A curated list of awesome resources for quantitative investment and trading strategies focusing on artificial intelligence and machine learning applications in finance.
- Introduction
- How to Contribute
- Quantitative Trading Strategies
- Tools and Platforms
- Learning Resources
- Books
- Research Papers
- Community and Conferences
- Reference
Quantitative investing uses mathematical models and algorithms to determine investment opportunities. This repository aims to provide a comprehensive resource for those interested in the intersection of AI, machine learning, and quantitative finance.
Contributions are welcome! Please read the contribution guidelines first.
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- Exploiting pricing inefficiencies among related financial instruments using advanced statistical models.
- Sub-strategies:
- Mean Reversion: Assuming asset prices will revert to their historical average.
- Pairs Trading: Taking long and short positions in correlated securities.
- Cointegration Analysis: Exploiting long-term price relationships.
- Investing in securities that exhibit characteristics associated with higher returns, such as value, momentum, or size.
- Factors:
- Value: Selecting undervalued stocks.
- Momentum: Buying recent winners and selling losers.
- Size: Investing in small-cap stocks.
- Quality: Selecting stocks based on financial health indicators.
- Low Volatility: Investing in stocks with lower price fluctuations.
- Rapid trading using powerful computers and algorithms.
- Approaches:
- Market Making: Providing liquidity by simultaneously placing buy and sell orders.
- Latency Arbitrage: Exploiting tiny price discrepancies.
- Order Flow Prediction: Anticipating and acting on order flow patterns.
- Trading based on the continuation of price trends.
- Methods:
- Moving Averages: Using price averages to identify trends.
- Breakout Trading: Entering positions when prices move beyond support/resistance levels.
- Momentum Indicators: Using technical indicators to measure price velocity.
- Strategies focused on market volatility rather than directional moves.
- Methods:
- Options Pricing: Using volatility models for options valuation.
- Volatility Arbitrage: Exploiting differences between implied and realized volatility.
- Allocating capital based on risk, balancing the contributions of different assets to overall portfolio volatility.
- Implementation:
- Balancing Risk Contributions: Across different asset classes.
- Leveraging Lower-Risk Assets: To achieve the desired risk/return profile.
- Trading based on macroeconomic factors and global market trends.
- Approaches:
- Global Macro: Trading based on broad economic trends.
- Asset Allocation: Dynamically adjusting portfolio composition based on market conditions.
- Trading based on specific events or news.
- Examples:
- Merger Arbitrage: Trading around M&A activities.
- Earnings Announcements: Trading based on financial report releases.
- Economic Data Releases: Trading on macroeconomic news.
- Utilizing AI to improve human decision-making processes and improve investment strategies. Deploying algorithms to analyze vast datasets and enhance the accuracy and efficiency of financial models.
- Techniques:
- Supervised Learning: Predicting outcomes using labeled data.
- Unsupervised Learning: Discovering hidden patterns in data.
- Reinforcement Learning: Learning optimal strategies through environment interaction.
- Natural Language Processing (NLP): Analyzing text data for trading signals.
- Combining multiple strategies to diversify and enhance performance.
- Examples:
- Multi-Factor Models: Integrating multiple factors in a single strategy.
- Strategy Allocation: Dynamically allocating capital across various quantitative strategies.
List of software tools and platforms used in quantitative finance.
- pybroker: focus on strategies that use machine learning https://github.com/edtechre/pybroker
Online courses, tutorials, and workshops focused on quantitative investing and machine learning in finance.
- Algorithmic Trading & Quantitative Analysis Using Python https://www.udemy.com/course/algorithmic-trading-quantitative-analysis-using-python/
- Quantitative Trading Strategies https://finmath.uchicago.edu/curriculum/degree-concentrations/trading/finm-33150/
- Oxford Algorithmic Trading Programme https://www.sbs.ox.ac.uk/programmes/executive-education/online-programmes/oxford-algorithmic-trading-programme
- https://orfe.princeton.edu/research/financial-mathematics
This section curates significant books in the realms of quantitative finance, algorithmic trading, and market data analysis. Each book listed has proven to be invaluable for learning and applying quantitative techniques in the financial markets.
- Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernest Chan - A great introduction to quantitative trading for retail traders.
