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Awesome Systematic Trading

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日本語版はこちら

We are collecting a list of resources papers, softwares, books, articles for finding, developing, and running systematic trading (quantitative trading) strategies.

What will you find here?

What the replication record looks like

We have coded and run 4,843 of these papers over their own full history. Some numbers worth knowing before you pick one to implement:

  • The median replication returns a Sharpe ratio of 0.37, and 48% clear a t-statistic of 1.96. Half the published record cannot be distinguished from zero on its own sample.
  • Median test window: 34 years. A strategy needs roughly (1.96 / Sharpe)² years to prove itself, so a Sharpe of 0.4 needs about 24 of them.
  • The median strategy carries a beta of +0.17 to the S&P 500. Removing it takes the median information ratio down to 0.21, so a meaningful slice of the published edge is index exposure rather than skill.
  • Across 2,838 papers with a record on both sides of their publication date, we could find no measurable decay after publication once the market period is controlled for, to within a fifth of a percentage point a year.

Method and caveats are written up on the wiki.

📈 Interested in trading strategies implemented in Python?

Visit our comprehensive collection at paperswithbacktest.com for exclusive content!

Click here to see the full table of content

How can I help?

You can help by submitting an issue with suggestions and by sharing on Twitter:

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Libraries and packages

List of 112 libraries and packages implementing trading bots, backtesters, indicators, pricers, etc. Each library is categorized by its programming language and ordered by descending populatrity (number of stars).

Backtesting and Live Trading

General - Event Driven Frameworks

Repository Description Stars Made with
vnpy Python-based open source quantitative trading system development framework, officially released in January 2015, has grown step by step into a full-featured quantitative trading platform GitHub stars made-with-python
zipline dormant since 2024-02 Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. GitHub stars made-with-python
backtrader dormant since 2024-08 Event driven Python Backtesting library for trading strategies GitHub stars made-with-python
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 GitHub stars made-with-python
QuantConnect Lean Algorithmic Trading Engine by QuantConnect (Python, C#) GitHub stars made-with-python
Rqalpha A extendable, replaceable Python algorithmic backtest && trading framework supporting multiple securities GitHub stars made-with-python
finmarketpy Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians) GitHub stars made-with-python
backtesting.py Backtesting.py is a Python framework for inferring viability of trading strategies on historical (past) data. Improved upon the vision of Backtrader, and by all means surpassingly comparable to other accessible alternatives, Backtesting.py is lightweight, fast, user-friendly, intuitive, interactive, intelligent and, hopefully, future-proof. GitHub stars made-with-python
zvt Modular quant framework GitHub stars made-with-python
WonderTrader WonderTrader——量化研发交易一站式框架 GitHub stars made-with-python
nautilus_trader A high-performance algorithmic trading platform and event-driven backtester GitHub stars made-with-python
PandoraTrader High-frequency quantitative trading platform based on c++ development, supporting multiple trading APIs and cross-platform GitHub stars made-with-c++
HFTBacktest Highly precise backtest on HFT data in Python+Numba GitHub stars made-with-python
aat An asynchronous, event-driven framework for writing algorithmic trading strategies in python with optional acceleration in C++. It is designed to be modular and extensible, with support for a wide variety of instruments and strategies, live trading across (and between) multiple exchanges. GitHub stars made-with-python
sdoosa-algo-trade-python dormant since 2023-09 This project is mainly for newbies into algo trading who are interested in learning to code their own trading algo using python interpreter. GitHub stars made-with-python
lumibot A very simple yet useful backtesting and sample based live trading framework (a bit slow to run...) GitHub stars made-with-python
quanttrader dormant since 2024-06 Backtest and live trading in Python. Event based. Similar to backtesting.py. GitHub stars made-with-python
gobacktest archived A Go implementation of event-driven backtesting framework GitHub stars made-with-go
PineForge Transpiles PineScript v6 strategies to C++ and runs deterministic offline backtests on user-provided OHLCV data. GitHub stars made-with-c++
FlashFunk High Performance Runtime in Rust GitHub stars made-with-rust

