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史上最全Quant资源整理 #16

Open ljk99 opened 3 years ago

ljk99 commented 3 years ago

https://bigquant.com/community/t/topic/168059

有些国外的平台、社区、博客如果连接无法打开，那说明可能需要“科学”上网

量化交易平台

国内在线量化平台：

国外量化平台：

相关平台：

  • 掘金量化 - 支持C/C++、C#、MATLAB、Python和R的量化交易平台

  • DigQuant - 提供基于matlab量化工具

  • SmartQuant - 策略交易平台

  • OpenQuant - 基于C#的开源量化回测平台

基于图表的量化交易平台

  • 文华赢智 、TB、金字塔、MultiCharts 中国版 - 程序化交易软件、MT4、TradeStation

  • Auto-Trader - 基于MATLAB的量化交易平台

  • BotVS - 首家支持传统期货与股票证券与数字货币的量化平台

开源框架

  • Pandas - 数据分析包

  • Zipline - 一个Python的回测框架

  • vnpy - 基于python的开源交易平台开发框架

  • tushare - 财经数据接口包

  • easytrader - 进行自动的程序化股票交易

  • pyalgotrade - 一个Python的事件驱动回测框架

  • 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。

  • zwPython - 基于winpython的集成式python开发平台

  • quantmod - 量化金融建模

  • rqalpha - 基于Python的回测引擎

  • quantdigger - 基于python的量化回测框架

  • 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 - Lean Algorithmic Trading Engine by QuantConnect (C#, Python, F#, VB, Java)

  • QUANTAXIS - 量化金融策略框架

其他量化交易平台：

Progress Apama、龙软DTS、国泰安量化投资平台、飞创STP、易盛程序化交易、盛立SPT平台、天软量化回测平台 、量邦天语、EQB-Quant

数据源

数据库

网站、论坛、社区、博客

国外：

国内：

交易API

编程

Python

安装

教程

库

R

安装

教程

库

C++

教程

库

Julia

教程

库

编程论坛

编程能力在线训练

  • Solve Programming Questions | HackerRank - 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 - C, C++, Java, Python, C#, JavaScript, Ruby, Bash, MySQL在线编程训练

Quant Books

  • 《投资学》第6版[美]兹维·博迪.文字版 (link)

  • 《打开量化投资的黑箱》 里什·纳兰

  • 《宽客》[美] 斯科特·帕特森（Scott Patterson） 著；译科，卢开济 译

  • 《解读量化投资：西蒙斯用公式打败市场的故事》 忻海

  • 《Trends in Quantitative Finance》 Frank J. Fabozzi, Sergio M. Focardi, Petter N. Kolm

  • 《漫步华尔街》麦基尔

  • 《海龟交易法则》柯蒂斯·费思

  • 《交易策略评估与最佳化》罗伯特·帕多

  • 《统计套利》 安德鲁·波尔《信号与噪声》纳特•西尔弗

  • 《期货截拳道》朱淋靖

  • 《量化投资—策略与技术》 丁鹏

  • 《量化投资—以matlab为工具》 李洋faruto

  • 《量化投资策略:如何实现超额收益Alpha》 吴冲锋

  • 《中低频量化交易策略研发（上）》 杨博理

  • 《走出幻觉走向成熟》 金融帝国

  • 《失控》凯文·凯利 《通往财务自由之路》范K撒普

  • 《以交易为生》 埃尔德

  • 《超越技术分析》图莎尔·钱德

  • 《高级技术分析》布鲁斯·巴布科克

  • 《积极型投资组合管理》格里纳德，卡恩

  • 《金融计量学:从初级到高级建模技术》 斯维特洛扎

  • 《投资革命》Bernstein

  • 《富可敌国》Sebastian Mallaby

  • 《量化交易——如何建立自己的算法交易事业》欧内斯特·陈

  • 《聪明的投资者》 巴菲特

  • 《黑天鹅·如何应对不可知的未来》 纳西姆·塔勒布

  • 《期权、期货和其他衍生品》 约翰·赫尔

  • 《Building Reliable Trading Systems: Tradable Strategies That Perform As They Backtest and Meet Your Risk-Reward Goals》 Keith Fitschen

  • 《Quantitative Equity Investing》by Frank J. Fabozzi, Sergio M. Focardi, Petter N. Kolm

  • Barra USE3 handbook

  • 《Quantitative Equity Portfolio Management》 Ludwig Chincarini

  • 《Quantitative Equity Portfolio Management》 Qian & Hua & Sorensen

Quant Papers

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)

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)

  • 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)

  • 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)

  • 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)

  • Creamer G, Freund Y. Automated trading with boosting and expert weighting. Quantitative Finance, 2010, 10(4):401–420. (link)

  • Batres-Estrada, Bilberto. “Deep learning for multivariate financial time series.” (2015). (link)

  • Xiong, Ruoxuan, Eric P. Nicholas, and Yuan Shen. “Deep Learning Stock Volatilities with Google Domestic Trends.” arXiv preprint arXiv:1512.04916 (2015).(link)

  • Sharang, Abhijit, and Chetan Rao. “Using machine learning for medium frequency derivative portfolio trading.” arXiv preprint arXiv:1512.06228 (2015).(link)

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)

  • 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)

  • 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)

  • Deng, Yue, et al. “Deep Direct Reinforcement Learning for Financial Signal Representation and Trading.” (2016).(link)

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)

  • 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)

  • Moat H S, Curme C, Avakian A, et al. Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Scientific Reports, 2013, 3:1–5. (link)

  • 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)

  • Fehrer, R., & Feuerriegel, S. (2015). Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures. arXiv preprint arXiv:1508.01993. (link)

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)

  • Ganchev K, Nevmyvaka Y, Kearns M, et al. Censored exploration and the dark pool problem. Communications of the ACM, 2010, 53(5):99. (link)

  • 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)

  • Sirignano, Justin A. “Deep Learning for Limit Order Books.” arXiv preprint arXiv:1601.01987 (2016). (link)

  • Deng, Yue, et al. “Sparse coding-inspired optimal trading system for HFT industry.” IEEE Transactions on Industrial Informatics 11.2 (2015): 467-475.(link)

  • Ahuja, Saran, et al. “Limit order trading with a mean reverting reference price.” arXiv preprint arXiv:1607.00454 (2016). (link)

  • 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)

Portfolio Management

  • B. Li and S. C. H. Hoi, “Online portfolio selection,” ACM Comput. Surv., vol. 46, no. 3, pp. 1–36, 2014. (link)

  • Heaton, J. B., Polson, N. G., & Witte, J. H. (2016). Deep Portfolio Theory. (link)

  • Eugene F. Fama, Kenneth R. French. The cross-section of expected stock returns. Journal of Finance, 47 (1992), pp. 427–465.

学术期刊

一堆学术期刊可以常常去浏览一下，也会有许多思路，作者常常看的有：

  • Journal of FinanceJournal of Financial Economics

  • Review of Financial Studies

  • Journal of Accounting and Economics

  • Review of Accounting Studies

  • Journal of Accounting Research

  • Accounting Review

  • Journal of Financial and Quantitative Analysis

  • Financial Analysts Journal

  • Financial Management

  • Journal of Empirical Finance

  • Quantitative Finance

  • Journal of Alternative Investments

  • Journal of Fixed Income

  • Journal of Investing

  • Journal of Portfolio Management

  • Journal of Trading

  • Review of Asset Pricing Studies

  • 经济研究

  • 经济学（季刊）

  • 金融研究

  • 管理世界

  • 会计研究

  • 投资研究

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