市场数据神经网络模型训练

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

1. Train the specified model:

```bash
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
```

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

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

1. Compare model performance across types:

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

1. 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" })

1. Train SONA on model outcomes:

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