General - Vector Based Frameworks

Repository Description Stars Made with
QTradeX A powerful and flexible Python framework for designing, backtesting, optimizing, and deploying algotrading bots GitHub stars made-with-python
vectorbt vectorbt takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds. GitHub stars made-with-python
pysystemtrade Systematic Trading in python from book Systematic Trading by Rob Carver GitHub stars made-with-python
bt Flexible backtesting for Python based on Algo and Strategy Tree GitHub stars made-with-python
ml-quant-trading PyTorch research stack for ML multi-factor trading with 213 factors, bias correction, portfolio optimization, vectorized backtesting, and public validation reports GitHub stars made-with-python

Cryptocurrencies

Repository Description Stars Made with
Freqtrade Freqtrade is a free and open source crypto trading bot written in Python. It is designed to support all major exchanges and be controlled via Telegram. It contains backtesting, plotting and money management tools as well as strategy optimization by machine learning. GitHub stars made-with-python
Jesse Jesse is an advanced crypto trading framework which aims to simplify researching and defining trading strategies. GitHub stars made-with-python
OctoBot Cryptocurrency trading bot for TA, arbitrage and social trading with an advanced web interface GitHub stars made-with-python
Kelp archived Kelp is a free and open-source trading bot for the Stellar DEX and 100+ centralized exchanges GitHub stars made-with-go
openlimits dormant since 2022-07 A Rust high performance cryptocurrency trading API with support for multiple exchanges and language wrappers. GitHub stars made-with-rust
bTrader archived Triangle arbitrage trading bot for Binance GitHub stars made-with-rust
crypto-crawler-rs dormant since 2023-03 Crawl orderbook and trade messages from crypto exchanges GitHub stars made-with-rust
Hummingbot A client for crypto market making GitHub stars made-with-python
cryptotrader-core dormant since 2019-06 Simple to use Crypto Exchange REST API client in rust. GitHub stars made-with-rust

Trading bots

Trading bots and alpha models. Some of them are old and not maintained.

Repository Description Stars Made with
Blackbird no longer available Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy GitHub stars made-with-c++
bitcoin-arbitrage Bitcoin arbitrage - opportunity detector GitHub stars made-with-python
ThetaGang ThetaGang is an IBKR bot for collecting money GitHub stars made-with-typescript
czsc 缠中说禅技术分析工具；缠论；股票；期货；Quant；量化交易 GitHub stars made-with-python
R2 Bitcoin Arbitrager dormant since 2023-04 R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript GitHub stars made-with-typescript
Intelligent Trading Bot Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering GitHub stars made-with-python
analyzingalpha dormant since 2023-08 Implementation of simple strategies GitHub stars made-with-python
PyTrendFollow dormant since 2018-04 PyTrendFollow - systematic futures trading using trend following GitHub stars made-with-python
TradeSight AI-powered algorithmic trading platform with RSI/MACD signals, overnight strategy tournaments, paper trading via Alpaca, multi-stock scanning, and web dashboard GitHub stars made-with-python
PRISM-INSIGHT AI-powered stock analysis with 13 specialized agents, automated trading via KIS API (Korean & US markets) GitHub stars made-with-python

Analytics

Indicators

Libraries of indicators to predict future price movements.

Repository Description Stars Made with
ta-lib Perform technical analysis of financial market data GitHub stars made-with-python
go-tart dormant since 2021-06 A Go implementation of the [ta-lib]((https://github.com/mrjbq7/ta-lib) with streaming update support GitHub stars made-with-go
pandas-ta no longer available Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns GitHub stars made-with-python
finta archived Common financial technical indicators implemented in Pandas GitHub stars made-with-python
ta-rust dormant since 2024-07 Technical analysis library for Rust language GitHub stars made-with-rust
wickra Streaming-first technical-analysis library with a Rust core and native Python/Node/WASM bindings plus a C ABI (C, C++, C#/.NET, Go, Java, R); 514 O(1)-per-tick indicators across 24 families, bit-exact batch and streaming GitHub stars made-with-rust

Metrics computation

Librairies of financial metrics.

