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. Feature engineering: build multi-dimensional factors from raw OHLCV data (momentum, volatility, RSI, moving-average ratios, volume ratio, and more)
2. Label construction: future N-day return > 0 is the positive class (1), < 0 is the negative class (0)
3. Walk-forward training: use an expanding window (not a fixed window), train on historical data only, and roll forward day by day for prediction
4. Signal generation: map predict_proba[:, 1] to [-1.0, 1.0], or use discrete signals from predict in {-1, 0, 1}

Feature Engineering Template

The following are commonly used features, all computed with pandas:

import pandas as pd
import numpy as np

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

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

    Returns:
        DataFrame with added 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)
    delta = c.diff()
    gain = delta.clip(lower=0).rolling(14).mean()
    loss = (-delta.clip(upper=0)).rolling(14).mean()
    rs = gain / loss
    features["f_rsi_14"] = 100 - (100 / (1 + rs))

    # Bollinger Band position
    ma20 = c.rolling(20).mean()
    std20 = c.rolling(20).std()
    bb_upper = ma20 + 2 * std20
    bb_lower = ma20 - 2 * std20
    features["f_bb_position"] = (c - bb_lower) / (bb_upper - bb_lower)

    # 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()

    return features

Model Selection Guide

Walk-Forward Training Template (Critical)

Core principle: it is strictly forbidden to train on the full dataset and then predict the full dataset. That is future data leakage.

• You must use an expanding window or rolling window
• StandardScaler must be fit on the training set only, then transform the test set
• Predict only the current day (or a small forward slice) each time, then roll the window forward

from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd

def walk_forward_predict(
    features: pd.DataFrame,
    labels: pd.Series,
    min_train_size: int = 252,
    retrain_freq: int = 20,
    model_type: str = "random_forest",
) -> 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: Model type, one of "random_forest" / "gradient_boosting" / "ridge".

    Returns:
        Predicted signal series with range [-1.0, 1.0].
    """
    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:
            # Expanding window: train on [0, i)
            X_train = features.iloc[:i].values
            y_train = labels.iloc[: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":
                from sklearn.ensemble import GradientBoostingClassifier
                model = GradientBoostingClassifier(
                    n_estimators=100, max_depth=3, learning_rate=0.05,
                    random_state=42
                )
            elif model_type == "ridge":
                from sklearn.linear_model import LogisticRegression
                model = LogisticRegression(penalty="l2", C=1.0, random_state=42)
            else:
                raise ValueError(f"Unsupported model_type: {model_type}")

            model.fit(X_train, y_train)

        # Predict today
        X_today = features.iloc[i : i + 1].values
        if np.isnan(X_today).any():
            predictions.iloc[i] = 0.0
            continue

        X_today = scaler.transform(X_today)

        if hasattr(model, "predict_proba"):
            # predict_proba[:, 1] is in [0, 1], map it to [-1, 1]
            prob = model.predict_proba(X_today)[0, 1]
            predictions.iloc[i] = prob * 2 - 1  # [0,1] -> [-1,1]
        else:
            predictions.iloc[i] = float(model.predict(X_today)[0])

    return predictions

Parameters

Common Pitfalls

1. Data leakage (most fatal): running fit_transform on the full dataset before backtesting means future information was used. Walk-forward is mandatory, and the training set must contain history only
2. Standardization leakage: using StandardScaler on the full dataset to compute means and standard deviations, then transforming everything. The correct approach is fit_transform on the training set, then transform only on the test set
3. Overfitting: trees that are too deep (max_depth > 10), too many features, or too small a training set. Keep max_depth=3~5 and feature count < 15
4. Class imbalance: in bull markets the up/down ratio may be 7:3, so the model may prefer predicting the majority class. Use class_weight="balanced" or SMOTE if needed
5. Feature noise: irrelevant features add noise and reduce performance. Check feature_importances_ after training and remove features with importance below 1%
6. Look-ahead bias (non-leakage form): computing features from today's close and predicting today's signal. Make sure features use only data from T-1 and earlier
7. Retraining frequency: too frequent (daily) makes training slow and overfits recent data; too sparse (yearly) makes the model stale. Recommended retrain_freq=20 (about one month)

Dependencies

pip install scikit-learn joblib pandas numpy

Signal Convention

• predict_proba[:, 1] mapped through prob * 2 - 1 to [-1.0, 1.0] (continuous-strength signal)
• Or discrete signals from predict() in {-1, 0, 1} (short, neutral, long)
• Positive values = bullish direction, negative values = bearish direction, absolute value = confidence strength

Source: JacobHsu/vibe-trading-agent — distributed by TomeVault.