Books on algorithmic trading#
See also AI and machine learning topics, as AI and machine learning books have their own section.
Advanced Futures Trading Strategies#
Our personal favourite.
In Advanced Futures Trading Strategies, Robert Carver provides a complete practical guide to 30 trading strategies for the futures markets. Advanced Futures Trading Strategies is the definitive practical guide to futures trading strategies.
Beating the Financial Futures Market#
Traders in today’s fast-moving financial futures markets must contend with market noise that is ambiguous, contradictory, and (here’s the worst part) almost entirely random. More than ninety percent of traders will eventually go broke trying to make sense of, and systemize, that noise. But for those that learn the tricks of survival, the rewards can be phenomenal. How can you become one of the five to ten percent of traders that survive the learning curve, design a profitable system, and turn financial futures trading into a profitable sideline or even a full-time career? Beating the Financial Futures Market provides you with a straightforward, historically proven program to cut through the noise, determine what bits of information are valuable, and integrate those bits into an overall trading program designed to jump on lucrative trading opportunities as they occur. Written by veteran commodities trader, systems designer, lecturer, author, and Chicago Board of Trade member Art Collins, this comprehensive trading handbook details: *Guidelines for overcoming dead-end discretionary trading “insights” to focus on market-tested, mechanical trading rules and knowledge *The four rules of prudent optimization, essential for identifying the best performing variables within your formulas *Eight consistent biases that, when followed, can lead you to more reliable and profitable trades *Statistically verifiable strategies for combining—and recombining—specific indicators based on prevailing market environments *Actual TradeStation© summaries, showing in black and white which concepts worked and which didn’t, and when and why The backroads of financial futures trading are littered with failed geniuses, traders who spent their days trying to outthink the markets. Beating the Financial Futures Market will show you how to simplify your trading program by strictly adhering, 100 percent of the time, to a focused roster of mechanical trading techniques. It will help you remove much of the difficulty from your trading day by developing a disciplined, turnkey system and letting the system do the work—leaving you to simply make trades as specified by your system, institute the necessary safeguards, and dramatically improve both your percentage of winning trades and the bottomline profitability of those winning trades.
Detecting Regime Change in Computational Finance#
Based on interdisciplinary research into “Directional Change”, a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarising price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction (“zigzags”). By sampling data in a different way, this book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics:
Data science: as an alternative to time series, price movements in a market can be summarised as directional changes
Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model
Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change
Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed
Algorithmic trading: regime tracking information can help us to design trading algorithms
It will be of great interest to researchers in computational finance, machine learning and data science.
Finding Alphas: A Quantitative Approach to Building Trading Strategies#
Drawing on the expertise of WorldQuant’s global network, this new edition of Finding Alphas: A Quantitative Approach to Building Trading Strategies contains significant changes and updates to the original material, with new and updated data and examples.
Nine chapters have been added about alphas – models used to make predictions regarding the prices of financial instruments. The new chapters cover topics including alpha correlation, controlling biases, exchange-traded funds, event-driven investing, index alphas, intraday data in alpha research, intraday trading, machine learning, and the triple axis plan for identifying alphas.
Provides more references to the academic literature
Includes new, high-quality material
Organizes content in a practical and easy-to-follow manner
-Adds new alpha examples with formulas and explanations
If you’re looking for the latest information on building trading strategies from a quantitative approach, this book has you covered.
Building Winning Algorithmic Trading Systems#
In Building Algorithmic Trading Systems: A Trader’s Journey From Data Mining to Monte Carlo Simulation to Live Training, award-winning trader Kevin Davey shares his secrets for developing trading systems that generate triple-digit returns. With both explanation and demonstration, Davey guides you step-by-step through the entire process of generating and validating an idea, setting entry and exit points, testing systems, and implementing them in live trading. You’ll find concrete rules for increasing or decreasing allocation to a system, and rules for when to abandon one. The companion website includes Davey’s own Monte Carlo simulator and other tools that will enable you to automate and test your own trading ideas.
A purely discretionary approach to trading generally breaks down over the long haul. With market data and statistics easily available, traders are increasingly opting to employ an automated or algorithmic trading system―enough that algorithmic trades now account for the bulk of stock trading volume. Building Algorithmic Trading Systems teaches you how to develop your own systems with an eye toward market fluctuations and the impermanence of even the most effective algorithm.
Learn the systems that generated triple-digit returns in the World Cup Trading Championship
Develop an algorithmic approach for any trading idea using off-the-shelf software or popular platforms
Test your new system using historical and current market data
Mine market data for statistical tendencies that may form the basis of a new system
Market patterns change, and so do system results. Past performance isn’t a guarantee of future success, so the key is to continually develop new systems and adjust established systems in response to evolving statistical tendencies. For individual traders looking for the next leap forward, Building Algorithmic Trading Systems provides expert guidance and practical advice.
