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Found 5 Skills
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Build trading systems in the style of Renaissance Technologies, the most successful quantitative hedge fund in history. Emphasizes statistical arbitrage, signal processing, and rigorous scientific methodology. Use when developing alpha research, signal extraction, or systematic trading strategies.
Academic backtesting framework for quantitative research. ~30 risk and performance ratios, 10 classes of indicators, event-driven engine with 6+ strategies, MPT optimizer, forward-looking simulation with Johnson SU + t-Copula, walk-forward CV, stress testing, fundamental analysis (Altman Z, Piotroski, DuPont). All flat Python + numpy.
RQAlpha 米筐开源事件驱动回测框架。支持A股和期货，模块化架构，可自由扩展；当用户需要使用 rqalpha 进行策略回测、模拟交易或Mod插件开发时使用。