Backtest Expert

Systematic approach to backtesting trading strategies based on professional methodology that prioritizes robustness over optimistic results.

Core Philosophy

Goal: Find strategies that "break the least", not strategies that "profit the most" on paper.

Principle: Add friction, stress test assumptions, and see what survives. If a strategy holds up under pessimistic conditions, it's more likely to work in live trading.

When to Use This Skill

Use this skill when:

Developing or validating systematic trading strategies
Evaluating whether a trading idea is robust enough for live implementation
Troubleshooting why a backtest might be misleading
Learning proper backtesting methodology
Avoiding common pitfalls (curve-fitting, look-ahead bias, survivorship bias)
Assessing parameter sensitivity and regime dependence
Setting realistic expectations for slippage and execution costs

Prerequisites

Python 3.9+ (for evaluation script)
No API keys required
No external data dependencies — metrics are user-provided

Workflow

1. State the Hypothesis

Define the edge in one sentence.

Example: "Stocks that gap up >3% on earnings and pull back to previous day's close within first hour provide mean-reversion opportunity."

If you can't articulate the edge clearly, don't proceed to testing.

2. Codify Rules with Zero Discretion

Define with complete specificity:

Entry: Exact conditions, timing, price type
Exit: Stop loss, profit target, time-based exit
Position sizing: Fixed $$, % of portfolio, volatility-adjusted
Filters: Market cap, volume, sector, volatility conditions
Universe: What instruments are eligible

Critical: No subjective judgment allowed. Every decision must be rule-based and unambiguous.

3. Run Initial Backtest

Test over:

Minimum 5 years (preferably 10+)
Multiple market regimes (bull, bear, high/low volatility)
Realistic costs: Commissions + conservative slippage

Examine initial results for basic viability. If fundamentally broken, iterate on hypothesis.

4. Stress Test the Strategy

This is where 80% of testing time should be spent.

Parameter sensitivity:

Test stop loss at 50%, 75%, 100%, 125%, 150% of baseline
Test profit target at 80%, 90%, 100%, 110%, 120% of baseline
Vary entry/exit timing by ±15-30 minutes
Look for "plateaus" of stable performance, not narrow spikes

Execution friction:

Increase slippage to 1.5-2x typical estimates
Model worst-case fills (buy at ask+1 tick, sell at bid-1 tick)
Add realistic order rejection scenarios
Test with pessimistic commission structures

Time robustness:

Analyze year-by-year performance
Require positive expectancy in majority of years
Ensure strategy doesn't rely on 1-2 exceptional periods
Test in different market regimes separately

Sample size:

Absolute minimum: 30 trades
Preferred: 100+ trades
High confidence: 200+ trades

5. Out-of-Sample Validation

Walk-forward analysis:

Optimize on training period (e.g., Year 1-3)
Test on validation period (Year 4)
Roll forward and repeat
Compare in-sample vs out-of-sample performance

Warning signs:

Out-of-sample <50% of in-sample performance
Need frequent parameter re-optimization
Parameters change dramatically between periods

6. Evaluate Results

Questions to answer:

Does edge survive pessimistic assumptions?
Is performance stable across parameter variations?
Does strategy work in multiple market regimes?
Is sample size sufficient for statistical confidence?
Are results realistic, not "too good to be true"?

Decision criteria:

✅ Deploy: Survives all stress tests with acceptable performance
🔄 Refine: Core logic sound but needs parameter adjustment
❌ Abandon: Fails stress tests or relies on fragile assumptions

Use the evaluation script for a structured, quantitative assessment:

python3 skills/backtest-expert/scripts/evaluate_backtest.py \
  --total-trades 150 \
  --win-rate 62 \
  --avg-win-pct 1.8 \
  --avg-loss-pct 1.2 \
  --max-drawdown-pct 15 \
  --years-tested 8 \
  --num-parameters 3 \
  --slippage-tested \
  --output-dir reports/

The script scores across 5 dimensions (Sample Size, Expectancy, Risk Management, Robustness, Execution Realism), detects red flags, and outputs a Deploy/Refine/Abandon verdict.

Key Testing Principles

Punish the Strategy

Add friction everywhere:

Commissions higher than reality
Slippage 1.5-2x typical
Worst-case fills
Order rejections
Partial fills

Rationale: Strategies that survive pessimistic assumptions often outperform in live trading.

Seek Plateaus, Not Peaks

Look for parameter ranges where performance is stable, not optimal values that create performance spikes.

Good: Strategy profitable with stop loss anywhere from 1.5% to 3.0% Bad: Strategy only works with stop loss at exactly 2.13%

Stable performance indicates genuine edge; narrow optima suggest curve-fitting.

Test All Cases, Not Cherry-Picked Examples

Wrong approach: Study hand-picked "market leaders" that worked Right approach: Test every stock that met criteria, including those that failed

Selective examples create survivorship bias and overestimate strategy quality.

Separate Idea Generation from Validation

Intuition: Useful for generating hypotheses Validation: Must be purely data-driven

Never let attachment to an idea influence interpretation of test results.

Common Failure Patterns

Recognize these patterns early to save time:

Parameter sensitivity: Only works with exact parameter values
Regime-specific: Great in some years, terrible in others
Slippage sensitivity: Unprofitable when realistic costs added
Small sample: Too few trades for statistical confidence
Look-ahead bias: "Too good to be true" results
Over-optimization: Many parameters, poor out-of-sample results

See references/failed_tests.md for detailed examples and diagnostic framework.

Output

reports/backtest_eval_<timestamp>.json — structured evaluation with per-dimension scores, red flags, and verdict
reports/backtest_eval_<timestamp>.md — human-readable report with dimension table, key metrics, and red flag details

Resources

Methodology Reference

File: references/methodology.md

When to read: For detailed guidance on specific testing techniques.

Contents:

Stress testing methods
Parameter sensitivity analysis
Slippage and friction modeling
Sample size requirements
Market regime classification
Common biases and pitfalls (survivorship, look-ahead, curve-fitting, etc.)

Failed Tests Reference

File: references/failed_tests.md

When to read: When strategy fails tests, or learning from past mistakes.

Contents:

Why failures are valuable
Common failure patterns with examples
Case study documentation framework
Red flags checklist for evaluating backtests

Critical Reminders

Time allocation: Spend 20% generating ideas, 80% trying to break them.

Context-free requirement: If strategy requires "perfect context" to work, it's not robust enough for systematic trading.

Red flag: If backtest results look too good (>90% win rate, minimal drawdowns, perfect timing), audit carefully for look-ahead bias or data issues.

Tool limitations: Understand your backtesting platform's quirks (interpolation methods, handling of low liquidity, data alignment issues).

Statistical significance: Small edges require large sample sizes to prove. 5% edge per trade needs 100+ trades to distinguish from luck.

Discretionary vs Systematic Differences

This skill focuses on systematic/quantitative backtesting where:

All rules are codified in advance
No discretion or "feel" in execution
Testing happens on all historical examples, not cherry-picked cases
Context (news, macro) is deliberately stripped out

Discretionary traders study differently—this skill may not apply to setups requiring subjective judgment.

backtest-expert