Mohammad Aghdam
44 lines
1022 B
Python
44 lines
1022 B
Python
# backtests/vectorbt_backtest.py
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import numpy as np
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import pandas as pd
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import vectorbt as vbt
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def run_vectorbt_backtest(
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model,
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X,
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selected_features,
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data,
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scaler,
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init_cash=10000,
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freq='4H',
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threshold=0.0
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):
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"""
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Simple, short-enabled backtest using target exposure (-1, 0, +1).
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"""
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# features -> scale -> predict
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X_sel = X[selected_features]
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preds = model.predict(scaler.transform(X_sel))
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# map preds -> {-1, 0, 1}
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if threshold > 0.0:
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exposure = np.where(preds > threshold, 1.0,
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np.where(preds < -threshold, -1.0, 0.0))
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else:
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exposure = np.sign(preds).astype(float)
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# align to prices
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close = data.loc[X_sel.index, "close"]
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target = pd.Series(exposure, index=close.index)
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# build portfolio: -1 short, 0 flat, +1 long
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pf = vbt.Portfolio.from_orders(
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close=close,
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size=target,
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size_type='targetpercent',
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init_cash=init_cash,
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freq=freq
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)
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return pf
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