real math strategy backtest lab

The problem

Vague trading ideas often fail when exposed to real-world costs and historical volatility. Manual backtesting is slow, prone to look-ahead bias, and frequently ignores critical risk metrics like drawdown or slippage.

What it does

• Translates plain-language strategy rules into executable Python/pandas backtesting code.
• Simulates entry/exit signals, position sizing, and stop-loss logic against historical OHLCV data.
• Calculates risk-adjusted performance including Sharpe ratio, Sortino ratio, and Maximum Drawdown.
• Quantifies the specific impact of transaction costs, commissions, and slippage on net returns.
• Validates data integrity by checking for gaps, duplicates, and missing values before execution.

Frameworks & tools

Python, pandas, and NumPy for vectorization and performance metrics.

Why this beats prompting it yourself

Generic prompts often forget to account for transaction-cost drag or accidentally introduce look-ahead bias by using future data points in current signals. This skill enforces a rigorous simulation framework that isolates costs and calculates downside deviation for a realistic view of risk.

Use cases

• Backtesting moving average or RSI-based indicators on daily or intraday CSV data.
• Analyzing the impact of a 0.1% commission fee on a high-frequency strategy.
• Comparing the Sortino ratio of a long-only strategy versus one that includes short selling.
• Identifying the maximum peak-to-trough decline to determine capital requirements.

Known limitations

Requires structured price data (timestamp, OHLCV). It does not fetch external market data; files must be provided or simulated.