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in AI and Deep Learning by (50.2k points)

Given a set of data very similar to the Motley Fool CAPS system, where individual users enter BUY and SELL recommendations on various equities. What I would like to do is show each recommendation and I guess somehow rate (1-5) as to whether it was good predictor<5> (ie. correlation coefficient = 1) of the future stock price (or eps or whatever) or a horrible predictor (ie. correlation coefficient = -1) or somewhere in between.

Each recommendation is tagged to a particular user, so that it can be tracked over time. I can also track market direction (bullish/bearish) based off of something like an sp500 price. The components I think that would make sense in the model would be:

user direction (long/short) market direction sector of the stock

The thought is that some users are better in bull markets than a bear (and vice versa), and some are better at shorts than longs- and then a combination the above. I can automatically tag the market direction and sector (based on the market at the time and the equity being recommended).

The thought is that I could present a series of screens and allow me to rank each individual recommendation by displaying available data absolutely, market and sector outperformance for a specific time period out. I would follow a detailed list for ranking the stocks so that the ranking is as objective as possible. My assumption is that a single user is right no more than 57% of the time - but who knows.

I could load the system and say "Let's rank the recommendation as a predictor of stock value 90 days forward"; and that would represent a very explicit set of rankings.

NOW here is the crux - I want to create some sort of machine learning algorithm that can identify patterns over a series of time so that as recommendations stream into the application we maintain a ranking of that stock (ie. similar to correlation coefficient) as to the likelihood of that recommendation (in addition to the past series of recommendations ) will affect the price.

Now here is the super crux. I have never taken an AI class / read an AI book / never mind specific to machine learning. So I am looking for guidance - sample or description of a similar system I could adapt. The place to look for info or any general help. Or even push me in the right direction to get started...

My hope is to implement this with F# and be able to impress my friends with a new skill set in F# with the implementation of machine learning and potentially something (application/source) I can include in a tech portfolio or blog space;

Thank you for any advice in advance.

1 Answer

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by (108k points)

F# has algebraic data types(particularly in functional programming and type theory).

An algebraic data type is a kind of composite type(a type formed by combining other types). F# has tuples, records, discriminated unions. 

Often values of algebraic types are analyzed with pattern matching. With pattern matching it becomes really easy to work with data structures and make a flow against specified parameters where deconstruction is done automatically.


For more information regarding creating machine learning model using F#, refer the following link:

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