# awesome-systematic-trading
**Repository Path**: charlesroswell/awesome-systematic-trading
## Basic Information
- **Project Name**: awesome-systematic-trading
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 7
- **Created**: 2024-11-13
- **Last Updated**: 2024-11-13
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
令人敬畏的系统化交易
我们正在收集一份关于寻找、开发和运行系统性交易（量化交易）策略的资源论文、软件、书籍、文章清单。
### 你在这里会发现什么？
- [97个](#库和包)用于研究和实际交易的[库和包](#库和包)
- 机构和学术界描述的[696项战略](#战略)
- [55本](#书籍)适合初学者和专业人士的[书籍](#书籍)
- [23个视频](#视频)和采访
- 还有一些[博客](#博客)和[课程](#课程)
点击这里查看完整的内容表
- [库和包](#库和包)
- [回溯测试和真实交易](#回溯测试和真实交易)
- [一般 - 事件驱动框架](#一般---事件驱动框架)
- [一般 - 基于矢量的框架](#一般---基于矢量的框架)
- [加密货币](#加密货币)
- [交易机器人](#交易机器人)
- [分析](#分析)
- [指标](#指标)
- [度量衡计算](#度量衡计算)
- [优化](#优化)
- [定价](#定价)
- [风险](#风险)
- [经纪人API](#经纪人api)
- [数据来源](#数据来源)
- [一般](#一般)
- [加密货币](#加密货币-1)
- [数据科学](#数据科学)
- [数据库](#数据库)
- [图形计算](#图形计算)
- [机器学习](#机器学习)
- [时间序列分析](#时间序列分析)
- [视觉化](#视觉化)
- [战略](#战略)
- [债券、商品、货币、股票](#债券商品货币股票)
- [债券、商品、股票、REITs](#债券商品股票reits)
- [债券、股票](#债券股票)
- [债券、股票、REITs](#债券股票reits)
- [商品](#商品)
- [加密货币](#加密货币-2)
- [货币](#货币)
- [股票](#股票)
- [书籍](#书籍)
- [初学者](#初学者)
- [传记](#传记)
- [编码](#编码)
- [隐蔽性](#隐蔽性)
- [一般](#一般-1)
- [高频交易](#高频交易)
- [机器学习](#机器学习-1)
- [视频](#视频)
- [博客](#博客)
- [课程](#课程)
> ### 我怎样才能提供帮助？
> 你可以通过提交带有建议的问题和在Twitter上分享来帮助。
>
> [](https://twitter.com/intent/tweet?text=A%20free%20and%20comprehensive%20list%20of%20papers%2C%20libraries%2C%20books%2C%20blogs%2C%20tutorials%20for%20quantitative%20traders.&url=https://github.com/edarchimbaud/awesome-systematic-trading)
# 库和包
*97个实现交易机器人、回溯测试器、指标、定价器等的库和包列表。每个库都按其编程语言分类，并按人口降序排列（星星的数量）。*
## 回溯测试和真实交易
### 一般 - 事件驱动框架
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [vnpy](https://github.com/vnpy/vnpy) | 基于Python的开源量化交易系统开发框架，于2015年1月正式发布，已经一步步成长为一个全功能的量化交易平台。 |  |  |
| [zipline](https://github.com/quantopian/zipline) | Zipline是一个Pythonic算法交易库。它是一个事件驱动的系统，用于回溯测试。 |  |  |
| [backtrader](https://github.com/mementum/backtrader) | 事件驱动的Python交易策略回测库 |  |  |
| [QUANTAXIS](https://github.com/QUANTAXIS/QUANTAXIS) | QUANTAXIS 支持任务调度 分布式部署的 股票/期货/期权/港股/虚拟货币 数据/回测/模拟/交易/可视化/多账户 纯本地量化解决方案 |  |  |
| [QuantConnect](https://github.com/QuantConnect/Lean) | QuantConnect的精益算法交易引擎（Python，C#）。 |  |  |
| [Rqalpha](https://github.com/ricequant/rqalpha) | 一个可扩展、可替换的Python算法回测和交易框架，支持多种证券 |  |  |
| [finmarketpy](https://github.com/cuemacro/finmarketpy) | 用于回测交易策略和分析金融市场的Python库（前身为pythalesians）。 |  |  |
| [backtesting.py](https://github.com/kernc/backtesting.py) | Backtesting.py是一个Python框架，用于根据历史（过去）数据推断交易策略的可行性。Backtesting.py在Backtrader的基础上进行了改进，并以各种方式超越了其他可获得的替代方案，Backtesting.py是轻量级的、快速的、用户友好的、直观的、互动的、智能的，并希望是面向未来的。 |  |  |
| [zvt](https://github.com/zvtvz/zvt) | 模块化的量化框架 |  |  |
| [WonderTrader](https://github.com/wondertrader/wondertrader) | WonderTrader——量化研发交易一站式框架 |  |  |
| [nautilus_trader](https://github.com/nautechsystems/nautilus_trader) | 一个高性能的算法交易平台和事件驱动的回测器 |  |  |
| [PandoraTrader](https://github.com/pegasusTrader/PandoraTrader) | 基于c++开发，支持多种交易API，跨平台的高频量化交易平台 |  |  |
[HFTBacktest](https://github.com/nkaz001/hftbacktest) | Python+Numba 对高频交易数据进行高精度回测 |  |  |
| [aat](https://github.com/AsyncAlgoTrading/aat) | 一个异步的、事件驱动的框架，用于用python编写算法交易策略，并可选择用C++进行加速。它的设计是模块化和可扩展的，支持各种工具和策略，在多个交易所之间进行实时交易。 |  |  |
| [sdoosa-algo-trade-python](https://github.com/sreenivasdoosa/sdoosa-algo-trade-python) | 这个项目主要是为那些有兴趣学习使用python解释器编写自己的交易算法的algo交易新手准备的。 |  |  |
| [lumibot](https://github.com/Lumiwealth/lumibot) | 一个非常简单而有用的回溯测试和基于样本的实时交易框架（运行速度有点慢......）。 |  |  |
| [quanttrader](https://github.com/letianzj/quanttrader) | 在Python中进行回测和实时交易。基于事件。类似于backtesting.py。 |  |  |
| [gobacktest](https://github.com/gobacktest/gobacktest) | 事件驱动的回溯测试框架的Go实现 |  |  |
| [FlashFunk](https://github.com/HFQR/FlashFunk) | Rust中的高性能运行时 |  |  |
### 一般 - 基于矢量的框架
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [vectorbt](https://github.com/polakowo/vectorbt) | vectorbt采取了一种新颖的回测方法：它完全在pandas和NumPy对象上运行，并由Numba加速，以速度和规模分析任何数据。这允许在几秒钟内对成千上万的策略进行测试。 |  |  |
| [pysystemtrade](https://github.com/robcarver17/pysystemtrade) | 罗布-卡弗的《系统交易》一书中的python系统交易 |  |  |
| [bt](https://github.com/pmorissette/bt) | 基于Algo和策略树的Python的灵活回测 |  |  |
### 加密货币
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [Freqtrade](https://github.com/freqtrade/freqtrade) | Freqtrade是一个用Python编写的免费和开源的加密货币交易机器人。它被设计为支持所有主要交易所，并通过Telegram进行控制。它包含回测、绘图和资金管理工具，以及通过机器学习进行策略优化。 |  |  |
| [Jesse](https://github.com/jesse-ai/jesse) | Jesse是一个先进的加密货币交易框架，旨在简化研究和定义交易策略。 |  |  |
| [OctoBot](https://github.com/Drakkar-Software/OctoBot) | 用于TA、套利和社会交易的加密货币交易机器人，具有先进的网络界面 |  |  |
| [Kelp](https://github.com/stellar/kelp) | Kelp是一个免费和开源的交易机器人，适用于Stellar DEX和100多个集中式交易所 |  |  |
| [openlimits](https://github.com/nash-io/openlimits) | 一个Rust高性能的加密货币交易API，支持多个交易所和语言封装器。 |  |  |
| [bTrader](https://github.com/gabriel-milan/btrader) | Binance的三角套利交易机器人 |  |  |
| [crypto-crawler-rs](https://github.com/crypto-crawler/crypto-crawler-rs) | 抓取加密货币交易所的订单簿和交易信息 |  |  |
| [Hummingbot](https://github.com/CoinAlpha/hummingbot) | 一个用于加密货币做市的客户 |  |  |
| [cryptotrader-core](https://github.com/monomadic/cryptotrader-core) | 简单的使用Rust中的加密货币交易所REST API客户端。 |  |  |
## 交易机器人
*交易机器人和阿尔法模型。其中一些是旧的，没有维护。*
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [Blackbird](https://github.com/butor/blackbird) | 黑鸟比特币套利：市场中立的多/空策略 |  |  |
