This repository contains the implementation of my MSc project titled Representation Learning for Financial Time-Series Forecasting. The primary goal of this project is to enhance the accuracy of financial time-series forecasting by leveraging Contrastive Predictive Coding (CPC) to generate feature embeddings.
- Contrastive Predictive Coding (CPC): Utilised for feature extraction from financial time series, improving downstream model performance.
- Baseline Models: Includes Persistence, Zero, Mean, Linear Regression and Buy-and-Hold models for performance benchmarking.
- LSTM Model Implementation: For comparison with CPC-enhanced models.
- Sharpe Ratio Optimization: Evaluates model performance using Sharpe ratios.
To get started with this project, clone the repository and set up the environment using conda and pip.
git clone https://github.com/ese-msc-2023/irp-agk123.git
cd irp-agk123Create a new Conda environment using the provided environment.yml file (note the environment is mac users on arm architecture who require tensorflow-macos):
conda env create -f environment.yml
conda activate cpc_financial- deliverables: Deliverables for my project. Inlcudes the project plan, final report and presentation.
- images: Images utilised in the deliverables.
- other_notebooks: Tried and failed attempts and different approaches.
- src: Where the main notebook lives. Utils folder with utils functions.
- tests: Tests for my utility functions and CPC data generator that are automatically run through the CI github action.
The below results all beat the classic benchmark of buy-and-hold. For more details, visit the CPC notebook.
