Welcome to your comprehensive guide on applying Bayesian Hierarchical Hidden Markov Models (HHMM) to financial time series! This fascinating area of research seeks to replicate and expand on innovative financial models that deal with the complexities of market behaviors.
Project Overview
This project is part of the Google Summer of Code 2017. The primary objective is to replicate existing research on Hierarchical Hidden Markov Models applied to financial data. Utilizing literature from several academic sources, we’ll demonstrate how these models can enhance predictions in financial forecasting.
Goals of the Project
- Primary Goals:
- To replicate the research in HHMMs applied to financial time series.
- Provide insights into the credibility of the results and facilitate future research integration.
- Summarize the mathematical treatment of the Hidden Markov Models.
- Secondary Goals:
- Enable detailed replication that enhances trading strategies using hidden states.
- Prepare for the future integration of HHMM logic into existing trading frameworks.
Understanding the Code Structure
The core of our work is organized in various folders, where each contains specific R and Stan code aimed at different aspects of the project. Think of it as a toolbox where every tool has its unique purpose:
- common: General purpose files that provide foundational functions.
- hmm: Simulation of HMM data with sample code.
- iohmm-reg: Simulation focused on Integrated IOHMMs with a focus on linear regression.
- hhmm: Structural components and methods for setting up HHMMs.
- hassan2005: Contains code and write-up for replicating research from Hassan (2005).
- tayal2009: Code and write-up for Tayal’s (2009) work.
Visualizing the Model
To help grasp the concept, imagine our Hidden Markov Models as a rich tapestry woven with discrete states of a market’s evolving dynamics. Each hidden state represents a texture that influences how the financial data unfolds, much like the way different threads contribute to the overall design. The goal is to identify these states and their transitions to predict future market behaviors effectively.
Running the Replications
We encourage you to utilize the provided code for your experiments. To get started, ensure you have the necessary tools:
- R Version: 3.3.3
- RStudio Desktop: 1.0.136
- Rtools: 3.3
- Stan: 2.14
- Required R Packages: RStan 2.14.2
Troubleshooting Tips
If you encounter problems while running the code, here are some common issues and their solutions:
- Issue: Code won’t compile.
- Ensure that you have the right versions of R and the required packages.
- Check for any dependency issues and install missing packages.
- Issue: Results don’t match expectations.
- Verify your input data and ensure it’s pre-processed correctly.
- Revisit your model assumptions; make sure they align with your data.
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Concluding Thoughts
At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.
