", "distribute_df", "composite", "composite_df", "composite_all"] ================================================ FILE: src/zvt/api/kdata.py ================================================ # -*- coding: utf-8 -*- from typing import Union import numpy as np import pandas as pd from zvt.contract import IntervalLevel, AdjustType, Mixin from zvt.contract.api import decode_entity_id, get_schema_by_name from zvt.domain import Index1dKdata, Indexus1dKdata, Indexhk1dKdata from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import ( to_date_time_str, TIME_FORMAT_DAY, TIME_FORMAT_ISO8601, to_pd_timestamp, date_time_by_interval, current_date, ) def get_trade_dates(entity_type, start, end=None): if entity_type == "stockus": df = Indexus1dKdata.query_data( entity_id="indexus_us_SPX", provider="em", columns=["timestamp"], start_timestamp=start, end_timestamp=end, order=Indexus1dKdata.timestamp.asc(), return_type="df", ) elif entity_type == "stockhk": df = Indexhk1dKdata.query_data( entity_id="indexhk_hk_HSI", provider="em", columns=["timestamp"], start_timestamp=start, end_timestamp=end, order=Indexhk1dKdata.timestamp.asc(), return_type="df", ) else: df = Index1dKdata.query_data( entity_id="index_sh_000001", provider="em", columns=["timestamp"], start_timestamp=start, end_timestamp=end, order=Index1dKdata.timestamp.asc(), return_type="df", ) return df["timestamp"].tolist() def get_recent_trade_dates(entity_type, target_date=current_date(), days_count=5): max_start = date_time_by_interval(target_date, -days_count - 15) dates = get_trade_dates(entity_type=entity_type, start=max_start) if days_count == 0: return dates[-1:] return dates[-days_count:] def get_latest_kdata_date( entity_type: str, provider: str = None, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY, adjust_type: Union[AdjustType, str] = None, ) -> pd.Timestamp: data_schema: Mixin = get_kdata_schema(entity_type, level=level, adjust_type=adjust_type) filters = None if entity_type == "indexus": filters = [data_schema.entity_id == "indexus_us_SPX"] latest_data = data_schema.query_data( provider=provider, filters=filters, order=data_schema.timestamp.desc(), limit=1, return_type="domain" ) return to_pd_timestamp(latest_data[0].timestamp) def get_kdata_schema( entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY, adjust_type: Union[AdjustType, str] = None, ) -> Mixin: if type(level) == str: level = IntervalLevel(level) if type(adjust_type) == str: adjust_type = AdjustType(adjust_type) # kdata schema rule # name:{entity_type.capitalize()}{IntervalLevel.value.capitalize()}Kdata if adjust_type and (adjust_type != AdjustType.qfq): schema_str = "{}{}{}Kdata".format( entity_type.capitalize(), level.value.capitalize(), adjust_type.value.capitalize(), ) else: schema_str = "{}{}Kdata".format(entity_type.capitalize(), level.value.capitalize()) return get_schema_by_name(schema_str) def get_kdata( entity_id=None, entity_ids=None, level=IntervalLevel.LEVEL_1DAY.value, provider=None, columns=None, return_type="df", start_timestamp=None, end_timestamp=None, filters=None, session=None, order=None, limit=None, index="timestamp", drop_index_col=False, adjust_type: AdjustType = None, ): assert not entity_id or not entity_ids if entity_ids: entity_id = entity_ids[0] else: entity_ids = [entity_id] entity_type, exchange, code = decode_entity_id(entity_id) data_schema: Mixin = get_kdata_schema(entity_type, level=level, adjust_type=adjust_type) return data_schema.query_data( entity_ids=entity_ids, level=level, provider=provider, columns=columns, return_type=return_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters, session=session, order=order, limit=limit, index=index, drop_index_col=drop_index_col, ) def default_adjust_type(entity_type: str) -> AdjustType: """ :type entity_type: entity type, e.g stock, stockhk, stockus """ if entity_type.lower().startswith("stock"): return AdjustType.hfq return AdjustType.qfq def generate_kdata_id(entity_id, timestamp, level): if level >= IntervalLevel.LEVEL_1DAY: return "{}_{}".format(entity_id, to_date_time_str(timestamp, fmt=TIME_FORMAT_DAY)) else: return "{}_{}".format(entity_id, to_date_time_str(timestamp, fmt=TIME_FORMAT_ISO8601)) def to_high_level_kdata(kdata_df: pd.DataFrame, to_level: IntervalLevel): def to_close(s): if pd_is_not_null(s): return s[-1] def to_open(s): if pd_is_not_null(s): return s[0] def to_high(s): return np.max(s) def to_low(s): return np.min(s) def to_sum(s): return np.sum(s) original_level = kdata_df["level"][0] entity_id = kdata_df["entity_id"][0] provider = kdata_df["provider"][0] name = kdata_df["name"][0] code = kdata_df["code"][0] entity_type, _, _ = decode_entity_id(entity_id=entity_id) assert IntervalLevel(original_level) <= IntervalLevel.LEVEL_1DAY assert IntervalLevel(original_level) < IntervalLevel(to_level) df: pd.DataFrame = None if to_level == IntervalLevel.LEVEL_1WEEK: # loffset='-2'　用周五作为时间标签 if entity_type == "stock": df = kdata_df.resample("W", offset=pd.Timedelta(days=-2)).apply( { "close": to_close, "open": to_open, "high": to_high, "low": to_low, "volume": to_sum, "turnover": to_sum, } ) else: df = kdata_df.resample("W", offset=pd.Timedelta(days=-2)).apply( { "close": to_close, "open": to_open, "high": to_high, "low": to_low, "volume": to_sum, "turnover": to_sum, } ) df = df.dropna() # id entity_id timestamp provider code name level df["entity_id"] = entity_id df["provider"] = provider df["code"] = code df["name"] = name return df if __name__ == "__main__": print(get_trade_dates(entity_type="stockhk", start="2025-07-01")) # the __all__ is generated __all__ = [ "get_trade_dates", "get_recent_trade_dates", "get_latest_kdata_date", "get_kdata_schema", "get_kdata", "default_adjust_type", "generate_kdata_id", "to_high_level_kdata", ] ================================================ FILE: src/zvt/api/portfolio.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.api.utils import get_recent_report_date from zvt.contract import PortfolioStockHistory from zvt.contract.api import get_schema_by_name from zvt.domain import ReportPeriod, Fund, Etf from zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp def portfolio_relate_stock(df, portfolio): df["entity_id"] = portfolio.entity_id df["entity_type"] = portfolio.entity_type df["exchange"] = portfolio.exchange df["code"] = portfolio.code df["name"] = portfolio.name return df # 季报只有前十大持仓，半年报和年报才有全量的持仓信息，故根据离timestamp最近的报表(年报 or 半年报)来确定持仓 def get_portfolio_stocks( portfolio_entity=Fund, code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None, ): portfolio_stock = f"{portfolio_entity.__name__}Stock" data_schema: PortfolioStockHistory = get_schema_by_name(portfolio_stock) latests: List[PortfolioStockHistory] = data_schema.query_data( provider=provider, code=code, end_timestamp=timestamp, order=data_schema.timestamp.desc(), limit=1, return_type="domain", ) if latests: latest_record = latests[0] # 获取最新的报表 df = data_schema.query_data( provider=provider, code=code, codes=codes, ids=ids, end_timestamp=timestamp, filters=[data_schema.report_date == latest_record.report_date], ) # 最新的为年报或者半年报 if latest_record.report_period == ReportPeriod.year or latest_record.report_period == ReportPeriod.half_year: return df # 季报，需要结合 年报或半年报 来算持仓 else: step = 0 while step <= 20: report_date = get_recent_report_date(latest_record.report_date, step=step) pre_df = data_schema.query_data( provider=provider, code=code, codes=codes, ids=ids, end_timestamp=timestamp, filters=[data_schema.report_date == to_pd_timestamp(report_date)], ) # df = df.append(pre_df) df = pd.concat([df, pre_df]) # 半年报和年报 if (ReportPeriod.half_year.value in pre_df["report_period"].tolist()) or ( ReportPeriod.year.value in pre_df["report_period"].tolist() ): # 保留最新的持仓 df = df.drop_duplicates(subset=["stock_code"], keep="first") return df step = step + 1 def get_etf_stocks(code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None): return get_portfolio_stocks( portfolio_entity=Etf, code=code, codes=codes, ids=ids, timestamp=timestamp, provider=provider, ) def get_fund_stocks(code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None): return get_portfolio_stocks( portfolio_entity=Fund, code=code, codes=codes, ids=ids, timestamp=timestamp, provider=provider, ) # the __all__ is generated __all__ = ["portfolio_relate_stock", "get_portfolio_stocks", "get_etf_stocks", "get_fund_stocks"] ================================================ FILE: src/zvt/api/selector.py ================================================ # -*- coding: utf-8 -*- import logging import pandas as pd from sqlalchemy import or_, and_ from zvt.api.kdata import default_adjust_type, get_kdata_schema, get_latest_kdata_date, get_recent_trade_dates from zvt.contract import IntervalLevel, AdjustType from zvt.contract.api import get_entity_ids, get_entity_schema from zvt.domain import DragonAndTiger, Stock1dHfqKdata, Stock, LimitUpInfo, StockQuote, Stock1mQuote from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import ( to_pd_timestamp, date_time_by_interval, current_date, next_date, to_date_time_str, TIME_FORMAT_MINUTE2, ) logger = logging.getLogger(__name__) # 500亿 BIG_CAP = 50000000000 # 150亿 MIDDLE_CAP = 15000000000 # 40亿 SMALL_CAP = 4000000000 # 买入榜单 IN_DEPS = ["dep1", "dep2", "dep3", "dep4", "dep5"] # 卖出入榜单 OUT_DEPS = ["dep_1", "dep_2", "dep_3", "dep_4", "dep_5"] def get_entity_ids_by_filter( provider="em", ignore_delist=True, ignore_st=False, ignore_new_stock=False, target_date=None, entity_type="stock", entity_ids=None, ignore_bj=False, exchange=None, ): entity_schema = get_entity_schema(entity_type=entity_type) filters = [] if ignore_new_stock: if not target_date: target_date = current_date() pre_year = date_time_by_interval(target_date, -365) filters += [entity_schema.timestamp <= pre_year] else: if target_date: filters += [entity_schema.timestamp <= target_date] if ignore_delist: filters += [ entity_schema.name.not_like("%退%"), entity_schema.name.not_like("%PT%"), ] if ignore_st: filters += [ entity_schema.name.not_like("%ST%"), entity_schema.name.not_like("%*ST%"), ] if entity_schema == Stock and ignore_bj: filters += [entity_schema.exchange != "bj"] if exchange: filters += [entity_schema.exchange == exchange] return get_entity_ids(provider=provider, entity_schema=entity_schema, filters=filters, entity_ids=entity_ids) def get_limit_up_stocks(timestamp): df = LimitUpInfo.query_data(start_timestamp=timestamp, end_timestamp=timestamp, columns=[LimitUpInfo.entity_id]) if pd_is_not_null(df): return df["entity_id"].tolist() def get_recent_trending_stocks(adjust_type=AdjustType.qfq, provider="em", recent_days=20): kdata_schema = get_kdata_schema("stock", level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type) recent_dates = get_recent_trade_dates(entity_type="stock", days_count=recent_days) start_date = recent_dates[0] df = kdata_schema.query_data( provider=provider, filters=[ kdata_schema.timestamp >= start_date, kdata_schema.turnover >= 500000000, kdata_schema.change_pct > 0.08, ], index="entity_id", ) entity_ids = list(set(df["entity_id"].tolist())) kdata_df = kdata_schema.query_data( provider=provider, entity_ids=entity_ids, filters=[kdata_schema.timestamp >= date_time_by_interval(start_date, -400)], index=["entity_id", "timestamp"], ) from zvt.factors.algorithm import MaTransformer t = MaTransformer(windows=[10, 20, 30, 60, 120, 250]) ma_df = t.transform(kdata_df) end_date = ma_df["timestamp"].max() result_df = ma_df[ (ma_df["timestamp"] == end_date) & (ma_df["turnover"] >= 500000000) & (ma_df["close"] > ma_df["ma20"]) & (ma_df["ma10"] > ma_df["ma20"]) & (ma_df["ma20"] > ma_df["ma30"]) & (ma_df["ma30"] > ma_df["ma60"]) & (ma_df["ma60"] > ma_df["ma120"]) & (ma_df["ma120"] > ma_df["ma250"]) ] return result_df["entity_id"].tolist() def get_recent_active_stocks(adjust_type=AdjustType.qfq, provider="em", recent_days=10): kdata_schema = get_kdata_schema("stock", level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type) recent_dates = get_recent_trade_dates(entity_type="stock", days_count=recent_days) start_date = recent_dates[0] df = kdata_schema.query_data( provider=provider, filters=[kdata_schema.timestamp >= start_date, kdata_schema.is_limit_up == True], index="entity_id", ) entity_ids = list(set(df["entity_id"].tolist())) kdata_df = kdata_schema.query_data( provider=provider, entity_ids=entity_ids, filters=[kdata_schema.timestamp >= start_date], index=["entity_id", "timestamp"], ) from zvt.factors.algorithm import MaTransformer t = MaTransformer(windows=[10]) ma_df = t.transform(kdata_df) end_date = ma_df["timestamp"].max() result_df = ma_df[ (ma_df["close"] > ma_df["ma10"]) & (ma_df["timestamp"] == end_date) & (ma_df["turnover"] >= 500000000) ] return result_df["entity_id"].tolist() def get_dragon_and_tigger_player(start_timestamp, end_timestamp=None, direction="in"): assert direction in ("in", "out") filters = None if direction == "in": filters = [DragonAndTiger.change_pct > 0] columns = ["dep1", "dep2", "dep3"] elif direction == "out": filters = [DragonAndTiger.change_pct > 0] columns = ["dep_1", "dep_2", "dep_3"] df = DragonAndTiger.query_data(start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters) counts = [] for col in columns: counts.append(df[[col, f"{col}_rate"]].groupby(col).count().sort_values(f"{col}_rate", ascending=False)) return counts def get_big_players(start_timestamp, end_timestamp=None, count=40): dep1, dep2, dep3 = get_dragon_and_tigger_player(start_timestamp=start_timestamp, end_timestamp=end_timestamp) # 榜1前40 bang1 = dep1.index.tolist()[:count] # 榜2前40 bang2 = dep2.index.tolist()[:count] # 榜3前40 bang3 = dep3.index.tolist()[:count] return list(set(bang1 + bang2 + bang3)) def get_player_performance(start_timestamp, end_timestamp=None, days=5, players="机构专用", provider="em", buy_rate=5): filters = [] if isinstance(players, str): players = [players] if isinstance(players, list): for player in players: filters.append( or_( and_(DragonAndTiger.dep1 == player, DragonAndTiger.dep1_rate >= buy_rate), and_(DragonAndTiger.dep2 == player, DragonAndTiger.dep2_rate >= buy_rate), and_(DragonAndTiger.dep3 == player, DragonAndTiger.dep3_rate >= buy_rate), and_(DragonAndTiger.dep4 == player, DragonAndTiger.dep4_rate >= buy_rate), and_(DragonAndTiger.dep5 == player, DragonAndTiger.dep5_rate >= buy_rate), ) ) else: raise AssertionError("players should be list or str type") df = DragonAndTiger.query_data( start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters, index=["entity_id", "timestamp"], provider=provider, ) df = df[~df.index.duplicated(keep="first")] records = [] for entity_id, timestamp in df.index: end_date = date_time_by_interval(timestamp, days + round(days + days * 2 / 5 + 30)) kdata = Stock1dHfqKdata.query_data( entity_id=entity_id, start_timestamp=timestamp, end_timestamp=end_date, provider=provider, index="timestamp", ) if len(kdata) <= days: logger.warning(f"ignore {timestamp} -> end_timestamp: {end_date}") break close = kdata["close"] change_pct = (close[days] - close[0]) / close[0] records.append({"entity_id": entity_id, "timestamp": timestamp, f"change_pct": change_pct}) return pd.DataFrame.from_records(records) def get_player_success_rate( start_timestamp, end_timestamp=None, intervals=(3, 5, 10, 60), players=("机构专用", "东方财富证券股份有限公司拉萨团结路第二证券营业部"), provider="em", ): records = [] for player in players: record = {"player": player} for days in intervals: df = get_player_performance( start_timestamp=start_timestamp, end_timestamp=end_timestamp, days=days, players=player, provider=provider, ) rate = len(df[df["change_pct"] > 0]) / len(df) record[f"rate_{days}"] = rate records.append(record) return pd.DataFrame.from_records(records, index="player") def get_players(entity_id, start_timestamp, end_timestamp, provider="em", direction="in", buy_rate=5): columns = ["entity_id", "timestamp"] if direction == "in": for i in range(5): columns.append(f"dep{i + 1}") columns.append(f"dep{i + 1}_rate") elif direction == "out": for i in range(5): columns.append(f"dep_{i + 1}") columns.append(f"dep_{i + 1}_rate") df = DragonAndTiger.query_data( entity_id=entity_id, start_timestamp=start_timestamp, end_timestamp=end_timestamp, provider=provider, columns=columns, index=["entity_id", "timestamp"], ) dfs = [] if direction == "in": for i in range(5): p_df = df[[f"dep{i + 1}", f"dep{i + 1}_rate"]].copy() p_df.columns = ["player", "buy_rate"] dfs.append(p_df) elif direction == "out": for i in range(5): p_df = df[[f"dep_{i + 1}", f"dep_{i + 1}_rate"]].copy() p_df.columns = ["player", "buy_rate"] dfs.append(p_df) player_df = pd.concat(dfs, sort=True) return player_df.sort_index(level=[0, 1]) def get_good_players(timestamp=current_date(), recent_days=400, intervals=(3, 5, 10)): end_timestamp = date_time_by_interval(timestamp, -intervals[-1] - 30) # recent year start_timestamp = date_time_by_interval(end_timestamp, -recent_days) print(f"{start_timestamp} to {end_timestamp}") # 最近一年牛x的营业部 players = get_big_players(start_timestamp=start_timestamp, end_timestamp=end_timestamp) logger.info(players) df = get_player_success_rate( start_timestamp=start_timestamp, end_timestamp=end_timestamp, intervals=intervals, players=players ) good_players = df[(df["rate_3"] > 0.4) & (df["rate_5"] > 0.3) & (df["rate_10"] > 0.3)].index.tolist() return good_players def get_entity_list_by_cap( timestamp, cap_start, cap_end, entity_type="stock", provider=None, adjust_type=None, retry_times=20 ): if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) kdata_schema = get_kdata_schema(entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type) df = kdata_schema.query_data( provider=provider, filters=[kdata_schema.timestamp == to_pd_timestamp(timestamp)], index="entity_id", ) if pd_is_not_null(df): df["cap"] = df["turnover"] / df["turnover_rate"] df_result = df.copy() if cap_start: df_result = df_result.loc[(df["cap"] >= cap_start)] if cap_end: df_result = df_result.loc[(df["cap"] <= cap_end)] return df_result.index.tolist() else: if retry_times == 0: return [] return get_entity_list_by_cap( timestamp=next_date(timestamp), cap_start=cap_start, cap_end=cap_end, entity_type=entity_type, provider=provider, adjust_type=adjust_type, retry_times=retry_times - 1, ) def get_big_cap_stock(timestamp, provider="em", adjust_type=None): return get_entity_list_by_cap( timestamp=timestamp, cap_start=BIG_CAP, cap_end=None, entity_type="stock", provider=provider, adjust_type=adjust_type, ) def get_middle_cap_stock(timestamp, provider="em", adjust_type=None): return get_entity_list_by_cap( timestamp=timestamp, cap_start=MIDDLE_CAP, cap_end=BIG_CAP, entity_type="stock", provider=provider, adjust_type=adjust_type, ) def get_small_cap_stock(timestamp, provider="em", adjust_type=None): return get_entity_list_by_cap( timestamp=timestamp, cap_start=SMALL_CAP, cap_end=MIDDLE_CAP, entity_type="stock", provider=provider, adjust_type=adjust_type, ) def get_mini_cap_stock(timestamp, provider="em", adjust_type=None): return get_entity_list_by_cap( timestamp=timestamp, cap_start=None, cap_end=SMALL_CAP, entity_type="stock", provider=provider, adjust_type=adjust_type, ) def get_mini_and_small_stock(timestamp, provider="em", adjust_type=None): return get_entity_list_by_cap( timestamp=timestamp, cap_start=None, cap_end=MIDDLE_CAP, entity_type="stock", provider=provider, adjust_type=adjust_type, ) def get_middle_and_big_stock(timestamp, provider="em", adjust_type=None): return get_entity_list_by_cap( timestamp=timestamp, cap_start=MIDDLE_CAP, cap_end=None, entity_type="stock", provider=provider, adjust_type=adjust_type, ) def get_limit_up_today(): df = StockQuote.query_data(filters=[StockQuote.is_limit_up], columns=[StockQuote.entity_id]) if pd_is_not_null(df): return df["entity_id"].to_list() def get_top_up_today(n=100): df = StockQuote.query_data(columns=[StockQuote.entity_id], order=StockQuote.change_pct.desc(), limit=n) if pd_is_not_null(df): return df["entity_id"].to_list() def get_shoot_today(up_change_pct=0.03, down_change_pct=-0.03, interval=1): latest = Stock1mQuote.query_data( columns=[Stock1mQuote.time], return_type="df", limit=1, order=Stock1mQuote.time.desc() ) latest_time = int(latest["time"][0]) # interval minutes start_time = latest_time - ((interval + 1) * 60 * 1000) filters = [Stock1mQuote.time > start_time, Stock1mQuote.turnover_rate > 0.02] df = Stock1mQuote.query_data( filters=filters, columns=[ Stock1mQuote.entity_id, Stock1mQuote.is_limit_up, Stock1mQuote.near_limit_up, Stock1mQuote.time, Stock1mQuote.price, ], return_type="df", ) if not pd_is_not_null(df): logger.warning( f"no data found in Stock1mQuote from {to_date_time_str(start_time,fmt=TIME_FORMAT_MINUTE2)} to {to_date_time_str(latest_time,fmt=TIME_FORMAT_MINUTE2)}" ) return None, None up_list = [] up_df = df[(~df["is_limit_up"]) & df["near_limit_up"]] if pd_is_not_null(up_df): up_list = up_df["entity_id"] up_list = list(set(up_list.to_list())) df.sort_values(by=["entity_id", "time"], inplace=True) g_df = df.groupby("entity_id").agg( first_price=("price", "first"), last_price=("price", "last"), last_time=("time", "last"), change_pct=("price", lambda x: (x.iloc[-1] - x.iloc[0]) / x.iloc[0]), ) up = g_df[g_df["change_pct"] >= up_change_pct] down = g_df[g_df["change_pct"] <= down_change_pct] if up_list: up_list = list(set(up_list + up.index.tolist())) else: up_list = up.index.tolist() return up_list, down.index.tolist() def get_top_vol( entity_ids, target_date=None, limit=500, provider="qmt", ): if provider == "qmt": df = StockQuote.query_data( entity_ids=entity_ids, columns=[StockQuote.entity_id], order=StockQuote.turnover.desc(), limit=limit, ) return df["entity_id"].to_list() else: if not target_date: target_date = get_latest_kdata_date(provider="em", entity_type="stock", adjust_type=AdjustType.hfq) df = Stock1dHfqKdata.query_data( provider="em", filters=[Stock1dHfqKdata.timestamp == to_pd_timestamp(target_date)], entity_ids=entity_ids, columns=[Stock1dHfqKdata.entity_id], order=Stock1dHfqKdata.turnover.desc(), limit=limit, ) return df["entity_id"].to_list() def get_top_down_today(n=100): df = StockQuote.query_data(columns=[StockQuote.entity_id], order=StockQuote.change_pct.asc(), limit=n) if pd_is_not_null(df): return df["entity_id"].to_list() def get_limit_down_today(): df = StockQuote.query_data(filters=[StockQuote.is_limit_down], columns=[StockQuote.entity_id]) if pd_is_not_null(df): return df["entity_id"].to_list() def get_high_days_count(entity_ids=None, target_date=current_date(), days_count=10, high_days_count=None): recent_days = get_recent_trade_dates(entity_type="stock", target_date=target_date, days_count=days_count) if recent_days: filters = [LimitUpInfo.timestamp >= recent_days[0]] else: filters = [LimitUpInfo.timestamp >= target_date] if high_days_count: filters = filters + [LimitUpInfo.high_days_count >= high_days_count] df = LimitUpInfo.query_data( entity_ids=entity_ids, filters=filters, columns=[LimitUpInfo.timestamp, LimitUpInfo.entity_id, LimitUpInfo.high_days, LimitUpInfo.high_days_count], ) df_sorted = df.sort_values(by=["entity_id", "timestamp"]) df_latest = df_sorted.drop_duplicates(subset="entity_id", keep="last").reset_index(drop=True) entity_id_to_high_days_map = df_latest.set_index("entity_id")["high_days"].to_dict() return entity_id_to_high_days_map if __name__ == "__main__": # stocks = get_top_vol(entity_ids=None, provider="em") # assert len(stocks) == 500 # Index1dKdata.record_data(provider="em",sleeping_time=0) # print(get_recent_trade_dates(days_count=10)) # print(get_high_days_count(days_count=3, high_days_count=3)) print(get_shoot_today()) # the __all__ is generated __all__ = [ "get_entity_ids_by_filter", "get_limit_up_stocks", "get_dragon_and_tigger_player", "get_big_players", "get_player_performance", "get_player_success_rate", "get_players", "get_good_players", "get_entity_list_by_cap", "get_big_cap_stock", "get_middle_cap_stock", "get_small_cap_stock", "get_mini_cap_stock", "get_mini_and_small_stock", "get_middle_and_big_stock", ] ================================================ FILE: src/zvt/api/stats.py ================================================ # -*- coding: utf-8 -*- import enum import itertools import logging from typing import Union import pandas as pd from zvt.api.kdata import get_kdata_schema, default_adjust_type, get_latest_kdata_date, get_trade_dates from zvt.api.selector import get_entity_ids_by_filter from zvt.api.utils import get_recent_report_date from zvt.contract import Mixin, AdjustType from zvt.contract.api import decode_entity_id, get_entity_schema, get_entity_ids from zvt.contract.drawer import Drawer from zvt.domain import FundStock, StockValuation, BlockStock, Block from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import ( month_start_end_ranges, to_date_time_str, is_same_date, now_pd_timestamp, date_time_by_interval, ) logger = logging.getLogger(__name__) class WindowMethod(enum.Enum): change = "change" avg = "avg" sum = "sum" class TopType(enum.Enum): positive = "positive" negative = "negative" def get_top_performance_by_month( entity_type="stock", start_timestamp="2015-01-01", end_timestamp=now_pd_timestamp(), list_days=None, data_provider=None, ): ranges = month_start_end_ranges(start_date=start_timestamp, end_date=end_timestamp) for month_range in ranges: start_timestamp = month_range[0] end_timestamp = month_range[1] top, _ = get_top_performance_entities( entity_type=entity_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp, list_days=list_days, data_provider=data_provider, ) yield start_timestamp, end_timestamp, top def get_top_performance_entities_by_periods( entity_provider, data_provider, target_date=None, periods=None, entity_ids=None, ignore_new_stock=True, ignore_st=True, entity_type="stock", adjust_type=None, top_count=50, turnover_threshold=100000000, turnover_rate_threshold=0.02, return_type=TopType.positive, ): if periods is None: periods = [*range(1, 21)] if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) kdata_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) entity_schema = get_entity_schema(entity_type=entity_type) if not target_date: target_date = get_latest_kdata_date(provider=data_provider, entity_type=entity_type, adjust_type=adjust_type) if not entity_ids: filter_entity_ids = get_entity_ids_by_filter( provider=entity_provider, ignore_st=ignore_st, ignore_new_stock=ignore_new_stock, entity_schema=entity_schema, target_date=target_date, ) else: filter_entity_ids = entity_ids if not filter_entity_ids: return [] filter_turnover_df = kdata_schema.query_data( filters=[ kdata_schema.turnover >= turnover_threshold, kdata_schema.turnover_rate >= turnover_rate_threshold, ], provider=data_provider, start_timestamp=date_time_by_interval(target_date, -7), end_timestamp=target_date, index="entity_id", columns=["entity_id", "code"], ) if filter_entity_ids: filter_entity_ids = set(filter_entity_ids) & set(filter_turnover_df.index.tolist()) else: filter_entity_ids = filter_turnover_df.index.tolist() if not filter_entity_ids: return [] logger.info(f"{entity_type} filter_entity_ids size: {len(filter_entity_ids)}") filters = [kdata_schema.entity_id.in_(filter_entity_ids)] selected = [] current_start = None real_period = 1 for i, period in enumerate(periods): real_period = max(real_period, period) while True: start = date_time_by_interval(target_date, -real_period) trade_days = get_trade_dates(entity_type=entity_type, start=start, end=target_date) if not trade_days: logger.info(f"no trade days in: {start} to {target_date}") real_period = real_period + 1 continue if current_start and is_same_date(current_start, trade_days[0]): logger.info("ignore same trade days") real_period = real_period + 1 continue break current_start = trade_days[0] current_end = trade_days[-1] logger.info(f"trade days in: {current_start} to {current_end}, real_period: {real_period} ") positive_df, negative_df = get_top_performance_entities( entity_type=entity_type, start_timestamp=current_start, end_timestamp=current_end, kdata_filters=filters, pct=1, show_name=True, entity_provider=entity_provider, data_provider=data_provider, adjust_type=adjust_type, return_type=return_type, ) if return_type == TopType.positive: df = positive_df else: df = negative_df if pd_is_not_null(df): selected = selected + df.index[:top_count].tolist() selected = list(dict.fromkeys(selected)) return selected, real_period def get_top_performance_entities( entity_type="stock", start_timestamp=None, end_timestamp=None, pct=0.1, return_type=None, adjust_type: Union[AdjustType, str] = None, entity_filters=None, kdata_filters=None, show_name=False, list_days=None, entity_provider=None, data_provider=None, ): if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) if not entity_filters: entity_filters = [] if list_days: entity_schema = get_entity_schema(entity_type=entity_type) list_date = date_time_by_interval(start_timestamp, -list_days) entity_filters += [entity_schema.list_date <= list_date] filter_entities = get_entity_ids( provider=entity_provider, entity_type=entity_type, filters=entity_filters, ) if not filter_entities: logger.warning(f"no entities selected") return None, None if not kdata_filters: kdata_filters = [] kdata_filters = kdata_filters + [data_schema.entity_id.in_(filter_entities)] return get_top_entities( data_schema=data_schema, start_timestamp=start_timestamp, end_timestamp=end_timestamp, column="close", pct=pct, method=WindowMethod.change, return_type=return_type, kdata_filters=kdata_filters, show_name=show_name, data_provider=data_provider, ) def get_top_fund_holding_stocks(timestamp=None, pct=0.3, by=None): if not timestamp: timestamp = now_pd_timestamp() # 季报一般在report_date后1个月内公布，年报2个月内，年报4个月内 # 所以取时间点的最近的两个公布点，保证取到数据 # 所以，这是个滞后的数据，只是为了看个大概，毕竟模糊的正确better than 精确的错误 report_date = get_recent_report_date(timestamp, 1) fund_cap_df = FundStock.query_data( filters=[ FundStock.report_date >= report_date, FundStock.timestamp <= timestamp, ], columns=["stock_id", "market_cap"], ) fund_cap_df = fund_cap_df.groupby("stock_id")["market_cap"].sum().sort_values(ascending=False) # 直接根据持有市值返回 if not by: s = fund_cap_df.iloc[: int(len(fund_cap_df) * pct)] return s.to_frame() # 按流通盘比例 if by == "trading": columns = ["entity_id", "circulating_market_cap"] # 按市值比例 elif by == "all": columns = ["entity_id", "market_cap"] entity_ids = fund_cap_df.index.tolist() start_timestamp = date_time_by_interval(timestamp, -30) cap_df = StockValuation.query_data( entity_ids=entity_ids, filters=[ StockValuation.timestamp >= start_timestamp, StockValuation.timestamp <= timestamp, ], columns=columns, ) if by == "trading": cap_df = cap_df.rename(columns={"circulating_market_cap": "cap"}) elif by == "all": cap_df = cap_df.rename(columns={"market_cap": "cap"}) cap_df = cap_df.groupby("entity_id").mean() result_df = pd.concat([cap_df, fund_cap_df], axis=1, join="inner") result_df["pct"] = result_df["market_cap"] / result_df["cap"] pct_df = result_df["pct"].sort_values(ascending=False) s = pct_df.iloc[: int(len(pct_df) * pct)] return s.to_frame() def get_performance( entity_ids, start_timestamp=None, end_timestamp=None, adjust_type: Union[AdjustType, str] = None, data_provider=None, ): entity_type, _, _ = decode_entity_id(entity_ids[0]) if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) result, _ = get_top_entities( data_schema=data_schema, column="close", start_timestamp=start_timestamp, end_timestamp=end_timestamp, pct=1, method=WindowMethod.change, return_type=TopType.positive, kdata_filters=[data_schema.entity_id.in_(entity_ids)], data_provider=data_provider, ) return result def get_performance_stats_by_month( entity_type="stock", start_timestamp="2015-01-01", end_timestamp=now_pd_timestamp(), adjust_type: Union[AdjustType, str] = None, data_provider=None, ): ranges = month_start_end_ranges(start_date=start_timestamp, end_date=end_timestamp) month_stats = {} for month_range in ranges: start_timestamp = month_range[0] end_timestamp = month_range[1] logger.info(f"calculate range [{start_timestamp}, {end_timestamp}]") stats = get_performance_stats( entity_type=entity_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp, adjust_type=adjust_type, data_provider=data_provider, ) if stats: month_stats[f"{to_date_time_str(start_timestamp)}"] = stats return pd.DataFrame.from_dict(data=month_stats, orient="index") def get_performance_stats( entity_type="stock", start_timestamp=None, end_timestamp=None, adjust_type: Union[AdjustType, str] = None, data_provider=None, changes=((-1, -0.5), (-0.5, -0.2), (-0.2, 0), (0, 0.2), (0.2, 0.5), (0.5, 1), (1, 1000)), ): if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) score_df, _ = get_top_entities( data_schema=data_schema, column="close", start_timestamp=start_timestamp, end_timestamp=end_timestamp, pct=1, method=WindowMethod.change, return_type=TopType.positive, data_provider=data_provider, ) if pd_is_not_null(score_df): result = {} for change in changes: range_start = change[0] range_end = change[1] key = f"pct_{range_start}_{range_end}" df = score_df[(score_df["score"] >= range_start) & (score_df["score"] < range_end)] result[key] = len(df) return result def get_top_volume_entities( entity_type="stock", entity_ids=None, start_timestamp=None, end_timestamp=None, pct=0.1, return_type=TopType.positive, adjust_type: Union[AdjustType, str] = None, method=WindowMethod.avg, data_provider=None, threshold=None, ): if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) filters = [] if entity_ids: filters.append(data_schema.entity_id.in_(entity_ids)) if threshold: filters.append(data_schema.turnover >= threshold) result, _ = get_top_entities( data_schema=data_schema, start_timestamp=start_timestamp, end_timestamp=end_timestamp, column="turnover", pct=pct, method=method, return_type=return_type, kdata_filters=filters, data_provider=data_provider, ) return result def get_top_turnover_rate_entities( entity_type="stock", entity_ids=None, start_timestamp=None, end_timestamp=None, pct=0.1, return_type=TopType.positive, adjust_type: Union[AdjustType, str] = None, method=WindowMethod.avg, data_provider=None, threshold=None, ): if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) filters = [] if entity_ids: filters.append(data_schema.entity_id.in_(entity_ids)) if threshold: filters.append(data_schema.turnover_rate >= threshold) result, _ = get_top_entities( data_schema=data_schema, start_timestamp=start_timestamp, end_timestamp=end_timestamp, column="turnover_rate", pct=pct, method=method, return_type=return_type, kdata_filters=filters, data_provider=data_provider, ) return result def get_top_entities( data_schema: Mixin, column: str, start_timestamp=None, end_timestamp=None, pct=0.1, method: WindowMethod = WindowMethod.change, return_type: TopType = None, kdata_filters=None, show_name=False, data_provider=None, ): """ get top entities in specific domain between time range :param data_schema: schema in domain :param column: schema column :param start_timestamp: :param end_timestamp: :param pct: range (0,1] :param method: :param return_type: :param entity_filters: :param kdata_filters: :param show_name: show entity name :return: """ if type(method) == str: method = WindowMethod(method) if type(return_type) == str: return_type = TopType(return_type) if show_name: columns = ["entity_id", column, "name"] else: columns = ["entity_id", column] all_df = data_schema.query_data( start_timestamp=start_timestamp, end_timestamp=end_timestamp, columns=columns, filters=kdata_filters, provider=data_provider, ) if not pd_is_not_null(all_df): return None, None g = all_df.groupby("entity_id") tops = {} names = {} for entity_id, df in g: if method == WindowMethod.change: start = df[column].iloc[0] end = df[column].iloc[-1] if start != 0: change = (end - start) / abs(start) else: change = 0 tops[entity_id] = change elif method == WindowMethod.avg: tops[entity_id] = df[column].mean() elif method == WindowMethod.sum: tops[entity_id] = df[column].sum() if show_name: names[entity_id] = df["name"].iloc[0] positive_df = None negative_df = None top_index = int(len(tops) * pct) if return_type is None or return_type == TopType.positive: # from big to small positive_tops = {k: v for k, v in sorted(tops.items(), key=lambda item: item[1], reverse=True)} positive_tops = dict(itertools.islice(positive_tops.items(), top_index)) positive_df = pd.DataFrame.from_dict(positive_tops, orient="index") col = "score" positive_df.columns = [col] positive_df.sort_values(by=col, ascending=False) if return_type is None or return_type == TopType.negative: # from small to big negative_tops = {k: v for k, v in sorted(tops.items(), key=lambda item: item[1])} negative_tops = dict(itertools.islice(negative_tops.items(), top_index)) negative_df = pd.DataFrame.from_dict(negative_tops, orient="index") col = "score" negative_df.columns = [col] negative_df.sort_values(by=col) if names: if pd_is_not_null(positive_df): positive_df["name"] = positive_df.index.map(lambda x: names[x]) if pd_is_not_null(negative_df): negative_df["name"] = negative_df.index.map(lambda x: names[x]) return positive_df, negative_df def show_month_performance(): dfs = [] for timestamp, df in get_top_performance_by_month(start_timestamp="2005-01-01", list_days=250): if pd_is_not_null(df): df = df.reset_index(drop=True) df["entity_id"] = "stock_cn_performance" df["timestamp"] = timestamp dfs.append(df) all_df = pd.concat(dfs) print(all_df) drawer = Drawer(main_df=all_df) drawer.draw_scatter(show=True) def show_industry_composition(entity_ids, timestamp): block_df = Block.query_data(provider="eastmoney", filters=[Block.category == "industry"], index="entity_id") block_ids = block_df.index.tolist() block_df = BlockStock.query_data(entity_ids=block_ids, filters=[BlockStock.stock_id.in_(entity_ids)]) s = block_df["name"].value_counts() cycle_df = pd.DataFrame(columns=s.index, data=[s.tolist()]) cycle_df["entity_id"] = "stock_cn_industry" cycle_df["timestamp"] = timestamp drawer = Drawer(main_df=cycle_df) drawer.draw_pie(show=True) def get_change_ratio( entity_type="stock", start_timestamp=None, end_timestamp=None, adjust_type: Union[AdjustType, str] = None, provider="em", ): def cal_ratio(df): positive = df[df["direction"]] other = df[~df["direction"]] return len(positive) / len(other) if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_type) data_schema = get_kdata_schema(entity_type=entity_type, adjust_type=adjust_type) kdata_df = data_schema.query_data(provider=provider, start_timestamp=start_timestamp, end_timestamp=end_timestamp) kdata_df["direction"] = kdata_df["change_pct"] > 0 ratio_df = kdata_df.groupby("timestamp").apply(lambda df: cal_ratio(df)) return ratio_df if __name__ == "__main__": normal_stock_ids = get_entity_ids_by_filter( entity_type="stockus", provider="em", ignore_delist=True, ignore_st=False, ignore_new_stock=False ) print( get_top_performance_entities_by_periods( entity_provider="em", data_provider="em", entity_ids=normal_stock_ids, entity_type="stockus", adjust_type=AdjustType.qfq, turnover_threshold=10000000, turnover_rate_threshold=0, ) ) # the __all__ is generated __all__ = [ "WindowMethod", "TopType", "get_top_performance_by_month", "get_top_performance_entities_by_periods", "get_top_performance_entities", "get_top_fund_holding_stocks", "get_performance", "get_performance_stats_by_month", "get_performance_stats", "get_top_volume_entities", "get_top_turnover_rate_entities", "get_top_entities", "show_month_performance", "show_industry_composition", "get_change_ratio", ] ================================================ FILE: src/zvt/api/utils.py ================================================ # -*- coding: utf-8 -*- from typing import Type from zvt.contract import Mixin from zvt.domain import ReportPeriod from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp def to_report_period_type(report_date): the_date = to_pd_timestamp(report_date) if the_date.month == 3 and the_date.day == 31: return ReportPeriod.season1.value if the_date.month == 6 and the_date.day == 30: return ReportPeriod.half_year.value if the_date.month == 9 and the_date.day == 30: return ReportPeriod.season3.value if the_date.month == 12 and the_date.day == 31: return ReportPeriod.year.value return None def get_recent_report_date(the_date=now_pd_timestamp(), step=0): the_date = to_pd_timestamp(the_date) assert step >= 0 if the_date.month >= 10: recent = "{}{}".format(the_date.year, "-09-30") elif the_date.month >= 7: recent = "{}{}".format(the_date.year, "-06-30") elif the_date.month >= 4: recent = "{}{}".format(the_date.year, "-03-31") else: recent = "{}{}".format(the_date.year - 1, "-12-31") if step == 0: return recent else: step = step - 1 return get_recent_report_date(recent, step) def get_recent_report_period(the_date=now_pd_timestamp(), step=0): return to_report_period_type(get_recent_report_date(the_date, step=step)) def get_china_exchange(code): code_ = int(code) if 800000 >= code_ >= 600000: return "sh" elif code_ >= 400000: return "bj" else: return "sz" def china_stock_code_to_id(code): return "{}_{}_{}".format("stock", get_china_exchange(code), code) def value_to_pct(value, default=0): return value / 100 if value else default def value_multiply(value, multiplier, default=0): return value * multiplier if value else default def float_to_pct_str(value): return f"{round(value * 100, 2)}%" def get_recent_report(data_schema: Type[Mixin], timestamp, entity_id=None, filters=None, max_step=2): i = 0 while i < max_step: report_date = get_recent_report_date(the_date=timestamp, step=i) if filters: filters = filters + [data_schema.report_date == to_pd_timestamp(report_date)] else: filters = [data_schema.report_date == to_pd_timestamp(report_date)] df = data_schema.query_data(entity_id=entity_id, filters=filters) if pd_is_not_null(df): return df i = i + 1 # the __all__ is generated __all__ = [ "to_report_period_type", "get_recent_report_date", "get_recent_report_period", "get_china_exchange", "china_stock_code_to_id", "value_to_pct", "value_multiply", "float_to_pct_str", "get_recent_report", ] ================================================ FILE: src/zvt/autocode/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule generator from .generator import * from .generator import __all__ as _generator_all __all__ += _generator_all # import all from submodule templates from .templates import * from .templates import __all__ as _templates_all __all__ += _templates_all ================================================ FILE: src/zvt/autocode/generator.py ================================================ # -*- coding: utf-8 -*- import logging import os from typing import List from zvt.autocode.templates import all_tpls from zvt.contract import IntervalLevel, AdjustType from zvt.utils.file_utils import list_all_files from zvt.utils.git_utils import get_git_user_name, get_git_user_email from zvt.utils.time_utils import now_pd_timestamp logger = logging.getLogger(__name__) def all_sub_modules(dir_path: str): """ list all module name in specific directory :param dir_path: :return: """ modules = [] for entry in os.scandir(dir_path): if entry.is_dir() or (entry.path.endswith(".py") and not entry.path.endswith("__init__.py")): module_name = os.path.splitext(os.path.basename(entry.path))[0] # ignore hidden if module_name.startswith(".") or not module_name[0].isalpha(): continue modules.append(module_name) return modules def _remove_start_end(line: str, start="class ", end="("): if line.startswith(start) and (end in line): start_index = len(start) end_index = line.index(end) return line[start_index:end_index] if not start and (end in line): end_index = line.index(end) return line[:end_index] def _get_interface_name(line: str): """ get interface name of the line :param line: the line of the source :return: """ if line.startswith("class "): return _remove_start_end(line, "class ", "(") elif line.startswith("def "): return _remove_start_end(line, "def ", "(") def _get_var_name(line: str): """ get var name of the line :param line: the line of the source :return: """ if not _get_interface_name(line): words = line.split(" ") if len(words) >= 2 and words[1] == "=": return words[0] def all_sub_all(sub_module): return """ # import all from submodule {0} from .{0} import * from .{0} import __all__ as _{0}_all __all__ += _{0}_all""".format( sub_module ) def fill_package_if_not_exist(dir_path: str): fill_package(dir_path) for entry in os.scandir(dir_path): if entry.is_dir(): fill_package(entry.path) fill_package_if_not_exist(entry.path) elif entry.is_file(): pass def fill_package(dir_path: str): base_name = os.path.basename(dir_path) if base_name[0].isalpha(): if os.path.isdir(dir_path): pkg_file = os.path.join(dir_path, "__init__.py") if not os.path.exists(pkg_file): package_template = "# -*- coding: utf-8 -*-\n" with open(pkg_file, "w", encoding="utf-8") as outfile: outfile.write(package_template) def gen_exports( dir_path="./domain", gen_flag="# the __all__ is generated", export_from_package=False, exclude_modules=None, export_modules=None, excludes=None, export_var=False, ): if not excludes: excludes = ["logger"] if os.path.isfile(dir_path): files = [dir_path] else: fill_package_if_not_exist(dir_path=dir_path) files = list_all_files(dir_path=dir_path) for file in files: exports = [] lines = [] # read and generate __all__ with open(file, encoding="utf-8") as fp: line = fp.readline() while line: if line.startswith(gen_flag): break lines.append(line) export = _get_interface_name(line) if export_var and not export: export = _get_var_name(line) if export and export[0].isalpha() and export not in excludes: exports.append(export) line = fp.readline() print(f"{file}:{exports}") end_empty_lines_count = 0 for i in range(-1, -len(lines) - 1, -1): if not lines[i].isspace(): break end_empty_lines_count = end_empty_lines_count + 1 lines = lines[: len(lines) - end_empty_lines_count] if not lines: lines.append("# -*- coding: utf-8 -*-#") lines.append("\n\n") lines.append(gen_flag) lines.append("\n") exports_str = f"__all__ = {exports}" exports_str = exports_str.replace("'", '"') if len(exports_str) > 120: exports_wrap = [f'\n "{item}",' for item in exports] exports_str = "__all__ = [" + "".join(exports_wrap) + "\n]" exports_str = exports_str.replace("'", '"') lines.append(exports_str) lines.append("\n") # the package module if export_from_package: basename = os.path.basename(file) if basename == "__init__.py": dir_path = os.path.dirname(file) modules = all_sub_modules(dir_path) if modules: if exclude_modules: modules = set(modules) - set(exclude_modules) if export_modules: modules = set(modules) & set(export_modules) lines.append( """ # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules""" ) for mod in modules: lines.append(all_sub_all(mod)) lines.append("\n") # write with __all__ with open(file, mode="w", encoding="utf-8") as fp: fp.writelines(lines) # kdata schema rule # 1)name:{entity_type.capitalize()}{IntervalLevel.value.capitalize()}Kdata # 2)one db file for one schema def gen_kdata_schema( pkg: str, entity_type: str, levels: List[IntervalLevel], adjust_types=None, entity_in_submodule: bool = False, kdata_module="quotes", ): if adjust_types is None: adjust_types = [None] tables = [] base_path = "./domain" if kdata_module: base_path = os.path.join(base_path, kdata_module) if entity_in_submodule: base_path = os.path.join(base_path, entity_type) if not os.path.exists(base_path): logger.info(f"create dir {base_path}") os.makedirs(base_path) for level in levels: for adjust_type in adjust_types: level = IntervalLevel(level) cap_entity_type = entity_type.capitalize() cap_level = level.value.capitalize() # you should define {EntityType}KdataCommon in kdata_module at first kdata_common = f"{cap_entity_type}KdataCommon" if adjust_type and (adjust_type != AdjustType.qfq): class_name = f"{cap_entity_type}{cap_level}{adjust_type.value.capitalize()}Kdata" table_name = f"{entity_type}_{level.value}_{adjust_type.value.lower()}_kdata" else: class_name = f"{cap_entity_type}{cap_level}Kdata" table_name = f"{entity_type}_{level.value}_kdata" tables.append(table_name) schema_template = f"""# -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from {pkg}.domain.{kdata_module} import {kdata_common} KdataBase = declarative_base() class {class_name}(KdataBase, {kdata_common}): __tablename__ = "{table_name}" register_schema(db_name="{table_name}", schema_base=KdataBase, entity_type="{entity_type}") """ # generate the schema with open(os.path.join(base_path, f"{table_name}.py"), "w", encoding="utf-8") as outfile: outfile.write(schema_template) # generate the package pkg_file = os.path.join(base_path, "__init__.py") if not os.path.exists(pkg_file): package_template = """# -*- coding: utf-8 -*- """ with open(pkg_file, "w", encoding="utf-8") as outfile: outfile.write(package_template) # generate exports gen_exports("./domain") def gen_plugin_project(entity_type, prefix: str = "zvt", dir_path: str = ".", providers=["joinquant"]): """ generate a standard plugin project :param entity_type: the entity type of the plugin project :param prefix: project prefix :param dir_path: the root path for the project :param providers: the supported providers """ # generate project files project = f"{prefix}_{entity_type}" entity_class = entity_type.capitalize() project_path = os.path.join(dir_path, project) if not os.path.exists(project_path): os.makedirs(project_path) current_time = now_pd_timestamp() user_name = get_git_user_name() user_email = get_git_user_email() for tpl in all_tpls(project=project, entity_type=entity_type): file_name = tpl[0] tpl_content = tpl[1].safe_substitute( project=project, entity_type=entity_type, entity_class=entity_class, providers=providers, provider=providers[0], Provider=providers[0].capitalize(), year=current_time.year, user=user_name, email=user_email, ) file_path = os.path.join(project_path, file_name) file_dir = os.path.dirname(file_path) if not os.path.exists(file_dir): os.makedirs(file_dir) with open(file_path, "w", encoding="utf-8") as fh: fh.write(tpl_content) # the __all__ is generated __all__ = [ "all_sub_modules", "all_sub_all", "fill_package_if_not_exist", "fill_package", "gen_exports", "gen_kdata_schema", "gen_plugin_project", ] ================================================ FILE: src/zvt/autocode/templates/.coveragerc.template ================================================ [run] omit = ${project}/recorders/* ================================================ FILE: src/zvt/autocode/templates/.gitignore.template ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so #idea .idea .vscode # Distribution / packaging .Python env/ build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ *.egg-info/ .installed.cfg *.egg # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *,cover .hypothesis/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # IPython Notebook .ipynb_checkpoints # pyenv .python-version # celery beat schedule file celerybeat-schedule # dotenv .env # virtualenv venv/ ENV/ /ve/ # Spyder project settings .spyderproject # Rope project settings .ropeproject /zvt-home /zvt-test-home *.tar.gz .pytest_cache/ # LibreOffice locks .~lock.*# node_modules/ *running.ipynb .DS_Store ================================================ FILE: src/zvt/autocode/templates/.travis.yml.template ================================================ language: python cache: pip python: - '3.6' install: - git fetch --tags --depth=500 - pip install 'pytest>=3.6' --force-reinstall - pip install pytest-cov codecov - pip install -r ./requirements.txt script: - pytest ./tests --cov-config=.coveragerc --cov-report term --cov=./${project} --ignore=tests/recorders/ after_success: - codecov deploy: provider: pypi user: ${user} password: secure: MvVCnUZUuTYxzs8R8kWTocOqZt2MGUX/W1hdKMxKJ9G9hFOYhzjEnbsF1Sc9yHB/0S1OY49068ScbN6iCuyKK2LZy/x3o7cKJgPnQt2LEjy6Yuu9MLxT6v8hJ0MTT3YCn0N8bNv4tOz7KkxxbZ8O/b5MgIKfdjBhVHEj92hhykYzyzlmG8mF+nxU/j0IGCAdxN9+IDioMIvCgnFqQhvkDwva4YbG6Uy+8YMVHFT3I+tSZRSmYxl/IwHJS+5tinI4TxX/ewrI5EznOe0HZvhF+eez+tGenS3pKF4hqF6t4RmKQX2kkdMuPFAvuveoMnPGiaSdoEMni1JPFnZL+3R4GVJPzk4F10v6AZPd10CARXqEwP23JCKAe0WvnbSBkV4iKpkvgxqPA59UwNQ90Jn4pDcTSao1WfRliAnBWCVj7S4x6xjEoNKPvOhXP3RPIYhgEFu4Ma4Cpihkof6VVtlg8VAJKH7j1vWmms3ShdddKXMeF2365sn5Owe671okYmMYMas/v47Y2Cz/0hwpVLuklNR5OYayXMXfUMIG2pH1pEFvg7y8v7ivy1EyCKGva+M/qEnoR1VGWNGSkypJHxG+w8dbtYwf3EYTA9fk/di3ygzepV7RrKJSDf8R+NwFrSfXeoEP07LonbtGN9iIz7Lp5PoB6rv99NHihlB47n1+PtM= on: tags: true notifications: email: recipients: - ${email} ================================================ FILE: src/zvt/autocode/templates/LICENSE.template ================================================ MIT License Copyright (c) ${year} zvtvz Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: src/zvt/autocode/templates/MANIFEST.in.template ================================================ include *.md include *.txt include LICENSE recursive-include ${project}/* * ================================================ FILE: src/zvt/autocode/templates/README-en.md.template ================================================ [![github](https://img.shields.io/github/stars/zvtvz/${project}.svg)](https://github.com/zvtvz/${project}) [![image](https://img.shields.io/pypi/v/${project}.svg)](https://pypi.org/project/${project}/) [![image](https://img.shields.io/pypi/l/${project}.svg)](https://pypi.org/project/${project}/) [![image](https://img.shields.io/pypi/pyversions/${project}.svg)](https://pypi.org/project/${project}/) [![Build Status](https://api.travis-ci.org/zvtvz/${project}.svg?branch=master)](https://travis-ci.org/zvtvz/${project}) ### introduction ### feature ### install ``` pip install ${project} pip show ${project} ``` upgrate to latest version ``` pip install --upgrade ${project} ``` ### usage ### contact wechat foolcage ================================================ FILE: src/zvt/autocode/templates/README.md.template ================================================ [![github](https://img.shields.io/github/stars/zvtvz/${project}.svg)](https://github.com/zvtvz/${project}) [![image](https://img.shields.io/pypi/v/${project}.svg)](https://pypi.org/project/${project}/) [![image](https://img.shields.io/pypi/l/${project}.svg)](https://pypi.org/project/${project}/) [![image](https://img.shields.io/pypi/pyversions/${project}.svg)](https://pypi.org/project/${project}/) [![Build Status](https://api.travis-ci.org/zvtvz/${project}.svg?branch=master)](https://travis-ci.org/zvtvz/${project}) ### 说明 ### 特性 ### 安装 ``` pip install ${project} pip show ${project} ``` 更新到最新版本 ``` pip install --upgrade ${project} ``` ### 使用 ### 联系 微信 foolcage ================================================ FILE: src/zvt/autocode/templates/__init__.py ================================================ # -*- coding: utf-8 -*- import os import string from pkg_resources import resource_string from zvt.utils.file_utils import list_all_files def all_tpls(project: str, entity_type: str): """ return list of templates(location,Template) :param project: :return: """ tpl_dir = os.path.join(os.path.dirname(__file__)) tpl_files = list_all_files(tpl_dir, ext="template", return_base_name=True) tpls = [] for tpl in tpl_files: data = resource_string(__name__, tpl) file_location = os.path.splitext(os.path.basename(tpl))[0] # we assure that line endings are converted to '\n' for all OS data = data.decode(encoding="utf-8").replace(os.linesep, "\n") # change path for specific file # domain if file_location == "kdata_common.py": file_location = f"{project}/domain/quotes/__init__.py" elif file_location == "meta.py": file_location = f"{project}/domain/{entity_type}_meta.py" # recorder elif file_location == "kdata_recorder.py": file_location = f"{project}/recorders/{entity_type}_kdata_recorder.py" elif file_location == "meta_recorder.py": file_location = f"{project}/recorders/{entity_type}_meta_recorder.py" # fill script elif file_location == "fill_project.py": file_location = f"{project}/fill_project.py" # tests elif file_location == "test_pass.py": file_location = f"tests/test_pass.py" elif file_location == "pkg_init.py": file_location = f"{project}/__init__.py" tpls.append((file_location, string.Template(data))) return tpls # the __all__ is generated __all__ = ["all_tpls"] ================================================ FILE: src/zvt/autocode/templates/fill_project.py.template ================================================ # script to auto generate some files from zvt.contract import IntervalLevel from zvt.autocode.generator import gen_exports, gen_kdata_schema if __name__ == '__main__': gen_kdata_schema(pkg='${project}', entity_type='${entity_type}', levels=[IntervalLevel.LEVEL_1DAY], adjust_types=[None]) gen_exports() ================================================ FILE: src/zvt/autocode/templates/kdata_common.py.template ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Float from zvt.domain import KdataCommon # ${entity_type} common kdata class ${entity_class}KdataCommon(KdataCommon): pass ================================================ FILE: src/zvt/autocode/templates/kdata_recorder.py.template ================================================ # -*- coding: utf-8 -*- # -*- coding: utf-8 -*- from zvt import IntervalLevel from zvt.api.kdata import get_kdata_schema from zvt.contract.recorder import FixedCycleDataRecorder from ${project}.domain import ${entity_class}, ${entity_class}KdataCommon class ${Provider}${entity_class}KdataRecorder(FixedCycleDataRecorder): entity_provider = '${provider}' entity_schema = ${entity_class} provider = '${provider}' # register the recorder to data_schema data_schema = ${entity_class}KdataCommon def __init__(self, entity_type='${entity_type}', exchanges=None, entity_ids=None, codes=None, day_data=False, force_update=True, sleeping_time=10, entity_filters=None, real_time=False, fix_duplicate_way='ignore', start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60) -> None: level = IntervalLevel(level) self.data_schema = get_kdata_schema(entity_type=entity_type, level=level, adjust_type=None) super().__init__(entity_type, exchanges, entity_ids, codes, day_data, force_update, sleeping_time, entity_filters, real_time, fix_duplicate_way, start_timestamp, end_timestamp, close_hour, close_minute, level, kdata_use_begin_time, one_day_trading_minutes) def record(self, entity, start, end, size, timestamps): pass ================================================ FILE: src/zvt/autocode/templates/meta.py.template ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, DateTime, Boolean from sqlalchemy.orm import declarative_base from zvt.contract import EntityMixin from zvt.contract.register import register_schema, register_entity ${entity_class}MetaBase = declarative_base() @register_entity(entity_type='${entity_type}') class ${entity_class}(EntityMixin, ${entity_class}MetaBase): __tablename__ = '${entity_type}' # 上市日 list_date = Column(DateTime) # 退市日 end_date = Column(DateTime) register_schema(db_name='${entity_type}_meta', schema_base=${entity_class}MetaBase) ================================================ FILE: src/zvt/autocode/templates/meta_recorder.py.template ================================================ # -*- coding: utf-8 -*- from zvt.contract.recorder import Recorder from ${project}.domain import ${entity_class} class ${Provider}${entity_class}Recorder(Recorder): data_schema = ${entity_class} provider = '${provider}' def run(self): pass ================================================ FILE: src/zvt/autocode/templates/pkg_init.py.template ================================================ # -*- coding: utf-8 -*- import functools from zvt import init_config ${project}_config = {} int_${project}_config = functools.partial(init_config, pkg_name='${project}', current_config=${project}_config) int_${project}_config() __all__ = ['int_${project}_config'] ================================================ FILE: src/zvt/autocode/templates/requirements.txt.template ================================================ zvt ================================================ FILE: src/zvt/autocode/templates/setup.py.template ================================================ #!/usr/bin/env python # To use a consistent encoding from codecs import open from os import path # Always prefer setuptools over distutils from setuptools import setup, find_packages try: # for pip >= 10 from pip._internal.req import parse_requirements except ImportError: # for pip <= 9.0.3 from pip.req import parse_requirements here = path.abspath(path.dirname(__file__)) # Get the long description from the README file with open(path.join(here, "README.md"), encoding="utf-8") as f: long_description = f.read() # Arguments marked as "Required" below must be included for upload to PyPI. # Fields marked as "Optional" may be commented out. install_reqs = parse_requirements("requirements.txt", session=False) try: requirements = [str(ir.req) for ir in install_reqs] except: requirements = [str(ir.requirement) for ir in install_reqs] setup( name="${project}", version="0.0.1", description="unified,modular quant framework for human beings ", long_description=long_description, url="https://github.com/zvtvz/${project}", author="${user}", author_email="${email}", classifiers=[ # Optional "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "Intended Audience :: Customer Service", "Intended Audience :: Education", "Intended Audience :: Financial and Insurance Industry", "Topic :: Software Development :: Build Tools", "Topic :: Office/Business :: Financial :: Investment", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", ], keywords="quant stock finance fintech big-data zvt ma-analysis trading-platform pandas fundamental-analysis", packages=find_packages(include=["${project}.*", "${project}"]), python_requires=">=3.5, <4", include_package_data=True, install_requires=requirements, project_urls={ "Bug Reports": "https://github.com/zvtvz/${project}/issues", "Funding": "https://github.com/zvtvz/${project}", "Say Thanks!": "https://saythanks.io/to/foolcage", "Source": "https://github.com/zvtvz/${project}", }, long_description_content_type="text/markdown", ) ================================================ FILE: src/zvt/autocode/templates/test_pass.py.template ================================================ # -*- coding: utf-8 -*- def test_pass(): pass # the __all__ is generated __all__ = ['test_pass'] ================================================ FILE: src/zvt/broker/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/broker/qmt/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/broker/qmt/context.py ================================================ # -*- coding: utf-8 -*- from typing import Optional from zvt import zvt_env from zvt.broker.qmt.qmt_account import QmtStockAccount class QmtContext(object): def __init__(self): self.qmt_account: Optional[QmtStockAccount] = None qmt_context = QmtContext() def init_qmt_account(qmt_mini_data_path=None, qmt_account_id=None): if not qmt_mini_data_path: qmt_mini_data_path = zvt_env["qmt_mini_data_path"] if not qmt_account_id: qmt_account_id = zvt_env["qmt_account_id"] qmt_context.qmt_account = QmtStockAccount( path=qmt_mini_data_path, account_id=qmt_account_id, trader_name="zvt", session_id=None ) init_qmt_account() # the __all__ is generated __all__ = ["QmtContext", "init_qmt_account"] ================================================ FILE: src/zvt/broker/qmt/errors.py ================================================ # -*- coding: utf-8 -*- class TraderError(Exception): """Base class for exceptions in this module.""" pass class QmtError(TraderError): def __init__(self, message="qmt error"): self.message = message class PositionOverflowError(TraderError): def __init__(self, message="超出仓位限制"): self.message = message # the __all__ is generated __all__ = ["TraderError", "QmtError", "PositionOverflowError"] ================================================ FILE: src/zvt/broker/qmt/qmt_account.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import List from xtquant import xtconstant, xtdata from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback from xtquant.xttype import StockAccount, XtPosition from zvt.broker.qmt.errors import QmtError, PositionOverflowError from zvt.broker.qmt.qmt_quote import _to_qmt_code from zvt.common.trading_models import BuyParameter, PositionType, SellParameter from zvt.trader import AccountService, TradingSignal, OrderType, trading_signal_type_to_order_type from zvt.utils.time_utils import now_pd_timestamp, to_pd_timestamp logger = logging.getLogger(__name__) def _to_qmt_order_type(order_type: OrderType): if order_type == OrderType.order_long: return xtconstant.STOCK_BUY elif order_type == OrderType.order_close_long: return xtconstant.STOCK_SELL class MyXtQuantTraderCallback(XtQuantTraderCallback): def on_connected(self): logger.info("qmt on_connected") def on_smt_appointment_async_response(self, response): logger.info(f"qmt on_smt_appointment_async_response: {vars(response)}") def on_cancel_order_stock_async_response(self, response): logger.info(f"qmt on_cancel_order_stock_async_response: {vars(response)}") def on_disconnected(self): """ 连接断开 :return: """ logger.info(f"qmt on_disconnected") def on_stock_order(self, order): """ 委托回报推送 :param order: XtOrder对象 :return: """ logger.info(f"qmt on_stock_order: {vars(order)}") def on_stock_asset(self, asset): """ 资金变动推送 :param asset: XtAsset对象 :return: """ logger.info(f"qmt on_stock_asset: {vars(asset)}") def on_stock_trade(self, trade): """ 成交变动推送 :param trade: XtTrade对象 :return: """ logger.info(f"qmt on_stock_trade: {vars(trade)}") def on_stock_position(self, position): """ 持仓变动推送 :param position: XtPosition对象 :return: """ logger.info(f"qmt on_stock_position: {vars(position)}") def on_order_error(self, order_error): """ 委托失败推送 :param order_error:XtOrderError 对象 :return: """ logger.info(f"qmt on_order_error: {vars(order_error)}") def on_cancel_error(self, cancel_error): """ 撤单失败推送 :param cancel_error: XtCancelError 对象 :return: """ logger.info(f"qmt on_cancel_error: {vars(cancel_error)}") def on_order_stock_async_response(self, response): """ 异步下单回报推送 :param response: XtOrderResponse 对象 :return: """ logger.info(f"qmt on_order_stock_async_response: {vars(response)}") def on_account_status(self, status): """ :param response: XtAccountStatus 对象 :return: """ logger.info(status.account_id, status.account_type, status.status) class QmtStockAccount(AccountService): def __init__(self, path, account_id, trader_name, session_id=None) -> None: if not session_id: session_id = int(time.time()) self.trader_name = trader_name logger.info(f"path: {path}, account: {account_id}, trader_name: {trader_name}, session: {session_id}") self.xt_trader = XtQuantTrader(path=path, session=session_id) # StockAccount可以用第二个参数指定账号类型，如沪港通传'HUGANGTONG'，深港通传'SHENGANGTONG' self.account = StockAccount(account_id=account_id, account_type="STOCK") # 创建交易回调类对象，并声明接收回调 callback = MyXtQuantTraderCallback() self.xt_trader.register_callback(callback) # 启动交易线程 self.xt_trader.start() # 建立交易连接，返回0表示连接成功 connect_result = self.xt_trader.connect() if connect_result != 0: logger.error(f"qmt trader 连接失败: {connect_result}") raise QmtError(f"qmt trader 连接失败: {connect_result}") logger.info("qmt trader 建立交易连接成功！") # 对交易回调进行订阅，订阅后可以收到交易主推，返回0表示订阅成功 subscribe_result = self.xt_trader.subscribe(self.account) if subscribe_result != 0: logger.error(f"账号订阅失败: {subscribe_result}") raise QmtError(f"账号订阅失败: {subscribe_result}") logger.info("账号订阅成功！") def get_positions(self): positions: List[XtPosition] = self.xt_trader.query_stock_positions(self.account) return positions def get_current_position(self, entity_id, create_if_not_exist=False): stock_code = _to_qmt_code(entity_id=entity_id) # 根据股票代码查询对应持仓 return self.xt_trader.query_stock_position(self.account, stock_code) def get_current_account(self): asset = self.xt_trader.query_stock_asset(self.account) return asset def order_by_amount(self, entity_id, order_price, order_timestamp, order_type, order_amount): stock_code = _to_qmt_code(entity_id=entity_id) fix_result_order_id = self.xt_trader.order_stock( account=self.account, stock_code=stock_code, order_type=_to_qmt_order_type(order_type=order_type), order_volume=order_amount, price_type=xtconstant.FIX_PRICE, price=order_price, strategy_name=self.trader_name, order_remark="order from zvt", ) logger.info(f"order result id: {fix_result_order_id}") def on_trading_signals(self, trading_signals: List[TradingSignal]): for trading_signal in trading_signals: try: self.handle_trading_signal(trading_signal) except Exception as e: logger.exception(e) self.on_trading_error(timestamp=trading_signal.happen_timestamp, error=e) def handle_trading_signal(self, trading_signal: TradingSignal): entity_id = trading_signal.entity_id happen_timestamp = trading_signal.happen_timestamp order_type = trading_signal_type_to_order_type(trading_signal.trading_signal_type) trading_level = trading_signal.trading_level.value # askPrice 多档委卖价 # bidPrice 多档委买价 # askVol 多档委卖量 # bidVol 多档委买量 if now_pd_timestamp() > to_pd_timestamp(trading_signal.due_timestamp): logger.warning( f"the signal is expired, now {now_pd_timestamp()} is after due time: {trading_signal.due_timestamp}" ) return quote = xtdata.get_l2_quote(stock_code=_to_qmt_code(entity_id=entity_id), start_time=happen_timestamp) if order_type == OrderType.order_long: price = quote["askPrice"] elif order_type == OrderType.order_close_long: price = quote["bidPrice"] else: assert False self.order_by_amount( entity_id=entity_id, order_price=price, order_timestamp=happen_timestamp, order_type=order_type, order_amount=trading_signal.order_amount, ) def on_trading_open(self, timestamp): pass def on_trading_close(self, timestamp): pass def on_trading_finish(self, timestamp): pass def on_trading_error(self, timestamp, error): pass def sell(self, position_strategy: SellParameter): # account_type int 账号类型，参见数据字典 # account_id str 资金账号 # stock_code str 证券代码 # volume int 持仓数量 # can_use_volume int 可用数量 # open_price float 开仓价 # market_value float 市值 # frozen_volume int 冻结数量 # on_road_volume int 在途股份 # yesterday_volume int 昨夜拥股 # avg_price float 成本价 # direction int 多空方向，股票不适用；参见数据字典 stock_codes = [_to_qmt_code(entity_id) for entity_id in position_strategy.entity_ids] for i, stock_code in enumerate(stock_codes): pct = position_strategy.sell_pcts[i] position = self.xt_trader.query_stock_position(self.account, stock_code) fix_result_order_id = self.xt_trader.order_stock( account=self.account, stock_code=stock_code, order_type=xtconstant.STOCK_SELL, order_volume=int(position.can_use_volume * pct), price_type=xtconstant.MARKET_SH_CONVERT_5_CANCEL, price=0, strategy_name=self.trader_name, order_remark="order from zvt", ) logger.info(f"order result id: {fix_result_order_id}") def buy(self, buy_parameter: BuyParameter): # account_type int 账号类型，参见数据字典 # account_id str 资金账号 # cash float 可用金额 # frozen_cash float 冻结金额 # market_value float 持仓市值 # total_asset float 总资产 acc = self.get_current_account() # 优先使用金额下单 if buy_parameter.money_to_use: money_to_use = buy_parameter.money_to_use if acc.cash < money_to_use: raise QmtError(f"可用余额不足 {acc.cash} < {money_to_use}") else: # 检查仓位 if buy_parameter.position_type == PositionType.normal: current_pct = round(acc.market_value / acc.total_asset, 2) if current_pct >= buy_parameter.position_pct: raise PositionOverflowError(f"目前仓位为{current_pct}, 已超过请求的仓位: {buy_parameter.position_pct}") money_to_use = acc.total_asset * (buy_parameter.position_pct - current_pct) elif buy_parameter.position_type == PositionType.cash: money_to_use = acc.cash * buy_parameter.position_pct else: assert False stock_codes = [_to_qmt_code(entity_id) for entity_id in buy_parameter.entity_ids] ticks = xtdata.get_full_tick(code_list=stock_codes) if not buy_parameter.weights: stocks_count = len(stock_codes) money_for_stocks = [round(money_to_use / stocks_count)] * stocks_count else: weights_sum = sum(buy_parameter.weights) money_for_stocks = [round(weight / weights_sum) for weight in buy_parameter.weights] for i, stock_code in enumerate(stock_codes): try_price = ticks[stock_code]["askPrice"][3] volume = money_for_stocks[i] / try_price fix_result_order_id = self.xt_trader.order_stock( account=self.account, stock_code=stock_code, order_type=xtconstant.STOCK_BUY, order_volume=volume, price_type=xtconstant.MARKET_SH_CONVERT_5_CANCEL, price=0, strategy_name=self.trader_name, order_remark="order from zvt", ) logger.info(f"order result id: {fix_result_order_id}") if __name__ == "__main__": account = QmtStockAccount(path=r"D:\qmt\userdata_mini", account_id="") account.get_positions() # the __all__ is generated __all__ = ["MyXtQuantTraderCallback", "QmtStockAccount"] ================================================ FILE: src/zvt/broker/qmt/qmt_quote.py ================================================ # -*- coding: utf-8 -*- import logging import time import pandas as pd from xtquant import xtdata from zvt.api.kdata import get_recent_trade_dates from zvt.api.selector import get_entity_ids_by_filter from zvt.contract import IntervalLevel, AdjustType from zvt.contract.api import decode_entity_id, df_to_db, get_db_session from zvt.domain import StockQuote, Stock, Stock1dKdata, StockQuoteLog, Stock1mQuote from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import ( to_date_time_str, current_date, to_pd_timestamp, now_pd_timestamp, date_time_by_interval, now_timestamp_ms, to_timestamp_ms, date_and_time, TIME_FORMAT_MINUTE, is_same_date, ) # https://dict.thinktrader.net/nativeApi/start_now.html?id=e2M5nZ logger = logging.getLogger(__name__) def _to_qmt_code(entity_id): _, exchange, code = decode_entity_id(entity_id=entity_id) return f"{code}.{exchange.upper()}" def _to_zvt_entity_id(qmt_code): code, exchange = qmt_code.split(".") exchange = exchange.lower() return f"stock_{exchange}_{code}" def _to_qmt_dividend_type(adjust_type: AdjustType): if adjust_type == AdjustType.qfq: return "front" elif adjust_type == AdjustType.hfq: return "back" else: return "none" def _qmt_instrument_detail_to_stock(stock_detail): exchange = stock_detail["ExchangeID"].lower() code = stock_detail["InstrumentID"] name = stock_detail["InstrumentName"] list_date = to_pd_timestamp(stock_detail["OpenDate"]) try: end_date = to_pd_timestamp(stock_detail["ExpireDate"]) except: end_date = None pre_close = stock_detail["PreClose"] limit_up_price = stock_detail["UpStopPrice"] limit_down_price = stock_detail["DownStopPrice"] float_volume = stock_detail["FloatVolume"] total_volume = stock_detail["TotalVolume"] entity_id = f"stock_{exchange}_{code}" return { "id": entity_id, "entity_id": entity_id, "timestamp": list_date, "entity_type": "stock", "exchange": exchange, "code": code, "name": name, "list_date": list_date, "end_date": end_date, "pre_close": pre_close, "limit_up_price": limit_up_price, "near_limit_up_price": limit_up_price * 0.98, "limit_down_price": limit_down_price, "float_volume": float_volume, "total_volume": total_volume, } def get_qmt_stocks(include_bj=True): stock_list = xtdata.get_stock_list_in_sector("沪深A股") if include_bj: bj_entity_ids = get_entity_ids_by_filter( provider="em", ignore_delist=True, ignore_st=False, entity_type="stock", exchange="bj" ) bj_stock_list = map(lambda x: _to_qmt_code(x), bj_entity_ids) stock_list += bj_stock_list return stock_list def _build_entity_list(qmt_stocks): entity_list = [] for stock in qmt_stocks: stock_detail = xtdata.get_instrument_detail(stock, False) if stock_detail: entity_list.append(_qmt_instrument_detail_to_stock(stock_detail)) else: code, exchange = stock.split(".") exchange = exchange.lower() entity_id = f"stock_{exchange}_{code}" # get from provider em datas = Stock.query_data(provider="em", entity_id=entity_id, return_type="dict") if datas: entity = datas[0] else: entity = { "id": _to_zvt_entity_id(stock), "entity_id": entity_id, "entity_type": "stock", "exchange": exchange, "code": code, "name": "未获取", } # Do this in other task in days timely # xtdata.download_financial_data(stock_list=[stock], table_list=["Capital"]) capital_datas = xtdata.get_financial_data( [stock], table_list=["Capital"], report_type="report_time", ) df = capital_datas[stock]["Capital"] if pd_is_not_null(df): latest_data = df.iloc[-1] entity["float_volume"] = latest_data["circulating_capital"] entity["total_volume"] = latest_data["total_capital"] tick = xtdata.get_full_tick(code_list=[stock]) if tick and tick[stock]: if exchange == "bj": # 北交所 limit_up_price = tick[stock]["lastClose"] * 1.29 limit_down_price = tick[stock]["lastClose"] * 0.71 elif code.startswith(("30", "68")): # 创业板和科创板 limit_up_price = tick[stock]["lastClose"] * 1.19 limit_down_price = tick[stock]["lastClose"] * 0.81 else: limit_up_price = tick[stock]["lastClose"] * 1.1 limit_down_price = tick[stock]["lastClose"] * 0.9 entity["limit_up_price"] = round(limit_up_price, 2) entity["near_limit_up_price"] = round(limit_up_price * 0.98, 2) entity["limit_down_price"] = round(limit_down_price, 2) entity_list.append(entity) return pd.DataFrame.from_records(data=entity_list) def get_entity_list(include_bj=True): stocks = get_qmt_stocks(include_bj=include_bj) return _build_entity_list(qmt_stocks=stocks) def get_kdata( entity_id, start_timestamp, end_timestamp, level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.qfq, download_history=True, ): code = _to_qmt_code(entity_id=entity_id) period = level.value start_time = to_date_time_str(start_timestamp, fmt="YYYYMMDDHHmmss") end_time = to_date_time_str(end_timestamp, fmt="YYYYMMDDHHmmss") # download比较耗时，建议单独定时任务来做 if download_history: print(f"download from {start_time} to {end_time}") xtdata.download_history_data(stock_code=code, period=period, start_time=start_time, end_time=end_time) records = xtdata.get_market_data( stock_list=[code], period=period, start_time=to_date_time_str(start_timestamp, fmt="YYYYMMDDHHmmss"), end_time=to_date_time_str(end_timestamp, fmt="YYYYMMDDHHmmss"), dividend_type=_to_qmt_dividend_type(adjust_type=adjust_type), fill_data=False, ) dfs = [] for col in records: df = records[col].T df.columns = [col] dfs.append(df) df = pd.concat(dfs, axis=1) df["volume"] = df["volume"] * 100 return df def tick_to_quote(entity_df): # def calculate_limit_up_amount(row): # if row["is_limit_up"]: # return row["price"] * row["bidVol"][0] * 100 # else: # return None # # def calculate_limit_down_amount(row): # if row["is_limit_down"]: # return row["price"] * row["askVol"][0] * 100 # else: # return None def on_data(datas, stock_df=entity_df): stock_finished = False if not Stock.in_trading_time(): stock_finished = True start_time = time.time() time_tag = False track_time = None for code in datas: tick_data = datas[code] if "timetag" in tick_data: time_tag = True track_time = to_timestamp_ms(tick_data["timetag"]) else: track_time = datas[code]["time"] delay = (now_timestamp_ms() - track_time) / 1000 logger.info(f"stock {code} delay {delay} seconds") if delay < 60: break else: logger.warning(f"delay {delay} seconds, may need to restart this script or qmt client") break tick_df = pd.DataFrame.from_records(data=[datas[code] for code in datas], index=list(datas.keys())) if time_tag: tick_df["time"] = tick_df["timetag"].apply(to_timestamp_ms) # 过滤无效tick,一般是退市的 tick_df = tick_df[tick_df["lastPrice"] != 0] tick_df.index = tick_df.index.map(_to_zvt_entity_id) # tick_df = tick_df[tick_df.index.isin(stock_df.index)] stock_df = stock_df[~stock_df.index.duplicated(keep="first")] # 保留首次出现的行 tick_df = tick_df[~tick_df.index.duplicated(keep="first")] df = pd.concat( [ stock_df, tick_df, ], axis=1, join="inner", ) df = df.rename(columns={"lastPrice": "price", "amount": "turnover"}) df["close"] = df["price"] if stock_finished: the_time = date_and_time(track_time, "15:00") df["time"] = to_timestamp_ms(the_time) df["timestamp"] = df["time"].apply(to_pd_timestamp) df["volume"] = df["pvolume"] df["avg_price"] = df["turnover"] / df["volume"] # 换手率 # df["turnover_rate"] = df["pvolume"] / df["float_volume"] # 涨跌幅 df["change_pct"] = (df["price"] - df["lastClose"]) / df["lastClose"] # # 盘口卖单金额 # df["ask_amount"] = df.apply( # lambda row: np.sum(np.array(row["askPrice"]) * (np.array(row["askVol"]) * 100)), axis=1 # ) # # 盘口买单金额 # df["bid_amount"] = df.apply( # lambda row: np.sum(np.array(row["bidPrice"]) * (np.array(row["bidVol"]) * 100)), axis=1 # ) # 涨停 df["is_limit_up"] = (df["price"] != 0) & (df["price"] >= df["limit_up_price"]) df["near_limit_up"] = (df["price"] != 0) & (df["price"] >= df["near_limit_up_price"]) # df["limit_up_amount"] = df.apply(lambda row: calculate_limit_up_amount(row), axis=1) # 跌停 df["is_limit_down"] = (df["price"] != 0) & (df["price"] <= df["limit_down_price"]) # df["limit_down_amount"] = df.apply(lambda row: calculate_limit_down_amount(row), axis=1) df["float_cap"] = df["float_volume"] * df["price"] df["total_cap"] = df["total_volume"] * df["price"] # 换手率 df["turnover_rate"] = df["turnover"] / df["float_cap"] df["provider"] = "qmt" # 实时行情统计，只保留最新 df["id"] = df[["entity_id", "timestamp"]].apply( lambda se: "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"])), axis=1 ) df_to_db(df, data_schema=StockQuote, provider="qmt", force_update=True, drop_duplicates=False) # 历史记录 # df["id"] = df[["entity_id", "timestamp"]].apply( # lambda se: "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], TIME_FORMAT_MINUTE2)), axis=1 # ) # df_to_db(df, data_schema=StockQuoteLog, provider="qmt", force_update=False, drop_duplicates=False) # 1分钟分时 df["id"] = df[["entity_id", "timestamp"]].apply( lambda se: "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_MINUTE)), axis=1, ) df_to_db(df, data_schema=Stock1mQuote, provider="qmt", force_update=True, drop_duplicates=False) # 日线行情 if stock_finished: df["timestamp"] = date_and_time(track_time, "00:00") df["id"] = df[["entity_id", "timestamp"]].apply( lambda se: "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"])), axis=1 ) df["level"] = "1d" df_to_db(df=df, data_schema=Stock1dKdata, provider="em", force_update=True, drop_duplicates=False) cost_time = time.time() - start_time logger.info(f"Quotes cost_time:{cost_time} for {len(datas.keys())} stocks") return track_time return on_data def clear_history_quote(target_date=current_date()): session = get_db_session("qmt", data_schema=StockQuote) session.query(StockQuote).filter(StockQuote.timestamp < target_date).delete() session.commit() logger.info(f"clear stock quote data before: {target_date}") def clear_history_1m_quote(target_date=current_date()): session = get_db_session("qmt", data_schema=StockQuote) dates = get_recent_trade_dates(entity_type="stock", target_date=target_date, days_count=5) if dates: start_date = dates[0] else: start_date = date_time_by_interval(target_date, -5) session.query(Stock1mQuote).filter(Stock1mQuote.timestamp < start_date).delete() session.commit() logger.info(f"clear stock 1m data before: {start_date}") def clear_history_quote_log(target_date): dates = get_recent_trade_dates(entity_type="stock", target_date=target_date, days_count=2) if dates: start_date = dates[0] else: start_date = date_time_by_interval(target_date, -2) session = get_db_session("qmt", data_schema=StockQuoteLog) session.query(StockQuoteLog).filter(StockQuoteLog.timestamp < start_date).delete() session.commit() logger.info(f"clear stock quote log data before: {target_date}") def record_stock_quote(subscribe=False): clear_history_quote(target_date=current_date()) qmt_stocks = get_qmt_stocks() entity_list = _build_entity_list(qmt_stocks=qmt_stocks) entity_df = entity_list[ [ "entity_id", "code", "name", "limit_up_price", "near_limit_up_price", "limit_down_price", "float_volume", "total_volume", ] ] entity_df = entity_df.set_index("entity_id", drop=False) on_data_func = tick_to_quote(entity_df=entity_df) if subscribe: logger.info(f"subscribe tick for {len(qmt_stocks)} stocks") sid = xtdata.subscribe_whole_quote(qmt_stocks, callback=on_data_func) """阻塞线程接收行情回调""" import time client = xtdata.get_client() while True: time.sleep(3) if not client.is_connected(): raise Exception("行情服务连接断开") current_timestamp = now_pd_timestamp() if not Stock.in_real_trading_time(): logger.info(f"record tick finished at: {current_timestamp}") break xtdata.unsubscribe_quote(sid) else: import time first_time = True last_time = False while True: if not first_time and Stock.in_trading_time() and not Stock.in_real_trading_time(): logger.info("Sleeping time......") time.sleep(30 * 1) continue if not Stock.in_trading_time(): logger.info("Not in trading time......") time.sleep(60 * 1) last_time = True datas = xtdata.get_full_tick(code_list=qmt_stocks) track_time = on_data_func(datas=datas) if first_time and not is_same_date(track_time, current_date()): last_time = True first_time = False if last_time: current_timestamp = now_pd_timestamp() logger.info(f"record tick finished at: {current_timestamp}") # clear_history_quote_log(target_date=current_date()) clear_history_1m_quote(target_date=current_date()) break if __name__ == "__main__": from apscheduler.schedulers.background import BackgroundScheduler sched = BackgroundScheduler() record_stock_quote() sched.add_job(func=record_stock_quote, trigger="cron", hour=9, minute=22, day_of_week="mon-fri") sched.start() sched._thread.join() # the __all__ is generated __all__ = [ "get_qmt_stocks", "get_entity_list", "get_kdata", "tick_to_quote", "clear_history_quote", ] ================================================ FILE: src/zvt/common/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/common/query_models.py ================================================ # -*- coding: utf-8 -*- from datetime import datetime from enum import Enum from typing import Optional from pydantic import BaseModel, Field class OrderByType(Enum): asc = "asc" desc = "desc" class TimeUnit(Enum): year = "year" month = "month" day = "day" hour = "hour" minute = "minute" second = "second" class AbsoluteTimeRange(BaseModel): start_timestamp: datetime end_timestamp: datetime class RelativeTimeRage(BaseModel): interval: int time_unit: TimeUnit class TimeRange(BaseModel): absolute_time_range: Optional[AbsoluteTimeRange] = Field(default=None) relative_time_range: Optional[RelativeTimeRage] = Field(default=None) # the __all__ is generated __all__ = ["OrderByType", "TimeUnit", "AbsoluteTimeRange", "RelativeTimeRage", "TimeRange"] ================================================ FILE: src/zvt/common/trading_models.py ================================================ # -*- coding: utf-8 -*- from enum import Enum from typing import List, Optional from pydantic import BaseModel, Field class PositionType(Enum): # 按整体仓位算 normal = "normal" # 不管整体仓位多少 # 按现金算 cash = "cash" class BuyParameter(BaseModel): entity_ids: List[str] position_type: PositionType = Field(default=PositionType.normal) position_pct: Optional[float] = Field(default=None) weights: Optional[List[float]] = Field(default=None) money_to_use: Optional[float] = Field(default=None) class SellParameter(BaseModel): entity_ids: List[str] sell_pcts: Optional[List[float]] = Field(default=None) class TradingResult(BaseModel): success_entity_ids: Optional[List[str]] = Field(default=None) failed_entity_ids: Optional[List[str]] = Field(default=None) # the __all__ is generated __all__ = ["PositionType", "BuyParameter", "SellParameter", "TradingResult"] ================================================ FILE: src/zvt/config.json ================================================ { "jq_username": "", "jq_password": "", "http_proxy": "127.0.0.1:1087", "https_proxy": "127.0.0.1:1087", "smtp_host": "smtpdm.aliyun.com", "smtp_port": "80", "email_username": "", "email_password": "", "wechat_app_id": "", "wechat_app_secrect": "", "qiye_wechat_bot_token": "", "qmt_mini_data_path": "D:\\qmt\\userdata_mini", "qmt_account_id": "", "moonshot_api_key": "", "qwen_api_key": "", "em_header": "", "storage": { "base_path": null, "path_template": null, "storage_routes": {}, "schema_providers": { "zvt_info": [ "zvt" ], "trader_info": [ "zvt" ], "stock_tags": [ "zvt" ], "stock_trading": [ "zvt" ], "stock_1d_ma_factor": [ "zvt" ], "stock_1d_ma_stats_factor": [ "zvt" ], "stock_1d_zen_factor": [ "zvt" ], "stock_1wk_zen_factor": [ "zvt" ], "index_1d_zen_factor": [ "zvt" ], "top_stocks": [ "zvt" ], "stock_quote": [ "qmt" ], "stock_quote_log": [ "qmt" ] } } } ================================================ FILE: src/zvt/consts.py ================================================ # -*- coding: utf-8 -*- import os from pathlib import Path # zvt home dir ZVT_HOME = os.environ.get("ZVT_HOME") if not ZVT_HOME: ZVT_HOME = os.path.abspath(os.path.join(Path.home(), "zvt-home")) # data for testing ZVT_TEST_HOME = os.path.abspath(os.path.join(Path.home(), "zvt-test-home")) ZVT_TEST_ZIP_DATA_PATH = os.path.join(ZVT_TEST_HOME, "data.zip") ZVT_TEST_DATA_PATH = os.path.join(ZVT_TEST_HOME, "data") DATA_SAMPLE_ZIP_PATH = os.path.abspath(os.path.join(os.path.dirname(__file__), "samples", "data.zip")) # ****** setting for stocks ****** # SAMPLE_STOCK_CODES = ["000001", "000002"] # 沪深300，证券，中证500，上证50，创业板，军工,传媒,资源 SAMPLE_ETF_CODES = ["510300", "512880", "510500", "510050", "159915", "512660", "512980", "510410"] # 上证指数 上证50 沪深300 中证500 中证1000 科创50 # 深证成指(399001) 创业板指(399006) 国证成长（399370）国证价值（399371）国证基金(399379) 国证ETF(399380) IMPORTANT_INDEX = [ "000001", "000016", "000300", "000905", "000852", "000688", "399001", "399006", "399370", "399371", "399379", "399380", ] ================================================ FILE: src/zvt/contract/__init__.py ================================================ # -*- coding: utf-8 -*- from enum import Enum class IntervalLevel(Enum): """ Repeated fixed time interval, e.g, 5m, 1d. """ #: level l2 quote LEVEL_L2_QUOTE = "l2quote" #: level tick LEVEL_TICK = "tick" #: 1 minute LEVEL_1MIN = "1m" #: 5 minutes LEVEL_5MIN = "5m" #: 15 minutes LEVEL_15MIN = "15m" #: 30 minutes LEVEL_30MIN = "30m" #: 1 hour LEVEL_1HOUR = "1h" #: 4 hours LEVEL_4HOUR = "4h" #: 1 day LEVEL_1DAY = "1d" #: 1 week LEVEL_1WEEK = "1wk" #: 1 month LEVEL_1MON = "1mon" def to_pd_freq(self): if self == IntervalLevel.LEVEL_1MIN: return "1min" if self == IntervalLevel.LEVEL_5MIN: return "5min" if self == IntervalLevel.LEVEL_15MIN: return "15min" if self == IntervalLevel.LEVEL_30MIN: return "30min" if self == IntervalLevel.LEVEL_1HOUR: return "1H" if self == IntervalLevel.LEVEL_4HOUR: return "4H" if self >= IntervalLevel.LEVEL_1DAY: return "1D" def floor_timestamp(self, pd_timestamp): if self == IntervalLevel.LEVEL_1MIN: return pd_timestamp.floor("1min") if self == IntervalLevel.LEVEL_5MIN: return pd_timestamp.floor("5min") if self == IntervalLevel.LEVEL_15MIN: return pd_timestamp.floor("15min") if self == IntervalLevel.LEVEL_30MIN: return pd_timestamp.floor("30min") if self == IntervalLevel.LEVEL_1HOUR: return pd_timestamp.floor("1h") if self == IntervalLevel.LEVEL_4HOUR: return pd_timestamp.floor("4h") if self == IntervalLevel.LEVEL_1DAY: return pd_timestamp.floor("1d") def to_minute(self): return int(self.to_second() / 60) def to_second(self): return int(self.to_ms() / 1000) def to_ms(self): """ To seconds count in the interval :return: seconds count in the interval """ #: we treat tick intervals is 5s, you could change it if self == IntervalLevel.LEVEL_TICK: return 5 * 1000 if self == IntervalLevel.LEVEL_1MIN: return 60 * 1000 if self == IntervalLevel.LEVEL_5MIN: return 5 * 60 * 1000 if self == IntervalLevel.LEVEL_15MIN: return 15 * 60 * 1000 if self == IntervalLevel.LEVEL_30MIN: return 30 * 60 * 1000 if self == IntervalLevel.LEVEL_1HOUR: return 60 * 60 * 1000 if self == IntervalLevel.LEVEL_4HOUR: return 4 * 60 * 60 * 1000 if self == IntervalLevel.LEVEL_1DAY: return 24 * 60 * 60 * 1000 if self == IntervalLevel.LEVEL_1WEEK: return 7 * 24 * 60 * 60 * 1000 if self == IntervalLevel.LEVEL_1MON: return 31 * 7 * 24 * 60 * 60 * 1000 def __ge__(self, other): if self.__class__ is other.__class__: return self.to_ms() >= other.to_ms() return NotImplemented def __gt__(self, other): if self.__class__ is other.__class__: return self.to_ms() > other.to_ms() return NotImplemented def __le__(self, other): if self.__class__ is other.__class__: return self.to_ms() <= other.to_ms() return NotImplemented def __lt__(self, other): if self.__class__ is other.__class__: return self.to_ms() < other.to_ms() return NotImplemented class AdjustType(Enum): """ split-adjusted type for :class:`~.zvt.contract.schema.TradableEntity` quotes """ #: not adjusted #: 不复权 bfq = "bfq" #: pre adjusted #: 前复权 qfq = "qfq" #: post adjusted #: 后复权 hfq = "hfq" class ActorType(Enum): #: 个人 individual = "individual" #: 公募基金 raised_fund = "raised_fund" #: 社保 social_security = "social_security" #: 保险 insurance = "insurance" #: 外资 qfii = "qfii" #: 信托 trust = "trust" #: 券商 broker = "qmt" #: 私募 private_equity = "private_equity" #: 公司(可能包括私募) corporation = "corporation" class TradableType(Enum): #: A股(中国) #: China stock stock = "stock" #: 可转债(中国) #: China convertible Bond cbond = "cbond" #: A股指数(中国) #: China index index = "index" #: A股板块(中国) #: China stock block block = "block" #: 美股 #: USA stock stockus = "stockus" #: 美股指数 #: USA index indexus = "indexus" #: USA block blockus = "blockus" #: 港股 #: Hongkong Stock stockhk = "stockhk" #: 港股指数 #: Hongkong Index indexhk = "indexhk" #: 期货(中国) #: China future future = "future" #: 数字货币 #: Cryptocurrency coin = "coin" #: 期权(中国) #: China option option = "option" #: 基金(中国) #: China fund fund = "fund" #: 货币汇率 #: currency exchange rate currency = "currency" class Exchange(Enum): #: 上证交易所 sh = "sh" #: 深证交易所 sz = "sz" #: 北交所 bj = "bj" #: 对于中国的非交易所的 标的 cn = "cn" #: 对于美国的非交易所的 标的 us = "us" #: 纳斯达克 nasdaq = "nasdaq" #: 纽交所 nyse = "nyse" #: 港交所 hk = "hk" #: 数字货币 binance = "binance" huobipro = "huobipro" #: 上海期货交易所 shfe = "shfe" #: 大连商品交易所 dce = "dce" #: 郑州商品交易所 czce = "czce" #: 中国金融期货交易所 cffex = "cffex" #: 上海国际能源交易中心 ine = "ine" #: 广州期货所 gfex = "gfex" #: 外汇交易所(虚拟) #: currency exchange(virtual) forex = "forex" #: 人民币中间价 tradable_type_map_exchanges = { TradableType.block: [Exchange.cn], TradableType.index: [Exchange.sh, Exchange.sz], TradableType.stock: [Exchange.sh, Exchange.sz, Exchange.bj], TradableType.cbond: [Exchange.sh, Exchange.sz], TradableType.stockhk: [Exchange.hk], TradableType.indexhk: [Exchange.hk], TradableType.stockus: [Exchange.nasdaq, Exchange.nyse], TradableType.blockus: [Exchange.us], TradableType.indexus: [Exchange.us], TradableType.future: [Exchange.shfe, Exchange.dce, Exchange.czce, Exchange.cffex, Exchange.ine], TradableType.coin: [Exchange.binance, Exchange.huobipro], TradableType.currency: [Exchange.forex], } def get_entity_exchanges(entity_type): entity_type = TradableType(entity_type) return tradable_type_map_exchanges.get(entity_type) from .context import zvt_context zvt_context = zvt_context # the __all__ is generated __all__ = ["IntervalLevel", "AdjustType", "ActorType", "TradableType", "Exchange", "get_entity_exchanges"] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule schema from .schema import * from .schema import __all__ as _schema_all __all__ += _schema_all ================================================ FILE: src/zvt/contract/api.py ================================================ # -*- coding: utf-8 -*- import logging import platform from typing import List, Union, Type import pandas as pd from sqlalchemy import func, exists, and_ from sqlalchemy.engine import Engine from sqlalchemy.ext.declarative import DeclarativeMeta from sqlalchemy.orm import Query from sqlalchemy.orm import sessionmaker, Session from sqlalchemy.sql.expression import text from zvt import zvt_env from zvt.contract import IntervalLevel from zvt.contract import zvt_context from zvt.contract.schema import Mixin, TradableEntity from zvt.contract.storage import get_storage_backend from zvt.contract.route_registry import get_route_registry from zvt.contract.register import ensure_schema_tables_and_indexes _initialized_storage_ids = set() def _storage_backend(): return zvt_context.storage_backend or get_storage_backend() def _route_registry(): return zvt_context.route_registry or get_route_registry() from zvt.utils.pd_utils import pd_is_not_null, index_df from zvt.utils.time_utils import to_pd_timestamp logger = logging.getLogger(__name__) def _get_db_name(data_schema: DeclarativeMeta) -> str: """ get db name of the domain schema :param data_schema: the data schema :return: db name """ for db_name, base in zvt_context.dbname_map_base.items(): if issubclass(data_schema, base): return db_name def get_db_engine( provider: str, db_name: str = None, data_schema: object = None, data_path: str = None ) -> Engine: """ get db engine from (provider,db_name) or (provider,data_schema). Creates tables and indexes on first use (lazy init). """ if data_schema: db_name = _get_db_name(data_schema=data_schema) if data_path is None: data_path = zvt_env.get("data_path", ".") storage_id = _route_registry().get_storage_id(provider, db_name) engine = _storage_backend().get_engine(storage_id, data_path) if storage_id not in _initialized_storage_ids: schema_base = zvt_context.dbname_map_base.get(db_name) if schema_base: ensure_schema_tables_and_indexes(engine, schema_base, db_name) _initialized_storage_ids.add(storage_id) return engine def _ensure_schema_providers_loaded(): """Populate zvt_context from config schema_providers for schemas without Recorders.""" if getattr(_ensure_schema_providers_loaded, "_done", False): return try: from zvt.contract.schema import _get_schema_providers schema_providers = _get_schema_providers() for db_name, providers in schema_providers.items(): for p in providers: if p not in zvt_context.providers: zvt_context.providers.append(p) if p not in zvt_context.provider_map_dbnames: zvt_context.provider_map_dbnames[p] = [] if db_name not in zvt_context.provider_map_dbnames[p]: zvt_context.provider_map_dbnames[p].append(db_name) except Exception: pass _ensure_schema_providers_loaded._done = True def get_providers() -> List[str]: _ensure_schema_providers_loaded() return zvt_context.providers def get_schemas(provider: str) -> List[DeclarativeMeta]: _ensure_schema_providers_loaded() schemas = [] for provider1, dbs in zvt_context.provider_map_dbnames.items(): if provider == provider1: for dbname in dbs: schemas1 = zvt_context.dbname_map_schemas.get(dbname) if schemas1: schemas += schemas1 return schemas def get_db_session(provider: str, db_name: str = None, data_schema: object = None, force_new: bool = False) -> Session: """ get db session from (provider,db_name) or (provider,data_schema) :param provider: data provider :param db_name: db name :param data_schema: data schema :param force_new: True for new session, otherwise use global session :return: db session """ _ensure_schema_providers_loaded() if data_schema: db_name = _get_db_name(data_schema=data_schema) data_path = zvt_env.get("data_path", ".") storage_id = _route_registry().get_storage_id(provider, db_name) engine = get_db_engine(provider=provider, db_name=db_name) session_fac = _storage_backend().get_session_factory(storage_id, data_path) session_fac.configure(bind=engine) if force_new: return session_fac() session_key = storage_id session = zvt_context.sessions.get(session_key) # FIXME: should not maintain global session if not session: session = session_fac() zvt_context.sessions[session_key] = session return session def get_db_session_factory(provider: str, db_name: str = None, data_schema: object = None): """ get db session factory from (provider,db_name) or (provider,data_schema) """ if data_schema: db_name = _get_db_name(data_schema=data_schema) engine = get_db_engine(provider=provider, db_name=db_name) data_path = zvt_env.get("data_path", ".") storage_id = _route_registry().get_storage_id(provider, db_name) session_fac = _storage_backend().get_session_factory(storage_id, data_path) session_fac.configure(bind=engine) return session_fac DBSession = get_db_session_factory def get_entity_schema(entity_type: str) -> Type[TradableEntity]: """ get entity schema from name :param entity_type: entity type, e.g. stock, stockus. :return: the Schema of the entity """ return zvt_context.tradable_schema_map[entity_type] def get_schema_by_name(name: str) -> DeclarativeMeta: """ get domain schema by the name :param name: schema name :return: schema """ for schema in zvt_context.schemas: if schema.__name__ == name: return schema def get_schema_columns(schema: DeclarativeMeta) -> List[str]: """ get all columns of the domain schema :param schema: data schema :return: columns of the schema """ return schema.__table__.columns.keys() def common_filter( query: Query, data_schema, start_timestamp=None, end_timestamp=None, filters=None, order=None, limit=None, distinct=None, time_field="timestamp", ): """ build filter by the arguments :param query: sql query :param data_schema: data schema :param start_timestamp: start timestamp :param end_timestamp: end timestamp :param filters: sql filters :param order: sql order :param limit: sql limit size :param time_field: time field in columns :return: result query """ assert data_schema is not None time_col = eval("data_schema.{}".format(time_field)) if start_timestamp: query = query.filter(time_col >= to_pd_timestamp(start_timestamp)) if end_timestamp: query = query.filter(time_col <= to_pd_timestamp(end_timestamp)) if filters: for filter in filters: query = query.filter(filter) if order is not None: query = query.order_by(order) else: query = query.order_by(time_col.asc()) if limit: query = query.limit(limit) if distinct: query = query.distinct(distinct) return query def del_data(data_schema: Type[Mixin], filters: List = None, provider=None): """ delete data by filters :param data_schema: data schema :param filters: filters :param provider: data provider """ if not provider: provider = data_schema.get_providers()[0] session = get_db_session(provider=provider, data_schema=data_schema) query = session.query(data_schema) if filters: for f in filters: query = query.filter(f) query.delete() session.commit() def get_by_id(data_schema, id: str, provider: str = None, session: Session = None): """ get one record by id from data schema :param data_schema: data schema :param id: the record id :param provider: data provider :param session: db session :return: the record of the id """ _ensure_schema_providers_loaded() providers = data_schema.get_providers() if not providers: raise ValueError(f"no provider registered for: {data_schema}") if not provider: provider = providers[0] if not session: session = get_db_session(provider=provider, data_schema=data_schema) return session.query(data_schema).get(id) def _row2dict(row): d = {} for column in row.__table__.columns: d[column.name] = getattr(row, column.name) return d def get_data( data_schema: Type[Mixin], ids: List[str] = None, entity_ids: List[str] = None, entity_id: str = None, codes: List[str] = None, code: str = None, level: Union[IntervalLevel, str] = None, provider: str = None, columns: List = None, col_label: dict = None, return_type: str = "df", start_timestamp: Union[pd.Timestamp, str] = None, end_timestamp: Union[pd.Timestamp, str] = None, filters: List = None, session: Session = None, order=None, limit: int = None, distinct=None, index: Union[str, list] = None, drop_index_col=False, time_field: str = "timestamp", ): """ query data by the arguments :param data_schema: :param ids: :param entity_ids: :param entity_id: :param codes: :param code: :param level: :param provider: :param columns: :param col_label: dict with key(column), value(label) :param return_type: df, domain or dict. default is df :param start_timestamp: :param end_timestamp: :param filters: :param session: :param order: :param limit: :param index: index field name, str for single index, str list for multiple index :param drop_index_col: whether drop the col if it's in index, default False :param time_field: :return: results basing on return_type. """ _ensure_schema_providers_loaded() providers = data_schema.get_providers() if not providers: raise ValueError(f"no provider registered for: {data_schema}") if not provider: provider = providers[0] if not session: session = get_db_session(provider=provider, data_schema=data_schema) time_col = eval("data_schema.{}".format(time_field)) if columns: # support str for i, col in enumerate(columns): if isinstance(col, str): columns[i] = eval("data_schema.{}".format(col)) # make sure get timestamp if time_col not in columns: columns.append(time_col) if col_label: columns_ = [] for col in columns: if col.name in col_label: columns_.append(col.label(col_label.get(col.name))) else: columns_.append(col) columns = columns_ query = session.query(*columns) else: query = session.query(data_schema) if entity_id: query = query.filter(data_schema.entity_id == entity_id) if entity_ids: query = query.filter(data_schema.entity_id.in_(entity_ids)) if code: query = query.filter(data_schema.code == code) if codes: query = query.filter(data_schema.code.in_(codes)) if ids: query = query.filter(data_schema.id.in_(ids)) # we always store different level in different schema,the level param is not useful now if level: try: #: some schema has no level,just ignore it data_schema.level if type(level) == IntervalLevel: level = level.value query = query.filter(data_schema.level == level) except Exception as e: pass query = common_filter( query, data_schema=data_schema, start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters, order=order, limit=limit, distinct=distinct, time_field=time_field, ) if return_type == "df": df = pd.read_sql(query.statement, query.session.bind) if pd_is_not_null(df): if index: df = index_df(df, index=index, drop=drop_index_col, time_field=time_field) return df elif return_type == "domain": return query.all() elif return_type == "dict": domains = query.all() return [_row2dict(item) for item in domains] elif return_type == "select": return query.selectable def data_exist(session, schema, id): """ whether exist data of the id :param session: :param schema: :param id: :return: """ return session.query(exists().where(and_(schema.id == id))).scalar() def get_data_count(data_schema, filters=None, provider=None, session=None): """ get record count basing on the filters :param data_schema: :param filters: :param session: :return: """ if not session: session = get_db_session(provider=provider, data_schema=data_schema) query = session.query(data_schema) if filters: for filter in filters: query = query.filter(filter) count_q = query.statement.with_only_columns(func.count(data_schema.id)).order_by(None) count = session.execute(count_q).scalar() return count def get_group(provider, data_schema, column, group_func=func.count, session=None): if not session: session = get_db_session(provider=provider, data_schema=data_schema) if group_func: query = session.query(column, group_func(column)).group_by(column) else: query = session.query(column).group_by(column) df = pd.read_sql(query.statement, session.bind) return df def decode_entity_id(entity_id: str): """ decode entity id to entity_type, exchange, code :param entity_id: :return: tuple with format (entity_type, exchange, code) """ result = entity_id.split("_") entity_type = result[0] exchange = result[1] code = "".join(result[2:]) return entity_type, exchange, code def get_entity_type(entity_id: str): """ get entity type by entity id :param entity_id: :return: entity type """ entity_type, _, _ = decode_entity_id(entity_id) return entity_type def get_entity_exchange(entity_id: str): """ get exchange by entity id :param entity_id: :return: exchange """ _, exchange, _ = decode_entity_id(entity_id) return exchange def get_entity_code(entity_id: str): """ get code by entity id :param entity_id: :return: code """ _, _, code = decode_entity_id(entity_id) return code def df_to_db( df: pd.DataFrame, data_schema: DeclarativeMeta, provider: str, force_update: bool = False, sub_size: int = 8000, drop_duplicates: bool = True, dtype=None, session=None, need_check=True, ) -> object: """ store the df to db :param df: data with columns of the schema :param data_schema: data schema :param provider: data provider :param force_update: whether update the data with id existed :param sub_size: update batch size :param drop_duplicates: whether drop duplicates :return: """ if not pd_is_not_null(df): return 0 if drop_duplicates and df.duplicated(subset="id").any(): logger.warning(f"remove duplicated:{df[df.duplicated()]}") df = df.drop_duplicates(subset="id", keep="last") schema_cols = get_schema_columns(data_schema) cols = set(df.columns.tolist()) & set(schema_cols) if not cols: print("wrong cols") return 0 cols = list(cols) df = df[cols] size = len(df) if platform.system() == "Windows": sub_size = 900 if size >= sub_size: step_size = int(size / sub_size) if size % sub_size: step_size = step_size + 1 else: step_size = 1 saved = 0 if not session: session = get_db_session(provider=provider, data_schema=data_schema) for step in range(step_size): df_current = df.iloc[sub_size * step : sub_size * (step + 1)] if need_check: if force_update: ids = df_current["id"].tolist() if len(ids) == 1: sql = text(f'delete from `{data_schema.__tablename__}` where id = "{ids[0]}"') else: sql = text(f"delete from `{data_schema.__tablename__}` where id in {tuple(ids)}") session.execute(sql) else: current = get_data( session=session, data_schema=data_schema, columns=[data_schema.id], provider=provider, ids=df_current["id"].tolist(), ) if pd_is_not_null(current): df_current = df_current[~df_current["id"].isin(current["id"])] if pd_is_not_null(df_current): saved = saved + len(df_current) df_current.to_sql( data_schema.__tablename__, session.connection(), index=False, if_exists="append", dtype=dtype ) session.commit() return saved def get_entities( entity_schema: Type[TradableEntity] = None, entity_type: str = None, exchanges: List[str] = None, ids: List[str] = None, entity_ids: List[str] = None, entity_id: str = None, codes: List[str] = None, code: str = None, provider: str = None, columns: List = None, col_label: dict = None, return_type: str = "df", start_timestamp: Union[pd.Timestamp, str] = None, end_timestamp: Union[pd.Timestamp, str] = None, filters: List = None, session: Session = None, order=None, limit: int = None, index: Union[str, list] = "code", ) -> List: """ get entities by the arguments :param entity_schema: :param entity_type: :param exchanges: :param ids: :param entity_ids: :param entity_id: :param codes: :param code: :param provider: :param columns: :param col_label: :param return_type: :param start_timestamp: :param end_timestamp: :param filters: :param session: :param order: :param limit: :param index: :return: """ if not entity_schema: entity_schema = zvt_context.tradable_schema_map[entity_type] if not provider: provider = entity_schema.get_providers()[0] if not order: order = entity_schema.code.asc() if exchanges: if filters: filters.append(entity_schema.exchange.in_(exchanges)) else: filters = [entity_schema.exchange.in_(exchanges)] return get_data( data_schema=entity_schema, ids=ids, entity_ids=entity_ids, entity_id=entity_id, codes=codes, code=code, level=None, provider=provider, columns=columns, col_label=col_label, return_type=return_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters, session=session, order=order, limit=limit, index=index, ) def get_entity_ids( entity_type="stock", entity_schema: TradableEntity = None, exchanges=None, codes=None, provider=None, filters=None, entity_ids=None, ): """ get entity ids by the arguments :param entity_type: :param entity_schema: :param exchanges: :param codes: :param provider: :param filters: :param entity_ids: :return: """ df = get_entities( entity_type=entity_type, entity_schema=entity_schema, exchanges=exchanges, codes=codes, provider=provider, filters=filters, entity_ids=entity_ids, ) if pd_is_not_null(df): return df["entity_id"].to_list() return None if __name__ == "__main__": print(get_entities(entity_type="block")) # the __all__ is generated __all__ = [ "get_db_engine", "get_providers", "get_schemas", "get_db_session", "get_db_session_factory", "get_entity_schema", "get_schema_by_name", "get_schema_columns", "common_filter", "del_data", "get_by_id", "get_data", "data_exist", "get_data_count", "get_group", "decode_entity_id", "get_entity_type", "get_entity_exchange", "get_entity_code", "df_to_db", "get_entities", "get_entity_ids", ] ================================================ FILE: src/zvt/contract/base_service.py ================================================ # -*- coding: utf-8 -*- import json from typing import Type, List from zvt.contract.api import del_data, get_db_session from zvt.contract.zvt_info import StateMixin from zvt.utils.str_utils import to_snake_str class StatefulService(object): """ Base service with state could be stored in state_schema """ #: state schema state_schema: Type[StateMixin] = None #: name of the service, default name of class if not set manually name = None def __init__(self) -> None: assert self.state_schema is not None if self.name is None: self.name = to_snake_str(type(self).__name__) self.state_session = get_db_session(data_schema=self.state_schema, provider="zvt") def clear_state_data(self, entity_id=None): """ clear state of the entity :param entity_id: entity id """ filters = [self.state_schema.state_name == self.name] if entity_id: filters = filters + [self.state_schema.entity_id == entity_id] del_data(self.state_schema, filters=filters) def decode_state(self, state: str): """ decode state :param state: :return: """ return json.loads(state, object_hook=self.state_object_hook()) def encode_state(self, state: object): """ encode state :param state: :return: """ return json.dumps(state, cls=self.state_encoder()) def state_object_hook(self): return None def state_encoder(self): return None class OneStateService(StatefulService): """ StatefulService which saving all states in one object """ def __init__(self) -> None: super().__init__() self.state_domain = self.state_schema.get_by_id(id=self.name) if self.state_domain: self.state: dict = self.decode_state(self.state_domain.state) else: self.state = None def persist_state(self): state_str = self.encode_state(self.state) if not self.state_domain: self.state_domain = self.state_schema(id=self.name, entity_id=self.name, state_name=self.name) self.state_domain.state = state_str self.state_session.add(self.state_domain) self.state_session.commit() class EntityStateService(StatefulService): """ StatefulService which saving one state one entity """ def __init__(self, entity_ids) -> None: super().__init__() self.entity_ids = entity_ids state_domains: List[StateMixin] = self.state_schema.query_data( filters=[self.state_schema.state_name == self.name], entity_ids=self.entity_ids, return_type="domain" ) #: entity_id:state self.states: dict = {} if state_domains: for state in state_domains: self.states[state.entity_id] = self.decode_state(state.state) def persist_state(self, entity_id): state = self.states.get(entity_id) if state: domain_id = f"{self.name}_{entity_id}" state_domain = self.state_schema.get_by_id(domain_id) state_str = self.encode_state(state) if not state_domain: state_domain = self.state_schema(id=domain_id, entity_id=entity_id, state_name=self.name) state_domain.state = state_str self.state_session.add(state_domain) self.state_session.commit() # the __all__ is generated __all__ = ["StatefulService", "OneStateService", "EntityStateService"] ================================================ FILE: src/zvt/contract/context.py ================================================ # -*- coding: utf-8 -*- """ ZVT global context. Phase 1: storage_backend and route_registry are optional injections; api uses get_storage_backend()/get_route_registry() when not set. """ from typing import Optional, TYPE_CHECKING if TYPE_CHECKING: from zvt.contract.storage import StorageBackend from zvt.contract.route_registry import RouteRegistry class Registry(object): """ Class storing zvt registering meta """ def __init__(self) -> None: #: all registered providers self.providers = [] #: all registered entity types(str) self.tradable_entity_types = [] #: all entity schemas self.tradable_entity_schemas = [] #: all registered schemas self.schemas = [] #: tradable entity type -> schema self.tradable_schema_map = {} #: global sessions self.sessions = {} #: provider_dbname -> engine (deprecated: engines now in StorageBackend) self.db_engine_map = {} #: provider_dbname -> session factory (deprecated: now in StorageBackend) self.db_session_map = {} #: provider -> [db_name1,db_name2...] self.provider_map_dbnames = {} #: db_name -> [declarative_base1,declarative_base2...] self.dbname_map_base = {} #: db_name -> [declarative_meta1,declarative_meta2...] self.dbname_map_schemas = {} #: entity_type -> related schemas self.entity_map_schemas = {} #: factor class registry self.factor_cls_registry = {} #: db_names of internal schemas (business logic data, not tied to external data source) self.internal_db_names = set() #: Optional StorageBackend; if set, api uses it instead of get_storage_backend() self.storage_backend: Optional["StorageBackend"] = None #: Optional RouteRegistry; if set, api uses it instead of get_route_registry() self.route_registry: Optional["RouteRegistry"] = None #: :class:`~.zvt.contract.context.Registry` instance zvt_context = Registry() # the __all__ is generated __all__ = ["Registry"] ================================================ FILE: src/zvt/contract/data_type.py ================================================ # -*- coding: utf-8 -*- class Bean(object): def __init__(self) -> None: super().__init__() self.__dict__ def dict(self): return self.__dict__ def from_dct(self, dct: dict): if dct: for k in dct: self.__dict__[k] = dct[k] # the __all__ is generated __all__ = ["Bean"] ================================================ FILE: src/zvt/contract/drawer.py ================================================ # -*- coding: utf-8 -*- import logging from enum import Enum from typing import List, Optional import numpy as np import pandas as pd import plotly.graph_objs as go from plotly.subplots import make_subplots from zvt.contract.api import decode_entity_id from zvt.contract.data_type import Bean from zvt.contract.normal_data import NormalData from zvt.utils.decorator import to_string from zvt.utils.pd_utils import pd_is_not_null logger = logging.getLogger(__name__) class ChartType(Enum): """ Chart type enum """ #: candlestick chart kline = "kline" #: line chart line = "line" #: area chart area = "area" #: scatter chart scatter = "scatter" #: histogram chart histogram = "histogram" #: pie chart pie = "pie" #: bar chart bar = "bar" _zvt_chart_type_map_scatter_mode = {ChartType.line: "lines", ChartType.area: "none", ChartType.scatter: "markers"} @to_string class Rect(Bean): """ rect struct with left-bottom(x0, y0), right-top(x1, y1) """ def __init__(self, x0=None, y0=None, x1=None, y1=None) -> None: #: left-bottom x0 self.x0 = x0 #: left-bottom y0 self.y0 = y0 #: right-top x1 self.x1 = x1 #: right-top y1 self.y1 = y1 class Draw(object): def draw_kline( self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs ): return self.draw( main_chart=ChartType.kline, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw_line( self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs ): return self.draw( main_chart=ChartType.line, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw_area( self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs ): return self.draw( main_chart=ChartType.area, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw_scatter( self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs ): return self.draw( main_chart=ChartType.scatter, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw_histogram( self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs ): return self.draw( ChartType.histogram, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw_bar(self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs): return self.draw( ChartType.bar, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw_pie(self, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs): return self.draw( ChartType.pie, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def draw( self, main_chart=ChartType.kline, sub_chart="bar", width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs, ): raise NotImplementedError() def default_layout(self, main_chart=None, width=None, height=None, title=None, keep_ui_state=True, **layout_params): if keep_ui_state: uirevision = True else: uirevision = None if main_chart == ChartType.histogram: xaxis = None else: xaxis = dict( linecolor="#BCCCDC", showgrid=False, showspikes=True, # Show spike line for X-axis # Format spike spikethickness=2, spikedash="dot", spikecolor="#999999", spikemode="across", rangeselector=dict( buttons=list( [ dict(count=1, label="1m", step="month", stepmode="backward"), dict(count=3, label="3m", step="month", stepmode="backward"), dict(count=6, label="6m", step="month", stepmode="backward"), dict(count=1, label="YTD", step="year", stepmode="todate"), dict(count=1, label="1y", step="year", stepmode="backward"), dict(step="all"), ] ) ), rangeslider=dict( visible=True, ), type="date", ) return dict( showlegend=True, plot_bgcolor="#FFF", hovermode="x", hoverdistance=100, # Distance to show hover label of data point spikedistance=1000, # Distance to show spike uirevision=uirevision, height=height, width=width, title=title, yaxis=dict( autorange=True, fixedrange=False, zeroline=False, linecolor="#BCCCDC", showgrid=False, ), xaxis=xaxis, legend_orientation="h", hoverlabel={"namelength": -1}, **layout_params, ) class Drawable(object): def drawer(self): drawer = Drawer( main_df=self.drawer_main_df(), main_data=self.drawer_main_data(), factor_df_list=self.drawer_factor_df_list(), factor_data_list=self.drawer_factor_data_list(), sub_df_list=self.drawer_sub_df_list(), sub_data_list=self.drawer_sub_data_list(), sub_col_chart=self.drawer_sub_col_chart(), annotation_df=self.drawer_annotation_df(), rects=self.drawer_rects(), ) return drawer def draw( self, main_chart=ChartType.kline, width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs, ): return self.drawer().draw( main_chart=main_chart, width=width, height=height, title=title, keep_ui_state=keep_ui_state, show=show, scale_value=scale_value, **kwargs, ) def drawer_main_df(self) -> Optional[pd.DataFrame]: return None def drawer_main_data(self) -> Optional[NormalData]: return None def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: return None def drawer_factor_data_list(self) -> Optional[List[NormalData]]: return None def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: return None def drawer_sub_data_list(self) -> Optional[List[NormalData]]: return None def drawer_annotation_df(self) -> Optional[pd.DataFrame]: return None def drawer_rects(self) -> Optional[List[Rect]]: return None def drawer_sub_col_chart(self) -> Optional[dict]: return None class StackedDrawer(Draw): def __init__(self, *drawers) -> None: super().