Repository Description Stars Made with
quantstats Portfolio analytics for quants, written in Python GitHub stars made-with-python
ffn A financial function library for Python GitHub stars made-with-python

Optimization

Repository Description Stars Made with
skfolio Portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models. GitHub stars made-with-python
PyPortfolioOpt Financial portfolio optimizations in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity GitHub stars made-with-python
Riskfolio-Lib Portfolio Optimization and Quantitative Strategic Asset Allocation in Python GitHub stars made-with-python
empyrial Empyrial is a Python-based open-source quantitative investment library dedicated to financial institutions and retail investors, officially released in March 2021 GitHub stars made-with-python
Deepdow dormant since 2024-01 Python package connecting portfolio optimization and deep learning. Its goal is to facilitate research of networks that perform weight allocation in one forward pass. GitHub stars made-with-python
spectre Portfolio Optimization and Quantitative Strategic Asset Allocation in Python GitHub stars made-with-python

Pricing

Repository Description Stars Made with
tf-quant-finance High-performance TensorFlow library for quantitative finance from Google GitHub stars made-with-python
FinancePy A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives GitHub stars made-with-python
PyQL Python wrapper of the famous pricing library QuantLib GitHub stars made-with-python

Risk

Repository Description Stars Made with
pyfolio dormant since 2023-12 Portfolio and risk analytics in Python GitHub stars made-with-python

Broker APIs

Repository Description Stars Made with
ccxt A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges GitHub stars made-with-python
Ib_insync archived Python sync/async framework for Interactive Brokers. GitHub stars made-with-python
Coinnect dormant since 2021-11 Coinnect is a Rust library aiming to provide a complete access to main crypto currencies exchanges via REST API. GitHub stars made-with-rust
PENDAX dormant since 2024-05 Javascript SDK for Trading, Data, and Websockets for FTX, FTXUS, OKX, Bybit, & More. GitHub stars made-with-javascript

Data Sources

General

Repository Description Stars Made with
Fincept Terminal Fincept Terminal is a comprehensive CLI tool that provides financial insights, market analysis, and a host of other financial services such as technical analysis, fundamental analysis, sentiment analysis, quantitative analysis, and economic data services. GitHub stars made-with-python
OpenBB Terminal Investment Research for Everyone, Anywhere. GitHub stars made-with-python
TuShare dormant since 2024-03 TuShare is a utility for crawling historical data of China stocks GitHub stars made-with-python
yfinance yfinance offers a threaded and Pythonic way to download market data from Yahoo!Ⓡ finance. GitHub stars made-with-python
AkShare AKShare is an elegant and simple financial data interface library for Python, built for human beings! GitHub stars made-with-python
pandas-datareader Up to date remote data access for pandas, works for multiple versions of pandas. GitHub stars made-with-python
Quandl archived Get millions of financial and economic dataset from hundreds of publishers via a single free API. GitHub stars made-with-python
findatapy findatapy creates an easy to use Python API to download market data from many sources including Quandl, Bloomberg, Yahoo, Google etc. using a unified high level interface. GitHub stars made-with-python
Investpy Financial Data Extraction from Investing.com with Python GitHub stars made-with-python
Fundamental Analysis Data Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies. GitHub stars made-with-python
Wallstreet dormant since 2024-07 Wallstreet: Real time Stock and Option tools GitHub stars made-with-python
pwb-toolbox Loader for the 32 Papers With Backtest datasets on Hugging Face: daily prices back to 1962 for stocks, ETFs, indices, currencies and commodities, sovereign yield curves, quarterly fundamentals, FRED-MD macro series, and 5.7 billion rows of 1-minute US equity bars. Cards and schemas are open to read, downloads are gated. GitHub stars made-with-python

