Performance Analytics

Daily: Quick P&L check (5 minutes). Weekly: Summary metrics and notable patterns (30 minutes). Monthly: Comprehensive analysis with charts (1-2 hours). Quarterly: Deep strategic review (2-4 hours). Don't over-analyze short periods - statistical significance requires sufficient data. But regular check-ins catch problems early.

Below 0.5: Poor - rethink strategy. 0.5-1.0: Acceptable - room for improvement. 1.0-2.0: Good - competitive with professional funds. 2.0+: Excellent - rare and exceptional. For retail traders, Sharpe above 1.0 is a reasonable goal. Note: Very high Sharpes (3+) over short periods often don't persist.

Win rate alone doesn't determine profitability. You could win 80% of trades but if average win is $1,000 and average loss is $5,000, you lose money: (0.8 × $1,000) - (0.2 × $5,000) = $800 - $1,000 = -$200 per trade. Payoff ratio (avg win / avg loss) matters as much as win rate.

Track both. Gross returns show your strategy's potential - the edge before costs. Net returns show actual results after commissions, taxes, slippage. Compare them: if gross is good but net is poor, costs are eating your edge. Focus on net returns for reality, but monitor gross to understand if cost reduction could help.

Use the 13-week (3-month) Treasury Bill rate or the Federal Funds Rate as reference. Currently approximately 3.75-4% annually. For monthly calculations, divide by 12. For daily, divide by 365. You can also use money market fund returns as a practical risk-free alternative.

Use statistical tests: T-test on returns (t-stat > 2 suggests skill), Monte Carlo simulation (compare to random), bootstrap analysis (test robustness). Also: longer track record = more confidence in skill. 1 year of data is suggestive, 3+ years is more conclusive. Consistent performance across different market conditions suggests skill.

Arithmetic: Simple average of periodic returns. Geometric: Compound return accounting for sequencing. Example: +50% then -50% = Arithmetic average 0%, but Geometric return is -25% ($100 → $150 → $75). Geometric is your actual growth rate. Always use geometric (CAGR) for true performance.

Use percentage returns, not absolute dollars. $100,000 profit on $1,000,000 capital (10%) is very different from $100,000 on $10,000,000 (1%). For deposits/withdrawals during period, use time-weighted return (TWR) which eliminates impact of cash flows. Money-weighted return (IRR) includes cash flow timing effects.

Depends on strategy and risk tolerance. Conservative: 10-15% max. Moderate: 15-25% max. Aggressive: 25-35% max. Professional funds typically target 15-20% max. Important: max drawdown will likely be exceeded in the future - historical max is not a ceiling. Plan for worse than you've experienced.

Complex because no perfect benchmark exists. Options: the S&P 500 (broad market), your strategy's underlying, an options-selling index such as the CBOE S&P 500 PutWrite (PUT) or BuyWrite (BXM) if selling premium, risk-adjusted comparison via Sharpe. For delta-neutral strategies, risk-free rate is appropriate benchmark. Match benchmark to your strategy's risk profile.

Use point-in-time data (what was actually tradeable at that moment). Include delisted stocks in universe. Don't use current index constituents historically. Recognize that live performance typically underperforms backtests by 30-50%. Apply haircuts to backtested metrics before making decisions.

Depends on return volatility and effect size you're trying to detect. Rule of thumb: Minimum 30 observations for basic statistics. 100+ trades for meaningful segmented analysis. 250+ for reliable factor analysis. For subtle effects (small alpha), need more data. Use power analysis to determine required sample size for your specific hypothesis.

Segment analysis by regime (bull/bear/sideways, high/low volatility). Use regime-switching models. Calculate regime-conditional metrics. Test if your edge persists across regimes. Be cautious of strategies that only work in one regime - may fail when regime changes. Regime-robust strategies are more reliable.

Normalize returns to equivalent leverage (e.g., 1x). Levered Sharpe = Unlevered Sharpe (leverage doesn't change Sharpe if borrowing at risk-free). But drawdowns scale with leverage. Report both levered and unlevered metrics. Be especially cautious of high Sharpe achieved through very high leverage.

Use confidence intervals, not point estimates. Apply shrinkage (blend historical with market average). Assume some mean reversion of exceptional returns. Account for volatility drag (geometric < arithmetic). Scenario analysis: bull, base, bear, tail. Monte Carlo for probability distributions. Be skeptical of projections from short histories.