├── LICENSE ├── README.md └── papers.md /LICENSE: -------------------------------------------------------------------------------- 1 | The MIT License (MIT) 2 | 3 | Copyright (c) 2016 thuquant 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Awesome Quant [![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome) 3 | 4 | 一份精心挑选的中文Quant相关资源索引。 5 | 6 | ## 目录 7 | 8 | * [数据源](#数据源) 9 | * [数据库](#数据库) 10 | * [量化交易平台](#量化交易平台) 11 | * [策略](#策略) 12 | * [回测](#回测) 13 | * [交易API](#交易api) 14 | * [编程](#编程) 15 | * [Python](#python) 16 | * [R](#r) 17 | * [C++](#c) 18 | * [Julia](#julia) 19 | * [论坛](#论坛) 20 | * [书籍](#书籍) 21 | * [论文](#论文) 22 | * [政策](#政策) 23 | * [值得关注的信息源](#值得关注的信息源) 24 | * [其他Quant资源索引](#其他quant资源索引) 25 | 26 | ## 数据源 27 | * [TuShare](http://tushare.org/) - 中文财经数据接口包 28 | * [Quandl](https://www.quandl.com/) - 国际金融和经济数据 29 | * [Wind资讯-经济数据库](http://www.wind.com.cn/NewSite/edb.html) - 收费 30 | * [锐思数据 - 首页](http://www.resset.cn/) - 收费 31 | * [国泰安数据服务中心](http://www.gtarsc.com/Home) - 收费 32 | * [恒生API](https://open.hscloud.cn/cloud/open/apilibrary/queryLibraryMenu.html?parent_id=100313&menu_id=100307) - 收费 33 | * [Bloomberg API](https://www.bloomberglabs.com/api/libraries/) - 收费 34 | * [数库金融数据和深度分析API服务](http://developer.chinascope.com/) - 收费 35 | * [Historical Data Sources](http://quantpedia.com/Links/HistoricalData) - 一个数据源索引 36 | * [Python通达信数据接口](https://github.com/rainx/pytdx) - 免费通达信数据源 37 | * [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 38 | * [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。 39 | * [JoinQuant/jqdatasdk](https://github.com/JoinQuant/jqdatasdk) - jqdatasdk是提供给用户获取聚宽金融数据的SDK 40 | * [米筐科技的RQData数据接口](https://www.ricequant.com/introduce_rqdata) - 收费 41 | * [AkShare](https://github.com/jindaxiang/akshare) - 免费开源财经数据接口库，目前包含中文领域最全的数据接口 42 | 43 | ## 数据库 44 | 45 | * [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 46 | * [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 47 | * [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 48 | * [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 49 | * [OpenTSDB/opentsdb: A scalable, distributed Time Series Database.](https://github.com/OpenTSDB/opentsdb) - 基于HBase的时间序列数据库 50 | * [kairosdb/kairosdb: Fast scalable time series database](https://github.com/kairosdb/kairosdb) - 基于Cassandra的时间序列数据库 51 | * [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 52 | 53 | ## 量化交易平台 54 | 55 | * [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 56 | * [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 57 | * [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 58 | * [掘金量化](http://www.myquant.cn/) - 支持C/C++、C#、MATLAB、Python和R的量化交易平台 59 | * [Auto-Trader](http://www.atrader.com.cn/portal.php) - 基于MATLAB的量化交易平台 60 | * [MultiCharts 中国版 - 程序化交易软件](https://www.multicharts.cn/) 61 | * [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/) 62 | * [Tradeblazer(TB) - 交易开拓者](http://www.tradeblazer.net/) - 期货程序化交易软件平台 63 | * [MetaTrader 5](https://www.metatrader5.com/en) - Multi-Asset Trading Platform 64 | * [BigQuant](https://bigquant.com) - 专注量化投资的人工智能/机器学习平台 65 | * [天勤量化（TqSdk）](https://github.com/shinnytech/tqsdk-python) - 快期出品的 Python 量化开发包，免费提供期货、期权、股票数据，支持实盘交易/历史回测 66 | * [果仁网](https://guorn.com/) - 一个以选股+量化为主要特色的平台，不需要写代码就能完成大部分的量化和回测操作 67 | 68 | ## 策略 69 | * [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) 70 | * [myquant/strategy: 掘金策略集锦](https://github.com/myquant/strategy) 71 | * [优矿社区内容索引](https://uqer.io/community/share/58243e7d228e5b91df6d5d19) 72 | * [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) 73 | * [雪球选股](https://xueqiu.com/9796081404) 74 | * [botvs/strategies: 用Javascript OR Python进行量化交易](https://github.com/botvs/strategies) 75 | 76 | ## 回测 77 | * [Zipline](https://github.com/quantopian/zipline) - 一个Python的回测框架 78 | * [pyalgotrade](https://github.com/gbeced/pyalgotrade) - 一个Python的事件驱动回测框架 79 | * [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。 