HFTFramework
Unverified ML strategy on Multi by javifalces. BotFinder score 18 out of 100.
HFTFramework utilized for research on " A reinforcement learning approach to improve the performance of the Avellaneda-Stoikov market-making algorithm "
Source: github
BotFinder analysis pending.
HFTFramework
HFT Framework This repository is home to a High-Frequency Trading (HFT) framework, developed using Java and Python, primarily for research applications. The framework is engineered to interface with live markets through the use of connectors, which can be integrated within the same process or remotely via the ZeroMQ networking library. A significant feature of this framework is its ability to perform backtesting at the L2 tick data level, utilizing the same codebase as that used for live market interfacing. This capability allows for a detailed and granular analysis of trading strategies, providing valuable insights into their potential performance in live markets. Feedback, suggestions, and modifications are welcomed and appreciated. Please note: This framework has not been validated in a live trading environment. Proceed with caution and assume all associated risks. HFT Framework How-to use 1. Create algorithm and backtest 1.1 Java Algorithms 1.2 Pure Python Strategies (pythonalgo) 2. Live trading Monitoring Web Monitoring UI 3. Market Engine XChangeEngine MetatraderEngine Arquitec
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