advanced-algorithmic-trading

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15.2 Machine Learning Domains
Machine learning tasks are generally categorised into three main areas: Supervised Learning,
Unsupervised Learning and Reinforcement Learning.
The methods all differ in how the machine learning algorithm is "rewarded" for being correct
in its predictions or classifications.
15.2.1 Supervised Learning
Supervised learning algorithms involve labelled data. That is, data annotated with values such
as categories (as in supervised classification) or numerical responses (as in supervised regression).
Such algorithms are "trained" on the data and learn which predictive factors influence the
responses.
When applied to unseen data supervised learning algorithms attempt to make predictions
based on their prior training experience. An example from the quantitative finance would be
using supervised regression to predict tomorrow’s stock price from the previous month’s worth
of price data.
15.2.2 Unsupervised Learning
Unsupervised learning algorithms do not make use of labelled data. Instead they utilise the
underlying structure of the data to identify patterns. The canonical method is unsupervised
clustering, which attempts to partition datasets into sub-clusters that are associated in some
manner. An example from quantitative finance would be the clustering of certain assets into
groups that behave similarly in order to optimise portfolio allocations.
15.2.3 Reinforcement Learning
Reinforcement learning algorithms attempt to perform a task within a certain dynamic environment,
by taking actions inside the environment that seek to maximise a reward mechanism.
These algorithms differ from supervised learning in that there is no direct set of input/output
pairs utilised within the data. Such algorithms have recently gained significant notoriety due
to their use by Google DeepMind[3]. DeepMind have utilised "deep reinforcement learning"
to exceed human performance in Atari video games[70] and the ancient game of Go[4]. Such
algorithms have been applied in quant finance to optimise investment portfolios.
Unfortunately it won’t be possible to consider Reinforcement Learning in this book as the
topic is extremely broad and constantly evolving, filling many books and research papers in its
own right.
15.3 Machine Learning Techniques
Due to its interdisciplinary nature there are a large number of differing machine learning algorithms.
Most have arisen from the computer science, engineering and statistics communities.
The list of machine learning algorithms is almost endless, as they include crossover techniques
and ensembles of many other algorithms. However, the algorithms frequently used within
quantitative finance are considered below.