80 | * [ricequant/rqalpha](https://github.com/ricequant/rqalpha) - RQalpha: Ricequant 开源的基于Python的回测引擎 81 | * [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 82 | * [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 83 | * [QuantConnect/Lean](https://github.com/QuantConnect/Lean) - Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#, VB, Java) 84 | * [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 85 | * [Hikyuu](http://hikyuu.org) - 基于Python/C++的开源量化交易研究框架 86 | * [StarQuant](https://github.com/physercoe/starquant) - 基于Python/C++的综合量化交易回测系统/平台 87 | 88 | ## 交易API 89 | * [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 90 | * [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 91 | * [大连飞创信息技术有限公司](http://www.dfitc.com.cn/portal/cate?cid=1364967839100#1) - 飞创 92 | * [vnpy](https://github.com/vnpy/vnpy) - 基于python的开源交易平台开发框架 93 | * [QuantBox/XAPI2](https://github.com/QuantBox/XAPI2) - 统一行情交易接口第2版 94 | * [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 95 | * [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 96 | * [IB API | Interactive Brokers](https://www.interactivebrokers.com.hk/cn/index.php?f=5234&ns=T) - 盈透证券的交易API 97 | * [FutunnOpen/futuquant](https://github.com/FutunnOpen/futuquant) - 富途量化平台 API 98 | 99 | 100 | ## 编程 101 | 102 | ### Python 103 | #### 安装 104 | * [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 105 | * [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 106 | 107 | #### 教程 108 | * [Python | Codecademy](https://www.codecademy.com/learn/python) 109 | * [用 Python 玩转数据 - 南京大学 | Coursera](https://www.coursera.org/learn/hipython) 110 | * [Introduction to Data Science in Python - University of Michigan | Coursera](https://www.coursera.org/learn/python-data-analysis) 111 | * [The Python Tutorial — Python 3.5.2 documentation](https://docs.python.org/3/tutorial/) 112 | * [Python for Finance](https://book.douban.com/subject/25921015/) 113 | * [Algorithmic Thinking](https://www.coursera.org/learn/algorithmic-thinking-1) - Python 算法思维训练 114 | 115 | #### 库 116 | * [awesome-python: A curated list of awesome Python frameworks, libraries, software and resources](https://github.com/vinta/awesome-python) 117 | * [pandas](http://pandas.pydata.org) - Python做数据分析的基础 118 | * [pyql: Cython QuantLib wrappers](https://github.com/enthought/pyql) 119 | * [ffn](http://pmorissette.github.io/ffn/quick.html) - 绩效评估 120 | * [ta-lib: Python wrapper for TA-Lib (http://ta-lib.org/).](https://github.com/mrjbq7/ta-lib) - 技术指标 121 | * [StatsModels: Statistics in Python — statsmodels documentation](http://statsmodels.sourceforge.net/) - 常用统计模型 122 | * [arch: ARCH models in Python](https://github.com/bashtage/arch) - 时间序列 123 | * [pyfolio: Portfolio and risk analytics in Python](https://github.com/quantopian/pyfolio) - 组合风险评估 124 | * [twosigma/flint: A Time Series Library for Apache Spark](https://github.com/twosigma/flint) - Apache Spark上的时间序列库 125 | * [PyFlux](https://github.com/RJT1990/pyflux) - Python 的时间序列建模(频率派和贝叶斯) 126 | 127 | ### R 128 | 129 | #### 安装 130 | * [The Comprehensive R Archive Network](https://mirrors.tuna.tsinghua.edu.cn/CRAN/) - 从国内清华镜像下载安装 131 | * [RStudio](https://www.rstudio.com/products/rstudio/download/) - R的常用开发平台下载 132 | 133 | #### 教程 134 | * [Free Introduction to R Programming Online Course](https://www.datacamp.com/courses/free-introduction-to-r) - datacamp的在线学习 135 | * [R Programming - 约翰霍普金斯大学 | Coursera](https://www.coursera.org/learn/r-programming) 136 | * [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 137 | 138 | #### 库 139 | * [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 140 | * [qinwf/awesome-R: A curated list of awesome R packages, frameworks and software.](https://github.com/qinwf/awesome-R) - R包的awesome 141 | 142 | ### C++ 143 | #### 教程 144 | * [C++程序设计](http://www.xuetangx.com/courses/course-v1:PekingX+04831750.1x+2015T1/about) - 北京大学 郭炜 145 | * [基于Linux的C++ ](http://www.xuetangx.com/courses/course-v1:TsinghuaX+20740084X+sp/about) - 清华大学 乔林 146 | * [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 147 | * [C++ Design Patterns and Derivatives Pricing ](https://book.douban.com/subject/1485468/) - C++设计模式 148 | * [C++ reference - cppreference.com](http://en.cppreference.com/w/cpp) - 在线文档 149 | 150 | #### 库 151 | * [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 152 | * [rigtorp/awesome-modern-cpp: A collection of resources on modern C++](https://github.com/rigtorp/awesome-modern-cpp) - 现代C++库整理 153 | * [QuantLib: a free/open-source library for quantitative finance](http://quantlib.org/index.shtml) 154 | * [libtrading/libtrading: Libtrading, an ultra low-latency trading connectivity library for C and C++.](https://github.com/libtrading/libtrading) 155 | 156 | ### Julia 157 | #### 教程 158 | * [Learning Julia](http://julialang.org/learning/) - 官方整理 159 | * [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。 160 | 161 | #### 库 162 | * [Quantitative Finance in Julia](https://github.com/JuliaQuant) - 多数为正在实现中，感兴趣的可以参与 163 | 164 | ### 编程论坛 165 | - [Stack Overflow](http://stackoverflow.com/) - 对应语言的tag 166 | - [SegmentFault](https://segmentfault.com/) - 对应语言的tag 167 | 168 | ### 编程能力在线训练 169 | 170 | * [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。 171 | * [LeetCode Online Judge](https://leetcode.com/) - C, C++, Java, Python, C#, JavaScript, Ruby, Bash, MySQL在线编程训练 172 | 173 | ## 论坛 174 | * [Quantitative Finance StackExchange](http://quant.stackexchange.com/) - stackexchange 系列的 quant 论坛 175 | * [JoinQuant社区](https://www.joinquant.com/community) - JoinQuant社区 176 | * [优矿社区](https://uqer.io/community/list) - 优矿社区 177 | * [RiceQuant量化社区](https://www.ricequant.com/community/) - RiceQuant量化社区 178 | * [掘金量化社区](http://forum.myquant.cn/) - 掘金量化社区 179 | * [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 180 | 181 | ## 书籍 182 | * [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. 183 | * [量化交易](https://book.douban.com/subject/25878150/) - Ernest P. Chan撰写的量化投资理论 184 | * [量化投资与对冲基金丛书：波动率交易](https://book.douban.com/subject/25711100/) 185 | * [Following the Trend](https://book.douban.com/subject/19990593/) 186 | * [Statistical Inference](https://book.douban.com/subject/1464795/) - 统计推断入门 187 | * [All of Nonparametric Statistics](https://book.douban.com/subject/4251603/) - 非参统计入门 188 | * [The Elements of Statistical Learning](https://book.douban.com/subject/3294335/) - Data Mining, Inference, and Prediction 189 | * [Analysis of Financial Time Series](https://book.douban.com/subject/4719140/) - Ruey S. Tsay 的时间序列分析 190 | * [Options, Futures, and Other Derivatives](https://book.douban.com/subject/6127888/) - 期权期货等衍生品 191 | 192 | 193 | 194 | ## 论文 195 | * [awesome-quant/papers.md](https://github.com/thuquant/awesome-quant/blob/master/papers.md) 196 | 197 | ## 值得关注的信息源 198 | * [Quantitative Finance arxiv](https://arxiv.org/archive/q-fin) 199 | * [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。 200 | * [Ricequant量化](http://xueqiu.com/ricequant) - Ricequant量化平台的雪球账号。 201 | * [量化哥-优矿Uqer](http://xueqiu.com/4105947155) - 优矿Uqer量化平台的雪球账号。 202 | * [宽客 (Quant) - 索引 - 知乎](https://www.zhihu.com/topic/19557481) 203 | * 量化投资与机器学习 - 微信公众号 204 | * THU量协 - 微信公众号 205 | * 优矿量化实验室 - 微信公众号 206 | * Ricequant - 微信公众号 207 | * 鲁明量化全视角 - 微信公众号 208 | 209 | 210 | ## 政策 211 | * [中国证券监督管理委员会](http://www.csrc.gov.cn/pub/newsite/) 212 | * [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 213 | * [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 214 | * [大连商品交易所](http://www.dce.com.cn/) 215 | * [上海期货交易所首页](http://www.shfe.com.cn/) 216 | * [郑州商品交易所网站](http://www.czce.com.cn/portal/index.htm) 217 | * [上海证券交易所](http://www.sse.com.cn/) 218 | * [深圳证券交易所](http://www.szse.cn/) 219 | 220 | # 其他Quant资源索引 221 | 222 | * [Quantitative Finance Reading List - QuantStart](https://www.quantstart.com/articles/Quantitative-Finance-Reading-List#general-quant-finance-reading) 223 | * [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/) 224 | 225 | # 其他 Awesome 列表 226 | * 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) 227 | * Other awesome lists [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness). 228 | * Even more lists [awesome](https://github.com/sindresorhus/awesome). 229 | * Another list? [list](https://github.com/jnv/lists). 