| [bitcoin-arbitrage](https://github.com/maxme/bitcoin-arbitrage) | 比特币套利 - 机会检测器 |  |  |
| [ThetaGang](https://github.com/brndnmtthws/thetagang) | ThetaGang是一个用于收集资金的IBKR机器人 |  |  |
| [czsc](https://github.com/waditu/czsc) | 缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易 |  |  |
| [R2 Bitcoin Arbitrager](https://github.com/bitrinjani/r2) | R2 Bitcoin Arbitrager是一个由Node.js + TypeScript驱动的自动套利交易系统。 |  |  |
| [analyzingalpha](https://github.com/leosmigel/analyzingalpha) | 实施简单的战略 |  |  |
| [PyTrendFollow](https://github.com/chrism2671/PyTrendFollow) | PyTrendFollow - 使用趋势跟踪的系统性期货交易 |  |  |
## 分析
### 指标
*预测未来价格走势的指标库。*
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [ta-lib](https://github.com/mrjbq7/ta-lib) | 对金融市场数据进行技术分析 |  |  |
| [go-tart](https://github.com/iamjinlei/go-tart) | 用Go实现的[ta-lib]((https://github.com/mrjbq7/ta-lib)，支持增量更新 |  |  |
| [pandas-ta](https://github.com/twopirllc/pandas-ta) | 潘达斯技术分析（Pandas TA）是一个易于使用的库，它利用潘达斯软件包的130多个指标和实用功能以及60多个TA Lib蜡烛图。 |  |  |
| [finta](https://github.com/peerchemist/finta) | 在Pandas中实施的共同财务技术指标 |  |  |
| [ta-rust](https://github.com/greyblake/ta-rs) | Rust语言的技术分析库 |  |  |
### 度量衡计算
*财务衡量标准。*
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [quantstats](https://github.com/ranaroussi/quantstats) | 用Python编写的面向量化投资人的投资组合分析方法 |  |  |
| [ffn](https://github.com/pmorissette/ffn) | 一个用于Python的金融函数库 |  |  |
### 优化
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt) | 在python中进行金融投资组合优化，包括经典的有效边界、Black-Litterman、分级风险平价等。 |  |  |
| [Riskfolio-Lib](https://github.com/dcajasn/Riskfolio-Lib) | Python中的投资组合优化和定量战略资产配置 |  |  |
| [empyrial](https://github.com/ssantoshp/Empyrial) | Empyrial是一个基于Python的开源量化投资库，专门为金融机构和零售投资者服务，于2021年3月正式发布。 |  |  |
| [Deepdow](https://github.com/jankrepl/deepdow) | 连接组合优化和深度学习的Python包。它的目标是促进研究在一次前进过程中进行权重分配的网络。 |  |  |
| [spectre](https://github.com/Heerozh/spectre) | Python中的投资组合优化和定量战略资产配置 |  |  |
### 定价
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [tf-quant-finance](https://github.com/google/tf-quant-finance) | 谷歌为量化金融提供的高性能TensorFlow库 |  |  |
| [FinancePy](https://github.com/domokane/FinancePy) | 一个Python金融库，专注于金融衍生品的定价和风险管理，包括固定收益、股票、外汇和信用衍生品。 |  |  |
| [PyQL](https://github.com/enthought/pyql) | 著名定价库QuantLib的Python封装器 |  |  |
### 风险
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [pyfolio](https://github.com/quantopian/pyfolio) | Python中的投资组合和风险分析 |  |  |
## 经纪人API
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [ccxt](https://github.com/ccxt/ccxt) | 一个JavaScript / Python / PHP加密货币交易API，支持100多个比特币/altcoin交易所 |  |  |
| [Ib_insync](https://github.com/erdewit/ib_insync) | 用于交互式经纪人的Python同步/async框架。 |  |  |
| [Coinnect](https://github.com/hugues31/coinnect) | Coinnect是一个Rust库，旨在通过REST API提供对主要加密货币交易所的完整访问。 |  |  |
## 数据来源
### 一般
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [OpenBB Terminal](https://github.com/OpenBB-finance/OpenBBTerminal) | 为每个人、在任何地方进行投资研究。 |  |  |
| [TuShare](https://github.com/waditu/tushare) | TuShare是一个用于抓取中国股票历史数据的工具。 |  |  |
| [yfinance](https://github.com/ranaroussi/yfinance) | yfinance提供了一个线程和Pythonic方式，从雅虎金融下载市场数据。 |  |  |
| [AkShare](https://github.com/akfamily/akshare) | AKShare是一个优雅而简单的Python金融数据接口库，它是为人类而建的！它是为人类服务的。 |  |  |
| [pandas-datareader](https://github.com/pydata/pandas-datareader) | 为pandas提供最新的远程数据访问，适用于多个版本的pandas。 |  |  |
| [Quandl](https://github.com/quandl/quandl-python) | 通过一个免费的API，从数百个出版商那里获得数以百万计的金融和经济数据集。 |  |  |
| [findatapy](https://github.com/cuemacro/findatapy) | findatapy创建了一个易于使用的Python API，使用统一的高级接口从许多来源下载市场数据，包括Quandl、彭博、雅虎、谷歌等。 |  |  |
| [Investpy](https://github.com/alvarobartt/investpy) | 用Python从Investing.com提取金融数据 |  |  |
| [Fundamental Analysis Data](https://github.com/JerBouma/FundamentalAnalysis) | 完整的基本面分析软件包能够收集20年的公司简介、财务报表、比率和20,000多家公司的股票数据。 |  |  |
| [Wallstreet](https://github.com/mcdallas/wallstreet) | 华尔街。实时股票和期权工具 |  |  |
### 加密货币
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [Cryptofeed](https://github.com/bmoscon/cryptofeed) | 使用Asyncio的加密货币交易所Websocket数据源处理程序 |  |  |
| [Gekko-Datasets](https://github.com/xFFFFF/Gekko-Datasets) | Gekko交易机器人数据集转储。下载和使用SQLite格式的历史文件。 |  |  |
| [CryptoInscriber](https://github.com/Optixal/CryptoInscriber) | 一个实时的加密货币历史交易数据图谱。从任何加密货币交易所下载实时历史交易数据。 |  |  |
[Crypto Lake](https://github.com/crypto-lake/lake-api) | 加密货币的高频订单簿和交易数据
|  |  |
## 数据科学
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [TensorFlow](https://github.com/tensorflow/tensorflow) | Python中科学计算的基本算法 |  |  |
| [Pytorch](https://github.com/pytorch/pytorch) | Python中的张量和动态神经网络具有强大的GPU加速功能 |  |  |
| [Keras](https://github.com/keras-team/keras) | 最具用户友好性的Python中的人类深度学习 |  |  |
| [Scikit-learn](https://github.com/scikit-learn/scikit-learn) | Python中的机器学习 |  |  |
| [Pandas](https://github.com/pandas-dev/pandas) | 灵活而强大的Python数据分析/操作库，提供类似于R data.frame对象的标记数据结构、统计函数以及更多。 |  |  |
| [Numpy](https://github.com/numpy/numpy) | 用Python进行科学计算的基本包 |  |  |
| [Scipy](https://github.com/scipy/scipy) | Python中科学计算的基本算法 |  |  |
| [PyMC](https://github.com/pymc-devs/pymc) | Python中的概率编程。用Aesara进行贝叶斯建模和概率机器学习 |  |  |
| [Cvxpy](https://github.com/cvxpy/cvxpy) | 一种用于凸优化问题的Python嵌入式建模语言。 |  |  |
## 数据库
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [Marketstore](https://github.com/alpacahq/marketstore) | 金融时序数据的DataFrame服务器 |  |  |
