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 [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