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We also recommend that a\n file or class name and description of purpose be included on the\n same \"printed page\" as the copyright notice for easier\n identification within third-party archives.\n\n Copyright [yyyy] [name of copyright owner]\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n" }, { "path": "README.md", "content": "# Wt4ElegantRL\nWt4ElegantRL = [WonderTrader](https://github.com/wondertrader/wtpy) + [ElegantRL](https://github.com/AI4Finance-Foundation/ElegantRL)\n\n# Document by panyunan\nhttps://gitee.com/panyunan/wt4elegantrl-doc\n\n## INSTALL\nclone\n```\ngit clone https://github.com/drlgistics/Wt4ElegantRL.git\ncd Wt4ElegantRL\n```\n\nwith cuda\n```\nconda env create -n wt4elegantrl --file ./requirements/full_with_cuda.yaml\n```\n\nor without cuda\n```\nconda env create -n wt4elegantrl --file ./requirements/full_without_cuda.yaml\n```\n\nactivate\n```\nconda activate wt4elegantrl\n```\n\n## RLLIB\ndemo\n```\npython ./compare_rllib.py test -p ./trained/rllib/TD3_2021-10-29_11-21-37/TD3_SimpleCTAEnv_536e7_00000_0_2021-10-29_11-21-37/checkpoint_000023/checkpoint-23\n```\n\ntrain\n```\npython ./compare_rllib.py train\n```\n\n## SB3\ndemo\n```\npython ./compare_sb3.py test -p ./trained/sb3/td3/best_model\n```\n\ntrain\n```\npython ./compare_sb3.py train\n```\n\n## ELEGANTRL\ntrain\n```\npython ./compare_elegantrl.py train\n```\n" }, { "path": "analysts.py", "content": "from ntpath import realpath\nfrom wtpy.apps import WtBtAnalyst\nfrom glob import glob\nfrom tqdm.auto import tqdm\nfrom os.path import dirname, basename\nfrom click import command, option\n\n# for i in tqdm(glob('./outputs_bt/*/funds.csv')):\n# folder = dirname(i)\n# name = folder[13:]\n# analyst = WtBtAnalyst()\n# folder = \"./outputs_bt/%s/\" % name\n# analyst.add_strategy(\n# name, folder=folder, init_capital=500000, rf=0.04, annual_trading_days=240)\n# try:\n# analyst.run_new('%s/%s_PnLAnalyzing.xlsx' % (folder, name))\n# # analyst.run('%s/%s_PnLAnalyzing.xlsx' % (folder, name))\n# except:\n# analyst.run('%s/%s_PnLAnalyzing.xlsx' % (folder, name))\n\n@command()\n@option('--path', '-p', 'path', default='./outputs_bt/*/')\ndef run(path):\n for i in tqdm(glob('%s/funds.csv'%path)):\n folder = dirname(i)\n name = basename(folder)\n analyst = WtBtAnalyst()\n analyst.add_strategy(\n name, folder='%s/'%folder, init_capital=500000, rf=0.04, annual_trading_days=240)\n try:\n analyst.run_new('%s/%s_PnLAnalyzing.xlsx' % (folder, name))\n # analyst.run('%s/%s_PnLAnalyzing.xlsx' % (folder, name))\n except:\n analyst.run('%s/%s_PnLAnalyzing.xlsx' % (folder, name))\n\nif __name__ == '__main__':\n run()" }, { "path": "assessments.py", "content": "from abc import abstractmethod\nimport numpy as np\nfrom wtpy.StrategyDefs import CtaContext, HftContext\n\n\nclass Assessment():\n def __init__(self, init_assets=1000000):\n self._init_assets_ = init_assets\n self.reset()\n\n @abstractmethod\n def reset(self):\n raise NotImplementedError\n\n @abstractmethod\n def calculate(self, context: CtaContext):\n raise NotImplementedError\n\n @abstractmethod\n def finish(self):\n raise NotImplementedError\n\n @property\n @abstractmethod\n def reward(self) -> float:\n raise NotImplementedError\n\n @property\n @abstractmethod\n def done(self) -> float:\n raise NotImplementedError\n\n @property\n @abstractmethod\n def curr_assets(self) -> float:\n raise NotImplementedError\n\n @property\n def init_assets(self) -> float:\n return self._init_assets_\n\n\nclass SimpleAssessment(Assessment): # 借鉴了neofinrl\n gamma = 0.99\n\n def reset(self):\n self.__assets__: list = [self._init_assets_]\n self.__reward__: list = [0]\n self.__done__: bool = False\n self.__successive__: int = 1\n # self.__gamma__ = 1-self.gamma\n\n def calculate(self, context: CtaContext):\n if self.__done__:\n return\n\n # 动态权益\n dynbalance = context.stra_get_fund_data(0)\n # 总平仓盈亏\n # closeprofit = context.stra_get_fund_data(1)\n # 总浮动盈亏\n # positionprofit = context.stra_get_fund_data(2)\n # 总手续费\n # fee = context.stra_get_fund_data(3)\n\n self.__assets__.append(self._init_assets_+dynbalance) # 账户实时的动态权益\n \n \n if len(self.__reward__) > 1:\n reward = (self.__assets__[-1]-self.__assets__[-2]) \\\n / self._init_assets_ * 12 #* 0.382\n\n # if (reward < 0 and self.__reward__[-1] < 0) or \\\n # (reward > 0 and self.__reward__[-1] > 0):\n # self.__successive__ += 1\n # else:\n # self.__successive__ = 1\n \n # reward *= self.__successive__\n\n # if self.__assets__[-1] > self.__assets__[-2]: #\n # reward += 0.0001*self.__successive__\n # else:\n # reward -= 0.0001*self.__successive__\n\n # reward += (self.__assets__[-1]-max(self.__assets__[:-1])) \\\n # / self._init_assets_ * 0.1\n # reward += (self.__assets__[-1]-min(self.__assets__[:-1])) \\\n # / self._init_assets_ * 0.1\n\n # reward = 0\n\n # returns = np.diff(np.array(self.__assets__))\n # reward = (np.where(returns < 0, 0, returns).sum()-1e-5) \\\n # / abs(np.where(returns > 0, 0, returns).sum()+1e-5) \\\n # - 1\n # reward *= 0.1\n\n # print(np.where(returns < 0, 0, returns).sum(), np.where(returns > 0, 0, returns).sum(), dynbalance, reward)\n\n # 情绪奖励\n # if (reward < 0 and self.__reward__[-1] < 0) or \\\n # (reward > 0 and self.__reward__[-1] > 0):\n # self.__successive__ += 1\n # else:\n # self.__successive__ = 1\n\n # #资金成本\n # if self.__assets__[-1] > self.__assets__[-2]: #\n # reward += 0.0001*self.__successive__\n # else:\n # reward -= 0.0001*self.__successive__\n\n # reward *= 0.01\n\n # #近期奖励\n # if self.__assets__[-1] > max(self.__assets__[:-1]):\n # reward += 0.02\n # if self.__assets__[-1] < min(self.__assets__[:-1]):\n # reward -= 0.01\n\n # 长期奖励\n # reward += (self.__assets__[-1]-max(self.__assets__[:-1])) \\\n # / self._init_assets_ * self.__successive__ * 0.382\n # reward += (self.__assets__[-1]-min(self.__assets__[:-1])) \\\n # / self._init_assets_ * self.__successive__ * 0.382\n\n # if (reward < 0 and self.__reward__[-1] < 0) or \\\n # (reward > 0 and self.__reward__[-1] > 0):\n # reward *= self.__successive__\n # self.__successive__ += 1\n # else:\n # self.__successive__ = 1\n # reward += (self.__assets__[-1]-max(self.__assets__[:-1])) \\\n # / self._init_assets_ * 0.382\n # reward += (self.__assets__[-1]-min(self.__assets__[:-1])) \\\n # / self._init_assets_ * 0.382\n\n '''\n 2021/11/01\n\n if self.__assets__[-1] > self.__assets__[-2]:\n self.__successive__ += 1\n else:\n self.__successive__ = 1\n \n reward = -0.00001\n reward += (self.__assets__[-1] /\n self.__assets__[-2] - 1) * 0.382 * self.__successive__\n max_assets = (self.__assets__[-1]/max(self.__assets__[:-1])-1) * 0.382\n reward += max_assets * (5 if max_assets > 0 else 1)\n min_assets = (self.__assets__[-1]/min(self.__assets__[:-1])-1) * 0.382\n reward += min_assets * (5 if min_assets < 0 else 1)\n '''\n else:\n reward = -0.01\n\n self.__reward__.append(reward) # 以动态权益差分设计reward\n self.__done__ = False # 此处可以根据控制任务结束状态\n\n def finish(self):\n if self.__done__:\n return\n\n # returns = np.add(1, self.__reward__).cumprod()\n # np.subtract(returns, 1, out=returns)\n\n # gamma = 0\n # for reward in np.diff(np.log(self.__assets__)):\n # gamma *= self.gamma\n # gamma += reward\n\n gamma = 0\n for reward in self.__reward__:\n gamma *= self.gamma\n gamma += reward\n\n # gamma = np.diff(np.array(self.__assets__))\n # gamma = (np.where(gamma < 0, 0, gamma).sum()-1e-5) \\\n # / abs(np.where(gamma > 0, 0, gamma).sum()+1e-5) \\\n # - 1\n\n # gamma = np.round(np.nanprod(np.array(self.__reward__)+1, axis=0)-1, 5)\n # gamma = self.__assets__[-1]/max(self.__assets__)-1\n # gamma = self.__assets__[-1]/self.init_assets-1\n self.__reward__.append(gamma) # 在结束的时候把过程奖励做处理，作为整个训练的奖励\n self.__done__ = True\n\n @property\n def reward(self) -> float:\n # return self.__reward__[-1]\n return float(self.__reward__[-1])\n\n @property\n def rewards(self) -> float:\n return self.__reward__\n\n @property\n def done(self) -> float:\n return self.__done__\n\n @property\n def curr_assets(self) -> float:\n return self.__assets__[-1]\n" }, { "path": "compare_elegantrl.py", "content": "from click import command, group, option\nfrom elegantrl.agent import AgentPPO as Agent\n# from elegantrl.agent import AgentSAC as Agent\n# from elegantrl.agent import AgentModSAC as Agent\n# from elegantrl.agent import AgentTD3 as Agent\n\n\n# from elegantrl.agent import AgentDoubleDQN as Agent\n# from elegantrl.agent import AgentDQN as Agent\n\n\nfrom elegantrl.run import Arguments, train_and_evaluate\nfrom envs_simple_cta import SimpleCTASubProcessEnv\nfrom gym import make, register\nfrom numpy import inf\nfrom os import getpid\n\n\nclass Wt4RLSimpleTrainer(SimpleCTASubProcessEnv):\n env_num = 1\n max_step = 1500\n if_discrete = False\n\n @property\n def state_dim(self):\n return self.observation_space.shape[0]\n\n @property\n def action_dim(self):\n # if len(self.action_space.shape) > 0 else 10\n return self.action_space.shape[0]\n\n def __init__(self):\n super().__init__(**{\n # 'time_start': 202108301600,\n # 'time_end': 202108311600,\n 'time_range': (\n # (201901011600, 202101011600),\n\n # (201901011600, 201906301600),\n # (201906301600, 202001011600),\n # (202001011600, 202006301600),\n # (202006301600, 202101011600),\n\n (201812311600, 201901311600),\n (201901311600, 201902311600),\n (201902311600, 201903311600),\n (201903311600, 201904311600),\n (201904311600, 201905311600),\n (201905311600, 201906311600),\n (201906311600, 201907311600),\n (201907311600, 201908311600),\n (201908311600, 201909311600),\n (201909311600, 201910311600),\n (201910311600, 201911311600),\n (201911311600, 201912311600),\n\n (201912311600, 202001311600),\n (202001311600, 202002311600),\n (202002311600, 202003311600),\n (202003311600, 202004311600),\n (202004311600, 202005311600),\n (202005311600, 202006311600),\n (202006311600, 202007311600),\n (202007311600, 202008311600),\n (202008311600, 202009311600),\n (202009311600, 202010311600),\n (202010311600, 202011311600),\n (202011311600, 202012311600),\n ),\n 'slippage': 0,\n 'mode': 1\n })\n\n\nclass Wt4RLSimpleEvaluator(SimpleCTASubProcessEnv):\n env_num = 1\n max_step = 1500\n if_discrete = False\n\n @property\n def state_dim(self):\n return self.observation_space.shape[0]\n\n @property\n def action_dim(self):\n # if len(self.action_space.shape) > 0 else 10\n return self.action_space.shape[0]\n\n def __init__(self): # mode=3可以打开详细调试模式\n super().__init__(**{\n 'time_range': (\n # (202101011600, 202106301600),\n # (201701011600, 201706301600),\n # (201706301600, 201801011600),\n # (201801011600, 201806301600),\n # (201806301600, 201901011600),\n\n\n (202012311600, 202101311600),\n (202101311600, 202102311600),\n (202102311600, 202103311600),\n (202103311600, 202104311600),\n (202104311600, 202105311600),\n (202105311600, 202106311600),\n\n (201612311600, 201701311600),\n (201701311600, 201702311600),\n (201702311600, 201703311600),\n (201703311600, 201704311600),\n (201704311600, 201705311600),\n (201705311600, 201706311600),\n (201706311600, 201707311600),\n (201707311600, 201708311600),\n (201708311600, 201709311600),\n (201709311600, 201710311600),\n (201710311600, 201711311600),\n (201711311600, 201712311600),\n\n (201712311600, 201801311600),\n (201801311600, 201802311600),\n (201802311600, 201803311600),\n (201803311600, 201804311600),\n (201804311600, 201805311600),\n (201805311600, 201806311600),\n (201806311600, 201807311600),\n (201807311600, 201808311600),\n (201808311600, 201809311600),\n (201809311600, 201810311600),\n (201810311600, 201811311600),\n (201811311600, 201812311600),\n ),\n 'slippage': 0,\n 'mode': 1\n })\n\n\nregister('wt4rl-simplecta-trainer-v0', entry_point=Wt4RLSimpleTrainer)\nregister('wt4rl-simplecta-evaluator-v0', entry_point=Wt4RLSimpleEvaluator)\n\n\nif __name__ == '__main__':\n @group()\n def run():\n pass\n\n @command()\n @option('--count', default=24)\n def debug(count):\n env: SimpleCTASubProcessEnv = make('wt4rl-simplecta-trainer-v0')\n print('状态空间', env.observation_space.shape)\n print('动作空间', env.action_space.shape)\n a = 0\n for i in range(1, int(count)+1): # 模拟训练10次\n obs = env.reset()\n done = False\n n = 0\n while not done:\n action = env.action_space.sample() # 模拟智能体产生动作\n obs, reward, done, info = env.step(action)\n n += 1\n a += 1\n # print('action:', action, 'obs:', obs, 'reward:', reward, 'done:', done)\n print('第%s次训练完成，执行%s步, 奖励%s, 盈亏%s。' % (i, n, reward, env.assets))\n env.close()\n print(a)\n\n @command()\n def train():\n args = Arguments(\n env='wt4rl-simplecta-trainer-v0',\n # env='wt4rl-simplecta-evaluator-v0',\n agent=Agent()\n )\n\n # args必须设置的参数\n args.eval_env = 'wt4rl-simplecta-evaluator-v0'\n args.max_step = 3000\n args.state_dim = 126\n args.action_dim = 3\n args.if_discrete = False\n args.target_return = 5 # inf\n args.if_overwrite = False\n args.eval_times1 = 15 # 待查明：为啥td3的评估器结果完全一致\n args.eval_times2 = 30 # 待查明：为啥td3的评估器结果完全一致\n\n args.worker_num = 1 # 内存小的注意别爆内存\n args.break_step = inf\n args.if_allow_break = True\n\n #\n args.gamma = 0.96 # 8小时会跨过一次隔夜风险，既96个bar\n args.learning_rate = 1e-5\n # args.gamma = 0.1 ** (1/12/8) # 8小时会跨过一次隔夜风险，既96个bar\n # args.learning_rate = 1e-3 # N15:294 Y14:292\n args.eval_gap = 2 ** 8\n args.net_dim = 2 ** 6\n args.batch_size = args.net_dim * 2\n args.max_memo = 2 ** 20\n args.target_step = args.max_step * 2\n args.if_per_or_gae = True\n # args.agent.if_use_cri_target = True\n # args.agent.if_use_dueling = True\n\n args.env_num = 1\n args.learner_gpus = (0,)\n args.workers_gpus = args.learner_gpus\n args.eval_gpu_id = 0\n args.cwd = './outputs_bt/elegantrl/%s_%s_%s' % (\n args.agent.__class__.__name__, args.gamma, args.learning_rate)\n # args.repeat_times = 0.01\n\n #args.net_dim = 2**9\n # args.net_dim = 2 ** 8\n #args.max_memo = 2 ** 22\n # args.break_step = args.max_step*1000\n # args.batch_size = 2 ** 11 # args.net_dim * 2\n # args.repeat_times = 1.5\n\n # args.eval_gap = 2 ** 9\n # args.eval_times1 = 2 ** 2\n # args.eval_times2 = 2 ** 5\n\n # args.worker_num = 4\n # args.target_step = args.env.max_step * 1\n # train_and_evaluate(args)\n\n train_and_evaluate(args)\n\n @command()\n def test():\n env = Wt4RLSimpleEvaluator(mode=3)\n agent = Agent()\n\n agent.init(net_dim=2 ** 8, state_dim=380, action_dim=10,\n learning_rate=0.1 ** (1/12/8), if_per_or_gae=True, env_num=1, gpu_id=0)\n agent.save_or_load_agent(\n cwd='./outputs_bt/elegantrl/AgentTD3_6.103515625e-05_8', if_save=False)\n\n # for i in range(10): # 模拟训练10次\n # obs = env.reset()\n # done = False\n # n = 0\n # while not done:\n # action = agent.select_action(obs) # 模拟智能体产生动作\n # obs, reward, done, info = env.step(action)\n # n += 1\n # # print('action:', action, 'obs:', obs,\n # # 'reward:', reward, 'done:', done)\n # print('第%s次训练完成，执行%s步, 盈亏%s。' % (i+1, n, env.assets))\n # env.close()\n\n run.add_command(debug)\n run.add_command(train)\n run.add_command(test)\n # run.add_command(eval)\n\n run()\n" }, { "path": "compare_rllib.py", "content": "from ray import tune, init\n# from ray.rllib.agents.sac import SACTrainer as Trainer\n# from ray.rllib.agents.ddpg import TD3Trainer as Trainer\n# from ray.rllib.agents.ddpg import ApexDDPGTrainer as Trainer\n\n\n# from ray.rllib.agents.a3c import A3CTrainer as Trainer\nfrom ray.rllib.agents.ppo import PPOTrainer as Trainer\n# from ray.rllib.agents.ppo import APPOTrainer as Trainer\n# from ray.rllib.agents.marwil import MARWILTrainer as Trainer\n# from ray.rllib.agents.impala import ImpalaTrainer as Trainer\n\n# from ray.rllib.agents.mbmpo import MBMPOTrainer as Trainer\n# from ray.rllib.agents.dreamer import DREAMERTrainer as Trainer\n# from ray.rllib.agents.pg import PGTrainer as Trainer\n\n\n# from ray.rllib.agents.pg import PGTrainer as Trainer\n# from ray.rllib.agents.dqn import R2D2Trainer as Trainer\n# from ray.rllib.agents.dqn import ApexTrainer as Trainer\n\n\n# from ray.tune.schedulers.pb2 import PB2\nfrom envs_simple_cta import SimpleCTAEnv\nimport click\n\ntune.register_env('SimpleCTAEnv',\n lambda env_config: SimpleCTAEnv(**env_config))\n\n\nif __name__ == '__main__':\n @click.group()\n def run():\n pass\n\n @run.command()\n def train():\n # pb2 = PB2(\n # time_attr=\"training_iteration\",\n # metric=\"episode_reward_mean\",\n # mode=\"max\",\n # perturbation_interval=50000,\n # quantile_fraction=0.25, # copy bottom % with top %\n # # Specifies the hyperparam search space\n # hyperparam_bounds={\n # \"lr\": [1e-3, 1e-5],\n # \"gamma\": [0.96, 0.99],\n # })\n\n nums_subproc = 5\n nums_gpu = 0.92/(nums_subproc+2)\n config = {\n 'env': 'SimpleCTAEnv',\n 'env_config': {\n # 'time_start': 202108301600,\n # 'time_end': 202108311600,\n 'time_range': (\n # (201901011600, 202101011600),\n\n # (201901011600, 201906301600),\n # (201906301600, 202001011600),\n # (202001011600, 202006301600),\n # (202006301600, 202101011600),\n\n (201812311600, 201901311600),\n (201901311600, 201902311600),\n (201902311600, 201903311600),\n (201903311600, 201904311600),\n (201904311600, 201905311600),\n (201905311600, 201906311600),\n (201906311600, 201907311600),\n (201907311600, 201908311600),\n (201908311600, 201909311600),\n (201909311600, 201910311600),\n (201910311600, 201911311600),\n (201911311600, 201912311600),\n\n (201912311600, 202001311600),\n (202001311600, 202002311600),\n (202002311600, 202003311600),\n (202003311600, 202004311600),\n (202004311600, 202005311600),\n (202005311600, 202006311600),\n (202006311600, 202007311600),\n (202007311600, 202008311600),\n (202008311600, 202009311600),\n (202009311600, 202010311600),\n (202010311600, 202011311600),\n (202011311600, 202012311600),\n ),\n 'slippage': 0,\n 'mode': 1\n },\n # 'rollout_fragment_length': 10156,\n 'framework': 'torch',\n 'num_workers': nums_subproc,\n 'num_gpus': nums_gpu,\n 'num_gpus_per_worker': nums_gpu,\n 'gamma': 0.99,\n 'lr': 1e-5,\n 'train_batch_size': 30465,\n # \"actor_lr\": 1e-5,\n # \"critic_lr\": 1e-5,\n # \"l2_reg\": 1e-6,\n # 'model': {\n # 'use_lstm': True,\n # # 'fcnet_hiddens': [64],\n # # 'fcnet_activation': 'linear',\n # # 'lstm_cell_size': 64,\n # # 'max_seq_len': 2,\n # },\n 'evaluation_interval': 24,\n \"evaluation_num_episodes\": 30,\n 'evaluation_parallel_to_training': False,\n 'evaluation_num_workers': 1,\n\n \"evaluation_config\": {\n \"env_config\": {\n 'time_range': (\n # (202101011600, 202106301600),\n # (201701011600, 201706301600),\n # (201706301600, 201801011600),\n # (201801011600, 201806301600),\n # (201806301600, 201901011600),\n\n\n (202012311600, 202101311600),\n (202101311600, 202102311600),\n (202102311600, 202103311600),\n (202103311600, 202104311600),\n (202104311600, 202105311600),\n (202105311600, 202106311600),\n\n (201612311600, 201701311600),\n (201701311600, 201702311600),\n (201702311600, 201703311600),\n (201703311600, 201704311600),\n (201704311600, 201705311600),\n (201705311600, 201706311600),\n (201706311600, 201707311600),\n (201707311600, 201708311600),\n (201708311600, 201709311600),\n (201709311600, 201710311600),\n (201710311600, 201711311600),\n (201711311600, 201712311600),\n\n (201712311600, 201801311600),\n (201801311600, 201802311600),\n (201802311600, 201803311600),\n (201803311600, 201804311600),\n (201804311600, 201805311600),\n (201805311600, 201806311600),\n (201806311600, 201807311600),\n (201807311600, 201808311600),\n (201808311600, 201809311600),\n (201809311600, 201810311600),\n (201810311600, 201811311600),\n (201811311600, 201812311600),\n ),\n 'slippage': 0,\n 'mode': 1\n },\n },\n # 'train_batch_size': 10156,\n \"batch_mode\": \"complete_episodes\",\n }\n\n # training and saving\n analysis = tune.run(\n Trainer,\n stop={\n \"timesteps_total\": 1e10,\n 'episode_reward_mean': 0.48,\n # 'episode_reward_min': 50,\n },\n # scheduler=pb2,\n # num_samples=nums_subproc,\n config=config,\n keep_checkpoints_num=10,\n checkpoint_freq=24,\n checkpoint_score_attr='episode_reward_mean',\n checkpoint_at_end=True,\n local_dir=\"./outputs_bt/rllib\",\n )\n # retrieve the checkpoint path\n analysis.default_metric = \"episode_reward_mean\"\n analysis.default_mode = \"max\"\n checkpoint_path = analysis.get_best_checkpoint(\n trial=analysis.get_best_trial())\n print(f\"Trained model saved at {checkpoint_path}\")\n\n @run.command()\n @click.option('--path', '-p', 'path')\n def test(path):\n\n config = {\n 'env': 'SimpleCTAEnv',\n 'env_config': {\n 'time_range': (\n (202106311600, 202107311600),\n (202107311600, 202108311600),\n (202108311600, 202109311600),\n (202109311600, 202110311600),\n (202110311600, 202111311600),\n (202106311600, 202111311600),\n ),\n 'slippage': 0,\n 'mode': 2\n },\n 'framework': 'torch',\n 'num_workers': 1,\n 'num_gpus': 1,\n 'num_gpus_per_worker': 1,\n }\n agent = Trainer(config=config)\n agent.restore(path)\n print(f\"Agent loaded from saved model at {path}\")\n\n env = SimpleCTAEnv(**config['env_config'])\n\n for i in range(len(config['env_config']['time_range'])*10): # 模拟训练10次\n obs = env.reset()\n done = False\n n = 0\n while not done:\n # action = env.action_space.sample() # 模拟智能体产生动作\n action = agent.compute_single_action(obs)\n obs, reward, done, info = env.step(action)\n n += 1\n # print(\n # # 'action:', action,\n # # 'obs:', obs,\n # 'reward:', reward,\n # # 'done:', done\n # )\n # break\n # break\n print('第%s次测试完成，执行%s步, 市值%s。' % (i+1, n, env.assets))\n env.close()\n del env\n\n run()\n" }, { "path": "compare_sb3.py", "content": "# from stable_baselines3 import SAC as Trainer\nfrom stable_baselines3 import TD3 as Trainer\n\n# from stable_baselines3 import PPO as Trainer\n# from stable_baselines3 import A2C as Trainer\n\nfrom envs_simple_cta import SimpleCTASubProcessEnv, SimpleCTAEnv\nfrom stable_baselines3.common.callbacks import EvalCallback, StopTrainingOnRewardThreshold\nfrom stable_baselines3.common.monitor import Monitor\nimport click\n\n\n@click.group()\ndef run():\n pass\n\n\n@run.command()\ndef debug():\n learner = SimpleCTASubProcessEnv(**{\n 'time_range': (\n # (201901011600, 202101011600),\n\n # (201901011600, 201906301600),\n # (201906301600, 202001011600),\n # (202001011600, 202006301600),\n # (202006301600, 202101011600),\n\n (201812311600, 201901311600),\n (201901311600, 201902311600),\n (201902311600, 201903311600),\n (201903311600, 201904311600),\n (201904311600, 201905311600),\n (201905311600, 201906311600),\n (201906311600, 201907311600),\n (201907311600, 201908311600),\n (201908311600, 201909311600),\n (201909311600, 201910311600),\n (201910311600, 201911311600),\n (201911311600, 201912311600),\n\n (201912311600, 202001311600),\n (202001311600, 202002311600),\n (202002311600, 202003311600),\n (202003311600, 202004311600),\n (202004311600, 202005311600),\n (202005311600, 202006311600),\n (202006311600, 202007311600),\n (202007311600, 202008311600),\n (202008311600, 202009311600),\n (202009311600, 202010311600),\n (202010311600, 202011311600),\n (202011311600, 202012311600),\n ),\n 'slippage': 0,\n 'mode': 1\n })\n\n evaluator = SimpleCTASubProcessEnv(**{\n 'time_range': (\n # (202101011600, 202106301600),\n # (201701011600, 201706301600),\n # (201706301600, 201801011600),\n # (201801011600, 201806301600),\n # (201806301600, 201901011600),\n\n\n (202012311600, 202101311600),\n (202101311600, 202102311600),\n (202102311600, 202103311600),\n (202103311600, 202104311600),\n (202104311600, 202105311600),\n (202105311600, 202106311600),\n\n (201612311600, 201701311600),\n (201701311600, 201702311600),\n (201702311600, 201703311600),\n (201703311600, 201704311600),\n (201704311600, 201705311600),\n (201705311600, 201706311600),\n (201706311600, 201707311600),\n (201707311600, 201708311600),\n (201708311600, 201709311600),\n (201709311600, 201710311600),\n (201710311600, 201711311600),\n (201711311600, 201712311600),\n\n (201712311600, 201801311600),\n (201801311600, 201802311600),\n (201802311600, 201803311600),\n (201803311600, 201804311600),\n (201804311600, 201805311600),\n (201805311600, 201806311600),\n (201806311600, 201807311600),\n (201807311600, 201808311600),\n (201808311600, 201809311600),\n (201809311600, 201810311600),\n (201810311600, 201811311600),\n (201811311600, 201812311600),\n ),\n 'slippage': 0,\n 'mode': 1\n })\n\n env = learner\n\n for i in range(1): # 模拟训练10次\n obs = env.reset()\n done = False\n n = 0\n while not done:\n action = env.action_space.sample() # 模拟智能体产生动作\n obs, reward, done, info = env.step(action)\n n += 1\n # print('action:', action, 'obs:', obs,\n # 'reward:', reward, 'done:', done)\n print('第%s次训练完成，执行%s步, 市值%s。' % (i+1, n, env.assets))\n learner.close()\n\n\n@run.command()\ndef train():\n learner = SimpleCTASubProcessEnv(**{\n 'time_range': (\n # (201901011600, 202101011600),\n\n # (201901011600, 201906301600),\n # (201906301600, 202001011600),\n # (202001011600, 202006301600),\n # (202006301600, 202101011600),\n\n (201812311600, 201901311600),\n (201901311600, 201902311600),\n (201902311600, 201903311600),\n (201903311600, 201904311600),\n (201904311600, 201905311600),\n (201905311600, 201906311600),\n (201906311600, 201907311600),\n (201907311600, 201908311600),\n (201908311600, 201909311600),\n (201909311600, 201910311600),\n (201910311600, 201911311600),\n (201911311600, 201912311600),\n\n (201912311600, 202001311600),\n (202001311600, 202002311600),\n (202002311600, 202003311600),\n (202003311600, 202004311600),\n (202004311600, 202005311600),\n (202005311600, 202006311600),\n (202006311600, 202007311600),\n (202007311600, 202008311600),\n (202008311600, 202009311600),\n (202009311600, 202010311600),\n (202010311600, 202011311600),\n (202011311600, 202012311600),\n ),\n 'slippage': 0,\n 'mode': 1\n })\n\n evaluator = SimpleCTASubProcessEnv(**{\n 'time_range': (\n # (202101011600, 202106301600),\n # (201701011600, 201706301600),\n # (201706301600, 201801011600),\n # (201801011600, 201806301600),\n # (201806301600, 201901011600),\n\n\n (202012311600, 202101311600),\n (202101311600, 202102311600),\n (202102311600, 202103311600),\n (202103311600, 202104311600),\n (202104311600, 202105311600),\n (202105311600, 202106311600),\n\n (201612311600, 201701311600),\n (201701311600, 201702311600),\n (201702311600, 201703311600),\n (201703311600, 201704311600),\n (201704311600, 201705311600),\n (201705311600, 201706311600),\n (201706311600, 201707311600),\n (201707311600, 201708311600),\n (201708311600, 201709311600),\n (201709311600, 201710311600),\n (201710311600, 201711311600),\n (201711311600, 201712311600),\n\n (201712311600, 201801311600),\n (201801311600, 201802311600),\n (201802311600, 201803311600),\n (201803311600, 201804311600),\n (201804311600, 201805311600),\n (201805311600, 201806311600),\n (201806311600, 201807311600),\n (201807311600, 201808311600),\n (201808311600, 201809311600),\n (201809311600, 201810311600),\n (201810311600, 201811311600),\n (201811311600, 201812311600),\n ),\n 'slippage': 0,\n 'mode': 2\n })\n\n n = 1500\n\n eval_callback = EvalCallback(\n eval_env=Monitor(evaluator),\n callback_on_new_best=StopTrainingOnRewardThreshold(\n reward_threshold=5, verbose=1),\n n_eval_episodes=30,\n eval_freq=24,\n log_path='./outputs_bt/sb3/%s'%Trainer.__name__,\n best_model_save_path='./outputs_bt/sb3/%s'%Trainer.__name__,\n verbose=1)\n\n model: Trainer = Trainer('MlpPolicy', learner,\n # gamma=0.1 ** (1/12/8),\n gamma=0.96,\n # learning_rate=2 ** -14, # 15: 167, 14:\n learning_rate=1e-5,\n # learning_starts=100,\n # batch_size=128,\n # ent_coef='auto_0.1',\n # policy_kwargs=dict(net_arch=[128, 128, 128]),\n tensorboard_log='./outputs_bt/sb3/%s'%Trainer.__name__,\n verbose=1,\n # device='cpu',\n )\n model.learn(\n total_timesteps=10000,\n callback=eval_callback,\n log_interval=1\n )\n model.save('SimpleTrainer')\n\n\n@run.command()\n@click.option('--path', '-p', 'path')\ndef test(path):\n env = SimpleCTAEnv(**{\n # 'time_start': 201701011600,\n # 'time_end': 201901011600,\n # 'time_start': 202001011600,\n # 'time_end': 202108311600,\n 'time_start': 202108311600,\n 'time_end': 202110131600,\n # 'time_end': 202110281600,\n 'slippage': 0,\n 'mode': 2,\n 'id': 2,\n })\n model = Trainer.load(path)\n\n for i in range(1): # 模拟训练10次\n obs = env.reset()\n done = False\n n = 0\n while not done:\n action = model.predict(obs)[0]\n obs, reward, done, info = env.step(action)\n n += 1\n # print(\n # # 'action:', action,\n # # 'obs:', obs,\n # 'reward:', reward,\n # # 'done:', done\n # )\n # break\n # break\n print('第%s次测试完成，执行%s步, 市值%s。' % (i+1, n, env.assets))\n env.close()\n\n\nif __name__ == '__main__':\n run()\n" }, { "path": "config/01commom/actpolicy.json", "content": "{\n \"default\":{\n \"order\":[\n {\n \"action\":\"close\",\n \"limit\":0\n },\n {\n \"action\":\"open\",\n \"limit\":0\n }\n ]\n },\n \"stockindex\":{\n \"order\":[\n {\n \"action\":\"closeyestoday\",\n \"limit\":0\n },\n {\n \"action\":\"open\",\n \"limit\":500\n },\n {\n \"action\":\"closetoday\",\n \"limit\":0\n }\n ],\n \"filters\":[\"CFFEX.IF\",\"CFFEX.IC\",\"CFFEX.IH\"]\n },\n \"lowinnerdayfee\":{\n \"order\":[\n {\n \"action\":\"closetoday\",\n \"limit\":0\n },\n {\n \"action\":\"closeyestoday\",\n \"limit\":0\n },\n {\n \"action\":\"open\",\n \"limit\":0\n }\n ],\n \"filters\":[\"DCE.a\",\"DCE.cs\",\"DCE.i\",\"DCE.j\",\"DCE.l\",\"DCE.m\",\"DCE.p\",\"DCE.pp\",\"SHFE.au\",\"SHFE.hc\",\"CZCE.ZC\",\"CZCE.CF\",\"CZCE.MA\",\"CZCE.SR\",\"CZCE.AP\"]\n }\n}" }, { "path": "config/01commom/commodities.json", "content": "{\n \"CFFEX\": {\n \"IC\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.2,\n \"volscale\": 200,\n \"name\": \"֤\",\n \"exchg\": \"CFFEX\",\n \"session\": \"SD0930\",\n \"holiday\": \"CHINA\"\n },\n \"IF\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.2,\n \"volscale\": 300,\n \"name\": \"\",\n \"exchg\": \"CFFEX\",\n \"session\": \"SD0930\",\n \"holiday\": \"CHINA\"\n },\n \"IH\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.2,\n \"volscale\": 300,\n \"name\": \"֤\",\n \"exchg\": \"CFFEX\",\n \"session\": \"SD0930\",\n \"holiday\": \"CHINA\"\n },\n \"T\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 3,\n \"pricetick\": 0.005,\n \"volscale\": 10000,\n \"name\": \"10ծ\",\n \"exchg\": \"CFFEX\",\n \"session\": \"FD0915\",\n \"holiday\": \"CHINA\"\n },\n \"TF\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 3,\n \"pricetick\": 0.005,\n \"volscale\": 10000,\n \"name\": \"5ծ\",\n \"exchg\": \"CFFEX\",\n \"session\": \"FD0915\",\n \"holiday\": \"CHINA\"\n },\n \"TS\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 3,\n \"pricetick\": 0.005,\n \"volscale\": 20000,\n \"name\": \"2ծ\",\n \"exchg\": \"CFFEX\",\n \"session\": \"FD0915\",\n \"holiday\": \"CHINA\"\n }\n },\n \"CZCE\": {\n \"AP\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"ƻ\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"CF\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"֣\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"CJ\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"CY\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"ɴ\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"FG\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"JR\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"LR\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"̵\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"MA\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"֣\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"OI\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"PF\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"PK\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"\",\n \"holiday\": \"CHINA\"\n },\n \"PM\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 50,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"RI\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"̵\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"RM\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"RS\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"SA\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"SF\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"SM\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 5,\n \"name\": \"̹\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"SR\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"TA\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 5,\n \"name\": \"PTA\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"UR\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"WH\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"֣\",\n \"exchg\": \"CZCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"ZC\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.2,\n \"volscale\": 100,\n \"name\": \"֣ú\",\n \"exchg\": \"CZCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n }\n },\n \"DCE\": {\n \"a\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"һ\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"b\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"bb\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 2,\n \"pricetick\": 0.05,\n \"volscale\": 500,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"c\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"cs\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"eb\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 5,\n \"name\": \"ϩ\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"eg\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"Ҷ\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"fb\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.5,\n \"volscale\": 10,\n \"name\": \"˰\",\n \"exchg\": \"DCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"i\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.5,\n \"volscale\": 100,\n \"name\": \"ʯ\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"j\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.5,\n \"volscale\": 100,\n \"name\": \"̿\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"jd\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"jm\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.5,\n \"volscale\": 60,\n \"name\": \"ú\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"l\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"lh\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 16,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"m\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"p\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"pg\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 20,\n \"name\": \"Һ\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"pp\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 5,\n \"name\": \"۱ϩ\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"rr\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"v\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"PVC\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"y\": {\n \"covermode\": 0,\n \"pricemode\": 0,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"DCE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n }\n },\n \"INE\": {\n \"bc\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 10.0,\n \"volscale\": 5,\n \"name\": \"ͭ\",\n \"exchg\": \"INE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"lu\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"INE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"nr\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 10,\n \"name\": \"20\",\n \"exchg\": \"INE\",\n \"session\": \"FN0230\",\n \"holiday\": \"CHINA\"\n },\n \"sc\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 1,\n \"pricetick\": 0.1,\n \"volscale\": 1000,\n \"name\": \"ԭ\",\n \"exchg\": \"INE\",\n \"session\": \"FN0230\",\n \"holiday\": \"CHINA\"\n }\n },\n \"SHFE\": {\n \"ag\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 15,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0230\",\n \"holiday\": \"CHINA\"\n },\n \"al\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"au\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 2,\n \"pricetick\": 0.02,\n \"volscale\": 1000,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0230\",\n \"holiday\": \"CHINA\"\n },\n \"bu\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"cu\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 10.0,\n \"volscale\": 5,\n \"name\": \"ͭ\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"fu\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"ȼ\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"hc\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"Ⱦ\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"ni\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 10.0,\n \"volscale\": 1,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"pb\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"Ǧ\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"rb\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"ru\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 10,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"sn\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 10.0,\n \"volscale\": 1,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"sp\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 2.0,\n \"volscale\": 10,\n \"name\": \"ֽ\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN2300\",\n \"holiday\": \"CHINA\"\n },\n \"ss\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n },\n \"wr\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 1.0,\n \"volscale\": 10,\n \"name\": \"߲\",\n \"exchg\": \"SHFE\",\n \"session\": \"FD0900\",\n \"holiday\": \"CHINA\"\n },\n \"zn\": {\n \"covermode\": 1,\n \"pricemode\": 1,\n \"category\": 1,\n \"precision\": 0,\n \"pricetick\": 5.0,\n \"volscale\": 5,\n \"name\": \"п\",\n \"exchg\": \"SHFE\",\n \"session\": \"FN0100\",\n \"holiday\": \"CHINA\"\n }\n }\n}" }, { "path": "config/01commom/contracts.json", "content": "{\n \"CFFEX\": {\n \"IC2110\": {\n \"name\": \"֤2110\",\n \"code\": \"IC2110\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IC\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 10\n },\n \"IC2111\": {\n \"name\": \"֤2111\",\n \"code\": \"IC2111\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IC\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 10\n },\n \"IC2112\": {\n \"name\": \"֤2112\",\n \"code\": \"IC2112\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IC\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 0\n },\n \"IC2203\": {\n \"name\": \"֤2203\",\n \"code\": \"IC2203\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IC\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 0\n },\n \"IF2110\": {\n \"name\": \"2110\",\n \"code\": \"IF2110\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IF\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 10\n },\n \"IF2111\": {\n \"name\": \"2111\",\n \"code\": \"IF2111\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IF\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 10\n },\n \"IF2112\": {\n \"name\": \"2112\",\n \"code\": \"IF2112\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IF\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 0\n },\n \"IF2203\": {\n \"name\": \"2203\",\n \"code\": \"IF2203\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IF\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 0\n },\n \"IH2110\": {\n \"name\": \"֤2110\",\n \"code\": \"IH2110\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IH\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 10\n },\n \"IH2111\": {\n \"name\": \"֤2111\",\n \"code\": \"IH2111\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IH\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 10\n },\n \"IH2112\": {\n \"name\": \"֤2112\",\n \"code\": \"IH2112\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IH\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 0\n },\n \"IH2203\": {\n \"name\": \"֤2203\",\n \"code\": \"IH2203\",\n \"exchg\": \"CFFEX\",\n \"product\": \"IH\",\n \"maxlimitqty\": 20,\n \"maxmarketqty\": 0\n },\n \"T2112\": {\n \"name\": \"10ծ2112\",\n \"code\": \"T2112\",\n \"exchg\": \"CFFEX\",\n \"product\": \"T\",\n \"maxlimitqty\": 50,\n \"maxmarketqty\": 0\n },\n \"T2203\": {\n \"name\": \"10ծ2203\",\n \"code\": \"T2203\",\n \"exchg\": \"CFFEX\",\n \"product\": \"T\",\n \"maxlimitqty\": 50,\n \"maxmarketqty\": 0\n },\n \"T2206\": {\n \"name\": \"10ծ2206\",\n \"code\": \"T2206\",\n \"exchg\": \"CFFEX\",\n \"product\": \"T\",\n \"maxlimitqty\": 50,\n \"maxmarketqty\": 0\n },\n \"TF2112\": {\n \"name\": \"5ծ2112\",\n \"code\": \"TF2112\",\n \"exchg\": \"CFFEX\",\n \"product\": \"TF\",\n \"maxlimitqty\": 50,\n \"maxmarketqty\": 0\n },\n \"TF2203\": {\n \"name\": 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1000\n },\n \"CY111\": {\n \"name\": \"ɴ111\",\n \"code\": \"CY111\",\n \"exchg\": \"CZCE\",\n \"product\": \"CY\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"CY112\": {\n \"name\": \"ɴ112\",\n \"code\": \"CY112\",\n \"exchg\": \"CZCE\",\n \"product\": \"CY\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"CY201\": {\n \"name\": \"ɴ201\",\n \"code\": \"CY201\",\n \"exchg\": \"CZCE\",\n \"product\": \"CY\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 200\n },\n \"CY202\": {\n \"name\": \"ɴ202\",\n \"code\": \"CY202\",\n \"exchg\": \"CZCE\",\n \"product\": \"CY\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 200\n },\n \"CY203\": {\n \"name\": \"ɴ203\",\n \"code\": \"CY203\",\n \"exchg\": \"CZCE\",\n \"product\": \"CY\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 200\n },\n \"CY204\": {\n \"name\": \"ɴ204\",\n \"code\": \"CY204\",\n \"exchg\": \"CZCE\",\n \"product\": \"CY\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 200\n },\n \"CY205\": {\n \"name\": 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},\n \"bb2207\": {\n \"name\": \"2207\",\n \"code\": \"bb2207\",\n \"exchg\": \"DCE\",\n \"product\": \"bb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"bb2208\": {\n \"name\": \"2208\",\n \"code\": \"bb2208\",\n \"exchg\": \"DCE\",\n \"product\": \"bb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"bb2209\": {\n \"name\": \"2209\",\n \"code\": \"bb2209\",\n \"exchg\": \"DCE\",\n \"product\": \"bb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"c2111\": {\n \"name\": \"2111\",\n \"code\": \"c2111\",\n \"exchg\": \"DCE\",\n \"product\": \"c\",\n \"maxlimitqty\": 2000,\n \"maxmarketqty\": 2000\n },\n \"c2201\": {\n \"name\": \"2201\",\n \"code\": \"c2201\",\n \"exchg\": \"DCE\",\n \"product\": \"c\",\n \"maxlimitqty\": 2000,\n \"maxmarketqty\": 2000\n },\n \"c2203\": {\n \"name\": \"2203\",\n \"code\": \"c2203\",\n \"exchg\": \"DCE\",\n \"product\": \"c\",\n \"maxlimitqty\": 2000,\n \"maxmarketqty\": 2000\n },\n \"c2205\": {\n \"name\": 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\"exchg\": \"DCE\",\n \"product\": \"cs\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"cs2207\": {\n \"name\": \"2207\",\n \"code\": \"cs2207\",\n \"exchg\": \"DCE\",\n \"product\": \"cs\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"cs2209\": {\n \"name\": \"2209\",\n \"code\": \"cs2209\",\n \"exchg\": \"DCE\",\n \"product\": \"cs\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2110\": {\n \"name\": \"ϩ2110\",\n \"code\": \"eb2110\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2111\": {\n \"name\": \"ϩ2111\",\n \"code\": \"eb2111\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2112\": {\n \"name\": \"ϩ2112\",\n \"code\": \"eb2112\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2201\": {\n \"name\": \"ϩ2201\",\n \"code\": \"eb2201\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2202\": {\n \"name\": \"ϩ2202\",\n \"code\": \"eb2202\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2203\": {\n \"name\": \"ϩ2203\",\n \"code\": \"eb2203\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2204\": {\n \"name\": \"ϩ2204\",\n \"code\": \"eb2204\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2205\": {\n \"name\": \"ϩ2205\",\n \"code\": \"eb2205\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2206\": {\n \"name\": \"ϩ2206\",\n \"code\": \"eb2206\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eb2207\": {\n \"name\": \"ϩ2207\",\n \"code\": \"eb2207\",\n \"exchg\": \"DCE\",\n \"product\": \"eb\",\n 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1000,\n \"maxmarketqty\": 1000\n },\n \"eg2202\": {\n \"name\": \"Ҷ2202\",\n \"code\": \"eg2202\",\n \"exchg\": \"DCE\",\n \"product\": \"eg\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eg2203\": {\n \"name\": \"Ҷ2203\",\n \"code\": \"eg2203\",\n \"exchg\": \"DCE\",\n \"product\": \"eg\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eg2204\": {\n \"name\": \"Ҷ2204\",\n \"code\": \"eg2204\",\n \"exchg\": \"DCE\",\n \"product\": \"eg\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eg2205\": {\n \"name\": \"Ҷ2205\",\n \"code\": \"eg2205\",\n \"exchg\": \"DCE\",\n \"product\": \"eg\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eg2206\": {\n \"name\": \"Ҷ2206\",\n \"code\": \"eg2206\",\n \"exchg\": \"DCE\",\n \"product\": \"eg\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"eg2207\": {\n \"name\": \"Ҷ2207\",\n \"code\": \"eg2207\",\n \"exchg\": \"DCE\",\n \"product\": \"eg\",\n \"maxlimitqty\": 1000,\n 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1000\n },\n \"fb2202\": {\n \"name\": \"˰2202\",\n \"code\": \"fb2202\",\n \"exchg\": \"DCE\",\n \"product\": \"fb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"fb2203\": {\n \"name\": \"˰2203\",\n \"code\": \"fb2203\",\n \"exchg\": \"DCE\",\n \"product\": \"fb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"fb2204\": {\n \"name\": \"˰2204\",\n \"code\": \"fb2204\",\n \"exchg\": \"DCE\",\n \"product\": \"fb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"fb2205\": {\n \"name\": \"˰2205\",\n \"code\": \"fb2205\",\n \"exchg\": \"DCE\",\n \"product\": \"fb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"fb2206\": {\n \"name\": \"˰2206\",\n \"code\": \"fb2206\",\n \"exchg\": \"DCE\",\n \"product\": \"fb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"fb2207\": {\n \"name\": \"˰2207\",\n \"code\": \"fb2207\",\n \"exchg\": \"DCE\",\n \"product\": \"fb\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n 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\"exchg\": \"DCE\",\n \"product\": \"i\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"i2209\": {\n \"name\": \"ʯ2209\",\n \"code\": \"i2209\",\n \"exchg\": \"DCE\",\n \"product\": \"i\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"j2110\": {\n \"name\": \"̿2110\",\n \"code\": \"j2110\",\n \"exchg\": \"DCE\",\n \"product\": \"j\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 500\n },\n \"j2111\": {\n \"name\": \"̿2111\",\n \"code\": \"j2111\",\n \"exchg\": \"DCE\",\n \"product\": \"j\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 500\n },\n \"j2112\": {\n \"name\": \"̿2112\",\n \"code\": \"j2112\",\n \"exchg\": \"DCE\",\n \"product\": \"j\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 500\n },\n \"j2201\": {\n \"name\": \"̿2201\",\n \"code\": \"j2201\",\n \"exchg\": \"DCE\",\n \"product\": \"j\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 500\n },\n \"j2202\": {\n \"name\": \"̿2202\",\n \"code\": \"j2202\",\n \"exchg\": \"DCE\",\n \"product\": \"j\",\n 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1000\n },\n \"l2204\": {\n \"name\": \"2204\",\n \"code\": \"l2204\",\n \"exchg\": \"DCE\",\n \"product\": \"l\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"l2205\": {\n \"name\": \"2205\",\n \"code\": \"l2205\",\n \"exchg\": \"DCE\",\n \"product\": \"l\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"l2206\": {\n \"name\": \"2206\",\n \"code\": \"l2206\",\n \"exchg\": \"DCE\",\n \"product\": \"l\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"l2207\": {\n \"name\": \"2207\",\n \"code\": \"l2207\",\n \"exchg\": \"DCE\",\n \"product\": \"l\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"l2208\": {\n \"name\": \"2208\",\n \"code\": \"l2208\",\n \"exchg\": \"DCE\",\n \"product\": \"l\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"l2209\": {\n \"name\": \"2209\",\n \"code\": \"l2209\",\n \"exchg\": \"DCE\",\n \"product\": \"l\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"lh2111\": {\n \"name\": 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1000\n },\n \"p2203\": {\n \"name\": \"2203\",\n \"code\": \"p2203\",\n \"exchg\": \"DCE\",\n \"product\": \"p\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"p2204\": {\n \"name\": \"2204\",\n \"code\": \"p2204\",\n \"exchg\": \"DCE\",\n \"product\": \"p\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"p2205\": {\n \"name\": \"2205\",\n \"code\": \"p2205\",\n \"exchg\": \"DCE\",\n \"product\": \"p\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"p2206\": {\n \"name\": \"2206\",\n \"code\": \"p2206\",\n \"exchg\": \"DCE\",\n \"product\": \"p\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"p2207\": {\n \"name\": \"2207\",\n \"code\": \"p2207\",\n \"exchg\": \"DCE\",\n \"product\": \"p\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"p2208\": {\n \"name\": \"2208\",\n \"code\": \"p2208\",\n \"exchg\": \"DCE\",\n \"product\": \"p\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"p2209\": {\n \"name\": 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\"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"pp2204\": {\n \"name\": \"۱ϩ2204\",\n \"code\": \"pp2204\",\n \"exchg\": \"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"pp2205\": {\n \"name\": \"۱ϩ2205\",\n \"code\": \"pp2205\",\n \"exchg\": \"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"pp2206\": {\n \"name\": \"۱ϩ2206\",\n \"code\": \"pp2206\",\n \"exchg\": \"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"pp2207\": {\n \"name\": \"۱ϩ2207\",\n \"code\": \"pp2207\",\n \"exchg\": \"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"pp2208\": {\n \"name\": \"۱ϩ2208\",\n \"code\": \"pp2208\",\n \"exchg\": \"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"pp2209\": {\n \"name\": \"۱ϩ2209\",\n \"code\": \"pp2209\",\n \"exchg\": \"DCE\",\n \"product\": \"pp\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"rr2110\": {\n \"name\": \"2110\",\n \"code\": \"rr2110\",\n \"exchg\": \"DCE\",\n \"product\": \"rr\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"rr2111\": {\n \"name\": \"2111\",\n \"code\": \"rr2111\",\n \"exchg\": \"DCE\",\n \"product\": \"rr\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"rr2112\": {\n \"name\": \"2112\",\n \"code\": \"rr2112\",\n \"exchg\": \"DCE\",\n \"product\": \"rr\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"rr2201\": {\n \"name\": \"2201\",\n \"code\": \"rr2201\",\n \"exchg\": \"DCE\",\n \"product\": \"rr\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"rr2202\": {\n \"name\": \"2202\",\n \"code\": \"rr2202\",\n \"exchg\": \"DCE\",\n \"product\": \"rr\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"rr2203\": {\n \"name\": \"2203\",\n \"code\": \"rr2203\",\n \"exchg\": \"DCE\",\n \"product\": \"rr\",\n 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\"exchg\": \"DCE\",\n \"product\": \"y\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n },\n \"y2209\": {\n \"name\": \"2209\",\n \"code\": \"y2209\",\n \"exchg\": \"DCE\",\n \"product\": \"y\",\n \"maxlimitqty\": 1000,\n \"maxmarketqty\": 1000\n }\n },\n \"INE\": {\n \"bc2110\": {\n \"name\": \"ͭ2110\",\n \"code\": \"bc2110\",\n \"exchg\": \"INE\",\n \"product\": \"bc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bc2111\": {\n \"name\": \"ͭ2111\",\n \"code\": \"bc2111\",\n \"exchg\": \"INE\",\n \"product\": \"bc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bc2112\": {\n \"name\": \"ͭ2112\",\n \"code\": \"bc2112\",\n \"exchg\": \"INE\",\n \"product\": \"bc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bc2201\": {\n \"name\": \"ͭ2201\",\n \"code\": \"bc2201\",\n \"exchg\": \"INE\",\n \"product\": \"bc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bc2202\": {\n \"name\": \"ͭ2202\",\n \"code\": \"bc2202\",\n \"exchg\": \"INE\",\n 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500,\n \"maxmarketqty\": 30\n },\n \"bc2209\": {\n \"name\": \"ͭ2209\",\n \"code\": \"bc2209\",\n \"exchg\": \"INE\",\n \"product\": \"bc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"lu2111\": {\n \"name\": \"2111\",\n \"code\": \"lu2111\",\n \"exchg\": \"INE\",\n \"product\": \"lu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"lu2112\": {\n \"name\": \"2112\",\n \"code\": \"lu2112\",\n \"exchg\": \"INE\",\n \"product\": \"lu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"lu2201\": {\n \"name\": \"2201\",\n \"code\": \"lu2201\",\n \"exchg\": \"INE\",\n \"product\": \"lu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"lu2202\": {\n \"name\": \"2202\",\n \"code\": \"lu2202\",\n \"exchg\": \"INE\",\n \"product\": \"lu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"lu2203\": {\n \"name\": \"2203\",\n \"code\": \"lu2203\",\n \"exchg\": \"INE\",\n \"product\": \"lu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n 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\"code\": \"lu2210\",\n \"exchg\": \"INE\",\n \"product\": \"lu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"nr2110\": {\n \"name\": \"202110\",\n \"code\": \"nr2110\",\n \"exchg\": \"INE\",\n \"product\": \"nr\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"nr2111\": {\n \"name\": \"202111\",\n \"code\": \"nr2111\",\n \"exchg\": \"INE\",\n \"product\": \"nr\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"nr2112\": {\n \"name\": \"202112\",\n \"code\": \"nr2112\",\n \"exchg\": \"INE\",\n \"product\": \"nr\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"nr2201\": {\n \"name\": \"202201\",\n \"code\": \"nr2201\",\n \"exchg\": \"INE\",\n \"product\": \"nr\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"nr2202\": {\n \"name\": \"202202\",\n \"code\": \"nr2202\",\n \"exchg\": \"INE\",\n \"product\": \"nr\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"nr2203\": {\n \"name\": \"202203\",\n \"code\": \"nr2203\",\n \"exchg\": 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{\n \"name\": \"ԭ2212\",\n \"code\": \"sc2212\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2303\": {\n \"name\": \"ԭ2303\",\n \"code\": \"sc2303\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2306\": {\n \"name\": \"ԭ2306\",\n \"code\": \"sc2306\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2309\": {\n \"name\": \"ԭ2309\",\n \"code\": \"sc2309\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2312\": {\n \"name\": \"ԭ2312\",\n \"code\": \"sc2312\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2403\": {\n \"name\": \"ԭ2403\",\n \"code\": \"sc2403\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2406\": {\n \"name\": \"ԭ2406\",\n \"code\": \"sc2406\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"sc2409\": {\n \"name\": \"ԭ2409\",\n \"code\": \"sc2409\",\n \"exchg\": \"INE\",\n \"product\": \"sc\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n }\n },\n \"SHFE\": {\n \"ag2110\": {\n \"name\": \"2110\",\n \"code\": \"ag2110\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2111\": {\n \"name\": \"2111\",\n \"code\": \"ag2111\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2112\": {\n \"name\": \"2112\",\n \"code\": \"ag2112\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2201\": {\n \"name\": \"2201\",\n \"code\": \"ag2201\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2202\": {\n \"name\": \"2202\",\n \"code\": \"ag2202\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2203\": {\n \"name\": \"2203\",\n \"code\": \"ag2203\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2204\": {\n \"name\": \"2204\",\n \"code\": \"ag2204\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2205\": {\n \"name\": \"2205\",\n \"code\": \"ag2205\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2206\": {\n \"name\": \"2206\",\n \"code\": \"ag2206\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2207\": {\n \"name\": \"2207\",\n \"code\": \"ag2207\",\n \"exchg\": \"SHFE\",\n \"product\": \"ag\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"ag2208\": {\n \"name\": \"2208\",\n \"code\": \"ag2208\",\n \"exchg\": \"SHFE\",\n \"product\": 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{\n \"name\": \"2209\",\n \"code\": \"al2209\",\n \"exchg\": \"SHFE\",\n \"product\": \"al\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2110\": {\n \"name\": \"2110\",\n \"code\": \"au2110\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2111\": {\n \"name\": \"2111\",\n \"code\": \"au2111\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2112\": {\n \"name\": \"2112\",\n \"code\": \"au2112\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2202\": {\n \"name\": \"2202\",\n \"code\": \"au2202\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2204\": {\n \"name\": \"2204\",\n \"code\": \"au2204\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2206\": {\n \"name\": \"2206\",\n \"code\": \"au2206\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2208\": {\n \"name\": \"2208\",\n \"code\": \"au2208\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"au2210\": {\n \"name\": \"2210\",\n \"code\": \"au2210\",\n \"exchg\": \"SHFE\",\n \"product\": \"au\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bu2110\": {\n \"name\": \"2110\",\n \"code\": \"bu2110\",\n \"exchg\": \"SHFE\",\n \"product\": \"bu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bu2111\": {\n \"name\": \"2111\",\n \"code\": \"bu2111\",\n \"exchg\": \"SHFE\",\n \"product\": \"bu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bu2112\": {\n \"name\": \"2112\",\n \"code\": \"bu2112\",\n \"exchg\": \"SHFE\",\n \"product\": \"bu\",\n \"maxlimitqty\": 500,\n \"maxmarketqty\": 30\n },\n \"bu2201\": {\n \"name\": \"2201\",\n \"code\": \"bu2201\",\n \"exchg\": \"SHFE\",\n 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\"byvolume\":false\n },\n\t\"SHFE.ag\":\n {\n \"open\":0.00005,\n \"close\":0.00005,\n \"closetoday\":0.00005,\n \"byvolume\":false\n },\n\t\"SHFE.au\":\n {\n \"open\":10.0,\n \"close\":10.0,\n \"closetoday\":0.0,\n \"byvolume\":true\n },\n\t\"SHFE.bu\":\n {\n \"open\":0.0001,\n \"close\":0.0001,\n \"closetoday\":0.0001,\n \"byvolume\":false\n },\n\t\"SHFE.cu\":\n {\n \"open\":0.0001,\n \"close\":0.0001,\n \"closetoday\":0.0001,\n \"byvolume\":false\n },\n\t\"SHFE.hc\":\n {\n \"open\":0.00005,\n \"close\":0.00005,\n \"closetoday\":0.0,\n \"byvolume\":false\n },\n\t\"SHFE.ni\":\n {\n \"open\":6.0,\n \"close\":6.0,\n \"closetoday\":6.0,\n \"byvolume\":true\n },\n\t\"SHFE.rb\":\n {\n \"open\":0.0001,\n \"close\":0.0001,\n \"closetoday\":0.0001,\n \"byvolume\":false\n },\n\t\"SHFE.ru\":\n {\n \"open\":3.01,\n \"close\":3.01,\n \"closetoday\":0,\n \"byvolume\":true\n },\n\t\"SHFE.sp\":\n {\n \"open\":0.000051,\n \"close\":0.000051,\n \"closetoday\":0,\n \"byvolume\":false\n },\n\t\"CZCE.AP\":\n {\n \"open\":0.5,\n \"close\":0.5,\n \"closetoday\":0.0,\n \"byvolume\":true\n },\n\t\"CZCE.CF\":\n {\n \"open\":4.31,\n \"close\":4.31,\n \"closetoday\":0.0,\n \"byvolume\":true\n },\n\t\"CZCE.MA\":\n {\n \"open\":1.4,\n \"close\":1.4,\n \"closetoday\":0,\n \"byvolume\":true\n },\n\t\"CZCE.TA\":\n {\n \"open\":3.01,\n \"close\":3.01,\n \"closetoday\":0,\n \"byvolume\":true\n },\n\t\"CZCE.SR\":\n {\n \"open\":3.0,\n \"close\":3.0,\n \"closetoday\":0.0,\n \"byvolume\":true\n },\n\t\"CZCE.ZC\":\n {\n \"open\":4.0,\n \"close\":4.0,\n \"closetoday\":0.0,\n \"byvolume\":true\n },\n\t\"CZCE.RM\":\n {\n \"open\":1.51,\n \"close\":1.51,\n \"closetoday\":0.0,\n \"byvolume\":true\n }\n}" }, { "path": "config/01commom/holidays.json", "content": "{\n \"CHINA\" : [\n 20180101,20180215,20181005,20181231,\n 20190101,20190913,20191001,20191002,20191003,20191004,20191007,\n 20200101,20200124,20200127,20200128,20200129,20200130,20200131,20200406,20200501,20200504,20200505,20200625,20200626,20201001,20201002,20201005,20201006,20201007,20201008,\n 20210101,20210211,20210212,20210215,20210216,20210217,20210405,20210503,20210504,20210505,20210614,20210920,20210921,20211001,20211004,20211005,20211006,20211007]\n}" }, { "path": "config/01commom/hots.json", "content": "{\n \"CFFEX\": {\n \"IC\": [\n {\n \"date\": 20160104,\n \"from\": \"\",\n \"to\": \"IC1601\",\n \"newClose\": 6881.2001953125,\n \"oldClose\": 0\n },\n {\n \"date\": 20160115,\n \"from\": \"IC1601\",\n \"to\": \"IC1602\",\n \"newClose\": 5570.0,\n \"oldClose\": 5942.7998046875\n },\n {\n \"date\": 20160218,\n \"from\": \"IC1602\",\n \"to\": \"IC1603\",\n \"newClose\": 5746.0,\n \"oldClose\": 5950.2001953125\n },\n {\n \"date\": 20160318,\n \"from\": \"IC1603\",\n \"to\": \"IC1604\",\n \"newClose\": 5799.60009765625,\n \"oldClose\": 5944.60009765625\n },\n {\n \"date\": 20160415,\n \"from\": 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\"basefiles\":{\n \"session\":\"./config/01commom/sessions.json\",\n \"commodity\":\"./config/01commom/commodities.json\",\n \"contract\":\"./config/01commom/contracts.json\",\n \"holiday\":\"./config/01commom/holidays.json\",\n \"hot\":\"./config/01commom/hots.json\"\n },\n \"fees\":\"./config/01commom/fees.json\"\n },\n \"env\":{\n \"mocker\":\"cta\"\n },\n \"bspolicy\":\"./config/01commom/actpolicy.json\"\n}" }, { "path": "config/03research/hft.json", "content": "{\n \"replayer\":{\n \"mode\":\"bin\",\n \"path\":\"./dataset/\",\n \"tick\":true,\n \"basefiles\":{\n \"session\":\"./config/01commom/sessions.json\",\n \"commodity\":\"./config/01commom/commodities.json\",\n \"contract\":\"./config/01commom/contracts.json\",\n \"holiday\":\"./config/01commom/holidays.json\",\n \"hot\":\"./config/01commom/hots.json\"\n },\n \"fees\":\"./config/01commom/fees.json\"\n },\n \"env\":{\n \"mocker\":\"hft\"\n },\n \"bspolicy\":\"./config/01commom/actpolicy.json\"\n}" }, { "path": "config/03research/log_debugger.json", "content": "{\n \"root\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"basic_file_sink\",\n \"filename\":\"outputs_bt/logs/runner.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S,%f - %-5l] %v\",\n \"truncate\":true\n },\n {\n \"type\":\"ostream_sink\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S,%f - %-5l] %v\"\n }\n ]\n },\n \"dyn_pattern\":{\n \"strategy\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"basic_file_sink\",\n \"filename\":\"outputs_bt/logs/strategy/%s.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S,%f - %-5l] %v\",\n \"truncate\":true\n }\n ]\n }\n }\n}" }, { "path": "config/03research/log_evaluator.json", "content": "{\n \"root\":{\n \"level\":\"info\",\n \"async\":false,\n \"sinks\":[]\n },\n \"dyn_pattern\":{\n \"strategy\":{\n \"level\":\"info\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"basic_file_sink\",\n \"filename\":\"outputs_bt/logs/strategy/%s.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S,%f - %-5l] %v\",\n \"truncate\":true\n }\n ]\n }\n }\n}" }, { "path": "config/03research/log_trainer.json", "content": "{\n \"root\":{\n \"level\":\"info\",\n \"async\":false,\n \"sinks\":[]\n },\n \"dyn_pattern\":{\n \"strategy\":{\n \"level\":\"info\",\n \"async\":false,\n \"sinks\":[]\n }\n }\n}" }, { "path": "config/04realtime/log.json", "content": "{\n \"root\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"daily_file_sink\",\n \"filename\":\"Logs/Runner.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S - %-5l] %v\",\n \"truncate\":true\n },\n {\n \"type\":\"ostream_sink\",\n \"pattern\":\"[%m.%d %H:%M:%S - %^%-5l%$] %v\"\n }\n ]\n },\n \"dyn_pattern\":{\n \"strategy\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"daily_file_sink\",\n \"filename\":\"Logs/Strategy/%s.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S - %-5l] %v\",\n \"truncate\":true\n }\n ]\n },\n \"parser\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"daily_file_sink\",\n \"filename\":\"Logs/Parser/%s.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S - %-5l] %v\",\n \"truncate\":true\n }\n ]\n },\n \"trader\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"daily_file_sink\",\n \"filename\":\"Logs/Trader/%s.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S - %-5l] %v\",\n \"truncate\":true\n }\n ]\n },\n \"executer\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"daily_file_sink\",\n \"filename\":\"Logs/Executer/%s.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S - %-5l] %v\",\n \"truncate\":true\n }\n ]\n }\n },\n \"risk\":{\n \"level\":\"debug\",\n \"async\":false,\n \"sinks\":[\n {\n \"type\":\"daily_file_sink\",\n \"filename\":\"Logs/Riskmon/Riskmon.log\",\n \"pattern\":\"[%Y.%m.%d %H:%M:%S - %-5l] %v\",\n \"truncate\":true\n }\n ]\n }\n}" }, { "path": "dataset_from_storage.py", "content": "import pandas as pd\nfrom ctypes import POINTER\nfrom os import makedirs\n\nfrom wtpy.wrapper.WtDtHelper import WtDataHelper\nfrom wtpy.WtCoreDefs import WTSBarStruct\nfrom wtpy import WtDtServo\n\n\nclass DataReader:\n df: pd.DataFrame\n\n def __len__(self) -> int:\n return len(self.df)\n\n def set_bars(self, data: pd.DataFrame) -> tuple:\n dtype = {\n 'date': int,\n 'time': int,\n 'open': float,\n 'high': float,\n 'low': float,\n 'close': float,\n 'money': float,\n 'vol': int,\n 'hold': int,\n 'diff': int,\n }\n self.df = data[dtype.keys()].astype(dtype)\n\n def get_bars(self, curBar: POINTER(WTSBarStruct), idx: int) -> bool:\n curBar.contents.date = self.df['date'].iloc[idx]\n curBar.contents.time = self.df['time'].iloc[idx]\n curBar.contents.open = self.df['open'].iloc[idx]\n curBar.contents.high = self.df['high'].iloc[idx]\n curBar.contents.low = self.df['low'].iloc[idx]\n curBar.contents.close = self.df['close'].iloc[idx]\n curBar.contents.money = self.df['money'].iloc[idx]\n curBar.contents.vol = self.df['vol'].iloc[idx]\n curBar.contents.hold = self.df['hold'].iloc[idx]\n curBar.contents.diff = self.df['diff'].iloc[idx]\n\n return True\n\n\nreader: DataReader = DataReader()\nhelper: WtDataHelper = WtDataHelper()\ndt: WtDtServo = WtDtServo()\ndt.setBasefiles(\n commfile='./config/01commom/commodities.json',\n contractfile='./config/01commom/contracts.json',\n holidayfile='./config/01commom/holidays.json',\n sessionfile='./config/01commom/sessions.json',\n hotfile='./config/01commom/hots.json'\n)\ndt.setStorage('D:/Github/test/Storage/')\ndt.commitConfig()\n\nsecurities: tuple = (\n 'CFFEX.IC.HOT',\n 'CFFEX.IF.HOT',\n 'CFFEX.IH.HOT',\n 'CFFEX.T.HOT',\n 'CFFEX.TF.HOT',\n 'CZCE.RM.HOT',#\n 'CZCE.JR.HOT',#\n 'CZCE.AP.HOT',\n 'CZCE.CF.HOT',\n 'CZCE.MA.HOT',\n 'CZCE.SF.HOT',\n 'CZCE.SR.HOT',\n 'CZCE.TA.HOT',#\n 'CZCE.ZC.HOT',\n 'DCE.a.HOT',\n 'DCE.c.HOT',#\n 'DCE.cs.HOT',#\n 'DCE.i.HOT',\n 'DCE.jd.HOT',\n 'DCE.l.HOT',\n 'DCE.m.HOT',#\n 'DCE.p.HOT',\n 'DCE.pp.HOT',\n 'DCE.y.HOT',\n 'DCE.rr.HOT',#\n 'INE.sc.HOT',\n 'INE.lu.HOT',\n 'SHFE.ag.HOT',\n 'SHFE.al.HOT',\n 'SHFE.bu.HOT',\n 'SHFE.cu.HOT',\n 'SHFE.fu.HOT',\n 'SHFE.hc.HOT',\n 'SHFE.ni.HOT',\n 'SHFE.pb.HOT',\n 'SHFE.rb.HOT',\n 'SHFE.zn.HOT',\n)\n\nfor period, name in {'d1': 'day', 'm5': 'min5', }.items():#, 'm1': 'min1'\n for code in securities:\n print(period, code)\n path = './dataset/his/%s/%s' % (name, code.split('.')[0])\n makedirs(path, exist_ok=True)\n df = dt.get_bars(code, period=period, fromTime=201601011600,\n endTime=202110131600).to_pandas()\n reader.set_bars(df)\n helper.trans_bars(barFile='%s/%s.dsb' % (path, code.replace('.HOT', '_HOT')), getter=reader.get_bars,\n count=len(reader), period='d' if period == 'd1' else period)\n # break\n # break\n" }, { "path": "elegantrl/__init__.py", "content": "\n" }, { "path": "elegantrl/agent.py", "content": "import os\nimport torch\nimport numpy as np\nimport numpy.random as rd\n\nfrom copy import deepcopy\nfrom torch.nn.utils import clip_grad_norm_\nfrom elegantrl.net import QNet, QNetDuel, QNetTwin, QNetTwinDuel\nfrom elegantrl.net import Actor, Critic, ShareDPG\nfrom elegantrl.net import ActorSAC, CriticTwin, ShareSPG\nfrom elegantrl.net import ActorPPO, ActorDiscretePPO, CriticPPO, SharePPO\nfrom elegantrl.net import ActorBiConv, CriticBiConv, ShareBiConv\n\n\"\"\"[ElegantRL.2021.11.05](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\n\nclass AgentBase:\n def __init__(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n \"\"\"initialize\n\n replace by different DRL algorithms\n explict call self.init() for multiprocessing.\n\n :param net_dim: the dimension of networks (the width of neural networks)\n :param state_dim: the dimension of state (the number of state vector)\n :param action_dim: the dimension of action (the number of discrete action)\n :param reward_scale: scale the reward to get a appropriate scale Q value\n :param gamma: the discount factor of Reinforcement Learning\n\n :param learning_rate: learning rate of optimizer\n :param if_per_or_gae: PER (off-policy) or GAE (on-policy) for sparse reward\n :param env_num: the env number of VectorEnv. env_num == 1 means don't use VectorEnv\n :param gpu_id: the gpu_id of the training device. Use CPU when cuda is not available.\n \"\"\"\n self.gamma = None\n self.states = None\n self.device = None\n self.traj_list = None\n self.action_dim = None\n self.reward_scale = None\n self.if_off_policy = True\n\n self.env_num = env_num\n self.explore_rate = 1.0\n self.explore_noise = 0.1\n self.clip_grad_norm = 4.0\n # self.amp_scale = None # automatic mixed precision\n\n '''attribute'''\n self.explore_env = None\n self.get_obj_critic = None\n\n self.criterion = torch.nn.SmoothL1Loss()\n self.cri = self.cri_target = self.if_use_cri_target = self.cri_optim = self.ClassCri = None\n self.act = self.act_target = self.if_use_act_target = self.act_optim = self.ClassAct = None\n\n assert isinstance(gpu_id, int)\n assert isinstance(env_num, int)\n assert isinstance(net_dim, int)\n assert isinstance(state_dim, int)\n assert isinstance(action_dim, int)\n assert isinstance(if_per_or_gae, bool)\n assert isinstance(gamma, float)\n assert isinstance(reward_scale, float)\n assert isinstance(learning_rate, float)\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n \"\"\"initialize the self.object in `__init__()`\n\n replace by different DRL algorithms\n explict call self.init() for multiprocessing.\n\n :param net_dim: the dimension of networks (the width of neural networks)\n :param state_dim: the dimension of state (the number of state vector)\n :param action_dim: the dimension of action (the number of discrete action)\n :param reward_scale: scale the reward to get a appropriate scale Q value\n :param gamma: the discount factor of Reinforcement Learning\n\n :param learning_rate: learning rate of optimizer\n :param if_per_or_gae: PER (off-policy) or GAE (on-policy) for sparse reward\n :param env_num: the env number of VectorEnv. env_num == 1 means don't use VectorEnv\n :param gpu_id: the gpu_id of the training device. Use CPU when cuda is not available.\n \"\"\"\n self.gamma = gamma\n self.action_dim = action_dim\n self.reward_scale = reward_scale\n # self.amp_scale = torch.cuda.amp.GradScaler()\n self.traj_list = [list() for _ in range(env_num)]\n self.device = torch.device(f\"cuda:{gpu_id}\" if (torch.cuda.is_available() and (gpu_id >= 0)) else \"cpu\")\n\n self.cri = self.ClassCri(int(net_dim * 1.25), state_dim, action_dim).to(self.device)\n self.act = self.ClassAct(net_dim, state_dim, action_dim).to(self.device) if self.ClassAct else self.cri\n self.cri_target = deepcopy(self.cri) if self.if_use_cri_target else self.cri\n self.act_target = deepcopy(self.act) if self.if_use_act_target else self.act\n\n self.cri_optim = torch.optim.Adam(self.cri.parameters(), learning_rate)\n self.act_optim = torch.optim.Adam(self.act.parameters(), learning_rate) if self.ClassAct else self.cri\n # del self.ClassCri, self.ClassAct\n\n assert isinstance(if_per_or_gae, bool)\n if env_num == 1:\n self.explore_env = self.explore_one_env\n else:\n self.explore_env = self.explore_vec_env\n\n def select_action(self, state: np.ndarray) -> np.ndarray:\n s_tensor = torch.as_tensor(state[np.newaxis], device=self.device)\n a_tensor = self.act(s_tensor)\n action = a_tensor.detach().cpu().numpy()\n return action\n\n def select_actions(self, state: torch.Tensor) -> torch.Tensor:\n \"\"\"Select continuous actions for exploration\n\n :param state: states.shape==(batch_size, state_dim, )\n :return: actions.shape==(batch_size, action_dim, ), -1 < action < +1\n \"\"\"\n\n action = self.act(state.to(self.device))\n if rd.rand() < self.explore_rate: # epsilon-greedy\n action = (action + torch.randn_like(action) * self.explore_noise).clamp(-1, 1)\n return action.detach().cpu()\n\n def explore_one_env(self, env, target_step: int) -> list:\n \"\"\"actor explores in single Env, then returns the trajectory (env transitions) for ReplayBuffer\n\n :param env: RL training environment. env.reset() env.step()\n :param target_step: explored target_step number of step in env\n :return: `[traj_env_0, ]`\n `traj_env_0 = [(state, reward, mask, action, noise), ...]` for on-policy\n `traj_env_0 = [(state, other), ...]` for off-policy\n \"\"\"\n state = self.states[0]\n traj = list()\n for _ in range(target_step):\n ten_state = torch.as_tensor(state, dtype=torch.float32)\n ten_action = self.select_actions(ten_state.unsqueeze(0))[0]\n action = ten_action.numpy()\n next_s, reward, done, _ = env.step(action)\n\n ten_other = torch.empty(2 + self.action_dim)\n ten_other[0] = reward\n ten_other[1] = done\n ten_other[2:] = ten_action\n traj.append((ten_state, ten_other))\n\n state = env.reset() if done else next_s\n\n self.states[0] = state\n\n traj_state = torch.stack([item[0] for item in traj])\n traj_other = torch.stack([item[1] for item in traj])\n traj_list = [(traj_state, traj_other), ]\n return self.convert_trajectory(traj_list) # [traj_env_0, ]\n\n def explore_vec_env(self, env, target_step: int) -> list:\n \"\"\"actor explores in VectorEnv, then returns the trajectory (env transitions) for ReplayBuffer\n\n :param env: RL training environment. env.reset() env.step(). It should be a vector env.\n :param target_step: explored target_step number of step in env\n :return: `[traj_env_0, ]`\n `traj_env_0 = [(state, reward, mask, action, noise), ...]` for on-policy\n `traj_env_0 = [(state, other), ...]` for off-policy\n \"\"\"\n ten_states = self.states\n\n traj = list()\n for _ in range(target_step):\n ten_actions = self.select_actions(ten_states)\n ten_next_states, ten_rewards, ten_dones = env.step(ten_actions)\n\n ten_others = torch.cat((ten_rewards.unsqueeze(0), ten_dones.unsqueeze(0), ten_actions))\n traj.append((ten_states, ten_others))\n ten_states = ten_next_states\n\n self.states = ten_states\n\n # traj = [(env_ten, ...), ...], env_ten = (env1_ten, env2_ten, ...)