Python For Finance: Algorithmic Trading#
This Python for Finance tutorial introduces you to algorithmic trading, and much more.
Python for Data Analysis#
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.9 and pandas 1.2, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
Practical Guide to Applied Conformal Prediction: Learn and apply the best uncertainty frameworks to your industry applications#
Embark on an insightful journey with ‘Practical Guide to Applied Conformal Prediction in Python’, a comprehensive resource that equips you with the latest techniques to quantify uncertainty in machine learning and computer vision models effectively.
This book covers a wide array of real-world applications, including Conformal Prediction for forecasting, computer vision, and NLP, as well as advanced examples for handling imbalanced data and multi-class classification problems. These practical case studies will enable you to apply your newfound knowledge to various industry scenarios.
Designed for data scientists, analysts, machine learning engineers, and industry professionals, this book caters to different skill levels - making it an ideal resource for both beginners and experienced practitioners. Delve into the latest Conformal Prediction techniques and elevate your machine learning expertise.
If you’re eager to manage uncertainty in industry applications using Python, ‘Practical Guide to Applied Conformal Prediction in Python’ is the ultimate guide for you. Order your copy today and propel your career to new heights!
Algorithmic Trading: Winning Strategies and Their Rationale#
Engaging and informative, Algorithmic Trading skillfully covers a wide array of strategies. Broadly divided into the mean-reverting and momentum camps, it lays out standard techniques for trading each category of strategies and, equally important, the fundamental reasons why a strategy should work. The emphasis throughout is on simple and linear strategies, as an antidote to the over-fitting and data-snooping biases that often plague complex strategies. Along the way, it provides comprehensive coverage of:
Choosing the right automated execution platform as well as a backtesting platform that will allow you to reduce or eliminate common pitfalls associated with algorithmic trading strategies
Multiple statistical techniques for detecting “time series” mean reversion or stationarity, and for detecting cointegration of a portfolio of instruments
Simple techniques for trading mean-reverting portfolios―linear, Bollinger band, and Kalman filter―and whether using raw prices, log prices, or ratios make the most sense as inputs to these tests and strategies
Mean-reverting strategies for stocks, ETFs, currencies, and futures calendar and intermarket spreads
The four main drivers of momentum in stocks and futures, and strategies that can extract time series and cross sectional momentum
Newer momentum strategies based on news events and sentiment, leveraged ETFs, order flow, and high-frequency trading
Issues involving risk and money management based on the Kelly formula, but tempered with the author’s practical experience in risk management involving black swans, Constant Proportion Portfolio Insurance, and stop losses
Mathematics and software are the twin languages of algorithmic trading. This book stays true to that view by using a level of mathematics that allows for a more precise discussion of the concepts involved in financial markets. And it includes illustrative examples that are built around MATLAB© codes, which are available for download.
Further reading lists#
Here are more books on quantitative finance and algorithmic trading topics by the author.
General Finance Textbooks#
Options, Futures and Other Derivatives, John Hull
The Concepts and Practice of Mathematical Finance, Mark Joshi
Paul Wilmott on Quantitative Finance, Paul Wilmott
Option Pricing Theory and Stochastic Calculus#
Financial Calculus: An Introduction to Derivative Pricing, Martin Baxter and Andrew Rennie
Arbitrage Theory in Continuous Time, Tomas Björk
Stochastic Calculus for Finance I: The Binomial Asset Pricing Model, Steven Shreve
Stochastic Calculus for Finance II: Continuous-Time Models, Steven Shreve
Martingale Methods in Financial Modelling, Marek Musiela and Marek Rutkowski
Mathematical Methods for Financial Markets, Monique Jeanblanc, Marc Yor, and Marc Chesney
Financial Modelling With Jump Processes, Rama Cont and Peter Tankov
Option Volatility and Pricing, Sheldon Natenberg
Quantitative Risk Management#
Risk Management and Financial Institutions, by John C. Hull
Quantitative Risk Management: Concepts, Techniques, and Tools” by Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts
Market Risk Analysis, Volume I: Quantitative Methods in Finance” by Carol Alexander
The Concepts and Practice of Mathematical Finance” by Mark S. Joshi
Asset Pricing#
Asset Pricing (Revised Edition), Cochrane, John H. Princeton University Press, 2009.
Financial Decisions and Markets: A Course in Asset Pricing, Campbell, John Y. Princeton University Press, 2017.
Asset pricing and portfolio choice theory, Back, Kerry. Oxford University Press, 2010.
Damodaran on Valuation, Damodaran, Aswath, Wiley Finance, 2006
Dynamic Asset Pricing Theory (Third Edition), Duffie, Darrell. Princeton University Press, 2001.
Machine Learning#
Machine Learning: A Probabilistic Perspective, Kevin P Murphy
Advances in Financial Machine Learning, Marcos Lopez de Prado