A rigorous portfolio-level backtest follows a structured pipeline: universe definition (which stocks are eligible), signal generation (ranking stocks by your criteria), portfolio construction (determining weights and position sizes), execution simulation (modeling transaction costs, slippage, and market impact), and performance evaluation.
Each stage introduces potential biases. The universe must account for survivorship bias. Signals must use point-in-time data. Portfolio construction must respect realistic constraints (position limits, sector caps, turnover limits). Execution must model realistic costs.
Key metrics for evaluating a backtested strategy include the Sharpe ratio (risk-adjusted return), maximum drawdown (worst peak-to-trough decline), Calmar ratio (return divided by max drawdown), turnover (how frequently the portfolio changes), and alpha (excess return over a benchmark).
No single metric tells the complete story. A strategy with a high Sharpe ratio but extreme drawdowns may be unacceptable. A strategy with modest returns but very low drawdowns and consistent alpha generation may be preferable for risk-averse investors.
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