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 overfitting, or systematic trading development.
Оригинален текст · няма потвърден превод на българскиПърво прочетете инструкцията.
Файлът се зарежда от посочения източник само по ваше действие. Можете да го прочетете тук, да го копирате или да го запазите като .md.
GitHub хранилище- Звезди на репото
- 2871
- Лиценз
- MIT
- Последна проверка на записа
- 22.09.2026 г.
Добавете в своя инструмент.
Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add tradermonty/claude-trading-skillsСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
Откъс от SKILL.md
Началото на файла на автора, без превод.
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