Initial commit

backtests/vectorbt_backtest.py

@@ -0,0 +1,80 @@
+# vectorbt_backtest.py
+
+import numpy as np
+import pandas as pd
+import vectorbt as vbt
+
+def run_vectorbt_backtest(
+    model,
+    X,
+    selected_features,
+    data,
+    scaler,
+    init_cash=10000,
+    freq='4H',
+    threshold=0.0
+):
+    """
+    Runs a vectorbt backtest for a given pre-trained model.
+
+    Parameters
+    ----------
+    model : fitted scikit-learn model
+        Already fitted model (e.g. RandomForestRegressor).
+    X : pd.DataFrame
+        The full feature DataFrame (or the portion you want to backtest).
+    selected_features : list
+        List of feature names used by the model.
+    data : pd.DataFrame
+        Original DataFrame containing at least a 'close' column.
+    scaler : fitted scaler
+        The StandardScaler (or other) used to scale features.
+    init_cash : float
+        Starting capital for the backtest.
+    freq : str
+        Frequency for vectorbt (e.g. '4H', '1D').
+    threshold : float
+        Minimum absolute predicted return to place a trade (optional).
+
+    Returns
+    -------
+    pf : vbt.Portfolio
+        The resulting vectorbt portfolio object.
+    """
+    # 1) Subset X to the selected features
+    X_sel = X[selected_features]
+
+    # 2) Scale
+    X_scaled = scaler.transform(X_sel)
+
+    # 3) Generate predictions
+    preds = model.predict(X_scaled)
+
+    # 4) Convert predictions to signals
+    #    Optionally use threshold to reduce whipsaws
+    if threshold > 0.0:
+        signals = np.where(preds > threshold, 1, np.where(preds < -threshold, -1, 0))
+    else:
+        signals = np.sign(preds)
+
+    # 5) Align signals with close prices
+    close_prices = data.loc[X_sel.index, "close"]
+    # If signals is shorter or the same length
+    if len(signals) < len(close_prices):
+        # Pad signals with 0 if needed
+        signals = np.append(signals, [0]*(len(close_prices)-len(signals)))
+
+    signals_s = pd.Series(signals, index=close_prices.index)
+    # Align if any missing indexes
+    close_prices, signals_s = close_prices.align(signals_s, join="inner", axis=0)
+
+    # 6) Run vectorbt Portfolio
+    pf = vbt.Portfolio.from_signals(
+        close_prices,
+        entries=signals_s > 0,
+        exits=signals_s < 0,
+        init_cash=init_cash,
+        freq=freq
+    )
+
+    return pf