Machine Learning (ML) using Supervised Modelling & Cyclical Regression
Collective Sentiment & Cycles
Trading the stock market is probably the hardest job in the world and as a result of that the most humbling experience one may ever have and yet again its rythms follow the aggregated sentiment of a collectivity. Is the stock market therefore really that random? What if we were to apply a set of mathematics and statistics alongside cyclical concepts? Or even better compute all that in real time across thousands of symbols and publish it in comprehensive and interacive dashboards with visual charts?Cyclical Regression
Cyclical regression analysis is a continuously evolving family of algorithms that provides the simultaneous estimation of all risk parameters of a repetitive oscillation model. We have spent 7+ years developing a cyclical regression model and platform that's able to track in real time more than 2700 instruments across an array of asset classes. Instead of leveraging existing libraries we have developed the algorithm from the ground up hence allowing for optimal performance and fine tuning as well over time. The Hedgtrade platform is a unique offering in that respect combining not only cyclical projections but also combining it with seasonal patterns, technical momentum and trend indicators, dark pool and order book data among many other data sets.
Machine Learning (ML) for Time & Price Projection
At a high level the computation of the cyclical regression starts with the idea similar to that of a Fourier transform which is a mathematical function that transforms a timeseries from the time domain to the frequency domain.
This is a very powerful transformation which gives us the ability to understand the harmonics that are inside the underlying data.
Complex signals made from the sum of sine waves are all around us and that includes stock market dataseries.
In fact, all signals in the real world can be represented as the sum of sine waves, and this is where the marriage of cyclical regression
along with the computation of the underlying dominant harmonics of any financial instrument starts to make sense in order to uncover alpha, but
not yet and one still has to extend that matrix into a time projection. This is where the world of machine learning comes into play
in order to generate and document projections across all kinds of timeframes, instruments, and asset classes.
As an example of what the Fourier-like Hedgtrade transform model does, let's look at the two graphs below:
One of the key steps is to break down the market data series into its own underlying dominant oscillating components.
And to finally filter it by its most dominant harmonics in order to feed the machine learning projection model
And the result of that is a projection into the future that correlates with the underlying matrix, given all things being equal and
updated in real time by the quant platform - example below of a projection with the Russell 2000 going into the next couple of weeks
along with the thought process that it's all about building the sum of the evidence from a statistical point of view along with Hedgtrade's supporting models including
but not limited to seasonality (aggregated, decennial, election, day of month, etc), order book stats, liquidity models, technicals (momentum & trends), VIX projection, US dollar projection,
and so on...
Among other data points, the regression model also offers points of reversals for placing T/Ps and S/Ls alongside its technical trends and momentum metrics
hence supporting the overall thought process for the investor to build the sum of the evidence and have a statisitcal bias that's removed from any emotion or as some
would call it FOMO among other reactions to market movements
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