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jblitzar
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github-python

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else:
pred = np.array(
[model.predict(X[i : i + 1], verbose=0)[0][0] for i in range(len(X))]
)
predictions.append(weight * pred)
return np.sum(predictions, axis=0)
# Calculate risk metrics (Sharpe ratio and max drawdown)
def calculate_risk_metrics(returns):
sharpe_ratio = (
np.mean(returns) / np.std(returns) * np.sqrt(252)
) # Assuming daily returns
max_drawdown = np.max(np.maximum.accumulate(returns) - returns)
return sharpe_ratio, max_drawdown
# Predict future stock prices using a trained model
def predict_future(model, last_sequence, scaler, days):
future_predictions = []
current_sequence = last_sequence.copy()
with tqdm(total=days, desc="Predicting future", leave=False) as pbar:
for _ in range(days):
if isinstance(model, (RandomForestRegressor, XGBRegressor)):
prediction = model.predict(current_sequence.reshape(1, -1))
future_predictions.append(
prediction[0]
) # prediction is already a scalar
else:
prediction = model.predict(
current_sequence.reshape(
1, current_sequence.shape[0], current_sequence.shape[1]
),
verbose=0,
)
future_predictions.append(
prediction[0][0]
) # Take only the first (and only) element
# Update the sequence for the next prediction
current_sequence = np.roll(current_sequence, -1, axis=0)
current_sequence[-1] = prediction # Use the full prediction for updating
pbar.update(1)
return np.array(future_predictions)
# Split data into training and testing sets, respecting temporal order
def time_based_train_test_split(X, y, test_size=0.2):
"""
Split the data into training and testing sets, respecting the temporal order.
"""
split_idx = int(len(X) * (1 - test_size))
X_train, X_test = X[:split_idx], X[split_idx:]
y_train, y_test = y[:split_idx], y[split_idx:]
return X_train, X_test, y_train, y_test
# Tune hyperparameters for Random Forest model
def tune_random_forest(X, y, quick_test=False):
# Define parameter distribution based on quick_test flag
if quick_test:
print("Quick test mode: Performing simplified Random Forest tuning...")
param_dist = {"n_estimators": randint(10, 50), "max_depth": randint(3, 10)}
n_iter = 5
else:
print("Full analysis mode: Performing comprehensive Random Forest tuning...")
param_dist = {
"n_estimators": randint(100, 500),
"max_depth": randint(5, 50),
"min_samples_split": randint(2, 20),
"min_samples_leaf": randint(1, 10),
"max_features": ["auto", "sqrt", "log2"],
"bootstrap": [True, False],
}
n_iter = 20
# Initialize Random Forest model
rf = RandomForestRegressor(random_state=42)
# Perform randomized search for best parameters
tscv = TimeSeriesSplit(n_splits=3 if quick_test else 5)
rf_random = RandomizedSearchCV(
estimator=rf,
param_distributions=param_dist,
n_iter=n_iter,
cv=tscv,
scoring="neg_mean_squared_error", # Change to MSE
verbose=2,
random_state=42,
n_jobs=-1,
)
rf_random.fit(X.reshape(X.shape[0], -1), y)
print(f"Best Random Forest parameters: {rf_random.best_params_}")
return rf_random.best_estimator_
# Tune hyperparameters for XGBoost model
def tune_xgboost(X, y, quick_test=False):
# Define parameter distribution based on quick_test flag