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Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
---
name: trader-train-c639ef19fdd2
description: Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
allowed-tools: Bash Read mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__neural_train
argument-hint: "<lstm|transformer|nbeats> --symbol <TICKER>"
---
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 neural-trader`
2. 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
```
3. Review training output: loss curves, validation metrics, prediction accuracy
4. Generate predictions with confidence intervals:
```bash
npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d
```
5. Compare model performance across types:
```bash
npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats"
```
6. Store model results:
`mcp__claude-flow__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-models" })`
7. Train SONA on model outcomes:
`mcp__claude-flow__neural_train({ patternType: "trading-model", epochs: 10 })`
---
name: trader-train-c639ef19fdd2
description: Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
allowed-tools: Bash Read mcp__claude-flow__memory_store mcp__claude-flow__memory_search mcp__claude-flow__neural_train
argument-hint: "<lstm|transformer|nbeats> --symbol <TICKER>"
---
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 neural-trader`
2. 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
```
3. Review training output: loss curves, validation metrics, prediction accuracy
4. Generate predictions with confidence intervals:
```bash
npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d
```
5. Compare model performance across types:
```bash
npx neural-trader --model-compare --symbol TICKER --models "lstm,transformer,nbeats"
```
6. Store model results:
`mcp__claude-flow__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-models" })`
7. Train SONA on model outcomes:
`mcp__claude-flow__neural_train({ patternType: "trading-model", epochs: 10 })`
Imported from the listed AgentSkill.sh category. Complete supplied package files and notices are retained. Required external tools and credentials are not installed. No bundled code was executed during import. Individual agent task behavior has not been tested.