ml-strategy

# Machine-Learning Predictive Strategy

## Purpose

Use sklearn machine-learning models (`RandomForest` / `GradientBoosting` / `Ridge`) to predict the direction of future returns and generate trading signals. Walk-forward training is used to avoid future data leakage, and feature engineering extracts useful factors from OHLCV data.

## Signal Logic

1. **Validate input**: check OHLCV columns, minimum row count, NaN ratio — skip symbols that fail
2. **Feature engineering**: build multi-dimensional factors from raw OHLCV data (momentum, volatility, RSI, moving-average ratios, volume ratio, and more). All features are sanitized (inf removed, division-by-zero guarded)
3. **Label construction**: future N-day return > 0 is the positive class (`1`), < 0 is the negative class (`0`)
4. **Walk-forward training**: use an expanding or sliding window, train on historical data only, and roll forward day by day for prediction
5. **Signal generation**: map `predict_proba[:, 1]` to `[-1.0, 1.0]`, or use discrete signals from `predict` in `{-1, 0, 1}`. Output is guaranteed clean (no NaN, clipped to range)

## Complete SignalEngine Example

This is the recommended full pipeline. Copy and customise — safety is built in.

```python
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler


def validate_data(df: pd.DataFrame, min_rows: int = 300) -> bool:
    """Check that OHLCV data meets minimum quality for ML training.

    Args:
        df: DataFrame with DatetimeIndex.
        min_rows: Minimum number of rows required.

    Returns:
        True if data is usable.
    """
    required = {"open", "high", "low", "close", "volume"}
    if not required.issubset(df.columns):
        return False
    if len(df) < min_rows:
        return False
    if df["close"].isnull().mean() > 0.2:
        return False
    return True


def build_features(df: pd.DataFrame) -> pd.DataFrame:
    """Build a machine-learning feature matrix from OHLCV data.

    All features are guarded against division-by-zero and sanitized
    (inf replaced with NaN) so downstream code never sees inf values.

    Args:
        df: DataFrame containing open, high, low, close, and volume columns.

    Returns:
        DataFrame with feature columns prefixed by 'f_'.
    """
    c = df["close"]
    v = df["volume"]
    ret = c.pct_change()

    features = pd.DataFrame(index=df.index)
    features["f_ret_5d"] = c.pct_change(5)
    features["f_ret_20d"] = c.pct_change(20)
    features["f_vol_20d"] = ret.rolling(20).std()
    features["f_ma_ratio"] = c / c.rolling(20).mean()
    features["f_volume_ratio"] = v / v.rolling(20).mean()

    # RSI(14) — guard: loss=0 in zero-volatility periods produces inf
    delta = c.diff()
    gain = delta.clip(lower=0).rolling(14).mean()
    loss = (-delta.clip(upper=0)).rolling(14).mean()
    rs = gain / loss.replace(0, np.nan)
    features["f_rsi_14"] = 100 - (100 / (1 + rs))

    # Bollinger Band position — guard: bb_upper == bb_lower when std=0
    ma20 = c.rolling(20).mean()
    std20 = c.rolling(20).std()
    bb_upper = ma20 + 2 * std20
    bb_lower = ma20 - 2 * std20
    bb_range = (bb_upper - bb_lower).replace(0, np.nan)
    features["f_bb_position"] = (c - bb_lower) / bb_range

    # Intraday features
    features["f_high_low_ratio"] = (df["high"] - df["low"]) / c
    features["f_close_open_ratio"] = (c - df["open"]) / df["open"]
    features["f_skew_20d"] = ret.rolling(20).skew()

    # Sanitize: replace all inf with NaN (NaN handled by walk-forward)
    features = features.replace([np.inf, -np.inf], np.nan)
    return features


def walk_forward_predict(
    features: pd.DataFrame,
    labels: pd.Series,
    min_train_size: int = 252,
    retrain_freq: int = 20,
    model_type: str = "random_forest",
    window_type: str = "expanding",
    sliding_size: int = 504,
) -> pd.Series:
    """Walk-forward training and prediction to avoid future data leakage.

    Args:
        features: Feature matrix aligned with labels by row index.
        labels: Binary labels (0/1), representing the direction of future N-day returns.
        min_train_size: Minimum training-set size in trading days.
        retrain_freq: Retrain the model every N days.
        model_type: One of "random_forest" / "gradient_boosting" / "ridge".
        window_type: "expanding" uses all history; "sliding" uses a fixed lookback.
        sliding_size: Lookback window size when window_type is "sliding".

    Returns:
        Predicted signal series with range [-1.0, 1.0], no NaN values.
    """
    predictions = pd.Series(0.0, index=features.index)
    model = None
    scaler = None

    for i in range(min_train_size, len(features)):
        # Retrain every retrain_freq days
        if model is None or (i - min_train_size) % retrain_freq == 0:
            start = max(0, i - sliding_size) if window_type == "sliding" else 0
            X_train = features.iloc[start:i].values
            y_train = labels.iloc[start:i].values

            # Drop rows with NaN
            valid = ~(np.isnan(X_train).any(axis=1) | np.isnan(y_train))
            X_train = X_train[valid]
            y_train = y_train[valid]

            if len(X_train) < 50:
                continue

            # Standardization: fit only on training set
            scaler = StandardScaler()
            X_train = scaler.fit_transform(X_train)

            # Build the model
            if model_type == "random_forest":
                model = RandomForestClassifier(
                    n_estimators=100, max_depth=5, random_state=42,
                )
            elif model_type == "gradient_boosting":
                model = GradientBoostingClassifier(
                    n_estimators=100, max_depth=3, learning_rate=0.05,
                    random_state=42,
                )
            elif model_type == "ridge":