| [Tectonicdb](https://github.com/0b01/tectonicdb) | Tectonicdb是一个快速、高度压缩的独立数据库和流媒体协议，用于订单簿上的点子。 |  |  |
| [ArcticDB (Man Group)](https://github.com/man-group/arcticdb) | 用于时间序列和tick数据的高性能数据存储 |  |  |
## 图形计算
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [Ray](https://github.com/ray-project/ray) | 一个开源框架，为构建分布式应用提供了一个简单、通用的API。 |  |  |
| [Dask](https://github.com/dask/dask) | 在Python中使用类似Pandas的API进行任务调度的并行计算 |  |  |
| [Incremental (JaneStreet)](https://github.com/janestreet/incremental) | Incremental是一个库，它为你提供了一种建立复杂计算的方法，可以根据输入的变化进行有效的更新，其灵感来自Umut Acar等人关于自我调整计算的工作。Incremental在许多应用中都很有用 |  |  |
| [Man MDF](https://github.com/man-group/mdf) | 用于Python的数据流编程工具包 |  |  |
| [GraphKit](https://github.com/yahoo/graphkit) | 一个轻量级的Python模块，用于创建和运行计算的有序图。 |  |  |
| [Tributary](https://github.com/timkpaine/tributary) | 在Python中流化反应式和数据流图 |  |  |
## 机器学习
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [QLib (Microsoft)](https://github.com/microsoft/qlib) | Qlib是一个以人工智能为导向的量化投资平台，旨在实现人工智能技术在量化投资中的潜力，授权研究，并创造价值。通过Qlib，你可以轻松尝试你的想法，创造更好的量化投资策略。越来越多的SOTA量化研究作品/论文在Qlib中发布。 |  |  |
| [FinRL](https://github.com/AI4Finance-Foundation/FinRL) | FinRL是第一个开源框架，展示了在量化金融中应用深度强化学习的巨大潜力。 |  |  |
| [MlFinLab (Hudson & Thames)](https://github.com/hudson-and-thames/mlfinlab) | MlFinLab通过提供可重复的、可解释的和易于使用的工具，帮助那些希望利用机器学习的力量的投资组合经理和交易者。 |  |  |
| [TradingGym](https://github.com/Yvictor/TradingGym) | 交易和回测环境，用于训练强化学习代理或简单的规则基础算法。 |  |  |
| [Stock Trading Bot using Deep Q-Learning](https://github.com/pskrunner14/trading-bot) | 使用深度Q-学习的股票交易机器人 |  |  |
## 时间序列分析
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [Facebook Prophet](https://github.com/facebook/prophet) | 对具有线性或非线性增长的多季节性的时间序列数据产生高质量的预测的工具。 |  |  |
| [statsmodels](https://github.com/statsmodels/statsmodels) | Python模块，允许用户探索数据，估计统计模型，并进行统计测试。 |  |  |
| [tsfresh](https://github.com/blue-yonder/tsfresh) | 从时间序列中自动提取相关特征。 |  |  |
| [pmdarima](https://github.com/alkaline-ml/pmdarima) | 一个统计库，旨在填补Python时间序列分析能力的空白，包括相当于R的auto.arima函数。 |  |  |
## 视觉化
| 存储库 | 描述 | 明星 | 使用方法 |
|------------|-------------|-------|-----------|
| [D-Tale (Man Group)](https://github.com/man-group/dtale) | D-Tale是Flask后端和React前端的结合，为你带来查看和分析Pandas数据结构的简单方法。 |  |  |
| [mplfinance](https://github.com/matplotlib/mplfinance) | 使用Matplotlib实现金融市场数据可视化 |  |  |
| [btplotting](https://github.com/happydasch/btplotting) | btplotting为回测、优化结果和backtrader的实时数据提供绘图。 |  |  |
# 战略
*696篇描述原始系统交易策略的学术论文列表。每种策略按其资产类别分类，并按夏普比率降序排列。*
👉策略现在托管在 [这里](https://paperswithbacktest.com)：
- 债券策略 (7)](https://paperswithbacktest.com/bonds)
- 商品策略 (50)](https://paperswithbacktest.com/commodities)
- 加密货币策略 (12)](https://paperswithbacktest.com/cryptocurrencies)
- [货币策略 (67)](https://paperswithbacktest.com/currencies)
- 股票策略 (471)](https://paperswithbacktest.com/equities)
- 期权策略 (8)](https://paperswithbacktest.com/options)
- 债券/商品/货币/股票策略 (22)](https://paperswithbacktest.com/bonds-commodities-currencies-equities)
- [债券/商品/股票策略 (6)](https://paperswithbacktest.com/bonds-commodities-equities)
- [债券/商品/股票/房地产投资信托策略 (6)](https://paperswithbacktest.com/bonds-commodities-equities-reits)
- [债券/股票策略 (13)](https://paperswithbacktest.com/bonds-equities)
- 债券/股票/房地产投资信托策略 (6)](https://paperswithbacktest.com/bonds-equities-reits)
- 商品/股票策略 (3)](https://paperswithbacktest.com/commodities-equities)
- 股票/期权策略 (24)](https://paperswithbacktest.com/equities-options)
- 股票/房地产投资信托策略 (1)](https://paperswithbacktest.com/equities-reits)
上一个策略列表：
## 债券、商品、货币、股票
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|-------------|--------------|------------|-------------|----------------|--------|
| 时间序列动量效应 | `0.576` | `20.5%` | `月度` | [QuantConnect](./static/strategies/time-series-momentum-effect.py) | [纸张](https://pages.stern.nyu.edu/~lpederse/papers/TimeSeriesMomentum.pdf) |
| 利用期货进行短期反转 | `-0.05` | `12.3%` | `每周` | [QuantConnect](./static/strategies/asset-class-momentum-rotational-system.py) | [纸张](https://ideas.repec.org/a/eee/jbfina/v28y2004i6p1337-1361.html) |
## 债券、商品、股票、REITs
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 资产类别的趋势跟踪 | `0.502` | `10.4%` | `月度` | [QuantConnect](./static/strategies/asset-class-trend-following.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=962461) |
| 动量资产配置策略 | `0.321` | `11%` | `月度` | [QuantConnect](./static/strategies/asset-class-trend-following.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1585517) |
## 债券、股票
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 成对切换 | `0.691` | `9.5%` | `季度` | [QuantConnect](./static/strategies/paired-switching.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1917044) |
| FED模式 | `0.369` | `14.3%` | `月度` | [QuantConnect](./static/strategies/fed-model.py) | [纸张](https://www.researchgate.net/publication/228267011_The_FED_Model_and_Expected_Asset_Returns) |
## 债券、股票、REITs
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 各类资产的价值和动量因素 | `0.155` | `9.8%` | `月度` | [QuantConnect](./static/strategies/value-and-momentum-factors-across-asset-classes.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1079975) |
## 商品
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 商品中的偏度效应 | `0.482` | `17.7%` | `月度` | [QuantConnect](./static/strategies/skewness-effect-in-commodities.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2671165) |