\n traj_state = torch.stack([item[0] for item in traj])\n traj_other = torch.stack([item[1] for item in traj])\n traj_list = [(traj_state[:, env_i, :], traj_other[:, env_i, :])\n for env_i in range(len(self.states))]\n # traj_list = [traj_env_0, ...], traj_env_0 = (ten_state, ten_other)\n return self.convert_trajectory(traj_list) # [traj_env_0, ...]\n\n def update_net(self, buffer, batch_size: int, repeat_times: float, soft_update_tau: float) -> tuple:\n \"\"\"update the neural network by sampling batch data from ReplayBuffer\n\n :param buffer: Experience replay buffer\n :param batch_size: sample batch_size of data for Stochastic Gradient Descent\n :param repeat_times: `batch_sampling_times = int(target_step * repeat_times / batch_size)`\n :param soft_update_tau: soft target update: `target_net = target_net * (1-tau) + current_net * tau`,\n \"\"\"\n\n def optim_update(self, optimizer, objective, params): # plan todo params generator -> list\n \"\"\"minimize the optimization objective via update the network parameters\n\n :param optimizer: `optimizer = torch.optim.SGD(net.parameters(), learning_rate)`\n :param objective: `objective = net(...)` the optimization objective, sometimes is a loss function.\n :param params: `params = net.parameters()` the network parameters which need to be updated.\n \"\"\"\n optimizer.zero_grad()\n objective.backward()\n clip_grad_norm_(params, max_norm=self.clip_grad_norm)\n optimizer.step()\n\n # def optim_update_amp(self, optimizer, objective): # automatic mixed precision\n # \"\"\"minimize the optimization objective via update the network parameters\n #\n # amp: Automatic Mixed Precision\n #\n # :param optimizer: `optimizer = torch.optim.SGD(net.parameters(), learning_rate)`\n # :param objective: `objective = net(...)` the optimization objective, sometimes is a loss function.\n # :param params: `params = net.parameters()` the network parameters which need to be updated.\n # \"\"\"\n # # self.amp_scale = torch.cuda.amp.GradScaler()\n #\n # optimizer.zero_grad()\n # self.amp_scale.scale(objective).backward() # loss.backward()\n # self.amp_scale.unscale_(optimizer) # amp\n #\n # # from torch.nn.utils import clip_grad_norm_\n # # clip_grad_norm_(model.parameters(), max_norm=3.0) # amp, clip_grad_norm_\n # self.amp_scale.step(optimizer) # optimizer.step()\n # self.amp_scale.update() # optimizer.step()\n\n @staticmethod\n def soft_update(target_net, current_net, tau):\n \"\"\"soft update target network via current network\n\n :param target_net: update target network via current network to make training more stable.\n :param current_net: current network update via an optimizer\n :param tau: tau of soft target update: `target_net = target_net * (1-tau) + current_net * tau`\n \"\"\"\n for tar, cur in zip(target_net.parameters(), current_net.parameters()):\n tar.data.copy_(cur.data * tau + tar.data * (1.0 - tau))\n\n def save_or_load_agent(self, cwd: str, if_save: bool):\n \"\"\"save or load training files for Agent\n\n :param cwd: Current Working Directory. ElegantRL save training files in CWD.\n :param if_save: True: save files. False: load files.\n \"\"\"\n\n def load_torch_file(model_or_optim, _path):\n state_dict = torch.load(_path, map_location=lambda storage, loc: storage)\n model_or_optim.load_state_dict(state_dict)\n\n name_obj_list = [('actor', self.act), ('act_target', self.act_target), ('act_optim', self.act_optim),\n ('critic', self.cri), ('cri_target', self.cri_target), ('cri_optim', self.cri_optim), ]\n name_obj_list = [(name, obj) for name, obj in name_obj_list if obj is not None]\n\n if if_save:\n for name, obj in name_obj_list:\n save_path = f\"{cwd}/{name}.pth\"\n torch.save(obj.state_dict(), save_path)\n else:\n for name, obj in name_obj_list:\n save_path = f\"{cwd}/{name}.pth\"\n load_torch_file(obj, save_path) if os.path.isfile(save_path) else None\n\n def convert_trajectory(self, traj_list: list) -> list: # off-policy\n \"\"\"convert trajectory (env exploration type) to trajectory (replay buffer type)\n\n convert `other = concat(( reward, done, ...))`\n to `other = concat((scale_reward, mask, ...))`\n\n :param traj_list: `traj_list = [(tensor_state, other_state), ...]`\n :return: `traj_list = [(tensor_state, other_state), ...]`\n \"\"\"\n for ten_state, ten_other in traj_list:\n ten_other[:, 0] = ten_other[:, 0] * self.reward_scale # ten_reward\n ten_other[:, 1] = (1.0 - ten_other[:, 1]) * self.gamma # ten_mask = (1.0 - ary_done) * gamma\n return traj_list\n\n\n\"\"\"Value-based Methods (Q network)\"\"\"\n\n\nclass AgentDQN(AgentBase): # [ElegantRL.2021.10.25]\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassCri = None # self.ClassCri = QNetDuel if self.if_use_dueling else QNet\n self.if_use_dueling = True # self.ClassCri = QNetDuel if self.if_use_dueling else QNet\n self.explore_rate = 0.25 # the probability of choosing action randomly in epsilon-greedy\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n self.ClassCri = QNetDuel if self.if_use_dueling else QNet\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.SmoothL1Loss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.SmoothL1Loss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def select_actions(self, states: torch.Tensor) -> torch.Tensor: # for discrete action space\n \"\"\"Select discrete actions for exploration\n\n `tensor states` states.shape==(batch_size, state_dim, )\n return `tensor a_ints` a_ints.shape==(batch_size, )\n \"\"\"\n if rd.rand() < self.explore_rate: # epsilon-greedy\n a_ints = torch.randint(self.action_dim, size=states.shape[0]) # choosing action randomly\n else:\n actions = self.act(states.to(self.device))\n a_ints = actions.argmax(dim=1)\n return a_ints.detach().cpu()\n\n def explore_one_env(self, env, target_step) -> list:\n traj = list()\n state = self.states[0]\n for _ in range(target_step):\n ten_state = torch.as_tensor(state, dtype=torch.float32)\n ten_action = self.select_actions(ten_state.unsqueeze(0))[0]\n action = ten_action.numpy() # isinstance(action, int)\n next_s, reward, done, _ = env.step(action)\n\n ten_other = torch.empty(2 + 1)\n ten_other[0] = reward\n ten_other[1] = done\n ten_other[2] = ten_action\n traj.append((ten_state, ten_other))\n\n state = env.reset() if done else next_s\n self.states[0] = state\n\n traj_state = torch.stack([item[0] for item in traj])\n traj_other = torch.stack([item[1] for item in traj])\n traj_list = [(traj_state, traj_other), ]\n return self.convert_trajectory(traj_list) # [traj_env_0, ...]\n\n def explore_vec_env(self, env, target_step) -> list:\n ten_states = self.states\n\n traj = list()\n for _ in range(target_step):\n ten_actions = self.select_actions(ten_states)\n ten_next_states, ten_rewards, ten_dones = env.step(ten_actions)\n\n ten_others = torch.cat((ten_rewards.unsqueeze(0),\n ten_dones.unsqueeze(0),\n ten_actions.unsqueeze(0)))\n traj.append((ten_states, ten_others))\n ten_states = ten_next_states\n\n self.states = ten_states\n\n traj_state = torch.stack([item[0] for item in traj])\n traj_other = torch.stack([item[1] for item in traj])\n traj_list = [(traj_state[:, env_i, :], traj_other[:, env_i, :])\n for env_i in range(len(self.states))]\n return self.convert_trajectory(traj_list) # [traj_env_0, ...]\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> tuple:\n buffer.update_now_len()\n obj_critic = q_value = None\n for _ in range(int(buffer.now_len / batch_size * repeat_times)):\n obj_critic, q_value = self.get_obj_critic(buffer, batch_size)\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n return obj_critic.item(), q_value.mean().item()\n\n def get_obj_critic_raw(self, buffer, batch_size):\n with torch.no_grad():\n reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n next_q = self.cri_target(next_s).max(dim=1, keepdim=True)[0]\n q_label = reward + mask * next_q\n\n q_value = self.cri(state).gather(1, action.long())\n obj_critic = self.criterion(q_value, q_label)\n return obj_critic, q_value\n\n def get_obj_critic_per(self, buffer, batch_size):\n with torch.no_grad():\n reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n next_q = self.cri_target(next_s).max(dim=1, keepdim=True)[0]\n q_label = reward + mask * next_q\n\n q_value = self.cri(state).gather(1, action.long())\n td_error = self.criterion(q_value, q_label) # or td_error = (q_value - q_label).abs()\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, q_value\n\n\nclass AgentDoubleDQN(AgentDQN): # [ElegantRL.2021.10.25]\n def __init__(self):\n AgentDQN.__init__(self)\n self.soft_max = torch.nn.Softmax(dim=1)\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n self.ClassCri = QNetTwinDuel if self.if_use_dueling else QNetTwin\n AgentDQN.init(self, net_dim, state_dim, action_dim, learning_rate, reward_scale, gamma,\n if_per_or_gae, env_num, gpu_id)\n\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.SmoothL1Loss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.SmoothL1Loss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def select_actions(self, states: torch.Tensor) -> torch.Tensor: # for discrete action space\n actions = self.act(states.to(self.device))\n if rd.rand() < self.explore_rate: # epsilon-greedy\n a_prob = self.soft_max(actions)\n a_ints = torch.multinomial(a_prob, num_samples=1, replacement=True)[:, 0]\n # a_int = rd.choice(self.action_dim, prob=a_prob) # numpy version\n else:\n a_ints = actions.argmax(dim=1)\n return a_ints.detach().cpu()\n\n def get_obj_critic_raw(self, buffer, batch_size) -> (torch.Tensor, torch.Tensor):\n with torch.no_grad():\n reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n next_q = torch.min(*self.cri_target.get_q1_q2(next_s)).max(dim=1, keepdim=True)[0]\n q_label = reward + mask * next_q\n\n q1, q2 = [qs.gather(1, action.long()) for qs in self.act.get_q1_q2(state)]\n obj_critic = self.criterion(q1, q_label) + self.criterion(q2, q_label)\n return obj_critic, q1\n\n def get_obj_critic_per(self, buffer, batch_size):\n with torch.no_grad():\n reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n next_q = torch.min(*self.cri_target.get_q1_q2(next_s)).max(dim=1, keepdim=True)[0]\n q_label = reward + mask * next_q\n\n q1, q2 = [qs.gather(1, action.long()) for qs in self.act.get_q1_q2(state)]\n td_error = self.criterion(q1, q_label) + self.criterion(q2, q_label)\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, q1\n\n\n'''Actor-Critic Methods (Policy Gradient)'''\n\n\nclass AgentDDPG(AgentBase):\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassAct = Actor\n self.ClassCri = Critic\n self.if_use_cri_target = True\n self.if_use_act_target = True\n\n self.explore_noise = 0.3 # explore noise of action (OrnsteinUhlenbeckNoise)\n self.ou_noise = None\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n self.ou_noise = OrnsteinUhlenbeckNoise(size=action_dim, sigma=self.explore_noise)\n\n if if_per_or_gae:\n self.criterion = torch.nn.SmoothL1Loss(reduction='none' if if_per_or_gae else 'mean')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.SmoothL1Loss(reduction='none' if if_per_or_gae else 'mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def select_actions(self, state: torch.Tensor) -> torch.Tensor:\n action = self.act(state.to(self.device))\n if rd.rand() < self.explore_rate: # epsilon-greedy\n ou_noise = torch.as_tensor(self.ou_noise(), dtype=torch.float32, device=self.device).unsqueeze(0)\n action = (action + ou_noise).clamp(-1, 1)\n return action.detach().cpu()\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> (float, float):\n buffer.update_now_len()\n\n obj_critic = None\n obj_actor = None\n for _ in range(int(buffer.now_len / batch_size * repeat_times)):\n obj_critic, state = self.get_obj_critic(buffer, batch_size)\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n action_pg = self.act(state) # policy gradient\n obj_actor = -self.cri(state, action_pg).mean()\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n return obj_critic.item(), obj_actor.item()\n\n def get_obj_critic_raw(self, buffer, batch_size):\n with torch.no_grad():\n reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n next_q = self.cri_target(next_s, self.act_target(next_s))\n q_label = reward + mask * next_q\n q_value = self.cri(state, action)\n obj_critic = self.criterion(q_value, q_label)\n return obj_critic, state\n\n def get_obj_critic_per(self, buffer, batch_size):\n with torch.no_grad():\n reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n next_q = self.cri_target(next_s, self.act_target(next_s))\n q_label = reward + mask * next_q\n\n q_value = self.cri(state, action)\n td_error = self.criterion(q_value, q_label) # or td_error = (q_value - q_label).abs()\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, state\n\n\nclass AgentTD3(AgentBase):\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassAct = Actor\n self.ClassCri = CriticTwin\n self.if_use_cri_target = True\n self.if_use_act_target = True\n\n self.explore_noise = 0.1 # standard deviation of exploration noise\n self.policy_noise = 0.2 # standard deviation of policy noise\n self.update_freq = 2 # delay update frequency\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.SmoothL1Loss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.SmoothL1Loss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> tuple:\n buffer.update_now_len()\n\n obj_critic = None\n obj_actor = None\n for update_c in range(int(buffer.now_len / batch_size * repeat_times)):\n obj_critic, state = self.get_obj_critic(buffer, batch_size)\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n\n if update_c % self.update_freq == 0: # delay update\n action_pg = self.act(state) # policy gradient\n obj_actor = -self.cri_target(state, action_pg).mean() # use cri_target is more stable than cri\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n return obj_critic.item() / 2, obj_actor.item()\n\n def get_obj_critic_raw(self, buffer, batch_size):\n with torch.no_grad():\n reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n next_a = self.act_target.get_action(next_s, self.policy_noise) # policy noise\n next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a)) # twin critics\n q_label = reward + mask * next_q\n q1, q2 = self.cri.get_q1_q2(state, action)\n obj_critic = self.criterion(q1, q_label) + self.criterion(q2, q_label) # twin critics\n return obj_critic, state\n\n def get_obj_critic_per(self, buffer, batch_size):\n \"\"\"Prioritized Experience Replay\n\n Contributor: Github GyChou\n \"\"\"\n with torch.no_grad():\n reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n next_a = self.act_target.get_action(next_s, self.policy_noise) # policy noise\n next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a)) # twin critics\n q_label = reward + mask * next_q\n\n q1, q2 = self.cri.get_q1_q2(state, action)\n td_error = self.criterion(q1, q_label) + self.criterion(q2, q_label)\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, state\n\n\nclass AgentSAC(AgentBase): # [ElegantRL.2021.10.25]\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassCri = CriticTwin\n self.ClassAct = ActorSAC\n self.if_use_cri_target = True\n self.if_use_act_target = False\n\n self.alpha_log = None\n self.alpha_optim = None\n self.target_entropy = None\n self.obj_critic = (-np.log(0.5)) ** 0.5 # for reliable_lambda\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n\n self.alpha_log = torch.tensor((-np.log(action_dim) * np.e,), dtype=torch.float32,\n requires_grad=True, device=self.device) # trainable parameter\n self.alpha_optim = torch.optim.Adam((self.alpha_log,), lr=learning_rate)\n self.target_entropy = np.log(action_dim)\n\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.SmoothL1Loss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.SmoothL1Loss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def select_actions(self, state: torch.Tensor) -> torch.Tensor:\n state = state.to(self.device)\n if rd.rand() < self.explore_rate: # epsilon-greedy\n actions = self.act.get_action(state)\n else:\n actions = self.act(state)\n return actions.detach().cpu()\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):\n buffer.update_now_len()\n\n obj_actor = None\n alpha = None\n for _ in range(int(buffer.now_len * repeat_times / batch_size)):\n alpha = self.alpha_log.exp()\n\n '''objective of critic (loss function of critic)'''\n obj_critic, state = self.get_obj_critic(buffer, batch_size, alpha)\n self.obj_critic = 0.995 * self.obj_critic + 0.0025 * obj_critic.item() # for reliable_lambda\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n '''objective of alpha (temperature parameter automatic adjustment)'''\n action_pg, logprob = self.act.get_action_logprob(state) # policy gradient\n obj_alpha = (self.alpha_log * (logprob - self.target_entropy).detach()).mean()\n self.optim_update(self.alpha_optim, obj_alpha, self.alpha_log)\n\n '''objective of actor'''\n with torch.no_grad():\n self.alpha_log[:] = self.alpha_log.clamp(-20, 2).detach()\n obj_actor = -(torch.min(*self.cri.get_q1_q2(state, action_pg)) + logprob * alpha).mean()\n # use self.cri_target.get_q1_q2 in above code for more stable training.\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n return self.obj_critic, obj_actor.item(), alpha.item()\n\n def get_obj_critic_raw(self, buffer, batch_size, alpha):\n with torch.no_grad():\n reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n\n next_a, next_log_prob = self.act_target.get_action_logprob(next_s) # stochastic policy\n next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a)) # twin critics\n\n q_label = reward + mask * (next_q + next_log_prob * alpha)\n q1, q2 = self.cri.get_q1_q2(state, action)\n obj_critic = self.criterion(q1, q_label) + self.criterion(q2, q_label)\n return obj_critic, state\n\n def get_obj_critic_per(self, buffer, batch_size, alpha):\n with torch.no_grad():\n reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n\n next_a, next_log_prob = self.act_target.get_action_logprob(next_s) # stochastic policy\n next_q = torch.min(*self.cri_target.get_q1_q2(next_s, next_a)) # twin critics\n\n q_label = reward + mask * (next_q + next_log_prob * alpha)\n\n q1, q2 = self.cri.get_q1_q2(state, action)\n td_error = self.criterion(q1, q_label) + self.criterion(q2, q_label)\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, state\n\n\nclass AgentModSAC(AgentSAC): # Modified SAC using reliable_lambda and TTUR (Two Time-scale Update Rule)\n def __init__(self):\n AgentSAC.__init__(self)\n self.if_use_act_target = True\n self.if_use_cri_target = True\n self.obj_critic = (-np.log(0.5)) ** 0.5 # for reliable_lambda\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):\n buffer.update_now_len()\n\n obj_actor = None\n update_a = 0\n alpha = None\n for update_c in range(1, int(buffer.now_len * repeat_times / batch_size)):\n alpha = self.alpha_log.exp()\n\n '''objective of critic (loss function of critic)'''\n obj_critic, state = self.get_obj_critic(buffer, batch_size, alpha)\n self.obj_critic = 0.995 * self.obj_critic + 0.0025 * obj_critic.item() # for reliable_lambda\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n a_noise_pg, logprob = self.act.get_action_logprob(state) # policy gradient\n '''objective of alpha (temperature parameter automatic adjustment)'''\n obj_alpha = (self.alpha_log * (logprob - self.target_entropy).detach()).mean()\n self.optim_update(self.alpha_optim, obj_alpha, self.alpha_log)\n with torch.no_grad():\n self.alpha_log[:] = self.alpha_log.clamp(-16, 2).detach()\n\n '''objective of actor using reliable_lambda and TTUR (Two Time-scales Update Rule)'''\n reliable_lambda = np.exp(-self.obj_critic ** 2) # for reliable_lambda\n if_update_a = update_a / update_c < 1 / (2 - reliable_lambda)\n if if_update_a: # auto TTUR\n update_a += 1\n\n q_value_pg = torch.min(*self.cri.get_q1_q2(state, a_noise_pg))\n obj_actor = -(q_value_pg + logprob * alpha).mean()\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n\n return self.obj_critic, obj_actor.item(), alpha.item()\n\n\nclass AgentPPO(AgentBase):\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassAct = ActorPPO\n self.ClassCri = CriticPPO\n\n self.if_off_policy = False\n self.ratio_clip = 0.2 # could be 0.00 ~ 0.50 ratio.clamp(1 - clip, 1 + clip)\n self.lambda_entropy = 0.02 # could be 0.00~0.10\n self.lambda_a_value = 1.00 # could be 0.25~8.00, the lambda of advantage value\n self.lambda_gae_adv = 0.98 # could be 0.95~0.99, GAE (Generalized Advantage Estimation. ICLR.2016.)\n self.get_reward_sum = None # self.get_reward_sum_gae if if_use_gae else self.get_reward_sum_raw\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n self.traj_list = [list() for _ in range(env_num)]\n self.env_num = env_num\n\n if if_per_or_gae: # if_use_gae\n self.get_reward_sum = self.get_reward_sum_gae\n else:\n self.get_reward_sum = self.get_reward_sum_raw\n if env_num == 1:\n self.explore_env = self.explore_one_env\n else:\n self.explore_env = self.explore_vec_env\n\n def select_action(self, state: np.ndarray) -> np.ndarray:\n s_tensor = torch.as_tensor(state[np.newaxis], device=self.device)\n a_tensor = self.act(s_tensor)\n action = a_tensor.detach().cpu().numpy()\n return action\n\n def select_actions(self, state: torch.Tensor) -> tuple:\n \"\"\"\n `tensor state` state.shape = (batch_size, state_dim)\n return `tensor action` action.shape = (batch_size, action_dim)\n return `tensor noise` noise.shape = (batch_size, action_dim)\n \"\"\"\n state = state.to(self.device)\n action, noise = self.act.get_action(state)\n return action.detach().cpu(), noise.detach().cpu()\n\n def explore_one_env(self, env, target_step):\n state = self.states[0]\n\n last_done = 0\n traj = list()\n for step_i in range(target_step):\n ten_states = torch.as_tensor(state, dtype=torch.float32).unsqueeze(0)\n ten_actions, ten_noises = self.select_actions(ten_states)\n action = ten_actions[0].numpy()\n next_s, reward, done, _ = env.step(np.tanh(action))\n\n traj.append((ten_states, reward, done, ten_actions, ten_noises))\n if done:\n state = env.reset()\n last_done = step_i\n else:\n state = next_s\n\n self.states[0] = state\n\n traj_list = self.splice_trajectory([traj, ], [last_done, ])\n return self.convert_trajectory(traj_list) # [traj_env_0, ]\n\n def explore_vec_env(self, env, target_step):\n ten_states = self.states\n\n env_num = len(self.traj_list)\n traj_list = [list() for _ in range(env_num)] # [traj_env_0, ..., traj_env_i]\n last_done_list = [0 for _ in range(env_num)]\n\n for step_i in range(target_step):\n ten_actions, ten_noises = self.select_actions(ten_states)\n tem_next_states, ten_rewards, ten_dones = env.step(ten_actions.tanh())\n\n for env_i in range(env_num):\n traj_list[env_i].append((ten_states[env_i], ten_rewards[env_i], ten_dones[env_i],\n ten_actions[env_i], ten_noises[env_i]))\n if ten_dones[env_i]:\n last_done_list[env_i] = step_i\n\n ten_states = tem_next_states\n\n self.states = ten_states\n\n traj_list = self.splice_trajectory(traj_list, last_done_list)\n return self.convert_trajectory(traj_list) # [traj_env_0, ...]\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):\n with torch.no_grad():\n buf_len = buffer[0].shape[0]\n buf_state, buf_reward, buf_mask, buf_action, buf_noise = [ten.to(self.device) for ten in buffer]\n\n '''get buf_r_sum, buf_logprob'''\n bs = 2 ** 10 # set a smaller 'BatchSize' when out of GPU memory.\n buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]\n buf_value = torch.cat(buf_value, dim=0)\n buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)\n\n buf_r_sum, buf_adv_v = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value) # detach()\n buf_adv_v = (buf_adv_v - buf_adv_v.mean()) * (self.lambda_a_value / (buf_adv_v.std() + 1e-5))\n # buf_adv_v: buffer data of adv_v value\n del buf_noise, buffer[:]\n\n obj_critic = None\n obj_actor = None\n update_times = int(buf_len / batch_size * repeat_times)\n for update_i in range(1, update_times + 1):\n indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)\n\n state = buf_state[indices]\n r_sum = buf_r_sum[indices]\n adv_v = buf_adv_v[indices]\n action = buf_action[indices]\n logprob = buf_logprob[indices]\n\n '''PPO: Surrogate objective of Trust Region'''\n new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action) # it is obj_actor\n ratio = (new_logprob - logprob.detach()).exp()\n surrogate1 = adv_v * ratio\n surrogate2 = adv_v * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)\n obj_surrogate = -torch.min(surrogate1, surrogate2).mean()\n obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n\n value = self.cri(state).squeeze(1) # critic network predicts the reward_sum (Q value) of state\n obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1)).mean()\n return obj_critic.item(), obj_actor.item(), a_std_log.item() # logging_tuple\n\n def get_reward_sum_raw(self, buf_len, buf_reward, buf_mask, buf_value) -> (torch.Tensor, torch.Tensor):\n buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device) # reward sum\n\n pre_r_sum = 0\n for i in range(buf_len - 1, -1, -1):\n buf_r_sum[i] = buf_reward[i] + buf_mask[i] * pre_r_sum\n pre_r_sum = buf_r_sum[i]\n buf_adv_v = buf_r_sum - buf_value[:, 0]\n return buf_r_sum, buf_adv_v\n\n def get_reward_sum_gae(self, buf_len, ten_reward, ten_mask, ten_value) -> (torch.Tensor, torch.Tensor):\n buf_r_sum = torch.empty(buf_len, dtype=torch.float32, device=self.device) # old policy value\n buf_adv_v = torch.empty(buf_len, dtype=torch.float32, device=self.device) # advantage value\n\n pre_r_sum = 0\n pre_adv_v = 0 # advantage value of previous step\n ten_bool = torch.not_equal(ten_mask, 0).float()\n for i in range(buf_len - 1, -1, -1):\n buf_r_sum[i] = ten_reward[i] + ten_mask[i] * pre_r_sum\n pre_r_sum = buf_r_sum[i]\n buf_adv_v[i] = ten_reward[i] + ten_bool[i] * (pre_adv_v - ten_value[i]) # todo need to check\n pre_adv_v = ten_value[i] + buf_adv_v[i] * self.lambda_gae_adv\n return buf_r_sum, buf_adv_v\n\n def splice_trajectory(self, traj_list, last_done_list):\n for env_i in range(self.env_num):\n last_done = last_done_list[env_i]\n traj_temp = traj_list[env_i]\n\n traj_list[env_i] = self.traj_list[env_i] + traj_temp[:last_done + 1]\n self.traj_list[env_i] = traj_temp[last_done:]\n return traj_list\n\n def convert_trajectory(self, traj_list):\n for traj in traj_list:\n temp = list(map(list, zip(*traj))) # 2D-list transpose\n\n ten_state = torch.stack(temp[0])\n ten_reward = torch.as_tensor(temp[1], dtype=torch.float32) * self.reward_scale\n ten_mask = (1.0 - torch.as_tensor(temp[2], dtype=torch.float32)) * self.gamma\n ten_action = torch.stack(temp[3])\n ten_noise = torch.stack(temp[4])\n\n traj[:] = (ten_state, ten_reward, ten_mask, ten_action, ten_noise)\n return traj_list\n\n\nclass AgentDiscretePPO(AgentPPO):\n def __init__(self):\n AgentPPO.__init__(self)\n self.ClassAct = ActorDiscretePPO\n\n def explore_one_env(self, env, target_step):\n state = self.states[0]\n\n last_done = 0\n traj = list()\n for step_i in range(target_step):\n ten_states = torch.as_tensor(state, dtype=torch.float32).unsqueeze(0)\n ten_a_ints, ten_probs = self.select_actions(ten_states)\n a_int = ten_a_ints[0].numpy()\n next_s, reward, done, _ = env.step(a_int) # only different\n\n traj.append((ten_states, reward, done, ten_a_ints, ten_probs))\n if done:\n state = env.reset()\n last_done = step_i\n else:\n state = next_s\n\n self.states[0] = state\n\n traj_list = self.splice_trajectory([traj, ], [last_done, ])\n return self.convert_trajectory(traj_list)\n\n def explore_vec_env(self, env, target_step):\n ten_states = self.states\n\n env_num = len(self.traj_list)\n traj_list = [list() for _ in range(env_num)] # [traj_env_0, ..., traj_env_i]\n last_done_list = [0 for _ in range(env_num)]\n\n for step_i in range(target_step):\n ten_a_ints, ten_probs = self.select_actions(ten_states)\n tem_next_states, ten_rewards, ten_dones = env.step(ten_a_ints.numpy())\n\n for env_i in range(env_num):\n traj_list[env_i].append((ten_states[env_i], ten_rewards[env_i], ten_dones[env_i],\n ten_a_ints[env_i], ten_probs[env_i]))\n if ten_dones[env_i]:\n last_done_list[env_i] = step_i\n\n ten_states = tem_next_states\n\n self.states = ten_states\n\n traj_list = self.splice_trajectory(traj_list, last_done_list)\n return self.convert_trajectory(traj_list) # [traj_env_0, ...]\n\n\nclass AgentA2C(AgentPPO): # A2C.2015, PPO.2016\n def __init__(self):\n AgentPPO.__init__(self)\n print('| AgentA2C: A2C or A3C is worse than PPO. We provide AgentA2C code just for teaching.'\n '| Without TrustRegion, A2C needs special hyper-parameters, such as smaller repeat_times.')\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):\n with torch.no_grad():\n buf_len = buffer[0].shape[0]\n buf_state, buf_reward, buf_mask, buf_action, buf_noise = [ten.to(self.device) for ten in buffer]\n\n '''get buf_r_sum, buf_logprob'''\n bs = 2 ** 10 # set a smaller 'BatchSize' when out of GPU memory.\n buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]\n buf_value = torch.cat(buf_value, dim=0)\n # buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)\n\n buf_r_sum, buf_adv_v = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value) # detach()\n buf_adv_v = (buf_adv_v - buf_adv_v.mean()) * (self.lambda_a_value / (buf_adv_v.std() + 1e-5))\n # buf_adv_v: advantage_value in ReplayBuffer\n del buf_noise, buffer[:]\n\n obj_critic = None\n obj_actor = None\n update_times = int(buf_len / batch_size * repeat_times)\n for update_i in range(1, update_times + 1):\n indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)\n\n state = buf_state[indices]\n r_sum = buf_r_sum[indices]\n adv_v = buf_adv_v[indices]\n action = buf_action[indices]\n # logprob = buf_logprob[indices]\n\n '''A2C: Advantage function'''\n new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action) # it is obj_actor\n obj_actor = -(adv_v * new_logprob.exp()).mean() + obj_entropy * self.lambda_entropy\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n\n value = self.cri(state).squeeze(1) # critic network predicts the reward_sum (Q value) of state\n obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1)).mean()\n return obj_critic.item(), obj_actor.item(), a_std_log.item() # logging_tuple\n\n\nclass AgentDiscreteA2C(AgentA2C):\n def __init__(self):\n AgentA2C.__init__(self)\n self.ClassAct = ActorDiscretePPO\n\n def explore_one_env(self, env, target_step):\n state = self.states[0]\n\n last_done = 0\n traj = list()\n for step_i in range(target_step):\n ten_states = torch.as_tensor(state, dtype=torch.float32).unsqueeze(0)\n ten_a_ints, ten_probs = self.select_actions(ten_states)\n a_int = ten_a_ints[0].numpy()\n next_s, reward, done, _ = env.step(a_int) # only different\n\n traj.append((ten_states, reward, done, ten_a_ints, ten_probs))\n if done:\n state = env.reset()\n last_done = step_i\n else:\n state = next_s\n\n self.states[0] = state\n\n traj_list = self.splice_trajectory([traj, ], [last_done, ])\n return self.convert_trajectory(traj_list)\n\n def explore_vec_env(self, env, target_step):\n ten_states = self.states\n\n env_num = len(self.traj_list)\n traj_list = [list() for _ in range(env_num)] # [traj_env_0, ..., traj_env_i]\n last_done_list = [0 for _ in range(env_num)]\n\n for step_i in range(target_step):\n ten_a_ints, ten_probs = self.select_actions(ten_states)\n tem_next_states, ten_rewards, ten_dones = env.step(ten_a_ints.numpy())\n\n for env_i in range(env_num):\n traj_list[env_i].append((ten_states[env_i], ten_rewards[env_i], ten_dones[env_i],\n ten_a_ints[env_i], ten_probs[env_i]))\n if ten_dones[env_i]:\n last_done_list[env_i] = step_i\n\n ten_states = tem_next_states\n\n self.states = ten_states\n\n traj_list = self.splice_trajectory(traj_list, last_done_list)\n return self.convert_trajectory(traj_list) # [traj_env_0, ...]\n\n\nclass AgentStep1AC(AgentBase):\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassAct = ActorBiConv\n self.ClassCri = CriticBiConv\n self.if_use_cri_target = False\n self.if_use_act_target = False\n self.explore_noise = 2 ** -8\n self.obj_critic = (-np.log(0.5)) ** 0.5 # for reliable_lambda\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.MSELoss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.MSELoss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n self.get_obj_critic = self.get_obj_critic_raw\n\n def select_actions(self, state: torch.Tensor) -> torch.Tensor:\n action = self.act.get_action(state.to(self.device), self.explore_noise)\n return action.detach().cpu()\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> (float, float):\n buffer.update_now_len()\n\n obj_actor = None\n update_a = 0\n for update_c in range(1, int(buffer.now_len / batch_size * repeat_times)):\n '''objective of critic (loss function of critic)'''\n obj_critic, state = self.get_obj_critic(buffer, batch_size)\n self.obj_critic = 0.99 * self.obj_critic + 0.01 * obj_critic.item() # for reliable_lambda\n self.optim_update(self.cri_optim, obj_critic, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n '''objective of actor using reliable_lambda and TTUR (Two Time-scales Update Rule)'''\n reliable_lambda = np.exp(-self.obj_critic ** 2) # for reliable_lambda\n if_update_a = update_a / update_c < 1 / (2 - reliable_lambda)\n if if_update_a: # auto TTUR\n update_a += 1\n\n obj_actor = -self.cri(state, self.act(state)).mean() # policy gradient\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n\n return self.obj_critic, obj_actor.item()\n\n def get_obj_critic_raw(self, buffer, batch_size):\n with torch.no_grad():\n # reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n q_label, action, state = buffer.sample_batch_one_step(batch_size)\n\n q_value = self.cri(state, action)\n obj_critic = self.criterion(q_value, q_label)\n return obj_critic, state\n\n def get_obj_critic_per(self, buffer, batch_size):\n with torch.no_grad():\n # reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n q_label, action, state, is_weights = buffer.sample_batch_one_step(batch_size)\n\n q_value = self.cri(state, action)\n td_error = self.criterion(q_value, q_label) # or td_error = (q_value - q_label).abs()\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, q_value\n\n\n'''Actor-Critic Methods (Parameter Sharing)'''\n\n\nclass AgentShareAC(AgentBase): # IAC (InterAC) waiting for check\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassCri = ShareDPG # self.Act = None\n\n self.explore_noise = 0.2 # standard deviation of explore noise\n self.policy_noise = 0.4 # standard deviation of policy noise\n self.update_freq = 2 ** 7 # delay update frequency, for hard target update\n self.avg_loss_c = (-np.log(0.5)) ** 0.5 # old version reliable_lambda\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> tuple:\n buffer.update_now_len()\n\n obj_critic = None\n obj_actor = None\n reliable_lambda = None\n k = 1.0 + buffer.now_len / buffer.max_len\n batch_size_ = int(batch_size * k)\n for i in range(int(buffer.now_len / batch_size * repeat_times)):\n with torch.no_grad():\n reward, mask, action, state, next_state = buffer.sample_batch(batch_size_)\n\n next_q_label, next_action = self.cri_target.next_q_action(state, next_state, self.policy_noise)\n q_label = reward + mask * next_q_label\n\n \"\"\"obj_critic\"\"\"\n q_eval = self.cri.critic(state, action)\n obj_critic = self.criterion(q_eval, q_label)\n\n '''auto reliable lambda'''\n self.avg_loss_c = 0.995 * self.avg_loss_c + 0.005 * obj_critic.item() / 2 # soft update, twin critics\n reliable_lambda = np.exp(-self.avg_loss_c ** 2)\n\n '''actor correction term'''\n actor_term = self.criterion(self.cri(next_state), next_action)\n\n if i % repeat_times == 0:\n '''actor obj'''\n action_pg = self.cri(state) # policy gradient\n obj_actor = -self.cri_target.critic(state, action_pg).mean() # policy gradient\n # NOTICE! It is very important to use act_target.critic here instead act.critic\n # Or you can use act.critic.deepcopy(). Whatever you cannot use act.critic directly.\n\n obj_united = obj_critic + actor_term * (1 - reliable_lambda) + obj_actor * (reliable_lambda * 0.5)\n else:\n obj_united = obj_critic + actor_term * (1 - reliable_lambda)\n\n \"\"\"united loss\"\"\"\n self.optim_update(self.cri_optim, obj_united, self.cri.parameters())\n if i % self.update_freq == self.update_freq and reliable_lambda > 0.1:\n self.cri_target.load_state_dict(self.cri.state_dict()) # Hard Target Update\n\n return obj_critic.item(), obj_actor.item(), reliable_lambda\n\n\nclass AgentShareSAC(AgentSAC): # Integrated Soft Actor-Critic\n def __init__(self):\n AgentSAC.__init__(self)\n self.obj_critic = (-np.log(0.5)) ** 0.5 # for reliable_lambda\n self.cri_optim = None\n\n self.target_entropy = None\n self.alpha_log = None\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n self.device = torch.device(f\"cuda:{gpu_id}\" if torch.cuda.is_available() else \"cpu\")\n self.alpha_log = torch.tensor((-np.log(action_dim) * np.e,), dtype=torch.float32,\n requires_grad=True, device=self.device) # trainable parameter\n self.target_entropy = np.log(action_dim)\n self.act = self.cri = ShareSPG(net_dim, state_dim, action_dim).to(self.device)\n self.act_target = self.cri_target = deepcopy(self.act)\n\n self.cri_optim = torch.optim.Adam(\n [{'params': self.act.enc_s.parameters(), 'lr': learning_rate * 1.5},\n {'params': self.act.enc_a.parameters(), },\n {'params': self.act.net.parameters(), 'lr': learning_rate * 1.5},\n {'params': self.act.dec_a.parameters(), },\n {'params': self.act.dec_d.parameters(), },\n {'params': self.act.dec_q1.parameters(), },\n {'params': self.act.dec_q2.parameters(), },\n {'params': (self.alpha_log,)}], lr=learning_rate)\n\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.SmoothL1Loss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.SmoothL1Loss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> tuple: # 1111\n buffer.update_now_len()\n\n obj_actor = None\n update_a = 0\n alpha = None\n for update_c in range(1, int(buffer.now_len / batch_size * repeat_times)):\n alpha = self.alpha_log.exp()\n\n '''objective of critic'''\n obj_critic, state = self.get_obj_critic(buffer, batch_size, alpha)\n self.obj_critic = 0.995 * self.obj_critic + 0.0025 * obj_critic.item() # for reliable_lambda\n reliable_lambda = np.exp(-self.obj_critic ** 2) # for reliable_lambda\n\n '''objective of alpha (temperature parameter automatic adjustment)'''\n a_noise_pg, logprob = self.act.get_action_logprob(state) # policy gradient\n obj_alpha = (self.alpha_log * (logprob - self.target_entropy).detach() * reliable_lambda).mean()\n with torch.no_grad():\n self.alpha_log[:] = self.alpha_log.clamp(-16, 2).detach()\n\n '''objective of actor using reliable_lambda and TTUR (Two Time-scales Update Rule)'''\n if_update_a = update_a / update_c < 1 / (2 - reliable_lambda)\n if if_update_a: # auto TTUR\n update_a += 1\n\n q_value_pg = torch.min(*self.act_target.get_q1_q2(state, a_noise_pg)).mean() # twin critics\n obj_actor = -(q_value_pg + logprob * alpha.detach()).mean() # policy gradient\n\n obj_united = obj_critic + obj_alpha + obj_actor * reliable_lambda\n else:\n obj_united = obj_critic + obj_alpha\n\n self.optim_update(self.cri_optim, obj_united, self.cri.parameters())\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n\n return self.obj_critic, obj_actor.item(), alpha.item()\n\n\nclass AgentSharePPO(AgentPPO):\n def __init__(self):\n AgentPPO.__init__(self)\n self.obj_c = (-np.log(0.5)) ** 0.5 # for reliable_lambda\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n self.device = torch.device(f\"cuda:{gpu_id}\" if torch.cuda.is_available() else \"cpu\")\n if if_per_or_gae:\n self.get_reward_sum = self.get_reward_sum_gae\n else:\n self.get_reward_sum = self.get_reward_sum_raw\n\n self.act = self.cri = SharePPO(state_dim, action_dim, net_dim).to(self.device)\n\n self.cri_optim = torch.optim.Adam([\n {'params': self.act.enc_s.parameters(), 'lr': learning_rate * 0.9},\n {'params': self.act.dec_a.parameters(), },\n {'params': self.act.a_std_log, },\n {'params': self.act.dec_q1.parameters(), },\n {'params': self.act.dec_q2.parameters(), },\n ], lr=learning_rate)\n self.criterion = torch.nn.SmoothL1Loss()\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):\n with torch.no_grad():\n buf_len = buffer[0].shape[0]\n buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]\n # (ten_state, ten_action, ten_noise, ten_reward, ten_mask) = buffer\n\n '''get buf_r_sum, buf_logprob'''\n bs = 2 ** 10 # set a smaller 'BatchSize' when out of GPU memory.\n buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]\n buf_value = torch.cat(buf_value, dim=0)\n buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)\n\n buf_r_sum, buf_adv_v = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value) # detach()\n buf_adv_v = (buf_adv_v - buf_adv_v.mean()) * (self.lambda_a_value / torch.std(buf_adv_v) + 1e-5)\n # buf_adv_v: buffer data of adv_v value\n del buf_noise, buffer[:]\n\n obj_critic = obj_actor = None\n for _ in range(int(buf_len / batch_size * repeat_times)):\n indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)\n\n state = buf_state[indices]\n r_sum = buf_r_sum[indices]\n adv_v = buf_adv_v[indices] # advantage value\n action = buf_action[indices]\n logprob = buf_logprob[indices]\n\n '''PPO: Surrogate objective of Trust Region'''\n new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action) # it is obj_actor\n ratio = (new_logprob - logprob.detach()).exp()\n surrogate1 = adv_v * ratio\n surrogate2 = adv_v * ratio.clamp(1 - self.ratio_clip, 1 + self.ratio_clip)\n obj_surrogate = -torch.min(surrogate1, surrogate2).mean()\n obj_actor = obj_surrogate + obj_entropy * self.lambda_entropy\n\n value = self.cri(state).squeeze(1) # critic network predicts the reward_sum (Q value) of state\n obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)\n\n obj_united = obj_critic + obj_actor\n self.optim_update(self.cri_optim, obj_united, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1)).mean()\n return obj_critic.item(), obj_actor.item(), a_std_log.item() # logging_tuple\n\n\nclass AgentShareA2C(AgentSharePPO):\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau):\n with torch.no_grad():\n buf_len = buffer[0].shape[0]\n buf_state, buf_action, buf_noise, buf_reward, buf_mask = [ten.to(self.device) for ten in buffer]\n # (ten_state, ten_action, ten_noise, ten_reward, ten_mask) = buffer\n\n '''get buf_r_sum, buf_logprob'''\n bs = 2 ** 10 # set a smaller 'BatchSize' when out of GPU memory.\n buf_value = [self.cri_target(buf_state[i:i + bs]) for i in range(0, buf_len, bs)]\n buf_value = torch.cat(buf_value, dim=0)\n # buf_logprob = self.act.get_old_logprob(buf_action, buf_noise)\n\n buf_r_sum, buf_adv_v = self.get_reward_sum(buf_len, buf_reward, buf_mask, buf_value) # detach()\n buf_adv_v = (buf_adv_v - buf_adv_v.mean()) * (self.lambda_a_value / torch.std(buf_adv_v) + 1e-5)\n # buf_adv_v: buffer data of adv_v value\n del buf_noise, buffer[:]\n\n obj_critic = obj_actor = None\n for _ in range(int(buf_len / batch_size * repeat_times)):\n indices = torch.randint(buf_len, size=(batch_size,), requires_grad=False, device=self.device)\n\n state = buf_state[indices]\n r_sum = buf_r_sum[indices]\n adv_v = buf_adv_v[indices] # advantage value\n action = buf_action[indices]\n # logprob = buf_logprob[indices]\n\n '''A2C: Advantage function'''\n new_logprob, obj_entropy = self.act.get_logprob_entropy(state, action) # it is obj_actor\n obj_actor = -(adv_v * new_logprob.exp()).mean() + obj_entropy * self.lambda_entropy\n self.optim_update(self.act_optim, obj_actor, self.act.parameters())\n\n value = self.cri(state).squeeze(1) # critic network predicts the reward_sum (Q value) of state\n obj_critic = self.criterion(value, r_sum) / (r_sum.std() + 1e-6)\n\n obj_united = obj_critic + obj_actor\n self.optim_update(self.cri_optim, obj_united, self.cri.parameters())\n if self.if_use_cri_target:\n self.soft_update(self.cri_target, self.cri, soft_update_tau)\n\n a_std_log = getattr(self.act, 'a_std_log', torch.zeros(1)).mean()\n return obj_critic.item(), obj_actor.item(), a_std_log.item() # logging_tuple\n\n\nclass AgentShareStep1AC(AgentBase):\n def __init__(self):\n AgentBase.__init__(self)\n self.ClassAct = ShareBiConv\n self.ClassCri = self.ClassAct\n self.if_use_cri_target = True\n self.if_use_act_target = True\n self.obj_critic = (-np.log(0.5)) ** 0.5 # for reliable_lambda\n\n def init(self, net_dim=256, state_dim=8, action_dim=2, reward_scale=1.0, gamma=0.99,\n learning_rate=1e-4, if_per_or_gae=False, env_num=1, gpu_id=0):\n AgentBase.init(self, net_dim=net_dim, state_dim=state_dim, action_dim=action_dim,\n reward_scale=reward_scale, gamma=gamma,\n learning_rate=learning_rate, if_per_or_gae=if_per_or_gae,\n env_num=env_num, gpu_id=gpu_id, )\n self.act = self.cri = self.ClassAct(net_dim, state_dim, action_dim).to(self.device)\n if self.if_use_act_target:\n self.act_target = self.cri_target = deepcopy(self.act)\n else:\n self.act_target = self.cri_target = self.act\n\n self.cri_optim = torch.optim.Adam(\n [{'params': self.act.enc_s.parameters(), 'lr': learning_rate * 1.5},\n {'params': self.act.enc_a.parameters(), },\n {'params': self.act.mid_n.parameters(), 'lr': learning_rate * 1.5},\n {'params': self.act.dec_a.parameters(), },\n {'params': self.act.dec_q.parameters(), },\n ], lr=learning_rate)\n self.act_optim = self.cri_optim\n\n if if_per_or_gae: # if_use_per\n self.criterion = torch.nn.MSELoss(reduction='none')\n self.get_obj_critic = self.get_obj_critic_per\n else:\n self.criterion = torch.nn.MSELoss(reduction='mean')\n self.get_obj_critic = self.get_obj_critic_raw\n\n def select_actions(self, state: torch.Tensor) -> torch.Tensor:\n action = self.act.get_action(state.to(self.device), self.explore_noise)\n return action.detach().cpu()\n\n def update_net(self, buffer, batch_size, repeat_times, soft_update_tau) -> (float, float):\n buffer.update_now_len()\n\n obj_critic = None\n obj_actor = None\n update_a = 0\n for update_c in range(1, int(buffer.now_len / batch_size * repeat_times)):\n '''objective of critic'''\n obj_critic, state = self.get_obj_critic(buffer, batch_size)\n self.obj_critic = 0.995 * self.obj_critic + 0.005 * obj_critic.item() # for reliable_lambda\n reliable_lambda = np.exp(-self.obj_critic ** 2) # for reliable_lambda\n\n '''objective of actor using reliable_lambda and TTUR (Two Time-scales Update Rule)'''\n if_update_a = update_a / update_c < 1 / (2 - reliable_lambda)\n if if_update_a: # auto TTUR\n update_a += 1\n\n action_pg = self.act(state) # policy gradient\n obj_actor = -self.act_target.critic(state, action_pg).mean()\n\n obj_united = obj_critic + obj_actor * reliable_lambda\n else:\n obj_united = obj_critic\n\n self.optim_update(self.act_optim, obj_united, self.act.parameters())\n if self.if_use_act_target:\n self.soft_update(self.act_target, self.act, soft_update_tau)\n\n return obj_critic.item(), obj_actor.item()\n\n def get_obj_critic_raw(self, buffer, batch_size):\n with torch.no_grad():\n # reward, mask, action, state, next_s = buffer.sample_batch(batch_size)\n q_label, action, state = buffer.sample_batch_one_step(batch_size)\n\n q_value = self.act.critic(state, action)\n obj_critic = self.criterion(q_value, q_label)\n return obj_critic, state\n\n def get_obj_critic_per(self, buffer, batch_size):\n with torch.no_grad():\n # reward, mask, action, state, next_s, is_weights = buffer.sample_batch(batch_size)\n q_label, action, state, is_weights = buffer.sample_batch_one_step(batch_size)\n\n q_value = self.act.critic(state, action)\n td_error = self.criterion(q_value, q_label) # or td_error = (q_value - q_label).abs()\n obj_critic = (td_error * is_weights).mean()\n\n buffer.td_error_update(td_error.detach())\n return obj_critic, q_value\n\n\n'''Utils'''\n\n\nclass OrnsteinUhlenbeckNoise: # NOT suggest to use it\n def __init__(self, size, theta=0.15, sigma=0.3, ou_noise=0.0, dt=1e-2):\n \"\"\"The noise of Ornstein-Uhlenbeck Process\n\n Source: https://github.com/slowbull/DDPG/blob/master/src/explorationnoise.py\n It makes Zero-mean Gaussian Noise more stable.\n It helps agent explore better in a inertial system.\n Don't abuse OU Process. OU process has too much hyper-parameters and over fine-tuning make no sense.\n\n :int size: the size of noise, noise.shape==(-1, action_dim)\n :float theta: related to the not independent of OU-noise\n :float sigma: related to action noise std\n :float ou_noise: initialize OU-noise\n :float dt: derivative\n \"\"\"\n self.theta = theta\n self.sigma = sigma\n self.ou_noise = ou_noise\n self.dt = dt\n self.size = size\n\n def __call__(self) -> float:\n \"\"\"output a OU-noise\n\n :return array ou_noise: a noise generated by Ornstein-Uhlenbeck Process\n \"\"\"\n noise = self.sigma * np.sqrt(self.dt) * rd.normal(size=self.size)\n self.ou_noise -= self.theta * self.ou_noise * self.dt + noise\n return self.ou_noise\n" }, { "path": "elegantrl/demo.py", "content": "from elegantrl.agent import *\nfrom elegantrl.env import build_env\nfrom elegantrl.run import Arguments, train_and_evaluate, train_and_evaluate_mp\n\n\"\"\"[ElegantRL.2021.10.10](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\n'''train'''\n\n\ndef demo_continuous_action_off_policy(): # [ElegantRL.2021.10.10]\n env_name = ['Pendulum-v1', 'LunarLanderContinuous-v2',\n 'BipedalWalker-v3', 'BipedalWalkerHardcore-v3'][0]\n agent_class = [AgentModSAC, AgentSAC,\n AgentTD3, AgentDDPG][0]\n args = Arguments(env=build_env(env_name), agent=agent_class())\n\n if env_name in {'Pendulum-v1', 'Pendulum-v0'}:\n \"\"\"EpisodeReturn: (-1800) -1000 ~ -200 (-50)\n Step 2e5, Reward -200, UsedTime 200s ModSAC\n \"\"\"\n # args = Arguments(env=build_env(env_name), agent=agent_class()) # One way to build env\n # args = Arguments(env=env_name, agent=agent_class()) # Another way to build env\n # args.env_num = 1\n # args.max_step = 200\n # args.state_dim = 3\n # args.action_dim = 1\n # args.if_discrete = False\n # args.target_return = -200\n\n args.gamma = 0.97\n args.net_dim = 2 ** 7\n args.worker_num = 2\n args.reward_scale = 2 ** -2\n args.target_step = 200 * 4 # max_step = 200\n if env_name in {'LunarLanderContinuous-v2', 'LunarLanderContinuous-v1'}:\n \"\"\"EpisodeReturn: (-800) -200 ~ 200 (302)\n Step 4e5, Reward 200, UsedTime 900s, TD3\n Step 5e5, Reward 200, UsedTime 1500s, ModSAC\n \"\"\"\n args.eval_times1 = 2 ** 4\n args.eval_times2 = 2 ** 6\n\n args.target_step = args.env.max_step\n if env_name in {'BipedalWalker-v3', 'BipedalWalker-v2'}:\n \"\"\"EpisodeReturn: (-200) -140 ~ 300 (341)\n Step 08e5, Reward 300, UsedTime 1800s TD3\n Step 11e5, Reward 329, UsedTime 6000s TD3\n Step 4e5, Reward 300, UsedTime 2000s ModSAC\n Step 8e5, Reward 330, UsedTime 5000s ModSAC\n \"\"\"\n args.eval_times1 = 2 ** 3\n args.eval_times2 = 2 ** 5\n\n args.gamma = 0.98\n args.target_step = args.env.max_step\n if env_name in {'BipedalWalkerHardcore-v3', 'BipedalWalkerHardcore-v2'}:\n '''EpisodeReturn: (-200) -150 ~ 300 (334)\n TotalStep (2e6) 4e6\n \n Step 12e5, Reward 20\n Step 18e5, Reward 135\n Step 25e5, Reward 202\n Step 43e5, Reward 309, UsedTime 68ks, ModSAC, worker_num=4\n \n Step 14e5, Reward 15\n Step 18e5, Reward 117\n Step 28e5, Reward 212\n Step 45e5, Reward 306, UsedTime 67ks, ModSAC, worker_num=4\n \n Step 8e5, Reward 13\n Step 16e5, Reward 136\n Step 23e5, Reward 219\n Step 38e5, Reward 302\n UsedTime 99ks ModSAC, worker_num=2\n '''\n args.gamma = 0.98\n args.net_dim = 2 ** 8\n args.max_memo = 2 ** 22\n args.break_step = int(80e6)\n args.batch_size = args.net_dim * 2\n args.repeat_times = 1.5\n args.learning_rate = 2 ** -15\n\n args.eval_gap = 2 ** 9\n args.eval_times1 = 2 ** 2\n args.eval_times2 = 2 ** 5\n\n args.worker_num = 4\n args.target_step = args.env.max_step * 1\n # args.learner_gpus = (0, ) # single GPU\n # args.learner_gpus = (0, 1) # multiple GPUs\n # train_and_evaluate(args) # single process\n train_and_evaluate_mp(args) # multiple process\n\n\ndef demo_continuous_action_on_policy(): # [ElegantRL.2021.10.13]\n env_name = ['Pendulum-v1', 'LunarLanderContinuous-v2',\n 'BipedalWalker-v3', 'BipedalWalkerHardcore-v3'][ENV_ID]\n agent_class = [AgentPPO, AgentA2C][0]\n args = Arguments(env=build_env(env_name), agent=agent_class())\n # args.if_per_or_gae = True\n\n if env_name in {'Pendulum-v1', 'Pendulum-v0'}:\n \"\"\"\n Step 45e4, Reward -138, UsedTime 373s PPO\n Step 40e4, Reward -200, UsedTime 400s PPO\n Step 46e4, Reward -213, UsedTime 300s PPO\n \"\"\"\n # args = Arguments(env=build_env(env_name), agent=agent_class()) # One way to build env\n # args = Arguments(env=env_name, agent=agent_class()) # Another way to build env\n # args.env_num = 1\n # args.max_step = 200\n # args.state_dim = 3\n # args.action_dim = 1\n # args.if_discrete = False\n # args.target_return = -200\n\n args.gamma = 0.97\n args.net_dim = 2 ** 8\n args.worker_num = 2\n args.reward_scale = 2 ** -2\n args.target_step = 200 * 16 # max_step = 200\n\n args.eval_gap = 2 ** 5\n if env_name in {'LunarLanderContinuous-v2', 'LunarLanderContinuous-v1'}:\n \"\"\"\n Step 9e5, Reward 210, UsedTime 1127s PPO\n Step 13e5, Reward 223, UsedTime 1416s PPO\n Step 15e5, Reward 250, UsedTime 1648s PPO\n Step 19e5, Reward 201, UsedTime 1880s PPO\n Step 43e5, Reward 224, UsedTime 3738s PPO\n Step 14e5, Reward 213, UsedTime 1654s PPO GAE\n Step 12e5, Reward 216, UsedTime 1710s PPO GAE\n \"\"\"\n args.eval_times1 = 2 ** 4\n args.eval_times2 = 2 ** 6\n\n args.target_step = args.env.max_step * 8\n if env_name in {'BipedalWalker-v3', 'BipedalWalker-v2'}:\n \"\"\"\n Step 51e5, Reward 300, UsedTime 2827s PPO\n Step 78e5, Reward 304, UsedTime 4747s PPO\n Step 61e5, Reward 300, UsedTime 3977s PPO GAE\n Step 95e5, Reward 291, UsedTime 6193s PPO GAE\n \"\"\"\n args.eval_times1 = 2 ** 3\n args.eval_times2 = 2 ** 5\n\n args.gamma = 0.98\n args.target_step = args.env.max_step * 16\n if env_name in {'BipedalWalkerHardcore-v3', 'BipedalWalkerHardcore-v2'}:\n \"\"\"\n Step 57e5, Reward 295, UsedTime 17ks PPO\n Step 70e5, Reward 300, UsedTime 21ks PPO\n \"\"\"\n args.gamma = 0.98\n args.net_dim = 2 ** 8\n args.max_memo = 2 ** 22\n args.batch_size = args.net_dim * 4\n args.repeat_times = 2 ** 4\n args.learning_rate = 2 ** -16\n\n args.eval_gap = 2 ** 8\n args.eval_times1 = 2 ** 2\n args.eval_times2 = 2 ** 5\n # args.break_step = int(80e5)\n\n args.worker_num = 4\n args.target_step = args.env.max_step * 16\n\n args.learner_gpus = (GPU_ID,) # single GPU\n # args.learner_gpus = (0, 1) # multiple GPUs\n # train_and_evaluate(args) # single process\n train_and_evaluate_mp(args) # multiple process\n\n\ndef demo_discrete_action_off_policy(): # [ElegantRL.2021.10.10]\n env_name = ['CartPole-v0', 'LunarLander-v2',\n 'SlimeVolley-v0', ][0]\n agent_class = [AgentDoubleDQN, AgentDQN][0]\n args = Arguments(env=build_env(env_name), agent=agent_class())\n args.agent.if_use_dueling = True # DuelingDQN\n\n if env_name in {'CartPole-v0', }:\n \"Step 1e5, Reward 200, UsedTime 40s, AgentD3QN\"\n args.target_return = 195\n\n args.reward_scale = 2 ** -1\n args.target_step = args.env.max_step * 4\n\n args.eval_gap = 2 ** 5\n if env_name in {'LunarLander-v2', }:\n \"Step 29e4, Reward 222, UsedTime 5811s D3QN\"\n args.max_memo = 2 ** 19\n\n args.reward_scale = 2 ** -1\n args.target_step = args.env.max_step\n\n # args.learner_gpus = (0, ) # single GPU\n # args.learner_gpus = (0, 1) # multiple GPUs\n # train_and_evaluate(args) # single process\n train_and_evaluate_mp(args) # multiple process\n\n\ndef demo_discrete_action_on_policy(): # [ElegantRL.2021.10.12]\n env_name = ['CartPole-v0', 'LunarLander-v2',\n 'SlimeVolley-v0', ][0]\n agent_class = [AgentDiscretePPO, AgentDiscreteA2C][0]\n args = Arguments(env=build_env(env_name), agent=agent_class())\n\n if env_name in {'CartPole-v0', }:\n \"Step 1e5, Reward 200, UsedTime 40s, DiscretePPO\"\n args.target_return = 195\n\n args.reward_scale = 2 ** -1\n args.target_step = args.env.max_step * 8\n\n args.eval_gap = 2 ** 5\n train_and_evaluate(args) # single process\n if env_name in {'LunarLander-v2', }:\n '''\n Step 70e5, Reward 110, UsedTime 9961s DiscretePPO, repeat_times = 2 ** 4\n Step 10e5, Reward 218, UsedTime 1336s DiscretePPO, repeat_times = 2 ** 5\n '''\n args.reward_scale = 2 ** -1\n args.repeat_times = 2 ** 5\n\n args.worker_num = 2\n args.target_step = args.env.max_step * 4\n train_and_evaluate_mp(args) # multiple process\n\n\ndef demo_pixel_level_on_policy(): # 2021-09-07\n env_name = ['CarRacingFix', ][ENV_ID]\n agent_class = [AgentPPO, AgentSharePPO, AgentShareA2C][0]\n # args = Arguments(env=build_env(env_name, if_print=True), agent=agent_class())\n args = Arguments(env=env_name, agent=agent_class())\n\n if env_name == 'CarRacingFix':\n args.state_dim = (112, 112, 6)\n args.action_dim = 6\n args.max_step = 512\n args.if_discrete = False\n args.target_return = 950\n\n \"Step 12e5, Reward 300, UsedTime 10ks PPO\"\n \"Step 20e5, Reward 700, UsedTime 25ks PPO\"\n \"Step 40e5, Reward 800, UsedTime 50ks PPO\"\n args.agent.ratio_clip = 0.5\n args.agent.explore_rate = 0.75\n args.agent.if_use_cri_target = True\n\n args.gamma = 0.98\n args.net_dim = 2 ** 8\n args.repeat_times = 2 ** 4\n args.learning_rate = 2 ** -17\n args.soft_update_tau = 2 ** -11\n args.batch_size = args.net_dim * 4\n args.if_per_or_gae = True\n args.agent.lambda_gae_adv = 0.96\n\n args.eval_gap = 2 ** 9\n args.eval_times1 = 2 ** 2\n args.eval_times1 = 2 ** 4\n args.if_allow_break = False\n args.break_step = int(2 ** 22)\n\n # args.worker_num = 6 # about 96 cores\n args.worker_num = 2 # about 32 cores\n args.target_step = int(args.max_step * 12 / args.worker_num)\n\n args.learner_gpus = (GPU_ID,) # single GPU\n args.eval_gpu_id = GPU_ID\n train_and_evaluate_mp(args)\n\n\ndef demo_isaac_gym_on_policy():\n env_name = ['IsaacVecEnvAnt', 'IsaacVecEnvHumanoid'][0]\n args = Arguments(env=env_name, agent=AgentPPO())\n args.learner_gpus = (0,)\n args.eval_gpu_id = 1\n\n if env_name in {'IsaacVecEnvAnt', 'IsaacOneEnvAnt'}:\n '''\n Step 21e7, Reward 8350, UsedTime 35ks\n Step 484e7, Reward 16206, UsedTime 960ks PPO\n Step 20e7, Reward 9196, UsedTime 35ks\n Step 471e7, Reward 15021, UsedTime 960ks PPO, if_use_cri_target = True\n Step 23e7, Reward 7111, UsedTime 12ks PPO\n Step 22e7, Reward 5412, UsedTime 12ks PPO, max_step * 2\n '''\n args.eval_env = 'IsaacOneEnvAnt'\n args.env = f'IsaacVecEnvAnt'\n args.env_num = 4096\n args.max_step = 1000\n args.state_dim = 60\n args.action_dim = 8\n args.if_discrete = False\n args.target_return = 8000\n\n args.agent.lambda_entropy = 0.05\n args.agent.lambda_gae_adv = 0.97\n args.agent.if_use_cri_target = False\n\n args.if_per_or_gae = True\n args.learning_rate = 2 ** -14\n\n args.net_dim = int(2 ** 8 * 1.5)\n args.batch_size = args.net_dim * 2 ** 4\n args.target_step = args.max_step * 1\n args.repeat_times = 2 ** 4\n args.reward_scale = 2 ** -2 # (-50) 0 ~ 2500 (3340)\n\n args.break_step = int(8e14)\n args.if_allow_break = False\n args.eval_times1 = 2 ** 1\n args.eval_times1 = 2 ** 3\n\n if env_name in {'IsaacVecEnvHumanoid', 'IsaacOneEnvHumanoid'}:\n '''\n Step 126e7, Reward 8021\n Step 216e7, Reward 9517\n Step 283e7, Reward 9998\n Step 438e7, Reward 10749, UsedTime 960ks PPO, env_num = 4096\n Step 71e7, Reward 7800\n Step 215e7, Reward 9794, UsedTime 465ks PPO, env_num = 2048\n Step 1e7, Reward 117\n Step 16e7, Reward 538\n Step 21e7, Reward 3044\n Step 38e7, Reward 5015\n Step 65e7, Reward 6010\n Step 72e7, Reward 6257, UsedTime 129ks PPO, if_use_cri_target = True\n Step 77e7, Reward 5399, UsedTime 143ks PPO\n Step 86e7, Reward 5822, UsedTime 157ks PPO, max_step * 2\n '''\n args.eval_env = 'IsaacOneEnvHumanoid'\n args.env = f'IsaacVecEnvHumanoid'\n args.env_num = 4096\n args.max_step = 1000\n args.state_dim = 108\n args.action_dim = 21\n args.if_discrete = False\n args.target_return = 7000\n\n args.agent.lambda_entropy = 0.05\n args.agent.lambda_gae_adv = 0.97\n args.agent.if_use_cri_target = True\n\n args.net_dim = int(2 ** 8 * 1.5)\n args.batch_size = args.net_dim * 2 ** 5\n args.target_step = args.max_step * 1\n args.repeat_times = 2 ** 4\n args.reward_scale = 2 ** -2 # (-50) 0 ~ 2500 (3340)\n args.if_per_or_gae = True\n args.learning_rate = 2 ** -15\n\n args.break_step = int(8e14)\n args.if_allow_break = False\n args.eval_times1 = 2 ** 1\n args.eval_times1 = 2 ** 3\n\n args.worker_num = 1\n args.workers_gpus = args.learner_gpus\n train_and_evaluate_mp(args) # train_and_evaluate(args)\n\n\ndef demo_pybullet_off_policy():\n env_name = ['AntBulletEnv-v0', 'HumanoidBulletEnv-v0',\n 'ReacherBulletEnv-v0', 'MinitaurBulletEnv-v0', ][0]\n agent_class = [AgentModSAC, AgentTD3,\n AgentShareSAC, AgentShareAC][0]\n args = Arguments(env=build_env(env_name, if_print=True), agent=agent_class())\n\n if env_name == 'AntBulletEnv-v0':\n \"\"\"EpisodeReturn (-50) 0 ~ 2500 (3340)\n TotalStep (8e5) 10e5\n 0 4.29e+06 2446.47 | 431.34 82.1 999 0 | 0.08 1.65-275.32 0.26 | UsedTime 14393 |\n 0 1.41e+07 3499.37 | 3317.42 5.9 999 0 | 0.24 0.06 -49.94 0.03 | UsedTime 70020 |\n 0 3.54e+06 2875.30 | 888.67 4.7 999 0 | 0.19 0.11 -69.10 0.05 | UsedTime 54701 |\n 0 2.00e+07 2960.38 | 698.58 42.5 999 0 | 0.08 0.05 -39.44 0.03 | UsedTime 53545 |\n \"\"\"\n args.agent.if_use_act_target = False\n\n args.net_dim = 2 ** 9\n args.max_memo = 2 ** 22\n args.repeat_times = 2 ** 1\n args.reward_scale = 2 ** -2\n args.batch_size = args.net_dim * 2\n args.target_step = args.env.max_step * 2\n\n args.eval_gap = 2 ** 8\n args.eval_times1 = 2 ** 1\n args.eval_times2 = 2 ** 4\n args.break_step = int(8e7)\n args.if_allow_break = False\n if env_name == 'HumanoidBulletEnv-v0':\n \"\"\"\n 0 1.50e+07 2571.46 | 53.63 66.8 128 58 | 0.04 0.96-153.29 0.06 | UsedTime 74470 |\n 0 1.51e+07 2822.93 | -1.51 27.1 99 36 | 0.03 0.58 -96.48 0.04 | UsedTime 74480 |\n 0 1.09e+06 66.96 | 58.69 8.2 58 12 | 0.22 0.28 -22.92 0.00\n 0 3.01e+06 129.69 | 101.39 40.6 96 33 | 0.14 0.28 -20.16 0.03\n 0 5.02e+06 263.13 | 208.69 122.6 195 59 | 0.11 0.29 -32.71 0.03\n 0 6.03e+06 791.89 | 527.79 282.7 360 144 | 0.21 0.26 -36.51 0.03\n 0 8.00e+06 2432.21 | 35.78 49.3 113 54 | -0.08 1.30-168.28 0.05\n 0 8.13e+06 2432.21 | 907.28 644.9 606 374 | 0.11 0.72-134.01 0.05\n 0 8.29e+06 2432.21 | 2341.30 39.4 999 0 | 0.41 0.41 -96.96 0.03\n 0 1.09e+07 2936.10 | 2936.10 24.8 999 0 | 0.60 0.13 -68.74 0.02\n 0 2.83e+07 2968.08 | 2737.18 15.9 999 0 | 0.57 0.21 -81.07 0.03 | UsedTime 74512 |\n \"\"\"\n args.net_dim = 2 ** 9\n args.reward_scale = 2 ** -2\n args.batch_size = args.net_dim * 2\n args.target_step = args.env.max_step * 2\n\n args.break_step = int(8e7)\n args.if_allow_break = False\n if env_name == 'ReacherBulletEnv-v0':\n \"\"\"EpisodeReturn (-37) 0 ~ 18 (29) \n TotalStep: (4e4) 5e4 # low eval_times\n \"\"\"\n args.explore_rate = 0.9\n args.learning_rate = 2 ** -15\n\n args.gamma = 0.99\n args.net_dim = 2 ** 8\n args.break_step = int(4e7)\n args.batch_size = args.net_dim * 2\n args.repeat_times = 2 ** 0\n args.reward_scale = 2 ** 2\n\n args.target_step = args.env.max_step * 4\n\n args.eval_gap = 2 ** 8\n args.eval_times1 = 2 ** 3\n args.eval_times2 = 2 ** 5\n if env_name == 'MinitaurBulletEnv-v0':\n \"\"\"EpisodeReturn (-2) 0 ~ 16 (20)\n TotalStep (2e6) 4e6\n 0 1.00e+06 0.46 | 0.24 0.0 98 37 | 0.06 0.06 -7.64 0.02\n 0 1.26e+06 1.36 | 1.36 0.7 731 398 | 0.10 0.08 -10.40 0.02\n 0 1.30e+06 3.18 | 3.18 0.8 999 0 | 0.13 0.08 -10.99 0.02\n 0 2.00e+06 3.18 | 0.04 0.0 28 0 | 0.13 0.09 -16.02 0.02\n 0 4.04e+06 7.11 | 6.68 0.6 999 0 | 0.17 0.08 -19.67 0.02\n 0 5.72e+06 9.79 | 9.28 0.1 999 0 | 0.22 0.03 -23.89 0.01\n 0 6.01e+06 10.69 | 10.09 0.8 999 0 | 0.22 0.03 -24.98 0.01\n \"\"\"\n\n args.net_dim = 2 ** 9\n args.reward_scale = 2 ** 5 # (-2) 0 ~ 16 (20)\n args.learning_rate = 2 ** -16\n args.batch_size = args.net_dim * 2\n args.target_step = args.env.max_step * 2\n\n args.eval_gap = 2 ** 8\n args.eval_times1 = 2 ** 2\n args.eval_times2 = 2 ** 4\n args.break_step = int(8e7)\n args.if_allow_break = False\n\n args.worker_num = 4\n args.learner_gpus = (0,)\n train_and_evaluate_mp(args)\n\n\ndef demo_pybullet_on_policy():\n env_name = ['AntBulletEnv-v0', 'HumanoidBulletEnv-v0',\n 'ReacherBulletEnv-v0', 'MinitaurBulletEnv-v0', ][0]\n agent_class = [AgentPPO, AgentSharePPO][0]\n args = Arguments(env=build_env(env_name, if_print=True), agent=agent_class())\n\n if env_name == 'AntBulletEnv-v0':\n \"\"\"\n 0 1.98e+07 3322.16 | 3322.16 48.7 999 0 | 0.78 0.48 -0.01 -0.80 | UsedTime 12380 PPO\n 0 1.99e+07 3104.05 | 3071.44 14.5 999 0 | 0.74 0.47 0.01 -0.79 | UsedTime 12976\n 0 1.98e+07 3246.79 | 3245.98 25.3 999 0 | 0.75 0.48 -0.02 -0.81 | UsedTime 13170\n 0 1.97e+07 3345.48 | 3345.48 29.0 999 0 | 0.80 0.49 -0.01 -0.81 | UsedTime 8169 PPO 2GPU\n 0 1.98e+07 3028.69 | 3004.67 10.3 999 0 | 0.72 0.48 0.05 -0.82 | UsedTime 8734 PPO 2GPU\n \"\"\"\n args.agent.lambda_entropy = 0.05\n args.agent.lambda_gae_adv = 0.97\n\n args.net_dim = 2 ** 9\n args.reward_scale = 2 ** -2 # (-50) 0 ~ 2500 (3340)\n args.repeat_times = 2 ** 3\n args.learning_rate = 2 ** -15\n args.if_per_or_gae = True\n args.batch_size = args.net_dim * 2 ** 3\n args.target_step = args.env.max_step * 2\n\n args.break_step = int(8e7)\n args.if_allow_break = False\n if env_name == 'HumanoidBulletEnv-v0':\n \"\"\"\n 0 2.00e+07 2049.87 | 1905.57 686.5 883 308 | 0.93 0.42 -0.02 -1.14 | UsedTime 15292\n 0 3.99e+07 2977.80 | 2611.64 979.6 879 317 | 1.29 0.46 -0.01 -1.16 | UsedTime 19685\n 0 7.99e+07 3047.88 | 3041.95 41.1 999 0 | 1.37 0.46 -0.04 -1.15 | UsedTime 38693\n \"\"\"\n args.agent.lambda_entropy = 0.02\n args.agent.lambda_gae_adv = 0.97\n\n args.net_dim = 2 ** 9\n args.batch_size = args.net_dim * 2 ** 3\n args.target_step = args.env.max_step * 4\n args.reward_scale = 2 ** -1\n args.repeat_times = 2 ** 3\n args.if_per_or_gae = True\n args.learning_rate = 2 ** -14\n\n args.break_step = int(8e7)\n args.if_allow_break = False\n if env_name == 'ReacherBulletEnv-v0':\n '''eval_times = 4\n Step 1e5, Return: 18, UsedTime 3ks, PPO eval_times =< 4\n Step 1e6, Return: 18, UsedTime 30ks, PPO eval_times =< 4\n\n eval_times = 64\n The probability of the following results is only 25%. \n 0 5.00e+05 3.23 | 3.23 12.6 149 0 | -0.03 0.64 -0.03 -0.51\n 0 3.55e+06 7.69 | 7.69 10.3 149 0 | -0.19 0.56 -0.04 -0.59\n 0 5.07e+06 9.72 | 7.89 7.6 149 0 | 0.27 0.24 0.02 -0.71\n 0 6.85e+06 11.89 | 6.52 12.3 149 0 | 0.22 0.18 -0.06 -0.85\n 0 7.87e+06 18.59 | 18.59 9.4 149 0 | 0.39 0.18 -0.01 -0.94\n 0 1.01e+06 -2.19 | -7.30 10.9 149 0 | -0.05 0.70 0.03 -0.52\n 0 4.05e+06 9.29 | -1.86 15.0 149 0 | 0.08 0.28 -0.05 -0.65\n 0 4.82e+06 11.12 | 11.12 12.4 149 0 | 0.13 0.26 -0.07 -0.71\n 0 6.07e+06 15.66 | 15.66 11.1 149 0 | 0.16 0.14 0.00 -0.81\n 0 9.46e+06 18.58 | 18.58 8.2 149 0 | 0.19 0.10 -0.06 -1.09\n 0 2.20e+06 3.63 | 3.26 7.3 149 0 | -0.05 0.43 -0.01 -0.55\n 0 4.19e+06 5.24 | 4.60 9.2 149 0 | 0.04 0.24 0.00 -0.66\n 0 6.16e+06 5.24 | 4.80 9.2 149 0 | 0.03 0.15 -0.00 -0.81\n 0 7.40e+06 12.99 | 12.99 13.2 149 0 | 0.07 0.19 -0.03 -0.91\n 0 1.01e+07 18.09 | 18.09 7.6 149 0 | 0.18 0.16 -0.00 -1.09\n 0 1.06e+06 3.25 | 3.25 7.6 149 0 | -0.21 0.72 -0.05 -0.51 \n 0 2.13e+06 3.56 | 0.94 6.1 149 0 | 0.08 0.54 0.02 -0.56\n 0 5.85e+06 11.61 | 11.61 11.0 149 0 | 0.13 0.22 -0.05 -0.78\n 0 9.04e+06 14.07 | 13.57 10.5 149 0 | 0.27 0.17 0.01 -1.05\n 0 1.01e+07 16.16 | 16.16 10.8 149 0 | 0.29 0.19 -0.08 -1.13\n 0 1.14e+07 21.33 | 21.33 7.8 149 0 | 0.21 0.24 -0.06 -1.21\n 0 1.02e+07 4.06 | -3.27 11.1 149 0 | -0.01 0.34 -0.03 -0.88\n 0 2.00e+07 9.23 | -1.57 7.6 149 0 | 0.06 0.12 -0.08 -1.26\n 0 3.00e+07 11.78 | 11.78 7.6 149 0 | 0.05 0.08 -0.04 -1.40\n 0 4.01e+07 13.20 | 12.35 7.8 149 0 | 0.14 0.08 0.01 -1.42\n 0 5.02e+07 14.13 | 11.53 6.5 149 0 | 0.10 0.03 0.03 -1.42\n 0 6.00e+07 15.75 | 6.33 6.1 149 0 | 0.18 0.13 -0.03 -1.43\n 0 7.29e+07 20.71 | 20.71 8.1 149 0 | 0.16 0.03 -0.00 -1.41\n '''\n args.agent.ratio_clip = 0.5\n args.agent.lambda_gae_adv = 0.97\n args.agent.if_use_cri_target = True\n\n args.gamma = 0.99\n args.reward_scale = 2 ** 1\n args.if_per_or_gae = True\n args.break_step = int(8e7)\n args.explore_rate = 0.9\n args.learning_rate = 2 ** -16\n\n args.net_dim = 2 ** 8\n args.batch_size = args.net_dim * 4\n args.repeat_times = 2 ** 4\n args.target_step = args.env.max_step * 4\n\n args.eval_gap = 2 ** 8\n args.eval_times1 = 2 ** 3\n args.eval_times2 = 2 ** 5\n if env_name == 'MinitaurBulletEnv-v0':\n \"\"\"EpisodeReturn (-2) 0 ~ 16 (PPO 34)\n TotalStep (PPO 3e5) 5e5\n 0 5.91e+05 10.59 | 10.59 3.9 727 282 | 0.27 0.69 -0.03 -0.52\n 0 1.15e+06 14.91 | 12.48 2.2 860 158 | 0.40 0.65 -0.02 -0.55\n 0 2.27e+06 25.38 | 22.54 4.7 968 54 | 0.75 0.61 -0.06 -0.60\n 0 4.13e+06 29.05 | 28.33 1.0 999 0 | 0.89 0.51 -0.07 -0.65\n 0 8.07e+06 32.66 | 32.17 0.9 999 0 | 0.97 0.45 -0.06 -0.73\n 0 1.10e+07 32.66 | 32.33 1.3 999 0 | 0.94 0.40 -0.07 -0.80 | UsedTime 20208 |\n\n 0 5.91e+05 5.48 | 5.48 1.5 781 219 | 0.24 0.66 -0.04 -0.52\n 0 1.01e+06 12.35 | 9.77 2.9 754 253 | 0.34 0.74 -0.05 -0.54\n 0 2.10e+06 12.35 | 12.21 4.8 588 285 | 0.60 0.65 -0.01 -0.58\n 0 4.09e+06 28.31 | 22.88 12.6 776 385 | 0.88 0.51 -0.03 -0.66\n 0 8.03e+06 30.96 | 28.32 6.8 905 163 | 0.93 0.52 -0.05 -0.76\n 0 1.09e+07 32.07 | 31.29 0.9 999 0 | 0.95 0.47 -0.07 -0.82 | UsedTime 20238 |\n \"\"\"\n args.agent.lambda_entropy = 0.05\n args.agent.lambda_gae_adv = 0.97\n\n args.net_dim = 2 ** 9\n args.reward_scale = 2 ** 5 # (-2) 0 ~ 16 (20)\n args.repeat_times = 2 ** 4\n args.batch_size = args.net_dim * 4\n args.target_step = args.env.max_step * 2\n args.if_per_or_gae = True\n args.learning_rate = 2 ** -15\n\n args.break_step = int(8e7)\n args.if_allow_break = False\n\n args.eval_gap = 2 ** 8\n args.eval_times1 = 2 ** 2\n args.eval_times2 = 2 ** 4\n args.worker_num = 4\n args.learner_gpus = (0,)\n train_and_evaluate_mp(args)\n\n\ndef demo_step1_off_policy():\n env_name = ['DownLinkEnv-v0', 'DownLinkEnv-v1'][ENV_ID]\n agent_class = [AgentStep1AC, AgentShareStep1AC][1]\n args = Arguments(env=build_env(env_name), agent=agent_class())\n args.random_seed += GPU_ID\n\n args.net_dim = 2 ** 8\n args.batch_size = int(args.net_dim * 2 ** -1)\n\n args.max_memo = 2 ** 17\n args.target_step = int(args.max_memo * 2 ** -4)\n args.repeat_times = 0.75\n args.reward_scale = 2 ** 2\n args.agent.exploration_noise = 2 ** -5\n\n args.eval_gpu_id = GPU_ID\n args.eval_gap = 2 ** 9\n args.eval_times1 = 2 ** 0\n args.eval_times2 = 2 ** 1\n\n args.learner_gpus = (GPU_ID,)\n\n if_use_single_process = 0\n if if_use_single_process:\n train_and_evaluate(args, )\n else:\n args.worker_num = 4\n train_and_evaluate_mp(args, )\n\n\n'''train and watch'''\n\nif __name__ == '__main__':\n GPU_ID = 0 # eval(sys.argv[1])\n ENV_ID = 0 # eval(sys.argv[2])\n # demo_continuous_action_off_policy()\n # demo_continuous_action_on_policy()\n # demo_discrete_action_off_policy()\n # demo_discrete_action_on_policy()\n # demo_pixel_level_on_policy()\n # demo_pybullet_off_policy()\n # demo_pybullet_on_policy()\n # demo_step1_off_policy()\n pass\n" }, { "path": "elegantrl/env.py", "content": "import os\nimport gym # not necessary\nimport numpy as np\nfrom copy import deepcopy\n\n\"\"\"[ElegantRL.2021.11.08](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\ngym.logger.set_level(40) # Block warning\n\n\"\"\"register your custom env here.\"\"\"\n\n\ndef build_env(env, if_print=False, env_num=1, device_id=None, args=None, ):\n if isinstance(env, str):\n env_name = env\n else:\n env_name = env.env_name\n env = None\n\n '''OpenAI gym classical control'''\n if env_name in {'CartPole-v0', 'CartPole-v1'}:\n env = gym.make(env_name)\n env = PreprocessEnv(env, if_print=if_print)\n elif env_name in {'Pendulum-v1', 'Pendulum-v0'}:\n env = PendulumEnv(env_name)\n\n '''OpenAI gym Box2D'''\n # pip3 install Box2D==2.3.8 or 2.3.10\n if env_name in {'LunarLander-v2', 'LunarLanderContinuous-v2',\n 'BipedalWalker-v3', 'BipedalWalkerHardcore-v3', }:\n env = gym.make(env_name)\n env = PreprocessEnv(env, if_print=if_print) # todo plan to be elegant\n elif env_name == 'CarRacingFix': # Box2D\n from envs.CarRacingFix import CarRacingFix\n env = CarRacingFix()\n if if_print: # todo plan to be elegant\n print(f\"\\n| env_name: {env.env_name}, action if_discrete: {env.if_discrete}\"\n f\"\\n| state_dim: {env.state_dim}, action_dim: {env.action_dim}\"\n f\"\\n| max_step: {env.max_step:4}, target_return: {env.target_return}\")\n\n '''PyBullet gym'''\n if env_name in {'ReacherBulletEnv-v0', 'AntBulletEnv-v0',\n 'HumanoidBulletEnv-v0', 'MinitaurBulletEnv-v0'}:\n import pybullet_envs\n dir(pybullet_envs)\n env = gym.make(env_name)\n env = PreprocessEnv(env, if_print=if_print)\n\n '''NVIDIA Isaac gym'''\n if env_name.find('Isaac') >= 0:\n from envs.IsaacGym import PreprocessIsaacOneEnv, PreprocessIsaacVecEnv\n\n env_last_name = env_name[11:]\n assert env_last_name in {'Ant', 'Humanoid'}\n\n if env_name.find('IsaacOneEnv') >= 0:\n env = PreprocessIsaacOneEnv(env_last_name, if_print=if_print, env_num=1, device_id=device_id)\n elif env_name.find('IsaacVecEnv') >= 0:\n env = PreprocessIsaacVecEnv(env_last_name, if_print=if_print, env_num=env_num, device_id=device_id)\n else:\n raise ValueError(f'| build_env_from_env_name: need register: {env_name}')\n return env\n\n # elif env_name[:10] in {'StockDOW5', 'StockDOW30', 'StockNAS74', 'StockNAS89'}:\n # if_eval = env_name.find('eval') != -1\n # gamma = 0.993\n # from elegantrl.envs.FinRL.StockTradingEnv import StockEnvDOW5, StockEnvDOW30, StockEnvNAS74, StockEnvNAS89\n # env_class = {'StockDOW5': StockEnvDOW5,\n # 'StockDOW30': StockEnvDOW30,\n # 'StockNAS74': StockEnvNAS74,\n # 'StockNAS89': StockEnvNAS89,\n # }[env_name[:10]]\n # env = env_class(if_eval=if_eval, gamma=gamma)\n\n if env_name in {'DownLinkEnv-v0', 'DownLinkEnv-v1'}:\n if env_name == 'DownLinkEnv-v0':\n from envs.DownLink import DownLinkEnv\n env = DownLinkEnv(bs_n=4, ur_n=8, power=1.0, csi_noise_var=0.1, csi_clip=3.0)\n elif env_name == 'DownLinkEnv-v1':\n from envs.DownLink import DownLinkEnv1\n env = DownLinkEnv1(bs_n=4, ur_n=8, power=1.0, csi_noise_var=0.1, csi_clip=3.0,\n env_cwd=getattr(args, 'cwd', '.'))\n else:\n raise ValueError(\"| env.py, build_env(), DownLinkEnv\")\n \n if env_name[:6]=='wt4rl-':\n env = gym.make(env_name)\n\n if env is None:\n try:\n env = deepcopy(env)\n print(f\"| build_env(): Warning. NOT suggest to use `deepcopy(env)`. env_name: {env_name}\")\n except Exception as error:\n print(f\"| build_env(): Error. {error}\")\n raise ValueError(\"| build_env(): register your custom env in this function.