longbridge-ml-strategy

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

1. Feature engineering (compute on rolling windows):
2. MACD line and signal (EMA12 − EMA26, signal EMA9)
3. RSI-14
4. Bollinger Band width: (upper − lower) / mid, window 20
5. Volume change rate: (vol_t − vol_{t-5}) / vol_{t-5}
6. 5-day price momentum: (close_t / close_{t-5}) − 1
7. Label: 1 if close_{t+5} > close_t × 1.01, 0 if < close_t × 0.99, else drop

1. Walk-forward training:
2. Training window: 252 days; retrain every 60 days
3. Model: RandomForestClassifier(n_estimators=100) or GradientBoostingClassifier
4. Predict probability for the current bar

1. Signal generation:
2. prob > 0.60 → 模型上涨概率偏高 / Model upside probability elevated
3. prob < 0.40 → 模型下跌概率偏高 / Model downside probability elevated
4. Otherwise → 模型无方向性预测 / No directional signal from model

1. Backtest metrics (on out-of-sample predictions):
2. Win rate (% correct directional calls)
3. Profit factor (gross profit / gross loss)
4. Annualised Sharpe ratio (assuming daily rebalance)
5. Max drawdown

1. Feature importance: rank top-5 features by mean decrease in impurity.

Run longbridge kline --help to confirm flag names before calling.

CLI

longbridge kline --help

longbridge kline <SYMBOL> --period day --count 504 --format json

Output

| Metric | 简体 | 繁體 | English | |---|---|---|---| | Current signal | 当前信号 | 當前訊號 | Current signal | | Signal probability | 预测概率 | 預測概率 | Signal probability | | Win rate | 胜率 | 勝率 | Win rate | | Profit factor | 盈亏比 | 盈虧比 | Profit factor | | Sharpe ratio | 夏普比率 | 夏普比率 | Sharpe ratio | | Max drawdown | 最大回撤 | 最大回撤 | Max drawdown | | Top features | 重要特征 | 重要特徵 | Top features |

Output: current signal box → backtest summary table → feature importance list → caveats (past performance, data snooping). Cite Longbridge Securities / 数据来源：长桥证券 / 數據來源：長橋證券.

以上内容仅供参考，不构成投资建议。投资决策请结合自身风险承受能力独立判断。

The above is for reference only and does not constitute investment advice. Investment decisions should be made based on your own risk tolerance.

Error handling

| Situation | 简体回复 | 繁體回復 | English reply | |---|---|---|---| | command not found: longbridge | 回退到 MCP 或提示安装 longbridge-terminal | 回退到 MCP 或提示安裝 longbridge-terminal | Fall back to MCP or install longbridge-terminal | | not logged in / unauthorized | 请运行 longbridge auth login | 請執行 longbridge auth login | Run longbridge auth login | | scikit-learn not found | 提示 pip install scikit-learn pandas numpy，并改用逻辑回归降级 | 提示安裝，降級至邏輯回歸 | Prompt install; degrade to logistic regression | | Fewer than 252 candles | 数据不足，无法完成 walk-forward 训练 | 數據不足 | Insufficient data for walk-forward | | Other stderr | 直接显示原始错误 | 直接顯示原始錯誤 | Surface verbatim |

MCP fallback

When the CLI is unavailable, fall back to the MCP server. Discover available tools from the MCP server's tool list at runtime.

Related skills

• longbridge-kline — OHLCV data source
• longbridge-volatility-strategy — vol regime as an additional feature
• longbridge-multifactor — cross-sectional factor signals complement

File layout

longbridge-ml-strategy/
└── SKILL.md