__init__() assert len(drawers) > 1 self.drawers: List[Drawer] = drawers def make_y_layout(self, index, total, start_index=1, domain_range=(0, 1)): part = (domain_range[1] - domain_range[0]) / total if index == 1: yaxis = "yaxis" y = "y" else: yaxis = f"yaxis{index}" y = f"y{index}" return ( yaxis, y, dict( anchor="x", autorange=True, fixedrange=False, zeroline=False, linecolor="#BCCCDC", showgrid=False, domain=[ domain_range[0] + part * (index - start_index), domain_range[0] + part * (index - start_index + 1), ], ), ) def draw( self, main_chart=ChartType.kline, sub_chart="bar", width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs, ): stacked_fig = go.Figure() total = len(self.drawers) start = 1 domain_range = (0, 1) for drawer in self.drawers: if drawer.has_sub_plot(): domain_range = (0.2, 1) start = 2 break for index, drawer in enumerate(self.drawers, start=start): traces, sub_traces = drawer.make_traces( main_chart=main_chart, sub_chart=sub_chart, scale_value=scale_value, **kwargs ) # fix sub traces as the bottom if sub_traces: yaxis, y, layout = self.make_y_layout(index=1, total=1, domain_range=(0, 0.2)) # update sub_traces with yaxis for trace in sub_traces: trace.yaxis = y stacked_fig.add_traces(sub_traces) stacked_fig.layout[yaxis] = layout # make y layouts yaxis, y, layout = self.make_y_layout( index=index, total=total, start_index=start, domain_range=domain_range ) stacked_fig.layout[yaxis] = layout # update traces with yaxis for trace in traces: trace.yaxis = y stacked_fig.add_traces(traces) # update shapes with yaxis if drawer.rects: for rect in drawer.rects: stacked_fig.add_shape( type="rect", x0=rect.x0, y0=rect.y0, x1=rect.x1, y1=rect.y1, line=dict(color="RoyalBlue", width=1), # fillcolor="LightSkyBlue", yref=y, ) # annotations if pd_is_not_null(drawer.annotation_df): stacked_fig.layout["annotations"] = annotations(drawer.annotation_df, yref=y) stacked_fig.update_layout( self.default_layout( main_chart=main_chart, width=width, height=height, title=title, keep_ui_state=keep_ui_state ) ) if show: stacked_fig.show() else: return stacked_fig class Drawer(Draw): def __init__( self, main_df: pd.DataFrame = None, factor_df_list: List[pd.DataFrame] = None, sub_df_list: pd.DataFrame = None, main_data: NormalData = None, factor_data_list: List[NormalData] = None, sub_data_list: NormalData = None, sub_col_chart: Optional[dict] = None, rects: List[Rect] = None, annotation_df: pd.DataFrame = None, scale_value: int = None, ) -> None: """ :param main_df: df for main chart :param factor_df_list: list of factor df on main chart :param sub_df_list: df for sub chart under main chart :param main_data: NormalData wrap main_df,use either :param factor_data_list: list of NormalData wrap factor_df,use either :param sub_data_list: NormalData wrap sub_df,use either :param annotation_df: """ #: 主图数据 if main_data is None: main_data = NormalData(main_df) self.main_data: NormalData = main_data #: 主图因子 if not factor_data_list and factor_df_list: factor_data_list = [] for df in factor_df_list: factor_data_list.append(NormalData(df)) #: 每一个df可能有多个column, 代表多个指标，对于连续型的，可以放在一个df里面 #: 对于离散型的，比如一些特定模式的连线，放在多个df里面较好，因为index不同 self.factor_data_list: List[NormalData] = factor_data_list #: 副图数据 if not sub_data_list and sub_df_list: sub_data_list = [] for df in sub_df_list: sub_data_list.append(NormalData(df)) #: 每一个df可能有多个column, 代表多个指标，对于连续型的，可以放在一个df里面 #: 对于离散型的，比如一些特定模式的连线，放在多个df里面较好，因为index不同 self.sub_data_list: List[NormalData] = sub_data_list #: 幅图col对应的图形，line or bar self.sub_col_chart = sub_col_chart #: 主图的标记数据 self.annotation_df = annotation_df #: list of rect self.rects = rects self.scale_value = scale_value def add_factor_df(self, df: pd.DataFrame): self.add_factor_data(NormalData(df)) def add_factor_data(self, data: NormalData): if not self.factor_data_list: self.factor_data_list = [] self.factor_data_list.append(data) def add_sub_df(self, df: pd.DataFrame): self.add_sub_data(NormalData(df)) def add_sub_data(self, data: NormalData): if not self.sub_data_list: self.sub_data_list = [] self.sub_data_list.append(data) def has_sub_plot(self): return self.sub_data_list is not None and not self.sub_data_list[0].empty() def make_traces(self, main_chart=ChartType.kline, sub_chart="bar", yaxis="y", scale_value=None, **kwargs): traces = [] sub_traces = [] for entity_id, df in self.main_data.entity_map_df.items(): df = df.select_dtypes(np.number) df = df.copy() if scale_value: for col in df.columns: first = None for i in range(0, len(df)): first = df[col][i] if first != 0: break if first == 0: continue scale = scale_value / first df[col] = df[col] * scale code = entity_id try: _, _, code = decode_entity_id(entity_id) except Exception: pass # 构造主图 if main_chart == ChartType.bar: for col in df.columns: trace_name = "{}_{}".format(code, col) ydata = df[col].values.tolist() traces.append(go.Bar(x=df.index, y=ydata, name=trace_name, yaxis=yaxis, **kwargs)) elif main_chart == ChartType.kline: trace_name = "{}_kdata".format(code) trace = go.Candlestick( x=df.index, open=df["open"], close=df["close"], low=df["low"], high=df["high"], name=trace_name, yaxis=yaxis, **kwargs, ) traces.append(trace) elif main_chart in [ChartType.scatter, ChartType.line, ChartType.area]: mode = _zvt_chart_type_map_scatter_mode.get(main_chart) for col in df.columns: trace_name = "{}_{}".format(code, col) ydata = df[col].values.tolist() traces.append(go.Scatter(x=df.index, y=ydata, mode=mode, name=trace_name, yaxis=yaxis, **kwargs)) elif main_chart == ChartType.histogram: for col in df.columns: trace_name = "{}_{}".format(code, col) x = df[col].tolist() trace = go.Histogram(x=x, name=trace_name, **kwargs) traces.append(trace) annotation = [ dict( entity_id=entity_id, timestamp=x[-1], value=0, flag=f"{trace_name}:{x[-1]}", ) ] annotation_df = pd.DataFrame.from_records(annotation, index=["entity_id", "timestamp"]) if pd_is_not_null(self.annotation_df): self.annotation_df = pd.concat([self.annotation_df, annotation_df]) else: self.annotation_df = annotation_df elif main_chart == ChartType.pie: for _, row in df.iterrows(): traces.append(go.Pie(name=entity_id, labels=df.columns.tolist(), values=row.tolist(), **kwargs)) else: assert False # 构造主图指标 if self.factor_data_list: for factor_data in self.factor_data_list: if not factor_data.empty(): factor_df = factor_data.entity_map_df.get(entity_id) factor_df = factor_df.select_dtypes(np.number) if pd_is_not_null(factor_df): for col in factor_df.columns: trace_name = "{}_{}".format(code, col) ydata = factor_df[col].values.tolist() line = go.Scatter( x=factor_df.index, y=ydata, mode="lines", name=trace_name, yaxis=yaxis, **kwargs ) traces.append(line) # 构造幅图 if self.has_sub_plot(): for sub_data in self.sub_data_list: sub_df = sub_data.entity_map_df.get(entity_id) if pd_is_not_null(sub_df): sub_df = sub_df.select_dtypes(np.number) for col in sub_df.columns: trace_name = "{}_{}".format(code, col) ydata = sub_df[col].values.tolist() def color(i): if i > 0: return "red" else: return "green" colors = [color(i) for i in ydata] the_sub_chart = None if self.sub_col_chart is not None: the_sub_chart = self.sub_col_chart.get(col) if not the_sub_chart: the_sub_chart = sub_chart if the_sub_chart == ChartType.line: sub_trace = go.Scatter( x=sub_df.index, y=ydata, name=trace_name, yaxis="y2", marker=dict(color=colors) ) else: sub_trace = go.Bar( x=sub_df.index, y=ydata, name=trace_name, yaxis="y2", marker=dict(color=colors) ) sub_traces.append(sub_trace) return traces, sub_traces def add_rects(self, fig, yaxis="y"): if self.rects: for rect in self.rects: fig.add_shape( type="rect", x0=rect.x0, y0=rect.y0, x1=rect.x1, y1=rect.y1, line=dict(color="RoyalBlue", width=1), # fillcolor="LightSkyBlue" ) fig.update_shapes(dict(xref="x", yref=yaxis)) def draw( self, main_chart=ChartType.kline, sub_chart="bar", width=None, height=None, title=None, keep_ui_state=True, show=False, scale_value=None, **kwargs, ): yaxis = "y" traces, sub_traces = self.make_traces( main_chart=main_chart, sub_chart=sub_chart, yaxis=yaxis, scale_value=scale_value, **kwargs ) if sub_traces: fig = make_subplots(rows=2, cols=1, row_heights=[0.8, 0.2], vertical_spacing=0.08, shared_xaxes=True) fig.add_traces(traces, rows=[1] * len(traces), cols=[1] * len(traces)) fig.add_traces(sub_traces, rows=[2] * len(sub_traces), cols=[1] * len(sub_traces)) else: fig = go.Figure() fig.add_traces(traces) # 绘制矩形 self.add_rects(fig, yaxis=yaxis) fig.update_layout( self.default_layout( main_chart=main_chart, width=width, height=height, title=title, keep_ui_state=keep_ui_state ) ) if sub_traces: fig.update_layout(xaxis_rangeslider_visible=False) fig.update_layout(xaxis2_rangeslider_visible=True, xaxis2_rangeslider_thickness=0.1) # 绘制标志 if pd_is_not_null(self.annotation_df): fig.layout["annotations"] = annotations(self.annotation_df, yref=yaxis) if show: fig.show() else: return fig def draw_table(self, width=None, height=None, title=None, keep_ui_state=True, **kwargs): cols = self.main_data.data_df.index.names + self.main_data.data_df.columns.tolist() index1 = self.main_data.data_df.index.get_level_values(0).tolist() index2 = self.main_data.data_df.index.get_level_values(1).tolist() values = [index1] + [index2] + [self.main_data.data_df[col] for col in self.main_data.data_df.columns] data = go.Table( header=dict( values=cols, fill_color=["#000080", "#000080"] + ["#0066cc"] * len(self.main_data.data_df.columns), align="left", font=dict(color="white", size=13), ), cells=dict(values=values, fill=dict(color="#F5F8FF"), align="left"), **kwargs, ) fig = go.Figure() fig.add_traces([data]) fig.update_layout(self.default_layout(width=width, height=height, title=title, keep_ui_state=keep_ui_state)) fig.show() def annotations(annotation_df: pd.DataFrame, yref="y"): """ annotation_df format:: value flag color entity_id timestamp :param annotation_df: :param yref: specific yaxis e.g, y,y2,y3 :return: """ if pd_is_not_null(annotation_df): annotations = [] for trace_name, df in annotation_df.groupby(level=0): if pd_is_not_null(df): for (_, timestamp), item in df.iterrows(): if "color" in item: color = item["color"] else: color = "#ec0000" value = round(item["value"], 2) annotations.append( dict( x=timestamp, y=value, xref="x", yref=yref, text=item["flag"], showarrow=True, align="center", arrowhead=2, arrowsize=1, arrowwidth=2, # arrowcolor='#030813', ax=-10, ay=-30, bordercolor="#c7c7c7", borderwidth=1, bgcolor=color, opacity=0.8, ) ) return annotations return None # the __all__ is generated __all__ = ["ChartType", "Rect", "Draw", "Drawable", "StackedDrawer", "Drawer", "annotations"] ================================================ FILE: src/zvt/contract/factor.py ================================================ # -*- coding: utf-8 -*- import json import logging import time from enum import Enum from typing import List, Union, Optional, Type import pandas as pd from zvt.contract import IntervalLevel from zvt.contract import zvt_context from zvt.contract.api import get_data, df_to_db, del_data from zvt.contract.base_service import EntityStateService from zvt.contract.reader import DataReader, DataListener from zvt.contract.schema import Mixin, TradableEntity from zvt.contract.zvt_info import FactorState from zvt.utils.pd_utils import pd_is_not_null, drop_continue_duplicate, is_filter_result_df, is_score_result_df from zvt.utils.str_utils import to_snake_str from zvt.utils.time_utils import to_pd_timestamp class TargetType(Enum): positive = "positive" negative = "negative" keep = "keep" class Indicator(object): def __init__(self) -> None: self.logger = logging.getLogger(self.__class__.__name__) self.indicators = [] class Transformer(Indicator): def __init__(self) -> None: super().__init__() def transform(self, input_df: pd.DataFrame) -> pd.DataFrame: """ input_df format:: col1 col2 col3 ... entity_id timestamp 1.2 0.5 0.3 ... 1.0 0.7 0.2 ... the return result would change the columns and keep the format :param input_df: :return: """ g = input_df.groupby(level=0) if len(g.groups) == 1: entity_id = input_df.index[0][0] df = input_df.reset_index(level=0, drop=True) ret_df = self.transform_one(entity_id=entity_id, df=df) ret_df["entity_id"] = entity_id return ret_df.set_index("entity_id", append=True).swaplevel(0, 1) else: return g.apply(lambda x: self.transform_one(x.index[0][0], x.reset_index(level=0, drop=True))) def transform_one(self, entity_id: str, df: pd.DataFrame) -> pd.DataFrame: """ df format:: col1 col2 col3 ... timestamp 1.2 0.5 0.3 ... 1.0 0.7 0.2 ... the return result would change the columns and keep the format :param entity_id: :param df: :return: """ return df class Accumulator(Indicator): def __init__(self, acc_window: int = 1) -> None: """ :param acc_window: the window size of acc for computing,default is 1 """ super().__init__() self.acc_window = acc_window def acc(self, input_df: pd.DataFrame, acc_df: pd.DataFrame, states: dict) -> (pd.DataFrame, dict): """ :param input_df: new input :param acc_df: previous result :param states: current states of the entity :return: new result and states """ g = input_df.groupby(level=0) if len(g.groups) == 1: entity_id = input_df.index[0][0] df = input_df.reset_index(level=0, drop=True) if pd_is_not_null(acc_df) and (entity_id == acc_df.index[0][0]): acc_one_df = acc_df.reset_index(level=0, drop=True) else: acc_one_df = None ret_df, state = self.acc_one(entity_id=entity_id, df=df, acc_df=acc_one_df, state=states.get(entity_id)) if pd_is_not_null(ret_df): ret_df["entity_id"] = entity_id ret_df = ret_df.set_index("entity_id", append=True).swaplevel(0, 1) ret_df["entity_id"] = entity_id return ret_df, {entity_id: state} return None, {entity_id: state} else: new_states = {} def cal_acc(x): entity_id = x.index[0][0] if pd_is_not_null(acc_df): acc_g = acc_df.groupby(level=0) acc_one_df = None if entity_id in acc_g.groups: acc_one_df = acc_g.get_group(entity_id) if pd_is_not_null(acc_one_df): acc_one_df = acc_one_df.reset_index(level=0, drop=True) else: acc_one_df = None one_result, state = self.acc_one( entity_id=entity_id, df=x.reset_index(level=0, drop=True), acc_df=acc_one_df, state=states.get(x.index[0][0]), ) new_states[entity_id] = state return one_result ret_df = g.apply(lambda x: cal_acc(x)) return ret_df, new_states def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame, state: dict) -> (pd.DataFrame, dict): """ df format:: col1 col2 col3 ... timestamp 1.2 0.5 0.3 ... 1.0 0.7 0.2 ... the new result and state :param df: current input df :param entity_id: current computing entity_id :param acc_df: current result of the entity_id :param state: current state of the entity_id :return: new result and state of the entity_id """ return acc_df, state class Scorer(object): def __init__(self) -> None: self.logger = logging.getLogger(self.__class__.__name__) def score(self, input_df: pd.DataFrame) -> pd.DataFrame: """ :param input_df: current input df :return: df with normal score """ return input_df def _register_class(target_class): if target_class.__name__ not in ("Factor", "FilterFactor", "ScoreFactor", "StateFactor"): zvt_context.factor_cls_registry[target_class.__name__] = target_class class FactorMeta(type): def __new__(meta, name, bases, class_dict): cls = type.__new__(meta, name, bases, class_dict) _register_class(cls) return cls class Factor(DataReader, EntityStateService, DataListener): #: Schema for storing states state_schema = FactorState #: define the schema for persist,its columns should be same as indicators in transformer or accumulator factor_schema: Type[Mixin] = None #: transformer for this factor if not passed as __init__ argument transformer: Transformer = None #: accumulator for this factor if not passed as __init__ argument accumulator: Accumulator = None def __init__( self, data_schema: Type[Mixin], entity_schema: Type[TradableEntity] = None, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, ) -> None: """ :param keep_all_timestamp: :param fill_method: :param effective_number: :param transformer: :param accumulator: :param need_persist: whether persist factor :param only_compute_factor: only compute factor nor result :param factor_name: :param clear_state: :param only_load_factor: only load factor and compute result """ self.only_load_factor = only_load_factor #: define unique name of your factor if you want to keep factor state #: the factor state is defined by factor_name and entity_id if not factor_name: self.name = to_snake_str(type(self).__name__) else: self.name = factor_name DataReader.__init__( self, data_schema, entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, ) EntityStateService.__init__(self, entity_ids=entity_ids) self.clear_state = clear_state self.keep_all_timestamp = keep_all_timestamp self.fill_method = fill_method self.effective_number = effective_number if transformer: self.transformer = transformer else: self.transformer = self.__class__.transformer if accumulator: self.accumulator = accumulator else: self.accumulator = self.__class__.accumulator self.need_persist = need_persist self.only_compute_factor = only_compute_factor #: 中间结果，不持久化 #: data_df->pipe_df self.pipe_df: pd.DataFrame = None #: 计算因子的结果，可持久化,通过对pipe_df的计算得到 #: pipe_df->factor_df self.factor_df: pd.DataFrame = None #: result_df是用于选股的标准df,通过对factor_df的计算得到 #: factor_df->result_df self.result_df: pd.DataFrame = None if self.clear_state: self.clear_state_data() elif self.need_persist or self.only_load_factor: self.load_factor() #: 根据已经计算的factor_df和computing_window来保留data_df #: 因为读取data_df的目的是为了计算factor_df,选股和回测只依赖factor_df #: 所以如果有持久化的factor_df,只需保留需要用于计算的data_df即可 if pd_is_not_null(self.data_df) and self.computing_window: dfs = [] for entity_id, df in self.data_df.groupby(level=0): latest_laved = get_data( provider="zvt", data_schema=self.factor_schema, entity_id=entity_id, order=self.factor_schema.timestamp.desc(), limit=1, index=[self.category_field, self.time_field], return_type="domain", ) if latest_laved: df1 = df[df.timestamp < latest_laved[0].timestamp].iloc[-self.computing_window :] if pd_is_not_null(df1): df = df[df.timestamp >= df1.iloc[0].timestamp] dfs.append(df) self.data_df = pd.concat(dfs) self.register_data_listener(self) #: the compute logic is not triggered from load data #: for the case:1)load factor from db 2)compute the result if self.only_load_factor: self.compute() def load_data(self): if self.only_load_factor: return super().load_data() def load_factor(self): if self.only_compute_factor: #: 如果只是为了计算因子，只需要读取acc_window的factor_df if self.accumulator is not None: self.factor_df = self.load_window_df( provider="zvt", data_schema=self.factor_schema, window=self.accumulator.acc_window ) else: self.factor_df = get_data( provider="zvt", data_schema=self.factor_schema, start_timestamp=self.start_timestamp, entity_ids=self.entity_ids, end_timestamp=self.end_timestamp, index=[self.category_field, self.time_field], ) self.decode_factor_df(self.factor_df) def decode_factor_df(self, df): col_map_object_hook = self.factor_col_map_object_hook() if pd_is_not_null(df) and col_map_object_hook: for col in col_map_object_hook: if col in df.columns: df[col] = df[col].apply( lambda x: json.loads(x, object_hook=col_map_object_hook.get(col)) if x else None ) def factor_col_map_object_hook(self) -> dict: """ :return:{col:object_hook} """ return {} def clear_state_data(self, entity_id=None): super().clear_state_data(entity_id=entity_id) if entity_id: del_data(self.factor_schema, filters=[self.factor_schema.entity_id == entity_id], provider="zvt") else: del_data(self.factor_schema, provider="zvt") def pre_compute(self): if not self.only_load_factor and not pd_is_not_null(self.pipe_df): self.pipe_df = self.data_df def do_compute(self): self.logger.info("compute factor start") self.compute_factor() self.logger.info("compute factor finish") self.logger.info("compute result start") self.compute_result() self.logger.info("compute result finish") def compute_factor(self): if self.only_load_factor: return #: 无状态的转换运算 if pd_is_not_null(self.data_df) and self.transformer: self.pipe_df = self.transformer.transform(self.data_df) else: self.pipe_df = self.data_df #: 有状态的累加运算 if pd_is_not_null(self.pipe_df) and self.accumulator: self.factor_df, self.states = self.accumulator.acc(self.pipe_df, self.factor_df, self.states) else: self.factor_df = self.pipe_df def compute_result(self): if pd_is_not_null(self.factor_df): cols = [] if is_filter_result_df(self.factor_df): cols.append("filter_result") if is_score_result_df(self.factor_df): cols.append("score_result") if cols: self.result_df = self.factor_df[cols] def after_compute(self): if self.only_load_factor: return if self.keep_all_timestamp: self.fill_gap() if self.need_persist and pd_is_not_null(self.factor_df): self.persist_factor() def compute(self): self.pre_compute() self.logger.info(f"[[[ ~~~~~~~~factor:{self.name} ~~~~~~~~]]]") self.logger.info("do_compute start") start_time = time.time() self.do_compute() cost_time = time.time() - start_time self.logger.info("do_compute finished,cost_time:{}s".format(cost_time)) self.logger.info("after_compute start") start_time = time.time() self.after_compute() cost_time = time.time() - start_time self.logger.info("after_compute finished,cost_time:{}s".format(cost_time)) self.logger.info(f"[[[ ^^^^^^^^factor:{self.name} ^^^^^^^^]]]") def drawer_main_df(self) -> Optional[pd.DataFrame]: if self.only_load_factor: return self.factor_df return self.data_df def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: if (self.transformer is not None or self.accumulator is not None) and pd_is_not_null(self.factor_df): indicators = None if self.transformer is not None: indicators = self.transformer.indicators elif self.accumulator is not None: indicators = self.accumulator.indicators if indicators: return [self.factor_df[indicators]] else: return [self.factor_df] return None def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: if (self.transformer is not None or self.accumulator is not None) and pd_is_not_null(self.result_df): return [self.result_df] return None def drawer_annotation_df(self) -> Optional[pd.DataFrame]: def order_type_flag(order_type): if order_type is None: return None if order_type: return "B" if not order_type: return "S" def order_type_color(order_type): if order_type: return "#ec0000" else: return "#00da3c" if is_filter_result_df(self.result_df): annotation_df = self.result_df[["filter_result"]].copy() annotation_df = annotation_df[~annotation_df["filter_result"].isna()] annotation_df = drop_continue_duplicate(annotation_df, "filter_result") annotation_df["value"] = self.factor_df.loc[annotation_df.index]["close"] annotation_df["flag"] = annotation_df["filter_result"].apply(lambda x: order_type_flag(x)) annotation_df["color"] = annotation_df["filter_result"].apply(lambda x: order_type_color(x)) return annotation_df def fill_gap(self): #: 该操作较慢，只适合做基本面的运算 idx = pd.date_range(self.start_timestamp, self.end_timestamp) new_index = pd.MultiIndex.from_product( [self.result_df.index.levels[0], idx], names=[self.category_field, self.time_field] ) self.result_df = self.result_df.loc[~self.result_df.index.duplicated(keep="first")] self.result_df = self.result_df.reindex(new_index) self.result_df = self.result_df.groupby(level=0).fillna(method=self.fill_method, limit=self.effective_number) def add_entities(self, entity_ids): if (self.entity_ids and entity_ids) and (set(self.entity_ids) == set(entity_ids)): self.logger.info(f"current: {self.entity_ids}") self.logger.info(f"refresh: {entity_ids}") return new_entity_ids = None if entity_ids: new_entity_ids = list(set(entity_ids) - set(self.entity_ids)) self.entity_ids = list(set(self.entity_ids + entity_ids)) if new_entity_ids: self.logger.info(f"added new entity: {new_entity_ids}") if not self.only_load_factor: new_data_df = self.data_schema.query_data( entity_ids=new_entity_ids, provider=self.provider, columns=self.columns, start_timestamp=self.start_timestamp, end_timestamp=self.end_timestamp, filters=self.filters, order=self.order, limit=self.limit, level=self.level, index=[self.category_field, self.time_field], time_field=self.time_field, ) self.data_df = pd.concat([self.data_df, new_data_df], sort=False) self.data_df.sort_index(level=[0, 1], inplace=True) new_factor_df = get_data( provider="zvt", data_schema=self.factor_schema, start_timestamp=self.start_timestamp, entity_ids=new_entity_ids, end_timestamp=self.end_timestamp, index=[self.category_field, self.time_field], ) self.decode_factor_df(new_factor_df) self.factor_df = pd.concat([self.factor_df, new_factor_df], sort=False) self.factor_df.sort_index(level=[0, 1], inplace=True) def on_data_loaded(self, data: pd.DataFrame): self.compute() def on_data_changed(self, data: pd.DataFrame): """ overwrite it for computing after data added :param data: """ self.compute() def on_entity_data_changed(self, entity, added_data: pd.DataFrame): """ overwrite it for computing after entity data added :param entity: :param added_data: """ pass def persist_factor(self): df = self.factor_df.copy() #: encode json columns if pd_is_not_null(df) and self.factor_col_map_object_hook(): for col in self.factor_col_map_object_hook(): if col in df.columns: df[col] = df[col].apply(lambda x: json.dumps(x, cls=self.state_encoder())) if self.states: g = df.groupby(level=0) for entity_id in self.states: state = self.states[entity_id] try: if state: self.persist_state(entity_id=entity_id) if entity_id in g.groups: df_to_db( df=df.loc[(entity_id,)], data_schema=self.factor_schema, provider="zvt", force_update=False ) except Exception as e: self.logger.error(f"{self.name} {entity_id} save state error") self.logger.exception(e) #: clear them if error happen self.clear_state_data(entity_id) else: df_to_db(df=df, data_schema=self.factor_schema, provider="zvt", force_update=False) def get_filter_df(self): if is_filter_result_df(self.result_df): return self.result_df[["filter_result"]] def get_score_df(self): if is_score_result_df(self.result_df): return self.result_df[["score_result"]] def get_trading_signal_df(self): df = self.result_df[["filter_result"]].copy() df = df[~df["filter_result"].isna()] df = drop_continue_duplicate(df, "filter_result") return df def get_targets( self, timestamp=None, start_timestamp=None, end_timestamp=None, target_type: TargetType = TargetType.positive, positive_threshold=0.8, negative_threshold=-0.8, ): if timestamp and (start_timestamp or end_timestamp): raise ValueError("Use timestamp or (start_timestamp, end_timestamp)") # select by filter filter_df = self.get_filter_df() selected_df = None target_df = None if pd_is_not_null(filter_df): if target_type == TargetType.positive: selected_df = filter_df[filter_df["filter_result"] == True] elif target_type == TargetType.negative: selected_df = filter_df[filter_df["filter_result"] == False] else: selected_df = filter_df[filter_df["filter_result"].isna()] # select by score score_df = self.get_score_df() if pd_is_not_null(score_df): if pd_is_not_null(selected_df): # filter at first score_df = score_df.loc[selected_df.index, :] if target_type == TargetType.positive: selected_df = score_df[score_df["score_result"] >= positive_threshold] elif target_type == TargetType.negative: selected_df = score_df[score_df["score_result"] <= negative_threshold] else: selected_df = score_df[ (score_df["score_result"] > negative_threshold) & (score_df["score"] < positive_threshold) ] self.logger.debug(selected_df) if pd_is_not_null(selected_df): selected_df = selected_df.reset_index(level="entity_id") if timestamp: if to_pd_timestamp(timestamp) in selected_df.index: target_df = selected_df.loc[[to_pd_timestamp(timestamp)], ["entity_id"]] else: target_df = selected_df.loc[ slice(to_pd_timestamp(start_timestamp), to_pd_timestamp(end_timestamp)), ["entity_id"] ] if pd_is_not_null(target_df): return target_df["entity_id"].tolist() return [] class ScoreFactor(Factor): scorer: Scorer = None def compute_result(self): super().compute_result() if pd_is_not_null(self.factor_df) and self.scorer: self.result_df = self.scorer.score(self.factor_df) # the __all__ is generated __all__ = ["TargetType", "Indicator", "Transformer", "Accumulator", "Scorer", "FactorMeta", "Factor", "ScoreFactor"] ================================================ FILE: src/zvt/contract/model.py ================================================ # -*- coding: utf-8 -*- from datetime import datetime from pydantic import BaseModel, ConfigDict class CustomModel(BaseModel): model_config = ConfigDict(from_attributes=True, allow_inf_nan=True) class MixinModel(CustomModel): id: str entity_id: str timestamp: datetime # the __all__ is generated __all__ = ["CustomModel", "MixinModel"] ================================================ FILE: src/zvt/contract/normal_data.py ================================================ # -*- coding: utf-8 -*- from zvt.utils.pd_utils import pd_is_not_null, fill_with_same_index, normal_index_df, is_normal_df class NormalData(object): table_type_sample = None def __init__(self, df, category_field="entity_id", time_field="timestamp", fill_index: bool = False) -> None: self.data_df = df self.category_field = category_field self.time_field = time_field self.fill_index = fill_index self.entity_ids = [] self.df_list = [] self.entity_map_df = {} self.normalize() def normalize(self): """ normalize data_df to:: col1 col2 col3 entity_id timestamp """ if pd_is_not_null(self.data_df): if not is_normal_df(self.data_df): self.data_df = normal_index_df(self.data_df, self.category_field, self.time_field) self.entity_ids = self.data_df.index.levels[0].to_list() for entity_id in self.entity_ids: df = self.data_df.loc[(entity_id,)] self.df_list.append(df) self.entity_map_df[entity_id] = df if len(self.df_list) > 1 and self.fill_index: self.df_list = fill_with_same_index(df_list=self.df_list) def empty(self): return not pd_is_not_null(self.data_df) # the __all__ is generated __all__ = ["NormalData"] ================================================ FILE: src/zvt/contract/reader.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import List, Union, Type, Optional import pandas as pd from zvt.contract import IntervalLevel from zvt.contract.api import get_entities from zvt.contract.drawer import Drawable from zvt.contract.schema import Mixin, TradableEntity from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp class DataListener(object): def on_data_loaded(self, data: pd.DataFrame) -> object: """ :param data: """ raise NotImplementedError def on_data_changed(self, data: pd.DataFrame) -> object: """ :param data: """ raise NotImplementedError def on_entity_data_changed(self, entity: str, added_data: pd.DataFrame) -> object: """ :param entity: the entity :param added_data: the data added for the entity """ pass class DataReader(Drawable): logger = logging.getLogger(__name__) def __init__( self, data_schema: Type[Mixin], entity_schema: Type[TradableEntity] = None, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = now_pd_timestamp(), columns: List = None, filters: List = None, order: object = None, limit: int = None, level: IntervalLevel = None, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, ) -> None: self.logger = logging.getLogger(self.__class__.__name__) self.data_schema = data_schema self.entity_schema = entity_schema self.provider = provider self.entity_provider = entity_provider self.start_timestamp = start_timestamp self.end_timestamp = end_timestamp self.start_timestamp = to_pd_timestamp(self.start_timestamp) self.end_timestamp = to_pd_timestamp(self.end_timestamp) self.exchanges = exchanges self.codes = codes self.entity_ids = entity_ids # 转换成标准entity_id if entity_schema and not self.entity_ids: df = get_entities( entity_schema=entity_schema, provider=self.entity_provider, exchanges=self.exchanges, codes=self.codes ) if pd_is_not_null(df): self.entity_ids = df["entity_id"].to_list() self.filters = filters self.order = order self.limit = limit if level: self.level = IntervalLevel(level) else: self.level = level self.category_field = category_field self.time_field = time_field self.computing_window = keep_window self.category_col = eval("self.data_schema.{}".format(self.category_field)) self.time_col = eval("self.data_schema.{}".format(self.time_field)) self.columns = columns if self.columns: # always add category_column and time_field for normalizing self.columns = list(set(self.columns) | {self.category_field, self.time_field}) self.data_listeners: List[DataListener] = [] self.data_df: pd.DataFrame = None self.load_data() def load_window_df(self, provider, data_schema, window): window_df = None dfs = [] for entity_id in self.entity_ids: df = data_schema.query_data( provider=provider, index=[self.category_field, self.time_field], order=data_schema.timestamp.desc(), entity_id=entity_id, limit=window, ) if pd_is_not_null(df): dfs.append(df) if dfs: window_df = pd.concat(dfs) window_df = window_df.sort_index(level=[0, 1]) return window_df def load_data(self): self.logger.info("load_data start") start_time = time.time() params = dict( entity_size=len(self.entity_ids) if self.entity_ids != None else None, provider=self.provider, columns=self.columns, start_timestamp=self.start_timestamp, end_timestamp=self.end_timestamp, filters=self.filters, order=self.order, limit=self.limit, level=self.level, index=[self.category_field, self.time_field], time_field=self.time_field, ) self.logger.info(f"query_data params:{params}") self.data_df = self.data_schema.query_data( entity_ids=self.entity_ids, provider=self.provider, columns=self.columns, start_timestamp=self.start_timestamp, end_timestamp=self.end_timestamp, filters=self.filters, order=self.order, limit=self.limit, level=self.level, index=[self.category_field, self.time_field], time_field=self.time_field, ) cost_time = time.time() - start_time self.logger.info("load_data finished, cost_time:{}".format(cost_time)) for listener in self.data_listeners: listener.on_data_loaded(self.data_df) def move_on(self, to_timestamp: Union[str, pd.Timestamp] = None, timeout: int = 20) -> object: """ using continual fetching data in realtime 1)get the data happened before to_timestamp,if not set,get all the data which means to now 2)if computing_window set,the data_df would be cut for saving memory :param to_timestamp: :type to_timestamp: :param timeout: :type timeout: int :return: :rtype: """ if not pd_is_not_null(self.data_df): self.load_data() return start_time = time.time() has_got = [] dfs = [] changed = False while True: for entity_id, df in self.data_df.groupby(level=0): if entity_id in has_got: continue recorded_timestamp = df.index.levels[1].max() #: move_on读取数据，表明之前的数据已经处理完毕，只需要保留computing_window的数据 if self.computing_window: df = df.iloc[-self.computing_window :] added_filter = [self.category_col == entity_id, self.time_col > recorded_timestamp] if self.filters: filters = self.filters + added_filter else: filters = added_filter added_df = self.data_schema.query_data( provider=self.provider, columns=self.columns, end_timestamp=to_timestamp, filters=filters, level=self.level, index=[self.category_field, self.time_field], ) if pd_is_not_null(added_df): self.logger.info(f'got new data:{df.to_json(orient="records", force_ascii=False)}') for listener in self.data_listeners: listener.on_entity_data_changed(entity=entity_id, added_data=added_df) #: if got data,just move to another entity_id changed = True has_got.append(entity_id) # df = df.append(added_df, sort=False) df = pd.concat([df, added_df], sort=False) dfs.append(df) else: cost_time = time.time() - start_time if cost_time > timeout: #: if timeout,just add the old data has_got.append(entity_id) dfs.append(df) self.logger.warning( "category:{} level:{} getting data timeout,to_timestamp:{},now:{}".format( entity_id, self.level, to_timestamp, now_pd_timestamp() ) ) continue if len(has_got) == len(self.data_df.index.levels[0]): break if dfs: self.data_df = pd.concat(dfs, sort=False) self.data_df.sort_index(level=[0, 1], inplace=True) if changed: for listener in self.data_listeners: listener.on_data_changed(self.data_df) def register_data_listener(self, listener): if listener not in self.data_listeners: self.data_listeners.append(listener) #: notify it once after registered if pd_is_not_null(self.data_df): listener.on_data_loaded(self.data_df) def deregister_data_listener(self, listener): if listener in self.data_listeners: self.data_listeners.remove(listener) def empty(self): return not pd_is_not_null(self.data_df) def drawer_main_df(self) -> Optional[pd.DataFrame]: return self.data_df if __name__ == "__main__": from zvt.domain import Stock1dKdata, Stock data_reader = DataReader( data_schema=Stock1dKdata, entity_schema=Stock, codes=["002572", "000338"], start_timestamp="2017-01-01", end_timestamp="2019-06-10", ) data_reader.draw(show=True) # the __all__ is generated __all__ = ["DataListener", "DataReader"] ================================================ FILE: src/zvt/contract/recorder.py ================================================ # -*- coding: utf-8 -*- import logging import time import uuid from typing import List import pandas as pd import requests from sqlalchemy.orm import Session from zvt.contract import IntervalLevel from zvt.contract.api import get_db_session, get_schema_columns from zvt.contract.api import get_entities, get_data from zvt.contract.base_service import OneStateService from zvt.contract.schema import Mixin, TradableEntity from zvt.contract.utils import is_in_same_interval, evaluate_size_from_timestamp from zvt.contract.zvt_info import RecorderState from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import ( to_pd_timestamp, TIME_FORMAT_DAY, to_date_time_str, now_pd_timestamp, now_date_time_str, ) from zvt.utils.utils import fill_domain_from_dict def _register_kdata_schemas_for_recorder(cls): """ Pre-register concrete kdata schemas (Stock1dKdata, etc.) for recorders that define supported_levels. KdataCommon is a placeholder; the real schemas are per (level, adjust_type). Must have: entity_schema, data_schema (KdataCommon), supported_levels, provider. """ if not ( hasattr(cls, "entity_schema") and cls.entity_schema and hasattr(cls, "data_schema") and cls.data_schema and cls.data_schema.__name__.endswith("KdataCommon") and hasattr(cls, "supported_levels") and cls.supported_levels and hasattr(cls, "provider") and cls.provider ): return False from zvt.api.kdata import get_kdata_schema from zvt.contract import zvt_context entity_type = cls.entity_schema.__name__.lower() adjust_types = getattr(cls, "supported_adjust_types", [None]) for level in cls.supported_levels: for adjust_type in adjust_types: try: schema = get_kdata_schema(entity_type=entity_type, level=level, adjust_type=adjust_type) if schema: schema.register_recorder_cls(cls.provider, cls) db_name = getattr(schema, "_zvt_db_name", None) if db_name: if cls.provider not in zvt_context.providers: zvt_context.providers.append(cls.provider) if not zvt_context.provider_map_dbnames.get(cls.provider): zvt_context.provider_map_dbnames[cls.provider] = [] if db_name not in zvt_context.provider_map_dbnames[cls.provider]: zvt_context.provider_map_dbnames[cls.provider].append(db_name) except Exception: pass return True class Meta(type): def __new__(meta, name, bases, class_dict): cls = type.__new__(meta, name, bases, class_dict) if hasattr(cls, "data_schema") and hasattr(cls, "provider"): if cls.data_schema and issubclass(cls.data_schema, Mixin): if _register_kdata_schemas_for_recorder(cls): pass else: cls.data_schema.register_recorder_cls(cls.provider, cls) from zvt.contract import zvt_context if cls.provider not in zvt_context.providers: zvt_context.providers.append(cls.provider) if not zvt_context.provider_map_dbnames.get(cls.provider): zvt_context.provider_map_dbnames[cls.provider] = [] db_name = getattr(cls.data_schema, "_zvt_db_name", None) if db_name and db_name not in zvt_context.provider_map_dbnames[cls.provider]: zvt_context.provider_map_dbnames[cls.provider].append(db_name) return cls class Recorder(OneStateService, metaclass=Meta): #: overwrite them to set up the data you want to record provider: str = None data_schema: Mixin = None #: original page url original_page_url = None #: request url url = None state_schema = RecorderState def __init__(self, force_update: bool = False, sleeping_time: int = 10) -> None: super().__init__() self.logger = logging.getLogger(self.__class__.__name__) assert self.provider is not None assert self.data_schema is not None # Register concrete schema for kdata Recorders that set data_schema in __init__ # (e.g. via get_kdata_schema); metaclass only runs at class-def time when data_schema may be None self.data_schema.register_recorder_cls(self.provider, self.__class__) from zvt.contract import zvt_context if self.provider not in zvt_context.providers: zvt_context.providers.append(self.provider) if not zvt_context.provider_map_dbnames.get(self.provider): zvt_context.provider_map_dbnames[self.provider] = [] db_name = getattr(self.data_schema, "_zvt_db_name", None) if db_name and db_name not in zvt_context.provider_map_dbnames[self.provider]: zvt_context.provider_map_dbnames[self.provider].append(db_name) if self.provider not in self.data_schema.get_providers(): self.logger.error( f"provider: {self.provider} is not registered for {self.data_schema}({self.data_schema.get_providers()})" ) assert False self.force_update = force_update self.sleeping_time = sleeping_time #: using to do db operations self.session = get_db_session(provider=self.provider, data_schema=self.data_schema) self.http_session = requests.Session() def run(self): raise NotImplementedError def sleep(self, seconds=None): if seconds: sleeping_time = seconds else: sleeping_time = self.sleeping_time if sleeping_time and sleeping_time > 0: self.logger.info(f"sleeping {sleeping_time} seconds") time.sleep(self.sleeping_time) class EntityEventRecorder(Recorder): #: overwrite them to fetch the entity list entity_provider: str = None entity_schema: TradableEntity = None def __init__( self, force_update=False, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, end_timestamp=None, entity_filters=None, ignore_failed=True, return_unfinished=False, ) -> None: """ :param latest_trading_date: :param code: :param ignore_failed: :param entity_filters: :param exchanges: :param entity_id: for record single entity :param entity_ids: set entity_ids or (entity_type,exchanges,codes) :param codes: :param day_data: one record per day,set to True if you want skip recording it when data of today exist :param force_update: :param sleeping_time: """ super().__init__(force_update=force_update, sleeping_time=sleeping_time) assert self.entity_provider is not None assert self.entity_schema is not None #: setup the entities you want to record self.exchanges = exchanges if codes is None and code is not None: self.codes = [code] else: self.codes = codes self.day_data = day_data self.end_timestamp = end_timestamp #: set entity_ids or (entity_type,exchanges,codes) self.entity_ids = None if entity_id: self.entity_ids = [entity_id] if entity_ids: self.entity_ids = entity_ids self.entity_filters = entity_filters self.ignore_failed = ignore_failed self.return_unfinished = return_unfinished self.entity_session: Session = None self.entities: List = None self.init_entities() def init_entities(self): """ init the entities which we would record data for """ if self.entity_provider == self.provider and self.entity_schema == self.data_schema: self.entity_session = self.session else: self.entity_session = get_db_session(provider=self.entity_provider, data_schema=self.entity_schema) if self.day_data: if not self.end_timestamp: self.end_timestamp = now_date_time_str() df = self.data_schema.query_data( start_timestamp=self.end_timestamp, columns=["entity_id", "timestamp"], provider=self.provider ) if pd_is_not_null(df): entity_ids = df["entity_id"].tolist() self.logger.info(f"ignore entity_ids:{entity_ids}") if self.entity_filters: self.entity_filters.append(self.entity_schema.entity_id.notin_(entity_ids)) else: self.entity_filters = [self.entity_schema.entity_id.notin_(entity_ids)] #: init the entity list self.entities = get_entities( session=self.entity_session, entity_schema=self.entity_schema, exchanges=self.exchanges, entity_ids=self.entity_ids, codes=self.codes, return_type="domain", provider=self.entity_provider, filters=self.entity_filters, ) class TimeSeriesDataRecorder(EntityEventRecorder): default_size = 2000 def __init__( self, force_update=False, sleeping_time=5, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, return_unfinished=False, ) -> None: self.start_timestamp = to_pd_timestamp(start_timestamp) self.end_timestamp = to_pd_timestamp(end_timestamp) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code=code, codes=codes, day_data=day_data, end_timestamp=self.end_timestamp, entity_filters=entity_filters, ignore_failed=ignore_failed, return_unfinished=return_unfinished, ) self.real_time = real_time self.close_hour, self.close_minute = self.entity_schema.get_close_hour_and_minute() self.fix_duplicate_way = fix_duplicate_way def get_latest_saved_record(self, entity): order = eval("self.data_schema.{}.desc()".format(self.get_evaluated_time_field())) records = get_data( entity_id=entity.id, provider=self.provider, data_schema=self.data_schema, order=order, limit=1, return_type="domain", session=self.session, ) if records: return records[0] return None def evaluate_start_end_size_timestamps(self, entity): #: not to list date yet if entity.timestamp and (entity.timestamp >= now_pd_timestamp()): self.logger.info("ignore entity: {} list date: {}", entity.id, entity.timestamp) return entity.timestamp, None, 0, None latest_saved_record = self.get_latest_saved_record(entity=entity) if latest_saved_record: latest_timestamp = eval("latest_saved_record.{}".format(self.get_evaluated_time_field())) else: latest_timestamp = entity.timestamp if not latest_timestamp: return self.start_timestamp, self.end_timestamp, self.default_size, None if self.start_timestamp: latest_timestamp = max(latest_timestamp, self.start_timestamp) size = self.default_size if self.end_timestamp: if latest_timestamp > self.end_timestamp: size = 0 return latest_timestamp, self.end_timestamp, size, None def get_data_map(self): """ {'original_field':('domain_field',transform_func)} """ return {} def record(self, entity, start, end, size, timestamps): """ implement the recording logic in this method, should return json or domain list :param entity: :type entity: :param start: :type start: :param end: :type end: :param size: :type size: :param timestamps: :type timestamps: """ raise NotImplementedError def get_evaluated_time_field(self): """ the timestamp field for evaluating time range of recorder,used in get_latest_saved_record """ return "timestamp" def get_original_time_field(self): return "timestamp" def generate_domain_id(self, entity, original_data, time_fmt=TIME_FORMAT_DAY): """ generate domain id from the entity and original data,the default id meaning:entity + event happen time :param entity: :type entity: :param original_data: :type original_data: :param time_fmt: :type time_fmt: :return: :rtype: """ timestamp = to_date_time_str(original_data[self.get_original_time_field()], fmt=time_fmt) return "{}_{}".format(entity.id, timestamp) def generate_domain(self, entity, original_data): """ generate the data_schema instance using entity and original_data,the original_data is from record result :param entity: :param original_data: """ got_new_data = False #: if the domain is directly generated in record method, we just return it if isinstance(original_data, self.data_schema): got_new_data = True return got_new_data, original_data the_id = self.generate_domain_id(entity, original_data) #: optional way #: item = self.session.query(self.data_schema).get(the_id) items = get_data( data_schema=self.data_schema, session=self.session, provider=self.provider, entity_id=entity.id, filters=[self.data_schema.id == the_id], return_type="domain", ) if items and not self.force_update: self.logger.info("ignore the data {}:{} saved before".format(self.data_schema, the_id)) return got_new_data, None if not items: timestamp_str = original_data[self.get_original_time_field()] timestamp = None try: timestamp = to_pd_timestamp(timestamp_str) except Exception as e: self.logger.exception(e) if "name" in get_schema_columns(self.data_schema): domain_item = self.data_schema( id=the_id, code=entity.code, name=entity.name, entity_id=entity.id, timestamp=timestamp ) else: domain_item = self.data_schema(id=the_id, code=entity.code, entity_id=entity.id, timestamp=timestamp) got_new_data = True else: domain_item = items[0] fill_domain_from_dict(domain_item, original_data, self.get_data_map()) return got_new_data, domain_item def persist(self, entity, domain_list): """ persist the domain list to db :param entity: :param domain_list: """ if domain_list: try: if domain_list[0].timestamp >= domain_list[-1].timestamp: first_timestamp = domain_list[-1].timestamp last_timestamp = domain_list[0].timestamp else: first_timestamp = domain_list[0].timestamp last_timestamp = domain_list[-1].timestamp except: first_timestamp = domain_list[0].timestamp last_timestamp = domain_list[-1].timestamp self.logger.info( "persist {} for entity_id:{},time interval:[{},{}]".format( self.data_schema, entity.id, first_timestamp, last_timestamp ) ) self.session.add_all(domain_list) self.session.commit() def on_finish(self): try: if self.session: self.session.close() if self.entity_session: self.entity_session.close() if self.http_session: self.http_session.close() except Exception as e: self.logger.error(e) def on_finish_entity(self, entity): pass def run(self): finished_items = [] unfinished_items = self.entities raising_exception = None while True: count = len(unfinished_items) for index, entity_item in enumerate(unfinished_items): try: self.logger.info(f"run to {index + 1}/{count}") start_timestamp, end_timestamp, size, timestamps = self.evaluate_start_end_size_timestamps( entity_item ) size = int(size) if timestamps: self.logger.info( "entity_id:{},evaluate_start_end_size_timestamps result:{},{},{},{}-{}".format( entity_item.id, start_timestamp, end_timestamp, size, timestamps[0], timestamps[-1] ) ) else: self.logger.info( "entity_id:{},evaluate_start_end_size_timestamps result:{},{},{},{}".format( entity_item.id, start_timestamp, end_timestamp, size, timestamps ) ) #: no more to record if size == 0: finished_items.append(entity_item) self.logger.info( "finish recording {} for entity_id:{},latest_timestamp:{}".format( self.data_schema, entity_item.id, start_timestamp ) ) self.on_finish_entity(entity_item) continue #: sleep for a while to next entity if index != 0: self.sleep() original_list = self.record( entity_item, start=start_timestamp, end=end_timestamp, size=size, timestamps=timestamps ) all_duplicated = True if original_list: domain_list = [] for original_item in original_list: got_new_data, domain_item = self.generate_domain(entity_item, original_item) if got_new_data: all_duplicated = False #: handle the case generate_domain_id generate duplicate id if domain_item: duplicate = [item for item in domain_list if item.id == domain_item.id] if duplicate: #: regenerate the id if self.fix_duplicate_way == "add": domain_item.id = "{}_{}".format(domain_item.id, uuid.uuid1()) #: ignore else: self.logger.info(f"ignore original duplicate item:{domain_item.id}") continue domain_list.append(domain_item) if domain_list: self.persist(entity_item, domain_list) else: self.logger.info("just got {} duplicated data in this cycle".format(len(original_list))) #: could not get more data entity_finished = False if not original_list or all_duplicated: #: not realtime if not self.real_time: entity_finished = True #: realtime and to the close time if self.real_time and (self.close_hour is not None) and (self.close_minute is not None): current_timestamp = pd.Timestamp.now() if current_timestamp.hour >= self.close_hour: if current_timestamp.minute - self.close_minute >= 5: self.logger.info( "{} now is the close time:{}".format(entity_item.id, current_timestamp) ) entity_finished = True #: add finished entity to finished_items if entity_finished: finished_items.append(entity_item) latest_saved_record = self.get_latest_saved_record(entity=entity_item) if latest_saved_record: start_timestamp = eval("latest_saved_record.{}".format(self.get_evaluated_time_field())) self.logger.info( "finish recording {} for entity_id:{},latest_timestamp:{}".format( self.data_schema, entity_item.id, start_timestamp ) ) self.on_finish_entity(entity_item) continue except Exception as e: self.logger.exception( "recording data for entity_id:{},{},error:{}".format(entity_item.id, self.data_schema, e) ) raising_exception = e if self.return_unfinished: self.on_finish() unfinished_items = set(unfinished_items) - set(finished_items) return [item.entity_id for item in unfinished_items] finished_items = unfinished_items break unfinished_items = set(unfinished_items) - set(finished_items) if len(unfinished_items) == 0: break self.on_finish() if self.return_unfinished: return [] if raising_exception: raise raising_exception class FixedCycleDataRecorder(TimeSeriesDataRecorder): def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, return_unfinished=False, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code=code, codes=codes, day_data=day_data, entity_filters=entity_filters, ignore_failed=ignore_failed, real_time=real_time, fix_duplicate_way=fix_duplicate_way, start_timestamp=start_timestamp, end_timestamp=end_timestamp, return_unfinished=return_unfinished, ) self.level = IntervalLevel(level) self.kdata_use_begin_time = kdata_use_begin_time self.one_day_trading_minutes = one_day_trading_minutes def get_latest_saved_record(self, entity): order = eval("self.data_schema.{}.desc()".format(self.get_evaluated_time_field())) #: 对于k线这种数据，最后一个记录有可能是没完成的，所以取两个 #: 同一周期内只保留最新的一个数据 records = get_data( entity_id=entity.id, provider=self.provider, data_schema=self.data_schema, order=order, limit=2, return_type="domain", session=self.session, level=self.level, ) if records: #: delete unfinished kdata if len(records) == 2: if is_in_same_interval(t1=records[0].timestamp, t2=records[1].timestamp, level=self.level): self.session.delete(records[1]) self.session.flush() return records[0] return None def evaluate_start_end_size_timestamps(self, entity): #: not to list date yet if entity.timestamp and (entity.timestamp >= now_pd_timestamp()): return entity.timestamp, None, 0, None #: get latest record latest_saved_record = self.get_latest_saved_record(entity=entity) if latest_saved_record: #: the latest saved timestamp latest_saved_timestamp = latest_saved_record.timestamp else: #: the list date latest_saved_timestamp = entity.timestamp if not latest_saved_timestamp: return None, None, self.default_size, None size = evaluate_size_from_timestamp( start_timestamp=latest_saved_timestamp, level=self.level, one_day_trading_minutes=self.one_day_trading_minutes, ) if self.start_timestamp: start = max(self.start_timestamp, latest_saved_timestamp) else: start = latest_saved_timestamp return start, None, size, None class TimestampsDataRecorder(TimeSeriesDataRecorder): def __init__( self, force_update=False, sleeping_time=5, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code=code, codes=codes, day_data=day_data, entity_filters=entity_filters, ignore_failed=ignore_failed, real_time=real_time, fix_duplicate_way=fix_duplicate_way, start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) self.security_timestamps_map = {} def init_timestamps(self, entity_item) -> List[pd.Timestamp]: raise NotImplementedError def evaluate_start_end_size_timestamps(self, entity): timestamps = self.security_timestamps_map.get(entity.id) if not timestamps: timestamps = self.init_timestamps(entity) if self.start_timestamp: timestamps = [t for t in timestamps if t >= self.start_timestamp] if self.end_timestamp: timestamps = [t for t in timestamps if t <= self.end_timestamp] self.security_timestamps_map[entity.id] = timestamps if not timestamps: return None, None, 0, timestamps timestamps.sort() self.logger.info("entity_id:{},timestamps start:{},end:{}".format(entity.id, timestamps[0], timestamps[-1])) latest_record = self.get_latest_saved_record(entity=entity) if latest_record: self.logger.info("latest record timestamp:{}".format(latest_record.timestamp)) timestamps = [t for t in timestamps if t >= latest_record.timestamp] if timestamps: return timestamps[0], timestamps[-1], len(timestamps), timestamps return None, None, 0, None return timestamps[0], timestamps[-1], len(timestamps), timestamps # the __all__ is generated __all__ = [ "Meta", "Recorder", "EntityEventRecorder", "TimeSeriesDataRecorder", "FixedCycleDataRecorder", "TimestampsDataRecorder", ] ================================================ FILE: src/zvt/contract/register.py ================================================ # -*- coding: utf-8 -*- """ Phase 3: providers removed from register_schema. Providers come from Recorder registration. """ import logging from typing import List import sqlalchemy from sqlalchemy import MetaData from sqlalchemy.ext.declarative import DeclarativeMeta from sqlalchemy.sql.ddl import CreateTable from sqlalchemy.sql.expression import text from zvt.contract import zvt_context from zvt.contract.schema import TradableEntity, Mixin from zvt.utils.utils import add_to_map_list logger = logging.getLogger(__name__) def ensure_schema_tables_and_indexes(engine, schema_base: DeclarativeMeta, db_name: str): """Create tables and indexes for schema. Used by lazy init.""" schema_base.metadata.create_all(bind=engine) for table_name, table in iter(schema_base.metadata.tables.items()): db_meta = MetaData() db_meta.reflect(bind=engine) db_table = db_meta.tables[table_name] existing_columns = [c.name for c in db_table.columns] added_columns = [c for c in table.columns if c.name not in existing_columns] index_list = [] with engine.connect() as con: if db_name in ("zvt_info", "stock_news", "stock_tags", "stock_quote"): con.execute(text("PRAGMA journal_mode=WAL;")) con.execute(text("PRAGMA journal_size_limit=1073741824;")) else: con.execute(text("PRAGMA journal_mode=DELETE;")) rs = con.execute(text("PRAGMA INDEX_LIST('{}')".format(table_name))) for row in rs: index_list.append(row[1]) try: if added_columns: ddl_c = engine.dialect.ddl_compiler(engine.dialect, CreateTable(table)) for added_column in added_columns: stmt = text( f"ALTER TABLE {table_name} ADD COLUMN {ddl_c.get_column_specification(added_column)}" ) logger.info(f"{engine.url} migrations:\n {stmt}") con.execute(stmt) for col in [ "timestamp", "entity_id", "code", "report_period", "created_timestamp", "updated_timestamp", ]: if col in table.c: column = getattr(table.c, col) index_name = "{}_{}_index".format(table_name, col) if index_name not in index_list: sqlalchemy.schema.Index(index_name, column).create(engine) for cols in [("timestamp", "entity_id"), ("timestamp", "code")]: if cols[0] in table.c and cols[1] in table.c: column0 = getattr(table.c, cols[0]) column1 = getattr(table.c, cols[1]) index_name = "{}_{}_{}_index".format(table_name, cols[0], cols[1]) if index_name not in index_list: sqlalchemy.schema.Index(index_name, column0, column1).create(engine) except Exception as e: logger.error(e) def register_entity(entity_type: str = None): """ function for register entity type :param entity_type: :type entity_type: :return: :rtype: """ def register(cls): # register the entity if issubclass(cls, TradableEntity): entity_type_ = entity_type if not entity_type: entity_type_ = cls.__name__.lower() if entity_type_ not in zvt_context.tradable_entity_types: zvt_context.tradable_entity_types.append(entity_type_) zvt_context.tradable_entity_schemas.append(cls) zvt_context.tradable_schema_map[entity_type_] = cls return cls return register def register_schema( db_name: str, schema_base: DeclarativeMeta, entity_type: str = None, internal: bool = False, ): """ Register schema. Providers come from Recorder registration (provider_map_recorder). Tables/engines are created lazily on first get_db_engine(provider, db_name). For schemas without Recorders, add to config: storage.schema_providers. :param internal: If True, schema is business/internal data, not tied to external data source. Internal schemas only care about storage routing, do not participate in provider switching (e.g. zvt_info, trader_info, stock_tags). """ schemas = [] for item in schema_base.registry.mappers: cls = item.class_ if type(cls) == DeclarativeMeta: if zvt_context.dbname_map_schemas.get(db_name): schemas = zvt_context.dbname_map_schemas[db_name] zvt_context.schemas.append(cls) if issubclass(cls, Mixin): cls._zvt_db_name = db_name cls._zvt_internal = internal # internal=True: business data, no external data source if entity_type: add_to_map_list(the_map=zvt_context.entity_map_schemas, key=entity_type, value=cls) schemas.append(cls) zvt_context.dbname_map_schemas[db_name] = schemas zvt_context.dbname_map_base[db_name] = schema_base if internal: zvt_context.internal_db_names.add(db_name) # the __all__ is generated __all__ = ["register_entity", "register_schema", "ensure_schema_tables_and_indexes"] ================================================ FILE: src/zvt/contract/route_registry.py ================================================ # -*- coding: utf-8 -*- """ Route registry: maps (provider, db_name) -> storage_id. Phase 1: uses deterministic rule storage_id = "{provider}_{db_name}". Phase 2: supports config file overrides via zvt_config["storage"]["storage_routes"]. """ from typing import Optional, Type def _get_db_name_from_context(data_schema) -> Optional[str]: """Resolve db_name for a schema from zvt_context.dbname_map_base.""" from zvt.contract import zvt_context for db_name, base in zvt_context.dbname_map_base.items(): if issubclass(data_schema, base): return db_name return None def _get_storage_config() -> dict: """Load storage config from zvt_config. Lazy import to avoid cycles.""" try: from zvt import zvt_config return zvt_config.get("storage") or {} except Exception: return {} class RouteRegistry: """ Maps (provider, db_name) or (provider, data_schema) to storage_id. Default: storage_id = "{provider}_{db_name}". Config override: zvt_config["storage"]["storage_routes"]["provider|db_name"] = storage_id. """ def __init__(self) -> None: self._route_overrides: dict = {} def register_route(self, provider: str, db_name: str, storage_id: str) -> None: """Register explicit route override. Takes precedence over config.""" key = f"{provider}|{db_name}" self._route_overrides[key] = storage_id def _get_route_overrides_from_config(self) -> dict: """Merge config storage_routes with programmatic overrides. Config is loaded once.""" config = _get_storage_config() routes = dict(config.get("storage_routes") or {}) routes.update(self._route_overrides) return routes def get_storage_id(self, provider: str, db_name: str) -> str: """ Get storage_id for (provider, db_name). Checks: 1) programmatic overrides 2) config storage_routes 3) default "{provider}_{db_name}". """ overrides = self._get_route_overrides_from_config() key = f"{provider}|{db_name}" if key in overrides: return overrides[key] return f"{provider}_{db_name}" def get_storage_id_for_schema( self, provider: str, data_schema: Type = None, db_name: str = None ) -> str: """ Get storage_id for (provider, data_schema) or (provider, db_name). :param provider: Data provider name. :param data_schema: Schema class (used to resolve db_name if db_name is None). :param db_name: DB name. If provided, data_schema is ignored. :return: storage_id string. """ if db_name is None: db_name = _get_db_name_from_context(data_schema) if db_name is None: raise ValueError(f"Cannot resolve db_name for schema {data_schema}") return self.get_storage_id(provider, db_name) # Default instance; can be replaced for custom routing _default_route_registry: Optional[RouteRegistry] = None def get_route_registry() -> RouteRegistry: """Get the default route registry.""" global _default_route_registry if _default_route_registry is None: _default_route_registry = RouteRegistry() return _default_route_registry def set_route_registry(registry: RouteRegistry): """Replace default route registry. Useful for tests.""" global _default_route_registry _default_route_registry = registry __all__ = [ "RouteRegistry", "get_route_registry", "set_route_registry", ] ================================================ FILE: src/zvt/contract/schema.py ================================================ # -*- coding: utf-8 -*- import inspect import json from datetime import timedelta from typing import Dict, List, Union import pandas as pd import pkg_resources from sqlalchemy import Column, String, DateTime, Float from sqlalchemy.orm import Session from zvt.contract import IntervalLevel def _get_schema_providers() -> Dict[str, List[str]]: """Get schema_providers merged with package default (ensures defaults are available).""" default = {} try: with open(pkg_resources.resource_filename("zvt", "config.json")) as f: default = (json.load(f).get("storage") or {}).get("schema_providers") or {} except Exception: pass try: from zvt import zvt_config user = (zvt_config.get("storage") or {}).get("schema_providers") or {} default = dict(default) default.update(user) except Exception: pass return default from zvt.utils.time_utils import date_and_time, is_same_date_time, now_pd_timestamp class Mixin(object): """ Base class of schema. """ #: id id = Column(String, primary_key=True) #: entity id entity_id = Column(String) #: the meaning could be different for different case,most time it means 'happen time' #: Need replace with your timezone timestamp = Column(DateTime) # unix epoch,same meaning with timestamp # ts = Column(BIGINT) @classmethod def help(cls): print(inspect.getsource(cls)) @classmethod def important_cols(cls): return [] @classmethod def time_field(cls): return "timestamp" @classmethod def register_recorder_cls(cls, provider, recorder_cls): """ register the recorder for the schema :param provider: :param recorder_cls: """ # don't make provider_map_recorder as class field,it should be created for the sub class as need if not hasattr(cls, "provider_map_recorder"): cls.provider_map_recorder = {} if provider not in cls.provider_map_recorder: cls.provider_map_recorder[provider] = recorder_cls @classmethod def register_provider(cls, provider): """(Deprecated) Providers now come from Recorder registration.""" if not hasattr(cls, "_zvt_providers_override"): cls._zvt_providers_override = [] if provider not in cls._zvt_providers_override: cls._zvt_providers_override.append(provider) @classmethod def is_internal(cls) -> bool: """True if schema is internal/business data, not tied to external data source.""" return getattr(cls, "_zvt_internal", False) @classmethod def get_providers(cls) -> List[str]: """ Providers: from provider_map_recorder (Recorder registration), or config storage.schema_providers. Only use provider_map_recorder when set on this concrete schema class (cls.__dict__), not inherited from a shared base like KdataCommon - otherwise StockQuote would wrongly get Stock1dKdata's providers. """ if "provider_map_recorder" in cls.__dict__ and cls.provider_map_recorder: return list(cls.provider_map_recorder.keys()) db_name = getattr(cls, "_zvt_db_name", None) if db_name: schema_providers = _get_schema_providers() if db_name in schema_providers: return schema_providers[db_name] return getattr(cls, "_zvt_providers_override", []) or [] @classmethod def test_data_correctness(cls, provider, data_samples): for data in data_samples: item = cls.query_data(provider=provider, ids=[data["id"]], return_type="dict") print(item) for k in data: if k == "timestamp": assert is_same_date_time(item[0][k], data[k]) else: assert item[0][k] == data[k] @classmethod def get_by_id(cls, id, provider_index: int = 0, provider: str = None): from .api import get_by_id if not provider: provider = cls.get_providers()[provider_index] return get_by_id(data_schema=cls, id=id, provider=provider) @classmethod def query_data( cls, provider_index: int = 0, ids: List[str] = None, entity_ids: List[str] = None, entity_id: str = None, codes: List[str] = None, code: str = None, level: Union[IntervalLevel, str] = None, provider: str = None, columns: List = None, col_label: dict = None, return_type: str = "df", start_timestamp: Union[pd.Timestamp, str] = None, end_timestamp: Union[pd.Timestamp, str] = None, filters: List = None, session: Session = None, order=None, limit: int = None, distinct=None, index: Union[str, list] = None, drop_index_col=False, time_field: str = "timestamp", ): """ query data by the arguments :param provider_index: :param data_schema: :param ids: :param entity_ids: :param entity_id: :param codes: :param code: :param level: :param provider: :param columns: :param col_label: dict with key(column), value(label) :param return_type: df, domain or dict. default is df :param start_timestamp: :param end_timestamp: :param filters: :param session: :param order: :param limit: :param index: index field name, str for single index, str list for multiple index :param drop_index_col: whether drop the col if it's in index, default False :param time_field: :return: results basing on return_type. """ from .api import get_data if not provider: provider = cls.get_providers()[provider_index] return get_data( data_schema=cls, ids=ids, entity_ids=entity_ids, entity_id=entity_id, codes=codes, code=code, level=level, provider=provider, columns=columns, col_label=col_label, return_type=return_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters, session=session, order=order, limit=limit, index=index, distinct=distinct, drop_index_col=drop_index_col, time_field=time_field, ) @classmethod def get_storages( cls, provider: str = None, ): """ get the storages info :param provider: provider :return: storages """ if not provider: providers = cls.get_providers() else: providers = [provider] from zvt.contract.api import get_db_engine engines = [] for p in providers: engines.append(get_db_engine(provider=p, data_schema=cls)) return engines @classmethod def record_data( cls, provider_index: int = 0, provider: str = None, force_update=None, sleeping_time=None, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, real_time=None, fix_duplicate_way=None, start_timestamp=None, end_timestamp=None, one_day_trading_minutes=None, **kwargs, ): """ record data by the arguments :param entity_id: :param provider_index: :param provider: :param force_update: :param sleeping_time: :param exchanges: :param entity_ids: :param code: :param codes: :param real_time: :param fix_duplicate_way: :param start_timestamp: :param end_timestamp: :param one_day_trading_minutes: :param kwargs: :return: """ if cls.provider_map_recorder: print(f"{cls.__name__} registered recorders:{cls.provider_map_recorder}") if provider: recorder_class = cls.provider_map_recorder[provider] else: recorder_class = cls.provider_map_recorder[cls.get_providers()[provider_index]] # get args for specific recorder class from zvt.contract.recorder import TimeSeriesDataRecorder if issubclass(recorder_class, TimeSeriesDataRecorder): args = [ item for item in inspect.getfullargspec(cls.record_data).args if item not in ("cls", "provider_index", "provider") ] else: args = ["force_update", "sleeping_time"] #: just fill the None arg to kw,so we could use the recorder_class default args kw = {} for arg in args: tmp = eval(arg) if tmp is not None: kw[arg] = tmp #: FixedCycleDataRecorder from zvt.contract.recorder import FixedCycleDataRecorder if issubclass(recorder_class, FixedCycleDataRecorder): #: contract: #: 1)use FixedCycleDataRecorder to record the data with IntervalLevel #: 2)the table of schema with IntervalLevel format is {entity}_{level}_[adjust_type]_{event} table: str = cls.__tablename__ try: items = table.split("_") if len(items) == 4: adjust_type = items[2] kw["adjust_type"] = adjust_type level = IntervalLevel(items[1]) except: #: for other schema not with normal format,but need to calculate size for remaining days level = IntervalLevel.LEVEL_1DAY kw["level"] = level #: add other custom args for k in kwargs: kw[k] = kwargs[k] r = recorder_class(**kw) return r.run() else: r = recorder_class(**kw) return r.run() else: print(f"no recorders for {cls.__name__}") class NormalMixin(Mixin): #: the record created time in db created_timestamp = Column(DateTime, default=pd.Timestamp.now()) #: the record updated time in db, some recorder would check it for whether need to refresh updated_timestamp = Column(DateTime) class Entity(Mixin): #: 标的类型 entity_type = Column(String(length=64)) #: 所属交易所 exchange = Column(String(length=32)) #: 编码 code = Column(String(length=64)) #: 名字 name = Column(String(length=128)) #: 上市日 list_date = Column(DateTime) #: 退市日 end_date = Column(DateTime) class TradableEntity(Entity): """ tradable entity """ @classmethod def get_timezone(cls): """ overwrite it to get the timezone of the entity :return: pytz timezone """ return None @classmethod def get_trading_dates(cls, start_date=None, end_date=None): """ overwrite it to get the trading dates of the entity :param start_date: :param end_date: :return: list of dates """ return pd.date_range(start_date, end_date, freq="B") @classmethod def get_trading_intervals(cls, include_bidding_time=False): """ overwrite it to get the trading intervals of the entity :return: list of time intervals, in format [(start,end)] """ if include_bidding_time: return [("09:15", "11:30"), ("13:00", "15:00")] else: return [("09:30", "11:30"), ("13:00", "15:00")] @classmethod def in_real_trading_time(cls, timestamp=None, include_bidding_time=True): if not timestamp: timestamp = now_pd_timestamp(tz=cls.get_timezone()) else: timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone()) for open_close in cls.get_trading_intervals(include_bidding_time=include_bidding_time): open_time = date_and_time(the_date=timestamp.date(), the_time=open_close[0], tz=cls.get_timezone()) close_time = date_and_time(the_date=timestamp.date(), the_time=open_close[1], tz=cls.get_timezone()) if open_time <= timestamp <= close_time: return True else: continue return False @classmethod def before_trading_time(cls, timestamp=None): if not timestamp: timestamp = now_pd_timestamp(tz=cls.get_timezone()) else: timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone()) open_time = date_and_time( the_date=timestamp.date(), the_time=cls.get_trading_intervals(include_bidding_time=True)[0][0], tz=cls.get_timezone(), ) return timestamp < open_time @classmethod def after_trading_time(cls, timestamp=None): if not timestamp: timestamp = now_pd_timestamp(tz=cls.get_timezone()) else: timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone()) close_time = date_and_time( the_date=timestamp.date(), the_time=cls.get_trading_intervals(include_bidding_time=True)[-1][1], tz=cls.get_timezone(), ) return timestamp > close_time @classmethod def in_trading_time(cls, timestamp=None): if not timestamp: timestamp = now_pd_timestamp(tz=cls.get_timezone()) else: timestamp = pd.Timestamp(timestamp, tz=cls.get_timezone()) open_time = date_and_time( the_date=timestamp.date(), the_time=cls.get_trading_intervals(include_bidding_time=True)[0][0], tz=cls.get_timezone(), ) close_time = date_and_time( the_date=timestamp.date(), the_time=cls.get_trading_intervals(include_bidding_time=True)[-1][1], tz=cls.get_timezone(), ) return open_time <= timestamp <= close_time @classmethod def get_close_hour_and_minute(cls): hour, minute = cls.get_trading_intervals()[-1][1].split(":") return int(hour), int(minute) @classmethod def get_interval_timestamps(cls, start_date, end_date, level: IntervalLevel): """ generate the timestamps for the level :param start_date: :param end_date: :param level: """ for current_date in cls.get_trading_dates(start_date=start_date, end_date=end_date): if level == IntervalLevel.LEVEL_1DAY: yield current_date elif level == IntervalLevel.LEVEL_1WEEK: if current_date.weekday() == 4: yield current_date else: start_end_list = cls.get_trading_intervals() for start_end in start_end_list: start = start_end[0] end = start_end[1] current_timestamp = date_and_time(the_date=current_date, the_time=start) end_timestamp = date_and_time(the_date=current_date, the_time=end) while current_timestamp <= end_timestamp: yield current_timestamp current_timestamp = current_timestamp + timedelta(minutes=level.to_minute()) @classmethod def is_open_timestamp(cls, timestamp): timestamp = pd.Timestamp(timestamp) return is_same_date_time( timestamp, date_and_time(the_date=timestamp.date(), the_time=cls.get_trading_intervals()[0][0]), ) @classmethod def is_close_timestamp(cls, timestamp): timestamp = pd.Timestamp(timestamp) return is_same_date_time( timestamp, date_and_time(the_date=timestamp.date(), the_time=cls.get_trading_intervals()[-1][1]), ) @classmethod def is_finished_kdata_timestamp(cls, timestamp: pd.Timestamp, level: IntervalLevel): """ :param timestamp: the timestamp could be recorded in kdata of the level :type timestamp: pd.Timestamp :param level: :type level: zvt.domain.common.IntervalLevel :return: :rtype: bool """ timestamp = pd.Timestamp(timestamp) for t in cls.get_interval_timestamps(timestamp.date(), timestamp.date(), level=level): if is_same_date_time(t, timestamp): return True return False @classmethod def could_short(cls): """ whether could be shorted :return: """ return False @classmethod def get_trading_t(cls): """ 0 means t+0 1 means t+1 :return: """ return 1 class ActorEntity(Entity): pass class NormalEntityMixin(TradableEntity): #: the record created time in db created_timestamp = Column(DateTime, default=pd.Timestamp.now()) #: the record updated time in db, some recorder would check it for whether need to refresh updated_timestamp = Column(DateTime) class Portfolio(TradableEntity): """ composition of tradable entities """ @classmethod def get_stocks( cls, code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None, ): """ the publishing policy of portfolio positions is different for different types, overwrite this function for get the holding stocks in specific date :param code: portfolio(etf/block/index...) code :param codes: portfolio(etf/block/index...) codes :param ids: portfolio(etf/block/index...) ids :param timestamp: the date of the holding stocks :param provider: the data provider :return: """ from zvt.contract.api import get_schema_by_name schema_str = f"{cls.__name__}Stock" portfolio_stock = get_schema_by_name(schema_str) return portfolio_stock.query_data(provider=provider, code=code, codes=codes, timestamp=timestamp, ids=ids) #: 组合(Fund,Etf,Index,Block等)和个股(Stock)的关系 应该继承自该类 #: 该基础类可以这样理解: #: entity为组合本身,其包含了stock这种entity,timestamp为持仓日期,从py的"你知道你在干啥"的哲学出发，不加任何约束 class PortfolioStock(Mixin): #: portfolio标的类型 entity_type = Column(String(length=64)) #: portfolio所属交易所 exchange = Column(String(length=32)) #: portfolio编码 code = Column(String(length=64)) #: portfolio名字 name = Column(String(length=128)) stock_id = Column(String) stock_code = Column(String(length=64)) stock_name = Column(String(length=128)) #: 支持时间变化,报告期标的调整 class PortfolioStockHistory(PortfolioStock): #: 报告期,season1,half_year,season3,year report_period = Column(String(length=32)) #: 3-31,6-30,9-30,12-31 report_date = Column(DateTime) #: 占净值比例 proportion = Column(Float) #: 持有股票的数量 shares = Column(Float) #: 持有股票的市值 market_cap = Column(Float) #: 交易标的和参与者的关系应该继承自该类, meet,遇见,恰如其分的诠释参与者和交易标的的关系 #: 市场就是参与者与交易标的的关系，类的命名规范为{Entity}{relation}{Entity}，entity_id代表"所"为的entity,"受"者entity以具体类别的id命名 #: 比如StockTopTenHolder:TradableMeetActor中entity_id和actor_id,分别代表股票和股东 class TradableMeetActor(Mixin): #: tradable code code = Column(String(length=64)) #: tradable name name = Column(String(length=128)) actor_id = Column(String) actor_type = Column(String) actor_code = Column(String(length=64)) actor_name = Column(String(length=128)) #: 也可以"所"为参与者，"受"为标的 class ActorMeetTradable(Mixin): #: actor code code = Column(String(length=64)) #: actor name name = Column(String(length=128)) tradable_id = Column(String) tradable_type = Column(String) tradable_code = Column(String(length=64)) tradable_name = Column(String(length=128)) # the __all__ is generated __all__ = [ "Mixin", "NormalMixin", "Entity", "TradableEntity", "ActorEntity", "NormalEntityMixin", "Portfolio", "PortfolioStock", "PortfolioStockHistory", "TradableMeetActor", "ActorMeetTradable", ] ================================================ FILE: src/zvt/contract/storage.py ================================================ # -*- coding: utf-8 -*- """ Storage abstraction for ZVT. Phase 1: StorageBackend interface + SqliteStorageBackend with existing path logic. Phase 2: path_template and base_path from config. """ import json import logging import os from abc import ABC, abstractmethod from typing import Optional from sqlalchemy import create_engine from sqlalchemy.engine import Engine from sqlalchemy.orm import sessionmaker logger = logging.getLogger(__name__) def _default_data_path() -> str: """Get data path from zvt_env, with fallback for tests.""" try: from zvt import zvt_env return zvt_env.get("data_path", ".") except Exception: return "." def _get_storage_config() -> dict: """Load storage config from zvt_config. Lazy import to avoid cycles.""" try: from zvt import zvt_config return zvt_config.get("storage") or {} except Exception: return {} class StorageBackend(ABC): """ Abstract storage backend. Decouples physical storage from domain/read/record logic. """ @abstractmethod def get_engine(self, storage_id: str, data_path: Optional[str] = None) -> Engine: """ Get or create SQLAlchemy engine for the given storage_id. :param storage_id: Logical storage identifier (e.g. "em_stock_meta"). :param data_path: Base path for data files. If None, uses zvt_env["data_path"]. :return: Engine instance. """ raise NotImplementedError @abstractmethod def get_session_factory(self, storage_id: str, data_path: Optional[str] = None): """ Get or create sessionmaker for the given storage_id. Must be configured with engine before use (caller's responsibility, e.g. in register_schema). :param storage_id: Logical storage identifier. :param data_path: Base path for data files. :return: sessionmaker instance. """ raise NotImplementedError class SqliteStorageBackend(StorageBackend): """ SQLite storage backend. Default path: {data_path}/{provider}/{provider}_{db_name}.db Config (zvt_config["storage"]): - base_path: override data path - path_template: format string with {base_path}, {provider}, {db_name}, {storage_id} """ def __init__(self, path_template: Optional[str] = None, base_path: Optional[str] = None): """ :param path_template: Optional. Overrides config. Placeholders: {base_path}, {provider}, {db_name}, {storage_id} :param base_path: Optional. Overrides config. """ self._path_template_override = path_template self._base_path_override = base_path self._engine_map = {} self._session_factory_map = {} def _get_path_template(self) -> Optional[str]: """Path template: constructor override > config > None (use default).""" if self._path_template_override is not None: return self._path_template_override config = _get_storage_config() return config.get("path_template") def _get_base_path(self, data_path: Optional[str]) -> str: """Base path: param > constructor override > config > zvt_env.""" if data_path is not None: return data_path if self._base_path_override is not None: return self._base_path_override config = _get_storage_config() cfg_path = config.get("base_path") if cfg_path is not None: return cfg_path return _default_data_path() def _storage_id_to_path(self, storage_id: str, data_path: str) -> str: """ Compute SQLite file path from storage_id. storage_id format: "{provider}_{db_name}" (e.g. "em_stock_meta"). Default path: {data_path}/{provider}/{provider}_{db_name}.db """ if "_" not in storage_id: raise ValueError(f"Invalid storage_id: {storage_id}, expected '{{provider}}_{{db_name}}'") provider, db_name = storage_id.split("_", 1) path_template = self._get_path_template() if path_template: return path_template.format( base_path=data_path, provider=provider, db_name=db_name, storage_id=storage_id ) provider_dir = os.path.join(data_path, provider) if not os.path.exists(provider_dir): os.makedirs(provider_dir) return os.path.join(provider_dir, f"{provider}_{db_name}.db?check_same_thread=False") def get_engine(self, storage_id: str, data_path: Optional[str] = None) -> Engine: data_path = self._get_base_path(data_path) if storage_id in self._engine_map: return self._engine_map[storage_id] db_path = self._storage_id_to_path(storage_id, data_path) url = "sqlite:///" + db_path engine = create_engine( url, echo=False, json_serializer=lambda obj: json.dumps(obj, ensure_ascii=False) ) self._engine_map[storage_id] = engine return engine def get_session_factory(self, storage_id: str, data_path: Optional[str] = None): data_path = self._get_base_path(data_path) if storage_id in self._session_factory_map: return self._session_factory_map[storage_id] fac = sessionmaker() self._session_factory_map[storage_id] = fac return fac # Default instance; can be replaced for testing or custom backends _default_storage_backend: Optional[StorageBackend] = None def get_storage_backend() -> StorageBackend: """Get the default storage backend. Creates SqliteStorageBackend on first use.""" global _default_storage_backend if _default_storage_backend is None: _default_storage_backend = SqliteStorageBackend() return _default_storage_backend def set_storage_backend(backend: StorageBackend): """Replace default storage backend. Useful for tests.""" global _default_storage_backend _default_storage_backend = backend __all__ = [ "StorageBackend", "SqliteStorageBackend", "get_storage_backend", "set_storage_backend", ] ================================================ FILE: src/zvt/contract/utils.py ================================================ # -*- coding: utf-8 -*- import math import pandas as pd from zvt.contract import IntervalLevel from zvt.utils.time_utils import to_pd_timestamp def is_in_same_interval(t1: pd.Timestamp, t2: pd.Timestamp, level: IntervalLevel): t1 = to_pd_timestamp(t1) t2 = to_pd_timestamp(t2) if level == IntervalLevel.LEVEL_1WEEK: return t1.week == t2.week if level == IntervalLevel.LEVEL_1MON: return t1.month == t2.month return level.floor_timestamp(t1) == level.floor_timestamp(t2) def evaluate_size_from_timestamp( start_timestamp, level: IntervalLevel, one_day_trading_minutes, end_timestamp: pd.Timestamp = None ): """ given from timestamp,level,one_day_trading_minutes,this func evaluate size of kdata to current. it maybe a little bigger than the real size for fetching all the kdata. :param start_timestamp: :type start_timestamp: pd.Timestamp :param level: :type level: IntervalLevel :param one_day_trading_minutes: :type one_day_trading_minutes: int """ if not end_timestamp: end_timestamp = pd.Timestamp.now() else: end_timestamp = to_pd_timestamp(end_timestamp) time_delta = end_timestamp - to_pd_timestamp(start_timestamp) one_day_trading_seconds = one_day_trading_minutes * 60 if level == IntervalLevel.LEVEL_1DAY: return time_delta.days + 1 if level == IntervalLevel.LEVEL_1WEEK: return int(math.ceil(time_delta.days / 7)) + 1 if level == IntervalLevel.LEVEL_1MON: return int(math.ceil(time_delta.days / 30)) + 1 if time_delta.days > 0: seconds = (time_delta.days + 1) * one_day_trading_seconds return int(math.ceil(seconds / level.to_second())) + 1 else: seconds = time_delta.total_seconds() return min(int(math.ceil(seconds / level.to_second())) + 1, one_day_trading_seconds / level.to_second() + 1) def next_timestamp_on_level(current_timestamp: pd.Timestamp, level: IntervalLevel) -> pd.Timestamp: current_timestamp = to_pd_timestamp(current_timestamp) return current_timestamp + pd.Timedelta(seconds=level.to_second()) def is_finished_kdata_timestamp(timestamp, level: IntervalLevel): timestamp = to_pd_timestamp(timestamp) if level.floor_timestamp(timestamp) == timestamp: return True return False ================================================ FILE: src/zvt/contract/zvt_info.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Text from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.contract.schema import Mixin ZvtInfoBase = declarative_base() class StateMixin(Mixin): #: the unique name of the service, e.g. recorder,factor,tag state_name = Column(String(length=128)) #: json string state = Column(Text()) class RecorderState(ZvtInfoBase, StateMixin): """ Schema for storing recorder state """ __tablename__ = "recorder_state" class TaggerState(ZvtInfoBase, StateMixin): """ Schema for storing tagger state """ __tablename__ = "tagger_state" class FactorState(ZvtInfoBase, StateMixin): """ Schema for storing factor state """ __tablename__ = "factor_state" register_schema(db_name="zvt_info", schema_base=ZvtInfoBase, internal=True) # the __all__ is generated __all__ = ["StateMixin", "RecorderState", "TaggerState", "FactorState"] ================================================ FILE: src/zvt/domain/__init__.py ================================================ # -*- coding: utf-8 -*- import enum class BlockCategory(enum.Enum): #: 行业版块 industry = "industry" #: 概念版块 concept = "concept" #: 区域版块 area = "area" class IndexCategory(enum.Enum): #: 中国指数提供商： #: 中证指数公司 http://www.csindex.com.cn/zh-CN #: 上证指数(上交所标的) 中证指数(沪深) #: 国证指数公司 http://www.cnindex.com.cn/index.html #: 深证指数(深交所标的) 国证指数(沪深) #: 规模指数 #: 常见的上证指数，深证指数等 scope = "scope" #: 行业指数 industry = "industry" #: 风格指数 style = "style" #: 主题指数 # #: 策略指数 # #: 综合指数 # #: 债券指数 # #: 基金指数 fund = "fund" #: 定制指数 # #: 人民币指数 # #: 跨境指数 # #: 其他指数 class ReportPeriod(enum.Enum): # 有些基金的2，4季报只有10大持仓，半年报和年报有详细持仓，需要区别对待 season1 = "season1" season2 = "season2" season3 = "season3" season4 = "season4" half_year = "half_year" year = "year" # 用于区分不同的财务指标 class CompanyType(enum.Enum): qiye = "qiye" baoxian = "baoxian" yinhang = "yinhang" quanshang = "quanshang" CHINA_FUTURE_CODE_MAP_NAME = { "I": "铁矿石", "RB": "螺纹钢", "HC": "热轧卷板", "SS": "不锈钢", "SF": "硅铁", "SM": "锰硅", "WR": "线材", "CU": "沪铜", "AL": "沪铝", "ZN": "沪锌", "PB": "沪铅", "NI": "沪镍", "SN": "沪锡", "BC": "国际铜", "AU": "沪金", "AG": "沪银", "A": "豆一", "B": "豆二", "Y": "豆油", "M": "豆粕", "RS": "菜籽", "OI": "菜油", "RM": "菜粕", "P": "棕榈油", "C": "玉米", "CS": "玉米淀粉", "JD": "鸡蛋", "CF": "一号棉花", "CY": "棉纱", "SR": "白糖", "AP": "苹果", "CJ": "红枣", "PK": "花生", "PM": "普麦", "WH": "强麦", "RR": "粳米", "JR": "粳稻", "RI": "早籼稻", "LR": "晚籼稻", "LH": "生猪", "SC": "原油", "FU": "燃油", "PG": "LPG", "LU": "低硫燃油", "BU": "石油沥青", "MA": "甲醇", "EG": "乙二醇", "L": "聚乙烯", "TA": "PTA", "V": "聚氯乙烯", "PP": "聚丙烯", "EB": "苯乙烯", "SA": "纯碱", "FG": "玻璃", "UR": "尿素", "RU": "橡胶", "NR": "20号胶", "SP": "纸浆", "FB": "纤维板", "BB": "胶合板", "PF": "短纤", "JM": "焦煤", "J": "焦炭", "ZC": "动力煤", "IC": "中证500指数", "IF": "沪深300指数", "IH": "上证50指数", "T": "10年期国债期货", "TF": "5年期国债期货", "TS": "2年期国债期货", } def get_future_name(code): simple_code = code[:-4] return f"{CHINA_FUTURE_CODE_MAP_NAME[simple_code]}{code[-4:]}" # the __all__ is generated __all__ = ["BlockCategory", "IndexCategory", "ReportPeriod", "CompanyType", "get_future_name"] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule misc from .misc import * from .misc import __all__ as _misc_all __all__ += _misc_all # import all from submodule quotes from .quotes import * from .quotes import __all__ as _quotes_all __all__ += _quotes_all # import all from submodule meta from .meta import * from .meta import __all__ as _meta_all __all__ += _meta_all # import all from submodule fundamental from .fundamental import * from .fundamental import __all__ as _fundamental_all __all__ += _fundamental_all # import all from submodule macro from .macro import * from .macro import __all__ as _macro_all __all__ += _macro_all # import all from submodule actor from .actor import * from .actor import __all__ as _actor_all __all__ += _actor_all # import all from submodule emotion from .emotion import * from .emotion import __all__ as _emotion_all __all__ += _emotion_all ================================================ FILE: src/zvt/domain/actor/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule stock_actor from .stock_actor import * from .stock_actor import __all__ as _stock_actor_all __all__ += _stock_actor_all # import all from submodule actor_meta from .actor_meta import * from .actor_meta import __all__ as _actor_meta_all __all__ += _actor_meta_all ================================================ FILE: src/zvt/domain/actor/actor_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.contract.schema import ActorEntity ActorMetaBase = declarative_base() #: 参与者 class ActorMeta(ActorMetaBase, ActorEntity): __tablename__ = "actor_meta" register_schema(db_name="actor_meta", schema_base=ActorMetaBase) # the __all__ is generated __all__ = ["ActorMeta"] ================================================ FILE: src/zvt/domain/actor/stock_actor.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, DateTime, Float, Boolean, Integer from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.contract.schema import TradableMeetActor StockActorBase = declarative_base() class StockTopTenFreeHolder(StockActorBase, TradableMeetActor): __tablename__ = "stock_top_ten_free_holder" report_period = Column(String(length=32)) report_date = Column(DateTime) #: 持股数 holding_numbers = Column(Float) #: 持股比例 holding_ratio = Column(Float) #: 持股市值 holding_values = Column(Float) class StockTopTenHolder(StockActorBase, TradableMeetActor): __tablename__ = "stock_top_ten_holder" report_period = Column(String(length=32)) report_date = Column(DateTime) #: 持股数 holding_numbers = Column(Float) #: 持股比例 holding_ratio = Column(Float) #: 持股市值 holding_values = Column(Float) class StockInstitutionalInvestorHolder(StockActorBase, TradableMeetActor): __tablename__ = "stock_institutional_investor_holder" report_period = Column(String(length=32)) report_date = Column(DateTime) #: 持股数 holding_numbers = Column(Float) #: 持股比例 holding_ratio = Column(Float) #: 持股市值 holding_values = Column(Float) class StockActorSummary(StockActorBase, TradableMeetActor): __tablename__ = "stock_actor_summary" #: tradable code code = Column(String(length=64)) #: tradable name name = Column(String(length=128)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 变动比例 change_ratio = Column(Float) #: 是否完成 is_complete = Column(Boolean) #: 持股市值 actor_type = Column(String) actor_count = Column(Integer) #: 持股数 holding_numbers = Column(Float) #: 持股比例 holding_ratio = Column(Float) #: 持股市值 holding_values = Column(Float) register_schema(db_name="stock_actor", schema_base=StockActorBase, entity_type="stock") # the __all__ is generated __all__ = ["StockTopTenFreeHolder", "StockTopTenHolder", "StockInstitutionalInvestorHolder", "StockActorSummary"] ================================================ FILE: src/zvt/domain/emotion/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule emotion from .emotion import * from .emotion import __all__ as _emotion_all __all__ += _emotion_all ================================================ FILE: src/zvt/domain/emotion/emotion.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Integer, DateTime, Boolean, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema EmotionBase = declarative_base() class LimitUpInfo(EmotionBase, Mixin): __tablename__ = "limit_up_info" code = Column(String(length=32)) name = Column(String(length=32)) #: 是否新股 is_new = Column(Boolean) #: 是否回封，是就是打开过，否相反 is_again_limit = Column(Boolean) #: 涨停打开次数,0代表封住就没开板 open_count = Column(Integer) #: 首次封板时间 first_limit_up_time = Column(DateTime) #: 最后封板时间 last_limit_up_time = Column(DateTime) #: 涨停类型:换手板，一字板 limit_up_type = Column(String) #: 封单金额 order_amount = Column(String) #: 最近一年封板成功率 success_rate = Column(Float) #: 流通市值 currency_value = Column(Float) #: 涨幅 change_pct = Column(Float) #: 换手率 turnover_rate = Column(Float) #: 涨停原因 reason = Column(String) #: 几天几板 high_days = Column(String) #: 最近几板，不一定是连板 high_days_count = Column(Integer) class LimitDownInfo(EmotionBase, Mixin): __tablename__ = "limit_down_info" code = Column(String(length=32)) name = Column(String(length=32)) #: 是否新股 is_new = Column(Boolean) #: 是否回封，是就是打开过，否相反 is_again_limit = Column(Boolean) #: 流通市值 currency_value = Column(Float) #: 涨幅 change_pct = Column(Float) #: 换手率 turnover_rate = Column(Float) class Emotion(EmotionBase, Mixin): __tablename__ = "emotion" #: 涨停数量 limit_up_count = Column(Integer) #: 炸板数 limit_up_open_count = Column(Integer) #: 涨停封板成功率 limit_up_success_rate = Column(Float) #: 连板高度 max_height = Column(Integer) #: 连板数x个数 相加 continuous_power = Column(Integer) #: 跌停数量 limit_down_count = Column(Integer) #: 跌停打开 limit_down_open_count = Column(Integer) #: 跌停封板成功率 limit_down_success_rate = Column(Float) register_schema(db_name="emotion", schema_base=EmotionBase) # the __all__ is generated __all__ = ["LimitUpInfo", "LimitDownInfo", "Emotion"] ================================================ FILE: src/zvt/domain/fundamental/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule dividend_financing from .dividend_financing import * from .dividend_financing import __all__ as _dividend_financing_all __all__ += _dividend_financing_all # import all from submodule finance from .finance import * from .finance import __all__ as _finance_all __all__ += _finance_all # import all from submodule trading from .trading import * from .trading import __all__ as _trading_all __all__ += _trading_all # import all from submodule valuation from .valuation import * from .valuation import __all__ as _valuation_all __all__ += _valuation_all ================================================ FILE: src/zvt/domain/fundamental/dividend_financing.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, DateTime, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema DividendFinancingBase = declarative_base() class DividendFinancing(DividendFinancingBase, Mixin): __tablename__ = "dividend_financing" provider = Column(String(length=32)) code = Column(String(length=32)) #: 分红总额 dividend_money = Column(Float) #: 新股 ipo_issues = Column(Float) ipo_raising_fund = Column(Float) #: 增发 spo_issues = Column(Float) spo_raising_fund = Column(Float) #: 配股 rights_issues = Column(Float) rights_raising_fund = Column(Float) class DividendDetail(DividendFinancingBase, Mixin): __tablename__ = "dividend_detail" provider = Column(String(length=32)) code = Column(String(length=32)) #: 公告日 announce_date = Column(DateTime) #: 股权登记日 record_date = Column(DateTime) #: 除权除息日 dividend_date = Column(DateTime) #: 方案 dividend = Column(String(length=128)) class SpoDetail(DividendFinancingBase, Mixin): __tablename__ = "spo_detail" provider = Column(String(length=32)) code = Column(String(length=32)) spo_issues = Column(Float) spo_price = Column(Float) spo_raising_fund = Column(Float) class RightsIssueDetail(DividendFinancingBase, Mixin): __tablename__ = "rights_issue_detail" provider = Column(String(length=32)) code = Column(String(length=32)) #: 配股 rights_issues = Column(Float) rights_issue_price = Column(Float) rights_raising_fund = Column(Float) register_schema( db_name="dividend_financing", schema_base=DividendFinancingBase, entity_type="stock" ) # the __all__ is generated __all__ = ["DividendFinancing", "DividendDetail", "SpoDetail", "RightsIssueDetail"] ================================================ FILE: src/zvt/domain/fundamental/finance.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, DateTime, Float, Integer from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema FinanceBase = declarative_base() class BalanceSheet(FinanceBase, Mixin): @classmethod def important_cols(cls): return [ "total_assets", "total_liabilities", "equity", "cash_and_cash_equivalents", "accounts_receivable", "inventories", "goodwill", ] __tablename__ = "balance_sheet" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 流动资产 # #: 货币资金 cash_and_cash_equivalents = Column(Float) #: 应收票据 note_receivable = Column(Float) #: 应收账款 accounts_receivable = Column(Float) #: 预付款项 advances_to_suppliers = Column(Float) #: 其他应收款 other_receivables = Column(Float) #: 存货 inventories = Column(Float) #: 一年内到期的非流动资产 current_portion_of_non_current_assets = Column(Float) #: 其他流动资产 other_current_assets = Column(Float) #: 流动资产合计 total_current_assets = Column(Float) #: 非流动资产 # #: 可供出售金融资产 fi_assets_saleable = Column(Float) #: 长期应收款 long_term_receivables = Column(Float) #: 长期股权投资 long_term_equity_investment = Column(Float) #: 投资性房地产 real_estate_investment = Column(Float) #: 固定资产 fixed_assets = Column(Float) #: 在建工程 construction_in_process = Column(Float) #: 无形资产 intangible_assets = Column(Float) #: 商誉 goodwill = Column(Float) #: 长期待摊费用 long_term_prepaid_expenses = Column(Float) #: 递延所得税资产 deferred_tax_assets = Column(Float) #: 其他非流动资产 other_non_current_assets = Column(Float) #: 非流动资产合计 total_non_current_assets = Column(Float) #: 资产总计 total_assets = Column(Float) #: 流动负债 # #: 短期借款 short_term_borrowing = Column(Float) #: 吸收存款及同业存放 accept_money_deposits = Column(Float) #: 应付账款 accounts_payable = Column(Float) #: 预收款项 advances_from_customers = Column(Float) #: 应付职工薪酬 employee_benefits_payable = Column(Float) #: 应交税费 taxes_payable = Column(Float) #: 应付利息 interest_payable = Column(Float) #: 其他应付款 other_payable = Column(Float) #: 一年内到期的非流动负债 current_portion_of_non_current_liabilities = Column(Float) #: 其他流动负债 other_current_liabilities = Column(Float) #: 流动负债合计 total_current_liabilities = Column(Float) #: 非流动负债 # #: 长期借款 long_term_borrowing = Column(Float) #: 长期应付款 long_term_payable = Column(Float) #: 递延收益 deferred_revenue = Column(Float) #: 递延所得税负债 deferred_tax_liabilities = Column(Float) #: 其他非流动负债 other_non_current_liabilities = Column(Float) #: 非流动负债合计 total_non_current_liabilities = Column(Float) #: 负债合计 total_liabilities = Column(Float) #: 所有者权益(或股东权益) # #: 实收资本（或股本） capital = Column(Float) #: 资本公积 capital_reserve = Column(Float) #: 专项储备 special_reserve = Column(Float) #: 盈余公积 surplus_reserve = Column(Float) #: 未分配利润 undistributed_profits = Column(Float) #: 归属于母公司股东权益合计 equity = Column(Float) #: 少数股东权益 equity_as_minority_interest = Column(Float) #: 股东权益合计 total_equity = Column(Float) #: 负债和股东权益合计 total_liabilities_and_equity = Column(Float) #: 银行相关 #: 资产 #: 现金及存放中央银行款项 fi_cash_and_deposit_in_central_bank = Column(Float) #: 存放同业款项 fi_deposit_in_other_fi = Column(Float) #: 贵金属 fi_expensive_metals = Column(Float) #: 拆出资金 fi_lending_to_other_fi = Column(Float) #: 以公允价值计量且其变动计入当期损益的金融资产 fi_financial_assets_effect_current_income = Column(Float) #: 衍生金融资产 fi_financial_derivative_asset = Column(Float) #: 买入返售金融资产 fi_buying_sell_back_fi__asset = Column(Float) #: 应收账款 # #: 应收利息 fi_interest_receivable = Column(Float) #: 发放贷款及垫款 fi_disbursing_loans_and_advances = Column(Float) #: 可供出售金融资产 # #: 持有至到期投资 fi_held_to_maturity_investment = Column(Float) #: 应收款项类投资 fi_account_receivable_investment = Column(Float) #: 投资性房地产 # #: 固定资产 # #: 无形资产 # #: 商誉 # #: 递延所得税资产 # #: 其他资产 fi_other_asset = Column(Float) #: 资产总计 # #: 负债 # #: 向中央银行借款 fi_borrowings_from_central_bank = Column(Float) #: 同业和其他金融机构存放款项 fi_deposit_from_other_fi = Column(Float) #: 拆入资金 fi_borrowings_from_fi = Column(Float) #: 以公允价值计量且其变动计入当期损益的金融负债 fi_financial_liability_effect_current_income = Column(Float) #: 衍生金融负债 fi_financial_derivative_liability = Column(Float) #: 卖出回购金融资产款 fi_sell_buy_back_fi_asset = Column(Float) #: 吸收存款 fi_savings_absorption = Column(Float) #: 存款证及应付票据 fi_notes_payable = Column(Float) #: 应付职工薪酬 # #: 应交税费 # #: 应付利息 # #: 预计负债 fi_estimated_liabilities = Column(Float) #: 应付债券 fi_bond_payable = Column(Float) #: 其他负债 fi_other_liability = Column(Float) #: 负债合计 # #: 所有者权益(或股东权益) #: 股本 fi_capital = Column(Float) #: 其他权益工具 fi_other_equity_instruments = Column(Float) #: 其中:优先股 fi_preferred_stock = Column(Float) #: 资本公积 # #: 盈余公积 # #: 一般风险准备 fi_generic_risk_reserve = Column(Float) #: 未分配利润 # #: 归属于母公司股东权益合计 # #: 股东权益合计 # #: 负债及股东权益总计 #: 券商相关 #: 资产 # #: 货币资金 # #: 其中: 客户资金存款 fi_client_fund = Column(Float) #: 结算备付金 fi_deposit_reservation_for_balance = Column(Float) #: 其中: 客户备付金 fi_client_deposit_reservation_for_balance = Column(Float) #: 融出资金 fi_margin_out_fund = Column(Float) #: 以公允价值计量且其变动计入当期损益的金融资产 # #: 衍生金融资产 # #: 买入返售金融资产 # #: 应收利息 # #: 应收款项 fi_receivables = Column(Float) #: 存出保证金 fi_deposit_for_recognizance = Column(Float) #: 可供出售金融资产 # #: 持有至到期投资 # #: 长期股权投资 # #: 固定资产 # #: 在建工程 # #: 无形资产 # #: 商誉 # #: 递延所得税资产 # #: 其他资产 # #: 资产总计 # #: 负债 # #: 短期借款 # #: 拆入资金 # #: 以公允价值计量且其变动计入当期损益的金融负债 # #: 衍生金融负债 # #: 卖出回购金融资产款 # #: 代理买卖证券款 fi_receiving_as_agent = Column(Float) #: 应付账款 # #: 应付职工薪酬 # #: 应交税费 # #: 应付利息 # #: 应付短期融资款 fi_short_financing_payable = Column(Float) #: 预计负债 # #: 应付债券 # #: 递延所得税负债 # #: 其他负债 # #: 负债合计 # #: 所有者权益(或股东权益) # #: 股本 # #: 资本公积 # #: 其他权益工具 # #: 盈余公积 # #: 一般风险准备 # #: 交易风险准备 fi_trade_risk_reserve = Column(Float) #: 未分配利润 # #: 归属于母公司股东权益合计 # #: 少数股东权益 # #: 股东权益合计 # #: 负债和股东权益总计 #: 保险相关 #: 资产 #: 应收保费 fi_premiums_receivable = Column(Float) #: 应收分保账款 fi_reinsurance_premium_receivable = Column(Float) #: 应收分保合同准备金 fi_reinsurance_contract_reserve = Column(Float) #: 保户质押贷款 fi_policy_pledge_loans = Column(Float) #: 发放贷款及垫款 #: 定期存款 fi_time_deposit = Column(Float) #: 可供出售金融资产 # #: 持有至到期投资 # #: 应收款项类投资 # #: 应收账款 # #: 长期股权投资 # #: 存出资本保证金 fi_deposit_for_capital_recognizance = Column(Float) #: 投资性房地产 # #: 固定资产 # #: 无形资产 # #: 商誉 # #: 递延所得税资产 # #: 其他资产 # #: 独立账户资产 fi_capital_in_independent_accounts = Column(Float) #: 资产总计 # #: 负债 # #: 短期借款 # #: 同业及其他金融机构存放款项 # #: 拆入资金 # #: 以公允价值计量且其变动计入当期损益的金融负债 # #: 衍生金融负债 # #: 卖出回购金融资产款 # #: 吸收存款 # #: 代理买卖证券款 # #: 应付账款 # #: 预收账款 fi_advance_from_customers = Column(Float) #: 预收保费 fi_advance_premium = Column(Float) #: 应付手续费及佣金 fi_fees_and_commissions_payable = Column(Float) #: 应付分保账款 fi_dividend_payable_for_reinsurance = Column(Float) #: 应付职工薪酬 # #: 应交税费 # #: 应付利息 # #: 预计负债 # #: 应付赔付款 fi_claims_payable = Column(Float) #: 应付保单红利 fi_policy_holder_dividend_payable = Column(Float) #: 保户储金及投资款 fi_policy_holder_deposits_and_investment_funds = Column(Float) #: 保险合同准备金 fi_contract_reserve = Column(Float) #: 长期借款 # #: 应付债券 # #: 递延所得税负债 # #: 其他负债 # #: 独立账户负债 fi_independent_liability = Column(Float) #: 负债合计 # #: 所有者权益(或股东权益) # #: 股本 # #: 资本公积 # #: 盈余公积 # #: 一般风险准备 # #: 未分配利润 # #: 归属于母公司股东权益总计 # #: 少数股东权益 # #: 股东权益合计 # #: 负债和股东权益总计 class IncomeStatement(FinanceBase, Mixin): @classmethod def important_cols(cls): return [ "operating_income", "investment_income", "total_operating_costs", "total_profits", "sales_costs", "managing_costs", "financing_costs", ] __tablename__ = "income_statement" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 营业总收入 # #: 营业收入 operating_income = Column(Float) #: 营业总成本 total_operating_costs = Column(Float) #: 营业成本 operating_costs = Column(Float) #: 研发费用 rd_costs = Column(Float) #: 提取保险合同准备金净额 net_change_in_insurance_contract_reserves = Column(Float) #: 营业税金及附加 business_taxes_and_surcharges = Column(Float) #: 销售费用 sales_costs = Column(Float) #: 管理费用 managing_costs = Column(Float) #: 财务费用 financing_costs = Column(Float) #: 资产减值损失 assets_devaluation = Column(Float) #: 其他经营收益 # #: 加: 投资收益 investment_income = Column(Float) #: 其中: 对联营企业和合营企业的投资收益 investment_income_from_related_enterprise = Column(Float) #: 营业利润 operating_profit = Column(Float) #: 加: 营业外收入 non_operating_income = Column(Float) #: 减: 营业外支出 non_operating_costs = Column(Float) #: 其中: 非流动资产处置净损失 loss_on_disposal_non_current_asset = Column(Float) #: 利润总额 total_profits = Column(Float) #: 减: 所得税费用 tax_expense = Column(Float) #: 净利润 net_profit = Column(Float) #: 其中: 归属于母公司股东的净利润 net_profit_as_parent = Column(Float) #: 少数股东损益 net_profit_as_minority_interest = Column(Float) #: 扣除非经常性损益后的净利润 deducted_net_profit = Column(Float) #: 每股收益 #: 基本每股收益 eps = Column(Float) #: 稀释每股收益 diluted_eps = Column(Float) #: 其他综合收益 other_comprehensive_income = Column(Float) #: 归属于母公司股东的其他综合收益 other_comprehensive_income_as_parent = Column(Float) #: 归属于少数股东的其他综合收益 other_comprehensive_income_as_minority_interest = Column(Float) #: 综合收益总额 total_comprehensive_income = Column(Float) #: 归属于母公司所有者的综合收益总额 total_comprehensive_income_as_parent = Column(Float) #: 归属于少数股东的综合收益总额 total_comprehensive_income_as_minority_interest = Column(Float) #: 银行相关 #: 利息净收入 fi_net_interest_income = Column(Float) #: 其中:利息收入 fi_interest_income = Column(Float) #: 利息支出 fi_interest_expenses = Column(Float) #: 手续费及佣金净收入 fi_net_incomes_from_fees_and_commissions = Column(Float) #: 其中:手续费及佣金收入 fi_incomes_from_fees_and_commissions = Column(Float) #: 手续费及佣金支出 fi_expenses_for_fees_and_commissions = Column(Float) #: 公允价值变动收益 fi_income_from_fair_value_change = Column(Float) #: 汇兑收益 fi_income_from_exchange = Column(Float) #: 其他业务收入 fi_other_income = Column(Float) #: 业务及管理费 fi_operate_and_manage_expenses = Column(Float) #: 保险相关 #: 已赚保费 fi_net_income_from_premium = Column(Float) #: 其中:保险业务收入 fi_income_from_premium = Column(Float) #: 分保费收入 fi_income_from_reinsurance_premium = Column(Float) #: 减:分出保费 fi_reinsurance_premium = Column(Float) #: 提取未到期责任准备金 fi_undue_duty_reserve = Column(Float) #: 银行业务利息净收入 fi_net_income_from_bank_interest = Column(Float) #: 其中:银行业务利息收入 fi_income_from_bank_interest = Column(Float) #: 银行业务利息支出 fi_expenses_for_bank_interest = Column(Float) #: 非保险业务手续费及佣金净收入 fi_net_incomes_from_fees_and_commissions_of_non_insurance = Column(Float) #: 非保险业务手续费及佣金收入 fi_incomes_from_fees_and_commissions_of_non_insurance = Column(Float) #: 非保险业务手续费及佣金支出 fi_expenses_for_fees_and_commissions_of_non_insurance = Column(Float) #: 退保金 fi_insurance_surrender_costs = Column(Float) #: 赔付支出 fi_insurance_claims_expenses = Column(Float) #: 减:摊回赔付支出 fi_amortized_insurance_claims_expenses = Column(Float) #: 提取保险责任准备金 fi_insurance_duty_reserve = Column(Float) #: 减:摊回保险责任准备金 fi_amortized_insurance_duty_reserve = Column(Float) #: 保单红利支出 fi_dividend_expenses_to_insured = Column(Float) #: 分保费用 fi_reinsurance_expenses = Column(Float) #: 减:摊回分保费用 fi_amortized_reinsurance_expenses = Column(Float) #: 其他业务成本 fi_other_op_expenses = Column(Float) #: 券商相关 #: 手续费及佣金净收入 # #: 其中:代理买卖证券业务净收入 fi_net_incomes_from_trading_agent = Column(Float) #: 证券承销业务净收入 fi_net_incomes_from_underwriting = Column(Float) #: 受托客户资产管理业务净收入 fi_net_incomes_from_customer_asset_management = Column(Float) #: 手续费及佣金净收入其他项目 fi_fees_from_other = Column(Float) #: 公允价值变动收益 # #: 其中:可供出售金融资产公允价值变动损益 fi_income_from_fair_value_change_of_fi_salable = Column(Float) class CashFlowStatement(FinanceBase, Mixin): @classmethod def important_cols(cls): return ["net_op_cash_flows", "net_investing_cash_flows", "net_financing_cash_flows", "cash"] __tablename__ = "cash_flow_statement" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 经营活动产生的现金流量 # #: 销售商品、提供劳务收到的现金 cash_from_selling = Column(Float) #: 收到的税费返还 tax_refund = Column(Float) #: 收到其他与经营活动有关的现金 cash_from_other_op = Column(Float) #: 经营活动现金流入小计 total_op_cash_inflows = Column(Float) #: 购买商品、接受劳务支付的现金 cash_to_goods_services = Column(Float) #: 支付给职工以及为职工支付的现金 cash_to_employees = Column(Float) #: 支付的各项税费 taxes_and_surcharges = Column(Float) #: 支付其他与经营活动有关的现金 cash_to_other_related_op = Column(Float) #: 经营活动现金流出小计 total_op_cash_outflows = Column(Float) #: 经营活动产生的现金流量净额 net_op_cash_flows = Column(Float) #: 投资活动产生的现金流量 #: 收回投资收到的现金 cash_from_disposal_of_investments = Column(Float) #: 取得投资收益收到的现金 cash_from_returns_on_investments = Column(Float) #: 处置固定资产、无形资产和其他长期资产收回的现金净额 cash_from_disposal_fixed_intangible_assets = Column(Float) #: 处置子公司及其他营业单位收到的现金净额 cash_from_disposal_subsidiaries = Column(Float) #: 收到其他与投资活动有关的现金 cash_from_other_investing = Column(Float) #: 投资活动现金流入小计 total_investing_cash_inflows = Column(Float) #: 购建固定资产、无形资产和其他长期资产支付的现金 cash_to_acquire_fixed_intangible_assets = Column(Float) #: 投资支付的现金 cash_to_investments = Column(Float) #: 取得子公司及其他营业单位支付的现金净额 cash_to_acquire_subsidiaries = Column(Float) #: 支付其他与投资活动有关的现金 cash_to_other_investing = Column(Float) #: 投资活动现金流出小计 total_investing_cash_outflows = Column(Float) #: 投资活动产生的现金流量净额 net_investing_cash_flows = Column(Float) #: 筹资活动产生的现金流量 # #: 吸收投资收到的现金 cash_from_accepting_investment = Column(Float) #: 子公司吸收少数股东投资收到的现金 cash_from_subsidiaries_accepting_minority_interest = Column(Float) #: 取得借款收到的现金 cash_from_borrowings = Column(Float) #: 发行债券收到的现金 cash_from_issuing_bonds = Column(Float) #: 收到其他与筹资活动有关的现金 cash_from_other_financing = Column(Float) #: 筹资活动现金流入小计 total_financing_cash_inflows = Column(Float) #: 偿还债务支付的现金 cash_to_repay_borrowings = Column(Float) #: 分配股利、利润或偿付利息支付的现金 cash_to_pay_interest_dividend = Column(Float) #: 子公司支付给少数股东的股利、利润 cash_to_pay_subsidiaries_minority_interest = Column(Float) #: 支付其他与筹资活动有关的现金 cash_to_other_financing = Column(Float) #: 筹资活动现金流出小计 total_financing_cash_outflows = Column(Float) #: 筹资活动产生的现金流量净额 net_financing_cash_flows = Column(Float) #: 汇率变动对现金及现金等价物的影响 foreign_exchange_rate_effect = Column(Float) #: 现金及现金等价物净增加额 net_cash_increase = Column(Float) #: 加: 期初现金及现金等价物余额 cash_at_beginning = Column(Float) #: 期末现金及现金等价物余额 cash = Column(Float) #: 银行相关 #: 客户存款和同业及其他金融机构存放款项净增加额 fi_deposit_increase = Column(Float) #: 向中央银行借款净增加额 fi_borrow_from_central_bank_increase = Column(Float) #: 存放中央银行和同业款项及其他金融机构净减少额 fi_deposit_in_others_decrease = Column(Float) #: 拆入资金及卖出回购金融资产款净增加额 fi_borrowing_and_sell_repurchase_increase = Column(Float) #: 其中:卖出回购金融资产款净增加额 fi_sell_repurchase_increase = Column(Float) #: 拆出资金及买入返售金融资产净减少额 fi_lending_and_buy_repurchase_decrease = Column(Float) #: 其中:拆出资金净减少额 fi_lending_decrease = Column(Float) #: 买入返售金融资产净减少额 fi_buy_repurchase_decrease = Column(Float) #: 收取的利息、手续费及佣金的现金 fi_cash_from_interest_commission = Column(Float) #: 客户贷款及垫款净增加额 fi_loan_advance_increase = Column(Float) #: 存放中央银行和同业及其他金融机构款项净增加额 fi_deposit_in_others_increase = Column(Float) #: 拆出资金及买入返售金融资产净增加额 fi_lending_and_buy_repurchase_increase = Column(Float) #: 其中:拆出资金净增加额 fi_lending_increase = Column(Float) #: 拆入资金及卖出回购金融资产款净减少额 fi_borrowing_and_sell_repurchase_decrease = Column(Float) #: 其中:拆入资金净减少额 fi_borrowing_decrease = Column(Float) #: 卖出回购金融资产净减少额 fi_sell_repurchase_decrease = Column(Float) #: 支付利息、手续费及佣金的现金 fi_cash_to_interest_commission = Column(Float) #: 应收账款净增加额 fi_account_receivable_increase = Column(Float) #: 偿付债券利息支付的现金 fi_cash_to_pay_interest = Column(Float) #: 保险相关 #: 收到原保险合同保费取得的现金 fi_cash_from_premium_of_original = Column(Float) #: 保户储金及投资款净增加额 fi_insured_deposit_increase = Column(Float) #: 银行及证券业务卖出回购资金净增加额 fi_bank_broker_sell_repurchase_increase = Column(Float) #: 银行及证券业务买入返售资金净减少额 fi_bank_broker_buy_repurchase_decrease = Column(Float) #: 支付原保险合同赔付等款项的现金 fi_cash_to_insurance_claim = Column(Float) #: 支付再保险业务现金净额 fi_cash_to_reinsurance = Column(Float) #: 银行业务及证券业务拆借资金净减少额 fi_lending_decrease = Column(Float) #: 银行业务及证券业务卖出回购资金净减少额 fi_bank_broker_sell_repurchase_decrease = Column(Float) #: 支付保单红利的现金 fi_cash_to_dividends = Column(Float) #: 保户质押贷款净增加额 fi_insured_pledge_loans_increase = Column(Float) #: 收购子公司及其他营业单位支付的现金净额 fi_cash_to_acquire_subsidiaries = Column(Float) #: 处置子公司及其他营业单位流出的现金净额 fi_cash_to_disposal_subsidiaries = Column(Float) #: 支付卖出回购金融资产款现金净额 fi_cash_to_sell_repurchase = Column(Float) #: 券商相关 #: 拆入资金净增加额 fi_borrowing_increase = Column(Float) #: 代理买卖证券收到的现金净额 fi_cash_from_trading_agent = Column(Float) #: 回购业务资金净增加额 fi_cash_from_repurchase_increase = Column(Float) #: 处置交易性金融资产的净减少额 fi_disposal_trade_asset_decrease = Column(Float) #: 回购业务资金净减少额 fi_repurchase_decrease = Column(Float) #: 代理买卖证券支付的现金净额（净减少额） fi_cash_to_agent_trade = Column(Float) #: 主要财务指标 class FinanceFactor(FinanceBase, Mixin): @classmethod def important_cols(cls): return [ "basic_eps", "total_op_income", "net_profit", "op_income_growth_yoy", "net_profit_growth_yoy", "roe", "rota", "gross_profit_margin", "net_margin", ] __tablename__ = "finance_factor" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 每股指标 # #: 基本每股收益(元) basic_eps = Column(Float) #: 扣非每股收益(元) deducted_eps = Column(Float) #: 稀释每股收益(元) diluted_eps = Column(Float) #: 每股净资产(元) bps = Column(Float) #: 每股资本公积(元) capital_reserve_ps = Column(Float) #: 每股未分配利润(元) undistributed_profit_ps = Column(Float) #: 每股经营现金流(元) op_cash_flow_ps = Column(Float) #: 成长能力指标 # #: 营业总收入(元) total_op_income = Column(Float) #: 毛利润(元) gross_profit = Column(Float) #: 归属净利润(元) net_profit = Column(Float) #: 扣非净利润(元) deducted_net_profit = Column(Float) #: 营业总收入同比增长 op_income_growth_yoy = Column(Float) #: 归属净利润同比增长 net_profit_growth_yoy = Column(Float) #: 扣非净利润同比增长 deducted_net_profit_growth_yoy = Column(Float) #: 营业总收入滚动环比增长 op_income_growth_qoq = Column(Float) #: 归属净利润滚动环比增长 net_profit_growth_qoq = Column(Float) #: 扣非净利润滚动环比增长 deducted_net_profit_growth_qoq = Column(Float) #: 盈利能力指标 # #: 净资产收益率(加权) roe = Column(Float) #: 净资产收益率(扣非/加权) deducted_roe = Column(Float) #: 总资产收益率(加权) rota = Column(Float) #: 毛利率 gross_profit_margin = Column(Float) #: 净利率 net_margin = Column(Float) #: 收益质量指标 # #: 预收账款/营业收入 advance_receipts_per_op_income = Column(Float) #: 销售净现金流/营业收入 sales_net_cash_flow_per_op_income = Column(Float) #: 经营净现金流/营业收入 op_net_cash_flow_per_op_income = Column(Float) #: 实际税率 actual_tax_rate = Column(Float) #: 财务风险指标 # #: 流动比率 current_ratio = Column(Float) #: 速动比率 quick_ratio = Column(Float) #: 现金流量比率 cash_flow_ratio = Column(Float) #: 资产负债率 debt_asset_ratio = Column(Float) #: 权益乘数 em = Column(Float) #: 产权比率 equity_ratio = Column(Float) #: 营运能力指标(一般企业) # #: 总资产周转天数(天) total_assets_turnover_days = Column(Integer) #: 存货周转天数(天) inventory_turnover_days = Column(Integer) #: 应收账款周转天数(天) receivables_turnover_days = Column(Integer) #: 总资产周转率(次) total_assets_turnover = Column(Float) #: 存货周转率(次) inventory_turnover = Column(Float) #: 应收账款周转率(次) receivables_turnover = Column(Float) #: 专项指标(银行) # #: 存款总额 fi_total_deposit = Column(Float) #: 贷款总额 fi_total_loan = Column(Float) #: 存贷款比例 fi_loan_deposit_ratio = Column(Float) #: 资本充足率 fi_capital_adequacy_ratio = Column(Float) #: 核心资本充足率 fi_core_capital_adequacy_ratio = Column(Float) #: 不良贷款率 fi_npl_ratio = Column(Float) #: 不良贷款拨备覆盖率 fi_npl_provision_coverage = Column(Float) #: 资本净额 fi_net_capital = Column(Float) #: 专项指标(保险) # #: 总投资收益率 insurance_roi = Column(Float) #: 净投资收益率 insurance_net_investment_yield = Column(Float) #: 已赚保费 insurance_earned_premium = Column(Float) #: 赔付支出 insurance_payout = Column(Float) #: 退保率 insurance_surrender_rate = Column(Float) #: 偿付能力充足率 insurance_solvency_adequacy_ratio = Column(Float) #: 专项指标(券商) # #: 净资本 broker_net_capital = Column(Float) #: 净资产 broker_net_assets = Column(Float) #: 净资本/净资产 broker_net_capital_assets_ratio = Column(Float) #: 自营固定收益类证券规模/净资本 broker_self_operated_fixed_income_securities_net_capital_ratio = Column(Float) register_schema(db_name="finance", schema_base=FinanceBase, entity_type="stock") # the __all__ is generated __all__ = ["BalanceSheet", "IncomeStatement", "CashFlowStatement", "FinanceFactor"] ================================================ FILE: src/zvt/domain/fundamental/trading.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema TradingBase = declarative_base() class ManagerTrading(TradingBase, Mixin): __tablename__ = "manager_trading" provider = Column(String(length=32)) code = Column(String(length=32)) #: 日期 变动人 变动数量(股) 交易均价(元) 结存股票(股) 交易方式 董监高管 高管职位 与高管关系 #: 2017-08-11 韦春 200 9.16 -- 竞价交易 刘韬 高管 兄弟姐妹 #: 变动人 trading_person = Column(String(length=32)) #: 变动数量 volume = Column(Float) #: 交易均价 price = Column(Float) #: 结存股票 holding = Column(Float) #: 交易方式 trading_way = Column(String(length=32)) #: 董监高管 manager = Column(String(length=32)) #: 高管职位 manager_position = Column(String(length=32)) #: 与高管关系 relationship_with_manager = Column(String(length=32)) class HolderTrading(TradingBase, Mixin): __tablename__ = "holder_trading" provider = Column(String(length=32)) code = Column(String(length=32)) #: 股东名称 holder_name = Column(String(length=32)) #: 变动数量 volume = Column(Float) #: 变动比例 change_pct = Column(Float) #: 变动后持股比例 holding_pct = Column(Float) class BigDealTrading(TradingBase, Mixin): __tablename__ = "big_deal_trading" provider = Column(String(length=32)) code = Column(String(length=32)) #: 成交额 turnover = Column(Float) #: 成交价 price = Column(Float) #: 卖出营业部 sell_broker = Column(String(length=128)) #: 买入营业部 buy_broker = Column(String(length=128)) #: 折/溢价率 compare_rate = Column(Float) class MarginTrading(TradingBase, Mixin): __tablename__ = "margin_trading" code = Column(String(length=32)) #: 融资余额(元） fin_value = Column(Float) #: 融资买入额（元） fin_buy_value = Column(Float) #: 融资偿还额（元） fin_refund_value = Column(Float) #: 融券余量（股） sec_value = Column(Float) #: 融券卖出量（股） sec_sell_value = Column(Float) #: 融券偿还量（股） sec_refund_value = Column(Float) #: 融资融券余额（元） fin_sec_value = Column(Float) class DragonAndTiger(TradingBase, Mixin): __tablename__ = "dragon_and_tiger" code = Column(String(length=32)) name = Column(String(length=32)) #: 异动原因 reason = Column(String(length=128)) #: 成交额 turnover = Column(Float) #: 涨幅 change_pct = Column(Float) #: 净买入 net_in = Column(Float) #: 买入营业部 dep1 = Column(String(length=128)) dep1_in = Column(Float) dep1_out = Column(Float) dep1_rate = Column(Float) dep2 = Column(String(length=128)) dep2_in = Column(Float) dep2_out = Column(Float) dep2_rate = Column(Float) dep3 = Column(String(length=128)) dep3_in = Column(Float) dep3_out = Column(Float) dep3_rate = Column(Float) dep4 = Column(String(length=128)) dep4_in = Column(Float) dep4_out = Column(Float) dep4_rate = Column(Float) dep5 = Column(String(length=128)) dep5_in = Column(Float) dep5_out = Column(Float) dep5_rate = Column(Float) #: 卖出营业部 dep_1 = Column(String(length=128)) dep_1_in = Column(Float) dep_1_out = Column(Float) dep_1_rate = Column(Float) dep_2 = Column(String(length=128)) dep_2_in = Column(Float) dep_2_out = Column(Float) dep_2_rate = Column(Float) dep_3 = Column(String(length=128)) dep_3_in = Column(Float) dep_3_out = Column(Float) dep_3_rate = Column(Float) dep_4 = Column(String(length=128)) dep_4_in = Column(Float) dep_4_out = Column(Float) dep_4_rate = Column(Float) dep_5 = Column(String(length=128)) dep_5_in = Column(Float) dep_5_out = Column(Float) dep_5_rate = Column(Float) register_schema( db_name="trading", schema_base=TradingBase, entity_type="stock" ) # the __all__ is generated __all__ = ["ManagerTrading", "HolderTrading", "BigDealTrading", "MarginTrading", "DragonAndTiger"] ================================================ FILE: src/zvt/domain/fundamental/valuation.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema ValuationBase = declarative_base() class StockValuation(ValuationBase, Mixin): __tablename__ = "stock_valuation" code = Column(String(length=32)) name = Column(String(length=32)) #: 总股本(股) capitalization = Column(Float) #: 公司已发行的普通股股份总数(包含A股，B股和H股的总股本) circulating_cap = Column(Float) #: 市值 market_cap = Column(Float) #: 流通市值 circulating_market_cap = Column(Float) #: 换手率 turnover_ratio = Column(Float) #: 静态pe pe = Column(Float) #: 动态pe pe_ttm = Column(Float) #: 市净率 pb = Column(Float) #: 市销率 ps = Column(Float) #: 市现率 pcf = Column(Float) class EtfValuation(ValuationBase, Mixin): __tablename__ = "etf_valuation" code = Column(String(length=32)) name = Column(String(length=32)) #: 静态pe pe = Column(Float) #: 加权 pe1 = Column(Float) #: 动态pe pe_ttm = Column(Float) #: 加权 pe_ttm1 = Column(Float) #: 市净率 pb = Column(Float) #: 加权 pb1 = Column(Float) #: 市销率 ps = Column(Float) #: 加权 ps1 = Column(Float) #: 市现率 pcf = Column(Float) #: 加权 pcf1 = Column(Float) register_schema(db_name="valuation", schema_base=ValuationBase, entity_type="stock") # the __all__ is generated __all__ = ["StockValuation", "EtfValuation"] ================================================ FILE: src/zvt/domain/macro/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule monetary from .monetary import * from .monetary import __all__ as _monetary_all __all__ += _monetary_all # import all from submodule macro from .macro import * from .macro import __all__ as _macro_all __all__ += _macro_all ================================================ FILE: src/zvt/domain/macro/macro.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float, BIGINT from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema MacroBase = declarative_base() class Economy(MacroBase, Mixin): # https://datatopics.worldbank.org/world-development-indicators//themes/economy.html __tablename__ = "economy" code = Column(String(length=32)) name = Column(String(length=32)) population = Column(BIGINT) gdp = Column(Float) gdp_per_capita = Column(Float) gdp_per_employed = Column(Float) gdp_growth = Column(Float) agriculture_growth = Column(Float) industry_growth = Column(Float) manufacturing_growth = Column(Float) service_growth = Column(Float) consumption_growth = Column(Float) capital_growth = Column(Float) exports_growth = Column(Float) imports_growth = Column(Float) gni = Column(Float) gni_per_capita = Column(Float) gross_saving = Column(Float) cpi = Column(Float) unemployment_rate = Column(Float) fdi_of_gdp = Column(Float) register_schema(db_name="macro", schema_base=MacroBase) # the __all__ is generated __all__ = ["Economy"] ================================================ FILE: src/zvt/domain/macro/monetary.