\")\n return env\n\n\ndef build_eval_env(eval_env, env, env_num, eval_gpu_id, args, ):\n if isinstance(eval_env, str):\n eval_env = build_env(env=eval_env, if_print=False, env_num=env_num, device_id=eval_gpu_id, args=args, )\n elif eval_env is None:\n eval_env = build_env(env=env, if_print=False, env_num=env_num, device_id=eval_gpu_id, args=args, )\n else:\n assert hasattr(eval_env, 'reset')\n assert hasattr(eval_env, 'step')\n return eval_env\n\n\n\"\"\"a demo tell you how to build a custom env\"\"\"\n\n\nclass PendulumEnv: # [ElegantRL.2021.10.10]\n def __init__(self, env_name):\n assert env_name in {'Pendulum-v1', 'Pendulum-v0'}\n try:\n env_name = 'Pendulum-v0' # gym.__version__ == 0.17.0\n self.env = gym.make(env_name)\n except KeyError:\n env_name = 'Pendulum-v1' # gym.__version__ == 0.21.0\n self.env = gym.make(env_name)\n self.env_name = env_name # assert isinstance(env_name, str)\n\n # from elegantrl.env import get_gym_env_info\n # get_gym_env_info(env, if_print=True) # use this function to see the env information\n self.env_num = 1 # the env number of VectorEnv is greater than 1\n self.max_step = 200 # the max step of each episode\n self.state_dim = 3 # feature number of state\n self.action_dim = 1 # feature number of action\n self.if_discrete = False # discrete action or continuous action\n self.target_return = -200 # episode return is between (-1600, 0)\n\n def reset(self):\n return self.env.reset()\n\n def step(self, action):\n # PendulumEnv set its action space as (-2, +2). It is bad. # https://github.com/openai/gym/wiki/Pendulum-v0\n # I suggest you to set action space as (-1, +1) when you design your own env.\n return self.env.step(action * 2) # state, reward, done, info_dict\n\n def render(self):\n self.env.render()\n\n\n\"\"\"Utils\"\"\"\n\n\nclass PreprocessEnv(gym.Wrapper): # environment wrapper\n def __init__(self, env, if_print=True, if_norm=False):\n \"\"\"Preprocess a standard OpenAI gym environment for training.\n\n `object env` a standard OpenAI gym environment, it has env.reset() and env.step()\n `bool if_print` print the information of environment. Such as env_name, state_dim ...\n `object data_type` convert state (sometimes float64) to data_type (float32).\n \"\"\"\n self.env = gym.make(env) if isinstance(env, str) else env\n super().__init__(self.env)\n\n (self.env_name, self.state_dim, self.action_dim, self.max_step,\n self.if_discrete, self.target_return) = get_gym_env_info(self.env, if_print)\n self.env.env_num = getattr(self.env, 'env_num', 1)\n self.env_num = 1\n\n if if_norm:\n state_avg, state_std = get_avg_std__for_state_norm(self.env_name)\n self.neg_state_avg = -state_avg\n self.div_state_std = 1 / (state_std + 1e-4)\n\n self.reset = self.reset_norm\n self.step = self.step_norm\n else:\n self.reset = self.reset_type\n self.step = self.step_type\n\n def reset_type(self):\n return self.env.reset()\n\n def step_type(self, action) -> (np.ndarray, float, bool, dict):\n return self.env.step(action)\n\n def reset_norm(self):\n \"\"\" convert the data type of state from float64 to float32\n do normalization on state\n\n return `array state` state.shape==(state_dim, )\n \"\"\"\n state = self.env.reset()\n return (state + self.neg_state_avg) * self.div_state_std\n\n def step_norm(self, action) -> (np.ndarray, float, bool, dict):\n \"\"\"convert the data type of state from float64 to float32,\n do normalization on state\n\n return `array state` state.shape==(state_dim, )\n return `float reward` reward of one step\n return `bool done` the terminal of an training episode\n return `dict info` the information save in a dict. OpenAI gym standard. Send a `None` is OK\n \"\"\"\n state, reward, done, info = self.env.step(action)\n state = (state + self.neg_state_avg) * self.div_state_std\n return state, reward, done, info\n\n\ndef get_gym_env_info(env, if_print) -> (str, int, int, int, bool, float): # [ElegantRL.2021.10.10]\n \"\"\"get information of a standard OpenAI gym env.\n\n The DRL algorithm AgentXXX need these env information for building networks and training.\n\n `object env` a standard OpenAI gym environment, it has env.reset() and env.step()\n `bool if_print` print the information of environment. Such as env_name, state_dim ...\n return `env_name` the environment name, such as XxxXxx-v0\n return `state_dim` the dimension of state\n return `action_dim` the dimension of continuous action; Or the number of discrete action\n return `max_step` the steps in an episode. (from env.reset to done). It breaks an episode when it reach max_step\n return `if_discrete` Is this env a discrete action space?\n return `target_return` the target episode return, if agent reach this score, then it pass this game (env).\n \"\"\"\n\n if isinstance(env, gym.Env):\n env_name = getattr(env, 'env_name', None)\n env_name = env.unwrapped.spec.id if env_name is None else env_name\n\n state_shape = env.observation_space.shape\n state_dim = state_shape[0] if len(state_shape) == 1 else state_shape # sometimes state_dim is a list\n\n target_return = getattr(env, 'target_return', None)\n target_return_default = getattr(env.spec, 'reward_threshold', None)\n if target_return is None:\n target_return = target_return_default\n if target_return is None:\n target_return = 2 ** 16\n\n max_step = getattr(env, 'max_step', None)\n max_step_default = getattr(env, '_max_episode_steps', None)\n if max_step is None:\n max_step = max_step_default\n if max_step is None:\n max_step = 2 ** 10\n\n if_discrete = isinstance(env.action_space, gym.spaces.Discrete)\n if if_discrete: # make sure it is discrete action space\n action_dim = env.action_space.bs_n\n elif isinstance(env.action_space, gym.spaces.Box): # make sure it is continuous action space\n action_dim = env.action_space.shape[0]\n assert not any(env.action_space.high - 1)\n assert not any(env.action_space.low + 1)\n else:\n raise RuntimeError('\\n| Error in get_gym_env_info()'\n '\\n Please set these value manually: if_discrete=bool, action_dim=int.'\n '\\n And keep action_space in (-1, 1).')\n else:\n env_name = env.env_name\n state_dim = env.state_dim\n action_dim = env.action_dim\n max_step = env.max_step\n if_discrete = env.if_discrete\n target_return = env.target_return\n\n if if_print:\n print(f\"\\n| env_name: {env_name}, action if_discrete: {if_discrete}\"\n f\"\\n| state_dim: {state_dim:4}, action_dim: {action_dim}\"\n f\"\\n| max_step: {max_step:4}, target_return: {target_return}\")\n return env_name, state_dim, action_dim, max_step, if_discrete, target_return\n\n\ndef get_avg_std__for_state_norm(env_name) -> (np.ndarray, np.ndarray):\n \"\"\"return the state normalization data: neg_avg and div_std\n\n ReplayBuffer.print_state_norm() will print `neg_avg` and `div_std`\n You can save these array to here. And PreprocessEnv will load them automatically.\n eg. `state = (state + self.neg_state_avg) * self.div_state_std` in `PreprocessEnv.step_norm()`\n neg_avg = -states.mean()\n div_std = 1/(states.std()+1e-5) or 6/(states.max()-states.min())\n\n `str env_name` the name of environment that helps to find neg_avg and div_std\n return `array avg` neg_avg.shape=(state_dim)\n return `array std` div_std.shape=(state_dim)\n \"\"\"\n avg = 0\n std = 1\n if env_name == 'LunarLanderContinuous-v2':\n avg = np.array([1.65470898e-02, -1.29684399e-01, 4.26883133e-03, -3.42124557e-02,\n -7.39076972e-03, -7.67103031e-04, 1.12640885e+00, 1.12409466e+00])\n std = np.array([0.15094465, 0.29366297, 0.23490797, 0.25931464, 0.21603736,\n 0.25886878, 0.277233, 0.27771219])\n elif env_name == \"BipedalWalker-v3\":\n avg = np.array([1.42211734e-01, -2.74547996e-03, 1.65104509e-01, -1.33418152e-02,\n -2.43243194e-01, -1.73886203e-02, 4.24114229e-02, -6.57800099e-02,\n 4.53460692e-01, 6.08022244e-01, -8.64884810e-04, -2.08789053e-01,\n -2.92092949e-02, 5.04791247e-01, 3.33571745e-01, 3.37325723e-01,\n 3.49106580e-01, 3.70363115e-01, 4.04074671e-01, 4.55838055e-01,\n 5.36685407e-01, 6.70771701e-01, 8.80356865e-01, 9.97987386e-01])\n std = np.array([0.84419678, 0.06317835, 0.16532085, 0.09356959, 0.486594,\n 0.55477525, 0.44076614, 0.85030824, 0.29159821, 0.48093035,\n 0.50323634, 0.48110776, 0.69684234, 0.29161077, 0.06962932,\n 0.0705558, 0.07322677, 0.07793258, 0.08624322, 0.09846895,\n 0.11752805, 0.14116005, 0.13839757, 0.07760469])\n elif env_name == 'ReacherBulletEnv-v0':\n avg = np.array([0.03149641, 0.0485873, -0.04949671, -0.06938662, -0.14157104,\n 0.02433294, -0.09097818, 0.4405931, 0.10299437], dtype=np.float32)\n std = np.array([0.12277275, 0.1347579, 0.14567468, 0.14747661, 0.51311225,\n 0.5199606, 0.2710207, 0.48395795, 0.40876198], dtype=np.float32)\n elif env_name == 'AntBulletEnv-v0':\n avg = np.array([-1.4400886e-01, -4.5074993e-01, 8.5741436e-01, 4.4249415e-01,\n -3.1593361e-01, -3.4174921e-03, -6.1666980e-02, -4.3752361e-03,\n -8.9226037e-02, 2.5108769e-03, -4.8667483e-02, 7.4835382e-03,\n 3.6160579e-01, 2.6877613e-03, 4.7474738e-02, -5.0628246e-03,\n -2.5761038e-01, 5.9789192e-04, -2.1119279e-01, -6.6801407e-03,\n 2.5196713e-01, 1.6556121e-03, 1.0365561e-01, 1.0219718e-02,\n 5.8209229e-01, 7.7563477e-01, 4.8815918e-01, 4.2498779e-01],\n dtype=np.float32)\n std = np.array([0.04128463, 0.19463477, 0.15422264, 0.16463493, 0.16640785,\n 0.08266512, 0.10606721, 0.07636797, 0.7229637, 0.52585346,\n 0.42947173, 0.20228386, 0.44787514, 0.33257666, 0.6440182,\n 0.38659114, 0.6644085, 0.5352245, 0.45194066, 0.20750992,\n 0.4599643, 0.3846344, 0.651452, 0.39733195, 0.49320385,\n 0.41713253, 0.49984455, 0.4943505], dtype=np.float32)\n elif env_name == 'HumanoidBulletEnv-v0':\n avg = np.array([-1.25880212e-01, -8.51390958e-01, 7.07488894e-01, -5.72232604e-01,\n -8.76260102e-01, -4.07587215e-02, 7.27005303e-04, 1.23370838e+00,\n -3.68912554e+00, -4.75829793e-03, -7.42472351e-01, -8.94218776e-03,\n 1.29535913e+00, 3.16205365e-03, 9.13809776e-01, -6.42679911e-03,\n 8.90435696e-01, -7.92571157e-03, 6.54826105e-01, 1.82383414e-02,\n 1.20868635e+00, 2.90832808e-03, -9.96598601e-03, -1.87555347e-02,\n 1.66691601e+00, 7.45300390e-03, -5.63859344e-01, 5.48619963e-03,\n 1.33900166e+00, 1.05895223e-02, -8.30249667e-01, 1.57017610e-03,\n 1.92912612e-02, 1.55787319e-02, -1.19833803e+00, -8.22103582e-03,\n -6.57119334e-01, -2.40323972e-02, -1.05282271e+00, -1.41856335e-02,\n 8.53593826e-01, -1.73063378e-03, 5.46878874e-01, 5.43514848e-01],\n dtype=np.float32)\n std = np.array([0.08138401, 0.41358876, 0.33958328, 0.17817754, 0.17003846,\n 0.15247536, 0.690917, 0.481272, 0.40543965, 0.6078898,\n 0.46960834, 0.4825346, 0.38099176, 0.5156369, 0.6534775,\n 0.45825616, 0.38340876, 0.89671516, 0.14449312, 0.47643778,\n 0.21150663, 0.56597894, 0.56706554, 0.49014297, 0.30507362,\n 0.6868296, 0.25598812, 0.52973163, 0.14948095, 0.49912784,\n 0.42137524, 0.42925757, 0.39722264, 0.54846555, 0.5816031,\n 1.139402, 0.29807225, 0.27311933, 0.34721208, 0.38530213,\n 0.4897849, 1.0748593, 0.30166605, 0.30824476], dtype=np.float32)\n elif env_name == 'MinitaurBulletEnv-v0': # need check\n avg = np.array([0.90172989, 1.54730119, 1.24560906, 1.97365306, 1.9413892,\n 1.03866835, 1.69646277, 1.18655352, -0.45842347, 0.17845232,\n 0.38784456, 0.58572877, 0.91414561, -0.45410697, 0.7591031,\n -0.07008998, 3.43842258, 0.61032482, 0.86689961, -0.33910894,\n 0.47030415, 4.5623528, -2.39108079, 3.03559422, -0.36328256,\n -0.20753499, -0.47758384, 0.86756409])\n std = np.array([0.34192648, 0.51169916, 0.39370621, 0.55568461, 0.46910769,\n 0.28387504, 0.51807949, 0.37723445, 13.16686185, 17.51240024,\n 14.80264211, 16.60461412, 15.72930229, 11.38926597, 15.40598346,\n 13.03124941, 2.47718145, 2.55088804, 2.35964651, 2.51025567,\n 2.66379017, 2.37224904, 2.55892521, 2.41716885, 0.07529733,\n 0.05903034, 0.1314812, 0.0221248])\n return avg, std\n\n\ndef demo_get_video_to_watch_gym_render():\n import cv2 # pip3 install opencv-python\n # import gym # pip3 install gym==0.17 pyglet==1.5.0 # env.render() bug in gym==0.18, pyglet==1.6\n import torch\n\n \"\"\"init env\"\"\"\n env = build_env(env='CarRacingFix')\n\n '''init agent'''\n # agent = None # means use random action\n from elegantrl.agent import AgentPPO\n agent = AgentPPO() # means use the policy network which saved in cwd\n agent_cwd = '/mnt/sdb1/Yonv/code/ElegantRL/AgentPPO_CarRacingFix_3'\n net_dim = 2 ** 8\n state_dim = env.state_dim\n action_dim = env.action_dim\n os.environ['CUDA_VISIBLE_DEVICES'] = '1'\n\n agent.init(net_dim, state_dim, action_dim)\n agent.save_or_load_agent(cwd=agent_cwd, if_save=False)\n device = agent.device\n\n '''initialize evaluete and env.render()'''\n save_frame_dir = '' # means don't save video, just open the env.render()\n # save_frame_dir = 'frames' # means save video in this directory\n if save_frame_dir:\n os.makedirs(save_frame_dir, exist_ok=True)\n\n state = env.reset()\n episode_return = 0\n step = 0\n for i in range(2 ** 10):\n print(i) if i % 128 == 0 else None\n for j in range(1):\n if agent is None:\n action = env.action_space.sample()\n else:\n s_tensor = torch.as_tensor((state,), dtype=torch.float32, device=device)\n a_tensor = agent.act(s_tensor)\n action = a_tensor.detach().cpu().numpy()[0] # if use 'with torch.no_grad()', then '.detach()' not need.\n next_state, reward, done, _ = env.step(action)\n\n episode_return += reward\n step += 1\n\n if done:\n print(f'\\t'\n f'TotalStep {i:>6}, epiStep {step:6.0f}, '\n f'Reward_T {reward:8.3f}, epiReward {episode_return:8.3f}')\n state = env.reset()\n episode_return = 0\n step = 0\n else:\n state = next_state\n\n if save_frame_dir:\n frame = env.render('rgb_array')\n cv2.imwrite(f'{save_frame_dir}/{i:06}.png', frame)\n cv2.imshow('OpenCV Window', frame)\n cv2.waitKey(1)\n else:\n env.render()\n env.close()\n\n '''convert frames png/jpg to video mp4/avi using ffmpeg'''\n if save_frame_dir:\n frame_shape = cv2.imread(f'{save_frame_dir}/{3:06}.png').shape\n print(f\"frame_shape: {frame_shape}\")\n\n save_video = 'gym_render.mp4'\n os.system(f\"| Convert frames to video using ffmpeg. Save in {save_video}\")\n os.system(f'ffmpeg -r 60 -f image2 -s {frame_shape[0]}x{frame_shape[1]} '\n f'-i ./{save_frame_dir}/%06d.png '\n f'-crf 25 -vb 20M -pix_fmt yuv420p {save_video}')\n\n\ndef train_save_eval_watch():\n # from elegantrl.env import build_env\n from elegantrl.run import Arguments, train_and_evaluate\n from elegantrl.agent import AgentDoubleDQN\n\n env = build_env('CartPole-v0')\n agent = AgentDoubleDQN(if_dueling=True)\n cwd = 'demo_CartPole_D3QN'\n\n print('train and save')\n args = Arguments(env=env, agent=agent)\n args.eval_gap = 2 ** 5\n args.target_return = 195\n train_and_evaluate(args) # single process\n\n print('evaluate and watch')\n agent.init(args.net_dim, args.state_dim, args.action_dim, gpu_id=0)\n agent.save_or_load_agent(cwd=cwd, if_save=False)\n state = env.reset()\n for i in range(2 ** 10):\n action = agent.select_action(state)\n next_state, reward, done, _ = env.step(action)\n state = env.reset() if done else next_state\n env.render()\n" }, { "path": "elegantrl/evaluator.py", "content": "import os\nimport time\nimport torch\nimport numpy as np\n\n\"\"\"[ElegantRL.2021.11.10](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\n\nclass Evaluator: # [ElegantRL.2021.10.13]\n def __init__(self, cwd, agent_id, eval_env, eval_gap, eval_times1, eval_times2, target_return, if_overwrite):\n self.recorder = list() # total_step, r_avg, r_std, obj_c, ...\n self.recorder_path = f'{cwd}/recorder.npy'\n\n self.cwd = cwd\n self.agent_id = agent_id\n self.eval_env = eval_env\n self.eval_gap = eval_gap\n self.eval_times1 = eval_times1\n self.eval_times2 = eval_times2\n self.if_overwrite = if_overwrite\n self.target_return = target_return\n\n self.r_max = -np.inf\n self.eval_time = 0\n self.used_time = 0\n self.total_step = 0\n self.start_time = time.time()\n print(f\"{'#' * 80}\\n\"\n f\"{'ID':<3}{'Step':>8}{'maxR':>8} |\"\n f\"{'avgR':>8}{'stdR':>7}{'avgS':>7}{'stdS':>6} |\"\n f\"{'expR':>8}{'objC':>7}{'etc.':>7}\")\n\n def evaluate_and_save(self, act, steps, r_exp, log_tuple) -> (bool, bool): # 2021-09-09\n self.total_step += steps # update total training steps\n\n if time.time() - self.eval_time < self.eval_gap:\n if_reach_goal = False\n if_save = False\n else:\n self.eval_time = time.time()\n\n '''evaluate first time'''\n rewards_steps_list = [get_episode_return_and_step(self.eval_env, act)\n for _ in range(self.eval_times1)]\n r_avg, r_std, s_avg, s_std = self.get_r_avg_std_s_avg_std(rewards_steps_list)\n\n '''evaluate second time'''\n if r_avg > self.r_max: # evaluate actor twice to save CPU Usage and keep precision\n rewards_steps_list += [get_episode_return_and_step(self.eval_env, act)\n for _ in range(self.eval_times2 - self.eval_times1)]\n r_avg, r_std, s_avg, s_std = self.get_r_avg_std_s_avg_std(rewards_steps_list)\n\n '''save the policy network'''\n if_save = r_avg > self.r_max\n if if_save: # save checkpoint with highest episode return\n self.r_max = r_avg # update max reward (episode return)\n\n act_name = 'actor' if self.if_overwrite else f'actor.{self.r_max:08.2f}'\n act_path = f\"{self.cwd}/{act_name}.pth\"\n torch.save(act.state_dict(), act_path) # save policy network in *.pth\n\n print(f\"{self.agent_id:<3}{self.total_step:8.2e}{self.r_max:8.2f} |\") # save policy and print\n\n self.recorder.append((self.total_step, r_avg, r_std, r_exp, *log_tuple)) # update recorder\n\n '''print some information to Terminal'''\n if_reach_goal = bool(self.r_max > self.target_return) # check if_reach_goal\n if if_reach_goal and self.used_time is None:\n self.used_time = int(time.time() - self.start_time)\n print(f\"{'ID':<3}{'Step':>8}{'TargetR':>8} |\"\n f\"{'avgR':>8}{'stdR':>7}{'avgS':>7}{'stdS':>6} |\"\n f\"{'UsedTime':>8} ########\\n\"\n f\"{self.agent_id:<3}{self.total_step:8.2e}{self.target_return:8.2f} |\"\n f\"{r_avg:8.2f}{r_std:7.1f}{s_avg:7.0f}{s_std:6.0f} |\"\n f\"{self.used_time:>8} ########\")\n\n print(f\"{self.agent_id:<3}{self.total_step:8.2e}{self.r_max:8.2f} |\"\n f\"{r_avg:8.2f}{r_std:7.1f}{s_avg:7.0f}{s_std:6.0f} |\"\n f\"{r_exp:8.2f}{''.join(f'{n:7.2f}' for n in log_tuple)}\")\n self.draw_plot()\n\n if hasattr(self.eval_env, 'curriculum_learning_for_evaluator'):\n self.eval_env.curriculum_learning_for_evaluator(r_avg)\n return if_reach_goal, if_save\n\n @staticmethod\n def get_r_avg_std_s_avg_std(rewards_steps_list):\n rewards_steps_ary = np.array(rewards_steps_list, dtype=np.float32)\n r_avg, s_avg = rewards_steps_ary.mean(axis=0) # average of episode return and episode step\n r_std, s_std = rewards_steps_ary.std(axis=0) # standard dev. of episode return and episode step\n return r_avg, r_std, s_avg, s_std\n\n def save_or_load_recoder(self, if_save):\n if if_save:\n np.save(self.recorder_path, self.recorder)\n elif os.path.exists(self.recorder_path):\n recorder = np.load(self.recorder_path)\n self.recorder = [tuple(i) for i in recorder] # convert numpy to list\n self.total_step = self.recorder[-1][0]\n\n def draw_plot(self):\n if len(self.recorder) == 0:\n print(\"| save_npy_draw_plot() WARNNING: len(self.recorder)==0\")\n return None\n\n np.save(self.recorder_path, self.recorder)\n\n '''draw plot and save as png'''\n train_time = int(time.time() - self.start_time)\n total_step = int(self.recorder[-1][0])\n save_title = f\"step_time_maxR_{int(total_step)}_{int(train_time)}_{self.r_max:.3f}\"\n\n save_learning_curve(self.recorder, self.cwd, save_title)\n\n\ndef get_episode_return_and_step(env, act) -> (float, int): # [ElegantRL.2021.10.13]\n device_id = next(act.parameters()).get_device() # net.parameters() is a python generator.\n device = torch.device('cpu' if device_id == -1 else f'cuda:{device_id}')\n\n episode_step = 1\n episode_return = 0.0 # sum of rewards in an episode\n\n max_step = env.max_step\n if_discrete = env.if_discrete\n if if_discrete:\n def get_action(_state):\n _state = torch.as_tensor(_state, dtype=torch.float32, device=device)\n _action = act(_state.unsqueeze(0))\n _action = _action.argmax(dim=1)[0]\n return _action.detach().cpu().numpy()\n else:\n def get_action(_state):\n _state = torch.as_tensor(_state, dtype=torch.float32, device=device)\n _action = act(_state.unsqueeze(0))[0]\n return _action.detach().cpu().numpy()\n\n state = env.reset()\n for episode_step in range(max_step):\n action = get_action(state)\n state, reward, done, _ = env.step(action)\n episode_return += reward\n\n if done:\n break\n episode_return = getattr(env, 'episode_return', episode_return)\n return episode_return, episode_step\n\n\ndef save_learning_curve(recorder=None, cwd='.', save_title='learning curve', fig_name='plot_learning_curve.jpg'):\n if recorder is None:\n recorder = np.load(f\"{cwd}/recorder.npy\")\n\n recorder = np.array(recorder)\n steps = recorder[:, 0] # x-axis is training steps\n r_avg = recorder[:, 1]\n r_std = recorder[:, 2]\n r_exp = recorder[:, 3]\n obj_c = recorder[:, 4]\n obj_a = recorder[:, 5]\n\n '''plot subplots'''\n import matplotlib as mpl\n mpl.use('Agg')\n \"\"\"Generating matplotlib graphs without a running X server [duplicate]\n write `mpl.use('Agg')` before `import matplotlib.pyplot as plt`\n https://stackoverflow.com/a/4935945/9293137\n \"\"\"\n import matplotlib.pyplot as plt\n fig, axs = plt.subplots(2)\n\n '''axs[0]'''\n ax00 = axs[0]\n ax00.cla()\n\n ax01 = axs[0].twinx()\n color01 = 'darkcyan'\n ax01.set_ylabel('Explore AvgReward', color=color01)\n ax01.plot(steps, r_exp, color=color01, alpha=0.5, )\n ax01.tick_params(axis='y', labelcolor=color01)\n\n color0 = 'lightcoral'\n ax00.set_ylabel('Episode Return')\n ax00.plot(steps, r_avg, label='Episode Return', color=color0)\n ax00.fill_between(steps, r_avg - r_std, r_avg + r_std, facecolor=color0, alpha=0.3)\n ax00.grid()\n\n '''axs[1]'''\n ax10 = axs[1]\n ax10.cla()\n\n ax11 = axs[1].twinx()\n color11 = 'darkcyan'\n ax11.set_ylabel('objC', color=color11)\n ax11.fill_between(steps, obj_c, facecolor=color11, alpha=0.2, )\n ax11.tick_params(axis='y', labelcolor=color11)\n\n color10 = 'royalblue'\n ax10.set_xlabel('Total Steps')\n ax10.set_ylabel('objA', color=color10)\n ax10.plot(steps, obj_a, label='objA', color=color10)\n ax10.tick_params(axis='y', labelcolor=color10)\n for plot_i in range(6, recorder.shape[1]):\n other = recorder[:, plot_i]\n ax10.plot(steps, other, label=f'{plot_i}', color='grey', alpha=0.5)\n ax10.legend()\n ax10.grid()\n\n '''plot save'''\n plt.title(save_title, y=2.3)\n plt.savefig(f\"{cwd}/{fig_name}\")\n plt.close('all') # avoiding warning about too many open figures, rcParam `figure.max_open_warning`\n # plt.show() # if use `mpl.use('Agg')` to draw figures without GUI, then plt can't plt.show()\n\n\n" }, { "path": "elegantrl/net.py", "content": "import numpy as np\nimport torch\nimport torch.nn as nn\n\n\"\"\"[ElegantRL.2021.09.01](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\n'''Q Network'''\n\n\nclass QNet(nn.Module): # nn.Module is a standard PyTorch Network\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim))\n\n def forward(self, state):\n return self.net(state) # Q value\n\n\nclass QNetDuel(nn.Module): # Dueling DQN\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.net_state = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU())\n self.net_adv = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # advantage function value 1\n self.net_val = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # Q value\n\n def forward(self, state):\n t_tmp = self.net_state(state) # tensor of encoded state\n q_adv = self.net_adv(t_tmp)\n q_val = self.net_val(t_tmp)\n return q_adv + q_val - q_val.mean(dim=1, keepdim=True) # dueling Q value\n\n\nclass QNetTwin(nn.Module): # Double DQN\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.net_state = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU())\n self.net_q1 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # q1 value\n self.net_q2 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # q2 value\n\n def forward(self, state):\n tmp = self.net_state(state)\n return self.net_q1(tmp) # one Q value\n\n def get_q1_q2(self, state):\n tmp = self.net_state(state)\n return self.net_q1(tmp), self.net_q2(tmp) # two Q values\n\n\nclass QNetTwinDuel(nn.Module): # D3QN: Dueling Double DQN\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.net_state = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU())\n self.net_adv1 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # q1 value\n self.net_adv2 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # q2 value\n self.net_val1 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # advantage function value 1\n self.net_val2 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # advantage function value 1\n\n def forward(self, state):\n t_tmp = self.net_state(state)\n q_adv = self.net_adv1(t_tmp)\n q_val = self.net_val1(t_tmp)\n return q_adv + q_val - q_val.mean(dim=1, keepdim=True) # one dueling Q value\n\n def get_q1_q2(self, state):\n tmp = self.net_state(state)\n\n adv1 = self.net_adv1(tmp)\n val1 = self.net_val1(tmp)\n q1 = adv1 + val1 - val1.mean(dim=1, keepdim=True)\n\n adv2 = self.net_adv2(tmp)\n val2 = self.net_val2(tmp)\n q2 = adv2 + val2 - val2.mean(dim=1, keepdim=True)\n return q1, q2 # two dueling Q values\n\n\n'''Policy Network (Actor)'''\n\n\nclass Actor(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.net = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim))\n\n def forward(self, state):\n return self.net(state).tanh() # action.tanh()\n\n def get_action(self, state, action_std):\n action = self.net(state).tanh()\n noise = (torch.randn_like(action) * action_std).clamp(-0.5, 0.5)\n return (action + noise).clamp(-1.0, 1.0)\n\n\nclass ActorSAC(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim, if_use_dn=False):\n super().__init__()\n if if_use_dn:\n nn_middle = DenseNet(mid_dim // 2)\n inp_dim = nn_middle.inp_dim\n out_dim = nn_middle.out_dim\n else:\n nn_middle = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(), )\n inp_dim = mid_dim\n out_dim = mid_dim\n\n self.net_state = nn.Sequential(nn.Linear(state_dim, inp_dim), nn.ReLU(),\n nn_middle, )\n self.net_a_avg = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # the average of action\n self.net_a_std = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim)) # the log_std of action\n self.log_sqrt_2pi = np.log(np.sqrt(2 * np.pi))\n\n def forward(self, state):\n tmp = self.net_state(state)\n return self.net_a_avg(tmp).tanh() # action\n\n def get_action(self, state):\n t_tmp = self.net_state(state)\n a_avg = self.net_a_avg(t_tmp) # NOTICE! it is a_avg without .tanh()\n a_std = self.net_a_std(t_tmp).clamp(-20, 2).exp()\n return torch.normal(a_avg, a_std).tanh() # re-parameterize\n\n def get_action_logprob(self, state):\n t_tmp = self.net_state(state)\n a_avg = self.net_a_avg(t_tmp) # NOTICE! it needs a_avg.tanh()\n a_std_log = self.net_a_std(t_tmp).clamp(-20, 2)\n a_std = a_std_log.exp()\n\n \"\"\"add noise to action in stochastic policy\"\"\"\n noise = torch.randn_like(a_avg, requires_grad=True)\n a_tan = (a_avg + a_std * noise).tanh() # action.tanh()\n # Can only use above code instead of below, because the tensor need gradients here.\n # a_noise = torch.normal(a_avg, a_std, requires_grad=True)\n\n '''compute log_prob according to mean and std of action (stochastic policy)'''\n # self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))\n log_prob = a_std_log + self.log_sqrt_2pi + noise.pow(2).__mul__(0.5) # noise.pow(2) * 0.5\n # same as below:\n # from torch.distributions.normal import Normal\n # log_prob_noise = Normal(a_avg, a_std).log_prob(a_noise)\n # log_prob = log_prob_noise + (-a_noise_tanh.pow(2) + 1.000001).log()\n # same as below:\n # a_delta = (a_avg - a_noise).pow(2) /(2*a_std.pow(2))\n # log_prob_noise = -a_delta - a_std.log() - np.log(np.sqrt(2 * np.pi))\n # log_prob = log_prob_noise + (-a_noise_tanh.pow(2) + 1.000001).log()\n\n log_prob = log_prob + (-a_tan.pow(2) + 1.000001).log() # fix log_prob using the derivative of action.tanh()\n # same as below:\n # epsilon = 1e-6\n # log_prob = log_prob_noise - (1 - a_noise_tanh.pow(2) + epsilon).log()\n return a_tan, log_prob.sum(1, keepdim=True)\n\n # def log_abs_det_jacobian(self, x, y):\n # # https://github.com/denisyarats/pytorch_sac/blob/81c5b536d3a1c5616b2531e446450df412a064fb/agent/actor.py#L37\n # # ↑ MIT License， Thanks for https://www.zhihu.com/people/Z_WXCY 2ez4U\n # # We use a formula that is more numerically stable, see details in the following link\n # # https://pytorch.org/docs/stable/_modules/torch/distributions/transforms.html#TanhTransform\n # # https://github.com/tensorflow/probability/commit/ef6bb176e0ebd1cf6e25c6b5cecdd2428c22963f\n # return 2. * (math.log(2.) - x - F.softplus(-2. * x))\n\n\nclass ActorPPO(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n if isinstance(state_dim, int):\n nn_middle = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(), )\n else:\n nn_middle = ConvNet(inp_dim=state_dim[2], out_dim=mid_dim, image_size=state_dim[0])\n\n self.net = nn.Sequential(nn_middle,\n nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim), )\n layer_norm(self.net[-1], std=0.1) # output layer for action\n\n # the logarithm (log) of standard deviation (std) of action, it is a trainable parameter\n self.a_std_log = nn.Parameter(torch.zeros((1, action_dim)) - 0.5, requires_grad=True)\n self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))\n\n def forward(self, state):\n return self.net(state).tanh() # action.tanh()\n\n def get_action(self, state):\n a_avg = self.net(state)\n a_std = self.a_std_log.exp()\n\n noise = torch.randn_like(a_avg)\n action = a_avg + noise * a_std\n return action, noise\n\n def get_logprob_entropy(self, state, action):\n a_avg = self.net(state)\n a_std = self.a_std_log.exp()\n\n delta = ((a_avg - action) / a_std).pow(2) * 0.5\n logprob = -(self.a_std_log + self.sqrt_2pi_log + delta).sum(1) # new_logprob\n\n dist_entropy = (logprob.exp() * logprob).mean() # policy entropy\n return logprob, dist_entropy\n\n def get_old_logprob(self, _action, noise): # noise = action - a_noise\n delta = noise.pow(2) * 0.5\n return -(self.a_std_log + self.sqrt_2pi_log + delta).sum(1) # old_logprob\n\n\nclass ActorDiscretePPO(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n if isinstance(state_dim, int):\n nn_middle = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(), )\n else:\n nn_middle = ConvNet(inp_dim=state_dim[2], out_dim=mid_dim, image_size=state_dim[0])\n\n self.net = nn.Sequential(nn_middle,\n nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim), )\n layer_norm(self.net[-1], std=0.1) # output layer for action\n\n self.action_dim = action_dim\n self.soft_max = nn.Softmax(dim=-1)\n self.Categorical = torch.distributions.Categorical\n\n def forward(self, state):\n return self.net(state) # action_prob without softmax\n\n def get_action(self, state):\n a_prob = self.soft_max(self.net(state))\n # dist = Categorical(a_prob)\n # a_int = dist.sample()\n a_int = torch.multinomial(a_prob, num_samples=1, replacement=True)[:, 0]\n # samples_2d = torch.multinomial(a_prob, num_samples=1, replacement=True)\n # samples_2d.shape == (batch_size, num_samples)\n return a_int, a_prob\n\n def get_logprob_entropy(self, state, a_int):\n a_prob = self.soft_max(self.net(state))\n dist = self.Categorical(a_prob)\n return dist.log_prob(a_int), dist.entropy().mean()\n\n def get_old_logprob(self, a_int, a_prob):\n dist = self.Categorical(a_prob)\n return dist.log_prob(a_int)\n\n\nclass ActorBiConv(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n\n self.net = nn.Sequential(\n BiConvNet(mid_dim, state_dim, mid_dim * 4),\n nn.Linear(mid_dim * 4, mid_dim * 1), nn.ReLU(),\n DenseNet(mid_dim * 1), nn.ReLU(),\n nn.Linear(mid_dim * 4, action_dim),\n )\n layer_norm(self.net[-1], std=0.1) # output layer for action\n\n def forward(self, state):\n action = self.net(state)\n return action * torch.pow((action ** 2).sum(), -0.5) # action / sqrt(L2_norm(action))\n\n def get_action(self, state, action_std):\n action = self.net(state)\n action = action + (torch.randn_like(action) * action_std)\n return action * torch.pow((action ** 2).sum(), -0.5) # action / sqrt(L2_norm(action))\n\n\n'''Value Network (Critic)'''\n\n\nclass Critic(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.net = nn.Sequential(nn.Linear(state_dim + action_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1))\n\n def forward(self, state, action):\n return self.net(torch.cat((state, action), dim=1)) # Q value\n\n\nclass CriticTwin(nn.Module): # shared parameter\n def __init__(self, mid_dim, state_dim, action_dim, if_use_dn=False):\n super().__init__()\n if if_use_dn:\n nn_middle = DenseNet(mid_dim // 2)\n inp_dim = nn_middle.inp_dim\n out_dim = nn_middle.out_dim\n else:\n nn_middle = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(), )\n inp_dim = mid_dim\n out_dim = mid_dim\n\n self.net_sa = nn.Sequential(nn.Linear(state_dim + action_dim, inp_dim), nn.ReLU(),\n nn_middle, ) # concat(state, action)\n self.net_q1 = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # q1 value\n self.net_q2 = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # q2 value\n\n def forward(self, state, action):\n tmp = self.net_sa(torch.cat((state, action), dim=1))\n return self.net_q1(tmp) # one Q value\n\n def get_q1_q2(self, state, action):\n tmp = self.net_sa(torch.cat((state, action), dim=1))\n return self.net_q1(tmp), self.net_q2(tmp) # two Q values\n\n\nclass CriticPPO(nn.Module):\n def __init__(self, mid_dim, state_dim, _action_dim):\n super().__init__()\n if isinstance(state_dim, int):\n nn_middle = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(), )\n else:\n nn_middle = ConvNet(inp_dim=state_dim[2], out_dim=mid_dim, image_size=state_dim[0])\n\n self.net = nn.Sequential(nn_middle,\n nn.Linear(mid_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1), )\n layer_norm(self.net[-1], std=0.5) # output layer for advantage value\n\n def forward(self, state):\n return self.net(state) # advantage value\n\n\nclass CriticTwinPPO(nn.Module):\n def __init__(self, mid_dim, state_dim, _action_dim):\n super().__init__()\n if isinstance(state_dim, int):\n nn_middle = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim), nn.ReLU(), )\n else:\n nn_middle = ConvNet(inp_dim=state_dim[2], out_dim=mid_dim, image_size=state_dim[0])\n\n self.net = nn.Sequential(nn_middle, )\n self.net_v1 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # advantage value1\n self.net_v2 = nn.Sequential(nn.Linear(mid_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, 1)) # advantage value2\n\n layer_norm(self.net[-1], std=0.5) # output layer for advantage value\n\n def forward(self, state):\n tmp = self.net(state)\n return self.net_v1(tmp) # one advantage value\n\n def get_q1_q2(self, state):\n tmp = self.net(state)\n return self.net_v1(tmp), self.net_v2(tmp) # two advantage values\n\n\nclass CriticBiConv(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n i_c_dim, i_h_dim, i_w_dim = state_dim # inp_for_cnn.shape == (N, C, H, W)\n assert action_dim == int(np.prod((i_c_dim, i_w_dim, i_h_dim)))\n action_dim = (i_c_dim, i_w_dim, i_h_dim) # (2, bs_n, ur_n)\n\n self.cnn_s = nn.Sequential(\n BiConvNet(mid_dim, state_dim, mid_dim * 4),\n nn.Linear(mid_dim * 4, mid_dim * 2), nn.ReLU(inplace=True),\n nn.Linear(mid_dim * 2, mid_dim * 1),\n )\n self.cnn_a = nn.Sequential(\n NnReshape(*action_dim),\n BiConvNet(mid_dim, action_dim, mid_dim * 4),\n nn.Linear(mid_dim * 4, mid_dim * 2), nn.ReLU(inplace=True),\n nn.Linear(mid_dim * 2, mid_dim * 1),\n )\n\n self.out_net = nn.Sequential(\n nn.Linear(mid_dim * 1, mid_dim * 1), nn.Hardswish(),\n nn.Linear(mid_dim * 1, 1),\n )\n layer_norm(self.out_net[-1], std=0.1) # output layer for action\n\n def forward(self, state, action):\n xs = self.cnn_s(state)\n xa = self.cnn_a(action)\n return self.out_net(xs + xa) # Q value\n\n\n'''Parameter sharing Network'''\n\n\nclass ShareDPG(nn.Module): # DPG means deterministic policy gradient\n def __init__(self, state_dim, action_dim, mid_dim):\n super().__init__()\n nn_dense = DenseNet(mid_dim // 2)\n inp_dim = nn_dense.inp_dim\n out_dim = nn_dense.out_dim\n\n self.enc_s = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, inp_dim))\n self.enc_a = nn.Sequential(nn.Linear(action_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, inp_dim))\n\n self.net = nn_dense\n\n self.dec_a = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.Hardswish(),\n nn.Linear(mid_dim, action_dim), nn.Tanh())\n self.dec_q = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.Hardswish(),\n nn.utils.spectral_norm(nn.Linear(mid_dim, 1)))\n\n @staticmethod\n def add_noise(a, noise_std):\n a_temp = torch.normal(a, noise_std)\n\n mask = torch.lt(a_temp, -1) + torch.gt(a_temp, 1) # mask = (a_temp < -1.0) + (a_temp > 1.0)\n mask = torch.tensor(mask, dtype=torch.float32).cuda()\n\n noise_uniform = torch.rand_like(a)\n a_noise = noise_uniform * mask + a_temp * (-mask + 1)\n return a_noise\n\n def forward(self, s, noise_std=0.0): # actor\n s_ = self.enc_s(s)\n a_ = self.net(s_)\n a = self.dec_a(a_)\n return a if noise_std == 0.0 else self.add_noise(a, noise_std)\n\n def critic(self, s, a):\n s_ = self.enc_s(s)\n a_ = self.enc_a(a)\n q_ = self.net(s_ + a_)\n q = self.dec_q(q_)\n return q\n\n def next_q_action(self, s, s_next, noise_std):\n s_ = self.enc_s(s)\n a_ = self.net(s_)\n a = self.dec_a(a_)\n\n '''q_target (without noise)'''\n a_ = self.enc_a(a)\n s_next_ = self.enc_s(s_next)\n q_target0_ = self.net(s_next_ + a_)\n q_target0 = self.dec_q(q_target0_)\n\n '''q_target (with noise)'''\n a_noise = self.add_noise(a, noise_std)\n a_noise_ = self.enc_a(a_noise)\n q_target1_ = self.net(s_next_ + a_noise_)\n q_target1 = self.dec_q(q_target1_)\n\n q_target = (q_target0 + q_target1) * 0.5\n return q_target, a\n\n\nclass ShareSPG(nn.Module): # SPG means stochastic policy gradient\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n self.log_sqrt_2pi_sum = np.log(np.sqrt(2 * np.pi)) * action_dim\n self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n nn_dense = DenseNet(mid_dim // 2)\n inp_dim = nn_dense.inp_dim\n out_dim = nn_dense.out_dim\n\n self.enc_s = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, inp_dim), ) # state\n self.enc_a = nn.Sequential(nn.Linear(action_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, inp_dim), ) # action without nn.Tanh()\n\n self.net = nn_dense\n\n self.dec_a = nn.Sequential(nn.Linear(out_dim, mid_dim // 2), nn.ReLU(),\n nn.Linear(mid_dim // 2, action_dim), ) # action_mean\n self.dec_d = nn.Sequential(nn.Linear(out_dim, mid_dim // 2), nn.ReLU(),\n nn.Linear(mid_dim // 2, action_dim), ) # action_std_log (d means standard deviation)\n self.dec_q1 = nn.Sequential(nn.Linear(out_dim, mid_dim // 2), nn.ReLU(),\n nn.Linear(mid_dim // 2, 1), ) # q1 value\n self.dec_q2 = nn.Sequential(nn.Linear(out_dim, mid_dim // 2), nn.ReLU(),\n nn.Linear(mid_dim // 2, 1), ) # q2 value\n self.log_alpha = nn.Parameter(torch.zeros((1, action_dim)) - np.log(action_dim), requires_grad=True)\n\n layer_norm(self.dec_a[-1], std=0.5)\n layer_norm(self.dec_d[-1], std=0.1)\n layer_norm(self.dec_q1[-1], std=0.5)\n layer_norm(self.dec_q2[-1], std=0.5)\n\n def forward(self, s):\n x = self.enc_s(s)\n x = self.net(x)\n a_avg = self.dec_a(x)\n return a_avg.tanh()\n\n def get_action(self, s):\n s_ = self.enc_s(s)\n a_ = self.net(s_)\n a_avg = self.dec_a(a_) # NOTICE! it is a_avg without tensor.tanh()\n\n a_std_log = self.dec_d(a_).clamp(-20, 2)\n a_std = a_std_log.exp()\n\n action = torch.normal(a_avg, a_std) # NOTICE! it is action without .tanh()\n return action.tanh()\n\n def get_action_logprob(self, state): # actor\n s_ = self.enc_s(state)\n a_ = self.net(s_)\n\n \"\"\"add noise to action, stochastic policy\"\"\"\n a_avg = self.dec_a(a_) # NOTICE! it is action without .tanh()\n a_std_log = self.dec_d(a_).clamp(-20, 2)\n a_std = a_std_log.exp()\n\n noise = torch.randn_like(a_avg, requires_grad=True)\n a_noise = a_avg + a_std * noise\n\n a_noise_tanh = a_noise.tanh()\n fix_term = (-a_noise_tanh.pow(2) + 1.00001).log()\n logprob = (noise.pow(2) / 2 + a_std_log + fix_term).sum(1, keepdim=True) + self.log_sqrt_2pi_sum\n return a_noise_tanh, logprob\n\n def get_q_logprob(self, state):\n s_ = self.enc_s(state)\n a_ = self.net(s_)\n\n \"\"\"add noise to action, stochastic policy\"\"\"\n a_avg = self.dec_a(a_) # NOTICE! it is action without .tanh()\n a_std_log = self.dec_d(a_).clamp(-20, 2)\n a_std = a_std_log.exp()\n\n noise = torch.randn_like(a_avg, requires_grad=True)\n a_noise = a_avg + a_std * noise\n\n a_noise_tanh = a_noise.tanh()\n fix_term = (-a_noise_tanh.pow(2) + 1.00001).log()\n logprob = (noise.pow(2) / 2 + a_std_log + fix_term).sum(1, keepdim=True) + self.log_sqrt_2pi_sum\n\n '''get q'''\n a_ = self.enc_a(a_noise_tanh)\n q_ = self.net(s_ + a_)\n q = torch.min(self.dec_q1(q_), self.dec_q2(q_))\n return q, logprob\n\n def get_q1_q2(self, s, a): # critic\n s_ = self.enc_s(s)\n a_ = self.enc_a(a)\n q_ = self.net(s_ + a_)\n q1 = self.dec_q1(q_)\n q2 = self.dec_q2(q_)\n return q1, q2\n\n\nclass SharePPO(nn.Module): # Pixel-level state version\n def __init__(self, state_dim, action_dim, mid_dim):\n super().__init__()\n if isinstance(state_dim, int):\n self.enc_s = nn.Sequential(nn.Linear(state_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, mid_dim)) # the only difference.\n else:\n self.enc_s = ConvNet(inp_dim=state_dim[2], out_dim=mid_dim, image_size=state_dim[0])\n out_dim = mid_dim\n\n self.dec_a = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, action_dim))\n self.a_std_log = nn.Parameter(torch.zeros(1, action_dim) - 0.5, requires_grad=True)\n\n self.dec_q1 = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, 1))\n self.dec_q2 = nn.Sequential(nn.Linear(out_dim, mid_dim), nn.ReLU(),\n nn.Linear(mid_dim, 1))\n\n layer_norm(self.dec_a[-1], std=0.01)\n layer_norm(self.dec_q1[-1], std=0.01)\n layer_norm(self.dec_q2[-1], std=0.01)\n\n self.sqrt_2pi_log = np.log(np.sqrt(2 * np.pi))\n\n def forward(self, s):\n s_ = self.enc_s(s)\n a_avg = self.dec_a(s_)\n return a_avg.tanh()\n\n def get_action_noise(self, state):\n s_ = self.enc_s(state)\n a_avg = self.dec_a(s_)\n a_std = self.a_std_log.exp()\n\n # a_noise = torch.normal(a_avg, a_std) # same as below\n noise = torch.randn_like(a_avg)\n a_noise = a_avg + noise * a_std\n return a_noise, noise\n\n def get_q_logprob(self, state, noise):\n s_ = self.enc_s(state)\n\n q = torch.min(self.dec_q1(s_), self.dec_q2(s_))\n logprob = -(noise.pow(2) / 2 + self.a_std_log + self.sqrt_2pi_log).sum(1)\n return q, logprob\n\n def get_q1_q2_logprob(self, state, action):\n s_ = self.enc_s(state)\n\n q1 = self.dec_q1(s_)\n q2 = self.dec_q2(s_)\n\n a_avg = self.dec_a(s_)\n a_std = self.a_std_log.exp()\n logprob = -(((a_avg - action) / a_std).pow(2) / 2 + self.a_std_log + self.sqrt_2pi_log).sum(1)\n return q1, q2, logprob\n\n\nclass ShareBiConv(nn.Module):\n def __init__(self, mid_dim, state_dim, action_dim):\n super().__init__()\n i_c_dim, i_h_dim, i_w_dim = state_dim # inp_for_cnn.shape == (N, C, H, W)\n assert action_dim == int(np.prod((i_c_dim, i_w_dim, i_h_dim)))\n\n state_tuple = (i_c_dim, i_h_dim, i_w_dim)\n self.enc_s = nn.Sequential(\n # NnReshape(*state_tuple),\n BiConvNet(mid_dim, state_tuple, mid_dim * 4),\n )\n action_tuple = (i_c_dim, i_w_dim, i_h_dim)\n self.enc_a = nn.Sequential(\n NnReshape(*action_tuple),\n BiConvNet(mid_dim, action_tuple, mid_dim * 4),\n )\n\n self.mid_n = nn.Sequential(\n nn.Linear(mid_dim * 4, mid_dim * 2), nn.ReLU(),\n nn.Linear(mid_dim * 2, mid_dim * 1), nn.ReLU(),\n DenseNet(mid_dim),\n )\n\n self.dec_a = nn.Sequential(\n nn.Linear(mid_dim * 4, mid_dim * 2), nn.Hardswish(),\n nn.Linear(mid_dim * 2, action_dim),\n )\n layer_norm(self.dec_a[-1], std=0.1) # output layer for action\n self.dec_q = nn.Sequential(\n nn.Linear(mid_dim * 4, mid_dim * 2), nn.Hardswish(),\n nn.Linear(mid_dim * 2, 1),\n )\n layer_norm(self.dec_q[-1], std=0.1) # output layer for action\n\n def forward(self, state): # actor\n xs = self.enc_s(state)\n xn = self.mid_n(xs)\n action = self.dec_a(xn)\n return action * torch.pow((action ** 2).sum(), -0.5) # action / sqrt(L2_norm(action))\n\n def critic(self, state, action):\n xs = self.enc_s(state)\n xa = self.enc_a(action)\n xn = self.mid_n(xs + xa)\n return self.dec_q(xn) # Q value\n\n def get_action(self, state, action_std): # actor, get noisy action\n xs = self.enc_s(state)\n xn = self.mid_n(xs)\n action = self.dec_a(xn)\n action = action + (torch.randn_like(action) * action_std)\n return action * torch.pow((action ** 2).sum(), -0.5) # action / sqrt(L2_norm(action))\n\n\n\"\"\"utils\"\"\"\n\n\nclass NnReshape(nn.Module):\n def __init__(self, *args):\n super().__init__()\n self.args = args\n\n def forward(self, x):\n return x.view((x.size(0),) + self.args)\n\n\nclass DenseNet(nn.Module): # plan to hyper-param: layer_number\n def __init__(self, lay_dim):\n super().__init__()\n self.dense1 = nn.Sequential(nn.Linear(lay_dim * 1, lay_dim * 1), nn.Hardswish())\n self.dense2 = nn.Sequential(nn.Linear(lay_dim * 2, lay_dim * 2), nn.Hardswish())\n self.inp_dim = lay_dim\n self.out_dim = lay_dim * 4\n\n def forward(self, x1): # x1.shape==(-1, lay_dim*1)\n x2 = torch.cat((x1, self.dense1(x1)), dim=1)\n x3 = torch.cat((x2, self.dense2(x2)), dim=1)\n return x3 # x2.shape==(-1, lay_dim*4)\n\n\nclass ConcatNet(nn.Module): # concatenate\n def __init__(self, lay_dim):\n super().__init__()\n self.dense1 = nn.Sequential(nn.Linear(lay_dim, lay_dim), nn.ReLU(),\n nn.Linear(lay_dim, lay_dim), nn.Hardswish(), )\n self.dense2 = nn.Sequential(nn.Linear(lay_dim, lay_dim), nn.ReLU(),\n nn.Linear(lay_dim, lay_dim), nn.Hardswish(), )\n self.dense3 = nn.Sequential(nn.Linear(lay_dim, lay_dim), nn.ReLU(),\n nn.Linear(lay_dim, lay_dim), nn.Hardswish(), )\n self.dense4 = nn.Sequential(nn.Linear(lay_dim, lay_dim), nn.ReLU(),\n nn.Linear(lay_dim, lay_dim), nn.Hardswish(), )\n self.inp_dim = lay_dim\n self.out_dim = lay_dim * 4\n\n def forward(self, x0):\n x1 = self.dense1(x0)\n x2 = self.dense2(x0)\n x3 = self.dense3(x0)\n x4 = self.dense4(x0)\n return torch.cat((x1, x2, x3, x4), dim=1)\n\n\nclass ConvNet(nn.Module): # pixel-level state encoder\n def __init__(self, inp_dim, out_dim, image_size=224):\n super().__init__()\n if image_size == 224:\n self.net = nn.Sequential( # size==(batch_size, inp_dim, 224, 224)\n nn.Conv2d(inp_dim, 32, (5, 5), stride=(2, 2), bias=False), nn.ReLU(inplace=True), # size=110\n nn.Conv2d(32, 48, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=54\n nn.Conv2d(48, 64, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=26\n nn.Conv2d(64, 96, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=12\n nn.Conv2d(96, 128, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=5\n nn.Conv2d(128, 192, (5, 5), stride=(1, 1)), nn.ReLU(inplace=True), # size=1\n NnReshape(-1), # size (batch_size, 1024, 1, 1) ==> (batch_size, 1024)\n nn.Linear(192, out_dim), # size==(batch_size, out_dim)\n )\n elif image_size == 112:\n self.net = nn.Sequential( # size==(batch_size, inp_dim, 112, 112)\n nn.Conv2d(inp_dim, 32, (5, 5), stride=(2, 2), bias=False), nn.ReLU(inplace=True), # size=54\n nn.Conv2d(32, 48, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=26\n nn.Conv2d(48, 64, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=12\n nn.Conv2d(64, 96, (3, 3), stride=(2, 2)), nn.ReLU(inplace=True), # size=5\n nn.Conv2d(96, 128, (5, 5), stride=(1, 1)), nn.ReLU(inplace=True), # size=1\n NnReshape(-1), # size (batch_size, 1024, 1, 1) ==> (batch_size, 1024)\n nn.Linear(128, out_dim), # size==(batch_size, out_dim)\n )\n else:\n assert image_size in {224, 112}\n\n def forward(self, x):\n # assert x.shape == (batch_size, inp_dim, image_size, image_size)\n x = x.permute(0, 3, 1, 2)\n x = x / 128.0 - 1.0\n return self.net(x)\n\n # @staticmethod\n # def check():\n # inp_dim = 3\n # out_dim = 32\n # batch_size = 2\n # image_size = [224, 112][1]\n # # from elegantrl.net import Conv2dNet\n # net = Conv2dNet(inp_dim, out_dim, image_size)\n #\n # x = torch.ones((batch_size, image_size, image_size, inp_dim), dtype=torch.uint8) * 255\n # print(x.shape)\n # y = net(x)\n # print(y.shape)\n\n\nclass BiConvNet(nn.Module):\n def __init__(self, mid_dim, inp_dim, out_dim):\n super().__init__()\n i_c_dim, i_h_dim, i_w_dim = inp_dim # inp_for_cnn.shape == (N, C, H, W)\n\n self.cnn_h = nn.Sequential(\n nn.Conv2d(i_c_dim * 1, mid_dim * 2, (1, i_w_dim), bias=True), nn.LeakyReLU(inplace=True),\n nn.Conv2d(mid_dim * 2, mid_dim * 1, (1, 1), bias=True), nn.ReLU(inplace=True),\n NnReshape(-1), # shape=(-1, i_h_dim * mid_dim)\n nn.Linear(i_h_dim * mid_dim, out_dim),\n )\n self.cnn_w = nn.Sequential(\n nn.Conv2d(i_c_dim * 1, mid_dim * 2, (i_h_dim, 1), bias=True), nn.LeakyReLU(inplace=True),\n nn.Conv2d(mid_dim * 2, mid_dim * 1, (1, 1), bias=True), nn.ReLU(inplace=True),\n NnReshape(-1), # shape=(-1, i_w_dim * mid_dim)\n nn.Linear(i_w_dim * mid_dim, out_dim),\n )\n\n def forward(self, state):\n xh = self.cnn_h(state)\n xw = self.cnn_w(state)\n return xw + xh\n\n\ndef layer_norm(layer, std=1.0, bias_const=1e-6):\n torch.nn.init.orthogonal_(layer.weight, std)\n torch.nn.init.constant_(layer.bias, bias_const)\n\n\nclass ActorSimplify:\n def __init__(self, gpu_id, actor_net):\n self.device = torch.device(f\"cuda:{gpu_id}\" if (torch.cuda.is_available() and gpu_id >= 0) else 'cpu')\n self.actor_net = actor_net.to(self.device)\n\n def get_action(self, state: np.ndarray) -> np.ndarray:\n states = torch.as_tensor(state[np.newaxis], dtype=torch.float32, device=self.device)\n action = self.actor_net(states)[0]\n return action.detach().cpu().numpy()\n\n\n\"\"\"check\"\"\"\n\n\ndef check_actor_network():\n batch_size = 3\n mid_dim = 2 ** 8\n state_dim = 24\n action_dim = 4\n\n if_check_actor_net = 0\n if if_check_actor_net:\n net = ActorPPO(mid_dim, state_dim, action_dim)\n\n inp = torch.ones((batch_size, state_dim), dtype=torch.float32)\n out = net(inp)\n print(out.shape)\n\n if_check_pixel_level = 1\n if if_check_pixel_level:\n img_channel = 6\n img_size = 112 # {112, 224}\n\n mid_dim = 2 ** 8\n state_dim = (img_size, img_size, img_channel)\n action_dim = 4\n\n net = ActorPPO(mid_dim, state_dim, action_dim)\n\n inp = torch.ones((batch_size, img_size, img_size, img_channel), dtype=torch.int8)\n out = net(inp)\n print(out.shape)\n\n\ndef check_network():\n from envs.DownLink import DownLinkEnv\n env = DownLinkEnv()\n\n gpu_id = 1\n mid_dim = 128\n state_dim = env.state_dim\n action_dim = env.action_dim\n\n if_check_actor = 0\n if if_check_actor:\n net = ActorBiConv(mid_dim, state_dim, action_dim)\n act = ActorSimplify(gpu_id, net)\n\n state = env.reset()\n\n action = env.get_action_mmse(state)\n reward = env.get_reward(action)\n print(f\"| mmse : {reward:8.3f}\")\n\n action = env.get_action_mmse(state)\n action = env.get_action_norm_power(action)\n reward = env.get_reward(action)\n print(f\"| mmse (max Power): {reward:8.3f}\")\n\n action = np.ones(env.action_dim)\n reward = env.get_reward(action)\n print(f\"| ones (max Power): {reward:8.3f}\")\n\n action = env.get_action_norm_power(action=None) # random.normal action\n reward = env.get_reward(action)\n print(f\"| rand (max Power): {reward:8.3f}\")\n\n action = act.get_action(state)\n action = env.get_action_norm_power(action)\n reward = env.get_reward(action)\n print(f\"| net (max Power): {reward:8.3f}\")\n\n # state, reward, done, _ = env.step(action)\n # print(reward)\n\n if_check_critic = 1\n if if_check_critic:\n batch_size = 3\n device = torch.device(f\"cuda:{gpu_id}\" if (torch.cuda.is_available() and gpu_id >= 0) else 'cpu')\n critic_net = CriticBiConv(mid_dim, state_dim, action_dim).to(device)\n\n ten_state = torch.randn(size=(batch_size, *state_dim), dtype=torch.float32, device=device)\n ten_action = torch.randn(size=(batch_size, action_dim), dtype=torch.float32, device=device)\n\n q_value = critic_net(ten_state, ten_action)\n print(q_value)\n" }, { "path": "elegantrl/replay.py", "content": "import os\nimport torch\nimport numpy as np\nimport numpy.random as rd\n\n\"\"\"[ElegantRL.2021.09.01](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\n\nclass ReplayBuffer:\n def __init__(self, max_len, state_dim, action_dim, if_use_per, gpu_id=0, state_type=torch.float32):\n \"\"\"Experience Replay Buffer\n\n save environment transition in a continuous RAM for high performance training\n we save trajectory in order and save state and other (action, reward, mask, ...) separately.\n\n `int max_len` the maximum capacity of ReplayBuffer. First In First Out\n `int state_dim` the dimension of state\n `int action_dim` the dimension of action (action_dim==1 for discrete action)\n `bool if_on_policy` on-policy or off-policy\n `bool if_gpu` create buffer space on CPU RAM or GPU\n `bool if_per` Prioritized Experience Replay for sparse reward\n \"\"\"\n self.now_len = 0\n self.next_idx = 0\n self.if_full = False\n self.max_len = max_len\n self.data_type = torch.float32\n self.action_dim = action_dim\n self.device = torch.device(f\"cuda:{gpu_id}\" if (torch.cuda.is_available() and (gpu_id >= 0)) else \"cpu\")\n\n self.per_tree = BinarySearchTree(max_len) if if_use_per else None\n\n other_dim = 1 + 1 + self.action_dim\n self.buf_other = torch.empty((max_len, other_dim), dtype=torch.float32, device=self.device)\n\n buf_state_shape = (max_len, state_dim) if isinstance(state_dim, int) else (max_len, *state_dim)\n self.buf_state = torch.empty(buf_state_shape, dtype=state_type, device=self.device)\n\n def append_buffer(self, state, other): # CPU array to CPU array\n self.buf_state[self.next_idx] = state\n self.buf_other[self.next_idx] = other\n\n if self.per_tree:\n self.per_tree.update_id(self.next_idx)\n\n self.next_idx += 1\n if self.next_idx >= self.max_len:\n self.if_full = True\n self.next_idx = 0\n\n def extend_buffer(self, state, other): # CPU array to CPU array\n size = len(other)\n next_idx = self.next_idx + size\n\n if self.per_tree:\n self.per_tree.update_ids(data_ids=np.arange(self.next_idx, next_idx) % self.max_len)\n\n if next_idx > self.max_len:\n self.buf_state[self.next_idx:self.max_len] = state[:self.max_len - self.next_idx]\n self.buf_other[self.next_idx:self.max_len] = other[:self.max_len - self.next_idx]\n self.if_full = True\n\n next_idx = next_idx - self.max_len\n self.buf_state[0:next_idx] = state[-next_idx:]\n self.buf_other[0:next_idx] = other[-next_idx:]\n else:\n self.buf_state[self.next_idx:next_idx] = state\n self.buf_other[self.next_idx:next_idx] = other\n self.next_idx = next_idx\n\n def sample_batch(self, batch_size) -> tuple:\n \"\"\"randomly sample a batch of data for training\n\n :int batch_size: the number of data in a batch for Stochastic Gradient Descent\n :return torch.Tensor reward: reward.shape==(now_len, 1)\n :return torch.Tensor mask: mask.shape ==(now_len, 1), mask = 0.0 if done else gamma\n :return torch.Tensor action: action.shape==(now_len, action_dim)\n :return torch.Tensor state: state.shape ==(now_len, state_dim)\n :return torch.Tensor state: state.shape ==(now_len, state_dim), next state\n \"\"\"\n if self.per_tree:\n beg = -self.max_len\n end = (self.now_len - self.max_len) if (self.now_len < self.max_len) else None\n\n indices, is_weights = self.per_tree.get_indices_is_weights(batch_size, beg, end)\n r_m_a = self.buf_other[indices]\n return (r_m_a[:, 0:1].type(torch.float32), # reward\n r_m_a[:, 1:2].type(torch.float32), # mask\n r_m_a[:, 2:].type(torch.float32), # action\n self.buf_state[indices].type(torch.float32), # state\n self.buf_state[indices + 1].type(torch.float32), # next state\n torch.as_tensor(is_weights, dtype=torch.float32, device=self.device)) # important sampling weights\n else:\n indices = rd.randint(self.now_len - 1, size=batch_size)\n r_m_a = self.buf_other[indices]\n return (r_m_a[:, 0:1], # reward\n r_m_a[:, 1:2], # mask\n r_m_a[:, 2:], # action\n self.buf_state[indices],\n self.buf_state[indices + 1])\n\n def sample_batch_one_step(self, batch_size) -> tuple:\n if self.per_tree:\n beg = -self.max_len\n end = (self.now_len - self.max_len) if (self.now_len < self.max_len) else None\n\n indices, is_weights = self.per_tree.get_indices_is_weights(batch_size, beg, end)\n r_m_a = self.buf_other[indices]\n return (r_m_a[:, 0:1].type(torch.float32), # reward\n r_m_a[:, 2:].type(torch.float32), # action\n self.buf_state[indices].type(torch.float32), # state\n torch.as_tensor(is_weights, dtype=torch.float32, device=self.device)) # important sampling weights\n else:\n indices = rd.randint(self.now_len - 1, size=batch_size)\n r_m_a = self.buf_other[indices]\n return (r_m_a[:, 0:1], # reward\n r_m_a[:, 2:], # action\n self.buf_state[indices],)\n\n def update_now_len(self):\n \"\"\"update the a pointer `now_len`, which is the current data number of ReplayBuffer\n \"\"\"\n self.now_len = self.max_len if self.if_full else self.next_idx\n\n def print_state_norm(self, neg_avg=None, div_std=None): # non-essential\n \"\"\"print the state norm information: state_avg, state_std\n\n We don't suggest to use running stat state.\n We directly do normalization on state using the historical avg and std\n eg. `state = (state + self.neg_state_avg) * self.div_state_std` in `PreprocessEnv.step_norm()`\n neg_avg = -states.mean()\n div_std = 1/(states.std()+1e-5) or 6/(states.max()-states.min())\n\n :array neg_avg: neg_avg.shape=(state_dim)\n :array div_std: div_std.shape=(state_dim)\n \"\"\"\n max_sample_size = 2 ** 14\n\n '''check if pass'''\n state_shape = self.buf_state.shape\n if len(state_shape) > 2 or state_shape[1] > 64:\n print(f\"| print_state_norm(): state_dim: {state_shape} is too large to print its norm. \")\n return None\n\n '''sample state'''\n indices = np.arange(self.now_len)\n rd.shuffle(indices)\n indices = indices[:max_sample_size] # len(indices) = min(self.now_len, max_sample_size)\n\n batch_state = self.buf_state[indices]\n\n '''compute state norm'''\n if isinstance(batch_state, torch.Tensor):\n batch_state = batch_state.cpu().data.numpy()\n assert isinstance(batch_state, np.ndarray)\n\n if batch_state.shape[1] > 64:\n print(f\"| _print_norm(): state_dim: {batch_state.shape[1]:.0f} is too large to print its norm. \")\n return None\n\n if np.isnan(batch_state).any(): # 2020-12-12\n batch_state = np.nan_to_num(batch_state) # nan to 0\n\n ary_avg = batch_state.mean(axis=0)\n ary_std = batch_state.std(axis=0)\n fix_std = ((np.max(batch_state, axis=0) - np.min(batch_state, axis=0)) / 6 + ary_std) / 2\n\n if neg_avg is not None: # norm transfer\n ary_avg = ary_avg - neg_avg / div_std\n ary_std = fix_std / div_std\n\n print(f\"print_state_norm: state_avg, state_std (fixed)\")\n print(f\"avg = np.{repr(ary_avg).replace('=float32', '=np.float32')}\")\n print(f\"std = np.{repr(ary_std).replace('=float32', '=np.float32')}\")\n\n def td_error_update(self, td_error):\n self.per_tree.td_error_update(td_error)\n\n def save_or_load_history(self, cwd, if_save, buffer_id=0):\n save_path = f\"{cwd}/replay_{buffer_id}.npz\"\n if_load = None\n\n if if_save:\n self.update_now_len()\n state_dim = self.buf_state.shape[1]\n other_dim = self.buf_other.shape[1]\n\n buf_state_data_type = np.float16 \\\n if self.buf_state.dtype in {np.float, np.float64, np.float32} \\\n else np.uint8\n\n buf_state = np.empty((self.max_len, state_dim), dtype=buf_state_data_type)\n buf_other = np.empty((self.max_len, other_dim), dtype=np.float16)\n\n temp_len = self.max_len - self.now_len\n buf_state[0:temp_len] = self.buf_state[self.now_len:self.max_len].detach().cpu().numpy()\n buf_other[0:temp_len] = self.buf_other[self.now_len:self.max_len].detach().cpu().numpy()\n\n buf_state[temp_len:] = self.buf_state[:self.now_len].detach().cpu().numpy()\n buf_other[temp_len:] = self.buf_other[:self.now_len].detach().cpu().numpy()\n\n np.savez_compressed(save_path, buf_state=buf_state, buf_other=buf_other)\n print(f\"| ReplayBuffer save in: {save_path}\")\n elif os.path.isfile(save_path):\n buf_dict = np.load(save_path)\n buf_state = buf_dict['buf_state']\n buf_other = buf_dict['buf_other']\n\n buf_state = torch.as_tensor(buf_state, dtype=torch.float32, device=self.device)\n buf_other = torch.as_tensor(buf_other, dtype=torch.float32, device=self.device)\n self.extend_buffer(buf_state, buf_other)\n self.update_now_len()\n print(f\"| ReplayBuffer load: {save_path}\")\n if_load = True\n else:\n # print(f\"| ReplayBuffer FileNotFound: {save_path}\")\n if_load = False\n return if_load\n\n\nclass ReplayBufferMP:\n def __init__(self, state_dim, action_dim, max_len, if_use_per, buffer_num, gpu_id):\n \"\"\"Experience Replay Buffer for Multiple Processing\n\n `int max_len` the max_len of ReplayBuffer, not the total len of ReplayBufferMP\n `int worker_num` the rollout workers number\n \"\"\"\n self.now_len = 0\n self.max_len = max_len\n self.worker_num = buffer_num\n\n buf_max_len = max_len // buffer_num\n self.buffers = [ReplayBuffer(max_len=buf_max_len, state_dim=state_dim, action_dim=action_dim,\n if_use_per=if_use_per, gpu_id=gpu_id)\n for _ in range(buffer_num)]\n\n def sample_batch(self, batch_size) -> list:\n bs = batch_size // self.worker_num\n list_items = [self.buffers[i].sample_batch(bs)\n for i in range(self.worker_num)]\n # list_items of reward, mask, action, state, next_state\n # list_items of reward, mask, action, state, next_state, is_weights (PER)\n\n list_items = list(map(list, zip(*list_items))) # 2D-list transpose\n return [torch.cat(item, dim=0) for item in list_items]\n\n def sample_batch_one_step(self, batch_size) -> list:\n bs = batch_size // self.worker_num\n list_items = [self.buffers[i].sample_batch_one_step(bs)\n for i in range(self.worker_num)]\n # list_items of reward, mask, action, state, next_state\n # list_items of reward, mask, action, state, next_state, is_weights (PER)\n\n list_items = list(map(list, zip(*list_items))) # 2D-list transpose\n return [torch.cat(item, dim=0) for item in list_items]\n\n def update_now_len(self):\n self.now_len = 0\n for buffer in self.buffers:\n buffer.update_now_len()\n self.now_len += buffer.now_len\n\n def print_state_norm(self, neg_avg=None, div_std=None): # non-essential\n # for buffer in self.l_buffer:\n self.buffers[0].print_state_norm(neg_avg, div_std)\n\n def td_error_update(self, td_error):\n td_errors = td_error.view(self.worker_num, -1, 1)\n for i in range(self.worker_num):\n self.buffers[i].per_tree.td_error_update(td_errors[i])\n\n def save_or_load_history(self, cwd, if_save):\n for i in range(self.worker_num):\n self.buffers[i].save_or_load_history(cwd, if_save, buffer_id=i)\n\n\nclass BinarySearchTree:\n \"\"\"Binary Search Tree for PER\n\n Contributor: Github GyChou, Github mississippiu\n Reference: https://github.com/kaixindelele/DRLib/tree/main/algos/pytorch/td3_sp\n Reference: https://github.com/jaromiru/AI-blog/blob/master/SumTree.py\n \"\"\"\n\n def __init__(self, memo_len):\n self.memo_len = memo_len # replay buffer len\n self.prob_ary = np.zeros((memo_len - 1) + memo_len) # parent_nodes_num + leaf_nodes_num\n self.max_len = len(self.prob_ary)\n self.now_len = self.memo_len - 1 # pointer\n self.indices = None\n self.depth = int(np.log2(self.max_len))\n\n # PER. Prioritized Experience Replay. Section 4\n # alpha, beta = 0.7, 0.5 for rank-based variant\n # alpha, beta = 0.6, 0.4 for proportional variant\n self.per_alpha = 0.6 # alpha = (Uniform:0, Greedy:1)\n self.per_beta = 0.4 # beta = (PER:0, NotPER:1)\n\n def update_id(self, data_id, prob=10): # 10 is max_prob\n tree_id = data_id + self.memo_len - 1\n if self.now_len == tree_id:\n self.now_len += 1\n\n delta = prob - self.prob_ary[tree_id]\n self.prob_ary[tree_id] = prob\n\n while tree_id != 0: # propagate the change through tree\n tree_id = (tree_id - 1) // 2 # faster than the recursive loop\n self.prob_ary[tree_id] += delta\n\n def update_ids(self, data_ids, prob=10): # 10 is max_prob\n ids = data_ids + self.memo_len - 1\n self.now_len += (ids >= self.now_len).sum()\n\n upper_step = self.depth - 1\n self.prob_ary[ids] = prob # here, ids means the indices of given children (maybe the right ones or left ones)\n p_ids = (ids - 1) // 2\n\n while upper_step: # propagate the change through tree\n ids = p_ids * 2 + 1 # in this while loop, ids means the indices of the left children\n self.prob_ary[p_ids] = self.prob_ary[ids] + self.prob_ary[ids + 1]\n p_ids = (p_ids - 1) // 2\n upper_step -= 1\n\n self.prob_ary[0] = self.prob_ary[1] + self.prob_ary[2]\n # because we take depth-1 upper steps, ps_tree[0] need to be updated alone\n\n def get_leaf_id(self, v):\n \"\"\"Tree structure and array storage:\n\n Tree index:\n 0 -> storing priority sum\n | |\n 1 2\n | | | |\n 3 4 5 6 -> storing priority for transitions\n Array type for storing: [0, 1, 2, 3, 4, 5, 6]\n \"\"\"\n parent_idx = 0\n while True:\n l_idx = 2 * parent_idx + 1 # the leaf's left node\n r_idx = l_idx + 1 # the leaf's right node\n if l_idx >= (len(self.prob_ary)): # reach bottom, end search\n leaf_idx = parent_idx\n break\n else: # downward search, always search for a higher priority node\n if v <= self.prob_ary[l_idx]:\n parent_idx = l_idx\n else:\n v -= self.prob_ary[l_idx]\n parent_idx = r_idx\n return min(leaf_idx, self.now_len - 2) # leaf_idx\n\n def get_indices_is_weights(self, batch_size, beg, end):\n self.per_beta = min(1., self.per_beta + 0.001)\n\n # get random values for searching indices with proportional prioritization\n values = (rd.rand(batch_size) + np.arange(batch_size)) * (self.prob_ary[0] / batch_size)\n\n # get proportional prioritization\n leaf_ids = np.array([self.get_leaf_id(v) for v in values])\n self.indices = leaf_ids - (self.memo_len - 1)\n\n prob_ary = self.prob_ary[leaf_ids] / self.prob_ary[beg:end].min()\n is_weights = np.power(prob_ary, -self.per_beta) # important sampling weights\n return self.indices, is_weights\n\n def td_error_update(self, td_error): # td_error = (q-q).detach_().abs()\n prob = td_error.squeeze().clamp(1e-6, 10).pow(self.per_alpha)\n prob = prob.cpu().numpy()\n self.update_ids(self.indices, prob)\n" }, { "path": "elegantrl/run.py", "content": "import os\nimport sys\nimport time\n\nimport torch\nimport numpy as np\nimport numpy.random as rd\nimport multiprocessing as mp\n\nfrom elegantrl.env import build_env, build_eval_env\nfrom elegantrl.replay import ReplayBuffer, ReplayBufferMP\nfrom elegantrl.evaluator import Evaluator\n\n\"\"\"[ElegantRL.2021.11.08](https://github.com/AI4Finance-Foundation/ElegantRL)\"\"\"\n\n\nclass Arguments: # [ElegantRL.2021.10.21]\n def __init__(self, env, agent):\n self.env = env # the environment for training\n self.env_num = getattr(env, 'env_num', 1) # env_num = 1. In vector env, env_num > 1.\n self.max_step = getattr(env, 'max_step', None) # the max step of an episode\n self.state_dim = getattr(env, 'state_dim', None) # vector dimension (feature number) of state\n self.action_dim = getattr(env, 'action_dim', None) # vector dimension (feature number) of action\n self.if_discrete = getattr(env, 'if_discrete', None) # discrete or continuous action space\n self.target_return = getattr(env, 'target_return', None) # target average episode return\n\n self.agent = agent # Deep Reinforcement Learning algorithm\n self.if_off_policy = agent.if_off_policy # agent is on-policy or off-policy\n if self.if_off_policy: # off-policy\n self.net_dim = 2 ** 8 # the network width\n self.max_memo = 2 ** 21 # capacity of replay buffer\n self.batch_size = self.net_dim # num of transitions sampled from replay buffer.\n self.target_step = 2 ** 10 # repeatedly update network to keep critic's loss small\n self.repeat_times = 2 ** 0 # collect target_step, then update network\n self.if_per_or_gae = False # use PER (Prioritized Experience Replay) for sparse reward\n else: # on-policy\n self.net_dim = 2 ** 9 # the network width\n self.max_memo = 2 ** 12 # capacity of replay buffer\n self.batch_size = self.net_dim * 2 # num of transitions sampled from replay buffer.\n self.target_step = self.max_memo # repeatedly update network to keep critic's loss small\n self.repeat_times = 2 ** 3 # collect target_step, then update network\n self.if_per_or_gae = False # use PER: GAE (Generalized Advantage Estimation) for sparse reward\n\n '''Arguments for training'''\n self.gamma = 0.99 # discount factor of future rewards\n self.reward_scale = 2 ** 0 # an approximate target reward usually be closed to 256\n self.learning_rate = 2 ** -15 # 2 ** -14 ~= 3e-5\n self.soft_update_tau = 2 ** -8 # 2 ** -8 ~= 5e-3\n\n '''Arguments for device'''\n self.worker_num = 2 # rollout workers number pre GPU (adjust it to get high GPU usage)\n self.thread_num = 8 # cpu_num for evaluate model, torch.set_num_threads(self.num_threads)\n self.random_seed = 0 # initialize random seed in self.init_before_training()\n self.learner_gpus = (0,) # for example: os.environ['CUDA_VISIBLE_DEVICES'] = '0, 2,'\n self.workers_gpus = self.learner_gpus # for GPU_VectorEnv (such as isaac gym)\n\n '''Arguments for evaluate and save'''\n self.cwd = None # the directory path to save the model\n self.if_remove = True # remove the cwd folder? (True, False, None:ask me)\n self.break_step = +np.inf # break training after 'total_step > break_step'\n self.if_allow_break = True # allow break training when reach goal (early termination)\n\n self.eval_env = None # the environment for evaluating. None means set automatically.\n self.eval_gap = 2 ** 8 # evaluate the agent per eval_gap seconds\n self.eval_times1 = 2 ** 2 # number of times that get episode return in first\n self.eval_times2 = 2 ** 4 # number of times that get episode return in second\n self.eval_gpu_id = None # -1 means use cpu, >=0 means use GPU, None means set as learner_gpus[0]\n self.if_overwrite = True # Save policy networks with different episode return or overwrite\n\n def init_before_training(self):\n np.random.seed(self.random_seed)\n torch.manual_seed(self.random_seed)\n torch.set_num_threads(self.thread_num)\n torch.set_default_dtype(torch.float32)\n\n '''env'''\n assert isinstance(self.env_num, int)\n assert isinstance(self.max_step, int)\n assert isinstance(self.state_dim, int) or isinstance(self.state_dim, tuple)\n assert isinstance(self.action_dim, int) or isinstance(self.action_dim, tuple)\n assert isinstance(self.if_discrete, bool)\n assert isinstance(self.target_return, int) or isinstance(self.target_return, float)\n\n '''agent'''\n assert hasattr(self.agent, 'init')\n assert hasattr(self.agent, 'update_net')\n assert hasattr(self.agent, 'explore_env')\n assert hasattr(self.agent, 'select_actions')\n\n '''auto set'''\n if self.cwd is None:\n agent_name = self.agent.__class__.__name__\n env_name = getattr(self.env, 'env_name', self.env)\n self.cwd = f'./{agent_name}_{env_name}_{self.learner_gpus}'\n if self.eval_gpu_id is None:\n self.eval_gpu_id = self.learner_gpus[0]\n\n '''remove history'''\n if self.if_remove is None:\n self.if_remove = bool(input(f\"| PRESS 'y' to REMOVE: {self.cwd}? \") == 'y')\n elif self.if_remove:\n import shutil\n shutil.rmtree(self.cwd, ignore_errors=True)\n print(f\"| Remove cwd: {self.cwd}\")\n else:\n print(f\"| Keep cwd: {self.cwd}\")\n os.makedirs(self.cwd, exist_ok=True)\n if not self.if_overwrite:\n os.makedirs('%s/best/'%self.cwd, exist_ok=True)\n\n\n'''single processing training'''\n\n\ndef train_and_evaluate(args, learner_id=0):\n args.init_before_training() # necessary!\n\n '''init: Agent'''\n agent = args.agent\n agent.init(net_dim=args.net_dim, state_dim=args.state_dim, action_dim=args.action_dim,\n gamma=args.gamma, reward_scale=args.reward_scale,\n learning_rate=args.learning_rate, if_per_or_gae=args.if_per_or_gae,\n env_num=args.env_num, gpu_id=args.learner_gpus[learner_id], )\n\n agent.save_or_load_agent(args.cwd, if_save=False)\n\n env = build_env(env=args.env, if_print=False,\n env_num=args.env_num, device_id=args.eval_gpu_id, args=args, )\n if env.env_num == 1:\n agent.states = [env.reset(), ]\n assert isinstance(agent.states[0], np.ndarray)\n assert agent.states[0].shape in {(env.state_dim,), env.state_dim}\n else:\n agent.states = env.reset()\n assert isinstance(agent.states, torch.Tensor)\n assert agent.states.shape == (env.env_num, env.state_dim)\n\n '''init Evaluator'''\n eval_env = build_eval_env(args.eval_env, args.env, args.env_num, args.eval_gpu_id, args)\n evaluator = Evaluator(cwd=args.cwd, agent_id=0,\n eval_env=eval_env, eval_gap=args.eval_gap,\n eval_times1=args.eval_times1, eval_times2=args.eval_times2,\n target_return=args.target_return, if_overwrite=args.if_overwrite)\n evaluator.save_or_load_recoder(if_save=False)\n\n '''init ReplayBuffer'''\n if args.if_off_policy:\n buffer = ReplayBuffer(max_len=args.max_memo, state_dim=env.state_dim,\n action_dim=1 if env.if_discrete else env.action_dim,\n if_use_per=args.if_per_or_gae, gpu_id=args.learner_gpus[learner_id])\n buffer.save_or_load_history(args.cwd, if_save=False)\n\n def update_buffer(_traj_list):\n ten_state, ten_other = _traj_list[0]\n buffer.extend_buffer(ten_state, ten_other)\n\n _steps, _r_exp = get_step_r_exp(ten_reward=ten_other[0]) # other = (reward, mask, action)\n return _steps, _r_exp\n else:\n buffer = list()\n\n def update_buffer(_traj_list):\n (ten_state, ten_reward, ten_mask, ten_action, ten_noise) = _traj_list[0]\n buffer[:] = (ten_state.squeeze(1),\n ten_reward,\n ten_mask,\n ten_action.squeeze(1),\n ten_noise.squeeze(1))\n\n _step, _r_exp = get_step_r_exp(ten_reward=buffer[1])\n return _step, _r_exp\n\n \"\"\"start training\"\"\"\n cwd = args.cwd\n gamma = args.gamma\n break_step = args.break_step\n batch_size = args.batch_size\n target_step = args.target_step\n repeat_times = args.repeat_times\n if_allow_break = args.if_allow_break\n soft_update_tau = args.soft_update_tau\n if_overwrite = args.if_overwrite\n del args\n\n '''init ReplayBuffer after training start'''\n if agent.if_off_policy:\n if_load = buffer.save_or_load_history(cwd, if_save=False)\n\n if not if_load:\n traj_list = agent.explore_env(env, target_step)\n steps, r_exp = update_buffer(traj_list)\n evaluator.total_step += steps\n\n '''start training loop'''\n if_train = True\n while if_train:\n with torch.no_grad():\n traj_list = agent.explore_env(env, target_step)\n steps, r_exp = update_buffer(traj_list)\n\n logging_tuple = agent.update_net(buffer, batch_size, repeat_times, soft_update_tau)\n with torch.no_grad():\n temp = evaluator.evaluate_and_save(agent.act, steps, r_exp, logging_tuple)\n if_reach_goal, if_save = temp\n\n if if_save and not if_overwrite:\n agent.save_or_load_agent('%s/best/'%(cwd), if_save=True)\n buffer.save_or_load_history('%s/best/'%(cwd), if_save=True) if agent.if_off_policy else None\n \n if_train = not ((if_allow_break and if_reach_goal)\n or evaluator.total_step > break_step\n or os.path.exists(f'{cwd}/stop'))\n\n print(f'| UsedTime: {time.time() - evaluator.start_time:>7.0f} | SavedDir: {cwd}')\n\n agent.save_or_load_agent(cwd, if_save=True)\n buffer.save_or_load_history(cwd, if_save=True) if agent.if_off_policy else None\n evaluator.save_or_load_recoder(if_save=True)\n\n\ndef get_step_r_exp(ten_reward):\n return len(ten_reward), ten_reward.mean().item()\n\n\n'''multiple processing training'''\n\n\ndef train_and_evaluate_mp(args, agent_id=0):\n args.init_before_training() # necessary!