ML HFT

High Frequency Trading Framework with Machine/Deep Learning

In this project, we provide a framework/pipeline for high frequency trading using machine/deep learning techniques. More advanced feature engineering (with depth trade and quote data) and models (such as pre-trained models) can be applied in this framework.

Target

• Extract trading signals from level-II orderbook data
• Predict orderbook dynamics using machine learning and deep learning techniques

Data

The SGX FTSE CHINA A50 INDEX Futures (新加坡交易所FTSE中国A50指数期货) tick depth data are used.

Strategy Pipline

<img src="./Graph/pipline.png" width="650">

Orderbook Signals

We use limit orderbook data to develop trading signals, including Depth Ratio, Rise Ratio, and Orderbook Imbalance (OBI).

<img src="./Graph/depth.png" width="650">

Price Series

<img src="./images/best_bid_ask.png" width="750">

Feature Engineering & HFT Factors Design

• Simple average depth ratio and OBI:

<img src="./images/depth_0915_1130.png" width="750"> <img src="./images/depth_1300_1600.png" width="750">

• Weighted average depth ratio, OBI, and rise ratio:

<img src="./images/rise_1300_1600_w.png" width="750">

Model Fitting

• Basic Models:

  • RandomForestClassifier
  • ExtraTreesClassifier
  • AdaBoostClassifier
  • GradientBoostingClassifier
  • Support Vector Machines
  • Other classifiers: Softmax, KNN, MLP, LSTM, etc.

• Hyperparameters:

  • Training window: 30min
  • Test window: 10sec
  • Prediction label: 15min forward

Performance Metrics

• Prediction accuracy:

<img src="./images/prediction.png" width="750">

• Prediction Accuracy Series:

  <img src="./images/single_day_accuracy.png" width="800">

• Cross Validation Mean Accuracy:

<img src="./images/CV_result.png" width="800">

• Best Model:

<img src="./images/best_CV_result.png" width="800">

PnL Visualization

<img src="./images/best_CV_result_all.png" width="800">

Improvements

Feature Engineering

Other potentially useful signals:

• volume imbalance signal
• trade imbalance signal
• technical indicators of bid and ask series (RSI, MACD...)
• WAP/WPR, weighted average price, VWAP, TWAP
• .....

Signal generating techniques:

• consider different weights on different level of orderbook data for a particular signal
• consider moving average with period n (hyperparameter)
• consider weighted average of signals, such as weighted average of trade imbalance and orderbook imbalance
• Lasso regression, genetic programming
• .....

Models

This project only provides a baseline. More advanced models are welcomed:

• CNN
• GRU/LSTM
• XGBoost, AdaBoost, GBDT, LightGBM
• Attention, Auto-encoder
• TabNet
• Pre-trained models
• .....

Performance Metrics

The performance metrics are subject to amendment, including the PnL calculation, commission fee consideration, etc.