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema MonetaryBase = declarative_base() class TreasuryYield(MonetaryBase, Mixin): __tablename__ = "treasury_yield" code = Column(String(length=32)) # 2年期 yield_2 = Column(Float) # 5年期 yield_5 = Column(Float) # 10年期 yield_10 = Column(Float) # 30年期 yield_30 = Column(Float) register_schema(db_name="monetary", schema_base=MonetaryBase) # the __all__ is generated __all__ = ["TreasuryYield"] ================================================ FILE: src/zvt/domain/meta/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule blockus_meta from .blockus_meta import * from .blockus_meta import __all__ as _blockus_meta_all __all__ += _blockus_meta_all # import all from submodule stockus_meta from .stockus_meta import * from .stockus_meta import __all__ as _stockus_meta_all __all__ += _stockus_meta_all # import all from submodule indexhk_meta from .indexhk_meta import * from .indexhk_meta import __all__ as _indexhk_meta_all __all__ += _indexhk_meta_all # import all from submodule stockhk_meta from .stockhk_meta import * from .stockhk_meta import __all__ as _stockhk_meta_all __all__ += _stockhk_meta_all # import all from submodule indexus_meta from .indexus_meta import * from .indexus_meta import __all__ as _indexus_meta_all __all__ += _indexus_meta_all # import all from submodule country_meta from .country_meta import * from .country_meta import __all__ as _country_meta_all __all__ += _country_meta_all # import all from submodule cbond_meta from .cbond_meta import * from .cbond_meta import __all__ as _cbond_meta_all __all__ += _cbond_meta_all # import all from submodule index_meta from .index_meta import * from .index_meta import __all__ as _index_meta_all __all__ += _index_meta_all # import all from submodule future_meta from .future_meta import * from .future_meta import __all__ as _future_meta_all __all__ += _future_meta_all # import all from submodule etf_meta from .etf_meta import * from .etf_meta import __all__ as _etf_meta_all __all__ += _etf_meta_all # import all from submodule currency_meta from .currency_meta import * from .currency_meta import __all__ as _currency_meta_all __all__ += _currency_meta_all # import all from submodule stock_meta from .stock_meta import * from .stock_meta import __all__ as _stock_meta_all __all__ += _stock_meta_all # import all from submodule block_meta from .block_meta import * from .block_meta import __all__ as _block_meta_all __all__ += _block_meta_all # import all from submodule fund_meta from .fund_meta import * from .fund_meta import __all__ as _fund_meta_all __all__ += _fund_meta_all ================================================ FILE: src/zvt/domain/meta/block_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio, PortfolioStock from zvt.contract.register import register_schema, register_entity BlockMetaBase = declarative_base() #: 板块 @register_entity(entity_type="block") class Block(BlockMetaBase, Portfolio): __tablename__ = "block" #: 板块类型，行业(industry),概念(concept) category = Column(String(length=64)) class BlockStock(BlockMetaBase, PortfolioStock): __tablename__ = "block_stock" register_schema(db_name="block_meta", schema_base=BlockMetaBase) # the __all__ is generated __all__ = ["Block", "BlockStock"] ================================================ FILE: src/zvt/domain/meta/blockus_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio, PortfolioStock from zvt.contract.register import register_schema, register_entity BlockusMetaBase = declarative_base() #: 板块 @register_entity(entity_type="blockus") class Blockus(BlockusMetaBase, Portfolio): __tablename__ = "block" #: 板块类型，行业(industry),概念(concept) category = Column(String(length=64)) class BlockusStockus(BlockusMetaBase, PortfolioStock): __tablename__ = "blockus_stockus" register_schema(db_name="blockus_meta", schema_base=BlockusMetaBase) # the __all__ is generated __all__ = ["Blockus", "BlockusStockus"] ================================================ FILE: src/zvt/domain/meta/cbond_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.orm import declarative_base from zvt.contract import TradableEntity from zvt.contract.register import register_schema, register_entity CBondBase = declarative_base() #: 美股 @register_entity(entity_type="cbond") class CBond(CBondBase, TradableEntity): __tablename__ = "cbond" register_schema(db_name="cbond_meta", schema_base=CBondBase) # the __all__ is generated __all__ = ["CBond"] ================================================ FILE: src/zvt/domain/meta/country_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema, register_entity from zvt.contract.schema import TradableEntity CountryMetaBase = declarative_base() @register_entity(entity_type="country") class Country(CountryMetaBase, TradableEntity): __tablename__ = "country" #: 区域 #: region region = Column(String(length=128)) #: 首都 #: capital city capital_city = Column(String(length=128)) #: 收入水平 #: income level income_level = Column(String(length=64)) #: 贷款类型 #: lending type lending_type = Column(String(length=64)) #: 经度 #: longitude longitude = Column(Float) #: 纬度 #: latitude latitude = Column(Float) register_schema(db_name="country_meta", schema_base=CountryMetaBase) # the __all__ is generated __all__ = ["Country"] ================================================ FILE: src/zvt/domain/meta/currency_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema, register_entity from zvt.contract.schema import TradableEntity CurrencyMetaBase = declarative_base() @register_entity(entity_type="currency") class Currency(CurrencyMetaBase, TradableEntity): __tablename__ = "currency" register_schema(db_name="currency_meta", schema_base=CurrencyMetaBase) # the __all__ is generated __all__ = ["Currency"] ================================================ FILE: src/zvt/domain/meta/etf_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio, PortfolioStockHistory from zvt.contract.register import register_schema, register_entity from zvt.utils.time_utils import now_pd_timestamp EtfMetaBase = declarative_base() #: etf @register_entity(entity_type="etf") class Etf(EtfMetaBase, Portfolio): __tablename__ = "etf" category = Column(String(length=64)) @classmethod def get_stocks(cls, code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None): from zvt.api.portfolio import get_etf_stocks return get_etf_stocks(code=code, codes=codes, ids=ids, timestamp=timestamp, provider=provider) class EtfStock(EtfMetaBase, PortfolioStockHistory): __tablename__ = "etf_stock" register_schema(db_name="etf_meta", schema_base=EtfMetaBase) # the __all__ is generated __all__ = ["Etf", "EtfStock"] ================================================ FILE: src/zvt/domain/meta/fund_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Integer from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio, PortfolioStockHistory from zvt.contract.register import register_entity, register_schema from zvt.utils.time_utils import now_pd_timestamp FundMetaBase = declarative_base() #: 个股 @register_entity(entity_type="fund") class Fund(FundMetaBase, Portfolio): __tablename__ = "fund" #: 基金管理人 advisor = Column(String(length=100)) #: 基金托管人 trustee = Column(String(length=100)) #: 编码 基金运作方式 #: 401001 开放式基金 #: 401002 封闭式基金 #: 401003 QDII #: 401004 FOF #: 401005 ETF #: 401006 LOF #: 基金运作方式编码 operate_mode_id = Column(Integer) #: 基金运作方式 operate_mode = Column(String(length=32)) #: 编码 基金类别 #: 402001 股票型 #: 402002 货币型 #: 402003 债券型 #: 402004 混合型 #: 402005 基金型 #: 402006 贵金属 #: 402007 封闭式 #: 投资标的类型编码 underlying_asset_type_id = Column(Integer) #: 投资标的类型 underlying_asset_type = Column(String(length=32)) @classmethod def get_stocks(cls, code=None, codes=None, ids=None, timestamp=now_pd_timestamp(), provider=None): from zvt.api.portfolio import get_fund_stocks return get_fund_stocks(code=code, codes=codes, ids=ids, timestamp=timestamp, provider=provider) class FundStock(FundMetaBase, PortfolioStockHistory): __tablename__ = "fund_stock" register_schema(db_name="fund_meta", schema_base=FundMetaBase) # the __all__ is generated __all__ = ["Fund", "FundStock"] ================================================ FILE: src/zvt/domain/meta/future_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema, register_entity from zvt.contract.schema import TradableEntity FutureMetaBase = declarative_base() @register_entity(entity_type="future") class Future(FutureMetaBase, TradableEntity): __tablename__ = "future" register_schema(db_name="future_meta", schema_base=FutureMetaBase) # the __all__ is generated __all__ = ["Future"] ================================================ FILE: src/zvt/domain/meta/index_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio, PortfolioStockHistory from zvt.contract.register import register_schema, register_entity IndexMetaBase = declarative_base() #: 指数 @register_entity(entity_type="index") class Index(IndexMetaBase, Portfolio): __tablename__ = "index" #: 发布商 publisher = Column(String(length=64)) #: 类别 #: see IndexCategory category = Column(String(length=64)) #: 基准点数 base_point = Column(Float) class IndexStock(IndexMetaBase, PortfolioStockHistory): __tablename__ = "index_stock" register_schema(db_name="index_meta", schema_base=IndexMetaBase) # the __all__ is generated __all__ = ["Index", "IndexStock"] ================================================ FILE: src/zvt/domain/meta/indexhk_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio from zvt.contract.register import register_schema, register_entity IndexhkMetaBase = declarative_base() #: 港股指数 @register_entity(entity_type="indexhk") class Indexhk(IndexhkMetaBase, Portfolio): __tablename__ = "indexhk" #: 发布商 publisher = Column(String(length=64)) #: 类别 #: see IndexCategory category = Column(String(length=64)) #: 基准点数 base_point = Column(Float) register_schema(db_name="indexhk_meta", schema_base=IndexhkMetaBase) # the __all__ is generated __all__ = ["Indexhk"] ================================================ FILE: src/zvt/domain/meta/indexus_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Portfolio from zvt.contract.register import register_schema, register_entity IndexusMetaBase = declarative_base() #: 美股指数 @register_entity(entity_type="indexus") class Indexus(IndexusMetaBase, Portfolio): __tablename__ = "indexus" #: 发布商 publisher = Column(String(length=64)) #: 类别 #: see IndexCategory category = Column(String(length=64)) #: 基准点数 base_point = Column(Float) register_schema(db_name="indexus_meta", schema_base=IndexusMetaBase) # the __all__ is generated __all__ = ["Indexus"] ================================================ FILE: src/zvt/domain/meta/stock_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, DateTime, BigInteger, Float from sqlalchemy.orm import declarative_base from zvt.contract import TradableEntity from zvt.contract.register import register_schema, register_entity StockMetaBase = declarative_base() #: 个股 @register_entity(entity_type="stock") class Stock(StockMetaBase, TradableEntity): __tablename__ = "stock" #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) #: 股东上次更新时间 holder_modified_date = Column(DateTime) #: 控股股东 controlling_holder = Column(String) #: 实际控制人 controlling_holder_parent = Column(String) #: 前十大股东占比 top_ten_ratio = Column(Float) #: 个股详情 class StockDetail(StockMetaBase, TradableEntity): __tablename__ = "stock_detail" #: 所属行业 industries = Column(String) #: 行业指数 industry_indices = Column(String) #: 所属板块 concept_indices = Column(String) #: 所属区域 area_indices = Column(String) #: 成立日期 date_of_establishment = Column(DateTime) #: 公司简介 profile = Column(String(length=1024)) #: 主营业务 main_business = Column(String(length=512)) #: 发行量(股) issues = Column(BigInteger) #: 发行价格 price = Column(Float) #: 募资净额(元) raising_fund = Column(Float) #: 发行市盈率 issue_pe = Column(Float) #: 网上中签率 net_winning_rate = Column(Float) register_schema( db_name="stock_meta", schema_base=StockMetaBase ) # the __all__ is generated __all__ = ["Stock", "StockDetail"] ================================================ FILE: src/zvt/domain/meta/stockhk_meta.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Boolean, Float, String from sqlalchemy.orm import declarative_base from zvt.contract import TradableEntity from zvt.contract.register import register_schema, register_entity StockhkMetaBase = declarative_base() #: 港股 @register_entity(entity_type="stockhk") class Stockhk(StockhkMetaBase, TradableEntity): __tablename__ = "stockhk" #: 是否属于港股通 south = Column(Boolean) #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) #: 所属行业 industry = Column(String) @classmethod def get_trading_t(cls): """ 0 means t+0 1 means t+1 :return: """ return 0 @classmethod def get_trading_intervals(cls, include_bidding_time=False): """ overwrite it to get the trading intervals of the entity :return: list of time intervals, in format [(start,end)] """ if include_bidding_time: return [("09:15", "12:00"), ("13:00", "16:00")] else: return [("09:30", "12:00"), ("13:00", "16:00")] register_schema(db_name="stockhk_meta", schema_base=StockhkMetaBase) # the __all__ is generated __all__ = ["Stockhk"] ================================================ FILE: src/zvt/domain/meta/stockus_meta.py ================================================ # -*- coding: utf-8 -*- import pytz from sqlalchemy import Column, Float, String from sqlalchemy.orm import declarative_base from zvt.contract import TradableEntity from zvt.contract.register import register_schema, register_entity StockusMetaBase = declarative_base() #: 美股 @register_entity(entity_type="stockus") class Stockus(StockusMetaBase, TradableEntity): __tablename__ = "stockus" #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) #: 所属行业 industry = Column(String) @classmethod def get_timezone(cls): return pytz.timezone("America/New_York") @classmethod def get_trading_t(cls): """ 0 means t+0 1 means t+1 :return: """ return 0 @classmethod def get_trading_intervals(cls, include_bidding_time=False): """ overwrite it to get the trading intervals of the entity :return: list of time intervals, in format [(start,end)] """ return [("09:30", "16:00")] register_schema(db_name="stockus_meta", schema_base=StockusMetaBase) # the __all__ is generated __all__ = ["Stockus"] ================================================ FILE: src/zvt/domain/misc/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule overall from .overall import * from .overall import __all__ as _overall_all __all__ += _overall_all # import all from submodule money_flow from .money_flow import * from .money_flow import __all__ as _money_flow_all __all__ += _money_flow_all # import all from submodule holder from .holder import * from .holder import __all__ as _holder_all __all__ += _holder_all # import all from submodule stock_news from .stock_news import * from .stock_news import __all__ as _stock_news_all __all__ += _stock_news_all ================================================ FILE: src/zvt/domain/misc/holder.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, DateTime, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema HolderBase = declarative_base() class HkHolder(HolderBase, Mixin): __tablename__ = "hk_holder" #: 股票代码 code = Column(String(length=32)) #: 股票名称 name = Column(String(length=32)) #: 市场通编码 三种类型：310001-沪股通，310002-深股通，310005-港股通 holder_code = Column(String(length=32)) #: 市场通名称 三种类型：沪股通，深股通，港股通 holder_name = Column(String(length=32)) #: 持股数量 share_number = Column(Float) #: 持股比例 share_ratio = Column(Float) class TopTenTradableHolder(HolderBase, Mixin): __tablename__ = "top_ten_tradable_holder" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 股东代码 holder_code = Column(String(length=32)) #: 股东名称 holder_name = Column(String(length=32)) #: 持股数 shareholding_numbers = Column(Float) #: 持股比例 shareholding_ratio = Column(Float) #: 变动 change = Column(Float) #: 变动比例 change_ratio = Column(Float) class TopTenHolder(HolderBase, Mixin): __tablename__ = "top_ten_holder" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 股东代码 holder_code = Column(String(length=32)) #: 股东名称 holder_name = Column(String(length=32)) #: 持股数 shareholding_numbers = Column(Float) #: 持股比例 shareholding_ratio = Column(Float) #: 变动 change = Column(Float) #: 变动比例 change_ratio = Column(Float) class InstitutionalInvestorHolder(HolderBase, Mixin): __tablename__ = "institutional_investor_holder" provider = Column(String(length=32)) code = Column(String(length=32)) report_period = Column(String(length=32)) report_date = Column(DateTime) #: 机构类型 institutional_investor_type = Column(String(length=64)) #: 股东代码 holder_code = Column(String(length=32)) #: 股东名称 holder_name = Column(String(length=32)) #: 持股数 shareholding_numbers = Column(Float) #: 持股比例 shareholding_ratio = Column(Float) register_schema(db_name="holder", schema_base=HolderBase, entity_type="stock") # the __all__ is generated __all__ = ["HkHolder", "TopTenTradableHolder", "TopTenHolder", "InstitutionalInvestorHolder"] ================================================ FILE: src/zvt/domain/misc/money_flow.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema MoneyFlowBase = declarative_base() #: 板块资金流向 class BlockMoneyFlow(MoneyFlowBase, Mixin): __tablename__ = "block_money_flow" code = Column(String(length=32)) name = Column(String(length=32)) #: 收盘价 close = Column(Float) change_pct = Column(Float) turnover_rate = Column(Float) #: 净流入 net_inflows = Column(Float) #: 净流入率 net_inflow_rate = Column(Float) #: 主力=超大单+大单 net_main_inflows = Column(Float) net_main_inflow_rate = Column(Float) #: 超大单 net_huge_inflows = Column(Float) net_huge_inflow_rate = Column(Float) #: 大单 net_big_inflows = Column(Float) net_big_inflow_rate = Column(Float) #: 中单 net_medium_inflows = Column(Float) net_medium_inflow_rate = Column(Float) #: 小单 net_small_inflows = Column(Float) net_small_inflow_rate = Column(Float) class StockMoneyFlow(MoneyFlowBase, Mixin): __tablename__ = "stock_money_flow" code = Column(String(length=32)) name = Column(String(length=32)) #: 收盘价 close = Column(Float) change_pct = Column(Float) turnover_rate = Column(Float) #: 净流入 net_inflows = Column(Float) #: 净流入率 net_inflow_rate = Column(Float) #: 主力=超大单+大单 net_main_inflows = Column(Float) net_main_inflow_rate = Column(Float) #: 超大单 net_huge_inflows = Column(Float) net_huge_inflow_rate = Column(Float) #: 大单 net_big_inflows = Column(Float) net_big_inflow_rate = Column(Float) #: 中单 net_medium_inflows = Column(Float) net_medium_inflow_rate = Column(Float) #: 小单 net_small_inflows = Column(Float) net_small_inflow_rate = Column(Float) class IndexMoneyFlow(MoneyFlowBase, Mixin): __tablename__ = "index_money_flow" code = Column(String(length=32)) name = Column(String(length=32)) #: 净流入 net_inflows = Column(Float) #: 净流入率 net_inflow_rate = Column(Float) #: 主力=超大单+大单 net_main_inflows = Column(Float) net_main_inflow_rate = Column(Float) #: 超大单 net_huge_inflows = Column(Float) net_huge_inflow_rate = Column(Float) #: 大单 net_big_inflows = Column(Float) net_big_inflow_rate = Column(Float) #: 中单 net_medium_inflows = Column(Float) net_medium_inflow_rate = Column(Float) #: 小单 net_small_inflows = Column(Float) net_small_inflow_rate = Column(Float) register_schema(db_name="money_flow", schema_base=MoneyFlowBase, entity_type="stock") # the __all__ is generated __all__ = ["BlockMoneyFlow", "StockMoneyFlow", "IndexMoneyFlow"] ================================================ FILE: src/zvt/domain/misc/overall.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, Float from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema OverallBase = declarative_base() #: 市场整体估值 class StockSummary(OverallBase, Mixin): __tablename__ = "stock_summary" provider = Column(String(length=32)) code = Column(String(length=32)) name = Column(String(length=32)) total_value = Column(Float) total_tradable_vaule = Column(Float) pe = Column(Float) pb = Column(Float) volume = Column(Float) turnover = Column(Float) turnover_rate = Column(Float) #: 融资融券概况 class MarginTradingSummary(OverallBase, Mixin): __tablename__ = "margin_trading_summary" provider = Column(String(length=32)) code = Column(String(length=32)) name = Column(String(length=32)) #: 融资余额 margin_value = Column(Float) #: 买入额 margin_buy = Column(Float) #: 融券余额 short_value = Column(Float) #: 卖出量 short_volume = Column(Float) #: 融资融券余额 total_value = Column(Float) #: 北向/南向成交概况 class CrossMarketSummary(OverallBase, Mixin): __tablename__ = "cross_market_summary" provider = Column(String(length=32)) code = Column(String(length=32)) name = Column(String(length=32)) buy_amount = Column(Float) buy_volume = Column(Float) sell_amount = Column(Float) sell_volume = Column(Float) quota_daily = Column(Float) quota_daily_balance = Column(Float) register_schema(db_name="overall", schema_base=OverallBase, entity_type="stock") # the __all__ is generated __all__ = ["StockSummary", "MarginTradingSummary", "CrossMarketSummary"] ================================================ FILE: src/zvt/domain/misc/stock_news.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, JSON, Boolean, DateTime, Integer from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema NewsBase = declarative_base() class StockNews(NewsBase, Mixin): __tablename__ = "stock_news" #: 新闻编号 news_code = Column(String) #: 新闻地址 news_url = Column(String) #: 新闻标题 news_title = Column(String) #: 新闻内容 news_content = Column(String) #: 新闻解读 news_analysis = Column(JSON) #: 用户设置为忽略 ignore_by_user = Column(Boolean, default=False) class StockHotTopic(NewsBase, Mixin): __tablename__ = "stock_hot_topic" #: 出现时间 created_timestamp = Column(DateTime) #: 热度排行 position = Column(Integer) #: 相关标的 entity_ids = Column(JSON) #: 新闻编号 news_code = Column(String) #: 新闻标题 news_title = Column(String) #: 新闻内容 news_content = Column(String) #: 新闻解读 news_analysis = Column(JSON) register_schema(db_name="stock_news", schema_base=NewsBase, entity_type="stock") # the __all__ is generated __all__ = ["StockNews", "StockHotTopic"] ================================================ FILE: src/zvt/domain/quotes/__init__.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import String, Column, Float, Integer, JSON, Boolean from zvt.contract import Mixin class KdataCommon(Mixin): provider = Column(String(length=32)) code = Column(String(length=32)) name = Column(String(length=32)) # Enum constraint is not extendable # level = Column(Enum(IntervalLevel, values_callable=enum_value)) level = Column(String(length=32)) # 开盘价 open = Column(Float) # 收盘价 close = Column(Float) # 最高价 high = Column(Float) # 最低价 low = Column(Float) # 成交量 volume = Column(Float) # 成交金额 turnover = Column(Float) # 涨跌幅 change_pct = Column(Float) # 换手率 turnover_rate = Column(Float) class TickCommon(Mixin): #: UNIX时间戳 time = Column(Integer) #: 开盘价 open = Column(Float) #: 收盘价/当前价格 close = Column(Float) #: 最高价 high = Column(Float) #: 最低价 low = Column(Float) #: 成交量 volume = Column(Float) #: 成交金额 turnover = Column(Float) #: 委卖价 ask_price = Column(Float) #: 委买价 bid_price = Column(Float) #: 委卖量 ask_vol = Column(JSON) #: 委买量 bid_vol = Column(JSON) #: 成交笔数 transaction_num = Column(Integer) class BlockKdataCommon(KdataCommon): pass class IndexKdataCommon(KdataCommon): pass class IndexhkKdataCommon(KdataCommon): pass class IndexusKdataCommon(KdataCommon): pass class EtfKdataCommon(KdataCommon): turnover_rate = Column(Float) # ETF 累计净值（货币 ETF 为七日年化) cumulative_net_value = Column(Float) class StockKdataCommon(KdataCommon): #: 是否涨停 is_limit_up = Column(Boolean) #: 是否跌停 is_limit_down = Column(Boolean) class StockusKdataCommon(KdataCommon): #: 是否涨停 is_limit_up = Column(Boolean) #: 是否跌停 is_limit_down = Column(Boolean) class StockhkKdataCommon(KdataCommon): pass # future common kdata class FutureKdataCommon(KdataCommon): #: 持仓量 interest = Column(Float) #: 结算价 settlement = Column(Float) #: 涨跌幅(按收盘价) # change_pct = Column(Float) #: 涨跌幅(按结算价) change_pct1 = Column(Float) class CurrencyKdataCommon(KdataCommon): #: 持仓量 interest = Column(Float) #: 结算价 settlement = Column(Float) #: 涨跌幅(按收盘价) # change_pct = Column(Float) #: 涨跌幅(按结算价) change_pct1 = Column(Float) # the __all__ is generated __all__ = [ "KdataCommon", "TickCommon", "BlockKdataCommon", "IndexKdataCommon", "IndexhkKdataCommon", "IndexusKdataCommon", "EtfKdataCommon", "StockKdataCommon", "StockusKdataCommon", "StockhkKdataCommon", "FutureKdataCommon", "CurrencyKdataCommon", ] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule indexhk from .indexhk import * from .indexhk import __all__ as _indexhk_all __all__ += _indexhk_all # import all from submodule trade_day from .trade_day import * from .trade_day import __all__ as _trade_day_all __all__ += _trade_day_all # import all from submodule indexus from .indexus import * from .indexus import __all__ as _indexus_all __all__ += _indexus_all # import all from submodule stockhk from .stockhk import * from .stockhk import __all__ as _stockhk_all __all__ += _stockhk_all # import all from submodule stockus from .stockus import * from .stockus import __all__ as _stockus_all __all__ += _stockus_all # import all from submodule index from .index import * from .index import __all__ as _index_all __all__ += _index_all # import all from submodule etf from .etf import * from .etf import __all__ as _etf_all __all__ += _etf_all # import all from submodule stock from .stock import * from .stock import __all__ as _stock_all __all__ += _stock_all # import all from submodule currency from .currency import * from .currency import __all__ as _currency_all __all__ += _currency_all # import all from submodule future from .future import * from .future import __all__ as _future_all __all__ += _future_all # import all from submodule block from .block import * from .block import __all__ as _block_all __all__ += _block_all ================================================ FILE: src/zvt/domain/quotes/block/__init__.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule block_1d_kdata from .block_1d_kdata import * from .block_1d_kdata import __all__ as _block_1d_kdata_all __all__ += _block_1d_kdata_all # import all from submodule block_1wk_kdata from .block_1wk_kdata import * from .block_1wk_kdata import __all__ as _block_1wk_kdata_all __all__ += _block_1wk_kdata_all # import all from submodule block_1mon_kdata from .block_1mon_kdata import * from .block_1mon_kdata import __all__ as _block_1mon_kdata_all __all__ += _block_1mon_kdata_all ================================================ FILE: src/zvt/domain/quotes/block/block_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import BlockKdataCommon KdataBase = declarative_base() class Block1dKdata(KdataBase, BlockKdataCommon): __tablename__ = "block_1d_kdata" register_schema(db_name="block_1d_kdata", schema_base=KdataBase, entity_type="block") # the __all__ is generated __all__ = ["Block1dKdata"] ================================================ FILE: src/zvt/domain/quotes/block/block_1mon_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import BlockKdataCommon KdataBase = declarative_base() class Block1monKdata(KdataBase, BlockKdataCommon): __tablename__ = "block_1mon_kdata" register_schema(db_name="block_1mon_kdata", schema_base=KdataBase, entity_type="block") # the __all__ is generated __all__ = ["Block1monKdata"] ================================================ FILE: src/zvt/domain/quotes/block/block_1wk_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import BlockKdataCommon KdataBase = declarative_base() class Block1wkKdata(KdataBase, BlockKdataCommon): __tablename__ = "block_1wk_kdata" register_schema(db_name="block_1wk_kdata", schema_base=KdataBase, entity_type="block") # the __all__ is generated __all__ = ["Block1wkKdata"] ================================================ FILE: src/zvt/domain/quotes/currency/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule currency_1d_kdata from .currency_1d_kdata import * from .currency_1d_kdata import __all__ as _currency_1d_kdata_all __all__ += _currency_1d_kdata_all ================================================ FILE: src/zvt/domain/quotes/currency/currency_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import CurrencyKdataCommon KdataBase = declarative_base() class Currency1dKdata(KdataBase, CurrencyKdataCommon): __tablename__ = "currency_1d_kdata" register_schema(db_name="currency_1d_kdata", schema_base=KdataBase, entity_type="currency") # the __all__ is generated __all__ = ["Currency1dKdata"] ================================================ FILE: src/zvt/domain/quotes/etf/__init__.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule etf_1d_kdata from .etf_1d_kdata import * from .etf_1d_kdata import __all__ as _etf_1d_kdata_all __all__ += _etf_1d_kdata_all ================================================ FILE: src/zvt/domain/quotes/etf/etf_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import EtfKdataCommon KdataBase = declarative_base() class Etf1dKdata(KdataBase, EtfKdataCommon): __tablename__ = "etf_1d_kdata" register_schema(db_name="etf_1d_kdata", schema_base=KdataBase, entity_type="etf") # the __all__ is generated __all__ = ["Etf1dKdata"] ================================================ FILE: src/zvt/domain/quotes/future/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule future_1d_kdata from .future_1d_kdata import * from .future_1d_kdata import __all__ as _future_1d_kdata_all __all__ += _future_1d_kdata_all ================================================ FILE: src/zvt/domain/quotes/future/future_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import FutureKdataCommon KdataBase = declarative_base() class Future1dKdata(KdataBase, FutureKdataCommon): __tablename__ = "future_1d_kdata" register_schema(db_name="future_1d_kdata", schema_base=KdataBase, entity_type="future") # the __all__ is generated __all__ = ["Future1dKdata"] ================================================ FILE: src/zvt/domain/quotes/index/__init__.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule index_1d_kdata from .index_1d_kdata import * from .index_1d_kdata import __all__ as _index_1d_kdata_all __all__ += _index_1d_kdata_all # import all from submodule index_1m_kdata from .index_1m_kdata import * from .index_1m_kdata import __all__ as _index_1m_kdata_all __all__ += _index_1m_kdata_all # import all from submodule index_1wk_kdata from .index_1wk_kdata import * from .index_1wk_kdata import __all__ as _index_1wk_kdata_all __all__ += _index_1wk_kdata_all ================================================ FILE: src/zvt/domain/quotes/index/index_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import IndexKdataCommon KdataBase = declarative_base() class Index1dKdata(KdataBase, IndexKdataCommon): __tablename__ = "index_1d_kdata" register_schema(db_name="index_1d_kdata", schema_base=KdataBase, entity_type="index") # the __all__ is generated __all__ = ["Index1dKdata"] ================================================ FILE: src/zvt/domain/quotes/index/index_1m_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract import TradableEntity from zvt.contract.register import register_schema from zvt.domain.quotes import IndexKdataCommon KdataBase = declarative_base() class Index1mKdata(KdataBase, IndexKdataCommon, TradableEntity): __tablename__ = "index_1m_kdata" register_schema(db_name="index_1m_kdata", schema_base=KdataBase, entity_type="index") # the __all__ is generated __all__ = ["Index1mKdata"] ================================================ FILE: src/zvt/domain/quotes/index/index_1wk_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import IndexKdataCommon KdataBase = declarative_base() class Index1wkKdata(KdataBase, IndexKdataCommon): __tablename__ = "index_1wk_kdata" register_schema(db_name="index_1wk_kdata", schema_base=KdataBase, entity_type="index") # the __all__ is generated __all__ = ["Index1wkKdata"] ================================================ FILE: src/zvt/domain/quotes/indexhk/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule indexhk_1d_kdata from .indexhk_1d_kdata import * from .indexhk_1d_kdata import __all__ as _indexhk_1d_kdata_all __all__ += _indexhk_1d_kdata_all ================================================ FILE: src/zvt/domain/quotes/indexhk/indexhk_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import IndexhkKdataCommon KdataBase = declarative_base() class Indexhk1dKdata(KdataBase, IndexhkKdataCommon): __tablename__ = "indexhk_1d_kdata" register_schema(db_name="indexhk_1d_kdata", schema_base=KdataBase, entity_type="indexhk") # the __all__ is generated __all__ = ["Indexhk1dKdata"] ================================================ FILE: src/zvt/domain/quotes/indexus/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule indexus_1d_kdata from .indexus_1d_kdata import * from .indexus_1d_kdata import __all__ as _indexus_1d_kdata_all __all__ += _indexus_1d_kdata_all ================================================ FILE: src/zvt/domain/quotes/indexus/indexus_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import IndexusKdataCommon KdataBase = declarative_base() class Indexus1dKdata(KdataBase, IndexusKdataCommon): __tablename__ = "indexus_1d_kdata" register_schema(db_name="indexus_1d_kdata", schema_base=KdataBase, entity_type="indexus") # the __all__ is generated __all__ = ["Indexus1dKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/__init__.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule stock_1h_kdata from .stock_1h_kdata import * from .stock_1h_kdata import __all__ as _stock_1h_kdata_all __all__ += _stock_1h_kdata_all # import all from submodule stock_15m_hfq_kdata from .stock_15m_hfq_kdata import * from .stock_15m_hfq_kdata import __all__ as _stock_15m_hfq_kdata_all __all__ += _stock_15m_hfq_kdata_all # import all from submodule stock_1wk_kdata from .stock_1wk_kdata import * from .stock_1wk_kdata import __all__ as _stock_1wk_kdata_all __all__ += _stock_1wk_kdata_all # import all from submodule stock_15m_kdata from .stock_15m_kdata import * from .stock_15m_kdata import __all__ as _stock_15m_kdata_all __all__ += _stock_15m_kdata_all # import all from submodule stock_1m_hfq_kdata from .stock_1m_hfq_kdata import * from .stock_1m_hfq_kdata import __all__ as _stock_1m_hfq_kdata_all __all__ += _stock_1m_hfq_kdata_all # import all from submodule stock_4h_hfq_kdata from .stock_4h_hfq_kdata import * from .stock_4h_hfq_kdata import __all__ as _stock_4h_hfq_kdata_all __all__ += _stock_4h_hfq_kdata_all # import all from submodule stock_5m_hfq_kdata from .stock_5m_hfq_kdata import * from .stock_5m_hfq_kdata import __all__ as _stock_5m_hfq_kdata_all __all__ += _stock_5m_hfq_kdata_all # import all from submodule stock_5m_kdata from .stock_5m_kdata import * from .stock_5m_kdata import __all__ as _stock_5m_kdata_all __all__ += _stock_5m_kdata_all # import all from submodule stock_1d_kdata from .stock_1d_kdata import * from .stock_1d_kdata import __all__ as _stock_1d_kdata_all __all__ += _stock_1d_kdata_all # import all from submodule stock_30m_hfq_kdata from .stock_30m_hfq_kdata import * from .stock_30m_hfq_kdata import __all__ as _stock_30m_hfq_kdata_all __all__ += _stock_30m_hfq_kdata_all # import all from submodule stock_1mon_hfq_kdata from .stock_1mon_hfq_kdata import * from .stock_1mon_hfq_kdata import __all__ as _stock_1mon_hfq_kdata_all __all__ += _stock_1mon_hfq_kdata_all # import all from submodule stock_1wk_hfq_kdata from .stock_1wk_hfq_kdata import * from .stock_1wk_hfq_kdata import __all__ as _stock_1wk_hfq_kdata_all __all__ += _stock_1wk_hfq_kdata_all # import all from submodule stock_1mon_kdata from .stock_1mon_kdata import * from .stock_1mon_kdata import __all__ as _stock_1mon_kdata_all __all__ += _stock_1mon_kdata_all # import all from submodule stock_1h_hfq_kdata from .stock_1h_hfq_kdata import * from .stock_1h_hfq_kdata import __all__ as _stock_1h_hfq_kdata_all __all__ += _stock_1h_hfq_kdata_all # import all from submodule stock_1m_kdata from .stock_1m_kdata import * from .stock_1m_kdata import __all__ as _stock_1m_kdata_all __all__ += _stock_1m_kdata_all # import all from submodule stock_4h_kdata from .stock_4h_kdata import * from .stock_4h_kdata import __all__ as _stock_4h_kdata_all __all__ += _stock_4h_kdata_all # import all from submodule stock_1d_hfq_kdata from .stock_1d_hfq_kdata import * from .stock_1d_hfq_kdata import __all__ as _stock_1d_hfq_kdata_all __all__ += _stock_1d_hfq_kdata_all # import all from submodule stock_quote from .stock_quote import * from .stock_quote import __all__ as _stock_quote_all __all__ += _stock_quote_all # import all from submodule stock_30m_kdata from .stock_30m_kdata import * from .stock_30m_kdata import __all__ as _stock_30m_kdata_all __all__ += _stock_30m_kdata_all # import all from submodule stock_quote_log from .stock_quote_log import * from .stock_quote_log import __all__ as _stock_quote_log_all __all__ += _stock_quote_log_all ================================================ FILE: src/zvt/domain/quotes/stock/stock_15m_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock15mHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_15m_hfq_kdata" register_schema( db_name="stock_15m_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock15mHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_15m_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock15mKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_15m_kdata" register_schema( db_name="stock_15m_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock15mKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1d_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1dHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1d_hfq_kdata" register_schema( db_name="stock_1d_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1dHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1dKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1d_kdata" register_schema( db_name="stock_1d_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1dKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1h_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1hHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1h_hfq_kdata" register_schema( db_name="stock_1h_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1hHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1h_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1hKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1h_kdata" register_schema( db_name="stock_1h_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1hKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1m_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1mHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1m_hfq_kdata" register_schema( db_name="stock_1m_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1mHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1m_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1mKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1m_kdata" register_schema( db_name="stock_1m_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1mKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1mon_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1monHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1mon_hfq_kdata" register_schema( db_name="stock_1mon_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1monHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1mon_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1monKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1mon_kdata" register_schema( db_name="stock_1mon_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1monKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1wk_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1wkHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1wk_hfq_kdata" register_schema( db_name="stock_1wk_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1wkHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_1wk_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock1wkKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_1wk_kdata" register_schema( db_name="stock_1wk_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock1wkKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_30m_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock30mHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_30m_hfq_kdata" register_schema( db_name="stock_30m_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock30mHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_30m_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock30mKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_30m_kdata" register_schema( db_name="stock_30m_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock30mKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_4h_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock4hHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_4h_hfq_kdata" register_schema( db_name="stock_4h_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock4hHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_4h_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock4hKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_4h_kdata" register_schema( db_name="stock_4h_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock4hKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_5m_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock5mHfqKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_5m_hfq_kdata" register_schema( db_name="stock_5m_hfq_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock5mHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_5m_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon KdataBase = declarative_base() class Stock5mKdata(KdataBase, StockKdataCommon): __tablename__ = "stock_5m_kdata" register_schema( db_name="stock_5m_kdata", schema_base=KdataBase, entity_type="stock" ) # the __all__ is generated __all__ = ["Stock5mKdata"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_quote.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import String, Column, Float, Integer, Boolean from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon StockQuoteBase = declarative_base() class StockQuote(StockQuoteBase, StockKdataCommon): __tablename__ = "stock_quote" #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 封涨停金额 # limit_up_amount = Column(Float) #: 封跌停金额 # limit_down_amount = Column(Float) #: 5挡卖单金额 # ask_amount = Column(Float) #: 5挡买单金额 # bid_amount = Column(Float) #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) class Stock1mQuote(StockQuoteBase, Mixin): __tablename__ = "stock_1m_quote" code = Column(String(length=32)) name = Column(String(length=32)) #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 均价 avg_price = Column(Float) # 涨跌幅 change_pct = Column(Float) # 成交量 volume = Column(Float) # 成交金额 turnover = Column(Float) # 换手率 turnover_rate = Column(Float) #: 是否涨停 is_limit_up = Column(Boolean) #: 冲涨停 near_limit_up = Column(Boolean) #: 是否跌停 is_limit_down = Column(Boolean) register_schema(db_name="stock_quote", schema_base=StockQuoteBase, entity_type="stock") # the __all__ is generated __all__ = ["StockQuote", "Stock1mQuote"] ================================================ FILE: src/zvt/domain/quotes/stock/stock_quote_log.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Float, Integer, Boolean from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockKdataCommon StockQuoteLogBase = declarative_base() class StockQuoteLog(StockQuoteLogBase, StockKdataCommon): __tablename__ = "stock_quote_log" #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 冲涨停 near_limit_up = Column(Boolean) #: 封涨停金额 # limit_up_amount = Column(Float) #: 封跌停金额 # limit_down_amount = Column(Float) #: 5挡卖单金额 # ask_amount = Column(Float) #: 5挡买单金额 # bid_amount = Column(Float) #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) register_schema(db_name="stock_quote_log", schema_base=StockQuoteLogBase, entity_type="stock") # the __all__ is generated __all__ = ["StockQuoteLog"] ================================================ FILE: src/zvt/domain/quotes/stockhk/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule stockhk_1d_kdata from .stockhk_1d_kdata import * from .stockhk_1d_kdata import __all__ as _stockhk_1d_kdata_all __all__ += _stockhk_1d_kdata_all # import all from submodule stockhk_1d_hfq_kdata from .stockhk_1d_hfq_kdata import * from .stockhk_1d_hfq_kdata import __all__ as _stockhk_1d_hfq_kdata_all __all__ += _stockhk_1d_hfq_kdata_all # import all from submodule stockhk_quote from .stockhk_quote import * from .stockhk_quote import __all__ as _stockhk_quote_all __all__ += _stockhk_quote_all ================================================ FILE: src/zvt/domain/quotes/stockhk/stockhk_1d_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockhkKdataCommon KdataBase = declarative_base() class Stockhk1dHfqKdata(KdataBase, StockhkKdataCommon): __tablename__ = "stockhk_1d_hfq_kdata" register_schema(db_name="stockhk_1d_hfq_kdata", schema_base=KdataBase, entity_type="stockhk") # the __all__ is generated __all__ = ["Stockhk1dHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stockhk/stockhk_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockhkKdataCommon KdataBase = declarative_base() class Stockhk1dKdata(KdataBase, StockhkKdataCommon): __tablename__ = "stockhk_1d_kdata" register_schema(db_name="stockhk_1d_kdata", schema_base=KdataBase, entity_type="stockhk") # the __all__ is generated __all__ = ["Stockhk1dKdata"] ================================================ FILE: src/zvt/domain/quotes/stockhk/stockhk_quote.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import String, Column, Float, Integer, Boolean from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema from zvt.domain.quotes import StockhkKdataCommon StockhkQuoteBase = declarative_base() class StockhkQuote(StockhkQuoteBase, StockhkKdataCommon): __tablename__ = "stockhk_quote" #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 是否涨停 is_limit_up = Column(Boolean) #: 封涨停金额 limit_up_amount = Column(Float) #: 是否跌停 is_limit_down = Column(Boolean) #: 封跌停金额 limit_down_amount = Column(Float) #: 5挡卖单金额 ask_amount = Column(Float) #: 5挡买单金额 bid_amount = Column(Float) #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) class Stockhk1mQuote(StockhkQuoteBase, Mixin): __tablename__ = "stockhk_1m_quote" code = Column(String(length=32)) name = Column(String(length=32)) #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 均价 avg_price = Column(Float) # 涨跌幅 change_pct = Column(Float) # 成交量 volume = Column(Float) # 成交金额 turnover = Column(Float) # 换手率 turnover_rate = Column(Float) #: 是否涨停 is_limit_up = Column(Boolean) #: 是否跌停 is_limit_down = Column(Boolean) register_schema(db_name="stockhk_quote", schema_base=StockhkQuoteBase, entity_type="stockhk") # the __all__ is generated __all__ = ["StockhkQuote", "Stockhk1mQuote"] ================================================ FILE: src/zvt/domain/quotes/stockus/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule stockus_1d_kdata from .stockus_1d_kdata import * from .stockus_1d_kdata import __all__ as _stockus_1d_kdata_all __all__ += _stockus_1d_kdata_all # import all from submodule stockus_quote from .stockus_quote import * from .stockus_quote import __all__ as _stockus_quote_all __all__ += _stockus_quote_all # import all from submodule stockus_1d_hfq_kdata from .stockus_1d_hfq_kdata import * from .stockus_1d_hfq_kdata import __all__ as _stockus_1d_hfq_kdata_all __all__ += _stockus_1d_hfq_kdata_all ================================================ FILE: src/zvt/domain/quotes/stockus/stockus_1d_hfq_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockusKdataCommon KdataBase = declarative_base() class Stockus1dHfqKdata(KdataBase, StockusKdataCommon): __tablename__ = "stockus_1d_hfq_kdata" register_schema(db_name="stockus_1d_hfq_kdata", schema_base=KdataBase, entity_type="stockus") # the __all__ is generated __all__ = ["Stockus1dHfqKdata"] ================================================ FILE: src/zvt/domain/quotes/stockus/stockus_1d_kdata.py ================================================ # -*- coding: utf-8 -*- # this file is generated by gen_kdata_schema function, dont't change it from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.domain.quotes import StockusKdataCommon KdataBase = declarative_base() class Stockus1dKdata(KdataBase, StockusKdataCommon): __tablename__ = "stockus_1d_kdata" register_schema(db_name="stockus_1d_kdata", schema_base=KdataBase, entity_type="stockus") # the __all__ is generated __all__ = ["Stockus1dKdata"] ================================================ FILE: src/zvt/domain/quotes/stockus/stockus_quote.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import String, Column, Float, Integer, Boolean from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema from zvt.domain.quotes import StockusKdataCommon StockusQuoteBase = declarative_base() class StockusQuote(StockusQuoteBase, StockusKdataCommon): __tablename__ = "stockus_quote" #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 是否涨停 is_limit_up = Column(Boolean) #: 封涨停金额 limit_up_amount = Column(Float) #: 是否跌停 is_limit_down = Column(Boolean) #: 封跌停金额 limit_down_amount = Column(Float) #: 5挡卖单金额 ask_amount = Column(Float) #: 5挡买单金额 bid_amount = Column(Float) #: 流通市值 float_cap = Column(Float) #: 总市值 total_cap = Column(Float) class Stockus1mQuote(StockusQuoteBase, Mixin): __tablename__ = "stockus_1m_quote" code = Column(String(length=32)) name = Column(String(length=32)) #: UNIX时间戳 time = Column(Integer) #: 最新价 price = Column(Float) #: 均价 avg_price = Column(Float) # 涨跌幅 change_pct = Column(Float) # 成交量 volume = Column(Float) # 成交金额 turnover = Column(Float) # 换手率 turnover_rate = Column(Float) #: 是否涨停 is_limit_up = Column(Boolean) #: 是否跌停 is_limit_down = Column(Boolean) register_schema(db_name="stockus_quote", schema_base=StockusQuoteBase, entity_type="stockus") # the __all__ is generated __all__ = ["StockusQuote", "Stockus1mQuote"] ================================================ FILE: src/zvt/domain/quotes/trade_day.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema TradeDayBase = declarative_base() class StockTradeDay(TradeDayBase, Mixin): __tablename__ = "stock_trade_day" register_schema(db_name="trade_day", schema_base=TradeDayBase) # the __all__ is generated __all__ = ["StockTradeDay"] ================================================ FILE: src/zvt/factors/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule algorithm from .algorithm import * from .algorithm import __all__ as _algorithm_all __all__ += _algorithm_all # import all from submodule top_stocks from .top_stocks import * from .top_stocks import __all__ as _top_stocks_all __all__ += _top_stocks_all # import all from submodule ma from .ma import * from .ma import __all__ as _ma_all __all__ += _ma_all # import all from submodule transformers from .transformers import * from .transformers import __all__ as _transformers_all __all__ += _transformers_all # import all from submodule macd from .macd import * from .macd import __all__ as _macd_all __all__ += _macd_all # import all from submodule zen from .zen import * from .zen import __all__ as _zen_all __all__ += _zen_all # import all from submodule technical_factor from .technical_factor import * from .technical_factor import __all__ as _technical_factor_all __all__ += _technical_factor_all # import all from submodule fundamental from .fundamental import * from .fundamental import __all__ as _fundamental_all __all__ += _fundamental_all # import all from submodule factor_service from .factor_service import * from .factor_service import __all__ as _factor_service_all __all__ += _factor_service_all # import all from submodule factor_models from .factor_models import * from .factor_models import __all__ as _factor_models_all __all__ += _factor_models_all # import all from submodule target_selector from .target_selector import * from .target_selector import __all__ as _target_selector_all __all__ += _target_selector_all # import all from submodule shape from .shape import * from .shape import __all__ as _shape_all __all__ += _shape_all ================================================ FILE: src/zvt/factors/algorithm.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract.factor import Scorer, Transformer from zvt.utils.pd_utils import normal_index_df, group_by_entity_id, normalize_group_compute_result def ma(s: pd.Series, window: int = 5) -> pd.Series: """ :param s: :param window: :return: """ return s.rolling(window=window, min_periods=window).mean() def ema(s: pd.Series, window: int = 12) -> pd.Series: return s.ewm(span=window, adjust=False, min_periods=window).mean() def live_or_dead(x): if x: return 1 else: return -1 def macd( s: pd.Series, slow: int = 26, fast: int = 12, n: int = 9, return_type: str = "df", normal: bool = False, count_live_dead: bool = False, ): # 短期均线 ema_fast = ema(s, window=fast) # 长期均线 ema_slow = ema(s, window=slow) # 短期均线 - 长期均线 = 趋势的力度 diff: pd.Series = ema_fast - ema_slow # 力度均线 dea: pd.Series = diff.ewm(span=n, adjust=False).mean() # 力度 的变化 m: pd.Series = (diff - dea) * 2 # normal it if normal: diff = diff / s dea = dea / s m = m / s if count_live_dead: live = (diff > dea).apply(lambda x: live_or_dead(x)) bull = (diff > 0) & (dea > 0) live_count = live * (live.groupby((live != live.shift()).cumsum()).cumcount() + 1) if return_type == "se": if count_live_dead: return diff, dea, m, live, bull, live_count return diff, dea, m else: if count_live_dead: return pd.DataFrame( {"diff": diff, "dea": dea, "macd": m, "live": live, "bull": bull, "live_count": live_count} ) return pd.DataFrame({"diff": diff, "dea": dea, "macd": m}) def point_in_range(point: float, range: tuple): """ :param point: one point :param range: (start,end) :return: """ return range[0] <= point <= range[1] def intersect_ranges(range_list): if len(range_list) == 1: return range_list[0] result = intersect(range_list[0], range_list[1]) for range_i in range_list[2:]: result = intersect(result, range_i) return result def combine(range_a, range_b): if intersect(range_a, range_b): return min(range_a[0], range_b[0]), max(range_a[1], range_b[1]) return None def distance(range_a, range_b, use_max=False): if use_max: # 上升 if range_b[0] >= range_a[1]: return (range_b[1] - range_a[0]) / range_a[0] # 下降 if range_b[1] <= range_a[0]: return (range_b[0] - range_a[1]) / range_a[1] else: middle_start = (range_a[0] + range_a[1]) / 2 middle_end = (range_b[0] + range_b[1]) / 2 return (middle_end - middle_start) / middle_start def intersect(range_a, range_b): """ range_a and range_b with format (start,end) in y axis :param range_a: :param range_b: :return: """ if not range_a or not range_b: return None # 包含 if point_in_range(range_a[0], range_b) and point_in_range(range_a[1], range_b): return range_a if point_in_range(range_b[0], range_a) and point_in_range(range_b[1], range_a): return range_b if point_in_range(range_a[0], range_b): return range_a[0], range_b[1] if point_in_range(range_b[0], range_a): return range_b[0], range_a[1] return None class RankScorer(Scorer): def __init__(self, ascending=True) -> None: self.ascending = ascending def score(self, input_df) -> pd.DataFrame: result_df = input_df.groupby(level=1).rank(ascending=self.ascending, pct=True) return result_df class MaTransformer(Transformer): def __init__(self, windows=None, cal_change_pct=False) -> None: super().__init__() if windows is None: windows = [5, 10] self.windows = windows self.cal_change_pct = cal_change_pct def transform(self, input_df: pd.DataFrame) -> pd.DataFrame: if self.cal_change_pct: group_pct = group_by_entity_id(input_df["close"]).pct_change() input_df["change_pct"] = normalize_group_compute_result(group_pct) for window in self.windows: col = "ma{}".format(window) self.indicators.append(col) group_ma = group_by_entity_id(input_df["close"]).rolling(window=window, min_periods=window).mean() input_df[col] = normalize_group_compute_result(group_ma) return input_df def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame: """ transform_one would not take effects if transform was implemented. Just show how to implement it here, most of time you should overwrite transform directly for performance. :param entity_id: :param df: :return: """ if self.cal_change_pct: df["change_pct"] = df["close"].pct_change() for window in self.windows: col = "ma{}".format(window) self.indicators.append(col) df[col] = df["close"].rolling(window=window, min_periods=window).mean() return df class IntersectTransformer(Transformer): def __init__(self, kdata_overlap=0) -> None: super().__init__() self.kdata_overlap = kdata_overlap def transform(self, input_df) -> pd.DataFrame: """ :param input_df: :return: """ if self.kdata_overlap > 0: # 没有重叠，区间就是(0,0) input_df["overlap"] = [(0, 0)] * len(input_df.index) def cal_overlap(s): high = input_df.loc[s.index, "high"] low = input_df.loc[s.index, "low"] intersection = intersect_ranges(list(zip(low.to_list(), high.to_list()))) if intersection: # 设置column overlap为intersection,即重叠区间 input_df.at[s.index[-1], "overlap"] = intersection return 0 input_df[["high", "low"]].groupby(level=0).rolling( window=self.kdata_overlap, min_periods=self.kdata_overlap ).apply(cal_overlap, raw=False) return input_df class MaAndVolumeTransformer(Transformer): def __init__(self, windows=None, vol_windows=None, kdata_overlap=0) -> None: super().__init__() if vol_windows is None: vol_windows = [30] if windows is None: windows = [5, 10] self.windows = windows self.vol_windows = vol_windows self.kdata_overlap = kdata_overlap def transform(self, input_df) -> pd.DataFrame: for window in self.windows: col = f"ma{window}" self.indicators.append(col) ma_df = input_df["close"].groupby(level=0).rolling(window=window, min_periods=window).mean() ma_df = ma_df.reset_index(level=0, drop=True) input_df[col] = ma_df for vol_window in self.vol_windows: col = "vol_ma{}".format(vol_window) vol_ma_df = input_df["volume"].groupby(level=0).rolling(window=vol_window, min_periods=vol_window).mean() vol_ma_df = vol_ma_df.reset_index(level=0, drop=True) input_df[col] = vol_ma_df if self.kdata_overlap > 0: input_df["overlap"] = [(0, 0)] * len(input_df.index) def cal_overlap(s): high = input_df.loc[s.index, "high"] low = input_df.loc[s.index, "low"] intersection = intersect_ranges(list(zip(low.to_list(), high.to_list()))) if intersection: input_df.at[s.index[-1], "overlap"] = intersection return 0 input_df[["high", "low"]].groupby(level=0).rolling( window=self.kdata_overlap, min_periods=self.kdata_overlap ).apply(cal_overlap, raw=False) return input_df class MacdTransformer(Transformer): def __init__(self, slow=26, fast=12, n=9, normal=False, count_live_dead=False) -> None: super().__init__() self.slow = slow self.fast = fast self.n = n self.normal = normal self.count_live_dead = count_live_dead self.indicators.append("diff") self.indicators.append("dea") self.indicators.append("macd") def transform(self, input_df) -> pd.DataFrame: macd_df = input_df.groupby(level=0)["close"].apply( lambda x: macd( x, slow=self.slow, fast=self.fast, n=self.n, return_type="df", normal=self.normal, count_live_dead=self.count_live_dead, ) ) macd_df = macd_df.reset_index(level=0, drop=True) input_df = pd.concat([input_df, macd_df], axis=1, sort=False, verify_integrity=True) return input_df def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame: print(f"transform_one {entity_id} {df}") return macd( df["close"], slow=self.slow, fast=self.fast, n=self.n, return_type="df", normal=self.normal, count_live_dead=self.count_live_dead, ) class QuantileScorer(Scorer): def __init__(self, score_levels=[0, 0.1, 0.3, 0.5, 0.7, 0.9, 1.0]) -> None: self.score_levels = score_levels def score(self, input_df): self.score_levels.sort(reverse=True) quantile_df = input_df.groupby(level=1).quantile(self.score_levels) quantile_df.index.names = [self.time_field, "score_result"] self.logger.info("factor:{},quantile:\n{}".format(self.factor_name, quantile_df)) result_df = input_df.copy() result_df.reset_index(inplace=True, level="entity_id") result_df["quantile"] = None for timestamp in quantile_df.index.levels[0]: length = len(result_df.loc[result_df.index == timestamp, "quantile"]) result_df.loc[result_df.index == timestamp, "quantile"] = [quantile_df.loc[timestamp].to_dict()] * length self.logger.info("factor:{},df with quantile:\n{}".format(self.factor_name, result_df)) # result_df = result_df.set_index(['entity_id'], append=True) # result_df = result_df.sort_index(level=[0, 1]) # # self.logger.info(result_df) # def calculate_score(df, factor_name, quantile): original_value = df[factor_name] score_map = quantile.get(factor_name) min_score = self.score_levels[-1] if original_value < score_map.get(min_score): return 0 for score in self.score_levels[:-1]: if original_value >= score_map.get(score): return score for factor in input_df.columns.to_list(): result_df[factor] = result_df.apply(lambda x: calculate_score(x, factor, x["quantile"]), axis=1) result_df = result_df.reset_index() result_df = normal_index_df(result_df) result_df = result_df.loc[:, self.factors] result_df = result_df.loc[~result_df.index.duplicated(keep="first")] self.logger.info("factor:{},df:\n{}".format(self.factor_name, result_df)) return result_df # the __all__ is generated __all__ = [ "ma", "ema", "live_or_dead", "macd", "point_in_range", "intersect_ranges", "combine", "distance", "intersect", "RankScorer", "MaTransformer", "IntersectTransformer", "MaAndVolumeTransformer", "MacdTransformer", "QuantileScorer", ] ================================================ FILE: src/zvt/factors/factor_models.py ================================================ # -*- coding: utf-8 -*- from datetime import datetime from typing import List, Optional from pydantic import BaseModel, Field from zvt.contract import IntervalLevel from zvt.trader import TradingSignalType from zvt.utils.time_utils import date_time_by_interval, current_date class FactorRequestModel(BaseModel): factor_name: str entity_ids: Optional[List[str]] data_provider: str = Field(default="em") start_timestamp: datetime = Field(default=date_time_by_interval(current_date(), -365)) level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY) class TradingSignalModel(BaseModel): entity_id: str happen_timestamp: datetime due_timestamp: datetime trading_level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY) trading_signal_type: TradingSignalType position_pct: Optional[float] = Field(default=0.2) order_amount: Optional[float] = Field(default=None) order_money: Optional[float] = Field(default=None) class FactorResultModel(BaseModel): entity_ids: Optional[List[str]] tag_reason: str # the __all__ is generated __all__ = ["FactorRequestModel", "TradingSignalModel", "FactorResultModel"] ================================================ FILE: src/zvt/factors/factor_service.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract import zvt_context from zvt.domain import Stock from zvt.factors.factor_models import FactorRequestModel from zvt.factors.technical_factor import TechnicalFactor from zvt.trader import TradingSignalType def query_factor_result(factor_request_model: FactorRequestModel): factor_name = factor_request_model.factor_name entity_ids = factor_request_model.entity_ids level = factor_request_model.level factor: TechnicalFactor = zvt_context.factor_cls_registry[factor_name]( provider="em", entity_provider="em", entity_schema=Stock, entity_ids=entity_ids, level=level, start_timestamp=factor_request_model.start_timestamp, ) df = factor.get_trading_signal_df() df = df.reset_index(drop=False) def to_trading_signal(order_type): if order_type is None: return None if order_type: return TradingSignalType.open_long if not order_type: return TradingSignalType.close_long df = df.rename(columns={"timestamp": "happen_timestamp"}) df["due_timestamp"] = df["happen_timestamp"] + pd.Timedelta(seconds=level.to_second()) df["trading_signal_type"] = df["filter_result"].apply(lambda x: to_trading_signal(x)) print(df) return df.to_dict(orient="records") # the __all__ is generated __all__ = ["query_factor_result"] ================================================ FILE: src/zvt/factors/fundamental/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule finance_factor from .finance_factor import * from .finance_factor import __all__ as _finance_factor_all __all__ += _finance_factor_all ================================================ FILE: src/zvt/factors/fundamental/finance_factor.py ================================================ # -*- coding: utf-8 -*- import operator from itertools import accumulate from typing import List, Union, Type import pandas as pd from zvt.contract import IntervalLevel, Mixin, TradableEntity from zvt.contract.factor import Factor, Transformer, Accumulator from zvt.domain import FinanceFactor, BalanceSheet, Stock class FinanceBaseFactor(Factor): def __init__( self, data_schema: Type[Mixin] = FinanceFactor, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = None, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, ) -> None: if not columns: columns = data_schema.important_cols() super().__init__( data_schema, entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, ) class GoodCompanyFactor(FinanceBaseFactor): def __init__( self, data_schema: Type[Mixin] = FinanceFactor, entity_schema: TradableEntity = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = ( FinanceFactor.roe, FinanceFactor.op_income_growth_yoy, FinanceFactor.net_profit_growth_yoy, FinanceFactor.report_period, FinanceFactor.op_net_cash_flow_per_op_income, FinanceFactor.sales_net_cash_flow_per_op_income, FinanceFactor.current_ratio, FinanceFactor.debt_asset_ratio, ), filters: List = ( FinanceFactor.roe >= 0.02, FinanceFactor.op_income_growth_yoy >= 0.05, FinanceFactor.net_profit_growth_yoy >= 0.05, FinanceFactor.op_net_cash_flow_per_op_income >= 0.1, FinanceFactor.sales_net_cash_flow_per_op_income >= 0.3, FinanceFactor.current_ratio >= 1, FinanceFactor.debt_asset_ratio <= 0.5, ), order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = True, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, window="1095d", count=8, col_period_threshold={"roe": 0.02}, ) -> None: self.window = window self.count = count # 对于根据年度计算才有意义的指标，比如roe,我们会对不同季度的值区别处理,传入的参数为季度值 self.col_period_threshold = col_period_threshold if self.col_period_threshold: if "report_period" not in columns and (data_schema.report_period not in columns): columns.append(data_schema.report_period) self.logger.info(f"using data_schema:{data_schema.__name__}") super().__init__( data_schema, entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, ) def compute_factor(self): def filter_df(df): se = pd.Series(index=df.index) for index, row in df.iterrows(): if row.report_period == "year": mul = 4 elif row.report_period == "season3": mul = 3 elif row.report_period == "half_year": mul = 2 else: mul = 1 filters = [] for col in self.col_period_threshold: col_se = eval(f"row.{col}") filters.append(col_se >= mul * self.col_period_threshold[col]) se[index] = list(accumulate(filters, func=operator.__and__))[-1] return se if self.col_period_threshold: self.factor_df = self.data_df.loc[lambda df: filter_df(df), :] self.factor_df = pd.DataFrame(index=self.data_df.index, columns=["count"], data=1) self.factor_df = self.factor_df.reset_index(level=1) self.factor_df = self.factor_df.groupby(level=0).rolling(window=self.window, on=self.time_field).count() self.factor_df = self.factor_df.reset_index(level=0, drop=True) self.factor_df = self.factor_df.set_index(self.time_field, append=True) self.factor_df = self.factor_df.loc[(slice(None), slice(self.start_timestamp, self.end_timestamp)), :] self.logger.info("factor:{},factor_df:\n{}".format(self.name, self.factor_df)) def compute_result(self): self.result_df = self.factor_df.apply(lambda x: x >= self.count) self.result_df.columns = ["filter_score"] self.logger.info("factor:{},result_df:\n{}".format(self.name, self.result_df)) if __name__ == "__main__": # f1 = GoodCompanyFactor(keep_all_timestamp=False) # print(f1.result_df) # 高股息 低应收 factor2 = GoodCompanyFactor( data_schema=BalanceSheet, columns=[BalanceSheet.accounts_receivable], filters=[BalanceSheet.accounts_receivable <= 0.2 * BalanceSheet.total_current_assets], keep_all_timestamp=False, col_period_threshold=None, ) print(factor2.result_df) # the __all__ is generated __all__ = ["FinanceBaseFactor", "GoodCompanyFactor"] ================================================ FILE: src/zvt/factors/ma/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule ma_stats_factor from .ma_stats_factor import * from .ma_stats_factor import __all__ as _ma_stats_factor_all __all__ += _ma_stats_factor_all # import all from submodule top_bottom_factor from .top_bottom_factor import * from .top_bottom_factor import __all__ as _top_bottom_factor_all __all__ += _top_bottom_factor_all # import all from submodule ma_factor from .ma_factor import * from .ma_factor import __all__ as _ma_factor_all __all__ += _ma_factor_all # import all from submodule domain from .domain import * from .domain import __all__ as _domain_all __all__ += _domain_all ================================================ FILE: src/zvt/factors/ma/domain/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule stock_1d_ma_stats_factor from .stock_1d_ma_stats_factor import * from .stock_1d_ma_stats_factor import __all__ as _stock_1d_ma_stats_factor_all __all__ += _stock_1d_ma_stats_factor_all # import all from submodule stock_1d_ma_factor from .stock_1d_ma_factor import * from .stock_1d_ma_factor import __all__ as _stock_1d_ma_factor_all __all__ += _stock_1d_ma_factor_all # import all from submodule common from .common import * from .common import __all__ as _common_all __all__ += _common_all ================================================ FILE: src/zvt/factors/ma/domain/common.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Float, Integer from zvt.contract import Mixin class MaStatsFactorCommon(Mixin): open = Column(Float) close = Column(Float) high = Column(Float) low = Column(Float) turnover = Column(Float) ma5 = Column(Float) ma10 = Column(Float) ma34 = Column(Float) ma55 = Column(Float) ma89 = Column(Float) ma144 = Column(Float) ma120 = Column(Float) ma250 = Column(Float) vol_ma30 = Column(Float) live = Column(Integer) count = Column(Integer) distance = Column(Float) area = Column(Float) # the __all__ is generated __all__ = ["MaStatsFactorCommon"] ================================================ FILE: src/zvt/factors/ma/domain/stock_1d_ma_factor.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Float, String from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema Stock1dMaFactorBase = declarative_base() class Stock1dMaFactor(Stock1dMaFactorBase, Mixin): __tablename__ = "Stock1dMaFactor" level = Column(String(length=32)) code = Column(String(length=32)) name = Column(String(length=32)) open = Column(Float) close = Column(Float) high = Column(Float) low = Column(Float) ma5 = Column(Float) ma10 = Column(Float) ma34 = Column(Float) ma55 = Column(Float) ma89 = Column(Float) ma144 = Column(Float) ma120 = Column(Float) ma250 = Column(Float) register_schema(db_name="stock_1d_ma_factor", schema_base=Stock1dMaFactorBase, internal=True) # the __all__ is generated __all__ = ["Stock1dMaFactor"] ================================================ FILE: src/zvt/factors/ma/domain/stock_1d_ma_stats_factor.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.orm import declarative_base from zvt.contract.register import register_schema from zvt.factors.ma.domain.common import MaStatsFactorCommon Stock1dMaStatsFactorBase = declarative_base() class Stock1dMaStatsFactor(Stock1dMaStatsFactorBase, MaStatsFactorCommon): __tablename__ = "stock_1d_ma_stats_factor" register_schema(db_name="stock_1d_ma_stats_factor", schema_base=Stock1dMaStatsFactorBase, internal=True) # the __all__ is generated __all__ = ["Stock1dMaStatsFactor"] ================================================ FILE: src/zvt/factors/ma/ma_factor.py ================================================ # -*- coding: utf-8 -*- from typing import List, Union, Type import pandas as pd from zvt.contract import IntervalLevel, TradableEntity, AdjustType from zvt.contract.api import get_schema_by_name from zvt.contract.factor import Accumulator from zvt.contract.factor import Transformer from zvt.domain import Stock from zvt.factors.algorithm import MaTransformer, MaAndVolumeTransformer from zvt.factors.technical_factor import TechnicalFactor def get_ma_factor_schema(entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY): if type(level) == str: level = IntervalLevel(level) schema_str = "{}{}MaFactor".format(entity_type.capitalize(), level.value.capitalize()) return get_schema_by_name(schema_str) class MaFactor(TechnicalFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, windows=None, ) -> None: if need_persist: self.factor_schema = get_ma_factor_schema(entity_type=entity_schema.__name__, level=level) if not windows: windows = [5, 10, 34, 55, 89, 144, 120, 250] self.windows = windows transformer: Transformer = MaTransformer(windows=windows) super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, None, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) class CrossMaFactor(MaFactor): def compute_result(self): super().compute_result() cols = [f"ma{window}" for window in self.windows] s = self.factor_df[cols[0]] > self.factor_df[cols[1]] current_col = cols[1] for col in cols[2:]: s = s & (self.factor_df[current_col] > self.factor_df[col]) current_col = col print(self.factor_df[s]) self.result_df = s.to_frame(name="filter_result") class VolumeUpMaFactor(TechnicalFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, windows=None, vol_windows=None, turnover_threshold=300000000, turnover_rate_threshold=0.02, up_intervals=40, over_mode="and", ) -> None: if not windows: windows = [250] if not vol_windows: vol_windows = [30] self.windows = windows self.vol_windows = vol_windows self.turnover_threshold = turnover_threshold self.turnover_rate_threshold = turnover_rate_threshold self.up_intervals = up_intervals self.over_mode = over_mode transformer: Transformer = MaAndVolumeTransformer(windows=windows, vol_windows=vol_windows) super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) def compute_result(self): super().compute_result() # 价格刚上均线 cols = [f"ma{window}" for window in self.windows] filter_up = (self.factor_df["close"] > self.factor_df[cols[0]]) & ( self.factor_df["close"] < 1.15 * self.factor_df[cols[0]] ) for col in cols[1:]: if self.over_mode == "and": filter_up = filter_up & ( (self.factor_df["close"] > self.factor_df[col]) & (self.factor_df["close"] < 1.1 * self.factor_df[col]) ) else: filter_up = filter_up | ( (self.factor_df["close"] > self.factor_df[col]) & (self.factor_df["close"] < 1.1 * self.factor_df[col]) ) # 放量 if self.vol_windows: vol_cols = [f"vol_ma{window}" for window in self.vol_windows] filter_vol = self.factor_df["volume"] > 2 * self.factor_df[vol_cols[0]] for col in vol_cols[1:]: filter_vol = filter_vol & (self.factor_df["volume"] > 2 * self.factor_df[col]) # 成交额，换手率过滤 filter_turnover = (self.factor_df["turnover"] > self.turnover_threshold) & ( self.factor_df["turnover_rate"] > self.turnover_rate_threshold ) s = filter_up & filter_vol & filter_turnover # 突破后的时间周期 up_intervals s[s == False] = None s = s.groupby(level=0).fillna(method="ffill", limit=self.up_intervals) s[s.isna()] = False # 还在均线附近 # 1)刚突破 # 2)突破后，回调到附近 filter_result = filter_up & s & filter_turnover self.result_df = filter_result.to_frame(name="filter_result") # self.result_df = self.result_df.replace(False, None) class CrossMaVolumeFactor(VolumeUpMaFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, windows=[5, 10, 250], vol_windows=None, turnover_threshold=300000000, turnover_rate_threshold=0.02, up_intervals=40, over_mode="and", ) -> None: super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, windows, vol_windows, turnover_threshold, turnover_rate_threshold, up_intervals, over_mode, ) def compute_result(self): # 均线多头排列 cols = [f"ma{window}" for window in self.windows] filter_se = self.factor_df[cols[0]] > self.factor_df[cols[1]] current_col = cols[1] for col in cols[2:]: filter_se = filter_se & (self.factor_df[current_col] > self.factor_df[col]) current_col = col filter_se = filter_se & (self.factor_df["turnover"] > self.turnover_threshold) self.result_df = filter_se.to_frame(name="filter_result") # self.result_df = self.result_df.replace(False, None) if __name__ == "__main__": provider = "em" target_date = get_latest_kdata_date(entity_type="stock", provider=provider, adjust_type=AdjustType.qfq) factor = VolumeUpMaFactor( entity_schema=Stock, entity_provider=provider, provider=provider, entity_ids=None, start_timestamp=date_time_by_interval(target_date, -600), end_timestamp=target_date, adjust_type=AdjustType.qfq, windows=[120, 250], over_mode="or", up_intervals=60, turnover_threshold=300000000, turnover_rate_threshold=0.02, ) stocks = factor.get_targets(timestamp=target_date, target_type=TargetType.positive) # factor = CrossMaVolumeFactor( print(stocks) # entity_provider="em", # provider="em", # entity_ids=["stock_sz_000338"], # start_timestamp="2020-01-01", # end_timestamp=now_pd_timestamp(), # need_persist=False, # ) # factor.drawer().draw(show=True) # the __all__ is generated __all__ = ["get_ma_factor_schema", "MaFactor", "CrossMaFactor", "VolumeUpMaFactor", "CrossMaVolumeFactor"] ================================================ FILE: src/zvt/factors/ma/ma_stats_factor.py ================================================ # -*- coding: utf-8 -*- from typing import List, Union, Type, Optional import pandas as pd from zvt.contract import IntervalLevel, TradableEntity, AdjustType from zvt.contract.api import get_schema_by_name from zvt.contract.factor import Accumulator from zvt.domain import Stock from zvt.factors.algorithm import live_or_dead from zvt.factors.technical_factor import TechnicalFactor from zvt.utils.pd_utils import pd_is_not_null def get_ma_stats_factor_schema(entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY): if type(level) == str: level = IntervalLevel(level) schema_str = "{}{}MaStatsFactor".format(entity_type.capitalize(), level.value.capitalize()) return get_schema_by_name(schema_str) class MaStatsAccumulator(Accumulator): def __init__(self, acc_window: int = 250, windows=None, vol_windows=None) -> None: super().__init__(acc_window) self.windows = windows self.vol_windows = vol_windows def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame, state: dict) -> (pd.DataFrame, dict): self.logger.info(f"acc_one:{entity_id}") if pd_is_not_null(acc_df): df = df[df.index > acc_df.index[-1]] if pd_is_not_null(df): self.logger.info(f'compute from {df.iloc[0]["timestamp"]}') acc_df = pd.concat([acc_df, df]) else: self.logger.info("no need to compute") return acc_df, state else: acc_df = df for window in self.windows: col = "ma{}".format(window) self.indicators.append(col) ma_df = acc_df["close"].rolling(window=window, min_periods=window).mean() acc_df[col] = ma_df acc_df["live"] = (acc_df["ma5"] > acc_df["ma10"]).apply(lambda x: live_or_dead(x)) acc_df["distance"] = (acc_df["ma5"] - acc_df["ma10"]) / acc_df["close"] live = acc_df["live"] acc_df["count"] = live * (live.groupby((live != live.shift()).cumsum()).cumcount() + 1) acc_df["bulk"] = (live != live.shift()).cumsum() area_df = acc_df[["distance", "bulk"]] acc_df["area"] = area_df.groupby("bulk").cumsum() for vol_window in self.vol_windows: col = "vol_ma{}".format(vol_window) self.indicators.append(col) vol_ma_df = acc_df["turnover"].rolling(window=vol_window, min_periods=vol_window).mean() acc_df[col] = vol_ma_df acc_df = acc_df.set_index("timestamp", drop=False) return acc_df, state class MaStatsFactor(TechnicalFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, need_persist: bool = True, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, windows=None, vol_windows=None, ) -> None: if need_persist: self.factor_schema = get_ma_stats_factor_schema(entity_type=entity_schema.__name__, level=level) if not windows: windows = [5, 10, 34, 55, 89, 144, 120, 250] self.windows = windows if not vol_windows: vol_windows = [30] self.vol_windows = vol_windows columns: List = ["id", "entity_id", "timestamp", "level", "open", "close", "high", "low", "turnover"] accumulator: Accumulator = MaStatsAccumulator(windows=self.windows, vol_windows=self.vol_windows) super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, None, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) class TFactor(MaStatsFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, need_persist: bool = True, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = True, adjust_type: Union[AdjustType, str] = None, windows=None, vol_windows=None, ) -> None: super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, windows, vol_windows, ) def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: return [self.factor_df[["area"]]] def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: return [self.factor_df[["ma5", "ma10"]]] if __name__ == "__main__": codes = ["000338"] f = TFactor(codes=codes, only_load_factor=False) # distribute(f.factor_df[['area']],'area') f.draw(show=True) # the __all__ is generated __all__ = ["get_ma_stats_factor_schema", "MaStatsAccumulator", "MaStatsFactor", "TFactor"] ================================================ FILE: src/zvt/factors/ma/top_bottom_factor.py ================================================ # -*- coding: utf-8 -*- from typing import List, Union import pandas as pd from zvt.contract import AdjustType from zvt.contract import IntervalLevel, TradableEntity from zvt.contract.drawer import Drawer from zvt.contract.factor import Accumulator from zvt.contract.factor import Transformer from zvt.contract.reader import DataReader from zvt.domain import Stock, Stock1dKdata from zvt.factors.technical_factor import TechnicalFactor from zvt.utils.time_utils import now_pd_timestamp class TopBottomTransformer(Transformer): def __init__(self, window=20) -> None: super().__init__() self.window = window def transform(self, input_df) -> pd.DataFrame: top_df = input_df["high"].groupby(level=0).rolling(window=self.window, min_periods=self.window).max() top_df = top_df.reset_index(level=0, drop=True) input_df["top"] = top_df bottom_df = input_df["low"].groupby(level=0).rolling(window=self.window, min_periods=self.window).min() bottom_df = bottom_df.reset_index(level=0, drop=True) input_df["bottom"] = bottom_df return input_df class TopBottomFactor(TechnicalFactor): def __init__( self, entity_schema: TradableEntity = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = ["id", "entity_id", "timestamp", "level", "open", "close", "high", "low"], filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, window=30, ) -> None: transformer = TopBottomTransformer(window=window) super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) if __name__ == "__main__": factor = TopBottomFactor( codes=["601318"], start_timestamp="2005-01-01", end_timestamp=now_pd_timestamp(), level=IntervalLevel.LEVEL_1DAY, window=120, ) print(factor.factor_df) data_reader1 = DataReader(data_schema=Stock1dKdata, entity_schema=Stock, codes=["601318"]) drawer = Drawer(main_df=data_reader1.data_df, factor_df_list=[factor.factor_df[["top", "bottom"]]]) drawer.draw_kline(show=True) # the __all__ is generated __all__ = ["TopBottomTransformer", "TopBottomFactor"] ================================================ FILE: src/zvt/factors/macd/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule macd_factor from .macd_factor import * from .macd_factor import __all__ as _macd_factor_all __all__ += _macd_factor_all ================================================ FILE: src/zvt/factors/macd/macd_factor.py ================================================ # -*- coding: utf-8 -*- from typing import List, Optional import numpy as np import pandas as pd from zvt.factors.algorithm import MacdTransformer from zvt.factors.technical_factor import TechnicalFactor class MacdFactor(TechnicalFactor): transformer = MacdTransformer(count_live_dead=True) def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: return None def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: return [self.factor_df[["diff", "dea", "macd"]]] def drawer_sub_col_chart(self) -> Optional[dict]: return {"diff": "line", "dea": "line", "macd": "bar"} class BullFactor(MacdFactor): def compute_result(self): super().compute_result() self.result_df = self.factor_df["bull"].to_frame(name="filter_result") class KeepBullFactor(BullFactor): keep_window = 10 def compute_result(self): super().compute_result() df = ( self.result_df["filter_result"] .groupby(level=0) .rolling(window=self.keep_window, min_periods=self.keep_window) .apply(lambda x: np.logical_and.reduce(x)) ) df = df.reset_index(level=0, drop=True) self.result_df["filter_result"] = df # 金叉 死叉 持续时间 切换点 class LiveOrDeadFactor(MacdFactor): pattern = [-5, 1] def compute_result(self): super().compute_result() self.factor_df["pre"] = self.factor_df["live_count"].shift() s = (self.factor_df["pre"] <= self.pattern[0]) & (self.factor_df["live_count"] >= self.pattern[1]) self.result_df = s.to_frame(name="filter_result") class GoldCrossFactor(MacdFactor): def compute_result(self): super().compute_result() s = self.factor_df["live"] == 1 self.result_df = s.to_frame(name="filter_result") if __name__ == "__main__": f = GoldCrossFactor(provider="em", entity_provider="em", entity_ids=["stock_sz_000338"]) f.drawer().draw(show=True) # the __all__ is generated __all__ = ["MacdFactor", "BullFactor", "KeepBullFactor", "LiveOrDeadFactor", "GoldCrossFactor"] ================================================ FILE: src/zvt/factors/shape.py ================================================ # -*- coding: utf-8 -*- import json import logging from enum import Enum from typing import List import pandas as pd from zvt.contract.data_type import Bean from zvt.contract.drawer import Rect from zvt.factors.algorithm import intersect from zvt.utils.time_utils import TIME_FORMAT_ISO8601, to_date_time_str logger = logging.getLogger(__name__) class Direction(Enum): up = "up" down = "down" def opposite(self): if self == Direction.up: return Direction.down if self == Direction.down: return Direction.up class Fenxing(Bean): def __init__(self, state, kdata, index) -> None: self.state = state self.kdata = kdata self.index = index def fenxing_power(left, middle, right, fenxing="tmp_ding"): if fenxing == "tmp_ding": a = middle["high"] - middle["close"] b = middle["high"] - left["high"] c = middle["high"] - right["high"] return -(a + b + c) / middle["close"] if fenxing == "tmp_di": a = abs(middle["low"] - middle["close"]) b = abs(middle["low"] - left["low"]) c = abs(middle["low"] - right["low"]) return (a + b + c) / middle["close"] def a_include_b(a: pd.Series, b: pd.Series) -> bool: """ kdata a includes kdata b :param a: :param b: :return: """ return (a["high"] >= b["high"]) and (a["low"] <= b["low"]) def get_direction(kdata, pre_kdata, current=Direction.up) -> Direction: if is_up(kdata, pre_kdata): return Direction.up if is_down(kdata, pre_kdata): return Direction.down return current def is_up(kdata, pre_kdata): return kdata["high"] > pre_kdata["high"] def is_down(kdata, pre_kdata): return kdata["low"] < pre_kdata["low"] def handle_first_fenxing(one_df, step=11): if step >= len(one_df): logger.info(f"coult not get fenxing by step {step}, len {len(one_df)}") return None, None, None, None logger.info(f"try to get first fenxing by step {step}") df = one_df.iloc[:step] ding_kdata = df[df["high"].max() == df["high"]] ding_index = int(ding_kdata.index[-1]) di_kdata = df[df["low"].min() == df["low"]] di_index = int(di_kdata.index[-1]) # 确定第一个分型 if abs(ding_index - di_index) >= 4: if ding_index > di_index: fenxing = "bi_di" fenxing_index = di_index one_df.loc[di_index, "bi_di"] = True # 确定第一个分型后，开始遍历的位置 start_index = ding_index # 目前的笔的方向，up代表寻找 can_ding;down代表寻找can_di direction = Direction.up interval = ding_index - di_index else: fenxing = "bi_ding" fenxing_index = ding_index one_df.loc[ding_index, "bi_ding"] = True start_index = di_index direction = Direction.down interval = di_index - ding_index return ( Fenxing( state=fenxing, index=fenxing_index, kdata={ "low": float(one_df.loc[fenxing_index]["low"]), "high": float(one_df.loc[fenxing_index]["high"]), }, ), start_index, direction, interval, ) else: logger.info("need add step") return handle_first_fenxing(one_df, step=step + 1) def handle_zhongshu(points: list, acc_df, end_index, zhongshu_col="zhongshu", zhongshu_change_col="zhongshu_change"): zhongshu = None zhongshu_change = None interval = None if len(points) == 4: x1 = points[0][0] x2 = points[3][0] interval = points[3][2] - points[0][2] if points[0][1] < points[1][1]: # 向下段 range = intersect((points[0][1], points[1][1]), (points[2][1], points[3][1])) if range: y1, y2 = range # 记录中枢 zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2) zhongshu_change = abs(y1 - y2) / y1 acc_df.loc[end_index, zhongshu_col] = zhongshu acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change points = points[-1:] else: points = points[1:] else: # 向上段 range = intersect((points[1][1], points[0][1]), (points[3][1], points[2][1])) if range: y1, y2 = range # 记录中枢 zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2) zhongshu_change = abs(y1 - y2) / y1 acc_df.loc[end_index, zhongshu_col] = zhongshu acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change points = points[-1:] else: points = points[1:] return points, zhongshu, zhongshu_change, interval def handle_duan(fenxing_list: List[Fenxing], pre_duan_state="yi"): state = fenxing_list[0].state # 1笔区间 bi1_start = fenxing_list[0].kdata bi1_end = fenxing_list[1].kdata # 3笔区间 bi3_start = fenxing_list[2].kdata bi3_end = fenxing_list[3].kdata if state == "bi_ding": # 向下段,下-上-下 # 第一笔区间 range1 = (bi1_end["low"], bi1_start["high"]) # 第三笔区间 range3 = (bi3_end["low"], bi3_start["high"]) # 1,3有重叠，认为第一个段出现 if intersect(range1, range3): return "down" else: # 向上段，上-下-上 # 第一笔区间 range1 = (bi1_start["low"], bi1_end["high"]) # 第三笔区间 range3 = (bi3_start["low"], bi3_end["high"]) # 1,3有重叠，认为第一个段出现 if intersect(range1, range3): return "up" return pre_duan_state def handle_including(one_df, index, kdata, pre_index, pre_kdata, tmp_direction: Direction): # 改kdata if a_include_b(kdata, pre_kdata): # 长的kdata变短 if tmp_direction == Direction.up: one_df.loc[index, "low"] = pre_kdata["low"] else: one_df.loc[index, "high"] = pre_kdata["high"] # 改pre_kdata elif a_include_b(pre_kdata, kdata): # 长的pre_kdata变短 if tmp_direction == Direction.down: one_df.loc[pre_index, "low"] = kdata["low"] else: one_df.loc[pre_index, "high"] = kdata["high"] class FactorStateEncoder(json.JSONEncoder): def default(self, object): if isinstance(object, pd.Series): return object.to_dict() elif isinstance(object, pd.Timestamp): return to_date_time_str(object, fmt=TIME_FORMAT_ISO8601) elif isinstance(object, Enum): return object.value elif isinstance(object, Bean): return object.dict() else: return super().default(object) def decode_rect(dct): return Rect(x0=dct["x0"], y0=dct["y0"], x1=dct["x1"], y1=dct["y1"]) def decode_fenxing(dct): return Fenxing(state=dct["state"], kdata=dct["kdata"], index=dct["index"]) # the __all__ is generated __all__ = [ "Direction", "Fenxing", "fenxing_power", "a_include_b", "get_direction", "is_up", "is_down", "handle_first_fenxing", "handle_zhongshu", "handle_duan", "handle_including", "FactorStateEncoder", "decode_rect", "decode_fenxing", ] ================================================ FILE: src/zvt/factors/target_selector.py ================================================ import operator from enum import Enum from itertools import accumulate from typing import List, Optional import pandas as pd from pandas import DataFrame from zvt.contract import IntervalLevel from zvt.contract.factor import Factor from zvt.domain.meta.stock_meta import Stock from zvt.utils.pd_utils import index_df, pd_is_not_null, is_filter_result_df, is_score_result_df from zvt.utils.time_utils import to_pd_timestamp, now_pd_timestamp class TradeType(Enum): # open_long 代表开多，并应该平掉相应标的的空单 open_long = "open_long" # open_short 代表开空，并应该平掉相应标的的多单 open_short = "open_short" # keep 代表保持现状，跟主动开仓有区别，有时有仓位是可以保持的，但不适合开新的仓 keep = "keep" class SelectMode(Enum): condition_and = "condition_and" condition_or = "condition_or" class TargetSelector(object): def __init__( self, entity_ids=None, entity_schema=Stock, exchanges=None, codes=None, start_timestamp=None, end_timestamp=None, long_threshold=0.8, short_threshold=0.2, level=IntervalLevel.LEVEL_1DAY, provider=None, select_mode: SelectMode = SelectMode.condition_and, ) -> None: self.entity_ids = entity_ids self.entity_schema = entity_schema self.exchanges = exchanges self.codes = codes self.provider = provider self.select_mode = select_mode if start_timestamp: self.start_timestamp = to_pd_timestamp(start_timestamp) if end_timestamp: self.end_timestamp = to_pd_timestamp(end_timestamp) else: self.end_timestamp = now_pd_timestamp() self.long_threshold = long_threshold self.short_threshold = short_threshold self.level = level self.factors: List[Factor] = [] self.filter_result = None self.score_result = None self.open_long_df: Optional[DataFrame] = None self.open_short_df: Optional[DataFrame] = None self.keep_df: Optional[DataFrame] = None self.init_factors( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, level=self.level, ) def init_factors(self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, level): pass def add_factor(self, factor: Factor): self.check_factor(factor) self.factors.append(factor) return self def check_factor(self, factor: Factor): assert factor.level == self.level def move_on(self, to_timestamp=None, kdata_use_begin_time=False, timeout=20): if self.factors: for factor in self.factors: factor.move_on(to_timestamp, timeout=timeout) self.run() def run(self): """ """ if self.factors: filters = [] scores = [] for factor in self.factors: if is_filter_result_df(factor.result_df): df = factor.result_df[["filter_result"]] if pd_is_not_null(df): df.columns = ["score"] filters.append(df) else: raise Exception("no data for factor:{},{}".format(factor.name, factor)) if is_score_result_df(factor.result_df): df = factor.result_df[["score_result"]] if pd_is_not_null(df): df.columns = ["score"] scores.append(df) else: raise Exception("no data for factor:{},{}".format(factor.name, factor)) if filters: if self.select_mode == SelectMode.condition_and: self.filter_result = list(accumulate(filters, func=operator.__and__))[-1] else: self.filter_result = list(accumulate(filters, func=operator.__or__))[-1] if scores: self.score_result = list(accumulate(scores, func=operator.__add__))[-1] / len(scores) self.generate_targets() def get_targets(self, timestamp, trade_type: TradeType = TradeType.open_long) -> List[str]: if trade_type == TradeType.open_long: df = self.open_long_df elif trade_type == TradeType.open_short: df = self.open_short_df elif trade_type == TradeType.keep: df = self.keep_df else: assert False if pd_is_not_null(df): if timestamp in df.index: target_df = df.loc[[to_pd_timestamp(timestamp)], :] return target_df["entity_id"].tolist() return [] def get_targets_between( self, start_timestamp, end_timestamp, trade_type: TradeType = TradeType.open_long ) -> List[str]: if trade_type == TradeType.open_long: df = self.open_long_df elif trade_type == TradeType.open_short: df = self.open_short_df elif trade_type == TradeType.keep: df = self.keep_df else: assert False if pd_is_not_null(df): index = pd.date_range(start_timestamp, end_timestamp, freq=self.level.to_pd_freq()) return list(set(df.loc[df.index & index]["entity_id"].tolist())) return [] def get_open_long_targets(self, timestamp): return self.get_targets(timestamp=timestamp, trade_type=TradeType.open_long) def get_open_short_targets(self, timestamp): return self.get_targets(timestamp=timestamp, trade_type=TradeType.open_short) # overwrite it to generate targets def generate_targets(self): keep_result = pd.DataFrame() long_result = pd.DataFrame() short_result = pd.DataFrame() if pd_is_not_null(self.filter_result): keep_result = self.filter_result[self.filter_result["score"].isna()] long_result = self.filter_result[self.filter_result["score"] == True] short_result = self.filter_result[self.filter_result["score"] == False] if pd_is_not_null(self.score_result): score_keep_result = self.score_result[ (self.score_result["score"] > self.short_threshold) & (self.score_result["score"] < self.long_threshold) ] if pd_is_not_null(keep_result): keep_result = score_keep_result.loc[keep_result.index, :] else: keep_result = score_keep_result score_long_result = self.score_result[self.score_result["score"] >= self.long_threshold] if pd_is_not_null(long_result): long_result = score_long_result.loc[long_result.index, :] else: long_result = score_long_result score_short_result = self.score_result[self.score_result["score"] <= self.short_threshold] if pd_is_not_null(short_result): short_result = score_short_result.loc[short_result.index, :] else: short_result = score_short_result self.keep_df = self.normalize_result_df(keep_result) self.open_long_df = self.normalize_result_df(long_result) self.open_short_df = self.normalize_result_df(short_result) def get_result_df(self): return self.open_long_df def normalize_result_df(self, df): if pd_is_not_null(df): df = df.reset_index() df = index_df(df) df = df.sort_values(by=["score", "entity_id"]) return df # the __all__ is generated __all__ = ["TradeType", "SelectMode", "TargetSelector"] ================================================ FILE: src/zvt/factors/technical_factor.py ================================================ from typing import List, Union, Type, Optional import pandas as pd from zvt.api.kdata import get_kdata_schema, default_adjust_type from zvt.contract import IntervalLevel, TradableEntity, AdjustType from zvt.contract.factor import Factor, Transformer, Accumulator, FactorMeta from zvt.domain import Stock class TechnicalFactor(Factor, metaclass=FactorMeta): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, ) -> None: if columns is None: columns = [ "id", "entity_id", "timestamp", "level", "open", "close", "high", "low", "volume", "turnover", "turnover_rate", ] # 股票默认使用后复权 if not adjust_type: adjust_type = default_adjust_type(entity_type=entity_schema.__name__) self.adjust_type = adjust_type self.data_schema = get_kdata_schema(entity_schema.__name__, level=level, adjust_type=adjust_type) if not factor_name: if type(level) == str: factor_name = f"{type(self).__name__.lower()}_{level}" else: factor_name = f"{type(self).__name__.lower()}_{level.value}" super().__init__( self.data_schema, entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, ) def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: return [self.factor_df[["volume"]]] # the __all__ is generated __all__ = ["TechnicalFactor"] ================================================ FILE: src/zvt/factors/top_stocks.py ================================================ # -*- coding: utf-8 -*- import json import logging from typing import List from sqlalchemy import Column, String, Integer from sqlalchemy.orm import declarative_base from zvt.api.kdata import get_trade_dates, get_kdata_schema from zvt.api.selector import ( get_entity_ids_by_filter, get_limit_up_stocks, get_mini_and_small_stock, get_middle_and_big_stock, get_recent_active_stocks, get_recent_trending_stocks, ) from zvt.api.stats import get_top_performance_entities_by_periods, TopType from zvt.contract import Mixin, AdjustType from zvt.contract.api import get_db_session from zvt.contract.factor import TargetType from zvt.contract.register import register_schema from zvt.domain import Stock from zvt.factors.ma.ma_factor import VolumeUpMaFactor from zvt.utils.time_utils import ( date_time_by_interval, to_pd_timestamp, to_date_time_str, TIME_FORMAT_DAY, next_date, ) logger = logging.getLogger(__name__) TopStocksBase = declarative_base() class TopStocks(TopStocksBase, Mixin): __tablename__ = "top_stocks" entity_type = Column(String(length=64)) short_count = Column(Integer) short_stocks = Column(String(length=2048)) long_count = Column(Integer) long_stocks = Column(String(length=2048)) small_vol_up_count = Column(Integer) small_vol_up_stocks = Column(String(length=2048)) big_vol_up_count = Column(Integer) big_vol_up_stocks = Column(String(length=2048)) all_stocks_count = Column(Integer) register_schema(db_name="top_stocks", schema_base=TopStocksBase, internal=True) def compute_vol_up_stocks(target_date, provider="em", adjust_type=AdjustType.qfq, stock_type="small", entity_ids=None): if stock_type == "small": current_entity_pool = get_mini_and_small_stock( timestamp=target_date, provider=provider, adjust_type=adjust_type ) turnover_threshold = 500000000 turnover_rate_threshold = 0.02 elif stock_type == "big": current_entity_pool = get_middle_and_big_stock( timestamp=target_date, provider=provider, adjust_type=adjust_type ) turnover_threshold = 500000000 turnover_rate_threshold = 0.01 else: assert False if entity_ids: current_entity_pool = set(current_entity_pool) & set(entity_ids) kdata_schema = get_kdata_schema(entity_type="stock", level="1d", adjust_type=adjust_type) filters = [ kdata_schema.timestamp == to_pd_timestamp(target_date), kdata_schema.turnover >= turnover_threshold, kdata_schema.turnover_rate >= turnover_rate_threshold, ] kdata_df = kdata_schema.query_data( provider=provider, filters=filters, columns=["entity_id", "timestamp"], index="entity_id" ) if current_entity_pool: current_entity_pool = set(current_entity_pool) & set(kdata_df.index.tolist()) else: current_entity_pool = kdata_df.index.tolist() factor = VolumeUpMaFactor( entity_schema=Stock, entity_provider=provider, provider=provider, entity_ids=current_entity_pool, start_timestamp=date_time_by_interval(target_date, -600), end_timestamp=target_date, adjust_type=adjust_type, windows=[120, 250], over_mode="or", up_intervals=60, turnover_threshold=turnover_threshold, turnover_rate_threshold=turnover_rate_threshold, ) stocks = factor.get_targets(timestamp=target_date, target_type=TargetType.positive) return stocks def compute_top_stocks(start_date, entity_type="stock", provider="em", force_update=False, adjust_type=AdjustType.qfq): session = get_db_session(provider="zvt", data_schema=TopStocks) if force_update: session.query(TopStocks).filter(TopStocks.timestamp >= start_date).filter( TopStocks.entity_type == entity_type ).delete() else: latest = TopStocks.query_data( limit=1, filters=[TopStocks.entity_type == entity_type], order=TopStocks.timestamp.desc(), return_type="domain", ) if latest: start_date = next_date(the_time=to_date_time_str(latest[0].timestamp, fmt=TIME_FORMAT_DAY)) trade_days = get_trade_dates(entity_type=entity_type, start=start_date) for target_date in trade_days: top_stocks = TopStocks( entity_type=entity_type, entity_id=f"{entity_type}_top", id=f"{entity_type}_top_{target_date}", timestamp=target_date, ) entity_ids = get_entity_ids_by_filter( entity_type=entity_type, target_date=target_date, provider="em", ) turnover_threshold = 0 if entity_type == "stockus" or entity_type == "stockhk": turnover_threshold = 10000000 short_selected, short_period = get_top_performance_entities_by_periods( entity_provider="em", data_provider=provider, target_date=target_date, periods=[*range(1, 20)], ignore_new_stock=False, ignore_st=False, entity_ids=entity_ids, entity_type=entity_type, adjust_type=adjust_type, top_count=30, turnover_threshold=turnover_threshold, turnover_rate_threshold=0, return_type=TopType.positive, ) if entity_type == "stock": limit_up_stocks = get_limit_up_stocks(timestamp=target_date) logger.info(f"limit_up_stocks got: {len(limit_up_stocks)}") recent_active_stocks = get_recent_active_stocks(adjust_type=adjust_type, provider=provider, recent_days=10) logger.info(f"recent_active_stocks got: {len(recent_active_stocks)}") recent_trending_stocks = get_recent_trending_stocks( adjust_type=adjust_type, provider=provider, recent_days=20 ) logger.info(f"recent_trending_stocks got: {len(recent_trending_stocks)}") short_selected = list(set(short_selected + limit_up_stocks + recent_active_stocks + recent_trending_stocks)) top_stocks.short_count = len(short_selected) top_stocks.short_stocks = json.dumps(short_selected, ensure_ascii=False) long_period_start = short_period + 1 long_selected, long_period = get_top_performance_entities_by_periods( entity_provider="em", data_provider=provider, target_date=target_date, periods=[*range(long_period_start, long_period_start + 30)], ignore_new_stock=False, ignore_st=False, entity_ids=entity_ids, entity_type=entity_type, adjust_type=adjust_type, top_count=30, turnover_threshold=turnover_threshold, turnover_rate_threshold=0, return_type=TopType.positive, ) top_stocks.long_count = len(long_selected) top_stocks.long_stocks = json.dumps(long_selected, ensure_ascii=False) if entity_type == "stock": small_vol_up_stocks = compute_vol_up_stocks( target_date=target_date, provider=provider, stock_type="small", entity_ids=entity_ids ) top_stocks.small_vol_up_count = len(small_vol_up_stocks) top_stocks.small_vol_up_stocks = json.dumps(small_vol_up_stocks, ensure_ascii=False) big_vol_up_stocks = compute_vol_up_stocks( target_date=target_date, provider=provider, stock_type="big", entity_ids=entity_ids ) top_stocks.big_vol_up_count = len(big_vol_up_stocks) top_stocks.big_vol_up_stocks = json.dumps(big_vol_up_stocks, ensure_ascii=False) top_stocks.all_stocks_count = len(entity_ids) session.add(top_stocks) session.commit() def get_top_stocks(target_date, entity_type="stock", return_type="short"): datas: List[TopStocks] = TopStocks.query_data( filters=[TopStocks.entity_type == entity_type, TopStocks.timestamp == to_pd_timestamp(target_date)], return_type="domain", ) stocks = [] if datas: assert len(datas) == 1 top_stock = datas[0] if return_type == "all": short_stocks = json.loads(top_stock.short_stocks) if top_stock.short_stocks else [] long_stocks = json.loads(top_stock.long_stocks) if top_stock.long_stocks else [] small_vol_up_stocks = json.loads(top_stock.small_vol_up_stocks) if top_stock.small_vol_up_stocks else [] big_vol_up_stocks = json.loads(top_stock.big_vol_up_stocks) if top_stock.big_vol_up_stocks else [] all_stocks = list(set(short_stocks + long_stocks + small_vol_up_stocks + big_vol_up_stocks)) return all_stocks elif return_type == "short": stocks = json.loads(top_stock.short_stocks) if top_stock.short_stocks else [] elif return_type == "long": stocks = json.loads(top_stock.long_stocks) if top_stock.long_stocks else [] elif return_type == "small_vol_up": stocks = json.loads(top_stock.small_vol_up_stocks) if top_stock.small_vol_up_stocks else [] elif return_type == "big_vol_up": stocks = json.loads(top_stock.big_vol_up_stocks) if top_stock.big_vol_up_stocks else [] else: assert False return stocks if __name__ == "__main__": print(get_top_stocks(entity_type="stockus", target_date="2025-07-25", return_type="all")) # the __all__ is generated __all__ = [ "TopStocks", "compute_vol_up_stocks", "compute_top_stocks", "get_top_stocks", ] ================================================ FILE: src/zvt/factors/transformers.py ================================================ # -*- coding: utf-8 -*- import numpy as np import pandas as pd from zvt.contract.factor import Transformer from zvt.factors.algorithm import MaTransformer from zvt.factors.technical_factor import TechnicalFactor from zvt.utils.pd_utils import group_by_entity_id, normalize_group_compute_result, merge_filter_result from zvt.utils.time_utils import to_pd_timestamp def _cal_state(s, df, pre, interval, col): assert len(s) == pre + interval s = df.loc[s.index, :] pre_df: pd.DataFrame = s.iloc[:pre, :] recent_df: pd.DataFrame = s.iloc[-interval:, :] if pre_df.isnull().values.any() or recent_df.isnull().values.any(): return np.nan pre_result = np.logical_and.reduce(pre_df["close"] > pre_df[col]) recent_result = np.logical_and.reduce(recent_df["close"] < recent_df[col]) if pre_result and recent_result: return True return np.nan class CrossMaTransformer(MaTransformer): def __init__(self, windows=None, cal_change_pct=False) -> None: super().__init__(windows, cal_change_pct) def transform(self, input_df: pd.DataFrame) -> pd.DataFrame: input_df = super().transform(input_df) cols = [f"ma{window}" for window in self.windows] s = input_df[cols[0]] > input_df[cols[1]] current_col = cols[1] for col in cols[2:]: s = s & (input_df[current_col] > input_df[col]) current_col = col input_df["filter_result"] = s return input_df class SpecificTransformer(Transformer): def __init__(self, buy_timestamp, sell_timestamp) -> None: self.buy_timestamp = to_pd_timestamp(buy_timestamp) self.sell_timestamp = to_pd_timestamp(sell_timestamp) def transform(self, input_df: pd.DataFrame) -> pd.DataFrame: s = input_df[input_df.get_level_values["timestamp"] == self.buy_timestamp] s[s == False] = None s[input_df.get_level_values["timestamp"] == self.sell_timestamp] = False input_df["filter_result"] = s return input_df class FallBelowTransformer(Transformer): def __init__(self, window=10, interval=3) -> None: super().__init__() self.window = window self.interval = interval def transform(self, input_df: pd.DataFrame) -> pd.DataFrame: col = f"ma{self.window}" if col not in input_df.columns: group_result = ( group_by_entity_id(input_df["close"]).rolling(window=self.window, min_periods=self.window).mean() ) group_result = normalize_group_compute_result(group_result=group_result) input_df[col] = group_result # 连续3(interval)日收在10(window)日线下 s = input_df["close"] < input_df[col] s = ( group_by_entity_id(s) .rolling(window=self.interval, min_periods=self.interval) .apply(lambda x: np.logical_and.reduce(x)) ) s = normalize_group_compute_result(group_result=s) # 构造卖点 s[s == False] = None s[s == True] = False input_df = merge_filter_result(input_df=input_df, filter_result=s) return input_df if __name__ == "__main__": # df = Stock1dHfqKdata.query_data(codes=["000338"], index=["entity_id", "timestamp"]) # df = FallBelowTransformer().transform(df) # print(df["filter_result"]) TechnicalFactor(transformer=SpecificTransformer(timestamp="2020-03-01")) # the __all__ is generated __all__ = ["CrossMaTransformer", "SpecificTransformer", "FallBelowTransformer"] ================================================ FILE: src/zvt/factors/zen/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule zen_factor from .zen_factor import * from .zen_factor import __all__ as _zen_factor_all __all__ += _zen_factor_all # import all from submodule base_factor from .base_factor import * from .base_factor import __all__ as _base_factor_all __all__ += _base_factor_all # import all from submodule domain from .domain import * from .domain import __all__ as _domain_all __all__ += _domain_all ================================================ FILE: src/zvt/factors/zen/base_factor.py ================================================ # -*- coding: utf-8 -*- import json import logging from enum import Enum from typing import List from typing import Union, Optional, Type import numpy as np import pandas as pd from zvt.contract import IntervalLevel, AdjustType from zvt.contract import TradableEntity from zvt.contract.api import get_schema_by_name from zvt.contract.data_type import Bean from zvt.contract.drawer import Rect from zvt.contract.factor import Accumulator from zvt.contract.factor import Transformer from zvt.domain import Stock, Index, Index1dKdata from zvt.factors.algorithm import intersect, combine from zvt.factors.shape import ( Fenxing, Direction, handle_first_fenxing, decode_rect, get_direction, handle_including, fenxing_power, handle_duan, ) from zvt.factors.technical_factor import TechnicalFactor from zvt.utils.decorator import to_string from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import TIME_FORMAT_ISO8601, to_date_time_str logger = logging.getLogger(__name__) class FactorStateEncoder(json.JSONEncoder): def default(self, object): if isinstance(object, pd.Series): return object.to_dict() elif isinstance(object, pd.Timestamp): return to_date_time_str(object, fmt=TIME_FORMAT_ISO8601) elif isinstance(object, Enum): return object.value elif isinstance(object, Bean): return object.dict() else: return super().default(object) def get_zen_factor_schema(entity_type: str, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY): if type(level) == str: level = IntervalLevel(level) # z factor schema rule # 1)name:{SecurityType.value.capitalize()}{IntervalLevel.value.upper()}ZFactor schema_str = "{}{}ZenFactor".format(entity_type.capitalize(), level.value.capitalize()) return get_schema_by_name(schema_str) @to_string class ZenState(Bean): def __init__(self, state: dict = None) -> None: super().__init__() if not state: state = dict() # 用于计算未完成段的分型 self.fenxing_list = state.get("fenxing_list", []) fenxing_list = [Fenxing(item["state"], item["kdata"], item["index"]) for item in self.fenxing_list] self.fenxing_list = fenxing_list # 目前的方向 if state.get("direction"): self.direction = Direction(state.get("direction")) else: self.direction = None # 候选分型(candidate) self.can_fenxing = state.get("can_fenxing") self.can_fenxing_index = state.get("can_fenxing_index") # 反方向count self.opposite_count = state.get("opposite_count", 0) # 目前段的方向 self.current_duan_state = state.get("current_duan_state", "yi") # 记录用于计算中枢的段 # list of (timestamp,value) self.duans = state.get("duans", []) self.bis = state.get("bis", []) # 前一个点 self.pre_bi = state.get("pre_bi") self.pre_duan = state.get("pre_duan") # 目前的merge_zhongshu self.merge_zhongshu = state.get("merge_zhongshu") self.merge_zhongshu_level = state.get("merge_zhongshu_level") self.merge_zhongshu_interval = state.get("merge_zhongshu_interval") def handle_zhongshu( points: list, acc_df, end_index, zhongshu_col="zhongshu", zhongshu_change_col="zhongshu_change", ): zhongshu = None zhongshu_change = None interval = None if len(points) == 4: x1 = points[0][0] x2 = points[3][0] interval = points[3][2] - points[0][2] if points[0][1] < points[1][1]: # 向下段 range = intersect((points[0][1], points[1][1]), (points[2][1], points[3][1])) if range: y1, y2 = range # 记录中枢 zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2) zhongshu_change = abs(y1 - y2) / abs(y1) acc_df.loc[end_index, zhongshu_col] = zhongshu acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change points = points[-1:] else: points = points[1:] else: # 向上段 range = intersect((points[1][1], points[0][1]), (points[3][1], points[2][1])) if range: y1, y2 = range # 记录中枢 zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2) zhongshu_change = abs(y1 - y2) / abs(y1) acc_df.loc[end_index, zhongshu_col] = zhongshu acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change points = points[-1:] else: points = points[1:] return points, zhongshu, zhongshu_change, interval class ZenAccumulator(Accumulator): def __init__(self, acc_window: int = 1) -> None: """ 算法和概念 <实体> 某种状态的k线 [实体] 连续实体排列 两k线的关系有三种: 上涨，下跌，包含 上涨: k线高点比之前高，低点比之前高 下跌: k线低点比之前低，高点比之前低 包含: k线高点比之前高，低点比之前低;反方向，即被包含 处理包含关系，长的k线缩短，上涨时，低点取max(low1,low2)；下跌时，高点取min(high1,high2) 第一个顶(底)分型: 出现连续4根下跌(上涨)k线 之后开始寻找 候选底(顶)分型，寻找的过程中有以下状态 <临时顶>: 中间k线比两边的高点高,是一条特定的k线 <临时底>: 中间k线比两边的高点高,是一条特定的k线 <候选顶分型>: 连续的<临时顶>取最大 <候选底分型>: 连续的<临时底>取最小 任何时刻只能有一个候选，其之前是一个确定的分型 <上升k线>: <下降k线>: <连接k线>: 分型之间的k线都可以认为是连接k线，以上为演化过程的中间态 distance(<候选顶分型>, <连接k线>)>=4 则 <候选顶分型> 变成顶分型 distance(<候选底分型>, <连接k线>)>=4 则 <候选底分型> 变成底分型 <顶分型><连接k线><候选底分型> <底分型><连接k线><候选顶分型> """ super().__init__(acc_window) def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame, state: dict) -> (pd.DataFrame, dict): self.logger.info(f"acc_one:{entity_id}") if pd_is_not_null(acc_df): df = df[df.index > acc_df.index[-1]] if pd_is_not_null(df): self.logger.info(f'compute from {df.iloc[0]["timestamp"]}') # 遍历的开始位置 start_index = len(acc_df) acc_df = pd.concat([acc_df, df]) zen_state = ZenState(state) acc_df = acc_df.reset_index(drop=True) current_interval = acc_df.iloc[start_index - 1]["current_interval"] else: self.logger.info("no need to compute") return acc_df, state else: acc_df = df # 笔的底 acc_df["bi_di"] = False # 笔的顶 acc_df["bi_ding"] = False # 记录笔顶/底分型的值，bi_di取low,bi_ding取high,其他为None,绘图时取有值的连线即为 笔 acc_df["bi_value"] = np.nan # 笔的变化 acc_df["bi_change"] = np.nan # 笔的斜率 acc_df["bi_slope"] = np.nan # 持续的周期 acc_df["bi_interval"] = np.nan # 记录临时分型，不变 acc_df["tmp_ding"] = False acc_df["tmp_di"] = False # 分型的力度 acc_df["fenxing_power"] = np.nan # 目前分型确定的方向 acc_df["current_direction"] = None acc_df["current_change"] = np.nan acc_df["current_interval"] = np.nan acc_df["current_slope"] = np.nan # 最近的一个笔中枢 # acc_df['current_zhongshu'] = np.nan acc_df["current_zhongshu_change"] = np.nan acc_df["current_zhongshu_y0"] = np.nan acc_df["current_zhongshu_y1"] = np.nan acc_df["current_merge_zhongshu_change"] = np.nan acc_df["current_merge_zhongshu_y0"] = np.nan acc_df["current_merge_zhongshu_y1"] = np.nan acc_df["current_merge_zhongshu_level"] = np.nan acc_df["current_merge_zhongshu_interval"] = np.nan # 目前走势的临时方向 其跟direction的的关系 确定了下一个分型 acc_df["tmp_direction"] = None acc_df["opposite_change"] = np.nan acc_df["opposite_interval"] = np.nan acc_df["opposite_slope"] = np.nan acc_df["duan_state"] = "yi" # 段的底 acc_df["duan_di"] = False # 段的顶 acc_df["duan_ding"] = False # 记录段顶/底的值，为duan_di时取low,为duan_ding时取high,其他为None,绘图时取有值的连线即为 段 acc_df["duan_value"] = np.nan # 段的变化 acc_df["duan_change"] = np.nan # 段的斜率 acc_df["duan_slope"] = np.nan # 持续的周期 acc_df["duan_interval"] = np.nan # 记录在确定中枢的最后一个段的终点x1，值为Rect(x0,y0,x1,y1) acc_df["zhongshu"] = None acc_df["zhongshu_change"] = np.nan acc_df["bi_zhongshu"] = None acc_df["bi_zhongshu_change"] = np.nan acc_df["merge_zhongshu"] = None acc_df["merge_zhongshu_change"] = np.nan acc_df["merge_zhongshu_level"] = np.nan acc_df["merge_zhongshu_interval"] = np.nan acc_df = acc_df.reset_index(drop=True) zen_state = ZenState( dict( fenxing_list=[], direction=None, can_fenxing=None, can_fenxing_index=None, opposite_count=0, current_duan_state="yi", duans=[], pre_bi=None, pre_duan=None, merge_zhongshu=None, ) ) zen_state.fenxing_list: List[Fenxing] = [] # 取前11条k线，至多出现一个顶分型+底分型 # 注:只是一种方便的确定第一个分型的办法，有了第一个分型，后面的处理就比较统一 # start_index 为遍历开始的位置 # direction为一个确定分型后的方向，即顶分型后为:down，底分型后为:up fenxing, start_index, direction, current_interval = handle_first_fenxing(acc_df, step=11) if not fenxing: return None, None zen_state.fenxing_list.append(fenxing) zen_state.direction = direction # list of (timestamp,value) zen_state.duans = [] zen_state.bis = [] pre_kdata = acc_df.iloc[start_index - 1] pre_index = start_index - 1 tmp_direction = zen_state.direction current_merge_zhongshu = decode_rect(zen_state.merge_zhongshu) if zen_state.merge_zhongshu else None current_merge_zhongshu_change = None current_merge_zhongshu_interval = zen_state.merge_zhongshu_interval current_merge_zhongshu_level = zen_state.merge_zhongshu_level current_zhongshu = None current_zhongshu_change = None for index, kdata in acc_df.iloc[start_index:].iterrows(): # print(f'timestamp: {kdata.timestamp}') # 临时方向 tmp_direction = get_direction(kdata, pre_kdata, current=tmp_direction) # current states current_interval = current_interval + 1 if zen_state.direction == Direction.up: pre_value = acc_df.loc[zen_state.fenxing_list[0].index, "low"] current_value = kdata["high"] else: pre_value = acc_df.loc[zen_state.fenxing_list[0].index, "high"] current_value = kdata["low"] acc_df.loc[index, "current_direction"] = zen_state.direction.value acc_df.loc[index, "current_interval"] = current_interval change = (current_value - pre_value) / abs(pre_value) acc_df.loc[index, "current_change"] = change acc_df.loc[index, "current_slope"] = change / current_interval if current_zhongshu: # acc_df.loc[index, 'current_zhongshu'] = current_zhongshu acc_df.loc[index, "current_zhongshu_y0"] = current_zhongshu.y0 acc_df.loc[index, "current_zhongshu_y1"] = current_zhongshu.y1 acc_df.loc[index, "current_zhongshu_change"] = current_zhongshu_change else: # acc_df.loc[index, 'current_zhongshu'] = acc_df.loc[index - 1, 'current_zhongshu'] acc_df.loc[index, "current_zhongshu_y0"] = acc_df.loc[index - 1, "current_zhongshu_y0"] acc_df.loc[index, "current_zhongshu_y1"] = acc_df.loc[index - 1, "current_zhongshu_y1"] acc_df.loc[index, "current_zhongshu_change"] = acc_df.loc[index - 1, "current_zhongshu_change"] if current_merge_zhongshu: # acc_df.loc[index, 'current_merge_zhongshu'] = current_merge_zhongshu acc_df.loc[index, "current_merge_zhongshu_y0"] = current_merge_zhongshu.y0 acc_df.loc[index, "current_merge_zhongshu_y1"] = current_merge_zhongshu.y1 acc_df.loc[index, "current_merge_zhongshu_change"] = current_merge_zhongshu_change acc_df.loc[index, "current_merge_zhongshu_level"] = current_merge_zhongshu_level acc_df.loc[index, "current_merge_zhongshu_interval"] = current_merge_zhongshu_interval else: # acc_df.loc[index, 'current_merge_zhongshu'] = acc_df.loc[index - 1, 'current_merge_zhongshu'] acc_df.loc[index, "current_merge_zhongshu_y0"] = acc_df.loc[index - 1, "current_merge_zhongshu_y0"] acc_df.loc[index, "current_merge_zhongshu_y1"] = acc_df.loc[index - 1, "current_merge_zhongshu_y1"] acc_df.loc[index, "current_merge_zhongshu_change"] = acc_df.loc[ index - 1, "current_merge_zhongshu_change" ] acc_df.loc[index, "current_merge_zhongshu_level"] = acc_df.loc[ index - 1, "current_merge_zhongshu_level" ] acc_df.loc[index, "current_merge_zhongshu_interval"] = acc_df.loc[ index - 1, "current_merge_zhongshu_interval" ] # 处理包含关系 handle_including( one_df=acc_df, index=index, kdata=kdata, pre_index=pre_index, pre_kdata=pre_kdata, tmp_direction=tmp_direction, ) # 根据方向，寻找对应的分型 和 段 if zen_state.direction == Direction.up: tmp_fenxing_col = "tmp_ding" fenxing_col = "bi_ding" else: tmp_fenxing_col = "tmp_di" fenxing_col = "bi_di" # 方向一致，延续中 if tmp_direction == zen_state.direction: zen_state.opposite_count = 0 # 反向，寻找反 分型 else: zen_state.opposite_count = zen_state.opposite_count + 1 # opposite states current_interval = zen_state.opposite_count if tmp_direction == Direction.up: pre_value = acc_df.loc[index - zen_state.opposite_count, "low"] current_value = kdata["high"] else: pre_value = acc_df.loc[index - zen_state.opposite_count, "high"] current_value = kdata["low"] acc_df.loc[index, "tmp_direction"] = tmp_direction.value acc_df.loc[index, "opposite_interval"] = current_interval change = (current_value - pre_value) / abs(pre_value) acc_df.loc[index, "opposite_change"] = change acc_df.loc[index, "opposite_slope"] = change / current_interval # 第一次反向 if zen_state.opposite_count == 1: acc_df.loc[pre_index, tmp_fenxing_col] = True acc_df.loc[pre_index, "fenxing_power"] = fenxing_power( acc_df.loc[pre_index - 1], pre_kdata, kdata, fenxing=tmp_fenxing_col, ) if zen_state.can_fenxing is not None: # 候选底分型 if tmp_direction == Direction.up: # 取小的 if pre_kdata["low"] <= zen_state.can_fenxing["low"]: zen_state.can_fenxing = pre_kdata[["low", "high"]] zen_state.can_fenxing_index = pre_index # 候选顶分型 else: # 取大的 if pre_kdata["high"] >= zen_state.can_fenxing["high"]: zen_state.can_fenxing = pre_kdata[["low", "high"]] zen_state.can_fenxing_index = pre_index else: zen_state.can_fenxing = pre_kdata[["low", "high"]] zen_state.can_fenxing_index = pre_index # 分型确立 if zen_state.can_fenxing is not None: if zen_state.opposite_count >= 4 or (index - zen_state.can_fenxing_index >= 8): acc_df.loc[zen_state.can_fenxing_index, fenxing_col] = True # 记录笔的值 if fenxing_col == "bi_ding": bi_value = acc_df.loc[zen_state.can_fenxing_index, "high"] else: bi_value = acc_df.loc[zen_state.can_fenxing_index, "low"] acc_df.loc[zen_state.can_fenxing_index, "bi_value"] = bi_value # 计算笔斜率 if zen_state.pre_bi: change = (bi_value - zen_state.pre_bi[1]) / abs(zen_state.pre_bi[1]) interval = zen_state.can_fenxing_index - zen_state.pre_bi[0] bi_slope = change / interval acc_df.loc[zen_state.can_fenxing_index, "bi_change"] = change acc_df.loc[zen_state.can_fenxing_index, "bi_slope"] = bi_slope acc_df.loc[zen_state.can_fenxing_index, "bi_interval"] = interval # 记录用于计算笔中枢的笔 zen_state.bis.append( ( acc_df.loc[zen_state.can_fenxing_index, "timestamp"], bi_value, zen_state.can_fenxing_index, ) ) # 计算笔中枢，当下来说这个 中枢 是确定的，并且是不可变的 # 但标记的点为 过去，注意在回测时最近的一个中枢可能用到未来函数，前一个才是 已知的 # 所以记了一个 current_zhongshu_y0 current_zhongshu_y1 这个是可直接使用的 end_index = zen_state.can_fenxing_index ( zen_state.bis, current_zhongshu, current_zhongshu_change, current_zhongshu_interval, ) = handle_zhongshu( points=zen_state.bis, acc_df=acc_df, end_index=end_index, zhongshu_col="bi_zhongshu", zhongshu_change_col="bi_zhongshu_change", ) if not current_merge_zhongshu: current_merge_zhongshu = current_zhongshu current_merge_zhongshu_change = current_zhongshu_change current_merge_zhongshu_level = 1 current_merge_zhongshu_interval = current_zhongshu_interval else: if current_zhongshu: range_a = ( current_merge_zhongshu.y0, current_merge_zhongshu.y1, ) range_b = (current_zhongshu.y0, current_zhongshu.y1) combine_range = combine(range_a, range_b) if combine_range: y0 = combine_range[0] y1 = combine_range[1] current_merge_zhongshu = Rect( x0=current_merge_zhongshu.x0, x1=current_zhongshu.x1, y0=y0, y1=y1, ) current_merge_zhongshu_change = abs(y0 - y1) / abs(y0) current_merge_zhongshu_level = current_merge_zhongshu_level + 1 current_merge_zhongshu_interval = ( current_merge_zhongshu_interval + current_zhongshu_interval ) else: current_merge_zhongshu = current_zhongshu current_merge_zhongshu_change = current_zhongshu_change current_merge_zhongshu_level = 1 current_merge_zhongshu_interval = current_zhongshu_interval acc_df.loc[end_index, "merge_zhongshu"] = current_merge_zhongshu acc_df.loc[end_index, "merge_zhongshu_change"] = current_merge_zhongshu_change acc_df.loc[end_index, "merge_zhongshu_level"] = current_merge_zhongshu_level acc_df.loc[end_index, "merge_zhongshu_interval"] = current_merge_zhongshu_interval zen_state.merge_zhongshu = current_merge_zhongshu zen_state.merge_zhongshu_interval = current_merge_zhongshu_interval zen_state.merge_zhongshu_level = current_merge_zhongshu_level zen_state.pre_bi = (zen_state.can_fenxing_index, bi_value) zen_state.opposite_count = 0 zen_state.direction = zen_state.direction.opposite() zen_state.can_fenxing = None # 确定第一个段 if zen_state.fenxing_list != None: zen_state.fenxing_list.append( Fenxing( state=fenxing_col, kdata={ "low": float(acc_df.loc[zen_state.can_fenxing_index]["low"]), "high": float(acc_df.loc[zen_state.can_fenxing_index]["high"]), }, index=zen_state.can_fenxing_index, ) ) if len(zen_state.fenxing_list) == 4: duan_state = handle_duan( fenxing_list=zen_state.fenxing_list, pre_duan_state=zen_state.current_duan_state, ) change = duan_state != zen_state.current_duan_state if change: zen_state.current_duan_state = duan_state # 确定状态 acc_df.loc[ zen_state.fenxing_list[0].index : zen_state.fenxing_list[-1].index, "duan_state", ] = zen_state.current_duan_state duan_index = zen_state.fenxing_list[0].index if zen_state.current_duan_state == "up": acc_df.loc[duan_index, "duan_di"] = True duan_value = acc_df.loc[duan_index, "low"] else: duan_index = zen_state.fenxing_list[0].index acc_df.loc[duan_index, "duan_ding"] = True duan_value = acc_df.loc[duan_index, "high"] # 记录段的值 acc_df.loc[duan_index, "duan_value"] = duan_value # 计算段斜率 if zen_state.pre_duan: change = (duan_value - zen_state.pre_duan[1]) / abs(zen_state.pre_duan[1]) interval = duan_index - zen_state.pre_duan[0] duan_slope = change / interval acc_df.loc[duan_index, "duan_change"] = change acc_df.loc[duan_index, "duan_slope"] = duan_slope acc_df.loc[duan_index, "duan_interval"] = interval zen_state.pre_duan = (duan_index, duan_value) # 记录用于计算中枢的段 zen_state.duans.append( ( acc_df.loc[duan_index, "timestamp"], duan_value, duan_index, ) ) # 计算中枢 zen_state.duans, _, _, _ = handle_zhongshu( points=zen_state.duans, acc_df=acc_df, end_index=duan_index, zhongshu_col="zhongshu", zhongshu_change_col="zhongshu_change", ) # 只留最后一个 zen_state.fenxing_list = zen_state.fenxing_list[-1:] else: # 保持之前的状态并踢出候选 acc_df.loc[zen_state.fenxing_list[0].index, "duan_state"] = ( zen_state.current_duan_state ) zen_state.fenxing_list = zen_state.fenxing_list[1:] pre_kdata = kdata pre_index = index acc_df = acc_df.set_index("timestamp", drop=False) return acc_df, zen_state class ZenFactor(TechnicalFactor): accumulator = ZenAccumulator() def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = False, adjust_type: Union[AdjustType, str] = None, ) -> None: self.factor_schema = get_zen_factor_schema(entity_type=entity_schema.