\n\n process = list()\n mp.set_start_method(method='spawn', force=True) # force all the multiprocessing to 'spawn' methods\n\n '''learner'''\n learner_num = len(args.learner_gpus)\n learner_pipe = PipeLearner(learner_num)\n for learner_id in range(learner_num):\n '''evaluator'''\n if learner_id == learner_num - 1:\n evaluator_pipe = PipeEvaluator()\n process.append(mp.Process(target=evaluator_pipe.run, args=(args, agent_id)))\n else:\n evaluator_pipe = None\n\n '''explorer'''\n worker_pipe = PipeWorker(args.env_num, args.worker_num)\n for worker_id in range(args.worker_num):\n # if args.env_num == 1:\n # env_pipe = None\n # else:\n # env_pipe = PipeVectorEnv(args)\n # process.extend(env_pipe.process)\n env_pipe = None\n process.append(mp.Process(target=worker_pipe.run, args=(args, env_pipe, worker_id, learner_id)))\n\n process.append(mp.Process(target=learner_pipe.run, args=(args, evaluator_pipe, worker_pipe, learner_id)))\n\n [(p.start(), time.sleep(0.1)) for p in process]\n process[-1].join()\n process_safely_terminate(process)\n\n\nclass PipeWorker:\n def __init__(self, env_num, worker_num):\n self.env_num = env_num\n self.worker_num = worker_num\n self.pipes = [mp.Pipe() for _ in range(worker_num)]\n self.pipe1s = [pipe[1] for pipe in self.pipes]\n\n def explore0(self, agent):\n act_dict = agent.act.state_dict()\n for worker_id in range(self.worker_num):\n self.pipe1s[worker_id].send(act_dict)\n\n traj_lists = [pipe1.recv() for pipe1 in self.pipe1s]\n return traj_lists\n\n def explore(self, agent):\n act_dict = agent.act.state_dict()\n\n if sys.platform == 'win32': # Avoid CUDA runtime error (801)\n # Python3.9< multiprocessing can't send torch.tensor_gpu in WinOS. So I send torch.tensor_cpu\n for key, value in act_dict.items():\n act_dict[key] = value.to(torch.device('cpu'))\n\n for worker_id in range(self.worker_num):\n self.pipe1s[worker_id].send(act_dict)\n\n traj_lists = [pipe1.recv() for pipe1 in self.pipe1s]\n return traj_lists\n\n def run(self, args, _comm_env, worker_id, learner_id): # not elegant: comm_env\n # print(f'| os.getpid()={os.getpid()} PipeExplore.run {learner_id}')\n env = build_env(env=args.env, if_print=False,\n env_num=args.env_num, device_id=args.workers_gpus[learner_id], args=args, )\n\n '''init Agent'''\n agent = args.agent\n agent.init(net_dim=args.net_dim, state_dim=args.state_dim, action_dim=args.action_dim,\n gamma=args.gamma, reward_scale=args.reward_scale,\n learning_rate=args.learning_rate, if_per_or_gae=args.if_per_or_gae,\n env_num=args.env_num, gpu_id=args.learner_gpus[learner_id], )\n if args.env_num == 1:\n agent.states = [env.reset(), ]\n else:\n agent.states = env.reset() # VecEnv\n\n '''loop'''\n target_step = args.target_step\n del args\n\n with torch.no_grad():\n while True:\n act_dict = self.pipes[worker_id][0].recv()\n\n if sys.platform == 'win32': # todo: not elegant. YonV1943. Avoid CUDA runtime error (801)\n # Python3.9< multiprocessing can't send torch.tensor_gpu in WinOS. So I send torch.tensor_cpu\n for key, value in act_dict.items():\n act_dict[key] = value.to(agent.device)\n\n agent.act.load_state_dict(act_dict)\n\n trajectory = agent.explore_env(env, target_step)\n if sys.platform == 'win32': # todo: not elegant. YonV1943. Avoid CUDA runtime error (801)\n # Python3.9< multiprocessing can't send torch.tensor_gpu in WinOS. So I send torch.tensor_cpu\n trajectory = [[item.to(torch.device('cpu'))\n for item in item_list]\n for item_list in trajectory]\n\n self.pipes[worker_id][0].send(trajectory)\n\n\nclass PipeLearner:\n def __init__(self, learner_num):\n self.learner_num = learner_num\n self.round_num = int(np.log2(learner_num))\n\n self.pipes = [mp.Pipe() for _ in range(learner_num)]\n pipes = [mp.Pipe() for _ in range(learner_num)]\n self.pipe0s = [pipe[0] for pipe in pipes]\n self.pipe1s = [pipe[1] for pipe in pipes]\n self.device_list = [torch.device(f'cuda:{i}') for i in range(learner_num)]\n\n if learner_num == 1:\n self.idx_l = None\n elif learner_num == 2:\n self.idx_l = [(1,), (0,), ]\n elif learner_num == 4:\n self.idx_l = [(1, 2), (0, 3),\n (3, 0), (2, 1), ]\n elif learner_num == 8:\n self.idx_l = [(1, 2, 4), (0, 3, 5),\n (3, 0, 6), (2, 1, 7),\n (5, 6, 0), (4, 7, 1),\n (7, 4, 2), (6, 5, 3), ]\n else:\n print(f\"| LearnerPipe, ERROR: learner_num {learner_num} should in (1, 2, 4, 8)\")\n exit()\n\n def comm_data(self, data, learner_id, round_id):\n if round_id == -1:\n learner_jd = self.idx_l[learner_id][round_id]\n self.pipes[learner_jd][0].send(data)\n return self.pipes[learner_id][1].recv()\n else:\n learner_jd = self.idx_l[learner_id][round_id]\n self.pipe0s[learner_jd].send(data)\n return self.pipe1s[learner_id].recv()\n\n def comm_network_optim(self, agent, learner_id):\n device = self.device_list[learner_id]\n\n for round_id in range(self.round_num):\n data = get_comm_data(agent)\n data = self.comm_data(data, learner_id, round_id)\n\n if data:\n avg_update_net(agent.act, data[0], device)\n avg_update_optim(agent.act_optim, data[1], device) if data[1] else None\n\n avg_update_net(agent.cri, data[2], device) if data[2] else None\n avg_update_optim(agent.cri_optim, data[3], device)\n\n avg_update_net(agent.act_target, data[4], device) if agent.if_use_act_target else None\n avg_update_net(agent.cri_target, data[5], device) if agent.if_use_cri_target else None\n\n def run0(self, args, comm_eva, comm_exp, learner_id=0):\n # print(f'| os.getpid()={os.getpid()} PipeLearn.run, {learner_id}')\n pass\n\n '''init Agent'''\n agent = args.agent\n agent.init(net_dim=args.net_dim, state_dim=args.state_dim, action_dim=args.action_dim,\n gamma=args.gamma, reward_scale=args.reward_scale,\n learning_rate=args.learning_rate, if_per_or_gae=args.if_per_or_gae,\n env_num=args.env_num, gpu_id=args.learner_gpus[learner_id], )\n\n agent.save_or_load_agent(args.cwd, if_save=False)\n\n '''init ReplayBuffer'''\n if agent.if_off_policy:\n buffer_num = args.worker_num * args.env_num\n if self.learner_num > 1:\n buffer_num *= 2\n\n buffer = ReplayBufferMP(max_len=args.max_memo, state_dim=args.state_dim,\n action_dim=1 if args.if_discrete else args.action_dim,\n if_use_per=args.if_per_or_gae,\n buffer_num=buffer_num, gpu_id=args.learner_gpus[learner_id])\n buffer.save_or_load_history(args.cwd, if_save=False)\n\n def update_buffer(_traj_list):\n step_sum = 0\n r_exp_sum = 0\n for buffer_i, (ten_state, ten_other) in enumerate(_traj_list):\n buffer.buffers[buffer_i].extend_buffer(ten_state, ten_other)\n\n step_r_exp = get_step_r_exp(ten_reward=ten_other[:, 0]) # other = (reward, mask, action)\n step_sum += step_r_exp[0]\n r_exp_sum += step_r_exp[1]\n return step_sum, r_exp_sum / len(_traj_list)\n else:\n buffer = list()\n\n def update_buffer(_traj_list):\n _traj_list = list(map(list, zip(*_traj_list)))\n _traj_list = [torch.cat(t, dim=0) for t in _traj_list]\n (ten_state, ten_reward, ten_mask, ten_action, ten_noise) = _traj_list\n buffer[:] = (ten_state.squeeze(1),\n ten_reward,\n ten_mask,\n ten_action.squeeze(1),\n ten_noise.squeeze(1))\n\n _step, _r_exp = get_step_r_exp(ten_reward=buffer[1])\n return _step, _r_exp\n\n '''start training'''\n cwd = args.cwd\n batch_size = args.batch_size\n repeat_times = args.repeat_times\n soft_update_tau = args.soft_update_tau\n del args\n\n if_train = True\n while if_train:\n traj_lists = comm_exp.explore(agent)\n if self.learner_num > 1:\n data = self.comm_data(traj_lists, learner_id, round_id=-1)\n traj_lists.extend(data)\n traj_list = sum(traj_lists, list())\n\n steps, r_exp = update_buffer(traj_list)\n del traj_lists\n logging_tuple = agent.update_net(buffer, batch_size, repeat_times, soft_update_tau)\n if self.learner_num > 1:\n self.comm_network_optim(agent, learner_id)\n\n if comm_eva:\n if_train, if_save = comm_eva.evaluate_and_save_mp(agent.act, steps, r_exp, logging_tuple)\n\n agent.save_or_load_agent(cwd, if_save=True)\n if agent.if_off_policy:\n print(f\"| LearnerPipe.run: ReplayBuffer saving in {cwd}\")\n buffer.save_or_load_history(cwd, if_save=True)\n\n def run(self, args, comm_eva, comm_exp, learner_id=0):\n # print(f'| os.getpid()={os.getpid()} PipeLearn.run, {learner_id}')\n pass\n\n '''init Agent'''\n agent = args.agent\n agent.init(net_dim=args.net_dim, state_dim=args.state_dim, action_dim=args.action_dim,\n gamma=args.gamma, reward_scale=args.reward_scale,\n learning_rate=args.learning_rate, if_per_or_gae=args.if_per_or_gae,\n env_num=args.env_num, gpu_id=args.learner_gpus[learner_id], )\n agent.save_or_load_agent(args.cwd, if_save=False)\n\n '''init ReplayBuffer'''\n if agent.if_off_policy:\n buffer_num = args.worker_num * args.env_num\n if self.learner_num > 1:\n buffer_num *= 2\n\n buffer = ReplayBufferMP(max_len=args.max_memo, state_dim=args.state_dim,\n action_dim=1 if args.if_discrete else args.action_dim,\n if_use_per=args.if_per_or_gae,\n buffer_num=buffer_num, gpu_id=args.learner_gpus[learner_id])\n buffer.save_or_load_history(args.cwd, if_save=False)\n\n def update_buffer(_traj_list):\n step_sum = 0\n r_exp_sum = 0\n for buffer_i, (ten_state, ten_other) in enumerate(_traj_list):\n buffer.buffers[buffer_i].extend_buffer(ten_state, ten_other)\n\n step_r_exp = get_step_r_exp(ten_reward=ten_other[:, 0]) # other = (reward, mask, action)\n step_sum += step_r_exp[0]\n r_exp_sum += step_r_exp[1]\n return step_sum, r_exp_sum / len(_traj_list)\n else:\n buffer = list()\n\n def update_buffer(_traj_list):\n _traj_list = list(map(list, zip(*_traj_list)))\n _traj_list = [torch.cat(t, dim=0) for t in _traj_list]\n (ten_state, ten_reward, ten_mask, ten_action, ten_noise) = _traj_list\n buffer[:] = (ten_state.squeeze(1),\n ten_reward,\n ten_mask,\n ten_action.squeeze(1),\n ten_noise.squeeze(1))\n\n _step, _r_exp = get_step_r_exp(ten_reward=buffer[1])\n return _step, _r_exp\n\n '''start training'''\n cwd = args.cwd\n batch_size = args.batch_size\n repeat_times = args.repeat_times\n soft_update_tau = args.soft_update_tau\n del args\n\n if_train = True\n while if_train:\n traj_lists = comm_exp.explore(agent)\n if self.learner_num > 1:\n data = self.comm_data(traj_lists, learner_id, round_id=-1)\n traj_lists.extend(data)\n traj_list = sum(traj_lists, list())\n\n if sys.platform == 'win32': # Avoid CUDA runtime error (801)\n # Python3.9< multiprocessing can't send torch.tensor_gpu in WinOS. So I send torch.tensor_cpu\n traj_list = [[item.to(torch.device('cpu'))\n for item in item_list]\n for item_list in traj_list]\n\n steps, r_exp = update_buffer(traj_list)\n del traj_lists\n\n logging_tuple = agent.update_net(buffer, batch_size, repeat_times, soft_update_tau)\n if self.learner_num > 1:\n self.comm_network_optim(agent, learner_id)\n\n if comm_eva:\n if_train, if_save = comm_eva.evaluate_and_save_mp(agent.act, steps, r_exp, logging_tuple)\n\n agent.save_or_load_agent(cwd, if_save=True)\n if agent.if_off_policy:\n print(f\"| LearnerPipe.run: ReplayBuffer saving in {cwd}\")\n buffer.save_or_load_history(cwd, if_save=True)\n\n\nclass PipeEvaluator: # [ElegantRL.10.21]\n def __init__(self):\n super().__init__()\n self.pipe0, self.pipe1 = mp.Pipe()\n\n def evaluate_and_save_mp(self, agent_act, steps, r_exp, logging_tuple):\n if self.pipe1.poll(): # if_evaluator_idle\n if_train, if_save = self.pipe1.recv()\n act_cpu_dict = {k: v.cpu() for k, v in agent_act.state_dict().items()}\n else:\n if_train, if_save = True, False\n act_cpu_dict = None\n\n self.pipe1.send((act_cpu_dict, steps, r_exp, logging_tuple))\n return if_train, if_save\n\n def run(self, args, _learner_id):\n # print(f'| os.getpid()={os.getpid()} PipeEvaluate.run {agent_id}')\n pass\n\n '''init: Agent'''\n agent = args.agent\n agent.init(net_dim=args.net_dim, state_dim=args.state_dim, action_dim=args.action_dim,\n gamma=args.gamma, reward_scale=args.reward_scale,\n learning_rate=args.learning_rate, if_per_or_gae=args.if_per_or_gae,\n env_num=args.env_num, gpu_id=args.eval_gpu_id, )\n\n agent.save_or_load_agent(args.cwd, if_save=False)\n\n act = agent.act\n [setattr(param, 'requires_grad', False) for param in agent.act.parameters()]\n del agent\n\n '''init Evaluator'''\n eval_env = build_eval_env(args.eval_env, args.env, args.env_num, args.eval_gpu_id, args)\n evaluator = Evaluator(cwd=args.cwd, agent_id=0,\n eval_env=eval_env, eval_gap=args.eval_gap,\n eval_times1=args.eval_times1, eval_times2=args.eval_times2,\n target_return=args.target_return, if_overwrite=args.if_overwrite)\n evaluator.save_or_load_recoder(if_save=False)\n\n '''loop'''\n cwd = args.cwd\n break_step = args.break_step\n if_allow_break = args.if_allow_break\n del args\n\n if_save = False\n if_train = True\n if_reach_goal = False\n with torch.no_grad():\n while if_train:\n act_dict, steps, r_exp, logging_tuple = self.pipe0.recv()\n\n if act_dict:\n act.load_state_dict(act_dict)\n if_reach_goal, if_save = evaluator.evaluate_and_save(act, steps, r_exp, logging_tuple)\n else:\n evaluator.total_step += steps\n\n if_train = not ((if_allow_break and if_reach_goal)\n or evaluator.total_step > break_step\n or os.path.exists(f'{cwd}/stop'))\n self.pipe0.send((if_train, if_save))\n\n print(f'| UsedTime: {time.time() - evaluator.start_time:>7.0f} | SavedDir: {cwd}')\n evaluator.save_or_load_recoder(if_save=True)\n\n\n# class PipeVectorEnv:\n# def __init__(self, args):\n# self.env_num = args.env_num\n# self.pipes = [mp.Pipe() for _ in range(self.env_num)]\n# self.pipe0s = [pipe[0] for pipe in self.pipes]\n#\n# env = build_env(args.eval_env)\n# self.max_step = env.max_step\n# self.env_name = env.env_name\n# self.state_dim = env.state_dim\n# self.action_dim = env.action_dim\n# self.action_max = env.action_max\n# self.if_discrete = env.if_discrete\n# self.target_return = env.target_return\n# del env\n#\n# self.process = list()\n# for env_id in range(args.env_num):\n# self.process.append(mp.Process(target=self.run, args=(args, env_id)))\n# args.random_seed += 1 # set different for each env\n# # [p.start() for p in self.process]\n#\n# def reset(self):\n# vec_state = [pipe0.recv() for pipe0 in self.pipe0s]\n# return vec_state\n#\n# def step(self, vec_action): # pipe0_step\n# for i in range(self.env_num):\n# self.pipe0s[i].send(vec_action[i])\n# return [pipe0.recv() for pipe0 in self.pipe0s] # list of (state, reward, done)\n#\n# def run(self, args, env_id):\n# np.random.seed(args.random_seed)\n#\n# env = build_env(args.eval_env, if_print=False)\n# pipe1 = self.pipes[env_id][1]\n# del args\n#\n# state = env.reset()\n# pipe1.send(state)\n#\n# while True:\n# action = pipe1.recv()\n# state, reward, done, _ = env.step(action)\n# pipe1.send((env.reset() if done else state, reward, done))\n#\n# # def check(self):\n# # vec_state = self.reset()\n# # ten_state = np.array(vec_state)\n# # print(ten_state.shape)\n# #\n# # vec_action = np.array(((0.0, 1.0, 0.0),\n# # (0.0, 0.5, 0.0),\n# # (0.0, 0.1, 0.0),))[:self.env_num]\n# # assert self.env_num <= 3\n# #\n# # trajectory_list = list()\n# # for _ in range(8):\n# # s_r_d_list = self.step(vec_action)\n# # ten_state = np.array([s_r_d[0] for s_r_d in s_r_d_list])\n# # print(ten_state.shape)\n# # trajectory_list.append(s_r_d_list)\n# #\n# # trajectory_list = list(map(list, zip(*trajectory_list))) # 2D-list transpose\n# # print('| shape of trajectory_list:', len(trajectory_list), len(trajectory_list[0]))\n\ndef get_comm_data(agent):\n act = list(agent.act.parameters())\n cri_optim = get_optim_parameters(agent.cri_optim)\n\n if agent.cri is agent.act:\n cri = None\n act_optim = None\n else:\n cri = list(agent.cri.parameters())\n act_optim = get_optim_parameters(agent.act_optim)\n\n act_target = list(agent.act_target.parameters()) if agent.if_use_act_target else None\n cri_target = list(agent.cri_target.parameters()) if agent.if_use_cri_target else None\n return act, act_optim, cri, cri_optim, act_target, cri_target # data\n\n\n\"\"\"Utils\"\"\"\n\n\ndef get_num_learner(visible_gpu):\n assert isinstance(visible_gpu, str) # visible_gpu may in {'0', '1', '1,', '1,2', '1,2,'}\n visible_gpu = eval(visible_gpu)\n num_learner = 1 if isinstance(visible_gpu, int) else len(visible_gpu)\n return num_learner\n\n\ndef process_safely_terminate(process):\n for p in process:\n try:\n p.kill()\n except OSError as e:\n print(e)\n pass\n\n\ndef get_optim_parameters(optim): # for avg_update_optim()\n params_list = list()\n for params_dict in optim.state_dict()['state'].values():\n params_list.extend([t for t in params_dict.values() if isinstance(t, torch.Tensor)])\n return params_list\n\n\ndef avg_update_optim(dst_optim, src_optim_param, device):\n for dst, src in zip(get_optim_parameters(dst_optim), src_optim_param):\n dst.data.copy_((dst.data + src.data.to(device)) * 0.5)\n # dst.data.copy_(src.data * tau + dst.data * (1 - tau))\n\n\ndef avg_update_net(dst_net, src_net_param, device):\n for dst, src in zip(dst_net.parameters(), src_net_param):\n dst.data.copy_((dst.data + src.data.to(device)) * 0.5)\n" }, { "path": "envs.py", "content": "from gym import Env\nfrom gym.spaces import Box, Space\nfrom features import Feature\nfrom stoppers import Stopper\nfrom assessments import Assessment\nfrom wtpy.apps import WtBtAnalyst\nfrom wtpy.WtBtEngine import WtBtEngine\nfrom strategies import StateTransfer, EngineType\nfrom multiprocessing import Pipe, Process\nfrom os import getpid\n\n# 一个进程只能有一个env\n\n\nclass WtEnv(Env):\n TRAINER = 1\n EVALUATOR = 2\n DEBUGGER = 3\n\n def __init__(self,\n strategy: StateTransfer,\n stopper: Stopper,\n feature: Feature,\n assessment: Assessment,\n time_range: tuple,\n slippage: int = 0,\n id: int = getpid(),\n mode=1,\n ):\n\n self.reward_range\n\n if mode == 3: # 调试模式\n self._log_: str = './config/03research/log_debugger.json'\n self._dump_: bool = True\n self._mode_: str = 'WtDebugger'\n elif mode == 2: # 评估模式\n self._log_: str = './config/03research/log_evaluator.json'\n self._dump_: bool = True\n self._mode_: str = 'WtEvaluator'\n else: # 训练模式\n self._log_: str = './config/03research/log_trainer.json'\n self._dump_: bool = False\n self._mode_: str = 'WtTrainer'\n\n self._id_: int = id\n self._iter_: int = 0\n self._run_: bool = False\n\n self.__strategy__ = strategy\n self._et_ = self.__strategy__.EngineType()\n self.__stopper__: Stopper = stopper\n self.__slippage__: int = slippage\n\n self.__feature__: Feature = feature\n self.observation_space: Box = Box(**self.__feature__.observation)\n self.action_space: Space = self.__strategy__.Action(\n len(self.__feature__.securities))\n\n self._assessment_: Assessment = assessment\n\n self.__time_range__ = time_range\n\n def _debug_(self):\n pass\n\n def __step__(self):\n finished = not self._cb_step_()\n if self._assessment_.done or finished:\n self._assessment_.finish()\n self._debug_()\n self.close()\n # if self._dump_:\n # self.analyst(self._iter_)\n\n def close(self):\n if self._run_ and hasattr(self, '_engine_'):\n self._engine_.stop_backtest()\n self._run_ = False\n\n def reset(self):\n self.close()\n time_start, time_end = self.__time_range__[self._iter_%len(self.__time_range__)]\n self._iter_ += 1\n\n if not hasattr(self, '_engine_'):\n # 创建一个运行环境\n self._engine_: WtBtEngine = WtBtEngine(\n eType=self._et_,\n logCfg=self._log_,\n )\n if self._et_ == EngineType.ET_CTA:\n self._engine_.init(\n './config/01commom/',\n './config/03research/cta.json')\n self._cb_step_ = self._engine_.cta_step\n elif self._et_ == EngineType.ET_HFT:\n self._engine_.init(\n './config/01commom/',\n './config/03research/hft.json')\n self._cb_step_ = self._engine_.hft_step\n else:\n raise AttributeError\n\n self._engine_.configBacktest(time_start, time_end)\n self._engine_.commitBTConfig()\n else:\n self._engine_.set_time_range(time_start, time_end)\n\n # 重置奖励\n self._assessment_.reset()\n\n # 创建一个策略并加入运行环境\n self._strategy_: StateTransfer = self.__strategy__(\n name=self._name_(self._iter_),\n feature=self.__feature__,\n stopper=self.__stopper__,\n assessment=self._assessment_,\n )\n\n # 设置策略的时候一定要安装钩子\n if self._et_ == EngineType.ET_CTA:\n self._engine_.set_cta_strategy(\n self._strategy_, slippage=self.__slippage__, hook=True, persistData=self._dump_)\n elif self._et_ == EngineType.ET_HFT:\n self._engine_.set_hft_strategy(self._strategy_, hook=True)\n else:\n raise AttributeError\n\n # 回测一定要异步运行\n self._engine_.run_backtest(bAsync=True, bNeedDump=self._dump_)\n self._run_ = True\n\n self.__step__()\n return self.__feature__.obs\n\n def step(self, action):\n assert hasattr(self, '_engine_')\n self._strategy_.setAction(action)\n self._cb_step_()\n\n self.__step__()\n return self.__feature__.obs, self._assessment_.reward, self._assessment_.done, {}\n\n @property\n def assets(self):\n return self._assessment_.curr_assets\n\n def analyst(self, iter: int):\n name = self._name_(iter)\n analyst = WtBtAnalyst()\n folder = \"./outputs_bt/%s/\" % name\n analyst.add_strategy(\n name, folder=folder, init_capital=self._assessment_._init_assets_, rf=0.02, annual_trading_days=240)\n try:\n analyst.run_new('%s/PnLAnalyzing.xlsx' % folder)\n except:\n analyst.run('%s/PnLAnalyzing.xlsx' % folder)\n\n def analysts(self):\n for iter in range(1, self._iter_+1):\n self.analysis(iter)\n\n def _name_(self, iter):\n time_start, time_end = self.__time_range__[(iter-1)%len(self.__time_range__)]\n return '%s%s_%s_%s_%s-%s' % (self._mode_, self._id_, self.__strategy__.Name(), iter, str(time_start)[:8], str(time_end)[:8])\n\n def __del__(self):\n if hasattr(self, '_engine_'):\n self._engine_.release_backtest()\n\n\ndef __sub_process_worker__(pipe: Pipe, _cmd_, _attr_, cli, kwargs):\n env = cli(**kwargs)\n while True:\n cmd, kwargs = pipe.recv()\n if cmd in _cmd_:\n if cmd == 'stop':\n pipe.send(True)\n pipe.close()\n break\n call = getattr(env, cmd)\n if kwargs:\n # print(cmd, kwargs)\n pipe.send(call(**kwargs))\n else:\n pipe.send(call())\n elif cmd in _attr_:\n pipe.send(getattr(env, cmd))\n else:\n pipe.send('unknow %s' % cmd)\n\n\nclass WtSubProcessEnv(Env):\n _cmd_ = ('reset', 'step', 'close', 'stop')\n _attr_ = ('reward_range', 'metadata',\n 'observation_space', 'action_space', 'assets')\n\n def __init__(self, cli, **kwargs):\n self._pipe_, pipe = Pipe()\n self._process_ = Process(\n target=__sub_process_worker__,\n args=(pipe, self._cmd_, self._attr_, cli, kwargs),\n daemon=True\n )\n self._process_.start()\n\n def __do__(self, cmd, **kwargs):\n self._pipe_.send((cmd, kwargs))\n return self._pipe_.recv()\n\n @property\n def metadata(self):\n return self.__do__('metadata')\n\n @property\n def reward_range(self):\n return self.__do__('reward_range')\n\n @property\n def observation_space(self):\n return self.__do__('observation_space')\n\n @property\n def action_space(self):\n return self.__do__('action_space')\n\n @property\n def assets(self):\n return self.__do__('assets')\n\n def reset(self):\n return self.__do__('reset')\n\n def step(self, action):\n # print(type(action))\n return self.__do__('step', action=action)\n\n def close(self):\n return self.__do__('close')\n\n def __del__(self):\n self.__do__('stop')\n self._process_.join()\n self._process_.close()\n" }, { "path": "envs_simple_cta.py", "content": "from features import Indicator\nfrom assessments import SimpleAssessment\nfrom stoppers import SimpleStopper\nfrom strategies import SimpleCTA\nfrom envs import WtEnv, WtSubProcessEnv\n\n\nclass SimpleCTAEnv(WtEnv):\n def __init__(self,\n time_range: tuple,\n slippage: int = 0,\n mode: int = 1\n ):\n assets = 180000\n\n # 角色：数据研究人员、强化学习研究人员、策略研究人员\n # 原则：每个角色的分工模拟交易机构做隔离\n\n # 特征工程组件, 滚动窗口=2，根据特征工程自动生成强化学习需要的observation\n # 特征工程的因子生成绝大多数情况下（舆情因子、周期因子）不是由env负责的，所以尽量使用特征工程组件而不要在env中定义因子\n # 特征工程的因子定义和生成，主要使用者是数据研究人员\n # 特征工程的因子后处理，主要使用者是强化学习研究人员\n feature: Indicator = Indicator(\n code='DCE.c.HOT', period=Indicator.M5, roll=1, assets=assets) # 每一个特征工程必须指定一个主要标的\n\n # 按需添加其他标的\n feature.addSecurity(code='DCE.cs.HOT')\n feature.addSecurity(code='DCE.m.HOT')\n # feature.addSecurity(code='CZCE.RM.HOT')\n # feature.addSecurity(code='CZCE.JR.HOT')\n # feature.addSecurity(code='CZCE.TA.HOT')\n # feature.addSecurity(code='DCE.jd.HOT')\n # feature.addSecurity(code='SHFE.rb.HOT')\n # feature.addSecurity(code='SHFE.hc.HOT')\n # feature.addSecurity(code='SHFE.bu.HOT')\n # feature.addSecurity(code='SHFE.fu.HOT')\n # feature.addSecurity(code='SHFE.ni.HOT')\n\n # 分别使用5分钟、15分钟、日线建立多周期因子\n for period in (feature.M5, ): # feature.M5, feature.M10,\n feature.price(period)\n feature.volume(period)\n # feature.roc(period)\n feature.bollinger(period) # 标准差通道\n # feature.sar(period)\n # feature.trange(period) # 波动率\n feature.macd(period) # 双均线强度\n # feature.rsi(period)\n # feature.dx(period)\n # feature.obv(period)\n feature.kdj(period)\n\n # 除上述特征，特征工程组件会自动加上 \"开仓的最大浮盈、开仓的最大亏损、开仓的浮动盈亏、当前持仓数\"4列，如果没有持仓则全部为0\n\n # 止盈止损组件，暂时是个摆设\n # 止盈止损组件的主要使用者是策略研究人员\n stopper: SimpleStopper = SimpleStopper()\n\n # 评估组件\n # 评估组件的主要使用者是强化学习研究人员定义reward\n assessment: SimpleAssessment = SimpleAssessment(init_assets=assets)\n super().__init__(\n # 策略只做跟交易模式相关的操作(如趋势策略、日内回转、配对交易、统计套利)，不参与特征生成和评估，主要使用者是策略研究人员\n strategy=SimpleCTA,\n stopper=stopper,\n feature=feature, # 特征计算\n assessment=assessment, # 评估计算\n time_range=time_range,\n slippage=slippage,\n mode=mode, # 1训练模式，2评估模式，3debug模式\n )\n\n def _debug_(self):\n print('%s: assets %s, reward %s, reward_sum %s' % (\n self._name_(self._iter_), self._assessment_.curr_assets-self._assessment_.init_assets, self._assessment_.reward, sum(self._assessment_.rewards)))\n\n\nclass SimpleCTASubProcessEnv(WtSubProcessEnv):\n def __init__(self,\n time_range: tuple,\n slippage: int = 0,\n mode: int = 1):\n super().__init__(\n cli=SimpleCTAEnv,\n time_range=time_range,\n slippage=slippage,\n mode=mode)\n\n\nif __name__ == '__main__':\n env: WtEnv = SimpleCTASubProcessEnv(time_start=201901011600,\n time_end=202001011600, mode=2)\n\n print(env.action_space.contains)\n\n for i in range(1): # 模拟训练10次\n obs = env.reset()\n done = False\n n = 0\n while not done:\n # box.contains of Box([-3. -3. -3. -3.], [3. 3. 3. 3.], (4,), float16)\n action = env.action_space.sample() # 模拟智能体产生动作\n obs, reward, done, info = env.step(action)\n n += 1\n print(\n # 'action:', action,\n # 'obs:', obs,\n 'reward:', reward,\n # 'done:', done\n )\n # break\n # break\n print('第%s次训练完成，执行%s步, 市值%s。' % (i+1, n, env.assets))\n env.close()\n" }, { "path": "features.py", "content": "import numpy as np\nimport talib as ta\nfrom reprocess import REPROCESS, ZFILTER, MAXMIN\nfrom wtpy.StrategyDefs import CtaContext, HftContext\n\n\nclass Feature():\n M1 = 'm1'\n M3 = 'm3'\n M5 = 'm5'\n M10 = 'm10'\n M15 = 'm15'\n M30 = 'm30'\n M60 = 'm60'\n D1 = 'd1'\n\n def __init__(self, code: str, period: str, roll: int, assets: float = 1000000) -> None:\n self.__shape__: tuple = tuple()\n self._roll_: int = int(roll)\n self._assets_: float = float(assets)\n\n self.__cb__: dict = {}\n\n self.__obs__: dict = {}\n self.__time__: int = 0\n\n self.__securities__: list = []\n self.addSecurity(code=code)\n\n self.__main__: tuple = (code, period)\n self.__subscribies__: dict = {}\n self._subscribe_(period=period, count=1)\n\n # self.__comminfo__: dict = {}\n\n @property\n def securities(self):\n return self.__securities__\n\n def addSecurity(self, code: str):\n if self.__shape__ or code in self.__securities__:\n return\n self.__securities__.append(code)\n\n def _subscribe_(self, period: str, count: int = 1):\n self.__subscribies__[period] = max(\n self.__subscribies__.get(period, 0),\n count+self._roll_\n )\n\n def subscribe(self, context: CtaContext):\n '''\n 根据特征需求订阅数据\n '''\n for code in self.__securities__:\n # comminfo = context.stra_get_comminfo(code) # 品种信息数据\n # self.__comminfo__[code] = (comminfo.pricetick, comminfo.volscale)\n for period, count in self.__subscribies__.items():\n context.stra_get_bars(\n stdCode=code,\n period=period,\n count=count,\n isMain=(code == self.__main__[0]\n and period == self.__main__[1])\n )\n\n def _callback_(self, space: int, period: str, callback, reprocess: REPROCESS, **kwargs):\n if self.__shape__ or space < 1:\n return\n if period not in self.__cb__:\n self.__cb__[period] = {}\n self.__cb__[period][callback.__name__] = (\n space, callback, reprocess, kwargs)\n\n def sigmoid(self, value, thresh=30):\n return (1 / (1 + np.exp(-(value/thresh) * np.e)) - 0.5)*thresh\n\n @property\n def observation(self) -> dict:\n '''\n 根据特征需求生成observation\n '''\n self.__shape__ = (\n len(self.securities),\n sum(c[0] for v in self.__cb__.values()\n for c in v.values())*self._roll_+4\n )\n self.__flatten__ = (self.__shape__[0]*self.__shape__[1],)\n return dict(low=-np.inf, high=np.inf, shape=self.__flatten__, dtype=np.float64)\n\n def calculate(self, context: CtaContext):\n self.__time__ = context.stra_get_date()*10000+context.stra_get_time()\n if self.__time__ not in self.__obs__:\n obs = np.full(shape=self.__shape__,\n fill_value=np.nan, dtype=np.float64)\n for i, code in enumerate(self.securities): # 处理每一个标的\n n = 0\n for period, v in self.__cb__.items(): # 处理每一个周期\n for space, callback, p, args in v.values(): # 处理每一个特征\n features = callback(\n context=context, code=code, period=period, args=args) # 通过回调函数计算特征\n if space == 1:\n features = (features, )\n for feature in features: # 处理每一个返回值\n # print(p.calculate(feature))\n # obs[i][n:n +self._roll_] = p.calculate(feature)[-self._roll_:]\n obs[i][n:n +self._roll_] = p.calculate(feature)[-self._roll_:]\n #np.clip(p.calculate(feature)[-self._roll_:], -1, 1)\n n += self._roll_\n # self.__obs__[self.__time__] = obs\n # self.__obs__[self.__time__] = self.sigmoid(obs)\n self.__obs__[self.__time__] = obs\n\n # 开仓最大浮盈\n self.__obs__[self.__time__][:, -4] = tuple(\n context.stra_get_detail_profit(\n # stdCode=code, usertag='', flag=1)/self.__comminfo__[code][1]/self.__comminfo__[code][0] for code in self.securities\n stdCode=code, usertag='', flag=1)/self._assets_ for code in self.securities\n )\n\n # 开仓最大亏损\n self.__obs__[self.__time__][:, -3] = tuple(\n context.stra_get_detail_profit(\n # stdCode=code, usertag='', flag=-1)/self.__comminfo__[code][1]/self.__comminfo__[code][0] for code in self.securities\n stdCode=code, usertag='', flag=-1)/self._assets_ for code in self.securities\n )\n\n # 开仓浮动盈亏\n self.__obs__[self.__time__][:, -2] = tuple(\n context.stra_get_detail_profit(\n # stdCode=code, usertag='', flag=0)/self.__comminfo__[code][1]/self.__comminfo__[code][0] for code in self.securities\n stdCode=code, usertag='', flag=0)/self._assets_ for code in self.securities\n )\n\n # 持仓数\n self.__obs__[self.__time__][:, -1] = tuple(\n context.stra_get_position(stdCode=code) for code in self.securities)\n\n # self.__obs__[self.__time__][:, -4:] = self.sigmoid(self.__obs__[self.__time__][:, -4:])\n # np.clip(\n # self.__obs__[self.__time__][:, -4:], -1, 1,\n # out=self.__obs__[self.__time__][:, -4:])\n\n @property\n def obs(self):\n # .astype(np.float64)\n return self.__obs__.get(self.__time__).reshape(self.__flatten__)\n\n def price(self, period: str, reprocess: REPROCESS = MAXMIN):\n def price(context: CtaContext, code: str, period: str, args: dict):\n return context.stra_get_bars(stdCode=code, period=period, count=self.__subscribies__[period]).closes\n\n self._subscribe_(period=period, count=2+reprocess.n())\n self._callback_(space=1, period=period,\n callback=price, reprocess=reprocess)\n\n def volume(self, period: str, reprocess: REPROCESS = MAXMIN):\n def volume(context: CtaContext, code: str, period: str, args: dict):\n return context.stra_get_bars(stdCode=code, period=period, count=self.__subscribies__[period]).volumes\n\n self._subscribe_(period=period, count=2+reprocess.n())\n self._callback_(space=1, period=period,\n callback=volume, reprocess=reprocess)\n\n\nclass Indicator(Feature):\n def roc(self, period: str, reprocess: REPROCESS = REPROCESS):\n def roc(context: CtaContext, code: str, period: str, args: dict):\n price = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period]).closes\n price = np.log(price)\n return (price[1:]/price[:-1]-1)\n return np.diff(context.stra_get_bars(stdCode=code, period=period, count=self.__subscribies__[period]).closes)\n\n self._subscribe_(period=period, count=2+reprocess.n())\n self._callback_(space=1, period=period,\n callback=roc, reprocess=reprocess)\n\n def bollinger(self, period: str, timeperiod=5, nbdevup=2, nbdevdn=2, reprocess: REPROCESS = MAXMIN):\n def bollinger(context: CtaContext, code: str, period: str, args: dict):\n closes = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period]).closes\n upperband, middleband, lowerband = ta.BBANDS(closes, **args)\n return upperband, middleband, lowerband\n return upperband/closes-1, middleband/closes-1, lowerband/closes-1\n\n self._subscribe_(period=period, count=timeperiod+reprocess.n())\n self._callback_(space=3, period=period, callback=bollinger, reprocess=reprocess,\n timeperiod=timeperiod, nbdevup=nbdevup, nbdevdn=nbdevdn)\n\n def sar(self, period: str, acceleration=0, maximum=0, reprocess: REPROCESS = MAXMIN):\n def sar(context: CtaContext, code: str, period: str, args: dict):\n bars = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period])\n return ta.SAR(high=bars.highs, low=bars.lows, **args)\n return ta.SAR(high=bars.highs, low=bars.lows, **args)/bars.closes-1\n self._subscribe_(period=period, count=10+reprocess.n())\n self._callback_(space=1, period=period, acceleration=acceleration, maximum=maximum,\n callback=sar, reprocess=reprocess)\n\n def trange(self, period: str, reprocess: REPROCESS = MAXMIN):\n def trange(context: CtaContext, code: str, period: str, args: dict):\n bars = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period])\n return ta.TRANGE(high=bars.highs, low=bars.lows, close=bars.closes)\n return ta.TRANGE(high=bars.highs, low=bars.lows, close=bars.closes)/bars.closes\n\n self._subscribe_(period=period, count=2+reprocess.n())\n self._callback_(space=1, period=period,\n callback=trange, reprocess=reprocess)\n\n def macd(self, period: str, fastperiod: int = 12, slowperiod: int = 26, signalperiod: int = 9, reprocess: REPROCESS = MAXMIN):\n def macd(context: CtaContext, code: str, period: str, args: dict):\n return ta.MACD(np.log(context.stra_get_bars(stdCode=code, period=period, count=self.__subscribies__[period]).closes), **args)\n\n self._subscribe_(period=period, count=slowperiod +\n signalperiod+reprocess.n())\n self._callback_(space=3, period=period, callback=macd, reprocess=reprocess,\n fastperiod=fastperiod, slowperiod=slowperiod, signalperiod=signalperiod)\n\n def rsi(self, period: str, fastperiod: int = 6, midperiod: int = 12, slowperiod: int = 24, reprocess: REPROCESS = MAXMIN):\n def rsi(context: CtaContext, code: str, period: str, args: dict):\n bars = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period])\n return ta.RSI(bars.closes, args['fastperiod']), ta.RSI(bars.closes, args['midperiod']), ta.RSI(bars.closes, args['slowperiod'])\n return ta.RSI(bars.closes, args['fastperiod'])/100, ta.RSI(bars.closes, args['midperiod'])/100, ta.RSI(bars.closes, args['slowperiod'])/100\n\n self._subscribe_(period=period, count=slowperiod + 1 + reprocess.n())\n self._callback_(space=3, period=period, callback=rsi, reprocess=reprocess,\n fastperiod=fastperiod, midperiod=midperiod, slowperiod=slowperiod)\n\n def dx(self, period: str, timeperiod=14, reprocess: REPROCESS = MAXMIN):\n def dx(context: CtaContext, code: str, period: str, args: dict):\n bars = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period])\n return ta.DX(high=bars.highs, low=bars.lows, close=bars.closes, **args)\n return ta.DX(high=bars.highs, low=bars.lows, close=bars.closes, **args)/100\n\n self._subscribe_(period=period, count=timeperiod+1+reprocess.n())\n self._callback_(space=1, period=period, callback=dx, reprocess=reprocess,\n timeperiod=timeperiod)\n\n def obv(self, period: str, reprocess: REPROCESS = MAXMIN):\n def obv(context: CtaContext, code: str, period: str, args: dict):\n bars = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period])\n return ta.OBV(bars.closes, bars.volumes, **args)\n\n self._subscribe_(period=period, count=10+reprocess.n())\n self._callback_(space=1, period=period,\n callback=obv, reprocess=reprocess)\n\n def kdj(self, period: str, fastk_period: int = 5, slowk_period: int = 3, reprocess: REPROCESS = MAXMIN):\n def kdj(context: CtaContext, code: str, period: str, args: dict):\n bars = context.stra_get_bars(\n stdCode=code, period=period, count=self.__subscribies__[period])\n k, d = ta.STOCH(high=bars.highs, low=bars.lows,\n close=bars.closes, **args)\n return k, d, (3*k-2*d)\n return k/100, d/100, (3*k-2*d)/100\n\n self._subscribe_(period=period, count=10 + 1 + reprocess.n())\n self._callback_(space=3, period=period, callback=kdj, reprocess=reprocess,\n fastk_period=fastk_period, slowk_period=slowk_period)\n\n # def weights(self, period: str, timeperiod:int=1, index:str='000300', reprocess:REPROCESS =REPROCESS):\n # def example(context: CtaContext, code: str, period: str, args: dict):\n # # 标的代码 code\n # # 标的周期 period\n # # 自定义参数 args['index']\n # # 日期int context.stra_get_date()\n # # 时间int context.stra_get_time()\n # return 查询代码(code, context.stra_get_date(), args['index'])\n\n # self._subscribe_(period=period, count=1+reprocess.n()) # 在什么周期的event触发，需要几根bar\n # self._callback_(\n # space=1, #查询代码有几个值，自动生成obs的占位空间\n # period=period,\n # callback=example,\n # reprocess=reprocess\n # timeperiod=timeperiod,\n # index=index)\n" }, { "path": "reprocess.py", "content": "import numpy as np\nimport talib as ta\n\n\nclass REPROCESS():\n @staticmethod\n def n() -> int: # 定义至少需要多少条数据才能计算\n return 0\n\n @staticmethod\n def calculate(data: np.ndarray) -> np.ndarray: # 计算方法\n return data\n\n\nclass ZSCORE(REPROCESS):\n @staticmethod\n def n() -> int:\n return 1200\n\n @staticmethod\n def calculate(data: np.ndarray) -> np.ndarray:\n return (data-ta.MA(data, __class__.n()))/(ta.STDDEV(data, __class__.n())+1e-5)\n\n\nclass ZFILTER(REPROCESS):\n '''\n https://github.com/zhangchuheng123/Reinforcement-Implementation/blob/master/code/ppo.py\n '''\n @staticmethod\n def n() -> int:\n return 1200\n\n @staticmethod\n def calculate(data: np.ndarray) -> np.ndarray:\n # 1e-8\n return (data-ta.MA(data, __class__.n()))/(ta.STDDEV(data, __class__.n())+1e-5)\n\n\nclass MAXMIN(REPROCESS):\n @staticmethod\n def n() -> int:\n return 1200\n\n @staticmethod\n def calculate(data: np.ndarray) -> np.ndarray:\n data = data[-__class__.n():]\n return (data-data.min())/(data.max()-data.min()+1e-5)*2-1\n" }, { "path": "requirements/full_with_cuda.yaml", "content": "channels:\n - pytorch\n - conda-forge\n - defaults\ndependencies:\n - python==3.8.12\n - pip\n\n # torch\n - pytorch\n - torchvision\n - torchaudio\n - cudatoolkit\n\n # drl\n - matplotlib\n - gym\n - stable-baselines3\n - ray-rllib\n - tensorboard\n - gputil\n - opencensus \n - prometheus_client \n - aiohttp-cors \n - aioredis==1.3.1\n - gpy\n - scikit-learn\n\n # wtpy\n - pandas\n - flask\n - flask-compress\n - xlsxwriter\n - pyquery\n - zstandard\n\n # \n - bottleneck\n - numexpr\n - tqdm\n\n # vscode\n - IPython\n - ipywidgets" }, { "path": "requirements/full_without_cuda.yaml", "content": "channels:\n - pytorch\n - conda-forge\n - defaults\ndependencies:\n - python==3.8.12\n - pip\n\n # torch\n - pytorch\n - torchvision\n - torchaudio\n - cpuonly\n\n # drl\n - matplotlib\n - gym\n - stable-baselines3\n - ray-rllib\n - tensorboard\n - gputil\n - opencensus \n - prometheus_client \n - aiohttp-cors \n - aioredis==1.3.1\n - gpy\n - scikit-learn\n\n # wtpy\n - pandas\n - flask\n - flask-compress\n - xlsxwriter\n - pyquery\n - zstandard\n\n # \n - bottleneck\n - numexpr\n - tqdm\n\n # vscode\n - IPython\n - ipywidgets" }, { "path": "run_toy.py", "content": "from runner import entry, Runner\n\nclass SimpleRunner(Runner):\n def test(self):\n print('test1')\n\n\nif __name__ == '__main__':\n entry(obj=SimpleRunner())\n" }, { "path": "runner.py", "content": "import click\n\nclass Runner():\n def debug(self):\n print('debug')\n def train(self):\n print('train')\n def test(self):\n print('test')\n\n\n@click.group()\n@click.pass_context\ndef entry(ctx, obj:Runner=Runner()):\n assert isinstance(obj, Runner)\n ctx.ensure_object(Runner)\n\n@entry.command()\n@click.pass_context\ndef debug(ctx):\n ctx.obj.debug()\n\n@entry.command()\n@click.pass_context\ndef train(ctx):\n ctx.obj.train()\n\n@entry.command()\n@click.pass_context\ndef test(ctx):\n ctx.obj.test()\n\nif __name__ == '__main__':\n entry()" }, { "path": "stoppers.py", "content": "class Stopper():\n pass\n\n\nclass SimpleStopper(Stopper):\n pass\n" }, { "path": "strategies.py", "content": "from features import Feature\nfrom stoppers import Stopper\nfrom abc import abstractmethod\nfrom gym.spaces import Space, Box, Discrete, MultiDiscrete\nfrom assessments import Assessment\nfrom wtpy.WtBtEngine import EngineType\nfrom wtpy.StrategyDefs import BaseCtaStrategy, CtaContext, BaseHftStrategy, HftContext\nfrom numpy import around, float32\n\n\nclass StateTransfer():\n @staticmethod\n @abstractmethod\n def Name() -> str:\n raise NotImplementedError\n\n @staticmethod\n @abstractmethod\n def EngineType() -> int:\n raise NotImplementedError\n\n @staticmethod\n @abstractmethod\n def Action(size: int) -> dict:\n raise NotImplementedError\n\n @staticmethod\n def setAction(self, action):\n raise NotImplementedError\n\n def __init__(self, feature: Feature, assessment: Assessment, stopper: Stopper):\n self._feature_: Feature = feature\n self._assessment_: Assessment = assessment\n self._stopper_: Stopper = stopper\n\n # print('StateTransfer')\n\n\nclass SimpleCTA(BaseCtaStrategy, StateTransfer):\n @staticmethod\n def Name() -> str:\n return __class__.__name__\n\n @staticmethod\n def EngineType() -> int:\n return EngineType.ET_CTA\n\n @staticmethod\n def Action(size: int) -> Space:\n # return Discrete(10)\n return Box(low=-1., high=1., shape=(size, ), dtype=float32)\n # return MultiDiscrete([11]*size)\n # return dict(low=-1., high=1., shape=(size, ), dtype=float32)\n\n def setAction(self, action):\n # print('setAction 1')\n # action -= 5\n # self._action_ = dict(zip(self._feature_.securities, [action-5]))\n self._action_ = dict(zip(self._feature_.securities, around(action*3, 0)))\n # print(self._action_)\n # try:\n # self._action_ = dict(zip(self._feature_.securities, around(action, 0)))\n # print(self.name(), action, type(action))\n # except:\n # print(self.name(), action, type(action))\n # print('setAction 2')\n\n def __init__(self, name: str, feature: Feature, assessment: Assessment, stopper: Stopper):\n super(BaseCtaStrategy, self).__init__(\n feature=feature, assessment=assessment, stopper=stopper)\n super().__init__(name)\n self._action_: dict = {}\n # print('TrainCTA')\n\n def on_init(self, context: CtaContext):\n # print('on_init 1')\n self._feature_.subscribe(context)\n # print('on_init 2')\n\n def on_session_begin(self, context: CtaContext, curTDate: int):\n # print('on_session_begin')\n pass\n\n def on_backtest_end(self, context: CtaContext):\n # print('on_backtest_end')\n pass\n\n def on_calculate(self, context: CtaContext):\n # print('on_calculate 1')\n self._feature_.calculate(context=context)\n self._assessment_.calculate(context=context)\n # print('on_calculate 2')\n\n def on_calculate_done(self, context: CtaContext):\n # print('on_calculate_done 1')\n for code in tuple(self._action_.keys()):\n qty = self._action_.pop(code)\n if qty != context.stra_get_position(stdCode=code):\n context.stra_set_position(stdCode=code, qty=qty)\n # print('stra_set_position %s'%code)\n # print('on_calculate_done 2')\n\n\n# class SimpleHFT(BaseHftStrategy, StateTransfer):\n# @staticmethod\n# def Name() -> str:\n# return __class__.__name__\n\n# @staticmethod\n# def EngineType() -> int:\n# return EngineType.ET_HFT\n\n# def on_tick(self, context: HftContext, stdCode: str, newTick: dict):\n# pass\n" }, { "path": "wtpy/CodeHelper.py", "content": "import re\n\nclass CodeHelper:\n\n @staticmethod\n def isStdStkCode(stdCode:str) -> bool:\n pattern = re.compile(\"^[A-Z]+.([A-Z]+.)?\\\\d{6}Q?$\")\n if re.match(pattern, stdCode) is not None:\n return True\n\n return False\n\n @staticmethod\n def stdCodeToStdCommID(stdCode:str) -> str:\n if CodeHelper.isStdStkCode(stdCode):\n return CodeHelper.stdStkCodeToStdCommID(stdCode)\n else:\n return CodeHelper.stdFutCodeToStdCommID(stdCode)\n\n @staticmethod\n def stdStkCodeToStdCommID(stdCode:str) -> str:\n ay = stdCode.split(\".\")\n return ay[0] + \".\" + \"STK\"\n\n @staticmethod\n def stdFutCodeToStdCommID(stdCode:str) -> str:\n ay = stdCode.split(\".\")\n return ay[0] + \".\" + ay[1]" }, { "path": "wtpy/ContractMgr.py", "content": "import json\n\nclass ContractInfo:\n\n def __init__(self):\n self.exchg = '' #交易所\n self.code = '' #合约代码\n self.name = '' #合约名称\n self.product = '' #品种代码\n self.stdCode = '' #标准代码\n\nclass ContractMgr:\n\n def __init__(self):\n self.__contracts__ = dict()\n\n def load(self, fname:str):\n '''\n 从文件加载品种信息\n '''\n f = open(fname, 'r', encoding=\"gbk\")\n content = f.read()\n f.close()\n\n exchgMap = json.loads(content)\n for exchg in exchgMap:\n exchgObj = exchgMap[exchg]\n\n for code in exchgObj:\n cObj = exchgObj[code]\n cInfo = ContractInfo()\n cInfo.exchg = exchg\n cInfo.code = code\n cInfo.name = cObj[\"name\"]\n cInfo.product = cObj[\"product\"]\n \n #股票标准代码为SSE.000001，期货标准代码为SHFE.rb.2010\n if cInfo.code[:len(cInfo.product)] == cInfo.product:\n cInfo.stdCode = exchg + \".\" + cInfo.product + \".\" + cInfo.code[len(cInfo.product):]\n else:\n cInfo.stdCode = exchg + \".\" + cInfo.code\n\n key = \"%s.%s\" % (exchg, code)\n self.__contracts__[key] = cInfo\n\n def getContractInfo(self, stdCode:str) -> ContractInfo:\n if stdCode[-1] == 'Q':\n stdCode = stdCode[:-1]\n else:\n items = stdCode.split(\".\")\n if len(items) == 3:\n stdCode = items[0] + \".\" + items[1] + items[2]\n if stdCode not in self.__contracts__:\n return None\n \n return self.__contracts__[stdCode]\n\n def getTotalCodes(self) -> list:\n codes = list()\n for code in self.__contracts__:\n codes.append(self.__contracts__[code].stdCode)\n return codes\n \n\n" }, { "path": "wtpy/CtaContext.py", "content": "from pandas import DataFrame as df\nimport pandas as pd\nimport os\nimport json\n\nfrom wtpy.wrapper import WtWrapper\nfrom wtpy.WtDataDefs import WtKlineData, WtHftData\n\nclass CtaContext:\n '''\n Context是策略可以直接访问的唯一对象\\n\n 策略所有的接口都通过Context对象调用\\n\n Context类包括以下几类接口：\\n\n 1、时间接口（日期、时间等），接口格式如：stra_xxx\\n\n 2、数据接口（K线、财务等），接口格式如：stra_xxx\\n\n 3、下单接口（设置目标仓位、直接下单等），接口格式如：stra_xxx\\n\n '''\n\n def __init__(self, id:int, stra, wrapper: WtWrapper, engine):\n self.__stra_info__ = stra #策略对象，对象基类BaseStrategy.py\n self.__wrapper__ = wrapper #底层接口转换器\n self.__id__ = id #策略ID\n self.__bar_cache__ = dict() #K线缓存\n self.__tick_cache__ = dict() #tTick缓存，每次都重新去拉取，这个只做中转用，不在python里维护副本\n self.__sname__ = stra.name() \n self.__engine__ = engine #交易环境\n self.__pos_cache__ = None\n\n self.is_backtest = self.__engine__.is_backtest\n\n @property\n def id(self):\n return self.__id__\n\n def write_indicator(self, tag:str, time:int, data:dict):\n '''\n 输出指标数据\n @tag 指标标签\n @time 输出时间\n @data 输出的指标数据，dict类型，会转成json以后保存\n '''\n self.__engine__.write_indicator(self.__stra_info__.name(), tag, time, data)\n\n def on_init(self):\n '''\n 初始化，一般用于系统启动的时候\n '''\n self.__stra_info__.on_init(self)\n\n def on_session_begin(self, curTDate:int):\n '''\n 交易日开始事件\\n\n\n @curTDate 交易日，格式为20210220\n '''\n self.__stra_info__.on_session_begin(self, curTDate)\n\n def on_session_end(self, curTDate:int):\n '''\n 交易日结束事件\\n\n\n @curTDate 交易日，格式为20210220\n '''\n self.__stra_info__.on_session_end(self, curTDate)\n\n def on_backtest_end(self):\n '''\n 回测结束事件\n '''\n self.__stra_info__.on_backtest_end(self)\n\n def on_getticks(self, stdCode:str, newTicks:list, isLast:bool):\n key = stdCode\n\n ticks = self.__tick_cache__[key]\n for newTick in newTicks:\n ticks.append_item(newTick)\n\n def on_getpositions(self, stdCode:str, qty:float, isLast:bool):\n if len(stdCode) == 0:\n return\n self.__pos_cache__[stdCode] = qty\n\n def on_getbars(self, stdCode:str, period:str, newBars:list, isLast:bool):\n key = \"%s#%s\" % (stdCode, period)\n\n bars = self.__bar_cache__[key]\n for newBar in newBars:\n bars.append_bar(newBar)\n\n def on_tick(self, stdCode:str, newTick):\n self.__stra_info__.on_tick(self, stdCode, newTick)\n\n\n def on_bar(self, stdCode:str, period:str, newBar:dict):\n '''\n K线闭合事件响应\n @stdCode 品种代码\n @period K线基础周期\n @times 周期倍数\n @newBar 最新K线\n ''' \n key = \"%s#%s\" % (stdCode, period)\n\n if key not in self.__bar_cache__:\n return\n\n try:\n self.__bar_cache__[key].append_bar(newBar)\n self.__bar_cache__[key].closed = True\n self.__stra_info__.on_bar(self, stdCode, period, newBar)\n except ValueError as ve:\n print(ve)\n else:\n return\n\n\n def on_calculate(self):\n self.__stra_info__.on_calculate(self)\n\n def on_calculate_done(self):\n self.__stra_info__.on_calculate_done(self)\n\n def stra_log_text(self, message:str):\n '''\n 输出日志\n @message 消息内容，最大242字符\\n\n '''\n self.__wrapper__.cta_log_text(self.__id__, message[:242])\n\n def stra_get_tdate(self):\n '''\n 获取当前交易日\\n\n @return int，格式如20180513\n '''\n return self.__wrapper__.cta_get_tdate()\n \n def stra_get_date(self):\n '''\n 获取当前日期\\n\n @return int，格式如20180513\n '''\n return self.__wrapper__.cta_get_date()\n\n def stra_get_position_avgpx(self, stdCode:str = \"\") -> float:\n '''\n 获取当前持仓均价\\n\n @stdCode 合约代码\n @return 持仓均价\n '''\n return self.__wrapper__.cta_get_position_avgpx(self.__id__, stdCode)\n\n def stra_get_position_profit(self, stdCode:str = \"\") -> float:\n '''\n 获取持仓浮动盈亏\n @stdCode 合约代码，为None时读取全部品种的浮动盈亏\n @return 浮动盈亏\n '''\n return self.__wrapper__.cta_get_position_profit(self.__id__, stdCode)\n\n def stra_get_fund_data(self, flag:int = 0) -> float:\n '''\n 获取资金数据\\n\n @flag 0-动态权益，1-总平仓盈亏，2-总浮动盈亏，3-总手续费\\n\n @return 资金数据\n '''\n return self.__wrapper__.cta_get_fund_data(self.__id__, flag)\n\n def stra_get_time(self):\n '''\n 获取当前时间，24小时制，精确到分\\n\n @return int，格式如1231\n '''\n return self.__wrapper__.cta_get_time()\n\n def stra_get_price(self, stdCode:str):\n '''\n 获取最新价格，一般在获取了K线以后再获取该价格\n @return 最新价格\n '''\n return self.__wrapper__.cta_get_price(stdCode)\n\n def stra_get_all_position(self):\n '''\n 获取全部持仓\n '''\n self.__pos_cache__ = dict() #\n self.__wrapper__.cta_get_all_position(self.__id__)\n return self.__pos_cache__\n\n def stra_get_bars(self, stdCode:str, period:str, count:int, isMain:bool = False) -> WtKlineData:\n '''\n 获取历史K线\n @stdCode 合约代码\n @period K线周期，如m3/d7\n @count 要拉取的K线条数\n @isMain 是否是主K线\n '''\n key = \"%s#%s\" % (stdCode, period)\n\n if key in self.__bar_cache__:\n #这里做一个数据长度处理\n return self.__bar_cache__[key]\n\n self.__bar_cache__[key] = WtKlineData(size=count)\n cnt = self.__wrapper__.cta_get_bars(self.__id__, stdCode, period, count, isMain)\n if cnt == 0:\n return None\n\n df_bars = self.__bar_cache__[key]\n df_bars.closed = False\n\n return df_bars\n\n def stra_get_ticks(self, stdCode:str, count:int) -> WtHftData:\n '''\n 获取tick数据\n @stdCode 合约代码\n @count 要拉取的tick数量\n '''\n self.__tick_cache__[stdCode] = WtHftData(capacity=count)\n cnt = self.__wrapper__.cta_get_ticks(self.__id__, stdCode, count)\n if cnt == 0:\n return None\n \n df_ticks = self.__tick_cache__[stdCode]\n return df_ticks\n\n def stra_sub_ticks(self, stdCode:str):\n '''\n 订阅实时行情\\n\n 获取K线和tick数据的时候会自动订阅，这里只需要订阅额外要检测的品种即可\\n\n @stdCode 合约代码\n '''\n self.__wrapper__.cta_sub_ticks(self.__id__, stdCode)\n\n def stra_get_position(self, stdCode:str = \"\", usertag:str = \"\"):\n '''\n 读取当前仓位\\n\n @stdCode 合约/股票代码\\n\n @usertag 入场标记\n @return 正为多仓，负为空仓\n '''\n return self.__wrapper__.cta_get_position(self.__id__, stdCode, usertag)\n\n def stra_set_position(self, stdCode:str, qty:float, usertag:str = \"\", limitprice:float = 0.0, stopprice:float = 0.0):\n '''\n 设置仓位\\n\n @stdCode 合约/股票代码\\n\n @qty 目标仓位，正为多仓，负为空仓\\n\n @return 设置结果TRUE/FALSE\n '''\n self.__wrapper__.cta_set_position(self.__id__, stdCode, qty, usertag, limitprice, stopprice)\n \n\n def stra_enter_long(self, stdCode:str, qty:float, usertag:str = \"\", limitprice:float = 0.0, stopprice:float = 0.0):\n '''\n 多仓进场，如果有空仓，则平空再开多\\n\n @stdCode 品种代码\\n\n @qty 数量\\n\n @limitprice 限价，默认为0\\n\n @stopprice 止价，默认为0\n '''\n self.__wrapper__.cta_enter_long(self.__id__, stdCode, qty, usertag, limitprice, stopprice)\n\n def stra_exit_long(self, stdCode:str, qty:float, usertag:str = \"\", limitprice:float = 0.0, stopprice:float = 0.0):\n '''\n 多仓出场，如果剩余多仓不够，则全部平掉即可\\n\n @stdCode 品种代码\\n\n @qty 数量\\n\n @limitprice 限价，默认为0\\n\n @stopprice 止价，默认为0\n '''\n self.__wrapper__.cta_exit_long(self.__id__, stdCode, qty, usertag, limitprice, stopprice)\n\n def stra_enter_short(self, stdCode:str, qty:float, usertag:str = \"\", limitprice:float = 0.0, stopprice:float = 0.0):\n '''\n 空仓进场，如果有多仓，则平多再开空\\n\n @stdCode 品种代码\\n\n @qty 数量\\n\n @limitprice 限价，默认为0\\n\n @stopprice 止价，默认为0\n '''\n self.__wrapper__.cta_enter_short(self.__id__, stdCode, qty, usertag, limitprice, stopprice)\n\n def stra_exit_short(self, stdCode:str, qty:float, usertag:str = \"\", limitprice:float = 0.0, stopprice:float = 0.0):\n '''\n 空仓出场，如果剩余空仓不够，则全部平掉即可\\n\n @stdCode 品种代码\\n\n @qty 数量\\n\n @limitprice 限价，默认为0\\n\n @stopprice 止价，默认为0\n '''\n self.__wrapper__.cta_exit_short(self.__id__, stdCode, qty, usertag, limitprice, stopprice)\n\n def stra_get_last_entrytime(self, stdCode:str):\n '''\n 获取当前持仓最后一次进场时间\\n\n @stdCode 品种代码\\n\n @return 返回最后一次开仓的时间，格式如201903121047\n '''\n return self.__wrapper__.cta_get_last_entertime(self.__id__, stdCode)\n\n def stra_get_last_exittime(self, stdCode:str):\n '''\n 获取当前持仓最后一次出场时间\\n\n @stdCode 品种代码\\n\n @return 返回最后一次开仓的时间，格式如201903121047\n '''\n return self.__wrapper__.cta_get_last_exittime(self.__id__, stdCode)\n\n def stra_get_first_entrytime(self, stdCode:str):\n '''\n 获取当前持仓第一次进场时间\\n\n @stdCode 品种代码\\n\n @return 返回最后一次开仓的时间，格式如201903121047\n '''\n return self.__wrapper__.cta_get_first_entertime(self.__id__, stdCode)\n\n\n def user_save_data(self, key:str, val):\n '''\n 保存用户数据\n @key 数据id\n @val 数据值，可以直接转换成str的数据均可\n '''\n self.__wrapper__.cta_save_user_data(self.__id__, key, str(val))\n\n def user_load_data(self, key:str, defVal = None, vType = float):\n '''\n 读取用户数据\n @key 数据id\n @defVal 默认数据，如果找不到则返回改数据，默认为None\n @return 返回值，默认处理为float数据\n '''\n ret = self.__wrapper__.cta_load_user_data(self.__id__, key, \"\")\n if ret == \"\":\n return defVal\n\n return vType(ret)\n\n def stra_get_detail_profit(self, stdCode:str, usertag:str, flag:int = 0):\n '''\n 获取指定标记的持仓的盈亏\n @stdCode 合约代码\\n\n @usertag 进场标记\\n\n @flag 盈亏记号，0-浮动盈亏，1-最大浮盈，-1-最大亏损（负数）\n @return 盈亏 \n '''\n return self.__wrapper__.cta_get_detail_profit(self.__id__, stdCode, usertag, flag)\n\n def stra_get_detail_cost(self, stdCode:str, usertag:str):\n '''\n 获取指定标记的持仓的开仓价\n @stdCode 合约代码\\n\n @usertag 进场标记\\n\n @return 开仓价 \n '''\n return self.__wrapper__.cta_get_detail_cost(self.__id__, stdCode, usertag)\n\n def stra_get_detail_entertime(self, stdCode:str, usertag:str):\n '''\n 获取指定标记的持仓的进场时间\\n\n @stdCode 合约代码\\n\n @usertag 进场标记\\n\n @return 进场时间，格式如201907260932 \n '''\n return self.__wrapper__.cta_get_detail_entertime(self.__id__, stdCode, usertag)\n\n def stra_get_comminfo(self, stdCode:str):\n '''\n 获取品种详情\\n\n @stdCode 合约代码如SHFE.ag.HOT，或者品种代码如SHFE.ag\\n\n @return 品种信息，结构请参考ProductMgr中的ProductInfo\n '''\n if self.__engine__ is None:\n return None\n return self.__engine__.getProductInfo(stdCode)\n\n def stra_get_sessinfo(self, stdCode:str):\n '''\n 获取交易时段详情\\n\n @stdCode 合约代码如SHFE.ag.HOT，或者品种代码如SHFE.ag\\n\n @return 品种信息，结构请参考SessionMgr中的SessionInfo\n '''\n if self.__engine__ is None:\n return None\n return self.__engine__.getSessionByCode(stdCode)" }, { "path": "wtpy/ExtModuleDefs.py", "content": "\n\nclass BaseExtParser:\n '''\n 扩展行情接入模块基类\n '''\n def __init__(self, id:str):\n '''\n 构造函数\n @id 解析器ID\n '''\n self.__id__ = id\n return\n\n def id(self) -> str:\n return self.__id__\n\n def init(self, engine):\n '''\n 初始化\n '''\n self.__engine__ = engine\n return\n\n def connect(self):\n '''\n 开始连接\n '''\n return\n\n def disconnect(self):\n '''\n 断开连接\n '''\n return\n\n def release(self):\n '''\n 释放，一般是进程退出时调用\n '''\n return\n\n def subscribe(self, fullCode:str):\n '''\n 订阅实时行情\\n\n @fullCode 合约代码，格式如CFFEX.IF2106\n '''\n return\n\n def unsubscribe(self, fullCode:str):\n '''\n 退订实时行情\\n\n @fullCode 合约代码，格式如CFFEX.IF2106\n '''\n return\n\n\nclass BaseExtExecuter:\n '''\n 扩展执行器基类\n '''\n\n def __init__(self, id:str, scale:float):\n '''\n 构造函数\\n\n @id 执行器ID\\n\n @scale 数量放大倍数\n '''\n self.__id__ = id\n self.__scale__ = scale\n self.__targets__ = dict()\n return\n\n def id(self):\n return self.__id__\n \n def init(self):\n return\n\n def set_position(self, stdCode:str, targetPos:float):\n '''\n 设置目标部位\\n\n @stdCode 合约代码，期货格式为CFFEX.IF.2106\\n\n @targetPos 目标仓位，浮点数\n '''\n\n # 确定原来的目标仓位\n oldPos = 0\n if stdCode in self.__targets__:\n oldPos = self.__targets__[stdCode]\n\n # 修改最新的目标仓位\n self.__targets__[stdCode] = targetPos\n return" }, { "path": "wtpy/ExtToolDefs.py", "content": "import json\nimport time\nfrom threading import Thread\n\ndef fileToJson(filename, encoding=\"utf-8\"):\n f = open(filename, 'r')\n content = f.read()\n f.close()\n try:\n return json.loads(content)\n except:\n return None\n\nclass BaseIndexWriter:\n '''\n 基础指标输出工具\n '''\n\n def __init__(self):\n return\n\n def write_indicator(self, id:str, tag:str, time:int, data:dict):\n '''\n 将指标数据出\\n\n @id 指标ID\\n\n @tag 数据标记\\n\n @time 指标时间\\n\n @data 数据对象，一个dict\n '''\n raise Exception(\"Basic writer cannot output index data to any media\")\n\n\nclass BaseDataReporter:\n '''\n 数据报告器\n '''\n TaskReportRTData = 1\n TaskReportSettleData = 2\n TaskReportInitData = 3\n\n def __init__(self, id:str):\n self.__inited__ = False\n self.__id__ = id\n return\n\n def init(self):\n self.__inited__ = True\n self.__thrd_task__ = None\n self.__tasks__ = list()\n self.__stopped__ = False\n\n #读取策略标记\n filename = \"./generated/marker.json\"\n obj = fileToJson(filename)\n if obj is not None:\n self.stra_names = obj[\"marks\"]\n\n def rpt_portfolio_rt_data_impl(self, rtData):\n raise Exception(\"this method has not been implemented\")\n\n def rpt_strategy_rt_data_impl(self, rtData):\n raise Exception(\"this method has not been implemented\")\n\n def rpt_init_data_impl(self, initData):\n raise Exception(\"this method has not been implemented\")\n\n def __do_report_rt_data__(self):\n print(\"settle data reporter triggered\")\n # 第一步，提交组合数据，读取portfolio\n filename = \"./generated/portfolio/datas.json\"\n objPort = fileToJson(filename)\n objPort[\"pid\"] = self.__id__\n # 开始提交组合数据\n self.rpt_portfolio_rt_data_impl(objPort)\n\n # 第二步，提交策略数据\n for sname in self.stra_names:\n filename = \"./generated/stradata/\" + sname + \".json\"\n objStra = fileToJson(filename)\n objStra[\"pid\"] = self.__id__\n objStra[\"sid\"] = sname\n\n self.rpt_strategy_rt_data_impl(objStra)\n\n def __task_loop__(self):\n while not self.__stopped__:\n if len(self.__tasks__) == 0:\n time.sleep(1)\n continue\n else:\n taskid = self.__tasks__.pop(0) \n if taskid == self.TaskReportRTData:\n self.__do_report_rt_data__()\n elif taskid == self.TaskReportSettleData:\n self.__do_report_settle_data__()\n elif taskid == self.TaskReportInitData:\n self.__do_report_init_data__()\n\n def __start__(self):\n if self.__thrd_task__ is None:\n self.__thrd_task__ = Thread(target=self.__task_loop__, name=\"reportthread\")\n # self.__thrd_task__.setDaemon(True)\n self.__thrd_task__.start()\n print(\"report thread started\")\n\n def __do_report_init_data__(self):\n objInitData = dict()\n objInitData[\"pid\"] = self.__id__\n objInitData[\"strategies\"] = self.stra_names\n self.rpt_init_data_impl(objInitData)\n\n def __do_report_settle_data__(self):\n print(\"settle data reporter triggered\")\n\n def report_rt_data(self):\n print(\"rt data reporter triggered\")\n self.__tasks__.append(self.TaskReportRTData)\n if self.__thrd_task__ is None:\n self.__start__()\n\n def report_settle_data(self):\n self.__tasks__.append(self.TaskReportSettleData)\n if self.__thrd_task__ is None:\n self.__start__()\n\n def report_init_data(self):\n self.__tasks__.append(self.TaskReportInitData)\n if self.__thrd_task__ is None:\n self.__start__()\n " }, { "path": "wtpy/HftContext.py", "content": "from pandas import DataFrame as df\nimport pandas as pd\nimport os\nimport json\n\nfrom wtpy.wrapper import WtWrapper\nfrom wtpy.WtDataDefs import WtKlineData, WtHftData\n\nclass HftContext:\n '''\n Context是策略可以直接访问的唯一对象\\n\n 策略所有的接口都通过Context对象调用\\n\n Context类包括以下几类接口：\\n\n 1、时间接口（日期、时间等）,接口格式如：stra_xxx\\n\n 2、数据接口（K线、财务等）,接口格式如：stra_xxx\\n\n 3、下单接口（设置目标仓位、直接下单等）,接口格式如：stra_xxx\\n\n '''\n\n def __init__(self, id:int, stra, wrapper: WtWrapper, engine):\n self.__stra_info__ = stra #策略对象,对象基类BaseStrategy.py\n self.__wrapper__ = wrapper #底层接口转换器\n self.__id__ = id #策略ID\n self.__bar_cache__ = dict() #K线缓存\n self.__tick_cache__ = dict() #Tick缓存,每次都重新去拉取,这个只做中转用,不在python里维护副本\n self.__ordque_cache__ = dict() #委托队列缓存，用法同__tick_cache__\n self.__orddtl_cache__ = dict() #逐笔委托缓存，用法同__tick_cache__\n self.__trans_cache__ = dict() #逐笔成交缓存，用法同__tick_cache__\n self.__sname__ = stra.name() \n self.__engine__ = engine #交易环境\n\n self.is_backtest = self.__engine__.is_backtest\n\n @property\n def id(self):\n return self.__id__\n\n def on_init(self):\n '''\n 初始化,一般用于系统启动的时候\n '''\n self.__stra_info__.on_init(self)\n\n def on_session_begin(self, curTDate:int):\n '''\n 交易日开始事件\\n\n\n @curTDate 交易日，格式为20210220\n '''\n self.__stra_info__.on_session_begin(self, curTDate)\n\n def on_session_end(self, curTDate:int):\n '''\n 交易日结束事件\\n\n\n @curTDate 交易日，格式为20210220\n '''\n self.__stra_info__.on_session_end(self, curTDate)\n\n def on_backtest_end(self):\n '''\n 回测结束事件\n '''\n self.__stra_info__.on_backtest_end(self)\n\n def on_getticks(self, stdCode:str, newTicks:list, isLast:bool):\n key = stdCode\n\n ticks = self.__tick_cache__[key]\n for newTick in newTicks:\n ticks.append_item(newTick)\n\n def on_getbars(self, stdCode:str, period:str, newBars:list, isLast:bool):\n key = \"%s#%s\" % (stdCode, period)\n\n bars = self.__bar_cache__[key]\n for newBar in newBars:\n bars.append_bar(newBar)\n\n def on_tick(self, stdCode:str, newTick:dict):\n self.__stra_info__.on_tick(self, stdCode, newTick)\n\n def on_order_queue(self, stdCode:str, newOrdQue:dict):\n self.__stra_info__.on_order_queue(self, stdCode, newOrdQue)\n\n def on_get_order_queue(self, stdCode:str, newOdrQues:list, isLast:bool):\n key = stdCode\n items = self.__ordque_cache__[key]\n for newItem in newOdrQues:\n items.append_item(newItem)\n\n def on_order_detail(self, stdCode:str, newOrdDtl:dict):\n self.__stra_info__.on_order_detail(self, stdCode, newOrdDtl)\n\n def on_get_order_detail(self, stdCode:str, newOrdDtls:list, isLast:bool):\n key = stdCode\n items = self.__orddtl_cache__[key]\n for newItem in newOrdDtls:\n items.append_item(newItem)\n\n def on_transaction(self, stdCode:str, newTrans:dict):\n self.__stra_info__.on_transaction(self, stdCode, newTrans)\n\n def on_get_transaction(self, stdCode:str, newTranses:list, isLast:bool):\n key = stdCode\n items = self.__trans_cache__[key]\n for newItem in newTranses:\n items.append_item(newItem)\n\n def on_channel_ready(self):\n self.__stra_info__.on_channel_ready(self)\n\n def on_channel_lost(self):\n self.__stra_info__.on_channel_lost(self)\n\n def on_entrust(self, localid:int, stdCode:str, bSucc:bool, msg:str, userTag:str):\n self.__stra_info__.on_entrust(self, localid, stdCode, bSucc, msg, userTag)\n\n def on_order(self, localid:int, stdCode:str, isBuy:bool, totalQty:float, leftQty:float, price:float, isCanceled:bool, userTag:str):\n self.__stra_info__.on_order(self, localid, stdCode, isBuy, totalQty, leftQty, price, isCanceled, userTag)\n\n def on_trade(self, localid:int, stdCode:str, isBuy:bool, qty:float, price:float, userTag:str):\n self.__stra_info__.on_trade(self, localid, stdCode, isBuy, qty, price, userTag)\n\n def on_bar(self, code:str, period:str, newBar:dict):\n '''\n K线闭合事件响应\n @code 品种代码\n @period K线基础周期\n @times 周期倍数\n @newBar 最新K线\n ''' \n key = \"%s#%s\" % (code, period)\n\n if key not in self.__bar_cache__:\n return\n\n try:\n self.__bar_cache__[key].append_bar(newBar)\n self.__bar_cache__[key].closed = True\n self.__stra_info__.on_bar(self, code, period, newBar)\n except ValueError as ve:\n print(ve)\n else:\n return\n\n def stra_log_text(self, message:str):\n '''\n 输出日志\n @message 消息内容\\n\n '''\n self.__wrapper__.hft_log_text(self.__id__, message)\n \n def stra_get_date(self):\n '''\n 获取当前日期\\n\n @return int,格式如20180513\n '''\n return self.__wrapper__.hft_get_date()\n\n def stra_get_time(self):\n '''\n 获取当前时间,24小时制,精确到分\\n\n @return int,格式如1231\n '''\n return self.__wrapper__.hft_get_time()\n\n def stra_get_secs(self):\n '''\n 获取当前秒数,精确到毫秒\\n\n @return int,格式如1231\n '''\n return self.__wrapper__.hft_get_secs()\n\n def stra_get_price(self, code):\n '''\n 获取最新价格,一般在获取了K线以后再获取该价格\n @return 最新价格\n '''\n return self.__wrapper__.hft_get_price(code)\n\n def stra_get_bars(self, code:str, period:str, count:int) -> WtKlineData:\n '''\n 获取历史K线\n @code 合约代码\n @period K线周期,如m3/d7\n @count 要拉取的K线条数\n @isMain 是否是主K线\n '''\n key = \"%s#%s\" % (code, period)\n\n if key in self.__bar_cache__:\n #这里做一个数据长度处理\n return self.__bar_cache__[key]\n\n self.__bar_cache__[key] = WtKlineData(size=count)\n cnt = self.__wrapper__.hft_get_bars(self.__id__, code, period, count)\n if cnt == 0:\n return None\n\n df_bars = self.__bar_cache__[key]\n df_bars.closed = False\n\n return df_bars\n\n def stra_get_ticks(self, code:str, count:int) -> WtHftData:\n '''\n 获取tick数据\n @code 合约代码\n @count 要拉取的tick数量\n '''\n self.__tick_cache__[code] = WtHftData(capacity=count)\n cnt = self.__wrapper__.hft_get_ticks(self.__id__, code, count)\n if cnt == 0:\n return None\n \n hftData = self.__tick_cache__[code]\n return hftData\n\n def stra_get_order_queue(self, code:str, count:int) -> WtHftData:\n '''\n 获取委托队列数据\n @code 合约代码\n @count 要拉取的tick数量\n '''\n self.__ordque_cache__[code] = WtHftData(capacity=count)\n cnt = self.__wrapper__.hft_get_ordque(self.__id__, code, count)\n if cnt == 0:\n return None\n \n hftData = self.__ordque_cache__[code]\n return hftData\n\n def stra_get_order_detail(self, code:str, count:int) -> WtHftData:\n '''\n 获取逐笔委托数据\n @code 合约代码\n @count 要拉取的tick数量\n '''\n self.__orddtl_cache__[code] = WtHftData(capacity=count)\n cnt = self.__wrapper__.hft_get_orddtl(self.__id__, code, count)\n if cnt == 0:\n return None\n \n hftData = self.__orddtl_cache__[code]\n return hftData\n\n def stra_get_transaction(self, code:str, count:int) -> WtHftData:\n '''\n 获取逐笔成交数据\n @code 合约代码\n @count 要拉取的tick数量\n '''\n self.__trans_cache__[code] = WtHftData(capacity=count)\n cnt = self.__wrapper__.hft_get_trans(self.__id__, code, count)\n if cnt == 0:\n return None\n \n hftData = self.__trans_cache__[code]\n return hftData\n\n def stra_get_position(self, code:str = \"\"):\n '''\n 读取当前仓位\\n\n @code 合约/股票代码\\n\n @return 正为多仓,负为空仓\n '''\n return self.__wrapper__.hft_get_position(self.__id__, code)\n\n def stra_get_position_profit(self, code:str = \"\"):\n '''\n 读取指定持仓的浮动盈亏\\n\n @code 合约/股票代码\\n\n @return 指定持仓的浮动盈亏\n '''\n return self.__wrapper__.hft_get_position_profit(self.__id__, code)\n\n def stra_get_undone(self, stdCode:str):\n return self.__wrapper__.hft_get_undone(self.__id__, stdCode)\n\n\n def user_save_data(self, key:str, val):\n '''\n 保存用户数据\n @key 数据id\n @val 数据值,可以直接转换成str的数据均可\n '''\n self.__wrapper__.hft_save_user_data(self.__id__, key, str(val))\n\n def user_load_data(self, key:str, defVal = None, vType = float):\n '''\n 读取用户数据\n @key 数据id\n @defVal 默认数据,如果找不到则返回改数据,默认为None\n @return 返回值,默认处理为float数据\n '''\n ret = self.__wrapper__.hft_load_user_data(self.__id__, key, \"\")\n if ret == \"\":\n return defVal\n\n return vType(ret)\n\n def stra_get_comminfo(self, code:str):\n '''\n 获取品种详情\\n\n @code 合约代码如SHFE.ag.HOT,或者品种代码如SHFE.ag\\n\n @return 品种信息,结构请参考ProductMgr中的ProductInfo\n '''\n if self.__engine__ is None:\n return None\n return self.__engine__.getProductInfo(code)\n\n def stra_sub_ticks(self, stdCode:str):\n '''\n 订阅实时行情数据\\n\n 获取K线和tick数据的时候会自动订阅，这里只需要订阅额外要检测的品种即可\\n\n @code 品种代码\n '''\n self.__wrapper__.hft_sub_ticks(self.__id__, stdCode)\n\n def stra_cancel(self, localid:int):\n '''\n 撤销指定订单\\n\n @id 策略ID\\n\n @localid 下单时返回的本地订单号\n '''\n return self.__wrapper__.hft_cancel(self.__id__, localid)\n\n def stra_cancel_all(self, stdCode:str, isBuy:bool):\n '''\n 撤销指定品种的全部买入订单or卖出订单\\n\n @id 策略ID\\n\n @stdCode 品种代码\\n\n @isBuy 买入or卖出\n '''\n idstr = self.__wrapper__.hft_cancel_all(self.__id__, stdCode, isBuy)\n if len(idstr) == 0:\n return list()\n\n ids = idstr.split(\",\")\n localids = list()\n for localid in ids:\n localids.append(int(localid))\n return localids\n\n def stra_buy(self, stdCode:str, price:float, qty:float, userTag:str):\n '''\n 买入指令\\n\n @id 策略ID\\n\n @stdCode 品种代码\\n\n @price 买入价格, 0为市价\\n\n @qty 买入数量\n '''\n idstr = self.__wrapper__.hft_buy(self.__id__, stdCode, price, qty, userTag)\n if len(idstr) == 0:\n return list()\n \n ids = idstr.split(\",\")\n localids = list()\n for localid in ids:\n localids.append(int(localid))\n return localids\n\n def stra_sell(self, stdCode:str, price:float, qty:float, userTag:str):\n '''\n 卖出指令\\n\n @id 策略ID\\n\n @stdCode 品种代码\\n\n @price 卖出价格, 0为市价\\n\n @qty 卖出数量\n '''\n idstr = self.__wrapper__.hft_sell(self.__id__, stdCode, price, qty, userTag)\n if len(idstr) == 0:\n return list()\n \n ids = idstr.split(\",\")\n localids = list()\n for localid in ids:\n localids.append(int(localid))\n return localids\n" }, { "path": "wtpy/ProductMgr.py", "content": "import json\n\nclass ProductInfo:\n '''\n 品种信息\n '''\n\n def __init__(self):\n self.exchg = '' #交易所\n self.product = '' #品种代码\n self.name = '' #品种名称\n self.session = '' #交易时段名\n self.covermode = 0 #平仓模式\n self.pricemode = 0 #价格模式\n self.precision = 0 #精度\n self.pricetick = 0 #价格变动单位\n self.volscale = 1 #数量乘数\n\nclass ProductMgr:\n '''\n 品种信息管理器\n '''\n def __init__(self):\n self.__products__ = dict()\n return\n\n def load(self, fname:str):\n '''\n 从文件加载品种信息\n '''\n f = open(fname, 'r', encoding=\"gbk\")\n content = f.read()\n f.close()\n\n exchgMap = json.loads(content)\n for exchg in exchgMap:\n exchgObj = exchgMap[exchg]\n for pid in exchgObj:\n pObj = exchgObj[pid]\n pInfo = ProductInfo()\n pInfo.exchg = exchg\n pInfo.product = pid\n pInfo.name = pObj[\"name\"]\n pInfo.session = pObj[\"session\"]\n pInfo.covermode = int(pObj[\"covermode\"])\n pInfo.pricemode = int(pObj[\"pricemode\"])\n pInfo.precision = int(pObj[\"precision\"])\n pInfo.volscale = int(pObj[\"volscale\"])\n pInfo.pricetick = float(pObj[\"pricetick\"])\n\n key = \"%s.%s\" % (exchg, pid)\n self.__products__[key] = pInfo\n\n\n def getProductInfo(self, pid:str) -> ProductInfo:\n #pid形式可能为SHFE.ag.HOT，或者SHFE.ag.1912，或者SHFE.ag\n items = pid.split(\".\")\n key = items[0] + \".\" + items[1]\n if key not in self.__products__:\n return None\n\n return self.__products__[key]" }, { "path": "wtpy/SelContext.py", "content": "from pandas import DataFrame as df\nimport pandas as pd\nimport os\nimport json\n\nfrom wtpy.wrapper import WtWrapper\nfrom wtpy.WtDataDefs import WtKlineData, WtHftData\n\nclass SelContext:\n '''\n Context是策略可以直接访问的唯一对象\\n\n 策略所有的接口都通过Context对象调用\\n\n Context类包括以下几类接口：\\n\n 1、时间接口（日期、时间等），接口格式如：stra_xxx\\n\n 2、数据接口（K线、财务等），接口格式如：stra_xxx\\n\n 3、下单接口（设置目标仓位、直接下单等），接口格式如：stra_xxx\\n\n '''\n\n def __init__(self, id:int, stra, wrapper: WtWrapper, engine):\n self.__stra_info__ = stra #策略对象，对象基类BaseStrategy.py\n self.__wrapper__ = wrapper #底层接口转换器\n self.__id__ = id #策略ID\n self.__bar_cache__ = dict() #K线缓存\n self.__tick_cache__ = dict() #tTick缓存，每次都重新去拉取，这个只做中转用，不在python里维护副本\n self.__sname__ = stra.name() \n self.__engine__ = engine #交易环境\n self.__pos_cache__ = None\n\n self.is_backtest = self.__engine__.is_backtest\n\n def write_indicator(self, tag, time, data):\n '''\n 输出指标数据\n @tag 指标标签\n @time 输出时间\n @data 输出的指标数据，dict类型，会转成json以后保存\n '''\n self.__engine__.write_indicator(self.__stra_info__.name(), tag, time, data)\n\n def on_init(self):\n '''\n 初始化，一般用于系统启动的时候\n '''\n self.__stra_info__.on_init(self)\n\n def on_session_begin(self, curTDate:int):\n '''\n 交易日开始事件\\n\n\n @curTDate 交易日，格式为20210220\n '''\n self.__stra_info__.on_session_begin(self, curTDate)\n\n def on_session_end(self, curTDate:int):\n '''\n 交易日结束事件\\n\n\n @curTDate 交易日，格式为20210220\n '''\n self.__stra_info__.on_session_end(self, curTDate)\n\n def on_backtest_end(self):\n '''\n 回测结束事件\n '''\n self.__stra_info__.on_backtest_end(self)\n\n def on_getticks(self, stdCode:str, newTicks:list, isLast:bool):\n key = stdCode\n\n ticks = self.__tick_cache__[key]\n for newTick in newTicks:\n ticks.append_item(newTick)\n\n def on_getpositions(self, stdCode:str, qty:float, isLast:bool):\n if len(stdCode) == 0:\n return\n self.__pos_cache__[stdCode] = qty\n\n def on_getbars(self, stdCode:str, period:str, newBars:list, isLast:bool):\n key = \"%s#%s\" % (stdCode, period)\n\n bars = self.__bar_cache__[key]\n for newBar in newBars:\n bars.append_bar(newBar)\n\n def on_tick(self, stdCode:str, newTick):\n self.__stra_info__.on_tick(self, stdCode, newTick)\n\n def on_bar(self, stdCode:str, period:str, newBar:dict):\n pass\n\n def on_calculate(self):\n self.__stra_info__.on_calculate(self)\n\n def on_calculate_done(self):\n self.__stra_info__.on_calculate_done(self)\n\n def stra_log_text(self, message:str):\n '''\n 输出日志\n @message 消息内容\\n\n '''\n self.__wrapper__.sel_log_text(self.__id__, message)\n \n def stra_get_date(self):\n '''\n 获取当前日期\\n\n @return int，格式如20180513\n '''\n return self.__wrapper__.sel_get_date()\n\n def stra_get_time(self):\n '''\n 获取当前时间，24小时制，精确到分\\n\n @return int，格式如1231\n '''\n return self.__wrapper__.sel_get_time()\n\n def stra_get_price(self, stdCode):\n '''\n 获取最新价格，一般在获取了K线以后再获取该价格\n @return 最新价格\n '''\n return self.__wrapper__.sel_get_price(stdCode)\n\n def stra_get_all_position(self):\n '''\n 获取全部持仓\n '''\n self.__pos_cache__ = dict() #\n self.__wrapper__.sel_get_all_position(self.__id__)\n return self.__pos_cache__\n\n def stra_get_bars(self, stdCode:str, period:str, count:int) -> WtKlineData:\n '''\n 获取历史K线\n @stdCode 合约代码\n @period K线周期，如m3/d7\n @count 要拉取的K线条数\n '''\n key = \"%s#%s\" % (stdCode, period)\n\n if key in self.