- Algorithmic Trading: Winning Strategies and Their Rationale by Ernest Chan - Advanced strategies for developing and testing algorithmic trading systems.
- Machine Trading: Deploying Computer Algorithms to Conquer the Markets by Ernest Chan - Introduction to strategies in factor models, AI, options, time series analysis, and intraday trading.
- Mechanical Trading Systems by Richard Weissman - Discusses momentum and mean reversion strategies across different time frames.
- Following the Trend by Andreas Clenow - Insightful read on trend following, a popular quantitative trading strategy.
- Trade Your Way to Financial Freedom by Van Tharp - Structured approaches to developing personal trading systems.
- The Mathematics of Money Management by Ralph Vince - Techniques on risk management and optimal portfolio configuration.
- Intermarket Trading Strategies by Markos Katsanos - Explores global market relationships for strategy development.
- Applied Quantitative Methods for Trading and Investment by Christian Dunis et al. - Practical applications of quantitative techniques in trading.
- Algorithmic Trading and DMA by Barry Johnson - An introduction to direct market access and trading strategies.
- Technical Analysis from A to Z by Steven Achelis - A comprehensive guide to technical analysis indicators.
- Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky - Discusses the process of finding trading strategies (alphas).
- Algorithmic and High-Frequency Trading by Álvaro Cartea, Sebastian Jaimungal, and José Penalva - Provides an in-depth understanding of high-frequency trading strategies.
- Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernest P. Chan - A comprehensive guide to starting a quantitative trading business.
- Building Reliable Trading Systems: Tradable Strategies That Perform As They Backtest and Meet Your Risk-Reward Goals by Keith Fitschen - Focuses on developing trading systems that perform well in real-world conditions.
- Professional Automated Trading: Theory and Practice by Eugene A. Durenard - A practical guide to automated trading systems.
- Reminiscences of a Stock Operator by Edwin Lefèvre - Classic insights into the life and trading psychology of Jesse Livermore.
- When Genius Failed by Roger Lowenstein - The rise and fall of Long-Term Capital Management.
- Predictably Irrational by Dan Ariely - A look at the forces that affect our decision-making processes.
- Behavioral Investing by James Montier - Strategies to overcome psychological barriers to successful investing.
- The Laws of Trading by Agustin Lebron - Decision-making strategies from a professional trader's perspective.
- Thinking, Fast and Slow by Daniel Kahneman - A classic on human decision-making and cognitive biases, crucial for understanding market behavior.
- The Undoing Project by Michael Lewis - Chronicles the collaboration between Daniel Kahneman and Amos Tversky and their contributions to behavioral economics.
- Machine Learning for Algorithmic Trading by Stefan Jansen - Techniques for developing automated trading strategies using machine learning.
- Advances in Financial Machine Learning by Marcos Lopez de Prado - Discusses the challenges and opportunities of applying ML/AI in trading.
- Machine Learning for Asset Managers by Marcos Lopez de Prado - Focuses on portfolio construction, feature selection, and identifying overfit models.
- Time Series Analysis by James Hamilton - Statistical methods for analyzing time series data in economics and finance.
- Econometric Analysis by William Greene - A fundamental textbook on econometric methods.
- Wavelet Methods for Time Series Analysis by Donald Percival and Andrew Walden - Utilizes wavelet analysis for financial time series.
- The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman - A comprehensive overview of statistical learning theory and its applications.
- Applied Econometric Time Series by Walter Enders - demonstrates modern techniques for developing models capable of forecasting, interpreting, and testing hypotheses concerning economic data.
- Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control by Steven L. Brunton and J. Nathan Kutz - Focuses on the application of machine learning in scientific and engineering contexts.
- Big Data and Machine Learning in Quantitative Investment by Tony Guida - Explores the role of big data and machine learning in quantitative investment.
- Big Data Science in Finance by Irene Aldridge and Marco Avellaneda - Provides insights into the application of big data science in the financial industry.
- Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices by Agostino Capponi and Charles-Albert Lehalle - A comprehensive guide to contemporary practices in machine learning and data sciences for financial markets.
- Machine Learning in Finance: From Theory to Practice by Matthew F. Dixon, Igor Halperin, and Paul Bilokon - Covers the theory and practice of applying machine learning in finance.
