backtest-expert

SKILL.md

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.