| 商品期货的收益不对称效应 | `0.239` | `13.4%` | `月度` | [QuantConnect](./static/strategies/return-asymmetry-effect-in-commodity-futures.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3918896) |
| 商品的动量效应 | `0.14` | `20.3%` | `月度` | [QuantConnect](./static/strategies/momentum-effect-in-commodities.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=702281) |
| 商品的期限结构效应 | `0.128` | `23.1%` | `月度` | [QuantConnect](./static/strategies/term-structure-effect-in-commodities.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1127213) |
| 交易WTI/BRENT价差 | `-0.199` | `11.6%` | `每日` | [QuantConnect](./static/strategies/trading-wti-brent-spread.py) | [纸张](https://link.springer.com/article/10.1057/jdhf.2009.24) |
## 加密货币
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 比特币的隔夜季节性 | `0.892` | `20.8%` | `日内交易` | [QuantConnect](./static/strategies/intraday-seasonality-in-bitcoin.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4081000) |
| 加密货币的再平衡溢价 | `0.698` | `27.5%` | `每日` | [QuantConnect](./static/strategies/rebalancing-premium-in-cryptocurrencies.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3982120) |
## 货币
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 外汇套利交易 | `0.254` | `7.8%` | `月度` | [QuantConnect](./static/strategies/fx-carry-trade.py) | [纸张](http://globalmarkets.db.com/new/docs/dbCurrencyReturns_March2009.pdf) |
| 美元套利交易 | `0.113` | `5.8%` | `月度` | [QuantConnect](./static/strategies/dollar-carry-trade.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1541230) |
| 货币动量因素 | `-0.01` | `6.7%` | `月度` | [QuantConnect](./static/strategies/currency-momentum-factor.py) | [纸张](http://globalmarkets.db.com/new/docs/dbCurrencyReturns_March2009.pdf) |
| 货币价值因素--PPP战略 | `-0.103` | `5%` | `季度` | [QuantConnect](./static/strategies/currency-value-factor-ppp-strategy.py) | [纸张](http://globalmarkets.db.com/new/docs/dbCurrencyReturns_March2009.pdf) |
## 股票
| 标题 | 夏普比率 | 挥发性 | 重新平衡 | 实施 | 来源 |
|--------------|--------------|------------|-------------|----------------|--------|
| 资产增长效应 | `0.835` | `10.2%` | `每年一次` | [QuantConnect](./static/strategies/asset-growth-effect.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1335524) |
| 股票的短期反转效应 | `0.816` | `21.4%` | `每周` | [QuantConnect](./static/strategies/short-term-reversal-in-stocks.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1605049) |
| 盈利期间的逆转-公告 | `0.785` | `25.7%` | `每日` | [QuantConnect](./static/strategies/reversal-during-earnings-announcements.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2275982) |
| 规模因素--小市值股票溢价 | `0.747` | `11.1%` | `每年一次` | [QuantConnect](./static/strategies/small-capitalization-stocks-premium-anomaly.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3177539) |
| 股票中的低波动因素效应 | `0.717` | `11.5%` | `月度` | [QuantConnect](./static/strategies/low-volatility-factor-effect-in-stocks.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=980865) |
| 如何使用公司文件的词汇密度 | `0.688` | `10.4%` | `月度` | [QuantConnect](./static/strategies/how-to-use-lexical-density-of-company-filings.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3921091) |
| 波动性风险溢价效应 | `0.637` | `13.2%` | `月度` | [QuantConnect](./static/strategies/volatility-risk-premium-effect.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=189840) |
| 与股票的配对交易 | `0.634` | `8.5%` | `每日` | [QuantConnect](./static/strategies/pairs-trading-with-stocks.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=141615) |
| 原油预示着股票收益 | `0.599` | `11.5%` | `月度` | [QuantConnect](./static/strategies/crude-oil-predicts-equity-returns.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=460500) |
| 对赌股票中的贝塔系数 | `0.594` | `18.9%` | `月度` | [QuantConnect](./static/strategies/betting-against-beta-factor-in-stocks.py) | [纸张](https://pages.stern.nyu.edu/~lpederse/papers/BettingAgainstBeta.pdf) |
| 股票中的趋势跟踪效应 | `0.569` | `15.2%` | `每日` | [QuantConnect](./static/strategies/trend-following-effect-in-stocks.py) | [纸张](https://www.cis.upenn.edu/~mkearns/finread/trend.pdf) |
| ESG因子动量策略 | `0.559` | `21.8%` | `月度` | [QuantConnect](./static/strategies/esg-factor-momentum-strategy.py) | [纸张](https://www.semanticscholar.org/paper/Can-ESG-Add-Alpha-An-Analysis-of-ESG-Tilt-and-Nagy-Kassam/64f77da4f8ce5906a73ffe4e9eec7c49c0960acc) |
| 价值（账面价值）因素 | `0.526` | `11.9%` | `月度` | [QuantConnect](./static/strategies/value-book-to-market-factor.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2595747) |
| 足球俱乐部的股票套利 | `0.515` | `14.2%` | `每日` | [QuantConnect](./static/strategies/soccer-clubs-stocks-arbitrage.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1343685) |
| 合成贷款利率预示着随后的市场回报 | `0.494` | `13.7%` | `每日` | [QuantConnect](./static/strategies/synthetic-lending-rates-predict-subsequent-market-return.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3976307) |
| 期权到期周效应 | `0.452` | `5%` | `每周` | [QuantConnect](./static/strategies/option-expiration-week-effect.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1571786) |
| 分散交易 | `0.432` | `8.1%` | `月度` | [QuantConnect](./static/strategies/dispersion-trading.py) | [纸张](https://www.academia.edu/16327015/EQUILIBRIUM_INDEX_AND_SINGLE_STOCK_VOLATILITY_RISK_PREMIA) |
| 共同基金回报的势头 | `0.414` | `13.6%` | `季度` | [QuantConnect](./static/strategies/momentum-in-mutual-fund-returns.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1462408) |