__name__, level=level) super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) def factor_col_map_object_hook(self) -> dict: return { "zhongshu": decode_rect, "bi_zhongshu": decode_rect, "merge_zhongshu": decode_rect, } def state_encoder(self): return FactorStateEncoder def drawer_factor_df_list(self) -> Optional[List[pd.DataFrame]]: bi_value = self.factor_df[["bi_value"]].dropna() # duan_value = self.factor_df[['duan_value']].dropna() return [bi_value] def drawer_rects(self) -> List[Rect]: df1 = self.factor_df[["merge_zhongshu"]].dropna() return df1["merge_zhongshu"].tolist() def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: # bi_slope = self.factor_df[['bi_slope']].dropna() # duan_slope = self.factor_df[['duan_slope']].dropna() # power = self.factor_df[['fenxing_power']].dropna() # zhongshu_change = self.factor_df[['zhongshu_change']].dropna() # return [bi_slope, duan_slope, power, zhongshu_change] # change1 = self.factor_df[['current_merge_zhongshu_level']].dropna() # change2 = self.factor_df[['opposite_change']].dropna() current_slope = self.factor_df[["current_slope"]].dropna() return [current_slope] if __name__ == "__main__": entity_ids = ["index_sh_000001"] Index1dKdata.record_data(entity_ids=entity_ids) f = ZenFactor( entity_schema=Index, entity_ids=entity_ids, need_persist=False, provider="em", entity_provider="exchange", ) f.draw(show=True) # the __all__ is generated __all__ = ["FactorStateEncoder", "get_zen_factor_schema", "ZenState", "handle_zhongshu", "ZenAccumulator", "ZenFactor"] ================================================ FILE: src/zvt/factors/zen/domain/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule index_1d_zen_factor from .index_1d_zen_factor import * from .index_1d_zen_factor import __all__ as _index_1d_zen_factor_all __all__ += _index_1d_zen_factor_all # import all from submodule stock_1wk_zen_factor from .stock_1wk_zen_factor import * from .stock_1wk_zen_factor import __all__ as _stock_1wk_zen_factor_all __all__ += _stock_1wk_zen_factor_all # import all from submodule common from .common import * from .common import __all__ as _common_all __all__ += _common_all # import all from submodule stock_1d_zen_factor from .stock_1d_zen_factor import * from .stock_1d_zen_factor import __all__ as _stock_1d_zen_factor_all __all__ += _stock_1d_zen_factor_all ================================================ FILE: src/zvt/factors/zen/domain/common.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Float, String, Boolean, Integer from zvt.contract import Mixin class ZenFactorCommon(Mixin): level = Column(String(length=32)) # 开盘价 open = Column(Float) # 收盘价 close = Column(Float) # 最高价 high = Column(Float) # 最低价 low = Column(Float) # 成交量 volume = Column(Float) # 成交金额 turnover = Column(Float) # 笔的底 bi_di = Column(Boolean) # 笔的顶 bi_ding = Column(Boolean) # 记录笔顶/底分型的值，bi_di取low,bi_ding取high,其他为None,绘图时取有值的连线即为 笔 bi_value = Column(Float) # 笔的变化 bi_change = Column(Float) # 笔的斜率 bi_slope = Column(Float) # 持续的周期 bi_interval = Column(Integer) # 记录临时分型，不变 tmp_ding = Column(Boolean) tmp_di = Column(Boolean) # 分型的力度 fenxing_power = Column(Float) # 目前分型确定的方向 current_direction = Column(String(length=16)) current_change = Column(Float) current_interval = Column(Integer) current_slope = Column(Float) # 最近的一个笔中枢 # current_zhongshu = Column(String(length=512)) current_zhongshu_y0 = Column(Float) current_zhongshu_y1 = Column(Float) current_zhongshu_change = Column(Float) current_merge_zhongshu_y0 = Column(Float) current_merge_zhongshu_y1 = Column(Float) current_merge_zhongshu_change = Column(Float) current_merge_zhongshu_level = Column(Integer) current_merge_zhongshu_interval = Column(Integer) # 目前走势的临时方向 其跟direction的的关系 确定了下一个分型 tmp_direction = Column(String(length=16)) # 已经确定分型，目前反向才有值 opposite_change = Column(Float) opposite_slope = Column(Float) opposite_interval = Column(Integer) duan_state = Column(String(length=32)) # 段的底 duan_di = Column(Boolean) # 段的顶 duan_ding = Column(Boolean) # 记录段顶/底的值，为duan_di时取low,为duan_ding时取high,其他为None,绘图时取有值的连线即为 段 duan_value = Column(Float) # 段的变化 duan_change = Column(Float) # 段的斜率 duan_slope = Column(Float) # 持续的周期 duan_interval = Column(Integer) # 记录在确定中枢的最后一个段的终点x1，值为Rect(x0,y0,x1,y1) zhongshu = Column(String(length=512)) zhongshu_change = Column(Float) # 记录在确定中枢的最后一个笔的终点x1，值为Rect(x0,y0,x1,y1) bi_zhongshu = Column(String(length=512)) bi_zhongshu_change = Column(Float) # 从前往后，合并相邻的有重叠的笔中枢 merge_zhongshu = Column(String(length=512)) merge_zhongshu_change = Column(Float) merge_zhongshu_level = Column(Integer) merge_zhongshu_interval = Column(Integer) # the __all__ is generated __all__ = ["ZenFactorCommon"] ================================================ FILE: src/zvt/factors/zen/domain/index_1d_zen_factor.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.ext.declarative import declarative_base from zvt.contract.register import register_schema from zvt.factors.zen.domain.common import ZenFactorCommon Index1dZenFactorBase = declarative_base() class Index1dZenFactor(Index1dZenFactorBase, ZenFactorCommon): __tablename__ = "index_1d_zen_factor" register_schema(db_name="index_1d_zen_factor", schema_base=Index1dZenFactorBase, entity_type="index", internal=True) # the __all__ is generated __all__ = ["Index1dZenFactor"] ================================================ FILE: src/zvt/factors/zen/domain/stock_1d_zen_factor.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.ext.declarative import declarative_base from zvt.contract.register import register_schema from zvt.factors.zen.domain.common import ZenFactorCommon Stock1dZenFactorBase = declarative_base() class Stock1dZenFactor(Stock1dZenFactorBase, ZenFactorCommon): __tablename__ = "stock_1d_zen_factor" register_schema(db_name="stock_1d_zen_factor", schema_base=Stock1dZenFactorBase, entity_type="stock", internal=True) # the __all__ is generated __all__ = ["Stock1dZenFactor"] ================================================ FILE: src/zvt/factors/zen/domain/stock_1wk_zen_factor.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy.ext.declarative import declarative_base from zvt.contract.register import register_schema from zvt.factors.zen.domain.common import ZenFactorCommon Stock1wkZenFactorBase = declarative_base() class Stock1wkZenFactor(Stock1wkZenFactorBase, ZenFactorCommon): __tablename__ = "stock_1wk_zen_factor" register_schema( db_name="stock_1wk_zen_factor", schema_base=Stock1wkZenFactorBase, entity_type="stock", internal=True ) # the __all__ is generated __all__ = ["Stock1wkZenFactor"] ================================================ FILE: src/zvt/factors/zen/zen_factor.py ================================================ # -*- coding: utf-8 -*- import logging import math from enum import Enum from typing import List, Optional from typing import Union, Type import pandas as pd from zvt.contract import IntervalLevel, AdjustType from zvt.contract import TradableEntity from zvt.contract.data_type import Bean from zvt.contract.drawer import Rect from zvt.contract.factor import Accumulator from zvt.contract.factor import Transformer from zvt.domain import Stock from zvt.factors.algorithm import distance, intersect from zvt.factors.zen.base_factor import ZenFactor from zvt.utils.pd_utils import ( group_by_entity_id, normalize_group_compute_result, pd_is_not_null, ) logger = logging.getLogger(__name__) class ZhongshuRange(Enum): # <=0.4 small = "small" # >0.4 big = "big" @classmethod def of(cls, change): if change <= 0.4: return ZhongshuRange.small else: return ZhongshuRange.big class ZhongshuLevel(Enum): # level <= 3 level1 = "level1" # 3 < level <=7 level2 = "level2" # level > 7 level3 = "level3" @classmethod def of(cls, level): if level <= 3: return ZhongshuLevel.level1 elif level <= 7: return ZhongshuLevel.level2 else: return ZhongshuLevel.level3 class ZhongshuDistance(Enum): big_up = "big_up" big_down = "big_down" small_up = "small_up" small_down = "small_down" @classmethod def of(cls, d): if d is None or math.isnan(d) or d == 0: zhongshu_distance = None elif d <= -0.5: zhongshu_distance = ZhongshuDistance.big_down elif d < 0: zhongshu_distance = ZhongshuDistance.small_down elif d <= 0.5: zhongshu_distance = ZhongshuDistance.small_up else: zhongshu_distance = ZhongshuDistance.big_up return zhongshu_distance class Zhongshu(object): def __str__(self) -> str: if self.zhongshu_distance: d = self.zhongshu_distance.value else: d = None return f"{self.zhongshu_range.value},{self.zhongshu_level.value},{d}" def __eq__(self, o: object) -> bool: if isinstance(o, self.__class__): return ( self.zhongshu_range == o.zhongshu_range and self.zhongshu_level == o.zhongshu_level and self.zhongshu_distance == o.zhongshu_distance ) return False def __init__( self, zhongshu_range: ZhongshuRange, zhongshu_level: ZhongshuLevel, zhongshu_distance: ZhongshuDistance, ) -> None: self.zhongshu_range = zhongshu_range self.zhongshu_level = zhongshu_level self.zhongshu_distance = zhongshu_distance def category_zen_state(): all_states = [] for zhongshu_range in ZhongshuRange: for zhongshu_level in ZhongshuLevel: for distance in ZhongshuDistance: pass class ZenState(Bean): def __eq__(self, o: object) -> bool: if isinstance(o, self.__class__): return self.zhongshu_list == o.zhongshu_list def __str__(self) -> str: return ",".join([f"{elem}" for elem in self.zhongshu_list]) def __init__(self, zhongshu_state_list: List) -> None: self.zhongshu_list: List[Zhongshu] = [] self.zhongshu_state_list = zhongshu_state_list pre_range = None for zhongshu_state in zhongshu_state_list: current_range = (zhongshu_state[0], zhongshu_state[1]) d = None if pre_range is None: pre_range = current_range else: d = distance(pre_range, current_range) pre_range = current_range change = zhongshu_state[2] level = zhongshu_state[3] zhongshu_range = ZhongshuRange.of(change=change) zhongshu_level = ZhongshuLevel.of(level=level) zhongshu_distance = ZhongshuDistance.of(d=d) zhongshu = Zhongshu( zhongshu_range=zhongshu_range, zhongshu_level=zhongshu_level, zhongshu_distance=zhongshu_distance, ) self.zhongshu_list.append(zhongshu) def cal_distance(s): d_list = [] current_range = None print(s) for idx, row in s.items(): d = None if row is not None: if current_range is None: current_range = row else: d = distance((current_range.y0, current_range.y1), (row.y0, row.y1)) current_range = row d_list.append(d) return pd.Series(index=s.index, data=d_list) def cal_zen_state(s): zen_states = [] zhongshu_state_list = [] current_zhongshu_state = None for idx, row in s.items(): # row # 0 current_merge_zhongshu_y0 # 1 current_merge_zhongshu_y1 # 2 current_merge_zhongshu_change # 3 current_merge_zhongshu_level # 4 current_merge_zhongshu_interval if row[0] is not None and not math.isnan(row[0]): if current_zhongshu_state != row: # 相同的中枢，保留最近的(包含关系时产生) if current_zhongshu_state != None and intersect( (current_zhongshu_state[0], current_zhongshu_state[1]), (row[0], row[1]), ): zhongshu_state_list = zhongshu_state_list[:-1] # 最多保留最近5个 zhongshu_state_list = zhongshu_state_list[-4:] + [row] current_zhongshu_state = row if len(zhongshu_state_list) == 5: zen_states.append(ZenState(zhongshu_state_list)) else: zen_states.append(None) return pd.Series(index=s.index, data=zen_states) def good_state(zen_state: ZenState): if zen_state: zhongshu0 = zen_state.zhongshu_list[0] zhongshu1 = zen_state.zhongshu_list[1] zhongshu2 = zen_state.zhongshu_list[2] zhongshu3 = zen_state.zhongshu_list[3] zhongshu4 = zen_state.zhongshu_list[4] # 没大涨过 if ZhongshuDistance.big_up not in ( zhongshu1.zhongshu_distance, zhongshu2.zhongshu_distance, zhongshu3.zhongshu_distance, zhongshu4.zhongshu_distance, ): if ZhongshuRange.big not in ( zhongshu3.zhongshu_range, zhongshu4.zhongshu_range, ): # 最近一个窄幅震荡 if ZhongshuRange.small == zhongshu4.zhongshu_range and ZhongshuLevel.level1 != zhongshu4.zhongshu_level: return True return False def trending_state(zen_state: ZenState): if zen_state: zhongshu0 = zen_state.zhongshu_list[0] zhongshu1 = zen_state.zhongshu_list[1] zhongshu2 = zen_state.zhongshu_list[2] zhongshu3 = zen_state.zhongshu_list[3] zhongshu4 = zen_state.zhongshu_list[4] # 没大涨过 if ZhongshuDistance.big_up not in ( zhongshu1.zhongshu_distance, zhongshu2.zhongshu_distance, zhongshu3.zhongshu_distance, ): if ZhongshuRange.big not in ( zhongshu3.zhongshu_range, zhongshu4.zhongshu_range, ): # 最近一个窄幅震荡 if ZhongshuRange.small == zhongshu4.zhongshu_range and ZhongshuLevel.level1 == zhongshu4.zhongshu_level: return True return False class TrendingFactor(ZenFactor): def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = True, adjust_type: Union[AdjustType, str] = None, ) -> None: super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) def compute_result(self): super().compute_result() if pd_is_not_null(self.factor_df): df = self.factor_df.apply( lambda x: ( x["current_merge_zhongshu_y0"], x["current_merge_zhongshu_y1"], x["current_merge_zhongshu_change"], x["current_merge_zhongshu_level"], x["current_merge_zhongshu_interval"], ), axis=1, ) state_df = group_by_entity_id(df).apply(cal_zen_state) print(self.factor_df) print(state_df) self.factor_df["zen_state"] = normalize_group_compute_result(state_df) self.factor_df["good_state"] = self.factor_df["zen_state"].apply(good_state) s = self.factor_df["good_state"] self.result_df = s.to_frame(name="filter_result") class ShakingFactor(ZenFactor): # 震荡区间 shaking_range = 0.5 def __init__( self, entity_schema: Type[TradableEntity] = Stock, provider: str = None, entity_provider: str = None, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, limit: int = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, category_field: str = "entity_id", time_field: str = "timestamp", keep_window: int = None, keep_all_timestamp: bool = False, fill_method: str = "ffill", effective_number: int = None, transformer: Transformer = None, accumulator: Accumulator = None, need_persist: bool = False, only_compute_factor: bool = False, factor_name: str = None, clear_state: bool = False, only_load_factor: bool = True, adjust_type: Union[AdjustType, str] = None, ) -> None: super().__init__( entity_schema, provider, entity_provider, entity_ids, exchanges, codes, start_timestamp, end_timestamp, columns, filters, order, limit, level, category_field, time_field, keep_window, keep_all_timestamp, fill_method, effective_number, transformer, accumulator, need_persist, only_compute_factor, factor_name, clear_state, only_load_factor, adjust_type, ) def drawer_sub_df_list(self) -> Optional[List[pd.DataFrame]]: df1 = self.factor_df[["current_merge_zhongshu_y1"]].dropna() df2 = self.factor_df[["current_merge_zhongshu_y0"]].dropna() return [df1, df2] def drawer_rects(self) -> List[Rect]: return super().drawer_rects() def compute_result(self): super().compute_result() # 窄幅震荡 s1 = self.factor_df["current_merge_zhongshu_change"] <= self.shaking_range # 中枢级别 s2 = self.factor_df["current_merge_zhongshu_level"] >= 2 s3 = self.factor_df["current_merge_zhongshu_interval"] >= 120 # 中枢上缘 s4 = self.factor_df["close"] <= 1.1 * self.factor_df["current_merge_zhongshu_y1"] s5 = self.factor_df["close"] >= 0.9 * self.factor_df["current_merge_zhongshu_y1"] # 中枢下缘 s6 = self.factor_df["close"] <= 1.1 * self.factor_df["current_merge_zhongshu_y0"] s7 = self.factor_df["close"] >= 0.9 * self.factor_df["current_merge_zhongshu_y0"] s = s1 & s2 & s3 & ((s4 & s5) | (s6 & s7)) # s = s.groupby(level=0).apply(drop_continue_duplicate) if s.index.nlevels == 3: s = s.reset_index(level=0, drop=True) self.result_df = s.to_frame(name="filter_result") print(self.result_df) if __name__ == "__main__": entity_ids = ["stock_sz_000338"] f = ZenFactor( provider="em", entity_schema=Stock, entity_ids=entity_ids, need_persist=True, ) f.draw(show=True) # the __all__ is generated __all__ = [ "ZhongshuRange", "ZhongshuLevel", "ZhongshuDistance", "Zhongshu", "category_zen_state", "ZenState", "cal_distance", "cal_zen_state", "good_state", "trending_state", "TrendingFactor", "ShakingFactor", ] ================================================ FILE: src/zvt/fill_project.py ================================================ # script to auto generate some files from zvt.autocode.generator import gen_kdata_schema, gen_exports from zvt.contract import AdjustType from zvt.contract import IntervalLevel def gen_kdata_schemas(): """ generate kdata(OHLC) schemas for tradable entity """ # A股行情 gen_kdata_schema( pkg="zvt", entity_type="stock", levels=[ level for level in IntervalLevel if level not in (IntervalLevel.LEVEL_L2_QUOTE, IntervalLevel.LEVEL_TICK) ], adjust_types=[None, AdjustType.hfq], entity_in_submodule=True, ) # 中国期货 gen_kdata_schema( pkg="zvt", entity_type="future", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True, ) # 美股 gen_kdata_schema( pkg="zvt", entity_type="stockus", levels=[IntervalLevel.LEVEL_1DAY], adjust_types=[None, AdjustType.hfq], entity_in_submodule=True, ) # 美指 gen_kdata_schema( pkg="zvt", entity_type="indexus", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True, ) # 港股 gen_kdata_schema( pkg="zvt", entity_type="stockhk", levels=[IntervalLevel.LEVEL_1DAY], adjust_types=[None, AdjustType.hfq], entity_in_submodule=True, ) # 板块行情 gen_kdata_schema( pkg="zvt", entity_type="block", levels=[IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK, IntervalLevel.LEVEL_1MON], entity_in_submodule=True, ) # A股指数行情 gen_kdata_schema( pkg="zvt", entity_type="index", levels=[IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK], entity_in_submodule=True, ) # etf行情 gen_kdata_schema( pkg="zvt", entity_type="etf", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True ) # currency行情 gen_kdata_schema( pkg="zvt", entity_type="currency", levels=[IntervalLevel.LEVEL_1DAY], entity_in_submodule=True ) if __name__ == "__main__": # gen_exports("api") # gen_exports("broker") # gen_exports("common") # gen_exports("contract", export_from_package=True, export_modules=["schema"]) gen_exports("domain", export_from_package=True) # gen_exports("factors", export_from_package=True) # gen_exports("trading") # gen_exports("ml") # gen_exports("utils", export_from_package=False, export_var=True) # gen_exports('informer') # gen_exports('trader') # gen_exports('autocode') # gen_exports("zhdate") gen_exports("recorders", export_from_package=True, exclude_modules=["qmt"]) # gen_exports("tag", export_from_package=False) # gen_exports("sso", export_from_package=False) # gen_kdata_schemas() # zip_dir(ZVT_TEST_DATA_PATH, zip_file_name=DATA_SAMPLE_ZIP_PATH) ================================================ FILE: src/zvt/informer/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule informer from .informer import * from .informer import __all__ as _informer_all __all__ += _informer_all ================================================ FILE: src/zvt/informer/inform_utils.py ================================================ # -*- coding: utf-8 -*- import eastmoneypy import requests from zvt import zvt_config from zvt.contract.api import get_entities from zvt.informer import EmailInformer def inform_email(entity_ids, entity_type, target_date, title, provider): msg = "no targets" if entity_ids: entities = get_entities(provider=provider, entity_type=entity_type, entity_ids=entity_ids, return_type="domain") assert len(entities) == len(entity_ids) infos = [f"{entity.name}({entity.code})" for entity in entities] msg = "\n".join(infos) + "\n" EmailInformer().send_message(zvt_config["email_username"], f"{target_date} {title}", msg) def add_to_eastmoney(codes, group, entity_type="stock", over_write=True, headers_list=None): if headers_list is None: headers_list = [None] for headers in headers_list: with requests.Session() as session: group_id = eastmoneypy.get_group_id(group, session=session, headers=headers) need_create_group = False if not group_id: need_create_group = True if group_id and over_write: eastmoneypy.del_group(group_name=group, session=session, headers=headers) need_create_group = True codes = set(codes) if need_create_group: result = eastmoneypy.create_group(group_name=group, session=session, headers=headers) group_id = result["gid"] else: current_codes = eastmoneypy.list_entities(group_id=group_id, session=session, headers=headers) if current_codes: codes = codes - set(current_codes) for code in codes: eastmoneypy.add_to_group( code=code, entity_type=entity_type, group_id=group_id, session=session, headers=headers ) def clean_eastmoney_groups(keep, headers_list=None): if headers_list is None: headers_list = [None] for headers in headers_list: if keep is None: keep = ["自选股"] with requests.Session() as session: groups = eastmoneypy.get_groups(session=session, headers=headers) groups_to_clean = [group["gid"] for group in groups if group["gname"] not in keep] for gid in groups_to_clean: eastmoneypy.del_group(group_id=gid, session=session, headers=headers) def delete_eastmoney_group(group_name, headers_list=None): if headers_list is None: headers_list = [None] for headers in headers_list: with requests.Session() as session: eastmoneypy.del_group(group_name=group_name, session=session, headers=headers) # the __all__ is generated __all__ = ["inform_email", "add_to_eastmoney", "clean_eastmoney_groups", "delete_eastmoney_group"] ================================================ FILE: src/zvt/informer/informer.py ================================================ # -*- coding: utf-8 -*- import email import json import logging import smtplib from email.header import Header from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText import requests from zvt import zvt_config logger = logging.getLogger(__name__) class Informer(object): def send_message(self, to_user, title, body, **kwargs): pass class EmailInformer(Informer): def __init__(self, ssl=True) -> None: super().__init__() self.ssl = ssl def send_message_(self, to_user, title, body, **kwargs): if ( not zvt_config["smtp_host"] or not zvt_config["smtp_port"] or not zvt_config["email_username"] or not zvt_config["email_password"] ): logger.warning(f"Please set smtp_host/smtp_port/email_username/email_password in ~/zvt-home/config.json") return host = zvt_config["smtp_host"] port = zvt_config["smtp_port"] smtp_client = None try: if self.ssl: try: smtp_client = smtplib.SMTP_SSL(host=host, port=port) except: smtp_client = smtplib.SMTP_SSL() else: try: smtp_client = smtplib.SMTP(host=host, port=port) except: smtp_client = smtplib.SMTP() smtp_client.connect(host=host, port=port) smtp_client.login(zvt_config["email_username"], zvt_config["email_password"]) msg = MIMEMultipart("alternative") msg["Subject"] = Header(title).encode() msg["From"] = "{} <{}>".format(Header("zvt").encode(), zvt_config["email_username"]) if type(to_user) is list: msg["To"] = ", ".join(to_user) else: msg["To"] = to_user msg["Message-id"] = email.utils.make_msgid() msg["Date"] = email.utils.formatdate() plain_text = MIMEText(body, _subtype="plain", _charset="UTF-8") msg.attach(plain_text) smtp_client.sendmail(zvt_config["email_username"], to_user, msg.as_string()) except Exception as e: logger.exception("send email failed", e) finally: if smtp_client: try: smtp_client.quit() except Exception as e: logger.exception("smtp_client quit failed", e) def send_message(self, to_user, title, body, sub_size=20, with_sender=True, **kwargs): if type(to_user) is list and sub_size: size = len(to_user) if size >= sub_size: step_size = int(size / sub_size) if size % sub_size: step_size = step_size + 1 else: step_size = 1 for step in range(step_size): sub_to_user = to_user[sub_size * step : sub_size * (step + 1)] if with_sender: sub_to_user.append(zvt_config["email_username"]) self.send_message_(sub_to_user, title, body, **kwargs) else: self.send_message_(to_user, title, body, **kwargs) class WechatInformer(Informer): GET_TOKEN_URL = "https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid={}&secret={}".format( zvt_config["wechat_app_id"], zvt_config["wechat_app_secrect"] ) GET_TEMPLATE_URL = "https://api.weixin.qq.com/cgi-bin/template/get_all_private_template?access_token={}" SEND_MSG_URL = "https://api.weixin.qq.com/cgi-bin/message/template/send?access_token={}" token = None def __init__(self) -> None: self.refresh_token() def refresh_token(self): resp = requests.get(self.GET_TOKEN_URL) logger.info("refresh_token resp.status_code:{}, resp.text:{}".format(resp.status_code, resp.text)) if resp.status_code == 200 and resp.json() and "access_token" in resp.json(): self.token = resp.json()["access_token"] else: logger.exception("could not refresh_token") def send_price_notification(self, to_user, security_name, current_price, change_pct): the_json = self._format_price_notification(to_user, security_name, current_price, change_pct) the_data = json.dumps(the_json, ensure_ascii=False).encode("utf-8") resp = requests.post(self.SEND_MSG_URL.format(self.token), the_data) logger.info("send_price_notification resp:{}".format(resp.text)) if resp.json() and resp.json()["errcode"] == 0: logger.info("send_price_notification to user:{} data:{} success".format(to_user, the_json)) def _format_price_notification(self, to_user, security_name, current_price, change_pct): if change_pct > 0: title = "吃肉喝汤" else: title = "关灯吃面" # 先固定一个template # { # "template_id": "mkqi-L1h56mH637vLXiuS_ulLTs1byDYYgLBbSXQ65U", # "title": "涨跌幅提醒", # "primary_industry": "金融业", # "deputy_industry": "证券|基金|理财|信托", # "content": "{{first.DATA}}\n股票名：{{keyword1.DATA}}\n最新价：{{keyword2.DATA}}\n涨跌幅：{{keyword3.DATA}}\n{{remark.DATA}}", # "example": "您好，腾新控股最新价130.50元，上涨达到设置的3.2%\r\n股票名：腾讯控股（00700）\r\n最新价：130.50元\r\n涨跌幅：+3.2%\r\n点击查看最新实时行情。" # } template_id = "mkqi-L1h56mH637vLXiuS_ulLTs1byDYYgLBbSXQ65U" the_json = { "touser": to_user, "template_id": template_id, "url": "http://www.foolcage.com", "data": { "first": {"value": title, "color": "#173177"}, "keyword1": {"value": security_name, "color": "#173177"}, "keyword2": {"value": current_price, "color": "#173177"}, "keyword3": {"value": "{:.2%}".format(change_pct), "color": "#173177"}, "remark": {"value": "会所嫩模 Or 下海干活?", "color": "#173177"}, }, } return the_json class QiyeWechatBot(Informer): def __init__(self, token=None) -> None: self.token = token def send_message(self, content): if not self.token: if not zvt_config["qiye_wechat_bot_token"]: logger.warning(f"Please set qiye_wechat_bot_token in ~/zvt-home/config.json") return self.token = zvt_config["qiye_wechat_bot_token"] msg = { "msgtype": "text", "text": {"content": content}, } requests.post(f"https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key={self.token}", json=msg) if __name__ == "__main__": # weixin_action = WechatInformer() # weixin_action.send_price_notification(to_user='oRvNP0XIb9G3g6a-2fAX9RHX5--Q', security_name='BTC/USDT', # current_price=1000, change_pct='0.5%') bot = QiyeWechatBot() bot.send_message(content="test") # the __all__ is generated __all__ = ["Informer", "EmailInformer", "WechatInformer"] ================================================ FILE: src/zvt/main.py ================================================ import dash_bootstrap_components as dbc from dash import html from dash.dependencies import Input, Output from zvt.ui import zvt_app from zvt.ui.apps import factor_app def serve_layout(): layout = html.Div( children=[ # banner html.Div(className="zvt-banner", children=html.H2(className="h2-title", children="ZVT")), dbc.CardHeader( dbc.Tabs( [dbc.Tab(label="factor", tab_id="tab-factor", label_style={}, tab_style={"width": "100px"})], id="card-tabs", active_tab="tab-factor", ) ), dbc.CardBody(html.P(id="card-content", className="card-text")), ] ) return layout @zvt_app.callback(Output("card-content", "children"), [Input("card-tabs", "active_tab")]) def tab_content(active_tab): if "tab-factor" == active_tab: return factor_app.factor_layout() zvt_app.layout = serve_layout def main(): # init_plugins() zvt_app.run_server(debug=True, host="0.0.0.0") # zvt_app.run_server() if __name__ == "__main__": main() ================================================ FILE: src/zvt/misc/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/misc/constants.py ================================================ # -*- coding: utf-8 -*- CHINESEYEARCODE = [ 19416, 19168, 42352, 21717, 53856, 55632, 91476, 22176, 39632, 21970, 19168, 42422, 42192, 53840, 119381, 46400, 54944, 44450, 38320, 84343, 18800, 42160, 46261, 27216, 27968, 109396, 11104, 38256, 21234, 18800, 25958, 54432, 59984, 92821, 23248, 11104, 100067, 37600, 116951, 51536, 54432, 120998, 46416, 22176, 107956, 9680, 37584, 53938, 43344, 46423, 27808, 46416, 86869, 19872, 42416, 83315, 21168, 43432, 59728, 27296, 44710, 43856, 19296, 43748, 42352, 21088, 62051, 55632, 23383, 22176, 38608, 19925, 19152, 42192, 54484, 53840, 54616, 46400, 46752, 103846, 38320, 18864, 43380, 42160, 45690, 27216, 27968, 44870, 43872, 38256, 19189, 18800, 25776, 29859, 59984, 27480, 23232, 43872, 38613, 37600, 51552, 55636, 54432, 55888, 30034, 22176, 43959, 9680, 37584, 51893, 43344, 46240, 47780, 44368, 21977, 19360, 42416, 86390, 21168, 43312, 31060, 27296, 44368, 23378, 19296, 42726, 42208, 53856, 60005, 54576, 23200, 30371, 38608, 19195, 19152, 42192, 118966, 53840, 54560, 56645, 46496, 22224, 21938, 18864, 42359, 42160, 43600, 111189, 27936, 44448, 84835, 37744, 18936, 18800, 25776, 92326, 59984, 27296, 108228, 43744, 37600, 53987, 51552, 54615, 54432, 55888, 23893, 22176, 42704, 21972, 21200, 43448, 43344, 46240, 46758, 44368, 21920, 43940, 42416, 21168, 45683, 26928, 29495, 27296, 44368, 84821, 19296, 42352, 21732, 53600, 59752, 54560, 55968, 92838, 22224, 19168, 43476, 41680, 53584, 62034, 54560, ] """ 从1900年到2100年的农历月份数据代码 20位二进制代码表示一个年份的数据。 前四位0:表示闰月为29天，1:表示闰月为30天 中间12位：从左起表示1-12月每月的大小，1为30天，0为29天 最后四位：表示闰月的月份，0表示当年无闰月 前四位和最后四位应该结合使用，如果最后四位为0，则不考虑前四位 例： 1901年代码为 19168，转成二进制为 0b100101011100000, 最后四位为0，当年无闰月，月份数据为 010010101110 分别代表12月的大小情况 1903年代码为 21717，转成二进制为 0b101010011010101，最后四位为5，当年为闰五月，首四位为0，闰月为29天，月份数据为 010101001101 分别代表12月的大小情况 """ CHINESENEWYEAR = [ "19000131", "19010219", "19020208", "19030129", "19040216", "19050204", "19060125", "19070213", "19080202", "19090122", "19100210", "19110130", "19120218", "19130206", "19140126", "19150214", "19160203", "19170123", "19180211", "19190201", "19200220", "19210208", "19220128", "19230216", "19240205", "19250124", "19260213", "19270202", "19280123", "19290210", "19300130", "19310217", "19320206", "19330126", "19340214", "19350204", "19360124", "19370211", "19380131", "19390219", "19400208", "19410127", "19420215", "19430205", "19440125", "19450213", "19460202", "19470122", "19480210", "19490129", "19500217", "19510206", "19520127", "19530214", "19540203", "19550124", "19560212", "19570131", "19580218", "19590208", "19600128", "19610215", "19620205", "19630125", "19640213", "19650202", "19660121", "19670209", "19680130", "19690217", "19700206", "19710127", "19720215", "19730203", "19740123", "19750211", "19760131", "19770218", "19780207", "19790128", "19800216", "19810205", "19820125", "19830213", "19840202", "19850220", "19860209", "19870129", "19880217", "19890206", "19900127", "19910215", "19920204", "19930123", "19940210", "19950131", "19960219", "19970207", "19980128", "19990216", "20000205", "20010124", "20020212", "20030201", "20040122", "20050209", "20060129", "20070218", "20080207", "20090126", "20100214", "20110203", "20120123", "20130210", "20140131", "20150219", "20160208", "20170128", "20180216", "20190205", "20200125", "20210212", "20220201", "20230122", "20240210", "20250129", "20260217", "20270206", "20280126", "20290213", "20300203", "20310123", "20320211", "20330131", "20340219", "20350208", "20360128", "20370215", "20380204", "20390124", "20400212", "20410201", "20420122", "20430210", "20440130", "20450217", "20460206", "20470126", "20480214", "20490202", "20500123", "20510211", "20520201", "20530219", "20540208", "20550128", "20560215", "20570204", "20580124", "20590212", "20600202", "20610121", "20620209", "20630129", "20640217", "20650205", "20660126", "20670214", "20680203", "20690123", "20700211", "20710131", "20720219", "20730207", "20740127", "20750215", "20760205", "20770124", "20780212", "20790202", "20800122", "20810209", "20820129", "20830217", "20840206", "20850126", "20860214", "20870203", "20880124", "20890210", "20900130", "20910218", "20920207", "20930127", "20940215", "20950205", "20960125", "20970212", "20980201", "20990121", "21000209", ] """ 从1900年，至2100年每年的农历春节的公历日期 """ # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/misc/misc_models.py ================================================ # -*- coding: utf-8 -*- from datetime import datetime from zvt.contract.model import CustomModel class TimeMessage(CustomModel): # 时间 timestamp: datetime # 信息 message: str # the __all__ is generated __all__ = ["TimeMessage"] ================================================ FILE: src/zvt/misc/misc_service.py ================================================ # -*- coding: utf-8 -*- from zvt.misc.zhdate import ZhDate from zvt.utils.time_utils import to_pd_timestamp, current_date, count_interval def holiday_distance(timestamp=None, consider_future_days=15): if not timestamp: the_date = current_date() else: the_date = to_pd_timestamp(timestamp) # 业绩预告 month = the_date.month infos = [f"今天是{the_date.date()}"] if month == 12: infos.append("业绩预告期，注意排雷") # 元旦 new_year = to_pd_timestamp(f"{the_date.year + 1}-01-01") distance = count_interval(the_date, new_year) if 0 < distance < consider_future_days: infos.append(f"距离元旦还有{distance}天") if month in (1, 2): # 春节 zh_date = ZhDate(lunar_year=the_date.year, lunar_month=1, lunar_day=1) spring_date = zh_date.newyear distance = count_interval(the_date, spring_date) if 0 < distance < consider_future_days: infos.append(f"距离春节还有{distance}天") # 两会 # 三月初 lianghui = to_pd_timestamp(f"{the_date.year}-03-01") distance = count_interval(the_date, lianghui) if 0 < distance < consider_future_days: infos.append(f"距离两会还有{distance}天") # 年报发布 if month in (3, 4): infos.append("年报发布期，注意排雷") # 五一 if month == 4: wuyi = to_pd_timestamp(f"{the_date.year}-05-01") distance = count_interval(the_date, wuyi) if 0 < distance < consider_future_days: infos.append(f"距离五一还有{distance}天") # 业绩发布 if month in (7, 8): infos.append("半年报发布期，注意排雷") if month == 9: # 国庆 shiyi = to_pd_timestamp(f"{the_date.year}-10-01") distance = count_interval(the_date, shiyi) if 0 < distance < consider_future_days: infos.append(f"距离国庆还有{distance}天") msg = "，".join(infos) return msg def get_time_message(): return {"timestamp": current_date(), "message": holiday_distance()} if __name__ == "__main__": print(get_time_message()) # the __all__ is generated __all__ = ["holiday_distance", "get_time_message"] ================================================ FILE: src/zvt/misc/zhdate.py ================================================ """ -*- coding: utf-8 -*- thanks to https://github.com/CutePandaSh/zhdate """ from datetime import datetime, timedelta from itertools import accumulate from zvt.misc.constants import CHINESEYEARCODE, CHINESENEWYEAR class ZhDate: def __init__(self, lunar_year, lunar_month, lunar_day, leap_month=False): """初始化函数 Arguments: lunar_year {int} -- 农历年 lunar_month {int} -- 农历月份 lunar_day {int} -- 农历日 Keyword Arguments: leap_month {bool} -- 是否是在农历闰月中 (default: {False}) """ self.lunar_year = lunar_year self.lunar_month = lunar_month self.lunar_day = lunar_day self.leap_month = leap_month self.year_code = CHINESEYEARCODE[self.lunar_year - 1900] self.newyear = datetime.strptime(CHINESENEWYEAR[self.lunar_year - 1900], "%Y%m%d") if not ZhDate.validate(lunar_year, lunar_month, lunar_day, leap_month): raise TypeError("农历日期不支持所谓“{}”，超出农历1900年1月1日至2100年12月29日，或日期不存在".format(self)) def to_datetime(self): """农历日期转换称公历日期 Returns: datetime -- 当前农历对应的公历日期 """ return self.newyear + timedelta(days=self.__days_passed()) @staticmethod def from_datetime(dt): """静态方法，从公历日期生成农历日期 Arguments: dt {datetime} -- 公历的日期 Returns: ZhDate -- 生成的农历日期对象 """ lunar_year = dt.year # 如果还没有到农历正月初一 农历年份减去1 lunar_year -= (datetime.strptime(CHINESENEWYEAR[lunar_year - 1900], "%Y%m%d") - dt).total_seconds() > 0 # 当时农历新年时的日期对象 newyear_dt = datetime.strptime(CHINESENEWYEAR[lunar_year - 1900], "%Y%m%d") # 查询日期距离当年的春节差了多久 days_passed = (dt - newyear_dt).days # 被查询日期的年份码 year_code = CHINESEYEARCODE[lunar_year - 1900] # 取得本年的月份列表 month_days = ZhDate.decode(year_code) for pos, days in enumerate(accumulate(month_days)): if days_passed + 1 <= days: month = pos + 1 lunar_day = month_days[pos] - (days - days_passed) + 1 break leap_month = False if (year_code & 0xF) == 0 or month <= (year_code & 0xF): lunar_month = month else: lunar_month = month - 1 if (year_code & 0xF) != 0 and month == (year_code & 0xF) + 1: leap_month = True return ZhDate(lunar_year, lunar_month, lunar_day, leap_month) @staticmethod def today(): return ZhDate.from_datetime(datetime.now()) def __days_passed(self): """私有方法，计算当前农历日期和当年农历新年之间的天数差值 Returns: int -- 差值天数 """ month_days = ZhDate.decode(self.year_code) # 当前农历年的闰月，为0表示无润叶 month_leap = self.year_code & 0xF # 当年无闰月，或者有闰月但是当前月小于闰月 if (month_leap == 0) or (self.lunar_month < month_leap): days_passed_month = sum(month_days[: self.lunar_month - 1]) # 当前不是闰月，并且当前月份和闰月相同 elif (not self.leap_month) and (self.lunar_month == month_leap): days_passed_month = sum(month_days[: self.lunar_month - 1]) else: days_passed_month = sum(month_days[: self.lunar_month]) return days_passed_month + self.lunar_day - 1 def chinese(self): ZHNUMS = "〇一二三四五六七八九十" zh_year = "" for i in range(0, 4): zh_year += ZHNUMS[int(str(self.lunar_year)[i])] if self.leap_month: zh_month = "闰" else: zh_month = "" if self.lunar_month == 1: zh_month += "正" elif self.lunar_month == 12: zh_month += "腊" elif self.lunar_month <= 10: zh_month += ZHNUMS[self.lunar_month] else: zh_month += "十{}".format(ZHNUMS[self.lunar_month - 10]) if self.lunar_day <= 10: zh_day = "初{}".format(ZHNUMS[self.lunar_day]) elif self.lunar_day < 20: zh_day = "十{}".format(ZHNUMS[self.lunar_day - 10]) elif self.lunar_day == 20: zh_day = "二十" elif self.lunar_day < 30: zh_day = "廿{}".format(ZHNUMS[self.lunar_day - 20]) else: zh_day = "三十" year_tiandi = ZhDate.__tiandi(self.lunar_year - 1900 + 36) shengxiao = "鼠牛虎兔龙蛇马羊猴鸡狗猪" return "{}年{}月{} {}{}年".format(zh_year, zh_month, zh_day, year_tiandi, shengxiao[(self.lunar_year - 1900) % 12]) def __str__(self): """打印字符串的方法 Returns: str -- 标准格式农历日期字符串 """ return "农历{}年{}{}月{}日".format(self.lunar_year, "闰" if self.leap_month else "", self.lunar_month, self.lunar_day) def __repr__(self): return self.__str__() def __eq__(self, another): if not isinstance(another, ZhDate): raise TypeError("比较必须都是ZhDate类型") cond1 = self.lunar_year == another.lunar_year cond2 = self.lunar_month == another.lunar_month cond3 = self.lunar_day == another.lunar_day cond4 = self.leap_month == another.leap_month return cond1 and cond2 and cond3 and cond4 def __add__(self, another): if not isinstance(another, int): raise TypeError("加法只支持整数天数相加") return ZhDate.from_datetime(self.to_datetime() + timedelta(days=another)) def __sub__(self, another): if isinstance(another, int): return ZhDate.from_datetime(self.to_datetime() - timedelta(days=another)) elif isinstance(another, ZhDate): return (self.to_datetime() - another.to_datetime()).days elif isinstance(another, datetime): return (self.to_datetime() - another).days else: raise TypeError("减法只支持整数，ZhDate, Datetime类型") """ 以下为帮助函数 """ @staticmethod def __tiandi(anum): tian = "甲乙丙丁戊己庚辛壬癸" di = "子丑寅卯辰巳午未申酉戌亥" return "{}{}".format(tian[anum % 10], di[anum % 12]) @staticmethod def validate(year, month, day, leap): """农历日期校验 Arguments: year {int} -- 农历年份 month {int} -- 农历月份 day {int} -- 农历日期 leap {bool} -- 农历是否为闰月日期 Returns: bool -- 校验是否通过 """ # 年份低于1900，大于2100，或者月份不属于 1-12，或者日期不属于 1-30，返回校验失败 if not (1900 <= year <= 2100 and 1 <= month <= 12 and 1 <= day <= 30): return False year_code = CHINESEYEARCODE[year - 1900] # 有闰月标志 if leap: if (year_code & 0xF) != month: # 年度闰月和校验闰月不一致的话，返回校验失败 return False elif day == 30: # 如果日期是30的话，直接返回年度代码首位是否为1，即闰月是否为大月 return (year_code >> 16) == 1 else: # 年度闰月和当前月份相同，日期不为30的情况，返回通过 return True elif day <= 29: # 非闰月，并且日期小于等于29，返回通过 return True else: # 非闰月日期为30，返回年度代码中的月份位是否为1，即是否为大月 return ((year_code >> (12 - month) + 4) & 1) == 1 @staticmethod def decode(year_code): """解析年度农历代码函数 Arguments: year_code {int} -- 从年度代码数组中获取的代码整数 Returns: list[int, ] -- 当前年度代码解析以后形成的每月天数数组，已将闰月嵌入对应位置，即有闰月的年份返回的列表长度为13，否则为12 """ # 请问您为什么不在这么重要的地方写注释？ month_days = [] for i in range(4, 16): # 向右移动相应的位数 # 1 这个数只有一位，与任何数进行 按位与 都只能获得其 # 从后往前第一位，对！是获得这一位 month_days.insert(0, 30 if (year_code >> i) & 1 else 29) # 0xf 即 15 即二进制的 1111 # 所以 1111 与任何数进行 按位与 # 都将获得其最后四位，对！是获得这最后四位 # 后四位非0则表示有闰月（多一月），则插入一次月份 # 而首四位表示闰月的天数 if year_code & 0xF: month_days.insert((year_code & 0xF), 30 if year_code >> 16 else 29) # 返回一个列表 return month_days @staticmethod def month_days(year): """根据年份返回当前农历月份天数list Arguments: year {int} -- 1900到2100的之间的整数 Returns: [int] -- 农历年份所对应的农历月份天数列表 """ return ZhDate.decode(CHINESEYEARCODE[year - 1900]) # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/ml/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule lables from .lables import * from .lables import __all__ as _lables_all __all__ += _lables_all # import all from submodule ml from .ml import * from .ml import __all__ as _ml_all __all__ += _ml_all ================================================ FILE: src/zvt/ml/lables.py ================================================ # -*- coding: utf-8 -*- from enum import Enum class BehaviorCategory(Enum): # 上涨 up = 1 # 下跌 down = -1 class RelativePerformance(Enum): # 表现比90%好 best = 0.9 ordinary = 0.5 poor = 0 # the __all__ is generated __all__ = ["BehaviorCategory", "RelativePerformance"] ================================================ FILE: src/zvt/ml/ml.py ================================================ # -*- coding: utf-8 -*- import logging from typing import Union, Type, List import pandas as pd from sklearn.linear_model import LinearRegression, SGDRegressor from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from zvt.api.kdata import default_adjust_type, get_kdata from zvt.contract import IntervalLevel, AdjustType from zvt.contract import TradableEntity from zvt.contract.drawer import Drawer from zvt.domain import Stock from zvt.factors.transformers import MaTransformer from zvt.ml.lables import RelativePerformance, BehaviorCategory from zvt.utils.pd_utils import group_by_entity_id, normalize_group_compute_result, pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp logger = logging.getLogger(__name__) def cal_change(s: pd.Series, predict_range): return s.pct_change(periods=-predict_range) def cal_behavior_cls(s: pd.Series, predict_range): return s.pct_change(periods=-predict_range).apply( lambda x: BehaviorCategory.up.value if x > 0 else BehaviorCategory.down.value ) def cal_predict(s: pd.Series, predict_range): return s.shift(periods=-predict_range) def cal_relative_performance(s: pd.Series): if s >= RelativePerformance.best.value: return RelativePerformance.best if s >= RelativePerformance.ordinary.value: return RelativePerformance.ordinary if s >= RelativePerformance.poor.value: return RelativePerformance.poor class MLMachine(object): entity_schema: Type[TradableEntity] = None def __init__( self, entity_ids: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = "2015-01-01", end_timestamp: Union[str, pd.Timestamp] = "2021-12-01", predict_start_timestamp: Union[str, pd.Timestamp] = "2021-06-01", predict_steps: int = 20, level: Union[IntervalLevel, str] = IntervalLevel.LEVEL_1DAY, adjust_type: Union[AdjustType, str] = None, data_provider: str = None, label_method: str = "raw", ) -> None: """ :param entity_ids: :param start_timestamp: :param end_timestamp: :param predict_start_timestamp: :param predict_steps: :param level: :param adjust_type: :param data_provider: :param label_method: raw, change, or behavior_cls """ super().__init__() self.entity_ids = entity_ids self.start_timestamp = to_pd_timestamp(start_timestamp) self.end_timestamp = to_pd_timestamp(end_timestamp) self.predict_start_timestamp = to_pd_timestamp(predict_start_timestamp) assert self.start_timestamp < self.predict_start_timestamp < self.end_timestamp self.predict_steps = predict_steps self.level = level if not adjust_type: adjust_type = default_adjust_type(entity_type=self.entity_schema.__name__) self.adjust_type = adjust_type self.data_provider = data_provider self.label_method = label_method self.kdata_df = self.build_kdata() if not pd_is_not_null(self.kdata_df): logger.error("not kdta") assert False self.feature_df = self.build_feature(self.entity_ids, self.start_timestamp, self.end_timestamp) # drop na in feature self.feature_df = self.feature_df.dropna() self.feature_names = list(set(self.feature_df.columns) - {"entity_id", "timestamp"}) self.feature_df = self.feature_df.loc[:, self.feature_names] self.label_ser = self.build_label() # keep same index with feature df self.label_ser = self.label_ser.loc[self.feature_df.index] self.label_name = self.label_ser.name self.training_X, self.training_y, self.testing_X, self.testing_y = self.split_data() logger.info(self.training_X) logger.info(self.training_y) self.model = None self.pred_y = None def split_data(self): training_x = self.feature_df[self.feature_df.index.get_level_values("timestamp") < self.predict_start_timestamp] training_y = self.label_ser[self.label_ser.index.get_level_values("timestamp") < self.predict_start_timestamp] testing_x = self.feature_df[self.feature_df.index.get_level_values("timestamp") >= self.predict_start_timestamp] testing_y = self.label_ser[self.label_ser.index.get_level_values("timestamp") >= self.predict_start_timestamp] return training_x, training_y, testing_x, testing_y def build_kdata(self): columns = ["entity_id", "timestamp", "close"] return get_kdata( entity_ids=self.entity_ids, start_timestamp=self.start_timestamp, end_timestamp=self.end_timestamp, columns=columns, level=self.level, adjust_type=self.adjust_type, provider=self.data_provider, index=["entity_id", "timestamp"], drop_index_col=True, ) def build_label(self): label_name = f"y_{self.predict_steps}" if self.label_method == "raw": y = ( group_by_entity_id(self.kdata_df["close"]) .apply(lambda x: cal_predict(x, self.predict_steps)) .rename(label_name) ) elif self.label_method == "change": y = ( group_by_entity_id(self.kdata_df["close"]) .apply(lambda x: cal_change(x, self.predict_steps)) .rename(label_name) ) elif self.label_method == "behavior_cls": y = ( group_by_entity_id(self.kdata_df["close"]) .apply(lambda x: cal_behavior_cls(x, self.predict_steps)) .rename(label_name) ) else: assert False y = normalize_group_compute_result(y) return y def train(self, model=LinearRegression(), **params): self.model = model.fit(self.training_X, self.training_y, **params) return self.model def draw_result(self, entity_id): if self.label_method == "raw": df = self.kdata_df.loc[[entity_id], ["close"]].copy() pred_df = self.pred_y.to_frame(name="pred_close") pred_df = pred_df.loc[[entity_id], :].shift(self.predict_steps) drawer = Drawer( main_df=df, factor_df_list=[pred_df], ) drawer.draw_line(show=True) else: pred_df = self.pred_y.to_frame(name="pred_result").loc[[entity_id], :] df = self.testing_y.to_frame(name="real_result").loc[[entity_id], :].join(pred_df, how="outer") drawer = Drawer(main_df=df) drawer.draw_table() def predict(self): predictions = self.model.predict(self.testing_X) self.pred_y = pd.Series(data=predictions, index=self.testing_y.index) # explained_variance_score(self.testing_y, self.pred_y) # mean_squared_error(self.testing_y, self.pred_y) def build_feature( self, entity_ids: List[str], start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp ) -> pd.DataFrame: """ result df format col1 col2 col3 ... entity_id timestamp 1.2 0.5 0.3 ... 1.0 0.7 0.2 ... :param entity_ids: entity id list :param start_timestamp: :param end_timestamp: :rtype: pd.DataFrame """ raise NotImplementedError class StockMLMachine(MLMachine): entity_schema = Stock class MaStockMLMachine(StockMLMachine): def build_feature( self, entity_ids: List[str], start_timestamp: pd.Timestamp, end_timestamp: pd.Timestamp ) -> pd.DataFrame: """ :param entity_ids: :param start_timestamp: :param end_timestamp: :return: """ t = MaTransformer(windows=[5, 10, 120, 250]) df = t.transform(self.kdata_df) return df if __name__ == "__main__": machine = MaStockMLMachine(entity_ids=["stock_sz_000001"]) reg = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3)) machine.train(model=reg) machine.predict() machine.draw_result(entity_id="stock_sz_000001") # the __all__ is generated __all__ = [ "cal_change", "cal_behavior_cls", "cal_predict", "cal_relative_performance", "MLMachine", "StockMLMachine", "MaStockMLMachine", ] ================================================ FILE: src/zvt/plugin.py ================================================ # -*- coding: utf-8 -*- import argparse from zvt.autocode import gen_exports from zvt.autocode.generator import gen_plugin_project def main(): parser = argparse.ArgumentParser() parser.add_argument("--entity", help="entity name", default="future") parser.add_argument("--prefix", help="project prefix", default="zvt") parser.add_argument("--dir", help="project directory", default=".") parser.add_argument("--providers", help="providers", default=["joinquant"], nargs="+") args = parser.parse_args() dir_path = args.dir entity = args.entity providers = args.providers prefix = args.prefix gen_plugin_project(prefix=prefix, dir_path=dir_path, entity_type=entity, providers=providers) def export(): parser = argparse.ArgumentParser() parser.add_argument("--dir", help="export directory", default=".") args = parser.parse_args() dir_path = args.dir gen_exports(dir_path=dir_path) if __name__ == "__main__": gen_plugin_project(dir_path="../../../", entity_type="macro", providers=["zvt"]) main() ================================================ FILE: src/zvt/recorders/__init__.py ================================================ # -*- coding: utf-8 -*- CHINA_STOCK_MAIN_INDEX = [ # # 聚宽编码 # # 市场通编码 市场通名称 # # 310001 沪股通 # # 310002 深股通 # # 310003 港股通（沪） # # 310004 港股通（深） { "id": "index_cn_310001", "entity_id": "index_cn_310001", "code": "310001", "name": "沪股通", "timestamp": "2014-11-17", "exchange": "cn", "entity_type": "index", "category": "other", }, { "id": "index_cn_310002", "entity_id": "index_cn_310002", "code": "310002", "name": "深股通", "timestamp": "2014-11-17", "exchange": "cn", "entity_type": "index", "category": "other", }, { "id": "index_cn_310003", "entity_id": "index_cn_310003", "code": "310003", "name": "港股通（沪）", "timestamp": "2014-11-17", "exchange": "cn", "entity_type": "index", "category": "other", }, { "id": "index_cn_310004", "entity_id": "index_cn_310004", "code": "310004", "name": "港股通（深）", "timestamp": "2014-11-17", "exchange": "cn", "entity_type": "index", "category": "other", }, ] def init_main_index(provider="exchange"): from zvt.utils.time_utils import to_pd_timestamp import pandas as pd from zvt.contract.api import df_to_db from zvt.domain.meta import Index for item in CHINA_STOCK_MAIN_INDEX: item["timestamp"] = to_pd_timestamp(item["timestamp"]) df = pd.DataFrame(CHINA_STOCK_MAIN_INDEX) # print(df) df_to_db(df=df, data_schema=Index, provider=provider, force_update=False) # the __all__ is generated __all__ = ["init_main_index"] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule consts from .consts import * from .consts import __all__ as _consts_all __all__ += _consts_all # import all from submodule jqka from .jqka import * from .jqka import __all__ as _jqka_all __all__ += _jqka_all # import all from submodule eastmoney from .eastmoney import * from .eastmoney import __all__ as _eastmoney_all __all__ += _eastmoney_all # import all from submodule em from .em import * from .em import __all__ as _em_all __all__ += _em_all # import all from submodule exchange from .exchange import * from .exchange import __all__ as _exchange_all __all__ += _exchange_all # import all from submodule wb from .wb import * from .wb import __all__ as _wb_all __all__ += _wb_all # import all from submodule joinquant from .joinquant import * from .joinquant import __all__ as _joinquant_all __all__ += _joinquant_all # import all from submodule sina from .sina import * from .sina import __all__ as _sina_all __all__ += _sina_all # Recorders must be imported before schema use (providers derived from Recorder registration) init_main_index(provider="exchange") ================================================ FILE: src/zvt/recorders/consts.py ================================================ # -*- coding: utf-8 -*- from zvt.utils.utils import chrome_copy_header_to_dict SSE_KDATA_HEADER = chrome_copy_header_to_dict( """ Host: yunhq.sse.com.cn:32041 Connection: keep-alive Pragma: no-cache Cache-Control: no-cache User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36 Accept: */* Referer: http://www.sse.com.cn/market/price/trends/ Accept-Encoding: gzip, deflate Accept-Language: zh-CN,zh;q=0.8,en;q=0.6 Cookie: yfx_c_g_u_id_10000042=_ck17072000172016360411059933357; yfx_f_l_v_t_10000042=f_t_1500481040618__r_t_1507560823182__v_t_1507561607501__r_c_1; VISITED_MENU=%5B%228451%22%2C%228453%22%5D """ ) DEFAULT_SH_HEADER = chrome_copy_header_to_dict( """ Accept: */* Accept-Encoding: gzip, deflate Accept-Language: zh-CN,zh;q=0.9,en;q=0.8 Connection: keep-alive Cookie: yfx_c_g_u_id_10000042=_ck21052517524516575117884257529; yfx_f_l_v_t_10000042=f_t_1621936365643__r_t_1621936365643__v_t_1621936365643__r_c_0; VISITED_MENU=%5B%228466%22%2C%228528%22%5D; JSESSIONID=3C970EA11443D2B7F3844558174C2CDB Host: query.sse.com.cn Referer: http://www.sse.com.cn/ User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/90.0.4430.212 Safari/537.36 """ ) DEFAULT_SZ_HEADER = chrome_copy_header_to_dict( """ Accept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8 Accept-Encoding:gzip, deflate, sdch Accept-Language:zh-CN,zh;q=0.8,en;q=0.6 Connection:keep-alive Host:www.szse.cn Referer:http://www.szse.cn/main/marketdata/jypz/colist/ Upgrade-Insecure-Requests:1 User-Agent:Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.76 Mobile Safari/537.36 """ ) DEFAULT_TICK_HEADER = chrome_copy_header_to_dict( """ Host: market.finance.sina.com.cn Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8 Accept-Encoding: gzip, deflate, sdch Accept-Language: zh-CN,zh;q=0.8,en;q=0.6 Cookie: U_TRS1=000000b3.fffc2f53.566f784b.2900c0a6; UOR=www.baidu.com,blog.sina.com.cn,; SINAGLOBAL=182.139.185.185_1450145868.176138; vjuids=-fde6fe2f2.151a36dec26.0.113c0d8; SGUID=1450145869010_36029656; Apache=182.139.185.132_1457087046.693218; ULV=1457087047846:3:2:2:182.139.185.132_1457087046.693218:1457087047557; U_TRS2=0000009f.95a9161b.57038c9e.11b3a69f; SessionID=e2ajs2t3k4emag633adfjho861; SINA_FINANCE_SELECT_TYPE=stock; SCF=AhFAeZgjMw_ozKbe2MXZtX2fG8ZrLemvX24bd57wciL98hfpOY_4hN8aYqvvaxZDbINdQq3XqZSrS-ubJo-5VUI.; sso_info=v02m6alo5qztbaYloWum6akpp2WpaSPk4S1jpOYsYyjlLONg5DA; 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SINA_PORTFOLIO=sh601600%2Csh600089%2Csz000528%2Csh600150%2Csz000338%2Csh600031%2Csh600199%2Csh000001%2Csz399001%2Csz000778%2Csh600315%2Csh600570%2Csh601608%2Csz000099%2Csh600439%2Csz000521%2Csz300276%2Csz000783%2Csh600396%2Csz300336%2Csz000751%2Csz000848%2Csh600711%2Csh600880%2Csh600362%2Csh600060%2Csz000680%2Csz002051; ArtiFSize=14; visited_uss=gb_goog%7Cgb_sina%7Cgb_.dji; FINA_DMHQ=0; hqEtagMode=1; VISITED_FUTURE=hf_CL_0%2Chf_GC_0%2Chf_SI_0%2Chf_CAD_0%2CCF1609_1%2CRB0_1%2Chf_DJS_0%2CCFF_RE_IF1606_2%2CCFF_RE_IF1510_2%2CCFF_RE_IC1606_2%2CRM0_1%2CRB1610_1; lxlrttp=1476092678; FINA_V5_HQ=0; SUB=_2A251BPWoDeTxGedL4lQQ8ivPzziIHXVWcGBgrDV_PUNbm9BeLXLzkW8iLp81bNoV5si_9RFc9X4XY1wBcg..; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9W5J7vXaOMNYJQ0d27EZPWIF5NHD95QpSK.ceKzfe0BXWs4Dqcj.i--fi-zRiKnEi--fiKLhi-iWi--Xi-isi-88i--Ri-2piKyh; ALF=1507965304; FIN_ALL_VISITED=sz000338%2Csh000001%2Csz000680%2Csz399006%2Csh600028%2Csh600315%2Csh600031%2Csh601608%2Csh601038%2Csh601628%2CCL%2Csh600150%2Csz300369%2Csz300079%2CAGTD%2Csz300336%2Csz300276%2Cgoog%2Csina%2CGC%2CSI%2Csh600004%2Csh600000%2Csz300374%2Csh600199%2Csh600439%2Csz000783%2Csz000912%2CCAD%2Csz002628%2Csh600362%2Csz002051%2Csz000930%2Csz000528%2Csh603568%2Csh600060%2Csh600635%2Csz150266%2Csz000651%2Csz000099%2Csh600570%2Csz000002%2Csh600048%2Csz000839%2Csz002223%2Csh600880%2Csz000521%2Csz000878%2Csz399001%2Csh600396%2Csz000751%2Csh600016%2Csz000915%2Csh600779%2Csh600497%2Csh601600%2Csh600111%2Csh600550%2Csz000426%2Csz002237; FINA_LV2_S_2=1596125344; FINA_LV2_S_2_B=0; rotatecount=1; vjlast=1476433318; FINA_V_S_2=sz000338,sh000001,sz000680,sz399006,sh600028,sh600315,sh600031,sh601608,sh601038,sh601628,sh600150,sz300369,sz300079,sz300336,sz300276,sh600004,sh600000,sz300374,sh600199,sh600439 Referer:market.finance.sina.com.cn Upgrade-Insecure-Requests: 1 User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36 """ ) DEFAULT_KDATA_HEADER = chrome_copy_header_to_dict( """ Accept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8 Accept-Encoding:gzip, deflate, sdch Accept-Language:zh-CN,zh;q=0.8,en;q=0.6 Cache-Control:max-age=0 Connection:keep-alive Host:vip.stock.finance.sina.com.cn Referer:vip.stock.finance.sina.com.cn Upgrade-Insecure-Requests:1 User-Agent:Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.76 Mobile Safari/537.36 """ ) TONGHUASHUN_GN_HEADER = chrome_copy_header_to_dict( """ Accept:text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8 Accept-Encoding:gzip, deflate, sdch Accept-Language:zh-CN,zh;q=0.8,en;q=0.6 Cache-Control:max-age=0 Connection:keep-alive Cookie:Hm_lvt_ab89213e83c551bf095446c08bf64988=1477116434; Hm_lpvt_ab89213e83c551bf095446c08bf64988=1477116434; historystock=600126; spversion=20130314; Hm_lvt_78c58f01938e4d85eaf619eae71b4ed1=1477105757,1477116434; Hm_lpvt_78c58f01938e4d85eaf619eae71b4ed1=1477362626 Host:q.10jqka.com.cn Upgrade-Insecure-Requests:1 User-Agent:Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.76 Mobile Safari/537.36 """ ) TONGHUASHUN_KDATA_HEADER = chrome_copy_header_to_dict( """ Accept-Encoding: gzip, deflate Accept-Language: zh-CN,zh;q=0.8,en;q=0.6 User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36 Accept: */* Referer: http://stockpage.10jqka.com.cn/HQ_v4.html Cookie: __utma=156575163.1843700306.1488352720.1499234323.1502172029.4; __utmc=156575163; __utmz=156575163.1488352720.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); spversion=20130314; Hm_lvt_78c58f01938e4d85eaf619eae71b4ed1=1507300869; Hm_lpvt_78c58f01938e4d85eaf619eae71b4ed1=1508464591; historystock=603189%7C*%7C300295%7C*%7C600839%7C*%7C000338%7C*%7C002194; log=; v=AREjaxfGLPcJoUDd5wHRp1QiKRaufoSAL_MpD_OlDDL35T_CO86VwL9CPa2D Connection: keep-alive """ ) DEFAULT_BALANCE_SHEET_HEADER = chrome_copy_header_to_dict( """ Host: money.finance.sina.com.cn User-Agent: Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:54.0) Gecko/20100101 Firefox/54.0 Accept: text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8 Accept-Language: en-US,en;q=0.5 Accept-Encoding: gzip, deflate Referer: http://vip.stock.finance.sina.com.cn/corp/go.php/vFD_CashFlow/stockid/000338/ctrl/part/displaytype/4.phtml Cookie: U_TRS1=000000be.c95848c3.59817e10.a54886e2; U_TRS2=000000be.c96a48c3.59817e10.a91795e2; UOR=,vip.stock.finance.sina.com.cn,; ULV=1501658645426:2:2:2:182.148.114.190_1501658642.469995:1501658642409; SINAGLOBAL=182.148.114.190_1501658642.469991; Apache=182.148.114.190_1501658642.469995; _s_upa=1; SUB=_2A250hQ5nDeRhGedL4lQQ8ivPzziIHXVX83ivrDV_PUNbm9BeLXigkW8-niaOks2yNkw8lYo-TvoqGk6nRA..; SUBP=0033WrSXqPxfM725Ws9jqgMF55529P9D9W5J7vXaOMNYJQ0d27EZPWIF5NHD95QpSK.ceKzfe0BXWs4Dqcj.i--fi-zRiKnEi--fiKLhi-iWi--Xi-isi-88i--Ri-2piKyh; SCF=At4whqZZyjTBTvcLfR0tyqIpfHUX2VOK-qvBVHkbyahiCVcr4-8NjJQGHwCaTtkQJ0SPmrzvZARwtEkL1I_46z8.; ALF=1533194679; sso_info=v02m6alo5qztbaYloWum6akpp2WpaSPk4S1jpOYsYyjlLONg5DA; FINANCE2=f7634b1d12920e2763ffc0dc463ef6bb Connection: keep-alive Upgrade-Insecure-Requests: 1 """ ) DEFAULT_SH_SUMMARY_HEADER = chrome_copy_header_to_dict( """ Host: query.sse.com.cn Connection: keep-alive User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/61.0.3163.100 Safari/537.36 Accept: */* Referer: http://www.sse.com.cn/market/stockdata/overview/day/ Accept-Encoding: gzip, deflate Accept-Language: zh-CN,zh;q=0.8,en;q=0.6 Cookie: yfx_c_g_u_id_10000042=_ck17122009304714819234313401740; VISITED_COMPANY_CODE=%5B%22000016%22%5D; VISITED_INDEX_CODE=%5B%22000016%22%5D; yfx_f_l_v_t_10000042=f_t_1513733447386__r_t_1515716891222__v_t_1515721033042__r_c_3; VISITED_MENU=%5B%228464%22%2C%229666%22%2C%229668%22%2C%229669%22%2C%228454%22%2C%228460%22%2C%229665%22%2C%228459%22%2C%229692%22%2C%228451%22%2C%228466%22%5D """ ) DEFAULT_SH_ETF_LIST_HEADER = chrome_copy_header_to_dict( """ Host: query.sse.com.cn User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36 Accept: */* Referer: http://www.sse.com.cn/assortment/fund/etf/list/ Accept-Encoding: gzip, deflate Accept-Language: zh-CN,zh;q=0.9 Cookie: yfx_c_g_u_id_10000042=_ck19062609443812815766114343798; VISITED_COMPANY_CODE=%5B%22510300%22%5D; VISITED_FUND_CODE=%5B%22510300%22%5D; VISITED_MENU=%5B%228307%22%2C%228823%22%2C%228547%22%2C%228556%22%2C%228549%22%2C%2210848%22%2C%228550%22%5D; yfx_f_l_v_t_10000042=f_t_1561513478278__r_t_1561692626758__v_t_1561695738302__r_c_1 Connection: keep-alive """ ) EASTMONEY_ETF_NET_VALUE_HEADER = chrome_copy_header_to_dict( """ User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36 Referer: http://fund.eastmoney.com/ """ ) DEFAULT_HEADER = chrome_copy_header_to_dict( """ User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36 """ ) # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/recorders/eastmoney/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule common from .common import * from .common import __all__ as _common_all __all__ += _common_all # import all from submodule trading from .trading import * from .trading import __all__ as _trading_all __all__ += _trading_all # import all from submodule holder from .holder import * from .holder import __all__ as _holder_all __all__ += _holder_all # import all from submodule finance from .finance import * from .finance import __all__ as _finance_all __all__ += _finance_all # import all from submodule meta from .meta import * from .meta import __all__ as _meta_all __all__ += _meta_all # import all from submodule dividend_financing from .dividend_financing import * from .dividend_financing import __all__ as _dividend_financing_all __all__ += _dividend_financing_all ================================================ FILE: src/zvt/recorders/eastmoney/common.py ================================================ # -*- coding: utf-8 -*- import logging import requests from zvt.contract.api import get_data_count, get_data from zvt.contract.recorder import TimestampsDataRecorder, TimeSeriesDataRecorder from zvt.domain import CompanyType from zvt.domain.meta.stock_meta import StockDetail from zvt.utils.time_utils import to_pd_timestamp logger = logging.getLogger(__name__) class ApiWrapper(object): def request(self, url=None, method="post", param=None, path_fields=None): raise NotImplementedError def get_fc(security_item): if security_item.exchange == "sh": fc = "{}01".format(security_item.code) if security_item.exchange == "sz": fc = "{}02".format(security_item.code) return fc def get_company_type(stock_domain: StockDetail): industries = stock_domain.industries.split(",") if ("银行" in industries) or ("信托" in industries): return CompanyType.yinhang if "保险" in industries: return CompanyType.baoxian if "证券" in industries: return CompanyType.quanshang return CompanyType.qiye def company_type_flag(security_item): try: company_type = get_company_type(security_item) if company_type == CompanyType.qiye: return "4" if company_type == CompanyType.quanshang: return "1" if company_type == CompanyType.baoxian: return "2" if company_type == CompanyType.yinhang: return "3" except Exception as e: logger.warning(e) param = {"color": "w", "fc": get_fc(security_item)} resp = requests.post("https://emh5.eastmoney.com/api/CaiWuFenXi/GetCompanyType", json=param) ct = resp.json().get("Result").get("CompanyType") logger.warning("{} not catching company type:{}".format(security_item, ct)) return ct def call_eastmoney_api(url=None, method="post", param=None, path_fields=None): if method == "post": resp = requests.post(url, json=param) resp.encoding = "utf-8" try: origin_result = resp.json().get("Result") except Exception as e: logger.exception("code:{},content:{}".format(resp.status_code, resp.text)) raise e if path_fields: the_data = get_from_path_fields(origin_result, path_fields) if not the_data: logger.warning( "url:{},param:{},origin_result:{},could not get data for nested_fields:{}".format( url, param, origin_result, path_fields ) ) return the_data return origin_result def get_from_path_fields(the_json, path_fields): the_data = the_json.get(path_fields[0]) if the_data: for field in path_fields[1:]: the_data = the_data.get(field) if not the_data: return None return the_data class EastmoneyApiWrapper(ApiWrapper): def request(self, url=None, method="post", param=None, path_fields=None): return call_eastmoney_api(url=url, method=method, param=param, path_fields=path_fields) class BaseEastmoneyRecorder(object): request_method = "post" path_fields = None api_wrapper = EastmoneyApiWrapper() def generate_request_param(self, security_item, start, end, size, timestamp): raise NotImplementedError def record(self, entity_item, start, end, size, timestamps): if timestamps: original_list = [] for the_timestamp in timestamps: param = self.generate_request_param(entity_item, start, end, size, the_timestamp) tmp_list = self.api_wrapper.request( url=self.url, param=param, method=self.request_method, path_fields=self.path_fields ) self.logger.info( "record {} for entity_id:{},timestamp:{}".format(self.data_schema, entity_item.id, the_timestamp) ) # fill timestamp field for tmp in tmp_list: tmp[self.get_evaluated_time_field()] = the_timestamp original_list += tmp_list if len(original_list) == 50: break return original_list else: param = self.generate_request_param(entity_item, start, end, size, None) return self.api_wrapper.request( url=self.url, param=param, method=self.request_method, path_fields=self.path_fields ) class EastmoneyTimestampsDataRecorder(BaseEastmoneyRecorder, TimestampsDataRecorder): entity_provider = "eastmoney" entity_schema = StockDetail provider = "eastmoney" timestamps_fetching_url = None timestamp_list_path_fields = None timestamp_path_fields = None def init_timestamps(self, entity): param = {"color": "w", "fc": get_fc(entity)} timestamp_json_list = call_eastmoney_api( url=self.timestamps_fetching_url, path_fields=self.timestamp_list_path_fields, param=param ) if self.timestamp_path_fields and timestamp_json_list: timestamps = [get_from_path_fields(data, self.timestamp_path_fields) for data in timestamp_json_list] return [to_pd_timestamp(t) for t in timestamps] return [] class EastmoneyPageabeDataRecorder(BaseEastmoneyRecorder, TimeSeriesDataRecorder): entity_provider = "eastmoney" entity_schema = StockDetail provider = "eastmoney" page_url = None def get_remote_count(self, security_item): param = {"color": "w", "fc": get_fc(security_item), "pageNum": 1, "pageSize": 1} return call_eastmoney_api(self.page_url, param=param, path_fields=["TotalCount"]) def evaluate_start_end_size_timestamps(self, entity): remote_count = self.get_remote_count(entity) if remote_count == 0: return None, None, 0, None # get local count local_count = get_data_count( data_schema=self.data_schema, session=self.session, filters=[self.data_schema.entity_id == entity.id] ) # FIXME:the > case if local_count >= remote_count: return None, None, 0, None return None, None, remote_count - local_count, None def generate_request_param(self, security_item, start, end, size, timestamp): return { "color": "w", "fc": get_fc(security_item), "pageNum": 1, # just get more for some fixed data "pageSize": size + 10, } class EastmoneyMoreDataRecorder(BaseEastmoneyRecorder, TimeSeriesDataRecorder): entity_provider = "eastmoney" entity_schema = StockDetail provider = "eastmoney" def get_remote_latest_record(self, security_item): param = {"color": "w", "fc": get_fc(security_item), "pageNum": 1, "pageSize": 1} results = call_eastmoney_api(self.url, param=param, path_fields=self.path_fields) _, result = self.generate_domain(security_item, results[0]) return result def evaluate_start_end_size_timestamps(self, entity): # get latest record latest_record = get_data( entity_id=entity.id, provider=self.provider, data_schema=self.data_schema, order=self.data_schema.timestamp.desc(), limit=1, return_type="domain", session=self.session, ) if latest_record: remote_record = self.get_remote_latest_record(entity) if not remote_record or (latest_record[0].id == remote_record.id): return None, None, 0, None else: return None, None, 10, None return None, None, 1000, None def generate_request_param(self, security_item, start, end, size, timestamp): return {"color": "w", "fc": get_fc(security_item), "pageNum": 1, "pageSize": size} # the __all__ is generated __all__ = [ "ApiWrapper", "get_fc", "get_company_type", "company_type_flag", "call_eastmoney_api", "get_from_path_fields", "EastmoneyApiWrapper", "BaseEastmoneyRecorder", "EastmoneyTimestampsDataRecorder", "EastmoneyPageabeDataRecorder", "EastmoneyMoreDataRecorder", ] ================================================ FILE: src/zvt/recorders/eastmoney/dividend_financing/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule eastmoney_dividend_detail_recorder from .eastmoney_dividend_detail_recorder import * from .eastmoney_dividend_detail_recorder import __all__ as _eastmoney_dividend_detail_recorder_all __all__ += _eastmoney_dividend_detail_recorder_all # import all from submodule eastmoney_rights_issue_detail_recorder from .eastmoney_rights_issue_detail_recorder import * from .eastmoney_rights_issue_detail_recorder import __all__ as _eastmoney_rights_issue_detail_recorder_all __all__ += _eastmoney_rights_issue_detail_recorder_all # import all from submodule eastmoney_dividend_financing_recorder from .eastmoney_dividend_financing_recorder import * from .eastmoney_dividend_financing_recorder import __all__ as _eastmoney_dividend_financing_recorder_all __all__ += _eastmoney_dividend_financing_recorder_all # import all from submodule eastmoney_spo_detail_recorder from .eastmoney_spo_detail_recorder import * from .eastmoney_spo_detail_recorder import __all__ as _eastmoney_spo_detail_recorder_all __all__ += _eastmoney_spo_detail_recorder_all ================================================ FILE: src/zvt/recorders/eastmoney/dividend_financing/eastmoney_dividend_detail_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import DividendDetail from zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder from zvt.utils.time_utils import to_pd_timestamp class DividendDetailRecorder(EastmoneyPageabeDataRecorder): data_schema = DividendDetail url = "https://emh5.eastmoney.com/api/FenHongRongZi/GetFenHongSongZhuanList" page_url = url path_fields = ["FenHongSongZhuanList"] def get_original_time_field(self): return "GongGaoRiQi" def get_data_map(self): return { # 公告日 "announce_date": ("GongGaoRiQi", to_pd_timestamp), # 股权登记日 "record_date": ("GuQuanDengJiRi", to_pd_timestamp), # 除权除息日 "dividend_date": ("ChuQuanChuXiRi", to_pd_timestamp), # 方案 "dividend": ("FengHongFangAn", str), } if __name__ == "__main__": # init_log('dividend_detail.log') recorder = DividendDetailRecorder(codes=["601318"]) recorder.run() # the __all__ is generated __all__ = ["DividendDetailRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/dividend_financing/eastmoney_dividend_financing_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain.fundamental.dividend_financing import DividendFinancing from zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder from zvt.utils.utils import second_item_to_float class DividendFinancingRecorder(EastmoneyPageabeDataRecorder): data_schema = DividendFinancing url = "https://emh5.eastmoney.com/api/FenHongRongZi/GetLiNianFenHongRongZiList" page_url = url path_fields = ["LiNianFenHongRongZiList"] def get_original_time_field(self): return "ShiJian" def get_data_map(self): return { # 分红总额 "dividend_money": ("FenHongZongE", second_item_to_float), # 新股 "ipo_issues": ("XinGu", second_item_to_float), # 增发 "spo_issues": ("ZengFa", second_item_to_float), # 配股 "rights_issues": ("PeiFa", second_item_to_float), } def on_finish(self): try: code_security = {} for item in self.entities: code_security[item.code] = item need_fill_items = DividendFinancing.query_data( provider=self.provider, codes=list(code_security.keys()), return_type="domain", session=self.session, filters=[DividendFinancing.ipo_raising_fund.is_(None), DividendFinancing.ipo_issues != 0], ) for need_fill_item in need_fill_items: if need_fill_item: need_fill_item.ipo_raising_fund = code_security[item.code].raising_fund self.session.commit() except Exception as e: self.logger.exception(e) super().on_finish() if __name__ == "__main__": # init_log('dividend_financing.log') recorder = DividendFinancingRecorder(codes=["000999"]) recorder.run() # the __all__ is generated __all__ = ["DividendFinancingRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/dividend_financing/eastmoney_rights_issue_detail_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.consts import SAMPLE_STOCK_CODES from zvt.domain import RightsIssueDetail, DividendFinancing from zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import now_pd_timestamp from zvt.utils.utils import to_float class RightsIssueDetailRecorder(EastmoneyPageabeDataRecorder): data_schema = RightsIssueDetail url = "https://emh5.eastmoney.com/api/FenHongRongZi/GetPeiGuMingXiList" page_url = url path_fields = ["PeiGuMingXiList"] def get_original_time_field(self): return "PeiGuGongGaoRi" def get_data_map(self): return { "rights_issues": ("ShiJiPeiGu", to_float), "rights_issue_price": ("PeiGuJiaGe", to_float), "rights_raising_fund": ("ShiJiMuJi", to_float), } def on_finish(self): last_year = str(now_pd_timestamp().year) codes = [item.code for item in self.entities] need_filleds = DividendFinancing.query_data( provider=self.provider, codes=codes, return_type="domain", session=self.session, filters=[DividendFinancing.rights_raising_fund.is_(None)], end_timestamp=last_year, ) for item in need_filleds: df = RightsIssueDetail.query_data( provider=self.provider, entity_id=item.entity_id, columns=[RightsIssueDetail.timestamp, RightsIssueDetail.rights_raising_fund], start_timestamp=item.timestamp, end_timestamp="{}-12-31".format(item.timestamp.year), ) if pd_is_not_null(df): item.rights_raising_fund = df["rights_raising_fund"].sum() self.session.commit() super().on_finish() if __name__ == "__main__": # init_log('rights_issue.log') recorder = RightsIssueDetailRecorder(codes=SAMPLE_STOCK_CODES) recorder.run() # the __all__ is generated __all__ = ["RightsIssueDetailRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/dividend_financing/eastmoney_spo_detail_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import SpoDetail, DividendFinancing from zvt.recorders.eastmoney.common import EastmoneyPageabeDataRecorder from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import now_pd_timestamp from zvt.utils.utils import to_float class SPODetailRecorder(EastmoneyPageabeDataRecorder): data_schema = SpoDetail url = "https://emh5.eastmoney.com/api/FenHongRongZi/GetZengFaMingXiList" page_url = url path_fields = ["ZengFaMingXiList"] def get_original_time_field(self): return "ZengFaShiJian" def get_data_map(self): return { "spo_issues": ("ShiJiZengFa", to_float), "spo_price": ("ZengFaJiaGe", to_float), "spo_raising_fund": ("ShiJiMuJi", to_float), } def on_finish(self): last_year = str(now_pd_timestamp().year) codes = [item.code for item in self.entities] need_filleds = DividendFinancing.query_data( provider=self.provider, codes=codes, return_type="domain", session=self.session, filters=[DividendFinancing.spo_raising_fund.is_(None)], end_timestamp=last_year, ) for item in need_filleds: df = SpoDetail.query_data( provider=self.provider, entity_id=item.entity_id, columns=[SpoDetail.timestamp, SpoDetail.spo_raising_fund], start_timestamp=item.timestamp, end_timestamp="{}-12-31".format(item.timestamp.year), ) if pd_is_not_null(df): item.spo_raising_fund = df["spo_raising_fund"].sum() self.session.commit() super().on_finish() if __name__ == "__main__": # init_log('spo_detail.log') recorder = SPODetailRecorder(codes=["000999"]) recorder.run() # the __all__ is generated __all__ = ["SPODetailRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/finance/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule eastmoney_cash_flow_recorder from .eastmoney_cash_flow_recorder import * from .eastmoney_cash_flow_recorder import __all__ as _eastmoney_cash_flow_recorder_all __all__ += _eastmoney_cash_flow_recorder_all # import all from submodule eastmoney_income_statement_recorder from .eastmoney_income_statement_recorder import * from .eastmoney_income_statement_recorder import __all__ as _eastmoney_income_statement_recorder_all __all__ += _eastmoney_income_statement_recorder_all # import all from submodule eastmoney_finance_factor_recorder from .eastmoney_finance_factor_recorder import * from .eastmoney_finance_factor_recorder import __all__ as _eastmoney_finance_factor_recorder_all __all__ += _eastmoney_finance_factor_recorder_all # import all from submodule base_china_stock_finance_recorder from .base_china_stock_finance_recorder import * from .base_china_stock_finance_recorder import __all__ as _base_china_stock_finance_recorder_all __all__ += _base_china_stock_finance_recorder_all # import all from submodule eastmoney_balance_sheet_recorder from .eastmoney_balance_sheet_recorder import * from .eastmoney_balance_sheet_recorder import __all__ as _eastmoney_balance_sheet_recorder_all __all__ += _eastmoney_balance_sheet_recorder_all ================================================ FILE: src/zvt/recorders/eastmoney/finance/base_china_stock_finance_recorder.py ================================================ # -*- coding: utf-8 -*- from jqdatapy.api import get_fundamentals, get_query_count from zvt.api.utils import to_report_period_type from zvt.contract.api import get_data from zvt.domain import FinanceFactor, ReportPeriod from zvt.recorders.eastmoney.common import ( company_type_flag, get_fc, EastmoneyTimestampsDataRecorder, call_eastmoney_api, get_from_path_fields, ) from zvt.recorders.joinquant.common import to_jq_entity_id from zvt.utils.pd_utils import index_df from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str, to_pd_timestamp def to_jq_report_period(timestamp): the_date = to_pd_timestamp(timestamp) report_period = to_report_period_type(timestamp) if report_period == ReportPeriod.year.value: return "{}".format(the_date.year) if report_period == ReportPeriod.season1.value: return "{}q1".format(the_date.year) if report_period == ReportPeriod.half_year.value: return "{}q2".format(the_date.year) if report_period == ReportPeriod.season3.value: return "{}q3".format(the_date.year) assert False class BaseChinaStockFinanceRecorder(EastmoneyTimestampsDataRecorder): finance_report_type = None data_type = 1 timestamps_fetching_url = "https://emh5.eastmoney.com/api/CaiWuFenXi/GetCompanyReportDateList" timestamp_list_path_fields = ["CompanyReportDateList"] timestamp_path_fields = ["ReportDate"] def __init__( self, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, force_update=False, sleeping_time=5, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, real_time=real_time, fix_duplicate_way=fix_duplicate_way, start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) try: self.logger.info(f"joinquant query count:{get_query_count()}") self.fetch_jq_timestamp = True except Exception as e: self.fetch_jq_timestamp = False self.logger.warning( f"joinquant account not ok,the timestamp(publish date) for finance would be not correct. {e}" ) def init_timestamps(self, entity): param = {"color": "w", "fc": get_fc(entity), "DataType": self.data_type} if self.finance_report_type == "LiRunBiaoList" or self.finance_report_type == "XianJinLiuLiangBiaoList": param["ReportType"] = 1 timestamp_json_list = call_eastmoney_api( url=self.timestamps_fetching_url, path_fields=self.timestamp_list_path_fields, param=param ) if self.timestamp_path_fields: timestamps = [get_from_path_fields(data, self.timestamp_path_fields) for data in timestamp_json_list] return [to_pd_timestamp(t) for t in timestamps] def generate_request_param(self, security_item, start, end, size, timestamps): if len(timestamps) <= 10: param = { "color": "w", "fc": get_fc(security_item), "corpType": company_type_flag(security_item), # 0 means get all types "reportDateType": 0, "endDate": "", "latestCount": size, } else: param = { "color": "w", "fc": get_fc(security_item), "corpType": company_type_flag(security_item), # 0 means get all types "reportDateType": 0, "endDate": to_date_time_str(timestamps[10]), "latestCount": 10, } if self.finance_report_type == "LiRunBiaoList" or self.finance_report_type == "XianJinLiuLiangBiaoList": param["reportType"] = 1 return param def generate_path_fields(self, security_item): comp_type = company_type_flag(security_item) if comp_type == "3": return ["{}_YinHang".format(self.finance_report_type)] elif comp_type == "2": return ["{}_BaoXian".format(self.finance_report_type)] elif comp_type == "1": return ["{}_QuanShang".format(self.finance_report_type)] elif comp_type == "4": return ["{}_QiYe".format(self.finance_report_type)] def record(self, entity, start, end, size, timestamps): # different with the default timestamps handling param = self.generate_request_param(entity, start, end, size, timestamps) self.logger.info("request param:{}".format(param)) return self.api_wrapper.request( url=self.url, param=param, method=self.request_method, path_fields=self.generate_path_fields(entity) ) def get_original_time_field(self): return "ReportDate" def fill_timestamp_with_jq(self, security_item, the_data): # get report published date from jq try: df = get_fundamentals( table="indicator", code=to_jq_entity_id(security_item), columns="pubDate", date=to_jq_report_period(the_data.report_date), count=None, parse_dates=["pubDate"], ) if pd_is_not_null(df): the_data.timestamp = to_pd_timestamp(df["pubDate"][0]) self.logger.info( "jq fill {} {} timestamp:{} for report_date:{}".format( self.data_schema, security_item.id, the_data.timestamp, the_data.report_date ) ) self.session.commit() except Exception as e: self.logger.error(f"Failed to fill timestamp(publish date) for finance data from joinquant {e}") def on_finish_entity(self, entity): super().on_finish_entity(entity) if not self.fetch_jq_timestamp: return # fill the timestamp for report published date the_data_list = get_data( data_schema=self.data_schema, provider=self.provider, entity_id=entity.id, order=self.data_schema.timestamp.asc(), return_type="domain", session=self.session, filters=[ self.data_schema.timestamp == self.data_schema.report_date, self.data_schema.timestamp >= to_pd_timestamp("2005-01-01"), ], ) if the_data_list: if self.data_schema == FinanceFactor: for the_data in the_data_list: self.fill_timestamp_with_jq(entity, the_data) else: df = FinanceFactor.query_data( entity_id=entity.id, columns=[FinanceFactor.timestamp, FinanceFactor.report_date, FinanceFactor.id], filters=[ FinanceFactor.timestamp != FinanceFactor.report_date, FinanceFactor.timestamp >= to_pd_timestamp("2005-01-01"), FinanceFactor.report_date >= the_data_list[0].report_date, FinanceFactor.report_date <= the_data_list[-1].report_date, ], ) if pd_is_not_null(df): index_df(df, index="report_date", time_field="report_date") for the_data in the_data_list: if (df is not None) and (not df.empty) and the_data.report_date in df.index: the_data.timestamp = df.at[the_data.report_date, "timestamp"] self.logger.info( "db fill {} {} timestamp:{} for report_date:{}".format( self.data_schema, entity.id, the_data.timestamp, the_data.report_date ) ) self.session.commit() else: # self.logger.info( # 'waiting jq fill {} {} timestamp:{} for report_date:{}'.format(self.data_schema, # security_item.id, # the_data.timestamp, # the_data.report_date)) self.fill_timestamp_with_jq(entity, the_data) # the __all__ is generated __all__ = ["to_jq_report_period", "BaseChinaStockFinanceRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/finance/eastmoney_balance_sheet_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.api.utils import to_report_period_type from zvt.domain import BalanceSheet from zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder from zvt.utils.time_utils import to_pd_timestamp from zvt.utils.utils import add_func_to_value, first_item_to_float balance_sheet_map = { # 流动资产 # # 货币资金 "cash_and_cash_equivalents": "Monetaryfund", # 应收票据 "note_receivable": "Billrec", # 应收账款 "accounts_receivable": "Accountrec", # 预付款项 "advances_to_suppliers": "Advancepay", # 其他应收款 "other_receivables": "Otherrec", # 存货 "inventories": "Inventory", # 一年内到期的非流动资产 "current_portion_of_non_current_assets": "Nonlassetoneyear", # 其他流动资产 "other_current_assets": "Otherlasset", # 流动资产合计 "total_current_assets": "Sumlasset", # 非流动资产 # # 可供出售金融资产 "fi_assets_saleable": "Saleablefasset", # 长期应收款 "long_term_receivables": "Ltrec", # 长期股权投资 "long_term_equity_investment": "Ltequityinv", # 投资性房地产 "real_estate_investment": "Estateinvest", # 固定资产 "fixed_assets": "Fixedasset", # 在建工程 "construction_in_process": "Constructionprogress", # 无形资产 "intangible_assets": "Intangibleasset", # 商誉 "goodwill": "Goodwill", # 长期待摊费用 "long_term_prepaid_expenses": "Ltdeferasset", # 递延所得税资产 "deferred_tax_assets": "Deferincometaxasset", # 其他非流动资产 "other_non_current_assets": "Othernonlasset", # 非流动资产合计 "total_non_current_assets": "Sumnonlasset", # 资产总计 "total_assets": "Sumasset", # 流动负债 # # 短期借款 "short_term_borrowing": "Stborrow", # 吸收存款及同业存放 "accept_money_deposits": "Deposit", # 应付账款 "accounts_payable": "Accountpay", # 预收款项 "advances_from_customers": "Advancereceive", # 应付职工薪酬 "employee_benefits_payable": "Salarypay", # 应交税费 "taxes_payable": "Taxpay", # 应付利息 "interest_payable": "Interestpay", # 其他应付款 "other_payable": "Otherpay", # 一年内到期的非流动负债 "current_portion_of_non_current_liabilities": "Nonlliaboneyear", # 其他流动负债 "other_current_liabilities": "Otherlliab", # 流动负债合计 "total_current_liabilities": "Sumlliab", # 非流动负债 # # 长期借款 "long_term_borrowing": "Ltborrow", # 长期应付款 "long_term_payable": "Ltaccountpay", # 递延收益 "deferred_revenue": "Deferincome", # 递延所得税负债 "deferred_tax_liabilities": "Deferincometaxliab", # 其他非流动负债 "other_non_current_liabilities": "Othernonlliab", # 非流动负债合计 "total_non_current_liabilities": "Sumnonlliab", # 负债合计 "total_liabilities": "Sumliab", # 所有者权益(或股东权益) # # 实收资本（或股本） "capital": "Sharecapital", # 资本公积 "capital_reserve": "Capitalreserve", # 专项储备 "special_reserve": "Specialreserve", # 盈余公积 "surplus_reserve": "Surplusreserve", # 未分配利润 "undistributed_profits": "Retainedearning", # 归属于母公司股东权益合计 "equity": "Sumparentequity", # 少数股东权益 "equity_as_minority_interest": "Minorityequity", # 股东权益合计 "total_equity": "Sumshequity", # 负债和股东权益合计 "total_liabilities_and_equity": "Sumliabshequity", # 银行相关 # 资产 # 现金及存放中央银行款项 "fi_cash_and_deposit_in_central_bank": "Cashanddepositcbank", # 存放同业款项 "fi_deposit_in_other_fi": "Depositinfi", # 贵金属 "fi_expensive_metals": "Preciousmetal", # 拆出资金 "fi_lending_to_other_fi": "Lendfund", # 以公允价值计量且其变动计入当期损益的金融资产 "fi_financial_assets_effect_current_income": "Fvaluefasset", # 衍生金融资产 "fi_financial_derivative_asset": "Derivefasset", # 买入返售金融资产 "fi_buying_sell_back_fi__asset": "Buysellbackfasset", # 应收账款 # # 应收利息 "fi_interest_receivable": "Interestrec", # 发放贷款及垫款 "fi_disbursing_loans_and_advances": "Loanadvances", # 可供出售金融资产 # # 持有至到期投资 "fi_held_to_maturity_investment": "Heldmaturityinv", # 应收款项类投资 "fi_account_receivable_investment": "Investrec", # 投资性房地产 # # 固定资产 # # 无形资产 # # 商誉 # # 递延所得税资产 # # 其他资产 "fi_other_asset": "Otherasset", # 资产总计 # # 负债 # # 向中央银行借款 "fi_borrowings_from_central_bank": "Borrowfromcbank", # 同业和其他金融机构存放款项 "fi_deposit_from_other_fi": "Fideposit", # 拆入资金 "fi_borrowings_from_fi": "Borrowfund", # 以公允价值计量且其变动计入当期损益的金融负债 "fi_financial_liability_effect_current_income": "Fvaluefliab", # 衍生金融负债 "fi_financial_derivative_liability": "Derivefliab", # 卖出回购金融资产款 "fi_sell_buy_back_fi_asset": "Sellbuybackfasset", # 吸收存款 "fi_savings_absorption": "Acceptdeposit", # 存款证及应付票据 "fi_notes_payable": "Cdandbillrec", # 应付职工薪酬 # # 应交税费 # # 应付利息 # # 预计负债 "fi_estimated_liabilities": "Anticipateliab", # 应付债券 "fi_bond_payable": "Bondpay", # 其他负债 "fi_other_liability": "Otherliab", # 负债合计 # # 所有者权益(或股东权益) # 股本 "fi_capital": "Shequity", # 其他权益工具 "fi_other_equity_instruments": "Otherequity", # 其中:优先股 "fi_preferred_stock": "Preferredstock", # 资本公积 # # 盈余公积 # # 一般风险准备 "fi_generic_risk_reserve": "Generalriskprepare", # 未分配利润 # # 归属于母公司股东权益合计 # # 股东权益合计 # # 负债及股东权益总计 # 券商相关 # 资产 # # 货币资金 # # 其中: 客户资金存款 "fi_client_fund": "Clientfund", # 结算备付金 "fi_deposit_reservation_for_balance": "Settlementprovision", # 其中: 客户备付金 "fi_client_deposit_reservation_for_balance": "Clientprovision", # 融出资金 "fi_margin_out_fund": "Marginoutfund", # 以公允价值计量且其变动计入当期损益的金融资产 # # 衍生金融资产 # # 买入返售金融资产 # # 应收利息 # # 应收款项 "fi_receivables": "Receivables", # 存出保证金 "fi_deposit_for_recognizance": "Gdepositpay", # 可供出售金融资产 # # 持有至到期投资 # # 长期股权投资 # # 固定资产 # # 在建工程 # # 无形资产 # # 商誉 # # 递延所得税资产 # # 其他资产 # # 资产总计 # # 负债 # # 短期借款 # # 拆入资金 # # 以公允价值计量且其变动计入当期损益的金融负债 # # 衍生金融负债 # # 卖出回购金融资产款 # # 代理买卖证券款 "fi_receiving_as_agent": "Agenttradesecurity", # 应付账款 # # 应付职工薪酬 # # 应交税费 # # 应付利息 # # 应付短期融资款 "fi_short_financing_payable": "Shortfinancing", # 预计负债 # # 应付债券 # # 递延所得税负债 # # 其他负债 # # 负债合计 # # 所有者权益(或股东权益) # # 股本 # # 资本公积 # # 其他权益工具 # # 盈余公积 # # 一般风险准备 # # 交易风险准备 "fi_trade_risk_reserve": "Traderiskprepare", # 未分配利润 # # 归属于母公司股东权益合计 # # 少数股东权益 # # 股东权益合计 # # 负债和股东权益总计 # 保险相关 # 应收保费 "fi_premiums_receivable": "Premiumrec", "fi_reinsurance_premium_receivable": "Rirec", # 应收分保合同准备金 "fi_reinsurance_contract_reserve": "Ricontactreserverec", # 保户质押贷款 "fi_policy_pledge_loans": "Insuredpledgeloan", # 定期存款 "fi_time_deposit": "Tdeposit", # 可供出售金融资产 # # 持有至到期投资 # # 应收款项类投资 # # 应收账款 # # 长期股权投资 # # 存出资本保证金 "fi_deposit_for_capital_recognizance": "Capitalgdepositpay", # 投资性房地产 # # 固定资产 # # 无形资产 # # 商誉 # # 递延所得税资产 # # 其他资产 # # 独立账户资产 "fi_capital_in_independent_accounts": "Independentasset", # 资产总计 # # 负债 # # 短期借款 # # 同业及其他金融机构存放款项 # # 拆入资金 # # 以公允价值计量且其变动计入当期损益的金融负债 # # 衍生金融负债 # # 卖出回购金融资产款 # # 吸收存款 # # 代理买卖证券款 # # 应付账款 # # 预收账款 "fi_advance_from_customers": "Advancerec", # 预收保费 "fi_advance_premium": "Premiumadvance", # 应付手续费及佣金 "fi_fees_and_commissions_payable": "Commpay", # 应付分保账款 "fi_dividend_payable_for_reinsurance": "Ripay", # 应付职工薪酬 # # 应交税费 # # 应付利息 # # 预计负债 # # 应付赔付款 "fi_claims_payable": "Claimpay", # 应付保单红利 "fi_policy_holder_dividend_payable": "Policydivipay", # 保户储金及投资款 "fi_policy_holder_deposits_and_investment_funds": "Insureddepositinv", # 保险合同准备金 "fi_contract_reserve": "Contactreserve", # 长期借款 # # 应付债券 # # 递延所得税负债 # # 其他负债 # # 独立账户负债 "fi_independent_liability": "Independentliab", # 负债合计 # # 所有者权益(或股东权益) # # 股本 # # 资本公积 # # 盈余公积 # # 一般风险准备 # # 未分配利润 # # 归属于母公司股东权益总计 # # 少数股东权益 # # 股东权益合计 # # 负债和股东权益总计 } add_func_to_value(balance_sheet_map, first_item_to_float) balance_sheet_map["report_period"] = ("ReportDate", to_report_period_type) balance_sheet_map["report_date"] = ("ReportDate", to_pd_timestamp) class ChinaStockBalanceSheetRecorder(BaseChinaStockFinanceRecorder): data_schema = BalanceSheet url = "https://emh5.eastmoney.com/api/CaiWuFenXi/GetZiChanFuZhaiBiaoList" finance_report_type = "ZiChanFuZhaiBiaoList" data_type = 3 def get_data_map(self): return balance_sheet_map if __name__ == "__main__": # init_log('blance_sheet.log') recorder = ChinaStockBalanceSheetRecorder(codes=["002572"]) recorder.run() # the __all__ is generated __all__ = ["ChinaStockBalanceSheetRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/finance/eastmoney_cash_flow_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.api.utils import to_report_period_type from zvt.domain import CashFlowStatement from zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder from zvt.utils.time_utils import to_pd_timestamp from zvt.utils.utils import add_func_to_value, first_item_to_float cash_flow_map = { # 经营活动产生的现金流量 # # 销售商品、提供劳务收到的现金 "cash_from_selling": "Salegoodsservicerec", # 收到的税费返还 "tax_refund": "Taxreturnrec", # 收到其他与经营活动有关的现金 "cash_from_other_op": "Otheroperaterec", # 经营活动现金流入小计 "total_op_cash_inflows": "Sumoperateflowin", # 购买商品、接受劳务支付的现金 "cash_to_goods_services": "Buygoodsservicepay", # 支付给职工以及为职工支付的现金 "cash_to_employees": "Employeepay", # 支付的各项税费 "taxes_and_surcharges": "Taxpay", # 支付其他与经营活动有关的现金 "cash_to_other_related_op": "Otheroperatepay", # 经营活动现金流出小计 "total_op_cash_outflows": "Sumoperateflowout", # 经营活动产生的现金流量净额 "net_op_cash_flows": "Netoperatecashflow", # 投资活动产生的现金流量 # 收回投资收到的现金 "cash_from_disposal_of_investments": "Disposalinvrec", # 取得投资收益收到的现金 "cash_from_returns_on_investments": "Invincomerec", # 处置固定资产、无形资产和其他长期资产收回的现金净额 "cash_from_disposal_fixed_intangible_assets": "Dispfilassetrec", # 处置子公司及其他营业单位收到的现金净额 "cash_from_disposal_subsidiaries": "Dispsubsidiaryrec", # 收到其他与投资活动有关的现金 "cash_from_other_investing": "Otherinvrec", # 投资活动现金流入小计 "total_investing_cash_inflows": "Suminvflowin", # 购建固定资产、无形资产和其他长期资产支付的现金 "cash_to_acquire_fixed_intangible_assets": "Buyfilassetpay", # 投资支付的现金 "cash_to_investments": "Invpay", # 取得子公司及其他营业单位支付的现金净额 "cash_to_acquire_subsidiaries": "Getsubsidiarypay", # 支付其他与投资活动有关的现金 "cash_to_other_investing": "Otherinvpay", # 投资活动现金流出小计 "total_investing_cash_outflows": "Suminvflowout", # 投资活动产生的现金流量净额 "net_investing_cash_flows": "Netinvcashflow", # 筹资活动产生的现金流量 # # 吸收投资收到的现金 "cash_from_accepting_investment": "Acceptinvrec", # 子公司吸收少数股东投资收到的现金 "cash_from_subsidiaries_accepting_minority_interest": "Subsidiaryaccept", # 取得借款收到的现金 "cash_from_borrowings": "Loanrec", # 发行债券收到的现金 "cash_from_issuing_bonds": "Issuebondrec", # 收到其他与筹资活动有关的现金 "cash_from_other_financing": "Otherfinarec", # 筹资活动现金流入小计 "total_financing_cash_inflows": "Sumfinaflowin", # 偿还债务支付的现金 "cash_to_repay_borrowings": "Repaydebtpay", # 分配股利、利润或偿付利息支付的现金 "cash_to_pay_interest_dividend": "Diviprofitorintpay", # 子公司支付给少数股东的股利、利润 "cash_to_pay_subsidiaries_minority_interest": "Subsidiarypay", # 支付其他与筹资活动有关的现金 "cash_to_other_financing": "Otherfinapay", # 筹资活动现金流出小计 "total_financing_cash_outflows": "Sumfinaflowout", # 筹资活动产生的现金流量净额 "net_financing_cash_flows": "Netfinacashflow", # 汇率变动对现金及现金等价物的影响 "foreign_exchange_rate_effect": "Effectexchangerate", # 现金及现金等价物净增加额 "net_cash_increase": "Nicashequi", # 加: 期初现金及现金等价物余额 "cash_at_beginning": "Cashequibeginning", # 期末现金及现金等价物余额 "cash": "Cashequiending", # 银行相关 # 客户存款和同业及其他金融机构存放款项净增加额 "fi_deposit_increase": "Nideposit", # 向中央银行借款净增加额 "fi_borrow_from_central_bank_increase": "Niborrowfromcbank", # 存放中央银行和同业款项及其他金融机构净减少额 "fi_deposit_in_others_decrease": "Nddepositincbankfi", # 拆入资金及卖出回购金融资产款净增加额 "fi_borrowing_and_sell_repurchase_increase": "Niborrowsellbuyback", # 其中:卖出回购金融资产款净增加额 "fi_sell_repurchase_increase": "Nisellbuybackfasset", # 拆出资金及买入返售金融资产净减少额 "fi_lending_and_buy_repurchase_decrease": "Ndlendbuysellback", # 其中:拆出资金净减少额 "fi_lending_decrease": "Ndlendfund", # 买入返售金融资产净减少额 "fi_buy_repurchase_decrease": "Ndbuysellbackfasset", # 收取的利息、手续费及佣金的现金 "fi_cash_from_interest_commission": "Intandcommrec", # 客户贷款及垫款净增加额 "fi_loan_advance_increase": "Niloanadvances", # 存放中央银行和同业及其他金融机构款项净增加额 "fi_deposit_in_others_increase": "Nidepositincbankfi", # 拆出资金及买入返售金融资产净增加额 "fi_lending_and_buy_repurchase_increase": "Nilendsellbuyback", # 其中:拆出资金净增加额 "fi_lending_increase": "Nilendfund", # 拆入资金及卖出回购金融资产款净减少额 "fi_borrowing_and_sell_repurchase_decrease": "Ndborrowsellbuyback", # 其中:拆入资金净减少额 "fi_borrowing_decrease": "Ndborrowfund", # 卖出回购金融资产净减少额 "fi_sell_repurchase_decrease": "Ndsellbuybackfasset", # 支付利息、手续费及佣金的现金 "fi_cash_to_interest_commission": "Intandcommpay", # 应收账款净增加额 "fi_account_receivable_increase": "Niaccountrec", # 偿付债券利息支付的现金 "fi_cash_to_pay_interest": "Bondintpay", # 保险相关 # 收到原保险合同保费取得的现金 "fi_cash_from_premium_of_original": "Premiumrec", # 保户储金及投资款净增加额 "fi_insured_deposit_increase": "Niinsureddepositinv", # 银行及证券业务卖出回购资金净增加额 "fi_bank_broker_sell_repurchase_increase": "Nisellbuyback", # 银行及证券业务买入返售资金净减少额 "fi_bank_broker_buy_repurchase_decrease": "Ndbuysellback", # 支付原保险合同赔付等款项的现金 "fi_cash_to_insurance_claim": "Indemnitypay", # 支付再保险业务现金净额 "fi_cash_to_reinsurance": "Netripay", # 银行业务及证券业务拆借资金净减少额 "fi_lending_decrease": "Ndlendfund", # 银行业务及证券业务卖出回购资金净减少额 "fi_bank_broker_sell_repurchase_decrease": "Ndsellbuyback", # 支付保单红利的现金 "fi_cash_to_dividends": "Divipay", # 保户质押贷款净增加额 "fi_insured_pledge_loans_increase": "Niinsuredpledgeloan", # 收购子公司及其他营业单位支付的现金净额 "fi_cash_to_acquire_subsidiaries": "Buysubsidiarypay", # 处置子公司及其他营业单位流出的现金净额 "fi_cash_to_disposal_subsidiaries": "Dispsubsidiarypay", # 支付卖出回购金融资产款现金净额 "fi_cash_to_sell_repurchase": "Netsellbuybackfassetpay", # 券商相关 # 拆入资金净增加额 "fi_borrowing_increase": "Niborrowfund", # 代理买卖证券收到的现金净额 "fi_cash_from_trading_agent": "Agenttradesecurityrec", # 回购业务资金净增加额 "fi_cash_from_repurchase_increase": "Nibuybackfund", # 处置交易性金融资产的净减少额 "fi_disposal_trade_asset_decrease": "Nddisptradefasset", # 回购业务资金净减少额 "fi_repurchase_decrease": "Ndbuybackfund", # 代理买卖证券支付的现金净额（净减少额） "fi_cash_to_agent_trade": "Agenttradesecuritypay", } add_func_to_value(cash_flow_map, first_item_to_float) cash_flow_map["report_period"] = ("ReportDate", to_report_period_type) cash_flow_map["report_date"] = ("ReportDate", to_pd_timestamp) class ChinaStockCashFlowRecorder(BaseChinaStockFinanceRecorder): data_schema = CashFlowStatement url = "https://emh5.eastmoney.com/api/CaiWuFenXi/GetXianJinLiuLiangBiaoList" finance_report_type = "XianJinLiuLiangBiaoList" data_type = 4 def get_data_map(self): return cash_flow_map if __name__ == "__main__": # init_log('cash_flow.log') recorder = ChinaStockCashFlowRecorder(codes=["002572"]) recorder.run() # the __all__ is generated __all__ = ["ChinaStockCashFlowRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/finance/eastmoney_finance_factor_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.api.utils import to_report_period_type from zvt.domain import FinanceFactor from zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder from zvt.utils.time_utils import to_pd_timestamp from zvt.utils.utils import add_func_to_value, to_float finance_factor_map = { # 基本每股收益(元) "basic_eps": "Epsjb", # 扣非每股收益(元) "deducted_eps": "Epskcjb", # 稀释每股收益(元) "diluted_eps": "Epsxs", # 每股净资产(元) "bps": "Bps", # 每股资本公积(元) "capital_reserve_ps": "Mgzbgj", # 每股未分配利润(元) "undistributed_profit_ps": "Mgwfplr", # 每股经营现金流(元) "op_cash_flow_ps": "Mgjyxjje", # 成长能力指标 # # 营业总收入(元) "total_op_income": "Totalincome", # 毛利润(元) "gross_profit": "Grossprofit", # 归属净利润(元) "net_profit": "Parentnetprofit", # 扣非净利润(元) "deducted_net_profit": "Bucklenetprofit", # 营业总收入同比增长 "op_income_growth_yoy": "Totalincomeyoy", # 归属净利润同比增长 "net_profit_growth_yoy ": "Parentnetprofityoy", # 扣非净利润同比增长 "deducted_net_profit_growth_yoy": "Bucklenetprofityoy", # 营业总收入滚动环比增长 "op_income_growth_qoq": "Totalincomerelativeratio", # 归属净利润滚动环比增长 "net_profit_growth_qoq": "Parentnetprofitrelativeratio", # 扣非净利润滚动环比增长 "deducted_net_profit_growth_qoq": "Bucklenetprofitrelativeratio", # 盈利能力指标 # # 净资产收益率(加权) "roe": "Roejq", # 净资产收益率(扣非/加权) "deducted_roe": "Roekcjq", # 总资产收益率(加权) "rota": "Allcapitalearningsrate", # 毛利率 "gross_profit_margin": "Grossmargin", # 净利率 "net_margin": "Netinterest", # 收益质量指标 # # 预收账款/营业收入 "advance_receipts_per_op_income": "Accountsrate", # 销售净现金流/营业收入 "sales_net_cash_flow_per_op_income": "Salesrate", # 经营净现金流/营业收入 "op_net_cash_flow_per_op_income": "Operatingrate", # 实际税率 "actual_tax_rate": "Taxrate", # 财务风险指标 # # 流动比率 "current_ratio": "Liquidityratio", # 速动比率 "quick_ratio": "Quickratio", # 现金流量比率 "cash_flow_ratio": "Cashflowratio", # 资产负债率 "debt_asset_ratio": "Assetliabilityratio", # 权益乘数 "em": "Equitymultiplier", # 产权比率 "equity_ratio": "Equityratio", # 营运能力指标(一般企业) # # 总资产周转天数(天) "total_assets_turnover_days": "Totalassetsdays", # 存货周转天数(天) "inventory_turnover_days": "Inventorydays", # 应收账款周转天数(天) "receivables_turnover_days": "Accountsreceivabledays", # 总资产周转率(次) "total_assets_turnover": "Totalassetrate", # 存货周转率(次) "inventory_turnover": "Inventoryrate", # 应收账款周转率(次) "receivables_turnover": "Accountsreceiveablerate", # 专项指标(银行) # # 存款总额 "fi_total_deposit": "Totaldeposit", # 贷款总额 "fi_total_loan": "Totalloan", # 存贷款比例 "fi_loan_deposit_ratio": "Depositloanratio", # 资本充足率 "fi_capital_adequacy_ratio": "Capitaladequacyratio", # 核心资本充足率 "fi_core_capital_adequacy_ratio": "Corecapitaladequacyratio", # 不良贷款率 "fi_npl_ratio": "Nplratio", # 不良贷款拨备覆盖率 "fi_npl_provision_coverage": "Nplprovisioncoverage", # 资本净额 "fi_net_capital": "Netcapital_b", # 专项指标(保险) # # 总投资收益率 "insurance_roi": "Tror", # 净投资收益率 "insurance_net_investment_yield": "Nror", # 已赚保费 "insurance_earned_premium": "Eapre", # 赔付支出 "insurance_payout": "Comexpend", # 退保率 "insurance_surrender_rate": "Surrate", # 偿付能力充足率 "insurance_solvency_adequacy_ratio": "Solvenra", # 专项指标(券商) # # 净资本 "broker_net_capital": "Netcapital", # 净资产 "broker_net_assets": "Netassets", # 净资本/净资产 "broker_net_capital_assets_ratio": "Captialrate", # 自营固定收益类证券规模/净资本 "broker_self_operated_fixed_income_securities_net_capital_ratio": "Incomesizerate", } add_func_to_value(finance_factor_map, to_float) finance_factor_map["report_period"] = ("ReportDate", to_report_period_type) finance_factor_map["report_date"] = ("ReportDate", to_pd_timestamp) class ChinaStockFinanceFactorRecorder(BaseChinaStockFinanceRecorder): url = "https://emh5.eastmoney.com/api/CaiWuFenXi/GetZhuYaoZhiBiaoList" finance_report_type = "ZhuYaoZhiBiaoList" data_schema = FinanceFactor data_type = 1 def get_data_map(self): return finance_factor_map if __name__ == "__main__": # init_log('finance_factor.log') recorder = ChinaStockFinanceFactorRecorder(codes=["000001"]) recorder.run() # the __all__ is generated __all__ = ["ChinaStockFinanceFactorRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/finance/eastmoney_income_statement_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.api.utils import to_report_period_type from zvt.domain import IncomeStatement from zvt.recorders.eastmoney.finance.base_china_stock_finance_recorder import BaseChinaStockFinanceRecorder from zvt.utils.time_utils import to_pd_timestamp from zvt.utils.utils import add_func_to_value, first_item_to_float income_statement_map = { # 营业总收入 # # 营业收入 "operating_income": "Operatereve", # 营业总成本 "total_operating_costs": "Totaloperateexp", # 营业成本 "operating_costs": "Operateexp", # 研发费用 "rd_costs": "Rdexp", # 提取保险合同准备金净额 "net_change_in_insurance_contract_reserves": "Netcontactreserve", # 营业税金及附加 "business_taxes_and_surcharges": "Operatetax", # 销售费用 "sales_costs": "Saleexp", # 管理费用 "managing_costs": "Manageexp", # 财务费用 "financing_costs": "Financeexp", # 资产减值损失 "assets_devaluation": "Assetdevalueloss", # 其他经营收益 # # 加: 投资收益 "investment_income": "Investincome", # 其中: 对联营企业和合营企业的投资收益 "investment_income_from_related_enterprise": "Investjointincome", # 营业利润 "operating_profit": "Operateprofit", # 加: 营业外收入 "non_operating_income": "Nonoperatereve", # 减: 营业外支出 "non_operating_costs": "Nonoperateexp", # 其中: 非流动资产处置净损失 "loss_on_disposal_non_current_asset": "Nonlassetnetloss", # 利润总额 "total_profits": "Sumprofit", # 减: 所得税费用 "tax_expense": "Incometax", # 净利润 "net_profit": "Netprofit", # 其中: 归属于母公司股东的净利润 "net_profit_as_parent": "Parentnetprofit", # 少数股东损益 "net_profit_as_minority_interest": "Minorityincome", # 扣除非经常性损益后的净利润 "deducted_net_profit": "Kcfjcxsyjlr", # 每股收益 # 基本每股收益 "eps": "Basiceps", # 稀释每股收益 "diluted_eps": "Dilutedeps", # 其他综合收益 "other_comprehensive_income": "Othercincome", # 归属于母公司股东的其他综合收益 "other_comprehensive_income_as_parent": "Parentothercincome", # 归属于少数股东的其他综合收益 "other_comprehensive_income_as_minority_interest": "Minorityothercincome", # 综合收益总额 "total_comprehensive_income": "Sumcincome", # 归属于母公司所有者的综合收益总额 "total_comprehensive_income_as_parent": "Parentcincome", # 归属于少数股东的综合收益总额 "total_comprehensive_income_as_minority_interest": "Minoritycincome", # 银行相关 # 利息净收入 "fi_net_interest_income": "Intnreve", # 其中:利息收入 "fi_interest_income": "Intreve", # 利息支出 "fi_interest_expenses": "Intexp", # 手续费及佣金净收入 "fi_net_incomes_from_fees_and_commissions": "Commnreve", # 其中:手续费及佣金收入 "fi_incomes_from_fees_and_commissions": "Commreve", # 手续费及佣金支出 "fi_expenses_for_fees_and_commissions": "Commexp", # 公允价值变动收益 "fi_income_from_fair_value_change": "Fvalueincome", # 汇兑收益 "fi_income_from_exchange": "Exchangeincome", # 其他业务收入 "fi_other_income": "Otherreve", # 业务及管理费 "fi_operate_and_manage_expenses": "Operatemanageexp", # 保险相关 # 已赚保费 "fi_net_income_from_premium": "Premiumearned", # 其中:保险业务收入 "fi_income_from_premium": "Insurreve", # 分保费收入 "fi_income_from_reinsurance_premium": "Rireve", # 减:分出保费 "fi_reinsurance_premium": "Ripremium", # 提取未到期责任准备金 "fi_undue_duty_reserve": "Unduereserve", # 银行业务利息净收入 "fi_net_income_from_bank_interest": "Bankintnreve", # 其中:银行业务利息收入 "fi_income_from_bank_interest": "Bankintreve", # 银行业务利息支出 "fi_expenses_for_bank_interest": "Bankintexp", # 非保险业务手续费及佣金净收入 "fi_net_incomes_from_fees_and_commissions_of_non_insurance": "Ninsurcommnreve", # 非保险业务手续费及佣金收入 "fi_incomes_from_fees_and_commissions_of_non_insurance": "Ninsurcommreve", # 非保险业务手续费及佣金支出 "fi_expenses_for_fees_and_commissions_of_non_insurance": "Ninsurcommexp", # 退保金 "fi_insurance_surrender_costs": "Surrenderpremium", # 赔付支出 "fi_insurance_claims_expenses": "Indemnityexp", # 减:摊回赔付支出 "fi_amortized_insurance_claims_expenses": "Amortiseindemnityexp", # 提取保险责任准备金 "fi_insurance_duty_reserve": "Dutyreserve", # 减:摊回保险责任准备金 "fi_amortized_insurance_duty_reserve": "Amortisedutyreserve", # 保单红利支出 "fi_dividend_expenses_to_insured": "Policydiviexp", # 分保费用 "fi_reinsurance_expenses": "Riexp", # 减:摊回分保费用 "fi_amortized_reinsurance_expenses": "Amortiseriexp", # 其他业务成本 "fi_other_op_expenses": "Otherexp", # 券商相关 # 手续费及佣金净收入 # # 其中:代理买卖证券业务净收入 "fi_net_incomes_from_trading_agent": "Agenttradesecurity", # 证券承销业务净收入 "fi_net_incomes_from_underwriting": "Securityuw", # 受托客户资产管理业务净收入 "fi_net_incomes_from_customer_asset_management": "Clientassetmanage", # 手续费及佣金净收入其他项目 "fi_fees_from_other": "Commnreveother", # 公允价值变动收益 # # 其中:可供出售金融资产公允价值变动损益 "fi_income_from_fair_value_change_of_fi_salable": "Fvalueosalable", } add_func_to_value(income_statement_map, first_item_to_float) income_statement_map["report_period"] = ("ReportDate", to_report_period_type) income_statement_map["report_date"] = ("ReportDate", to_pd_timestamp) class ChinaStockIncomeStatementRecorder(BaseChinaStockFinanceRecorder): data_schema = IncomeStatement url = "https://emh5.eastmoney.com/api/CaiWuFenXi/GetLiRunBiaoList" finance_report_type = "LiRunBiaoList" data_type = 2 def get_data_map(self): return income_statement_map if __name__ == "__main__": # init_log('income_statement.log') recorder = ChinaStockIncomeStatementRecorder(codes=["002572"]) recorder.run() # the __all__ is generated __all__ = ["ChinaStockIncomeStatementRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/holder/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule eastmoney_top_ten_tradable_holder_recorder from .eastmoney_top_ten_tradable_holder_recorder import * from .eastmoney_top_ten_tradable_holder_recorder import __all__ as _eastmoney_top_ten_tradable_holder_recorder_all __all__ += _eastmoney_top_ten_tradable_holder_recorder_all # import all from submodule eastmoney_stock_actor_recorder from .eastmoney_stock_actor_recorder import * from .eastmoney_stock_actor_recorder import __all__ as _eastmoney_stock_actor_recorder_all __all__ += _eastmoney_stock_actor_recorder_all # import all from submodule eastmoney_top_ten_holder_recorder from .eastmoney_top_ten_holder_recorder import * from .eastmoney_top_ten_holder_recorder import __all__ as _eastmoney_top_ten_holder_recorder_all __all__ += _eastmoney_top_ten_holder_recorder_all ================================================ FILE: src/zvt/recorders/eastmoney/holder/eastmoney_stock_actor_recorder.py ================================================ # -*- coding: utf-8 -*- import requests from zvt.api.utils import get_recent_report_date from zvt.contract.recorder import Recorder from zvt.domain.actor.actor_meta import ActorMeta from zvt.utils.time_utils import to_pd_timestamp class EastmoneyActorRecorder(Recorder): name = "eastmoney_actor_recorder" provider = "eastmoney" data_schema = ActorMeta url = "https://datacenter.eastmoney.com/securities/api/data/v1/get?reportName=RPT_FREEHOLDERS_BASIC_INFO&columns=HOLDER_NAME,END_DATE,HOLDER_NEW,HOLDER_NUM,HOLDER_CODE"eColumns=&filter=(END_DATE='{}')&pageNumber={}&pageSize={}&sortTypes=-1,-1&sortColumns=HOLDER_NUM,HOLDER_NAME&source=SECURITIES&client=SW" start = "2016-03-31" def get_data(self, end_date, pn, ps): resp = requests.get(url=self.url.format(end_date, pn, ps)) return resp.json() def run(self): current_date = get_recent_report_date() pn = 1 ps = 2000 while to_pd_timestamp(current_date) >= to_pd_timestamp(self.start): if not self.state: current_date = get_recent_report_date() result = self.get_data(end_date=current_date, pn=pn, ps=ps) print(result) self.state = {"end_date": current_date, "pages": result["result"]["pages"], "pn": pn, "ps": ps} self.persist_state("stock_sz_000001", self.state) else: if self.state["pn"] >= self.state["pages"]: current_date = get_recent_report_date(the_date=self.state["end_date"], step=1) pn = pn ps = ps else: pn = self.state["pn"] + 1 ps = self.state["ps"] current_date = self.state["end_date"] result = self.get_data(end_date=current_date, pn=pn, ps=ps) print(result) self.state = {"end_date": current_date, "pages": result["result"]["pages"], "pn": pn, "ps": ps} self.persist_state("stock_sz_000001", self.state) if __name__ == "__main__": EastmoneyActorRecorder().run() # the __all__ is generated __all__ = ["EastmoneyActorRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/holder/eastmoney_top_ten_holder_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.api.utils import to_report_period_type from zvt.domain.misc.holder import TopTenHolder from zvt.recorders.eastmoney.common import EastmoneyTimestampsDataRecorder, get_fc from zvt.utils.time_utils import to_date_time_str, to_pd_timestamp from zvt.utils.utils import to_float class TopTenHolderRecorder(EastmoneyTimestampsDataRecorder): provider = "eastmoney" data_schema = TopTenHolder url = "https://emh5.eastmoney.com/api/GuBenGuDong/GetShiDaGuDong" path_fields = ["ShiDaGuDongList"] timestamps_fetching_url = "https://emh5.eastmoney.com/api/GuBenGuDong/GetFirstRequest2Data" timestamp_list_path_fields = ["SDGDBGQ", "ShiDaGuDongBaoGaoQiList"] timestamp_path_fields = ["BaoGaoQi"] def get_data_map(self): return { "report_period": ("timestamp", to_report_period_type), "report_date": ("timestamp", to_pd_timestamp), # 股东代码 "holder_code": ("GuDongDaiMa", str), # 股东名称 "holder_name": ("GuDongMingCheng", str), # 持股数 "shareholding_numbers": ("ChiGuShu", to_float), # 持股比例 "shareholding_ratio": ("ChiGuBiLi", to_float), # 变动 "change": ("ZengJian", to_float), # 变动比例 "change_ratio": ("BianDongBiLi", to_float), } def generate_request_param(self, security_item, start, end, size, timestamp): return {"color": "w", "fc": get_fc(security_item), "BaoGaoQi": to_date_time_str(timestamp)} def generate_domain_id(self, entity, original_data): the_name = original_data.get("GuDongMingCheng") timestamp = original_data[self.get_original_time_field()] the_id = "{}_{}_{}".format(entity.id, timestamp, the_name) return the_id if __name__ == "__main__": # init_log('top_ten_holder.log') TopTenHolderRecorder(codes=["002572"]).run() # the __all__ is generated __all__ = ["TopTenHolderRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/holder/eastmoney_top_ten_tradable_holder_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import TopTenTradableHolder from zvt.recorders.eastmoney.holder.eastmoney_top_ten_holder_recorder import TopTenHolderRecorder class TopTenTradableHolderRecorder(TopTenHolderRecorder): provider = "eastmoney" data_schema = TopTenTradableHolder url = "https://emh5.eastmoney.com/api/GuBenGuDong/GetShiDaLiuTongGuDong" path_fields = ["ShiDaLiuTongGuDongList"] timestamps_fetching_url = "https://emh5.eastmoney.com/api/GuBenGuDong/GetFirstRequest2Data" timestamp_list_path_fields = ["SDLTGDBGQ", "ShiDaLiuTongGuDongBaoGaoQiList"] timestamp_path_fields = ["BaoGaoQi"] if __name__ == "__main__": # init_log('top_ten_tradable_holder.log') TopTenTradableHolderRecorder(codes=["002572"]).run() # the __all__ is generated __all__ = ["TopTenTradableHolderRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/meta/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule eastmoney_block_meta_recorder from .eastmoney_block_meta_recorder import * from .eastmoney_block_meta_recorder import __all__ as _eastmoney_block_meta_recorder_all __all__ += _eastmoney_block_meta_recorder_all # import all from submodule eastmoney_stock_meta_recorder from .eastmoney_stock_meta_recorder import * from .eastmoney_stock_meta_recorder import __all__ as _eastmoney_stock_meta_recorder_all __all__ += _eastmoney_stock_meta_recorder_all ================================================ FILE: src/zvt/recorders/eastmoney/meta/eastmoney_block_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd import requests from zvt.api.utils import china_stock_code_to_id from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder, TimeSeriesDataRecorder from zvt.domain import BlockStock, BlockCategory, Block from zvt.recorders.consts import DEFAULT_HEADER from zvt.utils.time_utils import now_pd_timestamp from zvt.utils.utils import json_callback_param class EastmoneyBlockRecorder(Recorder): provider = "eastmoney" data_schema = Block # 用于抓取行业/概念/地域列表 category_map_url = { BlockCategory.industry: "https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._BKHY&sty=DCRRBKCPAL&st=(ChangePercent)&sr=-1&p=1&ps=200&lvl=&cb=jsonp_F1A61014DE5E45B7A50068EA290BC918&token=4f1862fc3b5e77c150a2b985b12db0fd&_=08766", BlockCategory.concept: "https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._BKGN&sty=DCRRBKCPAL&st=(ChangePercent)&sr=-1&p=1&ps=300&lvl=&cb=jsonp_3071689CC1E6486A80027D69E8B33F26&token=4f1862fc3b5e77c150a2b985b12db0fd&_=08251", # BlockCategory.area: 'https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C._BKDY&sty=DCRRBKCPAL&st=(ChangePercent)&sr=-1&p=1&ps=200&lvl=&cb=jsonp_A597D4867B3D4659A203AADE5B3B3AD5&token=4f1862fc3b5e77c150a2b985b12db0fd&_=02443' } def run(self): for category, url in self.category_map_url.items(): resp = requests.get(url, headers=DEFAULT_HEADER) results = json_callback_param(resp.text) the_list = [] for result in results: items = result.split(",") code = items[1] name = items[2] entity_id = f"block_cn_{code}" the_list.append( { "id": entity_id, "entity_id": entity_id, "entity_type": "block", "exchange": "cn", "code": code, "name": name, "category": category.value, } ) if the_list: df = pd.DataFrame.from_records(the_list) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=self.force_update) self.logger.info(f"finish record eastmoney blocks:{category.value}") class EastmoneyBlockStockRecorder(TimeSeriesDataRecorder): entity_provider = "eastmoney" entity_schema = Block provider = "eastmoney" data_schema = BlockStock # 用于抓取行业包含的股票 category_stocks_url = "https://nufm.dfcfw.com/EM_Finance2014NumericApplication/JS.aspx?type=CT&cmd=C.{}{}&sty=SFCOO&st=(Close)&sr=-1&p=1&ps=300&cb=jsonp_B66B5BAA1C1B47B5BB9778045845B947&token=7bc05d0d4c3c22ef9fca8c2a912d779c" def record(self, entity, start, end, size, timestamps): resp = requests.get(self.category_stocks_url.format(entity.code, "1"), headers=DEFAULT_HEADER) try: results = json_callback_param(resp.text) the_list = [] for result in results: items = result.split(",") stock_code = items[1] stock_id = china_stock_code_to_id(stock_code) block_id = entity.id the_list.append( { "id": "{}_{}".format(block_id, stock_id), "entity_id": block_id, "entity_type": "block", "exchange": entity.exchange, "code": entity.code, "name": entity.name, "timestamp": now_pd_timestamp(), "stock_id": stock_id, "stock_code": stock_code, "stock_name": items[2], } ) if the_list: df = pd.DataFrame.from_records(the_list) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) self.logger.info("finish recording block:{},{}".format(entity.category, entity.name)) except Exception as e: self.logger.error("error:,resp.text:", e, resp.text) self.sleep() if __name__ == "__main__": # init_log('china_stock_category.log') EastmoneyBlockRecorder().run() recorder = EastmoneyBlockStockRecorder(code="BK1144") recorder.run() # the __all__ is generated __all__ = ["EastmoneyBlockRecorder", "EastmoneyBlockStockRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/meta/eastmoney_stock_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import requests from zvt.contract.api import get_entities from zvt.contract.recorder import Recorder from zvt.domain.meta.stock_meta import StockDetail, Stock from zvt.recorders.exchange.exchange_stock_meta_recorder import ExchangeStockMetaRecorder from zvt.utils.time_utils import to_pd_timestamp from zvt.utils.utils import to_float, pct_to_float class EastmoneyStockRecorder(ExchangeStockMetaRecorder): data_schema = Stock provider = "eastmoney" class EastmoneyStockDetailRecorder(Recorder): provider = "eastmoney" data_schema = StockDetail def __init__(self, force_update=False, sleeping_time=5, code=None, codes=None) -> None: super().__init__(force_update, sleeping_time) # get list at first EastmoneyStockRecorder().run() if codes is None and code is not None: self.codes = [code] else: self.codes = codes filters = None if not self.force_update: filters = [StockDetail.profile.is_(None)] self.entities = get_entities( session=self.session, entity_schema=StockDetail, exchanges=None, codes=self.codes, filters=filters, return_type="domain", provider=self.provider, ) def run(self): for security_item in self.entities: assert isinstance(security_item, StockDetail) if security_item.exchange == "sh": fc = "{}01".format(security_item.code) if security_item.exchange == "sz": fc = "{}02".format(security_item.code) # 基本资料 # param = {"color": "w", "fc": fc, "SecurityCode": "SZ300059"} securities_code = f"{security_item.code}.