ML-HFT

bradleyboyuyang/ML-HFT is ranked 16,090 among these 8 peers; cpaczek/any-buddy is ranked 16,091.

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. Strategy Pipline Orderbook Signals We use limit 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 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: Prediction Accuracy Series: Cross Validation Mean Accuracy: Best Model: PnL Visualization 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.