Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.
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Updated
Jun 3, 2024 - Python
Modular Python library that provides an advanced event driven backtester and a set of high quality tools for quantitative finance. Integrated with various data vendors and brokers, supports Crypto, Stocks and Futures.
A high-performance WebSocket integration library for streaming public market data. Used as a key dependency of the `barter-rs` project.
Fcore Is an AI Framework for Financial Markets Analysis (Active Development).
A股回测框架, 模拟实盘账户交易, 适合编写T+0策略
Backgommon is a backtesting and simulation framework for trading strategies, written in pure go. It aims to be fast, flexible and easy to use.
A personal automated trading system
A nimble options backtesting library for Python
🔎 📈 🐍 💰 Backtest trading strategies in Python.
Backtesting toolbox for trading strategies
Explore and leverage the correlation between oil price movements, energy sector, and transportation sector. This repository houses quantitative research findings and trading strategies that exploit this correlation to generate robust signals.
Trading strategy backtesting framework with focus on position adjustment in a session scope.
event-driven backtesting framework written in golang
Open-source Rust framework for building event-driven live-trading & backtesting systems
High-frequency statistical arbitrage
backtrader documentation
A Backtest or Trading Framework with C++
These are the code snippets used in the Backtrader for backtesting guide on the AlgoTrading101 website
Built a practical Multi-Factor Backtesting Framework from scratch based on Huatai Security's(One of China's largest sell side) financial engineering report. Steps include factor data collection and preprocessing, factor combination, portfolio optimization and risk return analysis.
A highly customizable framework designed for parallel tuning of trading algorithms by reproducing and simulating the trading history of exchanges and the behaviour of brokers.
A fast and simple backtest implementation for algorithmic trading in golang
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