strategy-evaluation

Strategy Evaluation Framework

When to Activate

• Conducting comprehensive evaluation of a trading strategy before deployment
• Adjusting Sharpe ratios for autocorrelation, non-normality, and multiple testing
• Assessing parameter stability and regime robustness
• Estimating strategy capacity and transaction cost sensitivity
• Making go/no-go decisions on strategy deployment

Core Concepts

Strategy Evaluation Checklist

Five Pillars of Strategy Evaluation:

1. Statistical Significance — Is the edge real or noise?
2. Robustness — Does it survive regime changes, parameter perturbation, and alternative data?
3. Capacity — How much capital can the strategy absorb before degrading?
4. Implementation — Can it be executed at realistic costs?
5. Risk Profile — Are the drawdowns and tail risks acceptable?

Sharpe Ratio Adjustments

Adjustment for Autocorrelation:

Many strategies have autocorrelated returns (momentum strategies: positive,
mean reversion: negative). Standard Sharpe assumes IID returns.

Adjusted annualization:
SR_adjusted = SR_daily * sqrt(252 / (1 + 2*sum(rho_k for k=1..n)))

Where rho_k is the autocorrelation at lag k

Positive autocorrelation (momentum): SR is OVERSTATED
- If lag-1 autocorrelation = 0.1:
  SR_adjusted ≈ SR_standard * sqrt(252/(1+2*0.1+2*0.05+...)) ≈ SR * 0.85
  15% overstatement from ignoring autocorrelation

Negative autocorrelation (mean reversion): SR is UNDERSTATED
- If lag-1 autocorrelation = -0.1:
  SR_adjusted ≈ SR_standard * sqrt(252/(1-0.2-0.1+...)) ≈ SR * 1.10
  Mean reversion strategies are slightly better than they appear

Always compute ACF of strategy returns and apply this correction.

Adjustment for Non-Normality:

Sharpe ratio assumes normally distributed returns.
Real trading returns have:
- Negative skewness (fat left tail, common in equity strategies)
- Excess kurtosis (fatter tails than normal in both directions)

Adjusted Sharpe (Lo, 2002):
SR_adjusted = SR * [1 - (skewness/6)*SR + ((kurtosis-3)/24)*SR^2]^(-1/2)

Example:
SR = 1.5, skewness = -1.0, kurtosis = 6 (excess = 3)
Adjustment factor ≈ 0.88
SR_adjusted = 1.5 * 0.88 = 1.32

Strategies that appear to have high Sharpe but negative skew
(selling options, carry trades) are less attractive after adjustment.

Adjustment for Multiple Testing:

See overfitting-prevention skill for full treatment.

Quick reference:
If N strategies tested, minimum Sharpe for significance:
N=1:   0.40
N=10:  0.70
N=50:  0.95
N=100: 1.10

Apply Deflated Sharpe Ratio (DSR) for formal testing.

Parameter Stability

Assessment Methods:

1. Walk-Forward Stability:

Record optimal parameters in each walk-forward window
Calculate coefficient of variation (CV) for each parameter
CV < 0.15: stable (good)
CV 0.15-0.30: moderately stable (acceptable)
CV > 0.30: unstable (concerning)

2. Parameter Heatmap:

For strategies with 2 parameters:
- Create a grid of parameter combinations
- Color by performance metric (Sharpe, return)
- Robust strategy: broad warm region (plateau)
- Overfit strategy: narrow hot spot (peak)

3. Performance Degradation Test:

Perturb each parameter by ±10%, ±20%
If performance drops >30% with ±20% perturbation: fragile
If performance drops <15% with ±20% perturbation: robust

Regime Robustness

Regime Classification:

Define regimes by:
1. Trend regime: trending (ADX>25) vs ranging (ADX<20)
2. Volatility regime: low vol (VIX<15), medium (15-25), high (>25)
3. Correlation regime: normal correlation vs correlation breakdown
4. Macro regime: expansion, slowdown, recession, recovery

Evaluate strategy in each regime independently:
- Sharpe ratio per regime
- Max drawdown per regime
- Hit rate per regime

Red flag: strategy performs well in only one regime
(e.g., only works in bull markets -> not a strategy, just beta exposure)

Regime-Conditional Analysis:

Regime         | Sharpe | Max DD | % of Time | Contribution to P&L
Bull/Low Vol   | 1.8    | 5%     | 35%       | 45%
Bull/High Vol  | 0.9    | 12%    | 15%       | 15%
Bear/Low Vol   | 0.3    | 8%     | 20%       | 5%
Bear/High Vol  | -0.2   | 22%    | 15%       | -10%
Sideways       | 0.6    | 10%    | 15%       | 10%

Assessment: Strategy is primarily a bull market strategy.
45% of P&L from 35% of time (bull/low vol). Negative in bear/high vol.
If deployed, needs bear market hedge or position reduction trigger.

Capacity Estimation

Definition: Maximum capital the strategy can manage before transaction costs, market impact, and liquidity constraints erode performance.

