/trader

name: trader
description: Neural trading via npx neural-trader — strategies, backtesting, signals, risk, portfolio optimization

$ARGUMENTS Manage neural trading strategies via the `neural-trader` npm package. Parse subcommand from $ARGUMENTS.

Usage: /trader <subcommand> [options]

Subcommands:

• `strategy create <name> --type <momentum|mean-reversion|pairs|adaptive>` -- Create a strategy
• `backtest <strategy> --symbol <TICKER> --period <range>` -- Run backtest (Rust/NAPI, 8-19x faster)
• `train <model> --symbol <TICKER>` -- Train neural model (lstm, transformer, nbeats)
• `signal scan [--strategy <name>]` -- Scan for trading signals via anomaly detection
• `risk assess [--symbol <TICKER>]` -- Calculate risk metrics (VaR, Sharpe, drawdown)
• `portfolio optimize [--risk-target <number>]` -- Optimize allocation via mean-variance
• `live --broker <name> [--swarm enabled]` -- Start live trading with optional swarm coordination
• `history` -- View trade history and performance summary
• `cloud <backtest|train|sweep> <strategy-or-model> --symbol <TICKER> [--period 2020-2024] [--mc-paths 1000]` -- Run a HEAVY job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on an Anthropic Managed Agent cloud container instead of locally. Needs `ANTHROPIC_API_KEY`. See the `trader-cloud-backtest` skill + ADR-117. (Cost: a cloud session bills container time + tokens until terminated — the skill installs neural-trader once, reuses the env, pre-flights cheap, terminates eagerly.)

Steps by subcommand:

**strategy create**: 1. Run: `npx neural-trader --strategy <type> --symbol <TICKER> --create` 2. Store strategy config in memory: `npx @claude-flow/cli@latest memory store --key "strategy-NAME" --value "CONFIG" --namespace trading-strategies`

**backtest**: 1. Run: `npx neural-trader --backtest --strategy <name> --symbol <TICKER> --period <range> --walk-forward` 2. Capture Sharpe ratio, max drawdown, win rate, profit factor from output 3. Store results: `npx @claude-flow/cli@latest memory store --key "backtest-ID" --value "RESULTS" --namespace trading-backtests` 4. If Sharpe > 1.5, train SONA: `npx @claude-flow/cli@latest neural train --pattern-type trading-strategy --epochs 10`

**train**: 1. Run: `npx neural-trader --model <lstm|transformer|nbeats> --symbol <TICKER> --confidence 0.95` 2. Capture predictions and confidence intervals from output

**signal scan**: 1. Run: `npx neural-trader --signal scan --symbols <TICKERS>` 2. If --strategy specified, run: `npx neural-trader --signal scan --strategy <name>` 3. Store signals: `npx @claude-flow/cli@latest memory store --key "signal-TIMESTAMP" --value "SIGNALS" --namespace trading-signals`

**risk assess**: 1. Run: `npx neural-trader --risk assess --symbol <TICKER>` or: `npx neural-trader --var --symbol <TICKER> --investment <amount>` 2. Run: `npx neural-trader --risk-tolerance 0.02 --symbol <TICKER>` for position sizing 3. Store assessment: `npx @claude-flow/cli@latest memory store --key "risk-ID" --value "METRICS" --namespace trading-risk`

**portfolio optimize**: 1. Run: `npx neural-trader --portfolio optimize` or: `npx neural-trader --portfolio optimize --risk-target <number>` 2. Run: `npx neural-trader --portfolio rebalance` to generate trade plan 3. Store allocation: `npx @claude-flow/cli@latest memory store --key "portfolio-TIMESTAMP" --value "ALLOCATION" --namespace trading-portfolio`

**live**: 1. Run: `npx neural-trader --broker <name> --strategy <name> --swarm enabled` 2. Monitor output for trade executions and risk alerts 3. Circuit breakers auto-enforce: daily 3% loss halt, weekly 5% size reduction

**history**: 1. Search memory: `npx @claude-flow/cli@latest memory search --query "trade history" --namespace trading-history` 2. Show recent trades with PnL, strategy attribution, and aggregate metrics