| 扇形动量--旋转系统 | `0.401` | `14.1%` | `月度` | [QuantConnect](./static/strategies/sector-momentum-rotational-system.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1585517) |
| 结合智能因素的势头和市场组合 | `0.388` | `8.2%` | `月度` | [QuantConnect](./static/strategies/combining-smart-factors-momentum-and-market-portfolio.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3745517) |
| 股票的动量和反转与波动效应的结合 | `0.375` | `17%` | `月度` | [QuantConnect](./static/strategies/momentum-and-reversal-combined-with-volatility-effect-in-stocks.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1679464) |
| 市场情绪和一夜之间的反常现象 | `0.369` | `3.6%` | `每日` | [QuantConnect](./static/strategies/market-sentiment-and-an-overnight-anomaly.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3829582) |
| 一月的晴雨表 | `0.365` | `7.4%` | `月度` | [QuantConnect](./static/strategies/january-barometer.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1436516) |
| 研发支出和股票收益 | `0.354` | `8.1%` | `每年一次` | [QuantConnect](./static/strategies/rd-expenditures-and-stock-returns.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=227564) |
| 价值因素 - 国家内部的CAPE效应 | `0.351` | `20.2%` | `每年一次` | [QuantConnect](./static/strategies/value-factor-effect-within-countries.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2129474) |
| 股票收益横截面的12个月周期 | `0.34` | `43.7%` | `月度` | [QuantConnect](./static/strategies/12-month-cycle-in-cross-section-of-stocks-returns.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=687022) |
| 股票指数的月度转折 | `0.305` | `7.2%` | `每日` | [QuantConnect](./static/strategies/turn-of-the-month-in-equity-indexes.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=917884) |
| 发薪日反常现象 | `0.269` | `3.8%` | `每日` | [QuantConnect](./static/strategies/payday-anomaly.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3257064) |
| 利用国家ETF进行对价交易 | `0.257` | `5.7%` | `每日` | [QuantConnect](./static/strategies/pairs-trading-with-country-etfs.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1958546) |
| 剩余动量系数 | `0.24` | `9.7%` | `月度` | [QuantConnect](./static/strategies/residual-momentum-factor.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2319861) |
| 盈利公告溢价 | `0.192` | `3.7%` | `月度` | [QuantConnect](./static/strategies/earnings-announcement-premium.py) | [纸张](https://www.nber.org/system/files/working_papers/w13090/w13090.pdf) |
| 股票内部的ROA效应 | `0.155` | `8.7%` | `月度` | [QuantConnect](./static/strategies/roa-effect-within-stocks.py) | [纸张](https://static1.squarespace.com/static/5e6033a4ea02d801f37e15bb/t/5f61583e88f43b7d5b7196b5/1600215105801/Chen_Zhang_JF.pdf) |
| 股票的52周高点效应 | `0.153` | `19%` | `月度` | [QuantConnect](./static/strategies/52-weeks-high-effect-in-stocks.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1787378) |
| 结合基本面FSCORE和股票短期逆转的情况 | `0.153` | `17.6%` | `月度` | [QuantConnect](./static/strategies/combining-fundamental-fscore-and-equity-short-term-reversals.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3097420) |
| 对抗国际股票中的贝塔系数的赌注 | `0.142` | `9.1%` | `月度` | [QuantConnect](./static/strategies/betting-against-beta-factor-in-country-equity-indexes.py) | [纸张](https://pages.stern.nyu.edu/~lpederse/papers/BettingAgainstBeta.pdf) |
| 一贯的动力策略 | `0.128` | `28.8%` | `6个月` | [QuantConnect](./static/strategies/consistent-momentum-strategy.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2652592) |
| 空头利息效应--多空版本 | `0.079` | `6.6%` | `月度` | [QuantConnect](./static/strategies/short-interest-effect-long-short-version.py) | [纸张](https://www.semanticscholar.org/paper/Why-Do-Short-Interest-Levels-Predict-Stock-Returns-Boehmer-Erturk/06418ef437dc7156229532a97d0f8392373eb297?p2df) |
| 动量因素与资产增长效应相结合 | `0.058` | `25.1%` | `月度` | [QuantConnect](./static/strategies/momentum-factor-combined-with-asset-growth-effect.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1684767) |
| 股票中的动量因素效应 | `-0.008` | `21.8%` | `月度` | [QuantConnect](./static/strategies/momentum-factor-effect-in-stocks.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2435323) |
| 动量因素和风格轮换效应 | `-0.056` | `10%` | `月度` | [QuantConnect](./static/strategies/momentum-factor-and-style-rotation-effect.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1276815) |
| 盈利公告与股票回购的结合 | `-0.16` | `0.1%` | `每日` | [QuantConnect](./static/strategies/earnings-announcements-combined-with-stock-repurchases.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2589966) |
| 盈利质量因素 | `-0.18` | `28.7%` | `每年一次` | [QuantConnect](./static/strategies/earnings-quality-factor.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2179247) |
| 应计项目的异常情况 | `-0.272` | `13.7%` | `每年一次` | [QuantConnect](./static/strategies/accrual-anomaly.py) | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=546108) |
| ESG、价格动量和随机优化 | `N/A` | `N/A` | `月度` | | [纸张](https://quantpedia.com/strategies/esg-price-momentum-and-stochastic-optimization/) |
| 公司申报和股票回报的正相似性 | `N/A` | `N/A` | `月度` | | [纸张](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3690461) |
# 书籍
为量化交易者提供的55本书的综合清单。
## 初学者
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [A Beginner’s Guide to the Stock Market: Everything You Need to Start Making Money Today - Matthew R. Kratter](https://amzn.to/3QN2VdU) |  |  |
| [How to Day Trade for a Living: A Beginner’s Guide to Trading Tools and Tactics, Money Management, Discipline and Trading Psychology - Andrew Aziz](https://amzn.to/3bmehFv) |  |  |
