// Analyzes backtest results in plain language, connecting metrics to what they mean for the strategy
| name | backtest-explain |
| description | Analyzes backtest results in plain language, connecting metrics to what they mean for the strategy |
| version | 0.1.0 |
| tags | ["backtest","explanation","metrics"] |
| user_invocable | true |
| model_invocable | true |
| requires_tools | ["backtest_result","explain_pnl"] |
Analyzes backtest results in plain language, connecting metrics to what they mean for the strategy.
backtest_result to load metrics, trade count, and equity context.explain_pnl when the discussion ties to PnL decomposition on paper accounts; otherwise focus on backtest metrics from step 2.backtest_result: Retrieve stored backtest output including metrics and identifiers.explain_pnl: Relate profit/loss composition when comparing live paper performance to backtest expectations.User: I ran a backtest yesterday—can you explain if the drawdown is acceptable?
Agent: I will pull the result with backtest_result, then walk through max drawdown, win rate, and trade count in context of your strategy goals.
Guides the user through exchange connectivity and configuration safely, without handling secrets in chat
Explains common technical indicators (SMA, EMA, RSI, MACD) with quantitative intuition and example calculations
Analyzes current market state and short-term trends using ticker, candle, and narrative context
Explains profit and loss composition and attribution for paper accounts using engine-calculated breakdowns
Generates a risk assessment for current or hypothetical positions using engine rules and paper state
Generate valid EngineStrategyConfig YAML for PnLClaw strategies