Cryptocurrencies

Repository Description Stars Made with
Cryptofeed Cryptocurrency Exchange Websocket Data Feed Handler with Asyncio GitHub stars made-with-python
Gekko-Datasets dormant since 2018-05 Gekko trading bot dataset dumps. Download and use history files in SQLite format. GitHub stars made-with-python
CryptoInscriber dormant since 2018-03 A live crypto currency historical trade data blotter. Download live historical trade data from any crypto exchange. GitHub stars made-with-python
Crypto Lake High frequency order book & trade data for crypto GitHub stars made-with-python

Data Science

Repository Description Stars Made with
TensorFlow Fundamental algorithms for scientific computing in Python GitHub stars made-with-python
Pytorch Tensors and Dynamic neural networks in Python with strong GPU acceleration GitHub stars made-with-python
Keras The most user friendly Deep Learning for humans in Python GitHub stars made-with-python
Scikit-learn Machine learning in Python GitHub stars made-with-python
Pandas Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more GitHub stars made-with-python
Numpy The fundamental package for scientific computing with Python GitHub stars made-with-python
Scipy Fundamental algorithms for scientific computing in Python GitHub stars made-with-python
PyMC Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara GitHub stars made-with-python
Cvxpy A Python-embedded modeling language for convex optimization problems. GitHub stars made-with-python

Databases

Repository Description Stars Made with
Marketstore no longer available DataFrame Server for Financial Timeseries Data GitHub stars made-with-go
Tectonicdb dormant since 2024-01 Tectonicdb is a fast, highly compressed standalone database and streaming protocol for order book ticks. GitHub stars made-with-rust
ArcticDB (Man Group) High performance datastore for time series and tick data GitHub stars made-with-python

Graph Computation

Repository Description Stars Made with
Ray An open source framework that provides a simple, universal API for building distributed applications. GitHub stars made-with-python
Dask Parallel computing with task scheduling in Python with a Pandas like API GitHub stars made-with-python
Incremental (JaneStreet) Incremental is a library that gives you a way of building complex computations that can update efficiently in response to their inputs changing, inspired by the work of Umut Acar et. al. on self-adjusting computations. Incremental can be useful in a number of applications GitHub stars made-with-ocaml
Man MDF dormant since 2016-12 Data-flow programming toolkit for Python GitHub stars made-with-python
GraphKit dormant since 2023-03 A lightweight Python module for creating and running ordered graphs of computations. GitHub stars made-with-python
Tributary Streaming reactive and dataflow graphs in Python GitHub stars made-with-python

Machine Learning

Repository Description Stars Made with
QLib (Microsoft) Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. GitHub stars made-with-python
FinRL FinRL is the first open-source framework to demonstrate the great potential of applying deep reinforcement learning in quantitative finance. GitHub stars made-with-python
MlFinLab (Hudson & Thames) dormant since 2023-10 MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools. GitHub stars made-with-python
TradingGym dormant since 2024-02 Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo. GitHub stars made-with-python
Stock Trading Bot using Deep Q-Learning dormant since 2023-12 Stock Trading Bot using Deep Q-Learning GitHub stars made-with-python

TimeSeries Analysis

Repository Description Stars Made with
Facebook Prophet Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth. GitHub stars made-with-python
statsmodels Python module that allows users to explore data, estimate statistical models, and perform statistical tests. GitHub stars made-with-python
tsfresh Automatic extraction of relevant features from time series. GitHub stars made-with-python
pmdarima A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function. GitHub stars made-with-python

Visualization

Repository Description Stars Made with
D-Tale (Man Group) D-Tale is the combination of a Flask back-end and a React front-end to bring you an easy way to view & analyze Pandas data structures. GitHub stars made-with-python
mplfinance dormant since 2024-08 Financial Markets Data Visualization using Matplotlib GitHub stars made-with-python
btplotting btplotting provides plotting for backtests, optimization results and live data from backtrader. GitHub stars made-with-python

Strategies

Every strategy below is a published paper that has been coded and run over its own full history. The table is regenerated from the replication catalogue by scripts/build_strategies_table.py, so the numbers move when the catalogue does.

