ML-HFT
Unverified ML strategy on Multi by bradleyboyuyang. BotFinder score 18 out of 100.
High frequency trading (HFT) framework built for futures using machine learning and deep learning techniques
Source: github
BotFinder analysis pending.
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 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:
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)