Repository: physercoe/awesome-quant Branch: master Commit: e146ca72aa8f Files: 3 Total size: 22.2 KB Directory structure: gitextract_4njzjmqg/ ├── LICENSE ├── README.md └── papers.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ The MIT License (MIT) Copyright (c) 2016 thuquant Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # Awesome Quant [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 一份精心挑选的中文Quant相关资源索引。 ## 目录 * [数据源](#数据源) * [数据库](#数据库) * [量化交易平台](#量化交易平台) * [策略](#策略) * [回测](#回测) * [交易API](#交易api) * [编程](#编程) * [Python](#python) * [R](#r) * [C++](#c) * [Julia](#julia) * [论坛](#论坛) * [书籍](#书籍) * [论文](#论文) * [政策](#政策) * [值得关注的信息源](#值得关注的信息源) * [其他Quant资源索引](#其他quant资源索引) ## 数据源 * [TuShare](http://tushare.org/) - 中文财经数据接口包 * [Quandl](https://www.quandl.com/) - 国际金融和经济数据 * [Wind资讯-经济数据库](http://www.wind.com.cn/NewSite/edb.html) - 收费 * [锐思数据 - 首页](http://www.resset.cn/) - 收费 * [国泰安数据服务中心](http://www.gtarsc.com/Home) - 收费 * [恒生API](https://open.hscloud.cn/cloud/open/apilibrary/queryLibraryMenu.html?parent_id=100313&menu_id=100307) - 收费 * [Bloomberg API](https://www.bloomberglabs.com/api/libraries/) - 收费 * [数库金融数据和深度分析API服务](http://developer.chinascope.com/) - 收费 * [Historical Data Sources](http://quantpedia.com/Links/HistoricalData) - 一个数据源索引 * [Python通达信数据接口](https://github.com/rainx/pytdx) - 免费通达信数据源 * [fooltrader](https://github.com/foolcage/fooltrader) - 大数据开源量化项目,自己维护了一个爬取整合的全市场数据源 * [zvt](https://github.com/zvtvz/zvt) - ZVT是在fooltrader的基础上重新思考后编写的量化项目，其包含可扩展的数据recorder，api，因子计算，选股，回测，定位为中低频 多级别 多标的 全市场分析和交易框架。 * [JoinQuant/jqdatasdk](https://github.com/JoinQuant/jqdatasdk) - jqdatasdk是提供给用户获取聚宽金融数据的SDK * [米筐科技的RQData数据接口](https://www.ricequant.com/introduce_rqdata) - 收费 ## 数据库 * [manahl/arctic: High performance datastore for time series and tick data](https://github.com/manahl/arctic) - 基于mongodb和python的高性能时间序列和tick数据存储 * [kdb | The Leader in High-Performance Tick Database Technology | Kx Systems](https://kx.com/) - 收费的高性能金融序列数据库解决方案 * [MongoDB Blog](http://blog.mongodb.org/post/65517193370/schema-design-for-time-series-data-in-mongodb) - 用mongodb存储时间序列数据 * [InfluxDB – Time-Series Data Storage | InfluxData](https://www.influxdata.com/time-series-platform/influxdb/) - Go写的分布式时间序列数据库 * [OpenTSDB/opentsdb: A scalable, distributed Time Series Database.](https://github.com/OpenTSDB/opentsdb) - 基于HBase的时间序列数据库 * [kairosdb/kairosdb: Fast scalable time series database](https://github.com/kairosdb/kairosdb) - 基于Cassandra的时间序列数据库 * [timescale/timescaledb: An open-source time-series database optimized for fast ingest and complex queries. Engineered up from PostgreSQL, packaged as an extension.](https://github.com/timescale/timescaledb) - 基于PostgreSQL的时间序列数据库 ## 量化交易平台 * [JoinQuant聚宽量化交易平台](https://www.joinquant.com/) - 一个基于Python的在线量化交易平台 * [优矿 - 通联量化实验室](https://uqer.io/home/) - 一个基于Python的在线量化交易平台 * [Ricequant 量化交易平台](https://www.ricequant.com/) - 支持Python和Java的在线量化交易平台 * [掘金量化](http://www.myquant.cn/) - 支持C/C++、C#、MATLAB、Python和R的量化交易平台 * [Auto-Trader](http://www.atrader.com.cn/portal.php) - 基于MATLAB的量化交易平台 * [MultiCharts 中国版 - 