Training Neural Models on Market Data

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:

   npx neural-trader --model lstm --symbol TICKER --confidence 0.95
   npx neural-trader --model transformer --symbol TICKER --predict
   npx neural-trader --model nbeats --symbol TICKER --decompose
   

3. Review training output: loss curves, validation metrics, prediction accuracy
4. Generate predictions with confidence intervals:

   npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d
   

5. Compare model performance across types:

   npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats"
   

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({ patternType: "trading-model", epochs: 10 })