trading-rules-using-machine-learning
Unverified ML strategy on Multi by jo-cho. BotFinder score 18 out of 100.
Machine learning-driven financial trading strategy: momentum prediction, regime detection, and enhanced trading decisions.
Source: github
BotFinder analysis pending.
trading-rules-using-machine-learning
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
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
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