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AI/MLruvnet

trader-train

Train neural models (LSTM, Transformer, N-BEATS) on market data using npx neural-trader with confidence intervals

Stars
56,726
Source
ruvnet/claude-flow
Updated
2026-05-31
Slug
ruvnet--claude-flow--trader-train
View on GitHubRaw SKILL.md

// install — copy + paste into any project

mkdir -p .claude/skills && curl -fsSL https://raw.githubusercontent.com/ruvnet/claude-flow/HEAD/plugins/ruflo-neural-trader/skills/trader-train/SKILL.md -o .claude/skills/trader-train.md

Drops the SKILL.md into .claude/skills/trader-train.md. Works with Claude Code, Cursor, and any agent that loads SKILL.md files from .claude/skills/.

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__claude-flow__memory_store({ key: "model-MODEL-TICKER-DATE", value: "TRAINING_RESULTS", namespace: "trading-analysis" })
  7. Train SONA on model outcomes: mcp__claude-flow__neural_train({ patternType: "trading-model", epochs: 10 })