7 boosters for "backtesting" — open source, verified from GitHub, ready to install
LumiBot has a first-class AI agent runtime inside the lifecycle. An AI agent reasons, calls tools, and makes trading decisions on every bar during a backtest. The same strategy code runs live with zero changes. A built-in replay cache makes warm backtest reruns deterministic and fast. The primary w
"name": "finlab-plugin", "description": "FinLab quantitative trading skills for Taiwan stock market (台股) - includes strategy development, backtesting, data analysis, and factor research", "name": "FinLab Community"
Compares multiple trading strategies (long, short, or both) on the same symbol and generates side-by-side performance stats tables. Useful for quantitative traders and backtesting analysts who need to evaluate strategy performance variations quickly.
"name": "polars-backtest", "description": "Polars backtest extension development helper - backtesting with polars expressions", "url": "https://github.com/Yvictor"
AlgoClash is a competitive platform where developers build and deploy autonomous AI trading agents that battle in simulated stock markets with live leaderboards and backtesting. It's useful for ML/AI engineers interested in algorithmic trading, agent design, and competitive benchmarking.
Access crowdsourced forecasting data from RAND's Forecasting Initiative for policy-relevant predictions on geopolitics, national security, and S&T policy. Ideal for developers building forecasting tools, decision-support systems, or needing calibrated base rates for predictions.
An MCP server that enables AI-powered trading strategy development with backtesting, market data integration, and portfolio analysis capabilities. Useful for traders and financial developers seeking to leverage AI for quantitative strategy development.