程序化交易软件](https://www.multicharts.cn/) * [BotVS - 首家支持传统期货与股票证券与数字货币的量化平台](https://www.botvs.com/) * [Tradeblazer(TB) - 交易开拓者](http://www.tradeblazer.net/) - 期货程序化交易软件平台 * [MetaTrader 5](https://www.metatrader5.com/en) - Multi-Asset Trading Platform * [BigQuant](https://bigquant.com) - 专注量化投资的人工智能/机器学习平台 ## 策略 * [JoinQuant聚宽: 量化学习资料、经典交易策略、Python入门 - 雪球](https://xueqiu.com/8287840120/65009358) * [myquant/strategy: 掘金策略集锦](https://github.com/myquant/strategy) * [优矿社区内容索引](https://uqer.io/community/share/58243e7d228e5b91df6d5d19) * [RiceQuant米筐量化社区 2016年4月以来优秀策略与研究汇总](https://www.ricequant.com/community/topic/1863//3) * [雪球选股](https://xueqiu.com/9796081404) * [botvs/strategies: 用Javascript OR Python进行量化交易](https://github.com/botvs/strategies) ## 回测 * [Zipline](https://github.com/quantopian/zipline) - 一个Python的回测框架 * [pyalgotrade](https://github.com/gbeced/pyalgotrade) - 一个Python的事件驱动回测框架 * [pyalgotrade-cn](https://github.com/Yam-cn/pyalgotrade-cn) - Pyalgotrade-cn在原版pyalgotrade的基础上加入了A股历史行情回测，并整合了tushare提供实时行情。 * [ricequant/rqalpha](https://github.com/ricequant/rqalpha) - RQalpha: Ricequant 开源的基于Python的回测引擎 * [quantdigger](https://github.com/QuantFans/quantdigger) - 基于python的量化回测框架,借鉴了主流商业软件（比如TB, 金字塔）简洁的策略语法 * [pyktrader](https://github.com/harveywwu/pyktrader) - 基于pyctp接口，并采用vnpy的eventEngine，使用tkinter作为GUI的python交易平台 * [QuantConnect/Lean](https://github.com/QuantConnect/Lean) - Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#, VB, Java) * [QUANTAXIS](https://github.com/yutiansut/QUANTAXIS) - QUANTAXIS 量化金融策略框架 - 中小型策略团队解决方案 * [Hikyuu](http://hikyuu.org) - 基于Python/C++的开源量化交易研究框架 * [StarQuant](https://github.com/physercoe/starquant) - 基于Python/C++的综合量化交易回测系统/平台 ## 交易API * [上海期货信息技术有限公司CTP API](http://www.sfit.com.cn/5_2_DocumentDown.htm) - 期货交易所提供的API * [飞马快速交易平台 - 上海金融期货信息技术有限公司](http://www.cffexit.com.cn/static/3000201.html) - 飞马 * [大连飞创信息技术有限公司](http://www.dfitc.com.cn/portal/cate?cid=1364967839100#1) - 飞创 * [vnpy](https://github.com/vnpy/vnpy) - 基于python的开源交易平台开发框架 * [QuantBox/XAPI2](https://github.com/QuantBox/XAPI2) - 统一行情交易接口第2版 * [easytrader](https://github.com/shidenggui/easytrader) - 提供券商华泰/佣金宝/银河/广发/雪球的基金、股票自动程序化交易，量化交易组件 * [策略易](http://www.iguuu.com/e)（[SDK](https://github.com/sinall/StrategyEase-Python-SDK)） - 管理交易客户端，提供基于 HTTP 协议的 RESTFul API；各大在线量化交易平台策略自动化解决方案 * [IB API | Interactive Brokers](https://www.interactivebrokers.com.hk/cn/index.php?f=5234&ns=T) - 盈透证券的交易API * [FutunnOpen/futuquant](https://github.com/FutunnOpen/futuquant) - 富途量化平台 API ## 编程 ### Python #### 安装 * [Anaconda](https://www.continuum.io/downloads) - 推荐通过[清华大学镜像 ](https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/)下载安装 * [Python Extension Packages for Windows - Christoph Gohlke](http://www.lfd.uci.edu/~gohlke/pythonlibs/) - Windows用户从这里可以下载许多python库的预编译包 #### 教程 * [Python | Codecademy](https://www.codecademy.com/learn/python) * [用 Python 玩转数据 - 南京大学 | Coursera](https://www.coursera.org/learn/hipython) * [Introduction to Data Science in Python - University of Michigan | Coursera](https://www.coursera.org/learn/python-data-analysis) * [The Python Tutorial — Python 3.5.2 