backtest-expert

Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies...

Security Scan

This is an instruction-only skill whose requirements, files, and runtime instructions are consistent with its stated purpose of providing backtesting methodology and contain no unexpected credential or install requests.

Purpose & Capability

Name, description, and included files (SKILL.md, README, methodology, failed tests) all describe a backtesting methodology; nothing in the manifest asks for unrelated credentials, binaries, or system access.

Instruction Scope

SKILL.md is a self-contained methodology guide and references bundled docs. It includes an external documentation link (https://skillboss.co/skill.md) but does not instruct the agent to read arbitrary system files, fetch secrets, or call unfamiliar endpoints. Note: if a future version told the agent to run code or access user files/data, that would need re-evaluation.

Install Mechanism

No install spec and no code files; this is instruction-only so nothing is downloaded or written to disk by the skill itself.

Credentials

No environment variables, credentials, or config paths are requested. The guidance discusses data hygiene (survivorship bias, delistings) but does not require secret tokens or external service keys.

Persistence & Privilege

Defaults apply (not always:true). The skill is user-invocable and may be invoked autonomously per platform defaults, which is normal for skills and acceptable given the limited scope and lack of sensitive requests.

Scan Findings in Context

[no-findings] expected: Regex-based scanner had nothing to analyze because this is an instruction-only skill with no executable code files; absence of findings is expected and not a proof of safety beyond coherence.

Assessment

This skill appears coherent and documentation-only: it gives methodology and examples for robust backtesting and asks for no credentials or installs. Before installing, consider: (1) only provide your own market/data files to the agent if you trust it — the skill itself doesn't require them; (2) be cautious if later prompts ask you to run arbitrary code, install packages, or paste API keys — those would be unrelated to the skill's stated purpose; (3) verify any external data provider you use for backtests (survivorship-free datasets, delistings, corporate actions) and prefer local or trusted services; (4) the skill links to an external URL for setup docs — inspect that page before following scripts or downloads. If the skill later requests access to data sources, credentials, or to execute code, re-evaluate for proportionality and risk.

📖 Complete setup guide: https://skillboss.co/skill.md

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

Backtesting 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:

1. Optimize on training period (e.g., Year 1-3)
2. Test on validation period (Year 4)
3. Roll forward and repeat
4. 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

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:

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

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

Available Reference Documentation

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.