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29.2 Building a Prediction Model on Historical Data
In this section a prediction model will be created using Scikit-Learn. As this is a supervised
machine learning task the model is trained on a subset of the historical data. In particular data
for the US equity AREX is used from late 2007 until the end of 2012, although the method will
work with any intraday bar data (including equities and forex).
In order to train the model it is necessary to specify the feature predictors and the response.
The features in this instance are lagged minutely returns. Feature p is the pth bar closing price
return behind the current bar’s closing price return. The response is either the "up/down factor"
as described above (+1 or -1 for True/False) or the directional change of the returns in the next
bar (+1 or -1 for up/down).
The code below generates both the "up/down factor" and the directional change, but in the
trading strategy below the directional change is used as the response. It is straightforward to
uncomment a line in the following snippets in order to change this to the "up/down factor" but
this comes at the price of increased class imbalance. There are fewer instances of the stock going
up by a certain factor and not down by another, compared to simple directional prediction.
Once the model is trained it is then serialised. This means that the Python object containing
the model itself, along with its associated set of trained parameters, is written to disk in a special
format. This allows the model to be deserialised in the QSTrader environment. This is known
as pickling within the Python ecosystem.
Pickling is a common mechanism for deploying machine learning models into production.
Scikit-Learn calls this model persistence. It provides a module called joblib for this purpose.
The first task in the code is to import the necessary libraries, including NumPy, Pandas and
Scikit-Learn. For this example the LinearDiscriminantAnalysis and RandomForestClassifier
are trained, but some other ensemble methods have also been imported and commented out.
These can be uncommented and trained if other predictive models are desired. Finally joblib
is imported for persistence and the confusion_matrix is imported to analyse class imbalance:
# intraday_ml_model_fit.py
import datetime
import numpy as np
import pandas as pd
import sklearn
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.ensemble import (
BaggingClassifier, RandomForestClassifier, GradientBoostingClassifier
)
from sklearn.externals import joblib
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
The following function create_up_down_dataframe is a little complex. It is designed to
create the necessary feature matrix X and the response vector y, although it concatenates them
into the same Pandas DataFrame initially.