Algorithmic Trading Strategy¶
by Your Name
Algorithmic Trading Strategy is a comprehensive guide to building, optimizing, and implementing automated trading systems using machine learning and quantitative methods.
You can order print and ebook versions of Algorithmic Trading Strategy from Bookshop.org and Amazon.
For each chapter, there is a Jupyter notebook, below, where you can read the text, run the examples, and work on the exercises.
You can read the free version of the book by following the links on the left.
If you are looking for solutions to the exercises, follow the links on the left.
Algorithmic Trading Strategy is a Free Book. It is available under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), which means that you are free to copy and modify it, as long as you attribute the work and don't use it for commercial purposes.
Other Books by the Author are available from Your Publishing Site.
Run the notebooks¶
Download the notebooks as a Zip file
Or use these links to run the notebooks on Colab:
• Chapter 1: Trend Trading • Chapter 2: Mean Reversion • Chapter 3: Statistical Arbitrage • Chapter 4: Only-Buy Portfolio • Chapter 5: Time Series Forecasting • Chapter 6: Reinforcement Learning • Chapter 7: Machine Learning Trading • Chapter 8: Market Trend Decision • Chapter 9: Regime Detection • Chapter 10: Backtesting Components • Chapter 11: Single Stock Selection • Chapter 12: Multiple Stocks • Chapter 13: ETF Index • Chapter 14: Commodities • Chapter 15: Currency Pairs • Chapter 16: Feature Selection • Chapter 17: Backtesting Timeframes • Chapter 18: Trading Frequency • Chapter 19: GA, PSO, ES • Chapter 20: NSGA-III • Chapter 21: AGE-MOEA2 • Chapter 22: DNSGA2 • Chapter 23: SMS-EMOA • Chapter 24: Multi-objective Portfolio • Chapter 25: Deep Learning Portfolio • Chapter 26: Optimization Tools • Chapter 27: Kelly Criteria • Chapter 28: 2% Rule • Chapter 29: Model-based VaR • Chapter 30: Rolling VaR • Chapter 31: Pain Index • Chapter 32: Downside Risk