ruflo-neural-trader

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

Designs and optimizes neural trading strategies using npx neural-trader — LSTM/Transformer models, Rust/NAPI backtesting, Z-score anomaly detection. Pipeline middle stage — receives RegimeVerdict from market-analyst, sends SignalProposal[] to risk-analyst, gated on RiskDecision approval (ADR-126 Phase 5)

Backtesting specialist using npx neural-trader Rust/NAPI engine — walk-forward validation, Monte Carlo simulation, parameter optimization. Orthogonal research lane (ADR-126 Phase 5) — produces signed promotion candidates, NOT a hot-path participant in live execution

Market regime detection and technical analysis using npx neural-trader — RSI, MACD, Bollinger Bands, volume profile, regime classification. Pipeline entry point — sends RegimeVerdict to trading-strategist (ADR-126 Phase 5)

Portfolio risk assessment and position sizing using npx neural-trader — VaR/CVaR, Kelly criterion, circuit breakers, correlation monitoring. Pipeline BLOCKING GATE — receives SignalProposal from trading-strategist, returns RiskDecision (ADR-126 Phase 5)

Run a historical backtest using npx neural-trader with Rust/NAPI engine (8-19x faster) and walk-forward validation; Ed25519-sign the result for paper→live tamper evidence (ADR-126 Phase 4)

Run a heavy neural-trader job (long walk-forward, big Monte-Carlo, parameter sweep, model training) on the Anthropic Managed Agent cloud runtime instead of locally

Regulator-grade feature attribution for any LSTM/Transformer signal — single-entry PageRank ranks the top-K features that drove the prediction (ADR-126 Phase 6, ADR-123 single-entry PR)

Mean-variance portfolio optimization via Conjugate Gradient — 40-60× faster than the legacy Neumann path (ADR-126 Phase 3, ADR-123 Wedge 8)

Optimize portfolio allocation using npx neural-trader mean-variance engine with risk constraints and rebalancing plan