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  0.30: unstable (concerning)

1. 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)

1. Performance Degradation Test:

Perturb each parameter by ±10%, ±20%
If performance drops >30% with ±20% perturbation: fragile
If performance drops 25) vs ranging (ADX25)
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  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  0.6
- 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  DSR requires SR > 1.05
- SR of 1.22 passes marginally, but:
- Walk-forward WFE: 0.32 (poor — significant OOS degradation)
- CPCV: 14/28 paths profitable (50% — coin flip)
- Regime analysis: SR=2.1 in bull markets, SR=-0.3 in bear markets
  -> Strategy is leveraged beta exposure, not alpha
- Cost sensitivity: at 30bps (realistic for small caps), Sharpe = 0.55

Decision: REJECT
Rationale: Poor WFE, regime-dependent performance (only works in bull),
and CPCV shows 50% chance of loss. The "edge" is market exposure
disguised as alpha by favorable backtest period.

Example 3: Transaction Cost Impact

Strategy: Intraday mean reversion on SPY (5-minute bars)
Backtest period: 2020-2024

Performance at different cost levels:
Cost (bps/side) | Sharpe | Trades/Year | Annual Return
0               | 3.20   | 12,000      | 35%
1               | 2.45   | 12,000      | 28%
2               | 1.70   | 12,000      | 21%
3               | 0.95   | 12,000      | 14%
5               | -0.55  | 12,000      | -1%

Analysis:
- Strategy is extremely cost-sensitive
- Each 1 bps increase in round-trip cost reduces Sharpe by ~0.75
- Break-even cost: ~4.2 bps round-trip
- Realistic cost for institutional: 2-3 bps (SPY is very liquid)
- Realistic cost for retail: 5-10 bps (wider spreads, worse execution)

Recommendation:
- Viable for institutional traders with direct market access (Sharpe ~1.7)
- NOT viable for retail traders (negative Sharpe at realistic costs)
- Capacity: ~$50M before market impact becomes significant
  (intraday strategies have limited capacity)

Quality Gate

Before deploying a strategy, verify:

• [ ] Sharpe ratio adjusted for autocorrelation and non-normality
• [ ] Deflated Sharpe Ratio is significant after accounting for ALL strategies tested
• [ ] Walk-forward efficiency ratio (WFE) exceeds 0.6
• [ ] CPCV shows >80% of paths are profitable
• [ ] Parameter stability verified — CV 0.5 at realistic costs
• [ ] Monte Carlo confidence intervals calculated — 90th percentile drawdown acceptable
• [ ] Economic rationale documented — strategy exploits an identifiable and persistent inefficiency
• [ ] Correlation with existing strategies assessed — adds diversification benefit
• [ ] Implementation plan documented — infrastructure, data, execution, monitoring requirements

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: brainbytes-dev
• Source: brainbytes-dev/everything-claude-trading
• License: MIT

Install and usage instructions live in the source repository linked above.