advanced-algorithmic-trading

Chapter 29
Supervised Learning for Intraday
Returns Prediction using QSTrader
All of the trading strategies discussed in the book so far using QSTrader have been carried out
on daily OHLCV "bar" equities data. In this chapter an intraday strategy is backtested. A
predictive machine learning based algorithm is outlined and implemented, which attempts to
predict directional changes of minutely equities returns.
The chapter begins with a discussion of prediction goals for asset pricing data and the issues
associated with classification class imbalance, which is a common situation in quantitative finance
returns prediction.
Attention then turns towards building a predictive model using the Python Scikit-Learn
library with a variety of machine learning based classifiers. Certain convenience functions are
presented that aid such models in an intraday "online" environment.
The implementation of a Strategy class and Backtest script is presented that provides
the full code for testing such a predictive model. The code is relatively straightforward and
encourages significant experimentation through parameter tweaking and modification of the asset
universe.
Finally backtested results are presented for a particular statistical ensemeble technique–the
Random Forest classifier–on an individual equity, AREX, for the period 2013-2014, trained on
data from 2007-2012.
Note that the full code is presented at the end of the chapter. It requires a working installation
of the latest version of QSTrader, which can be found at the Github project page.
29.1 Prediction Goals with Machine Learning
In this chapter the main focus is on using supervised machine learning to make predictions about
asset price directional changes. These methods have been covered before on QuantStart.com and
in previous books, but they have not been placed into a robust intraday event-driven backtest
system as of yet. This chapter changes that.
The first task is to determine exactly what is being predicted. There are a few of examples
to consider:
• Direction - Predict the directional change of the returns of an equity in the next bar
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