javifalces/HFTFramework
HFTFramework utilized for research on " A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm "
This framework helps high-frequency traders and quantitative researchers test and deploy market-making strategies. You feed it historical market data (L2 tick data) or connect it to a live market, along with your trading algorithm. It then simulates or executes trades, providing insights into your strategy's potential performance or managing live orders. It's designed for quantitative traders and researchers focused on automated, rapid trading.
289 stars.
Use this if you are a quantitative trader or researcher needing to backtest high-frequency trading strategies with granular L2 tick data or deploy them to live markets.
Not ideal if you are a retail investor looking for a simple, out-of-the-box trading bot or if you don't have experience with quantitative strategy development.
Stars
289
Forks
61
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Mar 13, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/javifalces/HFTFramework"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
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