{security_item.exchange.upper()}" param = { "type": "RPT_F10_ORG_BASICINFO", "sty": "ORG_PROFIE,MAIN_BUSINESS,FOUND_DATE,EM2016,BLGAINIAN,REGIONBK", "filter": f"(SECUCODE=\"{securities_code}\")", "client": "app", "source": "SECURITIES", "pageNumber": 1, "pageSize": 1 } resp = requests.get("https://datacenter.eastmoney.com/securities/api/data/get", params=param) resp.encoding = "utf8" resp_json = resp.json()["result"]["data"][0] security_item.profile = resp_json["ORG_PROFIE"] security_item.main_business = resp_json["MAIN_BUSINESS"] security_item.date_of_establishment = to_pd_timestamp(resp_json["FOUND_DATE"]) # 关联行业 industries = ",".join(resp_json["EM2016"].split("-")) security_item.industries = industries # 关联概念 security_item.concept_indices = resp_json["BLGAINIAN"] # 关联地区 security_item.area_indices = resp_json["REGIONBK"] self.sleep() # 发行相关 param = { "reportName": "RPT_F10_ORG_ISSUEINFO", "columns": "AFTER_ISSUE_PE,ISSUE_PRICE,TOTAL_ISSUE_NUM,NET_RAISE_FUNDS,ONLINE_ISSUE_LWR", "filter": f"(SECUCODE=\"{securities_code}\")(TYPENEW=\"4\")", "client": "app", "source": "SECURITIES", "pageNumber": 1, "pageSize": 1 } resp = requests.get("https://datacenter.eastmoney.com/securities/api/data/v1/get", params=param) resp.encoding = "utf8" resp_json = resp.json()["result"]["data"][0] security_item.issue_pe = resp_json["AFTER_ISSUE_PE"] security_item.price = resp_json["ISSUE_PRICE"] security_item.issues = resp_json["TOTAL_ISSUE_NUM"] security_item.raising_fund = resp_json.get("NET_RAISE_FUNDS") security_item.net_winning_rate = resp_json["ONLINE_ISSUE_LWR"] self.session.commit() self.logger.info("finish recording stock meta for:{}".format(security_item.code)) self.sleep() if __name__ == "__main__": # init_log('china_stock_meta.log') recorder = EastmoneyStockRecorder() recorder.run() StockDetail.record_data(codes=["000338", "000777"], provider="eastmoney") # the __all__ is generated __all__ = ["EastmoneyStockRecorder", "EastmoneyStockDetailRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/trading/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule eastmoney_manager_trading_recorder from .eastmoney_manager_trading_recorder import * from .eastmoney_manager_trading_recorder import __all__ as _eastmoney_manager_trading_recorder_all __all__ += _eastmoney_manager_trading_recorder_all # import all from submodule eastmoney_holder_trading_recorder from .eastmoney_holder_trading_recorder import * from .eastmoney_holder_trading_recorder import __all__ as _eastmoney_holder_trading_recorder_all __all__ += _eastmoney_holder_trading_recorder_all ================================================ FILE: src/zvt/recorders/eastmoney/trading/eastmoney_holder_trading_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import HolderTrading from zvt.recorders.eastmoney.common import EastmoneyMoreDataRecorder from zvt.utils.utils import to_float class HolderTradingRecorder(EastmoneyMoreDataRecorder): data_schema = HolderTrading url = "https://emh5.eastmoney.com/api/JiaoYiShuJu/GetGuDongZengJian" path_fields = ["GuDongZengJianList"] def get_original_time_field(self): return "RiQi" def get_data_map(self): return { "holder_name": ("GuDongMingCheng", str), "volume": ("BianDongShuLiang", to_float), "change_pct": ("BianDongBiLi", to_float), "holding_pct": ("BianDongHouChiGuBiLi", to_float), } def generate_domain_id(self, entity, original_data): the_name = original_data.get("GuDongMingCheng") timestamp = original_data[self.get_original_time_field()] the_id = "{}_{}_{}".format(entity.id, timestamp, the_name) return the_id if __name__ == "__main__": # init_log('holder_trading.log') recorder = HolderTradingRecorder(codes=["002572"]) recorder.run() # the __all__ is generated __all__ = ["HolderTradingRecorder"] ================================================ FILE: src/zvt/recorders/eastmoney/trading/eastmoney_manager_trading_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import ManagerTrading from zvt.recorders.eastmoney.common import EastmoneyMoreDataRecorder from zvt.utils.utils import to_float class ManagerTradingRecorder(EastmoneyMoreDataRecorder): data_schema = ManagerTrading url = "https://emh5.eastmoney.com/api/JiaoYiShuJu/GetGaoGuanZengJian" path_fields = ["GaoGuanZengJianList"] def get_original_time_field(self): return "RiQi" def get_data_map(self): return { "trading_person": ("BianDongRen", str), "volume": ("BianDongShuLiang", to_float), "price": ("JiaoYiJunJia", to_float), "holding": ("BianDongHouShuLiang", to_float), "trading_way": ("JiaoYiTuJing", str), "manager": ("GaoGuanMingCheng", str), "manager_position": ("GaoGuanZhiWei", str), "relationship_with_manager": ("GaoGuanGuanXi", str), } def generate_domain_id(self, entity, original_data): the_name = original_data.get("BianDongRen") timestamp = original_data[self.get_original_time_field()] the_id = "{}_{}_{}".format(entity.id, timestamp, the_name) return the_id if __name__ == "__main__": # init_log('manager_trading.log') recorder = ManagerTradingRecorder(codes=["002572"]) recorder.run() # the __all__ is generated __all__ = ["ManagerTradingRecorder"] ================================================ FILE: src/zvt/recorders/em/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule quotes from .quotes import * from .quotes import __all__ as _quotes_all __all__ += _quotes_all # import all from submodule trading from .trading import * from .trading import __all__ as _trading_all __all__ += _trading_all # import all from submodule actor from .actor import * from .actor import __all__ as _actor_all __all__ += _actor_all # import all from submodule em_api from .em_api import * from .em_api import __all__ as _em_api_all __all__ += _em_api_all # import all from submodule macro from .macro import * from .macro import __all__ as _macro_all __all__ += _macro_all # import all from submodule meta from .meta import * from .meta import __all__ as _meta_all __all__ += _meta_all # import all from submodule misc from .misc import * from .misc import __all__ as _misc_all __all__ += _misc_all ================================================ FILE: src/zvt/recorders/em/actor/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule em_stock_top_ten_recorder from .em_stock_top_ten_recorder import * from .em_stock_top_ten_recorder import __all__ as _em_stock_top_ten_recorder_all __all__ += _em_stock_top_ten_recorder_all # import all from submodule em_stock_top_ten_free_recorder from .em_stock_top_ten_free_recorder import * from .em_stock_top_ten_free_recorder import __all__ as _em_stock_top_ten_free_recorder_all __all__ += _em_stock_top_ten_free_recorder_all # import all from submodule em_stock_actor_summary_recorder from .em_stock_actor_summary_recorder import * from .em_stock_actor_summary_recorder import __all__ as _em_stock_actor_summary_recorder_all __all__ += _em_stock_actor_summary_recorder_all # import all from submodule em_stock_ii_recorder from .em_stock_ii_recorder import * from .em_stock_ii_recorder import __all__ as _em_stock_ii_recorder_all __all__ += _em_stock_ii_recorder_all ================================================ FILE: src/zvt/recorders/em/actor/em_stock_actor_summary_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.api.utils import to_report_period_type, value_to_pct from zvt.contract import ActorType from zvt.contract.api import df_to_db from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Stock from zvt.domain.actor.stock_actor import StockActorSummary from zvt.recorders.em.em_api import get_ii_holder_report_dates, actor_type_to_org_type, get_ii_summary from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str # [{'CHANGE_RATIO': -1.045966694333, # 'IS_COMPLETE': '1', # 'ORG_TYPE': '07', # 'REPORT_DATE': '2021-03-31 00:00:00', # 'SECUCODE': '000338.SZ', # 'SECURITY_CODE': '000338', # 'TOTAL_FREE_SHARES': 2598718411, # 'TOTAL_MARKET_CAP': 49999342227.64, # 'TOTAL_ORG_NUM': 5, # 'TOTAL_SHARES_RATIO': 29.51742666}] class EMStockActorSummaryRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "em" data_schema = StockActorSummary def init_timestamps(self, entity_item) -> List[pd.Timestamp]: result = get_ii_holder_report_dates(code=entity_item.code) if result: return [to_pd_timestamp(item["REPORT_DATE"]) for item in result] def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) self.logger.info(f"to {entity.code} {the_date}") for actor_type in ActorType: if actor_type == ActorType.private_equity or actor_type == ActorType.individual: continue result = get_ii_summary( code=entity.code, report_date=the_date, org_type=actor_type_to_org_type(actor_type) ) if result: summary_list = [ { "id": f"{entity.entity_id}_{the_date}_{actor_type.value}", "entity_id": entity.entity_id, "timestamp": timestamp, "code": entity.code, "name": entity.name, "actor_type": actor_type.value, "actor_count": item["TOTAL_ORG_NUM"], "report_date": timestamp, "report_period": to_report_period_type(timestamp), "change_ratio": value_to_pct(item["CHANGE_RATIO"], default=1), "is_complete": item["IS_COMPLETE"], "holding_numbers": item["TOTAL_FREE_SHARES"], "holding_ratio": value_to_pct(item["TOTAL_SHARES_RATIO"], default=0), "holding_values": item["TOTAL_MARKET_CAP"], } for item in result ] df = pd.DataFrame.from_records(summary_list) df_to_db( data_schema=self.data_schema, df=df, provider=self.provider, force_update=True, drop_duplicates=True, ) if __name__ == "__main__": EMStockActorSummaryRecorder(codes=["000338"]).run() # the __all__ is generated __all__ = ["EMStockActorSummaryRecorder"] ================================================ FILE: src/zvt/recorders/em/actor/em_stock_ii_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.api.utils import to_report_period_type, value_to_pct from zvt.contract import ActorType from zvt.contract.api import df_to_db from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Stock, ActorMeta from zvt.domain.actor.stock_actor import StockInstitutionalInvestorHolder from zvt.recorders.em.em_api import get_ii_holder_report_dates, get_ii_holder, actor_type_to_org_type from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str # {'END_DATE': '2021-03-31 00:00:00', # 'HOLDER_CODE': '10015776', # 'HOLDER_CODE_OLD': '80010104', # 'HOLDER_NAME': '香港中央结算代理人有限公司', # 'HOLDER_RANK': 1, # 'HOLD_NUM': 1938664086, # 'HOLD_NUM_RATIO': 24.44, # 'HOLD_RATIO_QOQ': '0.04093328', # 'IS_HOLDORG': '1', # 'SECUCODE': '000338.SZ'} # {'END_DATE': '2021-03-31 00:00:00', # 'FREE_HOLDNUM_RATIO': 0.631949916991, # 'FREE_RATIO_QOQ': '-5.33046217', # 'HOLDER_CODE': '161606', # 'HOLDER_CODE_OLD': '161606', # 'HOLDER_NAME': '交通银行-融通行业景气证券投资基金', # 'HOLDER_RANK': 10, # 'HOLD_NUM': 39100990, # 'IS_HOLDORG': '1', # 'SECUCODE': '000338.SZ'} class EMStockIIRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "em" data_schema = StockInstitutionalInvestorHolder def init_timestamps(self, entity_item) -> List[pd.Timestamp]: result = get_ii_holder_report_dates(code=entity_item.code) if result: return [to_pd_timestamp(item["REPORT_DATE"]) for item in result] def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) self.logger.info(f"to {entity.code} {the_date}") for actor_type in ActorType: if actor_type == ActorType.private_equity or actor_type == ActorType.individual: continue result = get_ii_holder( code=entity.code, report_date=the_date, org_type=actor_type_to_org_type(actor_type) ) if result: holders = [ { "id": f'{entity.entity_id}_{the_date}_{actor_type.value}_cn_{item["HOLDER_CODE"]}', "entity_id": entity.entity_id, "timestamp": timestamp, "code": entity.code, "name": entity.name, "actor_id": f'{actor_type.value}_cn_{item["HOLDER_CODE"]}', "actor_type": actor_type.value, "actor_code": item["HOLDER_CODE"], "actor_name": f'{item["HOLDER_NAME"]}', "report_date": timestamp, "report_period": to_report_period_type(timestamp), "holding_numbers": item["TOTAL_SHARES"], "holding_ratio": value_to_pct(item["FREESHARES_RATIO"], 0), "holding_values": item["HOLD_VALUE"], } for item in result ] df = pd.DataFrame.from_records(holders) df_to_db( data_schema=self.data_schema, df=df, provider=self.provider, force_update=True, drop_duplicates=True, ) # save the actors actors = [ { "id": f'{actor_type.value}_cn_{item["HOLDER_CODE"]}', "entity_id": f'{actor_type.value}_cn_{item["HOLDER_CODE"]}', "entity_type": actor_type.value, "exchange": "cn", "code": item["HOLDER_CODE"], "name": f'{item["HOLDER_NAME"]}', } for item in result ] df1 = pd.DataFrame.from_records(actors) df_to_db( data_schema=ActorMeta, df=df1, provider=self.provider, force_update=False, drop_duplicates=True ) if __name__ == "__main__": EMStockIIRecorder(codes=["000562"]).run() # the __all__ is generated __all__ = ["EMStockIIRecorder"] ================================================ FILE: src/zvt/recorders/em/actor/em_stock_top_ten_free_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.api.utils import to_report_period_type, value_to_pct from zvt.contract import ActorType from zvt.contract.api import df_to_db from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Stock, ActorMeta from zvt.domain.actor.stock_actor import StockTopTenFreeHolder, StockInstitutionalInvestorHolder from zvt.recorders.em.em_api import get_holder_report_dates, get_free_holders from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str class EMStockTopTenFreeRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "em" data_schema = StockTopTenFreeHolder def init_timestamps(self, entity_item) -> List[pd.Timestamp]: result = get_holder_report_dates(code=entity_item.code) if result: return [to_pd_timestamp(item["END_DATE"]) for item in result] def on_finish_entity(self, entity): super().on_finish_entity(entity) holders = StockTopTenFreeHolder.query_data( entity_id=entity.id, filters=[StockTopTenFreeHolder.holding_values == None], session=self.session, return_type="domain", ) for holder in holders: ii = StockInstitutionalInvestorHolder.query_data( entity_id=entity.id, filters=[ StockInstitutionalInvestorHolder.holding_values > 1, StockInstitutionalInvestorHolder.holding_ratio > 0.01, StockInstitutionalInvestorHolder.timestamp == holder.timestamp, ], limit=1, return_type="domain", ) if ii: holder.holding_values = holder.holding_ratio * ii[0].holding_values / ii[0].holding_ratio self.session.commit() def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) result = get_free_holders(code=entity.code, end_date=the_date) if result: holders = [] new_actors = [] for item in result: # {'END_DATE': '2021-03-31 00:00:00', # 'FREE_HOLDNUM_RATIO': 0.631949916991, # 'FREE_RATIO_QOQ': '-5.33046217', # 'HOLDER_CODE': '161606', # 'HOLDER_CODE_OLD': '161606', # 'HOLDER_NAME': '交通银行-融通行业景气证券投资基金', # 'HOLDER_RANK': 10, # 'HOLD_NUM': 39100990, # 'IS_HOLDORG': '1', # 'SECUCODE': '000338.SZ'} # 机构 if item["IS_HOLDORG"] == "1": domains: List[ActorMeta] = ActorMeta.query_data( filters=[ActorMeta.code == item["HOLDER_CODE"]], return_type="domain" ) if not domains: actor_type = ActorType.corporation.value actor = ActorMeta( entity_id=f'{actor_type}_cn_{item["HOLDER_CODE"]}', id=f'{actor_type}_cn_{item["HOLDER_CODE"]}', entity_type=actor_type, exchange="cn", code=item["HOLDER_CODE"], name=item["HOLDER_NAME"], ) else: actor = domains[0] else: actor_type = ActorType.individual.value actor = ActorMeta( entity_id=f'{actor_type}_cn_{item["HOLDER_NAME"]}', id=f'{actor_type}_cn_{item["HOLDER_NAME"]}', entity_type=actor_type, exchange="cn", code=item["HOLDER_NAME"], name=item["HOLDER_NAME"], ) new_actors.append(actor.__dict__) holder = { "id": f"{entity.entity_id}_{the_date}_{actor.entity_id}", "entity_id": entity.entity_id, "timestamp": timestamp, "code": entity.code, "name": entity.name, "actor_id": actor.entity_id, "actor_type": actor.entity_type, "actor_code": actor.code, "actor_name": actor.name, "report_date": timestamp, "report_period": to_report_period_type(timestamp), "holding_numbers": item["HOLD_NUM"], "holding_ratio": value_to_pct(item["FREE_HOLDNUM_RATIO"], 0), } holders.append(holder) if holders: df = pd.DataFrame.from_records(holders) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) if new_actors: df = pd.DataFrame.from_records(new_actors) df_to_db(data_schema=ActorMeta, df=df, provider=self.provider, force_update=False) if __name__ == "__main__": EMStockTopTenFreeRecorder(codes=["000338"]).run() # the __all__ is generated __all__ = ["EMStockTopTenFreeRecorder"] ================================================ FILE: src/zvt/recorders/em/actor/em_stock_top_ten_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.api.utils import to_report_period_type, value_to_pct from zvt.contract import ActorType from zvt.contract.api import df_to_db from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Stock, ActorMeta from zvt.domain.actor.stock_actor import StockTopTenHolder, StockInstitutionalInvestorHolder from zvt.recorders.em.em_api import get_holder_report_dates, get_holders from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str class EMStockTopTenRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "em" data_schema = StockTopTenHolder def init_timestamps(self, entity_item) -> List[pd.Timestamp]: result = get_holder_report_dates(code=entity_item.code) if result: return [to_pd_timestamp(item["END_DATE"]) for item in result] def on_finish_entity(self, entity): super().on_finish_entity(entity) holders = StockTopTenHolder.query_data( entity_id=entity.id, filters=[StockTopTenHolder.holding_values == None], session=self.session, return_type="domain", ) for holder in holders: ii = StockInstitutionalInvestorHolder.query_data( entity_id=entity.id, filters=[ StockInstitutionalInvestorHolder.holding_values > 1, StockInstitutionalInvestorHolder.holding_ratio > 0.01, StockInstitutionalInvestorHolder.timestamp == holder.timestamp, ], limit=1, return_type="domain", ) if ii: holder.holding_values = holder.holding_ratio * ii[0].holding_values / ii[0].holding_ratio self.session.commit() def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) result = get_holders(code=entity.code, end_date=the_date) if result: holders = [] new_actors = [] for item in result: # 机构 if item["IS_HOLDORG"] == "1": domains: List[ActorMeta] = ActorMeta.query_data( filters=[ActorMeta.code == item["HOLDER_CODE"]], return_type="domain" ) if not domains: actor_type = ActorType.corporation.value actor = ActorMeta( entity_id=f'{actor_type}_cn_{item["HOLDER_CODE"]}', id=f'{actor_type}_cn_{item["HOLDER_CODE"]}', entity_type=actor_type, exchange="cn", code=item["HOLDER_CODE"], name=item["HOLDER_NAME"], ) else: actor = domains[0] else: actor_type = ActorType.individual.value actor = ActorMeta( entity_id=f'{actor_type}_cn_{item["HOLDER_NAME"]}', id=f'{actor_type}_cn_{item["HOLDER_NAME"]}', entity_type=actor_type, exchange="cn", code=item["HOLDER_NAME"], name=item["HOLDER_NAME"], ) new_actors.append(actor.__dict__) holder = { "id": f"{entity.entity_id}_{the_date}_{actor.entity_id}", "entity_id": entity.entity_id, "timestamp": timestamp, "code": entity.code, "name": entity.name, "actor_id": actor.entity_id, "actor_type": actor.entity_type, "actor_code": actor.code, "actor_name": actor.name, "report_date": timestamp, "report_period": to_report_period_type(timestamp), "holding_numbers": item["HOLD_NUM"], "holding_ratio": value_to_pct(item["HOLD_NUM_RATIO"], default=0), } holders.append(holder) if holders: df = pd.DataFrame.from_records(holders) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) if new_actors: df = pd.DataFrame.from_records(new_actors) df_to_db(data_schema=ActorMeta, df=df, provider=self.provider, force_update=False) if __name__ == "__main__": EMStockTopTenRecorder(codes=["000002"]).run() # the __all__ is generated __all__ = ["EMStockTopTenRecorder"] ================================================ FILE: src/zvt/recorders/em/em_api.py ================================================ # -*- coding: utf-8 -*- import logging import random import time from typing import Union import demjson3 import pandas as pd import requests import sqlalchemy from requests import Session from zvt import zvt_config from zvt.api.kdata import generate_kdata_id from zvt.api.utils import value_to_pct, china_stock_code_to_id from zvt.contract import ( ActorType, AdjustType, IntervalLevel, Exchange, TradableType, get_entity_exchanges, tradable_type_map_exchanges, ) from zvt.contract.api import decode_entity_id, df_to_db from zvt.domain import BlockCategory, StockHotTopic from zvt.recorders.consts import DEFAULT_HEADER from zvt.utils.time_utils import ( to_pd_timestamp, now_timestamp_ms, to_date_time_str, current_date, now_pd_timestamp, ) from zvt.utils.utils import to_float, json_callback_param, chrome_copy_header_to_dict logger = logging.getLogger(__name__) EM_TOKEN_HEADER = None if zvt_config["em_header"]: try: EM_TOKEN_HEADER = chrome_copy_header_to_dict(zvt_config["em_header"]) except Exception as e: logger.error(e) if not EM_TOKEN_HEADER: EM_TOKEN_HEADER = DEFAULT_HEADER # 获取中美国债收益率 def get_treasury_yield(pn=1, ps=2000, fetch_all=True): results = get_em_data( request_type="RPTA_WEB_TREASURYYIELD", source=None, fields="ALL", sort_by="SOLAR_DATE", sort="desc", pn=pn, ps=ps, fetch_all=fetch_all, ) yields = [] for item in results: date = item["SOLAR_DATE"] # 中国 yields.append( { "id": f"country_galaxy_CN_{to_date_time_str(date)}", "entity_id": "country_galaxy_CN", "timestamp": to_pd_timestamp(date), "code": "CN", "yield_2": item.get("EMM00588704"), "yield_5": item.get("EMM00166462"), "yield_10": item.get("EMM00166466"), "yield_30": item.get("EMM00166469"), } ) yields.append( { "id": f"country_galaxy_US_{to_date_time_str(date)}", "entity_id": "country_galaxy_US", "timestamp": to_pd_timestamp(date), "code": "US", "yield_2": item.get("EMG00001306"), "yield_5": item.get("EMG00001308"), "yield_10": item.get("EMG00001310"), "yield_30": item.get("EMG00001312"), } ) return yields # 机构持仓日期 def get_ii_holder_report_dates(code): return get_em_data( request_type="RPT_F10_MAIN_ORGHOLD", fields="REPORT_DATE,IS_COMPLETE", filters=generate_filters(code=code), sort_by="REPORT_DATE", sort="desc", ) def get_dragon_and_tiger_list(start_date, end_date=None): start_date = to_date_time_str(start_date) if not end_date: end_date = now_timestamp_ms() end_date = to_date_time_str(end_date) return get_em_data( request_type="RPT_DAILYBILLBOARD_DETAILS", fields="ALL", source="DataCenter", filters=f"(TRADE_DATE>='{start_date}')(TRADE_DATE<='{end_date}')", sort_by="TRADE_DATE,SECURITY_CODE", sort="asc,asc", ) # 龙虎榜 def get_dragon_and_tiger(code, start_date=None): return get_em_data( request_type="RPT_OPERATEDEPT_TRADE", fields="TRADE_ID,TRADE_DATE,EXPLANATION,SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,ACCUM_AMOUNT,CHANGE_RATE,NET_BUY,BUY_BUY_TOTAL,BUY_SELL_TOTAL,BUY_RATIO_TOTAL,SELL_BUY_TOTAL,SELL_SELL_TOTAL,SELL_RATIO_TOTAL,TRADE_DIRECTION,RANK,OPERATEDEPT_NAME,BUY_AMT_REAL,SELL_AMT_REAL,BUY_RATIO,SELL_RATIO,BUY_TOTAL,SELL_TOTAL,BUY_TOTAL_NET,SELL_TOTAL_NET,NET", filters=generate_filters(code=code, trade_date=start_date, field_op={"trade_date": ">="}), params='(groupField=TRADE_ID)(groupedFields=TRADE_DIRECTION,RANK,OPERATEDEPT_NAME,BUY_AMT_REAL,SELL_AMT_REAL,BUY_RATIO,SELL_RATIO,NET")(groupListName="LIST")', sort_by="TRADE_DATE,RANK", sort="asc,asc", ) # 十大股东持仓日期 def get_holder_report_dates(code): return get_em_data( request_type="RPT_F10_EH_HOLDERSDATE", fields="END_DATE,IS_DEFAULT,IS_REPORTDATE", filters=generate_filters(code=code), sort_by="END_DATE", sort="desc", ) # 十大流通股东日期 def get_free_holder_report_dates(code): return get_em_data( request_type="RPT_F10_EH_FREEHOLDERSDATE", fields="END_DATE,IS_DEFAULT,IS_REPORTDATE", filters=generate_filters(code=code), sort_by="END_DATE", sort="desc", ) # https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_EH_RELATION&sty=SECUCODE%2CHOLDER_NAME%2CRELATED_RELATION%2CHOLD_RATIO&filter=(SECUCODE%3D%22601162.SH%22)&client=APP&source=SECURITIES&p=1&ps=200&rdm=rnd_01BE6995104944ED99B70EEB7FFC0353&v=012649539724458458 # https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_F10_FREE_TOTALHOLDNUM&sty=SECUCODE%2CSECURITY_CODE%2CEND_DATE%2CHOLD_NUM_COUNT%2CHOLD_RATIO_COUNT%2CHOLD_RATIO_CHANGE&filter=(SECUCODE%3D%22601162.SH%22)(END_DATE%3D%272024-09-30%27)&client=APP&source=SECURITIES&p=1&ps=200&sr=1&st=&rdm=rnd_FA1943FA30474E3AA0CCF206EA1B5749&v=032098454407366983 def get_controlling_shareholder(code): return get_em_data( request_type="RPT_F10_EH_RELATION", fields="SECUCODE,CHOLDER_NAME,CRELATED_RELATION,CHOLD_RATIO", filters=generate_filters(code=code), ) # 机构持仓 def get_ii_holder(code, report_date, org_type): return get_em_data( request_type="RPT_MAIN_ORGHOLDDETAIL", fields="SECURITY_CODE,REPORT_DATE,HOLDER_CODE,HOLDER_NAME,TOTAL_SHARES,HOLD_VALUE,FREESHARES_RATIO,ORG_TYPE,SECUCODE,FUND_DERIVECODE", filters=generate_filters(code=code, report_date=report_date, org_type=org_type), ) # 机构持仓汇总 def get_ii_summary(code, report_date, org_type): return get_em_data( request_type="RPT_F10_MAIN_ORGHOLDDETAILS", fields="SECURITY_CODE,SECUCODE,REPORT_DATE,ORG_TYPE,TOTAL_ORG_NUM,TOTAL_FREE_SHARES,TOTAL_MARKET_CAP,TOTAL_SHARES_RATIO,CHANGE_RATIO,IS_COMPLETE", filters=generate_filters(code=code, report_date=report_date, org_type=org_type), ) def get_free_holders(code, end_date): return get_em_data( request_type="RPT_F10_EH_FREEHOLDERS", fields="SECUCODE,END_DATE,HOLDER_NAME,HOLDER_CODE,HOLDER_CODE_OLD,HOLD_NUM,FREE_HOLDNUM_RATIO,FREE_RATIO_QOQ,IS_HOLDORG,HOLDER_RANK", filters=generate_filters(code=code, end_date=end_date), sort_by="HOLDER_RANK", ) def get_top_ten_free_holder_stats(code): datas = get_holder_report_dates(code=code) if datas: end_date = to_date_time_str(datas[0]["END_DATE"]) holders = get_em_data( request_type="RPT_F10_FREE_TOTALHOLDNUM", fields="SECUCODE,SECURITY_CODE,END_DATE,HOLD_NUM_COUNT,HOLD_RATIO_COUNT,HOLD_RATIO_CHANGE,", filters=generate_filters(code=code, end_date=end_date), ) if holders: holder = holders[0] ratio = 0 change = 0 try: if holder["HOLD_RATIO_COUNT"]: ratio = holder["HOLD_RATIO_COUNT"] / 100 if holder["HOLD_RATIO_CHANGE"]: change = holder["HOLD_RATIO_CHANGE"] / 100 except Exception as e: logger.warning(f"Wrong holder {holder}", e) return { "code": code, "timestamp": end_date, "ratio": ratio, "change": change, } def get_controlling_shareholder(code): holders = get_em_data( request_type="RPT_F10_EH_RELATION", fields="SECUCODE,HOLDER_NAME,RELATED_RELATION,HOLD_RATIO", filters=generate_filters(code=code), ) if holders: control = {"ratio": 0} for holder in holders: if holder["RELATED_RELATION"] == "控股股东": control["holder"] = holder["HOLDER_NAME"] elif holder["RELATED_RELATION"] == "实际控制人": control["parent"] = holder["HOLDER_NAME"] if holder["HOLD_RATIO"]: control["ratio"] = control["ratio"] + holder["HOLD_RATIO"] return control def get_holders(code, end_date): return get_em_data( request_type="RPT_F10_EH_HOLDERS", fields="SECUCODE,END_DATE,HOLDER_NAME,HOLDER_CODE,HOLDER_CODE_OLD,HOLD_NUM,HOLD_NUM_RATIO,HOLD_RATIO_QOQ,HOLDER_RANK,IS_HOLDORG", filters=generate_filters(code=code, end_date=end_date), sort_by="HOLDER_RANK", ) def get_basic_info(entity_id, session=None): entity_type, exchange, code = decode_entity_id(entity_id) if entity_type == "stock": request_type = "RPT_F10_ORG_BASICINFO" fields = "SECUCODE,LISTING_DATE,SECURITY_CODE,SECURITY_NAME_ABBR,ORG_NAME,FORMERNAME,CSRC_INDUSTRY_NAME,STR_CODEH,STR_NAMEH,STR_CODEA,STR_NAMEA,STR_CODEB,STR_NAMEB,REGIONBK,EM2016,BLGAINIAN,CHAIRMAN,LEGAL_PERSON,PRESIDENT,SECRETARY,FOUND_DATE,REG_CAPITAL,TOTAL_NUM,TATOLNUMBER,ORG_TEL,ORG_EMAIL,ORG_WEB,ADDRESS,REG_ADDRESS,ORG_PROFIE,MAIN_BUSINESS,SECURITY_TYPE_CODE,CURRENCY,ACCOUNT_FIRM,LEGAL_ADVISER,EXPAND_NAME_ABBR,ORG_PROFILE" filters = generate_filters(code=code, exchange=exchange.upper()) elif entity_type == "stockhk": request_type = "RPT_HKF10_INFO_ORGPROFILE;RPT_HKF10_INFO_SECURITYINFO" fields = "SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,ORG_NAME,CHAIRMAN,MAIN_BUSINESS,BELONG_INDUSTRY,BELONG_INDUSTRY,REG_PLACE,REG_CAPITAL,FOUND_DATE,LISTING_DATE,@SECURITY_INNER_CODE;@SECURITY_INNER_CODE,TRADE_UNIT,PAR_VALUE,ISSUE_PRICE,ISSUE_NUM" filters = generate_filters(code=code, exchange="HK") elif entity_type == "stockus": if exchange == "nasdaq": exchange_code = "O" elif exchange == "nyse": exchange_code = "N" else: raise Exception(f"unknow exchange: {exchange}") request_type = "RPT_USF10_INFO_SECURITYINFO;RPT_USF10_INFO_ORGPROFILE" fields = "SECUCODE,SECURITY_CODE,SECURITY_TYPE,LISTING_DATE,TRADE_MARKET,ISSUE_PRICE,ISSUE_NUM,@SECUCODE;@SECUCODE,ORG_NAME,ORG_EN_ABBR,BELONG_INDUSTRY,FOUND_DATE,CHAIRMAN,ADDRESS,ORG_WEB" filters = generate_filters(code=code, exchange=exchange_code) else: raise Exception(f"unknow entity_type: {entity_type}") datas = get_em_data( session=session, request_type=request_type, fields=fields, filters=filters, ) if datas: return datas[0] return None def _order_param(order: str): if order: orders = order.split(",") return ",".join(["1" if item == "asc" else "-1" for item in orders]) return order def get_url( request_type=None, fields=None, request_type_param_name="type", fields_param_name="sty", source="SECURITIES", filters=None, order_by="", order="asc", pn=1, ps=2000, pn_param_name="p", ps_param_name="ps", params=None, ): # 根据 url 映射如下 # type=RPT_F10_MAIN_ORGHOLDDETAILS # sty=SECURITY_CODE,SECUCODE,REPORT_DATE,ORG_TYPE,TOTAL_ORG_NUM,TOTAL_FREE_SHARES,TOTAL_MARKET_CAP,TOTAL_SHARES_RATIO,CHANGE_RATIO,IS_COMPLETE # filter=(SECUCODE="000338.SZ")(REPORT_DATE=\'2021-03-31\')(ORG_TYPE="01") # sr=1 # st= sr = _order_param(order=order) v = random.randint(1000000000000000, 9000000000000000) if filters or source: url = f"https://datacenter.eastmoney.com/securities/api/data/get?{request_type_param_name}={request_type}&{fields_param_name}={fields}&filter={filters}&client=APP&source={source}&{pn_param_name}={pn}&{ps_param_name}={ps}&sr={sr}&st={order_by}&v=0{v}" else: url = f"https://datacenter.eastmoney.com/api/data/get?{request_type_param_name}={request_type}&{fields_param_name}={fields}&st={order_by}&sr={sr}&{pn_param_name}={pn}&{ps_param_name}={ps}&_={now_timestamp_ms()}" if params: url = url + f"¶ms={params}" return url def get_exchange(code): code_ = int(code) if 800000 >= code_ >= 600000: return "SH" elif code_ >= 400000: return "BJ" else: return "SZ" def actor_type_to_org_type(actor_type: ActorType): if actor_type == ActorType.raised_fund: return "01" if actor_type == ActorType.qfii: return "02" if actor_type == ActorType.social_security: return "03" if actor_type == ActorType.broker: return "04" if actor_type == ActorType.insurance: return "05" if actor_type == ActorType.trust: return "06" if actor_type == ActorType.corporation: return "07" assert False def generate_filters( code=None, exchange=None, trade_date=None, report_date=None, end_date=None, org_type=None, field_op: dict = None ): args = [ item for item in locals().items() if item[1] and (item[0] not in ("code", "exchange", "org_type", "field_op")) ] result = "" if code: if exchange: result += f'(SECUCODE="{code}.{exchange}")' else: result += f'(SECUCODE="{code}.{get_exchange(code)}")' if org_type: result += f'(ORG_TYPE="{org_type}")' for arg in args: field = arg[0] value = arg[1] if field_op: op = field_op.get(field, "=") else: op = "=" result += f"({field.upper()}{op}'{value}')" return result def get_em_data( request_type, fields, request_type_param_name="type", fields_param_name="sty", session=None, source="SECURITIES", filters=None, sort_by="", sort="asc", pn=1, ps=2000, pn_param_name="p", ps_param_name="ps", fetch_all=True, fetch_count=1, params=None, ): url = get_url( request_type=request_type, fields=fields, request_type_param_name=request_type_param_name, fields_param_name=fields_param_name, source=source, filters=filters, order_by=sort_by, order=sort, pn=pn, ps=ps, pn_param_name=pn_param_name, ps_param_name=ps_param_name, params=params, ) if session: resp = session.get(url) else: resp = requests.get(url) if resp.status_code == 200: json_result = resp.json() resp.close() if json_result: if json_result.get("result"): data: list = json_result["result"]["data"] need_next = pn < json_result["result"]["pages"] elif json_result.get("data"): data: list = json_result["data"] need_next = json_result["hasNext"] == 1 else: data = [] need_next = False if fetch_all or fetch_count - 1 > 0: if need_next: next_data = get_em_data( session=session, request_type=request_type, fields=fields, request_type_param_name=request_type_param_name, fields_param_name=fields_param_name, source=source, filters=filters, sort_by=sort_by, sort=sort, pn=pn + 1, ps=ps, pn_param_name=pn_param_name, ps_param_name=ps_param_name, fetch_all=fetch_all, fetch_count=fetch_count - 1, params=params, ) if next_data: data = data + next_data return data else: return data else: return data else: return data return None raise RuntimeError(f"request em data code: {resp.status_code}, error: {resp.text}") # quote # url = 'https://push2his.eastmoney.com/api/qt/stock/kline/get?' # 日线 klt=101 # 周线 klt=102 # 月线 klt=103 # # limit lmt=2000 # # 结束时间 end=20500000 # # 复权 fqt 0 不复权 1 前复权 2 后复权 # iscca # # 字段 # f51,f52,f53,f54,f55, # timestamp,open,close,high,low # f56,f57,f58,f59,f60,f61,f62,f63,f64 # volume,turnover,震幅,change_pct,change,turnover_rate # 深圳 # secid=0.399001&klt=101&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1 # secid=0.399001&klt=102&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1 # secid=0.000338&klt=101&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1 # # 港股 # secid=116.01024&klt=102&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1 # 美股 # secid=106.BABA&klt=102&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1 # # 上海 # secid=1.512660&klt=101&fqt=1&lmt=66&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1 def get_kdata(entity_id, session=None, level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.qfq, limit=10000): entity_type, exchange, code = decode_entity_id(entity_id) level = IntervalLevel(level) sec_id = to_em_sec_id(entity_id) fq_flag = to_em_fq_flag(adjust_type) level_flag = to_em_level_flag(level) # f131 结算价 # f133 持仓 # 目前未获取 url = f"https://push2his.eastmoney.com/api/qt/stock/kline/get?secid={sec_id}&klt={level_flag}&fqt={fq_flag}&lmt={limit}&end=20500000&iscca=1&fields1=f1,f2,f3,f4,f5,f6,f7,f8&fields2=f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f62,f63,f64&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1" if session: resp = session.get(url, headers=DEFAULT_HEADER) else: resp = requests.get(url, headers=DEFAULT_HEADER) resp.raise_for_status() results = resp.json() resp.close() data = results["data"] kdatas = [] if data: klines = data["klines"] name = data["name"] for result in klines: # "2000-01-28,1005.26,1012.56,1173.12,982.13,3023326,3075552000.00" # "2021-08-27,19.39,20.30,20.30,19.25,1688497,3370240912.00,5.48,6.01,1.15,3.98,0,0,0" # time,open,close,high,low,volume,turnover # "2022-04-13,10708,10664,10790,10638,402712,43124771328,1.43,0.57,60,0.00,4667112399583576064,4690067230254170112,1169270784" fields = result.split(",") the_timestamp = to_pd_timestamp(fields[0]) the_id = generate_kdata_id(entity_id=entity_id, timestamp=the_timestamp, level=level) open = to_float(fields[1]) close = to_float(fields[2]) high = to_float(fields[3]) low = to_float(fields[4]) volume = to_float(fields[5]) turnover = to_float(fields[6]) # 7 振幅 change_pct = value_to_pct(to_float(fields[8])) # 9 变动 turnover_rate = value_to_pct(to_float(fields[10])) kdatas.append( dict( id=the_id, timestamp=the_timestamp, entity_id=entity_id, provider="em", code=code, name=name, level=level.value, open=open, close=close, high=high, low=low, volume=volume, turnover=turnover, turnover_rate=turnover_rate, change_pct=change_pct, ) ) if kdatas: df = pd.DataFrame.from_records(kdatas) return df def get_basic_info1(entity_id): entity_type, exchange, code = decode_entity_id(entity_id) if entity_type == "stock": url = "https://emh5.eastmoney.com/api/GongSiGaiKuang/GetJiBenZiLiao" result_field = "JiBenZiLiao" elif entity_type == "stockus": url = "https://emh5.eastmoney.com/api/MeiGu/GaiKuang/GetZhengQuanZiLiao" result_field = "ZhengQuanZiLiao" elif entity_type == "stockhk": url = "https://emh5.eastmoney.com/api/GangGu/GaiKuang/GetZhengQuanZiLiao" result_field = "ZhengQuanZiLiao" else: assert False data = {"fc": to_em_fc(entity_id=entity_id), "color": "w"} resp = requests.post(url=url, json=data, headers=DEFAULT_HEADER) resp.encoding = "utf-8" resp.raise_for_status() resp.close() return resp.json()["Result"][result_field] def get_future_list(): # 主连 url = f"https://futsseapi.eastmoney.com/list/filter/2?fid=sp_all&mktid=0&typeid=0&pageSize=1000&pageIndex=0&callbackName=jQuery34106875017735118845_1649736551642&sort=asc&orderBy=idx&_={now_timestamp_ms()}" resp = requests.get(url, headers=DEFAULT_HEADER) resp.raise_for_status() result = json_callback_param(resp.text) resp.close() # [['DCE', 'im'], ['SHFE', 'rbm'], ['SHFE', 'hcm'], ['SHFE', 'ssm'], ['CZCE', 'SFM'], ['CZCE', 'SMM'], ['SHFE', 'wrm'], ['SHFE', 'cum'], ['SHFE', 'alm'], ['SHFE', 'znm'], ['SHFE', 'pbm'], ['SHFE', 'nim'], ['SHFE', 'snm'], ['INE', 'bcm'], ['SHFE', 'aum'], ['SHFE', 'agm'], ['DCE', 'am'], ['DCE', 'bm'], ['DCE', 'ym'], ['DCE', 'mm'], ['CZCE', 'RSM'], ['CZCE', 'OIM'], ['CZCE', 'RMM'], ['DCE', 'pm'], ['DCE', 'cm'], ['DCE', 'csm'], ['DCE', 'jdm'], ['CZCE', 'CFM'], ['CZCE', 'CYM'], ['CZCE', 'SRM'], ['CZCE', 'APM'], ['CZCE', 'CJM'], ['CZCE', 'PKM'], ['CZCE', 'PMM'], ['CZCE', 'WHM'], ['DCE', 'rrm'], ['CZCE', 'JRM'], ['CZCE', 'RIM'], ['CZCE', 'LRM'], ['DCE', 'lhm'], ['INE', 'scm'], ['SHFE', 'fum'], ['DCE', 'pgm'], ['INE', 'lum'], ['SHFE', 'bum'], ['CZCE', 'MAM'], ['DCE', 'egm'], ['DCE', 'lm'], ['CZCE', 'TAM'], ['DCE', 'vm'], ['DCE', 'ppm'], ['DCE', 'ebm'], ['CZCE', 'SAM'], ['CZCE', 'FGM'], ['CZCE', 'URM'], ['SHFE', 'rum'], ['INE', 'nrm'], ['SHFE', 'spm'], ['DCE', 'fbm'], ['DCE', 'bbm'], ['CZCE', 'PFM'], ['DCE', 'jmm'], ['DCE', 'jm'], ['CZCE', 'ZCM'], ['8', '060120'], ['8', '040120'], ['8', '070120'], ['8', '110120'], ['8', '050120'], ['8', '130120']] futures = [] for item in result["list"]: entity = {} entity["exchange"], entity["code"] = item["uid"].split("|") # {'8', 'CZCE', 'DCE', 'INE', 'SHFE'} if entity["exchange"] == "8": entity["exchange"] = "cffex" entity["code"] = to_zvt_code(entity["code"]) else: try: entity["exchange"] = Exchange(entity["exchange"].lower()).value if entity["code"][-1].lower() == "m": entity["code"] = entity["code"][:-1] else: assert False entity["code"] = entity["code"].upper() except Exception as e: logger.error(f"wrong item: {item}", e) continue entity["entity_type"] = "future" entity["name"] = item["name"] entity["id"] = f"future_{entity['exchange']}_{entity['code']}" entity["entity_id"] = entity["id"] futures.append(entity) df = pd.DataFrame.from_records(data=futures) return df def _calculate_limit(row): code = row["code"] change_pct = row["change_pct"] if code.startswith(("83", "87", "88", "889", "82", "920")): return change_pct >= 0.29, change_pct <= -0.29 elif code.startswith("300") or code.startswith("301") or code.startswith("688"): return change_pct >= 0.19, change_pct <= -0.19 else: return change_pct > 0.09, change_pct < -0.09 def get_stock_turnover(): sz_url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get?fields1=f1,f2&fields2=f51,f57&ut=fa5fd1943c7b386f172d6893dbfba10b&iscr=0&iscca=0&secid=0.399001&time=0&ndays=2" resp = requests.get(sz_url, headers=DEFAULT_HEADER) resp.raise_for_status() data = resp.json()["data"]["trends"] resp.close() return data def get_top_tradable_list(entity_type, fields, limit, entity_flag, pn=1, exchange=None, return_quote=False): logger.info(f"get_top_tradable_list {entity_type} exchange: {exchange} return_quote: {return_quote} to pn: {pn}") url = f"https://push2.eastmoney.com/api/qt/clist/get?np=1&fltt=2&invt=2&fields={fields}&pn={pn}&pz={limit}&fid=f3&po=1&{entity_flag}&ut=f057cbcbce2a86e2866ab8877db1d059&forcect=1&cb=cbCallbackMore&&callback=jQuery34109676853980006124_{now_timestamp_ms() - 1}&_={now_timestamp_ms()}" resp = requests.get(url, headers=EM_TOKEN_HEADER) resp.raise_for_status() result = json_callback_param(resp.text) resp.close() if not result["data"]: return None total = result["data"]["total"] data = result["data"]["diff"] if pn != 1: return data data_size = len(data) if total != data_size and limit > data_size: pn_size = int(total / data_size) if total % data_size: pn_size = pn_size + 1 while pn < pn_size: pn = pn + 1 logger.info(f"sleep 3 seconds to request {pn}/{pn_size}") time.sleep(3) append_data = get_top_tradable_list( entity_type=entity_type, fields=fields, limit=limit, entity_flag=entity_flag, pn=pn, exchange=exchange, return_quote=return_quote, ) if append_data: data = data + append_data df = pd.DataFrame.from_records(data=data) if return_quote: df = df[["f12", "f13", "f14", "f2", "f3", "f5", "f8", "f6", "f15", "f16", "f17", "f20", "f21"]] df.columns = [ "code", "market_code", "name", "price", "change_pct", "volume", "turnover_rate", "turnover", "high", "low", "open", "total_cap", "float_cap", ] df = df.dropna() df = df[df.change_pct != "-"] df = df[df.turnover_rate != "-"] df = df[df.turnover != "-"] df = df[df.turnover != 0] df = df.astype({"change_pct": "float", "turnover_rate": "float", "turnover": "float", "volume": "float"}) df["change_pct"] = df["change_pct"] / 100 df["turnover_rate"] = df["turnover_rate"] / 100 df[["is_limit_up", "is_limit_down"]] = df.apply(lambda row: _calculate_limit(row), axis=1, result_type="expand") df["entity_id"] = df[["market_code", "code"]].apply( lambda row: market_code_to_entity_id(market=row["market_code"], code=row["code"]), axis=1 ) else: if entity_type in (TradableType.stock, TradableType.stockhk, TradableType.stockus): df = df[["f12", "f13", "f14", "f20", "f21", "f9", "f23"]] df.columns = ["code", "exchange", "name", "total_cap", "float_cap", "pe", "pb"] df[["total_cap", "float_cap", "pe", "pb"]] = df[["total_cap", "float_cap", "pe", "pb"]].apply( pd.to_numeric, errors="coerce" ) else: df = df[["f12", "f13", "f14"]] df.columns = ["code", "exchange", "name"] if exchange: df["exchange"] = exchange.value df["entity_type"] = entity_type.value df["id"] = df[["entity_type", "exchange", "code"]].apply(lambda x: "_".join(x.astype(str)), axis=1) df["entity_id"] = df["id"] return df def get_top_stocks(limit=100): # 沪深和北交所 entity_flag = "fs=m:0+t:6+f:!2,m:0+t:13+f:!2,m:0+t:80+f:!2,m:1+t:2+f:!2,m:1+t:23+f:!2,m:0+t:81+s:2048" fields = "f2,f3,f5,f6,f8,f12,f13,f14,f15,f16,f17,f20,f21" return get_top_tradable_list( entity_type=TradableType.stock, fields=fields, limit=limit, entity_flag=entity_flag, return_quote=True ) def get_top_stockhks(limit=20, hk_south=False): if hk_south: entity_flag = "fs=b:DLMK0144,b:DLMK0146" else: entity_flag = f"fs=m:116+t:3,m:116+t:4" fields = "f2,f3,f5,f6,f8,f12,f13,f14,f15,f16,f17,f20,f21" return get_top_tradable_list( entity_type=TradableType.stockhk, fields=fields, limit=limit, entity_flag=entity_flag, return_quote=True ) def get_top_stockuss(limit=50): entity_flag = "fs=m:105,m:106" fields = "f2,f3,f5,f6,f8,f12,f13,f14,f15,f16,f17,f20,f21" return get_top_tradable_list( entity_type=TradableType.stockus, fields=fields, limit=limit, entity_flag=entity_flag, return_quote=True ) def get_tradable_list( entity_type: Union[TradableType, str] = "stock", exchange: Union[Exchange, str] = None, limit: int = 10000, hk_south=False, block_category=BlockCategory.concept, ): entity_type = TradableType(entity_type) if entity_type == TradableType.future: return get_future_list() exchanges = get_entity_exchanges(entity_type=entity_type) if exchange is not None: assert exchange in exchanges exchanges = [exchange] dfs = [] for exchange in exchanges: exchange = Exchange(exchange) ex_flag = to_em_entity_flag(exchange=exchange) entity_flag = f"fs=m:{ex_flag}" if entity_type == TradableType.index: if exchange == Exchange.sh: entity_flag = "fs=i:1.000001,i:1.000002,i:1.000003,i:1.000009,i:1.000010,i:1.000011,i:1.000012,i:1.000016,i:1.000300,i:1.000903,i:1.000905,i:1.000906,i:1.000688,i:1.000852,i:2.932000" if exchange == Exchange.sz: entity_flag = "fs=i:0.399001,i:0.399002,i:0.399003,i:0.399004,i:0.399005,i:0.399006,i:0.399100,i:0.399106,i:0.399305,i:0.399550" elif entity_type == TradableType.currency: entity_flag = "fs=m:119,m:120" elif entity_type == TradableType.indexus: # 纳斯达克，道琼斯，标普500，美元指数 entity_flag = "fs=i:100.NDX,i:100.DJIA,i:100.SPX,i:100.UDI" elif entity_type == TradableType.blockus: # 美股板块 entity_flag = "fs=m:202" elif entity_type == TradableType.cbond: if exchange == Exchange.sz: entity_flag = "fs=m:0+e:11" elif exchange == Exchange.sh: entity_flag = "fs=m:1+e:11" else: assert False elif entity_type == TradableType.indexhk: entity_flag = "fs=i:305.HSI" # m为交易所代码，t为交易类型 elif entity_type in [TradableType.block, TradableType.stock, TradableType.stockus, TradableType.stockhk]: if exchange == Exchange.sh: # t=2 主板 # t=23 科创板 entity_flag = "fs=m:1+t:2,m:1+t:23" if exchange == Exchange.sz: # t=6 主板 # t=80 创业板 entity_flag = "fs=m:0+t:6,m:0+t:13,m:0+t:80" if exchange == Exchange.bj: entity_flag = "fs=m:0+t:81+s:2048" if exchange == Exchange.hk: if hk_south: # 港股通 entity_flag = "fs=b:DLMK0144,b:DLMK0146" else: # t=3 主板 # t=4 创业板 entity_flag = f"fs=m:116+t:3,m:116+t:4" if exchange == Exchange.nasdaq: # t=1 # t=3 中概股 entity_flag = f"fs=m:105+t:1,m:105+t:3" if exchange == Exchange.nyse: # t=1 # t=3 中概股 entity_flag = f"fs=m:106+t:1,m:105+t:3" if exchange == Exchange.cn: if block_category == BlockCategory.industry: entity_flag = entity_flag + "+t:2" elif block_category == BlockCategory.concept: entity_flag = entity_flag + "+t:3" else: assert False # f2, f3, f4, f12, f13, f14, f19, f111, f148 fields = "f1,f2,f3,f4,f12,f13,f14" if entity_type in (TradableType.stock, TradableType.stockhk, TradableType.stockus): # 市值,流通市值,pe,pb fields = fields + ",f20,f21,f9,f23" df = get_top_tradable_list( entity_type=entity_type, fields=fields, limit=limit, entity_flag=entity_flag, exchange=exchange ) if entity_type == TradableType.block: df["category"] = block_category.value dfs.append(df) return pd.concat(dfs) def get_block_stocks(block_id, name="", session=None): entity_type, exchange, code = decode_entity_id(block_id) category_stocks_url = f"http://48.push2.eastmoney.com/api/qt/clist/get?cb=jQuery11240710111145777397_{now_timestamp_ms() - 1}&pn=1&pz=1000&po=1&np=1&ut=bd1d9ddb04089700cf9c27f6f7426281&fltt=2&invt=2&wbp2u=4668014655929990|0|1|0|web&fid=f3&fs=b:{code}+f:!50&fields=f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152,f45&_={now_timestamp_ms()}" if session: resp = session.get(category_stocks_url, headers=DEFAULT_HEADER) else: resp = requests.get(category_stocks_url, headers=DEFAULT_HEADER) data = json_callback_param(resp.text)["data"] the_list = [] if data: results = data["diff"] for result in results: stock_code = result["f12"] stock_name = result["f14"] stock_id = china_stock_code_to_id(stock_code) the_list.append( { "id": "{}_{}".format(block_id, stock_id), "entity_id": block_id, "entity_type": "block", "exchange": exchange, "code": code, "name": name, "timestamp": current_date(), "stock_id": stock_id, "stock_code": stock_code, "stock_name": stock_name, } ) return the_list def market_code_to_entity_id(market, code): if market in (0, 1): return china_stock_code_to_id(code) elif market == 105: return f"stockus_nasdaq_{code}" elif market == 106: return f"stockus_nyse_{code}" elif market == 116: return f"stockhk_hk_{code}" else: for exchange, flag in exchange_map_em_flag.items(): if flag == market: for entity_type, exchanges in tradable_type_map_exchanges.items(): if exchange in exchanges: return f"{entity_type.value}_{exchange.value}_{code}" return code def get_hot_topic(session: Session = None): url = "https://emcreative.eastmoney.com/FortuneApi/GuBaApi/common" data = { "url": "newctopic/api/Topic/HomeTopicRead?deviceid=IPHONE&version=10001000&product=Guba&plat=Iphone&p=1&ps=20&needPkPost=true", "type": "get", "parm": "", } logger.debug(f"get hot topic from: {url}") if session: resp = session.post(url=url, json=data, headers=DEFAULT_HEADER) else: resp = requests.post(url=url, json=data, headers=DEFAULT_HEADER) if resp.status_code == 200: data_list = resp.json().get("re") if data_list: hot_topics = [] for position, data in enumerate(data_list): if data["stockList"]: entity_ids = [ market_code_to_entity_id(market=stock["qMarket"], code=stock["qCode"]) for stock in data["stockList"] ] else: entity_ids = [] topic_id = data["topicid"] entity_id = f"hot_topic_{topic_id}" hot_topics.append( { "id": entity_id, "entity_id": entity_id, "timestamp": now_pd_timestamp(), "created_timestamp": to_pd_timestamp(data["cTime"]), "position": position, "entity_ids": entity_ids, "news_code": topic_id, "news_title": data["name"], "news_content": data["summary"], } ) return hot_topics logger.error(f"request em data code: {resp.status_code}, error: {resp.text}") def record_hot_topic(): hot_topics = get_hot_topic() logger.debug(hot_topics) if hot_topics: df = pd.DataFrame.from_records(hot_topics) df_to_db( df=df, data_schema=StockHotTopic, provider="em", force_update=True, dtype={"entity_ids": sqlalchemy.JSON} ) def get_news(entity_id, ps=200, index=1, start_timestamp=None, session=None, latest_code=None): sec_id = to_em_sec_id(entity_id=entity_id) url = f"https://np-listapi.eastmoney.com/comm/wap/getListInfo?cb=callback&client=wap&type=1&mTypeAndCode={sec_id}&pageSize={ps}&pageIndex={index}&callback=jQuery1830017478247906740352_{now_timestamp_ms() - 1}&_={now_timestamp_ms()}" logger.debug(f"get news from: {url}") if session: resp = session.get(url) else: resp = requests.get(url) # { # "Art_ShowTime": "2022-02-11 14:29:25", # "Art_Image": "", # "Art_MediaName": "每日经济新闻", # "Art_Code": "202202112274017262", # "Art_Title": "潍柴动力：巴拉德和锡里斯不纳入合并财务报表范围", # "Art_SortStart": "1644560965017262", # "Art_VideoCount": 0, # "Art_OriginUrl": "http://finance.eastmoney.com/news/1354,202202112274017262.html", # "Art_Url": "http://finance.eastmoney.com/a/202202112274017262.html", # } if resp.status_code == 200: json_text = resp.text[resp.text.index("(") + 1 : resp.text.rindex(")")] if "list" in demjson3.decode(json_text)["data"]: json_result = demjson3.decode(json_text)["data"]["list"] resp.close() if json_result: news = [ { "id": f'{entity_id}_{item.get("Art_ShowTime", "")}', "entity_id": entity_id, "timestamp": to_pd_timestamp(item.get("Art_ShowTime", "")), "news_code": item.get("Art_Code", ""), "news_url": item.get("Art_Url", ""), "news_title": item.get("Art_Title", ""), "ignore_by_user": False, } for index, item in enumerate(json_result) if not start_timestamp or ( (to_pd_timestamp(item["Art_ShowTime"]) >= start_timestamp) and (item.get("Art_Code", "") != latest_code) ) ] if len(news) < len(json_result): return news next_data = get_news(entity_id=entity_id, ps=ps, index=index + 1) if next_data: return news + next_data else: return news else: return None logger.error(f"request em data code: {resp.status_code}, error: {resp.text}") # utils to transform zvt entity to em entity def to_em_fc(entity_id): entity_type, exchange, code = decode_entity_id(entity_id) if entity_type == "stock": if exchange == "sh": return f"{code}01" if exchange == "sz": return f"{code}02" if entity_type == "stockhk": return code if entity_type == "stockus": if exchange == "nyse": return f"{code}.N" if exchange == "nasdaq": return f"{code}.O" exchange_map_em_flag = { #: 深证交易所 Exchange.sz: 0, #: 上证交易所 Exchange.sh: 1, #: 北交所 Exchange.bj: 0, #: 纳斯达克 Exchange.nasdaq: 105, #: 纽交所 Exchange.nyse: 106, #: 中国金融期货交易所 Exchange.cffex: 8, #: 上海期货交易所 Exchange.shfe: 113, #: 大连商品交易所 Exchange.dce: 114, #: 郑州商品交易所 Exchange.czce: 115, #: 上海国际能源交易中心 Exchange.ine: 142, #: 港交所 Exchange.hk: 116, #: 中国行业/概念板块 Exchange.cn: 90, #: 美国指数 Exchange.us: 100, #: 汇率 Exchange.forex: 119, } def to_em_entity_flag(exchange: Union[Exchange, str]): exchange = Exchange(exchange) return exchange_map_em_flag.get(exchange, exchange) def to_em_fq_flag(adjust_type: AdjustType): adjust_type = AdjustType(adjust_type) if adjust_type == AdjustType.bfq: return 0 if adjust_type == AdjustType.qfq: return 1 if adjust_type == AdjustType.hfq: return 2 def to_em_level_flag(level: IntervalLevel): level = IntervalLevel(level) if level == IntervalLevel.LEVEL_1MIN: return 1 elif level == IntervalLevel.LEVEL_5MIN: return 5 elif level == IntervalLevel.LEVEL_15MIN: return 15 elif level == IntervalLevel.LEVEL_30MIN: return 30 elif level == IntervalLevel.LEVEL_1HOUR: return 60 elif level == IntervalLevel.LEVEL_1DAY: return 101 elif level == IntervalLevel.LEVEL_1WEEK: return 102 elif level == IntervalLevel.LEVEL_1MON: return 103 assert False def to_em_sec_id(entity_id): entity_type, exchange, code = decode_entity_id(entity_id) # 主力合约 if entity_type == "future" and code[-1].isalpha(): code = code + "m" if entity_type == "currency" and "CNYC" in code: return f"120.{code}" if entity_type == "indexhk": return f"100.{code}" return f"{to_em_entity_flag(exchange)}.{code}" def to_zvt_code(code): # ('中证当月连续', '8|060120'), # ('沪深当月连续', '8|040120'), # ('上证当月连续', '8|070120'), # ('十债当季连续', '8|110120'), # ('五债当季连续', '8|050120'), # ('二债当季连续', '8|130120')] if code == "060120": return "IC" elif code == "040120": return "IF" elif code == "070120": return "IH" elif code == "110120": return "T" elif code == "050120": return "TF" elif code == "130120": return "TS" return code if __name__ == "__main__": print(get_tradable_list(entity_type="stockhk")) # the __all__ is generated __all__ = [ "get_treasury_yield", "get_ii_holder_report_dates", "get_dragon_and_tiger_list", "get_dragon_and_tiger", "get_holder_report_dates", "get_free_holder_report_dates", "get_controlling_shareholder", "get_ii_holder", "get_ii_summary", "get_free_holders", "get_top_ten_free_holder_stats", "get_controlling_shareholder", "get_holders", "get_basic_info", "get_url", "get_exchange", "actor_type_to_org_type", "generate_filters", "get_em_data", "get_kdata", "get_basic_info1", "get_future_list", "get_stock_turnover", "get_top_tradable_list", "get_top_stocks", "get_top_stockhks", "get_top_stockuss", "get_tradable_list", "get_block_stocks", "market_code_to_entity_id", "get_hot_topic", "record_hot_topic", "get_news", "to_em_fc", "to_em_entity_flag", "to_em_fq_flag", "to_em_level_flag", "to_em_sec_id", "to_zvt_code", ] ================================================ FILE: src/zvt/recorders/em/macro/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule em_treasury_yield_recorder from .em_treasury_yield_recorder import * from .em_treasury_yield_recorder import __all__ as _em_treasury_yield_recorder_all __all__ += _em_treasury_yield_recorder_all ================================================ FILE: src/zvt/recorders/em/macro/em_treasury_yield_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract import IntervalLevel from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Country from zvt.domain.macro.monetary import TreasuryYield from zvt.recorders.em import em_api class EMTreasuryYieldRecorder(FixedCycleDataRecorder): entity_schema = Country data_schema = TreasuryYield entity_provider = "wb" provider = "em" def __init__( self, force_update=True, sleeping_time=10, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, return_unfinished=False, ) -> None: super().__init__( force_update, sleeping_time, None, None, None, None, ["CN"], True, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) def record(self, entity, start, end, size, timestamps): # record before if start: result = em_api.get_treasury_yield(pn=1, ps=size, fetch_all=False) else: result = em_api.get_treasury_yield(fetch_all=True) if result: df = pd.DataFrame.from_records(result) df_to_db( data_schema=self.data_schema, df=df, provider=self.provider, force_update=True, drop_duplicates=True, ) if __name__ == "__main__": r = EMTreasuryYieldRecorder() r.run() # the __all__ is generated __all__ = ["EMTreasuryYieldRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule em_index_meta_recorder from .em_index_meta_recorder import * from .em_index_meta_recorder import __all__ as _em_index_meta_recorder_all __all__ += _em_index_meta_recorder_all # import all from submodule em_future_meta_recorder from .em_future_meta_recorder import * from .em_future_meta_recorder import __all__ as _em_future_meta_recorder_all __all__ += _em_future_meta_recorder_all # import all from submodule em_indexus_meta_recorder from .em_indexus_meta_recorder import * from .em_indexus_meta_recorder import __all__ as _em_indexus_meta_recorder_all __all__ += _em_indexus_meta_recorder_all # import all from submodule em_indexhk_meta_recorder from .em_indexhk_meta_recorder import * from .em_indexhk_meta_recorder import __all__ as _em_indexhk_meta_recorder_all __all__ += _em_indexhk_meta_recorder_all # import all from submodule em_cbond_meta_recorder from .em_cbond_meta_recorder import * from .em_cbond_meta_recorder import __all__ as _em_cbond_meta_recorder_all __all__ += _em_cbond_meta_recorder_all # import all from submodule em_stockhk_meta_recorder from .em_stockhk_meta_recorder import * from .em_stockhk_meta_recorder import __all__ as _em_stockhk_meta_recorder_all __all__ += _em_stockhk_meta_recorder_all # import all from submodule em_stockus_meta_recorder from .em_stockus_meta_recorder import * from .em_stockus_meta_recorder import __all__ as _em_stockus_meta_recorder_all __all__ += _em_stockus_meta_recorder_all # import all from submodule em_stock_meta_recorder from .em_stock_meta_recorder import * from .em_stock_meta_recorder import __all__ as _em_stock_meta_recorder_all __all__ += _em_stock_meta_recorder_all # import all from submodule em_currency_meta_recorder from .em_currency_meta_recorder import * from .em_currency_meta_recorder import __all__ as _em_currency_meta_recorder_all __all__ += _em_currency_meta_recorder_all # import all from submodule em_block_meta_recorder from .em_block_meta_recorder import * from .em_block_meta_recorder import __all__ as _em_block_meta_recorder_all __all__ += _em_block_meta_recorder_all ================================================ FILE: src/zvt/recorders/em/meta/em_block_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder, TimeSeriesDataRecorder from zvt.domain import Block, BlockCategory, BlockStock from zvt.recorders.em import em_api class EMBlockRecorder(Recorder): provider = "em" data_schema = Block def run(self): for block_category in [BlockCategory.concept, BlockCategory.industry]: df = em_api.get_tradable_list(entity_type="block", block_category=block_category, limit=100) self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) class EMBlockStockRecorder(TimeSeriesDataRecorder): entity_provider = "em" entity_schema = Block provider = "em" data_schema = BlockStock def record(self, entity, start, end, size, timestamps): the_list = em_api.get_block_stocks(entity.id, entity.name) if the_list: df = pd.DataFrame.from_records(the_list) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) self.logger.info("finish recording block:{},{}".format(entity.category, entity.name)) self.sleep() if __name__ == "__main__": recorder = EMBlockStockRecorder(day_data=True, sleeping_time=0) recorder.run() # the __all__ is generated __all__ = ["EMBlockRecorder", "EMBlockStockRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_cbond_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.cbond_meta import CBond from zvt.recorders.em import em_api class EMCBondRecorder(Recorder): provider = "em" data_schema = CBond def run(self): df = em_api.get_tradable_list(entity_type="cbond") self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = EMCBondRecorder() recorder.run() # the __all__ is generated __all__ = ["EMCBondRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_currency_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.currency_meta import Currency from zvt.recorders.em import em_api class EMCurrencyRecorder(Recorder): provider = "em" data_schema = Currency def run(self): df = em_api.get_tradable_list(entity_type="currency") self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = EMCurrencyRecorder(force_update=True) recorder.run() # the __all__ is generated __all__ = ["EMCurrencyRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_future_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Future from zvt.recorders.em import em_api class EMFutureRecorder(Recorder): provider = "em" data_schema = Future def run(self): df = em_api.get_tradable_list(entity_type="future") self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = EMFutureRecorder(force_update=True) recorder.run() # the __all__ is generated __all__ = ["EMFutureRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_index_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Index from zvt.recorders.em import em_api class EMIndexRecorder(Recorder): provider = "em" data_schema = Index def run(self): df = em_api.get_tradable_list(entity_type="index", limit=100) self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = EMIndexRecorder() recorder.run() # the __all__ is generated __all__ = ["EMIndexRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_indexhk_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.indexhk_meta import Indexhk from zvt.recorders.em import em_api class EMIndexhkRecorder(Recorder): provider = "em" data_schema = Indexhk def run(self): df = em_api.get_tradable_list(entity_type="indexhk") self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = EMIndexhkRecorder() recorder.run() # the __all__ is generated __all__ = ["EMIndexhkRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_indexus_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.indexus_meta import Indexus from zvt.recorders.em import em_api class EMIndexusRecorder(Recorder): provider = "em" data_schema = Indexus def run(self): df = em_api.get_tradable_list(entity_type="indexus") self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = EMIndexusRecorder() recorder.run() # the __all__ is generated __all__ = ["EMIndexusRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_stock_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List from zvt.contract import Exchange from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Stock from zvt.recorders.em import em_api from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp class EMStockRecorder(Recorder): provider = "em" data_schema = Stock def run(self): for exchange in [Exchange.sh, Exchange.sz, Exchange.bj]: df = em_api.get_tradable_list(entity_type="stock", exchange=exchange, limit=100) if pd_is_not_null(df): df["total_cap"] = df["total_cap"].fillna(0) df["float_cap"] = df["float_cap"].fillna(0) self.logger.info(df.head()) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) for item in df[["id", "name", "total_cap", "float_cap"]].values.tolist(): entity_id = item[0] datas: List[Stock] = Stock.query_data( entity_id=entity_id, return_type="domain", session=self.session ) if datas: entity_domain = datas[0] if "退" in entity_domain.name: self.logger.info(f"Ignore: {entity_domain.entity_id} {entity_domain.name} {item}") continue entity_domain.name = item[1] entity_domain.total_cap = item[2] entity_domain.float_cap = item[3] if not entity_domain.list_date: basic_info = em_api.get_basic_info(entity_id=entity_id, session=self.http_session) self.logger.info(f"basic info: {basic_info}") if basic_info: entity_domain.timestamp = to_pd_timestamp(basic_info.get("LISTING_DATE")) entity_domain.list_date = to_pd_timestamp(basic_info.get("LISTING_DATE")) else: entity_domain.name = entity_domain.name + "退市" self.session.add(entity_domain) self.session.commit() if __name__ == "__main__": recorder = EMStockRecorder() recorder.run() # the __all__ is generated __all__ = ["EMStockRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_stockhk_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.stockhk_meta import Stockhk from zvt.recorders.em import em_api from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp class EMStockhkRecorder(Recorder): provider = "em" data_schema = Stockhk record_south_only = True def run(self): df_south = em_api.get_tradable_list(entity_type="stockhk", hk_south=True) if pd_is_not_null(df_south): df_south = df_south.set_index("code", drop=False) df_south["total_cap"] = df_south["total_cap"].fillna(0) df_south["float_cap"] = df_south["float_cap"].fillna(0) df_south["south"] = True df_to_db(df=df_south, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if not self.record_south_only: df = em_api.get_tradable_list(entity_type="stockhk") if pd_is_not_null(df): self.logger.info(f"stockhk count: {len(df)}") self.logger.info(df.head()) df = df.set_index("code", drop=False) df["total_cap"] = df["total_cap"].fillna(0) df["float_cap"] = df["float_cap"].fillna(0) df_other = df.loc[~df.index.isin(df_south.index)].copy() df_other["south"] = False df_to_db( df=df_other, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update, ) else: df = df_south for item in df[["id", "name", "total_cap", "float_cap"]].values.tolist(): entity_id = item[0] datas: List[Stockhk] = Stockhk.query_data( entity_id=entity_id, return_type="domain", session=self.session ) if datas: entity_domain = datas[0] if "退市" in entity_domain.name: self.logger.info(f"Ignore: {entity_domain.entity_id} {entity_domain.name} {item}") continue entity_domain.name = item[1] entity_domain.total_cap = item[2] entity_domain.float_cap = item[3] if not entity_domain.list_date or not entity_domain.industry: basic_info = em_api.get_basic_info(entity_id=entity_id, session=self.http_session) self.logger.info(f"basic info: {basic_info}") if basic_info: entity_domain.industry = basic_info.get("BELONG_INDUSTRY") entity_domain.timestamp = to_pd_timestamp(basic_info.get("LISTING_DATE")) entity_domain.list_date = to_pd_timestamp(basic_info.get("LISTING_DATE")) else: entity_domain.name = entity_domain.name + "退市" self.session.add(entity_domain) self.session.commit() if __name__ == "__main__": recorder = EMStockhkRecorder() recorder.run() # the __all__ is generated __all__ = ["EMStockhkRecorder"] ================================================ FILE: src/zvt/recorders/em/meta/em_stockus_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.stockus_meta import Stockus from zvt.recorders.em import em_api from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp class EMStockusRecorder(Recorder): provider = "em" data_schema = Stockus def run(self): df = em_api.get_tradable_list(entity_type="stockus") if pd_is_not_null(df): self.logger.info(f"stockus count: {len(df)}") self.logger.info(df.head()) df["total_cap"] = df["total_cap"].fillna(0) df["float_cap"] = df["float_cap"].fillna(0) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) for item in df[["id", "name", "total_cap", "float_cap"]].values.tolist(): entity_id = item[0] datas: List[Stockus] = Stockus.query_data( entity_id=entity_id, return_type="domain", session=self.session ) if datas: entity_domain = datas[0] if "退市" in entity_domain.name: self.logger.info(f"Ignore: {entity_domain.entity_id} {entity_domain.name} {item}") continue entity_domain.name = item[1] entity_domain.total_cap = item[2] entity_domain.float_cap = item[3] if not entity_domain.industry or not entity_domain.list_date: basic_info = em_api.get_basic_info(entity_id=entity_id, session=self.http_session) self.logger.info(f"basic info: {basic_info}") if basic_info: entity_domain.industry = basic_info.get("BELONG_INDUSTRY") entity_domain.timestamp = to_pd_timestamp(basic_info.get("LISTING_DATE")) entity_domain.list_date = to_pd_timestamp(basic_info.get("LISTING_DATE")) else: entity_domain.name = entity_domain.name + "退市" self.session.add(entity_domain) self.session.commit() if __name__ == "__main__": recorder = EMStockusRecorder() recorder.run() # the __all__ is generated __all__ = ["EMStockusRecorder"] ================================================ FILE: src/zvt/recorders/em/misc/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule em_stock_news_recorder from .em_stock_news_recorder import * from .em_stock_news_recorder import __all__ as _em_stock_news_recorder_all __all__ += _em_stock_news_recorder_all ================================================ FILE: src/zvt/recorders/em/misc/em_stock_news_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract.api import df_to_db, decode_entity_id from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Stock, Stockus, Stockhk from zvt.domain.misc.stock_news import StockNews from zvt.recorders.em import em_api from zvt.utils.time_utils import count_interval, now_pd_timestamp, recent_year_date class EMStockNewsRecorder(FixedCycleDataRecorder): original_page_url = "https://wap.eastmoney.com/quote/stock/0.002572.html" url = "https://np-listapi.eastmoney.com/comm/wap/getListInfo?cb=callback&client=wap&type=1&mTypeAndCode=0.002572&pageSize=200&pageIndex={}&callback=jQuery1830017478247906740352_1644568731256&_=1644568879493" entity_schema = Stock data_schema = StockNews entity_provider = "em" provider = "em" def init_entities(self): if self.entity_ids: entity_type, _, _ = decode_entity_id(self.entity_ids[0]) if entity_type == "stockus": self.entity_schema = Stockus elif entity_type == "stockhk": self.entity_schema = Stockhk super().init_entities() def record(self, entity, start, end, size, timestamps): from_date = recent_year_date() if not start or (start < from_date): start = from_date if count_interval(start, now_pd_timestamp()) <= 30: ps = 30 else: ps = 200 latest_news: StockNews = self.get_latest_saved_record(entity=entity) news = em_api.get_news( session=self.http_session, entity_id=entity.id, ps=ps, start_timestamp=start, latest_code=latest_news.news_code if latest_news else None, ) if news: df = pd.DataFrame.from_records(news) self.logger.info(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": # df = Stock.query_data(filters=[Stock.exchange == "bj"], provider="em") # entity_ids = df["entity_id"].tolist() r = EMStockNewsRecorder(entity_ids=["stock_sh_600345"], sleeping_time=0) r.run() # the __all__ is generated __all__ = ["EMStockNewsRecorder"] ================================================ FILE: src/zvt/recorders/em/quotes/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule em_kdata_recorder from .em_kdata_recorder import * from .em_kdata_recorder import __all__ as _em_kdata_recorder_all __all__ += _em_kdata_recorder_all ================================================ FILE: src/zvt/recorders/em/quotes/em_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.api.kdata import get_kdata_schema, get_kdata from zvt.api.selector import get_entity_ids_by_filter from zvt.contract import IntervalLevel, AdjustType from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import ( Stock, Index, Block, StockKdataCommon, IndexKdataCommon, StockhkKdataCommon, StockusKdataCommon, BlockKdataCommon, Indexus, IndexusKdataCommon, Future, FutureKdataCommon, Currency, CurrencyKdataCommon, IndexhkKdataCommon, ) from zvt.domain.meta.indexhk_meta import Indexhk from zvt.domain.meta.stockhk_meta import Stockhk from zvt.domain.meta.stockus_meta import Stockus from zvt.recorders.em import em_api from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import count_interval, now_pd_timestamp, current_date class BaseEMStockKdataRecorder(FixedCycleDataRecorder): default_size = 50000 entity_provider: str = "em" provider = "em" def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, adjust_type=AdjustType.qfq, return_unfinished=False, ) -> None: level = IntervalLevel(level) self.adjust_type = AdjustType(adjust_type) self.entity_type = self.entity_schema.__name__.lower() self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=self.adjust_type) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) def need_redownload_qfq(self, entity_id, df): if self.adjust_type == AdjustType.qfq and pd_is_not_null(df): datas = get_kdata( entity_id=entity_id, provider=self.provider, limit=1, level=self.level, adjust_type=self.adjust_type, order=self.data_schema.timestamp.desc(), return_type="domain", ) if datas: latest_kdata = datas[0] check_df = df[df["timestamp"] == latest_kdata.timestamp] if pd_is_not_null(check_df): old = latest_kdata.close new = check_df.iloc[0, :]["close"] # 相同时间的close不同，表明前复权需要重新计算 if round(old, 2) != round(new, 2): # 删掉重新获取 self.session.query(self.data_schema).filter(self.data_schema.entity_id == entity_id).delete() return True else: return False else: self.logger.warning(f"no data found for {entity_id} at {latest_kdata.timestamp}, 前复权检查失败") return False def record(self, entity, start, end, size, timestamps): df = em_api.get_kdata( session=self.http_session, entity_id=entity.id, limit=size, adjust_type=self.adjust_type, level=self.level ) if self.need_redownload_qfq(entity.id, df): df = em_api.get_kdata( session=self.http_session, entity_id=entity.id, limit=self.default_size, adjust_type=self.adjust_type, level=self.level, ) delisted = False if pd_is_not_null(df): df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) latest_timestamp = df.iloc[-1, :]["timestamp"] days = count_interval(latest_timestamp, now_pd_timestamp()) if days > 200: delisted = True else: self.logger.info(f"no kdata for {entity.id}") if delisted and ("退市" not in entity.name): entity.name = entity.name + "退市" self.logger.info(f"set {entity.id} name as {entity.name}") self.entity_session.add(entity) self.entity_session.commit() def on_finish_entity(self, entity): # fill timestamp if not entity.timestamp or not entity.list_date: # get the first kdatas = self.data_schema.query_data( provider=self.provider, entity_id=entity.id, order=self.data_schema.timestamp.asc(), limit=1, return_type="domain", ) if kdatas: timestamp = kdatas[0].timestamp self.logger.info(f"fill {entity.name} list_date as {timestamp}") if not entity.timestamp: entity.timestamp = timestamp if not entity.list_date: entity.list_date = timestamp self.entity_session.add(entity) self.entity_session.commit() class EMStockKdataRecorder(BaseEMStockKdataRecorder): entity_schema = Stock data_schema = StockKdataCommon supported_levels = [ level for level in IntervalLevel if level not in (IntervalLevel.LEVEL_L2_QUOTE, IntervalLevel.LEVEL_TICK) ] supported_adjust_types = [AdjustType.qfq, AdjustType.hfq] def on_finish_entity(self, entity): super().on_finish_entity(entity) # fill holder if not entity.holder_modified_date or (count_interval(entity.holder_modified_date, now_pd_timestamp()) > 30): holder = em_api.get_controlling_shareholder(code=entity.code) if holder: entity.controlling_holder = holder.get("holder") if holder.get("parent"): entity.controlling_holder_parent = holder.get("parent") else: entity.controlling_holder_parent = holder.get("holder") entity.holder_modified_date = current_date() self.entity_session.add(entity) self.entity_session.commit() holder_stats = em_api.get_top_ten_free_holder_stats(code=entity.code) if holder_stats: entity.top_ten_ratio = holder_stats.get("ratio") entity.holder_modified_date = current_date() self.entity_session.add(entity) self.entity_session.commit() class EMStockusKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Stockus data_schema = StockusKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] supported_adjust_types = [AdjustType.qfq, AdjustType.hfq] class EMStockhkKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Stockhk data_schema = StockhkKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] supported_adjust_types = [AdjustType.qfq, AdjustType.hfq] class EMIndexhkKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Indexhk data_schema = IndexhkKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] class EMIndexKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Index data_schema = IndexKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK] class EMIndexusKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Indexus data_schema = IndexusKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] class EMBlockKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Block data_schema = BlockKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK, IntervalLevel.LEVEL_1MON] class EMFutureKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Future data_schema = FutureKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] class EMCurrencyKdataRecorder(BaseEMStockKdataRecorder): entity_provider = "em" entity_schema = Currency data_schema = CurrencyKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] if __name__ == "__main__": normal_ids = get_entity_ids_by_filter(entity_type="indexhk", provider="em") recorder = EMIndexhkKdataRecorder( entity_ids=normal_ids, level=IntervalLevel.LEVEL_1DAY, sleeping_time=2, adjust_type=AdjustType.qfq, day_data=True, ) recorder.run() # the __all__ is generated __all__ = [ "BaseEMStockKdataRecorder", "EMStockKdataRecorder", "EMStockusKdataRecorder", "EMStockhkKdataRecorder", "EMIndexhkKdataRecorder", "EMIndexKdataRecorder", "EMIndexusKdataRecorder", "EMBlockKdataRecorder", "EMFutureKdataRecorder", "EMCurrencyKdataRecorder", ] ================================================ FILE: src/zvt/recorders/em/trading/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule em_dragon_and_tiger_recorder from .em_dragon_and_tiger_recorder import * from .em_dragon_and_tiger_recorder import __all__ as _em_dragon_and_tiger_recorder_all __all__ += _em_dragon_and_tiger_recorder_all ================================================ FILE: src/zvt/recorders/em/trading/em_dragon_and_tiger_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import DragonAndTiger from zvt.recorders.em import em_api from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, TIME_FORMAT_DAY, date_time_by_interval, current_date class EMDragonAndTigerRecorder(Recorder): provider = "em" data_schema = DragonAndTiger def run(self): latest_infos = DragonAndTiger.query_data( provider=self.provider, order=DragonAndTiger.timestamp.desc(), limit=1, return_type="domain" ) if latest_infos: start_date = latest_infos[0].timestamp else: start_date = date_time_by_interval(current_date(), -10) dragon_list = em_api.get_dragon_and_tiger_list(start_date=start_date) entity_list = [{"code": data["SECURITY_CODE"], "exchange": data["MARKET"].lower()} for data in dragon_list] df = pd.DataFrame(entity_list) unique_df = df.drop_duplicates(subset=["code"]) unique_list = unique_df.to_dict("records") self.logger.info("Get dragon and tiger for entities: %s", unique_list) for entity in unique_list: code = entity["code"] entity_id = f"stock_{entity['exchange']}_{entity['code']}" datas = em_api.get_dragon_and_tiger(code=entity["code"], start_date=start_date) if datas: records = [] for data in datas: timestamp = to_pd_timestamp(data["TRADE_DATE"]) record = { "id": f"{entity_id}_{data['TRADE_ID']}_{to_date_time_str(timestamp, fmt=TIME_FORMAT_DAY)}", "entity_id": entity_id, "timestamp": timestamp, "code": code, "name": data["SECURITY_NAME_ABBR"], "reason": data["EXPLANATION"], "turnover": data["ACCUM_AMOUNT"], "change_pct": data["CHANGE_RATE"], "net_in": data["NET_BUY"], } # 营业部列表 deps = data["LIST"] for dep in deps: flag = "" if dep["TRADE_DIRECTION"] == "0" else "_" rank = dep["RANK"] dep_name = f"dep{flag}{rank}" dep_in = f"{dep_name}_in" dep_out = f"{dep_name}_out" dep_rate = f"{dep_name}_rate" record[dep_name] = dep["OPERATEDEPT_NAME"] record[dep_in] = dep["BUY_AMT_REAL"] record[dep_out] = dep["SELL_AMT_REAL"] record[dep_rate] = (dep["BUY_RATIO"] if dep["BUY_RATIO"] else 0) - ( dep["SELL_RATIO"] if dep["SELL_RATIO"] else 0 ) records.append(record) df = pd.DataFrame.from_records(records) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) else: self.logger.info(f"no data for {entity_id}") if __name__ == "__main__": EMDragonAndTigerRecorder(sleeping_time=2).run() # the __all__ is generated __all__ = ["EMDragonAndTigerRecorder"] ================================================ FILE: src/zvt/recorders/exchange/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule api from .api import * from .api import __all__ as _api_all __all__ += _api_all # import all from submodule exchange_index_recorder from .exchange_index_recorder import * from .exchange_index_recorder import __all__ as _exchange_index_recorder_all __all__ += _exchange_index_recorder_all # import all from submodule exchange_stock_meta_recorder from .exchange_stock_meta_recorder import * from .exchange_stock_meta_recorder import __all__ as _exchange_stock_meta_recorder_all __all__ += _exchange_stock_meta_recorder_all # import all from submodule exchange_stock_summary_recorder from .exchange_stock_summary_recorder import * from .exchange_stock_summary_recorder import __all__ as _exchange_stock_summary_recorder_all __all__ += _exchange_stock_summary_recorder_all # import all from submodule exchange_etf_meta_recorder from .exchange_etf_meta_recorder import * from .exchange_etf_meta_recorder import __all__ as _exchange_etf_meta_recorder_all __all__ += _exchange_etf_meta_recorder_all # import all from submodule exchange_index_stock_recorder from .exchange_index_stock_recorder import * from .exchange_index_stock_recorder import __all__ as _exchange_index_stock_recorder_all __all__ += _exchange_index_stock_recorder_all ================================================ FILE: src/zvt/recorders/exchange/api/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule cs_index_stock_api from .cs_index_stock_api import * from .cs_index_stock_api import __all__ as _cs_index_stock_api_all __all__ += _cs_index_stock_api_all # import all from submodule cn_index_stock_api from .cn_index_stock_api import * from .cn_index_stock_api import __all__ as _cn_index_stock_api_all __all__ += _cn_index_stock_api_all # import all from submodule cn_index_api from .cn_index_api import * from .cn_index_api import __all__ as _cn_index_api_all __all__ += _cn_index_api_all # import all from submodule cs_index_api from .cs_index_api import * from .cs_index_api import __all__ as _cs_index_api_all __all__ += _cs_index_api_all ================================================ FILE: src/zvt/recorders/exchange/api/cn_index_api.py ================================================ # -*- coding: utf-8 -*- import logging import time import pandas as pd import requests from zvt.domain import IndexCategory from zvt.recorders.consts import DEFAULT_HEADER from zvt.utils.time_utils import to_pd_timestamp logger = logging.getLogger(__name__) original_page_url = "http://www.cnindex.com.cn/zh_indices/sese/index.html?act_menu=1&index_type=-1" url = "http://www.cnindex.com.cn/index/indexList?channelCode={}&rows=1000&pageNum=1" # 中证指数 抓取 风格指数 行业指数 规模指数 基金指数 cni_category_map_url = { IndexCategory.style: url.format("202"), IndexCategory.industry: url.format("201"), IndexCategory.scope: url.format("200"), IndexCategory.fund: url.format("207"), } # 深证指数 只取规模指数 sz_category_map_url = { IndexCategory.scope: url.format("100"), } def _get_resp_data(resp: requests.Response): resp.raise_for_status() return resp.json()["data"] def get_cn_index(index_type="cni", category=IndexCategory.style): if index_type == "cni": category_map_url = cni_category_map_url elif index_type == "sz": category_map_url = sz_category_map_url else: logger.error(f"not support index_type: {index_type}") assert False requests_session = requests.Session() url = category_map_url.get(category) resp = requests_session.get(url, headers=DEFAULT_HEADER) results = _get_resp_data(resp)["rows"] # e.g # amount: 277743699997.9 # closeingPoint: 6104.7592 # docchannel: 1039 # freeMarketValue: 10794695531696.15 # id: 142 # indexcode: "399370" # indexename: "CNI Growth" # indexfullcname: "国证1000成长指数" # indexfullename: "CNI 1000 Growth Index" # indexname: "国证成长" # indexsource: "1" # indextype: "202" # pb: 5.34 # peDynamic: 29.8607 # peStatic: 33.4933 # percent: 0.0022 # prefixmonth: null # realtimemarket: "1" # remark: "" # sampleshowdate: null # samplesize: 332 # showcnindex: "1" # totalMarketValue: 23113641352198.32 the_list = [] logger.info(f"category: {category} ") logger.info(f"results: {results} ") for i, result in enumerate(results): logger.info(f"to {i}/{len(results)}") code = result["indexcode"] info_resp = requests_session.get(f"http://www.cnindex.com.cn/index-intro?indexcode={code}") # fbrq: "2010-01-04" # jd: 1000 # jr: "2002-12-31" # jsfs: "自由流通市值" # jsjj: "国证成长由国证1000指数样本股中成长风格突出的股票组成，为投资者提供更丰富的指数化投资工具。" # qzsx: null # typl: 2 # xyfw: "沪深A股" # xygz: "在国证1000指数样本股中，选取主营业务收入增长率、净利润增长率和净资产收益率综合排名前332只" index_info = _get_resp_data(info_resp) name = result["indexname"] entity_id = f"index_sz_{code}" index_item = { "id": entity_id, "entity_id": entity_id, "timestamp": to_pd_timestamp(index_info["jr"]), "entity_type": "index", "exchange": "sz", "code": code, "name": name, "category": category.value, "list_date": to_pd_timestamp(index_info["fbrq"]), "base_point": index_info["jd"], "publisher": "cnindex", } logger.info(index_item) the_list.append(index_item) time.sleep(3) if the_list: return pd.DataFrame.from_records(the_list) if __name__ == "__main__": df = get_cn_index() print(df) # the __all__ is generated __all__ = ["get_cn_index"] ================================================ FILE: src/zvt/recorders/exchange/api/cn_index_stock_api.py ================================================ # -*- coding: utf-8 -*- import logging import pandas as pd import requests from zvt.api.utils import china_stock_code_to_id, value_to_pct, value_multiply from zvt.recorders.consts import DEFAULT_HEADER from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, TIME_FORMAT_MON logger = logging.getLogger(__name__) original_page_url = "http://www.cnindex.com.cn/module/index-detail.html?act_menu=1&indexCode=399001" url = "http://www.cnindex.com.cn/sample-detail/detail?indexcode={}&dateStr={}&pageNum=1&rows=5000" def _get_resp_data(resp: requests.Response): resp.raise_for_status() return resp.json()["data"] def get_cn_index_stock(code, timestamp, name=None): entity_type = "index" exchange = "sz" entity_id = f"{entity_type}_{exchange}_{code}" data_str = to_date_time_str(timestamp, fmt=TIME_FORMAT_MON) resp = requests.get(url.format(code, data_str), headers=DEFAULT_HEADER) data = _get_resp_data(resp) if not data: return results = _get_resp_data(resp)["rows"] the_list = [] for result in results: # date: 1614268800000 # dateStr: "2021-02-26" # freeMarketValue: 10610.8 # indexcode: "399370" # market: null # seccode: "600519" # secname: "贵州茅台" # totalMarketValue: 26666.32 # trade: "主要消费" # weight: 10.01 stock_code = result["seccode"] stock_name = result["secname"] stock_id = china_stock_code_to_id(stock_code) the_list.append( { "id": "{}_{}_{}".format(entity_id, result["dateStr"], stock_id), "entity_id": entity_id, "entity_type": entity_type, "exchange": exchange, "code": code, "name": name, "timestamp": to_pd_timestamp(result["dateStr"]), "stock_id": stock_id, "stock_code": stock_code, "stock_name": stock_name, "proportion": value_to_pct(result["weight"], 0), "market_cap": value_multiply(result["freeMarketValue"], 100000000, 0), } ) if the_list: df = pd.DataFrame.from_records(the_list) return df if __name__ == "__main__": df = get_cn_index_stock(timestamp="2021-08-01", code="399370", name="国证成长") print(df) # the __all__ is generated __all__ = ["get_cn_index_stock"] ================================================ FILE: src/zvt/recorders/exchange/api/cs_index_api.py ================================================ # -*- coding: utf-8 -*- import logging import pandas as pd import requests from zvt.domain import IndexCategory from zvt.recorders.consts import DEFAULT_HEADER from zvt.utils.time_utils import to_pd_timestamp logger = logging.getLogger(__name__) original_page_url = "https://www.csindex.com.cn/zh-CN#/indices/family/list?index_series=2" url = "https://www.csindex.com.cn/csindex-home/index-list/query-index-item" index_category_map = {IndexCategory.scope: "17", IndexCategory.industry: "18", IndexCategory.style: "19"} def _get_resp_data(resp: requests.Response): resp.raise_for_status() return resp.json()["data"] def _get_params(index_type, category: IndexCategory): if index_type == "csi": index_series = ["1"] elif index_type == "sh": index_series = ["2"] else: logger.warning(f"not support index type: {index_type}") assert False index_classify = index_category_map.get(category) return { "sorter": {"sortField": "index_classify", "sortOrder": "asc"}, "pager": {"pageNum": 1, "pageSize": 10}, "indexFilter": { "ifCustomized": None, "ifTracked": None, "ifWeightCapped": None, "indexCompliance": None, "hotSpot": None, "indexClassify": [index_classify], "currency": None, "region": None, "indexSeries": index_series, "undefined": None, }, } def get_cs_index(index_type="sh"): if index_type == "csi": category_list = [IndexCategory.scope, IndexCategory.industry, IndexCategory.style] elif index_type == "sh": category_list = [IndexCategory.scope] else: logger.warning(f"not support index type: {index_type}") assert False requests_session = requests.Session() for category in category_list: data = _get_params(index_type=index_type, category=category) print(data) resp = requests_session.post(url, headers=DEFAULT_HEADER, json=data) print(resp) results = _get_resp_data(resp) the_list = [] logger.info(f"category: {category} ") logger.info(f"results: {results} ") for i, result in enumerate(results): logger.info(f"to {i}/{len(results)}") code = result["indexCode"] info_url = f"https://www.csindex.com.cn/csindex-home/indexInfo/index-basic-info/{code}" info = _get_resp_data(requests_session.get(info_url)) name = result["indexName"] entity_id = f"index_sh_{code}" index_item = { "id": entity_id, "entity_id": entity_id, "timestamp": to_pd_timestamp(info["basicDate"]), "entity_type": "index", "exchange": "sh", "code": code, "name": name, "category": category.value, "list_date": to_pd_timestamp(result["publishDate"]), "base_point": info["basicIndex"], "publisher": "csindex", } logger.info(index_item) the_list.append(index_item) if the_list: return pd.DataFrame.from_records(the_list) if __name__ == "__main__": df = get_cs_index() print(df) # the __all__ is generated __all__ = ["get_cs_index"] ================================================ FILE: src/zvt/recorders/exchange/api/cs_index_stock_api.py ================================================ # -*- coding: utf-8 -*- import io import logging import pandas as pd import requests from zvt.api.utils import china_stock_code_to_id from zvt.recorders.consts import DEFAULT_HEADER from zvt.utils.time_utils import now_pd_timestamp logger = logging.getLogger(__name__) original_page_url = "http://www.csindex.com.cn/zh-CN/downloads/indices" url = "http://www.csindex.com.cn/uploads/file/autofile/cons/{}cons.xls" def get_cs_index_stock(code, timestamp, name=None): entity_type = "index" exchange = "sh" entity_id = f"{entity_type}_{exchange}_{code}" response = requests.get(url.format(code), headers=DEFAULT_HEADER) response.raise_for_status() df = pd.read_excel(io.BytesIO(response.content)) df = df[["日期Date", "成分券代码Constituent Code", "成分券名称Constituent Name"]].rename( columns={"日期Date": "timestamp", "成分券代码Constituent Code": "stock_code", "成分券名称Constituent Name": "stock_name"} ) df["entity_id"] = entity_id df["entity_type"] = "index" df["exchange"] = "sh" df["code"] = code df["name"] = name df["stock_id"] = df["stock_code"].apply(lambda x: china_stock_code_to_id(str(x))) # id format: {entity_id}_{timestamp}_{stock_id} df["id"] = df[["entity_id", "timestamp", "stock_id"]].apply(lambda x: "_".join(x.astype(str)), axis=1) df["timestamp"] = pd.to_datetime(df["timestamp"]) return df if __name__ == "__main__": df = get_cs_index_stock(code="000001", name="上证指数", timestamp=now_pd_timestamp()) print(df) # the __all__ is generated __all__ = ["get_cs_index_stock"] ================================================ FILE: src/zvt/recorders/exchange/exchange_etf_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import io import re import demjson3 import pandas as pd import requests from zvt.api.utils import china_stock_code_to_id from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import EtfStock, Etf from zvt.recorders.consts import DEFAULT_SH_ETF_LIST_HEADER from zvt.utils.time_utils import now_pd_timestamp class ChinaETFListSpider(Recorder): data_schema = EtfStock def __init__(self, force_update=False, sleeping_time=10.0, provider="exchange") -> None: self.provider = provider super().__init__(force_update, sleeping_time) def run(self): # 抓取沪市 ETF 列表 url = "http://query.sse.com.cn/commonQuery.do?sqlId=COMMON_SSE_ZQPZ_ETFLB_L_NEW" response = requests.get(url, headers=DEFAULT_SH_ETF_LIST_HEADER) response_dict = demjson3.decode(response.text) df = pd.DataFrame(response_dict.get("result", [])) self.persist_etf_list(df, exchange="sh") self.logger.info("沪市 ETF 列表抓取完成...") # 抓取沪市 ETF 成分股 self.download_sh_etf_component(df) self.logger.info("沪市 ETF 成分股抓取完成...") # 抓取深市 ETF 列表 url = "http://www.szse.cn/api/report/ShowReport?SHOWTYPE=xlsx&CATALOGID=1945" response = requests.get(url) df = pd.read_excel(io.BytesIO(response.content), dtype=str) self.persist_etf_list(df, exchange="sz") self.logger.info("深市 ETF 列表抓取完成...") # 抓取深市 ETF 成分股 self.download_sz_etf_component(df) self.logger.info("深市 ETF 成分股抓取完成...") def persist_etf_list(self, df: pd.DataFrame, exchange: str): if df is None: return df = df.copy() if exchange == "sh": df = df[["FUND_ID", "FUND_NAME"]] elif exchange == "sz": df = df[["证券代码", "证券简称"]] df.columns = ["code", "name"] df["id"] = df["code"].apply(lambda code: f"etf_{exchange}_{code}") df["entity_id"] = df["id"] df["exchange"] = exchange df["entity_type"] = "etf" df["category"] = "etf" df = df.dropna(axis=0, how="any") df = df.drop_duplicates(subset="id", keep="last") df_to_db(df=df, data_schema=Etf, provider=self.provider, force_update=False) def download_sh_etf_component(self, df: pd.DataFrame): query_url = ( "http://query.sse.com.cn/infodisplay/queryConstituentStockInfo.do?" "isPagination=false&type={}&etfClass={}" ) etf_df = df[(df["ETF_CLASS"] == "1") | (df["ETF_CLASS"] == "2")] etf_df = self.populate_sh_etf_type(etf_df) for _, etf in etf_df.iterrows(): url = query_url.format(etf["ETF_TYPE"], etf["ETF_CLASS"]) response = requests.get(url, headers=DEFAULT_SH_ETF_LIST_HEADER) response_dict = demjson3.decode(response.text) response_df = pd.DataFrame(response_dict.get("result", [])) etf_code = etf["FUND_ID"] etf_id = f"etf_sh_{etf_code}" response_df = response_df[["instrumentId", "instrumentName"]].copy() response_df.rename(columns={"instrumentId": "stock_code", "instrumentName": "stock_name"}, inplace=True) response_df["entity_id"] = etf_id response_df["entity_type"] = "etf" response_df["exchange"] = "sh" response_df["code"] = etf_code response_df["name"] = etf["FUND_NAME"] response_df["timestamp"] = now_pd_timestamp() response_df["stock_id"] = response_df["stock_code"].apply(lambda code: china_stock_code_to_id(code)) response_df["id"] = response_df["stock_id"].apply(lambda x: f"{etf_id}_{x}") df_to_db(data_schema=self.data_schema, df=response_df, provider=self.provider) self.logger.info(f'{etf["FUND_NAME"]} - {etf_code} 成分股抓取完成...') self.sleep() def download_sz_etf_component(self, df: pd.DataFrame): query_url = "http://vip.stock.finance.sina.com.cn/corp/go.php/vII_NewestComponent/indexid/{}.phtml" self.parse_sz_etf_underlying_index(df) for _, etf in df.iterrows(): underlying_index = etf["拟合指数"] etf_code = etf["证券代码"] if len(underlying_index) == 0: self.logger.info(f'{etf["证券简称"]} - {etf_code} 非 A 股市场指数，跳过...') continue url = query_url.format(underlying_index) response = requests.get(url) response.encoding = "gbk" try: dfs = pd.read_html(response.text, header=1) except ValueError as error: self.logger.error(f"HTML parse error: {error}, response: {response.text}") continue if len(dfs) < 4: continue response_df = dfs[3].copy() response_df = response_df.dropna(axis=1, how="any") response_df["品种代码"] = response_df["品种代码"].apply(lambda x: f"{x:06d}") etf_id = f"etf_sz_{etf_code}" response_df = response_df[["品种代码", "品种名称"]].copy() response_df.rename(columns={"品种代码": "stock_code", "品种名称": "stock_name"}, inplace=True) response_df["entity_id"] = etf_id response_df["entity_type"] = "etf" response_df["exchange"] = "sz" response_df["code"] = etf_code response_df["name"] = etf["证券简称"] response_df["timestamp"] = now_pd_timestamp() response_df["stock_id"] = response_df["stock_code"].apply(lambda code: china_stock_code_to_id(code)) response_df["id"] = response_df["stock_id"].apply(lambda x: f"{etf_id}_{x}") df_to_db(data_schema=self.data_schema, df=response_df, provider=self.provider) self.logger.info(f'{etf["证券简称"]} - {etf_code} 成分股抓取完成...') self.sleep() @staticmethod def populate_sh_etf_type(df: pd.DataFrame): """ 填充沪市 ETF 代码对应的 TYPE 到列表数据中 :param df: ETF 列表数据 :return: 包含 ETF 对应 TYPE 的列表数据 """ query_url = ( "http://query.sse.com.cn/infodisplay/queryETFNewAllInfo.do?" "isPagination=false&type={}&pageHelp.pageSize=25" ) type_df = pd.DataFrame() for etf_class in [1, 2]: url = query_url.format(etf_class) response = requests.get(url, headers=DEFAULT_SH_ETF_LIST_HEADER) response_dict = demjson3.decode(response.text) response_df = pd.DataFrame(response_dict.get("result", [])) response_df = response_df[["fundid1", "etftype"]] type_df = pd.concat([type_df, response_df]) result_df = df.copy() result_df = result_df.sort_values(by="FUND_ID").reset_index(drop=True) type_df = type_df.sort_values(by="fundid1").reset_index(drop=True) result_df["ETF_TYPE"] = type_df["etftype"] return result_df @staticmethod def parse_sz_etf_underlying_index(df: pd.DataFrame): """ 解析深市 ETF 对应跟踪的指数代码 :param df: ETF 列表数据 :return: 解析完成 ETF 对应指数代码的列表数据 """ def parse_index(text): if len(text) == 0: return "" result = re.search(r"(\d+).*", text) if result is None: return "" else: return result.group(1) df["拟合指数"] = df["拟合指数"].apply(parse_index) __all__ = ["ChinaETFListSpider"] if __name__ == "__main__": spider = ChinaETFListSpider(provider="exchange") spider.run() # the __all__ is generated __all__ = ["ChinaETFListSpider"] ================================================ FILE: src/zvt/recorders/exchange/exchange_index_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Index from zvt.recorders.exchange.api import cn_index_api, cs_index_api class ExchangeIndexRecorder(Recorder): provider = "exchange" data_schema = Index def run(self): # 深圳 self.record_cn_index("sz") # 国证 self.record_cn_index("cni") # 上海 self.record_cs_index("sh") # 中证 self.record_cs_index("csi") # 中证，上海 def record_cs_index(self, index_type): df = cs_index_api.get_cs_index(index_type=index_type) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) self.logger.info(f"finish record {index_type} index") # 国证，深圳 def record_cn_index(self, index_type): if index_type == "cni": category_map_url = cn_index_api.cni_category_map_url elif index_type == "sz": category_map_url = cn_index_api.sz_category_map_url else: self.logger.error(f"not support index_type: {index_type}") assert False for category, _ in category_map_url.items(): df = cn_index_api.get_cn_index(index_type=index_type, category=category) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) self.logger.info(f"finish record {index_type} index:{category.value}") if __name__ == "__main__": # init_log('china_stock_category.log') ExchangeIndexRecorder().run() # the __all__ is generated __all__ = ["ExchangeIndexRecorder"] ================================================ FILE: src/zvt/recorders/exchange/exchange_index_stock_recorder.py ================================================ # -*- coding: utf-8 -*- from typing import List import pandas as pd from zvt.contract.api import df_to_db from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Index, IndexStock from zvt.recorders.exchange.api import cs_index_stock_api, cn_index_stock_api from zvt.utils.time_utils import pre_month_start_date from zvt.utils.time_utils import to_pd_timestamp class ExchangeIndexStockRecorder(TimestampsDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "exchange" data_schema = IndexStock def __init__( self, force_update=False, sleeping_time=5, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, record_history=False, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, ) self.record_history = record_history def init_timestamps(self, entity_item) -> List[pd.Timestamp]: last_valid_date = pre_month_start_date() if self.record_history: # 每个月记录一次 return [to_pd_timestamp(item) for item in pd.date_range(entity_item.list_date, last_valid_date, freq="M")] else: return [last_valid_date] def record(self, entity, start, end, size, timestamps): if entity.publisher == "cnindex": for timestamp in timestamps: df = cn_index_stock_api.get_cn_index_stock(code=entity.code, timestamp=timestamp, name=entity.name) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) elif entity.publisher == "csindex": # cs index not support history data df = cs_index_stock_api.get_cs_index_stock(code=entity.code, timestamp=None, name=entity.name) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) if __name__ == "__main__": # ExchangeIndexMetaRecorder().run() ExchangeIndexStockRecorder(codes=["399370"]).run() # the __all__ is generated __all__ = ["ExchangeIndexStockRecorder"] ================================================ FILE: src/zvt/recorders/exchange/exchange_stock_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import io import pandas as pd import requests from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Stock, StockDetail from zvt.recorders.consts import DEFAULT_SH_HEADER, DEFAULT_SZ_HEADER from zvt.utils.time_utils import to_pd_timestamp class ExchangeStockMetaRecorder(Recorder): data_schema = Stock provider = "exchange" original_page_url = "http://www.sse.com.cn/assortment/stock/list/share/" def run(self): url = ( "http://query.sse.com.cn/security/stock/downloadStockListFile.do?csrcCode=&stockCode=&areaName=&stockType=1" ) resp = requests.get(url, headers=DEFAULT_SH_HEADER) self.download_stock_list(response=resp, exchange="sh") url = ( "http://query.sse.com.cn/security/stock/downloadStockListFile.do?csrcCode=&stockCode=&areaName=&stockType=8" ) resp = requests.get(url, headers=DEFAULT_SH_HEADER) self.download_stock_list(response=resp, exchange="sh") url = "http://www.szse.cn/api/report/ShowReport?SHOWTYPE=xlsx&CATALOGID=1110&TABKEY=tab1&random=0.20932135244582617" resp = requests.get(url, headers=DEFAULT_SZ_HEADER) self.download_stock_list(response=resp, exchange="sz") def download_stock_list(self, response, exchange): df = None if exchange == "sh": df = pd.read_csv( io.BytesIO(response.content), sep="\s+", encoding="GB2312", dtype=str, parse_dates=["上市日期"], date_format="%Y-m-d", on_bad_lines="skip", ) print(df) if df is not None: df = df.loc[:, ["公司代码", "公司简称", "上市日期"]] elif exchange == "sz": df = pd.read_excel( io.BytesIO(response.content), sheet_name="A股列表", dtype=str, parse_dates=["A股上市日期"], date_format="%Y-m-d", ) if df is not None: df = df.loc[:, ["A股代码", "A股简称", "A股上市日期"]] if df is not None: df.columns = ["code", "name", "list_date"] df = df.dropna(subset=["code"]) # handle the dirty data # 600996,贵广网络,2016-12-26,2016-12-26,sh,stock,stock_sh_600996,,次新股,贵州,, df.loc[df["code"] == "600996", "list_date"] = "2016-12-26" print(df[df["list_date"] == "-"]) print(df["list_date"]) df["list_date"] = df["list_date"].apply(lambda x: to_pd_timestamp(x)) df["exchange"] = exchange df["entity_type"] = "stock" df["id"] = df[["entity_type", "exchange", "code"]].apply(lambda x: "_".join(x.astype(str)), axis=1) df["entity_id"] = df["id"] df["timestamp"] = df["list_date"] df = df.dropna(axis=0, how="any") df = df.drop_duplicates(subset=("id"), keep="last") df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=False) # persist StockDetail too df_to_db(df=df, data_schema=StockDetail, provider=self.provider, force_update=False) self.logger.info(df.tail()) self.logger.info("persist stock list successs") __all__ = ["ExchangeStockMetaRecorder"] if __name__ == "__main__": recorder = ExchangeStockMetaRecorder() recorder.run() # the __all__ is generated __all__ = ["ExchangeStockMetaRecorder"] ================================================ FILE: src/zvt/recorders/exchange/exchange_stock_summary_recorder.py ================================================ import demjson3 import pandas as pd import requests from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Index from zvt.domain.misc import StockSummary from zvt.recorders.consts import DEFAULT_SH_SUMMARY_HEADER from zvt.utils.time_utils import to_date_time_str from zvt.utils.utils import to_float class ExchangeStockSummaryRecorder(TimestampsDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "exchange" data_schema = StockSummary original_page_url = "http://www.sse.com.cn/market/stockdata/overview/day/" url = "http://query.sse.com.cn/marketdata/tradedata/queryTradingByProdTypeData.do?jsonCallBack=jsonpCallback30731&searchDate={}&prodType=gp&_=1515717065511" def __init__( self, force_update=False, sleeping_time=5, exchanges=None, entity_id=None, entity_ids=None, code=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, ["000001"], day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, ) def init_timestamps(self, entity): return pd.date_range(start=entity.timestamp, end=pd.Timestamp.now(), freq="B").tolist() def record(self, entity, start, end, size, timestamps): json_results = [] for timestamp in timestamps: timestamp_str = to_date_time_str(timestamp) url = self.url.format(timestamp_str) response = requests.get(url=url, headers=DEFAULT_SH_SUMMARY_HEADER) results = demjson3.decode(response.text[response.text.index("(") + 1 : response.text.index(")")])["result"] result = [result for result in results if result["productType"] == "1"] if result and len(result) == 1: result_json = result[0] # 有些较老的数据不存在,默认设为0.0 json_results.append( { "provider": "exchange", "timestamp": timestamp, "name": "上证指数", "pe": to_float(result_json["profitRate"], 0.0), "total_value": to_float(result_json["marketValue1"] + "亿", 0.0), "total_tradable_vaule": to_float(result_json["negotiableValue1"] + "亿", 0.0), "volume": to_float(result_json["trdVol1"] + "万", 0.0), "turnover": to_float(result_json["trdAmt1"] + "亿", 0.0), "turnover_rate": to_float(result_json["exchangeRate"], 0.0), } ) if len(json_results) > 30: return json_results return json_results def get_data_map(self): return None if __name__ == "__main__": ExchangeStockSummaryRecorder().run() # the __all__ is generated __all__ = ["ExchangeStockSummaryRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule quotes from .quotes import * from .quotes import __all__ as _quotes_all __all__ += _quotes_all # import all from submodule common from .common import * from .common import __all__ as _common_all __all__ += _common_all # import all from submodule fundamental from .fundamental import * from .fundamental import __all__ as _fundamental_all __all__ += _fundamental_all # import all from submodule misc from .misc import * from .misc import __all__ as _misc_all __all__ += _misc_all # import all from submodule meta from .meta import * from .meta import __all__ as _meta_all __all__ += _meta_all # import all from submodule overall from .overall import * from .overall import __all__ as _overall_all __all__ += _overall_all ================================================ FILE: src/zvt/recorders/joinquant/common.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel from zvt.domain import ReportPeriod def to_jq_trading_level(trading_level: IntervalLevel): if trading_level < IntervalLevel.LEVEL_1HOUR: return trading_level.value if trading_level == IntervalLevel.LEVEL_1HOUR: return "60m" if trading_level == IntervalLevel.LEVEL_4HOUR: return "240m" if trading_level == IntervalLevel.LEVEL_1DAY: return "1d" if trading_level == IntervalLevel.LEVEL_1WEEK: return "1w" if trading_level == IntervalLevel.LEVEL_1MON: return "1M" def to_jq_entity_id(security_item): if security_item.entity_type == "stock" or security_item.entity_type == "index": if security_item.exchange == "sh": return "{}.XSHG".format(security_item.code) if security_item.exchange == "sz": return "{}.XSHE".format(security_item.code) def to_entity_id(jq_code: str, entity_type): try: code, exchange = jq_code.split(".") if exchange == "XSHG": exchange = "sh" elif exchange == "XSHE": exchange = "sz" except: code = jq_code exchange = "sz" return f"{entity_type}_{exchange}_{code}" def jq_to_report_period(jq_report_type): if jq_report_type == "第一季度": return ReportPeriod.season1.value if jq_report_type == "第二季度": return ReportPeriod.season2.value if jq_report_type == "第三季度": return ReportPeriod.season3.value if jq_report_type == "第四季度": return ReportPeriod.season4.value if jq_report_type == "半年度": return ReportPeriod.half_year.value if jq_report_type == "年度": return ReportPeriod.year.value assert False # the __all__ is generated __all__ = ["to_jq_trading_level", "to_jq_entity_id", "to_entity_id", "jq_to_report_period"] ================================================ FILE: src/zvt/recorders/joinquant/fundamental/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule jq_margin_trading_recorder from .jq_margin_trading_recorder import * from .jq_margin_trading_recorder import __all__ as _jq_margin_trading_recorder_all __all__ += _jq_margin_trading_recorder_all # import all from submodule jq_stock_valuation_recorder from .jq_stock_valuation_recorder import * from .jq_stock_valuation_recorder import __all__ as _jq_stock_valuation_recorder_all __all__ += _jq_stock_valuation_recorder_all # import all from submodule jq_etf_valuation_recorder from .jq_etf_valuation_recorder import * from .jq_etf_valuation_recorder import __all__ as _jq_etf_valuation_recorder_all __all__ += _jq_etf_valuation_recorder_all ================================================ FILE: src/zvt/recorders/joinquant/fundamental/jq_etf_valuation_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.api.portfolio import get_etf_stocks from zvt.contract.api import df_to_db from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import StockValuation, Etf, EtfValuation from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import now_pd_timestamp class JqChinaEtfValuationRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Etf # 数据来自jq provider = "joinquant" data_schema = EtfValuation def record(self, entity, start, end, size, timestamps): if not end: end = now_pd_timestamp() date_range = pd.date_range(start=start, end=end, freq="1D").tolist() for date in date_range: # etf包含的个股和比例 etf_stock_df = get_etf_stocks(code=entity.code, timestamp=date, provider=self.provider) if pd_is_not_null(etf_stock_df): all_pct = etf_stock_df["proportion"].sum() if all_pct >= 1.2 or all_pct <= 0.8: self.logger.error(f"ignore etf:{entity.id} date:{date} proportion sum:{all_pct}") break etf_stock_df.set_index("stock_id", inplace=True) # 个股的估值数据 stock_valuation_df = StockValuation.query_data( entity_ids=etf_stock_df.index.to_list(), filters=[StockValuation.timestamp == date], index="entity_id", ) if pd_is_not_null(stock_valuation_df): stock_count = len(etf_stock_df) valuation_count = len(stock_valuation_df) self.logger.info( f"etf:{entity.id} date:{date} stock count: {stock_count}," f"valuation count:{valuation_count}" ) pct = abs(stock_count - valuation_count) / stock_count if pct >= 0.2: self.logger.error(f"ignore etf:{entity.id} date:{date} pct:{pct}") break se = pd.Series( { "id": "{}_{}".format(entity.id, date), "entity_id": entity.id, "timestamp": date, "code": entity.code, "name": entity.name, } ) for col in ["pe", "pe_ttm", "pb", "ps", "pcf"]: # PE=P/E # 这里的算法为：将其价格都设为PE,那么Earning为1(亏钱为-1)，结果为 总价格(PE)/总Earning value = 0 price = 0 # 权重估值 positive_df = stock_valuation_df[[col]][stock_valuation_df[col] > 0] positive_df["count"] = 1 positive_df = positive_df.multiply(etf_stock_df["proportion"], axis="index") if pd_is_not_null(positive_df): value = positive_df["count"].sum() price = positive_df[col].sum() negative_df = stock_valuation_df[[col]][stock_valuation_df[col] < 0] if pd_is_not_null(negative_df): negative_df["count"] = 1 negative_df = negative_df.multiply(etf_stock_df["proportion"], axis="index") value = value - negative_df["count"].sum() price = price + negative_df[col].sum() se[f"{col}1"] = price / value # 简单算术平均估值 positive_df = stock_valuation_df[col][stock_valuation_df[col] > 0] positive_count = len(positive_df) negative_df = stock_valuation_df[col][stock_valuation_df[col] < 0] negative_count = len(negative_df) value = positive_count - negative_count price = positive_df.sum() + abs(negative_df.sum()) se[col] = price / value df = se.to_frame().T self.logger.info(df) df_to_db( df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update ) return None if __name__ == "__main__": # 上证50 JqChinaEtfValuationRecorder(codes=["512290"]).run() # the __all__ is generated __all__ = ["JqChinaEtfValuationRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/fundamental/jq_margin_trading_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy.api import get_mtss from zvt.contract.api import df_to_db from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import Stock, MarginTrading from zvt.recorders.joinquant.common import to_jq_entity_id from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str, TIME_FORMAT_DAY class MarginTradingRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Stock # 数据来自jq provider = "joinquant" data_schema = MarginTrading def record(self, entity, start, end, size, timestamps): df = get_mtss(code=to_jq_entity_id(entity), date=to_date_time_str(start)) if pd_is_not_null(df): df["entity_id"] = entity.id df["code"] = entity.code df.rename(columns={"date": "timestamp"}, inplace=True) df["timestamp"] = pd.to_datetime(df["timestamp"]) df["id"] = df[["entity_id", "timestamp"]].apply( lambda se: "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_DAY)), axis=1, ) print(df) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) return None if __name__ == "__main__": MarginTradingRecorder(codes=["000004"]).run() # the __all__ is generated __all__ = ["MarginTradingRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/fundamental/jq_stock_valuation_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy.api import get_fundamentals from pandas._libs.tslibs.timedeltas import Timedelta from zvt.contract.api import df_to_db from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import Stock, StockValuation, Etf from zvt.recorders.joinquant.common import to_jq_entity_id from zvt.utils.time_utils import now_pd_timestamp, to_date_time_str, to_pd_timestamp class JqChinaStockValuationRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Stock # 数据来自jq provider = "joinquant" data_schema = StockValuation def record(self, entity, start, end, size, timestamps): start = max(start, to_pd_timestamp("2005-01-01")) end = min(now_pd_timestamp(), start + Timedelta(days=500)) count: Timedelta = end - start # df = get_fundamentals_continuously(q, end_date=now_time_str(), count=count.days + 1, panel=False) df = get_fundamentals( table="valuation", code=to_jq_entity_id(entity), date=to_date_time_str(end), count=min(count.days, 500) ) df["entity_id"] = entity.id df["timestamp"] = pd.to_datetime(df["day"]) df["code"] = entity.code df["name"] = entity.name df["id"] = df["timestamp"].apply(lambda x: "{}_{}".format(entity.id, to_date_time_str(x))) df = df.rename( {"pe_ratio_lyr": "pe", "pe_ratio": "pe_ttm", "pb_ratio": "pb", "ps_ratio": "ps", "pcf_ratio": "pcf"}, axis="columns", ) df["market_cap"] = df["market_cap"] * 100000000 df["circulating_market_cap"] = df["circulating_market_cap"] * 100000000 df["capitalization"] = df["capitalization"] * 10000 df["circulating_cap"] = df["circulating_cap"] * 10000 df["turnover_ratio"] = df["turnover_ratio"] * 0.01 df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) return None if __name__ == "__main__": # 上证50 df = Etf.get_stocks(code="510050") stocks = df.stock_id.tolist() print(stocks) print(len(stocks)) JqChinaStockValuationRecorder(entity_ids=["stock_sz_300999"], force_update=True).run() # the __all__ is generated __all__ = ["JqChinaStockValuationRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/meta/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule jq_fund_meta_recorder from .jq_fund_meta_recorder import * from .jq_fund_meta_recorder import __all__ as _jq_fund_meta_recorder_all __all__ += _jq_fund_meta_recorder_all # import all from submodule jq_trade_day_recorder from .jq_trade_day_recorder import * from .jq_trade_day_recorder import __all__ as _jq_trade_day_recorder_all __all__ += _jq_trade_day_recorder_all # import all from submodule jq_stock_meta_recorder from .jq_stock_meta_recorder import * from .jq_stock_meta_recorder import __all__ as _jq_stock_meta_recorder_all __all__ += _jq_stock_meta_recorder_all ================================================ FILE: src/zvt/recorders/joinquant/meta/jq_fund_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy.api import run_query from zvt.api.portfolio import portfolio_relate_stock from zvt.api.utils import china_stock_code_to_id from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder, TimeSeriesDataRecorder from zvt.domain.meta.fund_meta import Fund, FundStock from zvt.recorders.joinquant.common import to_entity_id, jq_to_report_period from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str, date_time_by_interval, now_pd_timestamp, is_same_date class JqChinaFundRecorder(Recorder): provider = "joinquant" data_schema = Fund def run(self): # 按不同类别抓取 # 编码 基金运作方式 # 401001 开放式基金 # 401002 封闭式基金 # 401003 QDII # 401004 FOF # 401005 ETF # 401006 LOF for operate_mode_id in (401001, 401002, 401005): year_count = 2 while True: latest = Fund.query_data( filters=[Fund.operate_mode_id == operate_mode_id], order=Fund.timestamp.desc(), limit=1, return_type="domain", ) start_timestamp = "2000-01-01" if latest: start_timestamp = latest[0].timestamp end_timestamp = min(date_time_by_interval(start_timestamp, 365 * year_count), now_pd_timestamp()) df = run_query( table="finance.FUND_MAIN_INFO", conditions=f"operate_mode_id#=#{operate_mode_id}&start_date#>=#{to_date_time_str(start_timestamp)}&start_date#<=#{to_date_time_str(end_timestamp)}", parse_dates=["start_date", "end_date"], dtype={"main_code": str}, ) if not pd_is_not_null(df) or (df["start_date"].max().year < end_timestamp.year): year_count = year_count + 1 if pd_is_not_null(df): df.rename(columns={"start_date": "timestamp"}, inplace=True) df["timestamp"] = pd.to_datetime(df["timestamp"]) df["list_date"] = df["timestamp"] df["end_date"] = pd.to_datetime(df["end_date"]) df["code"] = df["main_code"] df["entity_id"] = df["code"].apply(lambda x: to_entity_id(entity_type="fund", jq_code=x)) df["id"] = df["entity_id"] df["entity_type"] = "fund" df["exchange"] = "sz" df_to_db(df, data_schema=Fund, provider=self.provider, force_update=self.force_update) self.logger.info( f"persist fund {operate_mode_id} list success {start_timestamp} to {end_timestamp}" ) if is_same_date(end_timestamp, now_pd_timestamp()): break class JqChinaFundStockRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Fund provider = "joinquant" data_schema = FundStock def init_entities(self): # 只抓股票型，混合型并且没退市的持仓, self.entities = Fund.query_data( entity_ids=self.entity_ids, codes=self.codes, return_type="domain", provider=self.entity_provider, filters=[Fund.underlying_asset_type.in_(("股票型", "混合型")), Fund.end_date.is_(None)], ) def record(self, entity, start, end, size, timestamps): # 忽略退市的 if entity.end_date: return None redundant_times = 1 while redundant_times > 0: df = run_query( table="finance.FUND_PORTFOLIO_STOCK", conditions=f"pub_date#>=#{to_date_time_str(start)}&code#=#{entity.code}", parse_dates=None, ) df = df.dropna() if pd_is_not_null(df): # data format # id code period_start period_end pub_date report_type_id report_type rank symbol name shares market_cap proportion # 0 8640569 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 1 601318 中国平安 19869239.0 1.361043e+09 7.09 # 1 8640570 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 2 600519 贵州茅台 921670.0 6.728191e+08 3.50 # 2 8640571 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 3 600036 招商银行 18918815.0 5.806184e+08 3.02 # 3 8640572 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 4 601166 兴业银行 22862332.0 3.646542e+08 1.90 df["timestamp"] = pd.to_datetime(df["pub_date"]) df.rename(columns={"symbol": "stock_code", "name": "stock_name"}, inplace=True) df["proportion"] = df["proportion"] * 0.01 df = portfolio_relate_stock(df, entity) df["stock_id"] = df["stock_code"].apply(lambda x: china_stock_code_to_id(x)) df["id"] = df[["entity_id", "stock_id", "pub_date", "id"]].apply( lambda x: "_".join(x.astype(str)), axis=1 ) df["report_date"] = pd.to_datetime(df["period_end"]) df["report_period"] = df["report_type"].apply(lambda x: jq_to_report_period(x)) saved = df_to_db( df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update ) # 取不到非重复的数据 if saved == 0: return None # self.logger.info(df.tail()) self.logger.info( f"persist fund {entity.code}({entity.name}) portfolio success {df.iloc[-1]['pub_date']}" ) latest = df["timestamp"].max() # 取到了最近两年的数据，再请求一次,确保取完最新的数据 if latest.year >= now_pd_timestamp().year - 1: redundant_times = redundant_times - 1 start = latest else: return None return None if __name__ == "__main__": # JqChinaFundRecorder().run() JqChinaFundStockRecorder(codes=["000053"]).run() # the __all__ is generated __all__ = ["JqChinaFundRecorder", "JqChinaFundStockRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/meta/jq_stock_meta_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy.api import get_all_securities, run_query from zvt.api.portfolio import portfolio_relate_stock from zvt.api.utils import china_stock_code_to_id from zvt.contract.api import df_to_db, get_entity_exchange, get_entity_code from zvt.contract.recorder import Recorder, TimeSeriesDataRecorder from zvt.domain import EtfStock, Stock, Etf, StockDetail from zvt.recorders.joinquant.common import to_entity_id, jq_to_report_period from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str class BaseJqChinaMetaRecorder(Recorder): provider = "joinquant" def __init__(self, force_update=True, sleeping_time=10) -> None: super().__init__(force_update, sleeping_time) def to_zvt_entity(self, df, entity_type, category=None): df = df.set_index("code") df.index.name = "entity_id" df = df.reset_index() # 上市日期 df.rename(columns={"start_date": "timestamp"}, inplace=True) df["timestamp"] = pd.to_datetime(df["timestamp"]) df["list_date"] = df["timestamp"] df["end_date"] = pd.to_datetime(df["end_date"]) df["entity_id"] = df["entity_id"].apply(lambda x: to_entity_id(entity_type=entity_type, jq_code=x)) df["id"] = df["entity_id"] df["entity_type"] = entity_type df["exchange"] = df["entity_id"].apply(lambda x: get_entity_exchange(x)) df["code"] = df["entity_id"].apply(lambda x: get_entity_code(x)) df["name"] = df["display_name"] if category: df["category"] = category return df class JqChinaStockRecorder(BaseJqChinaMetaRecorder): data_schema = Stock def run(self): # 抓取股票列表 df_stock = self.to_zvt_entity(get_all_securities(code="stock"), entity_type="stock") df_to_db(df_stock, data_schema=Stock, provider=self.provider, force_update=self.force_update) # persist StockDetail too df_to_db(df=df_stock, data_schema=StockDetail, provider=self.provider, force_update=self.force_update) # self.logger.info(df_stock) self.logger.info("persist stock list success") class JqChinaEtfRecorder(BaseJqChinaMetaRecorder): data_schema = Etf def run(self): # 抓取etf列表 df_index = self.to_zvt_entity(get_all_securities(code="etf"), entity_type="etf", category="etf") df_to_db(df_index, data_schema=Etf, provider=self.provider, force_update=self.force_update) # self.logger.info(df_index) self.logger.info("persist etf list success") class JqChinaStockEtfPortfolioRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Etf # 数据来自jq provider = "joinquant" data_schema = EtfStock def record(self, entity, start, end, size, timestamps): df = run_query( table="finance.FUND_PORTFOLIO_STOCK", conditions=f"pub_date#>=#{to_date_time_str(start)}&code#=#{entity.code}", parse_dates=None, ) if pd_is_not_null(df): # id code period_start period_end pub_date report_type_id report_type rank symbol name shares market_cap proportion # 0 8640569 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 1 601318 中国平安 19869239.0 1.361043e+09 7.09 # 1 8640570 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 2 600519 贵州茅台 921670.0 6.728191e+08 3.50 # 2 8640571 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 3 600036 招商银行 18918815.0 5.806184e+08 3.02 # 3 8640572 159919 2018-07-01 2018-09-30 2018-10-26 403003 第三季度 4 601166 兴业银行 22862332.0 3.646542e+08 1.90 df["timestamp"] = pd.to_datetime(df["pub_date"]) df.rename(columns={"symbol": "stock_code", "name": "stock_name"}, inplace=True) df["proportion"] = df["proportion"] * 0.01 df = portfolio_relate_stock(df, entity) df["stock_id"] = df["stock_code"].apply(lambda x: china_stock_code_to_id(x)) df["id"] = df[["entity_id", "stock_id", "pub_date", "id"]].apply(lambda x: "_".join(x.astype(str)), axis=1) df["report_date"] = pd.to_datetime(df["period_end"]) df["report_period"] = df["report_type"].apply(lambda x: jq_to_report_period(x)) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) # self.logger.info(df.tail()) self.logger.info(f"persist etf {entity.code} portfolio success {df.iloc[-1]['pub_date']}") return None if __name__ == "__main__": # JqChinaEtfRecorder().run() JqChinaStockEtfPortfolioRecorder(codes=["510050"]).run() # the __all__ is generated __all__ = ["BaseJqChinaMetaRecorder", "JqChinaStockRecorder", "JqChinaEtfRecorder", "JqChinaStockEtfPortfolioRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/meta/jq_trade_day_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy.api import get_trade_days from zvt.contract.api import df_to_db from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import StockTradeDay, Stock from zvt.utils.time_utils import to_date_time_str class StockTradeDayRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Stock provider = "joinquant" data_schema = StockTradeDay def __init__( self, exchanges=None, entity_id=None, entity_ids=None, day_data=False, force_update=False, sleeping_time=5, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, entity_filters=None, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, codes=["000001"], day_data=day_data, entity_filters=entity_filters, ignore_failed=True, real_time=real_time, fix_duplicate_way=fix_duplicate_way, start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) def record(self, entity, start, end, size, timestamps): df = pd.DataFrame() dates = get_trade_days(date=to_date_time_str(start)) dates = dates.iloc[:, 0] self.logger.info(f"add dates:{dates}") df["timestamp"] = pd.to_datetime(dates) df["id"] = [to_date_time_str(date) for date in dates] df["entity_id"] = "stock_sz_000001" df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": r = StockTradeDayRecorder() r.run() # the __all__ is generated __all__ = ["StockTradeDayRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/misc/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule jq_hk_holder_recorder from .jq_hk_holder_recorder import * from .jq_hk_holder_recorder import __all__ as _jq_hk_holder_recorder_all __all__ += _jq_hk_holder_recorder_all # import all from submodule jq_index_money_flow_recorder from .jq_index_money_flow_recorder import * from .jq_index_money_flow_recorder import __all__ as _jq_index_money_flow_recorder_all __all__ += _jq_index_money_flow_recorder_all # import all from submodule jq_stock_money_flow_recorder from .jq_stock_money_flow_recorder import * from .jq_stock_money_flow_recorder import __all__ as _jq_stock_money_flow_recorder_all __all__ += _jq_stock_money_flow_recorder_all ================================================ FILE: src/zvt/recorders/joinquant/misc/jq_hk_holder_recorder.py ================================================ import pandas as pd from jqdatapy.api import run_query from zvt.contract.api import df_to_db, get_data from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Index from zvt.domain.misc.holder import HkHolder from zvt.recorders.joinquant.common import to_entity_id from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str, TIME_FORMAT_DAY, to_pd_timestamp # 这里选择继承TimestampsDataRecorder是因为 # 1)时间上就是交易日的列表,这个是可知的，可以以此为增量计算点 # 2)HkHolder数据结构的设计： # 沪股通/深股通 每日 持有 标的(股票)的情况 # 抓取的角度是entity从Index中获取 沪股通/深股通，然后按 每日 去获取 class JoinquantHkHolderRecorder(TimestampsDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "joinquant" data_schema = HkHolder def __init__( self, day_data=False, force_update=False, sleeping_time=5, real_time=False, start_timestamp=None, end_timestamp=None, ) -> None: # 聚宽编码 # 市场通编码 市场通名称 # 310001 沪股通 # 310002 深股通 # 310003 港股通（沪） # 310004 港股通（深） codes = ["310001", "310002"] super().__init__( force_update, sleeping_time, ["cn"], None, codes, day_data, real_time=real_time, fix_duplicate_way="ignore", start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) def init_timestamps(self, entity): # 聚宽数据从2017年3月17开始 return pd.date_range(start=to_pd_timestamp("2017-3-17"), end=pd.Timestamp.now(), freq="B").tolist() # 覆盖这个方式是因为，HkHolder里面entity其实是股票，而recorder中entity是 Index类型(沪股通/深股通) def get_latest_saved_record(self, entity): order = eval("self.data_schema.{}.desc()".format(self.get_evaluated_time_field())) records = get_data( filters=[HkHolder.holder_code == entity.code], provider=self.provider, data_schema=self.data_schema, order=order, limit=1, return_type="domain", session=self.session, ) if records: return records[0] return None def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: df = run_query( table="finance.STK_HK_HOLD_INFO", conditions=f"link_id#=#{entity.code}&day#=#{to_date_time_str(timestamp)}", ) print(df) if pd_is_not_null(df): df.rename( columns={"day": "timestamp", "link_id": "holder_code", "link_name": "holder_name"}, inplace=True ) df["timestamp"] = pd.to_datetime(df["timestamp"]) df["entity_id"] = df["code"].apply(lambda x: to_entity_id(entity_type="stock", jq_code=x)) df["code"] = df["code"].apply(lambda x: x.split(".")