__bar_cache__:\n #这里做一个数据长度处理\n return self.__bar_cache__[key]\n\n self.__bar_cache__[key] = WtKlineData(size=count)\n cnt = self.__wrapper__.sel_get_bars(self.__id__, stdCode, period, count)\n if cnt == 0:\n return None\n\n df_bars = self.__bar_cache__[key]\n df_bars.closed = False\n\n return df_bars\n\n def stra_get_ticks(self, stdCode:str, count:int) -> WtHftData:\n '''\n 获取tick数据\n @stdCode 合约代码\n @count 要拉取的tick数量\n '''\n self.__bar_cache__[stdCode] = WtHftData(capacity=count)\n cnt = self.__wrapper__.cta_get_ticks(self.__id__, stdCode, count)\n if cnt == 0:\n return None\n \n df_ticks = self.__tick_cache__[stdCode]\n return df_ticks\n\n def stra_sub_ticks(self, stdCode:str):\n '''\n 订阅实时行情\\n\n @stdCode 合约代码\n '''\n self.__wrapper__.cta_sub_ticks(stdCode)\n\n def stra_get_position(self, stdCode:str = \"\", usertag:str = \"\") -> float:\n '''\n 读取当前仓位\\n\n @stdCode 合约/股票代码\\n\n @usertag 入场标记\n @return 正为多仓，负为空仓\n '''\n return self.__wrapper__.sel_get_position(self.__id__, stdCode, usertag)\n\n def stra_set_position(self, stdCode:str, qty:float, usertag:str = \"\"):\n '''\n 设置仓位\\n\n @stdCode 合约/股票代码\\n\n @qty 目标仓位，正为多仓，负为空仓\\n\n @return 设置结果TRUE/FALSE\n '''\n self.__wrapper__.sel_set_position(self.__id__, stdCode, qty, usertag)\n \n def user_save_data(self, key:str, val):\n '''\n 保存用户数据\n @key 数据id\n @val 数据值，可以直接转换成str的数据均可\n '''\n self.__wrapper__.sel_save_user_data(self.__id__, key, str(val))\n\n def user_load_data(self, key:str, defVal = None, vType = float):\n '''\n 读取用户数据\n @key 数据id\n @defVal 默认数据，如果找不到则返回改数据，默认为None\n @return 返回值，默认处理为float数据\n '''\n ret = self.__wrapper__.sel_load_user_data(self.__id__, key, \"\")\n if ret == \"\":\n return defVal\n\n return vType(ret)\n\n def stra_get_comminfo(self, stdCode:str):\n '''\n 获取品种详情\\n\n @stdCode 合约代码如SHFE.ag.HOT，或者品种代码如SHFE.ag\\n\n @return 品种信息，结构请参考ProductMgr中的ProductInfo\n '''\n if self.__engine__ is None:\n return None\n return self.__engine__.getProductInfo(stdCode)\n\n def stra_get_sessioninfo(self, stdCode:str):\n '''\n 获取品种详情\\n\n @stdCode 合约代码如SHFE.ag.HOT，或者品种代码如SHFE.ag\\n\n @return 品种信息，结构请参考ProductMgr中的ProductInfo\n '''\n if self.__engine__ is None:\n return None\n return self.__engine__.getSessionByCode(stdCode)\n\n def stra_get_contract(self, stdCode:str):\n '''\n 获取品种详情\\n\n @stdCode 合约代码如SHFE.ag.HOT，或者品种代码如SHFE.ag\\n\n @return 品种信息，结构请参考ProductMgr中的ProductInfo\n '''\n if self.__engine__ is None:\n return None\n return self.__engine__.getContractInfo(stdCode)\n\n def stra_get_all_codes(self):\n if self.__engine__ is None:\n return None\n\n return self.__engine__.getAllCodes()\n" }, { "path": "wtpy/SessionMgr.py", "content": "import math\nimport json\n\nclass SectionInfo:\n\n def __init__(self):\n self.stime = 0\n self.etime = 0\n\nclass SessionInfo:\n\n def __init__(self):\n self.id = \"\"\n self.name = \"\"\n self.auction = SectionInfo()\n self.sections = list()\n self.offset = 0\n self.totalMins = 0\n\n def toString(self):\n '''\n 将SessionInfo转换成json字符串\n '''\n obj = dict()\n obj[\"name\"] = self.name\n obj[\"offset\"] = self.offset\n obj[\"auction\"] = {\n \"from\": self.originalTime(self.auction.stime),\n \"to\": self.originalTime(self.auction.etime)\n }\n\n obj[\"sections\"] = list()\n for secInfo in self.sections:\n obj[\"sections\"].append({\n \"from\": self.originalTime(secInfo.stime),\n \"to\": self.originalTime(secInfo.etime)\n })\n\n return json.dumps(obj, ensure_ascii=True)\n\n\n def offsetTime(self, rawTime:int):\n curMinute = math.floor(rawTime/100)*60 + rawTime%100\n curMinute += self.offset\n if curMinute >= 1440:\n curMinute -= 1440\n elif curMinute < 0:\n curMinute += 1440\n \n return math.floor(curMinute/60)*100 + curMinute%60\n\n def originalTime(self, offTime:int):\n curMinute = math.floor(offTime/100)*60 + offTime%100\n curMinute -= self.offset\n if curMinute >= 1440:\n curMinute -= 1440\n elif curMinute < 0:\n curMinute += 1440\n \n return math.floor(curMinute/60)*100 + curMinute%60\n\n def getOpenTime(self, bOffset:bool = False):\n if len(self.sections) == 0:\n return 0\n\n opentm = self.sections[0].stime\n if not bOffset:\n return self.originalTime(opentm)\n else:\n return opentm\n\n def getCloseTime(self, bOffset:bool = False):\n if len(self.sections) == 0:\n return 0\n\n closetm = self.sections[-1].etime\n if not bOffset:\n return self.originalTime(closetm)\n else:\n return closetm\n\n def getTradingMins(self):\n if len(self.sections) == 0:\n return 0\n \n if self.totalMins == 0:\n for sec in self.sections:\n s = sec.stime\n e = sec.etime\n h = math.floor(e / 100) - math.floor(s / 100)\n m = (e%100) - (s%100)\n self.totalMins += (h*60 + m)\n return self.totalMins\n\n def getTradingSecs(self):\n return self.getTradingMins()*60\n\n\n def isLastOfSection(self, rawTime:int):\n offTime = self.offsetTime(rawTime)\n\n for sec in self.sections:\n if sec.etime == offTime:\n return True\n \n return False\n\n def\tisInTradingTime(self, rawTime:int, bStrict:bool = False):\n mins = self.timeToMinutes(rawTime)\n if mins == -1:\n return False\n\n if bStrict and self.isLastOfSection(rawTime):\n return False\n \n return True\n\n def isFirstOfSection(self, rawTime:int):\n offTime = self.offsetTime(rawTime)\n\n for sec in self.sections:\n if sec.stime == offTime:\n return True\n \n return False\n\n def timeToMinutes(self, rawTime:int):\n if len(self.sections) == 0:\n return -1\n\n offTime = self.offsetTime(rawTime)\n\n bFound = False\n offset = 0\n for sec in self.sections:\n if sec.stime <= offTime and offTime <= sec.etime:\n hour = math.floor(offTime / 100) - math.floor(sec.stime / 100)\n minute = offTime % 100 - sec.stime % 100\n offset += hour*60 + minute\n bFound = True\n break\n else:\n hour = math.floor(sec.etime / 100) - math.floor(sec.stime / 100)\n minute = sec.etime % 100 - sec.stime % 100\n offset += hour*60 + minute\n\n if not bFound:\n return -1\n\n return offset\n\n def minutesToTime(self, minutes:int, bHeadFirst:bool = False):\n if len(self.sections) == 0:\n return -1\n\n offset = minutes\n for sec in self.sections:\n startMin = math.floor(sec.stime / 100)*60 + sec.stime % 100\n stopMin = math.floor(sec.etime / 100)*60 + sec.etime % 100\n\n if not bHeadFirst:\n if startMin + offset >= stopMin:\n offset -= (stopMin - startMin)\n if offset == 0:\n return self.originalTime(math.floor(stopMin / 60) * 100 + stopMin % 60)\n else:\n desMin = startMin + offset\n if desMin > 1440:\n desMin -= 1440\n\n return self.originalTime(math.floor(desMin / 60) * 100 + desMin % 60)\n else:\n if startMin + offset >= stopMin:\n offset -= (stopMin - startMin)\n else:\n desMin = startMin + offset\n if desMin > 1440:\n desMin -= 1440\n\n return self.originalTime(math.floor(desMin / 60) * 100 + desMin % 60)\n\n return self.getCloseTime()\n\nclass SessionMgr:\n\n def __init__(self):\n self.__sessions__ = dict()\n return\n\n\n def load(self, fname:int):\n f = open(fname, 'r', encoding=\"gbk\")\n content = f.read()\n f.close()\n\n sessions_dict = json.loads(content)\n for sid in sessions_dict: \n if sid in self.__sessions__:\n continue\n\n sObj = sessions_dict[sid]\n sInfo = SessionInfo()\n sInfo.id = sid\n sInfo.offset = sObj[\"offset\"]\n sInfo.name = sObj[\"name\"]\n\n if \"auction\" in sObj:\n sInfo.auction.stime = sInfo.offsetTime(sObj[\"auction\"][\"from\"])\n sInfo.auction.etime = sInfo.offsetTime(sObj[\"auction\"][\"to\"])\n\n for secObj in sObj[\"sections\"]:\n secInfo = SectionInfo()\n secInfo.stime = sInfo.offsetTime(secObj[\"from\"])\n secInfo.etime = sInfo.offsetTime(secObj[\"to\"])\n sInfo.sections.append(secInfo)\n\n self.__sessions__[sid] = sInfo\n\n\n def getSession(self, sid:str) -> SessionInfo:\n if sid not in self.__sessions__:\n return None\n\n return self.__sessions__[sid]" }, { "path": "wtpy/StrategyDefs.py", "content": "from wtpy import CtaContext, SelContext, HftContext\n\nclass BaseCtaStrategy:\n '''\n CTA策略基础类，所有的策略都从该类派生\n 包含了策略的基本开发框架\n '''\n def __init__(self, name:str):\n self.__name__ = name\n \n \n def name(self) -> str:\n return self.__name__\n\n\n def on_init(self, context:CtaContext):\n '''\n 策略初始化，启动的时候调用\n 用于加载自定义数据\n\n @context 策略运行上下文\n '''\n return\n\n def on_session_begin(self, context:CtaContext, curTDate:int):\n '''\n 交易日开始事件\n\n @curTDate 交易日，格式为20210220\n '''\n return\n\n def on_session_end(self, context:CtaContext, curTDate:int):\n '''\n 交易日结束事件\n\n @curTDate 交易日，格式为20210220\n '''\n return\n \n def on_calculate(self, context:CtaContext):\n '''\n K线闭合时调用，一般作为策略的核心计算模块\n\n @context 策略运行上下文\n '''\n return\n\n def on_calculate_done(self, context:CtaContext):\n '''\n K线闭合时调用，一般作为策略的核心计算模块\n\n @context 策略运行上下文\n '''\n return\n\n\n def on_tick(self, context:CtaContext, stdCode:str, newTick:dict):\n '''\n 逐笔数据进来时调用\n 生产环境中，每笔行情进来就直接调用\n 回测环境中，是模拟的逐笔数据\n\n @context 策略运行上下文\n @stdCode 合约代码\n @newTick 最新逐笔\n '''\n return\n\n def on_bar(self, context:CtaContext, stdCode:str, period:str, newBar:dict):\n '''\n K线闭合时回调\n\n @context 策略上下文\n @stdCode 合约代码\n @period K线周期\n @newBar 最新闭合的K线\n '''\n return\n\n def on_backtest_end(self, context:CtaContext):\n '''\n 回测结束时回调，只在回测框架下会触发\n\n @context 策略上下文\n '''\n return\n\nclass BaseHftStrategy:\n '''\n HFT策略基础类，所有的策略都从该类派生\n 包含了策略的基本开发框架\n '''\n def __init__(self, name:str):\n self.__name__ = name\n \n \n def name(self) -> str:\n return self.__name__\n\n\n def on_init(self, context:HftContext):\n '''\n 策略初始化，启动的时候调用\n 用于加载自定义数据\n\n @context 策略运行上下文\n '''\n return\n \n def on_session_begin(self, context:HftContext, curTDate:int):\n '''\n 交易日开始事件\n\n @curTDate 交易日，格式为20210220\n '''\n return\n\n def on_session_end(self, context:HftContext, curTDate:int):\n '''\n 交易日结束事件\n\n @curTDate 交易日，格式为20210220\n '''\n return\n\n def on_backtest_end(self, context:CtaContext):\n '''\n 回测结束时回调，只在回测框架下会触发\n\n @context 策略上下文\n '''\n return\n\n def on_tick(self, context:HftContext, stdCode:str, newTick:dict):\n '''\n Tick数据进来时调用\n\n @context 策略运行上下文\n @stdCode 合约代码\n @newTick 最新Tick\n '''\n return\n\n def on_order_detail(self, context:HftContext, stdCode:str, newOrdQue:dict):\n '''\n 逐笔委托数据进来时调用\n\n @context 策略运行上下文\n @stdCode 合约代码\n @newOrdQue 最新逐笔委托\n '''\n return\n\n def on_order_queue(self, context:HftContext, stdCode:str, newOrdQue:dict):\n '''\n 委托队列数据进来时调用\n\n @context 策略运行上下文\n @stdCode 合约代码\n @newOrdQue 最新委托队列\n '''\n return\n\n def on_transaction(self, context:HftContext, stdCode:str, newTrans:dict):\n '''\n 逐笔成交数据进来时调用\n\n @context 策略运行上下文\n @stdCode 合约代码\n @newTrans 最新逐笔成交\n '''\n return\n\n def on_bar(self, context:HftContext, stdCode:str, period:str, newBar:dict):\n '''\n K线闭合时回调\n\n @context 策略上下文\n @stdCode 合约代码\n @period K线周期\n @newBar 最新闭合的K线\n '''\n return\n\n def on_channel_ready(self, context:HftContext):\n '''\n 交易通道就绪通知\n\n @context 策略上下文\n '''\n return\n\n def on_channel_lost(self, context:HftContext):\n '''\n 交易通道丢失通知\n\n @context 策略上下文\n '''\n return\n\n def on_entrust(self, context:HftContext, localid:int, stdCode:str, bSucc:bool, msg:str, userTag:str):\n '''\n 下单结果回报\n\n @context 策略上下文\n @localid 本地订单id\n @stdCode 合约代码\n @bSucc 下单结果\n @mes 下单结果描述\n '''\n return\n\n def on_order(self, context:HftContext, localid:int, stdCode:str, isBuy:bool, totalQty:float, leftQty:float, price:float, isCanceled:bool, userTag:str):\n '''\n 订单回报\n @context 策略上下文\n @localid 本地订单id\n @stdCode 合约代码\n @isBuy 是否买入\n @totalQty 下单数量\n @leftQty 剩余数量\n @price 下单价格\n @isCanceled 是否已撤单\n '''\n return\n\n def on_trade(self, context:HftContext, localid:int, stdCode:str, isBuy:bool, qty:float, price:float, userTag:str):\n '''\n 成交回报\n\n @context 策略上下文\n @stdCode 合约代码\n @isBuy 是否买入\n @qty 成交数量\n @price 成交价格\n '''\n return\n\nclass BaseSelStrategy:\n '''\n 选股策略基础类，所有的多因子策略都从该类派生\n 包含了策略的基本开发框架\n '''\n def __init__(self, name:str):\n self.__name__ = name\n \n \n def name(self) -> str:\n return self.__name__\n\n\n def on_init(self, context:SelContext):\n '''\n 策略初始化，启动的时候调用\n 用于加载自定义数据\n\n @context 策略运行上下文\n '''\n return\n \n def on_session_begin(self, context:SelContext, curTDate:int):\n '''\n 交易日开始事件\n\n @curTDate 交易日，格式为20210220\n '''\n return\n\n def on_session_end(self, context:SelContext, curTDate:int):\n '''\n 交易日结束事件\n\n @curTDate 交易日，格式为20210220\n '''\n return\n \n def on_calculate(self, context:SelContext):\n '''\n K线闭合时调用，一般作为策略的核心计算模块\n @context 策略运行上下文\n '''\n return\n\n def on_calculate_done(self, context:SelContext):\n '''\n K线闭合时调用，一般作为策略的核心计算模块\n @context 策略运行上下文\n '''\n return\n\n def on_backtest_end(self, context:CtaContext):\n '''\n 回测结束时回调，只在回测框架下会触发\n\n @context 策略上下文\n '''\n return\n\n def on_tick(self, context:SelContext, stdCode:str, newTick:dict):\n '''\n 逐笔数据进来时调用\n 生产环境中，每笔行情进来就直接调用\n 回测环境中，是模拟的逐笔数据\n @context 策略运行上下文\n @stdCode 合约代码\n @newTick 最新逐笔\n '''\n return\n\n def on_bar(self, context:SelContext, stdCode:str, period:str, newBar:dict):\n '''\n K线闭合时回调\n @context 策略上下文\n @stdCode 合约代码\n @period K线周期\n @newBar 最新闭合的K线\n '''\n return" }, { "path": "wtpy/WtBtEngine.py", "content": "from wtpy.wrapper import WtBtWrapper\nfrom wtpy.CtaContext import CtaContext\nfrom wtpy.SelContext import SelContext\nfrom wtpy.HftContext import HftContext\nfrom wtpy.StrategyDefs import BaseCtaStrategy, BaseSelStrategy, BaseHftStrategy\nfrom wtpy.ExtToolDefs import BaseIndexWriter\nfrom wtpy.WtCoreDefs import EngineType\nfrom wtpy.WtUtilDefs import singleton\n\nfrom .ProductMgr import ProductMgr, ProductInfo\nfrom .SessionMgr import SessionMgr, SessionInfo\nfrom .ContractMgr import ContractMgr, ContractInfo\n\nfrom .CodeHelper import CodeHelper\n\nimport os\nimport json\n\n\n@singleton\nclass WtBtEngine:\n\n def __init__(self, eType:EngineType = EngineType.ET_CTA, logCfg:str = \"logcfgbt.json\", isFile:bool = True, bDumpCfg:bool = False, outDir:str = \"./outputs_bt\"):\n self.is_backtest = True\n\n self.__wrapper__ = WtBtWrapper(self) #api接口转换器\n self.__context__ = None #策略ctx映射表\n self.__config__ = dict() #框架配置项\n self.__cfg_commited__ = False #配置是否已提交\n\n self.__idx_writer__ = None #指标输出模块\n\n self.__dump_config__ = bDumpCfg #是否保存最终配置\n\n if eType == eType.ET_CTA:\n self.__wrapper__.initialize_cta(logCfg, isFile, outDir) #初始化CTA环境\n elif eType == eType.ET_HFT:\n self.__wrapper__.initialize_hft(logCfg, isFile, outDir) #初始化HFT环境\n elif eType == eType.ET_SEL:\n self.__wrapper__.initialize_sel(logCfg, isFile, outDir) #初始化SEL环境\n\n def __check_config__(self):\n '''\n 检查设置项\\n\n 主要会补充一些默认设置项\n '''\n if \"replayer\" not in self.__config__:\n self.__config__[\"replayer\"] = dict()\n self.__config__[\"replayer\"][\"basefiles\"] = dict()\n\n if \"replayer\" not in self.__config__:\n self.__config__[\"replayer\"] = dict()\n self.__config__[\"replayer\"][\"mode\"] = \"csv\"\n self.__config__[\"replayer\"][\"path\"] = \"./storage/\"\n\n if \"env\" not in self.__config__:\n self.__config__[\"env\"] = dict()\n self.__config__[\"env\"][\"mocker\"] = \"cta\"\n\n def set_writer(self, writer:BaseIndexWriter):\n '''\n 设置指标输出模块\n '''\n self.__idx_writer__ = writer\n\n def write_indicator(self, id, tag, time, data):\n '''\n 写入指标数据\n '''\n if self.__idx_writer__ is not None:\n self.__idx_writer__.write_indicator(id, tag, time, data)\n\n def init(self, folder:str, cfgfile:str = \"configbt.json\", commfile:str=\"commodities.json\", contractfile:str=\"contracts.json\"):\n '''\n 初始化\\n\n @folder 基础数据文件目录，\\\\结尾\\n\n @cfgfile 配置文件，json格式\n '''\n f = open(cfgfile, \"r\")\n content =f.read()\n self.__config__ = json.loads(content)\n f.close()\n\n self.__check_config__()\n\n self.__config__[\"replayer\"][\"basefiles\"][\"commodity\"] = folder + commfile\n self.__config__[\"replayer\"][\"basefiles\"][\"contract\"] = folder + contractfile\n self.__config__[\"replayer\"][\"basefiles\"][\"holiday\"] = folder + \"holidays.json\"\n self.__config__[\"replayer\"][\"basefiles\"][\"session\"] = folder + \"sessions.json\"\n self.__config__[\"replayer\"][\"basefiles\"][\"hot\"] = folder + \"hots.json\"\n\n self.productMgr = ProductMgr()\n self.productMgr.load(folder + commfile)\n\n self.contractMgr = ContractMgr()\n self.contractMgr.load(folder + contractfile)\n\n self.sessionMgr = SessionMgr()\n self.sessionMgr.load(folder + \"sessions.json\")\n\n def configMocker(self, name:str):\n '''\n 设置模拟器\n '''\n self.__config__[\"env\"][\"mocker\"] = name\n\n def configBacktest(self, stime:int, etime:int):\n '''\n 配置回测设置项\\n\n @stime 开始时间\\n\n @etime 结束时间\n '''\n self.__config__[\"replayer\"][\"stime\"] = int(stime)\n self.__config__[\"replayer\"][\"etime\"] = int(etime)\n\n def configBTStorage(self, mode:str, path:str = None, dbcfg:dict = None):\n '''\n 配置数据存储\\n\n @mode 存储模式，csv-表示从csv直接读取，一般回测使用，wtp-表示使用wt框架自带数据存储\n '''\n self.__config__[\"replayer\"][\"mode\"] = mode\n if path is not None:\n self.__config__[\"replayer\"][\"path\"] = path\n if dbcfg is not None:\n self.__config__[\"replayer\"][\"db\"] = dbcfg\n\n def setExternalCtaStrategy(self, id:str, module:str, typeName:str, params:dict):\n '''\n 添加外部的CTA策略\n '''\n if \"cta\" not in self.__config__:\n self.__config__[\"cta\"] = dict()\n\n self.__config__[\"cta\"][\"module\"] = module\n\n if \"strategy\" not in self.__config__[\"cta\"]:\n self.__config__[\"cta\"][\"strategy\"] = dict()\n\n self.__config__[\"cta\"][\"strategy\"][\"id\"] = id\n self.__config__[\"cta\"][\"strategy\"][\"name\"] = typeName\n self.__config__[\"cta\"][\"strategy\"][\"params\"] = params\n \n\n def setExternalHftStrategy(self, id:str, module:str, typeName:str, params:dict):\n '''\n 添加外部的HFT策略\n '''\n if \"hft\" not in self.__config__:\n self.__config__[\"hft\"] = dict()\n\n self.__config__[\"hft\"][\"module\"] = module\n\n if \"strategy\" not in self.__config__[\"hft\"]:\n self.__config__[\"hft\"][\"strategy\"] = dict()\n\n self.__config__[\"hft\"][\"strategy\"][\"id\"] = id\n self.__config__[\"hft\"][\"strategy\"][\"name\"] = typeName\n self.__config__[\"hft\"][\"strategy\"][\"params\"] = params\n\n\n def commitBTConfig(self):\n '''\n 提交配置\\n\n 只有第一次调用会生效，不可重复调用\\n\n 如果执行run之前没有调用，run会自动调用该方法\n '''\n if self.__cfg_commited__:\n return\n\n cfgfile = json.dumps(self.__config__, indent=4, sort_keys=True)\n self.__wrapper__.config_backtest(cfgfile, False)\n self.__cfg_commited__ = True\n\n if self.__dump_config__:\n f = open(\"config_run.json\", 'w')\n f.write(cfgfile)\n f.close()\n\n def getSessionByCode(self, code:str) -> SessionInfo:\n '''\n 通过合约代码获取交易时间模板\\n\n @code 合约代码，格式如SHFE.rb.HOT\n '''\n pid = CodeHelper.stdCodeToStdCommID(code)\n\n pInfo = self.productMgr.getProductInfo(pid)\n if pInfo is None:\n return None\n\n return self.sessionMgr.getSession(pInfo.session)\n\n def getSessionByName(self, sname:str) -> SessionInfo:\n '''\n 通过模板名获取交易时间模板\\n\n @sname 模板名\n '''\n return self.sessionMgr.getSession(sname)\n\n def getProductInfo(self, code:str) -> ProductInfo:\n '''\n 获取品种信息\\n\n @code 合约代码，格式如SHFE.rb.HOT\n '''\n return self.productMgr.getProductInfo(code)\n\n def getContractInfo(self, code:str) -> ContractInfo:\n '''\n 获取品种信息\\n\n @code 合约代码，格式如SHFE.rb.HOT\n '''\n return self.contractMgr.getContractInfo(code)\n\n def getAllCodes(self) -> list:\n '''\n 获取全部合约代码\n '''\n return self.contractMgr.getTotalCodes()\n\n def set_time_range(self, beginTime:int, endTime:int):\n '''\n 设置回测时间\\r\n @beginTime 开始时间，格式如yyyymmddHHMM\n @endTime 结束时间，格式如yyyymmddHHMM\n '''\n self.__wrapper__.set_time_range(beginTime, endTime)\n\n def set_cta_strategy(self, strategy:BaseCtaStrategy, slippage:int = 0, hook:bool = False, persistData:bool = True):\n '''\n 添加策略\\n\n @strategy 策略对象\n @slippage 滑点大小\n @hook 是否安装钩子，主要用于单步控制重算\n '''\n ctxid = self.__wrapper__.init_cta_mocker(strategy.name(), slippage, hook, persistData)\n self.__context__ = CtaContext(ctxid, strategy, self.__wrapper__, self)\n\n def set_hft_strategy(self, strategy:BaseHftStrategy, hook:bool = False):\n '''\n 添加策略\\n\n @strategy 策略对象\n @hook 是否安装钩子，主要用于单步控制重算\n '''\n ctxid = self.__wrapper__.init_hft_mocker(strategy.name(), hook)\n self.__context__ = HftContext(ctxid, strategy, self.__wrapper__, self)\n\n def set_sel_strategy(self, strategy:BaseSelStrategy, date:int=0, time:int=0, period:str=\"d\", trdtpl:str=\"CHINA\", session:str=\"TRADING\", slippage:int = 0):\n '''\n 添加策略\\n\n @strategy 策略对象\n '''\n ctxid = self.__wrapper__.init_sel_mocker(strategy.name(), date, time, period, trdtpl, session, slippage)\n self.__context__ = SelContext(ctxid, strategy, self.__wrapper__, self)\n\n def get_context(self, id:int):\n return self.__context__\n\n def run_backtest(self, bAsync:bool = False, bNeedDump:bool = True):\n '''\n 运行框架\n\n @bAsync 是否异步运行，默认为false\n '''\n if not self.__cfg_commited__: #如果配置没有提交，则自动提交一下\n self.commitBTConfig()\n\n self.__wrapper__.run_backtest(bNeedDump = bNeedDump, bAsync = bAsync)\n\n def cta_step(self, remark:str = \"\") -> bool:\n '''\n CTA策略单步执行\n\n @remark 单步备注信息，没有实际作用，主要用于外部调用区分步骤\n '''\n return self.__wrapper__.cta_step(self.__context__.id)\n\n def hft_step(self):\n '''\n HFT策略单步执行\n '''\n self.__wrapper__.hft_step(self.__context__.id)\n\n def stop_backtest(self):\n '''\n 手动停止回测\n '''\n self.__wrapper__.stop_backtest()\n\n def release_backtest(self):\n '''\n 释放框架\n '''\n self.__wrapper__.release_backtest()\n\n def on_init(self):\n return\n\n def on_schedule(self, date:int, time:int, taskid:int = 0):\n return\n\n def on_session_begin(self, date:int):\n return\n\n def on_session_end(self, date:int):\n return\n\n def on_backtest_end(self):\n if self.__context__ is None:\n return\n\n self.__context__.on_backtest_end()\n\n def clear_cache(self):\n '''\n 清除缓存的数据，即加已经加载到内存中的数据全部清除\n '''\n self.__wrapper__.clear_cache()\n" }, { "path": "wtpy/WtCoreDefs.py", "content": "from ctypes import c_uint, c_void_p, CFUNCTYPE, POINTER, c_char_p, c_bool, c_ulong, c_double\nfrom ctypes import Structure, c_char, c_int32, c_uint16, c_uint32, c_uint64, addressof, sizeof\nfrom copy import copy\nimport numpy as np\nimport pandas as pd\nfrom typing import Any\n\nMAX_INSTRUMENT_LENGTH = c_char*32\nMAX_EXCHANGE_LENGTH = c_char*10\nPriceQueueType = c_double*10\nVolumeQueueType = c_uint32*10\n\nclass WTSStruct(Structure):\n @property\n def fields(self) -> list:\n return self._fields_\n\n @property\n def values(self) -> tuple:\n return tuple(getattr(self, i[0]) for i in self._fields_)\n\n @property\n def to_dict(self) -> dict:\n return {i[0]:getattr(self, i[0]) for i in self._fields_}\n\nclass WTSTickStruct(WTSStruct):\n '''\n C接口传递的tick数据结构\n '''\n _fields_ = [(\"exchg\", MAX_EXCHANGE_LENGTH),\n (\"code\", MAX_INSTRUMENT_LENGTH),\n (\"price\", c_double),\n (\"open\", c_double),\n (\"high\", c_double),\n (\"low\", c_double),\n (\"settle_price\", c_double),\n\n (\"upper_limit\", c_double),\n (\"lower_limit\", c_double),\n\n (\"total_volume\", c_uint32),\n (\"volume\", c_uint32),\n (\"total_turnover\", c_double),\n (\"turn_over\", c_double),\n (\"open_interest\", c_uint32),\n (\"diff_interest\", c_int32),\n\n (\"trading_date\", c_uint32),\n (\"action_date\", c_uint32),\n (\"action_time\", c_uint32),\n\n (\"pre_close\", c_double),\n (\"pre_settle\", c_double),\n (\"pre_interest\", c_uint32),\n\n (\"bid_prices\", PriceQueueType),\n (\"ask_prices\", PriceQueueType),\n (\"bid_qty\", VolumeQueueType),\n (\"ask_qty\", VolumeQueueType)]\n _pack_ = 1\n\n @property\n def fields(self) -> list:\n fields = self._fields_.copy()\n fields[0] = ('exchg', 'S10')\n fields[1] = ('code', 'S10')\n fields[-4] = ('bid_prices', 'O')\n fields[-3] = ('ask_prices', 'O')\n fields[-2] = ('bid_qty', 'O')\n fields[-1] = ('ask_qty', 'O')\n return fields\n\n\nclass WTSBarStruct(WTSStruct):\n '''\n C接口传递的bar数据结构\n '''\n _fields_ = [(\"date\", c_uint32),\n (\"time\", c_uint32),\n (\"open\", c_double),\n (\"high\", c_double),\n (\"low\", c_double),\n (\"close\", c_double),\n (\"settle\", c_double),\n (\"money\", c_double),\n (\"vol\", c_uint32),\n (\"hold\", c_uint32),\n (\"diff\", c_int32)]\n _pack_ = 1\n\nclass WTSTransStruct(WTSStruct):\n '''\n C接口传递的逐笔成交数据结构\n '''\n _fields_ = [(\"exchg\", MAX_EXCHANGE_LENGTH),\n (\"code\", MAX_INSTRUMENT_LENGTH),\n\n (\"trading_date\", c_uint32),\n (\"action_date\", c_uint32),\n (\"action_time\", c_uint32),\n\n (\"index\", c_uint32),\n (\"ttype\", c_int32),\n (\"side\", c_int32),\n\n (\"price\", c_double),\n (\"volume\", c_uint32),\n (\"askorder\", c_int32),\n (\"bidorder\", c_int32)]\n _pack_ = 1\n\nclass WTSOrdQueStruct(WTSStruct):\n '''\n C接口传递的委托队列数据结构\n '''\n _fields_ = [(\"exchg\", MAX_EXCHANGE_LENGTH),\n (\"code\", MAX_INSTRUMENT_LENGTH),\n\n (\"trading_date\", c_uint32),\n (\"action_date\", c_uint32),\n (\"action_time\", c_uint32),\n\n (\"side\", c_int32),\n (\"price\", c_double),\n (\"order_items\", c_uint32),\n (\"qsize\", c_uint32),\n (\"volumes\", c_uint32*50)]\n _pack_ = 1\n\nclass WTSOrdDtlStruct(WTSStruct):\n '''\n C接口传递的委托明细数据结构\n '''\n _fields_ = [(\"exchg\", MAX_EXCHANGE_LENGTH),\n (\"code\", MAX_INSTRUMENT_LENGTH),\n\n (\"trading_date\", c_uint32),\n (\"action_date\", c_uint32),\n (\"action_time\", c_uint32),\n\n (\"index\", c_uint32),\n (\"side\", c_int32),\n (\"price\", c_double),\n (\"volume\", c_uint32),\n (\"otype\", c_int32)]\n _pack_ = 1\n\n\nclass CacheList(list):\n def to_record(self) -> np.recarray:\n data = np.empty(len(self), dtype=self[0].fields)\n for k, v in enumerate(self):\n data[k] = v.values\n return data.view(np.recarray)\n\n def to_pandas(self) -> pd.DataFrame:\n return pd.DataFrame(self.to_record())\n\nclass BarList(CacheList):\n def on_read_bar(self, curBar:POINTER(WTSBarStruct), count:int, isLast:bool):\n bsSize = sizeof(WTSBarStruct)\n addr = addressof(curBar.contents)\n for i in range(count):\n thisBar = WTSBarStruct.from_address(addr)\n self.append(copy(thisBar))\n addr += bsSize\n\n def on_data_count(self, count:int):\n pass\n\nclass TickList(CacheList):\n def on_read_tick(self, curTick:POINTER(WTSTickStruct), count:int, isLast:bool):\n tsSize = sizeof(WTSTickStruct)\n addr = addressof(curTick.contents)\n for i in range(count):\n thisTick = WTSTickStruct.from_address(addr)\n self.append(copy(thisTick))\n addr += tsSize\n\n def on_data_count(self, count:int):\n pass\n\n# 回调函数定义\n#策略初始化回调\nCB_STRATEGY_INIT = CFUNCTYPE(c_void_p, c_ulong) \n#策略tick数据推送回调\nCB_STRATEGY_TICK = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSTickStruct))\n#策略获取tick数据的单条tick同步回调\nCB_STRATEGY_GET_TICK = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSTickStruct), c_uint32, c_bool)\n#策略重算回调(CTA/SEL策略)\nCB_STRATEGY_CALC = CFUNCTYPE(c_void_p, c_ulong, c_ulong, c_ulong)\n#策略订阅的K线闭合事件回调\nCB_STRATEGY_BAR = CFUNCTYPE(c_void_p, c_ulong, c_char_p, c_char_p, POINTER(WTSBarStruct))\n#策略获取K线数据的单条K线同步回调\nCB_STRATEGY_GET_BAR = CFUNCTYPE(c_void_p, c_ulong, c_char_p, c_char_p, POINTER(WTSBarStruct), c_uint32, c_bool)\n#策略获取全部持仓的同步回调\nCB_STRATEGY_GET_POSITION = CFUNCTYPE(c_void_p, c_ulong, c_char_p, c_double, c_bool)\n#交易日开始结束事件回调\nCB_SESSION_EVENT = CFUNCTYPE(c_void_p, c_ulong, c_ulong, c_bool) \n\n#引擎事件回调(交易日开启结束等)\nCB_ENGINE_EVENT = CFUNCTYPE(c_void_p, c_ulong, c_ulong, c_ulong)\n\n#HFT策略交易通道事件回调\nCB_HFTSTRA_CHNL_EVT = CFUNCTYPE(c_void_p, c_ulong, c_char_p, c_ulong)\n#HFT策略订单推送回报\nCB_HFTSTRA_ORD = CFUNCTYPE(c_void_p, c_ulong, c_ulong, c_char_p, c_bool, c_double, c_double, c_double, c_bool, c_char_p)\n#HFT策略成交推送回报\nCB_HFTSTRA_TRD = CFUNCTYPE(c_void_p, c_ulong, c_ulong, c_char_p, c_bool, c_double, c_double, c_char_p)\n#HFT策略下单结果回报\nCB_HFTSTRA_ENTRUST = CFUNCTYPE(c_void_p, c_ulong, c_ulong, c_char_p, c_bool, c_char_p, c_char_p)\n\n#策略委托队列推送回调\nCB_HFTSTRA_ORDQUE = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSOrdQueStruct))\n#策略获取委托队列数据的单条数据同步回调\nCB_HFTSTRA_GET_ORDQUE = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSOrdQueStruct), c_uint32, c_bool)\n#策略委托明细推送回调\nCB_HFTSTRA_ORDDTL = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSOrdDtlStruct))\n#策略获取委托明细数据的单条数据同步回调\nCB_HFTSTRA_GET_ORDDTL = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSOrdDtlStruct), c_uint32, c_bool)\n#策略成交明细推送回调\nCB_HFTSTRA_TRANS = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSTransStruct))\n#策略获取成交明细数据的单条数据同步回调\nCB_HFTSTRA_GET_TRANS = CFUNCTYPE(c_void_p, c_ulong, c_char_p, POINTER(WTSTransStruct), c_uint32, c_bool)\n\n\nEVENT_ENGINE_INIT\t= 1 #框架初始化\nEVENT_SESSION_BEGIN = 2 #交易日开始\nEVENT_SESSION_END\t= 3 #交易日结束\nEVENT_ENGINE_SCHDL\t= 4 #框架调度\nEVENT_BACKTEST_END = 5 #回测结束\n\nCHNL_EVENT_READY\t= 1000 #通道就绪事件\nCHNL_EVENT_LOST\t\t= 1001 #通道断开事件\n\nfrom enum import Enum\nclass EngineType(Enum):\n '''\n 引擎类型\n 枚举变量\n '''\n ET_CTA = 999\n ET_HFT = 1000\n ET_SEL = 1001\n\n \n'''\nParser外接实现\n'''\nEVENT_PARSER_INIT\t\t= 1;\t#Parser初始化\nEVENT_PARSER_CONNECT\t= 2;\t#Parser连接\nEVENT_PARSER_DISCONNECT = 3;\t#Parser断开连接\nEVENT_PARSER_RELEASE\t= 4;\t#Parser释放\nCB_PARSER_EVENT = CFUNCTYPE(c_void_p, c_ulong, c_char_p)\nCB_PARSER_SUBCMD = CFUNCTYPE(c_void_p, c_char_p, c_char_p, c_bool)\n\n'''\nExecuter外接实现\n'''\nCB_EXECUTER_INIT = CFUNCTYPE(c_void_p, c_char_p)\nCB_EXECUTER_CMD = CFUNCTYPE(c_void_p, c_char_p, c_char_p, c_double)\n" }, { "path": "wtpy/WtDataDefs.py", "content": "import numpy as np\nfrom pandas import DataFrame\n\nclass WtKlineData:\n def __init__(self, size:int, bAlloc:bool = True):\n self.capacity:int = size\n self.size:int = 0\n\n if bAlloc:\n self.bartimes = np.zeros(self.capacity, np.int64)\n self.opens = np.zeros(self.capacity)\n self.highs = np.zeros(self.capacity)\n self.lows = np.zeros(self.capacity)\n self.closes = np.zeros(self.capacity)\n self.volumes = np.zeros(self.capacity)\n else:\n self.bartimes = None\n self.opens = None\n self.highs = None\n self.lows = None\n self.closes = None\n self.volumes = None\n\n def append_bar(self, newBar:dict):\n\n pos = self.size\n if pos == self.capacity:\n self.bartimes[:-1] = self.bartimes[1:]\n self.opens[:-1] = self.opens[1:]\n self.highs[:-1] = self.highs[1:]\n self.lows[:-1] = self.lows[1:]\n self.closes[:-1] = self.closes[1:]\n self.volumes[:-1] = self.volumes[1:]\n\n pos = -1\n else:\n self.size += 1\n self.bartimes[pos] = newBar[\"bartime\"]\n self.opens[pos] = newBar[\"open\"]\n self.highs[pos] = newBar[\"high\"]\n self.lows[pos] = newBar[\"low\"]\n self.closes[pos] = newBar[\"close\"]\n self.volumes[pos] = newBar[\"volume\"]\n\n def is_empty(self) -> bool:\n return self.size==0\n\n def clear(self):\n self.size = 0\n\n self.bartimes:np.ndarray = np.zeros(self.capacity, np.int64)\n self.opens:np.ndarray = np.zeros(self.capacity)\n self.highs:np.ndarray = np.zeros(self.capacity)\n self.lows:np.ndarray = np.zeros(self.capacity)\n self.closes:np.ndarray = np.zeros(self.capacity)\n self.volumes:np.ndarray = np.zeros(self.capacity)\n\n def get_bar(self, iLoc:int = -1) -> dict:\n if self.is_empty():\n return None\n\n lastBar = dict()\n lastBar[\"bartime\"] = self.bartimes[iLoc]\n lastBar[\"open\"] = self.opens[iLoc]\n lastBar[\"high\"] = self.highs[iLoc]\n lastBar[\"low\"] = self.lows[iLoc]\n lastBar[\"close\"] = self.closes[iLoc]\n lastBar[\"volume\"] = self.volumes[iLoc]\n\n return lastBar\n\n def slice(self, iStart:int = 0, iEnd:int = -1, bCopy:bool = False):\n if self.is_empty():\n return None\n\n bartimes = self.bartimes[iStart:iEnd]\n cnt = len(bartimes)\n ret = WtKlineData(cnt, False)\n ret.size = cnt\n\n if bCopy:\n ret.bartimes = bartimes.copy()\n ret.opens = self.opens[iStart:iEnd].copy()\n ret.highs = self.highs[iStart:iEnd].copy()\n ret.lows = self.lows[iStart:iEnd].copy()\n ret.closes = self.closes[iStart:iEnd].copy()\n ret.volumes = self.volumes[iStart:iEnd].copy()\n else:\n ret.bartimes = bartimes\n ret.opens = self.opens[iStart:iEnd]\n ret.highs = self.highs[iStart:iEnd]\n ret.lows = self.lows[iStart:iEnd]\n ret.closes = self.closes[iStart:iEnd]\n ret.volumes = self.volumes[iStart:iEnd]\n\n return ret\n\n def to_df(self) -> DataFrame:\n ret = DataFrame({\n \"bartime\":self.bartimes,\n \"open\":self.opens,\n \"high\":self.highs,\n \"low\":self.lows,\n \"close\":self.closes,\n \"volume\":self.volumes\n })\n ret.set_index(self.bartimes)\n return ret\n\nclass WtHftData:\n def __init__(self, capacity:int):\n self.capacity:int = capacity\n self.size:int = 0\n\n self.items = [None]*capacity\n\n def append_item(self, newItem:dict):\n pos = self.size\n if pos == self.capacity:\n self.items[:-1] = self.items[1:]\n pos = -1\n else:\n self.size += 1\n\n self.items[pos] = newItem\n\n def is_empty(self) -> bool:\n return self.size==0\n\n def clear(self):\n self.size = 0\n self.items = []*self.capacity\n\n def get_item(self, iLoc:int=-1) -> dict:\n if self.is_empty():\n return None\n\n return self.items[iLoc]\n\n def to_df(self) -> DataFrame:\n ret = DataFrame(self.items)\n return ret" }, { "path": "wtpy/WtDtEngine.py", "content": "from wtpy.wrapper import WtDtWrapper\nfrom wtpy.ExtModuleDefs import BaseExtParser\nfrom wtpy.WtUtilDefs import singleton\n\n@singleton\nclass WtDtEngine:\n\n def __init__(self):\n self.__wrapper__ = WtDtWrapper() #api接口转换器\n self.__ext_parsers__ = dict() #外接的行情接入模块\n\n def initialize(self, cfgfile:str = \"dtcfg.json\", logprofile:str = \"logcfgdt.json\"):\n '''\n 数据引擎初始化\\n\n @cfgfile 配置文件\\n\n @logprofile 日志模块配置文件\n '''\n self.__wrapper__.initialize(cfgfile, logprofile)\n \n def run(self):\n '''\n 运行数据引擎\n '''\n self.__wrapper__.run_datakit()\n\n def add_exetended_parser(self, parser:BaseExtParser):\n '''\n 添加扩展parser\n '''\n id = parser.id()\n if id not in self.__ext_parsers__:\n self.__ext_parsers__[id] = parser\n if not self.__wrapper__.create_extended_parser(id):\n self.__ext_parsers__.pop(id)\n\n def get_extended_parser(self, id:str)->BaseExtParser:\n '''\n 根据id获取扩展parser\n '''\n if id not in self.__ext_parsers__:\n return None\n return self.__ext_parsers__[id]\n\n def push_quote_from_extended_parser(self, id:str, newTick, bNeedSlice:bool):\n '''\n 向底层推送tick数据\n '''\n self.__wrapper__.push_quote_from_exetended_parser(id, newTick, bNeedSlice)" }, { "path": "wtpy/WtDtServo.py", "content": "from wtpy.WtUtilDefs import singleton\nfrom wtpy.wrapper import WtDtServoApi\nfrom wtpy.WtCoreDefs import BarList, TickList, WTSBarStruct, WTSTickStruct\n\nfrom flask import Flask, session, redirect, request, make_response\nfrom flask_compress import Compress\n\nimport urllib.request\nimport io\nimport gzip\n\nimport json\n\ndef pack_rsp(obj):\n rsp = make_response(json.dumps(obj))\n rsp.headers[\"content-type\"]= \"text/json;charset=utf-8\"\n return rsp\n\ndef parse_data():\n try:\n data = request.get_data()\n json_data = json.loads(data.decode(\"utf-8\"))\n return True,json_data\n except:\n return False, {\n \"result\": -998,\n \"message\": \"请求数据解析失败\"\n }\n\ndef get_param(json_data, key:str, type=str, defVal = \"\"):\n if key not in json_data:\n return defVal\n else:\n return type(json_data[key])\n\ndef httpPost(url, datas:dict, encoding='utf-8') -> dict:\n headers = {\n 'User-Agent': 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)',\n 'Accept-encoding': 'gzip'\n }\n data = json.dumps(datas).encode(\"utf-8\")\n request = urllib.request.Request(url, data, headers)\n if True:\n f = urllib.request.urlopen(request)\n ec = f.headers.get('Content-Encoding')\n if ec == 'gzip':\n cd = f.read()\n cs = io.BytesIO(cd)\n f = gzip.GzipFile(fileobj=cs)\n\n ret = json.loads(f.read().decode(encoding))\n f.close()\n return ret\n else:\n return None\n\n@singleton\nclass WtDtServo:\n\n # 构造函数，传入动态库名\n def __init__(self):\n self.__config__ = None\n self.__cfg_commited__ = False\n self.local_api = None\n self.server_inst = None\n self.remote_api = None \n\n def __check_config__(self):\n '''\n 检查设置项\\n\n 主要会补充一些默认设置项\n '''\n if self.local_api is None:\n self.local_api = WtDtServoApi()\n\n if self.__config__ is None:\n self.__config__ = dict()\n\n if \"basefiles\" not in self.__config__:\n self.__config__[\"basefiles\"] = dict()\n\n if \"data\" not in self.__config__:\n self.__config__[\"data\"] = {\n \"store\":{\n \"path\":\"./storage/\"\n }\n }\n\n def setRemoteUrl(self, url:str=\"http://127.0.0.1:8081\"):\n if self.__config__ is not None:\n raise Exception('WtDtServo is already in local mode')\n return\n \n self.remote_api = WtDtRemoteServo(url)\n\n\n def setBasefiles(self, commfile:str=\"./common/commodities.json\", contractfile:str=\"./common/contracts.json\", \n holidayfile:str=\"./common/holidays.json\", sessionfile:str=\"./common/sessions.json\", hotfile:str=\"./common/hots.json\"):\n '''\n C接口初始化\n '''\n self.__check_config__()\n\n self.__config__[\"basefiles\"][\"commodity\"] = commfile\n self.__config__[\"basefiles\"][\"contract\"] = contractfile\n self.__config__[\"basefiles\"][\"holiday\"] = holidayfile\n self.__config__[\"basefiles\"][\"session\"] = sessionfile\n self.__config__[\"basefiles\"][\"hot\"] = hotfile\n\n def setStorage(self, path:str = \"./storage/\"):\n self.__config__[\"data\"][\"store\"][\"path\"] = path\n \n def commitConfig(self):\n if self.remote_api is not None:\n raise Exception('WtDtServo is already in remote mode')\n return\n \n if self.__cfg_commited__:\n return\n\n cfgfile = json.dumps(self.__config__, indent=4, sort_keys=True)\n try:\n self.local_api.initialize(cfgfile, False)\n self.__cfg_commited__ = True\n except OSError as oe:\n print(oe)\n\n def __server_impl__(self, port:int, host:str):\n self.server_inst.run(port = port, host = host)\n \n def runServer(self, port:int = 8081, host=\"0.0.0.0\", bSync:bool = True):\n if self.remote_api is not None:\n raise Exception('WtDtServo is already in remote mode')\n return\n\n app = Flask(__name__)\n app.secret_key = \"!@#$%^&*()\"\n Compress(app)\n\n self.server_inst = app\n\n @app.route(\"/getbars\", methods=[\"POST\"])\n def on_get_bars():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n stdCode = get_param(json_data, \"code\")\n period = get_param(json_data, \"period\")\n fromTime = get_param(json_data, \"stime\", int, None)\n dataCount = get_param(json_data, \"count\", int, None)\n endTime = get_param(json_data, \"etime\", int)\n\n if (fromTime is None and dataCount is None) or (fromTime is not None and dataCount is not None):\n ret = {\n \"result\":-1,\n \"message\":\"Only one of stime and count must be valid at the same time\"\n }\n else:\n bars = self.local_api.get_bars(stdCode=stdCode, period=period, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n if bars is None:\n ret = {\n \"result\":-2,\n \"message\":\"Data not found\"\n }\n else:\n bar_list = [curBar.to_dict for curBar in bars]\n \n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"bars\": bar_list\n }\n\n return pack_rsp(ret)\n\n @app.route(\"/getticks\", methods=[\"POST\"])\n def on_get_ticks():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n stdCode = get_param(json_data, \"code\")\n fromTime = get_param(json_data, \"stime\", int, None)\n dataCount = get_param(json_data, \"count\", int, None)\n endTime = get_param(json_data, \"etime\", int)\n\n if (fromTime is None and dataCount is None) or (fromTime is not None and dataCount is not None):\n ret = {\n \"result\":-1,\n \"message\":\"Only one of stime and count must be valid at the same time\"\n }\n else:\n ticks = self.local_api.get_ticks(stdCode=stdCode, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n if ticks is None:\n ret = {\n \"result\":-2,\n \"message\":\"Data not found\"\n }\n else:\n tick_list = list()\n for curTick in ticks:\n curTick = curTick.to_dict\n curTick[\"exchg\"] = curTick[\"exchg\"].decode()\n curTick[\"code\"] = curTick[\"code\"].decode()\n\n # TODO bid/ask还有问题，先剔除\n curTick.pop(\"bid_prices\")\n curTick.pop(\"ask_prices\")\n curTick.pop(\"bid_qty\")\n curTick.pop(\"ask_qty\")\n\n tick_list.append(curTick)\n \n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"ticks\": tick_list\n }\n\n return pack_rsp(ret)\n\n self.commitConfig()\n if bSync:\n self.__server_impl__(port, host)\n else:\n import threading\n self.worker = threading.Thread(target=self.__server_impl__, args=(port,host,))\n self.worker.setDaemon(True)\n self.worker.start()\n\n def get_bars(self, stdCode:str, period:str, fromTime:int = None, dataCount:int = None, endTime:int = 0) -> BarList:\n '''\n 获取K线数据\\n\n @stdCode 标准合约代码\\n\n @period 基础K线周期，m1/m5/d\\n\n @fromTime 开始时间，日线数据格式yyyymmdd，分钟线数据为格式为yyyymmddHHMM\\n\n @endTime 结束时间，日线数据格式yyyymmdd，分钟线数据为格式为yyyymmddHHMM，为0则读取到最后一条\n '''\n if self.remote_api is not None:\n return self.remote_api.get_bars(stdCode=stdCode, period=period, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n \n self.commitConfig()\n\n if (fromTime is None and dataCount is None) or (fromTime is not None and dataCount is not None):\n raise Exception('Only one of fromTime and dataCount must be valid at the same time')\n\n return self.local_api.get_bars(stdCode=stdCode, period=period, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n\n def get_ticks(self, stdCode:str, fromTime:int = None, dataCount:int = None, endTime:int = 0) -> TickList:\n '''\n 获取tick数据\\n\n @stdCode 标准合约代码\\n\n @fromTime 开始时间，格式为yyyymmddHHMM\\n\n @endTime 结束时间，格式为yyyymmddHHMM，为0则读取到最后一条\n '''\n if self.remote_api is not None:\n return self.remote_api.get_ticks(stdCode=stdCode, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n\n self.commitConfig()\n\n if (fromTime is None and dataCount is None) or (fromTime is not None and dataCount is not None):\n raise Exception('Only one of fromTime and dataCount must be valid at the same time')\n\n return self.local_api.get_ticks(stdCode=stdCode, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n\nclass WtDtRemoteServo:\n\n def __init__(self, url:str=\"http://127.0.0.1:8081\"):\n self.remote_url = url\n\n def get_bars(self, stdCode:str, period:str, fromTime:int = None, dataCount:int = None, endTime:int = 0) -> BarList:\n '''\n 获取K线数据\\n\n @stdCode 标准合约代码\\n\n @period 基础K线周期，m1/m5/d\\n\n @fromTime 开始时间，日线数据格式yyyymmdd，分钟线数据为格式为yyyymmddHHMM\\n\n @endTime 结束时间，日线数据格式yyyymmdd，分钟线数据为格式为yyyymmddHHMM，为0则读取到最后一条\n '''\n if (fromTime is None and dataCount is None) or (fromTime is not None and dataCount is not None):\n raise Exception('Only one of fromTime and dataCount must be valid at the same time')\n\n url = self.remote_url + \"/getbars\"\n data = {\n \"code\":stdCode,\n \"period\":period,\n \"etime\":endTime\n }\n\n if fromTime is not None:\n data[\"stime\"] = fromTime\n elif dataCount is not None:\n data[\"count\"] = dataCount\n\n resObj = httpPost(url, data)\n if resObj[\"result\"] < 0:\n print(resObj[\"message\"])\n return None\n\n barCache = BarList()\n for curBar in resObj[\"bars\"]:\n bs = WTSBarStruct()\n bs.date = curBar[\"date\"]\n bs.time = curBar[\"time\"]\n bs.open = curBar[\"open\"]\n bs.high = curBar[\"high\"]\n bs.low = curBar[\"low\"]\n bs.close = curBar[\"close\"]\n bs.settle = curBar[\"settle\"]\n bs.money = curBar[\"money\"]\n bs.vol = curBar[\"vol\"]\n bs.hold = curBar[\"hold\"]\n bs.diff = curBar[\"diff\"]\n barCache.append(bs)\n return barCache\n \n \n\n def get_ticks(self, stdCode:str, fromTime:int = None, dataCount:int = None, endTime:int = 0) -> TickList:\n '''\n 获取tick数据\\n\n @stdCode 标准合约代码\\n\n @fromTime 开始时间，格式为yyyymmddHHMM\\n\n @endTime 结束时间，格式为yyyymmddHHMM，为0则读取到最后一条\n '''\n if (fromTime is None and dataCount is None) or (fromTime is not None and dataCount is not None):\n raise Exception('Only one of fromTime and dataCount must be valid at the same time')\n\n url = self.remote_url + \"/getticks\"\n data = {\n \"code\":stdCode,\n \"etime\":endTime\n }\n\n if fromTime is not None:\n data[\"stime\"] = fromTime\n elif dataCount is not None:\n data[\"count\"] = dataCount\n\n resObj = httpPost(url, data)\n if resObj[\"result\"] < 0:\n print(resObj[\"message\"])\n return None\n\n tickCache = TickList()\n for curTick in resObj[\"ticks\"]:\n ts = WTSTickStruct()\n ts.exchg = curTick[\"exchg\"].encode('utf-8')\n ts.code = stdCode.encode('utf-8')\n ts.open = curTick[\"open\"]\n ts.high = curTick[\"high\"]\n ts.low = curTick[\"low\"]\n ts.price = curTick[\"price\"]\n ts.settle_price = curTick[\"settle_price\"]\n\n ts.upper_limit = curTick[\"upper_limit\"]\n ts.lower_limit = curTick[\"lower_limit\"]\n\n ts.total_volume = curTick[\"total_volume\"]\n ts.volume = curTick[\"volume\"]\n ts.total_turnover = curTick[\"total_turnover\"]\n ts.turn_over = curTick[\"turn_over\"]\n ts.open_interest = curTick[\"open_interest\"]\n ts.diff_interest = curTick[\"diff_interest\"]\n\n ts.trading_date = curTick[\"trading_date\"]\n ts.action_date = curTick[\"action_date\"]\n ts.action_time = curTick[\"action_time\"]\n\n ts.pre_close = curTick[\"pre_close\"]\n ts.pre_settle = curTick[\"pre_settle\"]\n ts.pre_interest = curTick[\"pre_interest\"]\n\n # TODO 还有bid和ask档位没处理\n\n tickCache.append(ts)\n return tickCache\n" }, { "path": "wtpy/WtEngine.py", "content": "from wtpy.wrapper import WtWrapper\nfrom wtpy.CtaContext import CtaContext\nfrom wtpy.SelContext import SelContext\nfrom wtpy.HftContext import HftContext\nfrom wtpy.StrategyDefs import BaseCtaStrategy, BaseSelStrategy, BaseHftStrategy\nfrom wtpy.ExtToolDefs import BaseIndexWriter, BaseDataReporter\nfrom wtpy.WtCoreDefs import EngineType\nfrom wtpy.ExtModuleDefs import BaseExtParser, BaseExtExecuter\nfrom wtpy.WtUtilDefs import singleton\n\nfrom .ProductMgr import ProductMgr, ProductInfo\nfrom .SessionMgr import SessionMgr, SessionInfo\nfrom .ContractMgr import ContractMgr, ContractInfo\nfrom .CodeHelper import CodeHelper\n\nimport json\n\n@singleton\nclass WtEngine:\n '''\n 实盘交易引擎\n '''\n\n def __init__(self, eType:EngineType, logCfg:str = \"logcfg.json\", genDir:str = \"generated\", bDumpCfg:bool = False):\n '''\n WtEngine构造函数\\n\n @eType 引擎类型：EngineType.ET_CTA、EngineType.ET_HFT、EngineType.ET_SEL\\n\n @logCfg 日志配置文件\\n\n @genDir 数据输出目录\\n\n @bDumpCfg 是否保存最终配置文件\n '''\n self.is_backtest = False\n\n self.__wrapper__ = WtWrapper(self) #api接口转换器\n self.__cta_ctxs__ = dict() #CTA策略ctx映射表\n self.__sel_ctxs__ = dict() #SEL策略ctx映射表\n self.__hft_ctxs__ = dict() #HFT策略ctx映射表\n self.__config__ = dict() #框架配置项\n self.__cfg_commited__ = False #配置是否已提交\n\n self.__writer__ = None #指标输出模块\n self.__reporter__ = None #数据提交模块\n\n self.__ext_parsers__ = dict() #外接的行情接入模块\n self.__ext_executers__ = dict() #外接的执行器\n\n self.__dump_config__ = bDumpCfg #是否保存最终配置\n\n self.__engine_type = eType\n if eType == EngineType.ET_CTA:\n self.__wrapper__.initialize_cta(logCfg=logCfg, isFile=True, genDir=genDir)\n elif eType == EngineType.ET_HFT:\n self.__wrapper__.initialize_hft(logCfg=logCfg, isFile=True, genDir=genDir)\n elif eType == EngineType.ET_SEL:\n self.__wrapper__.initialize_sel(logCfg=logCfg, isFile=True, genDir=genDir)\n\n def __check_config__(self):\n '''\n 检查设置项\\n\n 主要会补充一些默认设置项\n '''\n if \"basefiles\" not in self.__config__:\n self.__config__[\"basefiles\"] = dict()\n\n if \"env\" not in self.__config__:\n self.__config__[\"env\"] = dict()\n self.__config__[\"env\"][\"name\"] = \"cta\"\n self.__config__[\"env\"][\"mode\"] = \"product\"\n self.__config__[\"env\"][\"product\"] = {\n \"session\":\"TRADING\"\n }\n \n def getEngineType(self):\n return self.__engine_type\n\n def add_exetended_parser(self, parser:BaseExtParser):\n id = parser.id()\n if id not in self.__ext_parsers__:\n self.__ext_parsers__[id] = parser\n if not self.__wrapper__.create_extended_parser(id):\n self.__ext_parsers__.pop(id)\n\n def add_exetended_executer(self, executer:BaseExtExecuter):\n id = executer.id()\n if id not in self.__ext_executers__:\n self.__ext_executers__[id] = executer\n if not self.__wrapper__.create_extended_executer(id):\n self.__ext_executers__.pop(id)\n\n def get_extended_parser(self, id:str)->BaseExtParser:\n if id not in self.__ext_parsers__:\n return None\n return self.__ext_parsers__[id]\n\n def get_extended_executer(self, id:str)->BaseExtExecuter:\n if id not in self.__ext_executers__:\n return None\n return self.__ext_executers__[id]\n\n def push_quote_from_extended_parser(self, id:str, newTick, bNeedSlice:bool):\n self.__wrapper__.push_quote_from_exetended_parser(id, newTick, bNeedSlice)\n\n def set_writer(self, writer:BaseIndexWriter):\n '''\n 设置指标输出模块\n '''\n self.__writer__ = writer\n\n def write_indicator(self, id:str, tag:str, time:int, data:dict):\n '''\n 写入指标数据\n '''\n if self.__writer__ is not None:\n self.__writer__.write_indicator(id, tag, time, data)\n\n def set_data_reporter(self, reporter:BaseDataReporter):\n '''\n 设置数据报告器\n '''\n self.__reporter__ = reporter\n\n def init(self, folder:str, cfgfile:str = \"config.json\", commfile:str=\"commodities.json\", contractfile:str=\"contracts.json\"):\n '''\n 初始化\\n\n @folder 基础数据文件目录，\\\\结尾\\n\n @cfgfile 配置文件，json格式\n '''\n f = open(cfgfile, \"r\")\n content =f.read()\n self.__config__ = json.loads(content)\n f.close()\n\n self.__check_config__()\n\n self.__config__[\"basefiles\"][\"commodity\"] = folder + commfile\n self.__config__[\"basefiles\"][\"contract\"] = folder + contractfile\n self.__config__[\"basefiles\"][\"holiday\"] = folder + \"holidays.json\"\n self.__config__[\"basefiles\"][\"session\"] = folder + \"sessions.json\"\n self.__config__[\"basefiles\"][\"hot\"] = folder + \"hots.json\"\n\n self.productMgr = ProductMgr()\n self.productMgr.load(folder + commfile)\n\n self.contractMgr = ContractMgr()\n self.contractMgr.load(folder + contractfile)\n\n self.sessionMgr = SessionMgr()\n self.sessionMgr.load(folder + \"sessions.json\")\n\n def configEngine(self, name:str, mode:str = \"product\"):\n '''\n 设置引擎和运行模式\n '''\n self.__config__[\"env\"][\"name\"] = name\n self.__config__[\"env\"][\"mode\"] = mode\n\n def addExternalCtaStrategy(self, id:str, params:dict):\n '''\n 添加外部的CTA策略\n '''\n if \"strategies\" not in self.__config__:\n self.__config__[\"strategies\"] = dict()\n\n if \"cta\" not in self.__config__[\"strategies\"]:\n self.__config__[\"strategies\"][\"cta\"] = list()\n\n params[\"id\"] = id\n self.__config__[\"strategies\"][\"cta\"].append(params)\n\n def addExternalHftStrategy(self, id:str, params:dict):\n '''\n 添加外部的HFT策略\n '''\n if \"strategies\" not in self.__config__:\n self.__config__[\"strategies\"] = dict()\n\n if \"hft\" not in self.__config__[\"strategies\"]:\n self.__config__[\"strategies\"][\"hft\"] = list()\n\n params[\"id\"] = id\n self.__config__[\"strategies\"][\"hft\"].append(params)\n\n def configStorage(self, path:str, module:str=\"\"):\n '''\n 配置数据存储\\n\n @mode 存储模式，csv-表示从csv直接读取，一般回测使用，wtp-表示使用wt框架自带数据存储\n '''\n self.__config__[\"data\"][\"store\"][\"module\"] = module\n self.__config__[\"data\"][\"store\"][\"path\"] = path\n\n def commitConfig(self):\n '''\n 提交配置\\n\n 只有第一次调用会生效，不可重复调用\\n\n 如果执行run之前没有调用，run会自动调用该方法\n '''\n if self.__cfg_commited__:\n return\n\n cfgfile = json.dumps(self.__config__, indent=4, sort_keys=True)\n self.__wrapper__.config(cfgfile, False)\n self.__cfg_commited__ = True\n\n if self.__dump_config__:\n f = open(\"config_run.json\", 'w')\n f.write(cfgfile)\n f.close()\n\n def regCtaStraFactories(self, factFolder:str):\n '''\n 向底层模块注册CTA工厂模块目录\\n\n !!!CTA策略只会被CTA引擎加载!!!\\n\n @factFolder 工厂模块所在的目录\n '''\n return self.__wrapper__.reg_cta_factories(factFolder)\n\n def regHftStraFactories(self, factFolder:str):\n '''\n 向底层模块注册HFT工厂模块目录\\n\n !!!HFT策略只会被HFT引擎加载!!!\\n\n @factFolder 工厂模块所在的目录\n '''\n return self.__wrapper__.reg_hft_factories(factFolder)\n\n def regExecuterFactories(self, factFolder:str):\n '''\n 向底层模块注册执行器模块目录\\n\n !!!执行器只在CTA引擎有效!!!\\n\n @factFolder 工厂模块所在的目录\n '''\n return self.__wrapper__.reg_exe_factories(factFolder)\n\n def addExecuter(self, id:str, trader:str, policies:dict, scale:int = 1):\n if \"executers\" not in self.__config__:\n self.__config__[\"executers\"] = list()\n\n exeItem = {\n \"active\":True,\n \"id\": id,\n \"scale\": scale,\n \"policy\": policies,\n \"trader\":trader\n }\n\n self.__config__[\"executers\"].append(exeItem)\n\n def addTrader(self, id:str, params:dict):\n if \"traders\" not in self.__config__:\n self.__config__[\"traders\"] = list()\n\n tItem = params\n tItem[\"active\"] = True\n tItem[\"id\"] = id\n\n self.__config__[\"traders\"].append(tItem)\n\n def getSessionByCode(self, stdCode:str) -> SessionInfo:\n '''\n 通过合约代码获取交易时间模板\\n\n @stdCode 合约代码，格式如SHFE.rb.HOT\n '''\n pid = CodeHelper.stdCodeToStdCommID(stdCode)\n pInfo = self.productMgr.getProductInfo(pid)\n if pInfo is None:\n return None\n\n return self.sessionMgr.getSession(pInfo.session)\n\n def getSessionByName(self, sname:str) -> SessionInfo:\n '''\n 通过模板名获取交易时间模板\\n\n @sname 模板名\n '''\n return self.sessionMgr.getSession(sname)\n\n def getProductInfo(self, stdCode:str) -> ProductInfo:\n '''\n 获取品种信息\\n\n @stdCode 合约代码，格式如SHFE.rb.HOT\n '''\n return self.productMgr.getProductInfo(stdCode)\n\n def getContractInfo(self, stdCode:str) -> ContractInfo:\n '''\n 获取品种信息\\n\n @stdCode 合约代码，格式如SHFE.rb.HOT\n '''\n return self.contractMgr.getContractInfo(stdCode)\n\n def getAllCodes(self) -> list:\n '''\n 获取全部合约代码\n '''\n return self.contractMgr.getTotalCodes()\n\n def add_cta_strategy(self, strategy:BaseCtaStrategy):\n '''\n 添加CTA策略\\n\n @strategy 策略对象\n '''\n id = self.__wrapper__.create_cta_context(strategy.name())\n self.__cta_ctxs__[id] = CtaContext(id, strategy, self.__wrapper__, self)\n\n def add_hft_strategy(self, strategy:BaseHftStrategy, trader:str, agent:bool = True):\n '''\n 添加HFT策略\\n\n @strategy 策略对象\n '''\n id = self.__wrapper__.create_hft_context(strategy.name(), trader, agent)\n self.__hft_ctxs__[id] = HftContext(id, strategy, self.__wrapper__, self)\n\n def add_sel_strategy(self, strategy:BaseSelStrategy, date:int, time:int, period:str):\n id = self.__wrapper__.create_sel_context(strategy.name(), date, time, period)\n self.__sel_ctxs__[id] = SelContext(id, strategy, self.__wrapper__, self)\n\n def get_context(self, id:int):\n '''\n 根据ID获取策略上下文\\n\n @id 上下文id，一般添加策略的时候会自动生成一个唯一的上下文id\n '''\n if self.__engine_type == EngineType.ET_CTA:\n if id not in self.__cta_ctxs__:\n return None\n\n return self.__cta_ctxs__[id]\n elif self.__engine_type == EngineType.ET_HFT:\n if id not in self.__hft_ctxs__:\n return None\n\n return self.__hft_ctxs__[id]\n elif self.__engine_type == EngineType.ET_SEL:\n if id not in self.__sel_ctxs__:\n return None\n\n return self.__sel_ctxs__[id]\n\n def run(self):\n '''\n 运行框架\n '''\n if not self.__cfg_commited__: #如果配置没有提交，则自动提交一下\n self.commitConfig()\n\n self.__wrapper__.run()\n\n def release(self):\n '''\n 释放框架\n '''\n self.__wrapper__.release()\n\n def on_init(self):\n if self.__reporter__ is not None:\n self.__reporter__.report_init_data()\n return\n\n def on_schedule(self, date:int, time:int, taskid:int = 0):\n # print(\"engine scheduled\")\n if self.__reporter__ is not None:\n self.__reporter__.report_rt_data()\n\n def on_session_begin(self, date:int):\n # print(\"session begin\")\n return\n\n def on_session_end(self, date:int):\n if self.__reporter__ is not None:\n self.__reporter__.report_settle_data()\n return\n" }, { "path": "wtpy/WtMsgQue.py", "content": "from wtpy.wrapper.WtMQWrapper import WtMQWrapper, CB_ON_MSG\nfrom wtpy.WtUtilDefs import singleton\n\nclass WtMQServer:\n\n def __init__(self):\n self.id = None\n\n def init(self, wrapper:WtMQWrapper, id:int):\n self.id = id\n self.wrapper = wrapper\n\n def publish_message(self, topic:str, message:str):\n if self.id is None:\n raise Exception(\"MQServer not initialzied\")\n\n self.wrapper.publish_message(self.id, topic, message)\n\nclass WtMQClient:\n\n def __init__(self):\n return\n\n def init(self, wrapper:WtMQWrapper, id:int):\n self.id = id\n self.wrapper = wrapper\n\n def start(self):\n if self.id is None:\n raise Exception(\"MQClient not initialzied\")\n\n self.wrapper.start_client(self.id)\n\n def subscribe(self, topic:str):\n if self.id is None:\n raise Exception(\"MQClient not initialzied\")\n self.wrapper.subcribe_topic(self.id, topic)\n\n def on_mq_message(self, topic:str, message:str, dataLen:int):\n pass\n\n@singleton\nclass WtMsgQue:\n\n def __init__(self) -> None:\n self._servers = dict()\n self._clients = dict()\n self._wrapper = WtMQWrapper(self)\n\n self._cb_msg = CB_ON_MSG(self.on_mq_message)\n\n def get_client(self, client_id:int) -> WtMQClient:\n if client_id not in self._clients:\n return None\n \n return self._clients[client_id]\n\n def on_mq_message(self, client_id:int, topic:str, message:str, dataLen:int):\n client = self.get_client(client_id)\n if client is None:\n return\n\n client.on_mq_message(topic, message, dataLen)\n\n def add_mq_server(self, url:str, server:WtMQServer = None) -> WtMQServer:\n id = self._wrapper.create_server(url)\n if server is None:\n server = WtMQServer()\n\n server.init(self._wrapper, id)\n self._servers[id] = server\n return server\n\n def destroy_mq_server(self, server:WtMQServer):\n id = server.id\n if id not in self._servers:\n return\n \n self._wrapper.destroy_server(id)\n self._servers.pop(id)\n\n def add_mq_client(self, url:str, client:WtMQClient = None) -> WtMQClient:\n id = self._wrapper.create_client(url, self._cb_msg)\n if client is None:\n client = WtMQClient()\n client.init(self._wrapper, id)\n self._clients[id] = client\n return client\n\n def destroy_mq_client(self, client:WtMQClient):\n id = client.id\n if id not in self._clients:\n return\n \n self._wrapper.destroy_client(id)\n self._clients.pop(id)\n \n " }, { "path": "wtpy/WtUtilDefs.py", "content": "def singleton(cls):\n instances = {}\n def getinstance(*args,**kwargs):\n if cls not in instances:\n instances[cls] = cls(*args,**kwargs)\n return instances[cls]\n return getinstance\n" }, { "path": "wtpy/__init__.py", "content": "from .StrategyDefs import BaseCtaStrategy, BaseSelStrategy, BaseHftStrategy\nfrom .CtaContext import CtaContext\nfrom .SelContext import SelContext\nfrom .HftContext import HftContext\nfrom .WtEngine import WtEngine\nfrom .WtBtEngine import WtBtEngine\nfrom .WtDtEngine import WtDtEngine\nfrom .WtCoreDefs import WTSTickStruct,WTSBarStruct,EngineType\nfrom .WtDataDefs import WtKlineData,WtHftData\nfrom .ExtToolDefs import BaseDataReporter, BaseIndexWriter\nfrom .ExtModuleDefs import BaseExtExecuter, BaseExtParser\nfrom .WtMsgQue import WtMsgQue, WtMQClient, WtMQServer\nfrom .WtDtServo import WtDtServo\n\nfrom wtpy.wrapper.WtExecApi import WtExecApi\nfrom wtpy.wrapper.ContractLoader import ContractLoader,LoaderType\n\n__all__ = [\"BaseCtaStrategy\", \"BaseSelStrategy\", \"BaseHftStrategy\", \"WtEngine\", \"CtaContext\", \"SelContext\", \"HftContext\", \n \"WtBtEngine\", \"WtDtEngine\", \"WtExecApi\",\"WTSTickStruct\",\"WTSBarStruct\",\"BaseIndexWriter\",\"BaseIndexWriter\",\n \"EngineType\", \"WtKlineData\", \"WtHftData\",\"ContractLoader\", \"BaseDataReporter\", \"BaseExtParser\", \"BaseExtExecuter\",\n \"LoaderType\", \"WtDtServo\", \"WtMsgQue\", \"WtMQClient\", \"WtMQServer\"]" }, { "path": "wtpy/apps/WtBtAnalyst.py", "content": "from pandas import DataFrame as df\nimport pandas as pd\nimport numpy as np\nfrom dateutil.parser import parse\nfrom collections import Counter\nfrom datetime import datetime\n\nimport math\nimport json\nfrom xlsxwriter import Workbook\n\n\nclass Calculate():\n '''\n 绩效比率计算\n '''\n def __init__(self, ret, mar, rf, period, trade):\n \"\"\"\n :param ret: 收益率序列\n :param mar: 最低可接受回报\n :param rf: 无风险利率\n :param period: 年交易日（用来年化）240\n \"\"\"\n self.ret = ret\n self.mar = mar\n self.rf = rf\n self.trade = math.ceil(trade)\n self.period = period\n self.daily_rf = (self.rf + 1) ** (1 / self.period) - 1\n\n # 潜在上行比率\n def calculate_upside_ratio(self):\n acess_return = np.sum(self.ret - self.mar)\n downside_std = self.ret[self.ret.apply(lambda x: x < 0)].std()\n upside_ratio = acess_return / downside_std\n return upside_ratio\n\n # 夏普率\n def sharp_ratio(self):\n expect_return = self.ret.mean()\n std = self.ret.std()\n sharp_ratio = (expect_return - self.daily_rf) / std\n return sharp_ratio\n\n # 索提诺比率\n def sortion_ratio(self):\n expect_return = self.ret.mean()\n downside_std = self.ret[self.ret.apply(lambda x: x < 0)].std()\n sortion_ratio = (expect_return - self.daily_rf) / downside_std\n return sortion_ratio\n\n # 最大回撤\n def maxDrawdown(self):\n ret = (self.ret + 1).cumprod()\n i = np.argmax((np.maximum.accumulate(ret) - ret) / np.maximum.accumulate(ret))\n if i == 0:\n return 0\n j = np.argmax(ret[:i])\n return (ret[j] - ret[i]) / ret[j]\n\n def maxDrawdown_time(self):\n ret = (self.ret + 1).cumprod()\n i = np.argmax((np.maximum.accumulate(ret) - ret) / np.maximum.accumulate(ret))\n if i == 0:\n return 0\n j = np.argmax(ret[:i])\n return i, j\n\n # 卡尔马比率\n def calmar_ratio(self):\n maxdrawdown = Calculate.maxDrawdown(self)\n annual_return = Calculate.get_annual_return(self)\n calmar_ratio = annual_return / maxdrawdown\n return calmar_ratio\n\n # 斯特林比率\n def sterling_a_ratio(self):\n average_drawdown = abs(self.ret.where(self.ret < 0, 0).mean())\n annual_return = Calculate.get_annual_return(self)\n sterling_a_ratio = (annual_return - self.rf) / average_drawdown\n return sterling_a_ratio\n\n # 单笔最大回撤\n def single_largest_maxdrawdown(self):\n single_largest_mdd = self.ret[self.ret.apply(lambda x: x < 0)]\n if len(single_largest_mdd) == 0:\n single_largest_mxd = 0\n return single_largest_mxd\n single_largest_mxd = abs(single_largest_mdd.min())\n return single_largest_mxd\n\n # 单笔最大回撤索引\n def single_maxdrawdown_time(self):\n i = np.argmin(self.ret)\n return i\n\n # 年化收益率\n def get_annual_return(self):\n annual_return = 0 if self.trade==0 else ((1 + self.ret.sum()) ** (self.period / self.trade) - 1)\n return annual_return\n\n # 月化收益率\n def monthly_return(self):\n ann = Calculate.get_annual_return(self)\n monthly_return = (ann + 1) ** (1/12) - 1\n return monthly_return\n\n # 月平均收益\n def monthly_average_return(self):\n monthly = self.ret.mean() * (self.period / 12)\n return monthly\n\n # 衰落时间\n def decay_time(self):\n netvalue = (self.ret+1).cumprod()\n\n ser = []\n temp = netvalue[0]\n ss = 0\n for x in netvalue:\n if x >= temp:\n ss = 0\n ser.append(ss)\n temp = x\n\n else:\n ss = ss + 1\n ser.append(ss)\n ss = max(pd.Series(ser))\n return ss\n\ndef fmtNAN(val, defVal = 0):\n if math.isnan(val):\n return defVal\n\n return val\n\ndef continue_trading_analysis(data, x_value) -> dict:\n '''\n 连续交易分析\n '''\n mean = data['profit'].mean()\n std = data['profit'].std()\n z_score = (x_value - mean) / std\n times = 0\n win_time = 0\n ltimes = 0\n loss_time = 0\n for i in range(len(data)-1):\n sss = data['profit'][i]\n if sss > 0:\n times += 1\n ltimes = 0\n rem = i\n if times > win_time:\n win_time = times\n con_win_p_end = rem\n else:\n times = 0\n ltimes += 1\n rem = i\n if ltimes > loss_time:\n loss_time = ltimes\n con_loss_p_end = rem\n\n capital = 500000\n con_win_profit = (data['profit'].loc[con_win_p_end-win_time + 1:con_win_p_end]).sum()\n con_lose_loss = (data['profit'].loc[con_loss_p_end-loss_time + 1:con_loss_p_end]).sum()\n # con_win_p_end, win_time 连续盈利结束位置，连续盈利最大次数\n # con_loss_p_end，loss_time 连续亏损结束位置，连续亏损最大次数\n\n cot_profit_ratio = con_win_profit / capital\n loss_profit_ratio = con_lose_loss / capital\n\n result = {'z值': z_score,\n '最大连续盈利交易次数': win_time,\n '最大连续亏损交易次数': loss_time,\n '最大连续盈利额': con_win_profit,\n '最大连续亏损额': con_lose_loss,\n '最大连续盈利（%）': cot_profit_ratio,\n '最大连续亏损（%）': loss_profit_ratio}\n\n return result\n\ndef nomalize_val(val):\n if math.isnan(val):\n return 0\n else:\n return val\n\ndef extreme_trading(data, time_of_std=1):\n '''\n 极端交易分析\n '''\n std = data['profit'].std()\n df_wins = data[data[\"profit\"] > 0]\n df_loses = data[data[\"profit\"] <= 0]\n winamout = df_wins[\"profit\"].sum() # 毛盈利\n loseamount = df_loses[\"profit\"].sum() # 毛亏损\n trdnetprofit = winamout + loseamount # 交易净盈亏\n totaltimes = len(data) # 总交易次数\n avgprof = trdnetprofit / totaltimes if totaltimes > 0 else 0 # 单次平均盈亏\n\n # 单笔盈利 + 标准差\n sin_profit_plstd = avgprof + (std * time_of_std)\n # 单笔盈利 - 标准差\n sin_profit_mistd = avgprof - (std * time_of_std)\n\n # 极端交易数量\n extreme_result = data[data['profit'].apply(lambda x: x > sin_profit_plstd or x < sin_profit_mistd)]\n extreme_num = len(extreme_result)\n\n # 极端交易盈亏 1 Std. Deviation of Avg. Trade\n extreme_profit = 0 if extreme_num==0 else extreme_result['profit'].sum()\n\n result = {'1 Std. Deviation of Avg. Trade': nomalize_val(std),\n '单笔净利 +1倍标准差': nomalize_val(sin_profit_plstd),\n '单笔盈利 - 标准差': nomalize_val(sin_profit_mistd),\n '极端交易数量': extreme_num,\n '极端交易盈亏': extreme_profit\n }\n return result\n\n\ndef average_profit(data):\n '''\n 连续交易分析之平均收益\n '''\n data = data['profit']\n win = 0\n li = []\n lose = 0\n li_2 = []\n dic= []\n dicc = []\n for i in range(1, len(data) - 1):\n if (data[i] > 0) == (data[i - 1] > 0):\n if data[i] > 0:\n c = 1\n win = win + c\n else:\n c = 1\n lose = lose + c\n if (data[i] > 0) == ((data[i + 1]) > 0):\n pass\n else:\n if data[i] > 0:\n dis = {str(win): data[i-win:i+1].sum()}\n dic.append(dis)\n li.append(win)\n win = 0\n\n else:\n dis = {str(lose): data[i-lose:i+1].sum()}\n dicc.append(dis)\n li_2.append(lose)\n lose = 0\n else:\n win = 0\n lose = 0\n number_win = Counter(li)\n number_lose = Counter(li_2)\n ss = pd.DataFrame()\n for x in dic:\n df = pd.DataFrame([x])\n ss = pd.concat([ss, df.T])\n win_ss = ss.reset_index()\n win_ss = win_ss.groupby('index').mean()\n\n ss2 = pd.DataFrame()\n for y in dicc:\n df = pd.DataFrame([y])\n ss2 = pd.concat([ss2, df.T])\n lose_ss = ss2.reset_index()\n lose_ss = lose_ss.groupby('index').mean()\n result = {'连续盈利次数': number_win,\n '连续亏损次数': number_lose,\n '每个序列平均收益': win_ss,\n '每个序列平均亏损': lose_ss}\n return result\n\ndef stat_closes_by_day(df_closes:df, capital) -> df:\n '''\n 按天统计平仓数据\n '''\n df_closes['day'] = df_closes['opentime']\n df_closes['win'] = df_closes['profit'].apply(lambda x: 1 if x > 0 else 0)\n df_closes['times'] = 1\n df_closes['gross_profit'] = df_closes['profit'].apply(lambda x: x if x > 0 else 0)\n df_closes['gross_loss'] = df_closes['profit'].apply(lambda x: x if x < 0 else 0)\n profit = df_closes.groupby(df_closes['day']).sum()\n profit['win_rate'] = profit['win'] / profit['times']\n profit['profit_ratio'] = profit['profit']*100.0/capital\n res = profit[['profit', 'gross_profit', 'gross_loss', 'times', 'win_rate','profit_ratio']]\n return res.iloc[::-1]\n\ndef stat_closes_by_month(df_closes:df, capital) -> df:\n '''\n 按月统计平仓数据\n '''\n df_closes['month'] = df_closes['opentime'].apply(lambda x: x.strftime(\"%Y/%m\"))\n df_closes['win'] = df_closes['profit'].apply(lambda x: 1 if x > 0 else 0)\n df_closes['times'] = 1\n df_closes['gross_profit'] = df_closes['profit'].apply(lambda x: x if x > 0 else 0)\n df_closes['gross_loss'] = df_closes['profit'].apply(lambda x: x if x < 0 else 0)\n profit = df_closes.groupby(df_closes['month']).sum()\n profit['win_rate'] = profit['win'] / profit['times']\n profit['profit_ratio'] = profit['profit']*100.0/capital\n res = profit[['profit', 'gross_profit', 'gross_loss', 'times', 'win_rate','profit_ratio']]\n return res.iloc[::-1]\n\ndef stat_closes_by_year(df_closes:df, capital) -> df:\n '''\n 按年统计平仓数据\n '''\n df_closes['year'] = df_closes['opentime'].apply(lambda x: x.strftime(\"%Y\"))\n df_closes['win'] = df_closes['profit'].apply(lambda x: 1 if x > 0 else 0)\n df_closes['times'] = 1\n df_closes['gross_profit'] = df_closes['profit'].apply(lambda x: x if x > 0 else 0)\n df_closes['gross_loss'] = df_closes['profit'].apply(lambda x: x if x < 0 else 0)\n profit = df_closes.groupby(df_closes['year']).sum()\n profit['win_rate'] = profit['win'] / profit['times']\n profit['profit_ratio'] = profit['profit']*100.0/capital\n res = profit[['profit', 'gross_profit', 'gross_loss', 'times', 'win_rate','profit_ratio']]\n return res.iloc[::-1]\n\ndef time_analysis(df_closes:df) -> dict:\n '''\n 时间分析\n '''\n trading_time = df_closes['closebarno'][len(df_closes) - 1] * 5\n\n def time_trans(x):\n d = x // (24 * 60)\n h = x % (24 * 60) // 60\n m = x % (24 * 60) % 60\n return str(d), str(h), str(m)\n # 转化为日，时，分\n s_trading_time = time_trans(trading_time)\n\n # 策略运行时间\n str_time = (df_closes['closebarno'] - df_closes['openbarno']).sum() * 5\n s_str_time = time_trans(str_time)\n # 比率\n porition = str_time / trading_time * 100\n\n # 最大空仓时间\n empty_time = (df_closes['openbarno'].shift(-1) - df_closes['closebarno']).max() * 5\n s_empty_time = time_trans(empty_time)\n\n capital = 500000\n df_closes['principal'] = df_closes['totalprofit'] + capital\n df_closes['principal'] = df_closes['principal'].shift(1)\n input_data = df_closes.fillna(value=500000)\n ret = input_data['profit'] / input_data['principal']\n mar = 0\n rf = 0.02\n period = 240\n trade = input_data['closebarno'][len(input_data) - 1] / 47\n factors = Calculate(ret, mar, rf, period, trade)\n\n single_drawdown_date = factors.single_maxdrawdown_time()\n signe_drawdown_date = parse(str(input_data['opentime'][single_drawdown_date]))\n\n # 最大回撤区间\n maxdrawdown_time = factors.maxDrawdown_time()\n\n # 回撤开始日期\n start_time = maxdrawdown_time[1]\n start_time = parse(str(input_data['opentime'][start_time]))\n\n # 回撤结束日期\n end_time = maxdrawdown_time[0]\n end_time = parse(str(input_data['opentime'][end_time]))\n\n # 最大损失日期\n loss_time_index = np.argmin(input_data['profit'])\n loss_time = parse(str(input_data['opentime'][loss_time_index]))\n\n result = {'交易周期': '(%s)' % ','.join(s_trading_time),\n '策略运行时间': '(%s)' % ','.join(s_str_time),\n '策略运行时间%': porition,\n '最长空仓时间': '(%s)' % ','.join(s_empty_time),\n '策略最大回撤开始时间': start_time.strftime(\"%Y/%m/%d %H:%M\"),\n '策略最大回撤结束时间': end_time.strftime(\"%Y/%m/%d %H:%M\"),\n '单笔最大回撤时间': signe_drawdown_date.strftime(\"%Y/%m/%d %H:%M\"),\n '平仓交易最大损失日期': loss_time.strftime(\"%Y/%m/%d %H:%M\")}\n\n return result\n\ndef ratio_calculate(data, after_merge, capital = 500000, rf = 0, period = 240) -> dict:\n data['principal'] = data['totalprofit'] + capital\n data['principal'] = data['principal'].shift(1)\n input_data = data.fillna(value=capital)\n ret = input_data['profit'] / input_data['principal']\n mar = 0\n trade = input_data['closebarno'][len(input_data) - 1] / 47\n factors = Calculate(ret, mar, rf, period, trade)\n # 潜在上涨比率\n potential_upside_ratio = factors.calculate_upside_ratio()\n # 夏普比率\n sharpe_ratio = factors.sharp_ratio()\n # 索提诺比率\n sortino_ratio = factors.sortion_ratio()\n # 卡尔马比率(Calmar Ratio)\n calmar_ratio = factors.calmar_ratio()\n # 斯特林比率\n sterling_ratio = factors.sterling_a_ratio()\n result1 = performance_summary(data, after_merge)\n # 净利/单笔最大亏损\n net_s_loss = result1.get('净利') / result1.get('单笔最大亏损')\n # 净利/单笔最大回撤\n net_s_drawdown = result1.get('净利') / factors.single_largest_maxdrawdown()\n # 净利/ 策略最大回撤\n net_strategy_drawdown = result1.get('净利') / factors.maxDrawdown()\n # 调整净利/单笔最大亏损\n adjust_s_loss = result1.get('调整净利') / result1.get('单笔最大亏损')\n # 调整净利/单笔最大回撤\n adjust_s_drawdown = result1.get('调整净利') / factors.single_largest_maxdrawdown()\n # 调整净利/ 策略最大回撤\n adjust_strategy_drawdown = result1.get('调整净利') / factors.maxDrawdown()\n\n result = {'潜在上涨比率':potential_upside_ratio,\n '夏普比率':sharpe_ratio,\n '索提诺比率':sortino_ratio,\n '卡尔马比率':calmar_ratio,\n '斯特林比率':sterling_ratio,\n '净利/单笔最大亏损':net_s_loss,\n '净利/单笔最大回撤':net_s_drawdown,\n '净利/ 策略最大回撤':net_strategy_drawdown,\n '调整净利/单笔最大亏损':adjust_s_loss,\n '调整净利/单笔最大回撤':adjust_s_drawdown,\n '调整净利/ 策略最大回撤':adjust_strategy_drawdown}\n\n return result\n\ndef performance_summary(input_data, input_data1, capital = 500000, rf = 0.00, period = 240):\n '''\n 绩效统计\n '''\n # 指标计算准备\n input_data['principal'] = input_data['totalprofit'] + capital\n input_data['principal'] = input_data['principal'].shift(1)\n input_data = input_data.fillna(value=capital)\n ret = input_data['profit'] / input_data['principal']\n mar = 0\n trade = len(input_data)\n #trade = input_data['closebarno'][len(input_data)-1] / 47\n\n # 指标class\n factors = Calculate(ret, mar, rf, period, trade)\n # 毛利\n profit = input_data[input_data['profit'].apply(lambda x: x >= 0)]\n total_profit = 0 if len(profit)==0 else profit['profit'].sum()\n # 毛损\n loss = input_data[input_data['profit'].apply(lambda x: x < 0)]\n total_loss = 0 if len(loss)==0 else loss['profit'].sum()\n # 净利\n net_profit = total_profit + total_loss\n input_data1['adjust_profit'] = (input_data1['profit'] - input_data1['transaction_fee']) if len(input_data1)>0 else 0\n # 调整毛利\n adjust_profit = input_data1[input_data1['adjust_profit'].apply(lambda x: x >= 0)]\n total_adjust_profit = 0 if len(adjust_profit)==0 else adjust_profit['adjust_profit'].sum()\n # 调整毛损\n adjust_loss = input_data1[input_data1['adjust_profit'].apply((lambda x: x < 0))]\n total_adjust_loss = 0 if len(adjust_loss)==0 else adjust_loss['adjust_profit'].sum()\n # 调整净利\n adjust_net_profit = total_adjust_profit + total_adjust_loss\n # 盈利因子\n profit_factor = 0 if total_loss==0 else np.abs(total_profit / total_loss)\n # 调整盈利因子\n adjust_profit_factor = 0 if total_adjust_loss == 0 else np.abs(total_adjust_profit / total_adjust_loss)\n # 最大持有合约数量\n max_holding_number = 1\n # 已付手续费\n paid_trading_fee = input_data1['transaction_fee'].sum() if len(input_data1)>0 else 0\n # 单笔最大亏损\n single_loss = input_data[input_data['profit'].apply(lambda x: x < 0)]\n single_largest_loss = 0 if len(single_loss)==0 else abs(single_loss['profit'].min())\n # 平仓交易最大亏损\n trading_loss = single_largest_loss\n # 平仓交易最大亏损比\n trading_loss_rate = trading_loss / capital\n # 年化收益率\n annual_ret = factors.get_annual_return()\n # 月化收益率\n monthly_return = factors.monthly_return()\n # 月平均收益\n monthly_average_return = factors.monthly_average_return()\n\n # 结果封装字典\n result = {'毛利': total_profit,\n '毛损': total_loss,\n '净利': net_profit,\n '调整毛利': adjust_net_profit,\n '调整毛损': total_adjust_loss,\n '调整净利': adjust_net_profit,\n '盈利因子': profit_factor,\n '调整盈利因子': adjust_profit_factor,\n '最大持有合约数量': max_holding_number,\n '已付手续费': paid_trading_fee,\n '单笔最大亏损': single_largest_loss,\n '平仓交易最大亏损': trading_loss,\n '平仓交易最大亏损比': trading_loss_rate,\n '年化收益率': annual_ret,\n '月化收益率': monthly_return,\n '月平均收益': monthly_average_return}\n return result\n\ndef do_trading_analyze(df_closes, df_funds):\n \n df_wins = df_closes[df_closes[\"profit\"] > 0]\n df_loses = df_closes[df_closes[\"profit\"] <= 0]\n\n ay_WinnerBarCnts = df_wins[\"closebarno\"] - df_wins[\"openbarno\"]\n ay_LoserBarCnts = df_loses[\"closebarno\"] - df_loses[\"openbarno\"]\n\n total_winbarcnts = ay_WinnerBarCnts.sum()\n total_losebarcnts = ay_LoserBarCnts.sum()\n\n total_fee = df_funds.iloc[-1][\"fee\"]\n\n totaltimes = len(df_closes) # 总交易次数\n wintimes = len(df_wins) # 盈利次数\n losetimes = len(df_loses) # 亏损次数\n winamout = df_wins[\"profit\"].sum() # 毛盈利\n loseamount = df_loses[\"profit\"].sum() # 毛亏损\n trdnetprofit = winamout + loseamount # 交易净盈亏\n accnetprofit = trdnetprofit - total_fee # 账户净盈亏\n winrate = (wintimes / totaltimes) if totaltimes > 0 else 0 # 胜率\n avgprof = (trdnetprofit / totaltimes) if totaltimes > 0 else 0 # 单次平均盈亏\n avgprof_win = (winamout / wintimes) if wintimes > 0 else 0 # 单次盈利均值\n avgprof_lose = (loseamount / losetimes) if losetimes > 0 else 0 # 单次亏损均值\n winloseratio = abs(avgprof_win / avgprof_lose) if avgprof_lose != 0 else \"N/A\" # 单次盈亏均值比\n\n # 单笔最大盈利交易\n largest_profit = df_wins['profit'].max()\n # 单笔最大亏损交易\n largest_loss = df_loses['profit'].min()\n # 交易的平均持仓K线根数\n avgtrd_hold_bar = 0 if totaltimes==0 else ((df_closes['closebarno'] - df_closes['openbarno']).sum()) / totaltimes\n # 平均空仓K线根数\n avb = (df_closes['openbarno'].shift(-1) - df_closes['closebarno']).dropna()\n avgemphold_bar = 0 if len(df_closes)==0 else avb.sum() / len(df_closes)\n\n # 两笔盈利交易之间的平均空仓K线根数\n win_holdbar_situ = (df_wins['openbarno'].shift(-1) - df_wins['closebarno']).dropna()\n winempty_avgholdbar = 0 if len(df_wins)==0 else win_holdbar_situ.sum() / len(df_wins)\n # 两笔亏损交易之间的偶平均空仓K线根数\n loss_holdbar_situ = (df_loses['openbarno'].shift(-1) - df_loses['closebarno']).dropna()\n lossempty_avgholdbar = 0 if len(df_loses)==0 else loss_holdbar_situ.sum() / len(df_loses)\n\n max_consecutive_wins = 0 # 最大连续盈利次数\n max_consecutive_loses = 0 # 最大连续亏损次数\n\n avg_bars_in_winner = total_winbarcnts / wintimes if wintimes > 0 else \"N/A\"\n avg_bars_in_loser = total_losebarcnts / losetimes if losetimes > 0 else \"N/A\"\n\n consecutive_wins = 0\n consecutive_loses = 0\n\n for idx, row in df_closes.iterrows():\n profit = row[\"profit\"]\n if profit > 0:\n consecutive_wins += 1\n consecutive_loses = 0\n else:\n consecutive_wins = 0\n consecutive_loses += 1\n\n max_consecutive_wins = max(max_consecutive_wins, consecutive_wins)\n max_consecutive_loses = max(max_consecutive_loses, consecutive_loses)\n\n summary = dict()\n\n summary[\"交易总数量\"] = totaltimes\n summary[\"盈利交易次数\"] = wintimes\n summary[\"亏损交易次数\"] = losetimes\n summary[\"毛盈利\"] = float(winamout)\n summary[\"毛亏损\"] = float(loseamount)\n summary[\"交易净盈亏\"] = float(trdnetprofit)\n summary[\"% 胜率\"] = winrate * 100\n summary[\"单次平均盈亏\"] = avgprof\n summary[\"单次盈利均值\"] = avgprof_win\n summary[\"单次亏损均值\"] = avgprof_lose\n summary[\"% 单次盈亏均值比\"] = winloseratio\n summary[\"最大连续盈利次数\"] = max_consecutive_wins\n summary[\"最大连续亏损次数\"] = max_consecutive_loses\n summary[\"盈利交易的平均持仓K线根数\"] = avg_bars_in_winner\n summary[\"亏损交易的平均持仓K线根数\"] = avg_bars_in_loser\n summary[\"账户净盈亏\"] = 0 if totaltimes==0 else accnetprofit / totaltimes\n summary['单笔最大盈利交易'] = largest_profit\n summary['单笔最大亏损交易'] = largest_loss\n summary['交易的平均持仓K线根数'] = avgtrd_hold_bar\n summary['平均空仓K线根数'] = avgemphold_bar\n summary['两笔盈利交易之间的平均空仓K线根数'] = winempty_avgholdbar\n summary['两笔亏损交易之间的平均空仓K线根数'] = lossempty_avgholdbar\n summary = pd.DataFrame([summary]).T\n summary = summary.reset_index()\n return summary\n\ndef trading_analyze(workbook:Workbook, df_closes, df_funds, capital = 500000):\n '''\n 交易分析\n '''\n res = average_profit(df_closes)\n rr = res.get('连续盈利次数')\n df = pd.DataFrame([rr]).T\n df.columns = ['连续次数']\n df = df.reset_index()\n\n every_series_profit = res.get('每个序列平均收益')\n every_series_profit = every_series_profit.reset_index()\n df['index'] = df['index'].apply(lambda x: int(x))\n every_series_profit['index'] = every_series_profit['index'].apply(lambda x: int(x))\n f_result = df.merge(every_series_profit)\n f_result = f_result.sort_values('index')\n f_result.columns = ['连续次数', '出现次数', '每个序列平均收益']\n\n rr_2 = res.get('连续亏损次数')\n df_2 = pd.DataFrame([rr_2]).T\n df_2.columns = ['连续次数']\n df_2 = df_2.reset_index()\n\n every_series_loss = res.get('每个序列平均亏损')\n every_series_loss = every_series_loss.reset_index()\n df_2['index'] = df_2['index'].apply(lambda x: int(x))\n every_series_loss['index'] = every_series_loss['index'].apply(lambda x: int(x))\n f_2_result = df_2.merge(every_series_loss)\n f_2_result = f_2_result.sort_values('index')\n f_2_result.columns = ['连续次数', '出现次数', '每个序列平均亏损']\n\n ssaa = 1000\n # 连续交易系列分析\n s = continue_trading_analysis(df_closes, ssaa)\n # 极端交易\n ss = extreme_trading(df_closes)\n extre_pro = ss.get('单笔净利 +1倍标准差')\n extre_los = ss.get('单笔盈利 - 标准差')\n data1 = df_closes[df_closes['profit'].apply(lambda x: x > extre_pro)]\n data2 = df_closes[df_closes['profit'].apply(lambda x: x < extre_los)]\n ss_1 = extreme_trading(data1)\n ss_2 = extreme_trading(data2)\n ss_1 = pd.DataFrame([ss_1]).T\n ss_2 = pd.DataFrame([ss_2]).T\n sss = pd.concat([ss_1, ss_2], axis=1)\n ss = pd.DataFrame([ss]).T\n sss = pd.concat([ss, sss], axis=1)\n sss.columns = ['总计', '极端盈利', '极端亏损']\n\n title_format = workbook.add_format({\n 'font_size': 16,\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n index_format = workbook.add_format({\n 'font_size': 12,\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n value_format = workbook.add_format({\n 'align': 'right', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n date_format = workbook.add_format({\n 'num_format': 'yyyy/mm/dd',\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n worksheet = workbook.add_worksheet('交易分析')\n\n trade_s = do_trading_analyze(df_closes, df_funds)\n data_1 = df_closes[df_closes['direct'].apply(lambda x: 'LONG' in x)]\n trade_s_long = do_trading_analyze(data_1, df_funds)\n data_2 = df_closes[df_closes['direct'].apply(lambda x: 'SHORT' in x)]\n trade_s_short = do_trading_analyze(data_2, df_funds)\n trade_s = trade_s.merge(trade_s_long, how='inner', on='index')\n trade_s = trade_s.merge(trade_s_short,how='inner', on='index')\n trade_s.columns =['类别', '所有交易', '多头', '空头']\n trade_s.fillna(value=0, inplace=True)\n\n worksheet.write_row('A1', ['总体交易分析'], title_format)\n worksheet.write_row('B3', ['所有交易','多头交易','空头交易'], index_format)\n worksheet.write_column('A4', trade_s['类别'], index_format)\n worksheet.write_column('B4', trade_s['所有交易'], value_format)\n worksheet.write_column('C4', trade_s['多头'], value_format)\n worksheet.write_column('D4', trade_s['空头'], value_format)\n\n worksheet.write_row('A28', ['极端交易'], title_format)\n worksheet.write_row('B30', ['总计','极端盈利','极端亏损'], index_format)\n worksheet.write_column('A31', sss.index, index_format)\n worksheet.write_column('B31', sss['总计'], value_format)\n worksheet.write_column('C31', sss['极端盈利'], value_format)\n worksheet.write_column('D31', sss['极端亏损'], value_format)\n\n worksheet.write_row('A38', ['连续交易系列分析'], title_format)\n worksheet.write_column('A40', s.keys(), index_format)\n worksheet.write_column('B40', s.values(), value_format)\n\n worksheet.write_row('A49', ['连续交易系列统计'], title_format)\n worksheet.write_row('A51', ['连续盈利次数','出现次数','每个序列的平均收益'], index_format)\n worksheet.write_column('A52', f_result['连续次数'], value_format)\n worksheet.write_column('B52', f_result['出现次数'], value_format)\n worksheet.write_column('C52', f_result['每个序列平均收益'], value_format)\n\n win_cnt = len(f_result)\n next_row = win_cnt+52\n worksheet.write_row('A%d'%next_row, ['连续亏损次数','出现次数','每个序列的平均亏损'], index_format)\n worksheet.write_column('A%d'%(next_row+1), f_2_result['连续次数'], value_format)\n worksheet.write_column('B%d'%(next_row+1), f_2_result['出现次数'], value_format)\n worksheet.write_column('C%d'%(next_row+1), f_2_result['每个序列平均亏损'], value_format)\n\n # 这里开始画图\n next_row += len(f_2_result) + 3\n worksheet.write_row('A%d'%next_row, ['全部交易'], title_format)\n chart_col = workbook.add_chart({'type': 'scatter'})\n length = len(df_closes)\n sheetName = '交易列表'\n chart_col.add_series(\n {\n 'name': '收益分布',\n 'categories': '=%s!