6 boosters for "backtesting" — AI-graded, open source, ready to install
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.
A Cursor IDE rule set for developers working on OKX Trading, a Java Spring Boot cryptocurrency backtesting system with AI strategy generation, providing project-specific conventions for coding standards, API interactions, and full-stack development workflows.
A .windsurfrules collection that guides AI coding assistants in implementing rigorous backtesting techniques for trading strategies, helping developers avoid common pitfalls like lookahead bias and overfitting. Useful for quants and trading engineers building reliable strategy validation systems.
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.
