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Trading rules using machine learning

This is my financial trading using ML.

• Example
• Project

Momentum prediction and enhancing the strategy with machine learning

1. Financial Data and Bars

   • Form time/dollar bars with tick data

2. Get Buy/Sell Signals

   • Momentum strategy (RSI..)
   • Additional ML regime detector

3. Trading Rules

   • Set enter rules with trading signals from classifiers
   • Set exit rules with profit-taking, stop-loss rate, and maximum holding period
   • (For enhancing the strategy) Label the binary outcome (Profit or Loss)

4. Strategy-Enhancing ML Model

• Get Features (X)

  • Market data & Technical analysis
  • Microstructure features
  • Macroeconomic variables
  • Fundamentals
  • news/public sentiments (in progress)

• Feature Engineering

  • Feature selection, dimension reduction

• Machine Learning Model Optmization

  • Cross-validation (time-series cv / Purged k-fold)
  • Hyperparameter tuning
  • AutoML with autogluon (or simply using ensemble methods such as Random forest, LightGBM, or XGBoost)
  • Metrics (accuracy, f1 score, roc-auc)

• Outcome

  • Bet confidence (probability to accept a single trading signal)

4. Trading Decision
   • Decide to bet or pass for each trading signal from the momentum strategy. The ML model above will help you.
   • Bet sizing with some advanced models (in progress)
5. Backtesting
   • Cumulative returns, Sharpe ratio, max drawdown, win ratio

References:

• Advances in Financial Machine Learning, Lopez de Prado (2018)

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