documentation](https://docs.python.org/3/tutorial/) * [Python for Finance](https://book.douban.com/subject/25921015/) * [Algorithmic Thinking](https://www.coursera.org/learn/algorithmic-thinking-1) - Python 算法思维训练 #### 库 * [awesome-python: A curated list of awesome Python frameworks, libraries, software and resources](https://github.com/vinta/awesome-python) * [pandas](http://pandas.pydata.org) - Python做数据分析的基础 * [pyql: Cython QuantLib wrappers](https://github.com/enthought/pyql) * [ffn](http://pmorissette.github.io/ffn/quick.html) - 绩效评估 * [ta-lib: Python wrapper for TA-Lib (http://ta-lib.org/).](https://github.com/mrjbq7/ta-lib) - 技术指标 * [StatsModels: Statistics in Python — statsmodels documentation](http://statsmodels.sourceforge.net/) - 常用统计模型 * [arch: ARCH models in Python](https://github.com/bashtage/arch) - 时间序列 * [pyfolio: Portfolio and risk analytics in Python](https://github.com/quantopian/pyfolio) - 组合风险评估 * [twosigma/flint: A Time Series Library for Apache Spark](https://github.com/twosigma/flint) - Apache Spark上的时间序列库 * [PyFlux](https://github.com/RJT1990/pyflux) - Python 的时间序列建模(频率派和贝叶斯) ### R #### 安装 * [The Comprehensive R Archive Network](https://mirrors.tuna.tsinghua.edu.cn/CRAN/) - 从国内清华镜像下载安装 * [RStudio](https://www.rstudio.com/products/rstudio/download/) - R的常用开发平台下载 #### 教程 * [Free Introduction to R Programming Online Course](https://www.datacamp.com/courses/free-introduction-to-r) - datacamp的在线学习 * [R Programming - 约翰霍普金斯大学 | Coursera](https://www.coursera.org/learn/r-programming) * [Intro to Computational Finance with R](https://www.datacamp.com/community/open-courses/computational-finance-and-financial-econometrics-with-r) - 用R进行计算金融分析 #### 库 * [CRAN Task View: Empirical Finance](https://cran.r-project.org/web/views/Finance.html) - CRAN官方的R金融相关包整理 * [qinwf/awesome-R: A curated list of awesome R packages, frameworks and software.](https://github.com/qinwf/awesome-R) - R包的awesome ### C++ #### 教程 * [C++程序设计](http://www.xuetangx.com/courses/course-v1:PekingX+04831750.1x+2015T1/about) - 北京大学 郭炜 * [基于Linux的C++ ](http://www.xuetangx.com/courses/course-v1:TsinghuaX+20740084X+sp/about) - 清华大学 乔林 * [面向对象程序设计（C++）](http://www.xuetangx.com/courses/course-v1:TsinghuaX+30240532X+sp/about) - 清华大学 徐明星 * [C++ Design Patterns and Derivatives Pricing ](https://book.douban.com/subject/1485468/) - C++设计模式 * [C++ reference - cppreference.com](http://en.cppreference.com/w/cpp) - 在线文档 #### 库 * [fffaraz/awesome-cpp: A curated list of awesome C/C++ frameworks, libraries, resources, and shiny things.](https://github.com/fffaraz/awesome-cpp) - C++库整理 * [rigtorp/awesome-modern-cpp: A collection of resources on modern C++](https://github.com/rigtorp/awesome-modern-cpp) - 现代C++库整理 * [QuantLib: a free/open-source library for quantitative finance](http://quantlib.org/index.shtml) * [libtrading/libtrading: Libtrading, an ultra low-latency trading connectivity library for C and C++.](https://github.com/libtrading/libtrading) ### Julia #### 教程 * [Learning Julia](http://julialang.org/learning/) - 官方整理 * [QUANTITATIVE ECONOMICS with Julia](http://quant-econ.net/_static/pdfs/jl-quant-econ.pdf) - 经济学诺奖获得者Thomas Sargent教你[Julia](http://julialang.org/)在量化经济的应用。 #### 库 * [Quantitative Finance in Julia](https://github.com/JuliaQuant) - 多数为正在实现中，感兴趣的可以参与 ### 编程论坛 - [Stack Overflow](http://stackoverflow.com/) - 对应语言的tag - [SegmentFault](https://segmentfault.com/) - 对应语言的tag ### 编程能力在线训练 * [Solve Programming Questions | HackerRank](https://www.hackerrank.com/domains) - 包含常用语言(C++, Java, Python, Ruby, SQL)和相关计算机应用技术(算法、数据结构、数学、AI、Linux Shell、分布式系统、正则表达式、安全)的教程和挑战。 * [LeetCode Online Judge](https://leetcode.com/) - C, C++, Java, Python, C#, JavaScript, Ruby, Bash, MySQL在线编程训练 ## 论坛 * [Quantitative Finance StackExchange](http://quant.stackexchange.com/) - stackexchange 系列的 quant 论坛 * [JoinQuant社区](https://www.joinquant.com/community) - JoinQuant社区 * [优矿社区](https://uqer.io/community/list) - 优矿社区 * [RiceQuant量化社区](https://www.ricequant.com/community/) - RiceQuant量化社区 * [掘金量化社区](http://forum.myquant.cn/) - 掘金量化社区 * [清华大学学生经济金融论坛](http://forum.thuquant.com/) - 清华大学学生金融数据与量化投资协会主办 ## 书籍 * [My Life as a Quant: Reflections on Physics and Finance](http://www.amazon.com/My-Life-Quant-Reflections-Physics/dp/0470192739) - In My Life as a Quant, Emanuel Derman relives his exciting journey as one of the first high-energy particle physicists to migrate to Wall Street. * [量化交易](https://book.douban.com/subject/25878150/) - Ernest P. Chan撰写的量化投资理论 * [量化投资与对冲基金丛书：波动率交易](https://book.douban.com/subject/25711100/) * [Following the Trend](https://book.douban.com/subject/19990593/) * [Statistical Inference](https://book.douban.com/subject/1464795/) - 统计推断入门 * [All of Nonparametric Statistics](https://book.douban.com/subject/4251603/) - 非参统计入门 * [The Elements of Statistical Learning](https://book.douban.com/subject/3294335/) - Data Mining, Inference, and Prediction * [Analysis of Financial Time Series](https://book.douban.com/subject/4719140/) - Ruey S. Tsay 的时间序列分析 * [Options, Futures, and Other Derivatives](https://book.douban.com/subject/6127888/) - 期权期货等衍生品 ## 论文 * [awesome-quant/papers.md](https://github.com/thuquant/awesome-quant/blob/master/papers.md) ## 值得关注的信息源 * [Quantitative Finance arxiv](https://arxiv.org/archive/q-fin) * [雪球工程师1号](http://xueqiu.com/engineer) - 财经社交网络雪球的量化相关账号。 * [Ricequant量化](http://xueqiu.com/ricequant) - Ricequant量化平台的雪球账号。 * [量化哥-优矿Uqer](http://xueqiu.com/4105947155) - 优矿Uqer量化平台的雪球账号。 * [宽客 (Quant) - 索引 - 知乎](https://www.zhihu.com/topic/19557481) * 量化投资与机器学习 - 微信公众号 * THU量协 - 微信公众号 * 优矿量化实验室 - 微信公众号 * Ricequant - 微信公众号 * 鲁明量化全视角 - 微信公众号 ## 政策 * [中国证券监督管理委员会](http://www.csrc.gov.cn/pub/newsite/) * [考试报名-中国证券业协会](http://www.sac.net.cn/cyry/kspt/ksbm/) - 证券从业资格报名 * [中国证券投资基金业协会](http://www.amac.org.cn/) - 内有相关法规教育和从业资格报名入口 * [大连商品交易所](http://www.dce.com.cn/) * [上海期货交易所首页](http://www.shfe.com.cn/) * [郑州商品交易所网站](http://www.czce.com.cn/portal/index.htm) * [上海证券交易所](http://www.sse.com.cn/) * [深圳证券交易所](http://www.szse.cn/) # 其他Quant资源索引 * [Quantitative Finance Reading List - QuantStart](https://www.quantstart.com/articles/Quantitative-Finance-Reading-List#general-quant-finance-reading) * [Master reading list for Quants, MFE (Financial Engineering) students | QuantNet Community](https://www.quantnet.com/threads/master-reading-list-for-quants-mfe-financial-engineering-students.535/) # 其他 Awesome 列表 * 英文版 awesome-quant [wilsonfreitas/awesome-quant: A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)](https://github.com/wilsonfreitas/awesome-quant) * Other awesome lists [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness). * Even more lists [awesome](https://github.com/sindresorhus/awesome). * Another list? [list](https://github.com/jnv/lists). * WTF! [awesome-awesome-awesome](https://github.com/t3chnoboy/awesome-awesome-awesome). * Analytics [awesome-analytics](https://github.com/onurakpolat/awesome-analytics). ================================================ FILE: papers.md ================================================ # Quant Papers Collection This is a joint effort on collecting latest papers related to quantitative finance. Please fork to add your wisdom! ## Machine Learning Related * Cavalcante, Rodolfo C., et al. "Computational Intelligence and Financial Markets: A Survey and Future Directions." Expert Systems with Applications 55 (2016): 194-211.[(link)](http://www.sciencedirect.com/science/article/pii/S095741741630029X) ### Low Frequency Prediction * Atsalakis G S, Valavanis K P. Surveying stock market forecasting techniques Part II: Soft computing methods. Expert Systems with Applications, 2009, 36(3):5932–5941. [(link)](https://scholar.google.com/scholar_url?url=http://www.sciencedirect.com/science/article/pii/S0957417408004417&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=bx96WJysNMK7jAHry6ToBA&scisig=AAGBfm0ZeE3fEbS6P7zo9Ltcd9M0vtAu9w) * Cai X, Lin X. Feature Extraction Using Restricted Boltzmann Machine for Stock Price Predic- tion. 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), 2012. 80–83.[(link)](https://scholar.google.com/scholar_url?url=http://ieeexplore.ieee.org/xpls/abs_all.jsp%3Farnumber%3D6272913&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=uR96WN71F4nE2AaJsreoBQ&scisig=AAGBfm2biXd57RUWeaTdwuSosAyN-Lpkhg) * Nair B B, Dharini N M, Mohandas V P. A stock market trend prediction system using a hybrid decision tree-neuro-fuzzy system. Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, 2010. 381–385. [(link)](https://scholar.google.com/scholar_url?url=http://ieeexplore.ieee.org/xpls/abs_all.jsp%3Farnumber%3D5655295&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=zx96WKLSJsSV2AbjyIiACA&scisig=AAGBfm0GQbLhoeE6waU9eWWfsUTYba5FmQ) * Lu C J, Lee T S, Chiu C C. Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems, 2009, 47(2):115–125. [(link)](https://scholar.google.com/scholar_url?url=http://www.sciencedirect.com/science/article/pii/S0167923609000323&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=ByB6WNPSB4iYjAHl4regCA&scisig=AAGBfm1iHSydvwcYSUzCM3YXChNVYuoQYg) * Creamer G, Freund Y. Automated trading with boosting and expert weighting. Quantitative Finance, 2010, 10(4):401–420. [(link)](https://scholar.google.com/scholar_url?url=http://www.tandfonline.com/doi/abs/10.1080/14697680903104113&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=GCB6WO63JcPLjAGumbfwBw&scisig=AAGBfm3q4amcbTFxs2tl5yuLG_4hoLSAsw) * Batres-Estrada, Bilberto. "Deep learning for multivariate financial time series." (2015). [(link)](http://www.diva-portal.org/smash/record.jsf?pid=diva2:820891) * Xiong, Ruoxuan, Eric P. Nicholas, and Yuan Shen. "Deep Learning Stock Volatilities with Google Domestic Trends." arXiv preprint arXiv:1512.04916 (2015).