Trader Train
- 640 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
trader-train is a Claude Code skill that trains LSTM, Transformer, and N-BEATS neural prediction models on market symbols using the neural-trader CLI with configurable confidence intervals for developers building quantit
About
trader-train wraps the neural-trader npm package to train market prediction models from a Claude Code session. It verifies neural-trader is installed (installing via npm if missing), then runs npx neural-trader with a chosen model flag—lstm, transformer, or nbeats—plus a ticker symbol and confidence level such as 0.95. Training output is persisted through Claude Flow memory_store, memory_search, and the neural_train MCP tool for later inference workflows. Developers invoke trader-train when they need repeatable CLI-driven model training on ingested market data instead of hand-rolling PyTorch or TensorFlow scripts. The skill focuses on training orchestration, not live order execution.
- trader-train
Trader Train by the numbers
- 640 all-time installs (skills.sh)
- +10 installs in the week ending Jul 26, 2026 (Skillselion tracking)
- Ranked #580 of 4,347 Backend & APIs skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 640 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you train LSTM models on market tickers?
Use trader-train for development tasks
Who is it for?
Quant developers who want CLI-driven LSTM, Transformer, or N-BEATS training on tickers without writing custom training scripts from scratch.
Skip if: Teams needing real-time trade execution, non-market datasets, or training outside the neural-trader ecosystem.
When should I use this skill?
User asks to train lstm, transformer, or nbeats models on a market symbol with neural-trader.
What you get
Trained neural-trader model artifacts, stored training metadata, confidence-interval predictions
- Trained model output
- Stored training metadata
By the numbers
- Supports 3 neural-trader model types: LSTM, Transformer, and N-BEATS
- Documents default confidence interval flag --confidence 0.95
Files
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 --decompose3. Review training output: loss curves, validation metrics, prediction accuracy 4. Generate predictions with confidence intervals:
npx neural-trader --model MODEL --symbol TICKER --predict --horizon 5d5. 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 })
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Choose trader-train for npm neural-trader CLI orchestration; use custom ML skills when you need PyTorch notebooks or non-market datasets.
FAQ
Which model types does trader-train support?
trader-train supports three neural-trader architectures—LSTM, Transformer, and N-BEATS—selected via the model argument alongside a ticker symbol and optional confidence interval such as 0.95.
How does trader-train install neural-trader?
trader-train checks npm ls neural-trader and runs npm install --ignore-scripts neural-trader when the package is absent, then executes npx neural-trader with the requested model and symbol flags.