[0]) # id格式为:{holder_name}_{entity_id}_{timestamp} df["id"] = df[["holder_name", "entity_id", "timestamp"]].apply( lambda se: "{}_{}_{}".format( se["holder_name"], se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_DAY) ), axis=1, ) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": JoinquantHkHolderRecorder(sleeping_time=10).run() # the __all__ is generated __all__ = ["JoinquantHkHolderRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/misc/jq_index_money_flow_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.contract import IntervalLevel from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import IndexMoneyFlow, Index, StockMoneyFlow from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str class JoinquantIndexMoneyFlowRecorder(FixedCycleDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "joinquant" data_schema = IndexMoneyFlow def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, return_unfinished=False, ) -> None: # 上证指数，深证成指，创业板指，科创板 support_codes = ["000001", "399001", "399006", "000688"] if not codes: codes = support_codes else: codes = list(set(codes) & set(support_codes)) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) def record(self, entity, start, end, size, timestamps): # 上证 if entity.code == "000001": all_df = StockMoneyFlow.query_data( provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.entity_id.like("stock_sh%")] ) # 深证 elif entity.code == "399001": all_df = StockMoneyFlow.query_data( provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.entity_id.like("stock_sz%")] ) # 创业板 elif entity.code == "399006": all_df = StockMoneyFlow.query_data( provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.code.like("300%")] ) # 科创板 elif entity.code == "000688": all_df = StockMoneyFlow.query_data( provider=self.provider, start_timestamp=start, filters=[StockMoneyFlow.code.like("688%")] ) if pd_is_not_null(all_df): g = all_df.groupby("timestamp") for timestamp, df in g: se = pd.Series( { "id": "{}_{}".format(entity.id, to_date_time_str(timestamp)), "entity_id": entity.id, "timestamp": timestamp, "code": entity.code, "name": entity.name, } ) for col in [ "net_main_inflows", "net_huge_inflows", "net_big_inflows", "net_medium_inflows", "net_small_inflows", ]: se[col] = df[col].sum() for col in [ "net_main_inflow_rate", "net_huge_inflow_rate", "net_big_inflow_rate", "net_medium_inflow_rate", "net_small_inflow_rate", ]: se[col] = df[col].sum() / len(df) index_df = se.to_frame().T self.logger.info(index_df) df_to_db( df=index_df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update ) return None if __name__ == "__main__": JoinquantIndexMoneyFlowRecorder(start_timestamp="2020-12-01").run() # the __all__ is generated __all__ = ["JoinquantIndexMoneyFlowRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/misc/jq_stock_money_flow_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy import get_token, get_money_flow from zvt import zvt_config from zvt.api.kdata import generate_kdata_id from zvt.contract import IntervalLevel from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import StockMoneyFlow, Stock from zvt.recorders.joinquant.common import to_jq_entity_id from zvt.recorders.joinquant.misc.jq_index_money_flow_recorder import JoinquantIndexMoneyFlowRecorder from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import TIME_FORMAT_DAY, to_date_time_str class JoinquantStockMoneyFlowRecorder(FixedCycleDataRecorder): entity_provider = "joinquant" entity_schema = Stock provider = "joinquant" data_schema = StockMoneyFlow def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, compute_index_money_flow=False, return_unfinished=False, ) -> None: super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) self.compute_index_money_flow = compute_index_money_flow get_token(zvt_config["jq_username"], zvt_config["jq_password"], force=True) def generate_domain_id(self, entity, original_data): return generate_kdata_id(entity_id=entity.id, timestamp=original_data["timestamp"], level=self.level) def on_finish(self): # 根据 个股资金流 计算 大盘资金流 if self.compute_index_money_flow: JoinquantIndexMoneyFlowRecorder().run() def record(self, entity, start, end, size, timestamps): if not self.end_timestamp: df = get_money_flow(code=to_jq_entity_id(entity), date=to_date_time_str(start)) else: df = get_money_flow(code=to_jq_entity_id(entity), date=start, end_date=to_date_time_str(self.end_timestamp)) df = df.dropna() if pd_is_not_null(df): df["name"] = entity.name df.rename( columns={ "date": "timestamp", "net_amount_main": "net_main_inflows", "net_pct_main": "net_main_inflow_rate", "net_amount_xl": "net_huge_inflows", "net_pct_xl": "net_huge_inflow_rate", "net_amount_l": "net_big_inflows", "net_pct_l": "net_big_inflow_rate", "net_amount_m": "net_medium_inflows", "net_pct_m": "net_medium_inflow_rate", "net_amount_s": "net_small_inflows", "net_pct_s": "net_small_inflow_rate", }, inplace=True, ) # 转换到标准float inflows_cols = [ "net_main_inflows", "net_huge_inflows", "net_big_inflows", "net_medium_inflows", "net_small_inflows", ] for col in inflows_cols: df[col] = pd.to_numeric(df[col], errors="coerce") df = df.dropna() if not pd_is_not_null(df): return None df[inflows_cols] = df[inflows_cols].apply(lambda x: x * 10000) inflow_rate_cols = [ "net_main_inflow_rate", "net_huge_inflow_rate", "net_big_inflow_rate", "net_medium_inflow_rate", "net_small_inflow_rate", ] for col in inflow_rate_cols: df[col] = pd.to_numeric(df[col], errors="coerce") df = df.dropna() if not pd_is_not_null(df): return None df[inflow_rate_cols] = df[inflow_rate_cols].apply(lambda x: x / 100) # 计算总流入 df["net_inflows"] = ( df["net_huge_inflows"] + df["net_big_inflows"] + df["net_medium_inflows"] + df["net_small_inflows"] ) # 计算总流入率 amount = df["net_main_inflows"] / df["net_main_inflow_rate"] df["net_inflow_rate"] = df["net_inflows"] / amount df["entity_id"] = entity.id df["timestamp"] = pd.to_datetime(df["timestamp"]) df["provider"] = "joinquant" df["code"] = entity.code def generate_kdata_id(se): return "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_DAY)) df["id"] = df[["entity_id", "timestamp"]].apply(generate_kdata_id, axis=1) df = df.drop_duplicates(subset="id", keep="last") df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) return None if __name__ == "__main__": JoinquantStockMoneyFlowRecorder(codes=["000578"]).run() # the __all__ is generated __all__ = ["JoinquantStockMoneyFlowRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/overall/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule jq_margin_trading_recorder from .jq_margin_trading_recorder import * from .jq_margin_trading_recorder import __all__ as _jq_margin_trading_recorder_all __all__ += _jq_margin_trading_recorder_all # import all from submodule jq_stock_summary_recorder from .jq_stock_summary_recorder import * from .jq_stock_summary_recorder import __all__ as _jq_stock_summary_recorder_all __all__ += _jq_stock_summary_recorder_all # import all from submodule jq_cross_market_recorder from .jq_cross_market_recorder import * from .jq_cross_market_recorder import __all__ as _jq_cross_market_recorder_all __all__ += _jq_cross_market_recorder_all ================================================ FILE: src/zvt/recorders/joinquant/overall/jq_cross_market_recorder.py ================================================ from jqdatapy.api import run_query from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import Index, CrossMarketSummary from zvt.utils.time_utils import to_date_time_str from zvt.utils.utils import multiple_number class CrossMarketSummaryRecorder(TimeSeriesDataRecorder): entity_provider = "joinquant" entity_schema = Index provider = "joinquant" data_schema = CrossMarketSummary def __init__(self, force_update=False, sleeping_time=5, real_time=False, fix_duplicate_way="add") -> None: # 聚宽编码 # 市场通编码 市场通名称 # 310001 沪股通 # 310002 深股通 # 310003 港股通（沪） # 310004 港股通（深） codes = ["310001", "310002", "310003", "310004"] super().__init__( force_update, sleeping_time, ["cn"], None, codes=codes, day_data=True, real_time=real_time, fix_duplicate_way=fix_duplicate_way, ) def init_entities(self): super().init_entities() def record(self, entity, start, end, size, timestamps): df = run_query( table="finance.STK_ML_QUOTA", conditions=f"link_id#=#{entity.code}&day#>=#{to_date_time_str(start)}" ) print(df) json_results = [] for item in df.to_dict(orient="records"): result = { "provider": self.provider, "timestamp": item["day"], "name": entity.name, "buy_amount": multiple_number(item["buy_amount"], 100000000), "buy_volume": item["buy_volume"], "sell_amount": multiple_number(item["sell_amount"], 100000000), "sell_volume": item["sell_volume"], "quota_daily": multiple_number(item["quota_daily"], 100000000), "quota_daily_balance": multiple_number(item["quota_daily_balance"], 100000000), } json_results.append(result) if len(json_results) < 100: self.one_shot = True return json_results def get_data_map(self): return None if __name__ == "__main__": CrossMarketSummaryRecorder().run() # the __all__ is generated __all__ = ["CrossMarketSummaryRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/overall/jq_margin_trading_recorder.py ================================================ from jqdatapy.api import run_query from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import Index, MarginTradingSummary from zvt.utils.time_utils import to_date_time_str # 聚宽编码 # XSHG-上海证券交易所 # XSHE-深圳证券交易所 code_map_jq = {"000001": "XSHG", "399106": "XSHE"} class MarginTradingSummaryRecorder(TimeSeriesDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "joinquant" data_schema = MarginTradingSummary def __init__( self, force_update=False, sleeping_time=5, exchanges=None, entity_id=None, entity_ids=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, ) -> None: # 上海A股,深圳市场 codes = ["000001", "399106"] super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, codes=codes, day_data=day_data, entity_filters=entity_filters, ignore_failed=ignore_failed, real_time=real_time, fix_duplicate_way=fix_duplicate_way, start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) def record(self, entity, start, end, size, timestamps): jq_code = code_map_jq.get(entity.code) df = run_query( table="finance.STK_MT_TOTAL", conditions=f"exchange_code#=#{jq_code}&date#>=#{to_date_time_str(start)}", parse_dates=["date"], ) print(df) json_results = [] for item in df.to_dict(orient="records"): result = { "provider": self.provider, "timestamp": item["date"], "name": entity.name, "margin_value": item["fin_value"], "margin_buy": item["fin_buy_value"], "short_value": item["sec_value"], "short_volume": item["sec_sell_volume"], "total_value": item["fin_sec_value"], } json_results.append(result) if len(json_results) < 100: self.one_shot = True return json_results def get_data_map(self): return None if __name__ == "__main__": MarginTradingSummaryRecorder().run() # the __all__ is generated __all__ = ["MarginTradingSummaryRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/overall/jq_stock_summary_recorder.py ================================================ from jqdatapy.api import run_query from zvt.contract.recorder import TimeSeriesDataRecorder from zvt.domain import Index from zvt.domain import StockSummary from zvt.utils.time_utils import to_date_time_str from zvt.utils.utils import multiple_number # 聚宽编码 # 322001 上海市场 # 322002 上海A股 # 322003 上海B股 # 322004 深圳市场 该市场交易所未公布成交量和成交笔数 # 322005 深市主板 # 322006 中小企业板 # 322007 创业板 code_map_jq = {"000001": "322002", "399106": "322004", "399001": "322005", "399005": "322006", "399006": "322007"} class StockSummaryRecorder(TimeSeriesDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "joinquant" data_schema = StockSummary def __init__( self, force_update=False, sleeping_time=5, exchanges=None, entity_id=None, entity_ids=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="add", start_timestamp=None, end_timestamp=None, ) -> None: # 上海A股,深圳市场,深圳成指,中小板,创业板 codes = ["000001", "399106", "399001", "399005", "399006"] super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, codes=codes, day_data=day_data, entity_filters=entity_filters, ignore_failed=ignore_failed, real_time=real_time, fix_duplicate_way=fix_duplicate_way, start_timestamp=start_timestamp, end_timestamp=end_timestamp, ) def record(self, entity, start, end, size, timestamps): jq_code = code_map_jq.get(entity.code) df = run_query( table="finance.STK_EXCHANGE_TRADE_INFO", conditions=f"exchange_code#=#{jq_code}&date#>=#{to_date_time_str(start)}", parse_dates=["date"], ) print(df) json_results = [] for item in df.to_dict(orient="records"): result = { "provider": self.provider, "timestamp": item["date"], "name": entity.name, "pe": item["pe_average"], "total_value": multiple_number(item["total_market_cap"], 100000000), "total_tradable_vaule": multiple_number(item["circulating_market_cap"], 100000000), "volume": multiple_number(item["volume"], 10000), "turnover": multiple_number(item["money"], 100000000), "turnover_rate": item["turnover_ratio"], } json_results.append(result) if len(json_results) < 100: self.one_shot = True return json_results def get_data_map(self): return None if __name__ == "__main__": StockSummaryRecorder().run() # the __all__ is generated __all__ = ["StockSummaryRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/quotes/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule jq_index_kdata_recorder from .jq_index_kdata_recorder import * from .jq_index_kdata_recorder import __all__ as _jq_index_kdata_recorder_all __all__ += _jq_index_kdata_recorder_all # import all from submodule jq_stock_kdata_recorder from .jq_stock_kdata_recorder import * from .jq_stock_kdata_recorder import __all__ as _jq_stock_kdata_recorder_all __all__ += _jq_stock_kdata_recorder_all ================================================ FILE: src/zvt/recorders/joinquant/quotes/jq_index_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- import argparse import pandas as pd from jqdatapy.api import get_token, get_bars from zvt import init_log, zvt_config from zvt.api.kdata import generate_kdata_id, get_kdata_schema, get_kdata from zvt.contract import IntervalLevel from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Index, IndexKdataCommon from zvt.recorders.joinquant.common import to_jq_trading_level, to_jq_entity_id from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str, TIME_FORMAT_DAY, TIME_FORMAT_ISO8601 class JqChinaIndexKdataRecorder(FixedCycleDataRecorder): entity_provider = "joinquant" entity_schema = Index # 数据来自jq provider = "joinquant" # 只是为了把recorder注册到data_schema data_schema = IndexKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY, IntervalLevel.LEVEL_1WEEK] def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, return_unfinished=False, ) -> None: level = IntervalLevel(level) self.data_schema = get_kdata_schema(entity_type="index", level=level) self.jq_trading_level = to_jq_trading_level(level) get_token(zvt_config["jq_username"], zvt_config["jq_password"], force=True) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) def init_entities(self): super().init_entities() # ignore no data index self.entities = [ entity for entity in self.entities if entity.code not in ["310001", "310002", "310003", "310004"] ] def generate_domain_id(self, entity, original_data): return generate_kdata_id(entity_id=entity.id, timestamp=original_data["timestamp"], level=self.level) def record(self, entity, start, end, size, timestamps): if not self.end_timestamp: df = get_bars( to_jq_entity_id(entity), count=size, unit=self.jq_trading_level, # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'] ) else: end_timestamp = to_date_time_str(self.end_timestamp) df = get_bars( to_jq_entity_id(entity), count=size, unit=self.jq_trading_level, # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'], end_date=end_timestamp, ) if pd_is_not_null(df): df["name"] = entity.name df.rename(columns={"money": "turnover", "date": "timestamp"}, inplace=True) df["entity_id"] = entity.id df["timestamp"] = pd.to_datetime(df["timestamp"]) df["provider"] = "joinquant" df["level"] = self.level.value df["code"] = entity.code def generate_kdata_id(se): if self.level >= IntervalLevel.LEVEL_1DAY: return "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_DAY)) else: return "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_ISO8601)) df["id"] = df[["entity_id", "timestamp"]].apply(generate_kdata_id, axis=1) df = df.drop_duplicates(subset="id", keep="last") df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) return None if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--level", help="trading level", default="1d", choices=[item.value for item in IntervalLevel]) parser.add_argument("--codes", help="codes", default=["000001"], nargs="+") args = parser.parse_args() level = IntervalLevel(args.level) codes = args.codes init_log("jq_china_stock_{}_kdata.log".format(args.level)) JqChinaIndexKdataRecorder(level=level, sleeping_time=0, codes=codes, real_time=False).run() print(get_kdata(entity_id="index_sh_000001", limit=10)) # the __all__ is generated __all__ = ["JqChinaIndexKdataRecorder"] ================================================ FILE: src/zvt/recorders/joinquant/quotes/jq_stock_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from jqdatapy.api import get_token, get_bars from zvt import zvt_config from zvt.api.kdata import generate_kdata_id, get_kdata_schema, get_kdata from zvt.contract import IntervalLevel, AdjustType, AdjustType from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Stock, StockKdataCommon, Stock1wkHfqKdata from zvt.recorders.joinquant.common import to_jq_trading_level, to_jq_entity_id from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_date_time_str, now_pd_timestamp, TIME_FORMAT_DAY, TIME_FORMAT_ISO8601 class JqChinaStockKdataRecorder(FixedCycleDataRecorder): entity_provider = "joinquant" entity_schema = Stock # 数据来自jq provider = "joinquant" # 只是为了把recorder注册到data_schema data_schema = StockKdataCommon supported_levels = [ level for level in IntervalLevel if level not in (IntervalLevel.LEVEL_L2_QUOTE, IntervalLevel.LEVEL_TICK) ] supported_adjust_types = [AdjustType.qfq, AdjustType.hfq] def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, adjust_type=AdjustType.qfq, return_unfinished=False, ) -> None: level = IntervalLevel(level) adjust_type = AdjustType(adjust_type) self.data_schema = get_kdata_schema(entity_type="stock", level=level, adjust_type=adjust_type) self.jq_trading_level = to_jq_trading_level(level) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) self.adjust_type = adjust_type get_token(zvt_config["jq_username"], zvt_config["jq_password"], force=True) def init_entities(self): super().init_entities() # 过滤掉退市的 self.entities = [ entity for entity in self.entities if (entity.end_date is None) or (entity.end_date > now_pd_timestamp()) ] def generate_domain_id(self, entity, original_data): return generate_kdata_id(entity_id=entity.id, timestamp=original_data["timestamp"], level=self.level) def recompute_qfq(self, entity, qfq_factor, last_timestamp): # 重新计算前复权数据 if qfq_factor != 0: kdatas = get_kdata( provider=self.provider, entity_id=entity.id, level=self.level.value, order=self.data_schema.timestamp.asc(), return_type="domain", session=self.session, filters=[self.data_schema.timestamp < last_timestamp], ) if kdatas: self.logger.info("recomputing {} qfq kdata,factor is:{}".format(entity.code, qfq_factor)) for kdata in kdatas: kdata.open = round(kdata.open * qfq_factor, 2) kdata.close = round(kdata.close * qfq_factor, 2) kdata.high = round(kdata.high * qfq_factor, 2) kdata.low = round(kdata.low * qfq_factor, 2) self.session.add_all(kdatas) self.session.commit() def record(self, entity, start, end, size, timestamps): if self.adjust_type == AdjustType.hfq: fq_ref_date = "2000-01-01" else: fq_ref_date = to_date_time_str(now_pd_timestamp()) if not self.end_timestamp: df = get_bars( to_jq_entity_id(entity), count=size, unit=self.jq_trading_level, # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'], fq_ref_date=fq_ref_date, ) else: end_timestamp = to_date_time_str(self.end_timestamp) df = get_bars( to_jq_entity_id(entity), count=size, unit=self.jq_trading_level, # fields=['date', 'open', 'close', 'low', 'high', 'volume', 'money'], end_date=end_timestamp, fq_ref_date=fq_ref_date, ) if pd_is_not_null(df): df["name"] = entity.name df.rename(columns={"money": "turnover", "date": "timestamp"}, inplace=True) df["entity_id"] = entity.id df["timestamp"] = pd.to_datetime(df["timestamp"]) df["provider"] = "joinquant" df["level"] = self.level.value df["code"] = entity.code # 判断是否需要重新计算之前保存的前复权数据 if self.adjust_type == AdjustType.qfq: check_df = df.head(1) check_date = check_df["timestamp"][0] current_df = get_kdata( entity_id=entity.id, provider=self.provider, start_timestamp=check_date, end_timestamp=check_date, limit=1, level=self.level, adjust_type=self.adjust_type, ) if pd_is_not_null(current_df): old = current_df.iloc[0, :]["close"] new = check_df["close"][0] # 相同时间的close不同，表明前复权需要重新计算 if round(old, 2) != round(new, 2): qfq_factor = new / old last_timestamp = pd.Timestamp(check_date) self.recompute_qfq(entity, qfq_factor=qfq_factor, last_timestamp=last_timestamp) def generate_kdata_id(se): if self.level >= IntervalLevel.LEVEL_1DAY: return "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_DAY)) else: return "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"], fmt=TIME_FORMAT_ISO8601)) df["id"] = df[["entity_id", "timestamp"]].apply(generate_kdata_id, axis=1) df = df.drop_duplicates(subset="id", keep="last") df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) return None if __name__ == "__main__": Stock1wkHfqKdata.record_data(codes=["300999"]) # the __all__ is generated __all__ = ["JqChinaStockKdataRecorder"] ================================================ FILE: src/zvt/recorders/jqka/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule emotion from .emotion import * from .emotion import __all__ as _emotion_all __all__ += _emotion_all # import all from submodule jqka_api from .jqka_api import * from .jqka_api import __all__ as _jqka_api_all __all__ += _jqka_api_all ================================================ FILE: src/zvt/recorders/jqka/emotion/JqkaEmotionRecorder.py ================================================ # -*- coding: utf-8 -*- import re from typing import List import pandas as pd from zvt.api.utils import china_stock_code_to_id from zvt.contract.api import df_to_db from zvt.contract.recorder import TimestampsDataRecorder from zvt.domain import Stock from zvt.domain.emotion.emotion import LimitUpInfo, LimitDownInfo, Emotion from zvt.recorders.jqka import jqka_api from zvt.utils.time_utils import to_date_time_str, date_time_by_interval, current_date, to_pd_timestamp def _get_high_days_count(high_days_str: str): if not high_days_str or (high_days_str == "首板"): return 1 pattern = r"\d+" result = re.findall(pattern, high_days_str) return int(result[-1]) class JqkaLimitUpRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "jqka" data_schema = LimitUpInfo def init_entities(self): # fake entity to for trigger run self.entities = [Stock(id="stock_sz_000001")] def init_timestamps(self, entity_item) -> List[pd.Timestamp]: latest_infos = LimitUpInfo.query_data( provider=self.provider, order=LimitUpInfo.timestamp.desc(), limit=1, return_type="domain" ) if latest_infos and not self.force_update: start_date = latest_infos[0].timestamp else: # 最近一年的数据 start_date = date_time_by_interval(current_date(), -360) return pd.date_range(start=start_date, end=pd.Timestamp.now(), freq="B").tolist() def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) self.logger.info(f"record {self.data_schema} to {the_date}") limit_ups = jqka_api.get_limit_up(date=the_date) if limit_ups: records = [] for data in limit_ups: entity_id = china_stock_code_to_id(code=data["code"]) first_limit_up_time = current_date() try: first_limit_up_time = pd.Timestamp.fromtimestamp(int(data["first_limit_up_time"])) except Exception: pass last_limit_up_time = current_date() try: last_limit_up_time = pd.Timestamp.fromtimestamp(int(data["last_limit_up_time"])) except Exception: pass record = { "id": "{}_{}".format(entity_id, the_date), "entity_id": entity_id, "timestamp": to_pd_timestamp(the_date), "code": data["code"], "name": data["name"], "is_new": data["is_new"], "is_again_limit": data["is_again_limit"], "open_count": data["open_num"] if data["open_num"] else 0, "first_limit_up_time": first_limit_up_time, "last_limit_up_time": last_limit_up_time, "limit_up_type": data["limit_up_type"], "order_amount": data["order_amount"], "success_rate": data["limit_up_suc_rate"], "currency_value": data["currency_value"], "change_pct": data["change_rate"] / 100, "turnover_rate": data["turnover_rate"] / 100, "reason": data["reason_type"], "high_days": data["high_days"], "high_days_count": _get_high_days_count(data["high_days"]), } records.append(record) df = pd.DataFrame.from_records(records) df_to_db( data_schema=self.data_schema, df=df, provider=self.provider, force_update=True, drop_duplicates=True, ) class JqkaLimitDownRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "jqka" data_schema = LimitDownInfo def init_entities(self): # fake entity to for trigger run self.entities = [Stock(id="stock_sz_000001")] def init_timestamps(self, entity_item) -> List[pd.Timestamp]: latest_infos = LimitDownInfo.query_data( provider=self.provider, order=LimitDownInfo.timestamp.desc(), limit=1, return_type="domain" ) if latest_infos and not self.force_update: start_date = latest_infos[0].timestamp else: # 最近一年的数据 start_date = date_time_by_interval(current_date(), -360) return pd.date_range(start=start_date, end=pd.Timestamp.now(), freq="B").tolist() def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) self.logger.info(f"record {self.data_schema} to {the_date}") limit_downs = jqka_api.get_limit_down(date=the_date) if limit_downs: records = [] for idx, data in enumerate(limit_downs): entity_id = china_stock_code_to_id(code=data["code"]) record = { "id": "{}_{}".format(entity_id, the_date), "entity_id": entity_id, "timestamp": to_pd_timestamp(the_date), "code": data["code"], "name": data["name"], "is_new": data["is_new"], "is_again_limit": data["is_again_limit"], "currency_value": data["currency_value"], "change_pct": data["change_rate"] / 100, "turnover_rate": data["turnover_rate"] / 100, } records.append(record) df = pd.DataFrame.from_records(records) df_to_db( data_schema=self.data_schema, df=df, provider=self.provider, force_update=True, drop_duplicates=True, ) def _cal_power_and_max_height(continuous_limit_up: dict): max_height = 0 power = 0 for item in continuous_limit_up: if max_height < item["height"]: max_height = item["height"] power = power + item["height"] * item["number"] return max_height, power class JqkaEmotionRecorder(TimestampsDataRecorder): entity_provider = "em" entity_schema = Stock provider = "jqka" data_schema = Emotion def init_entities(self): # fake entity to for trigger run self.entities = [Stock(id="stock_sz_000001")] def init_timestamps(self, entity_item) -> List[pd.Timestamp]: latest_infos = Emotion.query_data( provider=self.provider, order=Emotion.timestamp.desc(), limit=1, return_type="domain" ) if latest_infos and not self.force_update: start_date = latest_infos[0].timestamp else: # 最近一年的数据 start_date = date_time_by_interval(current_date(), -365) return pd.date_range(start=start_date, end=pd.Timestamp.now(), freq="B").tolist() def record(self, entity, start, end, size, timestamps): for timestamp in timestamps: the_date = to_date_time_str(timestamp) self.logger.info(f"record {self.data_schema} to {the_date}") limit_stats = jqka_api.get_limit_stats(date=the_date) continuous_limit_up = jqka_api.get_continuous_limit_up(date=the_date) max_height, continuous_power = _cal_power_and_max_height(continuous_limit_up=continuous_limit_up) if limit_stats: # 大盘 entity_id = "stock_sh_000001" record = { "id": "{}_{}".format(entity_id, the_date), "entity_id": entity_id, "timestamp": to_pd_timestamp(the_date), "limit_up_count": limit_stats["limit_up_count"]["today"]["num"], "limit_up_open_count": limit_stats["limit_up_count"]["today"]["open_num"], "limit_up_success_rate": limit_stats["limit_up_count"]["today"]["rate"], "limit_down_count": limit_stats["limit_down_count"]["today"]["num"], "limit_down_open_count": limit_stats["limit_down_count"]["today"]["open_num"], "limit_down_success_rate": limit_stats["limit_down_count"]["today"]["rate"], "max_height": max_height, "continuous_power": continuous_power, } df = pd.DataFrame.from_records([record]) df_to_db( data_schema=self.data_schema, df=df, provider=self.provider, force_update=True, drop_duplicates=True, ) if __name__ == "__main__": # JqkaLimitDownRecorder().run() LimitDownInfo.record_data(start_timestamp="2024-02-02", end_timestamp="2024-02-16", force_update=True) # the __all__ is generated __all__ = ["JqkaLimitUpRecorder", "JqkaLimitDownRecorder", "JqkaEmotionRecorder"] ================================================ FILE: src/zvt/recorders/jqka/emotion/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule JqkaEmotionRecorder from .JqkaEmotionRecorder import * from .JqkaEmotionRecorder import __all__ as _JqkaEmotionRecorder_all __all__ += _JqkaEmotionRecorder_all ================================================ FILE: src/zvt/recorders/jqka/jqka_api.py ================================================ # -*- coding: utf-8 -*- import requests from zvt.utils.time_utils import now_timestamp_ms, to_date_time_str, TIME_FORMAT_DAY1 from zvt.utils.utils import chrome_copy_header_to_dict _JKQA_HEADER = chrome_copy_header_to_dict( """ Accept: application/json, text/plain, */* Accept-Encoding: gzip, deflate, br Accept-Language: zh-CN,zh;q=0.9,en;q=0.8 Connection: keep-alive Host: data.10jqka.com.cn Referer: https://data.10jqka.com.cn/datacenterph/limitup/limtupInfo.html?fontzoom=no&client_userid=cA2fp&share_hxapp=gsc&share_action=webpage_share.1&back_source=wxhy sec-ch-ua: "Not_A Brand";v="99", "Google Chrome";v="109", "Chromium";v="109" sec-ch-ua-mobile: ?1 sec-ch-ua-platform: "Android" Sec-Fetch-Dest: empty Sec-Fetch-Mode: cors Sec-Fetch-Site: same-origin User-Agent: Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Mobile Safari/537.36 """ ) def get_continuous_limit_up(date: str): date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1) url = f"https://data.10jqka.com.cn/dataapi/limit_up/continuous_limit_up?filter=HS,GEM2STAR&date={date_str}" resp = requests.get(url, headers=_JKQA_HEADER) if resp.status_code == 200: json_result = resp.json() if json_result: return json_result["data"] raise RuntimeError(f"request jkqa data code: {resp.status_code}, error: {resp.text}") def get_limit_stats(date: str): date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1) url = f"https://data.10jqka.com.cn/dataapi/limit_up/limit_up_pool?page=1&limit=1&field=199112,10,9001,330323,330324,330325,9002,330329,133971,133970,1968584,3475914,9003,9004&filter=HS,GEM2STAR&date={date_str}&order_field=330324&order_type=0&_={now_timestamp_ms()}" resp = requests.get(url, headers=_JKQA_HEADER) if resp.status_code == 200: json_result = resp.json() if json_result: return { "limit_up_count": json_result["data"]["limit_up_count"], "limit_down_count": json_result["data"]["limit_down_count"], } raise RuntimeError(f"request jkqa data code: {resp.status_code}, error: {resp.text}") def get_limit_up(date: str): date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1) url = f"https://data.10jqka.com.cn/dataapi/limit_up/limit_up_pool?field=199112,10,9001,330323,330324,330325,9002,330329,133971,133970,1968584,3475914,9003,9004&filter=HS,GEM2STAR&order_field=199112&order_type=0&date={date_str}" return get_jkqa_data(url=url) def get_limit_down(date: str): date_str = to_date_time_str(date_time=date, fmt=TIME_FORMAT_DAY1) url = f"https://data.10jqka.com.cn/dataapi/limit_up/lower_limit_pool?field=199112,10,9001,330323,330324,330325,9002,330329,133971,133970,1968584,3475914,9003,9004&filter=HS,GEM2STAR&order_field=199112&order_type=0&date={date_str}" return get_jkqa_data(url=url) def get_jkqa_data(url, pn=1, ps=200, fetch_all=True, headers=_JKQA_HEADER): requesting_url = url + f"&page={pn}&limit={ps}&_={now_timestamp_ms()}" print(requesting_url) resp = requests.get(requesting_url, headers=headers) if resp.status_code == 200: json_result = resp.json() if json_result and json_result["data"]: data: list = json_result["data"]["info"] if fetch_all: if pn < json_result["data"]["page"]["count"]: next_data = get_jkqa_data( pn=pn + 1, ps=ps, url=url, fetch_all=fetch_all, ) if next_data: data = data + next_data if pn == 1 and len(data) != json_result["data"]["page"]["total"]: raise RuntimeError( f"Assertion failed, the total length of data should be {json_result['data']['page']['total']}, only {len(data)} fetched" ) return data else: return data else: return data else: return data return None raise RuntimeError(f"request jkqa data code: {resp.status_code}, error: {resp.text}") if __name__ == "__main__": # result = get_limit_up(date="20210716") # print(result) # result = get_limit_stats(date="20210716") # print(result) # result = get_limit_down(date="20210716") # print(result) result = get_continuous_limit_up(date="20210716") print(result) # the __all__ is generated __all__ = ["get_continuous_limit_up", "get_limit_stats", "get_limit_up", "get_limit_down", "get_jkqa_data"] ================================================ FILE: src/zvt/recorders/qmt/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule index from .index import * from .index import __all__ as _index_all __all__ += _index_all # import all from submodule quotes from .quotes import * from .quotes import __all__ as _quotes_all __all__ += _quotes_all # import all from submodule meta from .meta import * from .meta import __all__ as _meta_all __all__ += _meta_all ================================================ FILE: src/zvt/recorders/qmt/index/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule qmt_index_recorder from .qmt_index_recorder import * from .qmt_index_recorder import __all__ as _qmt_index_recorder_all __all__ += _qmt_index_recorder_all ================================================ FILE: src/zvt/recorders/qmt/index/qmt_index_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.api.kdata import get_kdata_schema from zvt.broker.qmt import qmt_quote from zvt.consts import IMPORTANT_INDEX from zvt.contract import IntervalLevel from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.contract.utils import evaluate_size_from_timestamp from zvt.domain import Index, IndexKdataCommon from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import TIME_FORMAT_DAY, TIME_FORMAT_MINUTE, current_date, to_date_time_str class QmtIndexRecorder(FixedCycleDataRecorder): provider = "qmt" # class level kdata schema should always use common data_schema = IndexKdataCommon entity_provider = "em" entity_schema = Index download_history_data = False def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, return_unfinished=False, download_history_data=False, ) -> None: level = IntervalLevel(level) self.entity_type = "index" self.download_history_data = download_history_data self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=None) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) self.one_day_trading_minutes = 240 def record(self, entity, start, end, size, timestamps): if start and (self.level == IntervalLevel.LEVEL_1DAY): start = start.date() if not start: start = "2005-01-01" if not end: end = current_date() # 统一高频数据习惯，减小数据更新次数，分钟K线需要直接多读1根K线，以兼容start_timestamp=9:30, end_timestamp=15:00的情况 if self.level == IntervalLevel.LEVEL_1MIN: end += pd.Timedelta(seconds=1) df = qmt_quote.get_kdata( entity_id=entity.id, start_timestamp=start, end_timestamp=end, adjust_type=None, level=self.level, download_history=self.download_history_data, ) time_str_fmt = TIME_FORMAT_DAY if self.level == IntervalLevel.LEVEL_1DAY else TIME_FORMAT_MINUTE if pd_is_not_null(df): df["entity_id"] = entity.id df["timestamp"] = pd.to_datetime(df.index) df["id"] = df.apply( lambda row: f"{row['entity_id']}_{to_date_time_str(row['timestamp'], fmt=time_str_fmt)}", axis=1 ) df["provider"] = "qmt" df["level"] = self.level.value df["code"] = entity.code df["name"] = entity.name df.rename(columns={"amount": "turnover"}, inplace=True) df["change_pct"] = (df["close"] - df["preClose"]) / df["preClose"] df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) else: self.logger.info(f"no kdata for {entity.id}") def evaluate_start_end_size_timestamps(self, entity): if self.download_history_data and self.start_timestamp and self.end_timestamp: # 历史数据可能碎片化，允许按照实际start和end之间有没有写满数据 expected_size = evaluate_size_from_timestamp( start_timestamp=self.start_timestamp, end_timestamp=self.end_timestamp, level=self.level, one_day_trading_minutes=self.one_day_trading_minutes, ) recorded_size = ( self.session.query(self.data_schema) .filter( self.data_schema.entity_id == entity.id, self.data_schema.timestamp >= self.start_timestamp, self.data_schema.timestamp <= self.end_timestamp, ) .count() ) if expected_size != recorded_size: # print(f"expected_size: {expected_size}, recorded_size: {recorded_size}") return self.start_timestamp, self.end_timestamp, self.default_size, None start_timestamp, end_timestamp, size, timestamps = super().evaluate_start_end_size_timestamps(entity) # start_timestamp is the last updated timestamp if self.end_timestamp is not None: if start_timestamp >= self.end_timestamp: return start_timestamp, end_timestamp, 0, None else: size = evaluate_size_from_timestamp( start_timestamp=start_timestamp, level=self.level, one_day_trading_minutes=self.one_day_trading_minutes, end_timestamp=self.end_timestamp, ) return start_timestamp, self.end_timestamp, size, timestamps return start_timestamp, end_timestamp, size, timestamps # # 中证，上海 # def record_cs_index(self, index_type): # df = cs_index_api.get_cs_index(index_type=index_type) # df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) # self.logger.info(f"finish record {index_type} index") # # # 国证，深圳 # def record_cn_index(self, index_type): # if index_type == "cni": # category_map_url = cn_index_api.cni_category_map_url # elif index_type == "sz": # category_map_url = cn_index_api.sz_category_map_url # else: # self.logger.error(f"not support index_type: {index_type}") # assert False # # for category, _ in category_map_url.items(): # df = cn_index_api.get_cn_index(index_type=index_type, category=category) # df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) # self.logger.info(f"finish record {index_type} index:{category.value}") if __name__ == "__main__": # init_log('china_stock_category.log') start_timestamp = pd.Timestamp("2024-12-01") end_timestamp = pd.Timestamp("2024-12-03") QmtIndexRecorder( codes=IMPORTANT_INDEX, level=IntervalLevel.LEVEL_1MIN, sleeping_time=0, start_timestamp=start_timestamp, end_timestamp=end_timestamp, download_history_data=True, ).run() # the __all__ is generated __all__ = ["QmtIndexRecorder"] ================================================ FILE: src/zvt/recorders/qmt/meta/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule qmt_stock_meta_recorder from .qmt_stock_meta_recorder import * from .qmt_stock_meta_recorder import __all__ as _qmt_stock_meta_recorder_all __all__ += _qmt_stock_meta_recorder_all ================================================ FILE: src/zvt/recorders/qmt/meta/qmt_stock_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.broker.qmt import qmt_quote from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain import Stock class QMTStockRecorder(Recorder): provider = "qmt" data_schema = Stock def run(self): df = qmt_quote.get_entity_list() self.logger.info(df.tail()) df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=True) if __name__ == "__main__": recorder = QMTStockRecorder() recorder.run() # the __all__ is generated __all__ = ["QMTStockRecorder"] ================================================ FILE: src/zvt/recorders/qmt/quotes/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule qmt_kdata_recorder from .qmt_kdata_recorder import * from .qmt_kdata_recorder import __all__ as _qmt_kdata_recorder_all __all__ += _qmt_kdata_recorder_all ================================================ FILE: src/zvt/recorders/qmt/quotes/qmt_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.api.kdata import get_kdata_schema, get_kdata from zvt.broker.qmt import qmt_quote from zvt.contract import IntervalLevel, AdjustType from zvt.contract.api import df_to_db, get_db_session, get_entities from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import ( Stock, StockKdataCommon, ) from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import current_date, to_date_time_str, now_date_time_str class BaseQmtKdataRecorder(FixedCycleDataRecorder): default_size = 50000 entity_provider: str = "qmt" provider = "qmt" def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, adjust_type=AdjustType.qfq, return_unfinished=False, ) -> None: level = IntervalLevel(level) self.adjust_type = AdjustType(adjust_type) self.entity_type = self.entity_schema.__name__.lower() self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=self.adjust_type) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) def init_entities(self): """ init the entities which we would record data for """ if self.entity_provider == self.provider and self.entity_schema == self.data_schema: self.entity_session = self.session else: self.entity_session = get_db_session(provider=self.entity_provider, data_schema=self.entity_schema) if self.day_data: df = self.data_schema.query_data( start_timestamp=now_date_time_str(), columns=["entity_id", "timestamp"], provider=self.provider ) if pd_is_not_null(df): entity_ids = df["entity_id"].tolist() self.logger.info(f"ignore entity_ids:{entity_ids}") if self.entity_filters: self.entity_filters.append(self.entity_schema.entity_id.notin_(entity_ids)) else: self.entity_filters = [self.entity_schema.entity_id.notin_(entity_ids)] #: init the entity list self.entities = get_entities( session=self.entity_session, entity_schema=self.entity_schema, exchanges=self.exchanges, entity_ids=self.entity_ids, codes=self.codes, return_type="domain", provider=self.entity_provider, filters=self.entity_filters, ) def record(self, entity, start, end, size, timestamps): if start and (self.level == IntervalLevel.LEVEL_1DAY): start = start.date() # 判断是否需要重新计算之前保存的前复权数据 if start and (self.adjust_type == AdjustType.qfq): check_df = qmt_quote.get_kdata( entity_id=entity.id, start_timestamp=start, end_timestamp=start, adjust_type=self.adjust_type, level=self.level, download_history=False, ) if pd_is_not_null(check_df): current_df = get_kdata( entity_id=entity.id, provider=self.provider, start_timestamp=start, end_timestamp=start, limit=1, level=self.level, adjust_type=self.adjust_type, ) if pd_is_not_null(current_df): old = current_df["close"].iloc[0] new = check_df["close"].iloc[0] # 相同时间的close不同，表明前复权需要重新计算 if round(old, 2) != round(new, 2): # 删掉重新获取 self.session.query(self.data_schema).filter(self.data_schema.entity_id == entity.id).delete() start = "2005-01-01" else: self.logger.warning("前复权检查失败，无法获取存储的最新数据") if not start: start = "2005-01-01" if not end: end = current_date() df = qmt_quote.get_kdata( entity_id=entity.id, start_timestamp=start, end_timestamp=end, adjust_type=self.adjust_type, level=self.level, download_history=False, ) if pd_is_not_null(df): df["entity_id"] = entity.id df["timestamp"] = pd.to_datetime(df.index) df["id"] = df.apply(lambda row: f"{row['entity_id']}_{to_date_time_str(row['timestamp'])}", axis=1) df["provider"] = "qmt" df["level"] = self.level.value df["code"] = entity.code df["name"] = entity.name df.rename(columns={"amount": "turnover"}, inplace=True) df["change_pct"] = (df["close"] - df["preClose"]) / df["preClose"] df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) else: self.logger.info(f"no kdata for {entity.id}") class QMTStockKdataRecorder(BaseQmtKdataRecorder): entity_schema = Stock data_schema = StockKdataCommon supported_levels = [IntervalLevel.LEVEL_1DAY] supported_adjust_types = [AdjustType.qfq, AdjustType.hfq] if __name__ == "__main__": # Stock.record_data(provider="qmt") QMTStockKdataRecorder(entity_id="stock_sz_301611", adjust_type=AdjustType.qfq).run() # the __all__ is generated __all__ = ["BaseQmtKdataRecorder", "QMTStockKdataRecorder"] ================================================ FILE: src/zvt/recorders/sina/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule money_flow from .money_flow import * from .money_flow import __all__ as _money_flow_all __all__ += _money_flow_all # import all from submodule quotes from .quotes import * from .quotes import __all__ as _quotes_all __all__ += _quotes_all # import all from submodule meta from .meta import * from .meta import __all__ as _meta_all __all__ += _meta_all ================================================ FILE: src/zvt/recorders/sina/meta/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule sina_block_recorder from .sina_block_recorder import * from .sina_block_recorder import __all__ as _sina_block_recorder_all __all__ += _sina_block_recorder_all ================================================ FILE: src/zvt/recorders/sina/meta/sina_block_recorder.py ================================================ # -*- coding: utf-8 -*- import json import demjson3 import pandas as pd import requests from zvt.api.utils import china_stock_code_to_id from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder, TimeSeriesDataRecorder from zvt.domain import BlockStock, BlockCategory, Block from zvt.utils.time_utils import now_pd_timestamp class SinaBlockRecorder(Recorder): provider = "sina" data_schema = Block # 用于抓取行业/概念/地域列表 category_map_url = { BlockCategory.industry: "http://vip.stock.finance.sina.com.cn/q/view/newSinaHy.php", BlockCategory.concept: "http://money.finance.sina.com.cn/q/view/newFLJK.php?param=class" # StockCategory.area: 'http://money.finance.sina.com.cn/q/view/newFLJK.php?param=area', } def run(self): # get stock blocks from sina for category, url in self.category_map_url.items(): resp = requests.get(url) resp.encoding = "GBK" tmp_str = resp.text json_str = tmp_str[tmp_str.index("{") : tmp_str.index("}") + 1] tmp_json = json.loads(json_str) the_list = [] for code in tmp_json: name = tmp_json[code].split(",")[1] entity_id = f"block_cn_{code}" the_list.append( { "id": entity_id, "entity_id": entity_id, "entity_type": "block", "exchange": "cn", "code": code, "name": name, "category": category.value, } ) if the_list: df = pd.DataFrame.from_records(the_list) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) self.logger.info(f"finish record sina blocks:{category.value}") class SinaChinaBlockStockRecorder(TimeSeriesDataRecorder): entity_provider = "sina" entity_schema = Block provider = "sina" data_schema = BlockStock # 用于抓取行业包含的股票 category_stocks_url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData?page={}&num=5000&sort=symbol&asc=1&node={}&symbol=&_s_r_a=page" def record(self, entity, start, end, size, timestamps): for page in range(1, 5): resp = requests.get(self.category_stocks_url.format(page, entity.code)) try: if resp.text == "null" or resp.text is None: break category_jsons = demjson3.decode(resp.text) the_list = [] for category in category_jsons: stock_code = category["code"] stock_id = china_stock_code_to_id(stock_code) block_id = entity.id the_list.append( { "id": "{}_{}".format(block_id, stock_id), "entity_id": block_id, "entity_type": "block", "exchange": entity.exchange, "code": entity.code, "name": entity.name, "timestamp": now_pd_timestamp(), "stock_id": stock_id, "stock_code": stock_code, "stock_name": category["name"], } ) if the_list: df = pd.DataFrame.from_records(the_list) df_to_db(data_schema=self.data_schema, df=df, provider=self.provider, force_update=True) self.logger.info("finish recording BlockStock:{},{}".format(entity.category, entity.name)) except Exception as e: self.logger.error("error:,resp.text:", e, resp.text) self.sleep() if __name__ == "__main__": # init_log('sina_china_stock_category.log') SinaBlockRecorder().run() recorder = SinaChinaBlockStockRecorder(codes=["new_cbzz"]) recorder.run() # the __all__ is generated __all__ = ["SinaBlockRecorder", "SinaChinaBlockStockRecorder"] ================================================ FILE: src/zvt/recorders/sina/money_flow/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule sina_stock_money_flow_recorder from .sina_stock_money_flow_recorder import * from .sina_stock_money_flow_recorder import __all__ as _sina_stock_money_flow_recorder_all __all__ += _sina_stock_money_flow_recorder_all # import all from submodule sina_block_money_flow_recorder from .sina_block_money_flow_recorder import * from .sina_block_money_flow_recorder import __all__ as _sina_block_money_flow_recorder_all __all__ += _sina_block_money_flow_recorder_all ================================================ FILE: src/zvt/recorders/sina/money_flow/sina_block_money_flow_recorder.py ================================================ # -*- coding: utf-8 -*- import time import requests from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import BlockMoneyFlow, BlockCategory, Block from zvt.utils.time_utils import to_pd_timestamp from zvt.utils.utils import to_float # 实时资金流 # 'http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_bkzj_bk?page=1&num=20&sort=netamount&asc=0&fenlei=1' # 'http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_bkzj_bk?page=1&num=20&sort=netamount&asc=0&fenlei=0' class SinaBlockMoneyFlowRecorder(FixedCycleDataRecorder): # entity的信息从哪里来 entity_provider = "sina" # entity的schema entity_schema = Block # 记录的信息从哪里来 provider = "sina" # 记录的schema data_schema = BlockMoneyFlow url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_bkzj_zjlrqs?page=1&num={}&sort=opendate&asc=0&bankuai={}%2F{}" def generate_url(self, category, code, number): if category == BlockCategory.industry.value: block = 0 elif category == BlockCategory.concept.value: block = 1 return self.url.format(number, block, code) def get_data_map(self): return {} def record(self, entity, start, end, size, timestamps): url = self.generate_url(category=entity.category, code=entity.code, number=size) resp = requests.get(url) opendate = "opendate" avg_price = "avg_price" avg_changeratio = "avg_changeratio" turnover = "turnover" netamount = "netamount" ratioamount = "ratioamount" r0_net = "r0_net" r0_ratio = "r0_ratio" r0x_ratio = "r0x_ratio" cnt_r0x_ratio = "cnt_r0x_ratio" json_list = [] try: json_list = eval(resp.text) except Exception as e: resp.encoding = "GBK" self.logger.error(resp.text) time.sleep(60 * 5) result_list = [] for item in json_list: result_list.append( { "name": entity.name, "timestamp": to_pd_timestamp(item["opendate"]), "close": to_float(item["avg_price"]), "change_pct": to_float(item["avg_changeratio"]), "turnover_rate": to_float(item["turnover"]) / 10000, "net_inflows": to_float(item["netamount"]), "net_inflow_rate": to_float(item["ratioamount"]), "net_main_inflows": to_float(item["r0_net"]), "net_main_inflow_rate": to_float(item["r0_ratio"]), } ) return result_list if __name__ == "__main__": SinaBlockMoneyFlowRecorder(codes=["new_fjzz"]).run() # SinaIndexMoneyFlowRecorder().run() # the __all__ is generated __all__ = ["SinaBlockMoneyFlowRecorder"] ================================================ FILE: src/zvt/recorders/sina/money_flow/sina_stock_money_flow_recorder.py ================================================ # -*- coding: utf-8 -*- import time import requests from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import StockMoneyFlow, Stock, StockTradeDay from zvt.utils.time_utils import to_pd_timestamp, is_same_date, now_pd_timestamp from zvt.utils.utils import to_float class SinaStockMoneyFlowRecorder(FixedCycleDataRecorder): entity_provider = "joinquant" entity_schema = Stock provider = "sina" data_schema = StockMoneyFlow url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/MoneyFlow.ssl_qsfx_lscjfb?page=1&num={}&sort=opendate&asc=0&daima={}" def init_entities(self): super().init_entities() # 过滤掉退市的 self.entities = [ entity for entity in self.entities if (entity.end_date is None) or (entity.end_date > now_pd_timestamp()) ] # TODO:more general for the case using StockTradeDay def evaluate_start_end_size_timestamps(self, entity): start, end, size, timestamps = super().evaluate_start_end_size_timestamps(entity) if start: trade_day = StockTradeDay.query_data(limit=1, order=StockTradeDay.timestamp.desc(), return_type="domain") if trade_day: if is_same_date(trade_day[0].timestamp, start): size = 0 return start, end, size, timestamps def generate_url(self, code, number): return self.url.format(number, code) def get_data_map(self): return {} def record(self, entity, start, end, size, timestamps): param = { "url": self.generate_url(code="{}{}".format(entity.exchange, entity.code), number=size), "security_item": entity, } resp = requests.get(param["url"]) # {opendate:"2019-04-29",trade:"10.8700",changeratio:"-0.0431338",turnover:"74.924",netamount:"-2903349.8500", # ratioamount:"-0.155177",r0:"0.0000",r1:"2064153.0000",r2:"6485031.0000",r3:"10622169.2100",r0_net:"0.0000", # r1_net:"2064153.0000",r2_net:"-1463770.0000",r3_net:"-3503732.8500"} opendate = "opendate" trade = "trade" changeratio = "changeratio" turnover = "turnover" netamount = "netamount" ratioamount = "ratioamount" r0 = "r0" r1 = "r1" r2 = "r2" r3 = "r3" r0_net = "r0_net" r1_net = "r1_net" r2_net = "r2_net" r3_net = "r3_net" json_list = [] try: json_list = eval(resp.text) except Exception as e: resp.encoding = "GBK" self.logger.error(resp.text) time.sleep(60 * 5) result_list = [] for item in json_list: amount = to_float(item["r0"]) + to_float(item["r1"]) + to_float(item["r2"]) + to_float(item["r3"]) result = { "timestamp": to_pd_timestamp(item["opendate"]), "name": entity.name, "close": to_float(item["trade"]), "change_pct": to_float(item["changeratio"]), "turnover_rate": to_float(item["turnover"]) / 10000, "net_inflows": to_float(item["netamount"]), "net_inflow_rate": to_float(item["ratioamount"]), # # 主力=超大单+大单 # net_main_inflows = Column(Float) # net_main_inflow_rate = Column(Float) # # 超大单 # net_huge_inflows = Column(Float) # net_huge_inflow_rate = Column(Float) # # 大单 # net_big_inflows = Column(Float) # net_big_inflow_rate = Column(Float) # # # 中单 # net_medium_inflows = Column(Float) # net_medium_inflow_rate = Column(Float) # # 小单 # net_small_inflows = Column(Float) # net_small_inflow_rate = Column(Float) "net_main_inflows": to_float(item["r0_net"]) + to_float(item["r1_net"]), "net_huge_inflows": to_float(item["r0_net"]), "net_big_inflows": to_float(item["r1_net"]), "net_medium_inflows": to_float(item["r2_net"]), "net_small_inflows": to_float(item["r3_net"]), } if amount != 0: result["net_main_inflow_rate"] = (to_float(item["r0_net"]) + to_float(item["r1_net"])) / amount result["net_huge_inflow_rate"] = to_float(item["r0_net"]) / amount result["net_big_inflow_rate"] = to_float(item["r1_net"]) / amount result["net_medium_inflow_rate"] = to_float(item["r2_net"]) / amount result["net_small_inflow_rate"] = to_float(item["r3_net"]) / amount result_list.append(result) return result_list if __name__ == "__main__": SinaStockMoneyFlowRecorder(codes=["000406"]).run() # SinaStockMoneyFlowRecorder().run() # the __all__ is generated __all__ = ["SinaStockMoneyFlowRecorder"] ================================================ FILE: src/zvt/recorders/sina/quotes/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule sina_index_kdata_recorder from .sina_index_kdata_recorder import * from .sina_index_kdata_recorder import __all__ as _sina_index_kdata_recorder_all __all__ += _sina_index_kdata_recorder_all # import all from submodule sina_etf_kdata_recorder from .sina_etf_kdata_recorder import * from .sina_etf_kdata_recorder import __all__ as _sina_etf_kdata_recorder_all __all__ += _sina_etf_kdata_recorder_all ================================================ FILE: src/zvt/recorders/sina/quotes/sina_etf_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- import demjson3 import pandas as pd import requests from zvt import init_log from zvt.api.kdata import generate_kdata_id, get_kdata from zvt.contract import IntervalLevel from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Etf, Etf1dKdata from zvt.recorders.consts import EASTMONEY_ETF_NET_VALUE_HEADER from zvt.utils.time_utils import to_date_time_str class ChinaETFDayKdataRecorder(FixedCycleDataRecorder): entity_provider = "exchange" entity_schema = Etf provider = "sina" data_schema = Etf1dKdata url = ( "http://money.finance.sina.com.cn/quotes_service/api/json_v2.php/CN_MarketData.getKLineData?" "symbol={}{}&scale=240&&datalen={}&ma=no" ) def get_data_map(self): return {} def generate_domain_id(self, entity, original_data): return generate_kdata_id(entity_id=entity.id, timestamp=original_data["timestamp"], level=self.level) def on_finish_entity(self, entity): kdatas = get_kdata( entity_id=entity.id, level=IntervalLevel.LEVEL_1DAY.value, order=Etf1dKdata.timestamp.asc(), return_type="domain", session=self.session, filters=[Etf1dKdata.cumulative_net_value.is_(None)], ) if kdatas and len(kdatas) > 0: start = kdatas[0].timestamp end = kdatas[-1].timestamp # 从东方财富获取基金累计净值 df = self.fetch_cumulative_net_value(entity, start, end) if df is not None and not df.empty: for kdata in kdatas: if kdata.timestamp in df.index: kdata.cumulative_net_value = df.loc[kdata.timestamp, "LJJZ"] kdata.change_pct = df.loc[kdata.timestamp, "JZZZL"] self.session.commit() self.logger.info(f"{entity.code} - {entity.name}累计净值更新完成...") def fetch_cumulative_net_value(self, security_item, start, end) -> pd.DataFrame: query_url = ( "http://api.fund.eastmoney.com/f10/lsjz?" "fundCode={}&pageIndex={}&pageSize=200&startDate={}&endDate={}" ) page = 1 df = pd.DataFrame() while True: url = query_url.format(security_item.code, page, to_date_time_str(start), to_date_time_str(end)) response = requests.get(url, headers=EASTMONEY_ETF_NET_VALUE_HEADER) response_json = demjson3.decode(response.text) response_df = pd.DataFrame(response_json["Data"]["LSJZList"]) # 最后一页 if response_df.empty: break response_df["FSRQ"] = pd.to_datetime(response_df["FSRQ"]) response_df["JZZZL"] = pd.to_numeric(response_df["JZZZL"], errors="coerce") response_df["LJJZ"] = pd.to_numeric(response_df["LJJZ"], errors="coerce") response_df = response_df.fillna(0) response_df.set_index("FSRQ", inplace=True, drop=True) df = pd.concat([df, response_df]) page += 1 self.sleep() return df def record(self, entity, start, end, size, timestamps): # 此 url 不支持分页，如果超过我们想取的条数，则只能取最大条数 if start is None or size > self.default_size: size = 8000 param = {"security_item": entity, "level": self.level.value, "size": size} security_item = param["security_item"] size = param["size"] url = ChinaETFDayKdataRecorder.url.format(security_item.exchange, security_item.code, size) response = requests.get(url) response_json = demjson3.decode(response.text) if response_json is None or len(response_json) == 0: return [] df = pd.DataFrame(response_json) df.rename(columns={"day": "timestamp"}, inplace=True) df["timestamp"] = pd.to_datetime(df["timestamp"]) df["name"] = security_item.name df["provider"] = "sina" df["level"] = param["level"] return df.to_dict(orient="records") __all__ = ["ChinaETFDayKdataRecorder"] if __name__ == "__main__": init_log("sina_china_etf_day_kdata.log") ChinaETFDayKdataRecorder(level=IntervalLevel.LEVEL_1DAY).run() # the __all__ is generated __all__ = ["ChinaETFDayKdataRecorder"] ================================================ FILE: src/zvt/recorders/sina/quotes/sina_index_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- import time import pandas as pd import requests from zvt.api.kdata import generate_kdata_id, get_kdata_schema from zvt.contract import IntervalLevel, AdjustType from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Index, IndexKdataCommon from zvt.utils.time_utils import get_year_quarters, is_same_date class ChinaIndexDayKdataRecorder(FixedCycleDataRecorder): entity_provider = "exchange" entity_schema = Index provider = "sina" data_schema = IndexKdataCommon url = "http://vip.stock.finance.sina.com.cn/corp/go.php/vMS_MarketHistory/stockid/{}/type/S.phtml?year={}&jidu={}" def __init__( self, force_update=True, sleeping_time=10, exchanges=None, entity_id=None, entity_ids=None, code=None, codes=None, day_data=False, entity_filters=None, ignore_failed=True, real_time=False, fix_duplicate_way="ignore", start_timestamp=None, end_timestamp=None, level=IntervalLevel.LEVEL_1DAY, kdata_use_begin_time=False, one_day_trading_minutes=24 * 60, return_unfinished=False, ) -> None: level = IntervalLevel(level) self.adjust_type = AdjustType.qfq self.entity_type = self.entity_schema.__name__.lower() self.data_schema = get_kdata_schema(entity_type=self.entity_type, level=level, adjust_type=self.adjust_type) super().__init__( force_update, sleeping_time, exchanges, entity_id, entity_ids, code, codes, day_data, entity_filters, ignore_failed, real_time, fix_duplicate_way, start_timestamp, end_timestamp, level, kdata_use_begin_time, one_day_trading_minutes, return_unfinished, ) def get_data_map(self): return {} def generate_domain_id(self, entity, original_data): return generate_kdata_id(entity.id, timestamp=original_data["timestamp"], level=self.level) def record(self, entity, start, end, size, timestamps): the_quarters = get_year_quarters(start) if not is_same_date(entity.timestamp, start) and len(the_quarters) > 1: the_quarters = the_quarters[1:] param = {"security_item": entity, "quarters": the_quarters, "level": self.level.value} security_item = param["security_item"] quarters = param["quarters"] level = param["level"] result_df = pd.DataFrame() for year, quarter in quarters: query_url = self.url.format(security_item.code, year, quarter) response = requests.get(query_url) response.encoding = "gbk" try: dfs = pd.read_html(response.text) except ValueError as error: self.logger.error(f"skip ({year}-{quarter:02d}){security_item.code}{security_item.name}({error})") time.sleep(10.0) continue if len(dfs) < 5: time.sleep(10.0) continue df = dfs[4].copy() df = df.iloc[1:] df.columns = ["timestamp", "open", "high", "close", "low", "volume", "turnover"] df["name"] = security_item.name df["level"] = level df["timestamp"] = pd.to_datetime(df["timestamp"]) df["provider"] = "sina" result_df = pd.concat([result_df, df]) self.logger.info(f"({security_item.code}{security_item.name})({year}-{quarter:02d})") time.sleep(10.0) result_df = result_df.sort_values(by="timestamp") return result_df.to_dict(orient="records") __all__ = ["ChinaIndexDayKdataRecorder"] if __name__ == "__main__": ChinaIndexDayKdataRecorder().run() # the __all__ is generated __all__ = ["ChinaIndexDayKdataRecorder"] ================================================ FILE: src/zvt/recorders/wb/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule wb_economy_recorder from .wb_economy_recorder import * from .wb_economy_recorder import __all__ as _wb_economy_recorder_all __all__ += _wb_economy_recorder_all # import all from submodule wb_country_recorder from .wb_country_recorder import * from .wb_country_recorder import __all__ as _wb_country_recorder_all __all__ += _wb_country_recorder_all # import all from submodule wb_api from .wb_api import * from .wb_api import __all__ as _wb_api_all __all__ += _wb_api_all ================================================ FILE: src/zvt/recorders/wb/wb_api.py ================================================ # -*- coding: utf-8 -*- import itertools import re from copy import copy import pandas as pd import requests from zvt.contract.api import get_entity_code from zvt.utils.pd_utils import normal_index_df from zvt.utils.time_utils import to_pd_timestamp WORLD_BANK_URL = "http://api.worldbank.org/v2" # thanks to https://github.com/mwouts/world_bank_data _economy_indicator_map = { "population": "SP.POP.TOTL", "gdp": "NY.GDP.MKTP.CD", "gdp_per_capita": "NY.GDP.PCAP.CD", "gdp_per_employed": "SL.GDP.PCAP.EM.KD", "gdp_growth": "NY.GDP.MKTP.KD.ZG", "agriculture_growth": "NV.AGR.TOTL.KD.ZG", "industry_growth": "NV.IND.TOTL.KD.ZG", "manufacturing_growth": "NV.IND.MANF.KD.ZG", "service_growth": "NV.SRV.TOTL.KD.ZG", "consumption_growth": "NE.CON.TOTL.KD.ZG", "capital_growth": "NE.GDI.TOTL.KD.ZG", "exports_growth": "NE.EXP.GNFS.KD.ZG", "imports_growth": "NE.IMP.GNFS.KD.ZG", "gni": "NY.GNP.ATLS.CD", "gni_per_capita": "NY.GNP.PCAP.CD", "gross_saving": "NY.GNS.ICTR.ZS", "cpi": "FP.CPI.TOTL", "unemployment_rate": "SL.UEM.TOTL.ZS", "fdi_of_gdp": "BX.KLT.DINV.WD.GD.ZS", } def _collapse(values): """Collapse multiple values to a colon-separated list of values""" if isinstance(values, str): return values if values is None: return "all" if isinstance(values, list): return ";".join([_collapse(v) for v in values]) return str(values) def _extract_preferred_field(data, id_or_value): """In case the preferred representation of data when the latter has multiple representations""" if not id_or_value: return data if not data: return "" if isinstance(data, dict): if id_or_value in data: return data[id_or_value] if isinstance(data, list): return ",".join([_extract_preferred_field(i, id_or_value) for i in data]) return data def _wb_get(paths: dict = None, **kwargs): params = copy(kwargs) params.setdefault("format", "json") params.setdefault("per_page", 20000) url = "/".join([WORLD_BANK_URL] + list(itertools.chain.from_iterable([(k, _collapse(paths[k])) for k in paths]))) response = requests.get(url=url, params=params) response.raise_for_status() try: data = response.json() except ValueError: raise ValueError( "{msg}\nurl={url}\nparams={params}".format(msg=_extract_message(response.text), url=url, params=params) ) if isinstance(data, list) and data and "message" in data[0]: try: msg = data[0]["message"][0]["value"] except (KeyError, IndexError): msg = str(msg) raise ValueError("{msg}\nurl={url}\nparams={params}".format(msg=msg, url=url, params=params)) # Redo the request and get the full information when the first response is incomplete if isinstance(data, list): page_information, data = data if "page" not in params: current_page = 1 while current_page < int(page_information["pages"]): params["page"] = current_page = int(page_information["page"]) + 1 response = requests.get(url=url, params=params) response.raise_for_status() page_information, new_data = response.json() data.extend(new_data) if not data: raise RuntimeError("The request returned no data:\nurl={url}\nparams={params}".format(url=url, params=params)) return data def _extract_message(msg): """' The indicator was not found. It may have been deleted or archived. '""" if "wb:message" not in msg: return msg return re.sub( re.compile(".*]*>", re.DOTALL), "", re.sub(re.compile(".*", re.DOTALL), "", msg) ) def _get_meta(name, filters=None, expected=None, **params): """Request data and return it in the form of a data frame""" filters = _collapse(filters) id_or_value = "value" if expected and id_or_value not in expected: raise ValueError("'id_or_value' should be one of '{}'".format("', '".join(expected))) data = _wb_get(paths={name: filters}, **params) # We get a list (countries) of dictionary (properties) columns = data[0].keys() records = {} for col in columns: records[col] = [_extract_preferred_field(cnt[col], id_or_value) for cnt in data] return pd.DataFrame(records, columns=columns) def get_countries(): df = _get_meta("country", expected=["id", "iso2code", "value"]) for col in ["latitude", "longitude"]: df[col] = pd.to_numeric(df[col]) df.rename( columns={ "iso2Code": "code", "incomeLevel": "income_level", "lendingType": "lending_type", "capitalCity": "capital_city", }, inplace=True, ) df["entity_type"] = "country" df["exchange"] = "galaxy" df["entity_id"] = df[["entity_type", "exchange", "code"]].apply(lambda x: "_".join(x.astype(str)), axis=1) df["id"] = df["entity_id"] return df def get_indicators(indicator=None, language=None, id_or_value=None, **params): """Return a DataFrame that describes one, multiple or all indicators, indexed by the indicator id. :param indicator: None (all indicators), the id of an indicator, or a list of multiple ids :param language: Desired language :param id_or_value: Choose either 'id' or 'value' for columns 'source' and 'topics'""" if id_or_value == "iso2code": id_or_value = "id" return _get_meta( "indicator", indicator, language=language, id_or_value=id_or_value, expected=["id", "value"], **params ) def get_indicator_data(indicator, indicator_name=None, country=None, date=None): datas = _wb_get(paths={"country": country, "indicator": indicator}, date=date) records = [ { "code": item["country"]["id"], "timestamp": to_pd_timestamp(item["date"]), item["indicator"]["id"] if not indicator_name else indicator_name: item["value"], } for item in datas ] df = pd.DataFrame.from_records(data=records) df = df.set_index(["code", "timestamp"]) return df def get_regions(region=None, language=None, **params): """Return a DataFrame that describes one, multiple or all regions, indexed by the region id. :param region: None (all regions), the id of a region, or a list of multiple ids :param language: Desired language""" return _get_meta("region", region, language, **params) def get_sources(source=None, language=None, **params): """Return a DataFrame that describes one, multiple or all sources, indexed by the source id. :param source: None (all sources), the id of a source, or a list of multiple ids :param language: Desired language""" return _get_meta("source", source, language, **params) def get_topics(topic=None, language=None, **params): """Return a DataFrame that describes one, multiple or all sources, indexed by the source id. :param topic: None (all topics), the id of a topic, or a list of multiple ids :param language: Desired language""" return _get_meta("topic", topic, language, **params) def get_incomelevels(incomelevel=None, language=None, **params): """Return a DataFrame that describes one, multiple or all income levels, indexed by the IL id. :param incomelevel: None (all income levels), the id of an income level, or a list of multiple ids :param language: Desired language""" return _get_meta("incomelevel", incomelevel, language, **params) def get_lendingtypes(lendingtype=None, language=None, **params): """Return a DataFrame that describes one, multiple or all lending types, indexed by the LT id. :param lendingtype: None (all lending types), the id of a lending type, or a list of multiple ids :param language: Desired language""" return _get_meta("lendingtype", lendingtype, language, **params) def get_economy_data(entity_id, indicators=None, date=None): country = get_entity_code(entity_id=entity_id) if not indicators: indicators = _economy_indicator_map.keys() dfs = [] for indicator in indicators: data = get_indicator_data( indicator=_economy_indicator_map.get(indicator), indicator_name=indicator, country=country, date=date ) dfs.append(data) df = pd.concat(dfs, axis=1) df = df.reset_index(drop=False) df["entity_id"] = entity_id df["id"] = df[["entity_id", "timestamp"]].apply(lambda x: "_".join(x.astype(str)), axis=1) df = normal_index_df(df, drop=False) return df if __name__ == "__main__": # df = get_countries() # print(df) df = get_economy_data(entity_id="country_galaxy_CN") print(df) # df = get_sources() # print(df) # the __all__ is generated __all__ = [ "get_countries", "get_indicators", "get_indicator_data", "get_regions", "get_sources", "get_topics", "get_incomelevels", "get_lendingtypes", "get_economy_data", ] ================================================ FILE: src/zvt/recorders/wb/wb_country_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import Recorder from zvt.domain.meta.country_meta import Country from zvt.recorders.wb import wb_api class WBCountryRecorder(Recorder): provider = "wb" data_schema = Country def run(self): df = wb_api.get_countries() df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) if __name__ == "__main__": recorder = WBCountryRecorder() recorder.run() # the __all__ is generated __all__ = ["WBCountryRecorder"] ================================================ FILE: src/zvt/recorders/wb/wb_economy_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import df_to_db from zvt.contract.recorder import FixedCycleDataRecorder from zvt.domain import Country, Economy from zvt.recorders.wb import wb_api from zvt.utils.time_utils import current_date class WBEconomyRecorder(FixedCycleDataRecorder): entity_schema = Country data_schema = Economy entity_provider = "wb" provider = "wb" def record(self, entity, start, end, size, timestamps): date = None if start: date = f"{start.year}:{current_date().year}" try: df = wb_api.get_economy_data(entity_id=entity.id, date=date) df["name"] = entity.name df_to_db(df=df, data_schema=self.data_schema, provider=self.provider, force_update=self.force_update) # 一些地方获取不到数据会报错 except Exception as e: self.logger.warning(f"Failed to get {entity.name} economy data", e) if __name__ == "__main__": entity_ids = ["country_galaxy_CN", "country_galaxy_US"] r = WBEconomyRecorder(entity_ids=entity_ids) r.run() # the __all__ is generated __all__ = ["WBEconomyRecorder"] ================================================ FILE: src/zvt/resources/concept_main_tag_mapping.json ================================================ { "广电": "文化传媒", "数字阅读": "文化传媒", "网络游戏": "文化传媒", "手游概念": "文化传媒", "影视概念": "文化传媒", "稀土永磁": "资源", "可燃冰": "资源", "油价相关": "资源", "低碳冶金": "资源", "基本金属": "资源", "黄金概念": "资源", "天然气": "资源", "稀缺资源": "资源", "页岩气": "资源", "煤化工": "资源", "油气设服": "资源", "小金属概念": "资源", "新能源": "新能源", "BC电池": "新能源", "TOPCon电池": "新能源", "钒电池": "新能源", "钙钛矿电池": "新能源", "麒麟电池": "新能源", "动力电池回收": "新能源", "钠离子电池": "新能源", "固态电池": "新能源", "刀片电池": "新能源", "HIT电池": "新能源", "燃料电池": "新能源", "锂电池": "新能源", "储能": "新能源", "充电桩": "新能源", "熔盐储能": "新能源", "换电概念": "新能源", "盐湖提锂": "新能源", "风能": "新能源", "太阳能": "新能源", "氢能源": "新能源", "抽水蓄能": "新能源", "光伏建筑一体化": "新能源", "人造太阳": "新能源", "可控核聚变": "新能源", "核能核电": "电力", "绿色电力": "电力", "特高压": "电力", "虚拟电厂": "电力", "智能电网": "电力", "超超临界发电": "电力", "半导体概念": "半导体", "中芯概念": "半导体", "存储芯片": "半导体", "AI芯片": "半导体", "汽车芯片": "半导体", "国产芯片": "半导体", "第三代半导体": "半导体", "第四代半导体": "半导体", "英伟达概念": "半导体", "玻璃基板": "半导体", "高带宽内存": "半导体", "Chiplet概念": "半导体", "光刻胶": "半导体", "氮化镓": "半导体", "EDA概念": "半导体", "IGBT概念": "半导体", "PCB": "半导体", "新能源车": "车路云", "华为汽车": "车路云", "汽车拆解": "车路云", "汽车一体化压铸": "车路云", "小米汽车": "车路云", "汽车热管理": "车路云", "电子后视镜": "车路云", "高压快充": "车路云", "车联网": "车路云", "激光雷达": "车路云", "特斯拉": "车路云", "EDR概念": "车路云", "无人驾驶": "车路云", "ETC": "车路云", "物联网": "智能机器", "2025规划": "智能机器", "智能机器": "智能机器", "工业互联": "智能机器", "轮毂电机": "智能机器", "发电机概念": "智能机器", "同步磁阻电机": "智能机器", "机器人执行器": "智能机器", "新型工业化": "智能机器", "工业母机": "智能机器", "工业4.0": "智能机器", "减速器": "智能机器", "机器人概念": "智能机器", "PLC概念": "智能机器", "机器视觉": "智能机器", "生物医药": "医药", "痘病毒防治": "医药", "地塞米松": "医药", "消毒剂": "医药", "口罩": "医药", "肝素概念": "医药", "健康中国": "医药", "幽门螺杆菌概念": "医药", "代糖概念": "医药", "医疗器械概念": "医药", "生物疫苗": "医药", "维生素": "医药", "注射器概念": "医药", "流感": "医药", "AI制药": "医药", "中药概念": "医药", "减肥药": "医药", "创新药": "医药", "新冠药物": "医药", "长寿药": "医药", "独家药品": "医药", "病毒防治": "医药", "SPD概念": "医药", "辅助生殖": "医药", "肝炎概念": "医药", "蒙脱石散": "医药", "血氧仪": "医药", "熊去氧胆酸": "医药", "抗原检测": "医药", "抗菌面料": "医药", "千金藤素": "医药", "DRG/DIP": "医药", "CRO": "医药", "阿兹海默": "医药", "CAR-T细胞疗法": "医药", "新冠检测": "医药", "青蒿素": "医药", "超级真菌": "医药", "气溶胶检测": "医药", "重组蛋白": "医药", "疫苗冷链": "医药", "精准医疗": "医药", "单抗概念": "医药", "免疫治疗": "医药", "基因测序": "医药", "体外诊断": "医药", "互联医疗": "医药", "人脑工程": "医药", "啤酒概念": "大消费", "进口博览": "大消费", "退税商店": "大消费", "拼多多概念": "大消费", "抖音小店": "大消费", "乳业": "大消费", "C2M概念": "大消费", "调味品概念": "大消费", "毛发医疗": "大消费", "化妆品概念": "大消费", "白酒": "大消费", "医疗美容": "大消费", "户外露营": "大消费", "在线旅游": "大消费", "跨境电商": "大消费", "电商概念": "大消费", "新零售": "大消费", "智能家居": "大消费", "网红直播": "大消费", "免税概念": "大消费", "预制菜概念": "大消费", "培育钻石": "大消费", "婴童概念": "大消费", "托育服务": "大消费", "智慧灯杆": "消费电子", "UWB概念": "消费电子", "电子纸概念": "消费电子", "胎压监测": "消费电子", "3D玻璃": "消费电子", "屏下摄像": "消费电子", "超清视频": "消费电子", "植物照明": "消费电子", "LED": "消费电子", "3D摄像头": "消费电子", "eSIM": "消费电子", "蓝宝石": "消费电子", "无线耳机": "消费电子", "智能穿戴": "消费电子", "AI手机": "消费电子", "AIPC": "消费电子", "柔性屏(折叠屏)": "消费电子", "星闪概念": "消费电子", "传感器": "消费电子", "被动元件": "消费电子", "小米概念": "消费电子", "无线充电": "消费电子", "智能电视": "消费电子", "空间计算": "消费电子", "裸眼3D": "消费电子", "混合现实": "消费电子", "增强现实": "消费电子", "虚拟现实": "消费电子", "MicroLED": "消费电子", "MiniLED": "消费电子", "OLED": "消费电子", "VPN": "消费电子", "IPv6": "消费电子", "WiFi": "消费电子", "毫米波概念": "消费电子", "5G概念": "消费电子", "6G概念": "消费电子", "F5G概念": "消费电子", "量子通信": "消费电子", "化工原料": "化工", "环氧丙烷": "化工", "PVDF概念": "化工", "新材料": "化工", "MLCC": "化工", "碳纤维": "化工", "PEEK材料概念": "化工", "磷化工": "化工", "碳基材料": "化工", "纳米银": "化工", "碳化硅": "化工", "复合集流体": "化工", "有机硅": "化工", "石墨烯": "化工", "氟化工": "化工", "草甘膦": "化工", "钛白粉": "化工", "降解塑料": "化工", "工业气体": "化工", "氦气概念": "化工", "超级电容": "化工", "军民融合": "军工", "海工装备": "军工", "军工": "军工", "航母概念": "军工", "国家安防": "军工", "空间站概念": "军工", "大飞机": "军工", "铜缆高速连接": "AI", "ERP概念": "AI", "数字哨兵": "AI", "电子身份证": "AI", "电子车牌": "AI", "大数据": "AI", "智慧城市": "AI", "云计算": "AI", "国产软件": "AI", "生物识别": "AI", "RCS概念": "AI", "远程办公": "AI", "在线教育": "AI", "百度概念": "AI", "人工智能": "AI", "液冷概念": "AI", "光通信模块": "AI", "CPO概念": "AI", "AI语料": "AI", "Kimi概念": "AI", "Sora概念": "AI", "短剧互动游戏": "AI", "多模态AI": "AI", "数据要素": "AI", "算力概念": "AI", "MLOps概念": "AI", "ChatGPT概念": "AI", "AIGC概念": "AI", "数据确权": "AI", "Web3.0": "AI", "虚拟数字人": "AI", "数字水印": "AI", "数据安全": "AI", "云游戏": "AI", "数字孪生": "AI", "边缘计算": "AI", "数据中心": "AI", "华为概念": "AI", "鸿蒙概念": "AI", "华为欧拉": "AI", "华为昇腾": "AI", "国资云概念": "AI", "东数西算": "AI", "网络安全": "AI", "元宇宙概念": "AI", "NFT概念": "AI", "信创": "AI", "数字经济": "AI", "区块链": "AI", "智慧政务": "AI", "数字货币": "AI", "电子竞技": "AI", "知识产权": "AI", "时空大数据": "AI", "低空经济": "低空经济", "飞行汽车(eVTOL)": "低空经济", "无人机": "低空经济", "建筑节能": "房地产", "REITs概念": "房地产", "租售同权": "房地产", "铁路基建": "房地产", "PPP模式": "房地产", "工程机械概念": "房地产", "新型城镇化": "房地产", "装配建筑": "房地产", "地下管网": "房地产", "民爆概念": "房地产", "参股期货": "金融", "参股券商": "金融", "参股保险": "金融", "跨境支付": "金融", "互联金融": "金融", "券商概念": "金融", "移动支付": "金融", "参股银行": "金融", "粮食概念": "农业", "水产养殖": "农业", "生态农业": "农业", "蝗虫防治": "农业", "农业种植": "农业", "鸡肉概念": "农业", "转基因": "农业", "人造肉": "农业", "食品安全": "农业", "猪肉概念": "农业", "生物质能发电": "公用", "噪声防治": "公用", "土壤修复": "公用", "地热能": "公用", "海绵城市": "公用", "节能环保": "公用", "尾气治理": "公用", "职业教育": "公用", "医废处理": "公用", "快递概念": "物流", "RCEP概念": "物流", "央企改革": "国企", "中特估": "国企", "中字头": "国企", "沪企改革": "国企", "国企改革": "国企", "世界杯": "其他", "东盟自贸区概念": "其他", "娃哈哈概念": "其他", "空气能热泵": "其他", "核酸采样亭": "其他", "中俄贸易概念": "其他", "净水概念": "其他", "京津冀": "其他", "低价股": "其他", "商汤概念": "其他", "粤港自贸": "其他", "土地流转": "其他", "壳资源": "其他", "盲盒经济": "其他", "内贸流通": "其他", "京东金融": "其他", "乡村振兴": "其他", "东北振兴": "其他", "社区团购": "其他", "地摊经济": "其他", "快手概念": "其他", "蚂蚁概念": "其他", "证金持股": "其他", "养老概念": "其他", "冷链物流": "其他", "贬值受益": "其他", "纾困概念": "其他", "阿里概念": "其他", "深圳特区": "其他", "超级品牌": "其他", "中超概念": "其他", "养老金": "其他", "专精特新": "其他", "统一大市场": "其他", "光伏高速公路": "其他", "核污染防治": "其他", "磁悬浮概念": "其他", "垃圾分类": "其他", "电子烟": "其他", "工业大麻": "其他", "全息技术": "其他", "超导概念": "其他", "北交所概念": "其他", "赛马概念": "其他", "体育产业": "其他", "雄安新区": "其他", "共享经济": "其他", "彩票概念": "其他", "苹果概念": "其他", "供销社概念": "其他", "水利建设": "其他", "3D打印": "其他", "创投": "其他", "字节概念": "其他", "海洋经济": "其他", "上海自贸": "其他", "一带一路": "其他", "碳交易": "其他", "宠物经济": "其他", "商业航天": "商业航天", "航天概念": "商业航天", "天基互联": "商业航天", "北斗导航": "商业航天", "通用航空": "商业航天" } ================================================ FILE: src/zvt/resources/hk_industry_main_tag_mapping.json ================================================ { "地产": "房地产", "建筑": "基建", "工业工程": "基建", "其他金融": "金融", "药品及生物科技": "医药", "医疗保健设备和服务": "医药", "软件服务": "AI", "电讯": "AI", "专业零售": "大消费", "旅游及消闲设施": "大消费", "食物饮品": "大消费", "纺织及服饰": "大消费", "消费者主要零售商": "大消费", "媒体及娱乐": "文化传媒", "家庭电器及用品": "消费电子", "资讯科技器材": "消费电子", "工用运输": "物流", "一般金属及矿石": "资源", "原材料": "资源", "汽车": "车路云", "农业产品": "农业", "煤炭": "资源", "黄金及贵金属": "资源", "石油及天然气": "资源", "工用支援": "其他", "综合企业": "其他", "支援服务": "其他" } ================================================ FILE: src/zvt/resources/industry_main_tag_mapping.json ================================================ { "风电设备": "新能源", "电池": "新能源", "光伏设备": "新能源", "能源金属": "新能源", "电源设备": "新能源", "半导体": "半导体", "电子化学品": "半导体", "医疗服务": "医药", "中药": "医药", "化学制药": "医药", "生物制品": "医药", "医药商业": "医药", "医疗器械": "医药", "贸易行业": "大消费", "家用轻工": "大消费", "造纸印刷": "大消费", "酿酒行业": "大消费", "珠宝首饰": "大消费", "美容护理": "大消费", "食品饮料": "大消费", "旅游酒店": "大消费", "商业百货": "大消费", "纺织服装": "大消费", "家电行业": "大消费", "航空机场": "大消费", "小金属": "资源", "贵金属": "资源", "有色金属": "资源", "煤炭行业": "资源", "石油行业": "资源", "燃气": "资源", "采掘行业": "资源", "消费电子": "消费电子", "电子元件": "消费电子", "光学光电子": "消费电子", "汽车零部件": "车路云", "汽车服务": "车路云", "汽车整车": "车路云", "交运设备": "车路云", "电机": "智能机器", "通用设备": "智能机器", "专用设备": "智能机器", "仪器仪表": "智能机器", "电网设备": "电力", "电力行业": "电力", "房地产开发": "房地产", "房地产服务": "房地产", "工程建设": "房地产", "水泥建材": "房地产", "装修装饰": "房地产", "装修建材": "房地产", "工程咨询服务": "房地产", "钢铁行业": "房地产", "工程机械": "房地产", "非金属材料": "化工", "包装材料": "化工", "化学制品": "化工", "化肥行业": "化工", "化学原料": "化工", "化纤行业": "化工", "塑料制品": "化工", "玻璃玻纤": "化工", "橡胶制品": "化工", "证券": "金融", "保险": "金融", "银行": "金融", "多元金融": "金融", "通信服务": "AI", "通信设备": "AI", "互联网服务": "AI", "软件开发": "AI", "计算机设备": "AI", "文化传媒": "文化传媒", "教育": "文化传媒", "游戏": "文化传媒", "农牧饲渔": "农业", "农药兽药": "农业", "物流行业": "物流", "航运港口": "物流", "铁路公路": "物流", "航天航空": "军工", "船舶制造": "军工", "环保行业": "环保", "公用事业": "其他", "专业服务": "其他", "综合行业": "其他" } ================================================ FILE: src/zvt/resources/log_conf.yaml ================================================ version: 1 disable_existing_loggers: False formatters: default: # "()": uvicorn.logging.DefaultFormatter format: '%(asctime)s %(levelname)s %(threadName)s %(message)s' access: # "()": uvicorn.logging.AccessFormatter format: '%(asctime)s %(levelname)s %(threadName)s %(message)s' handlers: default: formatter: default class: logging.StreamHandler stream: ext://sys.stderr file: class: logging.handlers.RotatingFileHandler formatter: default filename: server.log maxBytes: 524288000 level: INFO backupCount: 10 access: formatter: access class: logging.StreamHandler stream: ext://sys.stdout loggers: uvicorn.error: level: INFO handlers: - default propagate: no uvicorn.access: level: INFO handlers: - access propagate: no root: level: INFO handlers: - default - file propagate: no ================================================ FILE: src/zvt/resources/missed_concept.json ================================================ [ "北京冬奥", "长江三角", "HS300_", "股权激励", "标准普尔", "预盈预增", "上证50_", "预亏预减", "上证180_", "滨海新区", "MSCI中国", "沪股通", "转债标的", "昨日涨停_含一字", "万达概念", "成渝特区", "ST股", "机构重仓", "IPO受益", "参股新三板", "破净股", "上证380", "宁组合", "茅指数", "深证100R", "深股通", "举牌", "债转股", "昨日连板_含一字", "融资融券", "央视50_", "昨日触板", "科创板做市股", "QFII重仓", "科创板做市商", "独角兽", "AB股", "基金重仓", "富士康", "创业板综", "中证500", "次新股", "富时罗素", "百元股", "创业成份", "送转预期", "B股", "杭州亚运会", "深成500", "股权转让", "社保重仓", "昨日连板", "微盘股", "昨日涨停", "GDR", "分拆预期", "高送转", "湖北自贸", "AH股" ] ================================================ FILE: src/zvt/resources/missed_industry.json ================================================ [] ================================================ FILE: src/zvt/resources/us_industry_main_tag_mapping.json ================================================ { "生物科技": "医药", "制药": "医药", "药品零售": "医药", "医疗保健设备": "医药", "医疗保健技术": "医药", "生命科学工具和服务": "医药", "医疗保健用品": "医药", "投资银行业与经纪业": "金融", "区域性银行": "金融", "互助储蓄与抵押信贷金融服务": "金融", "资产管理与托管银行": "金融", "综合金融服务": "金融", "综合性银行": "金融", "财产与意外伤害保险": "金融", "医疗保健房地产投资信托": "金融", "多样化房地产投资信托": "金融", "抵押房地产投资信托": "金融", "特殊金融服务": "金融", "金融交易所和数据": "金融", "消费信贷": "金融", "保险经纪商": "金融", "综合性资本市场": "金融", "人寿与健康保险": "金融", "再保险": "金融", "多元化保险": "金融", "多领域控股": "金融", "餐馆": "大消费", "啤酒酿造商": "大消费", "包装食品与肉类": "大消费", "食品零售": "大消费", "互联网与直销零售": "大消费", "服装、服饰与奢侈品": "大消费", "个人护理用品": "大消费", "食品分销商": "大消费", "服装零售": "大消费", "保健护理服务": "大消费", "消闲用品": "大消费", "软饮料与不含酒精饮料": "大消费", "酒店、度假村与豪华游轮": "大消费", "鞋类": "大消费", "酿酒商与葡萄酒商": "大消费", "综合货品商店": "大消费", "家用器具与特殊消费品": "大消费", "纺织品": "大消费", "日常消费品零售": "大消费", "百货商店": "大消费", "其他专卖店": "大消费", "保健护理产品经销商": "大消费", "经销商": "大消费", "居家用品": "大消费", "保健护理机构": "大消费", "家庭装饰零售": "大消费", "家庭装潢零售": "大消费", "烟草": "大消费", "特殊消费者服务": "大消费", "家庭装饰品": "大消费", "纸制品": "大消费", "机场服务": "大消费", "管理型保健护理": "大消费", "办公服务与用品": "大消费", "消费电子产品": "消费电子", "电子元件": "消费电子", "电脑与电子产品零售": "消费电子", "家用电器": "消费电子", "电气部件与设备": "消费电子", "电子设备和仪器": "消费电子", "电子制造服务": "消费电子", "应用软件": "AI", "互联网服务与基础设施": "AI", "数据处理与外包服务": "AI", "信息科技咨询与其它服务": "AI", "通信设备": "AI", "电脑硬件、储存设备及电脑周边": "AI", "系统软件": "AI", "综合电信业务": "AI", "无线电信业务": "AI", "技术产品经销商": "AI", "半导体产品": "半导体", "半导体材料与设备": "半导体", "石油与天然气的勘探与生产": "资源", "石油与天然气钻井": "资源", "石油天然气设备与服务": "资源", "石油与天然气的储存和运输": "资源", "石油与天然气的炼制和营销": "资源", "综合性石油与天然气企业": "资源", "多种金属与采矿": "资源", "贵重金属与矿石": "资源", "铝": "资源", "黄金": "资源", "煤与消费用燃料": "资源", "燃气公用事业": "资源", "水公用事业": "资源", "钢铁": "资源", "金属、玻璃及塑料器皿": "资源", "电力公用事业": "电力", "新能源发电业者": "电力", "独立电力生产商与能源贸易商": "电力", "重型电气设备": "电力", "航天航空与国防": "军工", "安全和报警服务": "军工", "电影与娱乐": "文化传媒", "赌场与赌博": "文化传媒", "互动媒体与服务": "文化传媒", "出版": "文化传媒", "广告": "文化传媒", "广播": "文化传媒", "教育服务": "文化传媒", "互动家庭娱乐": "文化传媒", "消闲设施": "文化传媒", "调查和咨询服务": "文化传媒", "有线和卫星电视": "文化传媒", "房地产开发": "房地产", "房地产服务": "房地产", "住宅建筑": "房地产", "建筑材料": "房地产", "建筑与工程": "房地产", "建筑产品": "房地产", "建筑机械与重型运输设备": "房地产", "房地产经营公司": "房地产", "零售业房地产投资信托": "房地产", "工业房地产投资信托": "房地产", "酒店及度假村房地产投资信托": "房地产", "特种房地产投资信托": "房地产", "办公房地产投资信托": "房地产", "多样化房地产活动": "房地产", "住宅房地产投资信托": "房地产", "汽车零售": "车路云", "汽车制造商": "车路云", "摩托车制造商": "车路云", "汽车零件与设备": "车路云", "陆运": "物流", "航空货运与物流": "物流", "客运航空公司": "物流", "铁路": "物流", "海上运输": "物流", "公路与铁路": "物流", "海港与服务": "物流", "特种化学制品": "化工", "多种化学制品": "化工", "商品化工": "化工", "化肥与农用药剂": "化工", "轮胎与橡胶": "化工", "工业气体": "化工", "农用农业机械": "农业", "林业产品": "农业", "农产品与服务": "农业", "环境与设施服务": "环保", "贸易公司与经销商": "其他", "工业机械、物料与部件": "其他", "人力资源与就业服务": "其他", "综合支持服务": "其他", "复合型公用事业": "其他", "纸质和塑料包装产品及材料": "其他", "商业印刷": "其他", "工业集团企业": "其他", "非传统电信运营商": "其他" } ================================================ FILE: src/zvt/rest/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: src/zvt/rest/data.py ================================================ # -*- coding: utf-8 -*- from fastapi import APIRouter from fastapi.encoders import jsonable_encoder import zvt.contract as contract import zvt.contract.api as contract_api data_router = APIRouter( prefix="/api/data", tags=["data"], responses={404: {"description": "Not found"}}, ) @data_router.get( "/providers", response_model=list, ) def get_data_providers(): """ Get data providers """ return contract_api.get_providers() @data_router.get( "/schemas", response_model=list, ) def get_data_schemas(provider): """ Get schemas by provider """ return [schema.__name__ for schema in contract_api.get_schemas(provider=provider)] @data_router.get( "/query_data", response_model=list, ) def query_data(provider: str, schema: str): """ Get schemas by provider """ model: contract.Mixin = contract_api.get_schema_by_name(schema) with contract_api.DBSession(provider=provider, data_schema=model)() as session: return jsonable_encoder(model.query_data(session=session, limit=100, return_type="domain")) ================================================ FILE: src/zvt/rest/factor.py ================================================ # -*- coding: utf-8 -*- from typing import List from fastapi import APIRouter from zvt.contract import zvt_context from zvt.factors import factor_service from zvt.factors.factor_models import FactorRequestModel, TradingSignalModel factor_router = APIRouter( prefix="/api/factor", tags=["factor"], responses={404: {"description": "Not found"}}, ) @factor_router.get("/get_factors", response_model=List[str]) def get_factors(): return [name for name in zvt_context.factor_cls_registry.keys()] @factor_router.post("/query_factor_result", response_model=List[TradingSignalModel]) def query_factor_result(factor_request_model: FactorRequestModel): return factor_service.query_factor_result(factor_request_model) ================================================ FILE: src/zvt/rest/misc.py ================================================ # -*- coding: utf-8 -*- from fastapi import APIRouter from zvt.misc import misc_service from zvt.misc.misc_models import TimeMessage misc_router = APIRouter( prefix="/api/misc", tags=["misc"], responses={404: {"description": "Not found"}}, ) @misc_router.get( "/time_message", response_model=TimeMessage, ) def get_time_message(): """ Get time message """ return misc_service.get_time_message() ================================================ FILE: src/zvt/rest/trading.py ================================================ import platform from typing import List, Optional from fastapi import APIRouter, HTTPException from fastapi_pagination import Page import zvt.contract.api as contract_api import zvt.trading.trading_service as trading_service from zvt.common.trading_models import BuyParameter, SellParameter, TradingResult from zvt.tag.tag_schemas import MainTagInfo from zvt.trading.trading_models import ( BuildTradingPlanModel, TradingPlanModel, QueryTradingPlanModel, QueryStockQuoteModel, StockQuoteStatsModel, QueryStockQuoteSettingModel, BuildQueryStockQuoteSettingModel, QueryTagQuoteModel, TagQuoteStatsModel, KdataModel, KdataRequestModel, TSModel, TSRequestModel, QuoteStatsModel, ) from zvt.trading.trading_schemas import QueryStockQuoteSetting trading_router = APIRouter( prefix="/api/trading", tags=["trading"], # dependencies=[Depends(get_current_user)], responses={404: {"description": "Not found"}}, ) @trading_router.post("/query_kdata", response_model=Optional[List[KdataModel]]) def query_kdata(kdata_request_model: KdataRequestModel): return trading_service.query_kdata(kdata_request_model) @trading_router.post("/query_ts", response_model=Optional[List[TSModel]]) def query_kdata(ts_request_model: TSRequestModel): return trading_service.query_ts(ts_request_model) @trading_router.get("/get_quote_stats", response_model=Optional[QuoteStatsModel]) def get_quote_stats(): return trading_service.query_quote_stats() @trading_router.get("/get_query_stock_quote_setting", response_model=Optional[QueryStockQuoteSettingModel]) def get_query_stock_quote_setting(): with contract_api.DBSession(provider="zvt", data_schema=QueryStockQuoteSetting)() as session: query_setting: List[QueryStockQuoteSetting] = QueryStockQuoteSetting.query_data( session=session, return_type="domain" ) df = MainTagInfo.query_data(return_type="df") tags = df["tag"].tolist() if query_setting: return QueryStockQuoteSettingModel(stock_pool_name=query_setting[0].stock_pool_name, main_tags=tags) return QueryStockQuoteSettingModel(stock_pool_name="A股", main_tags=tags) @trading_router.post("/build_query_stock_quote_setting", response_model=QueryStockQuoteSettingModel) def build_query_stock_quote_setting(build_query_stock_quote_setting_model: BuildQueryStockQuoteSettingModel): return trading_service.build_query_stock_quote_setting(build_query_stock_quote_setting_model) @trading_router.post("/query_tag_quotes", response_model=List[TagQuoteStatsModel]) def query_tag_quotes(query_tag_quote_model: QueryTagQuoteModel): return trading_service.query_tag_quotes(query_tag_quote_model) @trading_router.post("/query_stock_quotes", response_model=Optional[StockQuoteStatsModel]) def query_stock_quotes(query_stock_quote_model: QueryStockQuoteModel): return trading_service.query_stock_quotes(query_stock_quote_model) @trading_router.post("/build_trading_plan", response_model=TradingPlanModel) def build_trading_plan(build_trading_plan_model: BuildTradingPlanModel): return trading_service.build_trading_plan(build_trading_plan_model) @trading_router.post("/query_trading_plan", response_model=Page[TradingPlanModel]) def query_trading_plan(query_trading_plan_model: QueryTradingPlanModel): return trading_service.query_trading_plan(query_trading_plan_model) @trading_router.get("/get_current_trading_plan", response_model=List[TradingPlanModel]) def get_current_trading_plan(): return trading_service.get_current_trading_plan() @trading_router.get("/get_future_trading_plan", response_model=List[TradingPlanModel]) def get_future_trading_plan(): return trading_service.get_future_trading_plan() @trading_router.post("/buy", response_model=TradingResult) def buy(buy_position_strategy: BuyParameter): if platform.system() == "Windows": from zvt.broker.qmt.context import qmt_context return qmt_context.qmt_account.buy(buy_position_strategy) else: raise HTTPException(status_code=500, detail="Please use qmt in windows! ") @trading_router.post("/sell", response_model=TradingResult) def sell(sell_position_strategy: SellParameter): if platform.system() == "Windows": from zvt.broker.qmt.context import qmt_context return qmt_context.qmt_account.sell(sell_position_strategy) else: raise HTTPException(status_code=500, detail="Please use qmt in windows! ") ================================================ FILE: src/zvt/rest/work.py ================================================ # -*- coding: utf-8 -*- from typing import List, Optional from fastapi import APIRouter import zvt.contract.api as contract_api import zvt.tag.tag_service as tag_service from zvt.domain import Stock from zvt.tag.common import TagType from zvt.tag.tag_models import ( TagInfoModel, CreateTagInfoModel, StockTagsModel, SimpleStockTagsModel, SetStockTagsModel, CreateStockPoolInfoModel, StockPoolInfoModel, CreateStockPoolsModel, StockPoolsModel, QueryStockTagStatsModel, StockTagStatsModel, QueryStockTagsModel, QuerySimpleStockTagsModel, ActivateSubTagsResultModel, ActivateSubTagsModel, BatchSetStockTagsModel, StockTagOptions, MainTagIndustryRelation, MainTagSubTagRelation, IndustryInfoModel, ChangeMainTagModel, BuildMainTagIndustryRelationModel, BuildMainTagSubTagRelationModel, ) from zvt.tag.tag_schemas import ( StockTags, MainTagInfo, SubTagInfo, HiddenTagInfo, StockPoolInfo, StockPools, IndustryInfo, ) from zvt.utils.time_utils import current_date work_router = APIRouter( prefix="/api/work", tags=["work"], # dependencies=[Depends(get_current_user)], responses={404: {"description": "Not found"}}, ) @work_router.post("/create_stock_pool_info", response_model=StockPoolInfoModel) def create_stock_pool_info(create_stock_pool_info_model: CreateStockPoolInfoModel): return tag_service.build_stock_pool_info(create_stock_pool_info_model, timestamp=current_date()) @work_router.get("/get_stock_pool_info", response_model=List[StockPoolInfoModel]) def get_stock_pool_info(): with contract_api.DBSession(provider="zvt", data_schema=StockPoolInfo)() as session: stock_pool_info: List[StockPoolInfo] = StockPoolInfo.query_data(session=session, return_type="domain") return stock_pool_info @work_router.post("/create_stock_pools", response_model=StockPoolsModel) def create_stock_pools(create_stock_pools_model: CreateStockPoolsModel): return tag_service.build_stock_pool(create_stock_pools_model, current_date()) @work_router.delete("/delete_stock_pool", response_model=str) def delete_stock_pool(stock_pool_name: str): return tag_service.delete_stock_pool(stock_pool_name=stock_pool_name) @work_router.get("/get_stock_pools", response_model=Optional[StockPoolsModel]) def get_stock_pools(stock_pool_name: str): with contract_api.DBSession(provider="zvt", data_schema=StockPools)() as session: stock_pools: List[StockPools] = StockPools.query_data( session=session, filters=[StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if stock_pools: return stock_pools[0] return None @work_router.get("/get_main_tag_info", response_model=List[TagInfoModel]) def get_main_tag_info(): """ Get main_tag info """ with contract_api.DBSession(provider="zvt", data_schema=MainTagInfo)() as session: tags_info: List[MainTagInfo] = MainTagInfo.query_data(session=session, return_type="domain") return tags_info @work_router.get("/get_sub_tag_info", response_model=List[TagInfoModel]) def get_sub_tag_info(): """ Get sub_tag info """ with contract_api.DBSession(provider="zvt", data_schema=SubTagInfo)() as session: tags_info: List[SubTagInfo] = SubTagInfo.query_data(session=session, return_type="domain") return tags_info @work_router.get("/get_main_tag_sub_tag_relation", response_model=MainTagSubTagRelation) def get_main_tag_sub_tag_relation(main_tag): return tag_service.get_main_tag_sub_tag_relation(main_tag=main_tag) @work_router.get("/get_industry_info", response_model=List[IndustryInfoModel]) def get_industry_info(): """ Get industry info """ with contract_api.DBSession(provider="zvt", data_schema=IndustryInfo)() as session: industry_info: List[IndustryInfo] = IndustryInfo.query_data(session=session, return_type="domain") return industry_info @work_router.get("/get_main_tag_industry_relation", response_model=MainTagIndustryRelation) def get_main_tag_industry_relation(main_tag): return tag_service.get_main_tag_industry_relation(main_tag=main_tag) @work_router.get("/get_hidden_tag_info", response_model=List[TagInfoModel]) def get_hidden_tag_info(): """ Get hidden_tag info """ with contract_api.DBSession(provider="zvt", data_schema=MainTagInfo)() as session: tags_info: List[HiddenTagInfo] = HiddenTagInfo.query_data(session=session, return_type="domain") return tags_info @work_router.post("/create_main_tag_info", response_model=TagInfoModel) def create_main_tag_info(tag_info: CreateTagInfoModel): return tag_service.create_tag_info(tag_info, tag_type=TagType.main_tag) @work_router.delete("/delete_main_tag", response_model=str) def delete_main_tag(tag: str): tag_service.delete_tag(tag=tag, tag_type=TagType.main_tag) return "ok" @work_router.post("/create_sub_tag_info", response_model=TagInfoModel) def create_sub_tag_info(tag_info: CreateTagInfoModel): return tag_service.create_tag_info(tag_info, TagType.sub_tag) @work_router.delete("/delete_sub_tag", response_model=str) def delete_sub_tag(tag: str): tag_service.delete_tag(tag=tag, tag_type=TagType.sub_tag) return "ok" @work_router.post("/create_hidden_tag_info", response_model=TagInfoModel) def create_hidden_tag_info(tag_info: CreateTagInfoModel): return tag_service.create_tag_info(tag_info, TagType.hidden_tag) @work_router.delete("/delete_hidden_tag", response_model=str) def delete_hidden_tag(tag: str): tag_service.delete_tag(tag=tag, tag_type=TagType.hidden_tag) return "ok" @work_router.post("/query_stock_tags", response_model=List[StockTagsModel]) def query_stock_tags(query_stock_tags_model: QueryStockTagsModel): """ Get entity tags """ filters = [StockTags.entity_id.in_(query_stock_tags_model.entity_ids)] with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: tags: List[StockTags] = StockTags.query_data( session=session, filters=filters, return_type="domain", order=StockTags.timestamp.desc() ) tags_dict = {tag.entity_id: tag for tag in tags} sorted_tags = [tags_dict[entity_id] for entity_id in query_stock_tags_model.entity_ids] return sorted_tags @work_router.post("/query_simple_stock_tags", response_model=List[SimpleStockTagsModel]) def query_simple_stock_tags(query_simple_stock_tags_model: QuerySimpleStockTagsModel): """ Get simple entity tags """ entity_ids = query_simple_stock_tags_model.entity_ids filters = [StockTags.entity_id.in_(entity_ids)] with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: tags: List[dict] = StockTags.query_data( session=session, filters=filters, return_type="dict", order=StockTags.timestamp.desc() ) entity_tag_map = {item["entity_id"]: item for item in tags} result_tags = [] stocks = Stock.query_data(provider="em", entity_ids=[tag["entity_id"] for tag in tags], return_type="domain") stocks_map = {item.entity_id: item for item in stocks} for entity_id in entity_ids: tag = entity_tag_map.get(entity_id) if not tag: continue tag["name"] = stocks_map.get(entity_id).name if stocks_map.get(entity_id).controlling_holder_parent: tag["controlling_holder_parent"] = stocks_map.get(entity_id).controlling_holder_parent else: tag["controlling_holder_parent"] = stocks_map.get(entity_id).controlling_holder tag["top_ten_ratio"] = stocks_map.get(entity_id).top_ten_ratio result_tags.append(tag) return result_tags @work_router.get("/get_stock_tag_options", response_model=StockTagOptions) def get_stock_tag_options(entity_id: str): """ Get stock tag options """ return tag_service.get_stock_tag_options(entity_id=entity_id) @work_router.post("/set_stock_tags", response_model=StockTagsModel) def set_stock_tags(set_stock_tags_model: SetStockTagsModel): """ Set stock tags """ return tag_service.build_stock_tags( set_stock_tags_model=set_stock_tags_model, timestamp=current_date(), set_by_user=True ) @work_router.post("/build_stock_tags", response_model=List[StockTagsModel]) def build_stock_tags(set_stock_tags_model_list: List[SetStockTagsModel]): """ Set stock tags in batch """ return [ tag_service.build_stock_tags( set_stock_tags_model=set_stock_tags_model, timestamp=current_date(), set_by_user=True ) for set_stock_tags_model in set_stock_tags_model_list ] @work_router.post("/query_stock_tag_stats", response_model=List[StockTagStatsModel]) def query_stock_tag_stats(query_stock_tag_stats_model: QueryStockTagStatsModel): """ Get stock tag stats """ return tag_service.query_stock_tag_stats(query_stock_tag_stats_model=query_stock_tag_stats_model) @work_router.get("/get_main_tags_in_stock_pool", response_model=List[str]) def get_main_tags_in_stock_pool(stock_pool_name: str): """ Get stock tag stats """ return tag_service.get_main_tags_in_stock_pool(stock_pool_name) @work_router.post("/activate_sub_tags", response_model=ActivateSubTagsResultModel) def activate_sub_tags(activate_sub_tags_model: ActivateSubTagsModel): """ Activate sub tags """ return tag_service.activate_sub_tags(activate_sub_tags_model=activate_sub_tags_model) @work_router.post("/batch_set_stock_tags", response_model=List[StockTagsModel]) def batch_set_stock_tags(batch_set_stock_tags_model: BatchSetStockTagsModel): return tag_service.batch_set_stock_tags(batch_set_stock_tags_model=batch_set_stock_tags_model) @work_router.post("/build_main_tag_industry_relation", response_model=str) def build_main_tag_industry_relation(build_relation_model: BuildMainTagIndustryRelationModel): tag_service.build_main_tag_industry_relation(build_relation_model=build_relation_model) return "success" @work_router.post("/build_main_tag_sub_tag_relation", response_model=str) def build_main_tag_sub_tag_relation(build_relation_model: BuildMainTagSubTagRelationModel): tag_service.build_main_tag_sub_tag_relation(build_relation_model=build_relation_model) return "ok" @work_router.post("/change_main_tag", response_model=List[StockTagsModel]) def change_main_tag(change_main_tag_model: ChangeMainTagModel): return tag_service.change_main_tag(change_main_tag_model=change_main_tag_model) ================================================ FILE: src/zvt/samples/__init__.py ================================================ # -*- coding: utf-8 -*- from .stock_traders import * ================================================ FILE: src/zvt/samples/stock_traders.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel from zvt.factors.ma.ma_factor import CrossMaFactor from zvt.factors.macd.macd_factor import BullFactor from zvt.trader.trader import StockTrader class MyMaTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): return [ CrossMaFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, windows=[5, 10], need_persist=False, adjust_type=adjust_type, ) ] class MyBullTrader(StockTrader): def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): return [ BullFactor( entity_ids=entity_ids, entity_schema=entity_schema, exchanges=exchanges, codes=codes, start_timestamp=start_timestamp, end_timestamp=end_timestamp, adjust_type=adjust_type, ) ] if __name__ == "__main__": # single stock with cross ma factor MyMaTrader( codes=["000338"], level=IntervalLevel.LEVEL_1DAY, start_timestamp="2018-01-01", end_timestamp="2019-06-30", trader_name="000338_ma_trader", ).run() # single stock with bull factor # MyBullTrader(codes=['000338'], level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01', # end_timestamp='2019-06-30', trader_name='000338_bull_trader').run() # multiple stocks with cross ma factor # MyMaTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01', # end_timestamp='2019-06-30', trader_name='sample_stocks_ma_trader').run() # multiple stocks with bull factor # MyBullTrader(codes=SAMPLE_STOCK_CODES, level=IntervalLevel.LEVEL_1DAY, start_timestamp='2018-01-01', # end_timestamp='2019-06-30', trader_name='sample_stocks_bull_trader').run() ================================================ FILE: src/zvt/sched/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: src/zvt/sched/sched.py ================================================ # -*- coding: utf-8 -*- import logging import os from apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor from apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore from apscheduler.schedulers.background import BackgroundScheduler from zvt import ZVT_HOME logger = logging.getLogger(__name__) jobs_db_path = os.path.join(ZVT_HOME, "jobs.db") jobstores = {"default": SQLAlchemyJobStore(url=f"sqlite:///{jobs_db_path}")} executors = {"default": ThreadPoolExecutor(20), "processpool": ProcessPoolExecutor(5)} job_defaults = {"coalesce": False, "max_instances": 1} zvt_scheduler = BackgroundScheduler(jobstores=jobstores, executors=executors, job_defaults=job_defaults) def sched_tasks(): import platform if platform.system() == "Windows": try: from zvt.broker.qmt.qmt_quote import record_stock_quote zvt_scheduler.add_job(func=record_stock_quote, trigger="cron", hour=9, minute=19, day_of_week="mon-fri") except Exception as e: logger.error("QMT not work", e) else: logger.warning("QMT need run in Windows!") zvt_scheduler.start() if __name__ == "__main__": sched_tasks() ================================================ FILE: src/zvt/tag/__init__.py ================================================ # -*- coding: utf-8 -*-# # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/tag/ai_suggestion.py ================================================ # -*- coding: utf-8 -*- import json import logging import re from typing import List import pandas as pd from openai import OpenAI from sqlalchemy import func, or_ import zvt.contract.api as contract_api from zvt import zvt_config from zvt.domain import StockNews, Stock from zvt.tag.tag_utils import match_tag from zvt.utils.time_utils import date_time_by_interval, current_date logger = logging.getLogger(__name__) def normalize_tag_suggestions(tag_suggestions): for direction in ["up", "down"]: if direction in tag_suggestions: for item in tag_suggestions[direction]: tag_type, tag = match_tag(item["block"]) item["tag"] = tag item["tag_type"] = tag_type if item["stocks"]: stocks = Stock.query_data( filters=[Stock.name.in_(item["stocks"])], return_type="dict", provider="em" ) if len(stocks) != len(item["stocks"]): logger.warning( f"Stocks not found in zvt:{set(item['stocks']) - set([item['name'] for item in stocks])}" ) item["stocks"] = [{"entity_id": item["entity_id"], "name": item["name"]} for item in stocks] return tag_suggestions def set_stock_news_tag_suggestions(stock_news, tag_suggestions, session): if stock_news.news_analysis: stock_news.news_analysis = dict(stock_news.news_analysis) else: stock_news.news_analysis = {} result = normalize_tag_suggestions(tag_suggestions) logger.debug(result) stock_news.news_analysis["tag_suggestions"] = result session.add(stock_news) session.commit() def build_tag_suggestions(entity_id): with contract_api.DBSession(provider="em", data_schema=StockNews)() as session: start_date = date_time_by_interval(current_date(), -30) datas: List[StockNews] = StockNews.query_data( entity_id=entity_id, limit=1, order=StockNews.timestamp.desc(), filters=[ StockNews.timestamp >= start_date, func.json_extract(StockNews.news_analysis, f'$."tag_suggestions"') != None, ], return_type="domain", ) if datas: latest_data = datas[0] else: latest_data = None filters = [ or_( StockNews.news_title.like("%上涨%"), StockNews.news_title.like("%拉升%"), StockNews.news_title.like("%涨停%"), StockNews.news_title.like("%下跌%"), StockNews.news_title.like("%跌停%"), ), StockNews.timestamp >= start_date, func.json_extract(StockNews.news_analysis, f'$."tag_suggestions"') == None, ] if latest_data: filters = filters + [ StockNews.timestamp >= latest_data.timestamp, StockNews.news_code != latest_data.news_code, ] stock_news_list: List[StockNews] = StockNews.query_data( entity_id=entity_id, session=session, order=StockNews.news_code.asc(), return_type="domain", filters=filters, ) if not stock_news_list: logger.info("all stock news has been analyzed") return example = { "up": [{"block": "block_a", "stocks": ["stock_a", "stock_b"]}], "down": [{"block": "block_b", "stocks": ["stock_1", "stock_2"]}], } client = OpenAI( api_key=zvt_config["qwen_api_key"], base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", ) for stock_news in stock_news_list: # same news if latest_data and (stock_news.news_code == latest_data.news_code): tag_suggestions = latest_data.news_analysis.get("tag_suggestions") if tag_suggestions: set_stock_news_tag_suggestions(stock_news, tag_suggestions, session) continue news_title = stock_news.news_title news_content = stock_news.news_content logger.info(news_title) logger.info(news_content) completion = client.chat.completions.create( model="qwen-max", messages=[ { "role": "system", "content": f"请从新闻标题和内容中识别是上涨还是下跌，提取相应的板块和个股，按照格式: {example} 输出一个 JSON 对象", }, { "role": "user", "content": f"新闻标题:{news_title}, 新闻内容:{news_content}", }, ], temperature=0.2, ) content = completion.choices[0].message.content content = content.replace("```json", "") content = content.replace("```", "") content = re.sub(r"\s+", "", content) logger.info(f"message content: {content}") tag_suggestions = json.loads(content) set_stock_news_tag_suggestions(stock_news, tag_suggestions, session) def extract_info(tag_dict): extracted_info = [] for key, value in tag_dict.items(): for item in value: extracted_info.append({"tag": item["tag"], "stocks": [stock["name"] for stock in item["stocks"]]}) return extracted_info def build_tag_suggestions_stats(): with contract_api.DBSession(provider="em", data_schema=StockNews)() as session: start_date = date_time_by_interval(current_date(), -10) stock_news_list: List[StockNews] = StockNews.query_data( session=session, order=StockNews.timestamp.desc(), distinct=StockNews.news_code, return_type="dict", filters=[ StockNews.timestamp >= start_date, func.json_extract(StockNews.news_analysis, f'$."tag_suggestions"') != None, ], ) datas = [] for stock_news in stock_news_list: tag_suggestions = stock_news["news_analysis"].get("tag_suggestions") if tag_suggestions: for key in ("up", "down"): suggestions = tag_suggestions.get(key) if suggestions: datas = datas + [ { "tag": item["tag"], "tag_type": item["tag_type"], "entity_ids": [stock["entity_id"] for stock in item["stocks"]], "stock_names": [stock["name"] for stock in item["stocks"]], } for item in suggestions ] df = pd.DataFrame.from_records(data=datas) grouped_df = ( df.groupby("tag") .agg( tag_count=("tag", "count"), tag_type=("tag_type", "first"), entity_ids=("entity_ids", "sum"), stock_names=("stock_names", "sum"), ) .reset_index() ) grouped_df["entity_ids"] = grouped_df["entity_ids"].apply(set).apply(list) grouped_df["stock_names"] = grouped_df["stock_names"].apply(set).apply(list) grouped_df["entity_ids_count"] = grouped_df["entity_ids"].apply(len) sorted_df = grouped_df.sort_values(by=["tag_count", "entity_ids_count"], ascending=[False, False]) return sorted_df.to_dict(orient="records") if __name__ == "__main__": build_tag_suggestions_stats() ================================================ FILE: src/zvt/tag/common.py ================================================ # -*- coding: utf-8 -*- from enum import Enum class StockPoolType(Enum): system = "system" custom = "custom" dynamic = "dynamic" class DynamicPoolType(Enum): limit_up = "limit_up" limit_down = "limit_down" class InsertMode(Enum): overwrite = "overwrite" append = "append" class TagType(Enum): #: A tag is a main tag due to its extensive capacity. main_tag = "main_tag" sub_tag = "sub_tag" hidden_tag = "hidden_tag" class TagStatsQueryType(Enum): simple = "simple" details = "details" # the __all__ is generated __all__ = ["StockPoolType", "DynamicPoolType", "InsertMode", "TagType", "TagStatsQueryType"] ================================================ FILE: src/zvt/tag/tag_models.py ================================================ # -*- coding: utf-8 -*- from typing import Dict, Union, List, Optional from pydantic import field_validator, Field from pydantic_core.core_schema import ValidationInfo from zvt.contract.model import MixinModel, CustomModel from zvt.tag.common import StockPoolType, TagType, TagStatsQueryType, InsertMode from zvt.tag.tag_utils import get_stock_pool_names class TagInfoModel(MixinModel): tag: str tag_reason: Optional[str] = Field(default=None) main_tag: Optional[str] = Field(default=None) class CreateTagInfoModel(CustomModel): tag: str tag_reason: Optional[str] = Field(default=None) class IndustryInfoModel(MixinModel): industry_name: str description: str # related main tag main_tag: str class MainTagIndustryRelation(CustomModel): main_tag: str industry_list: List[str] class BuildMainTagIndustryRelationModel(CustomModel): main_tag: str industry_list: List[str] activate: bool = Field(default=False) class MainTagSubTagRelation(CustomModel): main_tag: str sub_tag_list: List[str] class BuildMainTagSubTagRelationModel(CustomModel): main_tag: str sub_tag_list: List[str] activate: bool = Field(default=False) class ChangeMainTagModel(CustomModel): current_main_tag: str new_main_tag: str class StockTagsModel(MixinModel): main_tag: Optional[str] = Field(default=None) main_tag_reason: Optional[str] = Field(default=None) main_tags: Dict[str, str] sub_tag: Optional[str] = Field(default=None) sub_tag_reason: Optional[str] = Field(default=None) sub_tags: Union[Dict[str, str], None] active_hidden_tags: Union[Dict[str, str], None] hidden_tags: Union[Dict[str, str], None] set_by_user: bool = False class SimpleStockTagsModel(CustomModel): entity_id: str name: str main_tag: Optional[str] = Field(default=None) main_tag_reason: Optional[str] = Field(default=None) main_tags: Dict[str, str] sub_tag: Union[str, None] sub_tag_reason: Optional[str] = Field(default=None) sub_tags: Union[Dict[str, str], None] active_hidden_tags: Union[Dict[str, str], None] controlling_holder_parent: Optional[str] = Field(default=None) top_ten_ratio: Optional[float] = Field(default=None) class QueryStockTagsModel(CustomModel): entity_ids: List[str] class QuerySimpleStockTagsModel(CustomModel): entity_ids: List[str] class BatchSetStockTagsModel(CustomModel): entity_ids: List[str] tag: str tag_reason: Optional[str] = Field(default=None) tag_type: TagType class TagParameter(CustomModel): main_tag: str main_tag_reason: Optional[str] = Field(default=None) sub_tag: Optional[str] = Field(default=None) sub_tag_reason: Optional[str] = Field(default=None) hidden_tag: Optional[str] = Field(default=None) hidden_tag_reason: Optional[str] = Field(default=None) class StockTagOptions(CustomModel): main_tag: Optional[str] = Field(default=None) sub_tag: Optional[str] = Field(default=None) # hidden_tags: Optional[List[str]] = Field(default=None) active_hidden_tags: Optional[Dict[str, str]] = Field(default=None) main_tag_options: List[CreateTagInfoModel] sub_tag_options: List[CreateTagInfoModel] hidden_tag_options: List[CreateTagInfoModel] class SetStockTagsModel(CustomModel): entity_id: str main_tag: str main_tag_reason: Optional[str] = Field(default=None) sub_tag: Optional[str] = Field(default=None) sub_tag_reason: Optional[str] = Field(default=None) active_hidden_tags: Optional[Dict[str, str]] = Field(default=None) # @field_validator("main_tag") # @classmethod # def main_tag_must_be_in(cls, v: str) -> str: # if v not in get_main_tags(): # raise ValueError(f"main_tag: {v} must be created at main_tag_info at first") # return v # # @field_validator("sub_tag") # @classmethod # def sub_tag_must_be_in(cls, v: str) -> str: # if v and (v not in get_sub_tags()): # raise ValueError(f"sub_tag: {v} must be created at sub_tag_info at first") # return v # # @field_validator("active_hidden_tags") # @classmethod # def hidden_tag_must_be_in(cls, v: Union[Dict[str, str], None]) -> Union[Dict[str, str], None]: # if v: # for item in v.keys(): # if item not in get_hidden_tags(): # raise ValueError(f"hidden_tag: {v} must be created at hidden_tag_info at first") # return v class StockPoolModel(MixinModel): stock_pool_name: str entity_ids: List[str] class StockPoolInfoModel(MixinModel): stock_pool_type: StockPoolType stock_pool_name: str class CreateStockPoolInfoModel(CustomModel): stock_pool_type: StockPoolType stock_pool_name: str @field_validator("stock_pool_name") @classmethod def stock_pool_name_existed(cls, v: str) -> str: if v in get_stock_pool_names(): raise ValueError(f"stock_pool_name: {v} has been used") return v class StockPoolsModel(MixinModel): stock_pool_name: str entity_ids: List[str] class CreateStockPoolsModel(CustomModel): entity_type: str = Field(default="stock") stock_pool_name: str entity_ids: List[str] insert_mode: InsertMode = Field(default=InsertMode.overwrite) # @field_validator("stock_pool_name") # @classmethod # def stock_pool_name_must_be_in(cls, v: str) -> str: # if v: # if v not in get_stock_pool_names(): # raise ValueError(f"stock_pool_name: {v} must be created at stock_pool_info at first") # return v class QueryStockTagStatsModel(CustomModel): stock_pool_name: Optional[str] = Field(default=None) entity_ids: Optional[List[str]] = Field(default=None) query_type: Optional[TagStatsQueryType] = Field(default=TagStatsQueryType.details) @field_validator("stock_pool_name", "entity_ids") @classmethod def phone_or_mobile_must_set_only_one(cls, v, validation_info: ValidationInfo, **kwargs): if validation_info.field_name == "stock_pool_name": other_field = "entity_ids" else: other_field = "stock_pool_name" other_value = kwargs.get(other_field) if v and other_value: raise ValueError(f"Only one of 'stock_pool_name' or 'entity_ids' should be set.") elif not v and not other_value: raise ValueError("Either 'stock_pool_name' or 'entity_ids' must be set.") return v @field_validator("stock_pool_name") @classmethod def stock_pool_name_must_be_in(cls, v: str) -> str: if v: if v not in get_stock_pool_names(): raise ValueError(f"stock_pool_name: {v} not existed") return v class StockTagDetailsModel(CustomModel): entity_id: str main_tag: Optional[str] = Field(default=None) sub_tag: Optional[str] = Field(default=None) hidden_tags: Union[List[str], None] #: 代码 code: str #: 名字 name: str #: 减持 recent_reduction: Optional[bool] = Field(default=None) #: 增持 recent_acquisition: Optional[bool] = Field(default=None) #: 解禁 recent_unlock: Optional[bool] = Field(default=None) #: 增发配股 recent_additional_or_rights_issue: Optional[bool] = Field(default=None) #: 业绩利好 recent_positive_earnings_news: Optional[bool] = Field(default=None) #: 业绩利空 recent_negative_earnings_news: Optional[bool] = Field(default=None) #: 上榜次数 recent_dragon_and_tiger_count: Optional[int] = Field(default=None) #: 违规行为 recent_violation_alert: Optional[bool] = Field(default=None) #: 利好 recent_positive_news: Optional[bool] = Field(default=None) #: 利空 recent_negative_news: Optional[bool] = Field(default=None) #: 新闻总结 recent_news_summary: Optional[Dict[str, str]] = Field(default=None) class StockTagStatsModel(MixinModel): main_tag: str turnover: Optional[float] = Field(default=None) entity_count: Optional[int] = Field(default=None) position: Optional[int] = Field(default=None) is_main_line: Optional[bool] = Field(default=None) main_line_continuous_days: Optional[int] = Field(default=None) entity_ids: Optional[List[str]] = Field(default=None) stock_details: Optional[List[StockTagDetailsModel]] = Field(default=None) class ActivateSubTagsModel(CustomModel): sub_tags: List[str] class ActivateSubTagsResultModel(CustomModel): tag_entity_ids: Dict[str, Union[List[str], None]] # the __all__ is generated __all__ = [ "TagInfoModel", "CreateTagInfoModel", "StockTagsModel", "SimpleStockTagsModel", "QueryStockTagsModel", "QuerySimpleStockTagsModel", "BatchSetStockTagsModel", "TagParameter", "StockTagOptions", "SetStockTagsModel", "StockPoolModel", "StockPoolInfoModel", "CreateStockPoolInfoModel", "StockPoolsModel", "CreateStockPoolsModel", "QueryStockTagStatsModel", "StockTagDetailsModel", "StockTagStatsModel", "ActivateSubTagsModel", "ActivateSubTagsResultModel", ] ================================================ FILE: src/zvt/tag/tag_schemas.