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Roy Yang
In progress

Algorithmic trading strategy guide

A guide from strategy building to portfolio optimisation and risk management, with Jupyter notebooks to run alongside the text.

  • Python
  • Jupyter
  • MkDocs Material
parts, from foundations to portfolio risk
10
parts, from foundations to portfolio risk
chapters, plus 4 appendices
25
chapters, plus 4 appendices

Personal research. Not investment advice. Backtested and paper-traded results are hypothetical.

What it covers

Strategies (trend following, mean reversion, statistical arbitrage, time-series forecasting, reinforcement learning), strategy development (regime detection, backtesting components, instruments, feature selection), optimisation (GA, PSO, NSGA-III and other multi-objective methods) and risk management (Kelly criterion, the 2% rule, VaR, pain index, downside risk).

The guide is online at royywn.github.io/algotrading-strategy.

How the book is organised

Part I: Introduction and Foundations

  • Chapter 1: Why Algorithmic Trading Matters
  • Chapter 2: Who This Book is For

Part II: Strategy Development Framework

  • Chapter 3: Market Trend Decision
  • Chapter 4: Regime Detection
  • Chapter 5: Backtesting Components

Part III: Investment Instruments and Data

  • Chapter 6: Single Stock Selection
  • Chapter 7: Multiple Stocks and Portfolios
  • Chapter 8: Alternative Instruments

Part IV: Feature Engineering and Data Science

  • Chapter 9: Feature Selection Techniques
  • Chapter 10: Backtesting Timeframes and Data

Part V: Trading Strategy Types

  • Chapter 11: Trend Trading Strategies
  • Chapter 12: Mean Reversion Strategies
  • Chapter 13: Machine Learning Trading
  • Chapter 14: Reinforcement Learning

Part VI: Single Objective Optimization

  • Chapter 15: Genetic Algorithms

Part VII: Multi-Objective Optimization (Core)

  • Chapter 16: NSGA-III Implementation
  • Chapter 17: Advanced Multi-Objective Methods
  • Chapter 18: Portfolio Optimization
  • Chapter 19: Deep Learning Portfolio Optimization

Part VIII: Risk and Cash Management

  • Chapter 20: Cash Management Systems
  • Chapter 21: Risk Management

Part IX: Market Phase Analysis

  • Chapter 22: Pre-Market Analysis
  • Chapter 23: In-Market Execution
  • Chapter 24: Post-Market Optimization

Part X: Advanced Topics

  • Chapter 25: Portfolio Risk Quantification

Appendices

  • Appendix A: Python Environment Setup
  • Appendix B: Data Sources and APIs
  • Appendix C: Performance Benchmarks
  • Appendix D: Regulatory Considerations

Skills this project shows