advanced-algorithmic-trading

425
if __name__ == "__main__":
random_state = 42
n_estimators = 400
n_jobs = 1
csv_filepath = "/path/to/your/AREX.csv"
lookback_minutes = 30
lookforward_minutes = 5
print("Importing and creating CSV DataFrame...")
start_date = datetime.datetime(2007, 11, 8)
end_date = datetime.datetime(2012, 12, 31)
ts = create_up_down_dataframe(
csv_filepath,
lookback_minutes=lookback_minutes,
lookforward_minutes=lookforward_minutes,
start=start_date, end=end_date
)
The following code uses the Pandas DataFrame generated above to create the X feature
matrix and the y response vector. In this instance only the first five prior lags are used, although
thirty are available in the initial data generation.
Once the data has been formatted into the X and y data-structures, a training-test split is
created, with the test set equalling 30% of the data.
Note that in the final model development the data should not be split in this fashion and all
of the data up to 2012 should be utilised to train the model, with the remaining 2013/2014 data
used for out of sample trading strategy validation:
# Use the first five daily lags of AREX closing prices
print("Preprocessing data...")
X = ts[
[
"Lookback%s" % str(i)
for i in range(0, 5)
]
]
y = ts["UpDown"]
# Use the training-testing split with 70% of data in the
# training data with the remaining 30% of data in the testing
print("Creating train/test split of data...")
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.3, random_state=random_state
)
In this section the model is actually fit to the data. The important parameter is max_depth,
which controls the maximum depth of the grown trees in the Random Forest. It controls the