Introduction

The market data is a sequence called time series. Usually, researchers use only price data (or asset returns) to create a model that forecasts the next price value, movement direction, or other output. I think the better way is to use more data for that. The idea is try to combine versatile market conditions (volatility, volumes, price changes, and etc.)

The first type of potential features are the various derivatives of price data. The second type is the set of the volume derivatives.

These features will describe the current market condition more complex than raw market data or simple returns.

You will see these features in the next part of the article. As for the modeling, we will use Hidden Markov Model.

Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. Observed data is our market features, hidden states are our market behavior.

Our goal is to interpret the hidden state after the modeling, and create the trading strategy based on this knowledge.

The basic figure of Hidden Markov Model looks like this

Hidden Markov Model

This article is practice-oriented. For more information you read the introduction to Hidden Markov Model in this article by Tomer Amit. Also, I recommend to start with this video