advanced-algorithmic-trading

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Figure 22.4: 3D scatterplot of normalised bars along with cluster membership
Figure 22.5: S&P500 candlestick bars for 2013-2015 inclusive ordered via cluster membership,
overlaid with cluster boundaries (blue dotted lines)
This analysis is certainly interesting and motivates further study. However a significant
amount of extra work is required to carry out any form of quantitative trading strategy. In
particular, the above is restricted to the two most recent full years of S&P500 daily data. It
could easily be extended further back in time, or across many more assets (equities or otherwise).
In addition it is not clear if the choice of K = 5 is a good one. Perhaps K = 4 or K = 6
might reveal more structure. Should K be chosen on an asset-by-asset basis and if so, under
what "goodness" metric?
Another problem is that all of this work is in-sample. Any future usage of this as a predictive
tool implicitly assumes that the distribution of clusters would remain similar to the past. A more
realistic implementation would consider some form of "rolling" or "online" clustering tool that
would produce a follow-on matrix for each rolling window.
It would be necessary for this matrix not to deviate too frequently otherwise its predictive
power is likely to be poor, but frequently enough that it can implicitly detect market regime