Backtesting
Showing 36 of 39 projects
A multi-agent LLM framework for financial trading that simulates real-world trading firms with specialized AI agents for market analysis and decision-making.
A proof-of-concept AI-powered hedge fund simulation using multiple specialized agents for stock analysis and trading decisions.
A curated list of insanely awesome libraries, packages, and resources for Quantitative Finance (Quants).
A production-grade Rust-native trading engine with deterministic event-driven architecture for multi-asset, multi-venue systems.
An open-source, event-driven algorithmic trading engine for backtesting and live trading across multiple financial markets.
An open-source Python quantitative trading system supporting stocks, options, futures, and cryptocurrencies with integrated machine learning.
An open-source framework for applying deep reinforcement learning to quantitative finance, featuring a train-test-trade pipeline for stock and crypto trading.
A collection of Python scripts for backtesting quantitative trading strategies, including technical indicators, options strategies, and quantamental analysis.
A Python library for user-friendly forecasting and anomaly detection on time series, from ARIMA to deep neural networks.
A command-line cryptocurrency trading bot using Node.js and MongoDB for automated technical-analysis-based trading.
A Python library for performance and risk analysis of financial portfolios, generating comprehensive tear sheets.
An open-source Python framework for building, training, and evaluating reinforcement learning agents for algorithmic trading.
A curated list of awesome resources for applying LLMs and deep learning to financial market analysis and algorithmic trading.
A modular quantitative finance framework for data collection, analysis, strategy backtesting, and machine learning across multiple markets.
A Python framework for developing and backtesting algorithmic trading strategies with machine learning.
An AI-native modular infrastructure for quantitative trading, featuring a weight-centric architecture for building, testing, and deploying algorithmic strategies.
A collection of Python notebooks and tools for quantitative finance research, including backtesting, machine learning, and portfolio optimization.
A Java library for building, testing, and deploying automated trading strategies with 200+ technical indicators and production-ready tooling.
A framework for autonomous AI trading agents that self-improve their prompts through market feedback and Darwinian selection.
A Python toolkit for training reinforcement learning agents and backtesting rule-based algorithms in financial markets.
A deep reinforcement learning framework for financial portfolio management with policy gradient optimization and backtesting tools.
A modern cryptocurrency trading bot framework written in Go, supporting multiple exchanges, backtesting, and built-in strategies.
A Python library for calculating common financial risk and performance metrics used in quantitative finance.
A curated collection of backtested trading strategies and tools for the Gekko cryptocurrency trading bot.
A comprehensive Go library for technical analysis, offering indicators, strategies, and backtesting with no external dependencies.
A scalable, event-driven backtesting library for reinforcement learning in algorithmic trading, built on Backtrader with OpenAI Gym API.
A Python live trading framework with a zipline-compatible API for executing algorithms via broker APIs.
A .NET library for computing technical indicators, building trading strategies, and backtesting automated stock trading systems.
Code repository for the second edition of Mastering Python for Finance, implementing advanced financial statistical applications using Python.
A comprehensive Rust library for technical analysis, providing common indicators, methods, and an interface for custom indicators.
A CLI tool for batch backtesting, dataset import, and strategy parameter optimization for the Gekko Trading Bot.
A Node.js native library that uses genetic algorithms to perform technical analysis on OHLC data and generate profitable trading strategies.
A blazingly fast, highly scalable graph-based stream processing framework for latency-critical applications like electronic trading and real-time AI.
A trading environment for reinforcement learning agents, supporting backtesting, live trading, and multiple RL algorithms.
A Quasar Framework-based UI replacement for the Gekko trading bot, featuring enhanced live monitoring, charting, and backtest analysis.
A collection of custom indicators, utilities, and configuration files for the Gekko cryptocurrency trading platform.
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