[(link)](http://arxiv.org/abs/1512.04916) * Sharang, Abhijit, and Chetan Rao. "Using machine learning for medium frequency derivative portfolio trading." arXiv preprint arXiv:1512.06228 (2015).[(link)](http://arxiv.org/abs/1512.06228) ### Reinforcement Learning * Dempster, Michael AH, and Vasco Leemans. "An automated FX trading system using adaptive reinforcement learning." Expert Systems with Applications 30.3 (2006): 543-552. [(link)](https://scholar.google.com/scholar_url?url=http://www.sciencedirect.com/science/article/pii/S0957417405003015&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=LiB6WNKmKoK3jAHjxJyABg&scisig=AAGBfm3bJLN34rsebvNGo6IUfeYxiIC15w) * Tan, Zhiyong, Chai Quek, and Philip YK Cheng. "Stock trading with cycles: A financial application of ANFIS and reinforcement learning." Expert Systems with Applications 38.5 (2011): 4741-4755. [(link)](https://scholar.google.com/scholar_url?url=http://www.sciencedirect.com/science/article/pii/S095741741000905X&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=PSB6WL_aKMK7jAHry6ToBA&scisig=AAGBfm1WRwH4660mqK7RE0Mua2EDpuxLlA) * Rutkauskas, Aleksandras Vytautas, and Tomas Ramanauskas. "Building an artificial stock market populated by reinforcement‐learning agents." Journal of Business Economics and Management 10.4 (2009): 329-341.[(link)](https://scholar.google.com/scholar_url?url=http://www.tandfonline.com/doi/abs/10.3846/1611-1699.2009.10.329-341&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=USB6WKWEM4WMjAHRpKKwBw&scisig=AAGBfm15PBF06_fqletDTDk80FrNiyoWJg) * Deng, Yue, et al. "Deep Direct Reinforcement Learning for Financial Signal Representation and Trading." (2016).[(link)](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7407387) ### Natual Language Processing Related * Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. Journal of Computational Science, 2011, 2(1):1–8. [(link)](https://scholar.google.com/scholar_url?url=http://www.sciencedirect.com/science/article/pii/S187775031100007X&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=hCB6WJf-F4nE2AaJsreoBQ&scisig=AAGBfm0-CdCSkIorraVT063nZXOMGZPVng) * Preis T, Moat H S, Stanley H E, et al. Quantifying trading behavior in financial markets using Google Trends. Scientific reports, 2013, 3:1684. [(link)](https://scholar.google.com/scholar_url?url=http://www.nature.com/srep/2013/130425/srep01684/full/srep01684.html&hl=zh-CN&sa=T&oi=gsb-ggp&ct=res&cd=0&ei=lCB6WMyLOMSV2AbjyIiACA&scisig=AAGBfm1Kw6QEU25rQIFN5NppvKpiaZzlFg) * Moat H S, Curme C, Avakian A, et al. Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Scientific Reports, 2013, 3:1–5. [(link)](https://scholar.google.com/scholar_url?url=http://www.nature.com/srep/2013/130508/srep01801/full/srep01801.html%3FWT.ec_id%3DSREP-20130514&hl=zh-CN&sa=T&oi=gsb-ggp&ct=res&cd=0&ei=oCB6WOnhJ4ufjAHc4L2ADA&scisig=AAGBfm2DeL0w8CD41aPbIs1V7GwAz8gOOg) * Ding, Xiao, et al. "Deep learning for event-driven stock prediction." Proceedings of the 24th International Joint Conference on Artificial Intelligence (ICJAI’15). 2015. [(link)](https://scholar.google.com/scholar_url?url=http://ijcai.org/papers15/Papers/IJCAI15-329.pdf&hl=zh-CN&sa=T&oi=gsb-ggp&ct=res&cd=0&ei=pCF6WOLxFcK7jAHry6ToBA&scisig=AAGBfm0xUNdATrhy1lLIFLzyxMswZU6ifg) * Fehrer, R., & Feuerriegel, S. (2015). Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures. arXiv preprint arXiv:1508.01993. [(link)](http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1018&context=ecis2016_rip) ### High Frequency Trading * Nevmyvaka Y, Feng Y, Kearns M. Reinforcement learning for optimized trade execution. Proceedings of the 23rd international conference on Machine learning ICML 06, 2006, 17(1):673–680. [(link)](https://scholar.google.com/scholar_url?url=http://dl.acm.org/citation.cfm%3Fid%3D1143929&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=ryB6WPTAMcaL2Abq5oagAQ&scisig=AAGBfm3zYhh3tFDl_ZwyF25UcRYUnbJAJg) * Ganchev K, Nevmyvaka Y, Kearns M, et al. Censored exploration and the dark pool problem. Communications of the ACM, 2010, 53(5):99. [(link)](https://scholar.google.com/scholar_url?url=http://dl.acm.org/citation.cfm%3Fid%3D1735247&hl=zh-CN&sa=T&oi=gsb&ct=res&cd=0&ei=vCB6WJnWIYiYjAHl4regCA&scisig=AAGBfm2UT7ekE1Wd-P_ZdJHt8TBs6hJFTg) * Kearns M, Nevmyvaka Y. Machine learning for market microstructure and high frequency trading. High frequency trading - New realities for traders, markets and regulators, 2013. 1–21. [(link)](https://scholar.google.com/scholar_url?url=http://www.smallake.kr/wp-content/uploads/2014/01/KearnsNevmyvakaHFTRiskBooks.pdf&hl=zh-CN&sa=T&oi=gsb-ggp&ct=res&cd=0&ei=zCB6WPToHsPLjAGumbfwBw&scisig=AAGBfm3POscrhMXvpJb5DBb5-oYsWlyzCw) * Sirignano, Justin A. "Deep Learning for Limit Order Books." arXiv preprint arXiv:1601.01987 (2016). [(link)](http://jasirign.github.io/pdf/DeepLearningLimitOrderBooks.pdf) * Deng, Yue, et al. "Sparse coding-inspired optimal trading system for HFT industry." IEEE Transactions on Industrial Informatics 11.2 (2015): 467-475.[(link)](http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7042734) * Ahuja, Saran, et al. "Limit order trading with a mean reverting reference price." arXiv preprint arXiv:1607.00454 (2016). [(link)](https://arxiv.org/abs/1607.00454) * Aït-Sahalia, Yacine, and Jean Jacod. "Analyzing the spectrum of asset returns: Jump and volatility components in high frequency data." Journal of Economic Literature 50.4 (2012): 1007-1050. [(link)](http://www.ingentaconnect.com/content/aea/jel/2012/00000050/00000004/art00002) ## Portfolio Management * B. Li and S. C. H. Hoi, “Online portfolio selection,” ACM Comput. Surv., vol. 46, no. 3, pp. 1–36, 2014. [(link)](http://dl.acm.org/citation.cfm?id=2512962) * Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Portfolio Theory. [(link)](http://arxiv.org/abs/1605.07230)