Maghdam
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
# 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
|