Alpha is the last man standing. More so, if you are an investor.
Alpha is loosely defined as the profits one makes above the average profits. So if the Index generated 20% a year, and your trading generated 24%, you had an alpha of 4%.
Alpha is nice. It has a little problem. It naturally reduces over a period of time.
Let me explain, alpha is a result of a special trading strategy that is not known to a lot of people. But over a period of time, in a complex ecosystem of investing, the trading strategy starts to lose its effectiveness. This always happens to every wonderful strategy.
Combine this with the fact that now there is so much data to be analyzed, so much testing to be done before one can zero down on a good strategy.
It is obvious that manually it will not be feasible.
That is why when we were approached by a small group of individual investing professionals who wanted their strategies to be enhanced through technology, we latched onto the opportunity.
Their methods were manual, so they could only analyze a few stocks. Doing things manually often meant execution delays, wrong decisions were just some of the problems.
The next few months resulted in creation of a cloud based, state of the art reinforcement learning model that could solve this problem for them.
A cloud based system could ingest and process price, volume and open interest data from third party APIs at any frequency they wanted. Post processing prepared data for the final analysis step that would be executed by a carefully tuned reinforcement learning model that took over 38 different dimensions before it could decide the trade strategy.
The output was an automated email that informed of the next trading decisions, and updates on existing ones.
Building this using reinforcement learning might seem like an overkill in the beginning but it allowed this solution to find new insights regularly, without losing so much of its alpha for long periods of time.
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