https://en.wikipedia.org/wiki/Hidden_Markov_model https://en.wikipedia.org/wiki/System_identification In a nutshell, my goal is to build a probabilistic markovian "state transition model", and than use Bayesian Inference to incorporate market "observations". A transition model can be built using a variety of methods. A popular choice is the EM algorithm. Correction...my goal is to make money. The above goal is a secondary goal used to achieve the primary.
Logical people do logical things. Your background in engineering or computer science? I'm a Control Systems guy...so state-space modeling and kalman filtering is at the forefront of how I approach problems.
Computer science, but I do gravitate towards signal processing. The only problem is I have no formal introduction to it and I don't really know how to become an expert in it. Is there a "signal processing" book that you would recommend?
"Fundamentals of Object Tracking" by Challa et al is what I used to use as a reference for bayesian filtering (Kalman,etc..). It explains the theory, but I primarily used it as a recipe book. Its applications are obviously geared towards aircraft tracking...but a Bayesian Filter is a Bayesian Filter...only the state transition and observations models change.
From the paper: "In the case of Naive model, since it is judged that Pt is the same at the time point t + 5, it cannot be determined whether it will rise at the time point t + 5. Therefore, we assumed trading at each time point and as a result we recorded 15.48% revenue." I don't get it. If P_{t+5}=P_t, i.e., the naive strategy says that the price will not change, then how exactly it can be used to make profit? "Buy and hold strategy is a simple trading method to buy shares at the time of starting trading and sell shares at the end of the trading period. We confirmed that revenue was 3.09% when we implemented this strategy." So it seems that the time interval is so small that the market is not really positive in that time window. In other words, even a few years of US equities would lead to a large positive % (except from 2008-2009). Short out-of-sample time series are not really convincing. Then, their approach shows 17.38% compared to 15.48% of the naive strategy. Can the difference can be attested to overfitting of hyperparameters? They don't show a single figure with evolution of training and validation curves, that is weird. I hope you find the paper useful but after my quick check it looks like a mess to me. Well, it is PLOS One, that is probably why.
Just looking at that paper raises several flags, and makes it not really worthy of continuing (other than academic theory)... Some examples 1) Use 1 year of data??? 2016 that's great for the one year of data. Too bad next year may be nothing like it. 2) RMSE is kind of a useless metric to your trading system. So, you got an RMSE improvement of .34%, so what. How does that translate to profit, or some other suitable metric? 3) Why compare system 1 RMSE to system 2 etc? It's ok, but in many ways, useless. Better to compare all to some kind of baseline (with a more profit oriented objective/loss function). They may have addressed some of this as I didn't read further. Markov chains are very useful. Defining the states and optimization criteria, are the tough parts (as is the case with all ML).
Like most papers, they probably have mistakes that prevent the results from being reliable. I usually read for ideas and assume if they don't provide all the code that they don't really believe the results work.