Showing the 61 strongest of 1,687 replications that clear a t-statistic of 1.96 over at least 10 years, up to 12 per asset class. Sharpe ratios are measured on each strategy's own active window, not on a common calendar, and are gross of trading costs. Series with an annualised volatility outside 1% to 100% are treated as degenerate and dropped. The t-statistic is shown because a Sharpe ratio without one says very little: half the catalogue does not clear it.

Equities

Strategy Sharpe t-stat Volatility Years tested
A Study Of Differences In Returns Between Large And Small Companies In Europe 1.89 11.4 6.4% 37
The Investment CAPM 1.80 11.0 2.9% 37
Important Characteristics, Weaknesses and Errors in German Equity Data from Thomson 1.68 10.2 6.4% 37
The Role of Beta and Size in the Cross-Section of European Stock Returns 1.63 9.6 5.3% 34
Systematic Abnormal Return Variation and Global Market Inefficiencies 1.57 8.2 9.5% 27
Value and Size Effect: Now You See It, Now You Don’t 1.52 9.3 5.9% 37
Properties of the Most Diversified Portfolio 1.50 9.0 14.3% 37
Understanding Momentum and Reversal? 1.36 8.2 5.1% 36
Fact, Fiction, and the Size Effect 1.33 8.1 2.6% 37
The cross-section of returns in frontier equity markets: Integrated or segmented pricing? 1.32 8.0 3.9% 36
Analytical Solution for Kelly’s Criterion for Multiple Outcomes 1.31 7.9 13.3% 37
End-To-End Large Portfolio Optimization For Variance Minimization With Neural Networks Through Covariance Cleaning 1.28 7.3 15.4% 33

Bonds

Strategy Sharpe t-stat Volatility Years tested
Statistical and Economic Benefits of Whitening Residuals in Bond Yields 0.90 5.0 18.6% 30
Dynamic Risk-Aware Yield Search: A Useful Tool for Fixed Income Investors 0.89 4.4 2.9% 25
Out-performing corporate bonds indices with factor investing 0.85 5.1 12.3% 36
Priced risk in corporate bonds 0.72 3.0 6.0% 17
Sitting Bucks: Stale Pricing in Fixed Income Funds 0.64 3.9 5.7% 37
Frontier and Emerging Government Bond Markets 0.62 3.7 13.7% 36
Regime-based portfolio optimisation: A Hidden Markov Model approach for fixed income portfolios 0.62 3.6 6.0% 33
Price Effects of Sovereign Debt Auctions in the Euro-zone: The Role of the Crisis 0.51 3.1 13.4% 37
Are Bond Returns Predictable with Real-Time Macro Data? 0.49 2.5 5.9% 26
Trading the Term Premium 0.45 2.8 5.0% 37
Banks’ exposure to interest rate risk, their earnings from term transformation, and the dynamics of the term structure 0.44 2.7 7.9% 38
Predictable End-of-Month Treasury Returns 0.43 2.6 3.8% 37

Commodities

Strategy Sharpe t-stat Volatility Years tested
How to Improve Commodity Momentum Using Intra-Market Correlation 0.65 2.8 8.7% 19
Long-Run Reversal in Commodity Returns: Insights from Seven Centuries of Evidence 0.63 3.8 20.7% 37
Rolling vs. Expanding Windows in Mean-Reversion Strategies: Evidence from Gold-Silver and Cross-Asset Validation 0.36 2.2 98.5% 37

Currencies

Strategy Sharpe t-stat Volatility Years tested
Good Carry, Bad Carry 1.74 10.6 4.6% 37
The Time-Varying Systematic Risk of 1.53 9.3 4.1% 36
Lessons from the Evolution of Foreign Exchange Trading Strategies 1.24 7.4 12.4% 36
Optimal Currency Shares In International Reserves The Impact Of The Euro And The Prospects For The Dollar 0.68 2.9 55.3% 19

