advanced-algorithmic-trading

223
While seemingly not a traditional area that might be applied to quantitative finance, it can
be used indirectly on alternative data sources such as satellite imagery, to produce information
on supply and demand. For instance, analysing oil storage tank height and maritime oil freight
traffic can lead to a better understanding of current supply and demand for crude oil.
15.4.5 Model Accuracy
One of the trickiest aspects of machine learning is determining which model is "best" for any
particular problem or dataset at hand. This is known as model selection.
For supervised learning models a major issue arises when the model flexibility is adjusted.
While this helps the model adapt to more complex datasets it is also increasing the likelihood of
overfitting.
This situation occurs when the model is more closely aligned to "noise" in the training data
than the underlying "signal". It has the effect of reduced generalisation performance of the
model on unseen data. This particular issue leads to a balancing act between flexibility and
performance known as the bias-variance tradeoff.
A technique to mitigate the effects of the bias-variance tradeoff is known as cross-validation.
It involves partitioning the data into random subsets and fitting the model on each. Each model
fit is then assessed and averaged over the entire set, with the goal of producing a more robust
model that is less prone to overfitting on unseen data.
The bias-variance tradeoff and cross-validation techniques will be discussed at length in subsequent
chapters.
15.4.6 Parametric and Non-Parametric Models
Statistical machine learning models can be categorised into parametric and non-parametric methods.
They each have their advantages and disadvantages when applied to quantitative finance
data.
Parametric Models
A parametric statistical learning model is one that involves a specified model form for f along
with a set of parameters that define its behaviour. The canonical example of a parametric
model is linear regression. It involves estimating a set of p + 1 coefficients, given by the vector
β = (β 0 , β 1 , ..., β p ) whereby the response Y is linearly proportional, via each proportionality
constant β j , to each feature x j , plus an "intercept" term β 0 . The parameters of the model are
the β j coefficients.
Most of the models considered in this book will be parametric. While linear parametric models
may not seemingly be flexible enough to handle the non-linearities of asset price data, they can
often form effective trading algorithms. As always, by increasing the number of parameters in
order to increase flexibility there is always the danger of overfitting as the model is following the
"noise" too closely and not the "signal".
Non-Parametric Models
The alternative is to consider a form for f that does not involve any parameters - it is nonparametric.
The benefit of using a non-parametric model is greater flexibility. Their main