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--- name: benchmark-selection-for-strategy-evaluation description: Choosing an appropriate benchmark against which to evaluate a strategy's risk-adjusted performance. domain: Backtesting subdomain: Evaluation tags: - backtesting - benchmark - evaluation - metrics brokers_frameworks: - Pandas - NumPy version: "1.0.0" author: System license: MIT --- # Benchmark Selection for Strategy Evaluation ## When to Use Use this skill when evaluating a new strategy. Selecting the right benchmark is crucial; if you run a tech-heavy long-only strategy, benchmarking it against a risk-free rate or a generic broad index will make it look artificially good during tech bull markets. ## Prerequisites - Time series of strategy returns. - Time series of candidate benchmark returns (e.g., SPY, QQQ, sector ETFs, risk-free rate). ## Workflow 1. Collect daily returns for the strategy and several candidate benchmarks. 2. Initialize `BenchmarkSelector` with the benchmark returns. 3. Call `evaluate_benchmarks(strategy_returns)` to calculate correlation, Information Ratio, and Tracking Error. 4. Select the benchmark that closely matches the strategy's risk profile to isolate true alpha. ## Common Pitfalls - **Defaulting to SPY:** Using SPY as a benchmark for everything (e.g., a bond strategy, a short-only strategy, a crypto strategy). - **Ignoring Risk-Free Rate:** For market-neutral absolute return strategies, the risk-free rate or a very low-volatility benchmark is appropriate. ## Verification - Verify that Information Ratio correctly rewards excess return relative to tracking error. - Check that highly correlated benchmarks yield lower tracking error. ## Related Skills - `backtest-reporting-standardized-tearsheet`