Cryptocurrencies

Strategy Sharpe t-stat Volatility Years tested
How to Design a Simple Multi-Timeframe Trend Strategy on Bitcoin 3.39 16.2 46.3% 23
‘Know When to Hodl ‘Em, Know When to Fodl ‘Em’: An Investigation of Factor Based Investing in the Cryptocurrency Space 1.53 6.2 9.6% 16
Seasonality, Trend-following, and Mean reversion in Bitcoin 1.11 4.5 49.5% 16
Do Risk Preferences Drive Momentum in Cryptocurrencies? 0.68 4.0 54.9% 34
The Blockchain Risk Parity Line: Moving From The Efficient Frontier To The Final Frontier Of Investments 0.58 3.4 54.1% 34
Price Overreactions in the Cryptocurrency Market 0.53 3.1 32.3% 35
Proof-of-What? Detecting original consensus algorithms in cryptocurrencies with a four-factor model 0.52 2.4 85.9% 22
Cryptocurrency as money: A trading strategy solution 0.47 2.8 15.4% 35

Derivatives

Strategy Sharpe t-stat Volatility Years tested
Media Tone Goes Viral: Global Evidence from the Currency Market 1.06 6.5 1.3% 38
Robust Portfolio Optimization with Value-At-Risk Adjusted Sharpe Ratios 0.92 5.6 17.1% 37
When Factor Timing Makes Sense 0.74 4.5 10.4% 37
Rational Decision-Making Under Uncertainty: Observed Betting Patterns on a Biased Coin 0.60 3.7 3.6% 38
Can Financial Innovation Succeed by Catering to Behavioral Preferences? Evidence from a Callable Options Market 0.55 3.4 17.7% 37
A Theory of Model Sophistication and Operational Risk 0.50 3.1 9.4% 37
Tail-Risk Protection Trading Strategies 0.49 3.0 13.6% 37
Is Media Tone just a Tone? Time-Series and Cross-Sectional Evidence from the Currency Market 0.35 2.1 3.1% 36
The Temporal Pattern of Trading Rule Returns and Central Bank Intervention: Intervention Does Not Generate Technical Trading Rule Profits 0.33 2.0 9.3% 37
Arbitrage in the Foreign Exchange Market: Turning on the Microscope 0.32 2.0 8.4% 37

Multi-asset

Strategy Sharpe t-stat Volatility Years tested
Optimal Annuity Risk Management 1.62 9.9 5.4% 38
Explaining low annuity demand: an optimal portfolio application to Japan 1.60 9.8 4.8% 38
Diverging roads: Theory-based vs. machine learning-implied stock risk premia 1.57 9.5 8.3% 37
The Anomalous Behavior of the S&P Covered Call Closed End Fund 1.36 8.3 18.6% 37
Any role for mean reversion in short term asset 1.30 7.4 8.6% 32
Inconsistent investment and consumption problems 1.26 7.7 2.6% 38
Heuristic Portfolio Rules with Labor Income 1.21 7.4 10.5% 38
Investing for the Long-Run in European Real Estate 1.11 4.5 5.5% 16
Regime-Aware Risk Management in Concentrated Equity Portfolios: Evidence from the Magnificent Seven 1.11 6.4 1.4% 33
Are Heuristics Better than Theory if Market Crashes Are 1.10 6.7 8.4% 37
Risk Parity Portfolios with Risk Factors 1.08 6.3 17.3% 34
A Risk Based Approach to Tactical Asset Allocation 1.06 6.4 5.0% 37

Older QuantConnect implementations of some of these papers are kept in static/strategies.

Books

A comprehensive list of 55 books for quantitative traders.

