backtesting-trading-strategies
Backtest crypto and traditional trading strategies against historical data. Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity curves, and optimizes strategy parameters. Use when user wants to test a trading strategy, validate signals, or compare approaches. Trigger with phrases like "backtest strategy", "test trading strategy", "historical performance", "simulate trades", "optimize parameters", or "validate signals".
Install with Tessl CLI
npx tessl i github:jeremylongshore/claude-code-plugins-plus-skills --skill backtesting-trading-strategies84
Does it follow best practices?
If you maintain this skill, you can automatically optimize it using the tessl CLI to improve its score:
npx tessl skill review --optimize ./path/to/skillValidation for skill structure
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is an excellent skill description that hits all the marks. It provides specific capabilities with concrete metrics, includes comprehensive natural trigger terms, explicitly addresses both what and when, and carves out a clear niche in trading strategy backtesting that distinguishes it from general financial or data analysis skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple concrete actions: 'Backtest crypto and traditional trading strategies', 'Calculates performance metrics (Sharpe, Sortino, max drawdown)', 'generates equity curves', and 'optimizes strategy parameters'. These are specific, actionable capabilities. | 3 / 3 |
Completeness | Clearly answers both what (backtest strategies, calculate metrics, generate curves, optimize parameters) AND when (explicit 'Use when' clause plus dedicated 'Trigger with phrases' section). Both components are explicit and well-defined. | 3 / 3 |
Trigger Term Quality | Excellent coverage of natural trigger terms including 'backtest strategy', 'test trading strategy', 'historical performance', 'simulate trades', 'optimize parameters', 'validate signals'. These are phrases users would naturally say when needing this functionality. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche combining trading/crypto domain with backtesting functionality. Specific metrics (Sharpe, Sortino, max drawdown) and domain-specific triggers make it unlikely to conflict with general data analysis or other financial skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a well-structured skill with excellent actionability - commands are concrete and executable with clear parameter examples. The main weaknesses are moderate verbosity (metrics tables explaining concepts Claude knows) and missing validation/error recovery steps in the workflow, which is particularly important for financial backtesting where data quality and parameter validity matter.
Suggestions
Add validation checkpoints after data fetch (verify data quality, check for gaps) and after backtest (verify results are reasonable before optimization)
Trim the metrics tables - Claude understands Sharpe ratio and max drawdown; keep only the target values or thresholds that are project-specific
Add a feedback loop for optimization: what to do if optimization finds no improvement or overfits
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The content is reasonably efficient but includes some unnecessary elements like the full metrics tables that Claude already understands, and the Resources section with basic library descriptions. The strategy table and configuration examples add value but could be more compact. | 2 / 3 |
Actionability | Provides fully executable bash commands with clear parameters, concrete examples with JSON parameter syntax, and copy-paste ready code. Each step has specific commands that can be run immediately. | 3 / 3 |
Workflow Clarity | Steps are clearly numbered and sequenced (fetch data → run backtest → analyze → optimize), but lacks explicit validation checkpoints. No feedback loops for handling failed backtests or invalid parameters, which is important for financial operations. | 2 / 3 |
Progressive Disclosure | Well-structured with clear overview, appropriately references external files for errors and examples (one level deep), and organizes content into logical sections. The Files table provides good navigation to implementation details. | 3 / 3 |
Total | 10 / 12 Passed |
Validation
75%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 12 / 16 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
allowed_tools_field | 'allowed-tools' contains unusual tool name(s) | Warning |
metadata_version | 'metadata' field is not a dictionary | Warning |
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
body_steps | No step-by-step structure detected (no ordered list); consider adding a simple workflow | Warning |
Total | 12 / 16 Passed | |
- Reviewed
Table of Contents
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