$A$4:$A$%s' % (sheetName, length+3),\n 'values': '=%s!$J$4:$J$%s' % (sheetName, length+3),\n 'marker': {\n 'type':\"circle\", \n 'size':3\n }\n }\n )\n chart_col.set_title({'name': '收益分布'})\n worksheet.insert_chart('A%d' % (next_row+2), chart_col)\n\n next_row += 30\n worksheet.write_row('A%d'%next_row, ['潜在盈利'], title_format)\n chart_col = workbook.add_chart({'type': 'scatter'})\n length = len(df_closes)\n sheetName = '交易列表'\n chart_col.add_series(\n {\n 'name': '潜在盈利',\n 'categories': '=%s!$A$4:$A$%s' % (sheetName, length+3),\n 'values': '=%s!$N$4:$N$%s' % (sheetName, length+3),\n 'marker': {\n 'type':\"diamond\", \n 'size':3, \n 'border': {'color': 'red'},\n 'fill': {'color': 'red'}\n }\n }\n )\n chart_col.set_title({'name': '潜在盈利'})\n worksheet.insert_chart('A%d' % (next_row+2), chart_col)\n\n next_row += 30\n worksheet.write_row('A%d'%next_row, ['潜在亏损'], title_format)\n chart_col = workbook.add_chart({'type': 'scatter'})\n length = len(df_closes)\n sheetName = '交易列表'\n chart_col.add_series(\n {\n 'name': '潜在亏损',\n 'categories': '=%s!$A$4:$A$%s' % (sheetName, length+3),\n 'values': '=%s!$P$4:$P$%s' % (sheetName, length+3),\n 'marker': {\n 'type':\"triangle\", \n 'size':3, \n 'border': {'color': 'green'},\n 'fill': {'color': 'green'}\n }\n }\n )\n chart_col.set_title({'name': '潜在亏损'})\n worksheet.insert_chart('A%d' % (next_row+2), chart_col)\n\n \n # 周期分析\n worksheet = workbook.add_worksheet('周期分析')\n\n df_closes['opentime'] = df_closes['opentime'].apply(lambda x: parse(str(int(x / 10000))))\n res = stat_closes_by_day(df_closes.copy(), capital) \n worksheet.write_row('A1', ['日度绩效分析'], title_format)\n worksheet.write_row('A3', ['期间','盈利(¤)','盈利(%)','毛利','毛损','交易次数','胜率(%)'], index_format)\n worksheet.write_column('A4', res.index, date_format)\n worksheet.write_column('B4', res[\"profit\"], value_format)\n worksheet.write_column('C4', res[\"profit_ratio\"], value_format)\n worksheet.write_column('D4', res[\"gross_profit\"], value_format)\n worksheet.write_column('E4', res[\"gross_loss\"], value_format)\n worksheet.write_column('F4', res[\"times\"], value_format)\n worksheet.write_column('G4', res[\"win_rate\"]*100, value_format)\n \n next_row = 5 + len(res)\n res = stat_closes_by_month(df_closes.copy(), capital)\n worksheet.write_row('A%d'%(next_row+1), ['月度绩效分析'], title_format)\n worksheet.write_row('A%d'%(next_row+3), ['期间','盈利(¤)','盈利(%)','毛利','毛损','交易次数','胜率(%)'], index_format)\n worksheet.write_column('A%d'%(next_row+4), res.index, index_format)\n worksheet.write_column('B%d'%(next_row+4), res[\"profit\"], value_format)\n worksheet.write_column('C%d'%(next_row+4), res[\"profit_ratio\"], value_format)\n worksheet.write_column('D%d'%(next_row+4), res[\"gross_profit\"], value_format)\n worksheet.write_column('E%d'%(next_row+4), res[\"gross_loss\"], value_format)\n worksheet.write_column('F%d'%(next_row+4), res[\"times\"], value_format)\n worksheet.write_column('G%d'%(next_row+4), res[\"win_rate\"]*100, value_format)\n\n next_row = next_row + 4 + len(res)\n res = stat_closes_by_year(df_closes.copy(), capital) \n worksheet.write_row('A%d'%(next_row+1), ['年度绩效分析'], title_format)\n worksheet.write_row('A%d'%(next_row+3), ['期间','盈利(¤)','盈利(%)','毛利','毛损','交易次数','胜率(%)'], index_format)\n worksheet.write_column('A%d'%(next_row+4), res.index, index_format)\n worksheet.write_column('B%d'%(next_row+4), res[\"profit\"], value_format)\n worksheet.write_column('C%d'%(next_row+4), res[\"profit_ratio\"], value_format)\n worksheet.write_column('D%d'%(next_row+4), res[\"gross_profit\"], value_format)\n worksheet.write_column('E%d'%(next_row+4), res[\"gross_loss\"], value_format)\n worksheet.write_column('F%d'%(next_row+4), res[\"times\"], value_format)\n worksheet.write_column('G%d'%(next_row+4), res[\"win_rate\"]*100, value_format)\n\ndef strategy_analyze(workbook:Workbook, df_closes, df_trades, capital, rf = 0.0, period = 240):\n '''\n 策略分析\n '''\n\n # 截取开仓明细\n data1_open = df_trades[df_trades['action'].apply(lambda x: 'OPEN' in x)].reset_index()\n data1_open = data1_open.drop(columns=['index'])\n # 截取平仓明细\n data1_close = df_trades[df_trades['action'].apply(lambda x: 'CLOSE' in x)].reset_index()\n data1_close = data1_close.drop(columns=['index'])\n\n # 将平仓明细字段重命名，并跟开仓明细合并成一个大表\n data1_close = data1_close.rename(columns={'code': 'code_1', 'time': 'time_1', 'direct': 'direct_1',\n 'action': 'action_1', 'price': 'price_1', 'qty': 'qty_1', 'tag': 'tag_1',\n 'fee': 'fee_1'})\n\n new_data = pd.concat([data1_open, data1_close], axis=1)\n new_data = new_data.dropna()\n new_data = new_data.drop(columns=['code_1', 'qty_1'])\n\n # 计算开仓平仓手续费\n new_data['transaction_fee'] = new_data['fee'] + new_data['fee_1']\n clean_data = new_data[['time', 'transaction_fee']]\n clean_data = clean_data.rename(columns={'time': 'opentime'})\n\n # 合并数据\n after_merge = pd.merge(df_closes, clean_data, how='inner', on='opentime')\n\n data_long = df_closes[df_closes['direct'].apply(lambda x:'LONG' in x )].reset_index()\n after_merge_long = after_merge[after_merge['direct'].apply(lambda x: 'LONG' in x)].reset_index()\n data_short = df_closes[df_closes['direct'].apply(lambda x: 'SHORT' in x)].reset_index()\n after_merge_short = after_merge[after_merge['direct'].apply(lambda x: 'SHORT' in x)].reset_index()\n\n # 全部平仓明细进行绩效分析\n result1 = performance_summary(df_closes, after_merge, capital=capital, rf=rf, period=period)\n # 做多平仓明细进行绩效分析\n result1_2 = performance_summary(data_long, after_merge_long, capital=capital, rf=rf, period=period)\n # 做空平仓明细进行绩效分析\n result1_3 = performance_summary(data_short,after_merge_short, capital=capital, rf=rf, period=period)\n # 绩效比率计算\n result2 = ratio_calculate(df_closes, after_merge, capital=capital, rf=rf, period=period)\n # 时间分析\n result3 = time_analysis(df_closes)\n\n result1 = pd.DataFrame(pd.Series(result1), columns=['所有交易'])\n result1 = result1.reset_index().rename(columns={'index': '策略绩效概要'})\n\n result1_2 = pd.DataFrame(pd.Series(result1_2), columns=['多头交易'])\n result1_2 = result1_2.reset_index().rename(columns={'index': '策略绩效概要'})\n\n result1_3 = pd.DataFrame(pd.Series(result1_3), columns=['空头交易'])\n result1_3 = result1_3.reset_index().rename(columns={'index': '策略绩效概要'})\n\n result1 = result1.merge(result1_2,how='inner',on='策略绩效概要')\n result1 = result1.merge(result1_3,how='inner',on='策略绩效概要')\n\n sheetName = '策略分析'\n worksheet = workbook.add_worksheet(sheetName)\n\n title_format = workbook.add_format({\n 'font_size': 16,\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n index_format = workbook.add_format({\n 'font_size': 12,\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n value_format = workbook.add_format({\n 'align': 'right', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n result1.fillna(value=0, inplace=True)\n worksheet.write_row('A1', ['策略绩效概要'], title_format) \n worksheet.write_row('B3', ['所有交易','多头交易','空头交易'], index_format)\n worksheet.write_column('A4', result1['策略绩效概要'], index_format)\n worksheet.write_column('B4', result1['所有交易'], value_format)\n worksheet.write_column('C4', result1['多头交易'], value_format)\n worksheet.write_column('D4', result1['空头交易'], value_format)\n\n worksheet.write_row('A22', ['绩效比率'], title_format) \n worksheet.write_column('A24', result2.keys(), index_format)\n worksheet.write_column('B24', result2.values(), value_format)\n\n worksheet.write_row('A37', ['时间分析'], title_format) \n worksheet.write_column('A39', result3.keys(), index_format)\n worksheet.write_column('B39', result3.values(), value_format)\n\n # 这里开始画图\n worksheet.write_row('A49', ['详细权益曲线'], title_format)\n chart_col = workbook.add_chart({'type': 'line'})\n length = len(df_closes)\n sheetName = '交易列表'\n chart_col.add_series(\n {\n 'name': '详细权益曲线',\n 'categories': '=%s!$G$4:$G$%s' % (sheetName, length+3),\n 'values': '=%s!$R$4:$R$%s' % (sheetName, length+3),\n 'line': {'color': 'red','width': 1}\n }\n )\n chart_col.set_title({'name': '详细权益曲线'})\n worksheet.insert_chart('A51', chart_col)\n\n worksheet.write_row('A79', ['详细权益曲线及潜在亏损'], title_format)\n chart_col = workbook.add_chart({'type': 'line'})\n chart_col.add_series(\n {\n 'name': '权益曲线',\n 'categories': '=%s!$G$4:$G$%s' % (sheetName, length+3),\n 'values': '=%s!$R$4:$R$%s' % (sheetName, length+3),\n 'line': {'color': 'red','width': 1}\n }\n )\n chart_col.add_series(\n {\n 'name': '潜在亏损',\n 'categories': '=%s!$G$4:$G$%s' % (sheetName, length+3),\n 'values': '=%s!$P$4:$P$%s' % (sheetName, length+3),\n 'line': {'color': 'green','width': 1}\n }\n )\n chart_col.set_title({'name': '详细权益曲线及潜在亏损'})\n worksheet.insert_chart('A81', chart_col)\n\n worksheet.write_row('A109', ['潜在盈利与亏损'], title_format)\n chart_col = workbook.add_chart({'type': 'line'})\n chart_col.add_series(\n {\n 'name': '潜在盈利',\n 'categories': '=%s!$G$4:$G$%s' % (sheetName, length+3),\n 'values': '=%s!$N$4:$N$%s' % (sheetName, length+3),\n 'line': {'color': 'red','width': 1}\n }\n )\n chart_col.add_series(\n {\n 'name': '潜在亏损',\n 'categories': '=%s!$G$4:$G$%s' % (sheetName, length+3),\n 'values': '=%s!$P$4:$P$%s' % (sheetName, length+3),\n 'line': {'color': 'green','width': 1}\n }\n )\n chart_col.set_title({'name': '潜在盈利与亏损'})\n worksheet.insert_chart('A111', chart_col)\n\ndef output_closes(workbook:Workbook, df_closes:df, capital = 500000):\n worksheet = workbook.add_worksheet('交易列表')\n title_format = workbook.add_format({\n 'font_size': 16,\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n index_format = workbook.add_format({\n 'font_size': 12,\n 'bold': True,\n 'align': 'left', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n value_format = workbook.add_format({\n 'align': 'right', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n\n time_format = workbook.add_format({\n 'num_format': 'yyyy/mm/dd HH:MM',\n 'align': 'right', # 水平居中\n 'valign': 'vcenter' # 垂直居中\n })\n \n\n df_closes['entrytime'] = df_closes['opentime'].apply(lambda x: datetime.strptime(str(x), '%Y%m%d%H%M'))\n df_closes['exittime'] = df_closes['closetime'].apply(lambda x: datetime.strptime(str(x), '%Y%m%d%H%M'))\n\n worksheet.write_row('A1', ['交易列表'], title_format) \n worksheet.write_row('A3', ['编号', '代码','方向','进场时间','进场价格','进场标记','出场时间','出场价格','出场标记',\n '盈利¤','盈利%','累计盈利¤','累计盈利%','潜在盈利¤','潜在盈利%','潜在亏损¤','潜在亏损%','累计权益'], index_format)\n df_closes[\"profit_ratio\"] = df_closes[\"profit\"]*100/capital\n df_closes[\"total_profit_ratio\"] = df_closes[\"totalprofit\"]*100/capital\n df_closes[\"max_profit_ratio\"] = df_closes[\"maxprofit\"]*100/capital\n df_closes[\"max_loss_ratio\"] = df_closes[\"maxloss\"]*100/capital\n\n worksheet.write_column('A4', df_closes.index+1, value_format)\n worksheet.write_column('B4', df_closes['code'], value_format)\n worksheet.write_column('C4', df_closes['direct'], value_format)\n worksheet.write_column('D4', df_closes['entrytime'], time_format)\n worksheet.write_column('E4', df_closes['openprice'], value_format)\n ay = df_closes['entertag'].apply(lambda x: x if type(x)==str else '' if math.isnan(x) else x)\n worksheet.write_column('F4', ay, value_format)\n worksheet.write_column('G4', df_closes['exittime'], time_format)\n worksheet.write_column('H4', df_closes['closeprice'], value_format)\n ay = df_closes['exittag'].apply(lambda x: x if type(x)==str else '' if math.isnan(x) else x)\n worksheet.write_column('I4', ay, value_format)\n\n worksheet.write_column('J4', df_closes['profit'], value_format)\n worksheet.write_column('K4', df_closes['profit_ratio'], value_format)\n worksheet.write_column('L4', df_closes['totalprofit'], value_format)\n worksheet.write_column('M4', df_closes['total_profit_ratio'], value_format)\n worksheet.write_column('N4', df_closes['maxprofit'], value_format)\n worksheet.write_column('O4', df_closes['max_profit_ratio'], value_format)\n worksheet.write_column('P4', df_closes['maxloss'], value_format)\n worksheet.write_column('Q4', df_closes['max_loss_ratio'], value_format)\n worksheet.write_column('R4', df_closes['totalprofit']+capital, value_format)\n\ndef summary_analyze(df_funds:df, capital = 5000000, rf = 0, period = 240) -> dict:\n '''\n 概要分析\n '''\n init_capital = capital\n annual_days = period\n days = len(df_funds)\n\n #先做资金统计吧\n print(\"anayzing fund data……\")\n df_funds[\"dynbalance\"] += init_capital\n ayBal = df_funds[\"dynbalance\"] # 每日期末动态权益\n\n #生成每日期初动态权益\n ayPreBal = np.array(ayBal.tolist()[:-1]) \n ayPreBal = np.insert(ayPreBal, 0, init_capital) #每日期初权益\n df_funds[\"prebalance\"] = ayPreBal\n\n #统计期末权益大于期初权益的天数，即盈利天数\n windays = len(df_funds[df_funds[\"dynbalance\"]>df_funds[\"prebalance\"]])\n\n #每日净值\n ayNetVals = (ayBal/init_capital)\n \n ar = math.pow(ayNetVals.iloc[-1], annual_days/days) - 1 #年化收益率=总收益率^(年交易日天数/统计天数)\n ayDailyReturn = ayBal/ayPreBal-1 #每日收益率\n delta = fmtNAN(ayDailyReturn.std(axis=0)*math.pow(annual_days,0.5),0) #年化标准差=每日收益率标准差*根号下(年交易日天数)\n down_delta = fmtNAN(ayDailyReturn[ayDailyReturn<0].std(axis=0)*math.pow(annual_days,0.5), 0) #下行标准差=每日亏损收益率标准差*根号下(年交易日天数)\n\n #sharpe率\n if delta != 0.0:\n sr = (ar-rf)/delta\n else:\n sr = 9999.0\n\n #计算最大回撤和最大上涨\n maxub = ayNetVals[0]\n minub = maxub\n mdd = 0.0\n midd = 0.0\n mup = 0.0\n for idx in range(1,len(ayNetVals)):\n maxub = max(maxub, ayNetVals[idx])\n minub = min(minub, ayNetVals[idx])\n profit = (ayNetVals[idx] - ayNetVals[idx-1])/ayNetVals[idx-1]\n falldown = (ayNetVals[idx] - maxub)/maxub\n riseup = (ayNetVals[idx] - minub)/minub\n if profit <= 0:\n midd = max(midd, abs(profit))\n mdd = max(mdd, abs(falldown))\n else:\n mup = max(mup, abs(riseup))\n #索提诺比率\n if down_delta != 0.0:\n sortino = (ar-rf)/down_delta\n else:\n sortino = 0.0\n if mdd != 0.0:\n calmar = ar/mdd\n else:\n calmar = 999999.0\n\n\n # key_indicator = ['交易天数', '累积收益（%）', '年化收益率（%）', '胜率（%）', '最大回撤（%）', '最大上涨（%）', '标准差（%）',\n # '下行波动率（%）', 'Sharpe比率', 'Sortino比率', 'Calmar比率']\n return {\n \"days\": days,\n \"total_return\":(ayNetVals.iloc[-1]-1)*100, \n \"annual_return\":ar*100, \n \"win_rate\":(windays/days)*100, \n \"max_falldown\":mdd*100, \n \"max_profratio\":mup*100, \n \"std\":delta*100, \n \"down_std\":down_delta*100, \n \"sharpe_ratio\":sr, \n \"sortino_ratio\":sortino, \n \"calmar_ratio\":calmar\n }\n\n\ndef funds_analyze(workbook:Workbook, df_funds:df, capital = 5000000, rf = 0, period = 240):\n '''\n 逐日资金分析\n '''\n init_capital = capital\n annual_days = period\n days = len(df_funds)\n\n #先做资金统计吧\n print(\"anayzing fund data……\")\n df_funds[\"dynbalance\"] += init_capital\n ayBal = df_funds[\"dynbalance\"] # 每日期末动态权益\n\n #生成每日期初动态权益\n ayPreBal = np.array(ayBal.tolist()[:-1]) \n ayPreBal = np.insert(ayPreBal, 0, init_capital) #每日期初权益\n df_funds[\"prebalance\"] = ayPreBal\n\n #统计期末权益大于期初权益的天数，即盈利天数\n windays = len(df_funds[df_funds[\"dynbalance\"]>df_funds[\"prebalance\"]])\n\n #每日净值\n ayNetVals = (ayBal/init_capital)\n \n ar = math.pow(ayNetVals.iloc[-1], annual_days/days) - 1 #年化收益率=总收益率^(年交易日天数/统计天数)\n ayDailyReturn = ayBal/ayPreBal-1 #每日收益率\n delta = fmtNAN(ayDailyReturn.std(axis=0)*math.pow(annual_days,0.5),0) #年化标准差=每日收益率标准差*根号下(年交易日天数)\n down_delta = fmtNAN(ayDailyReturn[ayDailyReturn<0].std(axis=0)*math.pow(annual_days,0.5), 0) #下行标准差=每日亏损收益率标准差*根号下(年交易日天数)\n\n #sharpe率\n if delta != 0.0:\n sr = (ar-rf)/delta\n else:\n sr = 9999.0\n\n #计算最大回撤和最大上涨\n maxub = ayNetVals[0]\n minub = maxub\n mdd = 0.0\n midd = 0.0\n mup = 0.0\n for idx in range(1,len(ayNetVals)):\n maxub = max(maxub, ayNetVals[idx])\n minub = min(minub, ayNetVals[idx])\n profit = (ayNetVals[idx] - ayNetVals[idx-1])/ayNetVals[idx-1]\n falldown = (ayNetVals[idx] - maxub)/maxub\n riseup = (ayNetVals[idx] - minub)/minub\n if profit <= 0:\n midd = max(midd, abs(profit))\n mdd = max(mdd, abs(falldown))\n else:\n mup = max(mup, abs(riseup))\n #索提诺比率\n if down_delta != 0.0:\n sortino = (ar-rf)/down_delta\n else:\n sortino = 0.0\n if mdd != 0.0:\n calmar = ar/mdd\n else:\n calmar = 999999.0\n\n #输出到excel\n sheetName = '逐日绩效概览'\n worksheet = workbook.add_worksheet(sheetName)\n\n # 设置合并单元格及格式 #\n # ~~~~~~ 写入数据 ~~~~~~ #\n title_format = workbook.add_format({\n 'bold': True,\n 'border': 1,\n 'align': 'center', # 水平居中\n 'valign': 'vcenter', # 垂直居中\n 'fg_color': '#bcbcbc'\n })\n fund_data_format = workbook.add_format({\n 'border': 1,\n 'align': 'right', # 右对齐\n 'valign': 'vcenter', # 垂直居中\n })\n \n fund_data_format_2 = workbook.add_format({\n 'border': 1,\n 'align': 'right', # 右对齐\n 'valign': 'vcenter', # 垂直居中\n 'num_format': '0.00'\n })\n\n fund_data_format_3 = workbook.add_format({\n 'border': 1,\n 'align': 'right', # 右对齐\n 'valign': 'vcenter', # 垂直居中\n 'num_format': '0.000'\n })\n\n fund_data_format_4 = workbook.add_format({\n 'border': 1,\n 'align': 'right', # 右对齐\n 'valign': 'vcenter', # 垂直居中\n 'num_format': '0.0000'\n })\n\n\n merge_format = workbook.add_format({\n 'font_size': 16,\n 'bold': True,\n 'align': 'center', # 水平居中\n 'valign': 'vcenter', # 垂直居中\n })\n indicator_format = workbook.add_format({\n 'font_size': 12,\n 'bold': True,\n 'align': 'center', # 水平居中\n 'valign': 'vcenter', # 垂直居中\n })\n worksheet.merge_range('A1:D1', '收益率统计指标', merge_format)\n worksheet.merge_range('E1:H1', '风险统计指标', merge_format)\n worksheet.merge_range('I1:K1', '综合指标', merge_format)\n\n key_indicator = ['交易天数', '累积收益（%）', '年化收益率（%）', '胜率（%）', '最大回撤（%）', '最大上涨（%）', '标准差（%）',\n '下行波动率（%）', 'Sharpe比率', 'Sortino比率', 'Calmar比率']\n key_data = [(ayNetVals.iloc[-1]-1)*100, ar*100, (windays/days)*100, mdd*100, mup*100, delta*100, down_delta*100, sr, sortino, calmar]\n worksheet.write_row('A2', key_indicator, indicator_format)\n worksheet.write_column('A3', [days], fund_data_format)\n worksheet.write_row('B3', key_data, fund_data_format_3)\n\n # 画图 #\n chart_col = workbook.add_chart({'type': 'line'})\n length = days\n chart_col.add_series( # 给图表设置格式，填充内容\n {\n 'name': '=逐日绩效分析!$B$1',\n 'categories': '=逐日绩效分析!$A$3:$A$%d' % (length+2),\n 'values': '=逐日绩效分析!$G$3:$G$%d' % (length+2),\n 'line': {'color': 'blue', 'width':1},\n }\n )\n chart_col.set_title({'name': '累计净值'})\n worksheet.insert_chart('A8', chart_col)\n\n # 准备第二张表格 #\n sheetName = '逐日绩效分析'\n worksheet = workbook.add_worksheet(sheetName)\n title_format2 = workbook.add_format({\n 'border': 1,\n 'align': 'center', # 水平居中\n 'valign': 'vcenter', # 垂直居中\n 'fg_color': '#D3D3D3',\n 'text_wrap': 1\n })\n worksheet.merge_range('A1:A2', '日期', title_format2)\n worksheet.merge_range('B1:B2', '统计时间', title_format2)\n worksheet.merge_range('C1:C2', '初始资金', title_format2)\n worksheet.merge_range('D1:D2', '出入金', title_format2)\n worksheet.merge_range('E1:E2', '当前权益', title_format2)\n worksheet.merge_range('F1:F2', '累计盈亏', title_format2)\n worksheet.merge_range('G1:G2', '累计净值', title_format2)\n worksheet.merge_range('H1:I1', '当日盈亏', title_format2)\n indicator = ['数值', '比例']\n worksheet.write_row('H2', indicator, title_format2)\n worksheet.merge_range('J1:J2', '峰值', title_format2)\n worksheet.merge_range('K1:K2', '当日累计回撤', title_format2)\n worksheet.merge_range('L1:L2', '历史最大累计回撤', title_format2)\n worksheet.merge_range('M1:M2', '最大单日回撤', title_format2)\n worksheet.merge_range('N1:N2', '衰落时间', title_format2)\n # worksheet.merge_range('O1:O2', 'IF指数', title_format2)\n # worksheet.merge_range('P1:P2', 'IF净值', title_format2)\n\n # 写入内容 #\n profit_format = workbook.add_format({\n 'border': 1,\n 'align': 'right', # 靠右\n 'valign': 'vcenter', # 垂直居中\n 'fg_color': '#FAFAD2',\n 'num_format': '0.00'\n })\n\n percent_format = workbook.add_format({\n 'border': 1,\n 'align': 'right', # 右对齐\n 'valign': 'vcenter', # 垂直居中\n 'num_format': '0.00%'\n })\n\n date_format = workbook.add_format({\n 'num_format': 'yyyy/mm/dd',\n 'border': 1,\n 'align': 'right', # 右对齐\n 'valign': 'vcenter', # 垂直居中\n })\n\n ayDates = df_funds['date'].apply(lambda x: str(x)[:4]+'/'+str(x)[4:6]+'/'+str(x)[6:8])\n worksheet.write_column('A3', ayDates, date_format)\n worksheet.write_column('B3', range(len(df_funds)), fund_data_format)\n initial = [init_capital]*len(df_funds)\n worksheet.write_column('C3', initial, fund_data_format)\n worksheet.write_column('D3', '/', fund_data_format)\n worksheet.write_column('E3', ayBal, fund_data_format)\n worksheet.write_column('F3', ayBal-init_capital, fund_data_format_2)\n worksheet.write_column('G3', ayNetVals, fund_data_format_4)\n worksheet.write_column('H3', ayBal-ayPreBal, profit_format)\n worksheet.write_column('I3', ayDailyReturn, percent_format)\n # 计算峰值\n upper = np.maximum.accumulate(ayNetVals)\n worksheet.write_column('J3', upper, fund_data_format_4)\n # 回撤指标\n temp = 1-(ayNetVals)/(np.maximum.accumulate(ayNetVals))\n worksheet.write_column('K3', temp, percent_format)\n worksheet.write_column('L3', np.maximum.accumulate(temp), percent_format)\n worksheet.write_column('M3', np.minimum.accumulate(ayDailyReturn), percent_format)\n # 计算衰落时间\n down_time = [0]\n for i in range(1, len(upper)):\n if upper[i] > upper[i-1]:\n down_time.append(0)\n else:\n l = down_time[i-1]\n down_time.append(l+1)\n worksheet.write_column('N3', down_time, fund_data_format)\n\nclass WtBtAnalyst:\n\n def __init__(self):\n self.__strategies__ = dict()\n return\n\n def add_strategy(self, sname:str, folder:str, init_capital:float, rf:float=0.02, annual_trading_days:int = 240):\n self.__strategies__[sname] = {\n \"folder\": folder,\n \"cap\":init_capital,\n \"rf\":rf,\n \"atd\":annual_trading_days\n }\n\n def run_new(self, outFileName:str = ''):\n if len(self.__strategies__.keys()) == 0:\n raise Exception(\"strategies is empty\")\n\n for sname in self.__strategies__:\n sInfo = self.__strategies__[sname]\n folder = sInfo[\"folder\"]\n print(\"start PnL analyzing for strategy %s……\" % (sname))\n\n df_funds = pd.read_csv(folder + \"funds.csv\")\n df_closes = pd.read_csv(folder + \"closes.csv\")\n df_trades = pd.read_csv(folder + \"trades.csv\")\n\n if len(outFileName) == 0:\n outFileName = 'Strategy[%s]_PnLAnalyzing_%s_%s.xlsx' % (sname, df_funds['date'][0], df_funds['date'].iloc[-1])\n\n workbook = Workbook(outFileName)\n init_capital = sInfo[\"cap\"]\n annual_days = sInfo[\"atd\"]\n rf = sInfo[\"rf\"]\n\n strategy_analyze(workbook, df_closes.copy(), df_trades.copy(), capital=init_capital, rf=rf, period=annual_days)\n output_closes(workbook, df_closes.copy(), capital=init_capital)\n trading_analyze(workbook, df_closes.copy(), df_funds.copy(), capital=init_capital)\n funds_analyze(workbook, df_funds.copy(), capital=init_capital, rf=rf, period=annual_days)\n workbook.close()\n\n print(\"PnL analyzing of strategy %s done\" % (sname))\n\n\n def run(self, outFileName:str = ''):\n if len(self.__strategies__.keys()) == 0:\n raise Exception(\"strategies is empty\")\n\n for sname in self.__strategies__:\n sInfo = self.__strategies__[sname]\n folder = sInfo[\"folder\"]\n print(\"start PnL analyzing for strategy %s……\" % (sname))\n\n df_funds = pd.read_csv(folder + \"funds.csv\")\n print(\"fund logs loaded……\")\n\n init_capital = sInfo[\"cap\"]\n annual_days = sInfo[\"atd\"]\n rf = sInfo[\"rf\"]\n \n if len(outFileName) == 0:\n outFileName = 'Strategy[%s]_PnLAnalyzing_%s_%s.xlsx' % (sname, df_funds['date'][0], df_funds['date'].iloc[-1])\n workbook = Workbook(outFileName)\n funds_analyze(workbook, df_funds, capital=init_capital, rf=rf, period=annual_days)\n workbook.close()\n\n print(\"PnL analyzing of strategy %s done\" % (sname))\n\n def run_simple(self):\n if len(self.__strategies__.keys()) == 0:\n raise Exception(\"strategies is empty\")\n\n for sname in self.__strategies__:\n sInfo = self.__strategies__[sname]\n folder = sInfo[\"folder\"]\n\n df_funds = pd.read_csv(folder + \"funds.csv\")\n\n init_capital = sInfo[\"cap\"]\n annual_days = sInfo[\"atd\"]\n rf = sInfo[\"rf\"]\n \n filename = folder + 'summary.json'\n sumObj = summary_analyze(df_funds, capital=init_capital, rf=rf, period=annual_days)\n sumObj[\"name\"] = sname\n f = open(filename,\"w\")\n f.write(json.dumps(sumObj, indent=4, ensure_ascii=True))\n f.close()" }, { "path": "wtpy/apps/WtCtaOptimizer.py", "content": "from json import encoder\nimport multiprocessing\nimport time\nimport threading\nimport json\n\nimport os\nimport math\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame as df\n\nfrom wtpy import WtBtEngine,EngineType\nfrom wtpy.apps import WtBtAnalyst\n\ndef fmtNAN(val, defVal = 0):\n if math.isnan(val):\n return defVal\n\n return val\n\nclass ParamInfo:\n '''\n 参数信息类\n '''\n def __init__(self, name:str, start_val = None, end_val = None, step_val = None, ndigits = 1, val_list:list = None):\n self.name = name #参数名\n self.start_val = start_val #起始值\n self.end_val = end_val #结束值\n self.step_val = step_val #变化步长\n self.ndigits = ndigits #小数位\n self.val_list = val_list #指定参数\n\n def gen_array(self):\n if self.val_list is not None:\n return self.val_list\n\n values = list()\n curVal = round(self.start_val, self.ndigits)\n while curVal < self.end_val:\n values.append(curVal)\n\n curVal += self.step_val\n curVal = round(curVal, self.ndigits)\n if curVal >= self.end_val:\n curVal = self.end_val\n break\n values.append(round(curVal, self.ndigits))\n return values\n\nclass WtCtaOptimizer:\n '''\n 参数优化器\\n\n 主要用于做策略参数优化的\n '''\n def __init__(self, worker_num:int = 8):\n '''\n 构造函数\\n\n\n @worker_num 工作进程个数，默认为8，可以根据CPU核心数设置\n '''\n self.worker_num = worker_num\n self.running_worker = 0\n self.mutable_params = dict()\n self.fixed_params = dict()\n self.env_params = dict()\n\n self.cpp_stra_module = None\n return\n\n def add_mutable_param(self, name:str, start_val, end_val, step_val, ndigits = 1):\n '''\n 添加可变参数\\n\n\n @name 参数名\\n\n @start_val 起始值\\n\n @end_val 结束值\\n\n @step_val 步长\\n\n @ndigits 小数位\n '''\n self.mutable_params[name] = ParamInfo(name=name, start_val=start_val, end_val=end_val, step_val=step_val, ndigits=ndigits)\n\n def add_listed_param(self, name:str, val_list:list):\n '''\n 添加限定范围的可变参数\\n\n\n @name 参数名\\n\n @val_list 参数值列表\n '''\n self.mutable_params[name] = ParamInfo(name=name, val_list=val_list)\n\n def add_fixed_param(self, name:str, val):\n '''\n 添加固定参数\\n\n\n @name 参数名\\n\n @val 值\\n\n '''\n self.fixed_params[name] = val\n return\n \n def set_strategy(self, typeName:type, name_prefix:str):\n '''\n 设置策略\\n\n\n @typeName 策略类名\\n\n @name_prefix 命名前缀，用于自动命名用，一般为格式为\"前缀_参数1名_参数1值_参数2名_参数2值\"\n '''\n self.strategy_type = typeName\n self.name_prefix = name_prefix\n return\n\n def set_cpp_strategy(self, module:str, type_name:type, name_prefix:str):\n '''\n 设置CPP策略\\n\n\n @module 模块文件\\n\n @typeName 策略类名\\n\n @name_prefix 命名前缀，用于自动命名用，一般为格式为\"前缀_参数1名_参数1值_参数2名_参数2值\"\n '''\n self.cpp_stra_module = module\n self.cpp_stra_type = type_name\n self.name_prefix = name_prefix\n return\n\n def config_backtest_env(self, deps_dir:str, cfgfile:str=\"configbt.json\", storage_type:str=\"csv\", storage_path:str = None, db_config:dict = None):\n '''\n 配置回测环境\\n\n\n @deps_dir 依赖文件目录\\n\n @cfgfile 配置文件名\\n\n @storage_type 存储类型，csv/bin等\\n\n @storage_path 存储路径\n '''\n self.env_params[\"deps_dir\"] = deps_dir\n self.env_params[\"cfgfile\"] = cfgfile\n self.env_params[\"storage_type\"] = storage_type\n\n if storage_path is None and db_config is None:\n raise Exception(\"storage_path and db_config cannot be both None!\")\n\n if storage_type == 'db' and db_config is None:\n raise Exception(\"db_config cannot be None while storage_type is db!\")\n\n self.env_params[\"storage_path\"] = storage_path\n self.env_params[\"db_config\"] = db_config\n\n def config_backtest_time(self, start_time:int, end_time:int):\n '''\n 配置回测时间，可多次调用配置多个回测时间区间\\n\n\n @start_time 开始时间，精确到分钟，格式如201909100930\\n\n @end_time 结束时间，精确到分钟，格式如201909100930\n '''\n if \"time_ranges\" not in self.env_params:\n self.env_params[\"time_ranges\"] = []\n\n self.env_params[\"time_ranges\"].append([start_time,end_time])\n\n def __gen_tasks__(self, markerfile:str = \"strategies.json\"):\n '''\n 生成回测任务\n '''\n param_names = self.mutable_params.keys()\n param_values = dict()\n # 先生成各个参数的变量数组\n # 并计算总的参数有多少组\n total_groups = 1\n for name in param_names:\n paramInfo = self.mutable_params[name]\n values = paramInfo.gen_array()\n param_values[name] = values\n total_groups *= len(values)\n\n #再生成最终每一组的参数dict\n param_groups = list()\n stra_names = dict()\n time_ranges = self.env_params[\"time_ranges\"]\n for time_range in time_ranges:\n start_time = time_range[0]\n end_time = time_range[1]\n for i in range(total_groups):\n k = i\n thisGrp = self.fixed_params.copy() #复制固定参数\n endix = ''\n for name in param_names:\n cnt = len(param_values[name])\n curVal = param_values[name][k%cnt]\n tname = type(curVal)\n if tname.__name__ == \"list\":\n val_str = ''\n for item in curVal:\n val_str += str(item)\n val_str += \"_\"\n\n val_str = val_str[:-1]\n thisGrp[name] = curVal\n endix += name \n endix += \"_\"\n endix += val_str\n endix += \"_\"\n else:\n thisGrp[name] = curVal\n endix += name \n endix += \"_\"\n endix += str(curVal)\n endix += \"_\"\n k = math.floor(k / cnt)\n\n endix = endix[:-1]\n straName = self.name_prefix + endix\n straName += \"_%d_%d\" % (start_time, end_time)\n thisGrp[\"name\"] = straName\n thisGrp[\"start_time\"] = start_time\n thisGrp[\"end_time\"] = end_time\n stra_names[straName] = thisGrp\n param_groups.append(thisGrp)\n \n # 将每一组参数和对应的策略ID落地到文件中，方便后续的分析\n f = open(markerfile, \"w\")\n f.write(json.dumps(obj=stra_names, sort_keys=True, indent=4))\n f.close()\n return param_groups\n\n def __ayalyze_result__(self, strName:str, time_range:tuple, params:dict):\n folder = \"./outputs_bt/%s/\" % (strName)\n df_closes = pd.read_csv(folder + \"closes.csv\")\n df_funds = pd.read_csv(folder + \"funds.csv\")\n\n df_wins = df_closes[df_closes[\"profit\"]>0]\n df_loses = df_closes[df_closes[\"profit\"]<=0]\n\n ay_WinnerBarCnts = df_wins[\"closebarno\"]-df_wins[\"openbarno\"]\n ay_LoserBarCnts = df_loses[\"closebarno\"]-df_loses[\"openbarno\"]\n\n total_winbarcnts = ay_WinnerBarCnts.sum()\n total_losebarcnts = ay_LoserBarCnts.sum()\n\n total_fee = df_funds.iloc[-1][\"fee\"]\n\n totaltimes = len(df_closes) # 总交易次数\n wintimes = len(df_wins) # 盈利次数\n losetimes = len(df_loses) # 亏损次数\n winamout = df_wins[\"profit\"].sum() #毛盈利\n loseamount = df_loses[\"profit\"].sum() #毛亏损\n trdnetprofit = winamout + loseamount #交易净盈亏\n accnetprofit = trdnetprofit - total_fee #账户净盈亏\n winrate = wintimes / totaltimes if totaltimes>0 else 0 # 胜率\n avgprof = trdnetprofit/totaltimes if totaltimes>0 else 0 # 单次平均盈亏\n avgprof_win = winamout/wintimes if wintimes>0 else 0 # 单次盈利均值\n avgprof_lose = loseamount/losetimes if losetimes>0 else 0 # 单次亏损均值\n winloseratio = abs(avgprof_win/avgprof_lose) if avgprof_lose!=0 else \"N/A\" # 单次盈亏均值比\n \n max_consecutive_wins = 0 # 最大连续盈利次数\n max_consecutive_loses = 0 # 最大连续亏损次数\n \n avg_bars_in_winner = total_winbarcnts/wintimes if wintimes>0 else \"N/A\"\n avg_bars_in_loser = total_losebarcnts/losetimes if losetimes>0 else \"N/A\"\n\n consecutive_wins = 0\n consecutive_loses = 0\n for idx, row in df_closes.iterrows():\n profit = row[\"profit\"]\n if profit > 0:\n consecutive_wins += 1\n consecutive_loses = 0\n else:\n consecutive_wins = 0\n consecutive_loses += 1\n \n max_consecutive_wins = max(max_consecutive_wins, consecutive_wins)\n max_consecutive_loses = max(max_consecutive_loses, consecutive_loses)\n\n summary = params.copy()\n summary[\"开始时间\"] = time_range[0]\n summary[\"结束时间\"] = time_range[1]\n summary[\"总交易次数\"] = totaltimes\n summary[\"盈利次数\"] = wintimes\n summary[\"亏损次数\"] = losetimes\n summary[\"毛盈利\"] = float(winamout)\n summary[\"毛亏损\"] = float(loseamount)\n summary[\"交易净盈亏\"] = float(trdnetprofit)\n summary[\"胜率\"] = winrate*100\n summary[\"单次平均盈亏\"] = avgprof\n summary[\"单次盈利均值\"] = avgprof_win\n summary[\"单次亏损均值\"] = avgprof_lose\n summary[\"单次盈亏均值比\"] = winloseratio\n summary[\"最大连续盈利次数\"] = max_consecutive_wins\n summary[\"最大连续亏损次数\"] = max_consecutive_loses\n summary[\"平均盈利周期\"] = avg_bars_in_winner\n summary[\"平均亏损周期\"] = avg_bars_in_loser\n summary[\"平均账户收益率\"] = accnetprofit/totaltimes\n\n f = open(folder+\"summary.json\", mode=\"w\")\n f.write(json.dumps(obj=summary, indent=4))\n f.close()\n\n return\n\n def __execute_task__(self, params:dict):\n '''\n 执行单个回测任务\\n\n\n @params kv形式的参数\n '''\n name = params[\"name\"]\n f = open(\"logcfg_tpl.json\", \"r\")\n content =f.read()\n f.close()\n content = content.replace(\"$NAME$\", name)\n engine = WtBtEngine(eType=EngineType.ET_CTA, logCfg=content, isFile=False)\n engine.init(self.env_params[\"deps_dir\"], self.env_params[\"cfgfile\"])\n engine.configBacktest(params[\"start_time\"], params[\"end_time\"])\n engine.configBTStorage(mode=self.env_params[\"storage_type\"], path=self.env_params[\"storage_path\"], dbcfg=self.env_params[\"db_config\"])\n\n time_range = (params[\"start_time\"], params[\"end_time\"])\n\n # 去掉多余的参数\n params.pop(\"start_time\")\n params.pop(\"end_time\")\n \n if self.cpp_stra_module is not None:\n params.pop(\"name\")\n engine.setExternalCtaStrategy(name, self.cpp_stra_module, self.cpp_stra_type, params)\n else:\n straInfo = self.strategy_type(**params)\n engine.set_cta_strategy(straInfo)\n\n engine.commitBTConfig()\n engine.run_backtest()\n engine.release_backtest()\n\n self.__ayalyze_result__(name, time_range, params)\n\n def __start_task__(self, params:dict):\n '''\n 启动单个回测任务\\n\n 这里用线程启动子进程的目的是为了可以控制总的工作进程个数\\n\n 可以在线程中join等待子进程结束，再更新running_worker变量\\n\n 如果在__execute_task__中修改running_worker，因为在不同进程中，数据并不同步\\n\n\n @params kv形式的参数\n '''\n p = multiprocessing.Process(target=self.__execute_task__, args=(params,))\n p.start()\n p.join()\n self.running_worker -= 1\n print(\"工作进程%d个\" % (self.running_worker))\n\n def go(self, interval:float = 0.2, out_marker_file:str = \"strategies.json\", out_summary_file:str = \"total_summary.csv\"):\n '''\n 启动优化器\\n\n @interval 时间间隔，单位秒\n @markerfile 标记文件名，回测完成以后分析会用到\n '''\n self.tasks = self.__gen_tasks__(out_marker_file)\n self.running_worker = 0\n total_task = len(self.tasks)\n left_task = total_task\n while True:\n if left_task == 0:\n break\n\n if self.running_worker < self.worker_num:\n params = self.tasks[total_task-left_task]\n left_task -= 1\n print(\"剩余任务%d个\" % (left_task))\n p = threading.Thread(target=self.__start_task__, args=(params,))\n p.start()\n self.running_worker += 1\n print(\"工作进程%d个\" % (self.running_worker))\n else:\n time.sleep(interval)\n\n #最后，全部任务都已经启动完了，再等待所有工作进程结束\n while True:\n if self.running_worker == 0:\n break\n else:\n time.sleep(interval)\n\n #开始汇总回测结果\n f = open(out_marker_file, \"r\")\n content = f.read()\n f.close()\n\n obj_stras = json.loads(content)\n total_summary = list()\n for straName in obj_stras:\n filename = \"./outputs_bt/%s/summary.json\" % (straName)\n if not os.path.exists(filename):\n print(\"%s不存在，请检查数据\" % (filename))\n continue\n \n f = open(filename, \"r\")\n content = f.read()\n f.close()\n obj_summary = json.loads(content)\n total_summary.append(obj_summary)\n\n df_summary = df(total_summary)\n # df_summary = df_summary.drop(labels=[\"name\"], axis='columns')\n df_summary.to_csv(out_summary_file, encoding='utf-8-sig')\n\n def analyze(self, out_marker_file:str = \"strategies.json\", out_summary_file:str = \"total_summary.csv\"):\n #开始汇总回测结果\n f = open(out_marker_file, \"r\")\n content = f.read()\n f.close()\n\n total_summary = list()\n obj_stras = json.loads(content)\n for straName in obj_stras:\n params = obj_stras[straName]\n filename = \"./outputs_bt/%s/summary.json\" % (straName)\n if not os.path.exists(filename):\n print(\"%s不存在，请检查数据\" % (filename))\n continue\n \n time_range = (params[\"start_time\"],params[\"end_time\"])\n self.__ayalyze_result__(straName, time_range, params)\n \n f = open(filename, \"r\")\n content = f.read()\n f.close()\n obj_summary = json.loads(content)\n total_summary.append(obj_summary)\n\n df_summary = df(total_summary)\n df_summary = df_summary.drop(labels=[\"name\"], axis='columns')\n df_summary.to_csv(out_summary_file)\n\n def analyzer(self, out_marker_file:str = \"strategies.json\", init_capital=500000, rf=0.02, annual_trading_days=240):\n for straname in json.load(open(out_marker_file, mode='r')).keys():\n try:\n analyst = WtBtAnalyst()\n analyst.add_strategy(straname, folder=\"./outputs_bt/%s/\"%straname, init_capital=init_capital, rf=rf, annual_trading_days=annual_trading_days)\n analyst.run()\n except:\n pass\n\n \n" }, { "path": "wtpy/apps/WtHotPicker.py", "content": "\nimport datetime\nimport time\nimport json\nimport os\nimport logging\nimport functools\n\nimport urllib.request\nimport io\nimport gzip\nimport xml.dom.minidom\nfrom pyquery import PyQuery as pq\nimport re\n\nclass DayData:\n '''\n 每日行情数据\n '''\n\n def __init__(self):\n self.pid = ''\n self.month = 0\n self.code = '' # 代码\n self.close = 0 # 今收盘(收盘价)\n self.volume = 0 # 成交量(手)\n self.hold = 0 # 空盘量(总持？持仓量)\n\ndef extractPID(code):\n \n for idx in range(0, len(code)):\n c = code[idx]\n if '0' <= c and c <= '9': \n break\n \n return code[:idx]\n\ndef readFileContent(filename):\n if not os.path.exists(filename):\n return \"\"\n f = open(filename, 'r')\n content = f.read()\n f.close()\n return content\n\ndef cmp_alg_01(left:DayData, right:DayData):\n if left.month > right.month:\n if left.hold > right.hold and left.volume > right.volume/3:\n return 1\n else:\n return -1\n else:\n if left.hold <= right.hold or left.volume <= right.volume/3:\n return -1\n else:\n return 1\n\ndef countFridays(curDate:datetime.datetime):\n '''\n 计算截止到当周的周五的天数\n '''\n wd = curDate.weekday()\n checkDate = datetime.datetime(year=curDate.year, month=curDate.month, day=1)\n count = 0\n while checkDate < curDate:\n if checkDate.weekday() == 4:\n count += 1\n \n checkDate += datetime.timedelta(days=1)\n\n if wd < 4:\n count += 1\n\n return count\n\ndef httpGet(url, encoding='utf-8'):\n request = urllib.request.Request(url)\n request.add_header('Accept-encoding', 'gzip')\n request.add_header(\n 'User-Agent', 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)')\n try:\n f = urllib.request.urlopen(request)\n ec = f.headers.get('Content-Encoding')\n if ec == 'gzip':\n cd = f.read()\n cs = io.BytesIO(cd)\n f = gzip.GzipFile(fileobj=cs)\n\n return f.read().decode(encoding)\n except:\n return \"\"\n\ndef httpPost(url, datas, encoding='utf-8'):\n headers = {\n 'User-Agent': 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT)',\n 'Accept-encoding': 'gzip'\n }\n data = urllib.parse.urlencode(datas).encode('utf-8')\n request = urllib.request.Request(url, data, headers)\n try:\n f = urllib.request.urlopen(request)\n ec = f.headers.get('Content-Encoding')\n if ec == 'gzip':\n cd = f.read()\n cs = io.BytesIO(cd)\n f = gzip.GzipFile(fileobj=cs)\n\n return f.read().decode(encoding)\n except:\n return \"\"\n\nclass WtCacheMon:\n '''\n 缓存管理器基类\n '''\n def __init__(self):\n self.day_cache = dict()\n\n def get_cache(self, exchg, curDT:datetime.datetime):\n pass\n\nclass WtCacheMonExchg(WtCacheMon):\n '''\n 交易所行情缓存器\n 通过到交易所官网上拉取当日的行情快照，缓存当日行情数据\n '''\n\n @staticmethod\n def getCffexData(curDT:datetime.datetime) -> dict:\n '''\n 读取CFFEX指定日期的行情快照\n\n @curDT 指定的日期\n '''\n\n dtStr = curDT.strftime('%Y%m%d')\n dtNum = int(dtStr)\n path = \"http://www.cffex.com.cn/fzjy/mrhq/%d/%02d/index.xml\" % (dtNum/100, dtNum % 100)\n content = httpGet(path)\n if len(content) == 0:\n return None\n\n try:\n dom = xml.dom.minidom.parseString(content)\n except:\n logging.info(\"[CFFEX]%s无数据，跳过\" % (dtStr))\n return None\n\n root = dom.documentElement\n \n items = {}\n days = root.getElementsByTagName(\"dailydata\")\n for day in days:\n pid = day.getElementsByTagName(\n \"productid\")[0].firstChild.data.strip()\n\n if pid not in [\"IF\",\"IH\",\"IC\",\"T\",'TF']:\n continue\n\n item = DayData()\n item.code = day.getElementsByTagName(\"instrumentid\")[\n 0].firstChild.data.strip()\n item.pid = pid\n item.hold = float(day.getElementsByTagName(\n \"openinterest\")[0].firstChild.data)\n item.close = float(day.getElementsByTagName(\n \"closeprice\")[0].firstChild.data)\n item.volume = int(day.getElementsByTagName(\n \"volume\")[0].firstChild.data)\n\n item.month = item.code[len(item.pid):]\n\n items[item.code] = item\n return items\n\n @staticmethod\n def getShfeData(curDT:datetime.datetime) -> dict:\n '''\n 读取SHFE指定日期的行情快照\n\n @curDT 指定的日期\n '''\n\n dtStr = curDT.strftime('%Y%m%d')\n content = httpGet(\"http://www.shfe.com.cn/data/dailydata/kx/kx%s.dat\" % (dtStr))\n if len(content) == 0:\n return None\n \n items = {}\n root = json.loads(content)\n for day in root['o_curinstrument']:\n pid = day['PRODUCTID'].strip().rstrip('_f')\n dm = day['DELIVERYMONTH']\n if len(str(day['CLOSEPRICE']).strip()) == 0:\n continue\n\n code = pid + dm\n\n item = DayData()\n item.pid = pid\n item.code = code\n item.hold = int(day['OPENINTEREST'])\n if day['VOLUME'] != '':\n item.volume = int(day['VOLUME'])\n item.close = float(day[\"CLOSEPRICE\"])\n item.month = item.code[len(item.pid):]\n items[code] = item\n return items\n\n @staticmethod\n def getCzceData(curDT:datetime.datetime) -> dict:\n '''\n 读取CZCE指定日期的行情快照\n\n @curDT 指定的日期\n '''\n\n dtStr = curDT.strftime('%Y%m%d')\n url = 'http://www.czce.com.cn/cn/DFSStaticFiles/Future/%s/%s/FutureDataDaily.htm' % (dtStr[0:4], dtStr)\n try:\n html = httpGet(url).strip()\n except urllib.error.HTTPError as httperror:\n print(httperror)\n return None\n\n if len(html) == 0:\n return None\n\n dataitems = {}\n doc = pq(html)\n # print(doc(#senfe .table table))\n items = doc('#senfe')\n # 去掉第一行标题\n items.remove('tr.tr0')\n # 获取tr items.find('tr')\n lis = items('tr')\n # print(lis)\n # tr行数\n trcount = len(lis)\n # 遍历行\n for tr in range(0, trcount-1):\n item = DayData()\n tdlis = doc(lis[tr])('td')\n\n item.code = doc(tdlis[0]).text()\n ay = re.compile('[A-Za-z]+').findall(item.code)\n if len(ay) == 0:\n continue\n\n item.pid = ay[0] \n\n close = doc(tdlis[5]).text()\n if close != '':\n item.close = float(close.replace(\",\",\"\"))\n\n volume = doc(tdlis[9]).text()\n if volume != '':\n item.volume = int(volume.replace(\",\",\"\"))\n\n hold = doc(tdlis[10]).text()\n if hold != '':\n item.hold = int(hold.replace(\",\",\"\"))\n\n item.month = item.code[len(item.pid):]\n if item.month[0] == '0':\n item.month = \"2\" + item.month\n else:\n item.month = \"1\" + item.month\n\n dataitems[item.code] = item\n # print(dataitems)\n return dataitems\n\n @staticmethod\n def getDceData(curDT:datetime.datetime) -> dict:\n '''\n 读取DCE指定日期的行情快照\n\n @curDT 指定的日期\n '''\n\n pname_map = {\n \"聚乙烯\": \"l\",\n \"鸡蛋\": \"jd\",\n \"焦煤\": \"jm\",\n \"豆二\": \"b\",\n \"胶合板\": \"bb\",\n \"玉米\": \"c\",\n \"豆粕\": \"m\",\n \"棕榈油\": \"p\",\n \"玉米淀粉\": \"cs\",\n \"纤维板\": \"fb\",\n \"铁矿石\": \"i\",\n \"焦炭\": \"j\",\n \"豆一\": \"a\",\n \"聚丙烯\": \"pp\",\n \"聚氯乙烯\": \"v\",\n \"豆油\": \"y\",\n \"乙二醇\":\"eg\",\n \"粳米\":\"rr\",\n \"苯乙烯\":\"eb\",\n \"液化石油气\":\"pg\",\n \"生猪\":\"lh\"\n }\n\n url = 'http://www.dce.com.cn/publicweb/quotesdata/dayQuotesCh.html'\n try:\n data = {}\n data['dayQuotes.variety'] = 'all'\n data['dayQuotes.trade_type'] = 0\n data['year'] = curDT.year\n data['month'] = curDT.month - 1\n data['day'] = curDT.day\n html = httpPost(url, data)\n except urllib.error.HTTPError as httperror:\n print(httperror)\n return None\n\n dataitems = {}\n doc = pq(html)\n items = doc('.dataArea') # doc('#printData')\n # # 获取tr items.find('tr')\n lis = items('tr')\n trcount = len(lis)\n # 遍历行\n for tr in range(1, trcount):\n\n tdlis = doc(lis[tr])('td')\n # 商品名称\n pzname = doc(tdlis[0]).text()\n if pzname not in pname_map:\n if \"小计\" not in pzname and \"总计\" not in pzname:\n logging.error(\"未知品种:\" + pzname)\n continue\n\n # 交割月份\n item = DayData()\n item.pid = pname_map[pzname]\n item.code = item.pid + doc(tdlis[1]).text()\n # 收盘价\n spj = doc(tdlis[5]).text()\n item.close = float(spj if spj != '' else 0)\n # 成交量\n item.volume = int(doc(tdlis[10]).text())\n # 持仓量\n item.hold = int(doc(tdlis[11]).text())\n item.month = item.code[len(item.pid):]\n dataitems[item.code] = item\n\n return dataitems\n\n @staticmethod\n def getIneData(curDT:datetime.datetime) -> dict:\n '''\n 读取INE指定日期的行情快照\n\n @curDT 指定的日期\n '''\n dtStr = curDT.strftime('%Y%m%d')\n content = httpGet(\"http://www.ine.cn/data/dailydata/kx/kx%s.dat\" % (dtStr))\n if len(content) == 0:\n return None\n\n items = {}\n root = json.loads(content)\n for day in root['o_curinstrument']:\n pid = day['PRODUCTID'].strip().rstrip('_f')\n dm = day['DELIVERYMONTH']\n if pid != 'sc' or dm == '' or dm == '小计':\n continue\n item = DayData()\n item.pid = pid\n item.code = pid + dm\n item.hold = int(day['OPENINTEREST'])\n item.close = float(day['CLOSEPRICE'])\n item.volume = int(day['VOLUME']) if day['VOLUME']!='' else 0\n item.month = item.code[len(item.pid):]\n items[item.code] = item\n return items\n\n\n def cache_by_date(self, exchg:str, curDT:datetime.datetime):\n '''\n 缓存指定日期指定交易所的行数据\n\n @exchg 交易所代码\n @curDT 指定日期\n '''\n dtStr = curDT.strftime('%Y%m%d')\n\n if dtStr not in self.day_cache:\n self.day_cache[dtStr] = dict()\n\n cacheItem = self.day_cache[dtStr]\n if exchg == 'CFFEX':\n cacheItem[exchg] = WtCacheMonExchg.getCffexData(curDT)\n elif exchg == 'SHFE':\n cacheItem[exchg] = WtCacheMonExchg.getShfeData(curDT)\n elif exchg == 'DCE':\n cacheItem[exchg] = WtCacheMonExchg.getDceData(curDT)\n elif exchg == 'CZCE':\n cacheItem[exchg] = WtCacheMonExchg.getCzceData(curDT)\n elif exchg == 'INE':\n cacheItem[exchg] = WtCacheMonExchg.getIneData(curDT)\n else:\n raise Exception(\"未知交易所代码\" + exchg)\n\n def get_cache(self, exchg:str, curDT:datetime.datetime):\n '''\n 获取指定日期的某个交易所合约的快照数据\n\n @exchg 交易所代码\n @curDT 指定日期\n '''\n dtStr = curDT.strftime('%Y%m%d')\n if dtStr not in self.day_cache or exchg not in self.day_cache[dtStr]:\n self.cache_by_date(exchg, curDT)\n\n if dtStr not in self.day_cache:\n return None\n\n if exchg not in self.day_cache[dtStr]:\n return None\n return self.day_cache[dtStr][exchg]\n\nclass WtCacheMonSS(WtCacheMon):\n '''\n 快照缓存管理器\n 通过读取wtpy的datakit当日生成的快照文件，缓存当日行情数据\n 一般目录为\"数据存储目录/his/snapshots/xxxxxxx.csv\"\n '''\n\n def __init__(self, snapshot_path:str):\n WtCacheMon.__init__(self)\n self.snapshot_path = snapshot_path\n\n def cache_snapshot(self, curDT:datetime):\n '''\n 缓存指定日期的快照数据\n\n @curDT 指定的日期\n '''\n dtStr = curDT.strftime('%Y%m%d')\n\n filename = \"%s%s.csv\" % (self.snapshot_path, dtStr)\n content = readFileContent(filename)\n lines = content.split(\"\\n\")\n\n if dtStr not in self.day_cache:\n self.day_cache[dtStr] = dict()\n\n cacheItem = self.day_cache[dtStr]\n for idx in range(1, len(lines)):\n line = lines[idx]\n if len(line) == 0:\n break\n items = line.split(\",\")\n \n exchg = items[1]\n if exchg not in cacheItem:\n cacheItem[exchg] = dict()\n\n day = DayData()\n day.pid = extractPID(items[2])\n day.code = items[2]\n # 收盘价\n day.close = float(items[6])\n # 成交量\n day.volume = int(items[8])\n # 持仓量\n day.hold = int(items[10])\n day.month = day.code[len(day.pid):]\n if len(day.month) == 3:\n if day.month[0] >= '0' and day.month[0] <= '5':\n day.month = \"2\" + day.month\n else:\n day.month = \"1\" + day.month\n cacheItem[exchg][day.code] = day\n\n def get_cache(self, exchg, curDT:datetime):\n '''\n 获取指定日期的某个交易所合约的快照数据\n\n @exchg 交易所代码\n @curDT 指定日期\n '''\n\n dtStr = curDT.strftime('%Y%m%d')\n if dtStr not in self.day_cache:\n self.cache_snapshot(curDT)\n\n if dtStr not in self.day_cache:\n return None\n\n if exchg not in self.day_cache[dtStr]:\n return None\n return self.day_cache[dtStr][exchg]\n\nclass WtMailNotifier:\n '''\n 邮件通知器\n '''\n def __init__(self, user:str, pwd:str, sender:str=None, host:str=\"smtp.exmail.qq.com\", port=465, isSSL:bool = True):\n self.user = user\n self.pwd = pwd\n self.sender = sender if sender is not None else \"WtHotNotifier<%s>\" % (user)\n self.receivers = list()\n\n self.mail_host = host\n self.mail_port = port\n self.mail_ssl = isSSL\n\n def add_receiver(self, name:str, addr:str):\n '''\n 添加收件人\n\n @name 收件人姓名\n @addr 收件人邮箱地址\n '''\n self.receivers.append({\n \"name\":name,\n \"addr\":addr\n })\n\n def notify(self, hot_changes:dict, sec_changes:dict, nextDT:datetime.datetime, hotFile:str, hotMap:str, secFile:str, secMap:str):\n '''\n 通知主力切换事件\n\n @hot_changes 当日主力切换的规则列表\n @sec_changes 当日次主力切换的规则列表\n @nextDT 生效日期\n @hotFile 主力规则文件\n @hotMap 主力映射文件\n '''\n dtStr = nextDT.strftime('%Y.%m.%d')\n \n import smtplib\n from email.mime.text import MIMEText\n from email.mime.multipart import MIMEMultipart\n from email.mime.application import MIMEApplication\n from email.header import Header\n\n sender = self.sender\n receivers = self.receivers\n\n content = ''\n for exchg in hot_changes:\n for pid in hot_changes[exchg]:\n item = hot_changes[exchg][pid][-1]\n content += '品种%s.%s的主力合约已切换,下个交易日(%s)生效, %s -> %s\\n' % (exchg, pid, dtStr, item[\"from\"], item[\"to\"])\n\n content += '\\n'\n for exchg in sec_changes:\n for pid in sec_changes[exchg]:\n item = sec_changes[exchg][pid][-1]\n content += '品种%s.%s的次主力合约已切换,下个交易日(%s)生效, %s -> %s\\n' % (exchg, pid, dtStr, item[\"from\"], item[\"to\"])\n\n msg_mp = MIMEMultipart()\n msg_mp['From'] = sender # 发送者 \n \n subject = '主力合约换月邮件<%s>' % (dtStr)\n msg_mp['Subject'] = Header(subject, 'utf-8')\n\n content = MIMEText(content, 'plain', 'utf-8')\n msg_mp.attach(content)\n\n xlspart = MIMEApplication(open(hotFile,'rb').read())\n xlspart[\"Content-Type\"] = 'application/octet-stream'\n xlspart.add_header('Content-Disposition','attachment', filename=os.path.basename(hotFile))\n msg_mp.attach(xlspart)\n\n xlspart = MIMEApplication(open(hotMap,'rb').read())\n xlspart[\"Content-Type\"] = 'application/octet-stream'\n xlspart.add_header('Content-Disposition','attachment', filename=os.path.basename(hotMap))\n msg_mp.attach(xlspart)\n\n xlspart = MIMEApplication(open(secFile,'rb').read())\n xlspart[\"Content-Type\"] = 'application/octet-stream'\n xlspart.add_header('Content-Disposition','attachment', filename=os.path.basename(secFile))\n msg_mp.attach(xlspart)\n\n xlspart = MIMEApplication(open(secMap,'rb').read())\n xlspart[\"Content-Type\"] = 'application/octet-stream'\n xlspart.add_header('Content-Disposition','attachment', filename=os.path.basename(secMap))\n msg_mp.attach(xlspart)\n\n if self.mail_ssl:\n smtpObj = smtplib.SMTP_SSL(self.mail_host, self.mail_port)\n else:\n smtpObj = smtplib.SMTP(self.mail_host, self.mail_port)\n\n try:\n smtpObj.ehlo()\n smtpObj.login(self.user, self.pwd) \n logging.info(\"%s 登录成功 %s:%d\", self.user, self.mail_host, self.mail_port)\n except smtplib.SMTPException as ex:\n logging.error(\"邮箱初始化失败：{}\".format(ex))\n\n for item in receivers:\n to = \"%s<%s>\" % (item[\"name\"], item[\"addr\"])\n msg_mp['To'] = Header(to, 'utf-8') # 接收者\n try:\n smtpObj.sendmail(sender, item[\"addr\"], msg_mp.as_string())\n logging.info(\"邮件发送失败，收件人: %s\", to)\n except smtplib.SMTPException as ex:\n logging.error(\"邮件发送失败，收件人：{}, {}\".format(to, ex))\n\nclass WtHotPicker:\n '''\n 主力选择器\n '''\n def __init__(self, markerFile:str = \"./marker.json\", hotFile:str = \"../Common/hots.json\", secFile:str = None):\n self.marker_file = markerFile\n self.hot_file = hotFile\n self.sec_file = secFile\n\n self.mail_notifier:WtMailNotifier = None\n self.cache_monitor:WtCacheMon = None\n\n self.current_hots = None\n self.current_secs = None\n\n def set_cacher(self, cacher:WtCacheMon):\n '''\n 设置日行情缓存器\n '''\n self.cache_monitor = cacher\n \n def set_mail_notifier(self, notifier:WtMailNotifier):\n '''\n 设置邮件通知器\n '''\n self.mail_notifier = notifier\n\n def pick_exchg_hots(self, exchg:str, beginDT:datetime.datetime, endDT:datetime.datetime, alg:int = 0):\n '''\n 确定指定市场的主力合约\n\n @exchg 交易所代码\n @beginDT 开始日期\n @endDT 截止日期\n @alg 切换规则算法，0-除中金所外，按成交量确定，1-中金所，按照成交量和总持共同确定\n '''\n\n cacheMon = self.cache_monitor\n current_hots = self.current_hots\n current_secs = self.current_secs\n\n if exchg not in current_hots:\n current_hots[exchg] = dict()\n\n if exchg not in current_secs:\n current_secs[exchg] = dict()\n\n lastHots = current_hots[exchg]\n lastSecs = current_secs[exchg]\n\n hot_switches = {}\n sec_switches = {}\n\n curDT = beginDT\n\n while curDT <= endDT:\n hots = {}\n seconds = {}\n logging.info(\"[%s]开始拉取%s数据\" % (exchg, curDT.strftime('%Y%m%d')))\n items = cacheMon.get_cache(exchg, curDT)\n if items is not None:\n wd = curDT.weekday()\n fri_cnt = countFridays(curDT)\n cur_month = curDT.strftime('%Y%m')[2:]\n next_month = int(cur_month)+1\n if next_month % 100 == 13:\n next_month = str(int(cur_month[:2])+1)+\"01\"\n else:\n next_month = str(next_month)\n\n items_by_pid = dict()\n for code in items:\n item = items[code]\n pid = item.pid\n\n if pid not in items_by_pid:\n items_by_pid[pid] = list()\n\n items_by_pid[pid].append(item)\n\n for pid in items_by_pid:\n ay = items_by_pid[pid]\n if alg == 1:\n #ay.sort(key=functools.cmp_to_key(cmp_alg_01)) #按总持排序\n ay.sort(key=lambda x : x.volume) #按成交量\n elif alg == 0:\n ay.sort(key=lambda x : x.hold) #按总持\n hot = ay[-1]\n\n if len(ay) > 1:\n sec = ay[-2]\n #中金所算法，如果是当月第三个周三，并且主力合约月份小于次主力合约月份，\n #说明没有根据数据自动换月，强制进行换月\n if alg == 1 and wd == 2 and fri_cnt == 3 and hot.month==cur_month:\n for item in ay:\n if item.month == next_month:\n hot = item\n break\n\n #如果主力合约月份大于等于次主力合约，则次主力递延一位\n if hot.month >= sec.month:\n sec = ay[-3]\n \n seconds[pid] = sec.code\n\n hots[pid] = hot.code\n \n for key in hots.keys():\n nextDT = curDT + datetime.timedelta(days=1)\n if key not in lastHots:\n item = {}\n item[\"date\"] = int(curDT.strftime('%Y%m%d'))\n item[\"from\"] = \"\"\n item[\"to\"] = hots[key]\n item[\"oldclose\"] = 0.0\n item[\"newclose\"] = items[hots[key]].close\n hot_switches[key] = [item]\n lastHots[key] = hots[key]\n logging.info(\"[%s]品种%s主力确认, 确认日期: %s, %s\", exchg,key, nextDT.strftime('%Y%m%d'), hots[key])\n else:\n oldcode = lastHots[key]\n newcode = hots[key]\n oldItem = None\n if oldcode in items:\n oldItem = items[oldcode]\n newItem = items[newcode]\n if oldItem is None or newItem.month > oldItem.month:\n item = {}\n item[\"date\"] = int(nextDT.strftime('%Y%m%d'))\n item[\"from\"] = oldcode\n item[\"to\"] = newcode\n if oldcode in items:\n item[\"oldclose\"] = items[oldcode].close\n else:\n item[\"oldclose\"] = 0.0\n item[\"date\"] = int(curDT.strftime('%Y%m%d'))\n item[\"newclose\"] = items[newcode].close\n if key not in hot_switches:\n hot_switches[key] = list()\n hot_switches[key].append(item)\n logging.info(\"[%s]品种%s主力切换 切换日期: %s，%s -> %s\", exchg, key, nextDT.strftime('%Y%m%d'), lastHots[key], hots[key])\n lastHots[key] = hots[key]\n\n for key in seconds.keys():\n nextDT = curDT + datetime.timedelta(days=1)\n if key not in lastSecs:\n item = {}\n item[\"date\"] = int(curDT.strftime('%Y%m%d'))\n item[\"from\"] = \"\"\n item[\"to\"] = seconds[key]\n item[\"oldclose\"] = 0.0\n item[\"newclose\"] = items[seconds[key]].close\n sec_switches[key] = [item]\n lastSecs[key] = seconds[key]\n logging.info(\"[%s]品种%s次主力确认, 确认日期: %s, %s\", exchg,key, nextDT.strftime('%Y%m%d'), seconds[key])\n else:\n oldcode = lastSecs[key]\n newcode = seconds[key]\n oldItem = None\n if oldcode in items:\n oldItem = items[oldcode]\n newItem = items[newcode]\n if oldItem is None or newItem.month > oldItem.month:\n item = {}\n item[\"date\"] = int(nextDT.strftime('%Y%m%d'))\n item[\"from\"] = oldcode\n item[\"to\"] = newcode\n if oldcode in items:\n item[\"oldclose\"] = items[oldcode].close\n else:\n item[\"oldclose\"] = 0.0\n item[\"date\"] = int(curDT.strftime('%Y%m%d'))\n item[\"newclose\"] = items[newcode].close\n if key not in sec_switches:\n sec_switches[key] = list()\n sec_switches[key].append(item)\n logging.info(\"[%s]品种%s次主力切换 切换日期: %s，%s -> %s\", exchg, key, nextDT.strftime('%Y%m%d'), lastSecs[key], seconds[key])\n lastSecs[key] = seconds[key]\n # 日期递增\n curDT = curDT + datetime.timedelta(days=1)\n return hot_switches,sec_switches\n \n def merge_switch_list(self, total, exchg, switch_list):\n '''\n 合并主力切换规则\n \n @total 已有的全部切换规则\n @exchg 交易所代码\n @switcg_list 新的切换规则\n '''\n if exchg not in total:\n total[exchg] = switch_list\n logging.info(\"[%s]全市场主力切换规则重构\" % (exchg))\n return True, total\n \n bChanged = False\n for pid in switch_list:\n if pid not in total[exchg]:\n total[exchg][pid] = switch_list[pid]\n logging.info(\"[%s]品种%s主力切换规则重构\" % (exchg, pid))\n bChanged = True\n else:\n total[exchg][pid].extend(switch_list[pid])\n logging.info(\"[%s]品种%s主力切换规则追加%d条\" % (exchg, pid, len(switch_list[pid])))\n bChanged = True\n return bChanged, total\n\n def execute_rebuild(self, beginDate:datetime.datetime = None, endDate:datetime.datetime = None, exchanges = [\"CFFEX\", \"SHFE\", \"CZCE\", \"DCE\", \"INE\"], wait=False):\n '''\n 重构全部的主力切换规则\n 不依赖现有数据，全部重新确定主力合约的切换规则\n\n @beginDate 开始日期\n @endDate 截止日期\n @exchanges 要重构的交易所列表\n @wait 每个日期切换是否等待，等待时间1s，主要针对从交易所官网拉取，防止被拉黑名单\n '''\n if endDate is None:\n endDate = datetime.datetime.now()\n\n if beginDate is None:\n beginDate = datetime.datetime.strptime(\"2016-01-01\", '%Y-%m-%d')\n \n total_hots = dict()\n total_secs = dict()\n\n self.current_hots = dict()\n self.current_secs = dict()\n\n for exchg in exchanges:\n self.current_hots[exchg] = dict()\n self.current_secs[exchg] = dict()\n \n hot_changes = dict()\n sec_changes = dict()\n curDate = beginDate\n while curDate <= endDate:\n if wait:\n time.sleep(1)\n for exchg in exchanges:\n alg = 1 if exchg=='CFFEX' else 0 # 中金所的换月算法和其他交易所不同\n hotRules,secRules = self.pick_exchg_hots(exchg, curDate, curDate, alg=alg)\n\n if len(hotRules.keys()) > 0:\n hasChange,total_hots = self.merge_switch_list(total_hots, exchg, hotRules)\n\n if exchg not in hot_changes:\n hot_changes[exchg] = dict()\n hot_changes[exchg].update(hotRules)\n\n if len(secRules.keys()) > 0:\n hasChange,total_secs = self.merge_switch_list(total_secs, exchg, secRules)\n\n if exchg not in sec_changes:\n sec_changes[exchg] = dict()\n sec_changes[exchg].update(secRules)\n\n curDate = curDate + datetime.timedelta(days=1)\n\n #日期标记要保存\n marker = dict()\n marker[\"date\"] = int(endDate.strftime('%Y%m%d'))\n output = open(self.marker_file, 'w')\n output.write(json.dumps(marker, sort_keys=True, indent = 4))\n output.close()\n \n logging.info(\"主力切换规则已更新\")\n\n output = open(self.hot_file, 'w')\n output.write(json.dumps(total_hots, sort_keys=True, indent = 4))\n output.close()\n\n if self.sec_file is not None:\n output = open(self.sec_file, 'w')\n output.write(json.dumps(total_secs, sort_keys=True, indent = 4))\n output.close()\n\n output = open(\"hotmap.json\", 'w')\n output.write(json.dumps(self.current_hots, sort_keys=True, indent = 4))\n output.close()\n\n output = open(\"secmap.json\", 'w')\n output.write(json.dumps(self.current_secs, sort_keys=True, indent = 4))\n output.close()\n\n if self.mail_notifier is not None:\n self.mail_notifier.notify(hot_changes, sec_changes, endDate, hotFile, \"hotmap.json\", secFile, \"secmap.json\")\n\n return total_hots,total_secs\n \n def execute_increment(self, endDate:datetime.datetime = None, exchanges = [\"CFFEX\", \"SHFE\", \"CZCE\", \"DCE\", \"INE\"]):\n '''\n 增量更新主力切换规则\n 会自动加载marker.json取得上次更新的日期，并读取hots.json确定当前的映射规则\n\n @endDate 截止日期\n @exchanges 要重构的交易所列表\n '''\n\n if endDate is None:\n endDate = datetime.datetime.now()\n\n markerFile = self.marker_file\n hotFile = self.hot_file\n secFile = self.sec_file\n\n marker = {\"date\":\"0\"}\n c = readFileContent(markerFile)\n if len(c) > 0:\n marker = json.loads(c)\n\n c = readFileContent(hotFile)\n total_hots = dict()\n if len(c) > 0:\n total_hots = json.loads(c)\n else:\n marker[\"date\"] = \"0\"\n\n c = readFileContent(secFile)\n total_secs = dict()\n if len(c) > 0:\n total_secs = json.loads(c)\n else:\n marker[\"date\"] = \"0\"\n\n lastDate = str(marker[\"date\"])\n if lastDate >= endDate.strftime('%Y%m%d'):\n logging.info(\"上次更新日期%s大于结束日期%s，退出更新\" % (lastDate, endDate.strftime('%Y%m%d')))\n exit()\n elif lastDate != \"0\":\n beginDT = datetime.datetime.strptime(lastDate, \"%Y%m%d\") + datetime.timedelta(days=1)\n else:\n beginDT = datetime.datetime.strptime(\"2016-01-01\", '%Y-%m-%d')\n \n self.current_hots = dict()\n self.current_secs = dict()\n\n for exchg in total_hots:\n if exchg not in self.current_hots:\n self.current_hots[exchg] = dict()\n\n for pid in total_hots[exchg]:\n ay = total_hots[exchg][pid]\n self.current_hots[exchg][pid] = ay[-1][\"to\"]\n\n for exchg in total_secs:\n if exchg not in self.current_secs:\n self.current_secs[exchg] = dict()\n\n for pid in total_secs[exchg]:\n ay = total_secs[exchg][pid]\n self.current_secs[exchg][pid] = ay[-1][\"to\"]\n \n bChanged = False\n hot_changes = dict()\n sec_changes = dict()\n for exchg in exchanges:\n logging.info(\"[%s]开始分析主力换月数据\" % exchg)\n alg = 1 if exchg=='CFFEX' else 0 # 中金所的换月算法和其他交易所不同\n hotRules,secRules = self.pick_exchg_hots(exchg, beginDT, endDate, alg=alg)\n\n if len(hotRules.keys()) > 0:\n hasChange,total_hots = self.merge_switch_list(total_hots, exchg, hotRules)\n bChanged = bChanged or hasChange\n hot_changes[exchg] = hotRules\n\n if len(secRules.keys()) > 0:\n hasChange,total_secs = self.merge_switch_list(total_secs, exchg, secRules)\n bChanged = bChanged or hasChange\n sec_changes[exchg] = secRules\n\n\n #日期标记要保存\n marker = dict()\n marker[\"date\"] = int(endDate.strftime('%Y%m%d'))\n output = open(markerFile, 'w')\n output.write(json.dumps(marker, sort_keys=True, indent = 4))\n output.close()\n \n if bChanged:\n logging.info(\"主力切换规则已更新\")\n\n output = open(hotFile, 'w')\n output.write(json.dumps(total_hots, sort_keys=True, indent = 4))\n output.close()\n\n output = open(secFile, 'w')\n output.write(json.dumps(total_secs, sort_keys=True, indent = 4))\n output.close()\n\n output = open(\"hotmap.json\", 'w')\n output.write(json.dumps(self.current_hots, sort_keys=True, indent = 4))\n output.close()\n\n output = open(\"secmap.json\", 'w')\n output.write(json.dumps(self.current_secs, sort_keys=True, indent = 4))\n output.close()\n\n if self.mail_notifier is not None:\n self.mail_notifier.notify(hot_changes, sec_changes, endDate, hotFile, \"hotmap.json\", secFile, \"secmap.json\")\n else:\n logging.info(\"主力切换规则未更新，不保存数据\")\n" }, { "path": "wtpy/apps/__init__.py", "content": "from .WtBtAnalyst import WtBtAnalyst\nfrom .WtCtaOptimizer import WtCtaOptimizer\nfrom .WtHotPicker import WtHotPicker, WtCacheMonExchg, WtCacheMonSS, WtMailNotifier, WtCacheMon\n\n__all__ = [\"WtBtAnalyst\",\"WtCtaOptimizer\", \"WtHotPicker\", \"WtCacheMonExchg\", \"WtCacheMonSS\", \"WtMailNotifier\", \"WtCacheMon\"]" }, { "path": "wtpy/apps/datahelper/DHBaostock.py", "content": "from wtpy.apps.datahelper.DHDefs import BaseDataHelper, DBHelper\nimport baostock as bs\nfrom datetime import datetime\nimport json\nimport os\n\ndef transCodes(codes:list) -> list:\n ret = list()\n for code in codes:\n items = code.split(\".\")\n exchg = items[0]\n if exchg == \"SSE\":\n ret.append(\"sh.\"+items[1])\n else:\n ret.append(\"sz.\"+items[1])\n\n return ret\n\nclass DHBaostock(BaseDataHelper):\n\n def __init__(self):\n BaseDataHelper.__init__(self)\n print(\"Baostock helper has been created.\")\n return\n\n def auth(self, **kwargs):\n if self.isAuthed:\n return\n\n bs.login()\n self.isAuthed = True\n print(\"Baostock has been authorized.\")\n\n def dmpCodeListToFile(self, filename:str, hasIndex:bool=True, hasStock:bool=True):\n raise Exception(\"Baostock has not code list api\")\n\n def dmpAdjFactorsToFile(self, codes:list, filename:str):\n codes = transCodes(codes)\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n count = 0\n length = len(codes)\n for code in codes:\n exchg = code[:2]\n if exchg == 'sh':\n exchg = 'SSE'\n else:\n exchg = 'SZSE'\n count += 1\n\n stocks[exchg][code[3:]] = list()\n print(\"Fetching adjust factors of %s(%d/%s)...\" % (code, count, length))\n rs = bs.query_adjust_factor(code=code, start_date=\"1990-01-01\")\n\n if rs.error_code != '0':\n print(\"Error occured: %s\" % (rs.error_msg))\n continue\n \n while rs.next():\n items = rs.get_row_data()\n date = int(items[1].replace(\"-\",\"\"))\n factor = float(items[4])\n stocks[exchg][code[3:]].append({\n \"date\": date,\n \"factor\": factor\n })\n \n print(\"Writing adjust factors into file %s...\" % (filename))\n f = open(filename, 'w+')\n f.write(json.dumps(stocks, sort_keys=True, indent=4, ensure_ascii=False))\n f.close()\n\n def dmpBarsToFile(self, folder:str, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n codes = transCodes(codes)\n\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n start_date = start_date.strftime(\"%Y-%m-%d\")\n end_date = end_date.strftime(\"%Y-%m-%d\")\n\n freq = ''\n isDay = False\n filetag = ''\n fields = \"\"\n if period == 'day':\n freq = 'd'\n isDay = True\n filetag = 'd'\n fields = \"date,open,high,low,close,volume,amount\"\n elif period == \"min5\":\n freq = '5'\n filetag = 'm5'\n fields = \"date,time,open,high,low,close,volume,amount\"\n else:\n raise Exception(\"Baostock has only bars of frequency day and min5\")\n\n count = 0\n length = len(codes)\n for code in codes:\n exchg = code[:2]\n if exchg == 'sh':\n exchg = 'SSE'\n else:\n exchg = 'SZSE'\n count += 1\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, code, count, length))\n rs = bs.query_history_k_data_plus(code=code, fields=fields, start_date=start_date, end_date=end_date, frequency=freq)\n content = \"date,time,open,high,low,close,volume,turnover\\n\"\n if rs.error_code != '0':\n print(\"Error occured: %s\" % (rs.error_msg))\n continue\n\n while rs.next():\n items = rs.get_row_data().copy()\n if isDay:\n items.insert(1, \"0\")\n else:\n time = items[1][-9:-3]\n items[1] = time[:2]+\":\"+time[2:4]+\":\"+time[4:]\n content += \",\".join(items) + \"\\n\"\n\n filename = \"%s.%s_%s.csv\" % (exchg, code[3:], filetag)\n filepath = os.path.join(folder, filename)\n print(\"Writing bars into file %s...\" % (filepath))\n f = open(filepath, \"w\", encoding=\"utf-8\")\n f.write(content)\n f.close()\n\n def dmpAdjFactorsToDB(self, dbHelper:DBHelper, codes:list):\n codes = transCodes(codes)\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n\n count = 0\n length = len(codes)\n for code in codes:\n exchg = code[:2]\n if exchg == 'sh':\n exchg = 'SSE'\n else:\n exchg = 'SZSE'\n count += 1\n \n print(\"Fetching adjust factors of %s(%d/%s)...\" % (code, count, length))\n stocks[exchg][code[3:]] = list()\n rs = bs.query_adjust_factor(code=code, start_date=\"1990-01-01\")\n\n if rs.error_code == '0':\n print(\"Error occured: %s\" % (rs.error_msg))\n continue\n \n while rs.next():\n items = rs.get_row_data()\n date = int(items[1].replace(\"-\",\"\"))\n factor = float(items[4])\n stocks[exchg][code[3:]].append({\n \"date\": date,\n \"factor\": factor\n })\n \n print(\"Writing adjust factors into database...\")\n dbHelper.writeFactors(stocks)\n\n def dmpBarsToDB(self, dbHelper:DBHelper, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n codes = transCodes(codes)\n\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n start_date = start_date.strftime(\"%Y-%m-%d\")\n end_date = end_date.strftime(\"%Y-%m-%d\")\n\n freq = ''\n isDay = False\n fields = \"\"\n if period == 'day':\n freq = 'd'\n isDay = True\n fields = \"date,open,high,low,close,volume,amount\"\n elif period == \"min5\":\n freq = '5'\n fields = \"date,time,open,high,low,close,volume,amount\"\n else:\n raise Exception(\"Baostock has only bars of frequency day and min5\")\n\n count = 0\n length = len(codes)\n for code in codes:\n exchg = code[:2]\n if exchg == 'sh':\n exchg = 'SSE'\n else:\n exchg = 'SZSE'\n count += 1\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, code, count, length))\n rs = bs.query_history_k_data_plus(code=code, fields=fields, start_date=start_date, end_date=end_date, frequency=freq)\n bars = []\n while (rs.error_code == '0') & rs.next():\n items = rs.get_row_data()\n if isDay:\n bars.append({\n \"exchange\":exchg,\n \"code\":code[3:],\n \"date\": int(items[0].replace(\"-\",\"\")),\n \"time\": 0,\n \"open\": float(items[1]),\n \"high\": float(items[2]),\n \"low\": float(items[3]),\n \"close\": float(items[4]),\n \"volume\": float(items[5]),\n \"turnover\": float(items[6])\n })\n else:\n time = int(items[1][-9:-5])\n bars.append({\n \"exchange\":exchg,\n \"code\":code[3:],\n \"date\": int(items[0].replace(\"-\",\"\")),\n \"time\": time,\n \"open\": float(items[2]),\n \"high\": float(items[3]),\n \"low\": float(items[4]),\n \"close\": float(items[5]),\n \"volume\": float(items[6]),\n \"turnover\": float(items[7])\n })\n\n print(\"Writing bars into database...