Beginner

Title Reviews Rating
A Beginner’s Guide to the Stock Market: Everything You Need to Start Making Money Today - Matthew R. Kratter
How to Day Trade for a Living: A Beginner’s Guide to Trading Tools and Tactics, Money Management, Discipline and Trading Psychology - Andrew Aziz
The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns - John C. Bogle
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
Day Trading QuickStart Guide: The Simplified Beginner’s Guide to Winning Trade Plans, Conquering the Markets, and Becoming a Successful Day Trader - Troy Noonan
Introduction To Algo Trading: How Retail Traders Can Successfully Compete With Professional Traders - Kevin J Davey
Algorithmic Trading and DMA: An introduction to direct access trading strategies - Barry Johnson

Biography

Title Reviews Rating
My Life as a Quant: Reflections on Physics and Finance - Emanuel Derman
How I Became a Quant: Insights from 25 of Wall Street’s Elite: - Barry Schachter

Coding

Title Reviews Rating
Python for Finance: Mastering Data-Driven Finance - Yves Hilpisch
Trading Evolved: Anyone can Build Killer Trading Strategies in Python - Andreas F. Clenow
Python for Algorithmic Trading: From Idea to Cloud Deployment - Yves Hilpisch
Algorithmic Trading with Python: Quantitative Methods and Strategy Development - Chris Conlan
Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis - Sebastien Donadio

Crypto

Title Reviews Rating
The Bitcoin Standard: The Decentralized Alternative to Central Banking - Saifedean Ammous
Bitcoin Billionaires: A True Story of Genius, Betrayal, and Redemption - Ben Mezrich
Mastering Bitcoin: Programming the Open Blockchain - Andreas M. Antonopoulos
Why Buy Bitcoin: Investing Today in the Money of Tomorrow - Andy Edstrom

General

Title Reviews Rating
The Intelligent Investor: The Definitive Book on Value Investing - Benjamin Graham, Jason Zweig
How I Invest My Money: Finance experts reveal how they save, spend, and invest - Joshua Brown, Brian Portnoy
Naked Forex: High-Probability Techniques for Trading Without Indicators - Alex Nekritin
The Four Pillars of Investing: Lessons for Building a Winning Portfolio - William J. Bernstein
Option Volatility and Pricing: Advanced Trading Strategies and Techniques, 2nd Edition - Sheldon Natenberg
The Art and Science of Technical Analysis: Market Structure, Price Action, and Trading Strategies - Adam Grimes
The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management (Wiley Trading) - Alexander Elder
Building Winning Algorithmic Trading Systems: A Trader’s Journey From Data Mining to Monte Carlo Simulation to Live Trading (Wiley Trading) - Kevin J Davey
Systematic Trading: A unique new method for designing trading and investing systems - Robert Carver
Quantitative Momentum: A Practitioner’s Guide to Building a Momentum-Based Stock Selection System (Wiley Finance) - Wesley R. Gray, Jack R. Vogel
Algorithmic Trading: Winning Strategies and Their Rationale - Ernest P. Chan
Leveraged Trading: A professional approach to trading FX, stocks on margin, CFDs, spread bets and futures for all traders - Robert Carver
Trading Systems: A New Approach to System Development and Portfolio Optimisation - Emilio Tomasini, Urban Jaekle
Trading and Exchanges: Market Microstructure for Practitioners - Larry Harris
Trading Systems 2nd edition: A new approach to system development and portfolio optimisation - Emilio Tomasini, Urban Jaekle
Machine Trading: Deploying Computer Algorithms to Conquer the Markets - Ernest P. Chan
Quantitative Equity Portfolio Management: An Active Approach to Portfolio Construction and Management (McGraw-Hill Library of Investment and Finance) - Ludwig B Chincarini, Daehwan Kim
Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk - Richard Grinold, Ronald Kahn
Quantitative Technical Analysis: An integrated approach to trading system development and trading management - Dr Howard B Bandy
Advances in Active Portfolio Management: New Developments in Quantitative Investing - Richard Grinold, Ronald Kahn
Professional Automated Trading: Theory and Practice - Eugene A. Durenard
Algorithmic Trading and Quantitative Strategies (Chapman and Hall/CRC Financial Mathematics Series) - Raja Velu, Maxence Hardy, Daniel Nehren
Quantitative Trading: Algorithms, Analytics, Data, Models, Optimization - Xin Guo, Tze Leung Lai, Howard Shek, Samuel Po-Shing Wong