| [The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns - John C. Bogle](https://amzn.to/3A4mgkR) |  |  |
| [Investing QuickStart Guide: The Simplified Beginner’s Guide to Successfully Navigating the Stock Market, Growing Your Wealth & Creating a Secure Financial Future - Ted D. Snow](https://amzn.to/3A5aRkX) |  |  |
| [Day Trading QuickStart Guide: The Simplified Beginner’s Guide to Winning Trade Plans, Conquering the Markets, and Becoming a Successful Day Trader - Troy Noonan](https://amzn.to/3HPZijw) |  |  |
| [Introduction To Algo Trading: How Retail Traders Can Successfully Compete With Professional Traders - Kevin J Davey](https://amzn.to/39Tf7JC) |  |  |
| [Algorithmic Trading and DMA: An introduction to direct access trading strategies - Barry Johnson](https://amzn.to/3xYb0UN) |  |  |
## 传记
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [My Life as a Quant: Reflections on Physics and Finance - Emanuel Derman](https://amzn.to/3A8KudR) |  |  |
| [How I Became a Quant: Insights from 25 of Wall Street’s Elite: - Barry Schachter](https://amzn.to/3Alf8kz) |  |  |
## 编码
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [Python for Finance: Mastering Data-Driven Finance - Yves Hilpisch](https://amzn.to/3NhkTlP) |  |  |
| [Trading Evolved: Anyone can Build Killer Trading Strategies in Python - Andreas F. Clenow](https://amzn.to/3A0jcGB) |  |  |
| [Python for Algorithmic Trading: From Idea to Cloud Deployment - Yves Hilpisch](https://amzn.to/3bpkd0C) |  |  |
| [Algorithmic Trading with Python: Quantitative Methods and Strategy Development - Chris Conlan](https://amzn.to/3u3cxYo) |  |  |
| [Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis - Sebastien Donadio](https://amzn.to/3NqNghA) |  |  |
## 隐蔽性
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [The Bitcoin Standard: The Decentralized Alternative to Central Banking - Saifedean Ammous](https://amzn.to/3QMJgec) |  |  |
| [Bitcoin Billionaires: A True Story of Genius, Betrayal, and Redemption - Ben Mezrich](https://amzn.to/39SkdWt) |  |  |
| [Mastering Bitcoin: Programming the Open Blockchain - Andreas M. Antonopoulos](https://amzn.to/3NniZ3p) |  |  |
| [Why Buy Bitcoin: Investing Today in the Money of Tomorrow - Andy Edstrom](https://amzn.to/3OMcKqZ) |  |  |
## 一般
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [The Intelligent Investor: The Definitive Book on Value Investing - Benjamin Graham, Jason Zweig](https://www.amazon.fr/gp/product/0060555661/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0060555661&linkId=aba73910e4e3873b6cc8364487662bd6) |  |  |
| [How I Invest My Money: Finance experts reveal how they save, spend, and invest - Joshua Brown, Brian Portnoy](https://amzn.to/3A4rsoU) |  |  |
| [Naked Forex: High-Probability Techniques for Trading Without Indicators - Alex Nekritin](https://amzn.to/3NkrAUj) |  |  |
| [The Four Pillars of Investing: Lessons for Building a Winning Portfolio - William J. Bernstein](https://www.amazon.fr/gp/product/B0041842TW/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=B0041842TW&linkId=d9bc2fec4f3faa41ca4f24aed3c72122) |  |  |
| [Option Volatility and Pricing: Advanced Trading Strategies and Techniques, 2nd Edition - Sheldon Natenberg](https://amzn.to/3btOxXL) |  |  |
| [The Art and Science of Technical Analysis: Market Structure, Price Action, and Trading Strategies - Adam Grimes](https://www.amazon.fr/gp/product/1118115120/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1118115120&linkId=d5dc1f0e6727b2663d2186a110a31ad0) |  |  |
| [The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management (Wiley Trading) - Alexander Elder](https://www.amazon.fr/gp/product/1118467450/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1118467450&linkId=67ee502653bc52a5240ced9fc88eb76d) |  |  |
| [Building Winning Algorithmic Trading Systems: A Trader’s Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading) - Kevin J Davey](https://amzn.to/39QnsxA) |  |  |
| [Systematic Trading: A unique new method for designing trading and investing systems - Robert Carver](https://www.amazon.fr/gp/product/0857194453/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0857194453&linkId=32d8bffc32c01041cde066bacab76c04) |  |  |
| [Quantitative Momentum: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System (Wiley Finance) - Wesley R. Gray, Jack R. Vogel](https://www.amazon.fr/gp/product/111923719X/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=111923719X&linkId=b825cb65462a4a9254af3b7dc5328131) |  |  |
| [Algorithmic Trading: Winning Strategies and Their Rationale - Ernest P. Chan](https://amzn.to/3xWi8kd) |  |  |
| [Leveraged Trading: A professional approach to trading FX, stocks on margin, CFDs, spread bets and futures for all traders - Robert Carver](https://amzn.to/3Nhl6p7) |  |  |
| [Trading Systems: A New Approach to System Development and Portfolio Optimisation - Emilio Tomasini, Urban Jaekle](https://www.amazon.fr/gp/product/1905641796/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1905641796&linkId=61e6634242c497498338f73641ce0a80) |  |  |
| [Trading and Exchanges: Market Microstructure for Practitioners - Larry Harris](https://www.amazon.fr/gp/product/0195144708/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0195144708&linkId=e47e596fc0696cbd624726cce05b4500) |  |  |
