SKILL

Run a historical backtest using the neural-trader Rust/NAPI engine.

Steps:

1. Ensure neural-trader is available: npm ls neural-trader 2>/dev/null || npm install neural-trader
2. Check for saved strategy config: mcp__claude-flow__memory_retrieve({ key: "strategy-STRATEGY_NAME", namespace: "trading-strategies" }) If not found, list available: mcp__claude-flow__memory_search({ query: "strategy", namespace: "trading-strategies", limit: 10 })
3. Run backtest via neural-trader CLI:
   bashCopy

   npx neural-trader --backtest --strategy <name> --symbol <TICKER> --period <range> --walk-forward
   

   For multi-indicator strategies:
   bashCopy

   npx neural-trader --backtest --strategy multi-indicator --position-sizing kelly --symbol SPY --period 2020-2024
   

4. Capture performance metrics from output: total return, annualized return, Sharpe ratio, Sortino ratio, max drawdown, win rate, profit factor, number of trades
5. Store backtest results: mcp__claude-flow__memory_store({ key: "backtest-STRATEGY-TIMESTAMP", value: "RESULTS_JSON", namespace: "trading-backtests" })
6. If Sharpe > 1.5, store as successful pattern: mcp__claude-flow__agentdb_pattern-store({ pattern: "profitable-STRATEGY_TYPE", data: "PARAMS_AND_RESULTS" })
7. Train SONA on the outcome: mcp__claude-flow__neural_train({ patternType: "trading-strategy", epochs: 10 })