trader-train
Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
来源信息
- 仓库
- ruvnet/ruflo
- 最近来源活动
- 2026年7月17日 03:06
- 检测到的 SKILL.md 语言
- 英语
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- 分支
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安装方式
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
检查来源文件
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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SKILL.md
来源说明 · 只读预览- name
- trader-train
- description
- Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals
- allowed-tools
- Bash Read mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__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 --ignore-scripts 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 (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 })`
在 GitHub 查看