High Frequency Trading

Title Reviews Rating
Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading - Rishi K. Narang
Algorithmic and High-Frequency Trading (Mathematics, Finance and Risk) - Álvaro Cartea, Sebastian Jaimungal, José Penalva
The Problem of HFT – Collected Writings on High Frequency Trading & Stock Market Structure Reform - Haim Bodek
An Introduction to High-Frequency Finance - Ramazan Gençay, Michel Dacorogna, Ulrich A. Muller, Olivier Pictet, Richard Olsen
Market Microstructure in Practice - Charles-Albert Lehalle, Sophie Laruelle
The Financial Mathematics of Market Liquidity - Olivier Gueant
High-Frequency Trading - Maureen O’Hara, David Easley, Marcos M López de Prado

Machine Learning

Title Reviews Rating
Dark Pools: The rise of A.I. trading machines and the looming threat to Wall Street - Scott Patterson
Advances in Financial Machine Learning - Marcos Lopez de Prado
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
Machine Learning for Asset Managers (Elements in Quantitative Finance) - Marcos M López de Prado
Machine Learning in Finance: From Theory to Practice - Matthew F. Dixon, Igor Halperin, Paul Bilokon
Artificial Intelligence in Finance: A Python-Based Guide - Yves Hilpisch
Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques - Robert Kissell

Videos

Title Likes
Krish Naik - Machine learning tutorials and their Application in Stock Prediction
QuantInsti Youtube - webinars about Machine Learning for trading
Siraj Raval - Videos about stock market prediction using Deep Learning
Quantopian - Webinars about Machine Learning for trading
Sentdex - Machine Learning for Forex and Stock analysis and algorithmic trading
QuantNews - Machine Learning for Algorithmic Trading 3 part series
Sentdex - Python programming for Finance (a few videos including Machine Learning)
Chat with Traders EP042 - Machine learning for algorithmic trading with Bert Mouler
Tucker Balch - Applying Deep Reinforcement Learning to Trading
Ernie Chan - Machine Learning for Quantitative Trading Webinar
Chat with Traders EP147 - Detective work leading to viable trading strategies with Tom Starke
Chat with Traders EP142 - Algo trader using automation to bypass human flaws with Bert Mouler
Master Thesis presentation, Uni of Essex - Analyzing the Limit Order Book, A Deep Learning Approach
Howard Bandy - Machine Learning Trading System Development Webinar
Chat With Traders EP131 - Trading strategies, powered by machine learning with Morgan Slade
Chat with Traders Quantopian 5 - Good Uses of Machine Learning in Finance with Max Margenot
Hitoshi Harada, CTO at Alpaca - Deep Learning in Finance Talk
Better System Trader EP028 - David Aronson shares research into indicators that identify Bull and Bear markets.
Prediction Machines - Deep Learning with Python in Finance Talk
Better System Trader EP064 - Cryptocurrencies and Machine Learning with Bert Mouler
Better System Trader EP023 - Portfolio manager Michael Himmel talks AI and machine learning in trading
Better System Trader EP082 - Machine Learning With Kris Longmore

Blogs

Title
AAA Quants, Tom Starke Blog
AI & Systematic Trading
Blackarbs blog
Hardikp, Hardik Patel blog
Max Dama on Automated Trading
Medallion.Club on Systematic Trading (FR)
Proof Engineering: The Algorithmic Trading Platform
Quantsportal, Jacques Joubert's Blog
Quantstart - Machine Learning for Trading articles
RobotWealth, Kris Longmore Blog

Courses

Title
AI in Finance
AI & Systematic Trading
Algorithmic Trading for Cryptocurrencies in Python
Coursera, NYU - Guided Tour of Machine Learning in Finance
Coursera, NYU - Fundamentals of Machine Learning in Finance
Coursera, NYU - Reinforcement Learning in Finance
Coursera, NYU - Overview of Advanced Methods for Reinforcement Learning in Finance
Hudson and Thames Quantitative Research
NYU: Overview of Advanced Methods of Reinforcement Learning in Finance
Udacity: Artificial Intelligence for Trading
Udacity, Georgia Tech - Machine Learning for Trading

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A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.

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