bradleyboyuyang/ML-HFT
High frequency trading (HFT) framework built for futures using machine learning and deep learning techniques
This framework helps quantitative traders and fund managers develop and test high-frequency trading strategies for futures markets. By inputting real-time Level II order book data, it extracts trading signals and predicts order book dynamics using machine and deep learning techniques. The output is a robust, data-driven trading strategy pipeline that can inform automated trading decisions and visualize potential profits and losses (PnL).
558 stars. No commits in the last 6 months.
Use this if you are a quantitative trader or fund manager looking to build, evaluate, and deploy high-frequency trading strategies for futures using advanced machine learning.
Not ideal if you are a discretionary trader or primarily interested in long-term investment strategies, as this is specifically designed for very short-term, automated trading based on granular market data.
Stars
558
Forks
128
Language
Jupyter Notebook
License
—
Category
Last pushed
Sep 20, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/bradleyboyuyang/ML-HFT"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
polakowo/vectorbt
⚡️ Lightning-fast backtesting engine to find your trading edge.
asavinov/intelligent-trading-bot
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning...
Drakkar-Software/OctoBot-Script
Quant trading framework by OctoBot. Write, backtest & automate Python trading strategies like...
paperswithbacktest/pwb-toolbox
The toolbox for developing systematic trading strategies. It includes datasets and strategy...
ScottfreeLLC/AlphaPy
Python AutoML for Trading Systems and Sports Betting