Agent skill · data analytics · tradermonty
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.
Why this skill is useful
Provides domain-specific knowledge on systematic backtesting methodologies and robust testing practices that the AI wouldn't reliably generate on its own.
What it needs
Requires python installed locally. About 5k tokens when loaded. Last updated 2026-08-06. 2,581 stars on the source repository.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills tradermonty/backtest-expert