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Backtest stock and crypto strategies with VectorBT, costs, indicators, and performance analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "vectorbt-backtesting-skills" yet — see the docs or source repo.
Use VectorBT to backtest a dual moving average strategy for US stock AAPL with a 20-day fast MA and 50-day slow MA over the last 5 years. Include commissions and slippage. Output total return, max drawdown, Sharpe ratio, and a performance summary.
An executable backtest plan or code with transaction cost settings, key performance metrics, and a strategy performance summary.
Create a backtest for a BTC/USDT RSI trading strategy: buy when RSI is below 30 and sell when it is above 70 using daily data. Include trading fees, and provide parameter optimization suggestions and risk analysis.
A complete output including RSI strategy logic, backtest results, parameter tuning directions, and risk metric explanations.
I already have a VectorBT strategy result. Generate a QuantStats-style performance report based on the backtest output, highlighting cumulative returns, drawdown periods, monthly return distribution, and key risk-return metrics, and explain the strategy’s strengths and weaknesses.
A structured performance report covering chart highlights, interpretation of core metrics, and analysis of the strategy’s strengths and weaknesses.
Run AI-powered quantitative research, backtesting, and live trading across markets.
Run quantitative strategy backtests and analyze results through the Backtest360 engine.
Query strategy leaderboards, backtests, and export Pine scripts for trading analysis.
Run deterministic, reproducible backtests for AI trading strategies.
Turn plain-English strategies into quant research with screening, backtests, and factor analysis.
Analyze markets, build trading strategies, and track opportunities with an AI agent.