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.
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.
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.