Estimation Framework:

Capacity factors:
1. Average daily volume of traded instruments
2. Strategy turnover (annual turnover = trades per year * average position)
3. Market impact model
4. Target impact threshold (e.g., max 10% of daily volume)

Simple capacity estimate:
Capacity = ADV * max_participation_rate / daily_turnover

Where:
- ADV = average daily dollar volume of traded instrument
- max_participation_rate = 1-5% (higher for liquid markets)
- daily_turnover = strategy's daily trading volume as % of AUM

Example:
- Trading SPY: ADV = $30B
- Participation rate: 1%
- Daily turnover: 20% of AUM
- Capacity = $30B * 0.01 / 0.20 = $1.5B

For less liquid instruments:
- Small cap stocks: capacity might be $10-50M
- Crypto altcoins: capacity might be $1-5M
- FX G10: capacity effectively unlimited for most strategies

Market Impact Models:

Square root impact model:
Impact (bps) = sigma * sqrt(V_trade / V_daily) * C

Where:
sigma = daily volatility (bps)
V_trade = trade volume
V_daily = average daily volume
C = constant (~1 for equities)

Example: sigma=100bps, V_trade=1M shares, V_daily=10M shares
Impact = 100 * sqrt(0.1) * 1 = 31.6 bps one-way
Round-trip impact: ~63 bps

For 200% annual turnover (daily turnover ≈ 0.8%):
Annual impact cost: 63 bps * 200% / 100 = 126 bps
If gross Sharpe = 1.5 and vol = 15%: gross return = 22.5%
Net of impact: 22.5% - 1.26% = 21.24%
Impact is manageable at this AUM level.

Transaction Cost Sensitivity

Cost Components:

Total round-trip cost = commission + spread + market impact + slippage

Commission: mostly negligible now ($0 for retail, $0.002-0.005/share institutional)
Spread: bid-ask spread, varies by instrument
  - SPY: ~$0.01 (~0.002%)
  - Small caps: $0.05-0.50 (0.1-1.0%)
  - Crypto (BTC): ~0.01% on major venues
  - Crypto (altcoins): 0.1-1.0%
Market impact: from square root model above
Slippage: execution price vs decision price (latency-dependent)

Sensitivity Analysis:

Run backtest at multiple cost assumptions:
Cost scenario | Sharpe | CAGR  | Assessment
Zero cost     | 1.80   | 25%   | Theoretical upper bound (meaningless)
Low (5 bps)   | 1.55   | 21%   | Best case realistic
Medium (10bps)| 1.30   | 18%   | Base case
High (20 bps) | 0.85   | 12%   | Conservative / stress case
Very high(50) | 0.15   | 3%    | Capacity-constrained scenario

Critical cost threshold: the cost level where Sharpe drops below 0.5
If critical threshold < 15 bps: strategy is cost-sensitive, needs low-cost execution
If critical threshold > 50 bps: strategy is cost-robust

Methodology

Comprehensive Evaluation Process

Phase 1: Statistical Validation

1. Calculate raw performance metrics (Sharpe, drawdown, etc.)
2. Adjust Sharpe for autocorrelation and non-normality
3. Apply DSR for multiple testing correction
4. Run walk-forward analysis and calculate WFE
5. Run CPCV and assess path distribution
6. Determine statistical significance

Phase 2: Robustness Testing

1. Parameter stability across walk-forward windows
2. Parameter sensitivity (perturbation analysis)
3. Regime-conditional performance breakdown
4. Alternative asset/universe testing
5. Alternative time period testing
6. Monte Carlo simulation for confidence intervals

Phase 3: Practical Assessment

1. Capacity estimation
2. Transaction cost sensitivity
3. Execution feasibility (latency, infrastructure requirements)
4. Operational risk assessment
5. Regulatory and compliance review
6. Correlation with existing portfolio strategies

Phase 4: Go/No-Go Decision

Deploy (full allocation) when ALL of:
- Adjusted Sharpe > 0.8
- DSR significant at p < 0.05
- WFE > 0.6
- CPCV: >80% paths profitable
- Robust across 2+ regimes
- Capacity > 2x planned allocation
- Net-of-cost Sharpe > 0.5
- Economic rationale documented

Paper trade when:
- Adjusted Sharpe > 0.5
- Some robustness concerns
- Limited out-of-sample history
- Capacity may be constrained

Reject when:
- Adjusted Sharpe < 0.5 or DSR not significant
- WFE < 0.4
- Only works in one regime
- Transaction costs erode most of the edge
- No clear economic rationale

Correlation with Existing Strategies

Before adding a new strategy to a portfolio:
1. Calculate correlation of returns with each existing strategy
2. Calculate correlation during stress periods (may differ from average)
3. Assess marginal Sharpe contribution:
   Marginal SR = (SR_new - rho * SR_existing) / sqrt(1 - rho^2)
   If marginal SR < 0.3: strategy does not add enough diversification
4. Run portfolio optimization including new strategy
5. Check if new strategy displaces existing strategies or adds new capacity

Examples

Example 1: Full Evaluation Report

Strategy: Mean reversion on S&P 500 sector ETFs
Backtest: Jan 2010 - Dec 2024 (15 years, daily)

Raw metrics:
- CAGR: 14.2%, Vol: 10.5%, Sharpe: 1.35
- Max DD: 11.3%, Calmar: 1.26
- Win rate: 54%, Avg Win/Loss: 1.3x

Adjustments:
- Autocorrelation (lag-1 = -0.08): adjusted SR = 1.42 (slight boost, mean reversion)
- Non-normality (skew=-0.5, kurt=4.2): adjusted SR = 1.28
- Multiple testing (15 variants tested): DSR significant at p=0.03

Walk-forward (4Y IS, 1Y OOS, rolling):
- Average OOS Sharpe: 0.88
- WFE: 0.65 (acceptable)
- All 11 OOS windows positive

Parameter stability:
- Lookback: CV=0.12 (stable)
- Threshold: CV=0.18 (moderately 

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