statistics time-series probability hidden-markov-models algorithmic-trading

Finding probabilities of patterns in asset price movements based on multiple variables

I am seeking a method to allow me to analyse/search for patterns in asset price movements using 5 variables that move and change with price (from historical data).

I'd like to be able to assign a probability to a forecasted price move when for example, var1 and var2 do this and var3..5 do this, then price should do this with x amount of certainty.

Q1: Could someone point me in the right direction as to what framework / technique can help me achieve this?

Q2: Would this be a multivariate continuous random series analysis?

Q3: A Hidden Markov modelling?

Q4: Or perhaps is it a data-mining problem?

I'm looking for what rather then how.

Solution

One may opt to use Machine-Learning tools to build a learner to either

both classify of what kind the said "asset price movement" will be
and
serve also statistical probability measures for such a Classifier prediction
both regress a real target value, to which the asset price will move
and
serve also statistical probability measures for such a Regressor prediction

A1: ^{( while Stack Overflow discourages users to ask about an opinion about a tool or a particular framework )} there would be not much damages or extra time to be spent, if one performs academia papers research and there would be quite a remarkable list of repeatedly used tools, used for ML in the context of academic R&D. For a reason, there would not be a surprise to meet scikit-learn ML-classes a lot, some other papers may work with R-based quantitative finance / statistical libraries. The tools, however, with all due respect, are not the core to answer all the doubts and inital confusion present in a mix of your questions. The subject confusion is.

A2: No, it would not. Well, unless you beat all the advanced quantitative research and happen to prove that the Market exhibits a random behaviour ( which it is not and for which it would be waste of time to re-cite remarkable research published about why it is not indeed a random process ).

A3: Do not try to jump on any wagon just because of it's attractive Tag or "contemporary popularity" in marketing minded texts. With all due respect, understanding HMM is outside of your sight while you now appear to move just to the nearest horizons to first understand what to look for.

A4: This is a nice proof of a missed target. Your question shows in this particular point better than in others, how small amount of own research efforts were put into covering the problem-domain and acquiring at least some elementary knowledge before typing the last two questions.

If in a need for an inspiration, try to review a nice and a powerful approach for a fast Machine Learning process, where both Classification and Regression tasks obtain also probability estimates for each predicted target value.

To have some idea about highly performant ML-predictors, these typically operate on much more than a set of 5 variables _{( called in the ML-domain "features" )}. ( Think rather about some large hundreds to small thousands features, typically heavily non-linear transformations from the original TimeSeries' data ).

There you go, if indeed willing to master ML for algorithmic trading.

Finding probabilities of patterns in asset price movements based on multiple variables

You may like to read about a state-of-art research in this direction: