ruvnet / trader-train

Train neural prediction models using neural-trader's ML engine.

Steps:

1. Ensure neural-trader is available:
   npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader

2. Train the specified model:
   `bash
   npx neural-trader --train --model lstm --symbol TICKER --epochs 50
   `
   --train is required; --model X --symbol T alone is rejected ("Unknown command"), and --confidence and --decompose do not exist. In 2.8.11 the result carries upstreamStub: true ("neuralTrain returns a placeholder shape (no real LSTM/Transformer fit yet)"). Report that no model was fitted; never describe the output as a trained model or quote its numbers as accuracy.

3. Review training output: loss curves, validation metrics, prediction accuracy

4. Generate predictions with confidence intervals:
   `bash
   npx neural-trader --predict --model MODEL --symbol TICKER [--gpu]
   `
   --horizon is not supported, and neuralPredict is also a placeholder (upstreamStub: true) in 2.8.11.

5. Model comparison (--model-compare) does not exist in the CLI. Without a real fit there is nothing to compare; say so rather than fabricating a ranking.

6. Store model results (canonical trading-analysis namespace per ADR-126 Phase 1 — was previously stored to undeclared trading-models):
   mcp__plugin_ruflo-core_ruflo__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-analysis" })

7. Train SONA on model outcomes:
   mcp__plugin_ruflo-core_ruflo__neural_train({ modelType: "moe", epochs: 10 }) (modelType is required; there is no patternType). Skip this step when step 2 returned a stub, so placeholder runs do not train SONA.