- Machine Learning For Financial Engineering by László Györfi, György Ottucsák - Focuses on the application of machine learning techniques in financial engineering.
- Convex Optimization by Stephen Boyd and Lieven Vandenberghe - A detailed guide on convex optimization techniques used in finance.
- Financial Calculus by Martin Baxter and Andrew Rennie - An introduction to derivatives pricing using stochastic calculus.
- Stochastic Calculus for Finance I by Steven Shreve - Introduction to stochastic calculus for financial modeling.
- Stochastic Calculus for Finance II by Steven Shreve - Advanced concepts in stochastic calculus for complex financial models.
- Optimization Methods in Finance by Gérard Cornuéjols and Reha Tütüncü - Introduces optimization techniques and their applications in finance.
- Kalman Filtering: with Real-Time Applications by Charles K. Chui and Guanrong Chen - A practical guide to the application of Kalman filtering in real-time systems.
- Modern Portfolio Theory and Investment Analysis by Elton et al. - An in-depth look at Modern Portfolio Theory and its practical applications.
- Options, Futures, and Other Derivatives by John Hull - Essential reading on derivatives trading.
- Asset Management: A Systematic Approach to Factor Investing by Andrew Ang - Discusses a systematic approach to factor investing.
- Portfolio Management under Stress: A Bayesian-Net Approach to Coherent Asset Allocation by Riccardo Rebonato and Alexander Denev - Focuses on portfolio management strategies under stressful market conditions.
- Quantitative Equity Portfolio Management by Ludwig Chincarini and Daehwan Kim - Advanced techniques focused on quantitative equity portfolio management.
- Volatility and Correlation by Riccardo Rebonato - Discusses volatility and correlation in financial markets and their use in risk management.
- Study Guide for Options as a Strategic Investment by Lawrence McMillan - A comprehensive analysis of options strategies for various market conditions.
- Volatility Trading by Euan Sinclair - Practical strategies for trading volatility.
- The Volatility Surface by Jim Gatheral - Properties of the volatility surface and its implications for pricing derivatives.
- Dynamic Hedging: Managing Vanilla and Exotic Options by Nassim Nicholas Taleb - Introduces dynamic hedging strategies and their applications in managing standard and exotic options.
- Python for Finance by Yves Hilpisch - Essential techniques for algorithmic trading and derivatives pricing.
- Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch - Comprehensive guide on implementing trading strategies in Python, from data handling to cloud deployment.
- Python for Finance Cookbook - Second Edition by Eryk Lewinson - Over 80 powerful recipes for effective financial data analysis, using modern Python libraries such as pandas, NumPy, and scikit-learn.
- Python for Data Analysis by Wes McKinney - Written by the creator of the Pandas library, this book is essential for financial data analysis.
- The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman - The unbelievable story of Jim Simons, a secretive mathematician who pioneered the era of algorithmic trading and made $23 billion doing it, whose Renaissance’s Medallion fund has generated average annual returns of 66 percent since 1988.
- Poor Charlie’s Almanack: The Essential Wit and Wisdom of Charles T. Munger by Charles T. Munger, Peter D. Kaufman (Editor), Warren Buffett (Foreword), John Collison (Foreword) - This book offers lessons in investment strategy, philanthropy, and living a rational and ethical life.
- More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby - Details the history of hedge funds and their impact on financial markets.
Seminal and recent research that advances the field of quantitative finance.
Information on communities, meetups, and conferences dedicated to quantitative finance.
Feel free to explore these resources to deepen your understanding of quantitative finance and improve your trading strategies.
- 46 awesome books for quant finance, algo trading, and market data analysis https://www.pyquantnews.com/the-pyquant-newsletter/46-books-quant-finance-algo-trading-market-data
- 10 awesome books for Quantitative Trading https://medium.com/@mlblogging.k/10-awesome-books-for-quantitative-trading-fc0d6aa7e6d8
- Books for Algorithmic Trading I Wish I Had Read Sooner https://www.youtube.com/watch?v=ftFptCxm5ZU
- Awesome Systematic Trading https://github.com/paperswithbacktest/awesome-systematic-trading