230 | * WTF! [awesome-awesome-awesome](https://github.com/t3chnoboy/awesome-awesome-awesome). 231 | * Analytics [awesome-analytics](https://github.com/onurakpolat/awesome-analytics). 232 | -------------------------------------------------------------------------------- /papers.md: -------------------------------------------------------------------------------- 1 | # Quant Papers Collection 2 | 3 | This is a joint effort on collecting latest papers related to quantitative finance. Please fork to add your wisdom! 4 | 5 | ## Machine Learning Related 6 | 7 | * 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) 8 | 9 | ### Low Frequency Prediction 10 | 11 | * Atsalakis G S, Valavanis K P. Surveying stock market forecasting techniques Part II: Soft 12 | 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) 13 | 14 | * Cai X, Lin X. Feature Extraction Using Restricted Boltzmann Machine for Stock Price Predic- 15 | tion. 2012 IEEE International Conference on Computer Science and Automation Engineering 16 | (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) 17 | 18 | * Nair B B, Dharini N M, Mohandas V P. A stock market trend prediction system using a hybrid 19 | decision tree-neuro-fuzzy system. Proceedings - 2nd International Conference on Advances in 20 | 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) 21 | 22 | * Lu C J, Lee T S, Chiu C C. Financial time series forecasting using independent component 23 | 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) 24 | 25 | * Creamer G, Freund Y. Automated trading with boosting and expert weighting. Quantitative 26 | 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) 27 | 28 | * Batres-Estrada, Bilberto. "Deep learning for multivariate financial time series." (2015). [(link)](http://www.diva-portal.org/smash/record.jsf?pid=diva2:820891) 29 | 30 | * 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) 31 | 32 | * 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) 33 | 34 | 35 | ### Reinforcement Learning 36 | 37 | * 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) 38 | 39 | * 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) 40 | 41 | * 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) 42 | 43 | * 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) 44 | 45 | 46 | ### Natual Language Processing Related 47 | 48 | * Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. Journal of Computational 49 | 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) 50 | 51 | * Preis T, Moat H S, Stanley H E, et al. Quantifying trading behavior in financial markets using 52 | 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) 53 | 54 | * Moat H S, Curme C, Avakian A, et al. Quantifying Wikipedia Usage Patterns Before Stock 55 | 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) 56 | 57 | * 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) 58 | 59 | * 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) 60 | 61 | 62 | ### High Frequency Trading 63 | 64 | * 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) 65 | 66 | * Ganchev K, Nevmyvaka Y, Kearns M, et al. Censored exploration and the dark pool problem. 67 | 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) 68 | 69 | * Kearns M, Nevmyvaka Y. Machine learning for market microstructure and high frequency 70 | 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) 71 | 72 | * Sirignano, Justin A. "Deep Learning for Limit Order Books." arXiv preprint arXiv:1601.01987 (2016). [(link)](http://jasirign.github.io/pdf/DeepLearningLimitOrderBooks.pdf) 73 | 74 | * 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) 75 | 76 | * 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) 77 | 78 | * 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) 79 | 80 | ## Portfolio Management 81 | 82 | * 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) 83 | 84 | * Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Portfolio Theory. [(link)](http://arxiv.org/abs/1605.07230) 85 | 86 | 87 | --------------------------------------------------------------------------------