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, JSON, Boolean, Float, Integer from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema StockTagsBase = declarative_base() class IndustryInfo(StockTagsBase, Mixin): __tablename__ = "industry_info" industry_name = Column(String, unique=True) description = Column(String) # related main tag main_tag = Column(String) class MainTagInfo(StockTagsBase, Mixin): __tablename__ = "main_tag_info" tag = Column(String, unique=True) tag_reason = Column(String) class SubTagInfo(StockTagsBase, Mixin): __tablename__ = "sub_tag_info" tag = Column(String, unique=True) tag_reason = Column(String) # related main tag main_tag = Column(String) class HiddenTagInfo(StockTagsBase, Mixin): __tablename__ = "hidden_tag_info" tag = Column(String, unique=True) tag_reason = Column(String) class StockTags(StockTagsBase, Mixin): """ Schema for storing stock tags """ __tablename__ = "stock_tags" entity_type = Column(String(length=64)) code = Column(String(length=64)) name = Column(String(length=128)) main_tag = Column(String) main_tag_reason = Column(String) main_tags = Column(JSON) sub_tag = Column(String) sub_tag_reason = Column(String) sub_tags = Column(JSON) active_hidden_tags = Column(JSON) hidden_tags = Column(JSON) set_by_user = Column(Boolean, default=False) class StockSystemTags(StockTagsBase, Mixin): __tablename__ = "stock_system_tags" #: 编码 code = Column(String(length=64)) #: 名字 name = Column(String(length=128)) #: 减持 recent_reduction = Column(Boolean) #: 增持 recent_acquisition = Column(Boolean) #: 解禁 recent_unlock = Column(Boolean) #: 增发配股 recent_additional_or_rights_issue = Column(Boolean) #: 业绩利好 recent_positive_earnings_news = Column(Boolean) #: 业绩利空 recent_negative_earnings_news = Column(Boolean) #: 上榜次数 recent_dragon_and_tiger_count = Column(Integer) #: 违规行为 recent_violation_alert = Column(Boolean) #: 利好 recent_positive_news = Column(Boolean) #: 利空 recent_negative_news = Column(Boolean) #: 新闻总结 recent_news_summary = Column(JSON) class StockPoolInfo(StockTagsBase, Mixin): __tablename__ = "stock_pool_info" stock_pool_type = Column(String) stock_pool_name = Column(String, unique=True) class StockPools(StockTagsBase, Mixin): __tablename__ = "stock_pools" entity_type = Column(String(length=64)) stock_pool_name = Column(String) entity_ids = Column(JSON) class TagStats(StockTagsBase, Mixin): __tablename__ = "tag_stats" entity_type = Column(String(length=64)) stock_pool_name = Column(String) main_tag = Column(String) turnover = Column(Float) entity_count = Column(Integer) position = Column(Integer) is_main_line = Column(Boolean) main_line_continuous_days = Column(Integer) entity_ids = Column(JSON) register_schema(db_name="stock_tags", schema_base=StockTagsBase, internal=True) # the __all__ is generated __all__ = [ "IndustryInfo", "MainTagInfo", "SubTagInfo", "HiddenTagInfo", "StockTags", "StockSystemTags", "StockPoolInfo", "StockPools", "TagStats", ] ================================================ FILE: src/zvt/tag/tag_service.py ================================================ # -*- coding: utf-8 -*- import logging from typing import List import pandas as pd from fastapi import HTTPException from sqlalchemy import func, text import zvt.contract.api as contract_api from zvt.api.selector import get_entity_ids_by_filter from zvt.contract.api import decode_entity_id from zvt.domain import BlockStock, Block, Stock, Stockus, Stockhk from zvt.tag.common import TagType, TagStatsQueryType, StockPoolType, InsertMode from zvt.tag.tag_models import ( SetStockTagsModel, CreateStockPoolInfoModel, CreateStockPoolsModel, QueryStockTagStatsModel, ActivateSubTagsModel, BatchSetStockTagsModel, TagParameter, CreateTagInfoModel, StockTagOptions, ChangeMainTagModel, BuildMainTagIndustryRelationModel, ) from zvt.tag.tag_schemas import ( StockTags, StockPools, StockPoolInfo, TagStats, StockSystemTags, MainTagInfo, SubTagInfo, HiddenTagInfo, IndustryInfo, ) from zvt.tag.tag_utils import ( get_sub_tags, get_stock_pool_names, get_main_tag_by_sub_tag, get_main_tag_by_industry, ) from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, current_date, now_pd_timestamp from zvt.utils.utils import fill_dict, compare_dicts, flatten_list logger = logging.getLogger(__name__) def _stock_tags_need_update(stock_tags: StockTags, set_stock_tags_model: SetStockTagsModel): if ( stock_tags.main_tag != set_stock_tags_model.main_tag or stock_tags.main_tag_reason != set_stock_tags_model.main_tag_reason or stock_tags.sub_tag != set_stock_tags_model.sub_tag or stock_tags.sub_tag_reason != set_stock_tags_model.sub_tag_reason or not compare_dicts(stock_tags.active_hidden_tags, set_stock_tags_model.active_hidden_tags) ): return True return False def get_stock_tag_options(entity_id: str) -> StockTagOptions: with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: datas: List[StockTags] = StockTags.query_data( entity_id=entity_id, order=StockTags.timestamp.desc(), limit=1, return_type="domain", session=session ) main_tag_options = [] sub_tag_options = [] hidden_tag_options = [] main_tag = None sub_tag = None active_hidden_tags = None stock_tags = None if datas: stock_tags = datas[0] main_tag = stock_tags.main_tag sub_tag = stock_tags.sub_tag if stock_tags.main_tags: main_tag_options = [ CreateTagInfoModel(tag=tag, tag_reason=tag_reason) for tag, tag_reason in stock_tags.main_tags.items() ] if stock_tags.sub_tags: sub_tag_options = [ CreateTagInfoModel(tag=tag, tag_reason=tag_reason) for tag, tag_reason in stock_tags.sub_tags.items() ] if stock_tags.active_hidden_tags: active_hidden_tags = stock_tags.active_hidden_tags if stock_tags.hidden_tags: hidden_tag_options = [ CreateTagInfoModel(tag=tag, tag_reason=tag_reason) for tag, tag_reason in stock_tags.hidden_tags.items() ] main_tags_info: List[MainTagInfo] = MainTagInfo.query_data(session=session, return_type="domain") if not main_tag: main_tag = main_tags_info[0].tag main_tag_options = main_tag_options + [ CreateTagInfoModel(tag=item.tag, tag_reason=item.tag_reason) for item in main_tags_info if not stock_tags or (not stock_tags.main_tags) or (item.tag not in stock_tags.main_tags) ] sub_tags_info: List[SubTagInfo] = SubTagInfo.query_data(session=session, return_type="domain") if not sub_tag: sub_tag = sub_tags_info[0].tag sub_tag_options = sub_tag_options + [ CreateTagInfoModel(tag=item.tag, tag_reason=item.tag_reason) for item in sub_tags_info if not stock_tags or (not stock_tags.sub_tags) or (item.tag not in stock_tags.sub_tags) ] hidden_tags_info: List[HiddenTagInfo] = HiddenTagInfo.query_data(session=session, return_type="domain") hidden_tag_options = hidden_tag_options + [ CreateTagInfoModel(tag=item.tag, tag_reason=item.tag_reason) for item in hidden_tags_info if not stock_tags or (not stock_tags.hidden_tags) or (item.tag not in stock_tags.hidden_tags) ] return StockTagOptions( main_tag=main_tag, sub_tag=sub_tag, active_hidden_tags=active_hidden_tags, main_tag_options=main_tag_options, sub_tag_options=sub_tag_options, hidden_tag_options=hidden_tag_options, ) def build_stock_tags( set_stock_tags_model: SetStockTagsModel, timestamp: pd.Timestamp, set_by_user: bool, keep_current=False ): logger.info(set_stock_tags_model) main_tag_info = CreateTagInfoModel( tag=set_stock_tags_model.main_tag, tag_reason=set_stock_tags_model.main_tag_reason ) if not is_tag_info_existed(tag=main_tag_info.tag, tag_type=TagType.main_tag): create_tag_info(tag_info=main_tag_info, tag_type=TagType.main_tag) if set_stock_tags_model.sub_tag: sub_tag_info = CreateTagInfoModel( tag=set_stock_tags_model.sub_tag, tag_reason=set_stock_tags_model.sub_tag_reason ) if not is_tag_info_existed(tag=sub_tag_info.tag, tag_type=TagType.sub_tag): create_tag_info(tag_info=sub_tag_info, tag_type=TagType.sub_tag) if set_stock_tags_model.active_hidden_tags: for tag in set_stock_tags_model.active_hidden_tags: hidden_tag_info = CreateTagInfoModel(tag=tag, tag_reason=set_stock_tags_model.active_hidden_tags.get(tag)) if not is_tag_info_existed(tag=hidden_tag_info.tag, tag_type=TagType.hidden_tag): create_tag_info(tag_info=hidden_tag_info, tag_type=TagType.hidden_tag) with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: entity_id = set_stock_tags_model.entity_id main_tags = {} sub_tags = {} hidden_tags = {} entity_type, _, _ = decode_entity_id(entity_id) datas = StockTags.query_data( session=session, entity_id=entity_id, filters=[StockTags.entity_type == entity_type], limit=1, return_type="domain", ) if datas: current_stock_tags: StockTags = datas[0] # nothing change if not _stock_tags_need_update(current_stock_tags, set_stock_tags_model): logger.info(f"Not change stock_tags for {set_stock_tags_model.entity_id}") return current_stock_tags if current_stock_tags.main_tags: main_tags = dict(current_stock_tags.main_tags) if current_stock_tags.sub_tags: sub_tags = dict(current_stock_tags.sub_tags) if current_stock_tags.hidden_tags: hidden_tags = dict(current_stock_tags.hidden_tags) else: current_stock_tags = StockTags( entity_type=entity_type, id=f"{entity_id}_tags", entity_id=entity_id, timestamp=timestamp, ) # update tag if not keep_current: current_stock_tags.main_tag = set_stock_tags_model.main_tag current_stock_tags.main_tag_reason = set_stock_tags_model.main_tag_reason if set_stock_tags_model.sub_tag: current_stock_tags.sub_tag = set_stock_tags_model.sub_tag if set_stock_tags_model.sub_tag_reason: current_stock_tags.sub_tag_reason = set_stock_tags_model.sub_tag_reason # could update to None current_stock_tags.active_hidden_tags = set_stock_tags_model.active_hidden_tags # update tags main_tags[set_stock_tags_model.main_tag] = set_stock_tags_model.main_tag_reason if set_stock_tags_model.sub_tag: sub_tags[set_stock_tags_model.sub_tag] = set_stock_tags_model.sub_tag_reason if set_stock_tags_model.active_hidden_tags: for k, v in set_stock_tags_model.active_hidden_tags.items(): hidden_tags[k] = v current_stock_tags.main_tags = main_tags current_stock_tags.sub_tags = sub_tags current_stock_tags.hidden_tags = hidden_tags current_stock_tags.set_by_user = set_by_user session.add(current_stock_tags) session.commit() session.refresh(current_stock_tags) return current_stock_tags def build_tag_parameter(tag_type: TagType, tag, tag_reason, stock_tag: StockTags): hidden_tag = None hidden_tag_reason = None if tag_type == TagType.main_tag: main_tag = tag if main_tag in stock_tag.main_tags: main_tag_reason = stock_tag.main_tags.get(main_tag, tag_reason) else: main_tag_reason = tag_reason sub_tag = stock_tag.sub_tag sub_tag_reason = stock_tag.sub_tag_reason elif tag_type == TagType.sub_tag: sub_tag = tag if sub_tag in stock_tag.sub_tags: sub_tag_reason = stock_tag.sub_tags.get(sub_tag, tag_reason) else: sub_tag_reason = tag_reason main_tag = stock_tag.main_tag main_tag_reason = stock_tag.main_tag_reason elif tag_type == TagType.hidden_tag: hidden_tag = tag if stock_tag.hidden_tags and (hidden_tag in stock_tag.hidden_tags): hidden_tag_reason = stock_tag.hidden_tags.get(hidden_tag, tag_reason) else: hidden_tag_reason = tag_reason sub_tag = stock_tag.sub_tag sub_tag_reason = stock_tag.sub_tag_reason main_tag = stock_tag.main_tag main_tag_reason = stock_tag.main_tag_reason else: assert False return TagParameter( main_tag=main_tag, main_tag_reason=main_tag_reason, sub_tag=sub_tag, sub_tag_reason=sub_tag_reason, hidden_tag=hidden_tag, hidden_tag_reason=hidden_tag_reason, ) def batch_set_stock_tags(batch_set_stock_tags_model: BatchSetStockTagsModel): if not batch_set_stock_tags_model.entity_ids: return [] tag_info = CreateTagInfoModel(tag=batch_set_stock_tags_model.tag, tag_reason=batch_set_stock_tags_model.tag_reason) if not is_tag_info_existed(tag=tag_info.tag, tag_type=batch_set_stock_tags_model.tag_type): create_tag_info(tag_info=tag_info, tag_type=batch_set_stock_tags_model.tag_type) with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: tag_type = batch_set_stock_tags_model.tag_type if tag_type == TagType.main_tag: main_tag = batch_set_stock_tags_model.tag stock_tags: List[StockTags] = StockTags.query_data( entity_ids=batch_set_stock_tags_model.entity_ids, filters=[StockTags.main_tag != main_tag], session=session, return_type="domain", ) elif tag_type == TagType.sub_tag: sub_tag = batch_set_stock_tags_model.tag stock_tags: List[StockTags] = StockTags.query_data( entity_ids=batch_set_stock_tags_model.entity_ids, filters=[StockTags.sub_tag != sub_tag], session=session, return_type="domain", ) elif tag_type == TagType.hidden_tag: hidden_tag = batch_set_stock_tags_model.tag stock_tags: List[StockTags] = StockTags.query_data( entity_ids=batch_set_stock_tags_model.entity_ids, # 需要sqlite3版本>=3.37.0 filters=[func.json_extract(StockTags.active_hidden_tags, f'$."{hidden_tag}"') == None], session=session, return_type="domain", ) for stock_tag in stock_tags: tag_parameter: TagParameter = build_tag_parameter( tag_type=tag_type, tag=batch_set_stock_tags_model.tag, tag_reason=batch_set_stock_tags_model.tag_reason, stock_tag=stock_tag, ) if tag_type == TagType.hidden_tag: active_hidden_tags = {batch_set_stock_tags_model.tag: batch_set_stock_tags_model.tag_reason} else: active_hidden_tags = stock_tag.active_hidden_tags set_stock_tags_model = SetStockTagsModel( entity_id=stock_tag.entity_id, main_tag=tag_parameter.main_tag, main_tag_reason=tag_parameter.main_tag_reason, sub_tag=tag_parameter.sub_tag, sub_tag_reason=tag_parameter.sub_tag_reason, active_hidden_tags=active_hidden_tags, ) build_stock_tags( set_stock_tags_model=set_stock_tags_model, timestamp=now_pd_timestamp(), set_by_user=True, keep_current=False, ) session.refresh(stock_tag) return stock_tags def build_default_main_tag(entity_type="stock", entity_ids=None, force_rebuild=False): """ build default main tag by industry :param entity_type: :param entity_ids: entity ids :param force_rebuild: always rebuild it if True otherwise only build which not existed """ if not entity_ids: entity_ids = get_entity_ids_by_filter( entity_type=entity_type, provider="em", ignore_delist=True, ignore_st=False, ignore_new_stock=False ) if entity_type == "stock": df_block = Block.query_data(provider="em", filters=[Block.category == "industry"]) industry_codes = df_block["code"].tolist() block_stocks: List[BlockStock] = BlockStock.query_data( provider="em", filters=[BlockStock.code.in_(industry_codes), BlockStock.stock_id.in_(entity_ids)], return_type="domain", ) entity_industry_mapping = {block_stock.stock_id: block_stock.name for block_stock in block_stocks} elif entity_type == "stockus": datas: List[Stockus] = Stockus.query_data(entity_ids=entity_ids, return_type="domain") entity_industry_mapping = {item.entity_id: item.industry for item in datas} elif entity_type == "stockhk": datas: List[Stockhk] = Stockhk.query_data(entity_ids=entity_ids, return_type="domain") entity_industry_mapping = {item.entity_id: item.industry for item in datas} else: raise ValueError(f"Unsupported entity_type: {entity_type}") for entity_id in entity_ids: stock_tags: List[StockTags] = StockTags.query_data(entity_id=entity_id, return_type="domain") if not force_rebuild and stock_tags: logger.info(f"{entity_id} main tag has been set.") continue logger.info(f"build main tag for: {entity_id}") industry = entity_industry_mapping.get(entity_id) if industry: main_tag = get_main_tag_by_industry(industry_name=industry) main_tag_reason = f"来自行业:{industry}" else: main_tag = "其他" main_tag_reason = "其他" build_stock_tags( set_stock_tags_model=SetStockTagsModel( entity_id=entity_id, main_tag=main_tag, main_tag_reason=main_tag_reason, sub_tag=None, sub_tag_reason=None, active_hidden_tags=None, ), timestamp=now_pd_timestamp(), set_by_user=False, keep_current=False, ) def build_default_sub_tags(entity_ids=None): if not entity_ids: entity_ids = get_entity_ids_by_filter( provider="em", ignore_delist=True, ignore_st=False, ignore_new_stock=False ) for entity_id in entity_ids: logger.info(f"build sub tag for: {entity_id}") datas = StockTags.query_data(entity_id=entity_id, limit=1, return_type="domain") if not datas: raise AssertionError(f"Main tag must be set at first for {entity_id}") current_stock_tags: StockTags = datas[0] keep_current = False if current_stock_tags.set_by_user: logger.info(f"keep current tags set by user for: {entity_id}") keep_current = True current_sub_tag = current_stock_tags.sub_tag filters = [BlockStock.stock_id == entity_id] if current_sub_tag: logger.info(f"{entity_id} current_sub_tag: {current_sub_tag}") current_sub_tags = current_stock_tags.sub_tags.keys() filters = filters + [BlockStock.name.notin_(current_sub_tags)] df_block = Block.query_data(provider="em", filters=[Block.category == "concept"]) concept_codes = df_block["code"].tolist() filters = filters + [BlockStock.code.in_(concept_codes)] block_stocks: List[BlockStock] = BlockStock.query_data( provider="em", filters=filters, return_type="domain", ) if not block_stocks: logger.info(f"no block_stocks for: {entity_id}") continue for block_stock in block_stocks: sub_tag = block_stock.name if sub_tag in get_sub_tags(): sub_tag_reason = f"来自概念:{sub_tag}" main_tag = get_main_tag_by_sub_tag(sub_tag) main_tag_reason = sub_tag_reason if (main_tag == "其他" or not main_tag) and current_stock_tags.main_tag: main_tag = current_stock_tags.main_tag main_tag_reason = current_stock_tags.main_tag_reason build_stock_tags( set_stock_tags_model=SetStockTagsModel( entity_id=entity_id, main_tag=main_tag, main_tag_reason=main_tag_reason, sub_tag=sub_tag, sub_tag_reason=sub_tag_reason, active_hidden_tags=current_stock_tags.active_hidden_tags, ), timestamp=now_pd_timestamp(), set_by_user=False, keep_current=keep_current, ) else: logger.info(f"ignore {sub_tag} not in sub_tag_info yet") def get_tag_info_schema(tag_type: TagType): if tag_type == TagType.main_tag: data_schema = MainTagInfo elif tag_type == TagType.sub_tag: data_schema = SubTagInfo elif tag_type == TagType.hidden_tag: data_schema = HiddenTagInfo else: assert False return data_schema def is_tag_info_existed(tag: str, tag_type: TagType): data_schema = get_tag_info_schema(tag_type=tag_type) with contract_api.DBSession(provider="zvt", data_schema=data_schema)() as session: current_tags_info = data_schema.query_data( session=session, filters=[data_schema.tag == tag], return_type="domain" ) if current_tags_info: return True return False def create_tag_info(tag_info: CreateTagInfoModel, tag_type: TagType): """ Create tags info """ if is_tag_info_existed(tag=tag_info.tag, tag_type=tag_type): raise HTTPException(status_code=409, detail=f"This tag has been registered in {tag_type}") data_schema = get_tag_info_schema(tag_type=tag_type) with contract_api.DBSession(provider="zvt", data_schema=data_schema)() as session: timestamp = current_date() entity_id = "admin" tag_info_db = data_schema( id=f"admin_{tag_info.tag}", entity_id=entity_id, timestamp=timestamp, tag=tag_info.tag, tag_reason=tag_info.tag_reason, ) session.add(tag_info_db) session.commit() session.refresh(tag_info_db) return tag_info_db def build_stock_pool_info(create_stock_pool_info_model: CreateStockPoolInfoModel, timestamp): with contract_api.DBSession(provider="zvt", data_schema=StockPoolInfo)() as session: stock_pool_info = StockPoolInfo( entity_id="admin", timestamp=to_pd_timestamp(timestamp), id=f"admin_{create_stock_pool_info_model.stock_pool_name}", stock_pool_type=create_stock_pool_info_model.stock_pool_type.value, stock_pool_name=create_stock_pool_info_model.stock_pool_name, ) session.add(stock_pool_info) session.commit() session.refresh(stock_pool_info) return stock_pool_info def build_stock_pool(create_stock_pools_model: CreateStockPoolsModel, target_date=current_date()): with contract_api.DBSession(provider="zvt", data_schema=StockPools)() as session: entity_type = create_stock_pools_model.entity_type stock_pool_name = create_stock_pools_model.stock_pool_name if stock_pool_name not in get_stock_pool_names(): build_stock_pool_info( CreateStockPoolInfoModel(stock_pool_type=StockPoolType.custom, stock_pool_name=stock_pool_name), timestamp=target_date, ) # one instance per day for entity_type stock_pool_id = f"{entity_type}_{stock_pool_name}_{to_date_time_str(target_date)}" datas: List[StockPools] = StockPools.query_data( session=session, filters=[ StockPools.id == stock_pool_id, ], return_type="domain", ) if datas: stock_pool = datas[0] if create_stock_pools_model.insert_mode == InsertMode.overwrite: stock_pool.entity_ids = create_stock_pools_model.entity_ids else: stock_pool.entity_ids = list(set(stock_pool.entity_ids + create_stock_pools_model.entity_ids)) else: stock_pool = StockPools( entity_id=f"{entity_type}_{stock_pool_name}", timestamp=to_pd_timestamp(target_date), id=stock_pool_id, entity_type=entity_type, stock_pool_name=stock_pool_name, entity_ids=create_stock_pools_model.entity_ids, ) session.add(stock_pool) session.commit() session.refresh(stock_pool) return stock_pool def delete_stock_pool(stock_pool_name: str): with contract_api.DBSession(provider="zvt", data_schema=StockPoolInfo)() as session: stock_pool_info = StockPoolInfo.query_data( session=session, filters=[StockPoolInfo.stock_pool_name == stock_pool_name], return_type="domain", ) contract_api.del_data(data_schema=StockPools, filters=[StockPools.stock_pool_name == stock_pool_name]) if stock_pool_info: session.delete(stock_pool_info[0]) session.commit() return "success" return "not found" def get_main_tags_in_stock_pool(stock_pool_name: str) -> List[str]: with contract_api.DBSession(provider="zvt", data_schema=StockPools)() as session: stock_pool_info = StockPoolInfo.query_data( session=session, filters=[StockPoolInfo.stock_pool_name == stock_pool_name], return_type="domain", ) if not stock_pool_info: raise HTTPException(status_code=404, detail=f"Stock pool info {stock_pool_name} not found") entity_ids = None if stock_pool_name != "all": stock_pools: List[StockPools] = StockPools.query_data( session=session, filters=[StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if not stock_pools: raise HTTPException(status_code=404, detail=f"Stock pool {stock_pool_name} not found") entity_ids = stock_pools[0].entity_ids if not entity_ids: return [] df = StockTags.query_data( session=session, entity_ids=entity_ids, columns=["main_tag", "entity_id"], return_type="df", ) grouped_df = df.groupby("main_tag").agg(entity_count=("entity_id", "count")).reset_index() sorted_df = grouped_df.sort_values(by=["entity_count"], ascending=[False]) return sorted_df["main_tag"].tolist() def query_stock_tag_stats(query_stock_tag_stats_model: QueryStockTagStatsModel): with contract_api.DBSession(provider="zvt", data_schema=TagStats)() as session: datas = TagStats.query_data( session=session, filters=[TagStats.stock_pool_name == query_stock_tag_stats_model.stock_pool_name], order=TagStats.timestamp.desc(), limit=1, return_type="domain", ) if not datas: return [] target_date = datas[0].timestamp tag_stats_list: List[dict] = TagStats.query_data( session=session, filters=[ TagStats.stock_pool_name == query_stock_tag_stats_model.stock_pool_name, TagStats.timestamp == target_date, ], return_type="dict", order=TagStats.position.asc(), ) if query_stock_tag_stats_model.query_type == TagStatsQueryType.simple: return tag_stats_list entity_ids = flatten_list([tag_stats["entity_ids"] for tag_stats in tag_stats_list]) # get stocks meta stocks = Stock.query_data(provider="em", entity_ids=entity_ids, return_type="domain") entity_map = {item.entity_id: item for item in stocks} # get stock tags tags_dict = StockTags.query_data( session=session, filters=[StockTags.entity_id.in_(entity_ids)], return_type="dict", ) entity_tags_map = {item["entity_id"]: item for item in tags_dict} # get stock system tags system_tags_dict = StockSystemTags.query_data( session=session, filters=[StockSystemTags.timestamp == target_date, StockSystemTags.entity_id.in_(entity_ids)], return_type="dict", ) entity_system_tags_map = {item["entity_id"]: item for item in system_tags_dict} for tag_stats in tag_stats_list: stock_details = [] for entity_id in tag_stats["entity_ids"]: stock_details_model = { "entity_id": entity_id, "main_tag": tag_stats["main_tag"], "code": entity_map.get(entity_id).code, "name": entity_map.get(entity_id).name, } stock_tags = entity_tags_map.get(entity_id) stock_details_model["sub_tag"] = stock_tags["sub_tag"] if stock_tags["active_hidden_tags"] is not None: stock_details_model["hidden_tags"] = stock_tags["active_hidden_tags"].keys() else: stock_details_model["hidden_tags"] = None stock_system_tags = entity_system_tags_map.get(entity_id) stock_details_model = fill_dict(stock_system_tags, stock_details_model) stock_details.append(stock_details_model) tag_stats["stock_details"] = stock_details return tag_stats_list def refresh_main_tag_by_sub_tag(stock_tag: StockTags, set_by_user=False) -> StockTags: if not stock_tag.sub_tags: logger.warning(f"{stock_tag.entity_id} has no sub_tags yet") return stock_tag sub_tag = stock_tag.sub_tag sub_tag_reason = stock_tag.sub_tags[sub_tag] main_tag = get_main_tag_by_sub_tag(sub_tag) main_tag_reason = sub_tag_reason if main_tag == "其他": main_tag = stock_tag.main_tag main_tag_reason = stock_tag.main_tag_reason set_stock_tags_model = SetStockTagsModel( entity_id=stock_tag.entity_id, main_tag=main_tag, main_tag_reason=main_tag_reason, sub_tag=sub_tag, sub_tag_reason=sub_tag_reason, active_hidden_tags=stock_tag.active_hidden_tags, ) logger.info(f"set_stock_tags_model:{set_stock_tags_model}") return build_stock_tags( set_stock_tags_model=set_stock_tags_model, timestamp=stock_tag.timestamp, set_by_user=set_by_user, keep_current=False, ) def refresh_all_main_tag_by_sub_tag(): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: stock_tags = StockTags.query_data( session=session, return_type="domain", ) for stock_tag in stock_tags: refresh_main_tag_by_sub_tag(stock_tag) def reset_to_default_main_tag(current_main_tag: str): df = StockTags.query_data( filters=[StockTags.main_tag == current_main_tag], columns=[StockTags.entity_id], return_type="df", ) entity_ids = df["entity_id"].tolist() if not entity_ids: logger.info(f"all stocks with main_tag: {current_main_tag} has been reset") return build_default_main_tag(entity_ids=entity_ids, force_rebuild=True) def activate_industry_list(industry_list: List[str]): df_block = Block.query_data(provider="em", filters=[Block.category == "industry", Block.name.in_(industry_list)]) industry_codes = df_block["code"].tolist() block_stocks: List[BlockStock] = BlockStock.query_data( provider="em", filters=[BlockStock.code.in_(industry_codes)], return_type="domain", ) entity_ids = [block_stock.stock_id for block_stock in block_stocks] if not entity_ids: logger.info(f"No stocks in {industry_list}") return build_default_main_tag(entity_ids=entity_ids, force_rebuild=True) def activate_sub_tags(activate_sub_tags_model: ActivateSubTagsModel): sub_tags = activate_sub_tags_model.sub_tags with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: result = {} for sub_tag in sub_tags: # df = StockTags.query_data( # session=session, # filters=[StockTags.sub_tag != sub_tag], # columns=[StockTags.entity_id], # return_type="df", # ) # entity_ids = df["entity_id"].tolist() entity_ids = None # stock_tag with sub_tag but not set to related main_tag yet stock_tags = StockTags.query_data( session=session, entity_ids=entity_ids, # 需要sqlite3版本>=3.37.0 filters=[func.json_extract(StockTags.sub_tags, f'$."{sub_tag}"') != None], return_type="domain", ) if not stock_tags: logger.info(f"all stocks with sub_tag: {sub_tag} has been activated") continue for stock_tag in stock_tags: stock_tag.sub_tag = sub_tag session.commit() session.refresh(stock_tag) result[stock_tag.entity_id] = refresh_main_tag_by_sub_tag(stock_tag, set_by_user=True) return result def delete_main_tag(main_tag: str): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: stock_tags = StockTags.query_data( session=session, # 需要sqlite3版本>=3.37.0 filters=[func.json_extract(StockTags.main_tags, f'$."{main_tag}"') != None], return_type="domain", ) sql = text(f'update industry_info set main_tag = "其他" where main_tag = "{main_tag}"') session.execute(sql) session.commit() sql = text(f'update sub_tag_info set main_tag = "其他" where main_tag = "{main_tag}"') session.execute(sql) session.commit() for stock_tag in stock_tags: logger.info(f"remove main_tag: {main_tag} for {stock_tag.entity_id}") main_tags = dict(stock_tag.main_tags) logger.info(f"main_tags before deleted: {main_tags}") main_tags.pop(main_tag) logger.info(f"main_tags after deleted: {main_tags}") stock_tag.main_tags = main_tags if main_tag == stock_tag.main_tag: logger.info(f"main_tag before deleted: {stock_tag.main_tag}") if main_tags: # set to first main tag stock_tag.main_tag = list(main_tags.keys())[0] stock_tag.main_tag_reason = main_tags[stock_tag.main_tag] else: stock_tag.main_tag = "其他" stock_tag.main_tag_reason = "其他" logger.info(f"main_tag after deleted: {stock_tag.main_tag}") session.commit() def delete_sub_tag(sub_tag: str): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: stock_tags = StockTags.query_data( session=session, # 需要sqlite3版本>=3.37.0 filters=[func.json_extract(StockTags.sub_tags, f'$."{sub_tag}"') != None], return_type="domain", ) for stock_tag in stock_tags: logger.info(f"remove sub_tag: {sub_tag} for {stock_tag.entity_id}") sub_tags = dict(stock_tag.sub_tags) logger.info(f"sub_tags before deleted: {sub_tags}") sub_tags.pop(sub_tag) logger.info(f"sub_tags after deleted: {sub_tags}") stock_tag.sub_tags = sub_tags if sub_tag == stock_tag.sub_tag: logger.info(f"sub_tag before deleted: {stock_tag.sub_tag}") if sub_tags: # set to first main tag stock_tag.sub_tag = list(sub_tags.keys())[0] stock_tag.sub_tag_reason = sub_tags[stock_tag.sub_tag] else: stock_tag.sub_tag = "其他" stock_tag.sub_tag_reason = "其他" logger.info(f"sub_tag after deleted: {stock_tag.sub_tag}") session.commit() def delete_hidden_tag(hidden_tag: str): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: stock_tags = StockTags.query_data( session=session, # 需要sqlite3版本>=3.37.0 filters=[func.json_extract(StockTags.hidden_tags, f'$."{hidden_tag}"') != None], return_type="domain", ) for stock_tag in stock_tags: logger.info(f"delete hidden_tag: {hidden_tag} for {stock_tag.entity_id}") hidden_tags = dict(stock_tag.hidden_tags) logger.info(f"hidden_tags before deleted: {hidden_tags}") hidden_tags.pop(hidden_tag) stock_tag.hidden_tags = hidden_tags logger.info(f"hidden_tags after deleted: {hidden_tags}") if stock_tag.active_hidden_tags and (hidden_tag in stock_tag.active_hidden_tags): active_hidden_tags = dict(stock_tag.active_hidden_tags) logger.info(f"active_hidden_tags before deleted: {active_hidden_tags}") active_hidden_tags.pop(hidden_tag) stock_tag.active_hidden_tags = active_hidden_tags logger.info(f"active_hidden_tags after deleted: {active_hidden_tags}") session.commit() def delete_tag(tag: str, tag_type: TagType): data_schema = get_tag_info_schema(tag_type=tag_type) with contract_api.DBSession(provider="zvt", data_schema=data_schema)() as session: current_tags_info = data_schema.query_data( session=session, filters=[data_schema.tag == tag], return_type="domain" ) if not current_tags_info: logger.info(f"tag_type: {tag_type}, tag: {tag} not exists, ignore delete tag info") else: logger.info(f"delete tag info, tag_type: {tag_type}, tag: {tag} ") session.delete(current_tags_info[0]) session.commit() if tag_type == TagType.main_tag: delete_main_tag(main_tag=tag) elif tag_type == TagType.sub_tag: delete_sub_tag(sub_tag=tag) elif tag_type == TagType.hidden_tag: delete_hidden_tag(hidden_tag=tag) else: raise HTTPException(status_code=400, detail=f"Unsupported tag type: {tag_type}") def _create_main_tag_if_not_existed(main_tag, main_tag_reason): main_tag_info = CreateTagInfoModel(tag=main_tag, tag_reason=main_tag_reason) if not is_tag_info_existed(tag=main_tag_info.tag, tag_type=TagType.main_tag): create_tag_info(tag_info=main_tag_info, tag_type=TagType.main_tag) def get_main_tag_industry_relation(main_tag): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: df = IndustryInfo.query_data( session=session, columns=[IndustryInfo.industry_name], filters=[IndustryInfo.main_tag == main_tag], return_type="df", ) return {"main_tag": main_tag, "industry_list": df["industry_name"].tolist()} def get_main_tag_sub_tag_relation(main_tag): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: df = SubTagInfo.query_data( session=session, columns=[SubTagInfo.tag], filters=[SubTagInfo.main_tag == main_tag], return_type="df", ) return {"main_tag": main_tag, "sub_tag_list": df["tag"].tolist()} def build_main_tag_industry_relation(build_relation_model: BuildMainTagIndustryRelationModel): with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: main_tag = build_relation_model.main_tag _create_main_tag_if_not_existed(main_tag=main_tag, main_tag_reason=main_tag) industry_list = build_relation_model.industry_list datas: List[IndustryInfo] = IndustryInfo.query_data( session=session, filters=[IndustryInfo.main_tag == main_tag, IndustryInfo.industry_name.notin_(industry_list)], return_type="domain", ) for data in datas: data.main_tag = "其他" session.commit() industry_info_list: List[IndustryInfo] = IndustryInfo.query_data( session=session, filters=[IndustryInfo.industry_name.in_(industry_list)], return_type="domain", ) for industry_info in industry_info_list: industry_info.main_tag = main_tag session.commit() if build_relation_model.activate: # activate industry activate_industry_list(industry_list=industry_list) def build_main_tag_sub_tag_relation(build_relation_model: BuildMainTagIndustryRelationModel): with contract_api.DBSession(provider="zvt", data_schema=SubTagInfo)() as session: main_tag = build_relation_model.main_tag _create_main_tag_if_not_existed(main_tag=main_tag, main_tag_reason=main_tag) sub_tag_list = build_relation_model.sub_tag_list # update others to "其他" datas: List[SubTagInfo] = SubTagInfo.query_data( session=session, filters=[SubTagInfo.main_tag == main_tag, SubTagInfo.tag.notin_(sub_tag_list)], return_type="domain", ) for data in datas: data.main_tag = "其他" session.commit() # update sub tag info sub_tag_info_list: List[SubTagInfo] = SubTagInfo.query_data( session=session, filters=[SubTagInfo.tag.in_(sub_tag_list)], return_type="domain", ) for sub_tag_info in sub_tag_info_list: sub_tag_info.main_tag = main_tag session.commit() if build_relation_model.activate: # activate sub tags activate_sub_tags(ActivateSubTagsModel(sub_tags=sub_tag_list)) def change_main_tag(change_main_tag_model: ChangeMainTagModel): current_main_tag = change_main_tag_model.current_main_tag new_main_tag = change_main_tag_model.new_main_tag _create_main_tag_if_not_existed(main_tag=new_main_tag, main_tag_reason=new_main_tag) with contract_api.DBSession(provider="zvt", data_schema=StockTags)() as session: stock_tags: List[StockTags] = StockTags.query_data( filters=[StockTags.main_tag == current_main_tag], session=session, return_type="domain", ) for stock_tag in stock_tags: tag_parameter: TagParameter = build_tag_parameter( tag_type=TagType.main_tag, tag=new_main_tag, tag_reason=new_main_tag, stock_tag=stock_tag, ) set_stock_tags_model = SetStockTagsModel( entity_id=stock_tag.entity_id, main_tag=tag_parameter.main_tag, main_tag_reason=tag_parameter.main_tag_reason, sub_tag=tag_parameter.sub_tag, sub_tag_reason=tag_parameter.sub_tag_reason, active_hidden_tags=stock_tag.active_hidden_tags, ) build_stock_tags( set_stock_tags_model=set_stock_tags_model, timestamp=now_pd_timestamp(), set_by_user=True, keep_current=False, ) session.refresh(stock_tag) return stock_tags if __name__ == "__main__": # print(get_main_tags_in_stock_pool("涨停梯队")) # print(delete_tag(tag="赛马概念", tag_type=TagType.sub_tag)) activate_industry_list(industry_list=["航天航空"]) # activate_sub_tags(ActivateSubTagsModel(sub_tags=["航天概念", "天基互联", "北斗导航", "通用航空"])) # build_default_main_tag(entity_type="stockhk") # the __all__ is generated __all__ = [ "get_stock_tag_options", "build_stock_tags", "build_tag_parameter", "batch_set_stock_tags", "build_default_main_tag", "build_default_sub_tags", "get_tag_info_schema", "is_tag_info_existed", "create_tag_info", "build_stock_pool_info", "build_stock_pool", "query_stock_tag_stats", "refresh_main_tag_by_sub_tag", "refresh_all_main_tag_by_sub_tag", "reset_to_default_main_tag", "activate_industry_list", "activate_sub_tags", ] ================================================ FILE: src/zvt/tag/tag_stats.py ================================================ # -*- coding: utf-8 -*- import logging from typing import List import pandas as pd import sqlalchemy from zvt.api.kdata import get_kdata_schema from zvt.contract import AdjustType, IntervalLevel from zvt.contract.api import df_to_db, get_db_session from zvt.domain.quotes import KdataCommon from zvt.factors.top_stocks import TopStocks, get_top_stocks from zvt.tag.common import InsertMode from zvt.tag.tag_models import CreateStockPoolsModel from zvt.tag.tag_schemas import TagStats, StockTags, StockPools from zvt.tag.tag_service import build_stock_pool from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp, current_date, next_date logger = logging.getLogger(__name__) def build_system_stock_pools(start_date, entity_type, force_update=False): if entity_type == "stock": stock_pool_names = ["主线", "年线", "大局"] elif entity_type == "stockus": stock_pool_names = ["美股主线"] elif entity_type == "stockhk": stock_pool_names = ["港股主线"] else: raise ValueError(f"entity_type {entity_type} not supported") for stock_pool_name in stock_pool_names: if not force_update: datas = StockPools.query_data( limit=1, filters=[StockPools.entity_type == entity_type, StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.desc(), return_type="domain", ) if datas: start_date = max(next_date(the_time=datas[0].timestamp), start_date) df = TopStocks.query_data( start_timestamp=start_date, filters=[TopStocks.entity_type == entity_type], columns=[TopStocks.timestamp], order=TopStocks.timestamp.asc(), ) if not pd_is_not_null(df): logger.info(f"no data for top_stocks {entity_type} {start_date}") continue dates = df["timestamp"].tolist() for target_date in dates: logger.info(f"build_system_stock_pools {entity_type} {stock_pool_name} to {target_date}") if stock_pool_name == "主线" or stock_pool_name == "美股主线" or stock_pool_name == "港股主线": short_stocks = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type="short") long_stocks = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type="long") entity_ids = list(set(short_stocks + long_stocks)) elif stock_pool_name == "年线": small_stocks = get_top_stocks( entity_type=entity_type, target_date=target_date, return_type="small_vol_up" ) big_stocks = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type="big_vol_up") entity_ids = list(set(small_stocks + big_stocks)) elif stock_pool_name == "大局": entity_ids = get_top_stocks(entity_type=entity_type, target_date=target_date, return_type="all") else: assert False if not entity_ids: logger.info(f"no data for {entity_type} {stock_pool_name} {target_date}") break create_stock_pools_model: CreateStockPoolsModel = CreateStockPoolsModel( entity_type=entity_type, stock_pool_name=stock_pool_name, entity_ids=entity_ids, insert_mode=InsertMode.overwrite, ) build_stock_pool(create_stock_pools_model, target_date=target_date) def build_stock_pool_tag_stats( stock_pool_name, entity_type="stock", force_rebuild_latest=False, target_date=None, adjust_type=AdjustType.qfq, provider="em", ): datas = TagStats.query_data( limit=1, filters=[TagStats.entity_type == entity_type, TagStats.stock_pool_name == stock_pool_name], order=TagStats.timestamp.desc(), return_type="domain", ) start = target_date current_df = None if datas: if force_rebuild_latest: session = get_db_session("zvt", data_schema=TagStats) session.query(TagStats).filter(TagStats.entity_type == entity_type).filter( TagStats.stock_pool_name == stock_pool_name ).filter(TagStats.timestamp == datas[0].timestamp).delete() session.commit() return build_stock_pool_tag_stats(stock_pool_name=stock_pool_name, force_rebuild_latest=False) latest_tag_stats_timestamp = datas[0].timestamp current_df = TagStats.query_data( filters=[ TagStats.entity_type == entity_type, TagStats.stock_pool_name == stock_pool_name, TagStats.timestamp == latest_tag_stats_timestamp, ] ) start = next_date(the_time=latest_tag_stats_timestamp) stock_pools: List[StockPools] = StockPools.query_data( start_timestamp=start, filters=[StockPools.entity_type == entity_type, StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.asc(), return_type="domain", ) if not stock_pools: logger.info(f"no data to build tag stats: {entity_type} {stock_pool_name} {start}") return None for stock_pool in stock_pools: target_date = stock_pool.timestamp logger.info(f"build_stock_pool_tag_stats for {entity_type} {stock_pool_name} {target_date}") entity_ids = stock_pool.entity_ids tags_df = StockTags.query_data( entity_ids=entity_ids, filters=[StockTags.entity_type == entity_type], return_type="df", index="entity_id" ) kdata_schema: KdataCommon = get_kdata_schema( entity_type=entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=adjust_type ) kdata_df = kdata_schema.query_data( provider=provider, entity_ids=entity_ids, filters=[kdata_schema.timestamp == to_pd_timestamp(target_date)], columns=[kdata_schema.entity_id, kdata_schema.name, kdata_schema.turnover], index="entity_id", ) df = pd.concat([tags_df, kdata_df[["turnover", "name"]]], axis=1) grouped_df = ( df.groupby("main_tag") .agg( turnover=("turnover", "sum"), entity_count=("entity_id", "count"), entity_ids=("entity_id", lambda entity_id: list(entity_id)), ) .reset_index() ) sorted_df = grouped_df.sort_values(by=["turnover", "entity_count"], ascending=[False, False]) # sorted_df = grouped_df.sort_values(by=["entity_count"], ascending=[False]) sorted_df = sorted_df.reset_index(drop=True) sorted_df["position"] = sorted_df.index sorted_df["is_main_line"] = sorted_df.index < 5 sorted_df["main_line_continuous_days"] = sorted_df["is_main_line"].apply(lambda x: 1 if x else 0) # logger.info(f"current_df\n: {current_df}") if pd_is_not_null(current_df): sorted_df.set_index("main_tag", inplace=True, drop=False) current_df.set_index("main_tag", inplace=True, drop=False) common_index = sorted_df[sorted_df["is_main_line"]].index.intersection( current_df[current_df["is_main_line"]].index ) pre_selected = current_df.loc[common_index] if pd_is_not_null(pre_selected): pre_selected = pre_selected.reindex(sorted_df.index, fill_value=0) sorted_df["main_line_continuous_days"] = ( sorted_df["main_line_continuous_days"] + pre_selected["main_line_continuous_days"] ) sorted_df["entity_id"] = f"{entity_type}_{stock_pool_name}" sorted_df["timestamp"] = target_date sorted_df["entity_type"] = entity_type sorted_df["stock_pool_name"] = stock_pool_name sorted_df["id"] = sorted_df[["entity_id", "timestamp", "main_tag"]].apply( lambda x: "_".join(x.astype(str)), axis=1 ) df_to_db( provider="zvt", df=sorted_df, data_schema=TagStats, force_update=True, dtype={"entity_ids": sqlalchemy.JSON}, ) current_df = sorted_df def refresh_stock_pool(stock_pool_name, entity_ids, insert_mode=InsertMode.append, target_date=current_date()): create_stock_pools_model: CreateStockPoolsModel = CreateStockPoolsModel( stock_pool_name=stock_pool_name, entity_ids=entity_ids, insert_mode=insert_mode ) build_stock_pool(create_stock_pools_model, target_date=target_date) def get_tags_order_by_position(stock_pool_name, entity_type="stock"): latest_data = TagStats.query_data( filters=[TagStats.entity_type == entity_type, TagStats.stock_pool_name == stock_pool_name], order=TagStats.timestamp.desc(), limit=1, return_type="domain", ) target_date = to_pd_timestamp(latest_data[0].timestamp) tag_stats_list: List[TagStats] = TagStats.query_data( filters=[ TagStats.timestamp == to_pd_timestamp(target_date), TagStats.entity_type == entity_type, TagStats.stock_pool_name == stock_pool_name, ], order=TagStats.position.asc(), return_type="domain", ) all_tags = [item.main_tag for item in tag_stats_list] main_line_tag1 = all_tags[0:1] main_line_tag2 = all_tags[1:2] sub_line_tags1 = all_tags[2:4] sub_line_tags2 = all_tags[4:6] following_tags = all_tags[6:8] other_tags = all_tags[8:] return main_line_tag1, main_line_tag2, sub_line_tags1, sub_line_tags2, following_tags, other_tags if __name__ == "__main__": # build_system_stock_pools() # build_stock_pool_tag_stats(stock_pool_name="主线", force_rebuild_latest=True) # build_stock_pool_tag_stats(stock_pool_name="年线") print(get_tags_order_by_position(stock_pool_name="主线", entity_type="stockus")) # the __all__ is generated __all__ = ["build_system_stock_pools", "build_stock_pool_tag_stats", "refresh_stock_pool"] ================================================ FILE: src/zvt/tag/tag_utils.py ================================================ # -*- coding: utf-8 -*- import json import os from typing import List, Dict import pandas as pd from zvt import zvt_env from zvt.contract.api import df_to_db from zvt.domain import Block from zvt.tag.common import StockPoolType from zvt.tag.tag_schemas import MainTagInfo, SubTagInfo, HiddenTagInfo, StockPoolInfo, IndustryInfo from zvt.utils.time_utils import now_pd_timestamp def _get_default_industry_main_tag_mapping() -> Dict[str, str]: result = {} with open(os.path.join(zvt_env["resource_path"], "industry_main_tag_mapping.json"), encoding="utf-8") as f: result = json.load(f) with open(os.path.join(zvt_env["resource_path"], "us_industry_main_tag_mapping.json"), encoding="utf-8") as f: result.update(json.load(f)) with open(os.path.join(zvt_env["resource_path"], "hk_industry_main_tag_mapping.json"), encoding="utf-8") as f: result.update(json.load(f)) return result def _get_default_main_tag_industry_mapping() -> Dict[str, List[str]]: mapping = _get_default_industry_main_tag_mapping() result = {} for industry, main_tag in mapping.items(): result.setdefault(main_tag, []) result.get(main_tag).append(industry) return result def _get_default_concept_main_tag_mapping() -> Dict[str, str]: with open(os.path.join(zvt_env["resource_path"], "concept_main_tag_mapping.json"), encoding="utf-8") as f: return json.load(f) def _get_default_main_tag_concept_mapping() -> Dict[str, List[str]]: mapping = _get_default_concept_main_tag_mapping() result = {} for concept, main_tag in mapping.items(): result.setdefault(main_tag, []) result.get(main_tag).append(concept) return result def _get_initial_sub_tags() -> List[str]: return list(_get_default_concept_main_tag_mapping().keys()) def _get_industry_list(): df = Block.query_data( filters=[Block.category == "industry"], columns=[Block.name], return_type="df", order=Block.timestamp.desc() ) return df["name"].tolist() def _get_concept_list(): df = Block.query_data( filters=[Block.category == "concept"], columns=[Block.name], return_type="df", order=Block.timestamp.desc() ) return df["name"].tolist() def _check_missed_industry(): current_industry_list = _get_default_industry_main_tag_mapping().keys() return list(set(_get_industry_list()) - set(current_industry_list)) def _check_missed_concept(): current_concept_list = _get_default_concept_main_tag_mapping().keys() return list(set(_get_concept_list()) - set(current_concept_list)) def _get_initial_main_tag_info(): timestamp = now_pd_timestamp() entity_id = "admin" from_industry = [ { "id": f"{entity_id}_{main_tag}", "entity_id": entity_id, "timestamp": timestamp, "tag": main_tag, "tag_reason": f"来自这些行业:{industry}", } for main_tag, industry in _get_default_main_tag_industry_mapping().items() ] from_concept = [] for tag, concepts in _get_default_main_tag_concept_mapping().items(): if tag not in _get_default_main_tag_industry_mapping(): from_concept.append( { "id": f"{entity_id}_{tag}", "entity_id": entity_id, "timestamp": timestamp, "tag": tag, "tag_reason": f"来自这些概念:{','.join(concepts)}", } ) return from_industry + from_concept def _get_initial_industry_info(): timestamp = now_pd_timestamp() entity_id = "admin" industry_info = [ { "id": f"{entity_id}_{industry}", "entity_id": entity_id, "timestamp": timestamp, "industry_name": industry, "description": industry, "main_tag": main_tag, } for industry, main_tag in _get_default_industry_main_tag_mapping().items() ] return industry_info def _get_initial_sub_tag_info(): timestamp = now_pd_timestamp() entity_id = "admin" return [ { "id": f"{entity_id}_{sub_tag}", "entity_id": entity_id, "timestamp": timestamp, "tag": sub_tag, "tag_reason": sub_tag, "main_tag": main_tag, } for sub_tag, main_tag in _get_default_concept_main_tag_mapping().items() ] def _get_initial_stock_pool_info(): timestamp = now_pd_timestamp() entity_id = "admin" return [ { "id": f"{entity_id}_{stock_pool_name}", "entity_id": entity_id, "timestamp": timestamp, "stock_pool_type": StockPoolType.system.value, "stock_pool_name": stock_pool_name, } for stock_pool_name in ["主线", "年线", "大局", "A股", "美股主线", "港股主线"] ] _hidden_tags = { "中字头": "央企，国资委控股", "核心资产": "高ROE 高现金流 高股息 低应收 低资本开支 低财务杠杆 有增长", "高股息": "高股息", "微盘股": "市值50亿以下", "次新股": "上市未满两年", } def _get_initial_hidden_tag_info(): timestamp = now_pd_timestamp() entity_id = "admin" return [ { "id": f"{entity_id}_{tag}", "entity_id": entity_id, "timestamp": timestamp, "tag": tag, "tag_reason": tag_reason, } for tag, tag_reason in _hidden_tags.items() ] def build_initial_main_tag_info(force_update=False): main_tag_info_list = _get_initial_main_tag_info() df = pd.DataFrame.from_records(main_tag_info_list) df_to_db(df=df, data_schema=MainTagInfo, provider="zvt", force_update=force_update) def build_initial_industry_info(force_update=False): initial_industry_info = _get_initial_industry_info() df = pd.DataFrame.from_records(initial_industry_info) df_to_db(df=df, data_schema=IndustryInfo, provider="zvt", force_update=force_update) def build_initial_sub_tag_info(force_update=False): sub_tag_info_list = _get_initial_sub_tag_info() df = pd.DataFrame.from_records(sub_tag_info_list) df_to_db(df=df, data_schema=SubTagInfo, provider="zvt", force_update=force_update) def build_initial_stock_pool_info(force_update=False): stock_pool_info_list = _get_initial_stock_pool_info() df = pd.DataFrame.from_records(stock_pool_info_list) df_to_db(df=df, data_schema=StockPoolInfo, provider="zvt", force_update=force_update) def build_initial_hidden_tag_info(force_update=False): hidden_tag_info_list = _get_initial_hidden_tag_info() df = pd.DataFrame.from_records(hidden_tag_info_list) df_to_db(df=df, data_schema=HiddenTagInfo, provider="zvt", force_update=force_update) def get_main_tags(): df = MainTagInfo.query_data(columns=[MainTagInfo.tag]) return df["tag"].tolist() def get_main_tag_by_sub_tag(sub_tag): datas: List[SubTagInfo] = SubTagInfo.query_data(filters=[SubTagInfo.tag == sub_tag], return_type="domain") if datas: return datas[0].main_tag else: return _get_default_concept_main_tag_mapping().get(sub_tag, "其他") def get_main_tag_by_industry(industry_name): datas: List[IndustryInfo] = IndustryInfo.query_data( filters=[IndustryInfo.industry_name == industry_name], return_type="domain" ) if datas: return datas[0].main_tag else: _get_default_industry_main_tag_mapping().get(industry_name, "其他") def get_sub_tags(): df = SubTagInfo.query_data(columns=[SubTagInfo.tag]) return df["tag"].tolist() def get_hidden_tags(): df = HiddenTagInfo.query_data(columns=[HiddenTagInfo.tag]) return df["tag"].tolist() def get_stock_pool_names(): df = StockPoolInfo.query_data(columns=[StockPoolInfo.stock_pool_name]) return df["stock_pool_name"].tolist() def match_tag_by_type(alias, tag_type="main_tag"): if tag_type == "main_tag": tags = get_main_tags() elif tag_type == "sub_tag": tags = get_sub_tags() elif tag_type == "industry": tags = _get_industry_list() else: assert False max_intersection_length = 0 max_tag = None for tag in tags: intersection_length = len(set(alias) & set(tag)) # at least 2 same chars if intersection_length < 2: continue if intersection_length > max_intersection_length: max_intersection_length = intersection_length max_tag = tag return max_tag def match_tag(alias): tag = match_tag_by_type(alias, tag_type="main_tag") if tag: return "main_tag", tag tag = match_tag_by_type(alias, tag_type="sub_tag") if tag: return "sub_tag", tag tag = match_tag_by_type(alias, tag_type="industry") if tag: return "main_tag", get_main_tag_by_industry(tag) return "new_tag", alias if __name__ == "__main__": print(build_initial_industry_info()) print(build_initial_main_tag_info()) # the __all__ is generated __all__ = [ "build_initial_main_tag_info", "build_initial_industry_info", "build_initial_sub_tag_info", "build_initial_stock_pool_info", "build_initial_hidden_tag_info", "get_main_tags", "get_main_tag_by_sub_tag", "get_main_tag_by_industry", "get_sub_tags", "get_hidden_tags", "get_stock_pool_names", "match_tag_by_type", "match_tag", ] ================================================ FILE: src/zvt/tag/tagger.py ================================================ # -*- coding: utf-8 -*- import logging from typing import Type from zvt.contract import Mixin from zvt.contract import TradableEntity from zvt.contract.api import get_db_session from zvt.contract.base_service import OneStateService from zvt.contract.zvt_info import TaggerState from zvt.domain import Stock from zvt.tag.tag_schemas import StockTags logger = logging.getLogger(__name__) class Tagger(OneStateService): state_schema = TaggerState entity_schema: Type[TradableEntity] = None data_schema: Type[Mixin] = None start_timestamp = "2018-01-01" def __init__(self, force=False) -> None: super().__init__() assert self.entity_schema is not None assert self.data_schema is not None self.force = force self.session = get_db_session(provider="zvt", data_schema=self.data_schema) if self.state and not self.force: logger.info(f"get start_timestamp from state") self.start_timestamp = self.state["current_timestamp"] logger.info(f"tag start_timestamp: {self.start_timestamp}") def tag(self): raise NotImplementedError class StockTagger(Tagger): data_schema = StockTags entity_schema = Stock def tag(self): raise NotImplementedError # the __all__ is generated __all__ = ["Tagger", "StockTagger"] ================================================ FILE: src/zvt/tasks/init_tag_system.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import Block, BlockStock, Stock from zvt.tag.tag_service import build_default_main_tag, build_default_sub_tags from zvt.tag.tag_utils import ( build_initial_stock_pool_info, build_initial_main_tag_info, build_initial_sub_tag_info, build_initial_industry_info, ) from zvt.trading.trading_service import build_default_query_stock_quote_setting if __name__ == "__main__": # init industry info build_initial_industry_info() # init tag info build_initial_main_tag_info() build_initial_sub_tag_info() build_initial_stock_pool_info() build_default_query_stock_quote_setting() Stock.record_data(provider="em") Block.record_data(provider="em", sleeping_time=0) BlockStock.record_data(provider="em", sleeping_time=0) # init default main tag build_default_main_tag() # init default sub tags build_default_sub_tags() ================================================ FILE: src/zvt/tasks/qmt_data_runner.py ================================================ # -*- coding: utf-8 -*- import logging import time import pandas as pd from xtquant import xtdata from zvt import init_log from zvt.broker.qmt.qmt_quote import get_qmt_stocks from zvt.contract import AdjustType from zvt.recorders.qmt.meta import QMTStockRecorder from zvt.recorders.qmt.quotes import QMTStockKdataRecorder logger = logging.getLogger(__name__) def download_data(download_tick=False): period = "1d" xtdata.download_sector_data() stock_codes = get_qmt_stocks() stock_codes = sorted(stock_codes) count = len(stock_codes) download_status = {"ok": False} def update_progress(data, download_status: dict = download_status): logger.info(data) finished = data["finished"] total = data["total"] download_status["finished"] = finished download_status["total"] = total if finished == total: download_status["ok"] = True start_time = time.time() xtdata.download_history_data2(stock_list=stock_codes, period=period, callback=update_progress) while True: logger.info(f"current download_status:{download_status}") if download_status["ok"]: logger.info(f"finish download 1d kdata") break cost_time = time.time() - start_time if cost_time >= 60 * 30: logger.info(f"timeout download 1d kdata") break time.sleep(10) QMTStockRecorder().run() QMTStockKdataRecorder(adjust_type=AdjustType.qfq, sleeping_time=0).run() xtdata.download_financial_data2( stock_list=stock_codes, table_list=["Capital"], start_time="", end_time="", callback=lambda x: print(x) ) logger.info("download capital data ok") if download_tick: for index, stock_code in enumerate(stock_codes): logger.info(f"run to {index + 1}/{count}") records = xtdata.get_market_data( stock_list=[stock_code], period=period, count=5, dividend_type="front", fill_data=False, ) dfs = [] for col in records: df = records[col].T df.columns = [col] dfs.append(df) kdatas = pd.concat(dfs, axis=1) start_time = kdatas.index.to_list()[0] xtdata.download_history_data(stock_code, period="tick", start_time=start_time) logger.info(f"download {stock_code} tick from {start_time} ok") if __name__ == "__main__": init_log("qmt_data_runner.log") from apscheduler.schedulers.background import BackgroundScheduler sched = BackgroundScheduler() download_data() sched.add_job(func=download_data, trigger="cron", hour=15, minute=30, day_of_week="mon-fri") sched.start() sched._thread.join() ================================================ FILE: src/zvt/tasks/qmt_tick_runner.py ================================================ # -*- coding: utf-8 -*- from zvt import init_log from zvt.broker.qmt.qmt_quote import record_stock_quote if __name__ == "__main__": init_log("qmt_tick_runner.log") from apscheduler.schedulers.background import BackgroundScheduler sched = BackgroundScheduler() record_stock_quote() sched.add_job(func=record_stock_quote, trigger="cron", hour=9, minute=18, day_of_week="mon-fri") sched.start() sched._thread.join() ================================================ FILE: src/zvt/tasks/stock_pool_runner.py ================================================ # -*- coding: utf-8 -*- import logging from apscheduler.schedulers.background import BackgroundScheduler from zvt import zvt_config, init_log from zvt.api.kdata import get_latest_kdata_date, get_kdata_schema from zvt.api.selector import get_entity_ids_by_filter from zvt.contract import AdjustType from zvt.contract.api import get_entity_ids, get_data_count from zvt.domain import ( Stock, Block, Block1dKdata, BlockCategory, Index, Index1dKdata, LimitUpInfo, ) from zvt.factors import compute_top_stocks from zvt.informer import EmailInformer from zvt.informer.inform_utils import inform_email from zvt.informer.informer import QiyeWechatBot from zvt.tag.tag_stats import build_system_stock_pools, build_stock_pool_tag_stats from zvt.utils.recorder_utils import run_data_recorder from zvt.utils.time_utils import current_date logger = logging.getLogger(__name__) sched = BackgroundScheduler() email_informer = EmailInformer() bot = QiyeWechatBot() def report_limit_up(): latest_data = LimitUpInfo.query_data(order=LimitUpInfo.timestamp.desc(), limit=1, return_type="domain") timestamp = latest_data[0].timestamp df = LimitUpInfo.query_data(start_timestamp=timestamp, end_timestamp=timestamp, columns=["code", "name", "reason"]) df["reason"] = df["reason"].str.split("+") print(df) email_informer.send_message(zvt_config["email_username"], f"{timestamp} 热门报告", f"{df}") def record_stock_data(data_provider="em", entity_provider="em", sleeping_time=0, adjust_type=AdjustType.qfq): # 涨停数据 run_data_recorder(domain=LimitUpInfo, data_provider=None, force_update=False) report_limit_up() # A股指数 run_data_recorder(domain=Index, data_provider=data_provider, force_update=False) # A股指数行情 run_data_recorder( domain=Index1dKdata, data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=0, ) # 板块(概念，行业) run_data_recorder(domain=Block, entity_provider=entity_provider, data_provider=entity_provider, force_update=False) entity_ids = get_entity_ids(entity_schema=Block, filters=[Block.timestamp == None], provider=entity_provider) if entity_ids: # 板块行情(概念，行业) run_data_recorder( entity_ids=entity_ids, split_entity_ids_size=0, domain=Block1dKdata, entity_provider=entity_provider, data_provider=entity_provider, day_data=True, sleeping_time=0, ) # 报告新概念和行业 df = Block.query_data( filters=[Block.category == BlockCategory.concept.value], order=Block.list_date.desc(), index="entity_id", limit=7, ) inform_email( entity_ids=df.index.tolist(), entity_type="block", target_date=current_date(), title="report 新概念", provider="em", ) target_date = get_latest_kdata_date(entity_type="index", provider=data_provider) # A股标的 run_data_recorder(domain=Stock, data_provider=data_provider, force_update=False) # A股行情 normal_stock_ids = get_entity_ids_by_filter(entity_type="stock", provider="em", ignore_delist=True) kdata_schema = get_kdata_schema(entity_type="stock", level="1d", adjust_type=adjust_type) run_data_recorder( entity_ids=normal_stock_ids, split_entity_ids_size=0, domain=kdata_schema, data_provider=data_provider, entity_provider=entity_provider, day_data=True, sleeping_time=5, return_unfinished=True, end_timestamp=target_date, ) def record_stock_data_and_build_stock_pools(provider="em", adjust_type=AdjustType.qfq): # 获取 涨停 指数 板块(概念) 个股行情数据 record_stock_data(data_provider=provider, adjust_type=adjust_type) kdata_date = get_latest_kdata_date(provider=provider, entity_type="stock", adjust_type=adjust_type) kdata_schema = get_kdata_schema(entity_type="stock", level="1d", adjust_type=adjust_type) kdata_size = get_data_count(kdata_schema, filters=[kdata_schema.timestamp == kdata_date], provider=provider) if kdata_size < 5000: logger.warning(f"当前行情数据量过少: {kdata_size}, 不进行股票池构建") email_informer.send_message(zvt_config["email_username"], f"{kdata_date} k线数据不完整", "k线数据不完整") bot.send_message(f"{kdata_date} k线数据不完整") return # 计算短期/中期最强 放量突破年线半年线个股 compute_top_stocks(start_date=kdata_date, entity_type="stock", force_update=False, adjust_type=adjust_type) # 放入股票池 build_system_stock_pools(start_date=kdata_date, entity_type="stock", force_update=False) for stock_pool_name in ["主线", "年线", "大局"]: build_stock_pool_tag_stats(stock_pool_name=stock_pool_name, entity_type="stock", force_rebuild_latest=True) bot.send_message(f"{kdata_date} 股票池构建完成") if __name__ == "__main__": init_log("stock_pool_runner.log") record_stock_data_and_build_stock_pools() sched.add_job( func=record_stock_data_and_build_stock_pools, trigger="cron", hour=16, minute=00, day_of_week="mon-fri" ) sched.start() sched._thread.join() ================================================ FILE: src/zvt/tasks/today_shoot_runner.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import List from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.api.selector import get_shoot_today from zvt.domain import Stock from zvt.informer.inform_utils import add_to_eastmoney from zvt.tag.common import InsertMode from zvt.tag.tag_schemas import StockPools from zvt.tag.tag_stats import refresh_stock_pool from zvt.utils.time_utils import ( now_pd_timestamp, current_date, ) logger = logging.getLogger(__name__) sched = BackgroundScheduler() def calculate_shoot(): first_time = True stock_pool_name = "今日异动" while True: if not first_time and Stock.in_trading_time() and not Stock.in_real_trading_time(): logger.info(f"Sleeping time......") time.sleep(30 * 1) continue try: shoot_up, shoot_down = get_shoot_today() if len(shoot_up) > 80: logger.warning(f"shoot_up count size: {len(shoot_up)}") shoot_up = shoot_up[:80] if shoot_up: over_write = False stock_pools: List[StockPools] = StockPools.query_data( filters=[StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if stock_pools and (len(stock_pools[0].entity_ids) > 80): over_write = True refresh_stock_pool( entity_ids=shoot_up, stock_pool_name=stock_pool_name, insert_mode=InsertMode.overwrite if over_write else InsertMode.append, target_date=current_date(), ) add_to_eastmoney( codes=[entity_id.split("_")[2] for entity_id in shoot_up], group=stock_pool_name, over_write=over_write, ) except Exception as e: logger.error("Failed to handle shoot today", exc_info=True) first_time = False if not Stock.in_trading_time(): current_timestamp = now_pd_timestamp() logger.info(f"calculate shoot finished at: {current_timestamp}") break logger.info(f"Sleep 10 seconds") time.sleep(10) if __name__ == "__main__": init_log("today_shoot_runner.log") calculate_shoot() sched.add_job(func=calculate_shoot, trigger="cron", hour=9, minute=50, day_of_week="mon-fri") sched.start() sched._thread.join() ================================================ FILE: src/zvt/tasks/today_top_runner.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import List import eastmoneypy from apscheduler.schedulers.background import BackgroundScheduler from zvt import init_log from zvt.api.selector import get_top_up_today, get_top_down_today, get_top_vol from zvt.domain import Stock from zvt.informer.inform_utils import add_to_eastmoney from zvt.recorders.em.em_api import record_hot_topic from zvt.tag.common import InsertMode from zvt.tag.tag_schemas import StockPools from zvt.tag.tag_stats import refresh_stock_pool from zvt.utils.time_utils import now_pd_timestamp, current_date logger = logging.getLogger(__name__) sched = BackgroundScheduler() def calculate_top(clear_em=True): if clear_em: try: eastmoneypy.del_group("今日强势") except: pass seed = 0 add_all_to_em = False while True: current_timestamp = now_pd_timestamp() if not Stock.in_trading_time(): logger.info(f"calculate top finished at: {current_timestamp}") break if Stock.in_trading_time() and not Stock.in_real_trading_time(): logger.info(f"Sleeping time......") time.sleep(60 * 1) continue if seed == 0: record_hot_topic() seed = seed + 1 if seed == 5: seed = 0 target_date = current_date() top_up_entity_ids = get_top_up_today() if top_up_entity_ids: refresh_stock_pool( entity_ids=top_up_entity_ids, stock_pool_name="今日强势", insert_mode=InsertMode.append, target_date=target_date, ) try: to_added = top_up_entity_ids if add_all_to_em: stock_pools: List[StockPools] = StockPools.query_data( filters=[StockPools.stock_pool_name == "今日强势"], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if stock_pools: to_added = stock_pools[0].entity_ids if len(to_added) > 500: to_added = get_top_vol(entity_ids=to_added, limit=500) add_to_eastmoney( codes=[entity_id.split("_")[2] for entity_id in to_added], group="今日强势", over_write=False ) add_all_to_em = False except Exception as e: logger.error(e) add_all_to_em = True top_down_entity_ids = get_top_down_today() if top_down_entity_ids: refresh_stock_pool( entity_ids=top_down_entity_ids, stock_pool_name="今日弱势", insert_mode=InsertMode.append, target_date=target_date, ) logger.info(f"Sleep 2 minutes to compute {target_date} top stock tag stats") time.sleep(60 * 2) if __name__ == "__main__": init_log("today_top_runner.log") calculate_top(clear_em=False) sched.add_job(func=calculate_top, trigger="cron", hour=9, minute=26, day_of_week="mon-fri") sched.start() sched._thread.join() ================================================ FILE: src/zvt/trader/__init__.py ================================================ # -*- coding: utf-8 -*- from enum import Enum from typing import Union, List import pandas as pd from zvt.contract import IntervalLevel from zvt.utils.decorator import to_string class TradingSignalType(Enum): open_long = "open_long" open_short = "open_short" keep_long = "keep_long" keep_short = "keep_short" close_long = "close_long" close_short = "close_short" class OrderType(Enum): order_long = "order_long" order_short = "order_short" order_close_long = "order_close_long" order_close_short = "order_close_short" def trading_signal_type_to_order_type(trading_signal_type): if trading_signal_type == TradingSignalType.open_long: return OrderType.order_long elif trading_signal_type == TradingSignalType.open_short: return OrderType.order_short elif trading_signal_type == TradingSignalType.close_long: return OrderType.order_close_long elif trading_signal_type == TradingSignalType.close_short: return OrderType.order_close_short @to_string class TradingSignal: def __init__( self, entity_id: str, due_timestamp: Union[str, pd.Timestamp], happen_timestamp: Union[str, pd.Timestamp], trading_level: IntervalLevel, trading_signal_type: TradingSignalType, position_pct: float = None, order_money: float = None, order_amount: int = None, ): """ :param entity_id: the entity id :param due_timestamp: the signal due time :param happen_timestamp: the time when generating the signal :param trading_level: the level :param trading_signal_type: :param position_pct: percentage of account to order :param order_money: money to order :param order_amount: amount to order """ self.entity_id = entity_id self.due_timestamp = due_timestamp self.happen_timestamp = happen_timestamp self.trading_level = trading_level self.trading_signal_type = trading_signal_type if len([x for x in (position_pct, order_money, order_amount) if x is not None]) != 1: assert False # use position_pct or order_money or order_amount self.position_pct = position_pct # when close the position,just use position_pct self.order_money = order_money self.order_amount = order_amount class TradingListener(object): def on_trading_open(self, timestamp): raise NotImplementedError def on_trading_signals(self, trading_signals: List[TradingSignal]): raise NotImplementedError def on_trading_close(self, timestamp): raise NotImplementedError def on_trading_finish(self, timestamp): raise NotImplementedError def on_trading_error(self, timestamp, error): raise NotImplementedError class AccountService(TradingListener): def get_positions(self): pass def get_current_position(self, entity_id, create_if_not_exist=False): """ overwrite it to provide your real position :param entity_id: """ pass def get_current_account(self): pass def order_by_position_pct( self, entity_id, order_price, order_timestamp, order_type, order_position_pct: float, ): pass def order_by_money( self, entity_id, order_price, order_timestamp, order_type, order_money, ): pass def order_by_amount( self, entity_id, order_price, order_timestamp, order_type, order_amount, ): pass # the __all__ is generated __all__ = ["TradingSignalType", "TradingListener", "OrderType", "AccountService", "trading_signal_type_to_order_type"] # __init__.py structure: # common code of the package # export interface in __all__ which contains __all__ of its sub modules # import all from submodule trader from .trader import * from .trader import __all__ as _trader_all __all__ += _trader_all # import all from submodule errors from .errors import * from .errors import __all__ as _errors_all __all__ += _errors_all # import all from submodule account from .sim_account import * from .sim_account import __all__ as _account_all __all__ += _account_all ================================================ FILE: src/zvt/trader/errors.py ================================================ # -*- coding: utf-8 -*- class TraderError(Exception): """Base class for exceptions in this module.""" pass class InvalidOrderParamError(TraderError): def __init__(self, message="invalid order param"): self.message = message class NotEnoughMoneyError(TraderError): def __init__(self, message="not enough money"): self.message = message class NotEnoughPositionError(TraderError): def __init__(self, message="not enough position"): self.message = message class InvalidOrderError(TraderError): def __init__(self, message="invalid order"): self.message = message class WrongKdataError(TraderError): def __init__(self, message="wrong kdata"): self.message = message # the __all__ is generated __all__ = [ "TraderError", "InvalidOrderParamError", "NotEnoughMoneyError", "NotEnoughPositionError", "InvalidOrderError", "WrongKdataError", ] ================================================ FILE: src/zvt/trader/sim_account.py ================================================ # -*- coding: utf-8 -*- import logging import math from typing import List, Optional from zvt.api.kdata import get_kdata, get_kdata_schema from zvt.contract import IntervalLevel, TradableEntity, AdjustType from zvt.contract.api import get_db_session, decode_entity_id from zvt.trader import TradingSignal, AccountService, OrderType, trading_signal_type_to_order_type from zvt.trader.errors import ( NotEnoughMoneyError, InvalidOrderError, NotEnoughPositionError, InvalidOrderParamError, WrongKdataError, ) from zvt.trader.trader_info_api import get_trader_info, clear_trader from zvt.trader.trader_models import AccountStatsModel, PositionModel from zvt.trader.trader_schemas import AccountStats, Position, Order, TraderInfo from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import to_pd_timestamp, to_date_time_str, TIME_FORMAT_ISO8601, is_same_date from zvt.utils.utils import fill_domain_from_dict class SimAccountService(AccountService): def __init__( self, entity_schema: TradableEntity, trader_name, timestamp, provider=None, level=IntervalLevel.LEVEL_1DAY, base_capital=1000000, buy_cost=0.001, sell_cost=0.001, slippage=0.001, rich_mode=True, adjust_type: AdjustType = None, keep_history=False, real_time=False, kdata_use_begin_time=False, ): self.logger = logging.getLogger(self.__class__.__name__) self.entity_schema = entity_schema self.base_capital = base_capital self.buy_cost = buy_cost self.sell_cost = sell_cost self.slippage = slippage self.rich_mode = rich_mode self.adjust_type = adjust_type self.trader_name = trader_name self.session = get_db_session("zvt", data_schema=TraderInfo) self.provider = provider self.level = level self.start_timestamp = timestamp self.keep_history = keep_history self.real_time = real_time self.kdata_use_begin_time = kdata_use_begin_time self.account = self.init_account() account_info = ( f"init_account,holding size:{len(self.account.positions)} profit:{self.account.profit} input_money:{self.account.input_money} " f"cash:{self.account.cash} value:{self.account.value} all_value:{self.account.all_value}" ) self.logger.info(account_info) def input_money(self, money=1000000): self.account.input_money += money self.account.cash += money def clear_account(self): trader_info = get_trader_info(session=self.session, trader_name=self.trader_name, return_type="domain", limit=1) if trader_info: self.logger.warning("trader:{} has run before,old result would be deleted".format(self.trader_name)) clear_trader(session=self.session, trader_name=self.trader_name) def init_account(self) -> AccountStats: # 清除历史数据 if not self.keep_history: self.clear_account() # 读取之前保存的账户 if self.keep_history: self.account = self.load_account() if self.account: return self.account # init trader info entity_type = self.entity_schema.__name__.lower() sim_account = TraderInfo( id=self.trader_name, entity_id=f"trader_zvt_{self.trader_name}", timestamp=self.start_timestamp, trader_name=self.trader_name, entity_type=entity_type, start_timestamp=self.start_timestamp, provider=self.provider, level=self.level.value, real_time=self.real_time, kdata_use_begin_time=self.kdata_use_begin_time, kdata_adjust_type=self.adjust_type.value, ) self.session.add(sim_account) self.session.commit() return AccountStats( entity_id=f"trader_zvt_{self.trader_name}", timestamp=self.start_timestamp, trader_name=self.trader_name, cash=self.base_capital, input_money=self.base_capital, all_value=self.base_capital, value=0, closing=False, ) def load_account(self) -> AccountStats: records = AccountStats.query_data( filters=[AccountStats.trader_name == self.trader_name], order=AccountStats.timestamp.desc(), limit=1, return_type="domain", ) if not records: return self.account latest_record: AccountStats = records[0] # create new orm object from latest record account_stats_model = AccountStatsModel.from_orm(latest_record) account = AccountStats() fill_domain_from_dict(account, account_stats_model.model_dump(exclude={"id", "positions"})) positions: List[Position] = [] for position_domain in latest_record.positions: position_model = PositionModel.from_orm(position_domain) self.logger.debug("current position:{}".format(position_model)) position = Position() fill_domain_from_dict(position, position_model.model_dump()) positions.append(position) account.positions = positions return account def on_trading_open(self, timestamp): self.logger.info("on_trading_open:{}".format(timestamp)) if is_same_date(timestamp, self.start_timestamp): return self.account = self.load_account() def on_trading_error(self, timestamp, error): pass def on_trading_finish(self, timestamp): pass def on_trading_signals(self, trading_signals: List[TradingSignal]): for trading_signal in trading_signals: try: self.handle_trading_signal(trading_signal) except Exception as e: self.logger.exception(e) self.on_trading_error(timestamp=trading_signal.happen_timestamp, error=e) def handle_trading_signal(self, trading_signal: TradingSignal): entity_id = trading_signal.entity_id happen_timestamp = trading_signal.happen_timestamp order_type = trading_signal_type_to_order_type(trading_signal.trading_signal_type) trading_level = trading_signal.trading_level.value if order_type: try: kdata = get_kdata( provider=self.provider, entity_id=entity_id, level=trading_level, start_timestamp=happen_timestamp, end_timestamp=happen_timestamp, limit=1, adjust_type=self.adjust_type, ) except Exception as e: self.logger.error(e) raise WrongKdataError("could not get kdata") if pd_is_not_null(kdata): entity_type, _, _ = decode_entity_id(kdata["entity_id"][0]) the_price = kdata["close"][0] if the_price: if trading_signal.position_pct: self.order_by_position_pct( entity_id=entity_id, order_price=the_price, order_timestamp=happen_timestamp, order_position_pct=trading_signal.position_pct, order_type=order_type, ) elif trading_signal.order_money: self.order_by_money( entity_id=entity_id, order_price=the_price, order_timestamp=happen_timestamp, order_money=trading_signal.order_money, order_type=order_type, ) elif trading_signal.order_amount: self.order_by_amount( entity_id=entity_id, order_price=the_price, order_timestamp=happen_timestamp, order_amount=trading_signal.order_amount, order_type=order_type, ) else: assert False else: self.logger.warning( "ignore trading signal,wrong kdata,entity_id:{},timestamp:{},kdata:{}".format( entity_id, happen_timestamp, kdata.to_dict(orient="records") ) ) else: self.logger.warning( "ignore trading signal,could not get kdata,entity_id:{},timestamp:{}".format( entity_id, happen_timestamp ) ) def on_trading_close(self, timestamp): self.logger.info("on_trading_close:{}".format(timestamp)) # remove the empty position self.account.positions = [ position for position in self.account.positions if position.long_amount > 0 or position.short_amount > 0 ] # clear the data which need recomputing the_id = "{}_{}".format(self.trader_name, to_date_time_str(timestamp, TIME_FORMAT_ISO8601)) self.account.value = 0 self.account.all_value = 0 for position in self.account.positions: entity_type, _, _ = decode_entity_id(position.entity_id) data_schema = get_kdata_schema(entity_type, level=IntervalLevel.LEVEL_1DAY, adjust_type=self.adjust_type) kdata = get_kdata( provider=self.provider, level=IntervalLevel.LEVEL_1DAY, entity_id=position.entity_id, order=data_schema.timestamp.desc(), end_timestamp=timestamp, limit=1, adjust_type=self.adjust_type, ) closing_price = kdata["close"][0] position.available_long = position.long_amount position.available_short = position.short_amount if closing_price: if (position.long_amount is not None) and position.long_amount > 0: position.value = position.long_amount * closing_price self.account.value += position.value elif (position.short_amount is not None) and position.short_amount > 0: position.value = 2 * (position.short_amount * position.average_short_price) position.value -= position.short_amount * closing_price self.account.value += position.value # refresh profit position.profit = (closing_price - position.average_long_price) * position.long_amount position.profit_rate = position.profit / (position.average_long_price * position.long_amount) else: self.logger.warning( "could not refresh close value for position:{},timestamp:{}".format(position.entity_id, timestamp) ) position.id = "{}_{}_{}".format( self.trader_name, position.entity_id, to_date_time_str(timestamp, TIME_FORMAT_ISO8601) ) position.timestamp = to_pd_timestamp(timestamp) position.account_stats_id = the_id self.account.id = the_id self.account.all_value = self.account.value + self.account.cash self.account.closing = True self.account.timestamp = to_pd_timestamp(timestamp) self.account.profit = self.account.all_value - self.account.input_money self.account.profit_rate = self.account.profit / self.account.input_money self.session.add(self.account) self.session.commit() account_info = ( f"on_trading_close,holding size:{len(self.account.positions)} profit:{self.account.profit} input_money:{self.account.input_money} " f"cash:{self.account.cash} value:{self.account.value} all_value:{self.account.all_value}" ) self.logger.info(account_info) def get_current_position(self, entity_id, create_if_not_exist=False) -> Optional[Position]: """ get position for entity_id :param entity_id: the entity id :param create_if_not_exist: create an empty position if not exist in current account :return: """ for position in self.account.positions: if position.entity_id == entity_id: return position if create_if_not_exist: trading_t = self.entity_schema.get_trading_t() current_position = Position( trader_name=self.trader_name, entity_id=entity_id, long_amount=0, available_long=0, average_long_price=0, short_amount=0, available_short=0, average_short_price=0, profit=0, value=0, trading_t=trading_t, ) # add it to account self.account.positions.append(current_position) return current_position return None def get_current_account(self): return self.account def update_position(self, current_position, order_amount, current_price, order_type, timestamp): """ :param timestamp: :type timestamp: :param current_position: :type current_position: Position :param order_amount: :type order_amount: :param current_price: :type current_price: :param order_type: :type order_type: """ if order_type == OrderType.order_long: need_money = (order_amount * current_price) * (1 + self.slippage + self.buy_cost) if self.account.cash < need_money: if self.rich_mode: self.input_money() else: raise NotEnoughMoneyError() self.account.cash -= need_money # 计算平均价 long_amount = current_position.long_amount + order_amount if long_amount == 0: current_position.average_long_price = 0 current_position.average_long_price = ( current_position.average_long_price * current_position.long_amount + current_price * order_amount ) / long_amount current_position.long_amount = long_amount if current_position.trading_t == 0: current_position.available_long += order_amount elif order_type == OrderType.order_short: need_money = (order_amount * current_price) * (1 + self.slippage + self.buy_cost) if self.account.cash < need_money: if self.rich_mode: self.input_money() else: raise NotEnoughMoneyError() self.account.cash -= need_money short_amount = current_position.short_amount + order_amount current_position.average_short_price = ( current_position.average_short_price * current_position.short_amount + current_price * order_amount ) / short_amount current_position.short_amount = short_amount if current_position.trading_t == 0: current_position.available_short += order_amount elif order_type == OrderType.order_close_long: self.account.cash += order_amount * current_price * (1 - self.slippage - self.sell_cost) # FIXME:如果没卖完，重新计算计算平均价 current_position.available_long -= order_amount current_position.long_amount -= order_amount elif order_type == OrderType.order_close_short: self.account.cash += 2 * (order_amount * current_position.average_short_price) self.account.cash -= order_amount * current_price * (1 + self.slippage + self.sell_cost) current_position.available_short -= order_amount current_position.short_amount -= order_amount else: assert False # save the order info to db order_id = "{}_{}_{}_{}".format( self.trader_name, order_type, current_position.entity_id, to_date_time_str(timestamp, TIME_FORMAT_ISO8601) ) order = Order( id=order_id, timestamp=to_pd_timestamp(timestamp), trader_name=self.trader_name, entity_id=current_position.entity_id, order_price=current_price, order_amount=order_amount, order_type=order_type.value, level=self.level.value, status="success", ) self.session.add(order) self.session.commit() def cal_amount_by_money( self, order_price: float, order_money: float, ): if order_money > self.account.cash: if self.rich_mode: self.input_money() else: raise NotEnoughMoneyError() cost = order_price * (1 + self.slippage + self.buy_cost) order_amount = order_money // cost return order_amount def cal_amount_by_position_pct(self, entity_id, order_price: float, order_position_pct: float, order_type): if order_type == OrderType.order_long or order_type == OrderType.order_short: cost = order_price * (1 + self.slippage + self.buy_cost) want_pay = self.account.cash * order_position_pct order_amount = want_pay // cost if order_amount < 1: if self.rich_mode: self.input_money() order_amount = max((self.account.cash * order_position_pct) // cost, 1) else: raise NotEnoughMoneyError() return order_amount elif order_type == OrderType.order_close_long or order_type == OrderType.order_close_short: current_position = self.get_current_position(entity_id=entity_id, create_if_not_exist=True) if order_type == OrderType.order_close_long: available = current_position.available_long else: available = current_position.available_short if available > 0: if order_position_pct == 1.0: order_amount = available else: order_amount = math.floor(available * order_position_pct) return order_amount else: raise NotEnoughPositionError() def order_by_position_pct( self, entity_id, order_timestamp, order_price: float, order_type: OrderType, order_position_pct: float = 0.2, ): order_amount = self.cal_amount_by_position_pct( entity_id=entity_id, order_price=order_price, order_position_pct=order_position_pct, order_type=order_type ) self.order_by_amount( entity_id=entity_id, order_price=order_price, order_amount=order_amount, order_timestamp=order_timestamp, order_type=order_type, ) def order_by_money( self, entity_id, order_timestamp, order_price: float, order_type: OrderType, order_money: float, ): if order_type not in (OrderType.order_long, OrderType.order_short): raise InvalidOrderParamError(f"order type: {order_type.value} not support order_by_money") order_amount = self.cal_amount_by_money(order_price=order_price, order_money=order_money) self.order_by_amount( entity_id=entity_id, order_price=order_price, order_amount=order_amount, order_timestamp=order_timestamp, order_type=order_type, ) def order_by_amount( self, entity_id, order_price, order_timestamp, order_type, order_amount, ): current_position = self.get_current_position(entity_id=entity_id, create_if_not_exist=True) # 开多 if order_type == OrderType.order_long: if current_position.short_amount > 0: raise InvalidOrderError("close the short position before open long") self.update_position(current_position, order_amount, order_price, order_type, order_timestamp) # 开空 elif order_type == OrderType.order_short: if current_position.long_amount > 0: raise InvalidOrderError("close the long position before open short") self.update_position(current_position, order_amount, order_price, order_type, order_timestamp) # 平多 elif order_type == OrderType.order_close_long: if current_position.available_long >= order_amount: self.update_position(current_position, order_amount, order_price, order_type, order_timestamp) else: raise NotEnoughPositionError() # 平空 elif order_type == OrderType.order_close_short: if current_position.available_short >= order_amount: self.update_position(current_position, order_amount, order_price, order_type, order_timestamp) else: raise Exception("not enough position") # the __all__ is generated __all__ = ["AccountService", "SimAccountService"] ================================================ FILE: src/zvt/trader/trader.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import List, Union, Type, Tuple import pandas as pd from zvt.contract import IntervalLevel, TradableEntity, AdjustType from zvt.contract.drawer import Drawer from zvt.contract.factor import Factor, TargetType from zvt.contract.normal_data import NormalData from zvt.domain import Stock from zvt.trader import TradingSignal, TradingSignalType, TradingListener from zvt.trader.sim_account import SimAccountService from zvt.trader.trader_info_api import AccountStatsReader from zvt.trader.trader_schemas import AccountStats, Position from zvt.utils.time_utils import ( to_pd_timestamp, now_pd_timestamp, to_date_time_str, is_same_date, date_time_by_interval, ) class Trader(object): entity_schema: Type[TradableEntity] = None def __init__( self, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, provider: str = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, trader_name: str = None, real_time: bool = False, kdata_use_begin_time: bool = False, draw_result: bool = True, rich_mode: bool = False, adjust_type: AdjustType = None, profit_threshold=(3, -0.3), keep_history=False, pre_load_days=365, ) -> None: assert self.entity_schema is not None assert start_timestamp is not None assert end_timestamp is not None self.logger = logging.getLogger(__name__) if trader_name: self.trader_name = trader_name else: self.trader_name = type(self).__name__.lower() self.entity_ids = entity_ids self.exchanges = exchanges self.codes = codes self.provider = provider # make sure the min level factor correspond to the provider and level self.level = IntervalLevel(level) self.real_time = real_time self.start_timestamp = to_pd_timestamp(start_timestamp) self.end_timestamp = to_pd_timestamp(end_timestamp) self.pre_load_days = pre_load_days self.trading_dates = self.entity_schema.get_trading_dates( start_date=self.start_timestamp, end_date=self.end_timestamp ) if real_time: self.logger.info( "real_time mode, end_timestamp should be future,you could set it big enough for running forever" ) assert self.end_timestamp >= now_pd_timestamp() # false: 收到k线时，该k线已完成 # true: 收到k线时，该k线可能未完成 self.kdata_use_begin_time = kdata_use_begin_time self.draw_result = draw_result self.rich_mode = rich_mode self.adjust_type = AdjustType(adjust_type) self.profit_threshold = profit_threshold self.keep_history = keep_history self.level_map_long_targets = {} self.level_map_short_targets = {} self.trading_signals: List[TradingSignal] = [] self.trading_signal_listeners: List[TradingListener] = [] self.account_service = SimAccountService( entity_schema=self.entity_schema, trader_name=self.trader_name, timestamp=self.start_timestamp, provider=self.provider, level=self.level, rich_mode=self.rich_mode, adjust_type=self.adjust_type, keep_history=self.keep_history, ) self.register_trading_signal_listener(self.account_service) self.factors = self.init_factors( entity_ids=self.entity_ids, entity_schema=self.entity_schema, exchanges=self.exchanges, codes=self.codes, start_timestamp=date_time_by_interval(self.start_timestamp, -self.pre_load_days), end_timestamp=self.end_timestamp, adjust_type=self.adjust_type, ) if self.factors: self.trading_level_asc = list(set([IntervalLevel(factor.level) for factor in self.factors])) self.trading_level_asc.sort() self.logger.info(f"trader level:{self.level},factors level:{self.trading_level_asc}") if self.level != self.trading_level_asc[0]: raise Exception("trader level should be the min of the factors") self.trading_level_desc = list(self.trading_level_asc) self.trading_level_desc.reverse() else: self.trading_level_asc = [self.level] self.trading_level_desc = [self.level] self.on_init() def on_init(self): self.logger.info(f"trader:{self.trader_name} on_start") def init_entities(self, timestamp): """ init the entities for timestamp :param timestamp: :return: """ self.logger.info(f"timestamp: {timestamp} init_entities") return self.entity_ids def init_factors( self, entity_ids, entity_schema, exchanges, codes, start_timestamp, end_timestamp, adjust_type=None ): """ overwrite it to init factors if you want to use factor computing model :param adjust_type: """ return [] def update_targets_by_level( self, level: IntervalLevel, long_targets: List[str], short_targets: List[str], ) -> None: """ the trading signals is generated in min level,before that,we should cache targets of all levels :param level: :param long_targets: :param short_targets: """ self.logger.debug( f"level:{level},old long targets:{self.level_map_long_targets.get(level)},new long targets:{long_targets}" ) self.level_map_long_targets[level] = long_targets self.logger.debug( f"level:{level},old short targets:{self.level_map_short_targets.get(level)},new short targets:{short_targets}" ) self.level_map_short_targets[level] = short_targets def get_long_targets_by_level(self, level: IntervalLevel) -> List[str]: return self.level_map_long_targets.get(level) def get_short_targets_by_level(self, level: IntervalLevel) -> List[str]: return self.level_map_short_targets.get(level) def on_targets_selected_from_levels(self, timestamp) -> Tuple[List[str], List[str]]: """ this method's called in every min level cycle to select targets in all levels generated by the previous cycle the default implementation is selecting the targets in all levels overwrite it for your custom logic :param timestamp: current event time :return: long targets, short targets """ long_selected = None short_selected = None for level in self.trading_level_desc: long_targets = self.level_map_long_targets.get(level) # long must in all if long_targets: long_targets = set(long_targets) if long_selected is None: long_selected = long_targets else: long_selected = long_selected & long_targets else: long_selected = set() short_targets = self.level_map_short_targets.get(level) # short any if short_targets: short_targets = set(short_targets) if short_selected is None: short_selected = short_targets else: short_selected = short_selected | short_targets return long_selected, short_selected def get_current_account(self) -> AccountStats: return self.account_service.get_current_account() def get_current_positions(self) -> List[Position]: return self.get_current_account().positions def long_position_control(self): positions = self.get_current_positions() position_pct = 1.0 if not positions: # 没有仓位，买2成 position_pct = 0.2 elif len(positions) <= 10: # 小于10个持仓，买5成 position_pct = 0.5 # 买完 return position_pct def short_position_control(self): # 卖完 return 1.0 def on_profit_control(self): if self.profit_threshold and self.get_current_positions(): positive = self.profit_threshold[0] negative = self.profit_threshold[1] close_long_entity_ids = [] for position in self.get_current_positions(): if position.available_long > 1: # 止盈 if position.profit_rate >= positive: close_long_entity_ids.append(position.entity_id) self.logger.info(f"close profit {position.profit_rate} for {position.entity_id}") # 止损 if position.profit_rate <= negative: close_long_entity_ids.append(position.entity_id) self.logger.info(f"cut lost {position.profit_rate} for {position.entity_id}") return close_long_entity_ids, None return None, None def buy(self, timestamp, entity_ids, ignore_in_position=True): if ignore_in_position: account = self.get_current_account() current_holdings = [] if account.positions: current_holdings = [ position.entity_id for position in account.positions if position != None and position.available_long > 0 ] entity_ids = set(entity_ids) - set(current_holdings) if entity_ids: position_pct = self.long_position_control() position_pct = (1.0 / len(entity_ids)) * position_pct due_timestamp = to_pd_timestamp(timestamp) + pd.Timedelta(seconds=self.level.to_second()) for entity_id in entity_ids: trading_signal = TradingSignal( entity_id=entity_id, due_timestamp=due_timestamp, happen_timestamp=timestamp, trading_signal_type=TradingSignalType.open_long, trading_level=self.level, position_pct=position_pct, ) self.trading_signals.append(trading_signal) def sell(self, timestamp, entity_ids): # current position account = self.get_current_account() current_holdings = [] if account.positions: current_holdings = [ position.entity_id for position in account.positions if position != None and position.available_long > 0 ] shorted = set(current_holdings) & set(entity_ids) if shorted: position_pct = self.short_position_control() due_timestamp = to_pd_timestamp(timestamp) + pd.Timedelta(seconds=self.level.to_second()) for entity_id in shorted: trading_signal = TradingSignal( entity_id=entity_id, due_timestamp=due_timestamp, happen_timestamp=timestamp, trading_signal_type=TradingSignalType.close_long, trading_level=self.level, position_pct=position_pct, ) self.trading_signals.append(trading_signal) def on_finish(self, timestamp): self.on_trading_finish(timestamp) # show the result if self.draw_result: reader = AccountStatsReader(trader_names=[self.trader_name]) df = reader.data_df drawer = Drawer( main_data=NormalData(df.copy()[["trader_name", "timestamp", "all_value"]], category_field="trader_name") ) drawer.draw_line(show=True) def on_factor_targets_filtered( self, timestamp, level, factor: Factor, long_targets: List[str], short_targets: List[str] ) -> Tuple[List[str], List[str]]: """ overwrite it to filter the targets from factor :param timestamp: the event time :param level: the level :param factor: the factor :param long_targets: the long targets from the factor :param short_targets: the short targets from the factor :return: filtered long targets, filtered short targets """ self.logger.info(f"on_targets_filtered {level} long:{long_targets}") if len(long_targets) > 10: long_targets = long_targets[0:10] self.logger.info(f"on_targets_filtered {level} filtered long:{long_targets}") return long_targets, short_targets def in_trading_date(self, timestamp): return to_date_time_str(timestamp) in self.trading_dates def on_time(self, timestamp: pd.Timestamp): """ called in every min level cycle :param timestamp: event time """ self.logger.debug(f"current timestamp:{timestamp}") def on_trading_signals(self, trading_signals: List[TradingSignal]): for l in self.trading_signal_listeners: l.on_trading_signals(trading_signals) # clear after all listener handling self.trading_signals = [] def on_trading_open(self, timestamp): for l in self.trading_signal_listeners: l.on_trading_open(timestamp) def on_trading_close(self, timestamp): for l in self.trading_signal_listeners: l.on_trading_close(timestamp) def on_trading_finish(self, timestamp): for l in self.trading_signal_listeners: l.on_trading_finish(timestamp) def on_trading_error(self, timestamp, error): for l in self.trading_signal_listeners: l.on_trading_error(timestamp, error) def on_non_trading_day(self, timestamp): self.logger.info(f"on_non_trading_day: {timestamp}") def get_factors_by_level(self, level): return [factor for factor in self.factors if factor.level == level] def handle_factor_targets(self, timestamp: pd.Timestamp): """ select targets from factors :param timestamp: the timestamp for next kdata coming """ # 一般来说factor计算 多标的 历史数据比较快，多级别的计算也比较方便，常用于全市场标的粗过滤 # 更细节的控制可以在on_targets_filtered里进一步处理 # 也可以在on_time里面设计一些自己的逻辑配合过滤 # 多级别的遍历算法要点: # 1)计算各级别的 标的，通过 on_factor_targets_filtered 过滤，缓存在level_map_long_targets，level_map_short_targets # 2)在最小的level通过 on_targets_selected_from_levels 根据多级别的缓存标的，生成最终的选中标的 # 这里需要注意的是，小级别拿到上一个周期的大级别的标的，这是合理的 for level in self.trading_level_asc: self.logger.info(f"level: {level}") # in every cycle, all level factor do its job in its time if self.entity_schema.is_finished_kdata_timestamp(timestamp=timestamp, level=level): all_long_targets = [] all_short_targets = [] # 从该level的factor中过滤targets current_level_factors = self.get_factors_by_level(level=level) for factor in current_level_factors: long_targets = factor.get_targets(timestamp=timestamp, target_type=TargetType.positive) short_targets = factor.get_targets(timestamp=timestamp, target_type=TargetType.negative) if long_targets or short_targets: long_targets, short_targets = self.on_factor_targets_filtered( timestamp=timestamp, level=level, factor=factor, long_targets=long_targets, short_targets=short_targets, ) if long_targets: all_long_targets += long_targets if short_targets: all_short_targets += short_targets # 将各级别的targets缓存在level_map_long_targets，level_map_short_targets self.update_targets_by_level(level, all_long_targets, all_short_targets) def run(self): # iterate timestamp of the min level,e.g,9:30,9:35,9.40...for 5min level # timestamp represents the timestamp in kdata for timestamp in self.entity_schema.get_interval_timestamps( start_date=self.start_timestamp, end_date=self.end_timestamp, level=self.level ): self.logger.info(f">>>>>>>>>>") self.entity_ids = self.init_entities(timestamp=timestamp) self.logger.info(f"current entities: {self.entity_ids}") if not self.in_trading_date(timestamp=timestamp): self.on_non_trading_day(timestamp=timestamp) continue # on_trading_open to set the account if self.level >= IntervalLevel.LEVEL_1DAY or ( self.level != IntervalLevel.LEVEL_1DAY and self.entity_schema.is_open_timestamp(timestamp) ): self.on_trading_open(timestamp=timestamp) # the signals were generated by previous timestamp kdata if self.trading_signals: self.logger.info("current signals:") for signal in self.trading_signals: self.logger.info(str(signal)) self.on_trading_signals(self.trading_signals) for factor in self.factors: factor.add_entities(entity_ids=self.entity_ids) waiting_seconds = 0 if self.level == IntervalLevel.LEVEL_1DAY: if is_same_date(timestamp, now_pd_timestamp()): while True: self.logger.info(f"time is:{now_pd_timestamp()},just smoke for minutes") time.sleep(600) current = now_pd_timestamp() if current.hour >= 19: waiting_seconds = 20 break elif self.real_time: # all factor move on to handle the coming data if self.kdata_use_begin_time: real_end_timestamp = timestamp + pd.Timedelta(seconds=self.level.to_second()) else: real_end_timestamp = timestamp seconds = (now_pd_timestamp() - real_end_timestamp).total_seconds() waiting_seconds = self.level.to_second() - seconds # meaning the future kdata not ready yet,we could move on to check if waiting_seconds > 0: # iterate the factor from min to max which in finished timestamp kdata for level in self.trading_level_asc: if self.entity_schema.is_finished_kdata_timestamp(timestamp=timestamp, level=level): factors = self.get_factors_by_level(level=level) for factor in factors: factor.move_on(to_timestamp=timestamp, timeout=waiting_seconds + 20) if self.factors: self.handle_factor_targets(timestamp=timestamp) self.on_time(timestamp=timestamp) long_selected, short_selected = self.on_targets_selected_from_levels(timestamp) # 处理 止赢 止损 passive_short, _ = self.on_profit_control() if passive_short: if not short_selected: short_selected = passive_short else: short_selected = list(set(short_selected) | set(passive_short)) if short_selected: self.sell(timestamp=timestamp, entity_ids=short_selected) if long_selected: self.buy(timestamp=timestamp, entity_ids=long_selected) # on_trading_close to calculate date account if self.level >= IntervalLevel.LEVEL_1DAY or ( self.level != IntervalLevel.LEVEL_1DAY and self.entity_schema.is_close_timestamp(timestamp) ): self.on_trading_close(timestamp) self.logger.info(f"<<<<<<<<<<\n") self.on_finish(timestamp) def register_trading_signal_listener(self, listener): if listener not in self.trading_signal_listeners: self.trading_signal_listeners.append(listener) def deregister_trading_signal_listener(self, listener): if listener in self.trading_signal_listeners: self.trading_signal_listeners.remove(listener) class StockTrader(Trader): entity_schema = Stock def __init__( self, entity_ids: List[str] = None, exchanges: List[str] = None, codes: List[str] = None, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, provider: str = None, level: Union[str, IntervalLevel] = IntervalLevel.LEVEL_1DAY, trader_name: str = None, real_time: bool = False, kdata_use_begin_time: bool = False, draw_result: bool = True, rich_mode: bool = False, adjust_type: AdjustType = AdjustType.hfq, profit_threshold=(3, -0.3), keep_history=False, ) -> None: super().__init__( entity_ids, exchanges, codes, start_timestamp, end_timestamp, provider, level, trader_name, real_time, kdata_use_begin_time, draw_result, rich_mode, adjust_type, profit_threshold, keep_history, ) # the __all__ is generated __all__ = ["Trader", "StockTrader"] ================================================ FILE: src/zvt/trader/trader_info_api.py ================================================ # -*- coding: utf-8 -*- from typing import List, Union import pandas as pd from zvt.contract import IntervalLevel from zvt.contract.api import get_data, get_db_session from zvt.contract.drawer import Drawer from zvt.contract.normal_data import NormalData from zvt.contract.reader import DataReader from zvt.trader.trader_schemas import AccountStats, Order, TraderInfo, Position def clear_trader(trader_name, session=None): if not session: session = get_db_session("zvt", data_schema=TraderInfo) session.query(TraderInfo).filter(TraderInfo.trader_name == trader_name).delete() session.query(AccountStats).filter(AccountStats.trader_name == trader_name).delete() session.query(Position).filter(Position.trader_name == trader_name).delete() session.query(Order).filter(Order.trader_name == trader_name).delete() session.commit() def get_trader_info( trader_name=None, return_type="df", start_timestamp=None, end_timestamp=None, filters=None, session=None, order=None, limit=None, ) -> List[TraderInfo]: if trader_name: if filters: filters = filters + [TraderInfo.trader_name == trader_name] else: filters = [TraderInfo.trader_name == trader_name] return get_data( data_schema=TraderInfo, entity_id=None, codes=None, level=None, provider="zvt", columns=None, return_type=return_type, start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=filters, session=session, order=order, limit=limit, ) def get_order_securities(trader_name): items = ( get_db_session(provider="zvt", data_schema=Order) .query(Order.entity_id) .filter(Order.trader_name == trader_name) .group_by(Order.entity_id) .all() ) return [item[0] for item in items] class AccountStatsReader(DataReader): def __init__( self, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, level: IntervalLevel = IntervalLevel.LEVEL_1DAY, trader_names: List[str] = None, ) -> None: self.trader_names = trader_names self.filters = filters if self.trader_names: filter = [AccountStats.trader_name == name for name in self.trader_names] if self.filters: self.filters += filter else: self.filters = filter super().__init__( AccountStats, None, None, None, None, None, None, start_timestamp, end_timestamp, columns, self.filters, order, None, level, category_field="trader_name", time_field="timestamp", keep_window=None, ) def draw_line(self, show=True): drawer = Drawer( main_data=NormalData( self.data_df.copy()[["trader_name", "timestamp", "all_value"]], category_field="trader_name" ) ) return drawer.draw_line(show=show) class OrderReader(DataReader): def __init__( self, start_timestamp: Union[str, pd.Timestamp] = None, end_timestamp: Union[str, pd.Timestamp] = None, columns: List = None, filters: List = None, order: object = None, level: IntervalLevel = None, trader_names: List[str] = None, ) -> None: self.trader_names = trader_names self.filters = filters if self.trader_names: filter = [Order.trader_name == name for name in self.trader_names] if self.filters: self.filters += filter else: self.filters = filter super().__init__( Order, None, None, None, None, None, None, start_timestamp, end_timestamp, columns, self.filters, order, None, level, category_field="trader_name", time_field="timestamp", keep_window=None, ) if __name__ == "__main__": reader = AccountStatsReader(trader_names=["000338_ma_trader"]) drawer = Drawer( main_data=NormalData( reader.data_df.copy()[["trader_name", "timestamp", "all_value"]], category_field="trader_name" ) ) drawer.draw_line() # the __all__ is generated __all__ = ["clear_trader", "get_trader_info", "get_order_securities", "AccountStatsReader", "OrderReader"] ================================================ FILE: src/zvt/trader/trader_models.py ================================================ # -*- coding: utf-8 -*- from typing import List from zvt.contract.model import MixinModel class PositionModel(MixinModel): #: 机器人名字 trader_name: str #: 做多数量 long_amount: float #: 可平多数量 available_long: float #: 平均做多价格 average_long_price: float #: 做空数量 short_amount: float #: 可平空数量 available_short: float #: 平均做空价格 average_short_price: float #: 盈亏 profit: float #: 盈亏比例 profit_rate: float #: 市值 或者 占用的保证金(方便起见，总是100%) value: float #: 交易类型(0代表T+0,1代表T+1) trading_t: int class AccountStatsModel(MixinModel): #: 投入金额 input_money: float #: 机器人名字 trader_name: str #: 具体仓位 positions: List[PositionModel] #: 市值 value: float #: 可用现金 cash: float #: value + cash all_value: float #: 盈亏 profit: float #: 盈亏比例 profit_rate: float #: 收盘计算 closing: bool ================================================ FILE: src/zvt/trader/trader_schemas.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, String, DateTime, Boolean, Float, Integer, ForeignKey from sqlalchemy.orm import declarative_base from sqlalchemy.orm import relationship from zvt.contract import Mixin from zvt.contract.register import register_schema from zvt.utils.decorator import to_string TraderBase = declarative_base() class TraderInfo(TraderBase, Mixin): """ trader info """ __tablename__ = "trader_info" #: 机器人名字 trader_name = Column(String(length=128)) entity_type = Column(String(length=128)) start_timestamp = Column(DateTime) end_timestamp = Column(DateTime) provider = Column(String(length=32)) level = Column(String(length=32)) real_time = Column(Boolean) kdata_use_begin_time = Column(Boolean) kdata_adjust_type = Column(String(length=32)) @to_string class AccountStats(TraderBase, Mixin): """ account stats of every day """ __tablename__ = "account_stats" input_money = Column(Float) #: 机器人名字 trader_name = Column(String(length=128)) #: 可用现金 cash = Column(Float) #: 具体仓位 positions = relationship("Position", back_populates="account_stats") #: 市值 value = Column(Float) #: 市值+cash all_value = Column(Float) #: 盈亏 profit = Column(Float) #: 盈亏比例 profit_rate = Column(Float) #: 收盘计算 closing = Column(Boolean) #: the position for specific entity of every day class Position(TraderBase, Mixin): __tablename__ = "position" #: 机器人名字 trader_name = Column(String(length=128)) #: 账户id account_stats_id = Column(Integer, ForeignKey("account_stats.id")) account_stats = relationship("AccountStats", back_populates="positions") #: 做多数量 long_amount = Column(Float) #: 可平多数量 available_long = Column(Float) #: 平均做多价格 average_long_price = Column(Float) #: 做空数量 short_amount = Column(Float) #: 可平空数量 available_short = Column(Float) #: 平均做空价格 average_short_price = Column(Float) #: 盈亏 profit = Column(Float) #: 盈亏比例 profit_rate = Column(Float) #: 市值 或者 占用的保证金(方便起见，总是100%) value = Column(Float) #: 交易类型(0代表T+0,1代表T+1) trading_t = Column(Integer) #: 委托单 class Order(TraderBase, Mixin): __tablename__ = "order" #: 机器人名字 trader_name = Column(String(length=128)) #: 订单价格 order_price = Column(Float) #: 订单数量 order_amount = Column(Float) #: 订单类型 order_type = Column(String(length=64)) #: 订单状态 status = Column(String(length=64)) #: 产生订单的selector/factor level level = Column(String(length=32)) register_schema(db_name="trader_info", schema_base=TraderBase, internal=True) # the __all__ is generated __all__ = ["TraderInfo", "AccountStats", "Position", "Order"] ================================================ FILE: src/zvt/trading/__init__.py ================================================ # -*- coding: utf-8 -*- # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/trading/common.py ================================================ # -*- coding: utf-8 -*- from enum import Enum class ExecutionStatus(Enum): init = "init" success = "success" failed = "failed" # the __all__ is generated __all__ = ["ExecutionStatus"] ================================================ FILE: src/zvt/trading/trading_models.py ================================================ # -*- coding: utf-8 -*- from datetime import datetime from typing import List, Optional from typing import Union from pydantic import BaseModel, Field from pydantic import field_validator from zvt.common.query_models import TimeRange, OrderByType from zvt.contract import IntervalLevel, AdjustType from zvt.contract.model import MixinModel, CustomModel from zvt.tag.tag_utils import get_stock_pool_names from zvt.trader import TradingSignalType from zvt.trading.common import ExecutionStatus from zvt.utils.time_utils import date_time_by_interval, current_date from zvt.utils.time_utils import tomorrow_date, to_pd_timestamp class KdataRequestModel(BaseModel): entity_ids: List[str] data_provider: str = Field(default="em") start_timestamp: datetime = Field(default=date_time_by_interval(current_date(), -500)) end_timestamp: Optional[datetime] = Field(default=None) level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY) adjust_type: AdjustType = Field(default=AdjustType.qfq) class KdataModel(BaseModel): entity_id: str code: str name: str level: IntervalLevel = Field(default=IntervalLevel.LEVEL_1DAY) datas: List class TSRequestModel(BaseModel): entity_ids: List[str] data_provider: str = Field(default="qmt") days_count: int = Field(default=5) class TSModel(BaseModel): entity_id: str code: str name: str datas: List class QuoteStatsModel(BaseModel): #: UNIX时间戳 time: int #: 涨停数 limit_up_count: int #: 跌停数 limit_down_count: int #: 上涨数 up_count: int #: 下跌数 down_count: int #: 涨幅 change_pct: float #: 成交额 turnover: float #: 昨日成交额 pre_turnover: Optional[float] = Field(default=None) #: 同比 turnover_change: Optional[float] = Field(default=None) class QueryStockQuoteSettingModel(CustomModel): stock_pool_name: Optional[str] = Field(default=None) main_tags: Optional[List[str]] = Field(default=None) class BuildQueryStockQuoteSettingModel(CustomModel): stock_pool_name: str main_tags: Optional[List[str]] = Field(default=None) @field_validator("stock_pool_name") @classmethod def stock_pool_name_existed(cls, v: str) -> str: if v not in get_stock_pool_names(): raise ValueError(f"Invalid stock_pool_name: {v}") return v class QueryTagQuoteModel(CustomModel): stock_pool_name: str main_tags: List[str] class QueryStockQuoteModel(CustomModel): main_tag: Optional[str] = Field(default=None) entity_ids: Optional[List[str]] = Field(default=None) stock_pool_name: Optional[str] = Field(default=None) # the amount is not huge, just ignore now limit: int = Field(default=100) order_by_type: Optional[OrderByType] = Field(default=OrderByType.desc) order_by_field: Optional[str] = Field(default="change_pct") class StockQuoteModel(MixinModel): #: 代码 code: str #: 名字 name: str #: UNIX时间戳 time: int #: 最新价 price: float # 涨跌幅 change_pct: float # 成交金额 turnover: float # 换手率 turnover_rate: float #: 是否涨停 is_limit_up: bool #: 封涨停金额 # limit_up_amount: Optional[float] = Field(default=None) #: 是否跌停 is_limit_down: bool #: 封跌停金额 # limit_down_amount: Optional[float] = Field(default=None) #: 5挡卖单金额 # ask_amount: float #: 5挡买单金额 # bid_amount: float #: 流通市值 float_cap: float #: 总市值 total_cap: float main_tag: Optional[str] = Field(default=None) sub_tag: Union[str, None] = Field(default=None) hidden_tags: Union[List[str], None] = Field(default=None) class TagQuoteStatsModel(CustomModel): main_tag: str #: 涨停数 limit_up_count: int #: 跌停数 limit_down_count: int #: 上涨数 up_count: int #: 下跌数 down_count: int #: 涨幅 change_pct: float #: 成交额 turnover: float class StockQuoteStatsModel(CustomModel): #: 涨停数 limit_up_count: int #: 跌停数 limit_down_count: int #: 上涨数 up_count: int #: 下跌数 down_count: int #: 涨幅 change_pct: float #: 成交额 turnover: float quotes: List[StockQuoteModel] class TradingPlanModel(MixinModel): stock_id: str stock_code: str stock_name: str # 执行交易日 trading_date: datetime # 预期开盘涨跌幅 expected_open_pct: float buy_price: Optional[float] sell_price: Optional[float] # 操作理由 trading_reason: str # 交易信号 trading_signal_type: TradingSignalType # 执行状态 status: ExecutionStatus = Field(default=ExecutionStatus.init) # 复盘 review: Optional[str] class BuildTradingPlanModel(BaseModel): stock_id: str # 执行交易日 trading_date: datetime # 预期开盘涨跌幅 expected_open_pct: float buy_price: Optional[float] sell_price: Optional[float] # 操作理由 trading_reason: str # 交易信号 trading_signal_type: TradingSignalType @field_validator("trading_date") @classmethod def trading_date_must_be_future(cls, v: str) -> str: if to_pd_timestamp(v) < tomorrow_date(): raise ValueError(f"trading_date: {v} must set to future trading date") return v class QueryTradingPlanModel(BaseModel): time_range: TimeRange # the __all__ is generated __all__ = [ "QueryTagQuoteModel", "QueryStockQuoteSettingModel", "BuildQueryStockQuoteSettingModel", "QueryStockQuoteModel", "StockQuoteModel", "StockQuoteStatsModel", "TradingPlanModel", "BuildTradingPlanModel", "QueryTradingPlanModel", ] ================================================ FILE: src/zvt/trading/trading_schemas.py ================================================ # -*- coding: utf-8 -*- from sqlalchemy import Column, Float, DateTime, Integer from sqlalchemy import String, JSON from sqlalchemy.orm import declarative_base from zvt.contract import Mixin from zvt.contract.register import register_schema TradingBase = declarative_base() class TagQuoteStats(Mixin, TradingBase): __tablename__ = "tag_quote_stats" stock_pool_name = Column(String) main_tag = Column(String) limit_up_count = Column(Integer) limit_down_count = Column(Integer) up_count = Column(Integer) down_count = Column(Integer) change_pct = Column(Float) turnover = Column(Float) class TradingPlan(TradingBase, Mixin): __tablename__ = "trading_plan" stock_id = Column(String) stock_code = Column(String) stock_name = Column(String) trading_date = Column(DateTime) # 预期开盘涨跌幅 expected_open_pct = Column(Float, nullable=False) buy_price = Column(Float) sell_price = Column(Float) # 操作理由 trading_reason = Column(String) # 交易信号 trading_signal_type = Column(String) # 执行状态 status = Column(String) # 复盘 review = Column(String) class QueryStockQuoteSetting(TradingBase, Mixin): __tablename__ = "query_stock_quote_setting" stock_pool_name = Column(String) main_tags = Column(JSON) register_schema(db_name="stock_trading", schema_base=TradingBase, internal=True) # the __all__ is generated __all__ = ["TradingPlan", "QueryStockQuoteSetting"] ================================================ FILE: src/zvt/trading/trading_service.py ================================================ # -*- coding: utf-8 -*- import logging from typing import List import pandas as pd from fastapi import HTTPException from fastapi_pagination.ext.sqlalchemy import paginate import zvt.api.kdata as kdata_api import zvt.contract.api as contract_api from zvt.common.query_models import TimeUnit from zvt.domain import Stock, StockQuote, Stock1mQuote from zvt.domain.quotes.stockhk.stockhk_quote import StockhkQuote from zvt.domain.quotes.stockus.stockus_quote import StockusQuote from zvt.tag.tag_schemas import StockTags, StockPools, StockPoolInfo from zvt.trading.common import ExecutionStatus from zvt.trading.trading_models import ( BuildTradingPlanModel, QueryTradingPlanModel, QueryTagQuoteModel, QueryStockQuoteModel, BuildQueryStockQuoteSettingModel, KdataRequestModel, TSRequestModel, ) from zvt.trading.trading_schemas import TradingPlan, QueryStockQuoteSetting, TagQuoteStats from zvt.utils.pd_utils import pd_is_not_null from zvt.utils.time_utils import ( to_date_time_str, to_pd_timestamp, now_pd_timestamp, date_time_by_interval, current_date, date_and_time, ) logger = logging.getLogger(__name__) def query_kdata(kdata_request_model: KdataRequestModel): kdata_df = kdata_api.get_kdata( entity_ids=kdata_request_model.entity_ids, provider=kdata_request_model.data_provider, start_timestamp=kdata_request_model.start_timestamp, end_timestamp=kdata_request_model.end_timestamp, adjust_type=kdata_request_model.adjust_type, ) if pd_is_not_null(kdata_df): kdata_df["timestamp"] = kdata_df["timestamp"].apply(lambda x: int(x.timestamp())) kdata_df["data"] = kdata_df.apply( lambda x: x[ ["timestamp", "open", "high", "low", "close", "volume", "turnover", "change_pct", "turnover_rate"] ].values.tolist(), axis=1, ) df = kdata_df.groupby("entity_id").agg( code=("code", "first"), name=("name", "first"), level=("level", "first"), datas=("data", lambda data: list(data)), ) df = df.reset_index(drop=False) return df.to_dict(orient="records") def query_ts(ts_request_model: TSRequestModel): trading_dates = kdata_api.get_recent_trade_dates(entity_type="stock", days_count=ts_request_model.days_count) ts_df = Stock1mQuote.query_data( entity_ids=ts_request_model.entity_ids, provider=ts_request_model.data_provider, start_timestamp=trading_dates[0], ) if pd_is_not_null(ts_df): ts_df["data"] = ts_df.apply( lambda x: x[ ["time", "price", "avg_price", "change_pct", "volume", "turnover", "turnover_rate"] ].values.tolist(), axis=1, ) df = ts_df.groupby("entity_id").agg( code=("code", "first"), name=("name", "first"), datas=("data", lambda data: list(data)), ) df = df.reset_index(drop=False) return df.to_dict(orient="records") def build_trading_plan(build_trading_plan_model: BuildTradingPlanModel): with contract_api.DBSession(provider="zvt", data_schema=TradingPlan)() as session: stock_id = build_trading_plan_model.stock_id trading_date_str = to_date_time_str(build_trading_plan_model.trading_date) trading_date = to_pd_timestamp(trading_date_str) signal = build_trading_plan_model.trading_signal_type.value plan_id = f"{stock_id}_{trading_date_str}_{signal}" datas = TradingPlan.query_data( session=session, filters=[TradingPlan.id == plan_id], limit=1, return_type="domain" ) if datas: assert len(datas) == 1 plan = datas[0] else: datas = Stock.query_data(provider="em", entity_id=stock_id, return_type="domain") stock = datas[0] plan = TradingPlan( id=plan_id, entity_id=stock_id, stock_id=stock_id, stock_code=stock.code, stock_name=stock.name, trading_date=trading_date, expected_open_pct=build_trading_plan_model.expected_open_pct, buy_price=build_trading_plan_model.buy_price, sell_price=build_trading_plan_model.sell_price, trading_reason=build_trading_plan_model.trading_reason, trading_signal_type=signal, status=ExecutionStatus.init.value, ) plan.timestamp = now_pd_timestamp() session.add(plan) session.commit() session.refresh(plan) return plan def query_trading_plan(query_trading_plan_model: QueryTradingPlanModel): with contract_api.DBSession(provider="zvt", data_schema=TradingPlan)() as session: time_range = query_trading_plan_model.time_range if time_range.relative_time_range: start_timestamp = date_time_by_interval( current_date(), time_range.relative_time_range.interval, time_range.relative_time_range.time_unit ) end_timestamp = None else: start_timestamp = time_range.absolute_time_range.start_timestamp end_timestamp = time_range.absolute_time_range.end_timestamp selectable = TradingPlan.query_data( session=session, start_timestamp=start_timestamp, end_timestamp=end_timestamp, return_type="select" ) return paginate(session, selectable) def get_current_trading_plan(): with contract_api.DBSession(provider="zvt", data_schema=TradingPlan)() as session: return TradingPlan.query_data( session=session, filters=[TradingPlan.status == ExecutionStatus.pending.value], order=TradingPlan.trading_date.asc(), return_type="domain", ) def get_future_trading_plan(): with contract_api.DBSession(provider="zvt", data_schema=TradingPlan)() as session: return TradingPlan.query_data( session=session, filters=[TradingPlan.status == ExecutionStatus.init.value], order=TradingPlan.trading_date.asc(), return_type="domain", ) def check_trading_plan(): with contract_api.DBSession(provider="zvt", data_schema=TradingPlan)() as session: plans = TradingPlan.query_data( session=session, filters=[TradingPlan.status == ExecutionStatus.init.value, TradingPlan.trading_date == current_date()], order=TradingPlan.trading_date.asc(), return_type="domain", ) logger.debug(f"current plans:{plans}") def query_quote_stats(): quote_df = StockQuote.query_data( return_type="df", filters=[StockQuote.change_pct >= -0.31, StockQuote.change_pct <= 0.31], columns=["timestamp", "entity_id", "time", "change_pct", "turnover", "is_limit_up", "is_limit_down"], ) current_stats = cal_quote_stats(quote_df) start_timestamp = current_stats["timestamp"] pre_date_df = Stock1mQuote.query_data( filters=[Stock1mQuote.timestamp < to_date_time_str(start_timestamp)], order=Stock1mQuote.timestamp.desc(), limit=1, columns=["timestamp"], ) if pd_is_not_null(pre_date_df): pre_date = pre_date_df["timestamp"].tolist()[0] if start_timestamp.hour >= 15: start_timestamp = date_and_time(pre_date, "15:00") else: start_timestamp = date_and_time(pre_date, f"{start_timestamp.hour}:{start_timestamp.minute}") end_timestamp = date_time_by_interval(start_timestamp, 1, TimeUnit.minute) pre_df = Stock1mQuote.query_data( return_type="df", start_timestamp=start_timestamp, end_timestamp=end_timestamp, filters=[Stock1mQuote.change_pct >= -0.31, Stock1mQuote.change_pct <= 0.31], columns=["timestamp", "entity_id", "time", "change_pct", "turnover", "is_limit_up", "is_limit_down"], ) if pd_is_not_null(pre_df): pre_df = pre_df.drop_duplicates(subset=["entity_id"], keep="first") pre_stats = cal_quote_stats(pre_df) current_stats["pre_turnover"] = pre_stats["turnover"] current_stats["turnover_change"] = current_stats["turnover"] - current_stats["pre_turnover"] else: current_stats["pre_turnover"] = current_stats["turnover"] current_stats["turnover_change"] = 0 return current_stats def cal_quote_stats(quote_df): quote_df["ss"] = 1 df = ( quote_df.groupby("ss") .agg( timestamp=("timestamp", "last"), time=("time", "last"), up_count=("change_pct", lambda x: (x > 0).sum()), down_count=("change_pct", lambda x: (x <= 0).sum()), turnover=("turnover", "sum"), change_pct=("change_pct", "mean"), limit_up_count=("is_limit_up", "sum"), limit_down_count=("is_limit_down", lambda x: (x == True).sum()), ) .reset_index(drop=True) ) return df.to_dict(orient="records")[0] def cal_tag_quote_stats(stock_pool_name): stock_pools: List[StockPools] = StockPools.query_data( filters=[StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if stock_pools: entity_ids = stock_pools[0].entity_ids else: entity_ids = None tag_df = StockTags.query_data( entity_ids=entity_ids, filters=[StockTags.main_tag.isnot(None)], columns=[StockTags.entity_id, StockTags.main_tag], return_type="df", index="entity_id", ) entity_ids = tag_df["entity_id"].tolist() quote_df = StockQuote.query_data(entity_ids=entity_ids, return_type="df", index="entity_id") timestamp = quote_df["timestamp"].tolist()[0] df = pd.concat([tag_df, quote_df], axis=1) grouped_df = ( df.groupby("main_tag") .agg( up_count=("change_pct", lambda x: (x > 0).sum()), down_count=("change_pct", lambda x: (x <= 0).sum()), turnover=("turnover", "sum"), change_pct=("change_pct", "mean"), limit_up_count=("is_limit_up", "sum"), limit_down_count=("is_limit_down", lambda x: (x == True).sum()), total_count=("main_tag", "size"), # 添加计数，计算每个分组的总行数 ) .reset_index(drop=False) ) grouped_df["stock_pool_name"] = stock_pool_name grouped_df["entity_id"] = grouped_df[["stock_pool_name", "main_tag"]].apply( lambda se: "{}_{}".format(se["stock_pool_name"], se["main_tag"]), axis=1 ) grouped_df["timestamp"] = timestamp grouped_df["id"] = grouped_df[["entity_id", "timestamp"]].apply( lambda se: "{}_{}".format(se["entity_id"], to_date_time_str(se["timestamp"])), axis=1 ) print(grouped_df) contract_api.df_to_db( df=grouped_df, data_schema=TagQuoteStats, provider="zvt", force_update=True, drop_duplicates=False ) def query_tag_quotes(query_tag_quote_model: QueryTagQuoteModel): stock_pools: List[StockPools] = StockPools.query_data( filters=[StockPools.stock_pool_name == query_tag_quote_model.stock_pool_name], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if stock_pools: entity_ids = stock_pools[0].entity_ids else: entity_ids = None entity_type = "stock" if entity_ids: entity_id = entity_ids[0] entity_type, _, _ = contract_api.decode_entity_id(entity_id) tag_df = StockTags.query_data( entity_ids=entity_ids, filters=[StockTags.entity_type == entity_type], columns=[StockTags.entity_id, StockTags.main_tag], return_type="df", index="entity_id", ) entity_ids = tag_df["entity_id"].tolist() if entity_type == "stock": quote_df = StockQuote.query_data(entity_ids=entity_ids, return_type="df", index="entity_id") elif entity_type == "stockus": quote_df = StockusQuote.query_data(entity_ids=entity_ids, return_type="df", index="entity_id") elif entity_type == "stockhk": quote_df = StockhkQuote.query_data(entity_ids=entity_ids, return_type="df", index="entity_id") else: raise HTTPException(status_code=400, detail=f"Unsupported entity type: {entity_type}") df = pd.concat([tag_df, quote_df], axis=1) grouped_df = ( df.groupby("main_tag") .agg( up_count=("change_pct", lambda x: (x > 0).sum()), down_count=("change_pct", lambda x: (x <= 0).sum()), turnover=("turnover", "sum"), change_pct=("change_pct", "mean"), limit_up_count=("is_limit_up", "sum"), limit_down_count=("is_limit_down", lambda x: (x == True).sum()), total_count=("main_tag", "size"), # 添加计数，计算每个分组的总行数 ) .reset_index(drop=False) ) sorted_df = grouped_df.sort_values(by=["turnover", "total_count"], ascending=[False, False]) return sorted_df.to_dict(orient="records") def query_stock_quotes(query_stock_quote_model: QueryStockQuoteModel): entity_ids = None if query_stock_quote_model.stock_pool_name: stock_pool_name = query_stock_quote_model.stock_pool_name stock_pool_info = StockPoolInfo.query_data( filters=[StockPoolInfo.stock_pool_name == stock_pool_name], return_type="domain", ) if not stock_pool_info: raise HTTPException(status_code=404, detail=f"Stock pool info {stock_pool_name} not found") if stock_pool_name != "A股": stock_pools: List[StockPools] = StockPools.query_data( filters=[StockPools.stock_pool_name == stock_pool_name], order=StockPools.timestamp.desc(), limit=1, return_type="domain", ) if not stock_pools: raise HTTPException(status_code=404, detail=f"Stock pool {stock_pool_name} not found") if stock_pools: entity_ids = stock_pools[0].entity_ids else: entity_ids = query_stock_quote_model.entity_ids entity_type = "stock" if entity_ids: entity_id = entity_ids[0] entity_type, _, _ = contract_api.decode_entity_id(entity_id) if query_stock_quote_model.main_tag: tags_dict = StockTags.query_data( entity_ids=entity_ids, filters=[StockTags.entity_type == entity_type, StockTags.main_tag == query_stock_quote_model.main_tag], return_type="dict", ) if not tags_dict: return None entity_ids = [item["entity_id"] for item in tags_dict] else: tags_dict = StockTags.query_data( return_type="dict", ) entity_tags_map = {item["entity_id"]: item for item in tags_dict} if entity_type == "stock": order = eval( f"StockQuote.