# Backtest Expert > Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding… - **Type:** Skill - **Install:** `agentstack add skill-tradermonty-claude-trading-skills-backtest-expert` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [tradermonty](https://agentstack.voostack.com/s/tradermonty) - **Installs:** 0 - **Category:** [Finance & Payments](https://agentstack.voostack.com/c/finance-and-payments) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [tradermonty](https://github.com/tradermonty) - **Source:** https://github.com/tradermonty/claude-trading-skills/tree/main/skills/backtest-expert - **Website:** https://tradermonty.github.io/claude-trading-skills/ ## Install ```sh agentstack add skill-tradermonty-claude-trading-skills-backtest-expert ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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**: 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 .json` — structured evaluation with per-dimension scores, red flags, and verdict - `reports/backtest_eval_.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. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [tradermonty](https://github.com/tradermonty) - **Source:** [tradermonty/claude-trading-skills](https://github.com/tradermonty/claude-trading-skills) - **License:** MIT - **Homepage:** https://tradermonty.github.io/claude-trading-skills/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-tradermonty-claude-trading-skills-backtest-expert - Seller: https://agentstack.voostack.com/s/tradermonty - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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