HFT-ML-Strategy
- Extract trading signals from multi-level orderbook data
- Replicate well-designed high frequency trading (HFT) strategies using machine learning and deep learning techniques
Data
The SGX FTSE CHINA A50 INDEX Futures (新加坡交易所FTSE**A50指数期货) tick depth data are used.
Strategy Pipline
Orderbook Signals
We use level-3 deep orderbook data to develop trading signals, including Depth Ratio, Rise Ratio, and Orderbook Imbalance (OBI).
Price Series
Feature Engineering & HFT Factors Design
- Simple average depth ratio and OBI:
- Weighted average depth ratio, OBI, and rise ratio:
Model Fitting
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Models:
- RandomForestClassifier
- ExtraTreesClassifier
- AdaBoostClassifier
- GradientBoostingClassifier
- Support Vector Machines
- Other classifiers: Softmax, KNN, MLP, LSTM, etc.
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Hyperparameters:
- Training window: 30min
- Test window: 10sec
- Prediction label: 15min forward
Performance Metrics
- Prediction accuracy:
- Prediction Accuracy Series:
- Cross Validation Mean Accuracy:
- Best Model:
PnL Visualization
