advanced-algorithmic-trading

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bias-variance trade-off of the model. A smaller max_depth will have a reduced predictive capability,
but will produce a much smaller file size and will execute much faster in QSTrader.
Correspondingly, a larger max_depth will potentially overfit on the training data, will have a
significant filesize (the default value leads to a model of 4.1Gb in size!) and will execute slowly
in QSTrader.
The Hit Rate and confusion matrix for the testing set are also output here if the training/test
split is chosen as above.
Note that it is also necessary to modify the path to your data in the application of the joblib
serialisation mechanism ( joblib.dump(model, ’/path/to/your/ml_model_rf.pkl’)):
print("Fitting classifier model...")
model = RandomForestClassifier(
n_estimators=n_estimators,
n_jobs=n_jobs,
random_state=random_state,
max_depth=10
)
model.fit(X_train, y_train)
print("Outputting metrics...")
print("Hit-Rate: %s" % model.score(X_test, y_test))
print("%s\n" % confusion_matrix(model.predict(X_test), y_test))
print("Pickling model...")
joblib.dump(model, ’/path/to/your/ml_model_rf.pkl’)
This concludes the model generation script. The pickled model file is stored in ml_model_rf.pkl
and will be used in the following QSTrader Strategy object.
29.3 QSTrader Strategy Object
Now that the predictive model has been succesfully pickled by joblib, attention will turn to using
it in an "online" predictive manner within a QSTrader Strategy subclass.
The strategy itself is relatively simple. Every bar received via the event is used to form a
rolling lag-length array of the previous bars’ closing prices. This is then turned into a set of
returns and multiplied by 100, as in the predictive model fitting stage.
For ease of understanding the procedure the strategy itself has been set to only go long and
exit. It does not go short and exit, although this is a simple modification if so desired.
For every bar the models predict method is called, which returns +1 or -1 depending upon
predicted direction for the next bar. If the strategy is not invested and receives a +1 then it goes
long 10,000 units of AREX. If the strategy is invested and receives a -1 then it closes the 10,000
unit position.
NumPy and Pandas are both used within the class, as is joblib, for unpickling the predictive
model. In addition, the usual QSTrader objects for Strategy classes are imported:
# intraday_ml_strategy.py
import numpy as np
import pandas as pd