Backtesting
Showing 21 of 21 projects
A proof-of-concept AI-powered hedge fund simulation using multiple specialized agents for stock analysis and trading decisions.
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 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 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.
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