| [Trading Systems 2nd edition: A new approach to system development and portfolio optimisation - Emilio Tomasini, Urban Jaekle](https://www.amazon.fr/gp/product/085719755X/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=085719755X&linkId=97aa558484a8dc2bf57a5296e7f38cad) |  |  |
| [Machine Trading: Deploying Computer Algorithms to Conquer the Markets - Ernest P. Chan](https://amzn.to/3OIBe4o) |  |  |
| [Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management (McGraw-Hill Library of Investment and Finance) - Ludwig B Chincarini, Daehwan Kim](https://amzn.to/3yl9u0c) |  |  |
| [Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk - Richard Grinold, Ronald Kahn](https://amzn.to/3xMKaic) |  |  |
| [Quantitative Technical Analysis: An integrated approach to trading system development and trading management - Dr Howard B Bandy](https://www.amazon.fr/gp/product/0979183855/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0979183855&linkId=8ef7bda69477bdccf90f5ac02ee495b0) |  |  |
| [Advances in Active Portfolio Management: New Developments in Quantitative Investing - Richard Grinold, Ronald Kahn](https://amzn.to/3xUTK2z) |  |  |
| [Professional Automated Trading: Theory and Practice - Eugene A. Durenard](https://amzn.to/3yhfOpw) |  |  |
| [Algorithmic Trading and Quantitative Strategies (Chapman and Hall/CRC Financial Mathematics Series) - Raja Velu, Maxence Hardy, Daniel Nehren](https://amzn.to/3xUTQXZ) |  |  |
| [Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization - Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po-Shing Wong](https://www.amazon.fr/gp/product/0367871815/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0367871815&linkId=3f2ba1cbc0e1fe02e255da740423b2fb) |  |  |
## 高频交易
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading - Rishi K. Narang](https://www.amazon.fr/gp/product/1118362411/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1118362411&linkId=35e02d4e636350366531a5033597a541) |  |  |
| [Algorithmic and High-Frequency Trading (Mathematics, Finance and Risk) - Álvaro Cartea, Sebastian Jaimungal, José Penalva](https://www.amazon.fr/gp/product/1107091144/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1107091144&linkId=64e3ceb66482d8db6827830964b85613) |  |  |
| [The Problem of HFT – Collected Writings on High Frequency Trading & Stock Market Structure Reform - Haim Bodek](https://www.amazon.fr/gp/product/1481978357/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1481978357&linkId=2f3acf998de645990b681e2ac9f0217c) |  |  |
| [An Introduction to High-Frequency Finance - Ramazan Gençay, Michel Dacorogna, Ulrich A. Muller, Olivier Pictet, Richard Olsen](https://www.amazon.fr/gp/product/0122796713/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0122796713&linkId=7e6c098026204f399e45d7fbb803dcca) |  |  |
| [Market Microstructure in Practice - Charles-Albert Lehalle, Sophie Laruelle](https://www.amazon.fr/Market-Microstructure-Practice-Sophie-Laruelle/dp/9813231122) |  |  |
| [The Financial Mathematics of Market Liquidity - Olivier Gueant](https://www.amazon.com/Financial-Mathematics-Market-Liquidity-Execution/dp/1498725473) |  |  |
| [High-Frequency Trading - Maureen O’Hara, David Easley, Marcos M López de Prado](https://www.amazon.fr/gp/product/178272009X/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=178272009X&linkId=082f861ff6bbe4cca4ef7ccbe620a2c4) |  |  |
## 机器学习
| 标题 | 评论 | 评价 |
|----------|---------|--------|
| [Dark Pools: The rise of A.I. trading machines and the looming threat to Wall Street - Scott Patterson](https://www.amazon.fr/gp/product/0307887189/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0307887189&linkId=2572cae24ed7de0b279580312daf0f03) |  |  |
| [Advances in Financial Machine Learning - Marcos Lopez de Prado](https://www.amazon.fr/gp/product/1119482089/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1119482089&linkId=7eff4d3f3d9f2d00d05032f726386e53) |  |  |
| [Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition - Stefan Jansen](https://www.amazon.fr/gp/product/1839217715/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1839217715&linkId=80e3e93e1b6027596858ed0f1fbf10c2) |  |  |
| [Machine Learning for Asset Managers (Elements in Quantitative Finance) - Marcos M López de Prado](https://www.amazon.fr/gp/product/1108792898/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1108792898&linkId=8eb7e3c369d38b36df8dfecf05a622db) |  |  |
| [Machine Learning in Finance: From Theory to Practice - Matthew F. Dixon, Igor Halperin, Paul Bilokon](https://www.amazon.fr/gp/product/3030410676/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=3030410676&linkId=5f5f1df6be62ae96ef7a0c536c3ecdb4) |  |  |
| [Artificial Intelligence in Finance: A Python-Based Guide - Yves Hilpisch](https://www.amazon.fr/gp/product/1492055433/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=1492055433&linkId=7c20249be4d35badb127d6a5423fc495) |  |  |
| [Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques - Robert Kissell](https://www.amazon.fr/gp/product/0128156309/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=darchimbaud-21&creative=6746&linkCode=as2&creativeASIN=0128156309&linkId=0a197c0b547a0ee63ccd19389bb42edd) |  |  |
# 视频
| 标题 | 喜欢 |
|--------------------------------------------------------------------|-------|