{query_stock_quote_model.order_by_field}.{query_stock_quote_model.order_by_type.value}()" ) df = StockQuote.query_data(order=order, entity_ids=entity_ids, return_type="df") elif entity_type == "stockus": order = eval( f"StockusQuote.{query_stock_quote_model.order_by_field}.{query_stock_quote_model.order_by_type.value}()" ) df = StockusQuote.query_data(order=order, entity_ids=entity_ids, return_type="df") elif entity_type == "stockhk": order = eval( f"StockhkQuote.{query_stock_quote_model.order_by_field}.{query_stock_quote_model.order_by_type.value}()" ) df = StockhkQuote.query_data(order=order, entity_ids=entity_ids, return_type="df") else: raise HTTPException(status_code=400, detail=f"Unsupported entity type: {entity_type}") if not pd_is_not_null(df): return None def set_tags(quote): entity_id = quote["entity_id"] main_tag = entity_tags_map.get(entity_id, {}).get("main_tag", None) sub_tag = entity_tags_map.get(entity_id, {}).get("sub_tag", None) active_hidden_tags = entity_tags_map.get(entity_id, {}).get("active_hidden_tags", None) if active_hidden_tags: hidden_tags = list(active_hidden_tags.keys()) else: hidden_tags = None return pd.Series({"main_tag": main_tag, "sub_tag": sub_tag, "hidden_tags": hidden_tags}) df[["main_tag", "sub_tag", "hidden_tags"]] = df.apply(set_tags, axis=1) up_count = (df["change_pct"] > 0).sum() down_count = (df["change_pct"] < 0).sum() turnover = df["turnover"].sum() change_pct = df["change_pct"].mean() limit_up_count = df["is_limit_up"].sum() limit_down_count = df["is_limit_down"].sum() quotes = df.to_dict(orient="records") result = { "up_count": up_count, "down_count": down_count, "turnover": turnover, "change_pct": change_pct, "limit_up_count": limit_up_count, "limit_down_count": limit_down_count, "quotes": quotes[: query_stock_quote_model.limit], } return result def buy_stocks(): pass def sell_stocks(): pass def build_query_stock_quote_setting(build_query_stock_quote_setting_model: BuildQueryStockQuoteSettingModel): with contract_api.DBSession(provider="zvt", data_schema=QueryStockQuoteSetting)() as session: the_id = "admin_setting" datas = QueryStockQuoteSetting.query_data(ids=[the_id], session=session, return_type="domain") if datas: query_setting = datas[0] else: query_setting = QueryStockQuoteSetting(entity_id="admin", id=the_id) query_setting.timestamp = current_date() query_setting.stock_pool_name = build_query_stock_quote_setting_model.stock_pool_name query_setting.main_tags = build_query_stock_quote_setting_model.main_tags session.add(query_setting) session.commit() session.refresh(query_setting) return query_setting def build_default_query_stock_quote_setting(): datas = QueryStockQuoteSetting.query_data(ids=["admin_setting"], return_type="domain") if datas: return build_query_stock_quote_setting(BuildQueryStockQuoteSettingModel(stock_pool_name="all", main_tags=["消费电子"])) if __name__ == "__main__": # print(query_tag_quotes(QueryTagQuoteModel(stock_pool_name="all", main_tags=["低空经济", "半导体", "化工", "消费电子"]))) # print(query_stock_quotes(QueryStockQuoteModel(stock_pool_name="all", main_tag="半导体"))) print(query_quote_stats()) # the __all__ is generated __all__ = [ "build_trading_plan", "query_trading_plan", "get_current_trading_plan", "get_future_trading_plan", "check_trading_plan", "query_stock_quotes", "buy_stocks", "sell_stocks", "build_query_stock_quote_setting", ] ================================================ FILE: src/zvt/ui/__init__.py ================================================ # -*- coding: utf-8 -*- import os import dash import dash_bootstrap_components as dbc assets_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "assets")) zvt_app = dash.Dash( __name__, meta_tags=[{"name": "viewport", "content": "width=device-width"}], assets_folder=assets_path, external_stylesheets=[dbc.themes.BOOTSTRAP], ) zvt_app.config.suppress_callback_exceptions = True server = zvt_app.server ================================================ FILE: src/zvt/ui/apps/factor_app.py ================================================ # -*- coding: utf-8 -*- from typing import List import dash_daq as daq from dash import dash from dash import dcc from dash import html from dash.dependencies import Input, Output, State from zvt.contract import Mixin from zvt.contract import zvt_context, IntervalLevel from zvt.contract.api import get_entities, get_schema_by_name, get_schema_columns from zvt.contract.drawer import StackedDrawer from zvt.trader.trader_info_api import AccountStatsReader, OrderReader, get_order_securities from zvt.trader.trader_info_api import get_trader_info from zvt.trader.trader_schemas import TraderInfo from zvt.ui import zvt_app from zvt.ui.components.dcc_components import get_account_stats_figure from zvt.utils.pd_utils import pd_is_not_null account_readers = [] order_readers = [] # init the data traders: List[TraderInfo] = [] trader_names: List[str] = [] def order_type_flag(order_type): if order_type == "order_long" or order_type == "order_close_short": return "B" else: return "S" def order_type_color(order_type): if order_type == "order_long" or order_type == "order_close_short": return "#ec0000" else: return "#00da3c" def load_traders(): global traders global trader_names traders = get_trader_info(return_type="domain") account_readers.clear() order_readers.clear() for trader in traders: account_readers.append(AccountStatsReader(level=trader.level, trader_names=[trader.trader_name])) order_readers.append( OrderReader(start_timestamp=trader.start_timestamp, level=trader.level, trader_names=[trader.trader_name]) ) trader_names = [item.trader_name for item in traders] load_traders() def factor_layout(): layout = html.Div( [ # controls html.Div( className="three columns card", children=[ html.Div( className="bg-white user-control", children=[ html.Div( className="padding-top-bot", children=[ html.H6("select trader:"), dcc.Dropdown( id="trader-selector", placeholder="select the trader", options=[{"label": item, "value": i} for i, item in enumerate(trader_names)], ), ], ), # select entity type html.Div( className="padding-top-bot", children=[ html.H6("select entity type:"), dcc.Dropdown( id="entity-type-selector", placeholder="select entity type", options=[ {"label": name, "value": name} for name in zvt_context.tradable_schema_map.keys() ], value="stock", clearable=False, ), ], ), # select entity provider html.Div( className="padding-top-bot", children=[ html.H6("select entity provider:"), dcc.Dropdown(id="entity-provider-selector", placeholder="select entity provider"), ], ), # select entity html.Div( className="padding-top-bot", children=[ html.H6("select entity:"), dcc.Dropdown(id="entity-selector", placeholder="select entity"), ], ), # select levels html.Div( className="padding-top-bot", children=[ html.H6("select levels:"), dcc.Dropdown( id="levels-selector", options=[ {"label": level.name, "value": level.value} for level in (IntervalLevel.LEVEL_1WEEK, IntervalLevel.LEVEL_1DAY) ], value="1d", multi=True, ), ], ), # select factor html.Div( className="padding-top-bot", children=[ html.H6("select factor:"), dcc.Dropdown( id="factor-selector", placeholder="select factor", options=[ {"label": name, "value": name} for name in zvt_context.factor_cls_registry.keys() ], value="TechnicalFactor", ), ], ), # select data html.Div( children=[ html.Div( [ html.H6( "related/all data to show in sub graph", style={"display": "inline-block"}, ), daq.BooleanSwitch( id="data-switch", on=True, style={ "display": "inline-block", "float": "right", "vertical-align": "middle", "padding": "8px", }, ), ], ), dcc.Dropdown(id="data-selector", placeholder="schema"), ], style={"padding-top": "12px"}, ), # select properties html.Div( children=[dcc.Dropdown(id="schema-column-selector", placeholder="properties")], style={"padding-top": "6px"}, ), ], ) ], ), # Graph html.Div( className="nine columns card-left", children=[ html.Div( id="trader-details", className="bg-white", ), html.Div(id="factor-details"), ], ), ] ) return layout @zvt_app.callback( [ Output("trader-details", "children"), Output("entity-type-selector", "options"), Output("entity-provider-selector", "options"), Output("entity-selector", "options"), ], [ Input("trader-selector", "value"), Input("entity-type-selector", "value"), Input("entity-provider-selector", "value"), ], ) def update_trader_details(trader_index, entity_type, entity_provider): if trader_index is not None: # change entity_type options entity_type = traders[trader_index].entity_type if not entity_type: entity_type = "stock" entity_type_options = [{"label": entity_type, "value": entity_type}] # account stats account_stats = get_account_stats_figure(account_stats_reader=account_readers[trader_index]) providers = zvt_context.tradable_schema_map.get(entity_type).get_providers() entity_provider_options = [{"label": name, "value": name} for name in providers] # entities entity_ids = get_order_securities(trader_name=trader_names[trader_index]) df = get_entities( provider=entity_provider, entity_type=entity_type, entity_ids=entity_ids, columns=["entity_id", "code", "name"], index="entity_id", ) entity_options = [ {"label": f'{entity_id}({entity["name"]})', "value": entity_id} for entity_id, entity in df.iterrows() ] return account_stats, entity_type_options, entity_provider_options, entity_options else: entity_type_options = [{"label": name, "value": name} for name in zvt_context.tradable_schema_map.keys()] account_stats = None providers = zvt_context.tradable_schema_map.get(entity_type).get_providers() entity_provider_options = [{"label": name, "value": name} for name in providers] df = get_entities( provider=entity_provider, entity_type=entity_type, columns=["entity_id", "code", "name"], index="entity_id" ) entity_options = [ {"label": f'{entity_id}({entity["name"]})', "value": entity_id} for entity_id, entity in df.iterrows() ] return account_stats, entity_type_options, entity_provider_options, entity_options @zvt_app.callback( Output("data-selector", "options"), [Input("entity-type-selector", "value"), Input("data-switch", "on")] ) def update_entity_selector(entity_type, related): if entity_type is not None: if related: schemas = zvt_context.entity_map_schemas.get(entity_type) else: schemas = zvt_context.schemas return [{"label": schema.__name__, "value": schema.__name__} for schema in schemas] raise dash.PreventUpdate() @zvt_app.callback(Output("schema-column-selector", "options"), [Input("data-selector", "value")]) def update_column_selector(schema_name): if schema_name: schema = get_schema_by_name(name=schema_name) cols = get_schema_columns(schema=schema) return [{"label": col, "value": col} for col in cols] raise dash.PreventUpdate() @zvt_app.callback( Output("factor-details", "children"), [ Input("factor-selector", "value"), Input("entity-type-selector", "value"), Input("entity-selector", "value"), Input("levels-selector", "value"), Input("schema-column-selector", "value"), ], [State("trader-selector", "value"), State("data-selector", "value")], ) def update_factor_details(factor, entity_type, entity, levels, columns, trader_index, schema_name): if factor and entity_type and entity and levels: sub_df = None # add sub graph if columns: if type(columns) == str: columns = [columns] columns = columns + ["entity_id", "timestamp"] schema: Mixin = get_schema_by_name(name=schema_name) sub_df = schema.query_data(entity_id=entity, columns=columns) # add trading signals as annotation annotation_df = None if trader_index is not None: order_reader = order_readers[trader_index] annotation_df = order_reader.data_df.copy() annotation_df = annotation_df[annotation_df.entity_id == entity].copy() if pd_is_not_null(annotation_df): annotation_df["value"] = annotation_df["order_price"] annotation_df["flag"] = annotation_df["order_type"].apply(lambda x: order_type_flag(x)) annotation_df["color"] = annotation_df["order_type"].apply(lambda x: order_type_color(x)) print(annotation_df.tail()) if type(levels) is list and len(levels) >= 2: levels.sort() drawers = [] for level in levels: drawers.append( zvt_context.factor_cls_registry[factor]( entity_schema=zvt_context.tradable_schema_map[entity_type], level=level, entity_ids=[entity] ).drawer() ) stacked = StackedDrawer(*drawers) return dcc.Graph(id=f"{factor}-{entity_type}-{entity}", figure=stacked.draw_kline(show=False, height=900)) else: if type(levels) is list: level = levels[0] else: level = levels drawer = zvt_context.factor_cls_registry[factor]( entity_schema=zvt_context.tradable_schema_map[entity_type], level=level, entity_ids=[entity], need_persist=False, ).drawer() if pd_is_not_null(sub_df): drawer.add_sub_df(sub_df) if pd_is_not_null(annotation_df): drawer.annotation_df = annotation_df return dcc.Graph(id=f"{factor}-{entity_type}-{entity}", figure=drawer.draw_kline(show=False, height=800)) raise dash.PreventUpdate() ================================================ FILE: src/zvt/ui/assets/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: src/zvt/ui/assets/base.css ================================================ /* Table of contents –––––––––––––––––––––––––––––––––––––––––––––––––– - Plotly.js - Grid - Base Styles - Typography - Links - Buttons - Forms - Lists - Code - Tables - Spacing - Utilities - Clearing - Media Queries */ /* PLotly.js –––––––––––––––––––––––––––––––––––––––––––––––––– */ /* plotly.js's modebar's z-index is 1001 by default * https://github.com/plotly/plotly.js/blob/7e4d8ab164258f6bd48be56589dacd9bdd7fded2/src/css/_modebar.scss#L5 * In case a dropdown is above the graph, the dropdown's options * will be rendered below the modebar * Increase the select option's z-index */ /* This was actually not quite right - dropdowns were overlapping each other (edited October 26) .Select { z-index: 1002; }*/ /* Grid –––––––––––––––––––––––––––––––––––––––––––––––––– */ .container { position: relative; width: 100%; max-width: 960px; margin: 0 auto; padding: 0 20px; box-sizing: border-box; } .column, .columns { width: 100%; float: left; box-sizing: border-box; } /* For devices larger than 400px */ @media (min-width: 400px) { .container { width: 85%; padding: 0; } } /* For devices larger than 550px */ @media (min-width: 550px) { .container { width: 80%; } .column, .columns { margin-left: 4%; } .column:first-child, .columns:first-child { margin-left: 0; } .one.column, .one.columns { width: 4.66666666667%; } .two.columns { width: 13.3333333333%; } .three.columns { width: 22%; } .four.columns { width: 30.6666666667%; } .five.columns { width: 39.3333333333%; } .six.columns { width: 48%; } .seven.columns { width: 56.6666666667%; } .eight.columns { width: 65.3333333333%; } .nine.columns { width: 74.0%; } .ten.columns { width: 82.6666666667%; } .eleven.columns { width: 91.3333333333%; } .twelve.columns { width: 100%; margin-left: 0; } .one-third.column { width: 30.6666666667%; } .two-thirds.column { width: 65.3333333333%; } .one-half.column { width: 48%; } /* Offsets */ .offset-by-one.column, .offset-by-one.columns { margin-left: 8.66666666667%; } .offset-by-two.column, .offset-by-two.columns { margin-left: 17.3333333333%; } .offset-by-three.column, .offset-by-three.columns { margin-left: 26%; } .offset-by-four.column, .offset-by-four.columns { margin-left: 34.6666666667%; } .offset-by-five.column, .offset-by-five.columns { margin-left: 43.3333333333%; } .offset-by-six.column, .offset-by-six.columns { margin-left: 52%; } .offset-by-seven.column, .offset-by-seven.columns { margin-left: 60.6666666667%; } .offset-by-eight.column, .offset-by-eight.columns { margin-left: 69.3333333333%; } .offset-by-nine.column, .offset-by-nine.columns { margin-left: 78.0%; } .offset-by-ten.column, .offset-by-ten.columns { margin-left: 86.6666666667%; } .offset-by-eleven.column, .offset-by-eleven.columns { margin-left: 95.3333333333%; } .offset-by-one-third.column, .offset-by-one-third.columns { margin-left: 34.6666666667%; } .offset-by-two-thirds.column, .offset-by-two-thirds.columns { margin-left: 69.3333333333%; } .offset-by-one-half.column, .offset-by-one-half.columns { margin-left: 52%; } } /* Base Styles –––––––––––––––––––––––––––––––––––––––––––––––––– */ /* NOTE html is set to 62.5% so that all the REM measurements throughout Skeleton are based on 10px sizing. So basically 1.5rem = 15px :) */ html { font-size: 62.5%; } body { font-size: 1.5em; /* currently ems cause chrome bug misinterpreting rems on body element */ line-height: 1.6; font-weight: 400; font-family: "Open Sans", "HelveticaNeue", "Helvetica Neue", Helvetica, Arial, sans-serif; color: rgb(50, 50, 50); } /* Typography –––––––––––––––––––––––––––––––––––––––––––––––––– */ h1, h2, h3, h4, h5, h6 { margin-top: 0; margin-bottom: 0; font-weight: 300; } h1 { font-size: 4.5rem; line-height: 1.2; letter-spacing: -.1rem; margin-bottom: 2rem; } h2 { font-size: 3.6rem; line-height: 1.25; letter-spacing: -.1rem; margin-bottom: 1.8rem; margin-top: 1.8rem; } h3 { font-size: 3.0rem; line-height: 1.3; letter-spacing: -.1rem; margin-bottom: 1.5rem; margin-top: 1.5rem; } h4 { font-size: 2.6rem; line-height: 1.35; letter-spacing: -.08rem; margin-bottom: 1.2rem; margin-top: 1.2rem; } h5 { font-size: 2.2rem; line-height: 1.5; letter-spacing: -.05rem; margin-bottom: 0.6rem; margin-top: 0.6rem; } h6 { font-size: 2.0rem; line-height: 1.6; letter-spacing: 0; margin-bottom: 0.75rem; margin-top: 0.75rem; } p { margin-top: 0; } /* Blockquotes –––––––––––––––––––––––––––––––––––––––––––––––––– */ blockquote { border-left: 4px lightgrey solid; padding-left: 1rem; margin-top: 2rem; margin-bottom: 2rem; margin-left: 0rem; } /* Links –––––––––––––––––––––––––––––––––––––––––––––––––– */ a { color: #1EAEDB; text-decoration: underline; cursor: pointer; } a:hover { color: #0FA0CE; } /* Buttons –––––––––––––––––––––––––––––––––––––––––––––––––– */ .button, button, input[type="submit"], input[type="reset"], input[type="button"] { display: inline-block; height: 38px; padding: 0 30px; color: #555; text-align: center; font-size: 11px; font-weight: 600; line-height: 38px; letter-spacing: .1rem; text-transform: uppercase; text-decoration: none; white-space: nowrap; background-color: transparent; border-radius: 4px; border: 1px solid #bbb; cursor: pointer; box-sizing: border-box; } .button:hover, button:hover, input[type="submit"]:hover, input[type="reset"]:hover, input[type="button"]:hover, .button:focus, button:focus, input[type="submit"]:focus, input[type="reset"]:focus, input[type="button"]:focus { color: #333; border-color: #888; outline: 0; } .button.button-primary, button.button-primary, input[type="submit"].button-primary, input[type="reset"].button-primary, input[type="button"].button-primary { color: #FFF; background-color: #33C3F0; border-color: #33C3F0; } .button.button-primary:hover, button.button-primary:hover, input[type="submit"].button-primary:hover, input[type="reset"].button-primary:hover, input[type="button"].button-primary:hover, .button.button-primary:focus, button.button-primary:focus, input[type="submit"].button-primary:focus, input[type="reset"].button-primary:focus, input[type="button"].button-primary:focus { color: #FFF; background-color: #1EAEDB; border-color: #1EAEDB; } /* Forms –––––––––––––––––––––––––––––––––––––––––––––––––– */ input[type="email"], input[type="number"], input[type="search"], input[type="text"], input[type="tel"], input[type="url"], input[type="password"], textarea, select { height: 38px; padding: 6px 10px; /* The 6px vertically centers text on FF, ignored by Webkit */ background-color: #fff; border: 1px solid #D1D1D1; border-radius: 4px; box-shadow: none; box-sizing: border-box; font-family: inherit; font-size: inherit; /*https://stackoverflow.com/questions/6080413/why-doesnt-input-inherit-the-font-from-body*/ } /* Removes awkward default styles on some inputs for iOS */ input[type="email"], input[type="number"], input[type="search"], input[type="text"], input[type="tel"], input[type="url"], input[type="password"], textarea { -webkit-appearance: none; -moz-appearance: none; appearance: none; } textarea { min-height: 65px; padding-top: 6px; padding-bottom: 6px; } input[type="email"]:focus, input[type="number"]:focus, input[type="search"]:focus, input[type="text"]:focus, input[type="tel"]:focus, input[type="url"]:focus, input[type="password"]:focus, textarea:focus, select:focus { border: 1px solid #33C3F0; outline: 0; } label, legend { display: block; margin-bottom: 0px; } fieldset { padding: 0; border-width: 0; } input[type="checkbox"], input[type="radio"] { display: inline; } label > .label-body { display: inline-block; margin-left: .5rem; font-weight: normal; } /* Lists –––––––––––––––––––––––––––––––––––––––––––––––––– */ ul { list-style: circle inside; } ol { list-style: decimal inside; } ol, ul { padding-left: 0; margin-top: 0; } ul ul, ul ol, ol ol, ol ul { margin: 1.5rem 0 1.5rem 3rem; font-size: 90%; } li { margin-bottom: 1rem; } /* Tables –––––––––––––––––––––––––––––––––––––––––––––––––– */ table { border-collapse: collapse; } th, td { padding: 12px 15px; text-align: left; border-bottom: 1px solid #E1E1E1; } th:first-child, td:first-child { padding-left: 0; } th:last-child, td:last-child { padding-right: 0; } /* Spacing –––––––––––––––––––––––––––––––––––––––––––––––––– */ button, .button { margin-bottom: 0rem; } input, textarea, select, fieldset { margin-bottom: 0rem; } pre, dl, figure, table, form { margin-bottom: 0rem; } p, ul, ol { margin-bottom: 0.75rem; } /* Utilities –––––––––––––––––––––––––––––––––––––––––––––––––– */ .u-full-width { width: 100%; box-sizing: border-box; } .u-max-full-width { max-width: 100%; box-sizing: border-box; } .u-pull-right { float: right; } .u-pull-left { float: left; } /* Misc –––––––––––––––––––––––––––––––––––––––––––––––––– */ hr { margin-top: 3rem; margin-bottom: 3.5rem; border-width: 0; border-top: 1px solid #E1E1E1; } /* Clearing –––––––––––––––––––––––––––––––––––––––––––––––––– */ /* Self Clearing Goodness */ .container:after, .row:after, .u-cf { content: ""; display: table; clear: both; } /* Media Queries –––––––––––––––––––––––––––––––––––––––––––––––––– */ /* Note: The best way to structure the use of media queries is to create the queries near the relevant code. For example, if you wanted to change the styles for buttons on small devices, paste the mobile query code up in the buttons section and style it there. */ /* Larger than mobile */ @media (min-width: 400px) { } /* Larger than phablet (also point when grid becomes active) */ @media (min-width: 550px) { } /* Larger than tablet */ @media (min-width: 750px) { } /* Larger than desktop */ @media (min-width: 1000px) { } /* Larger than Desktop HD */ @media (min-width: 1200px) { } ================================================ FILE: src/zvt/ui/assets/custom.css ================================================ /*Fonts ––––––––––––––––––––––––––––––––––––––––––––––––––*/ @import url('https://fonts.googleapis.com/css?family=Roboto&display=swap'); body { margin: 0px; padding: 0px; background-color: #F3F4F9; font-family: 'Roboto'; color: #203cb3; } .zvt-banner { color: #7f7f7f; font-weight: 600; font-size: 20px; background: #fafbfc; padding: 12px; padding-left: 24px; border-bottom: 2px solid lightgray; } .zvt-nav { color: #7f7f7f; background: #fafbfc; padding: 12px; padding-left: 36px; } .div-logo { display: inline-block; float: right; } .logo { height: 35px; padding: 6px; margin-top: 3px; } .h2-title, .h2-title-mobile { font-family: 'Roboto'; display: inline-block; letter-spacing: 3.8px; font-weight: 800; font-size: 20px; } .h2-title-mobile { display: none; } h5, h6 { font-family: 'Roboto'; font-weight: 600; font-size: 16px; } h5 { padding-left: 42px; } .alert { padding: 20px; background-color: #f44336; color: white; } .bg-white { background-color: white; padding: 24px 32px; } .card { padding: 24px 12px 24px 12px; margin-left: 4%; } .card-left { padding: 24px 12px 24px 12px; margin-left: 0px; } .padding-top-bot { padding-top: 12px; padding-bottom: 18px; } .upload { width: 100%; line-height: 60px; border-width: 1px; border-style: dashed; border-radius: 5px; text-align: center; } .upload p, .upload a { display: inline; } .Select-control { border: 1px solid #203cb3; } @media only screen and (max-width: 320px) { .Select-menu-outer, .Select-value { font-size: 10.5px; } .upload { padding: 5px; } } /* mobile */ @media only screen and (max-width: 768px) { .upload { line-height: 60px; border-width: 1px; border-style: dashed; border-radius: 5px; text-align: center; font-size: small; } .columns { width: 100%; } .card, .card-left { padding: 12px; margin: 0px; } .bg-white { height: auto; } .logo { height: 28px; padding-left: 0px; padding-bottom: 0px; } .div-logo { float: left; display: block; width: 100%; } .h2-title { display: none; } .h2-title-mobile { display: block; float: left; } .app-body { margin-left: 0px; } .columns { text-align: center; } .user-control { padding-top: 24px; padding-bottom: 24px; } } ================================================ FILE: src/zvt/ui/components/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: src/zvt/ui/components/dcc_components.py ================================================ # -*- coding: utf-8 -*- from dash import dcc from zvt.api.kdata import get_kdata_schema from zvt.contract import zvt_context from zvt.contract.api import decode_entity_id from zvt.contract.drawer import Drawer from zvt.contract.reader import DataReader from zvt.trader.trader_info_api import OrderReader, AccountStatsReader from zvt.utils.pd_utils import pd_is_not_null def order_type_color(order_type): if order_type == "order_long" or order_type == "order_close_short": return "#ec0000" else: return "#00da3c" def order_type_flag(order_type): if order_type == "order_long" or order_type == "order_close_short": return "B" else: return "S" def get_trading_signals_figure( order_reader: OrderReader, entity_id: str, start_timestamp=None, end_timestamp=None, adjust_type=None ): entity_type, _, _ = decode_entity_id(entity_id) data_schema = get_kdata_schema(entity_type=entity_type, level=order_reader.level, adjust_type=adjust_type) if not start_timestamp: start_timestamp = order_reader.start_timestamp if not end_timestamp: end_timestamp = order_reader.end_timestamp kdata_reader = DataReader( data_schema=data_schema, entity_schema=zvt_context.tradable_schema_map.get(entity_type), entity_ids=[entity_id], start_timestamp=start_timestamp, end_timestamp=end_timestamp, level=order_reader.level, ) # generate the annotation df order_reader.move_on(timeout=0) df = order_reader.data_df.copy() df = df[df.entity_id == entity_id].copy() if pd_is_not_null(df): df["value"] = df["order_price"] df["flag"] = df["order_type"].apply(lambda x: order_type_flag(x)) df["color"] = df["order_type"].apply(lambda x: order_type_color(x)) print(df.tail()) drawer = Drawer(main_df=kdata_reader.data_df, annotation_df=df) return drawer.draw_kline(show=False, height=800) def get_account_stats_figure(account_stats_reader: AccountStatsReader): graph_list = [] # 账户统计曲线 if account_stats_reader: fig = account_stats_reader.draw_line(show=False) for trader_name in account_stats_reader.trader_names: graph_list.append(dcc.Graph(id="{}-account".format(trader_name), figure=fig)) return graph_list ================================================ FILE: src/zvt/utils/__init__.py ================================================ # the __all__ is generated __all__ = [] ================================================ FILE: src/zvt/utils/decorator.py ================================================ # -*- coding: utf-8 -*- def to_string(cls): def __str__(self): return "%s(%s)" % (type(self).__name__, ", ".join("%s=%s" % item for item in vars(self).items())) cls.__str__ = __str__ return cls # the __all__ is generated __all__ = ["to_string"] ================================================ FILE: src/zvt/utils/file_utils.py ================================================ # -*- coding: utf-8 -*- import os from typing import List, Optional def list_all_files( dir_path: str = "./domain", ext: Optional[str] = ".py", excludes=None, includes=None, return_base_name=False ) -> List[str]: """ list all files with extension in specific directory recursively :param includes: including files, None means all :param dir_path: the directory path :param ext: file extension :param excludes: excluding files :param return_base_name: return file name if True otherwise abs path :return: """ files = [] for entry in os.scandir(dir_path): if entry.is_dir(): files += list_all_files(entry.path, ext=ext, excludes=excludes, return_base_name=return_base_name) elif entry.is_file(): if not ext or (ext and entry.path.endswith(ext)): if excludes and entry.path.endswith(excludes): continue if includes and not entry.path.endswith(includes): continue if return_base_name: files.append(os.path.basename(entry.path)) else: files.append(entry.path) else: pass return files # the __all__ is generated __all__ = ["list_all_files"] ================================================ FILE: src/zvt/utils/git_utils.py ================================================ # -*- coding: utf-8 -*- import subprocess def get_git_user_name(): try: return subprocess.check_output(["git", "config", "--get", "user.name"]).decode("utf8").strip() except: return "foolcage" def get_git_user_email(): try: return subprocess.check_output(["git", "config", "--get", "user.email"]).decode("utf8").strip() except: return "" # the __all__ is generated __all__ = ["get_git_user_name", "get_git_user_email"] ================================================ FILE: src/zvt/utils/model_utils.py ================================================ # -*- coding: utf-8 -*- def update_model(db_model, schema): for key, value in schema.dict().items(): if value is not None: setattr(db_model, key, value) # the __all__ is generated __all__ = ["update_model"] ================================================ FILE: src/zvt/utils/pd_utils.py ================================================ # -*- coding: utf-8 -*- from typing import List, Union import pandas as pd def drop_continue_duplicate(s: Union[pd.Series, pd.DataFrame], col=None): if type(s) == pd.Series: return s[s.shift() != s] if type(s) == pd.DataFrame: ss = s[col] selected = ss[ss.shift() != ss] return s.loc[selected.index, :] def is_filter_result_df(df: pd.DataFrame): return pd_is_not_null(df) and "filter_result" in df.columns def is_score_result_df(df: pd.DataFrame): return pd_is_not_null(df) and "score_result" in df.columns def pd_is_not_null(df: Union[pd.DataFrame, pd.Series]): return df is not None and not df.empty def group_by_entity_id(input_df: pd.DataFrame): return input_df.groupby(level=0) def normalize_group_compute_result(group_result): if group_result.index.nlevels == 3: return group_result.reset_index(level=0, drop=True) return group_result def merge_filter_result(input_df: pd.DataFrame, filter_result: pd.Series): if is_filter_result_df(input_df): input_df["filter_result"] = input_df["filter_result"] & filter_result else: input_df["filter_result"] = filter_result return input_df def index_df(df, index="timestamp", inplace=True, drop=False, time_field="timestamp"): if time_field: df[time_field] = pd.to_datetime(df[time_field]) if inplace: df.set_index(index, drop=drop, inplace=inplace) else: df = df.set_index(index, drop=drop, inplace=inplace) if type(index) == str: df = df.sort_index() elif type(index) == list: df.index.names = index level = list(range(len(index))) df = df.sort_index(level=level) return df def normal_index_df(df, category_field="entity_id", time_filed="timestamp", drop=True, default_entity="entity"): if type(df) == pd.Series: df = df.to_frame(name="value") index = [category_field, time_filed] if is_normal_df(df): return df if df.index.nlevels == 1: if (time_filed != df.index.name) and (time_filed not in df.columns): assert False if category_field not in df.columns: df[category_field] = default_entity if time_filed not in df.columns: df = df.reset_index() return index_df(df=df, index=index, drop=drop, time_field="timestamp") def is_normal_df(df, category_field="entity_id", time_filed="timestamp"): if pd_is_not_null(df) and df.index.nlevels == 2: names = df.index.names if len(names) == 2 and names[0] == category_field and names[1] == time_filed: return True return False def df_subset(df, columns=None): if columns: return df.loc[:, columns] return df def fill_with_same_index(df_list: List[pd.DataFrame]): idx = None for df in df_list: if idx is None: idx = df.index else: idx = idx.append(df.index).drop_duplicates() idx = idx.sort_values() result = [] for df in df_list: # print(df[df.index.duplicated()]) added_index = idx.difference(df.index.drop_duplicates()) added_df = pd.DataFrame(index=added_index, columns=df.columns) # df1 = df.reindex(idx) # df1 = df.append(added_df) df1 = pd.concat([df, added_df]) df1 = df1.sort_index() result.append(df1) return result # the __all__ is generated __all__ = [ "drop_continue_duplicate", "is_filter_result_df", "is_score_result_df", "pd_is_not_null", "group_by_entity_id", "normalize_group_compute_result", "merge_filter_result", "index_df", "normal_index_df", "is_normal_df", "df_subset", "fill_with_same_index", ] ================================================ FILE: src/zvt/utils/recorder_utils.py ================================================ # -*- coding: utf-8 -*- import logging import time from typing import Type import zvt as zvt from zvt import zvt_config from zvt.informer import EmailInformer logger = logging.getLogger("__name__") def run_data_recorder( domain: Type["zvt.contract.Mixin"], entity_provider=None, data_provider=None, entity_ids=None, split_entity_ids_size=0, retry_times=10, sleeping_time=10, return_unfinished=False, **recorder_kv, ): if (entity_ids is not None) and split_entity_ids_size > 0: import numpy as np size = len(entity_ids) if size >= split_entity_ids_size: step_size = int(size / split_entity_ids_size) if size % split_entity_ids_size: step_size = step_size + 1 else: step_size = 1 entity_ids_list = np.array_split(entity_ids, step_size) for split_entity_ids in entity_ids_list: run_data_recorder( domain=domain, entity_provider=entity_provider, data_provider=data_provider, entity_ids=list(split_entity_ids), split_entity_ids_size=0, retry_times=retry_times, sleeping_time=sleeping_time, return_unfinished=return_unfinished, **recorder_kv, ) time.sleep(90) return logger.info(f" record data: {domain.__name__}, entity_provider: {entity_provider}, data_provider: {data_provider}") unfinished_entity_ids = entity_ids email_action = EmailInformer() while retry_times > 0: try: entity_size = len(entity_ids) if entity_ids else "all" if return_unfinished: unfinished_entity_ids = domain.record_data( entity_ids=unfinished_entity_ids, provider=data_provider, sleeping_time=sleeping_time, return_unfinished=return_unfinished, **recorder_kv, ) if unfinished_entity_ids: logger.info(f"unfinished_entity_ids({len(unfinished_entity_ids)}): {unfinished_entity_ids}") raise Exception("Would retry with unfinished latter!") else: domain.record_data( entity_ids=entity_ids, provider=data_provider, sleeping_time=sleeping_time, return_unfinished=return_unfinished, **recorder_kv, ) msg = f"record {domain.__name__} {entity_size} success" logger.info(msg) email_action.send_message(zvt_config["email_username"], msg, msg) break except Exception as e: logger.exception("report error:{}".format(e)) time.sleep(60 * 2) retry_times = retry_times - 1 if retry_times == 0: email_action.send_message( zvt_config["email_username"], f"record {domain.__name__} error", f"record {domain.__name__} error: {e}", ) if __name__ == "__main__": run_data_recorder() # the __all__ is generated __all__ = ["run_data_recorder"] ================================================ FILE: src/zvt/utils/str_utils.py ================================================ # -*- coding: utf-8 -*- def to_snake_str(input: str) -> str: parts = [] part = "" for c in input: if c.isupper() or c.isdigit(): if part: parts.append(part) part = c.lower() else: part = part + c parts.append(part) if len(parts) > 1: return "_".join(parts) elif parts: return parts[0] def to_camel_str(input: str) -> str: parts = input.split("_") domain_name = "" for part in parts: domain_name = domain_name + part.capitalize() return domain_name # the __all__ is generated __all__ = ["to_snake_str", "to_camel_str"] ================================================ FILE: src/zvt/utils/time_utils.py ================================================ # -*- coding: utf-8 -*- import calendar import datetime import arrow import pandas as pd from zvt.common.query_models import TimeUnit CHINA_TZ = "Asia/Shanghai" US_TZ = "America/New_York" TIME_FORMAT_ISO8601 = "YYYY-MM-DDTHH:mm:ss.SSS" TIME_FORMAT_MON = "YYYY-MM" TIME_FORMAT_DAY = "YYYY-MM-DD" TIME_FORMAT_DAY1 = "YYYYMMDD" TIME_FORMAT_MINUTE = "YYYYMMDDHHmm" TIME_FORMAT_SECOND = "YYYYMMDDHHmmss" TIME_FORMAT_MINUTE1 = "HH:mm" TIME_FORMAT_MINUTE2 = "YYYY-MM-DD HH:mm:ss" # ms(int) or second(float) or str def to_pd_timestamp(the_time, tz=None) -> pd.Timestamp | None: if the_time is None: return None # treat int as milliseconds, e.g. return from js if type(the_time) == int: return pd.Timestamp.fromtimestamp(the_time / 1000) # treat float as seconds, e.g. return from # timestamp_seconds = time.time() if type(the_time) == float: return pd.Timestamp.fromtimestamp(the_time) return pd.Timestamp(the_time, tz=tz) def get_local_timezone(): now = datetime.datetime.now() local_now = now.astimezone() local_tz = local_now.tzinfo return local_tz def to_timestamp_ms(the_time, tz=None) -> int: """ Convert a time to a timestamp in milliseconds. :param the_time: :param tz: :return: """ if not tz: tz = get_local_timezone() return int(to_pd_timestamp(the_time).tz_localize(tz).timestamp() * 1000) def now_timestamp_ms(): return int(pd.Timestamp.utcnow().timestamp() * 1000) def now_pd_timestamp(tz=None) -> pd.Timestamp: if tz: return pd.Timestamp(arrow.now(tz=tz).datetime) else: return pd.Timestamp.now() def current_date(tz=None) -> pd.Timestamp: return to_pd_timestamp(now_pd_timestamp(tz=tz).date()) def tomorrow_date(tz=None): return to_pd_timestamp(date_time_by_interval(now_pd_timestamp(tz=tz), 1).date()) def to_date_time_str(date_time, fmt=TIME_FORMAT_DAY, tz=None): try: return arrow.get(to_pd_timestamp(date_time, tz=tz)).format(fmt) except Exception as e: return date_time def now_date_time_str(fmt=TIME_FORMAT_DAY, tz=None): return to_date_time_str(date_time=now_pd_timestamp(), tz=tz, fmt=fmt) def recent_year_date(tz=None): return date_time_by_interval(the_time=current_date(tz=tz), interval=-365) def next_date(the_time, tz=None): """ Get the next date from the given time. :param the_time: The time to get the next date from. :param tz: The timezone to use. :return: The next date as a pd.Timestamp. """ return date_time_by_interval(the_time=the_time, interval=1, unit=TimeUnit.day, tz=tz) def date_time_by_interval(the_time, interval=1, unit: TimeUnit = TimeUnit.day, tz=None): time_delta = None if unit == TimeUnit.year: time_delta = datetime.timedelta(days=interval * 365) elif unit == TimeUnit.month: time_delta = datetime.timedelta(days=interval * 30) elif unit == TimeUnit.day: time_delta = datetime.timedelta(days=interval) elif unit == TimeUnit.minute: time_delta = datetime.timedelta(minutes=interval) elif unit == TimeUnit.second: time_delta = datetime.timedelta(seconds=interval) return to_pd_timestamp(the_time, tz=tz) + time_delta def pre_month(t=None, tz=None): if not t: t = current_date() else: t = to_pd_timestamp(t, tz=tz) t = t.replace(day=1) if t.month > 1: year = t.year month = t.month - 1 else: year = t.year - 1 month = 12 last_valid_date = t.replace(year=year, month=month) return last_valid_date def pre_month_start_date(t=current_date()): return month_start_date(pre_month(t)) def pre_month_end_date(t=current_date()): return month_end_date(pre_month(t)) def month_start_date(the_date): the_date = to_pd_timestamp(the_date) return the_date.replace(day=1) def month_end_date(the_date): the_date = to_pd_timestamp(the_date) _, day = calendar.monthrange(the_date.year, the_date.month) return the_date.replace(day=day) def month_start_end_ranges(start_date, end_date): days = pd.date_range(start=start_date, end=end_date, freq="M") return [(month_start_date(d), month_end_date(d)) for d in days] def is_same_date(one, two): return to_pd_timestamp(one).date() == to_pd_timestamp(two).date() def is_same_date_time(one, two): return to_timestamp_ms(one) == to_timestamp_ms(two) def get_year_quarter(time): time = to_pd_timestamp(time) return time.year, ((time.month - 1) // 3) + 1 def day_offset_today(offset=0): return now_pd_timestamp() + datetime.timedelta(days=offset) def get_year_quarters(start, end=pd.Timestamp.now()): start_year_quarter = get_year_quarter(start) current_year_quarter = get_year_quarter(end) if current_year_quarter[0] == start_year_quarter[0]: return [(current_year_quarter[0], x) for x in range(start_year_quarter[1], current_year_quarter[1] + 1)] elif current_year_quarter[0] - start_year_quarter[0] == 1: return [(start_year_quarter[0], x) for x in range(start_year_quarter[1], 5)] + [ (current_year_quarter[0], x) for x in range(1, current_year_quarter[1] + 1) ] elif current_year_quarter[0] - start_year_quarter[0] > 1: return ( [(start_year_quarter[0], x) for x in range(start_year_quarter[1], 5)] + [(x, y) for x in range(start_year_quarter[0] + 1, current_year_quarter[0]) for y in range(1, 5)] + [(current_year_quarter[0], x) for x in range(1, current_year_quarter[1] + 1)] ) else: raise Exception("wrong start time:{}".format(start)) def date_and_time(the_date, the_time, tz=None): time_str = "{}T{}:00.000".format(to_date_time_str(the_date), the_time) return to_pd_timestamp(time_str, tz=tz) def split_time_interval(start, end, method=None, interval=30, freq="D"): start = to_pd_timestamp(start) end = to_pd_timestamp(end) if not method: while start < end: interval_end = min(date_time_by_interval(the_time=start, interval=interval), end) yield pd.date_range(start=start, end=interval_end, freq=freq) start = date_time_by_interval(interval_end, 1) if method == "month": while start <= end: _, day = calendar.monthrange(start.year, start.month) interval_end = min(to_pd_timestamp(f"{start.year}-{start.month}-{day}"), end) yield pd.date_range(start=start, end=interval_end, freq=freq) start = date_time_by_interval(interval_end, 1) def count_interval(start_date, end_date): start_date = to_pd_timestamp(start_date) end_date = to_pd_timestamp(end_date) delta = end_date - start_date return delta.days if __name__ == "__main__": print(recent_year_date(tz=CHINA_TZ)) print(recent_year_date(tz=US_TZ)) # the __all__ is generated __all__ = [ "CHINA_TZ", "TIME_FORMAT_ISO8601", "TIME_FORMAT_MON", "TIME_FORMAT_DAY", "TIME_FORMAT_DAY1", "TIME_FORMAT_MINUTE", "TIME_FORMAT_SECOND", "TIME_FORMAT_MINUTE1", "TIME_FORMAT_MINUTE2", "to_pd_timestamp", "get_local_timezone", "to_timestamp_ms", "now_timestamp_ms", "now_pd_timestamp", "current_date", "tomorrow_date", "to_date_time_str", "now_date_time_str", "recent_year_date", "date_time_by_interval", "pre_month", "pre_month_start_date", "pre_month_end_date", "month_start_date", "month_end_date", "month_start_end_ranges", "is_same_date", "is_same_date_time", "get_year_quarter", "day_offset_today", "get_year_quarters", "date_and_time", "split_time_interval", "count_interval", ] ================================================ FILE: src/zvt/utils/utils.py ================================================ # -*- coding: utf-8 -*- import logging import numbers from decimal import * from urllib import parse import pandas as pd getcontext().prec = 16 logger = logging.getLogger(__name__) none_values = ["不变", "--", "-", "新进"] zero_values = ["不变", "--", "-", "新进"] def first_item_to_float(the_list): return to_float(the_list[0]) def second_item_to_float(the_list): return to_float(the_list[1]) def add_func_to_value(the_map, the_func): for k, v in the_map.items(): the_map[k] = (v, the_func) return the_map def to_float(the_str, default=None): if not the_str: return default if the_str in none_values: return None if "%" in the_str: return pct_to_float(the_str) try: scale = 1.0 if the_str[-2:] == "万亿": the_str = the_str[0:-2] scale = 1000000000000 elif the_str[-1] == "亿": the_str = the_str[0:-1] scale = 100000000 elif the_str[-1] == "万": the_str = the_str[0:-1] scale = 10000 if not the_str: return default return float(Decimal(the_str.replace(",", "")) * Decimal(scale)) except Exception as e: logger.error("the_str:{}".format(the_str)) logger.exception(e) return default def pct_to_float(the_str, default=None): if the_str in none_values: return None try: return float(Decimal(the_str.replace("%", "")) / Decimal(100)) except Exception as e: logger.exception(e) return default def float_to_pct(input_float: float) -> str: # Convert the float to a percentage and format it to two decimal places return f"{input_float * 100:.2f}%" def format_number_to_yi(number): return f"{number / 1e8:.2f}亿" def json_callback_param(the_str): json_str = the_str[the_str.index("(") + 1 : the_str.rindex(")")].replace("null", "None") return eval(json_str) def fill_domain_from_dict(the_domain, the_dict: dict, the_map: dict = None, default_func=lambda x: x): """ use field map and related func to fill properties from the dict to the domain :param the_domain: :type the_domain: DeclarativeMeta :param the_dict: :type the_dict: dict :param the_map: :type the_map: dict :param default_func: :type default_func: function """ if not the_map: the_map = {} for k in the_dict: the_map[k] = (k, default_func) for k, v in the_map.items(): if isinstance(v, tuple): field_in_dict = v[0] the_func = v[1] else: field_in_dict = v the_func = default_func the_value = the_dict.get(field_in_dict) if the_value is not None: to_value = the_value if to_value in none_values: setattr(the_domain, k, None) else: result_value = the_func(to_value) setattr(the_domain, k, result_value) exec("the_domain.{}=result_value".format(k)) SUPPORT_ENCODINGS = ["GB2312", "GBK", "GB18030", "UTF-8"] def read_csv(f, encoding, sep=None, na_values=None): encodings = [encoding] + SUPPORT_ENCODINGS for encoding in encodings: try: if sep: return pd.read_csv(f, sep=sep, encoding=encoding, na_values=na_values) else: return pd.read_csv(f, encoding=encoding, na_values=na_values) except UnicodeDecodeError as e: logger.warning("read_csv failed by using encoding:{}".format(encoding), e) f.seek(0) continue return None def chrome_copy_header_to_dict(src): lines = src.split("\n") header = {} if lines: for line in lines: try: index = line.index(":") key = line[:index] value = line[index + 1 :] if key and value: header.setdefault(key.strip(), value.strip()) except Exception: pass return header def to_positive_number(number): if isinstance(number, numbers.Number): return abs(number) return 0 def multiple_number(number, factor): try: return number * factor except: return number def add_to_map_list(the_map, key, value): result = [] if key in the_map: result = the_map[key] else: the_map[key] = result if value not in result: result.append(value) def iterate_with_step(data, sub_size=100): size = len(data) if size >= sub_size: step_count = int(size / sub_size) if size % sub_size: step_count = step_count + 1 else: step_count = 1 for step in range(step_count): if type(data) == pd.DataFrame or type(data) == pd.Series: yield data.iloc[sub_size * step : sub_size * (step + 1)] else: yield data[sub_size * step : sub_size * (step + 1)] def url_unquote(url): return parse.unquote(url) def parse_url_params(url): url = url_unquote(url) return parse.parse_qs(parse.urlsplit(url).query) def set_one_and_only_one(**kwargs): all_none = all(kwargs[v] is None for v in kwargs) if all_none: raise ValueError(f"{kwargs} must be set one at least") set_size = len([v for v in kwargs if kwargs[v] is not None]) if set_size != 1: raise ValueError(f"{kwargs} could only set one") return True def flatten_list(input_list): if not input_list: return input_list result = [] for item in input_list: if isinstance(item, list): result.extend(item) elif isinstance(item, dict): result.append(item) else: result.append(item) return result def to_str(str_or_list): if not str_or_list: return None if isinstance(str_or_list, str): return str_or_list if isinstance(str_or_list, list): str_list = [str(item) for item in str_or_list] return ";".join(str_list) def compare_dicts(dict1, dict2): # Check if both dictionaries are None if dict1 is None and dict2 is None: return True # Check if only one dictionary is None if dict1 is None or dict2 is None: return False # Check if the keys are the same if set(dict1.keys()) != set(dict2.keys()): return False # Check if the values are the same for each key for key in dict1: if dict1[key] != dict2[key]: return False # If all keys and values match, return True return True def fill_dict(src, dst): """ Fills items from the source dictionary (src) into the destination dictionary (dst) if the keys are not already present in dst. Args: src (dict): The source dictionary to copy items from. dst (dict): The destination dictionary to copy items into. Returns: dict: The updated destination dictionary with new items from the source dictionary. """ if not src: return dst for key, value in src.items(): if key not in dst: dst[key] = value return dst if __name__ == "__main__": url = url_unquote( "https://datacenter.eastmoney.com/securities/api/data/get?type=RPT_DAILYBILLBOARD_DETAILS&sty=ALL&source=DataCenter&client=WAP&p=1&ps=20&sr=-1,1&st=TRADE_DATE,SECURITY_CODE&filter=(TRADE_DATE%3E=%272022-04-01%27)(TRADE_DATE%3C=%272022-04-29%27)(MARKET=%22SH%22)&?v=05160638952989893" ) print(url) # the __all__ is generated __all__ = [ "none_values", "zero_values", "first_item_to_float", "second_item_to_float", "add_func_to_value", "to_float", "pct_to_float", "float_to_pct", "json_callback_param", "fill_domain_from_dict", "SUPPORT_ENCODINGS", "read_csv", "chrome_copy_header_to_dict", "to_positive_number", "multiple_number", "add_to_map_list", "iterate_with_step", "url_unquote", "parse_url_params", "set_one_and_only_one", "flatten_list", "to_str", "compare_dicts", "fill_dict", ] ================================================ FILE: src/zvt/utils/zip_utils.py ================================================ # -*- coding: utf-8 -*- import datetime import os import pathlib import zipfile def zip_dir(src_dir, dst_dir=None, zip_file_name=None): if not zip_file_name: zip_file_name = "data-{}.zip".format(datetime.datetime.today()) if dst_dir: dst_path = os.path.join(dst_dir, zip_file_name) else: dst_path = zip_file_name # os.remove(dst_path) the_zip_file = zipfile.ZipFile(dst_path, "w") for folder, subfolders, files in os.walk(src_dir): for file in files: the_path = os.path.join(folder, file) # if 'zvt_business.db' in the_path: # continue print("zip {}".format(the_path)) the_zip_file.write(the_path, os.path.relpath(the_path, src_dir), compress_type=zipfile.ZIP_DEFLATED) the_zip_file.close() def unzip(zip_file, dst_dir): the_zip_file = zipfile.ZipFile(zip_file) print("start unzip {} to {}".format(zip_file, dst_dir)) for name in the_zip_file.namelist(): extracted_path = pathlib.Path(the_zip_file.extract(name, path=dst_dir)) extracted_path.rename(f"{extracted_path}".encode("cp437").decode("gbk")) print("finish unzip {} to {}".format(zip_file, dst_dir)) the_zip_file.close() # the __all__ is generated __all__ = ["zip_dir", "unzip"] ================================================ FILE: src/zvt/zvt_server.py ================================================ # -*- coding: utf-8 -*- import os import uvicorn from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import ORJSONResponse from fastapi_pagination import add_pagination from zvt import zvt_env from zvt.rest.data import data_router from zvt.rest.factor import factor_router from zvt.rest.misc import misc_router from zvt.rest.trading import trading_router from zvt.rest.work import work_router app = FastAPI(default_response_class=ORJSONResponse) origins = ["*"] app.add_middleware( CORSMiddleware, allow_origins=origins, allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) @app.get("/") async def root(): return {"message": "Hello World"} app.include_router(data_router) app.include_router(factor_router) app.include_router(work_router) app.include_router(trading_router) app.include_router(misc_router) add_pagination(app) def main(): log_config = os.path.join(zvt_env["resource_path"], "log_conf.yaml") uvicorn.run("zvt_server:app", host="0.0.0.0", reload=True, port=8090, log_config=log_config) if __name__ == "__main__": main() ================================================ FILE: tests/__init__.py ================================================ # -*- coding: utf-8 -*- import os import sys os.environ.setdefault("TESTING_ZVT", "True") os.environ.setdefault("SQLALCHEMY_WARN_20", "1") sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../src"))) ================================================ FILE: tests/api/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/api/test_common.py ================================================ # -*- coding: utf-8 -*- from zvt.api.utils import get_recent_report_date from zvt.contract import IntervalLevel from zvt.api.kdata import get_kdata, to_high_level_kdata from ..context import init_test_context init_test_context() def test_to_high_level_kdata(): day_df = get_kdata(provider="joinquant", level=IntervalLevel.LEVEL_1DAY, entity_id="stock_sz_000338") print(day_df) df = to_high_level_kdata(kdata_df=day_df.loc[:"2019-09-01", :], to_level=IntervalLevel.LEVEL_1WEEK) print(df) def test_get_recent_report_date(): assert "2018-12-31" == get_recent_report_date("2019-01-01", 0) assert "2018-09-30" == get_recent_report_date("2019-01-01", 1) assert "2018-06-30" == get_recent_report_date("2019-01-01", 2) assert "2018-03-31" == get_recent_report_date("2019-01-01", 3) ================================================ FILE: tests/api/test_dividend_financing.py ================================================ from ..context import init_test_context init_test_context() from zvt.domain import SpoDetail, RightsIssueDetail, DividendFinancing from zvt.contract.api import get_db_session from zvt.utils.time_utils import to_pd_timestamp session = get_db_session(provider="eastmoney", db_name="dividend_financing") # type: sqlalchemy.orm.Session # 增发详情 def test_000778_spo_detial(): result = SpoDetail.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", order=SpoDetail.timestamp.desc(), ) assert len(result) == 4 latest: SpoDetail = result[0] assert latest.timestamp == to_pd_timestamp("2017-04-01") assert latest.spo_issues == 347600000 assert latest.spo_price == 5.15 assert latest.spo_raising_fund == 1766000000 # 配股详情 def test_000778_rights_issue_detail(): result = RightsIssueDetail.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", order=RightsIssueDetail.timestamp.desc(), ) assert len(result) == 2 latest: RightsIssueDetail = result[0] assert latest.timestamp == to_pd_timestamp("2001-09-10") assert latest.rights_issues == 43570000 assert latest.rights_raising_fund == 492300000 assert latest.rights_issue_price == 11.3 # 分红融资 def test_000778_dividend_financing(): result = DividendFinancing.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", order=DividendFinancing.timestamp.desc(), ) assert len(result) == 22 latest: DividendFinancing = result[1] assert latest.timestamp == to_pd_timestamp("2017") assert latest.dividend_money == 598632026.4 assert latest.spo_issues == 347572815.0 assert latest.rights_issues == 0 assert latest.ipo_issues == 0 ================================================ FILE: tests/api/test_finance.py ================================================ from ..context import init_test_context init_test_context() from zvt.domain import FinanceFactor, BalanceSheet, IncomeStatement, CashFlowStatement from zvt.contract.api import get_db_session from zvt.utils.time_utils import to_date_time_str session = get_db_session(provider="eastmoney", db_name="finance") # type: sqlalchemy.orm.Session # 银行指标 def test_000001_finance_factor(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-09-30", "2002-06-30", "2002-03-31", "2001-12-31", "2001-09-30", "2001-06-30", "2001-03-31", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", "1997-12-31", "1997-06-30", "1996-12-31", "1996-06-30", "1995-12-31", "1995-06-30", "1994-12-31", "1994-06-30", "1993-12-31", "1993-06-30", "1992-12-31", "1991-12-31", "1990-12-31", "1989-12-31", ] result = FinanceFactor.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000001"], end_timestamp="2018-12-30", order=FinanceFactor.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: FinanceFactor = result[0] assert latest.basic_eps == 1.14 assert latest.deducted_eps == 1.13 assert latest.diluted_eps == 1.14 assert latest.bps == 12.538 assert latest.capital_reserve_ps == 3.2886 assert latest.undistributed_profit_ps == 5.3566 assert latest.op_cash_flow_ps == -0.6587 assert latest.total_op_income == 86660000000 assert latest.net_profit == 20460000000 assert latest.deducted_net_profit == 20350000000 assert latest.op_income_growth_yoy == 0.0856 assert latest.net_profit_growth_yoy == 0.068 assert latest.deducted_net_profit_growth_yoy == 0.0636 assert latest.op_income_growth_qoq == 0.0336 assert latest.net_profit_growth_qoq == 0.0202 assert latest.deducted_net_profit_growth_qoq == 0.0168 assert latest.roe == 0.0948 assert latest.deducted_roe == 0.0943 assert latest.rota == 0.0062 assert latest.net_margin == 0.2360 assert latest.debt_asset_ratio == 0.9298 assert latest.em == 14.25 assert latest.equity_ratio == 13.25 assert latest.fi_total_deposit == 2130000000000 assert latest.fi_total_loan == 1920000000000 assert latest.fi_loan_deposit_ratio == 0.9004 assert latest.fi_npl_ratio == 0.0168 assert latest.fi_npl_provision_coverage == 1.6914 # 银行资产负债表 def test_000001_balance_sheet(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-09-30", "2002-06-30", "2002-03-31", "2001-12-31", "2001-06-30", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", "1997-12-31", "1997-06-30", "1996-12-31", "1996-06-30", "1995-12-31", "1995-06-30", "1994-12-31", "1994-06-30", "1993-12-31", "1992-12-31", "1991-12-31", "1990-12-31", "1989-12-31", ] result = BalanceSheet.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000001"], end_timestamp="2018-12-30", order=BalanceSheet.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: BalanceSheet = result[0] assert latest.fi_cash_and_deposit_in_central_bank == 287600000000 assert latest.fi_deposit_in_other_fi == 95310000000 assert latest.fi_expensive_metals == 72020000000 assert latest.fi_lending_to_other_fi == 88100000000 assert latest.fi_financial_assets_effect_current_income == 103500000000 assert latest.fi_financial_derivative_asset == 25650000000 assert latest.fi_buying_sell_back_fi__asset == 32760000000 assert latest.accounts_receivable == 22830000000 assert latest.fi_interest_receivable == 18310000000 assert latest.fi_disbursing_loans_and_advances == 1870000000000 assert latest.real_estate_investment == 194000000 assert latest.fixed_assets == 9374000000 assert latest.intangible_assets == 4722000000 assert latest.goodwill == 7568000000 assert latest.deferred_tax_assets == 28880000000 assert latest.fi_other_asset == 15630000000 assert latest.total_assets == 3350000000000 assert latest.fi_borrowings_from_central_bank == 149900000000 assert latest.fi_deposit_from_other_fi == 402300000000 assert latest.fi_borrowings_from_fi == 17830000000 assert latest.fi_financial_liability_effect_current_income == 9599000000 assert latest.fi_financial_derivative_liability == 20730000000 assert latest.fi_sell_buy_back_fi_asset == 2000000000 assert latest.fi_savings_absorption == 2130000000000 assert latest.fi_notes_payable == 0 assert latest.employee_benefits_payable == 10550000000 assert latest.taxes_payable == 7595000000 assert latest.interest_payable == 27670000000 assert latest.fi_estimated_liabilities == 24000000 assert latest.fi_bond_payable == 321500000000 assert latest.fi_other_liability == 12570000000 assert latest.total_liabilities == 3120000000000 assert latest.fi_capital == 17170000000 assert latest.fi_other_equity_instruments == 19950000000 assert latest.fi_preferred_stock == 19950000000 assert latest.capital_reserve == 56470000000 assert latest.surplus_reserve == 10780000000 assert latest.fi_generic_risk_reserve == 38550000000 assert latest.undistributed_profits == 91970000000 assert latest.equity == 235200000000 assert latest.total_liabilities_and_equity == 3350000000000 # 银行利润表 def test_000001_income_statement(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-09-30", "2002-06-30", "2002-03-31", "2001-12-31", "2001-09-30", "2001-06-30", "2001-03-31", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", "1997-12-31", "1997-06-30", "1996-12-31", "1996-06-30", "1995-12-31", "1995-06-30", "1994-12-31", "1994-06-30", "1993-12-31", "1993-06-30", "1992-12-31", "1991-12-31", "1990-12-31", "1989-12-31", ] result = IncomeStatement.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000001"], end_timestamp="2018-12-30", order=IncomeStatement.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: IncomeStatement = result[0] assert latest.operating_income == 86660000000 assert latest.fi_net_interest_income == 54530000000 assert latest.fi_interest_income == 121700000000 assert latest.fi_interest_expenses == 67130000000 assert latest.fi_net_incomes_from_fees_and_commissions == 23710000000 assert latest.fi_incomes_from_fees_and_commissions == 28920000000 assert latest.fi_expenses_for_fees_and_commissions == 5218000000 assert latest.investment_income == 7099000000 assert latest.fi_income_from_fair_value_change == 1047000000 assert latest.fi_income_from_exchange == -40000000 assert latest.fi_other_income == 144000000 assert latest.operating_costs == 26430000000 assert latest.business_taxes_and_surcharges == 847000000 assert latest.fi_operate_and_manage_expenses == 25580000000 assert latest.operating_profit == 26610000000 assert latest.non_operating_income == 14000000 assert latest.non_operating_costs == 62000000 assert latest.total_profits == 26570000000 # 银行现金流量表 def test_000001_cash_flow_statement(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-06-30", "2001-12-31", "2001-06-30", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", ] result = CashFlowStatement.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000001"], end_timestamp="2018-12-30", order=CashFlowStatement.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: CashFlowStatement = result[0] # 00000000 assert latest.fi_deposit_increase == 104700000000 assert latest.fi_borrow_from_central_bank_increase == 18960000000 assert latest.fi_deposit_in_others_decrease == 60880000000 assert latest.fi_lending_and_buy_repurchase_decrease == 12330000000 assert latest.fi_lending_decrease == 12270000000 assert latest.fi_buy_repurchase_decrease == 56000000 assert latest.fi_cash_from_interest_commission == 133700000000 assert latest.cash_from_other_op == 20540000000 assert latest.total_op_cash_inflows == 381200000000 assert latest.fi_loan_advance_increase == 250300000000 assert latest.fi_borrowing_and_sell_repurchase_decrease == 14350000000 assert latest.fi_borrowing_decrease == 10190000000 assert latest.fi_sell_repurchase_decrease == 4155000000 assert latest.fi_cash_to_interest_commission == 56760000000 assert latest.cash_to_employees == 12930000000 assert latest.taxes_and_surcharges == 20010000000 assert latest.cash_to_other_related_op == 38150000000 assert latest.total_op_cash_outflows == 392500000000 assert latest.net_op_cash_flows == -11310000000 assert latest.cash_from_disposal_of_investments == 348900000000 assert latest.cash_from_returns_on_investments == 21080000000 assert latest.cash_from_disposal_fixed_intangible_assets == 108000000 assert latest.total_investing_cash_inflows == 370100000000 assert latest.cash_to_investments == 294600000000 assert latest.cash_to_acquire_fixed_intangible_assets == 1518000000 assert latest.total_investing_cash_outflows == 296100000000 assert latest.net_investing_cash_flows == 73960000000 assert latest.cash_from_issuing_bonds == 581200000000 assert latest.total_financing_cash_inflows == 581200000000 assert latest.cash_to_repay_borrowings == 612500000000 assert latest.fi_cash_to_pay_interest == 2511000000 assert latest.cash_to_pay_interest_dividend == 3209000000 assert latest.total_financing_cash_outflows == 618300000000 assert latest.net_financing_cash_flows == -37080000000 assert latest.foreign_exchange_rate_effect == 2018000000 assert latest.net_cash_increase == 27590000000 assert latest.cash_at_beginning == 137000000000 assert latest.cash == 164600000000 # 企业指标 def test_000778_finance_factor(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-09-30", "2002-06-30", "2002-03-31", "2001-12-31", "2001-06-30", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", "1997-12-31", "1997-06-30", "1996-12-31", "1995-12-31", "1994-12-31", ] result = FinanceFactor.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-12-30", order=FinanceFactor.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: FinanceFactor = result[0] assert latest.basic_eps == 0.4537 assert latest.diluted_eps == 0.4537 assert latest.bps == 5.0919 assert latest.capital_reserve_ps == 2.1769 assert latest.undistributed_profit_ps == 1.8132 assert latest.op_cash_flow_ps == 1.0148 assert latest.total_op_income == 31710000000 assert latest.gross_profit == 5491000000 assert latest.net_profit == 1811000000 assert latest.deducted_net_profit == 1897000000 assert latest.op_income_growth_yoy == -0.1024 assert latest.net_profit_growth_yoy == 1.2404 assert latest.deducted_net_profit_growth_yoy == 1.4813 assert latest.op_income_growth_qoq == 0.0408 assert latest.net_profit_growth_qoq == 0.2143 assert latest.deducted_net_profit_growth_qoq == 0.2955 assert latest.roe == 0.0882 assert latest.rota == 0.0376 assert latest.gross_profit_margin == 0.1731 assert latest.net_margin == 0.0591 assert latest.advance_receipts_per_op_income == 0.1 assert latest.sales_net_cash_flow_per_op_income == 0.88 assert latest.op_net_cash_flow_per_op_income == 0.13 assert latest.actual_tax_rate == 0.2362 assert latest.current_ratio == 1 assert latest.quick_ratio == 0.84 assert latest.cash_flow_ratio == 0.17 assert latest.debt_asset_ratio == 0.5766 assert latest.em == 2.36 assert latest.equity_ratio == 1.43 assert latest.total_assets_turnover_days == 423.91 assert latest.inventory_turnover_days == 32.88 assert latest.receivables_turnover_days == 52.23 assert latest.total_assets_turnover == 0.64 assert latest.inventory_turnover == 8.21 assert latest.receivables_turnover == 5.17 # 企业资产负债表 def test_000778_balance_sheet(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-09-30", "2002-06-30", "2002-03-31", "2001-12-31", "2001-06-30", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", "1997-12-31", "1997-06-30", "1996-12-31", "1995-12-31", "1994-12-31", ] result = BalanceSheet.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-12-30", order=BalanceSheet.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: BalanceSheet = result[0] assert latest.cash_and_cash_equivalents == 6381000000 assert latest.note_receivable == 4655000000 assert latest.accounts_receivable == 1920000000 assert latest.advances_to_suppliers == 2112000000 assert latest.other_receivables == 4671000000 assert latest.inventories == 3891000000 assert latest.current_portion_of_non_current_assets == 270000000 assert latest.other_current_assets == 220100000 assert latest.total_current_assets == 24120000000 assert latest.fi_assets_saleable == 914900000 assert latest.long_term_receivables == 1078000000 assert latest.long_term_equity_investment == 6071000000 assert latest.real_estate_investment == 24130000 assert latest.fixed_assets == 15440000000 assert latest.construction_in_process == 1196000000 assert latest.intangible_assets == 853800000 assert latest.goodwill == 32990000 assert latest.long_term_prepaid_expenses == 2527000 assert latest.deferred_tax_assets == 292300000 assert latest.other_non_current_assets == 332800000 assert latest.total_assets == 50350000000 assert latest.short_term_borrowing == 9104000000 assert latest.accept_money_deposits == 0 assert latest.advances_from_customers == 3199000000 assert latest.employee_benefits_payable == 207800000 assert latest.taxes_payable == 735200000 assert latest.other_payable == 2022000000 assert latest.current_portion_of_non_current_liabilities == 1099000000 assert latest.other_current_liabilities == 1000000000 assert latest.total_current_liabilities == 24050000000 assert latest.long_term_borrowing == 914400000 assert latest.fi_bond_payable == 2992000000 assert latest.long_term_payable == 636800000 assert latest.deferred_tax_liabilities == 314800000 assert latest.other_non_current_liabilities == 127800000 assert latest.total_non_current_liabilities == 4986000000 assert latest.total_liabilities == 29040000000 assert latest.capital == 3991000000 assert latest.capital_reserve == 8688000000 assert latest.special_reserve == 40730000 assert latest.surplus_reserve == 1286000000 assert latest.undistributed_profits == 7236000000 assert latest.equity == 20320000000 assert latest.equity_as_minority_interest == 997400000 assert latest.total_equity == 21320000000 assert latest.total_liabilities_and_equity == 50350000000 # 企业利润表 def test_000778_income_statement(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-09-30", "2002-06-30", "2002-03-31", "2001-12-31", "2001-06-30", "2000-12-31", "2000-06-30", "1999-12-31", "1999-06-30", "1998-12-31", "1998-06-30", "1997-12-31", "1997-06-30", "1996-12-31", "1995-12-31", "1994-12-31", ] result = IncomeStatement.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-12-30", order=IncomeStatement.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: IncomeStatement = result[0] assert latest.operating_income == 31710000000 assert latest.total_operating_costs == 29230000000 assert latest.operating_costs == 26220000000 assert latest.rd_costs == 185500000 assert latest.net_change_in_insurance_contract_reserves == 0 assert latest.business_taxes_and_surcharges == 359700000 assert latest.sales_costs == 771400000 assert latest.managing_costs == 472900000 assert latest.financing_costs == 397500000 assert latest.assets_devaluation == 824400000 assert latest.investment_income == 104100000 assert latest.investment_income_from_related_enterprise == 61290000 assert latest.operating_profit == 2637000000 assert latest.non_operating_income == 38340000 assert latest.non_operating_costs == 221700000 assert latest.total_profits == 2454000000 assert latest.tax_expense == 579600000 assert latest.net_profit == 1874000000 assert latest.net_profit_as_parent == 1811000000 assert latest.net_profit_as_minority_interest == 63570000 assert latest.deducted_net_profit == 1897000000 assert latest.eps == 0.4537 assert latest.diluted_eps == 0.4537 assert latest.other_comprehensive_income == -521000000 assert latest.other_comprehensive_income_as_parent == -522400000 assert latest.other_comprehensive_income_as_minority_interest == 1403000 assert latest.total_comprehensive_income == 1353000000 assert latest.total_comprehensive_income_as_parent == 1288000000 assert latest.total_comprehensive_income_as_minority_interest == 64980000 # 银行现金流量表 def test_000778_cash_flow_statement(): correct_timestamps = [ "2018-09-30", "2018-06-30", "2018-03-31", "2017-12-31", "2017-09-30", "2017-06-30", "2017-03-31", "2016-12-31", "2016-09-30", "2016-06-30", "2016-03-31", "2015-12-31", "2015-09-30", "2015-06-30", "2015-03-31", "2014-12-31", "2014-09-30", "2014-06-30", "2014-03-31", "2013-12-31", "2013-09-30", "2013-06-30", "2013-03-31", "2012-12-31", "2012-09-30", "2012-06-30", "2012-03-31", "2011-12-31", "2011-09-30", "2011-06-30", "2011-03-31", "2010-12-31", "2010-09-30", "2010-06-30", "2010-03-31", "2009-12-31", "2009-09-30", "2009-06-30", "2009-03-31", "2008-12-31", "2008-09-30", "2008-06-30", "2008-03-31", "2007-12-31", "2007-09-30", "2007-06-30", "2007-03-31", "2006-12-31", "2006-09-30", "2006-06-30", "2006-03-31", "2005-12-31", "2005-09-30", "2005-06-30", "2005-03-31", "2004-12-31", "2004-09-30", "2004-06-30", "2004-03-31", "2003-12-31", "2003-09-30", "2003-06-30", "2003-03-31", "2002-12-31", "2002-06-30", "2001-12-31", "2001-06-30", "2000-12-31", "2000-06-30", "1999-12-31", "1998-12-31", "1998-06-30", ] result = CashFlowStatement.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-12-30", order=CashFlowStatement.report_date.desc(), time_field="report_date", ) assert len(correct_timestamps) == len(result) timestamps = [to_date_time_str(item.report_date) for item in result] assert set(correct_timestamps) == set(timestamps) latest: CashFlowStatement = result[0] assert latest.cash_from_selling == 27784000000 assert latest.tax_refund == 60700000 assert latest.cash_from_other_op == 1463000000 assert latest.total_op_cash_inflows == 29310000000 assert latest.cash_to_goods_services == 21210000000 assert latest.cash_to_employees == 1460000000 assert latest.taxes_and_surcharges == 2016000000 assert latest.cash_to_other_related_op == 573700000 assert latest.total_op_cash_outflows == 25260000000 assert latest.net_op_cash_flows == 4050000000 assert latest.cash_from_disposal_of_investments == 556500000 assert latest.cash_from_returns_on_investments == 44180000 assert latest.cash_from_disposal_fixed_intangible_assets == 457200 assert latest.cash_from_disposal_subsidiaries == 1046000000 assert latest.cash_from_other_investing == 553000000 assert latest.total_investing_cash_inflows == 2201000000 assert latest.cash_to_acquire_fixed_intangible_assets == 2521000000 assert latest.cash_to_investments == 1808000000 assert latest.total_investing_cash_outflows == 4329000000 assert latest.net_investing_cash_flows == -2128000000 assert latest.cash_from_accepting_investment == 24500000 assert latest.cash_from_subsidiaries_accepting_minority_interest == 24500000 assert latest.cash_from_borrowings == 10080000000 assert latest.cash_from_issuing_bonds == 997000000 assert latest.cash_from_other_financing == 200000000 assert latest.total_financing_cash_inflows == 11300000000 assert latest.cash_to_repay_borrowings == 11940000000 assert latest.cash_to_pay_interest_dividend == 892100000 assert latest.cash_to_other_financing == 328500000 assert latest.total_financing_cash_outflows == 13160000000 assert latest.net_financing_cash_flows == -1862000000 assert latest.foreign_exchange_rate_effect == 21350000 assert latest.net_cash_increase == 81240000 assert latest.cash_at_beginning == 5078000000 assert latest.cash == 5159000000 ================================================ FILE: tests/api/test_holder.py ================================================ from zvt.contract.api import get_db_session from ..context import init_test_context init_test_context() from typing import List from zvt.domain import TopTenHolder, TopTenTradableHolder session = get_db_session(provider="eastmoney", db_name="holder") # type: sqlalchemy.orm.Session # 十大股东 def test_000778_top_ten_holder(): result: List[TopTenHolder] = TopTenHolder.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", start_timestamp="2018-09-30", order=TopTenHolder.shareholding_ratio.desc(), ) assert len(result) == 10 assert result[0].holder_name == "新兴际华集团有限公司" assert result[0].shareholding_numbers == 1595000000 assert result[0].shareholding_ratio == 0.3996 assert result[0].change == 32080000 assert result[0].change_ratio == 0.0205 def test_000778_top_ten_tradable_holder(): result: List[TopTenHolder] = TopTenTradableHolder.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", start_timestamp="2018-09-30", order=TopTenTradableHolder.shareholding_ratio.desc(), ) assert len(result) == 10 assert result[0].holder_name == "新兴际华集团有限公司" assert result[0].shareholding_numbers == 1525000000 assert result[0].shareholding_ratio == 0.389 assert result[0].change == 38560000 assert result[0].change_ratio == 0.0259 ================================================ FILE: tests/api/test_intent.py ================================================ # -*- coding: utf-8 -*- from zvt.api.intent import compare, distribute, composite, composite_all from zvt.contract.drawer import ChartType from zvt.domain import FinanceFactor, CashFlowStatement, BalanceSheet, Stock1dKdata from zvt.utils.time_utils import to_pd_timestamp def test_compare_kdata(): entity_ids = ["stock_sz_000338", "stock_sh_601318"] compare(entity_ids=entity_ids, scale_value=10) compare(entity_ids=entity_ids, start_timestamp="2010-01-01") def test_compare_line(): entity_ids = ["stock_sz_000338", "stock_sh_601318"] compare(entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}) def test_compare_scatter(): entity_ids = ["stock_sz_000338", "stock_sh_601318"] compare( entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}, chart_type=ChartType.scatter ) def test_compare_area(): entity_ids = ["stock_sz_000338", "stock_sh_601318"] compare(entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}, chart_type=ChartType.area) def test_compare_bar(): entity_ids = ["stock_sz_000338", "stock_sh_601318"] compare(entity_ids=entity_ids, schema_map_columns={FinanceFactor: [FinanceFactor.roe]}, chart_type=ChartType.bar) def test_distribute(): distribute(entity_ids=None, data_schema=FinanceFactor, columns=["roe"]) def test_composite(): composite( entity_id="stock_sz_000338", data_schema=CashFlowStatement, columns=[ CashFlowStatement.net_op_cash_flows, CashFlowStatement.net_investing_cash_flows, CashFlowStatement.net_financing_cash_flows, ], filters=[ CashFlowStatement.report_period == "year", CashFlowStatement.report_date == to_pd_timestamp("2016-12-31"), ], ) composite( entity_id="stock_sz_000338", data_schema=BalanceSheet, columns=[ BalanceSheet.total_current_assets, BalanceSheet.total_non_current_assets, BalanceSheet.total_current_liabilities, BalanceSheet.total_non_current_liabilities, ], filters=[BalanceSheet.report_period == "year", BalanceSheet.report_date == to_pd_timestamp("2016-12-31")], ) def test_composite_all(): composite_all( provider="joinquant", entity_ids=None, data_schema=Stock1dKdata, column=Stock1dKdata.turnover, timestamp=to_pd_timestamp("2016-12-02"), ) ================================================ FILE: tests/api/test_joinquant_quotes.py ================================================ from zvt.api.kdata import get_kdata from zvt.contract import IntervalLevel from zvt.contract.api import get_db_session from ..context import init_test_context init_test_context() day_k_session = get_db_session(provider="joinquant", db_name="stock_1d_kdata") # type: sqlalchemy.orm.Session day_1h_session = get_db_session(provider="joinquant", db_name="stock_1h_kdata") # type: sqlalchemy.orm.Session def test_jq_603220_kdata(): df = get_kdata( entity_id="stock_sh_603220", session=day_k_session, level=IntervalLevel.LEVEL_1DAY, provider="joinquant" ) print(df) df = get_kdata( entity_id="stock_sh_603220", session=day_1h_session, level=IntervalLevel.LEVEL_1HOUR, provider="joinquant" ) print(df) ================================================ FILE: tests/api/test_kdata.py ================================================ # -*- coding: utf-8 -*- from zvt.api.kdata import get_kdata from zvt.api.kdata import get_latest_kdata_date from zvt.contract import IntervalLevel, AdjustType def test_jq_1mon_kdata(): df = get_kdata(entity_id="stock_sz_000338", provider="joinquant", level=IntervalLevel.LEVEL_1MON) se = df.loc["2010-01-29"] # make sure our fq is ok assert round(se["open"], 2) <= 5.44 assert round(se["high"], 2) <= 6.43 assert round(se["low"], 2) <= 5.2 assert round(se["close"], 2) <= 5.45 def test_jq_1wk_kdata(): df = get_kdata(entity_id="stock_sz_000338", provider="joinquant", level=IntervalLevel.LEVEL_1WEEK) print(df) def test_jq_1d_kdata(): df = get_kdata(entity_id="stock_sz_000338", provider="joinquant", level=IntervalLevel.LEVEL_1DAY) print(df) se = df.loc["2019-04-08"] # make sure our fq is ok assert round(se["open"], 2) <= 12.86 assert round(se["high"], 2) <= 14.16 assert round(se["low"], 2) <= 12.86 assert round(se["close"], 2) <= 14.08 def test_jq_1d_hfq_kdata(): df = get_kdata(entity_id="stock_sz_000338", provider="joinquant", level=IntervalLevel.LEVEL_1DAY, adjust_type="hfq") se = df.loc["2019-04-08"] print(se) assert round(se["open"], 2) == 249.29 assert round(se["high"], 2) == 273.68 assert round(se["low"], 2) == 249.29 assert round(se["close"], 2) == 272.18 def test_get_latest_kdata_date(): date = get_latest_kdata_date(provider="joinquant", entity_type="stock", adjust_type=AdjustType.hfq) assert date is not None ================================================ FILE: tests/api/test_technical.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import get_entities from ..context import init_test_context init_test_context() def test_basic_get_securities(): items = get_entities(entity_type="stock", provider="eastmoney") print(items) items = get_entities(entity_type="index", provider="exchange") print(items) ================================================ FILE: tests/api/test_trading.py ================================================ from zvt.contract.api import get_db_session from ..context import init_test_context init_test_context() from typing import List from zvt.domain import HolderTrading, ManagerTrading session = get_db_session(provider="eastmoney", db_name="trading") # type: sqlalchemy.orm.Session # 股东交易 def test_000778_holder_trading(): result: List[HolderTrading] = HolderTrading.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", start_timestamp="2018-09-30", order=HolderTrading.holding_pct.desc(), ) assert len(result) == 6 assert result[0].holder_name == "新兴际华集团有限公司" assert result[0].change_pct == 0.0205 assert result[0].volume == 32080000 assert result[0].holding_pct == 0.3996 # 高管交易 def test_000778_manager_trading(): result: List[ManagerTrading] = ManagerTrading.query_data( session=session, provider="eastmoney", return_type="domain", codes=["000778"], end_timestamp="2018-09-30", start_timestamp="2017-09-30", order=ManagerTrading.holding.desc(), ) assert len(result) == 1 assert result[0].trading_person == "巩国平" assert result[0].volume == 8400 assert result[0].price == None assert result[0].holding == 18700 assert result[0].trading_way == "增持" assert result[0].manager_position == "职工监事" assert result[0].manager == "巩国平" assert result[0].relationship_with_manager == "本人" ================================================ FILE: tests/context.py ================================================ # -*- coding: utf-8 -*- def init_test_context(): import os import sys sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../src"))) ================================================ FILE: tests/contract/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/contract/test_add_provider_plugin.py ================================================ # -*- coding: utf-8 -*- def test_add_tushare_provider(): pass # register_schema(providers=["tushare"], db_name="stock_meta", schema_base=StockMetaBase) # # from zvt.domain import Stock # # Stock.query_data(provider="tushare") # try: # Stock.record_data(provider="tushare") # assert False # except Exception as e: # print(e) ================================================ FILE: tests/contract/test_entity.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import TradableEntity, IntervalLevel from zvt.utils.time_utils import to_pd_timestamp def test_get_1min_timestamps(): timestamps = [] for timestamp in TradableEntity.get_interval_timestamps( start_date="2020-06-17", end_date="2020-06-18", level=IntervalLevel.LEVEL_1MIN ): timestamps.append(timestamp) assert to_pd_timestamp("2020-06-17 09:31:00") in timestamps assert to_pd_timestamp("2020-06-17 11:30:00") in timestamps assert to_pd_timestamp("2020-06-17 13:01:00") in timestamps assert to_pd_timestamp("2020-06-17 15:00:00") in timestamps assert to_pd_timestamp("2020-06-17 09:31:00") in timestamps assert to_pd_timestamp("2020-06-17 11:30:00") in timestamps assert to_pd_timestamp("2020-06-17 13:01:00") in timestamps assert to_pd_timestamp("2020-06-18 15:00:00") in timestamps def test_get_1h_timestamps(): timestamps = [] for timestamp in TradableEntity.get_interval_timestamps( start_date="2020-06-17", end_date="2020-06-18", level=IntervalLevel.LEVEL_1HOUR ): timestamps.append(timestamp) assert to_pd_timestamp("2020-06-17 10:30:00") in timestamps assert to_pd_timestamp("2020-06-17 11:30:00") in timestamps assert to_pd_timestamp("2020-06-17 14:00:00") in timestamps assert to_pd_timestamp("2020-06-17 15:00:00") in timestamps assert to_pd_timestamp("2020-06-17 10:30:00") in timestamps assert to_pd_timestamp("2020-06-17 11:30:00") in timestamps assert to_pd_timestamp("2020-06-17 14:00:00") in timestamps assert to_pd_timestamp("2020-06-18 15:00:00") in timestamps def test_is_finished_kdata_timestamp(): assert TradableEntity.is_finished_kdata_timestamp("2020-06-17 10:30", IntervalLevel.LEVEL_30MIN) assert not TradableEntity.is_finished_kdata_timestamp("2020-06-17 10:30", IntervalLevel.LEVEL_1DAY) assert TradableEntity.is_finished_kdata_timestamp("2020-06-17 11:30", IntervalLevel.LEVEL_30MIN) assert not TradableEntity.is_finished_kdata_timestamp("2020-06-17 11:30", IntervalLevel.LEVEL_1DAY) assert TradableEntity.is_finished_kdata_timestamp("2020-06-17 13:30", IntervalLevel.LEVEL_30MIN) assert not TradableEntity.is_finished_kdata_timestamp("2020-06-17 13:30", IntervalLevel.LEVEL_1DAY) def test_open_close(): assert TradableEntity.is_open_timestamp("2020-06-17 09:30") assert TradableEntity.is_close_timestamp("2020-06-17 15:00") timestamps = [] for timestamp in TradableEntity.get_interval_timestamps( start_date="2020-06-17", end_date="2020-06-18", level=IntervalLevel.LEVEL_1HOUR ): timestamps.append(timestamp) assert TradableEntity.is_open_timestamp(timestamps[0]) assert TradableEntity.is_close_timestamp(timestamps[-1]) ================================================ FILE: tests/contract/test_reader.py ================================================ # -*- coding: utf-8 -*- from ..context import init_test_context init_test_context() import time from zvt.domain import Stock1dKdata, Stock from zvt.utils.time_utils import to_date_time_str from zvt.contract.reader import DataReader from zvt.contract import IntervalLevel def test_china_stock_reader(): data_reader = DataReader( provider="joinquant", data_schema=Stock1dKdata, entity_schema=Stock, entity_provider="eastmoney", codes=["002572", "000338"], start_timestamp="2019-01-01", end_timestamp="2019-06-10", ) categories = data_reader.data_df.index.levels[0].to_list() df = data_reader.data_df assert "stock_sz_002572" in categories assert "stock_sz_000338" in categories assert ("stock_sz_002572", "2019-01-02") in df.index assert ("stock_sz_000338", "2019-01-02") in df.index assert ("stock_sz_002572", "2019-06-10") in df.index assert ("stock_sz_000338", "2019-06-10") in df.index for timestamp in Stock.get_interval_timestamps( start_date="2019-06-11", end_date="2019-06-14", level=IntervalLevel.LEVEL_1DAY ): data_reader.move_on(to_timestamp=timestamp) df = data_reader.data_df assert ("stock_sz_002572", timestamp) in df.index assert ("stock_sz_000338", to_date_time_str(timestamp)) in df.index def test_reader_move_on(): data_reader = DataReader( data_schema=Stock1dKdata, entity_schema=Stock, entity_provider="eastmoney", codes=["002572", "000338"], start_timestamp="2019-06-13", end_timestamp="2019-06-14", ) data_reader.move_on(to_timestamp="2019-06-15") assert ("stock_sz_002572", "2019-06-15") not in data_reader.data_df.index assert ("stock_sz_000338", "2019-06-15") not in data_reader.data_df.index start_time = time.time() data_reader.move_on(to_timestamp="2019-06-20", timeout=5) assert time.time() - start_time < 5 ================================================ FILE: tests/contract/test_schema.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import Stock, Stockhk, Stockus def test_stock_trading_time(): assert Stock.before_trading_time(timestamp="2024-09-02 08:00") is True assert Stock.before_trading_time(timestamp="2024-09-02 12:00") is False assert Stock.after_trading_time(timestamp="2024-09-02 08:00") is False assert Stock.after_trading_time(timestamp="2024-09-02 12:00") is False assert Stock.after_trading_time(timestamp="2024-09-02 14:00") is False assert Stock.after_trading_time(timestamp="2024-09-02 16:00") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 08:00") is False assert Stock.in_real_trading_time(timestamp="2024-09-02 09:20") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 09:30") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 11:00") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 11:30") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 11:40") is False assert Stock.in_real_trading_time(timestamp="2024-09-02 13:00") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 15:00") is True assert Stock.in_real_trading_time(timestamp="2024-09-02 15:10") is False assert Stock.in_real_trading_time(timestamp="2024-09-02 16:10") is False assert Stock.in_trading_time(timestamp="2024-09-02 08:00") is False assert Stock.in_trading_time(timestamp="2024-09-02 09:20") is True assert Stock.in_trading_time(timestamp="2024-09-02 09:30") is True assert Stock.in_trading_time(timestamp="2024-09-02 11:00") is True assert Stock.in_trading_time(timestamp="2024-09-02 11:30") is True assert Stock.in_trading_time(timestamp="2024-09-02 11:40") is True assert Stock.in_trading_time(timestamp="2024-09-02 13:00") is True assert Stock.in_trading_time(timestamp="2024-09-02 15:00") is True assert Stock.in_trading_time(timestamp="2024-09-02 15:10") is False assert Stock.in_trading_time(timestamp="2024-09-02 16:10") is False def test_stock_hk_trading_time(): assert Stockhk.before_trading_time(timestamp="2024-09-02 08:00") is True assert Stockhk.before_trading_time(timestamp="2024-09-02 12:00") is False assert Stockhk.after_trading_time(timestamp="2024-09-02 08:00") is False assert Stockhk.after_trading_time(timestamp="2024-09-02 12:00") is False assert Stockhk.after_trading_time(timestamp="2024-09-02 14:00") is False assert Stockhk.after_trading_time(timestamp="2024-09-02 17:00") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 08:00") is False assert Stockhk.in_real_trading_time(timestamp="2024-09-02 09:15") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 09:30") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 11:00") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 12:00") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 12:40") is False assert Stockhk.in_real_trading_time(timestamp="2024-09-02 13:00") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 15:00") is True assert Stockhk.in_real_trading_time(timestamp="2024-09-02 16:10") is False assert Stockhk.in_real_trading_time(timestamp="2024-09-02 17:10") is False assert Stockhk.in_trading_time(timestamp="2024-09-02 08:00") is False assert Stockhk.in_trading_time(timestamp="2024-09-02 09:20") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 09:30") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 11:00") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 11:30") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 11:40") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 12:00") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 13:00") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 15:00") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 16:00") is True assert Stockhk.in_trading_time(timestamp="2024-09-02 16:10") is False assert Stockhk.in_trading_time(timestamp="2024-09-02 17:10") is False def test_stock_us_trading_time(): assert Stockus.before_trading_time(timestamp="2024-09-02 08:00") is True assert Stockus.before_trading_time(timestamp="2024-09-02 11:00") is False assert Stockus.after_trading_time(timestamp="2024-09-02 17:00") is True assert Stockus.after_trading_time(timestamp="2024-09-02 15:00") is False assert Stockus.in_real_trading_time(timestamp="2024-09-02 08:00") is False assert Stockus.in_real_trading_time(timestamp="2024-09-02 09:30") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 11:00") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 12:00") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 12:40") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 13:00") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 15:00") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 16:00") is True assert Stockus.in_real_trading_time(timestamp="2024-09-02 16:10") is False assert Stockus.in_real_trading_time(timestamp="2024-09-02 17:10") is False assert Stockus.in_trading_time(timestamp="2024-09-02 08:00") is False assert Stockus.in_trading_time(timestamp="2024-09-02 09:20") is False assert Stockus.in_trading_time(timestamp="2024-09-02 09:30") is True assert Stockus.in_trading_time(timestamp="2024-09-02 11:00") is True assert Stockus.in_trading_time(timestamp="2024-09-02 11:30") is True assert Stockus.in_trading_time(timestamp="2024-09-02 11:40") is True assert Stockus.in_trading_time(timestamp="2024-09-02 12:00") is True assert Stockus.in_trading_time(timestamp="2024-09-02 13:00") is True assert Stockus.in_trading_time(timestamp="2024-09-02 15:00") is True assert Stockus.in_trading_time(timestamp="2024-09-02 16:00") is True assert Stockus.in_trading_time(timestamp="2024-09-02 16:10") is False assert Stockus.in_trading_time(timestamp="2024-09-02 17:10") is False ================================================ FILE: tests/factors/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/factors/test_algorithm.py ================================================ # -*- coding: utf-8 -*- from zvt.factors.algorithm import point_in_range, intersect, intersect_ranges, combine, distance def test_point_in_range(): assert point_in_range(1, (1, 2)) assert point_in_range(2, (1, 2)) assert point_in_range(1.5, (1, 2)) assert not point_in_range(0.5, (1, 2)) assert not point_in_range(2.4, (1, 2)) def test_intersect(): a = (1, 2) b = (1.5, 3) assert intersect(a, b) == (1.5, 2) assert intersect(b, a) == (1.5, 2) a = (1, 2) b = (3, 4) assert intersect(a, b) == None assert intersect(b, a) == None a = (1, 4) b = (2, 3) assert intersect(a, b) == (2, 3) assert intersect(b, a) == (2, 3) def test_intersect_ranges(): a = (1, 2) b = (1.5, 3) c = (1.5, 5) assert intersect_ranges([a, b, c]) == (1.5, 2) assert intersect_ranges([b, a, c]) == (1.5, 2) assert intersect_ranges([a, c, b]) == (1.5, 2) a = (1, 2) b = (1.5, 3) c = (4, 5) assert intersect_ranges([a, b, c]) == None assert intersect_ranges([b, a, c]) == None assert intersect_ranges([a, c, b]) == None a = (1, 10) b = (1.5, 3) c = (2, 5) assert intersect_ranges([a, b, c]) == (2, 3) assert intersect_ranges([b, a, c]) == (2, 3) assert intersect_ranges([a, c, b]) == (2, 3) def test_combine(): a = (1, 2) b = (1.5, 3) assert combine(a, b) == (1, 3) assert combine(b, a) == (1, 3) a = (1, 2) b = (3, 4) assert combine(a, b) == None assert combine(b, a) == None a = (1, 4) b = (2, 3) assert combine(a, b) == (1, 4) assert combine(b, a) == (1, 4) def test_distance(): a = (1, 2) b = (1.5, 3) assert distance(a, b) == (4.5 / 2 - 1.5) / 1.5 assert distance(b, a) == (1.5 - 4.5 / 2) / 2.25 a = (1, 2) b = (3, 4) assert distance(a, b) == (3.5 - 1.5) / 1.5 assert distance(b, a) == (1.5 - 3.5) / 3.5 assert distance(a, b, use_max=True) == 3 assert distance(b, a, use_max=True) == -3 / 4 a = (1, 4) b = (2, 3) assert distance(a, b) == 0 assert distance(b, a) == 0 ================================================ FILE: tests/factors/test_factor_select_targets.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel from zvt.factors.ma.ma_factor import CrossMaFactor from zvt.contract.factor import TargetType from zvt.factors.macd.macd_factor import BullFactor from ..context import init_test_context init_test_context() def test_cross_ma_select_targets(): entity_ids = ["stock_sz_000338"] start_timestamp = "2018-01-01" end_timestamp = "2019-06-30" factor = CrossMaFactor( provider="joinquant", entity_ids=entity_ids, start_timestamp=start_timestamp, end_timestamp=end_timestamp, keep_window=10, windows=[5, 10], need_persist=False, level=IntervalLevel.LEVEL_1DAY, adjust_type="hfq", ) assert "stock_sz_000338" in factor.get_targets(timestamp="2018-01-19") def test_bull_select_targets(): factor = BullFactor( start_timestamp="2019-01-01", end_timestamp="2019-06-10", level=IntervalLevel.LEVEL_1DAY, provider="joinquant" ) targets = factor.get_targets(timestamp="2019-05-08", target_type=TargetType.positive) assert "stock_sz_000338" not in targets assert "stock_sz_002572" not in targets targets = factor.get_targets("2019-05-08", target_type=TargetType.negative) assert "stock_sz_000338" in targets assert "stock_sz_002572" not in targets factor.move_on(timeout=0) targets = factor.get_targets(timestamp="2019-06-19", target_type=TargetType.positive) assert "stock_sz_000338" in targets assert "stock_sz_002572" not in targets ================================================ FILE: tests/factors/test_factors.py ================================================ # -*- coding: utf-8 -*- from zvt.factors.zen.zen_factor import ZenFactor def test_zen_factor(): z = ZenFactor( codes=["000338"], need_persist=False, provider="joinquant", ) z.draw(show=True) z = ZenFactor( codes=["000338", "601318"], need_persist=True, provider="joinquant", ) z.draw(show=True) ================================================ FILE: tests/factors/test_technical_factor.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel from zvt.factors.algorithm import MaTransformer, MacdTransformer from zvt.factors.ma.ma_factor import CrossMaFactor from ..context import init_test_context init_test_context() from zvt.factors.technical_factor import TechnicalFactor def test_ma(): factor = TechnicalFactor( provider="joinquant", codes=["000338"], start_timestamp="2019-01-01", end_timestamp="2019-06-10", level=IntervalLevel.LEVEL_1DAY, keep_window=30, transformer=MaTransformer(windows=[5, 10, 30]), adjust_type="qfq", ) print(factor.factor_df.tail()) # compare with east money manually ma5 = factor.factor_df["ma5"] ma10 = factor.factor_df["ma10"] ma30 = factor.factor_df["ma30"] assert round(ma5.loc[("stock_sz_000338", "2019-06-10")], 2) <= 11.23 assert round(ma10.loc[("stock_sz_000338", "2019-06-10")], 2) <= 11.43 assert round(ma30.loc[("stock_sz_000338", "2019-06-10")], 2) <= 11.52 factor.move_on(to_timestamp="2019-06-17") ma5 = factor.factor_df["ma5"] ma10 = factor.factor_df["ma10"] ma30 = factor.factor_df["ma30"] assert round(ma5.loc[("stock_sz_000338", "2019-06-17")], 2) <= 12.06 assert round(ma10.loc[("stock_sz_000338", "2019-06-17")], 2) <= 11.64 assert round(ma30.loc[("stock_sz_000338", "2019-06-17")], 2) <= 11.50 def test_macd(): factor = TechnicalFactor( provider="joinquant", codes=["000338"], start_timestamp="2019-01-01", end_timestamp="2019-06-10", level=IntervalLevel.LEVEL_1DAY, keep_window=None, transformer=MacdTransformer(), adjust_type="qfq", ) print(factor.factor_df.tail()) # compare with east money manually diff = factor.factor_df["diff"] dea = factor.factor_df["dea"] macd = factor.factor_df["macd"] assert round(diff.loc[("stock_sz_000338", "2019-06-10")], 2) == -0.14 assert round(dea.loc[("stock_sz_000338", "2019-06-10")], 2) == -0.15 assert round(macd.loc[("stock_sz_000338", "2019-06-10")], 2) == 0.02 factor.move_on(to_timestamp="2019-06-17") diff = factor.factor_df["diff"] dea = factor.factor_df["dea"] macd = factor.factor_df["macd"] assert round(diff.loc[("stock_sz_000338", "2019-06-17")], 2) == 0.06 assert round(dea.loc[("stock_sz_000338", "2019-06-17")], 2) == -0.03 assert round(macd.loc[("stock_sz_000338", "2019-06-17")], 2) <= 0.19 def test_cross_ma(): factor = CrossMaFactor( codes=["000338"], start_timestamp="2019-01-01", end_timestamp="2019-06-10", level=IntervalLevel.LEVEL_1DAY, provider="joinquant", windows=[5, 10], adjust_type="qfq", ) print(factor.factor_df.tail()) print(factor.result_df.tail()) score = factor.result_df["filter_result"] assert score[("stock_sz_000338", "2019-06-03")] == True assert score[("stock_sz_000338", "2019-06-04")] == True assert ("stock_sz_000338", "2019-06-05") not in score or score[("stock_sz_000338", "2019-06-05")] == False assert ("stock_sz_000338", "2019-06-06") not in score or score[("stock_sz_000338", "2019-06-06")] == False assert ("stock_sz_000338", "2019-06-10") not in score or score[("stock_sz_000338", "2019-06-10")] == False factor.move_on() score = factor.result_df["filter_result"] assert score[("stock_sz_000338", "2019-06-17")] == True ================================================ FILE: tests/factors/test_transformers.py ================================================ # -*- coding: utf-8 -*- from zvt.api.kdata import get_kdata from zvt.factors.algorithm import MaTransformer, MacdTransformer def test_ma_transformer(): df = get_kdata( entity_id="stock_sz_000338", start_timestamp="2019-01-01", provider="joinquant", index=["entity_id", "timestamp"], ) t = MaTransformer(windows=[5, 10]) result_df = t.transform(df) print(result_df) def test_MacdTransformer(): df = get_kdata( entity_id="stock_sz_000338", start_timestamp="2019-01-01", provider="joinquant", index=["entity_id", "timestamp"], ) t = MacdTransformer() result_df = t.transform(df) print(result_df) ================================================ FILE: tests/ml/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/ml/test_sgd.py ================================================ # -*- coding: utf-8 -*- from sklearn.linear_model import SGDClassifier, SGDRegressor from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler from zvt.contract import AdjustType from zvt.ml import MaStockMLMachine start_timestamp = "2015-01-01" end_timestamp = "2019-01-01" predict_start_timestamp = "2018-06-01" def test_sgd_classification(): machine = MaStockMLMachine( data_provider="joinquant", start_timestamp=start_timestamp, end_timestamp=end_timestamp, predict_start_timestamp=predict_start_timestamp, entity_ids=["stock_sz_000001"], label_method="behavior_cls", adjust_type=AdjustType.qfq, ) clf = make_pipeline(StandardScaler(), SGDClassifier(max_iter=1000, tol=1e-3)) machine.train(model=clf) machine.predict() machine.draw_result(entity_id="stock_sz_000001") def test_sgd_regressor(): machine = MaStockMLMachine( data_provider="joinquant", start_timestamp=start_timestamp, end_timestamp=end_timestamp, predict_start_timestamp=predict_start_timestamp, entity_ids=["stock_sz_000001"], label_method="raw", adjust_type=AdjustType.qfq, ) reg = make_pipeline(StandardScaler(), SGDRegressor(max_iter=1000, tol=1e-3)) machine.train(model=reg) machine.predict() machine.draw_result(entity_id="stock_sz_000001") ================================================ FILE: tests/recorders/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/common/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/common/test_china_stock_list_recorder.py ================================================ # -*- coding: utf-8 -*- from ...context import init_test_context init_test_context() from zvt.recorders.eastmoney import EastmoneyStockRecorder def test_china_stock_recorder(): recorder = EastmoneyStockRecorder() try: recorder.run() except: assert False ================================================ FILE: tests/recorders/eastmoney/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/eastmoney/test_dividend_financing_recorder.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import DividendDetail, RightsIssueDetail, SpoDetail, DividendFinancing from ...context import init_test_context init_test_context() from zvt.consts import SAMPLE_STOCK_CODES def test_dividend_detail(): try: DividendDetail.record_data(provider="eastmoney", codes=SAMPLE_STOCK_CODES) except: assert False def test_rights_issue_detail(): try: RightsIssueDetail.record_data(provider="eastmoney", codes=SAMPLE_STOCK_CODES) except: assert False def test_spo_detail(): try: SpoDetail.record_data(provider="eastmoney", codes=SAMPLE_STOCK_CODES) except: assert False def test_dividend_financing(): try: DividendFinancing.record_data(provider="eastmoney", codes=SAMPLE_STOCK_CODES) except: assert False ================================================ FILE: tests/recorders/eastmoney/test_finance.py ================================================ # -*- coding: utf-8 -*- from ...context import init_test_context init_test_context() from zvt.consts import SAMPLE_STOCK_CODES from zvt.recorders.eastmoney.finance.eastmoney_finance_factor_recorder import ChinaStockFinanceFactorRecorder from zvt.recorders.eastmoney.finance.eastmoney_cash_flow_recorder import ChinaStockCashFlowRecorder from zvt.recorders.eastmoney.finance.eastmoney_balance_sheet_recorder import ChinaStockBalanceSheetRecorder from zvt.recorders.eastmoney.finance.eastmoney_income_statement_recorder import ChinaStockIncomeStatementRecorder def test_finance_factor_recorder(): recorder = ChinaStockFinanceFactorRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False def test_cash_flow_recorder(): recorder = ChinaStockCashFlowRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False def test_balance_sheet_recorder(): recorder = ChinaStockBalanceSheetRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False def test_income_statement_recorder(): recorder = ChinaStockIncomeStatementRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False ================================================ FILE: tests/recorders/eastmoney/test_holder_recorder.py ================================================ # -*- coding: utf-8 -*- from ...context import init_test_context init_test_context() from zvt.consts import SAMPLE_STOCK_CODES from zvt.recorders.eastmoney.holder.eastmoney_top_ten_holder_recorder import TopTenHolderRecorder from zvt.recorders.eastmoney.holder.eastmoney_top_ten_tradable_holder_recorder import TopTenTradableHolderRecorder def test_top_ten_holder_recorder(): recorder = TopTenHolderRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False def test_top_ten_tradable_holder_recorder(): recorder = TopTenTradableHolderRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False ================================================ FILE: tests/recorders/eastmoney/test_meta_recorder.py ================================================ # -*- coding: utf-8 -*- from ...context import init_test_context init_test_context() from zvt.recorders.eastmoney.meta.eastmoney_stock_meta_recorder import EastmoneyStockDetailRecorder from zvt.consts import SAMPLE_STOCK_CODES def test_meta_recorder(): recorder = EastmoneyStockDetailRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False ================================================ FILE: tests/recorders/eastmoney/test_trading_recorder.py ================================================ # -*- coding: utf-8 -*- from ...context import init_test_context init_test_context() from zvt.consts import SAMPLE_STOCK_CODES from zvt.recorders.eastmoney.trading.eastmoney_manager_trading_recorder import ManagerTradingRecorder from zvt.recorders.eastmoney.trading.eastmoney_holder_trading_recorder import HolderTradingRecorder def test_manager_trading_recorder(): recorder = ManagerTradingRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False def test_holder_trading_recorder(): recorder = HolderTradingRecorder(codes=SAMPLE_STOCK_CODES) try: recorder.run() except: assert False ================================================ FILE: tests/recorders/em/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/em/test_em_api.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel, AdjustType from zvt.recorders.em import em_api import requests def test_get_kdata(): # 上证A股 session = requests.Session() df = em_api.get_kdata( session=session, entity_id="stock_sh_601318", level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.qfq, limit=5, ) print(df) df = em_api.get_kdata( session=session, entity_id="stock_sh_601318", level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.hfq, limit=5, ) print(df) df = em_api.get_kdata( session=session, entity_id="stock_sh_601318", level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.bfq, limit=5, ) print(df) # 深圳A股 df = em_api.get_kdata( session=session, entity_id="stock_sz_000338", level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.qfq, limit=5, ) print(df) df = em_api.get_kdata( session=session, entity_id="stock_sz_000338", level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.hfq, limit=5, ) print(df) df = em_api.get_kdata( session=session, entity_id="stock_sz_000338", level=IntervalLevel.LEVEL_1DAY, adjust_type=AdjustType.bfq, limit=5, ) print(df) ================================================ FILE: tests/recorders/em/test_kdata_recorder.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/joinquant/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/joinquant/test_quote_recorder.py ================================================ # -*- coding: utf-8 -*- from ...context import init_test_context init_test_context() from zvt.contract import IntervalLevel from zvt.consts import SAMPLE_STOCK_CODES from zvt.recorders.joinquant.quotes.jq_stock_kdata_recorder import JqChinaStockKdataRecorder def test_1wk_kdata_recorder(): recorder = JqChinaStockKdataRecorder( codes=SAMPLE_STOCK_CODES, sleeping_time=0, level=IntervalLevel.LEVEL_1WEEK, real_time=False ) try: recorder.run() except: assert False def test_1mon_kdata_recorder(): recorder = JqChinaStockKdataRecorder( codes=SAMPLE_STOCK_CODES, sleeping_time=0, level=IntervalLevel.LEVEL_1MON, real_time=False ) try: recorder.run() except: assert False def test_1d_kdata_recorder(): recorder = JqChinaStockKdataRecorder( codes=SAMPLE_STOCK_CODES, sleeping_time=0, level=IntervalLevel.LEVEL_1DAY, real_time=False ) try: recorder.run() except: assert False def test_1d_hfq_kdata_recorder(): recorder = JqChinaStockKdataRecorder( codes=["000338"], sleeping_time=0, level=IntervalLevel.LEVEL_1DAY, real_time=False, adjust_type="hfq" ) try: recorder.run() except: assert False def test_1h_kdata_recorder(): recorder = JqChinaStockKdataRecorder( codes=["000338"], sleeping_time=0, level=IntervalLevel.LEVEL_1HOUR, real_time=False, start_timestamp="2019-01-01", ) try: recorder.run() except: assert False def test_5m_kdata_recorder(): recorder = JqChinaStockKdataRecorder( codes=["000338"], sleeping_time=0, level=IntervalLevel.LEVEL_5MIN, real_time=False, start_timestamp="2019-01-01" ) try: recorder.run() except: assert False ================================================ FILE: tests/recorders/joinquant/test_stock_money_flow.py ================================================ # -*- coding: utf-8 -*- from zvt.domain import StockMoneyFlow, Stock def test_stock_money_flow(): provider = "joinquant" # Stock.record_data(provider=provider) StockMoneyFlow.record_data( codes=["300999", "688981"], provider=provider, start_timestamp="2020-12-14", compute_index_money_flow=False ) data_samples = [ { "id": "stock_sz_300999_2020-12-14", "timestamp": "2020-12-14", "code": "300999", "net_main_inflows": 46378.96 * 10000, "net_main_inflow_rate": 9.3 / 100, "net_huge_inflows": 50111.54 * 10000, "net_huge_inflow_rate": 10.04 / 100, "net_big_inflows": -3732.58 * 10000, "net_big_inflow_rate": -0.75 / 100, "net_medium_inflows": -23493.71 * 10000, "net_medium_inflow_rate": -4.71 / 100, "net_small_inflows": -22885.25 * 10000, "net_small_inflow_rate": -4.59 / 100, }, { "id": "stock_sh_688981_2020-12-14", "timestamp": "2020-12-14", "code": "688981", "net_main_inflows": -14523.55 * 10000, "net_main_inflow_rate": -10.77 / 100, "net_huge_inflows": -17053.72 * 10000, "net_huge_inflow_rate": -12.65 / 100, "net_big_inflows": 2530.17 * 10000, "net_big_inflow_rate": 1.88 / 100, "net_medium_inflows": 6945.23 * 10000, "net_medium_inflow_rate": 5.15 / 100, "net_small_inflows": 7578.32 * 10000, "net_small_inflow_rate": 5.62 / 100, }, ] StockMoneyFlow.test_data_correctness(provider=provider, data_samples=data_samples) ================================================ FILE: tests/recorders/sina/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/recorders/sina/test_money_flow_recorder.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/test_generator.py ================================================ # -*- coding: utf-8 -*- from zvt.autocode.generator import _remove_start_end def test_remove_start_end(): cls = _remove_start_end("class A(object)", "class ", "(") assert cls == "A" func = _remove_start_end("def aaa(arg1, arg2)", "def ", "(") assert func == "aaa" var = _remove_start_end("zvt_env = ", "", " =") assert var == "zvt_env" ================================================ FILE: tests/trader/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/trader/test_trader.py ================================================ # -*- coding: utf-8 -*- from zvt.api.kdata import get_kdata from zvt.contract import IntervalLevel, AdjustType from zvt.samples import MyBullTrader, StockTrader from zvt.utils.time_utils import is_same_date buy_timestamp = "2019-05-29" sell_timestamp = "2020-01-06" class SingleTrader(StockTrader): def on_time(self, timestamp): if is_same_date(buy_timestamp, timestamp): self.buy(timestamp=buy_timestamp, entity_ids=["stock_sz_000338"]) if is_same_date(sell_timestamp, timestamp): self.sell(timestamp=sell_timestamp, entity_ids=["stock_sz_000338"]) def long_position_control(self): return 1 def test_single_trader(): trader = SingleTrader( provider="joinquant", codes=["000338"], level=IntervalLevel.LEVEL_1DAY, start_timestamp="2019-01-01", end_timestamp="2020-01-10", trader_name="000338_single_trader", draw_result=True, ) trader.run() positions = trader.get_current_account().positions print(positions) account = trader.get_current_account() print(account) buy_price = get_kdata( provider="joinquant", entity_id="stock_sz_000338", start_timestamp=buy_timestamp, end_timestamp=buy_timestamp, return_type="domain", )[0] sell_price = get_kdata( provider="joinquant", entity_id="stock_sz_000338", start_timestamp=sell_timestamp, end_timestamp=sell_timestamp, return_type="domain", )[0] sell_lost = trader.account_service.slippage + trader.account_service.sell_cost buy_lost = trader.account_service.slippage + trader.account_service.buy_cost pct = (sell_price.close * (1 - sell_lost) - buy_price.close * (1 + buy_lost)) / buy_price.close * (1 + buy_lost) profit_rate = (account.all_value - account.input_money) / account.input_money assert round(profit_rate, 2) == round(pct, 2) class MultipleTrader(StockTrader): has_buy = False def on_time(self, timestamp): if is_same_date(buy_timestamp, timestamp): self.buy(timestamp=timestamp, entity_ids=["stock_sz_000338"]) self.has_buy = True self.buy(timestamp=timestamp, entity_ids=["stock_sh_601318"]) if is_same_date(sell_timestamp, timestamp): self.sell( timestamp=timestamp, entity_ids=["stock_sz_000338", "stock_sh_601318"], ) def long_position_control(self): if self.has_buy: position_pct = 1.0 else: position_pct = 0.5 return position_pct def test_multiple_trader(): trader = MultipleTrader( provider="joinquant", codes=["000338", "601318"], level=IntervalLevel.LEVEL_1DAY, start_timestamp="2019-01-01", end_timestamp="2020-01-10", trader_name="multiple_trader", draw_result=False, adjust_type=AdjustType.qfq, ) trader.run() positions = trader.get_current_account().positions print(positions) account = trader.get_current_account() print(account) # 000338 buy_price = get_kdata( provider="joinquant", entity_id="stock_sz_000338", start_timestamp=buy_timestamp, end_timestamp=buy_timestamp, return_type="domain", )[0] sell_price = get_kdata( provider="joinquant", entity_id="stock_sz_000338", start_timestamp=sell_timestamp, end_timestamp=sell_timestamp, return_type="domain", )[0] sell_lost = trader.account_service.slippage + trader.account_service.sell_cost buy_lost = trader.account_service.slippage + trader.account_service.buy_cost pct1 = (sell_price.close * (1 - sell_lost) - buy_price.close * (1 + buy_lost)) / buy_price.close * (1 + buy_lost) # 601318 buy_price = get_kdata( provider="joinquant", entity_id="stock_sh_601318", start_timestamp=buy_timestamp, end_timestamp=buy_timestamp, return_type="domain", )[0] sell_price = get_kdata( provider="joinquant", entity_id="stock_sh_601318", start_timestamp=sell_timestamp, end_timestamp=sell_timestamp, return_type="domain", )[0] pct2 = (sell_price.close * (1 - sell_lost) - buy_price.close * (1 + buy_lost)) / buy_price.close * (1 + buy_lost) profit_rate = (account.all_value - account.input_money) / account.input_money assert profit_rate - (pct1 + pct2) / 2 <= 0.2 def test_basic_trader(): try: MyBullTrader( provider="joinquant", codes=["000338"], level=IntervalLevel.LEVEL_1DAY, start_timestamp="2018-01-01", end_timestamp="2019-06-30", trader_name="000338_bull_trader", draw_result=False, ).run() except: assert False ================================================ FILE: tests/utils/__init__.py ================================================ # -*- coding: utf-8 -*- ================================================ FILE: tests/utils/test_pd_utils.py ================================================ # -*- coding: utf-8 -*- import pandas as pd from zvt.utils.pd_utils import drop_continue_duplicate def test_drop_continue_duplicate(): data1 = [1, 2, 2, 3, 4, 4, 5] s = pd.Series(data=data1) s1 = drop_continue_duplicate(s=s) assert s1.tolist() == [1, 2, 3, 4, 5] data2 = [1, 2, 2, 2, 4, 4, 5] df = pd.DataFrame(data={"A": data1, "B": data2}) print(df) df1 = drop_continue_duplicate(s=df, col="A") assert df1["A"].tolist() == [1, 2, 3, 4, 5] df2 = drop_continue_duplicate(s=df, col="B") assert df2["A"].tolist() == [1, 2, 4, 5] ================================================ FILE: tests/utils/test_time_utils.py ================================================ # -*- coding: utf-8 -*- from zvt.contract import IntervalLevel from zvt.contract.utils import evaluate_size_from_timestamp, next_timestamp_on_level, is_finished_kdata_timestamp from zvt.utils.time_utils import ( to_pd_timestamp, split_time_interval, is_same_date, month_start_end_ranges, count_interval, ) def test_evaluate_size_from_timestamp(): size = evaluate_size_from_timestamp( start_timestamp="2019-01-01", end_timestamp="2019-01-02", level=IntervalLevel.LEVEL_1MON, one_day_trading_minutes=4 * 60, ) assert size == 2 size = evaluate_size_from_timestamp( start_timestamp="2019-01-01", end_timestamp="2019-01-02", level=IntervalLevel.LEVEL_1WEEK, one_day_trading_minutes=4 * 60, ) assert size == 2 size = evaluate_size_from_timestamp( start_timestamp="2019-01-01", end_timestamp="2019-01-02", level=IntervalLevel.LEVEL_1DAY, one_day_trading_minutes=4 * 60, ) assert size == 2 size = evaluate_size_from_timestamp( start_timestamp="2019-01-01", end_timestamp="2019-01-02", level=IntervalLevel.LEVEL_1HOUR, one_day_trading_minutes=4 * 60, ) assert size == 9 size = evaluate_size_from_timestamp( start_timestamp="2019-01-01", end_timestamp="2019-01-02", level=IntervalLevel.LEVEL_1MIN, one_day_trading_minutes=4 * 60, ) assert size == 481 def test_next_timestamp(): current = "2019-01-10 13:15" assert next_timestamp_on_level(current, level=IntervalLevel.LEVEL_1MIN) == to_pd_timestamp("2019-01-10 13:16") assert next_timestamp_on_level(current, level=IntervalLevel.LEVEL_5MIN) == to_pd_timestamp("2019-01-10 13:20") assert next_timestamp_on_level(current, level=IntervalLevel.LEVEL_15MIN) == to_pd_timestamp("2019-01-10 13:30") def test_is_finished_kdata_timestamp(): timestamp = "2019-01-10 13:05" assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1DAY) assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1HOUR) assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_30MIN) assert not is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_15MIN) assert is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_5MIN) assert is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1MIN) timestamp = "2019-01-10" assert is_finished_kdata_timestamp(timestamp, level=IntervalLevel.LEVEL_1DAY) def test_split_time_interval(): first = None last = None start = "2020-01-01" end = "2021-01-01" for interval in split_time_interval(start, end, interval=30): if first is None: first = interval last = interval print(first) print(last) assert is_same_date(first[0], start) assert is_same_date(first[-1], "2020-01-31") assert is_same_date(last[-1], end) def test_split_time_interval_month(): first = None last = None start = "2020-01-01" end = "2021-01-01" for interval in split_time_interval(start, end, method="month"): if first is None: first = interval last = interval print(first) print(last) assert is_same_date(first[0], start) assert is_same_date(first[-1], "2020-01-31") assert is_same_date(last[0], "2021-01-01") assert is_same_date(last[-1], "2021-01-01") def test_month_start_end_range(): start = "2020-01-01" end = "2021-01-01" ranges = month_start_end_ranges(start_date=start, end_date=end) print(ranges) assert is_same_date(ranges[0][0], "2020-01-01") assert is_same_date(ranges[0][1], "2020-01-31") assert is_same_date(ranges[-1][0], "2020-12-01") assert is_same_date(ranges[-1][1], "2020-12-31") start = "2020-01-01" end = "2021-01-31" ranges = month_start_end_ranges(start_date=start, end_date=end) print(ranges) assert is_same_date(ranges[0][0], "2020-01-01") assert is_same_date(ranges[0][1], "2020-01-31") assert is_same_date(ranges[-1][0], "2021-01-01") assert is_same_date(ranges[-1][1], "2021-01-31") def test_count_interval(): start = "2020-01-01" end = "2021-01-01" print(count_interval(start_date=start, end_date=end)) ================================================ FILE: tests/utils/test_utils.py ================================================ # -*- coding: utf-8 -*- from zvt.contract.api import get_entities from zvt.utils.utils import iterate_with_step, to_str, float_to_pct def test_iterate_with_step(): data = range(1000) first = None last = None for sub_data in iterate_with_step(data): if not first: first = sub_data last = sub_data assert first[0] == 0 assert first[-1] == 99 assert last[0] == 900 assert last[-1] == 999 def test_iterate_entities(): data = get_entities(entity_type="stock") first = None last = None for sub_data in iterate_with_step(data): if first is None: first = sub_data last = sub_data assert len(first) == 100 assert len(last) <= 100 def test_to_str(): assert to_str(None) is None assert to_str("") is None assert to_str("a") == "a" assert to_str(["a", "b"]) == "a;b" assert to_str([1, 2]) == "1;2" def test_float_to_pct(): assert float_to_pct(0.1) == "10.00%" assert float_to_pct(0.111) == "11.10%" assert float_to_pct(0.8) == "80.00%" assert float_to_pct(0.555) == "55.50%" assert float_to_pct(0.33333) == "33.33%"