\")\n dbHelper.writeBars(bars, period)" }, { "path": "wtpy/apps/datahelper/DHDefs.py", "content": "from datetime import datetime\n\nclass DBHelper:\n\n def __init__(self):\n pass\n\n def initDB(self):\n '''\n 初始化数据库，主要是建表等工作\n '''\n pass\n\n def writeBars(self, bars:list, period=\"day\"):\n '''\n 将K线存储到数据库中\\n\n @bars K线序列\\n\n @period K线周期\n '''\n pass\n\n def writeFactors(self, factors:dict):\n '''\n 将复权因子存储到数据库中\\n\n @factors 复权因子\n '''\n pass\n\n\nclass BaseDataHelper:\n\n def __init__(self):\n self.isAuthed = False\n pass\n\n def __check__(self):\n if not self.isAuthed:\n raise Exception(\"This module has not authorized yet!\")\n\n def auth(self, **kwargs):\n '''\n 模块认证\n '''\n pass\n\n def dmpCodeListToFile(self, filename:str, hasIndex:bool=True, hasStock:bool=True):\n '''\n 将代码列表导出到文件\\n\n @filename 要输出的文件名，json格式\\n\n @hasIndex 是否包含指数\\n\n @hasStock 是否包含股票\\n\n '''\n pass\n\n def dmpAdjFactorsToFile(self, codes:list, filename:str):\n '''\n 将除权因子导出到文件\\n\n @codes 股票列表，格式如[\"SSE.600000\",\"SZSE.000001\"]\\n\n @filename 要输出的文件名，json格式\n '''\n pass\n\n def dmpBarsToFile(self, folder:str, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n '''\n 将K线导出到指定的目录下的csv文件，文件名格式如SSE.600000_d.csv\\n\n @folder 要输出的文件夹\\n\n @codes 股票列表，格式如[\"SSE.600000\",\"SZSE.000001\"]\\n\n @start_date 开始日期，datetime类型，传None则自动设置为1990-01-01\\n\n @end_date 结束日期，datetime类型，传None则自动设置为当前日期\\n\n @period K线周期，支持day、min1、min5\\n\n '''\n pass\n\n def dmpAdjFactorsToDB(self, dbHelper:DBHelper, codes:list):\n '''\n 将除权因子导出到数据库\\n\n @codes 股票列表，格式如[\"SSE.600000\",\"SZSE.000001\"]\\n\n @dbHelper 数据库辅助模块\n '''\n pass\n\n def dmpBarsToDB(self, dbHelper:DBHelper, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n '''\n 将K线导出到数据库\\n\n @dbHelper 数据库辅助模块\\n\n @codes 股票列表，格式如[\"SSE.600000\",\"SZSE.000001\"]\\n\n @start_date 开始日期，datetime类型，传None则自动设置为1990-01-01\\n\n @end_date 结束日期，datetime类型，传None则自动设置为当前日期\\n\n @period K线周期，支持day、min1、min5\\n\n '''\n pass" }, { "path": "wtpy/apps/datahelper/DHFactory.py", "content": "from wtpy.apps.datahelper.DHDefs import BaseDataHelper\nfrom wtpy.apps.datahelper.DHBaostock import DHBaostock\nfrom wtpy.apps.datahelper.DHTushare import DHTushare\nfrom wtpy.apps.datahelper.DHRqData import DHRqData\n\nclass DHFactory:\n \n @staticmethod\n def createHelper(name:str) -> BaseDataHelper:\n '''\n 创建数据辅助模块\\n\n @name 模块名称，目前支持的有tushare、baostock、rqdata\n '''\n name = name.lower()\n if name == \"baostock\":\n return DHBaostock()\n elif name == \"tushare\":\n return DHTushare()\n elif name == \"rqdata\":\n return DHRqData()\n else:\n raise Exception(\"Cannot recognize helper with name %s\" % (name))\n" }, { "path": "wtpy/apps/datahelper/DHRqData.py", "content": "from wtpy.apps.datahelper.DHDefs import BaseDataHelper, DBHelper\nimport rqdatac as rq\nfrom datetime import datetime, timedelta\nimport json\nimport os\n\ndef exchgStdToRQ(exchg:str) -> str:\n if exchg == 'SSE':\n return \"XSHG\"\n elif exchg == 'SZSE':\n return \"XSHE\"\n else:\n return exchg\n\ndef exchgRQToStd(exchg:str) -> str:\n if exchg == 'XSHG':\n return \"SSE\"\n elif exchg == 'XSHE':\n return \"SZSE\"\n else:\n return exchg\n\ndef stdCodeToRQ(stdCode:str):\n stdCode = stdCode.upper()\n items = stdCode.split(\".\")\n exchg = exchgStdToRQ(items[0])\n if len(items) == 2:\n # 简单股票代码，格式如SSE.600000\n return items[1] + \".\" + exchg\n elif items[1] in [\"IDX\",\"ETF\",\"STK\",\"OPT\"]:\n # 标准股票代码，格式如SSE.IDX.000001\n return items[2] + \".\" + exchg\n elif len(items) == 3:\n # 标准期货代码，格式如CFFEX.IF.2103\n if items[2] != 'HOT':\n return ''.join(items[1:])\n else:\n return items[1] + \"88\"\n\n \n\n\nclass DHRqData(BaseDataHelper):\n\n def __init__(self):\n BaseDataHelper.__init__(self)\n print(\"Rqdata helper has been created.\")\n return\n\n def auth(self, **kwargs):\n if self.isAuthed:\n return\n\n rq.init(**kwargs)\n self.isAuthed = True\n print(\"Rqdata has been authorized.\")\n\n def dmpCodeListToFile(self, filename:str, hasIndex:bool=True, hasStock:bool=True):\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n \n #个股列表\n if hasStock:\n print(\"Fetching stock list...\")\n df_stocks = rq.all_instruments(type='CS', market=\"cn\")\n for idx, row in df_stocks.iterrows():\n rawcode = row[\"order_book_id\"][:6]\n exchg = row[\"exchange\"]\n if exchg == 'XSHG':\n exchg = \"SSE\"\n else:\n exchg = \"SZSE\"\n sInfo = dict()\n sInfo[\"exchg\"] = exchg \n sInfo[\"code\"] = rawcode\n sInfo[\"name\"] = row[\"symbol\"]\n sInfo[\"product\"] = \"STK\" \n \n stocks[sInfo[\"exchg\"]][rawcode] = sInfo\n\n if hasIndex:\n #上证指数列表\n print(\"Fetching index list...\")\n df_stocks = rq.all_instruments(type='INDX', market=\"cn\")\n for idx, row in df_stocks.iterrows():\n rawcode = row[\"order_book_id\"][:6]\n exchg = row[\"exchange\"]\n if exchg == 'XSHG':\n exchg = \"SSE\"\n else:\n exchg = \"SZSE\"\n sInfo = dict()\n sInfo[\"exchg\"] = exchg \n sInfo[\"code\"] = rawcode\n sInfo[\"name\"] = row[\"symbol\"]\n sInfo[\"product\"] = \"IDX\" \n \n stocks[sInfo[\"exchg\"]][rawcode] = sInfo\n\n print(\"Writing code list into file %s...\" % (filename))\n f = open(filename, 'w')\n f.write(json.dumps(stocks, sort_keys=True, indent=4, ensure_ascii=False))\n f.close()\n\n def dmpAdjFactorsToFile(self, codes:list, filename:str):\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n count += 1\n rq_code = code + \".\" + exchgStdToRQ(exchg)\n\n stocks[exchg][code] = list()\n print(\"Fetching adjust factors of %s(%d/%s)...\" % (code, count, length))\n df_factors = rq.get_ex_factor(order_book_ids=rq_code, start_date=\"1990-01-01\")\n \n for idx, row in df_factors.iterrows():\n date = row['announcement_date'].to_pydatetime()\n date = date + timedelta(days=1)\n factor = float(row['ex_cum_factor'])\n stocks[exchg][code].append({\n \"date\": int(date.strftime(\"%Y%m%d\")),\n \"factor\": factor\n })\n \n print(\"Writing adjust factors into file %s...\" % (filename))\n f = open(filename, 'w+')\n f.write(json.dumps(stocks, sort_keys=True, indent=4, ensure_ascii=False))\n f.close()\n\n def dmpBarsToFile(self, folder:str, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n freq = ''\n isDay = False\n filetag = ''\n if period == 'day':\n freq = '1d'\n isDay = True\n filetag = 'd'\n elif period == \"min5\":\n freq = '5m'\n filetag = 'm5'\n elif period == \"min1\":\n freq = '1m'\n filetag = 'm1'\n else:\n raise Exception(\"Unrecognized period\")\n \n count = 0\n length = len(codes)\n for stdCode in codes:\n count += 1\n rq_code = stdCodeToRQ(stdCode)\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, stdCode, count, length))\n df_bars = rq.get_price(order_book_ids = rq_code,start_date=start_date, end_date=end_date,frequency=freq,adjust_type='none',expect_df=True)\n content = \"date,time,open,high,low,close,volume,turnover,hold\\n\"\n total_nums = len(df_bars)\n cur_num = 0\n for idx, row in df_bars.iterrows():\n trade_date = row.name[1].to_pydatetime()\n date = trade_date.strftime(\"%Y-%m-%d\")\n if isDay:\n time = '0'\n else:\n time = trade_date.strftime(\"%H:%M:%S\")\n o = str(row[\"open\"])\n h = str(row[\"high\"])\n l = str(row[\"low\"])\n c = str(row[\"close\"])\n v = str(row[\"volume\"])\n t = str(row[\"total_turnover\"])\n items = [date, time, o, h, l, c, v, t]\n if \"open_interest\" in row:\n items.append(str(row[\"open_interest\"]))\n\n content += \",\".join(items) + \"\\n\"\n\n cur_num += 1\n if cur_num % 500 == 0:\n print(\"Processing bars %d/%d...\" % (cur_num, total_nums))\n\n filename = \"%s_%s.csv\" % (stdCode, filetag)\n filepath = os.path.join(folder, filename)\n print(\"Writing bars into file %s...\" % (filepath))\n f = open(filepath, \"w\", encoding=\"utf-8\")\n f.write(content)\n f.close()\n\n def dmpAdjFactorsToDB(self, dbHelper:DBHelper, codes:list):\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n count += 1\n rq_code = code + \".\" + exchgStdToRQ(exchg)\n\n stocks[exchg][code] = list()\n print(\"Fetching adjust factors of %s(%d/%s)...\" % (code, count, length))\n df_factors = rq.get_ex_factor(order_book_ids=rq_code, start_date=\"1990-01-01\")\n \n for idx, row in df_factors.iterrows():\n date = row['announcement_date'].to_pydatetime()\n date = date + timedelta(days=1)\n factor = float(row['ex_cum_factor'])\n stocks[exchg][code].append({\n \"date\": int(date.strftime(\"%Y%m%d\")),\n \"factor\": factor\n })\n \n print(\"Writing adjust factors into database...\")\n dbHelper.writeFactors(stocks)\n\n def dmpBarsToDB(self, dbHelper:DBHelper, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n freq = ''\n isDay = False\n if period == 'day':\n freq = '1d'\n isDay = True\n elif period == \"min5\":\n freq = '5m'\n elif period == \"min1\":\n freq = '1m'\n else:\n raise Exception(\"Unrecognized period\")\n \n count = 0\n length = len(codes)\n for stdCode in codes:\n items = stdCode.split(\".\")\n exchg = items[0]\n code = stdCode[(len(exchg)+1):]\n rq_code = stdCodeToRQ(stdCode)\n count += 1\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, stdCode, count, length))\n df_bars = rq.get_price(order_book_ids = rq_code,start_date=start_date, end_date=end_date,frequency=freq,adjust_type='none',expect_df=True)\n bars = list()\n total_nums = len(df_bars)\n cur_num = 0\n for idx, row in df_bars.iterrows():\n trade_date = row.name[1].to_pydatetime()\n date = int(trade_date.strftime(\"%Y%m%d\"))\n if isDay:\n time = 0\n else:\n time = int(trade_date.strftime(\"%H%M\"))\n curBar = {\n \"exchange\":exchg,\n \"code\": code,\n \"date\": date,\n \"time\": time,\n \"open\": row[\"open\"],\n \"high\": row[\"open\"],\n \"low\": row[\"open\"],\n \"close\": row[\"open\"],\n \"volume\": row[\"volume\"],\n \"turnover\": row[\"total_turnover\"]\n }\n\n if \"settlement\" in row:\n curBar[\"settle\"] = row[\"settlement\"]\n\n if \"open_interest\" in row:\n curBar[\"interest\"] = row[\"open_interest\"]\n\n bars.append(curBar)\n cur_num += 1\n if cur_num % 500 == 0:\n print(\"Processing bars %d/%d...\" % (cur_num, total_nums))\n\n print(\"Writing bars into database...\")\n dbHelper.writeBars(bars, period)" }, { "path": "wtpy/apps/datahelper/DHTushare.py", "content": "from wtpy.apps.datahelper.DHDefs import BaseDataHelper, DBHelper\nimport tushare as ts\nfrom datetime import datetime\nimport json\nimport os\n\ndef transCode(stdCode:str) -> str:\n items = stdCode.split(\".\")\n exchg = items[0]\n if exchg == \"SSE\":\n exchg = \"SH\"\n elif exchg == \"SZSE\":\n exchg = \"SZ\"\n \n if exchg in ['SH','SZ']:\n rawCode = ''\n if len(items) > 2:\n rawCode = items[2]\n else:\n rawCode = items[1]\n else:\n # 期货合约代码，格式为DCE.a.2018\n rawCode = ''\n if exchg == \"CZCE\":\n rawCode = items[1] + items[2][1:]\n else:\n rawCode = ''.join(items[1:])\n return rawCode.upper() + \".\" + exchg\n\n \n\nclass DHTushare(BaseDataHelper):\n\n def __init__(self):\n BaseDataHelper.__init__(self)\n self.api = None\n self.use_pro = True\n\n print(\"Tushare helper has been created.\")\n return\n\n def auth(self, **kwargs):\n if self.isAuthed:\n return\n\n if \"use_pro\" in kwargs:\n self.use_pro = kwargs[\"use_pro\"]\n kwargs.pop(\"use_pro\")\n\n self.api = ts.pro_api(**kwargs)\n self.isAuthed = True\n print(\"Tushare has been authorized, use_pro is %s.\" % (\"enabled\" if self.use_pro else \"disabled\"))\n\n def dmpCodeListToFile(self, filename:str, hasIndex:bool=True, hasStock:bool=True):\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n \n #个股列表\n if hasStock:\n print(\"Fetching stock list...\")\n df_stocks = self.api.stock_basic(exchange='', list_status='L', fields='ts_code,symbol,name,area,industry,list_date')\n for idx, row in df_stocks.iterrows():\n code = row[\"ts_code\"]\n rawcode = row[\"symbol\"]\n sInfo = dict()\n pid = \"STK\"\n if code[-2:] == \"SH\":\n sInfo[\"exchg\"] = \"SSE\"\n else:\n sInfo[\"exchg\"] = \"SZSE\"\n code = rawcode #code[-2:] + rawcode\n sInfo[\"code\"] = code\n sInfo[\"name\"] = row[\"name\"]\n sInfo[\"product\"] = pid \n \n stocks[sInfo[\"exchg\"]][code] = sInfo\n\n if hasIndex:\n #上证指数列表\n print(\"Fetching index list of SSE...\")\n df_stocks = self.api.index_basic(market='SSE')\n for idx, row in df_stocks.iterrows():\n code = row[\"ts_code\"]\n rawcode = code[:6]\n if rawcode[0] != '0':\n continue\n \n sInfo = dict()\n sInfo[\"exchg\"] = \"SSE\"\n code = rawcode #\"SH\" + rawcode\n sInfo[\"code\"] = code\n sInfo[\"name\"] = row[\"name\"]\n sInfo[\"product\"] = \"IDX\" \n \n stocks[sInfo[\"exchg\"]][code] = sInfo\n\n #深证指数列表\n print(\"Fetching index list of SZSE...\")\n df_stocks = self.api.index_basic(market='SZSE')\n for idx, row in df_stocks.iterrows():\n code = row[\"ts_code\"]\n rawcode = code[:6]\n if rawcode[:3] != '399':\n continue\n \n sInfo = dict()\n sInfo[\"exchg\"] = \"SZSE\"\n code = rawcode #\"SZ\" + rawcode\n sInfo[\"code\"] = code\n sInfo[\"name\"] = row[\"name\"]\n sInfo[\"product\"] = \"IDX\" \n \n stocks[sInfo[\"exchg\"]][code] = sInfo\n\n print(\"Writing code list into file %s...\" % (filename))\n f = open(filename, 'w')\n f.write(json.dumps(stocks, sort_keys=True, indent=4, ensure_ascii=False))\n f.close()\n\n\n def dmpAdjFactorsToFile(self, codes:list, filename:str):\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n ts_code = transCode(stdCode)\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n count += 1\n\n print(\"Fetching adjust factors of %s(%d/%s)...\" % (stdCode, count, length))\n stocks[exchg][code] = list()\n df_factors = self.api.adj_factor(ts_code=ts_code)\n\n items = list()\n for idx, row in df_factors.iterrows():\n date = row[\"trade_date\"]\n factor = row[\"adj_factor\"]\n items.append({\n \"date\": int(date),\n \"factor\": float(factor)\n })\n\n items.reverse()\n pre_factor = 0\n for item in items:\n if item[\"factor\"] != pre_factor:\n stocks[exchg][code].append(item)\n pre_factor = item[\"factor\"]\n\n print(\"Writing adjust factors into file %s...\" % (filename))\n f = open(filename, 'w+')\n f.write(json.dumps(stocks, sort_keys=True, indent=4, ensure_ascii=False))\n f.close()\n\n def __dmp_bars_to_file_from_pro__(self, folder:str, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n freq = ''\n isDay = False\n filetag = ''\n if period == 'day':\n freq = 'D'\n isDay = True\n filetag = 'd'\n elif period == \"min5\":\n freq = '5min'\n filetag = 'm5'\n elif period == \"min1\":\n freq = '1min'\n filetag = 'm1'\n else:\n raise Exception(\"Unrecognized period\")\n\n if isDay:\n start_date = start_date.strftime(\"%Y%m%d\")\n end_date = end_date.strftime(\"%Y%m%d\")\n else:\n start_date = start_date.strftime(\"%Y-%m-%d\") + \" 09:00:00\"\n end_date = end_date.strftime(\"%Y-%m-%d\") + \" 15:15:00\"\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n ts_code = transCode(stdCode)\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n asset_type = \"E\"\n if (exchg == 'SSE' and code[0] == '0') | (exchg == 'SZSE' and code[:3] == '399'):\n asset_type = \"I\"\n elif exchg not in ['SSE','SZSE']:\n asset_type = \"FT\"\n count += 1\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, code, count, length))\n df_bars = ts.pro_bar(api=self.api, ts_code=ts_code, start_date=start_date, end_date=end_date, freq=freq, asset=asset_type)\n df_bars = df_bars.iloc[::-1]\n content = \"date,time,open,high,low,close,volume,turnover\\n\"\n for idx, row in df_bars.iterrows():\n if isDay:\n trade_date = row[\"trade_date\"]\n date = trade_date + ''\n time = '0'\n else:\n trade_time = row[\"trade_time\"]\n date = trade_time.split(' ')[0]\n time = trade_time.split(' ')[1]\n o = str(row[\"open\"])\n h = str(row[\"high\"])\n l = str(row[\"low\"])\n c = str(row[\"close\"])\n v = str(row[\"vol\"]*100)\n t = str(row[\"amount\"]*100)\n items = [date, time, o, h, l, c, v, t]\n\n content += \",\".join(items) + \"\\n\"\n\n filename = \"%s.%s_%s.csv\" % (exchg, code, filetag)\n filepath = os.path.join(folder, filename)\n print(\"Writing bars into file %s...\" % (filepath))\n f = open(filepath, \"w\", encoding=\"utf-8\")\n f.write(content)\n f.close()\n\n def __dmp_bars_to_file_from_old__(self, folder:str, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n freq = ''\n isDay = False\n filetag = ''\n if period == 'day':\n freq = 'D'\n isDay = True\n filetag = 'd'\n elif period == \"min5\":\n freq = '5'\n filetag = 'm5'\n else:\n raise Exception(\"Unrecognized period\")\n\n start_date = start_date.strftime(\"%Y-%m-%d\")\n end_date = end_date.strftime(\"%Y-%m-%d\")\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n count += 1\n if (exchg == 'SSE' and code[0] == '0') | (exchg == 'SZSE' and code[:3] == '399'):\n raise Exception(\"Old api only supports stocks\")\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, code, count, length))\n df_bars = ts.get_k_data(code, start=start_date, end=end_date, ktype=freq)\n content = \"date,time,open,high,low,close,volume\\n\"\n for idx, row in df_bars.iterrows():\n if isDay:\n date = row[\"date\"]\n time = '0'\n else:\n trade_time = row[\"date\"]\n date = trade_time.split(' ')[0]\n time = trade_time.split(' ')[1] + \":00\"\n o = str(row[\"open\"])\n h = str(row[\"high\"])\n l = str(row[\"low\"])\n c = str(row[\"close\"])\n v = str(row[\"volume\"])\n items = [date, time, o, h, l, c, v]\n\n content += \",\".join(items) + \"\\n\"\n\n filename = \"%s.%s_%s.csv\" % (exchg, code, filetag)\n filepath = os.path.join(folder, filename)\n print(\"Writing bars into file %s...\" % (filepath))\n f = open(filepath, \"w\", encoding=\"utf-8\")\n f.write(content)\n f.close()\n\n def dmpBarsToFile(self, folder:str, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if self.use_pro:\n self.__dmp_bars_to_file_from_pro__(folder=folder, codes=codes, start_date=start_date, end_date=end_date, period=period)\n else:\n self.__dmp_bars_to_file_from_old__(folder=folder, codes=codes, start_date=start_date, end_date=end_date, period=period)\n\n def dmpAdjFactorsToDB(self, dbHelper:DBHelper, codes:list):\n stocks = {\n \"SSE\":{},\n \"SZSE\":{}\n }\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n ts_code = transCode(stdCode)\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n count += 1\n\n print(\"Fetching adjust factors of %s(%d/%s)...\" % (stdCode, count, length))\n stocks[exchg][code] = list()\n df_factors = self.api.adj_factor(ts_code=ts_code)\n\n items = list()\n for idx, row in df_factors.iterrows():\n date = row[\"trade_date\"]\n factor = row[\"adj_factor\"]\n items.append({\n \"date\": int(date),\n \"factor\": factor\n })\n\n items.reverse()\n pre_factor = 0\n for item in items:\n if item[\"factor\"] != pre_factor:\n stocks[exchg][code].append(item)\n pre_factor = item[\"factor\"]\n\n print(\"Writing adjust factors into database...\")\n dbHelper.writeFactors(stocks)\n\n def __dmp_bars_to_db_from_pro__(self, dbHelper:DBHelper, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n freq = ''\n isDay = False\n filetag = ''\n if period == 'day':\n freq = 'D'\n isDay = True\n filetag = 'd'\n elif period == \"min5\":\n freq = '5min'\n filetag = 'm5'\n elif period == \"min1\":\n freq = '1min'\n filetag = 'm1'\n else:\n raise Exception(\"Unrecognized period\")\n\n if isDay:\n start_date = start_date.strftime(\"%Y%m%d\")\n end_date = end_date.strftime(\"%Y%m%d\")\n else:\n start_date = start_date.strftime(\"%Y-%m-%d\") + \" 09:00:00\"\n end_date = end_date.strftime(\"%Y-%m-%d\") + \" 15:15:00\"\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n ts_code = transCode(stdCode)\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n asset_type = \"E\"\n if (exchg == 'SSE' and code[0] == '0') | (exchg == 'SZSE' and code[:3] == '399'):\n asset_type = \"I\"\n elif exchg not in ['SSE','SZSE']:\n asset_type = \"FT\"\n count += 1\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, code, count, length))\n df_bars = ts.pro_bar(api=self.api, ts_code=ts_code, start_date=start_date, end_date=end_date, freq=freq, asset=asset_type)\n bars = []\n for idx, row in df_bars.iterrows(): \n if isDay:\n trade_date = row[\"trade_date\"]\n bars.append({\n \"exchange\":exchg,\n \"code\": code,\n \"date\": int(trade_date),\n \"time\": 0,\n \"open\": row[\"open\"],\n \"high\": row[\"high\"],\n \"low\": row[\"low\"],\n \"close\": row[\"close\"],\n \"volume\": row[\"vol\"]*100,\n \"turnover\": row[\"amount\"]*100\n })\n else:\n trade_time = row[\"trade_time\"]\n date = int(trade_time.split(' ')[0].replace(\"-\",\"\"))\n time = int(trade_time.split(' ')[1].replace(\":\",\"\")[:4])\n bars.append({\n \"exchange\":exchg,\n \"code\":code,\n \"date\": date,\n \"time\": time,\n \"open\": row[\"open\"],\n \"high\": row[\"high\"],\n \"low\": row[\"low\"],\n \"close\": row[\"close\"],\n \"volume\": row[\"vol\"]*100,\n \"turnover\": row[\"amount\"]*100\n })\n\n print(\"Writing bars into database...\")\n dbHelper.writeBars(bars, period)\n\n def __dmp_bars_to_db_from_old__(self, dbHelper:DBHelper, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if start_date is None:\n start_date = datetime(year=1990, month=1, day=1)\n \n if end_date is None:\n end_date = datetime.now()\n\n freq = ''\n isDay = False\n if period == 'day':\n freq = 'D'\n isDay = True\n elif period == \"min5\":\n freq = '5'\n else:\n raise Exception(\"Unrecognized period\")\n\n start_date = start_date.strftime(\"%Y-%m-%d\")\n end_date = end_date.strftime(\"%Y-%m-%d\")\n\n count = 0\n length = len(codes)\n for stdCode in codes:\n exchg = stdCode.split(\".\")[0]\n code = stdCode[-6:]\n if (exchg == 'SSE' and code[0] == '0') | (exchg == 'SZSE' and code[:3] == '399'):\n raise Exception(\"Old api only supports stocks\")\n count += 1\n \n print(\"Fetching %s bars of %s(%d/%s)...\" % (period, code, count, length))\n df_bars = ts.get_k_data(code, start=start_date, end=end_date, ktype=freq)\n bars = []\n for idx, row in df_bars.iterrows(): \n if isDay:\n trade_date = row[\"date\"]\n bars.append({\n \"exchange\":exchg,\n \"code\": code,\n \"date\": int(trade_date.replace('-','')),\n \"time\": 0,\n \"open\": row[\"open\"],\n \"high\": row[\"high\"],\n \"low\": row[\"low\"],\n \"close\": row[\"close\"],\n \"volume\": row[\"volume\"]\n })\n else:\n trade_time = row[\"date\"]\n date = int(trade_time.split(' ')[0].replace(\"-\",\"\"))\n time = int(trade_time.split(' ')[1].replace(\":\",\"\"))\n bars.append({\n \"exchange\":exchg,\n \"code\":code,\n \"date\": date,\n \"time\": time,\n \"open\": row[\"open\"],\n \"high\": row[\"high\"],\n \"low\": row[\"low\"],\n \"close\": row[\"close\"],\n \"volume\": row[\"volume\"]\n })\n\n print(\"Writing bars into database...\")\n dbHelper.writeBars(bars, period)\n\n def dmpBarsToDB(self, dbHelper:DBHelper, codes:list, start_date:datetime=None, end_date:datetime=None, period:str=\"day\"):\n if self.use_pro:\n self.__dmp_bars_to_db_from_pro__(dbHelper=dbHelper, codes=codes, start_date=start_date, end_date=end_date, period=period)\n else:\n self.__dmp_bars_to_db_from_old__(dbHelper=dbHelper, codes=codes, start_date=start_date, end_date=end_date, period=period)" }, { "path": "wtpy/apps/datahelper/__init__.py", "content": "from .DHFactory import DHFactory\nfrom .db.MysqlHelper import MysqlHelper\n\n__all__ = [\"DHFactory\",\"MysqlHelper\"]" }, { "path": "wtpy/apps/datahelper/db/MysqlHelper.py", "content": "from wtpy.apps.datahelper.DHDefs import DBHelper\nimport pymysql\nimport math\nimport os\n\nclass MysqlHelper(DBHelper):\n def __init__(self, host:str, user:str, pwd:str, dbname:str, port:int=3306):\n self.params = {\n \"host\":host,\n 'user':user,\n 'password':pwd,\n 'database':dbname,\n 'port':port\n }\n self.conn:pymysql.Connection = None\n \n def __get_conn__(self):\n if self.conn is None:\n self.conn = pymysql.connect(**self.params)\n \n try:\n self.conn.ping()\n except:\n self.conn = pymysql.connect(**self.params)\n\n return self.conn \n\n def initDB(self):\n paths = os.path.split(__file__)\n a = (paths[:-1] + ('initdb_mysql.sql',))\n _path = os.path.join(*a)\n f = open(_path, \"r\", encoding=\"UTF-8\")\n content = f.read()\n f.close()\n conn = self.__get_conn__()\n cursor = conn.cursor()\n items = content.split(\";\")\n for item in items:\n item = item.strip()\n if len(item) == 0:\n continue\n cursor.execute(item+\";\")\n conn.commit()\n cursor.close()\n\n def writeBars(self, bars:list, period=\"day\"):\n count = 0\n sql = \"\"\n isDay = (period=='day')\n tbname = \"tb_kline_%s\" % (period)\n for curBar in bars:\n if count == 0:\n if isDay:\n sql = \"REPLACE INTO %s(exchange,`code`,`date`,open,high,low,close,settle,volume,turnover,interest,diff_interest) VALUES\" % (tbname)\n else:\n sql = \"REPLACE INTO %s(exchange,`code`,`date`,`time`,open,high,low,close,volume,turnover,interest,diff_interest) VALUES\" % (tbname)\n \n if isDay:\n subsql = \"('%s','%s',%d,%f,%f,%f,%f,\" % (curBar[\"exchange\"], curBar[\"code\"], curBar[\"date\"], curBar[\"open\"], curBar[\"high\"], curBar[\"low\"], curBar[\"close\"])\n if \"settle\" in curBar:\n subsql += str(curBar[\"settle\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"volume\" in curBar:\n subsql += str(curBar[\"volume\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"turnover\" in curBar:\n subsql += str(curBar[\"turnover\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"interest\" in curBar:\n subsql += str(curBar[\"interest\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"diff_interest\" in curBar:\n subsql += str(curBar[\"diff_interest\"]) + \",\"\n else:\n subsql += \"0,\"\n subsql = subsql[:-1] + \"),\"\n sql += subsql\n else:\n barTime = (curBar[\"date\"] - 19900000)*10000 + curBar[\"time\"]\n subsql = \"('%s','%s',%d,%d,%f,%f,%f,%f,\" % (curBar[\"exchange\"], curBar[\"code\"], curBar[\"date\"], barTime, curBar[\"open\"], curBar[\"high\"], curBar[\"low\"], curBar[\"close\"])\n if \"volume\" in curBar:\n subsql += str(curBar[\"volume\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"turnover\" in curBar:\n subsql += str(curBar[\"turnover\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"interest\" in curBar:\n subsql += str(curBar[\"interest\"]) + \",\"\n else:\n subsql += \"0,\"\n if \"diff_interest\" in curBar:\n subsql += str(curBar[\"diff_interest\"]) + \",\"\n else:\n subsql += \"0,\"\n subsql = subsql[:-1] + \"),\"\n sql += subsql\n\n count += 1\n if count == 500:\n count = 0\n sql = sql[:-1] + \";\"\n conn = self.__get_conn__()\n cursor = conn.cursor()\n cursor.execute(sql)\n conn.commit()\n cursor.close()\n\n # 循环完了，再做一次提交\n if count > 0:\n sql = sql[:-1] + \";\"\n conn = self.__get_conn__()\n cursor = conn.cursor()\n cursor.execute(sql)\n conn.commit()\n cursor.close()\n\n\n def writeFactors(self, factors:dict):\n for exchg in factors:\n codelist = factors[exchg]\n for code in codelist:\n items = codelist[code]\n sql = 'REPLACE INTO tb_adj_factors(exchange,`code`,`date`,factor) VALUES'\n for item in items:\n sql += \"('%s','%s',%d,%f),\" % (exchg, code, item[\"date\"], item[\"factor\"])\n\n sql = sql[:-1] + \";\"\n conn = self.__get_conn__()\n cursor = conn.cursor()\n cursor.execute(sql)\n conn.commit()\n cursor.close()" }, { "path": "wtpy/apps/datahelper/db/__init__.py", "content": "" }, { "path": "wtpy/apps/datahelper/db/initdb_mysql.sql", "content": "CREATE TABLE IF NOT EXISTS `tb_adj_factors` (\n `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT,\n `exchange` varchar(20) NOT NULL DEFAULT '',\n `code` varchar(32) NOT NULL DEFAULT '',\n `date` int(10) unsigned NOT NULL DEFAULT 0,\n `factor` double(22,12) NOT NULL DEFAULT 0.000000000000,\n `createtime` datetime NOT NULL DEFAULT current_timestamp(),\n `updatetime` datetime NOT NULL DEFAULT current_timestamp() ON UPDATE current_timestamp(),\n PRIMARY KEY (`id`),\n UNIQUE KEY `exchange_code_date` (`exchange`,`code`,`date`),\n KEY `exchange_code` (`exchange`,`code`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='除权因子表';\nCREATE TABLE IF NOT EXISTS `tb_kline_day` (\n `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT,\n `exchange` varchar(20) NOT NULL DEFAULT '',\n `code` varchar(30) NOT NULL DEFAULT '',\n `date` int(10) unsigned NOT NULL DEFAULT 0,\n `open` double(22,4) NOT NULL DEFAULT 0.0000,\n `high` double(22,4) NOT NULL DEFAULT 0.0000,\n `low` double(22,4) NOT NULL DEFAULT 0.0000,\n `close` double(22,4) NOT NULL DEFAULT 0.0000,\n `settle` double(22,4) NOT NULL DEFAULT 0.0000,\n `volume` double(22,6) unsigned NOT NULL DEFAULT 0.000000,\n `turnover` double(22,4) NOT NULL DEFAULT 0.0000,\n `interest` bigint(20) unsigned NOT NULL DEFAULT 0,\n `diff_interest` bigint(20) NOT NULL DEFAULT 0,\n `createtime` datetime NOT NULL DEFAULT current_timestamp(),\n `updatetime` datetime NOT NULL DEFAULT current_timestamp() ON UPDATE current_timestamp(),\n PRIMARY KEY (`id`),\n UNIQUE KEY `exchange_code_date` (`exchange`,`code`,`date`),\n KEY `exchange_code` (`exchange`,`code`),\n KEY `date` (`date`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='日线表';\nCREATE TABLE IF NOT EXISTS `tb_kline_min1` (\n `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT,\n `exchange` varchar(20) NOT NULL DEFAULT '',\n `code` varchar(30) NOT NULL DEFAULT '',\n `date` int(10) unsigned NOT NULL DEFAULT 0,\n `time` int(10) unsigned NOT NULL DEFAULT 0,\n `open` double(22,4) NOT NULL DEFAULT 0.0000,\n `high` double(22,4) NOT NULL DEFAULT 0.0000,\n `low` double(22,4) NOT NULL DEFAULT 0.0000,\n `close` double(22,4) NOT NULL DEFAULT 0.0000,\n `volume` double(22,6) unsigned NOT NULL DEFAULT 0.000000,\n `turnover` double(22,4) NOT NULL DEFAULT 0.0000,\n `interest` bigint(20) unsigned NOT NULL DEFAULT 0,\n `diff_interest` bigint(20) NOT NULL DEFAULT 0,\n `createtime` datetime NOT NULL DEFAULT current_timestamp(),\n `updatetime` datetime NOT NULL DEFAULT current_timestamp() ON UPDATE current_timestamp(),\n PRIMARY KEY (`id`) USING BTREE,\n UNIQUE KEY `exchange_code_date_time` (`exchange`,`code`,`date`,`time`),\n KEY `exchange_code` (`exchange`,`code`),\n KEY `time` (`time`),\n KEY `date` (`date`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='1分钟线表';\nCREATE TABLE IF NOT EXISTS `tb_kline_min5` (\n `id` bigint(20) unsigned NOT NULL AUTO_INCREMENT,\n `exchange` varchar(20) NOT NULL DEFAULT '',\n `code` varchar(30) NOT NULL DEFAULT '',\n `date` int(10) unsigned NOT NULL DEFAULT 0,\n `time` int(10) unsigned NOT NULL DEFAULT 0,\n `open` double(22,4) NOT NULL DEFAULT 0.0000,\n `high` double(22,4) NOT NULL DEFAULT 0.0000,\n `low` double(22,4) NOT NULL DEFAULT 0.0000,\n `close` double(22,4) NOT NULL DEFAULT 0.0000,\n `volume` double(22,6) unsigned NOT NULL DEFAULT 0.000000,\n `turnover` double(22,4) NOT NULL DEFAULT 0.0000,\n `interest` bigint(20) unsigned NOT NULL DEFAULT 0,\n `diff_interest` bigint(20) NOT NULL DEFAULT 0,\n `createtime` datetime NOT NULL DEFAULT current_timestamp(),\n `updatetime` datetime NOT NULL DEFAULT current_timestamp() ON UPDATE current_timestamp(),\n PRIMARY KEY (`id`) USING BTREE,\n UNIQUE KEY `exchange_code_date_time` (`exchange`,`code`,`date`,`time`) USING BTREE,\n KEY `exchange_code` (`exchange`,`code`),\n KEY `date` (`date`),\n KEY `time` (`time`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8 COMMENT='5分钟线表';" }, { "path": "wtpy/monitor/DataMgr.py", "content": "import json\nimport os\nimport sqlite3\nimport hashlib\nimport datetime\nfrom .WtLogger import WtLogger\n\ndef backup_file(filename):\n if not os.path.exists(filename):\n return\n\n items = filename.split(\".\")\n ext = items[-1]\n prefix = \".\".join(items[:-1])\n\n now = datetime.datetime.now()\n timetag = now.strftime(\"%Y%m%d_%H%M%S\")\n target = prefix + \"_\" + timetag + \".\" + ext\n import shutil\n shutil.copy(filename, target)\n\nclass DataMgr:\n\n def __init__(self, datafile:str=\"mondata.db\", logger:WtLogger=None):\n self.__grp_cache__ = dict()\n self.__logger__ = logger\n\n self.__db_conn__ = sqlite3.connect(datafile, check_same_thread=False)\n self.__check_db__()\n\n #加载组合列表\n cur = self.__db_conn__.cursor()\n self.__config__ = {\n \"groups\":{},\n \"users\":{}\n }\n\n for row in cur.execute(\"SELECT * FROM groups;\"):\n grpInfo = dict()\n grpInfo[\"id\"] = row[1]\n grpInfo[\"name\"] = row[2]\n grpInfo[\"path\"] = row[3]\n grpInfo[\"info\"] = row[4]\n grpInfo[\"gtype\"] = row[5]\n grpInfo[\"datmod\"] = row[6]\n grpInfo[\"env\"] = row[7]\n grpInfo[\"mqurl\"] = row[8]\n self.__config__[\"groups\"][grpInfo[\"id\"]] = grpInfo\n\n for row in cur.execute(\"SELECT * FROM users;\"):\n usrInfo = dict()\n usrInfo[\"loginid\"] = row[1]\n usrInfo[\"name\"] = row[2]\n usrInfo[\"role\"] = row[3]\n usrInfo[\"passwd\"] = row[4]\n usrInfo[\"iplist\"] = row[5]\n usrInfo[\"remark\"] = row[6]\n usrInfo[\"createby\"] = row[7]\n usrInfo[\"createtime\"] = row[8]\n usrInfo[\"modifyby\"] = row[9]\n usrInfo[\"modifytime\"] = row[10]\n self.__config__[\"users\"][usrInfo[\"loginid\"]] = usrInfo\n\n def get_db(self):\n return self.__db_conn__\n\n def __check_db__(self):\n if self.__db_conn__ is None:\n return\n\n cur = self.__db_conn__.cursor()\n tables = []\n for row in cur.execute(\"select name from sqlite_master where type='table' order by name\"):\n tables.append(row[0])\n \n if \"actions\" not in tables:\n sql = \"CREATE TABLE [actions] (\\n\"\n sql += \"[id] INTEGER PRIMARY KEY autoincrement, \\n\"\n sql += \"[loginid] VARCHAR(20) NOT NULL DEFAULT '', \\n\"\n sql += \"[actiontime] DATETIME default (datetime('now', 'localtime')), \\n\"\n sql += \"[actionip] VARCHAR(30) NOT NULL DEFAULT '', \\n\"\n sql += \"[actiontype] VARCHAR(20) NOT NULL DEFAULT '',\\n\"\n sql += \"[remark] TEXT default '');\"\n cur.execute(sql)\n cur.execute(\"CREATE INDEX [idx_actions_loginid] ON [actions] ([loginid]);\")\n cur.execute(\"CREATE INDEX [idx_actions_actiontime] ON [actions] ([actiontime]);\")\n self.__db_conn__.commit()\n\n if \"groups\" not in tables:\n sql = \"CREATE TABLE [groups] (\\n\"\n sql += \"[id] INTEGER PRIMARY KEY autoincrement,\\n\"\n sql += \"[groupid] VARCHAR(20) NOT NULL DEFAULT '',\\n\"\n sql += \"[name] VARCHAR(30) NOT NULL DEFAULT '',\\n\"\n sql += \"[path] VARCHAR(256) NOT NULL DEFAULT '',\\n\"\n sql += \"[info] TEXT DEFAULT '',\\n\"\n sql += \"[gtype] VARCHAR(10) NOT NULL DEFAULT 'cta',\\n\"\n sql += \"[datmod] VARCHAR(10) NOT NULL DEFAULT 'mannual',\\n\"\n sql += \"[env] VARCHAR(20) NOT NULL DEFAULT 'product',\\n\"\n sql += \"[mqurl] VARCHAR(255) NOT NULL DEFAULT '',\\n\"\n sql += \"[createtime] DATETIME default (datetime('now', 'localtime')),\\n\"\n sql += \"[modifytime] DATETIME default (datetime('now', 'localtime')));\"\n cur.execute(sql)\n cur.execute(\"CREATE UNIQUE INDEX [idx_groupid] ON [groups] ([groupid]);\")\n self.__db_conn__.commit()\n\n if \"schedules\" not in tables:\n sql = \"CREATE TABLE [schedules] (\\n\"\n sql += \"[id] INTEGER PRIMARY KEY autoincrement,\\n\"\n sql += \"[appid] VARCHAR(20) NOT NULL DEFAULT '',\\n\"\n sql += \"[path] VARCHAR(256) NOT NULL DEFAULT '',\\n\"\n sql += \"[folder] VARCHAR(256) NOT NULL DEFAULT '',\\n\"\n sql += \"[param] VARCHAR(50) NOT NULL DEFAULT '',\\n\"\n sql += \"[type] INTEGER DEFAULT 0,\\n\"\n sql += \"[span] INTEGER DEFAULT 3,\\n\"\n sql += \"[guard] VARCHAR(20) DEFAULT 'false',\\n\"\n sql += \"[redirect] VARCHAR(20) DEFAULT 'false',\\n\"\n sql += \"[schedule] VARCHAR(20) DEFAULT 'false',\\n\"\n sql += \"[weekflag] VARCHAR(20) DEFAULT '000000',\\n\"\n sql += \"[mqurl] VARCHAR(255) NOT NULL DEFAULT '',\\n\"\n sql += \"[task1] VARCHAR(100) NOT NULL DEFAULT '{\\\"active\\\": true,\\\"time\\\": 0,\\\"action\\\": 0}',\\n\"\n sql += \"[task2] VARCHAR(100) NOT NULL DEFAULT '{\\\"active\\\": true,\\\"time\\\": 0,\\\"action\\\": 0}',\\n\"\n sql += \"[task3] VARCHAR(100) NOT NULL DEFAULT '{\\\"active\\\": true,\\\"time\\\": 0,\\\"action\\\": 0}',\\n\"\n sql += \"[task4] VARCHAR(100) NOT NULL DEFAULT '{\\\"active\\\": true,\\\"time\\\": 0,\\\"action\\\": 0}',\\n\"\n sql += \"[task5] VARCHAR(100) NOT NULL DEFAULT '{\\\"active\\\": true,\\\"time\\\": 0,\\\"action\\\": 0}',\\n\"\n sql += \"[task6] VARCHAR(100) NOT NULL DEFAULT '{\\\"active\\\": true,\\\"time\\\": 0,\\\"action\\\": 0}',\\n\"\n sql += \"[createtime] DATETIME default (datetime('now', 'localtime')),\\n\"\n sql += \"[modifytime] DATETIME default (datetime('now', 'localtime')));\"\n cur.execute(sql)\n cur.execute(\"CREATE UNIQUE INDEX [idx_appid] ON [schedules] ([appid]);\")\n self.__db_conn__.commit()\n\n if \"users\" not in tables:\n sql = \"CREATE TABLE [users] (\\n\"\n sql += \"[id] INTEGER PRIMARY KEY autoincrement,\\n\"\n sql += \"[loginid] VARCHAR(20) NOT NULL DEFAULT '',\\n\"\n sql += \"[name] VARCHAR(30) NOT NULL DEFAULT '',\\n\"\n sql += \"[role] VARCHAR(10) NOT NULL DEFAULT '',\\n\"\n sql += \"[passwd] VARCHAR(30) NOT NULL DEFAULT 'cta',\\n\"\n sql += \"[iplist] VARCHAR(100) NOT NULL DEFAULT 'mannual',\\n\"\n sql += \"[remark] VARCHAR(256) NOT NULL DEFAULT '',\\n\"\n sql += \"[createby] VARCHAR(20) NOT NULL DEFAULT '',\\n\"\n sql += \"[createtime] DATETIME default (datetime('now', 'localtime')),\\n\"\n sql += \"[modifyby] VARCHAR(20) NOT NULL DEFAULT '',\\n\"\n sql += \"[modifytime] DATETIME default (datetime('now', 'localtime')));\"\n cur.execute(sql)\n cur.execute(\"CREATE UNIQUE INDEX [idx_loginid] ON [users] ([loginid]);\")\n self.__db_conn__.commit()\n\n def __check_cache__(self, grpid, grpInfo):\n now = datetime.datetime.now()\n if grpid not in self.__grp_cache__:\n self.__grp_cache__[grpid] = dict()\n self.__grp_cache__[grpid][\"cachetime\"] = None\n else:\n cache_time = self.__grp_cache__[grpid][\"cachetime\"]\n bNeedReset = False\n if cache_time is None:\n bNeedReset = True\n else:\n td = now - cache_time\n if td.total_seconds() >= 60:# 上次缓存时间超过60s，则重新读取\n bNeedReset = True\n\n if bNeedReset:\n self.__grp_cache__[grpid] = dict()\n self.__grp_cache__[grpid][\"cachetime\"] = None\n\n if \"strategies\" not in self.__grp_cache__[grpid]:\n filepath = \"./generated/marker.json\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n try:\n f = open(filepath, \"r\")\n content = f.read()\n marker = json.loads(content)\n f.close()\n\n self.__grp_cache__[grpid] = {\n \"strategies\":marker[\"marks\"],\n \"channels\":marker[\"channels\"]\n } \n\n if \"executers\" in marker:\n self.__grp_cache__[grpid][\"executers\"] = marker[\"executers\"]\n else:\n self.__grp_cache__[grpid][\"executers\"] = []\n\n except:\n self.__grp_cache__[grpid] = {\n \"strategies\":[],\n \"channels\":[],\n \"executers\":[]\n } \n self.__grp_cache__[grpid][\"strategies\"].sort()\n self.__grp_cache__[grpid][\"channels\"].sort()\n self.__grp_cache__[grpid][\"executers\"].sort()\n self.__grp_cache__[grpid][\"cachetime\"] = now\n\n def get_groups(self, tpfilter:str=''):\n ret = []\n for grpid in self.__config__[\"groups\"]:\n grpinfo = self.__config__[\"groups\"][grpid]\n if tpfilter == '':\n ret.append(grpinfo)\n elif grpinfo[\"gtype\"] == tpfilter:\n ret.append(grpinfo)\n \n return ret\n\n def has_group(self, grpid:str):\n return (grpid in self.__config__[\"groups\"])\n\n def get_group(self, grpid:str):\n if grpid in self.__config__[\"groups\"]:\n return self.__config__[\"groups\"][grpid]\n else:\n return None\n\n def get_group_cfg(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return \"{}\"\n else:\n grpInfo = self.__config__[\"groups\"][grpid]\n filepath = \"./config.json\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n f = open(filepath, \"r\")\n content = f.read()\n f.close()\n return json.loads(content)\n\n def set_group_cfg(self, grpid:str, config:dict):\n if grpid not in self.__config__[\"groups\"]:\n return False\n else:\n grpInfo = self.__config__[\"groups\"][grpid]\n filepath = \"./config.json\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n backup_file(filepath)\n f = open(filepath, \"w\")\n f.write(json.dumps(config, indent=4))\n f.close()\n return True\n\n def get_group_entry(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return \"{}\"\n else:\n grpInfo = self.__config__[\"groups\"][grpid]\n filepath = \"./run.py\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n f = open(filepath, \"r\", encoding=\"utf-8\")\n content = f.read()\n f.close()\n return content\n\n def set_group_entry(self, grpid:str, content:str):\n if grpid not in self.__config__[\"groups\"]:\n return False\n else:\n grpInfo = self.__config__[\"groups\"][grpid]\n filepath = \"./run.py\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n backup_file(filepath)\n f = open(filepath, \"w\", encoding=\"utf-8\")\n f.write(content)\n f.close()\n return True\n\n def add_group(self, grpInfo:dict):\n grpid = grpInfo[\"id\"]\n isNewGrp = not (grpid in self.__config__[\"groups\"])\n\n bSucc = False\n try:\n cur = self.__db_conn__.cursor()\n sql = ''\n if isNewGrp:\n sql = \"INSERT INTO groups(groupid,name,path,info,gtype,datmod,env,mqurl) VALUES('%s','%s','%s','%s','%s','%s','%s','%s');\" \\\n % (grpid, grpInfo[\"name\"], grpInfo[\"path\"], grpInfo[\"info\"], grpInfo[\"gtype\"], grpInfo[\"datmod\"], grpInfo[\"env\"], grpInfo[\"mqurl\"])\n else:\n sql = \"UPDATE groups SET name='%s',path='%s',info='%s',gtype='%s',datmod='%s',env='%s',mqurl='%s',modifytime=datetime('now','localtime') WHERE groupid='%s';\" \\\n % (grpInfo[\"name\"], grpInfo[\"path\"], grpInfo[\"info\"], grpInfo[\"gtype\"], grpInfo[\"datmod\"], grpInfo[\"env\"], grpInfo[\"mqurl\"], grpid)\n cur.execute(sql)\n self.__db_conn__.commit()\n bSucc = True\n except sqlite3.Error as e:\n print(e)\n\n if bSucc:\n self.__config__[\"groups\"][grpid] = grpInfo\n\n return bSucc\n\n def del_group(self, grpid:str):\n if grpid in self.__config__[\"groups\"]:\n self.__config__[\"groups\"].pop(grpid)\n \n cur = self.__db_conn__.cursor()\n cur.execute(\"DELETE FROM groups WHERE groupid='%s';\" % (grpid))\n self.__db_conn__.commit()\n\n def get_users(self):\n ret = []\n for loginid in self.__config__[\"users\"]:\n usrInfo = self.__config__[\"users\"][loginid]\n ret.append(usrInfo.copy()) \n \n return ret\n\n def add_user(self, usrInfo, admin):\n loginid = usrInfo[\"loginid\"]\n isNewUser = not (loginid in self.__config__[\"users\"])\n\n cur = self.__db_conn__.cursor()\n now = datetime.datetime.now()\n if isNewUser:\n encpwd = hashlib.md5((loginid+usrInfo[\"passwd\"]).encode(\"utf-8\")).hexdigest()\n usrInfo[\"passwd\"] = encpwd\n usrInfo[\"createby\"] = admin\n usrInfo[\"modifyby\"] = admin\n usrInfo[\"createtime\"] = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n usrInfo[\"modifytime\"] = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n cur.execute(\"INSERT INTO users(loginid,name,role,passwd,iplist,remark,createby,modifyby) VALUES(?,?,?,?,?,?,?,?);\", \n (loginid, usrInfo[\"name\"], usrInfo[\"role\"], encpwd, usrInfo[\"iplist\"], usrInfo[\"remark\"], admin, admin))\n else:\n usrInfo[\"modifyby\"] = admin\n usrInfo[\"modifytime\"] = now.strftime(\"%Y-%m-%d %H:%M:%S\")\n cur.execute(\"UPDATE users SET name=?,role=?,iplist=?,remark=?,modifyby=?,modifytime=datetime('now','localtime') WHERE loginid=?;\", \n (usrInfo[\"name\"], usrInfo[\"role\"], usrInfo[\"iplist\"], usrInfo[\"remark\"], admin, loginid))\n self.__db_conn__.commit()\n\n self.__config__[\"users\"][loginid] = usrInfo\n\n def mod_user_pwd(self, loginid:str, newpwd:str, admin:str):\n cur = self.__db_conn__.cursor()\n cur.execute(\"UPDATE users SET passwd=?,modifyby=?,modifytime=datetime('now','localtime') WHERE loginid=?;\", \n (newpwd,admin,loginid))\n self.__db_conn__.commit()\n self.__config__[\"users\"][loginid][\"passwd\"]=newpwd\n\n\n def del_user(self, loginid, admin):\n if loginid in self.__config__[\"users\"]:\n self.__config__[\"users\"].pop(loginid)\n \n cur = self.__db_conn__.cursor()\n cur.execute(\"DELETE FROM users WHERE loginid='%s';\" % (loginid))\n self.__db_conn__.commit()\n return True\n else:\n return False\n\n def log_action(self, adminInfo, atype, remark):\n cur = self.__db_conn__.cursor()\n sql = \"INSERT INTO actions(loginid,actiontime,actionip,actiontype,remark) VALUES('%s',datetime('now','localtime'),'%s','%s','%s');\" % (\n adminInfo[\"loginid\"], adminInfo[\"loginip\"], atype, remark)\n cur.execute(sql)\n self.__db_conn__.commit()\n\n def get_user(self, loginid:str):\n if loginid in self.__config__[\"users\"]:\n return self.__config__[\"users\"][loginid].copy()\n elif loginid == 'superman':\n return {\n \"loginid\":loginid,\n \"name\":\"超管\",\n \"role\":\"superman\",\n \"passwd\":\"25ed305a56504e95fd1ca9900a1da174\",\n \"iplist\":\"\",\n \"remark\":\"内置超管账号\",\n 'builtin':True\n }\n else:\n return None\n\n def get_strategies(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n return self.__grp_cache__[grpid][\"strategies\"]\n\n def get_channels(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n return self.__grp_cache__[grpid][\"channels\"]\n\n def get_trades(self, grpid:str, straid:str, limit:int = 200):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n if straid not in self.__grp_cache__[grpid][\"strategies\"]:\n return []\n\n if \"trades\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"trades\"] = dict()\n \n if straid not in self.__grp_cache__[grpid][\"trades\"]:\n filepath = \"./generated/outputs/%s/trades.csv\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"trades\"] = list()\n self.__grp_cache__[grpid][\"trades\"][straid] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"trades\"][straid]\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n if len(cells) > 10:\n continue\n\n tItem = {\n \"strategy\":straid,\n \"code\": cells[0],\n \"time\": int(cells[1]),\n \"direction\": cells[2],\n \"offset\": cells[3],\n \"price\": float(cells[4]),\n \"volume\": float(cells[5]),\n \"tag\": cells[6],\n \"fee\": 0\n }\n\n if len(cells) > 7:\n tItem[\"fee\"] = float(cells[7])\n\n trdCache[\"trades\"].append(tItem)\n trdCache[\"lastrow\"] += 1\n \n return trdCache[\"trades\"][-limit:]\n\n def get_funds(self, grpid:str, straid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n if straid not in self.__grp_cache__[grpid][\"strategies\"]:\n return []\n\n if \"funds\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"funds\"] = dict()\n \n if straid not in self.__grp_cache__[grpid][\"funds\"]:\n filepath = \"./generated/outputs/%s/funds.csv\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"funds\"] = list()\n self.__grp_cache__[grpid][\"funds\"][straid] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"funds\"][straid]\n\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n if len(cells) > 10:\n continue\n\n tItem = {\n \"strategy\":straid,\n \"date\": int(cells[0]),\n \"closeprofit\": float(cells[1]),\n \"dynprofit\": float(cells[2]),\n \"dynbalance\": float(cells[3]),\n \"fee\": 0\n }\n\n if len(cells) > 4:\n tItem[\"fee\"] = float(cells[4])\n\n trdCache[\"funds\"].append(tItem)\n trdCache[\"lastrow\"] += 1\n\n ret = trdCache[\"funds\"].copy()\n\n if len(ret) > 0:\n last_date = ret[-1][\"date\"]\n else:\n last_date = 0\n\n # 这里再更新一条实时数据\n filepath = \"./generated/stradata/%s.json\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n fund = json_data[\"fund\"]\n if fund[\"tdate\"] > last_date:\n ret.append({\n \"strategy\":straid,\n \"date\": fund[\"tdate\"],\n \"closeprofit\": fund[\"total_profit\"],\n \"dynprofit\": fund[\"total_dynprofit\"],\n \"dynbalance\": fund[\"total_profit\"] + fund[\"total_dynprofit\"] - fund[\"total_fees\"],\n \"fee\": fund[\"total_fees\"]\n })\n except:\n pass\n f.close()\n \n return ret\n\n def get_signals(self, grpid:str, straid:str, limit:int = 200):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n if straid not in self.__grp_cache__[grpid][\"strategies\"]:\n return []\n\n if \"signals\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"signals\"] = dict()\n \n if straid not in self.__grp_cache__[grpid][\"signals\"]:\n filepath = \"./generated/outputs/%s/signals.csv\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"signals\"] = list()\n self.__grp_cache__[grpid][\"signals\"][straid] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"signals\"][straid]\n\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"strategy\":straid,\n \"code\": cells[0],\n \"target\": float(cells[1]),\n \"sigprice\": float(cells[2]),\n \"gentime\": cells[3],\n \"tag\": cells[4]\n }\n\n trdCache[\"signals\"].append(tItem)\n\n trdCache[\"lastrow\"] += len(lines) \n return trdCache[\"signals\"][-limit:]\n\n def get_rounds(self, grpid:str, straid:str, limit:int = 200):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n if straid not in self.__grp_cache__[grpid][\"strategies\"]:\n return []\n\n if \"rounds\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"rounds\"] = dict()\n \n if straid not in self.__grp_cache__[grpid][\"rounds\"]:\n filepath = \"./generated/outputs/%s/closes.csv\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"rounds\"] = list()\n self.__grp_cache__[grpid][\"rounds\"][straid] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"rounds\"][straid]\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"strategy\":straid,\n \"code\": cells[0],\n \"direct\": cells[1],\n \"opentime\": int(cells[2]),\n \"openprice\": float(cells[3]),\n \"closetime\": int(cells[4]),\n \"closeprice\": float(cells[5]),\n \"qty\": float(cells[6]),\n \"profit\": float(cells[7]),\n \"entertag\": cells[9],\n \"exittag\": cells[10]\n }\n\n trdCache[\"rounds\"].append(tItem)\n trdCache[\"lastrow\"] += len(lines)\n \n return trdCache[\"rounds\"][-limit:]\n\n def get_positions(self, grpid:str, straid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n ret = list()\n if straid != \"all\":\n if straid not in self.__grp_cache__[grpid][\"strategies\"]:\n return []\n \n filepath = \"./generated/stradata/%s.json\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n \n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n\n positions = json_data[\"positions\"]\n for pItem in positions:\n tag = \"volumn\" if \"volume\" not in pItem else \"volume\"\n if pItem[tag] == 0.0:\n continue\n\n for dItem in pItem[\"details\"]:\n dItem[\"code\"] = pItem[\"code\"]\n dItem[\"strategy\"] = straid\n if \"volumn\" in dItem:\n dItem[\"volume\"] = dItem[\"volumn\"]\n dItem.pop(\"volumn\")\n ret.append(dItem)\n except:\n pass\n\n f.close()\n else:\n for straid in self.__grp_cache__[grpid][\"strategies\"]:\n filepath = \"./generated/stradata/%s.json\" % (straid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n \n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n\n positions = json_data[\"positions\"]\n for pItem in positions:\n tag = \"volumn\" if \"volume\" not in pItem else \"volume\"\n if pItem[tag] == 0.0:\n continue\n\n for dItem in pItem[\"details\"]:\n dItem[\"code\"] = pItem[\"code\"]\n dItem[\"strategy\"] = straid\n if \"volumn\" in dItem:\n dItem[\"volume\"] = dItem[\"volumn\"]\n dItem.pop(\"volumn\")\n ret.append(dItem)\n except:\n pass\n\n f.close()\n return ret\n\n def get_channel_orders(self, grpid:str, chnlid:str, limit:int = 200):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n if chnlid not in self.__grp_cache__[grpid][\"channels\"]:\n return []\n\n if \"corders\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"corders\"] = dict()\n \n if chnlid not in self.__grp_cache__[grpid][\"corders\"]:\n filepath = \"./generated/traders/%s/orders.csv\" % (chnlid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"corders\"] = list()\n self.__grp_cache__[grpid][\"corders\"][chnlid] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"corders\"][chnlid]\n\n f = open(trdCache[\"file\"], \"r\",encoding=\"gb2312\",errors=\"ignore\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"channel\":chnlid,\n \"localid\":int(cells[0]),\n \"time\":int(cells[2]),\n \"code\": cells[3],\n \"action\": cells[4],\n \"total\": float(cells[5]),\n \"traded\": float(cells[6]),\n \"price\": float(cells[7]),\n \"orderid\": cells[8],\n \"canceled\": cells[9],\n \"remark\": cells[10]\n }\n\n trdCache[\"corders\"].append(tItem)\n \n return trdCache[\"corders\"][-limit:]\n\n def get_channel_trades(self, grpid:str, chnlid:str, limit:int = 200):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n if chnlid not in self.__grp_cache__[grpid][\"channels\"]:\n return []\n\n if \"ctrades\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"ctrades\"] = dict()\n \n if chnlid not in self.__grp_cache__[grpid][\"ctrades\"]:\n filepath = \"./generated/traders/%s/trades.csv\" % (chnlid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"ctrades\"] = list()\n self.__grp_cache__[grpid][\"ctrades\"][chnlid] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"ctrades\"][chnlid]\n\n f = open(trdCache[\"file\"], \"r\",encoding=\"gb2312\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"channel\":chnlid,\n \"localid\":int(cells[0]),\n \"time\":int(cells[2]),\n \"code\": cells[3],\n \"action\": cells[4],\n \"volume\": float(cells[5]),\n \"price\": float(cells[6]),\n \"tradeid\": cells[7],\n \"orderid\": cells[8]\n }\n\n trdCache[\"ctrades\"].append(tItem)\n \n return trdCache[\"ctrades\"][-limit:]\n\n def get_channel_positions(self, grpid:str, chnlid:str):\n if self.__config__ is None:\n return []\n\n if \"groups\" not in self.__config__:\n return []\n\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n ret = list()\n channels = list()\n if chnlid != 'all':\n channels.append(chnlid)\n else:\n channels = self.__grp_cache__[grpid][\"channels\"]\n\n for cid in channels:\n if cid not in self.__grp_cache__[grpid][\"channels\"]:\n continue\n \n filepath = \"./generated/traders/%s/rtdata.json\" % (cid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n \n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n\n positions = json_data[\"positions\"]\n for pItem in positions:\n pItem[\"channel\"] = cid\n ret.append(pItem)\n except:\n pass\n\n f.close()\n return ret\n\n def get_channel_funds(self, grpid:str, chnlid:str):\n if self.__config__ is None:\n return []\n\n if \"groups\" not in self.__config__:\n return []\n\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n ret = dict()\n channels = list()\n if chnlid != 'all':\n channels.append(chnlid)\n else:\n channels = self.__grp_cache__[grpid][\"channels\"]\n print(channels)\n\n for cid in channels:\n if cid not in self.__grp_cache__[grpid][\"channels\"]:\n continue\n \n filepath = \"./generated/traders/%s/rtdata.json\" % (cid)\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n continue\n \n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n\n funds = json_data[\"funds\"]\n ret[cid] = funds\n except:\n pass\n\n f.close()\n print(ret)\n return ret\n\n def get_actions(self, sdate, edate):\n ret = list()\n\n cur = self.__db_conn__.cursor()\n for row in cur.execute(\"SELECT id,loginid,actiontime,actionip,actiontype,remark FROM actions WHERE actiontime>=? and actiontime<=?;\", (sdate, edate)):\n aInfo = dict()\n aInfo[\"id\"] = row[0]\n aInfo[\"loginid\"] = row[1]\n aInfo[\"actiontime\"] = row[2]\n aInfo[\"actionip\"] = row[3]\n aInfo[\"action\"] = row[4]\n aInfo[\"remark\"] = row[5]\n\n ret.append(aInfo)\n\n return ret\n\n def get_group_trades(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n\n if \"grptrades\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"grptrades\"] = dict()\n \n filepath = \"./generated/portfolio/trades.csv\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n print(filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"trades\"] = list()\n self.__grp_cache__[grpid][\"grptrades\"][\"cache\"] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"grptrades\"]['cache']\n\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n print(lines)\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"code\": cells[0],\n \"time\": int(cells[1]),\n \"direction\": cells[2],\n \"offset\": cells[3],\n \"price\": float(cells[4]),\n \"volume\": float(cells[5]),\n \"fee\": float(cells[6])\n }\n\n trdCache[\"trades\"].append(tItem)\n trdCache[\"lastrow\"] += 1\n \n return trdCache[\"trades\"]\n\n def get_group_rounds(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n\n if \"grprounds\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"grprounds\"] = dict()\n \n filepath = \"./generated/portfolio/closes.csv\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"rounds\"] = list()\n self.__grp_cache__[grpid][\"grprounds\"][\"cache\"] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"grprounds\"]['cache']\n\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n print(lines)\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"code\": cells[0],\n \"direct\": cells[1],\n \"opentime\": int(cells[2]),\n \"openprice\": float(cells[3]),\n \"closetime\": int(cells[4]),\n \"closeprice\": float(cells[5]),\n \"qty\": float(cells[6]),\n \"profit\": float(cells[7])\n }\n\n trdCache[\"rounds\"].append(tItem)\n trdCache[\"lastrow\"] += 1\n \n return trdCache[\"rounds\"]\n\n def get_group_funds(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n\n if \"grpfunds\" not in self.__grp_cache__[grpid]:\n self.__grp_cache__[grpid][\"grpfunds\"] = dict()\n \n filepath = \"./generated/portfolio/funds.csv\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n trdCache = dict()\n trdCache[\"file\"] = filepath\n trdCache[\"lastrow\"] = 0\n trdCache[\"funds\"] = list()\n self.__grp_cache__[grpid][\"grpfunds\"][\"cache\"] = trdCache\n\n trdCache = self.__grp_cache__[grpid][\"grpfunds\"]['cache']\n\n f = open(trdCache[\"file\"], \"r\")\n last_row = trdCache[\"lastrow\"]\n lines = f.readlines()\n f.close()\n lines = lines[1+last_row:]\n\n for line in lines:\n cells = line.split(\",\")\n\n tItem = {\n \"date\": int(cells[0]),\n \"predynbalance\": float(cells[1]),\n \"prebalance\": float(cells[2]),\n \"balance\": float(cells[3]),\n \"closeprofit\": float(cells[4]),\n \"dynprofit\": float(cells[5]),\n \"fee\": float(cells[6]),\n \"maxdynbalance\": float(cells[7]),\n \"maxtime\": float(cells[8]),\n \"mindynbalance\": float(cells[9]),\n \"mintime\": float(cells[10]),\n \"mdmaxbalance\": float(cells[11]),\n \"mdmaxdate\": float(cells[12]),\n \"mdminbalance\": float(cells[13]),\n \"mdmindate\": float(cells[14])\n }\n\n trdCache[\"funds\"].append(tItem)\n trdCache[\"lastrow\"] += 1\n \n ret = trdCache[\"funds\"].copy()\n\n if len(ret) > 0:\n last_date = ret[-1][\"date\"]\n else:\n last_date = 0\n\n # 这里再更新一条实时数据\n filepath = \"./generated/portfolio/datas.json\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n fund = json_data[\"fund\"]\n if [\"date\"] > last_date:\n ret.append({\n \"date\": fund[\"date\"],\n \"predynbalance\": fund[\"predynbal\"],\n \"prebalance\": fund[\"prebalance\"],\n \"balance\": fund[\"balance\"],\n \"closeprofit\": fund[\"profit\"],\n \"dynprofit\": fund[\"dynprofit\"],\n \"fee\": fund[\"fees\"],\n \"maxdynbalance\": fund[\"max_dyn_bal\"],\n \"maxtime\": fund[\"max_time\"],\n \"mindynbalance\": fund[\"min_dyn_bal\"],\n \"mintime\": fund[\"min_time\"],\n \"mdmaxbalance\": fund[\"maxmd\"][\"dyn_balance\"],\n \"mdmaxdate\": fund[\"maxmd\"][\"date\"],\n \"mdminbalance\": fund[\"minmd\"][\"dyn_balance\"],\n \"mdmindate\": fund[\"minmd\"][\"date\"]\n })\n except:\n pass\n f.close()\n return ret\n\n def get_group_positions(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return []\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n filepath = \"./generated/portfolio/datas.json\"\n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return []\n else:\n ret = list()\n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n\n positions = json_data[\"positions\"]\n for pItem in positions:\n if pItem[\"volume\"] == 0:\n continue\n\n for dItem in pItem[\"details\"]:\n dItem[\"code\"] = pItem[\"code\"]\n ret.append(dItem)\n except:\n pass\n\n f.close()\n return ret\n\n def get_group_performances(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return {}\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n filepath = \"./generated/portfolio/datas.json\" \n filepath = os.path.join(grpInfo[\"path\"], filepath)\n if not os.path.exists(filepath):\n return {}\n else:\n perf = dict()\n f = open(filepath, \"r\")\n try:\n content = f.read()\n json_data = json.loads(content)\n\n positions = json_data[\"positions\"]\n for pItem in positions:\n code = pItem['code']\n ay = code.split(\".\")\n pid = code\n if len(ay) > 2:\n if ay[1] not in ['IDX','STK','ETF']:\n pid = ay[0] + \".\" + ay[1]\n else:\n pid = ay[0] + \".\" + ay[2]\n\n if pid not in perf:\n perf[pid] = {\n 'closeprofit':0,\n 'dynprofit':0\n }\n\n perf[pid]['closeprofit'] += pItem['closeprofit']\n perf[pid]['dynprofit'] += pItem['dynprofit']\n \n except:\n pass\n\n f.close()\n return perf\n\n def get_group_filters(self, grpid:str):\n if grpid not in self.__config__[\"groups\"]:\n return {}\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n \n filepath = os.path.join(grpInfo[\"path\"], 'filters.json')\n if not os.path.exists(filepath):\n filters = {}\n else:\n filters = {}\n f = open(filepath, \"r\")\n try:\n content = f.read()\n filters = json.loads(content)\n except:\n pass\n\n f.close()\n\n gpCache = self.__grp_cache__[grpid]\n if \"executer_filters\" not in filters:\n filters[\"executer_filters\"] = dict()\n if \"strategy_filters\" not in filters:\n filters[\"strategy_filters\"] = dict()\n if \"code_filters\" not in filters:\n filters[\"code_filters\"] = dict()\n\n for sid in gpCache[\"strategies\"]:\n if sid not in filters['strategy_filters']:\n filters['strategy_filters'][sid] = False\n \n for eid in gpCache[\"executers\"]:\n if eid not in filters['executer_filters']:\n filters['executer_filters'][eid] = False\n\n for id in filters['strategy_filters'].keys():\n if type(filters['strategy_filters'][id]) != bool:\n filters['strategy_filters'][id] = True\n\n for id in filters['code_filters'].keys():\n if type(filters['code_filters'][id]) != bool:\n filters['code_filters'][id] = True\n\n return filters\n\n def set_group_filters(self, grpid:str, filters:dict):\n if grpid not in self.__config__[\"groups\"]:\n return False\n\n grpInfo = self.__config__[\"groups\"][grpid]\n self.__check_cache__(grpid, grpInfo)\n\n realfilters = {\n \"strategy_filters\":{},\n \"code_filters\":{},\n \"executer_filters\":{}\n }\n\n if \"strategy_filters\" in filters:\n for sid in filters[\"strategy_filters\"]:\n if filters[\"strategy_filters\"][sid]:\n realfilters[\"strategy_filters\"][sid] = {\n \"action\":\"redirect\",\n \"target\":0\n }\n\n if \"code_filters\" in filters:\n for sid in filters[\"code_filters\"]:\n if filters[\"code_filters\"][sid]:\n realfilters[\"code_filters\"][sid] = {\n \"action\":\"redirect\",\n \"target\":0\n }\n\n if \"executer_filters\" in filters:\n realfilters[\"executer_filters\"] = filters[\"executer_filters\"]\n \n filepath = os.path.join(grpInfo[\"path\"], 'filters.json')\n backup_file(filepath)\n f = open(filepath, \"w\")\n f.write(json.dumps(realfilters, indent=4))\n f.close()\n return True\n " }, { "path": "wtpy/monitor/EventReceiver.py", "content": "import threading\nimport struct\nimport json\nimport chardet\n\nfrom wtpy import WtMsgQue, WtMQClient\n\nmq = WtMsgQue()\n\nTOPIC_RT_TRADE = \"TRD_TRADE\" # 生产环境下的成交通知\nTOPIC_RT_ORDER = \"TRD_ORDER\" # 生产环境下的订单通知\nTOPIC_RT_NOTIFY = \"TRD_NOTIFY\" # 生产环境下的普通通知\nTOPIC_RT_LOG = \"LOG\" # 生产环境下的日志通知\n\nclass EventSink:\n def __init__(self):\n pass\n\n def on_order(self, chnl:str, ordInfo:dict):\n pass\n\n def on_trade(self, chnl:str, trdInfo:dict):\n pass\n \n def on_notify(self, chnl:str, message:str):\n pass\n\n def on_log(self, tag:str, time:int, message:str):\n pass\n\ndef decode_bytes(data:bytes):\n ret = chardet.detect(data)\n if ret is not None:\n encoding = ret[\"encoding\"]\n if encoding is not None:\n return data.decode(encoding)\n else:\n return data.decode()\n else:\n return data.decode()\n\nclass EventReceiver(WtMQClient):\n\n def __init__(self, url:str, topics:list = [], sink:EventSink = None, logger = None):\n self.url = url\n self.logger = logger\n mq.add_mq_client(url, self)\n for topic in topics:\n self.subscribe(topic)\n\n self._stopped = False\n self._worker = None\n self._sink = sink\n\n def on_mq_message(self, topic:str, message:str, dataLen:int):\n topic = decode_bytes(topic)\n message = decode_bytes(message[:dataLen])\n if self._sink is not None:\n if topic == TOPIC_RT_TRADE:\n msgObj = json.loads(message)\n trader = msgObj[\"trader\"]\n msgObj.pop(\"trader\")\n self._sink.on_trade(trader, msgObj)\n elif topic == TOPIC_RT_ORDER:\n msgObj = json.loads(message)\n trader = msgObj[\"trader\"]\n msgObj.pop(\"trader\")\n self._sink.on_order(trader, msgObj)\n elif topic == TOPIC_RT_NOTIFY:\n trader = msgObj[\"trader\"]\n self._sink.on_notify(trader, msgObj[\"message\"])\n elif topic == TOPIC_RT_LOG:\n msgObj = json.loads(message)\n self._sink.on_log(msgObj[\"tag\"], msgObj[\"time\"], msgObj[\"message\"])\n\n def run(self):\n self.start()\n\n def release(self):\n mq.destroy_mq_client(self)\n\nTOPIC_BT_EVENT = \"BT_EVENT\" # 回测环境下的事件，主要通知回测的启动和结束\nTOPIC_BT_STATE = \"BT_STATE\" # 回测的状态\nTOPIC_BT_FUND = \"BT_FUND\" # 每日资金变化\n\nclass BtEventSink:\n def __init__(self):\n pass\n \n def on_begin(self):\n pass\n \n def on_finish(self):\n pass\n\n def on_fund(self, fundInfo:dict):\n pass\n\n def on_state(self, statInfo:float):\n pass\n\nclass BtEventReceiver(WtMQClient):\n\n def __init__(self, url:str, topics:list = [], sink:BtEventSink = None, logger = None):\n self.url = url\n self.logger = logger\n mq.add_mq_client(url, self)\n for topic in topics:\n self.subscribe(topic)\n\n self._stopped = False\n self._worker = None\n self._sink = sink\n\n def on_mq_message(self, topic:str, message:str, dataLen:int):\n topic = decode_bytes(topic)\n message = decode_bytes(message[:dataLen])\n if self._sink is not None:\n if topic == TOPIC_BT_EVENT:\n if message == 'BT_START':\n self._sink.on_begin()\n else:\n self._sink.on_finish()\n elif topic == TOPIC_BT_STATE:\n msgObj = json.loads(message)\n self._sink.on_state(msgObj)\n elif topic == TOPIC_BT_FUND:\n msgObj = json.loads(message)\n self._sink.on_fund(msgObj)\n\n def run(self):\n self.start()\n\n def release(self):\n mq.destroy_mq_client(self)\n" }, { "path": "wtpy/monitor/PushSvr.py", "content": "from flask_socketio import SocketIO, emit\nfrom flask import session, sessions\n\nfrom .WtLogger import WtLogger\n\ndef get_param(json_data, key:str, type=str, defVal = \"\"):\n if key not in json_data:\n return defVal\n else:\n return type(json_data[key])\n\nclass PushServer:\n\n def __init__(self, app, dataMgr, logger:WtLogger = None):\n sockio:SocketIO = SocketIO(app)\n self.sockio = sockio\n self.app = app\n self.dataMgr = dataMgr\n self.logger = logger\n\n @sockio.on('connect', namespace='/')\n def on_connect():\n usrInfo = session.get(\"userinfo\")\n if usrInfo is not None:\n self.logger.info(\"%s connected\" % usrInfo[\"loginid\"])\n\n @sockio.on('disconnect', namespace='/')\n def on_disconnect():\n usrInfo = session.get(\"userinfo\")\n if usrInfo is not None:\n self.logger.info(\"%s disconnected\" % usrInfo[\"loginid\"])\n\n @sockio.on('setgroup', namespace='/')\n def set_group(data):\n groupid = get_param(data, \"groupid\")\n if len(groupid) == 0:\n emit('setgroup', {\"result\":-2, \"message\":\"组合ID不能为空\"})\n else:\n session[\"groupid\"] = groupid \n\n def run(self, port:int, host:str):\n self.sockio.run(self.app, host, port)\n\n def notifyGrpLog(self, groupid, tag:str, time:int, message):\n self.sockio.emit(\"notify\", {\"type\":\"gplog\", \"groupid\":groupid, \"tag\":tag, \"time\":time, \"message\":message}, broadcast=True)\n\n def notifyGrpEvt(self, groupid, evttype):\n self.sockio.emit(\"notify\", {\"type\":\"gpevt\", \"groupid\":groupid, \"evttype\":evttype}, broadcast=True)\n\n def notifyGrpChnlEvt(self, groupid, chnlid, evttype, data):\n self.sockio.emit(\"notify\", {\"type\":\"chnlevt\", \"groupid\":groupid, \"channel\":chnlid, \"data\":data, \"evttype\":evttype}, broadcast=True)" }, { "path": "wtpy/monitor/WatchDog.py", "content": "import threading\nimport time\nimport subprocess\nimport os\nimport datetime\nimport json\nimport copy\nimport platform\nimport psutil\n\nfrom .EventReceiver import EventReceiver, EventSink\nfrom .WtLogger import WtLogger\n\nfrom enum import Enum\n\ndef isWindows():\n if \"windows\" in platform.system().lower():\n return True\n\n return False\n\nclass WatcherSink:\n\n def __init__(self):\n pass\n\n def on_start(self, appid:str):\n pass\n\n def on_stop(self, appid:str):\n pass\n\n def on_output(self, appid:str, tag:str, time:int, message:str):\n pass\n\n def on_order(self, appid:str, chnl:str, ordInfo:dict):\n pass\n\n def on_trade(self, appid:str, chnl:str, trdInfo:dict):\n pass\n \n def on_notify(self, appid:str, chnl:str, message:str):\n pass\n\n\nclass ActionType(Enum):\n '''\n 操作类型\n 枚举变量\n '''\n AT_START = 0\n AT_STOP = 1\n AT_RESTART = 2\n\nclass AppState(Enum):\n '''\n app状态\n 枚举变量\n '''\n AS_NotExist = 901\n AS_NotRunning = 902\n AS_Running = 903\n AS_Closed = 904\n\nclass AppInfo(EventSink):\n def __init__(self, appConf:dict, sink:WatcherSink = None, logger:WtLogger=None):\n self.__info__ = appConf\n\n self._cmd_line = None\n\n self.__logger__ = logger\n\n self._lock = threading.Lock()\n self._id = appConf[\"id\"]\n self._check_span = appConf[\"span\"]\n self._guard = appConf[\"guard\"]\n self._redirect = appConf[\"redirect\"]\n self._mq_url = appConf[\"mqurl\"].strip()\n self._schedule = appConf[\"schedule\"][\"active\"]\n self._weekflag = appConf[\"schedule\"][\"weekflag\"]\n\n self._ticks = 0\n self._state = AppState.AS_NotRunning\n self._procid = None\n self._sink = sink\n\n self._evt_receiver = None\n\n if not os.path.exists(appConf[\"folder\"]) or not os.path.exists(appConf[\"path\"]):\n self._state == AppState.AS_NotExist\n\n def applyConf(self, appConf:dict):\n self._lock.acquire()\n self.__info__ = appConf\n self._check_span = appConf[\"span\"]\n self._guard = appConf[\"guard\"]\n old_mqurl = self._mq_url\n self._mq_url = appConf[\"mqurl\"]\n self._redirect = appConf[\"redirect\"]\n self._schedule = appConf[\"schedule\"][\"active\"]\n self._weekflag = appConf[\"schedule\"][\"weekflag\"]\n self._ticks = 0\n self._lock.release()\n self.__logger__.info(\"应用%s的调度设置已更新\" % (self._id))\n\n if self._mq_url != old_mqurl:\n if self._evt_receiver is not None:\n self._evt_receiver.release()\n\n if self._mq_url != '':\n self._evt_receiver = EventReceiver(url=self._mq_url, logger=self.__logger__)\n self._evt_receiver.run()\n self.__logger__.info(\"应用%s开始接收%s的通知信息\" % (self._id, self._mq_url))\n\n def getConf(self):\n self._lock.acquire()\n ret = copy.copy(self.__info__)\n self._lock.release()\n return ret\n\n @property\n def cmd_line(self) -> str:\n fullPath = os.path.join(self.__info__[\"folder\"], self.__info__[\"param\"])\n if self._cmd_line is None:\n self._cmd_line = self.__info__[\"path\"] + \" \" + fullPath\n return self._cmd_line\n\n def is_running(self, pids) -> bool:\n bNeedCheck = (self._procid is None) or (not psutil.pid_exists(self._procid))\n if bNeedCheck:\n for pid in pids:\n try:\n pInfo = psutil.Process(pid)\n cmdLine = pInfo.cmdline()\n if len(cmdLine) == 0:\n continue\n # print(cmdLine)\n cmdLine = ' '.join(cmdLine)\n if self.cmd_line.upper() == cmdLine.upper():\n self._procid = pid\n self.__logger__.info(\"应用%s挂载成功，进程ID: %d\" % (self._id, self._procid))\n \n if self._mq_url != '':\n # 如果事件接收器为空或者url发生了改变，则需要重新创建\n bNeedCreate = self._evt_receiver is None or self._evt_receiver.url != self._mq_url\n if bNeedCreate:\n if self._evt_receiver is not None:\n self._evt_receiver.release()\n self._evt_receiver = EventReceiver(url=self._mq_url, logger=self.__logger__, sink=self)\n self._evt_receiver.run()\n self.__logger__.info(\"应用%s开始接收%s的通知信息\" % (self._id, self._mq_url))\n except:\n pass\n return False\n\n return True\n\n def run(self):\n if self._state == AppState.AS_Running:\n return\n\n if self._mq_url != '':\n # 每次启动都重新创建接收器\n if self._evt_receiver is not None:\n self._evt_receiver.release()\n self._evt_receiver = EventReceiver(url=self._mq_url, logger=self.__logger__, sink=self)\n self._evt_receiver.run()\n self.__logger__.info(\"应用%s开始接收%s的通知信息\" % (self._id, self._mq_url))\n\n try:\n fullPath = os.path.join(self.__info__[\"folder\"], self.__info__[\"param\"])\n if isWindows():\n self._procid = subprocess.Popen([self.__info__[\"path\"], fullPath], # 需要执行的文件路径\n cwd=self.__info__[\"folder\"], creationflags=subprocess.CREATE_NEW_CONSOLE).pid\n else:\n self._procid = subprocess.Popen([self.__info__[\"path\"], fullPath], # 需要执行的文件路径\n cwd=self.__info__[\"folder\"]).pid\n\n self._cmd_line = self.__info__[\"path\"] + \" \" + fullPath\n except:\n self.__logger__.info(\"应用%s启动异常\" % (self._id))\n\n self._state = AppState.AS_Running\n\n self.__logger__.info(\"应用%s的已启动，进程ID: %d\" % (self._id, self._procid))\n if self._sink is not None:\n self._sink.on_start(self._id)\n\n def stop(self):\n if self._state != AppState.AS_Running:\n return\n\n try:\n if isWindows():\n os.system(\"taskkill /pid \" + str(self._procid))\n else:\n os.system(\"kill -9 \" + str(self._procid))\n except e as SystemError:\n self.__logger__.error(\"关闭异常: {}\" % (e))\n pass\n self.__logger__.info(\"应用%s的已停止，进程ID: %d\" % (self._id, self._procid))\n if self._sink is not None:\n self._sink.on_stop(self._id)\n self._procid = None\n self._state = AppState.AS_Closed\n\n def restart(self):\n if self._procid is not None:\n self.stop()\n \n self.run()\n\n def update_state(self, pids):\n if self.is_running(pids):\n self._state = AppState.AS_Running\n elif self._state == AppState.AS_Running:\n self._state = AppState.AS_NotRunning\n self.__logger__.info(\"应用%s的已停止\" % (self._id))\n self._procid = None\n if self._sink is not None:\n self._sink.on_stop(self._id)\n \n\n def tick(self, pids):\n self._ticks += 1\n\n if self._ticks == self._check_span:\n self.update_state(pids)\n if self._state == AppState.AS_NotRunning and self._guard:\n self.__logger__.info(\"应用%s未启动，正在自动重启\" % (self._id))\n self.run()\n elif self._schedule:\n self.__schedule__()\n\n self._ticks = 0\n \n def __schedule__(self):\n weekflag = self._weekflag\n\n now = datetime.datetime.now()\n # python中周一是0，周天是6\n # 但是web端沿用了C++里的规则，周日是0，周六是6，所以做一个变换\n wd = now.weekday() + 1\n if wd == 7:\n wd = 0\n if weekflag[wd] != \"1\":\n return\n\n appid = self.__info__[\"id\"]\n\n curMin = int(now.strftime(\"%H%M\"))\n curDt = int(now.strftime(\"%y%m%d\"))\n self._lock.acquire()\n for tInfo in self.__info__[\"schedule\"][\"tasks\"]:\n if not tInfo[\"active\"]:\n continue\n \n if \"lastDate\" in tInfo:\n lastDate = tInfo[\"lastDate\"]\n else:\n lastDate = 0\n\n if \"lastTime\" in tInfo:\n lastTime = tInfo[\"lastTime\"]\n else:\n lastTime = 0\n targetTm = tInfo[\"time\"]\n action = tInfo[\"action\"]\n\n if curMin == targetTm and (curMin != lastTime or curDt != lastDate):\n if action == ActionType.AT_START.value:\n if self._state not in [AppState.AS_NotExist, AppState.AS_Running]:\n self.