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Found 67 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".
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.
Test trading strategies on historical data to evaluate performance, risks, and profitability.
Set up the Python backtesting environment. Detects OS, creates virtual environment, installs dependencies (openalgo, ta-lib, vectorbt, plotly), and creates the backtesting folder structure.
VectorBT backtesting expert. Use when user asks to backtest strategies, create entry/exit signals, analyze portfolio performance, optimize parameters, fetch historical data, use VectorBT/vectorbt, compare strategies, position sizing, equity curves, drawdown charts, or trade analysis. Also triggers for openalgo.ta helpers (exrem, crossover, crossunder, flip, donchian, supertrend).
Optimize strategy parameters using VectorBT. Tests parameter combinations and generates heatmaps.
Quick backtest a strategy on a symbol. Creates a complete .py script with data fetch, signals, backtest, stats, and plots.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
Quickly fetch data and print key backtest stats for a symbol with a default EMA crossover strategy. No file creation needed - runs inline in a notebook cell or prints to console.
Comprehensive guide for FinLab quantitative trading package for Taiwan stock market (台股). Use when working with trading strategies, backtesting, Taiwan stock data, FinLabDataFrame, factor analysis, stock selection, or when the user mentions FinLab, trading, 回測, 策略, 台股, quant trading, or stock market analysis. Includes data access, strategy development, backtesting workflows, and best practices.
World-class systematic trading research - backtesting, alpha generation, factor models, statistical arbitrage. Transform hypotheses into edges. Use when "backtest, alpha, factor model, statistical arbitrage, quant research, systematic trading, mean reversion, momentum strategy, regime detection, walk forward, " mentioned.