advanced-algorithmic-trading

259
amzn = create_lagged_series("AMZN", start, end, lags=3)
amzn.dropna(inplace=True)
# Use the first three daily lags of AMZN closing prices
# and scale the data to lie within -1 and +1 for comparison
X = amzn[["Lag1", "Lag2", "Lag3"]]
y = amzn["Today"]
X = scale(X)
y = scale(y)
# Use the training-testing split with 70% of data in the
# training data with the remaining 30% of data in the testing
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=random_state
)
# Pre-create the arrays which will contain the MSE for
# each particular ensemble method
estimators = np.zeros(axis_step)
bagging_mse = np.zeros(axis_step)
rf_mse = np.zeros(axis_step)
boosting_mse = np.zeros(axis_step)
# Estimate the Bagging MSE over the full number
# of estimators, across a step size ("step_factor")
for i in range(0, axis_step):
print("Bagging Estimator: %d of %d..." % (
step_factor*(i+1), n_estimators)
)
bagging = BaggingRegressor(
DecisionTreeRegressor(),
n_estimators=step_factor*(i+1),
n_jobs=n_jobs,
random_state=random_state
)
bagging.fit(X_train, y_train)
mse = mean_squared_error(y_test, bagging.predict(X_test))
estimators[i] = step_factor*(i+1)
bagging_mse[i] = mse
# Estimate the Random Forest MSE over the full number
# of estimators, across a step size ("step_factor")
for i in range(0, axis_step):
print("Random Forest Estimator: %d of %d..." % (
step_factor*(i+1), n_estimators)