__logger__.info(\"自动启动应用%s\" % (appid))\n self.run()\n elif action == ActionType.AT_STOP.value:\n if self._state == AppState.AS_Running:\n self.__logger__.info(\"自动停止应用%s\" % (appid))\n self.stop()\n elif action == ActionType.AT_RESTART.value:\n self.__logger__.info(\"自动重启应用%s\" % (appid))\n self.restart()\n\n tInfo[\"lastDate\"] = curDt\n tInfo[\"lastTime\"] = curMin\n self._lock.release()\n\n def isRunning(self):\n return self._state == AppState.AS_Running\n\n # EventSink.on_order\n def on_order(self, chnl:str, ordInfo:dict):\n if self._sink is not None:\n self._sink.on_order(self._id, chnl, ordInfo)\n\n # EventSink.on_trade\n def on_trade(self, chnl:str, trdInfo:dict):\n if self._sink is not None:\n self._sink.on_trade(self._id, chnl, trdInfo)\n \n # EventSink.on_notify\n def on_notify(self, chnl:str, message:str):\n if self._sink is not None:\n self._sink.on_notify(self._id, chnl, message)\n\n # EventSink.on_log\n def on_log(self, tag:str, time:int, message:str):\n if self._sink is not None:\n self._sink.on_output(self._id, tag, time, message)\n pass\n\nclass WatchDog:\n\n def __init__(self, db, sink:WatcherSink = None, logger:WtLogger=None):\n self.__db_conn__ = db\n self.__apps__ = dict()\n self.__app_conf__ = dict()\n self.__stopped__ = False\n self.__worker__ = None\n self.__sink__ = sink\n self.__logger__ = logger\n\n #加载调度列表\n cur = self.__db_conn__.cursor()\n for row in cur.execute(\"SELECT * FROM schedules;\"):\n appConf = dict()\n appConf[\"id\"] = row[1]\n appConf[\"path\"] = row[2]\n appConf[\"folder\"] = row[3]\n appConf[\"param\"] = row[4]\n appConf[\"type\"] = row[5]\n appConf[\"span\"] = row[6]\n appConf[\"guard\"] = row[7]=='true'\n appConf[\"redirect\"] = row[8]=='true'\n appConf[\"mqurl\"] = row[11]\n appConf[\"schedule\"] = dict()\n appConf[\"schedule\"][\"active\"] = row[9]=='true'\n appConf[\"schedule\"][\"weekflag\"] = row[10]\n appConf[\"schedule\"][\"tasks\"] = list()\n appConf[\"schedule\"][\"tasks\"].append(json.loads(row[12]))\n appConf[\"schedule\"][\"tasks\"].append(json.loads(row[13]))\n appConf[\"schedule\"][\"tasks\"].append(json.loads(row[14]))\n appConf[\"schedule\"][\"tasks\"].append(json.loads(row[15]))\n appConf[\"schedule\"][\"tasks\"].append(json.loads(row[16]))\n appConf[\"schedule\"][\"tasks\"].append(json.loads(row[17]))\n self.__app_conf__[appConf[\"id\"]] = appConf\n self.__apps__[appConf[\"id\"]] = AppInfo(appConf, sink, self.__logger__)\n\n\n def __watch_impl__(self):\n while not self.__stopped__:\n time.sleep(1)\n pids = psutil.pids()\n for appid in self.__apps__:\n appInfo = self.__apps__[appid]\n\n appInfo.tick(pids)\n\n def get_apps(self):\n ret = {}\n for appid in self.__app_conf__:\n bRunning = self.__apps__[appid].isRunning()\n conf = copy.copy(self.__app_conf__[appid])\n conf[\"running\"] = bRunning\n ret[appid] = conf\n return ret\n\n def run(self):\n if self.__worker__ is None:\n self.__worker__ = threading.Thread(target=self.__watch_impl__, name=\"WatchDog\", daemon=True)\n self.__worker__.start()\n self.__logger__.info(\"自动调度服务已启动\")\n\n def start(self, appid:str):\n if appid not in self.__apps__:\n return\n\n self.__logger__.info(\"手动启动%s\" % (appid))\n appInfo = self.__apps__[appid]\n appInfo.run()\n\n def stop(self, appid:str):\n if appid not in self.__apps__:\n return\n\n self.__logger__.info(\"手动停止%s\" % (appid))\n appInfo = self.__apps__[appid]\n appInfo.stop()\n\n def has_app(self, appid:str):\n return appid in self.__apps__\n\n def restart(self, appid:str):\n if appid not in self.__apps__:\n return\n\n appInfo = self.__apps__[appid]\n appInfo.restart()\n \n def isRunning(self, appid:str):\n if appid not in self.__apps__:\n return False\n\n appInfo = self.__apps__[appid]\n return appInfo.isRunning()\n\n def getAppConf(self, appid:str):\n if appid not in self.__apps__:\n return None\n \n appInfo = self.__apps__[appid]\n return appInfo.getConf()\n\n def delApp(self, appid:str):\n if appid not in self.__apps__:\n return\n\n self.__apps__.pop(appid)\n\n cur = self.__db_conn__.cursor()\n cur.execute(\"DELETE FROM schedules WHERE appid='%s';\" % (appid))\n self.__db_conn__.commit()\n self.__logger__.info(\"应用%s自动调度已删除\" % (appid))\n\n def updateMQURL(self, appid:str, mqurl:str):\n if appid not in self.__apps__:\n return\n\n self.__app_conf__[appid][\"mqurl\"] = mqurl\n appConf = self.__app_conf__[appid]\n appInst = self.__apps__[appid]\n appInst.applyConf(appConf)\n \n cur = self.__db_conn__.cursor()\n sql = \"UPDATE schedules SET mqurl='%s',modifytime=datetime('now','localtime') WHERE appid='%s';\" % (mqurl, appid)\n print(sql)\n cur.execute(sql)\n self.__db_conn__.commit()\n\n def applyAppConf(self, appConf:dict, isGroup:bool = False):\n appid = appConf[\"id\"]\n self.__app_conf__[appid] = appConf\n isNewApp = False\n if appid not in self.__apps__:\n isNewApp = True\n self.__apps__[appid] = AppInfo(appConf, self.__sink__, self.__logger__)\n else:\n appInst = self.__apps__[appid]\n appInst.applyConf(appConf)\n\n guard = 'true' if appConf[\"guard\"] else 'false'\n redirect = 'true' if appConf[\"redirect\"] else 'false'\n schedule = 'true' if appConf[\"schedule\"] else 'false'\n\n stype = 1 if isGroup else 0\n\n cur = self.__db_conn__.cursor()\n sql = ''\n if isNewApp:\n sql = \"INSERT INTO schedules(appid,path,folder,param,type,span,guard,redirect,schedule,weekflag,task1,task2,task3,task4,task5,task6,mqurl) \\\n VALUES('%s','%s','%s','%s',%d, %d,'%s','%s','%s','%s','%s','%s','%s','%s','%s','%s','%s');\" % (\n appid, appConf[\"path\"], appConf[\"folder\"], appConf[\"param\"], stype, appConf[\"span\"], guard, redirect, schedule, appConf[\"schedule\"][\"weekflag\"],\n json.dumps(appConf[\"schedule\"][\"tasks\"][0]),json.dumps(appConf[\"schedule\"][\"tasks\"][1]),json.dumps(appConf[\"schedule\"][\"tasks\"][2]),\n json.dumps(appConf[\"schedule\"][\"tasks\"][3]),json.dumps(appConf[\"schedule\"][\"tasks\"][4]),json.dumps(appConf[\"schedule\"][\"tasks\"][5]),\n appConf[\"mqurl\"])\n else:\n sql = \"UPDATE schedules SET path='%s',folder='%s',param='%s',type=%d,span='%s',guard='%s',redirect='%s',schedule='%s',weekflag='%s',task1='%s',task2='%s',\\\n task3='%s',task4='%s',task5='%s',task6='%s',mqurl='%s',modifytime=datetime('now','localtime') WHERE appid='%s';\" % (\n appConf[\"path\"], appConf[\"folder\"], appConf[\"param\"], stype, appConf[\"span\"], guard, redirect, schedule, appConf[\"schedule\"][\"weekflag\"],\n json.dumps(appConf[\"schedule\"][\"tasks\"][0]),json.dumps(appConf[\"schedule\"][\"tasks\"][1]),json.dumps(appConf[\"schedule\"][\"tasks\"][2]),\n json.dumps(appConf[\"schedule\"][\"tasks\"][3]),json.dumps(appConf[\"schedule\"][\"tasks\"][4]),json.dumps(appConf[\"schedule\"][\"tasks\"][5]), \n appConf[\"mqurl\"], appid)\n cur.execute(sql)\n self.__db_conn__.commit()" }, { "path": "wtpy/monitor/WtBtMon.py", "content": "'''\nDescripttion: 回测管理模块\nversion: \nAuthor: Wesley\nDate: 2021-08-11 14:03:33\nLastEditors: Wesley\nLastEditTime: 2021-09-02 14:18:50\n'''\nimport os\nimport json\nimport subprocess\nimport platform\nimport sys\nimport psutil\nimport hashlib\nimport datetime\nimport shutil\nimport json\nimport threading\nimport time\n\nfrom wtpy import WtDtServo\nfrom .WtLogger import WtLogger\nfrom .EventReceiver import BtEventReceiver, BtEventSink\n\ndef isWindows():\n if \"windows\" in platform.system().lower():\n return True\n\n return False\n\ndef md5_str(v:str) -> str:\n return hashlib.md5(v.encode()).hexdigest()\n\ndef gen_btid(user:str, straid:str) -> str:\n now = datetime.datetime.now()\n s = user + \"_\" + straid + \"_\" + str(now.timestamp())\n return md5_str(s)\n\ndef gen_straid(user:str) -> str:\n now = datetime.datetime.now()\n s = user + \"_\" + str(now.timestamp())\n return md5_str(s)\n\nclass BtTaskSink:\n\n def __init__(self):\n pass\n\n def on_start(self, user:str, straid:str, btid:str):\n pass\n\n def on_stop(self, user:str, straid:str, btid:str):\n pass\n\n def on_state(self, user:str, straid:str, btid:str, statInfo:dict):\n pass\n\n def on_fund(self, user:str, straid:str, btid:str, fundInfo:dict):\n pass\n\nclass WtBtTask(BtEventSink):\n '''\n 回测任务类\n '''\n def __init__(self, user:str, straid:str, btid:str, folder:str, logger:WtLogger = None, sink:BtTaskSink = None):\n self.user = user\n self.straid = straid\n self.btid = btid\n self.logger = logger\n self.folder = folder\n self.sink = sink\n \n self._cmd_line = None\n self._mq_url = \"ipc:///wtpy/bt_%s.ipc\" % (btid)\n self._ticks = 0\n self._state = 0\n self._procid = None\n self._evt_receiver = None\n\n def __check__(self):\n while True:\n time.sleep(1)\n pids = psutil.pids()\n if psutil.pid_exists(self._procid):\n continue\n else:\n print(\"%s process %d finished\" % (self.btid, self._procid))\n if self.sink is not None:\n self.sink.on_stop(self.user, self.straid, self.btid)\n break\n\n def run(self):\n if self._state != 0:\n return\n\n self._evt_receiver = BtEventReceiver(url=self._mq_url, logger=self.logger, sink=self)\n self._evt_receiver.run()\n self.logger.info(\"回测%s开始接收%s的通知信息\" % (self.btid, self._mq_url))\n\n try:\n fullPath = os.path.join(self.folder, \"runBT.py\")\n if isWindows():\n self._procid = subprocess.Popen([sys.executable, fullPath], # 需要执行的文件路径\n cwd=self.folder, creationflags=subprocess.CREATE_NEW_CONSOLE).pid\n else:\n self._procid = subprocess.Popen([sys.executable, fullPath], # 需要执行的文件路径\n cwd=self.folder).pid\n\n self._cmd_line = sys.executable + \" \" + fullPath\n except:\n self.logger.info(\"回测%s启动异常\" % (self.btid))\n\n self._state = 1\n\n self.logger.info(\"回测%s的已启动，进程ID: %d\" % (self.btid, self._procid))\n\n self.watcher = threading.Thread(target=self.__check__, name=self.btid, daemon=True)\n self.watcher.start()\n\n @property\n def cmd_line(self) -> str:\n fullPath = os.path.join(self.folder, \"runBT.py\")\n if self._cmd_line is None:\n self._cmd_line = sys.executable + \" \" + fullPath\n return self._cmd_line\n\n def is_running(self, pids) -> bool:\n bNeedCheck = (self._procid is None) or (not psutil.pid_exists(self._procid))\n if bNeedCheck:\n for pid in pids:\n try:\n pInfo = psutil.Process(pid)\n cmdLine = pInfo.cmdline()\n if len(cmdLine) == 0:\n continue\n # print(cmdLine)\n cmdLine = ' '.join(cmdLine)\n if self.cmd_line.upper() == cmdLine.upper():\n self._procid = pid\n self.logger.info(\"回测%s挂载成功，进程ID: %d\" % (self.btid, self._procid))\n\n if self._mq_url != '':\n self._evt_receiver = BtEventReceiver(url=self._mq_url, logger=self.logger, sink=self)\n self._evt_receiver.run()\n self.logger.info(\"回测%s开始接收%s的通知信息\" % (self.btid, self._mq_url))\n\n self.watcher = threading.Thread(target=self.__check__, name=self.btid, daemon=True)\n self.watcher.run()\n except:\n pass\n return False\n\n return True\n\n def on_begin(self):\n if self.sink is not None:\n self.sink.on_start(self.user, self.straid, self.btid)\n\n def on_finish(self):\n pass\n\n def on_state(self, statInfo:dict):\n if self.sink is not None:\n self.sink.on_state(self.user, self.straid, self.btid, statInfo)\n print(statInfo)\n\n def on_fund(self, fundInfo:dict):\n if self.sink is not None:\n self.sink.on_fund(self.user, self.straid, self.btid, fundInfo)\n print(fundInfo)\n\n\nclass WtBtMon(BtTaskSink):\n '''\n 回测管理器\n '''\n def __init__(self, deploy_folder:str, dtServo:WtDtServo = None, logger:WtLogger = None):\n self.path = deploy_folder\n self.user_stras = dict()\n self.user_bts = dict()\n self.logger = logger\n self.dt_servo = dtServo\n\n self.task_infos = dict()\n self.task_map = dict()\n\n self.__load_tasks__()\n\n def __load_user_data__(self, user:str):\n folder = os.path.join(self.path, user)\n if not os.path.exists(folder):\n os.mkdir(folder)\n\n filepath = os.path.join(folder, \"marker.json\")\n if not os.path.exists(filepath):\n return False\n\n f = open(filepath, \"r\")\n content = f.read()\n f.close()\n\n obj = json.loads(content)\n self.user_stras[user] = obj[\"strategies\"]\n self.user_bts[user] = obj[\"backtests\"]\n return True\n\n def __save_user_data__(self, user):\n folder = os.path.join(self.path, user)\n if not os.path.exists(folder):\n os.mkdir(folder)\n\n obj = {\n \"strategies\":{},\n \"backtests\":{}\n }\n\n if user in self.user_stras:\n obj[\"strategies\"] = self.user_stras[user]\n\n if user in self.user_bts:\n obj[\"backtests\"] = self.user_bts[user]\n\n filepath = os.path.join(folder, \"marker.json\")\n f = open(filepath, \"w\")\n f.write(json.dumps(obj, indent=4, ensure_ascii=False))\n f.close()\n return True\n\n def get_strategies(self, user:str) -> list:\n if user not in self.user_stras:\n bSucc = self.__load_user_data__(user)\n \n if not bSucc:\n return None\n\n ay = list()\n for straid in self.user_stras[user]:\n ay.append(self.user_stras[user][straid])\n return ay\n\n def add_strategy(self, user:str, name:str) -> dict:\n if user not in self.user_stras:\n self.__load_user_data__(user)\n\n if user not in self.user_stras:\n self.user_stras[user] = dict()\n\n straid = gen_straid(user)\n self.user_stras[user][straid] = {\n \"id\":straid,\n \"name\":name,\n \"perform\":{\n \"days\": 0,\n \"total_return\": 0,\n \"annual_return\": 0,\n \"win_rate\": 0,\n \"max_falldown\": 0,\n \"max_profratio\": 0,\n \"std\": 0,\n \"down_std\": 0,\n \"sharpe_ratio\": 0,\n \"sortino_ratio\": 0,\n \"calmar_ratio\": 0\n }\n }\n\n folder = os.path.join(self.path, user, straid)\n if not os.path.exists(folder):\n os.mkdir(folder)\n\n fname = os.path.join(folder, \"MyStrategy.py\")\n srcfname = os.path.join(self.path, \"template/MyStrategy.py\")\n shutil.copyfile(srcfname, fname)\n\n self.__save_user_data__(user)\n\n return self.user_stras[user][straid]\n\n def del_strategy(self, user:str, straid:str):\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return False\n\n if straid not in self.user_stras[user]:\n return True\n\n folder = os.path.join(self.path, user, straid)\n if not os.path.exists(folder):\n return True\n\n delFolder = os.path.join(self.path, user, \".del\")\n if not os.path.exists(delFolder):\n os.mkdir(delFolder)\n shutil.move(folder, delFolder)\n self.user_stras[user].pop(straid)\n self.__save_user_data__(user)\n return True\n \n def has_strategy(self, user:str, straid:str, btid:str = None) -> bool:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return False\n\n if btid is None:\n return straid in self.user_stras[user]\n else:\n return btid in self.user_bts[user]\n\n def get_strategy_code(self, user:str, straid:str, btid:str = None) -> str:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n if btid is None:\n path = os.path.join(self.path, user, straid, \"MyStrategy.py\")\n if not os.path.exists(path):\n return None\n\n f = open(path, \"r\", encoding=\"UTF-8\")\n content = f.read()\n f.close()\n return content\n else:\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n bt_path = os.path.join(self.path, \"%s/%s/backtests/%s/runBT.py\" % (user, straid, btid))\n f = open(bt_path, \"r\")\n content = f.read()\n f.close()\n return content\n\n def set_strategy_code(self, user:str, straid:str, content:str) -> bool:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return False\n\n path = os.path.join(self.path, user, straid, \"MyStrategy.py\")\n if not os.path.exists(path):\n return None\n\n f = open(path, \"w\", encoding=\"UTF-8\")\n f.write(content)\n f.close()\n return True\n\n def get_backtests(self, user:str, straid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n if user not in self.user_bts:\n return None\n\n ay = list()\n for btid in self.user_bts[user]:\n ay.append(self.user_bts[user][btid])\n\n return ay\n\n def del_backtest(self, user:str, btid:str):\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return\n\n if user not in self.user_bts:\n return\n\n if btid in self.user_bts[user]:\n self.user_bts[user].pop(btid)\n self.logger.info(\"Backtest %s of %s deleted\" % (btid, user))\n\n self.__save_user_data__(user)\n\n def get_bt_funds(self, user:str, straid:str, btid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/funds.csv\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n lines = f.readlines()\n f.close()\n lines = lines[1:]\n\n funds = list()\n for line in lines:\n cells = line.split(\",\")\n if len(cells) > 10:\n continue\n\n tItem = {\n \"date\": int(cells[0]),\n \"closeprofit\": float(cells[1]),\n \"dynprofit\": float(cells[2]),\n \"dynbalance\": float(cells[3]),\n \"fee\": 0\n }\n\n if len(cells) > 4:\n tItem[\"fee\"] = float(cells[4])\n\n funds.append(tItem)\n \n return funds\n\n def get_bt_trades(self, user:str, straid:str, btid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/trades.csv\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n lines = f.readlines()\n f.close()\n lines = lines[1:]\n\n items = list()\n for line in lines:\n cells = line.split(\",\")\n if len(cells) > 10:\n continue\n\n item = {\n \"code\": cells[0],\n \"time\": int(cells[1]),\n \"direction\": cells[2],\n \"offset\": cells[3],\n \"price\": float(cells[4]),\n \"volume\": float(cells[5]),\n \"tag\": cells[6],\n \"fee\": 0\n }\n\n if len(cells) > 7:\n item[\"fee\"] = float(cells[7])\n\n if len(cells) > 4:\n item[\"fee\"] = float(cells[4])\n\n items.append(item)\n \n return items\n\n def get_bt_rounds(self, user:str, straid:str, btid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/closes.csv\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n lines = f.readlines()\n f.close()\n lines = lines[1:]\n\n items = list()\n for line in lines:\n cells = line.split(\",\")\n\n item = {\n \"code\": cells[0],\n \"direct\": cells[1],\n \"opentime\": int(cells[2]),\n \"openprice\": float(cells[3]),\n \"closetime\": int(cells[4]),\n \"closeprice\": float(cells[5]),\n \"qty\": float(cells[6]),\n \"profit\": float(cells[7]),\n \"maxprofit\": float(cells[8]),\n \"maxloss\": float(cells[9]),\n \"entertag\": cells[11],\n \"exittag\": cells[12]\n }\n\n items.append(item)\n \n return items\n\n def get_bt_signals(self, user:str, straid:str, btid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/signals.csv\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n lines = f.readlines()\n f.close()\n lines = lines[1:]\n\n items = list()\n for line in lines:\n cells = line.split(\",\")\n if len(cells) > 10:\n continue\n\n item = {\n \"code\": cells[0],\n \"target\": float(cells[1]),\n \"sigprice\": float(cells[2]),\n \"gentime\": cells[3],\n \"tag\": cells[4]\n }\n\n items.append(item)\n \n return items\n\n def get_bt_summary(self, user:str, straid:str, btid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/summary.json\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n content = f.read()\n f.close()\n\n obj = json.loads(content)\n return obj\n\n def get_bt_state(self, user:str, straid:str, btid:str) -> list:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/btenv.json\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n content = f.read()\n f.close()\n\n obj = json.loads(content)\n return obj\n\n def get_bt_state(self, user:str, straid:str, btid:str) -> dict:\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n filename = \"%s/%s/backtests/%s/outputs_bt/%s/btenv.json\" % (user, straid, btid, btid)\n filename = os.path.join(self.path, filename)\n if not os.path.exists(filename):\n return None\n\n f = open(filename, \"r\")\n content = f.read()\n f.close()\n\n thisBts[btid][\"state\"] = json.loads(content)\n\n return thisBts[btid][\"state\"]\n\n def update_bt_state(self, user:str, straid:str, btid:str, stateObj:dict):\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n\n thisBts = self.user_bts[user]\n if btid not in thisBts:\n return None\n\n thisBts[btid][\"state\"] = stateObj\n\n def get_bt_kline(self, user:str, straid:str, btid:str) -> list:\n if self.dt_servo is None:\n return None\n\n if user not in self.user_bts:\n bSucc = self.__load_user_data__(user)\n\n if not bSucc:\n return None\n \n btState = self.get_bt_state(user, straid, btid)\n if btState is None:\n return None\n\n thisBts = self.user_bts[user]\n if \"kline\" not in thisBts[btid]:\n code = btState[\"code\"]\n period = btState[\"period\"]\n stime = btState[\"stime\"]\n etime = btState[\"etime\"]\n barList = self.dt_servo.get_bars(stdCode=code, period=period, fromTime=stime, endTime=etime)\n if barList is None:\n return None\n\n bars = list()\n for realBar in barList:\n bar = dict()\n if period[0] == 'd':\n bar[\"time\"] = realBar.date\n else:\n bar[\"time\"] = 1990*100000000 + realBar.time\n bar[\"bartime\"] = bar[\"time\"]\n bar[\"open\"] = realBar.open\n bar[\"high\"] = realBar.high\n bar[\"low\"] = realBar.low\n bar[\"close\"] = realBar.close\n bar[\"volume\"] = realBar.vol\n bars.append(bar)\n thisBts[btid][\"kline\"] = bars\n\n return thisBts[btid][\"kline\"]\n\n def run_backtest(self, user:str, straid:str, fromTime:int, endTime:int, capital:float, slippage:int=0) -> dict:\n if user not in self.user_bts:\n self.__load_user_data__(user)\n\n if user not in self.user_bts:\n self.user_bts[user] = dict()\n \n btid = gen_btid(user, straid)\n\n # 生成回测目录\n folder = os.path.join(self.path, user, straid, \"backtests\")\n if not os.path.exists(folder):\n os.mkdir(folder)\n \n folder = os.path.join(folder, btid)\n os.mkdir(folder)\n\n # 将策略文件复制到该目录下\n old_path = os.path.join(self.path, user, straid, \"MyStrategy.py\")\n new_path = os.path.join(folder, \"MyStrategy.py\")\n shutil.copyfile(old_path, new_path)\n\n # 初始化目录下的配置文件\n old_path = os.path.join(self.path, \"template/configbt.json\")\n new_path = os.path.join(folder, \"configbt.json\")\n f = open(old_path, \"r\", encoding=\"UTF-8\")\n content = f.read()\n f.close()\n content = content.replace(\"$BTID$\", btid)\n\n f = open(new_path, \"w\", encoding=\"UTF-8\")\n f.write(content)\n f.close()\n\n old_path = os.path.join(self.path, \"template/logcfgbt.json\")\n new_path = os.path.join(folder, \"logcfgbt.json\")\n shutil.copyfile(old_path, new_path)\n\n old_path = os.path.join(self.path, \"template/fees.json\")\n new_path = os.path.join(folder, \"fees.json\")\n shutil.copyfile(old_path, new_path)\n\n old_path = os.path.join(self.path, \"template/runBT.py\")\n new_path = os.path.join(folder, \"runBT.py\")\n\n f = open(old_path, \"r\", encoding=\"UTF-8\")\n content = f.read()\n f.close()\n content = content.replace(\"$FROMTIME$\", str(fromTime))\n content = content.replace(\"$ENDTIME$\", str(endTime))\n content = content.replace(\"$STRAID$\", btid)\n content = content.replace(\"$CAPITAL$\", str(capital))\n content = content.replace(\"$SLIPPAGE$\", str(slippage))\n\n f = open(new_path, \"w\", encoding=\"UTF-8\")\n f.write(content)\n f.close()\n\n btInfo = {\n \"id\":btid,\n \"capital\":capital,\n \"runtime\":datetime.datetime.now().strftime(\"%Y.%m.%d %H:%M:%S\"),\n \"state\":{\n \"code\": \"\",\n \"period\": \"\",\n \"stime\": fromTime,\n \"etime\": endTime,\n \"progress\": 0,\n \"elapse\": 0\n },\n \"perform\":{\n \"days\": 0,\n \"total_return\": 0,\n \"annual_return\": 0,\n \"win_rate\": 0,\n \"max_falldown\": 0,\n \"max_profratio\": 0,\n \"std\": 0,\n \"down_std\": 0,\n \"sharpe_ratio\": 0,\n \"sortino_ratio\": 0,\n \"calmar_ratio\": 0\n }\n }\n\n self.user_bts[user][btid] = btInfo\n\n self.__save_user_data__(user)\n\n # 添加\n btTask = WtBtTask(user, straid, btid, folder, self.logger, sink=self)\n btTask.run()\n\n self.task_map[btid] = btTask\n\n # 这里还需要记录一下回测的任务，不然如果重启就恢复不了了\n taskInfo = {\n \"user\":user,\n \"straid\":straid,\n \"btid\":btid,\n \"folder\":folder\n }\n self.task_infos[btid]= taskInfo\n self.__save_tasks__()\n\n return btInfo\n\n def __update_bt_result__(self, user:str, straid:str, btid:str):\n if user not in self.user_bts:\n self.__load_user_data__(user)\n\n if user not in self.user_bts:\n self.user_bts[user] = dict()\n\n # 更新回测状态\n stateObj = self.get_bt_state(user, straid, btid)\n self.user_bts[user][btid][\"state\"] = stateObj\n\n # 更新回测结果摘要\n summaryObj = self.get_bt_summary(user, straid, btid)\n self.user_bts[user][btid][\"perform\"] = summaryObj\n self.user_stras[user][straid][\"perform\"] = summaryObj\n\n self.__save_user_data__(user)\n \n def __save_tasks__(self):\n obj = self.task_infos\n\n filename = os.path.join(self.path, \"tasks.json\")\n f = open(filename, \"w\")\n f.write(json.dumps(obj, indent=4))\n f.close()\n\n def __load_tasks__(self):\n filename = os.path.join(self.path, \"tasks.json\")\n if not os.path.exists(filename):\n return\n\n f = open(filename, \"r\")\n content = f.read()\n f.close()\n\n task_infos = json.loads(content)\n pids = psutil.pids()\n for btid in task_infos:\n tInfo = task_infos[btid].copy()\n tInfo[\"logger\"] = self.logger\n btTask = WtBtTask(**tInfo)\n\n if btTask.is_running(pids):\n self.task_map[btid] = btTask\n self.task_infos[btid] = task_infos[btid]\n self.logger.info(\"回测任务%s已恢复\")\n else:\n # 之前记录过测回测任务，执行完成了，要更新回测数据\n self.__update_bt_result__(tInfo[\"user\"], tInfo[\"straid\"], btid)\n \n self.__save_tasks__()\n \n\n def on_start(self, user:str, straid:str, btid:str):\n pass\n\n def on_stop(self, user:str, straid:str, btid:str):\n self.__update_bt_result__(user, straid, btid)\n\n def on_state(self, user:str, straid:str, btid:str, statInfo:dict):\n self.user_bts[user][btid][\"state\"] = statInfo\n\n def on_fund(self, user:str, straid:str, btid:str, fundInfo:dict):\n pass" }, { "path": "wtpy/monitor/WtLogger.py", "content": "import logging\nimport os\n\nclass WtLogger:\n\n def __init__(self, catName:str='', filename:str=\"out.log\"):\n self.logger = logging.getLogger(catName)\n self.logger.setLevel(logging.DEBUG)\n\n #创建一个handler，用于写入日志文件\n log_path = os.getcwd()+\"/logs/\" # 指定文件输出路径，注意logs是个文件夹，一定要加上/，不然会导致输出路径错误，把logs变成文件名的一部分了\n if not os.path.exists(log_path):\n os.mkdir(log_path)\n logname = log_path + filename #指定输出的日志文件名\n fh = logging.FileHandler(logname,encoding = 'utf-8',mode='a') # 指定utf-8格式编码，避免输出的日志文本乱码\n fh.setLevel(logging.INFO)\n\n #创建一个handler，用于将日志输出到控制台\n ch = logging.StreamHandler()\n ch.setLevel(logging.INFO)\n\n # 定义handler的输出格式\n formatter = logging.Formatter('[%(asctime)s - %(levelname)s] %(message)s', datefmt='%Y-%m-%d %H:%M:%S')\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n\n # 给logger添加handler\n self.logger.addHandler(fh)\n self.logger.addHandler(ch)\n\n def info(self, message:str):\n self.logger.info(message)\n\n def warn(self, message:str):\n self.logger.warn(message)\n\n def error(self, message:str):\n self.logger.error(message)\n\n def fatal(self, message:str):\n self.logger.fatal(message)\n" }, { "path": "wtpy/monitor/WtMonSvr.py", "content": "from flask import Flask, session, redirect, request, make_response\nfrom flask_compress import Compress\nimport json\nimport datetime\nimport os\nimport hashlib\nimport sys\nimport base64\nimport chardet\nimport pytz\n\nfrom .WtLogger import WtLogger\nfrom .DataMgr import DataMgr, backup_file\nfrom .PushSvr import PushServer\nfrom .WatchDog import WatchDog, WatcherSink\nfrom .EventReceiver import EventReceiver, EventSink\nfrom .WtBtMon import WtBtMon\nfrom wtpy import WtDtServo\n\ndef pack_rsp(obj):\n rsp = make_response(json.dumps(obj))\n rsp.headers[\"content-type\"]= \"text/json;charset=utf-8\"\n return rsp\n\ndef parse_data():\n try:\n data = request.get_data()\n json_data = json.loads(data.decode(\"utf-8\"))\n return True,json_data\n except:\n return False, {\n \"result\": -998,\n \"message\": \"请求数据解析失败\"\n }\n\ndef get_param(json_data, key:str, type=str, defVal = \"\"):\n if key not in json_data:\n return defVal\n else:\n return type(json_data[key])\n\n#获取文件最后N行的函数\ndef get_tail(filename, N:int = 100, encoding=\"GBK\") :\n filesize = os.path.getsize(filename)\n blocksize = 10240\n dat_file = open(filename, 'r', encoding=encoding)\n last_line = \"\"\n if filesize > blocksize :\n maxseekpoint = (filesize // blocksize)\n dat_file.seek((maxseekpoint-1)*blocksize)\n elif filesize :\n dat_file.seek(0, 0)\n lines = dat_file.readlines()\n if lines :\n last_line = lines[-N:]\n dat_file.close()\n return ''.join(last_line), len(last_line)\n\ndef check_auth():\n usrInfo = session.get(\"userinfo\")\n # session里没有用户信息\n if usrInfo is None:\n \n return False, {\n \"result\":-999,\n \"message\":\"请先登录\"\n }\n\n # session里有用户信息，则要读取\n exptime = session.get(\"expiretime\")\n now = datetime.datetime.now().replace(tzinfo=pytz.timezone('UTC')).strftime(\"%Y.%m.%d %H:%M:%S\")\n if now > exptime:\n return False, {\n \"result\":-999,\n \"message\":\"登录已超时，请重新登录\"\n }\n\n return True, usrInfo\n\ndef get_cfg_tree(root:str, name:str):\n if not os.path.exists(root):\n return {\n \"label\":name,\n \"path\":root,\n \"exist\":False,\n \"isfile\":False,\n \"children\":[]\n }\n\n if os.path.isfile(root):\n return {\n \"label\":name,\n \"path\":root,\n \"exist\":False,\n \"isfile\":True\n }\n\n ret = {\n \"label\":name,\n \"path\":root,\n \"exist\":True,\n \"isfile\":False,\n \"children\":[]\n }\n\n filepath = os.path.join(root, \"run.py\")\n ret['children'].append({\n \"label\":\"run.py\",\n \"path\":filepath,\n \"exist\":True,\n \"isfile\":True,\n \"children\":[]\n })\n\n filepath = os.path.join(root, \"config.json\")\n ret['children'].append({\n \"label\":\"config.json\",\n \"path\":filepath,\n \"exist\":True,\n \"isfile\":True,\n \"children\":[]\n })\n\n f = open(filepath, \"r\")\n content = f.read()\n f.close()\n cfgObj = json.loads(content)\n if \"executers\" in cfgObj:\n filename = cfgObj[\"executers\"]\n if type(filename) == str:\n filepath = os.path.join(root, filename)\n ret['children'].append({\n \"label\":filename,\n \"path\":filepath,\n \"exist\":True,\n \"isfile\":True,\n \"children\":[]\n })\n\n if \"parsers\" in cfgObj:\n filename = cfgObj[\"parsers\"]\n if type(filename) == str:\n filepath = os.path.join(root, filename)\n ret['children'].append({\n \"label\":filename,\n \"path\":filepath,\n \"exist\":True,\n \"isfile\":True,\n \"children\":[]\n })\n\n if \"traders\" in cfgObj:\n filename = cfgObj[\"traders\"]\n if type(filename) == str:\n filepath = os.path.join(root, filename)\n ret['children'].append({\n \"label\":filename,\n \"path\":filepath,\n \"exist\":True,\n \"isfile\":True,\n \"children\":[]\n })\n\n filepath = os.path.join(root, 'generated')\n ret[\"children\"].append(get_path_tree(filepath, 'generated', True))\n \n return ret\n\ndef get_path_tree(root:str, name:str, hasFile:bool = True):\n if not os.path.exists(root):\n return {\n \"label\":name,\n \"path\":root,\n \"exist\":False,\n \"isfile\":False,\n \"children\":[]\n }\n\n if os.path.isfile(root):\n return {\n \"label\":name,\n \"path\":root,\n \"exist\":False,\n \"isfile\":True\n }\n\n ret = {\n \"label\":name,\n \"path\":root,\n \"exist\":True,\n \"isfile\":False,\n \"children\":[]\n }\n files = os.listdir(root, )\n for filename in files:\n if filename in ['__pycache__', '.vscode', 'wtpy', '__init__.py']:\n continue\n if filename[-3:] == 'pyc':\n continue\n\n filepath = os.path.join(root, filename)\n if os.path.isfile(filepath):\n if not hasFile:\n continue\n else:\n ret[\"children\"].append({\n \"label\":filename,\n \"path\":filepath,\n \"exist\":True,\n \"isfile\":True})\n else:\n ret[\"children\"].append(get_path_tree(filepath, filename, hasFile))\n\n ay1 = list()\n ay2 = list()\n for item in ret[\"children\"]:\n if item[\"isfile\"]:\n ay2.append(item)\n else:\n ay1.append(item)\n ay = ay1 + ay2\n ret[\"children\"] = ay\n return ret\n\nclass WtMonSvr(WatcherSink):\n\n def __init__(self, static_folder:str=\"\", static_url_path=\"/\", deploy_dir=\"C:/\"):\n if len(static_folder) == 0:\n static_folder = 'static'\n\n self.logger = WtLogger(__name__, \"WtMonSvr.log\")\n\n # 数据管理器，主要用于缓存各组合的数据\n self.__data_mgr__ = DataMgr('data.db', logger=self.logger)\n\n self.__bt_mon__:WtBtMon = None\n self.__dt_servo__:WtDtServo = None\n\n # 看门狗模块，主要用于调度各个组合启动关闭\n self._dog = WatchDog(sink=self, db=self.__data_mgr__.get_db(), logger=self.logger)\n\n app = Flask(__name__, instance_relative_config=True, static_folder=static_folder, static_url_path=static_url_path)\n app.secret_key = \"!@#$%^&*()\"\n Compress(app)\n # app.debug = True\n self.app = app\n self.worker = None\n self.deploy_dir = deploy_dir\n self.deploy_tree = None\n\n self.push_svr = PushServer(app, self.__data_mgr__, self.logger)\n\n self.init_mgr_apis(app)\n\n def set_bt_mon(self, btMon:WtBtMon):\n self.__bt_mon__ = btMon\n self.init_bt_apis(self.app)\n\n def set_dt_servo(self, dtServo:WtDtServo):\n self.__dt_servo__ = dtServo\n\n def init_bt_apis(self, app:Flask):\n\n # 拉取K线数据\n @app.route(\"/bt/qrybars\", methods=[\"POST\"])\n def qry_bt_bars():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n if self.__dt_servo__ is None:\n ret = {\n \"result\":-2,\n \"message\":\"没有配置数据伺服\"\n }\n return pack_rsp(ret)\n\n stdCode = get_param(json_data, \"code\")\n period = get_param(json_data, \"period\")\n fromTime = get_param(json_data, \"stime\", int, None)\n dataCount = get_param(json_data, \"count\", int, None)\n endTime = get_param(json_data, \"etime\", int)\n\n bars = self.__dt_servo__.get_bars(stdCode=stdCode, period=period, fromTime=fromTime, dataCount=dataCount, endTime=endTime)\n if bars is None:\n ret = {\n \"result\":-2,\n \"message\":\"Data not found\"\n }\n else:\n bar_list = [curBar.to_dict for curBar in bars]\n \n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"bars\": bar_list\n }\n\n return pack_rsp(ret)\n\n \n # 拉取用户策略列表\n @app.route(\"/bt/qrystras\", methods=[\"POST\"])\n def qry_my_stras():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"strategies\": self.__bt_mon__.get_strategies(user)\n }\n\n return pack_rsp(ret)\n\n # 拉取策略代码\n @app.route(\"/bt/qrycode\", methods=[\"POST\"])\n def qry_stra_code():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略代码不存在\"\n }\n else:\n content = self.__bt_mon__.get_strategy_code(user, straid)\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"content\":content\n }\n\n return pack_rsp(ret)\n\n # 提交策略代码\n @app.route(\"/bt/setcode\", methods=[\"POST\"])\n def set_stra_code():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n content = get_param(json_data, \"content\")\n if len(content) == 0 or len(straid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID和代码不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = self.__bt_mon__.set_strategy_code(user, straid, content)\n if ret:\n ret = {\n \"result\":0,\n \"message\":\"OK\"\n }\n else:\n ret = {\n \"result\":-3,\n \"message\":\"保存策略代码失败\"\n }\n\n return pack_rsp(ret)\n\n # 添加用户策略\n @app.route(\"/bt/addstra\", methods=[\"POST\"])\n def cmd_add_stra():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n name = get_param(json_data, \"name\")\n if len(name) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略名称不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-3,\n \"message\":\"回测管理器未配置\"\n }\n return pack_rsp(ret)\n\n straInfo = self.__bt_mon__.add_strategy(user, name)\n if straInfo is None:\n ret = {\n \"result\":-4,\n \"message\":\"策略添加失败\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"strategy\": straInfo\n }\n\n return pack_rsp(ret)\n\n # 删除用户策略\n @app.route(\"/bt/delstra\", methods=[\"POST\"])\n def cmd_del_stra():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n if len(straid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = self.__bt_mon__.del_strategy(user, straid)\n if ret:\n ret = {\n \"result\":0,\n \"message\":\"OK\"\n }\n else:\n ret = {\n \"result\":-3,\n \"message\":\"保存策略代码失败\"\n }\n\n return pack_rsp(ret)\n\n # 获取策略回测列表\n @app.route(\"/bt/qrystrabts\", methods=[\"POST\"])\n def qry_stra_bts():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n if len(straid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"backtests\":self.__bt_mon__.get_backtests(user, straid)\n }\n\n return pack_rsp(ret)\n\n # 获取策略回测信号\n @app.route(\"/bt/qrybtsigs\", methods=[\"POST\"])\n def qry_stra_bt_signals():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n btid = get_param(json_data, \"btid\")\n if len(straid) == 0 or len(btid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID和回测ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"signals\":self.__bt_mon__.get_bt_signals(user, straid, btid)\n }\n\n return pack_rsp(ret)\n\n # 删除策略回测列表\n @app.route(\"/bt/delstrabt\", methods=[\"POST\"])\n def cmd_del_stra_bt():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n btid = get_param(json_data, \"btid\")\n if len(btid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"回测ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n self.__bt_mon__.del_backtest(user, btid)\n ret = {\n \"result\":0,\n \"message\":\"OK\"\n }\n\n return pack_rsp(ret)\n\n # 获取策略回测成交\n @app.route(\"/bt/qrybttrds\", methods=[\"POST\"])\n def qry_stra_bt_trades():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n btid = get_param(json_data, \"btid\")\n if len(straid) == 0 or len(btid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID和回测ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"trades\":self.__bt_mon__.get_bt_trades(user, straid, btid)\n }\n\n return pack_rsp(ret)\n\n # 获取策略回测资金\n @app.route(\"/bt/qrybtfunds\", methods=[\"POST\"])\n def qry_stra_bt_funds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n btid = get_param(json_data, \"btid\")\n if len(straid) == 0 or len(btid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID和回测ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"funds\":self.__bt_mon__.get_bt_funds(user, straid, btid)\n }\n\n return pack_rsp(ret)\n\n # 获取策略回测回合\n @app.route(\"/bt/qrybtrnds\", methods=[\"POST\"])\n def qry_stra_bt_rounds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n straid = get_param(json_data, \"straid\")\n btid = get_param(json_data, \"btid\")\n if len(straid) == 0 or len(btid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID和回测ID不能为空\"\n }\n return pack_rsp(ret)\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"rounds\":self.__bt_mon__.get_bt_rounds(user, straid, btid)\n }\n\n return pack_rsp(ret)\n\n # 启动策略回测\n @app.route(\"/bt/runstrabt\", methods=[\"POST\"])\n def cmd_run_stra_bt():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, userInfo = check_auth()\n if not bSucc:\n return pack_rsp(userInfo)\n\n user = userInfo[\"loginid\"]\n role = userInfo[\"role\"]\n if role not in ['researcher','superman']:\n ret = {\n \"result\":-1,\n \"message\":\"没有权限\"\n }\n return pack_rsp(ret)\n\n curDt = int(datetime.datetime.now().strftime(\"%Y%m%d\"))\n\n straid = get_param(json_data, \"straid\")\n fromtime = get_param(json_data, \"stime\", int, defVal=curDt)\n endtime = get_param(json_data, \"etime\", int, defVal=curDt)\n capital = get_param(json_data, \"capital\", float, defVal=500000)\n slippage = get_param(json_data, \"slippage\", int, defVal=0)\n if len(straid) == 0:\n ret = {\n \"result\":-2,\n \"message\":\"策略ID不能为空\"\n }\n return pack_rsp(ret)\n\n if fromtime > endtime:\n fromtime,endtime = endtime,fromtime\n\n fromtime = fromtime*10000 + 900\n endtime = endtime*10000 + 1515\n\n if self.__bt_mon__ is None:\n ret = {\n \"result\":-1,\n \"message\":\"回测管理器未配置\"\n }\n else:\n if not self.__bt_mon__.has_strategy(user, straid):\n ret = {\n \"result\":-2,\n \"message\":\"策略不存在\"\n }\n else:\n btInfo = self.__bt_mon__.run_backtest(user,straid,fromtime,endtime,capital,slippage)\n ret = {\n \"result\":0,\n \"message\":\"OK\",\n \"backtest\": btInfo\n }\n\n return pack_rsp(ret)\n\n def init_mgr_apis(self, app:Flask):\n\n @app.route(\"/console\", methods=[\"GET\"])\n def stc_console_index():\n return redirect(\"./console/index.html\")\n\n @app.route(\"/mobile\", methods=[\"GET\"])\n def stc_mobile_index():\n return redirect(\"./mobile/index.html\")\n\n\n '''下面是API接口的编写'''\n @app.route(\"/mgr/login\", methods=[\"POST\"])\n def cmd_login():\n \n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n if True:\n user = get_param(json_data, \"loginid\")\n pwd = get_param(json_data, \"passwd\")\n\n if len(user) == 0 or len(pwd) == 0:\n ret = {\n \"result\":-1,\n \"message\":\"用户名和密码不能为空\"\n }\n else:\n encpwd = hashlib.md5((user+pwd).encode(\"utf-8\")).hexdigest()\n now = datetime.datetime.now()\n usrInf = self.__data_mgr__.get_user(user)\n if usrInf is None:\n ret = {\n \"result\":-1,\n \"message\":\"用户不存在\"\n }\n elif encpwd != usrInf[\"passwd\"]:\n ret = {\n \"result\":-1,\n \"message\":\"登录密码错误\"\n }\n else:\n usrInf.pop(\"passwd\")\n usrInf[\"loginip\"]=request.remote_addr\n usrInf[\"logintime\"]=now.strftime(\"%Y/%m/%d %H:%M:%S\")\n\n exptime = now + datetime.timedelta(minutes=360) #360分钟令牌超时\n session[\"userinfo\"] = usrInf\n session[\"expiretime\"] = exptime.replace(tzinfo=pytz.timezone('UTC')).strftime(\"%Y.%m.%d %H:%M:%S\")\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"userinfo\":usrInf\n }\n\n self.__data_mgr__.log_action(usrInf, \"login\", json.dumps(request.headers.get('User-Agent')))\n else:\n ret = {\n \"result\":-1,\n \"message\":\"请求处理出现异常\",\n }\n if session.get(\"userinfo\") is not None:\n session.pop(\"userinfo\")\n\n return pack_rsp(ret)\n\n # 修改密码\n @app.route(\"/mgr/modpwd\", methods=[\"POST\"])\n def mod_pwd():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n oldpwd = get_param(json_data, \"oldpwd\")\n newpwd = get_param(json_data, \"newpwd\")\n\n if len(oldpwd) == 0 or len(newpwd) == 0:\n ret = {\n \"result\":-1,\n \"message\":\"新旧密码都不能为空\"\n }\n else:\n user = adminInfo[\"loginid\"]\n oldencpwd = hashlib.md5((user+oldpwd).encode(\"utf-8\")).hexdigest()\n usrInf = self.__data_mgr__.get_user(user)\n if usrInf is None:\n ret = {\n \"result\":-1,\n \"message\":\"用户不存在\"\n }\n else:\n if oldencpwd != usrInf[\"passwd\"]:\n ret = {\n \"result\":-1,\n \"message\":\"旧密码错误\"\n }\n else:\n if 'builtin' in usrInf and usrInf[\"builtin\"]:\n #如果是内建账号要改密码，则先添加用户\n usrInf[\"passwd\"] = oldpwd\n self.__data_mgr__.add_user(usrInf, user)\n print(\"%s是内建账户，自动添加到数据库中\" % user)\n\n newencpwd = hashlib.md5((user+newpwd).encode(\"utf-8\")).hexdigest()\n self.__data_mgr__.mod_user_pwd(user, newencpwd, user)\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n return pack_rsp(ret)\n\n # 添加组合\n @app.route(\"/mgr/addgrp\", methods=[\"POST\"])\n def cmd_add_group():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n id = get_param(json_data, \"groupid\")\n name = get_param(json_data, \"name\")\n path = get_param(json_data, \"path\")\n info = get_param(json_data, \"info\")\n gtype = get_param(json_data, \"gtype\")\n env = get_param(json_data, \"env\")\n datmod = get_param(json_data, \"datmod\")\n mqurl = get_param(json_data, \"mqurl\")\n\n action = get_param(json_data, \"action\")\n if action == \"\":\n action = \"add\"\n\n if len(id) == 0 or len(name) == 0 or len(gtype) == 0:\n ret = {\n \"result\":-1,\n \"message\":\"组合ID、名称、类型都不能为空\"\n }\n elif not os.path.exists(path) or not os.path.isdir(path):\n ret = {\n \"result\":-2,\n \"message\":\"组合运行目录不正确\"\n }\n elif action == \"add\" and self.__data_mgr__.has_group(id):\n ret = {\n \"result\":-3,\n \"message\":\"组合ID不能重复\"\n }\n else:\n try:\n grpInfo = {\n \"id\":id,\n \"name\":name,\n \"path\":path,\n \"info\":info,\n \"gtype\":gtype,\n \"datmod\":datmod,\n \"env\":env,\n \"mqurl\":mqurl\n } \n\n if self.__data_mgr__.add_group(grpInfo):\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n if action == \"add\":\n self.__data_mgr__.log_action(adminInfo, \"addgrp\", json.dumps(grpInfo))\n else:\n self.__data_mgr__.log_action(adminInfo, \"modgrp\", json.dumps(grpInfo))\n\n self._dog.updateMQURL(id, mqurl)\n else:\n ret = {\n \"result\":-2,\n \"message\":\"添加用户失败\"\n }\n except:\n ret = {\n \"result\":-1,\n \"message\":\"请求解析失败\"\n }\n\n return pack_rsp(ret)\n\n # 删除组合\n @app.route(\"/mgr/delgrp\", methods=[\"POST\"])\n def cmd_del_group():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n id = get_param(json_data, \"groupid\")\n\n if len(id) == 0:\n ret = {\n \"result\":-1,\n \"message\":\"组合ID不能为空\"\n }\n elif not self.__data_mgr__.has_group(id):\n ret = {\n \"result\":-3,\n \"message\":\"该组合不存在\"\n }\n elif self._dog.isRunning(id):\n ret = {\n \"result\":-3,\n \"message\":\"请先停止该组合\"\n }\n else:\n if True:\n self._dog.delApp(id)\n self.__data_mgr__.del_group(id)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n self.__data_mgr__.log_action(adminInfo, \"delgrp\", id)\n else:\n ret = {\n \"result\":-1,\n \"message\":\"请求解析失败\"\n }\n\n return pack_rsp(ret)\n\n # 组合停止\n @app.route(\"/mgr/stopgrp\", methods=[\"POST\"])\n def cmd_stop_group():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n \n grpid = get_param(json_data, \"groupid\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n if self._dog.isRunning(grpid):\n self._dog.stop(grpid)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n self.__data_mgr__.log_action(adminInfo, \"stopgrp\", grpid)\n\n return pack_rsp(ret)\n \n # 组合启动\n @app.route(\"/mgr/startgrp\", methods=[\"POST\"])\n def cmd_start_group():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n \n grpid = get_param(json_data, \"groupid\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n if not self._dog.isRunning(grpid):\n self._dog.start(grpid)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n self.__data_mgr__.log_action(adminInfo, \"startgrp\", grpid)\n\n return pack_rsp(ret)\n\n # 获取执行的python进程的路径\n @app.route(\"/mgr/qryexec\", methods=[\"POST\"])\n def qry_exec_path():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"path\": sys.executable\n }\n\n return pack_rsp(ret)\n\n # 配置监控\n @app.route(\"/mgr/qrymon\", methods=[\"POST\"])\n def qry_mon_cfg():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n grpid = get_param(json_data, \"groupid\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n monCfg = self._dog.getAppConf(grpid)\n if monCfg is None:\n ret = {\n \"result\":0,\n \"message\":\"ok\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"ok\",\n \"config\":monCfg\n }\n\n return pack_rsp(ret)\n\n # 配置监控\n @app.route(\"/mgr/cfgmon\", methods=[\"POST\"])\n def cmd_config_monitor():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n #这里本来是要做检查的，算了，先省事吧\n isGrp = get_param(json_data, \"group\", bool, False)\n \n self._dog.applyAppConf(json_data, isGrp)\n ret = {\n \"result\":0,\n \"message\":\"ok\"\n }\n self.__data_mgr__.log_action(adminInfo, \"cfgmon\", json.dumps(json_data))\n\n return pack_rsp(ret)\n\n # 查询目录结构\n @app.route(\"/mgr/qrydir\", methods=[\"POST\"])\n def qry_directories():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n if True:\n if self.deploy_tree is None:\n self.deploy_tree = get_path_tree(self.deploy_dir, \"root\")\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"tree\":self.deploy_tree\n }\n else:\n ret = {\n \"result\":-1,\n \"message\":\"请求解析失败\"\n }\n\n return pack_rsp(ret)\n\n # 查询目录结构\n @app.route(\"/mgr/qrygrpdir\", methods=[\"POST\"])\n def qry_grp_directories():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"groupid\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n monCfg = self.__data_mgr__.get_group(grpid)\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"tree\": get_cfg_tree(monCfg[\"path\"], \"root\")\n }\n\n return pack_rsp(ret)\n\n # 查询组合列表\n @app.route(\"/mgr/qrygrp\", methods=[\"POST\"])\n def qry_groups():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n try:\n groups = self.__data_mgr__.get_groups()\n for grpInfo in groups:\n grpInfo[\"running\"] = self._dog.isRunning(grpInfo[\"id\"])\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"groups\":groups\n }\n except:\n ret = {\n \"result\":-1,\n \"message\":\"请求解析失败\"\n }\n\n return pack_rsp(ret)\n\n # 查询文件信息\n @app.route(\"/mgr/qrygrpfile\", methods=[\"POST\"])\n def qry_group_file():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"groupid\")\n path = get_param(json_data, \"path\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n monCfg = self.__data_mgr__.get_group(grpid)\n root = monCfg[\"path\"]\n if path[:len(root)] != root:\n ret = {\n \"result\":-1,\n \"message\":\"目标文件不在当前组合下\"\n }\n else:\n f = open(path,'rb')\n content = f.read()\n f.close()\n\n encoding = chardet.detect(content)[\"encoding\"]\n content = content.decode(encoding)\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"content\": content\n }\n\n return pack_rsp(ret)\n\n # 提交组合文件\n @app.route(\"/mgr/cmtgrpfile\", methods=[\"POST\"])\n def cmd_commit_group_file():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"groupid\")\n content = get_param(json_data, \"content\")\n path = get_param(json_data, \"path\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n monCfg = self.__data_mgr__.get_group(grpid)\n root = monCfg[\"path\"]\n if path[:len(root)] != root:\n ret = {\n \"result\":-1,\n \"message\":\"目标文件不在当前组合下\"\n }\n else:\n try:\n f = open(path,'rb')\n old_content = f.read()\n f.close()\n encoding = chardet.detect(old_content)[\"encoding\"]\n\n backup_file(path)\n\n f = open(path,'wb')\n f.write(content.encode(encoding))\n f.close()\n\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n except:\n ret = {\n \"result\":-1,\n \"message\":\"文件保存失败\"\n }\n\n return pack_rsp(ret)\n \n # 查询策略列表\n @app.route(\"/mgr/qrystras\", methods=[\"POST\"])\n def qry_strategys():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"groupid\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"strategies\":self.__data_mgr__.get_strategies(grpid)\n }\n\n return pack_rsp(ret)\n\n # 查询通道列表\n @app.route(\"/mgr/qrychnls\", methods=[\"POST\"])\n def qry_channels():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"groupid\")\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"channels\":self.__data_mgr__.get_channels(grpid)\n }\n\n return pack_rsp(ret)\n\n # 查询组合日志\n @app.route(\"/mgr/qrylogs\", methods=[\"POST\"])\n def qry_logs():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"id\")\n logtype = get_param(json_data, \"type\")\n\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n grpInfo = self.__data_mgr__.get_group(grpid)\n try:\n logfolder = os.path.join(grpInfo[\"path\"], \"./Logs/\")\n file_list = os.listdir(logfolder)\n targets = list()\n for fname in file_list:\n if fname[:6] == \"Runner\":\n targets.append(fname)\n\n targets.sort()\n filename = os.path.join(logfolder, targets[-1])\n content,lines = get_tail(filename, 100)\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"content\":content,\n \"lines\":lines\n }\n except:\n ret = {\n \"result\":-1,\n \"message\":\"请求解析失败\"\n }\n\n return pack_rsp(ret)\n\n # 查询策略成交\n @app.route(\"/mgr/qrytrds\", methods=[\"POST\"])\n def qry_trades():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n sid = get_param(json_data, \"strategyid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"trades\": self.__data_mgr__.get_trades(gid, sid)\n }\n \n\n return pack_rsp(ret)\n\n # 查询策略信号\n @app.route(\"/mgr/qrysigs\", methods=[\"POST\"])\n def qry_signals():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n sid = get_param(json_data, \"strategyid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"signals\": self.__data_mgr__.get_signals(gid, sid)\n }\n \n\n return pack_rsp(ret)\n\n # 查询策略回合\n @app.route(\"/mgr/qryrnds\", methods=[\"POST\"])\n def qry_rounds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n sid = get_param(json_data, \"strategyid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"rounds\": self.__data_mgr__.get_rounds(gid, sid)\n }\n\n return pack_rsp(ret)\n\n # 查询策略持仓\n @app.route(\"/mgr/qrypos\", methods=[\"POST\"])\n def qry_positions():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n sid = get_param(json_data, \"strategyid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"positions\": self.__data_mgr__.get_positions(gid, sid)\n }\n\n return pack_rsp(ret)\n\n # 查询策略持仓\n @app.route(\"/mgr/qryfunds\", methods=[\"POST\"])\n def qry_funds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n sid = get_param(json_data, \"strategyid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"funds\": self.__data_mgr__.get_funds(gid, sid)\n }\n\n return pack_rsp(ret)\n\n # 查询通道订单\n @app.route(\"/mgr/qrychnlords\", methods=[\"POST\"])\n def qry_channel_orders():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n cid = get_param(json_data, \"channelid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"orders\": self.__data_mgr__.get_channel_orders(gid, cid)\n }\n\n return pack_rsp(ret)\n\n # 查询通道成交\n @app.route(\"/mgr/qrychnltrds\", methods=[\"POST\"])\n def qry_channel_trades():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n cid = get_param(json_data, \"channelid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"trades\": self.__data_mgr__.get_channel_trades(gid, cid)\n }\n\n return pack_rsp(ret)\n\n # 查询通道持仓\n @app.route(\"/mgr/qrychnlpos\", methods=[\"POST\"])\n def qry_channel_position():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n cid = get_param(json_data, \"channelid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"positions\": self.__data_mgr__.get_channel_positions(gid, cid)\n }\n\n return pack_rsp(ret)\n\n # 查询通道资金\n @app.route(\"/mgr/qrychnlfund\", methods=[\"POST\"])\n def qry_channel_funds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n cid = get_param(json_data, \"channelid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"funds\": self.__data_mgr__.get_channel_funds(gid, cid)\n }\n\n return pack_rsp(ret)\n\n # 查询用户列表\n @app.route(\"/mgr/qryusers\", methods=[\"POST\"])\n def qry_users():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n users = self.__data_mgr__.get_users()\n for usrInfo in users:\n usrInfo.pop(\"passwd\")\n \n ret = {\n \"result\":0,\n \"message\":\"\",\n \"users\": users\n }\n \n\n return pack_rsp(ret)\n\n # 提交用户信息\n @app.route(\"/mgr/cmtuser\", methods=[\"POST\"])\n def cmd_commit_user():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n self.__data_mgr__.add_user(json_data, adminInfo[\"loginid\"])\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n self.__data_mgr__.log_action(adminInfo, \"cmtuser\", json.dumps(json_data))\n\n return pack_rsp(ret)\n\n # 删除用户\n @app.route(\"/mgr/deluser\", methods=[\"POST\"])\n def cmd_delete_user():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n loginid = get_param(json_data, \"loginid\")\n\n if self.__data_mgr__.del_user(loginid, adminInfo[\"loginid\"]):\n self.__data_mgr__.log_action(adminInfo, \"delusr\", loginid)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n return pack_rsp(ret)\n\n # 修改密码\n @app.route(\"/mgr/resetpwd\", methods=[\"POST\"])\n def reset_pwd():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n user = get_param(json_data, \"loginid\")\n pwd = get_param(json_data, \"passwd\")\n\n if len(pwd) == 0 or len(user) == 0:\n ret = {\n \"result\":-1,\n \"message\":\"密码都不能为空\"\n }\n else:\n encpwd = hashlib.md5((user+pwd).encode(\"utf-8\")).hexdigest()\n usrInf = self.__data_mgr__.get_user(user)\n if usrInf is None:\n ret = {\n \"result\":-1,\n \"message\":\"用户不存在\"\n }\n else:\n self.__data_mgr__.mod_user_pwd(user, encpwd, adminInfo[\"loginid\"])\n self.__data_mgr__.log_action(adminInfo, \"resetpwd\", loginid)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n return pack_rsp(ret)\n\n # 查询操作记录\n @app.route(\"/mgr/qryacts\", methods=[\"POST\"])\n def qry_actions():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n sdate = get_param(json_data, \"sdate\")\n edate = get_param(json_data, \"edate\")\n\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"actions\": self.__data_mgr__.get_actions(sdate, edate)\n } \n\n return pack_rsp(ret)\n\n # 查询全部调度\n @app.route(\"/mgr/qrymons\", methods=[\"POST\"])\n def qry_mon_apps():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n schedules = self._dog.get_apps()\n for appid in schedules:\n schedules[appid][\"group\"] = self.__data_mgr__.has_group(appid) \n\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"schedules\": schedules\n } \n\n return pack_rsp(ret)\n\n @app.route(\"/mgr/startapp\", methods=[\"POST\"])\n def cmd_start_app():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n \n appid = get_param(json_data, \"appid\")\n if not self._dog.has_app(appid):\n ret = {\n \"result\":-1,\n \"message\":\"App不存在\"\n }\n else:\n if not self._dog.isRunning(appid):\n self._dog.start(appid)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n self.__data_mgr__.log_action(adminInfo, \"startapp\", appid)\n\n return pack_rsp(ret)\n\n # 组合停止\n @app.route(\"/mgr/stopapp\", methods=[\"POST\"])\n def cmd_stop_app():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n \n appid = get_param(json_data, \"appid\")\n if not self._dog.has_app(appid):\n ret = {\n \"result\":-1,\n \"message\":\"App不存在\"\n }\n else:\n if self._dog.isRunning(appid):\n self._dog.stop(appid)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n self.__data_mgr__.log_action(adminInfo, \"stopapp\", appid)\n\n return pack_rsp(ret)\n\n # 查询调度日志\n @app.route(\"/mgr/qrymonlog\", methods=[\"POST\"])\n def qry_mon_logs():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n \n filename = os.getcwd() + \"/logs/WtMonSvr.log\"\n content,lines = get_tail(filename, 100, \"UTF-8\")\n ret = {\n \"result\":0,\n \"message\":\"Ok\",\n \"content\":content,\n \"lines\":lines\n }\n\n return pack_rsp(ret)\n\n # 删除调度任务\n @app.route(\"/mgr/delapp\", methods=[\"POST\"])\n def cmd_del_app():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, adminInfo = check_auth()\n if not bSucc:\n return pack_rsp(adminInfo)\n\n id = get_param(json_data, \"appid\")\n\n if len(id) == 0:\n ret = {\n \"result\":-1,\n \"message\":\"组合ID不能为空\"\n }\n elif self.__data_mgr__.has_group(id):\n ret = {\n \"result\":-2,\n \"message\":\"该调度任务是策略组合，请从组合管理删除\"\n }\n elif not self._dog.has_app(id):\n ret = {\n \"result\":-3,\n \"message\":\"该调度任务不存在\"\n }\n elif self._dog.isRunning(id):\n ret = {\n \"result\":-4,\n \"message\":\"请先停止该任务\"\n }\n else:\n if True:\n self._dog.delApp(id)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n\n self.__data_mgr__.log_action(adminInfo, \"delapp\", id)\n else:\n ret = {\n \"result\":-1,\n \"message\":\"请求解析失败\"\n }\n\n return pack_rsp(ret)\n\n # 查询组合持仓\n @app.route(\"/mgr/qryportpos\", methods=[\"POST\"])\n def qry_group_positions():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"positions\": self.__data_mgr__.get_group_positions(gid)\n }\n \n\n return pack_rsp(ret)\n\n # 查询组合成交\n @app.route(\"/mgr/qryporttrd\", methods=[\"POST\"])\n def qry_group_trades():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"trades\": self.__data_mgr__.get_group_trades(gid)\n }\n \n\n return pack_rsp(ret)\n\n # 查询组合回合\n @app.route(\"/mgr/qryportrnd\", methods=[\"POST\"])\n def qry_group_rounds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"rounds\": self.__data_mgr__.get_group_rounds(gid)\n }\n \n\n return pack_rsp(ret)\n \n # 查询组合资金\n @app.route(\"/mgr/qryportfunds\", methods=[\"POST\"])\n def qry_group_funds():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"funds\": self.__data_mgr__.get_group_funds(gid)\n }\n \n\n return pack_rsp(ret)\n\n # 查询组合绩效分析\n @app.route(\"/mgr/qryportperfs\", methods=[\"POST\"])\n def qry_group_perfs():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"performance\": self.__data_mgr__.get_group_performances(gid)\n }\n \n return pack_rsp(ret)\n\n # 查询组合过滤器\n @app.route(\"/mgr/qryportfilters\", methods=[\"POST\"])\n def qry_group_filters():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n gid = get_param(json_data, \"groupid\")\n\n if not self.__data_mgr__.has_group(gid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n ret = {\n \"result\":0,\n \"message\":\"\",\n \"filters\": self.__data_mgr__.get_group_filters(gid)\n }\n \n return pack_rsp(ret)\n\n # 提交组合过滤器\n @app.route(\"/mgr/cmtgrpfilters\", methods=[\"POST\"])\n def cmd_commit_group_filters():\n bSucc, json_data = parse_data()\n if not bSucc:\n return pack_rsp(json_data)\n\n bSucc, usrInfo = check_auth()\n if not bSucc:\n return pack_rsp(usrInfo)\n\n grpid = get_param(json_data, \"groupid\")\n filters = get_param(json_data, \"filters\", type=dict)\n if not self.__data_mgr__.has_group(grpid):\n ret = {\n \"result\":-1,\n \"message\":\"组合不存在\"\n }\n else:\n try:\n self.__data_mgr__.set_group_filters(grpid, filters)\n ret = {\n \"result\":0,\n \"message\":\"Ok\"\n }\n except:\n ret = {\n \"result\":-1,\n \"message\":\"过滤器保存失败\"\n }\n\n return pack_rsp(ret)\n \n \n def __run_impl__(self, port:int, host:str):\n self._dog.run()\n self.push_svr.run(port = port, host = host)\n \n def run(self, port:int = 8080, host=\"0.0.0.0\", bSync:bool = True):\n if bSync:\n self.__run_impl__(port, host)\n else:\n import threading\n self.worker = threading.Thread(target=self.__run_impl__, args=(port,host,))\n self.worker.setDaemon(True)\n self.worker.start()\n\n def init_logging(self):\n pass\n\n def on_start(self, grpid:str):\n if self.__data_mgr__.has_group(grpid):\n self.push_svr.notifyGrpEvt(grpid, 'start')\n\n def on_stop(self, grpid:str):\n if self.__data_mgr__.has_group(grpid):\n self.push_svr.notifyGrpEvt(grpid, 'stop')\n \n def on_output(self, grpid:str, tag:str, time:int, message:str):\n if self.__data_mgr__.has_group(grpid):\n self.push_svr.notifyGrpLog(grpid, tag, time, message)\n\n def on_order(self, grpid:str, chnl:str, ordInfo:dict):\n self.push_svr.notifyGrpChnlEvt(grpid, chnl, 'order', ordInfo)\n\n def on_trade(self, grpid:str, chnl:str, trdInfo:dict):\n self.push_svr.notifyGrpChnlEvt(grpid, chnl, 'trade', trdInfo)\n \n def on_notify(self, grpid:str, chnl:str, message:str):\n self.push_svr.notifyGrpChnlEvt(grpid, chnl, 'notify', message)" }, { "path": "wtpy/monitor/__init__.py", "content": "'''\nDescripttion: Automatically generated file comment\nversion: \nAuthor: Wesley\nDate: 2020-08-25 15:38:28\nLastEditors: Wesley\nLastEditTime: 2021-08-16 17:29:42\n'''\nfrom .WtMonSvr import WtMonSvr\nfrom .WtBtMon import WtBtMon\nfrom .WtLogger import WtLogger\n\n__all__ = [\"WtMonSvr\",\"WtBtMon\",\"WtLogger\"]" }, { "path": "wtpy/monitor/static/__init__.py", "content": "" }, { "path": "wtpy/monitor/static/console/__init__.py", "content": "" }, { "path": "wtpy/monitor/static/console/index.html", "content": "策略控制台
" }, { "path": "wtpy/monitor/static/console/static/__init__.py", "content": "" }, { "path": "wtpy/monitor/static/console/static/css/__init__.py", "content": "" }, { "path": "wtpy/monitor/static/console/static/css/app.7b6729291f05cec91b99cfd44c17df6b.css", "content": "#app{font-family:Avenir,Helvetica,Arial,sans-serif;-webkit-font-smoothing:antialiased;-moz-osx-font-smoothing:grayscale;height:100vh;width:100%}body{margin:0}.el-main{padding:4px!important}.box{border:1px solid #e4e7ed;margin:4px;border-radius:4px}::-webkit-scrollbar{width:6px;height:6px}::-webkit-scrollbar-track-piece{background-color:#f1f1f1;border-radius:2px}::-webkit-scrollbar-thumb:vertical{height:6px;background-color:#ccc;border-radius:2px}::-webkit-scrollbar-thumb:horizontal{width:6px;background-color:#ccc;border-radius:2px}.el-table td,.el-table th{padding:4px 0}.text-danger{color:#f56c6c;font-weight:700}.text-warning{color:#e6a23c;font-weight:700}.text-success{color:#67c23a;font-weight:700}.text-info{color:#909399;font-weight:700}[data-v-02eab36d]{margin:0}.el-input__inner[data-v-02eab36d]{border-radius:10px!important;height:35px!important;line-height:35px!important;font-size:12px}.el-button[data-v-02eab36d]{width:100%;border-radius:10px;height:35px!important;line-height:35px!important;padding:0}.el-checkbox__label[data-v-02eab36d]{font-size:12px;color:#a7aab2;padding-left:5px}.loginBox[data-v-02eab36d]{-webkit-box-flex:1;-ms-flex:1;flex:1;display:-webkit-box;display:-ms-flexbox;display:flex;-webkit-box-pack:center;-ms-flex-pack:center;justify-content:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;height:100vh;width:100%}.login[data-v-02eab36d]{width:240px;height:400px;text-align:center}.logo[data-v-02eab36d]{width:100px;margin:0 auto}.log 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t=this,e=this.straInfo.id,s=this.btInfo.id;this.loading.fund=!0,this.$api.getBtFunds(e,s,function(e){e.result<0?t.$notify.error(\"查询绩效出错：\"+e.message):(t.funds=e.funds,t.funds.reverse(),t.paintTrend()),t.loading.fund=!1})},getTrdSum:function(t){var e=t.columns,s=t.data,a=[];return e.forEach(function(t,e){if(e<3||e>5)a[e]=\"\";else if(3!=e){if(4==e)a[e]=s.length+\"笔\";else if(5==e){var i=s.map(function(t){return Number(t.volume)});i.every(function(t){return isNaN(t)})?a[e]=\"N/A\":a[e]=i.reduce(function(t,e){var s=Number(e);return isNaN(s)?t:t+e},0)+\"手\"}}else a[e]=\"总计\"}),a},getSigSum:function(t){var e=t.columns,s=t.data,a=[];return e.forEach(function(t,e){e>1?a[e]=\"\":0!=e?1==e&&(a[e]=s.length+\"笔\"):a[e]=\"总计\"}),a},getRndSum:function(t){var e=t.columns,s=t.data,a=[];return e.forEach(function(t,e){if(5==e||6==e||7==e||8==e)if(5!=e)if(6!=e){if(7==e){var i=s.map(function(t){return Number(t.qty)});i.every(function(t){return isNaN(t)})?a[e]=\"N/A\":a[e]=i.reduce(function(t,e){var 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e=this.group,t=this._location,n=this._orient,i=this.dataZoomModel.getFirstTargetAxisModel(),r=i&&i.get(\"inverse\"),o=this._displayables.barGroup,a=(this._dataShadowInfo||{}).otherAxisInverse;o.attr(n!==m||r?n===m&&r?{scale:a?[-1,1]:[-1,-1]}:\"vertical\"!==n||r?{scale:a?[-1,-1]:[-1,1],rotation:Math.PI/2}:{scale:a?[1,-1]:[1,1],rotation:Math.PI/2}:{scale:a?[1,1]:[1,-1]});var s=e.getBoundingRect([o]);e.attr(\"position\",[t.x-s.x,t.y-s.y])},_getViewExtent:function(){return[0,this._size[0]]},_renderBackground:function(){var e=this.dataZoomModel,t=this._size,n=this._displayables.barGroup;n.add(new h({silent:!0,shape:{x:0,y:0,width:t[0],height:t[1]},style:{fill:e.get(\"backgroundColor\")},z2:-40})),n.add(new h({shape:{x:0,y:0,width:t[0],height:t[1]},style:{fill:\"transparent\"},z2:0,onclick:i.bind(this._onClickPanelClick,this)}))},_renderDataShadow:function(){var e=this._dataShadowInfo=this._prepareDataShadowInfo();if(e){var t=this._size,n=e.series,r=n.getRawData(),a=n.getShadowDim?n.getShadowDim():e.otherDim;if(null!=a){var s=r.getDataExtent(a),l=.3*(s[1]-s[0]);s=[s[0]-l,s[1]+l];var u,c=[0,t[1]],h=[0,t[0]],f=[[t[0],0],[0,0]],p=[],g=h[1]/(r.count()-1),m=0,v=Math.round(r.count()/t[0]);r.each([a],function(e,t){if(v>0&&t%v)m+=g;else{var n=null==e||isNaN(e)||\"\"===e,i=n?0:d(e,s,c,!0);n&&!u&&t?(f.push([f[f.length-1][0],0]),p.push([p[p.length-1][0],0])):!n&&u&&(f.push([m,0]),p.push([m,0])),f.push([m,i]),p.push([m,i]),m+=g,u=n}});var y=this.dataZoomModel;this._displayables.barGroup.add(new o.Polygon({shape:{points:f},style:i.defaults({fill:y.get(\"dataBackgroundColor\")},y.getModel(\"dataBackground.areaStyle\").getAreaStyle()),silent:!0,z2:-20})),this._displayables.barGroup.add(new o.Polyline({shape:{points:p},style:y.getModel(\"dataBackground.lineStyle\").getLineStyle(),silent:!0,z2:-19}))}}},_prepareDataShadowInfo:function(){var e=this.dataZoomModel,t=e.get(\"showDataShadow\");if(!1!==t){var n,r=this.ecModel;return e.eachTargetAxis(function(o,a){var s=e.getAxisProxy(o.name,a).getTargetSeriesModels();i.each(s,function(e){if(!(n||!0!==t&&i.indexOf(y,e.get(\"type\"))<0)){var s,l=r.getComponent(o.axis,a).axis,u={x:\"y\",y:\"x\",radius:\"angle\",angle:\"radius\"}[o.name],c=e.coordinateSystem;null!=u&&c.getOtherAxis&&(s=c.getOtherAxis(l).inverse),u=e.getData().mapDimension(u),n={thisAxis:l,series:e,thisDim:o.name,otherDim:u,otherAxisInverse:s}}},this)},this),n}},_renderHandle:function(){var e=this._displayables,t=e.handles=[],n=e.handleLabels=[],i=this._displayables.barGroup,r=this._size,a=this.dataZoomModel;i.add(e.filler=new h({draggable:!0,cursor:x(this._orient),drift:p(this._onDragMove,this,\"all\"),ondragstart:p(this._showDataInfo,this,!0),ondragend:p(this._onDragEnd,this),onmouseover:p(this._showDataInfo,this,!0),onmouseout:p(this._showDataInfo,this,!1),style:{fill:a.get(\"fillerColor\"),textPosition:\"inside\"}})),i.add(new h({silent:!0,subPixelOptimize:!0,shape:{x:0,y:0,width:r[0],height:r[1]},style:{stroke:a.get(\"dataBackgroundColor\")||a.get(\"borderColor\"),lineWidth:1,fill:\"rgba(0,0,0,0)\"}})),g([0,1],function(e){var r=o.createIcon(a.get(\"handleIcon\"),{cursor:x(this._orient),draggable:!0,drift:p(this._onDragMove,this,e),ondragend:p(this._onDragEnd,this),onmouseover:p(this._showDataInfo,this,!0),onmouseout:p(this._showDataInfo,this,!1)},{x:-1,y:0,width:2,height:2}),s=r.getBoundingRect();this._handleHeight=l.parsePercent(a.get(\"handleSize\"),this._size[1]),this._handleWidth=s.width/s.height*this._handleHeight,r.setStyle(a.getModel(\"handleStyle\").getItemStyle());var u=a.get(\"handleColor\");null!=u&&(r.style.fill=u),i.add(t[e]=r);var c=a.textStyleModel;this.group.add(n[e]=new o.Text({silent:!0,invisible:!0,style:{x:0,y:0,text:\"\",textVerticalAlign:\"middle\",textAlign:\"center\",textFill:c.getTextColor(),textFont:c.getFont()},z2:10}))},this)},_resetInterval:function(){var e=this._range=this.dataZoomModel.getPercentRange(),t=this._getViewExtent();this._handleEnds=[d(e[0],[0,100],t,!0),d(e[1],[0,100],t,!0)]},_updateInterval:function(e,t){var n=this.dataZoomModel,i=this._handleEnds,r=this._getViewExtent(),o=n.findRepresentativeAxisProxy().getMinMaxSpan(),a=[0,100];c(t,i,r,n.get(\"zoomLock\")?\"all\":e,null!=o.minSpan?d(o.minSpan,a,r,!0):null,null!=o.maxSpan?d(o.maxSpan,a,r,!0):null);var s=this._range,l=this._range=f([d(i[0],r,a,!0),d(i[1],r,a,!0)]);return!s||s[0]!==l[0]||s[1]!==l[1]},_updateView:function(e){var t=this._displayables,n=this._handleEnds,i=f(n.slice()),r=this._size;g([0,1],function(e){var i=t.handles[e],o=this._handleHeight;i.attr({scale:[o/2,o/2],position:[n[e],r[1]/2-o/2]})},this),t.filler.setShape({x:i[0],y:0,width:i[1]-i[0],height:r[1]}),this._updateDataInfo(e)},_updateDataInfo:function(e){var t=this.dataZoomModel,n=this._displayables,i=n.handleLabels,r=this._orient,a=[\"\",\"\"];if(t.get(\"showDetail\")){var s=t.findRepresentativeAxisProxy();if(s){var l=s.getAxisModel().axis,u=this._range,c=e?s.calculateDataWindow({start:u[0],end:u[1]}).valueWindow:s.getDataValueWindow();a=[this._formatLabel(c[0],l),this._formatLabel(c[1],l)]}}var h=f(this._handleEnds.slice());function d(e){var t=o.getTransform(n.handles[e].parent,this.group),s=o.transformDirection(0===e?\"right\":\"left\",t),l=this._handleWidth/2+v,u=o.applyTransform([h[e]+(0===e?-l:l),this._size[1]/2],t);i[e].setStyle({x:u[0],y:u[1],textVerticalAlign:r===m?\"middle\":s,textAlign:r===m?s:\"center\",text:a[e]})}d.call(this,0),d.call(this,1)},_formatLabel:function(e,t){var n=this.dataZoomModel,r=n.get(\"labelFormatter\"),o=n.get(\"labelPrecision\");null!=o&&\"auto\"!==o||(o=t.getPixelPrecision());var a=null==e||isNaN(e)?\"\":\"category\"===t.type||\"time\"===t.type?t.scale.getLabel(Math.round(e)):e.toFixed(Math.min(o,20));return i.isFunction(r)?r(e,a):i.isString(r)?r.replace(\"{value}\",a):a},_showDataInfo:function(e){e=this._dragging||e;var t=this._displayables.handleLabels;t[0].attr(\"invisible\",!e),t[1].attr(\"invisible\",!e)},_onDragMove:function(e,t,n,i){this._dragging=!0,r.stop(i.event);var a=this._displayables.barGroup.getLocalTransform(),s=o.applyTransform([t,n],a,!0),l=this._updateInterval(e,s[0]),u=this.dataZoomModel.get(\"realtime\");this._updateView(!u),l&&u&&this._dispatchZoomAction()},_onDragEnd:function(){this._dragging=!1,this._showDataInfo(!1),!this.dataZoomModel.get(\"realtime\")&&this._dispatchZoomAction()},_onClickPanelClick:function(e){var t=this._size,n=this._displayables.barGroup.transformCoordToLocal(e.offsetX,e.offsetY);if(!(n[0]<0||n[0]>t[0]||n[1]<0||n[1]>t[1])){var i=this._handleEnds,r=(i[0]+i[1])/2,o=this._updateInterval(\"all\",n[0]-r);this._updateView(),o&&this._dispatchZoomAction()}},_dispatchZoomAction:function(){var 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this.each(function(t){C(this).addClass(e.call(this,t,yt(this)))});if((t=bt(e)).length)for(;n=this[l++];)if(r=yt(n),i=1===n.nodeType&&\" \"+vt(r)+\" \"){for(a=0;o=t[a++];)i.indexOf(\" \"+o+\" \")<0&&(i+=o+\" \");r!==(s=vt(i))&&n.setAttribute(\"class\",s)}return this},removeClass:function(e){var t,n,i,r,o,a,s,l=0;if(v(e))return this.each(function(t){C(this).removeClass(e.call(this,t,yt(this)))});if(!arguments.length)return this.attr(\"class\",\"\");if((t=bt(e)).length)for(;n=this[l++];)if(r=yt(n),i=1===n.nodeType&&\" \"+vt(r)+\" \"){for(a=0;o=t[a++];)for(;i.indexOf(\" \"+o+\" \")>-1;)i=i.replace(\" \"+o+\" \",\" \");r!==(s=vt(i))&&n.setAttribute(\"class\",s)}return this},toggleClass:function(e,t){var n=typeof e,i=\"string\"===n||Array.isArray(e);return\"boolean\"==typeof t&&i?t?this.addClass(e):this.removeClass(e):v(e)?this.each(function(n){C(this).toggleClass(e.call(this,n,yt(this),t),t)}):this.each(function(){var t,r,o,a;if(i)for(r=0,o=C(this),a=bt(e);t=a[r++];)o.hasClass(t)?o.removeClass(t):o.addClass(t);else void 0!==e&&\"boolean\"!==n||((t=yt(this))&&Z.set(this,\"__className__\",t),this.setAttribute&&this.setAttribute(\"class\",t||!1===e?\"\":Z.get(this,\"__className__\")||\"\"))})},hasClass:function(e){var t,n,i=0;for(t=\" \"+e+\" \";n=this[i++];)if(1===n.nodeType&&(\" \"+vt(yt(n))+\" \").indexOf(t)>-1)return!0;return!1}});var xt=/\\r/g;C.fn.extend({val:function(e){var t,n,i,r=this[0];return arguments.length?(i=v(e),this.each(function(n){var r;1===this.nodeType&&(null==(r=i?e.call(this,n,C(this).val()):e)?r=\"\":\"number\"==typeof r?r+=\"\":Array.isArray(r)&&(r=C.map(r,function(e){return null==e?\"\":e+\"\"})),(t=C.valHooks[this.type]||C.valHooks[this.nodeName.toLowerCase()])&&\"set\"in t&&void 0!==t.set(this,r,\"value\")||(this.value=r))})):r?(t=C.valHooks[r.type]||C.valHooks[r.nodeName.toLowerCase()])&&\"get\"in t&&void 0!==(n=t.get(r,\"value\"))?n:\"string\"==typeof(n=r.value)?n.replace(xt,\"\"):null==n?\"\":n:void 0}}),C.extend({valHooks:{option:{get:function(e){var t=C.find.attr(e,\"value\");return null!=t?t:vt(C.text(e))}},select:{get:function(e){var t,n,i,r=e.options,o=e.selectedIndex,a=\"select-one\"===e.type,s=a?null:[],l=a?o+1:r.length;for(i=o<0?l:a?o:0;i-1)&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),C.each([\"radio\",\"checkbox\"],function(){C.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=C.inArray(C(e).val(),t)>-1}},m.checkOn||(C.valHooks[this].get=function(e){return null===e.getAttribute(\"value\")?\"on\":e.value})}),m.focusin=\"onfocusin\"in n;var _t=/^(?:focusinfocus|focusoutblur)$/,wt=function(e){e.stopPropagation()};C.extend(C.event,{trigger:function(e,t,i,r){var o,a,s,l,u,c,h,d,p=[i||b],g=f.call(e,\"type\")?e.type:e,m=f.call(e,\"namespace\")?e.namespace.split(\".\"):[];if(a=d=s=i=i||b,3!==i.nodeType&&8!==i.nodeType&&!_t.test(g+C.event.triggered)&&(g.indexOf(\".\")>-1&&(g=(m=g.split(\".\")).shift(),m.sort()),u=g.indexOf(\":\")<0&&\"on\"+g,(e=e[C.expando]?e:new C.Event(g,\"object\"==typeof e&&e)).isTrigger=r?2:3,e.namespace=m.join(\".\"),e.rnamespace=e.namespace?new RegExp(\"(^|\\\\.)\"+m.join(\"\\\\.(?:.*\\\\.|)\")+\"(\\\\.|$)\"):null,e.result=void 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