longbridge-ml-strategy
Machine-learning prediction strategy framework via Longbridge Securities — walk-forward rolling training with feature engineering (MACD, RSI, Bollinger Band width, volume change rate) and a scikit-learn classifier (Random Forest / Gradient Boosting); retrains every 60 days, predicts 5-day direction probability; evaluates win rate, profit factor, and Sharpe ratio. For reference only — not investment advice. Triggers: "机器学习", "ML策略", "预测模型", "随机森林", "梯度提升", "深度学习", "AI选股", "walk-forward", "機器學習", "ML策略", "預測模型", "隨機森林", "梯度提升", "machine learning", "ML strategy", "predictive model", "random fores
npx skills add majiayu000/claude-skill-registry --skill longbridge-ml-strategy --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# longbridge-ml-strategy Walk-forward machine-learning framework for stock direction prediction. Fetches historical OHLCV data, engineers technical features, trains a rolling classifier (Random Forest or Gradient Boosting), generates probabilistic buy/sell signals, and evaluates backtest performance. > **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English. ## Dependencies Requires: `scikit-learn`, `pandas`, `numpy` (usually pre-installed). Optional: `xgboost` or `lightgbm` for gradient-boosting models. If unavailable, fall back to a simpler logistic-regression model. ## When to use - User asks for ML-based prediction, rolling model training, feature-importance analysis, or AI-driven entry/exit signals for a single stock. - Triggers: "用机器学习预测 TSLA 涨跌", "NVDA random forest strategy", "walk-forward backtest AAPL". ## Workflow 1. Fetch 504 daily candles (≈ 2 years): `longbridge kline <SYMBOL> --period day --count 504 --format json` 2. **Feature engineering** (compute on rolling windows): - MACD line and signal (EMA12 − EMA26, signal EMA9) - RSI-14 - Bollinger Band width: (upper − lower) / mid, window 20 - Volume change rate: (vol_t
- Dependencies
- When to use
- Workflow
- CLI
- Output
- Error handling
- MCP fallback
- Related skills
- File layout
longbridge kline --help longbridge kline <SYMBOL> --period day --count 504 --format json
What does the longbridge-ml-strategy skill do?
Machine-learning prediction strategy framework via Longbridge Securities — walk-forward rolling training with feature engineering (MACD, RSI, Bollinger Band width, volume change rate) and a scikit-learn classifier (Random Forest / Gradient Boosting); retrains every 60 days, predicts 5-day direction probability; evaluates win rate, profit factor, and Sharpe ratio. For reference only — not investment advice. Triggers: "机器学习", "ML策略", "预测模型", "随机森林", "梯度提升", "深度学习", "AI选股", "walk-forward", "機器學習", "ML策略", "預測模型", "隨機森林", "梯度提升", "machine learning", "ML strategy", "predictive model", "random fores
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill longbridge-ml-strategy --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.