| [Krish Naik - Machine learning tutorials and their Application in Stock Prediction](https://www.youtube.com/watch?v=H6du_pfuznE) |  |
| [QuantInsti Youtube - webinars about Machine Learning for trading](https://www.youtube.com/user/quantinsti/search?query=machine+learning) |  |
| [Siraj Raval - Videos about stock market prediction using Deep Learning](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/search?query=trading) |  |
| [Quantopian - Webinars about Machine Learning for trading](https://www.youtube.com/channel/UC606MUq45P3zFLa4VGKbxsg/search?query=machine+learning) |  |
| [Sentdex - Machine Learning for Forex and Stock analysis and algorithmic trading](https://www.youtube.com/watch?v=v_L9jR8P-54&list=PLQVvvaa0QuDe6ZBtkCNWNUbdaBo2vA4RO) |  |
| [QuantNews - Machine Learning for Algorithmic Trading 3 part series](https://www.youtube.com/playlist?list=PLHJACfjILJ-91qkw5YC83S6COKGscctzz) |  |
| [Sentdex - Python programming for Finance (a few videos including Machine Learning)](https://www.youtube.com/watch?v=Z-5wNWgRJpk&index=9&list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ) |  |
| [Chat with Traders EP042 - Machine learning for algorithmic trading with Bert Mouler](https://www.youtube.com/watch?v=i8FNO8r7PaE) |  |
| [Tucker Balch - Applying Deep Reinforcement Learning to Trading](https://www.youtube.com/watch?v=Pka0DC_P17k) |  |
| [Ernie Chan - Machine Learning for Quantitative Trading Webinar](https://www.youtube.com/watch?v=72aEDjwGMr8&t=1023s) |  |
| [Chat with Traders EP147 - Detective work leading to viable trading strategies with Tom Starke](https://www.youtube.com/watch?v=JjXw9Mda7eY) |  |
| [Chat with Traders EP142 - Algo trader using automation to bypass human flaws with Bert Mouler](https://www.youtube.com/watch?v=ofL66mh6Tw0) |  |
| [Master Thesis presentation, Uni of Essex - Analyzing the Limit Order Book, A Deep Learning Approach](https://www.youtube.com/watch?v=qxSh2VFmRGw) |  |
| [Howard Bandy - Machine Learning Trading System Development Webinar](https://www.youtube.com/watch?v=v729evhMpYk&t=1s) |  |
| [Chat With Traders EP131 - Trading strategies, powered by machine learning with Morgan Slade](https://www.youtube.com/watch?v=EbWbeYu8zwg) |  |
| [Chat with Traders Quantopian 5 - Good Uses of Machine Learning in Finance with Max Margenot](https://www.youtube.com/watch?v=Zj5sXWv9SDM) |  |
| [Hitoshi Harada, CTO at Alpaca - Deep Learning in Finance Talk](https://www.youtube.com/watch?v=FoQKCeDuPiY) |  |
| [Better System Trader EP028 - David Aronson shares research into indicators that identify Bull and Bear markets.](https://www.youtube.com/watch?v=Q4rV0Y9NokI) |  |
| [Prediction Machines - Deep Learning with Python in Finance Talk](https://www.youtube.com/watch?v=xvm-M-R2fZY) |  |
| [Better System Trader EP064 - Cryptocurrencies and Machine Learning with Bert Mouler](https://www.youtube.com/watch?v=YgRTd4nLJoU) |  |
| [Better System Trader EP023 - Portfolio manager Michael Himmel talks AI and machine learning in trading](https://www.youtube.com/watch?v=9tZjeyhfG0g) |  |
| [Better System Trader EP082 - Machine Learning With Kris Longmore](https://www.youtube.com/watch?v=0syNgsd635M) |  |
# 博客
| 标题 |
|--------------------------------------------------------------------|
| [AAA Quants, Tom Starke Blog](http://aaaquants.com/category/blog/) |
| [AI & Systematic Trading](https://blog.paperswithbacktest.com/) |
| [Blackarbs blog](http://www.blackarbs.com/blog/) |
| [Hardikp, Hardik Patel blog](https://www.hardikp.com/) |
| [Max Dama on Automated Trading](https://bit.ly/3wVZbh9) |
| [Medallion.Club on Systematic Trading (FR)](https://medallion.club/trading-algorithmique-quantitatif-systematique/) |
| [Proof Engineering: The Algorithmic Trading Platform](https://bit.ly/3lX7zYN) |
| [Quantsportal, Jacques Joubert's Blog](http://www.quantsportal.com/blog-page/) |
| [Quantstart - Machine Learning for Trading articles](https://www.quantstart.com/articles) |
| [RobotWealth, Kris Longmore Blog](https://robotwealth.com/blog/) |
# 课程
| 标题 |
|--------------------------------------------------------------------|
| [AI in Finance](https://cfte.education/) |
| [AI & Systematic Trading](https://paperswithbacktest.com/) |
| [Algorithmic Trading for Cryptocurrencies in Python](https://github.com/tudorelu/tudorials/tree/master/trading) |
| [Coursera, NYU - Guided Tour of Machine Learning in Finance](https://www.coursera.org/learn/guided-tour-machine-learning-finance) |
| [Coursera, NYU - Fundamentals of Machine Learning in Finance](https://www.coursera.org/learn/fundamentals-machine-learning-in-finance) |
| [Coursera, NYU - Reinforcement Learning in Finance](https://www.coursera.org/learn/reinforcement-learning-in-finance) |
| [Coursera, NYU - Overview of Advanced Methods for Reinforcement Learning in Finance](https://www.coursera.org/learn/advanced-methods-reinforcement-learning-finance) |
| [Hudson and Thames Quantitative Research](https://github.com/hudson-and-thames) |
| [NYU: Overview of Advanced Methods of Reinforcement Learning in Finance](https://www.coursera.org/learn/advanced-methods-reinforcement-learning-finance/home/welcome) |
| [Udacity: Artificial Intelligence for Trading](https://www.udacity.com/course/ai-for-trading--nd880) |
| [Udacity, Georgia Tech - Machine Learning for Trading](https://www.udacity.com/course/machine-learning-for-trading--ud501) |