trader-backtest

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Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation

6installs

Added on2026-05-03

NPX Install

npx skill4agent add ruvnet/ruflo trader-backtest

Run a historical backtest using the

neural-trader

Rust/NAPI engine.

Steps:

Ensure neural-trader is available:

npm ls neural-trader 2>/dev/null || npm install neural-trader

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 })

Run backtest via neural-trader CLI:

bash

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

For multi-indicator strategies:

bash

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

Capture performance metrics from output: total return, annualized return, Sharpe ratio, Sortino ratio, max drawdown, win rate, profit factor, number of trades

Store backtest results:

mcp__claude-flow__memory_store({ key: "backtest-STRATEGY-TIMESTAMP", value: "RESULTS_JSON", namespace: "trading-backtests" })

If Sharpe > 1.5, store as successful pattern:

mcp__claude-flow__agentdb_pattern-store({ pattern: "profitable-STRATEGY_TYPE", data: "PARAMS_AND_RESULTS" })

Train SONA on the outcome:

mcp__claude-flow__neural_train({ patternType: "trading-strategy", epochs: 10 })