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Performance of Market States Based Trading Strategies (EPFL - Financial Big Data)

Jupyter Notebook 98.27% Python 1.73%
clustering louvain-algorithm trading-strategies market-states

market-states-trading-strategy's Introduction

EPFL - Financial Big Data Project - Autumn 2023

Performance of Market States Based Trading Strategies

Final project of the course MATH-525 Financial Big Data (EPFL, Fall 2023).

The report is available here.

Installation and Requirements

The requirements are listed in the requirements.txt file. To install them, run the following command in the root directory of the project:

pip install -r requirements.txt

To run the code of the project one has to run the notebooks described below with the data provided in the hard-disk.

Brief description of the repository

fin-big-data_project/
│
├── [upload_data.py](upload_data.py)                            # Functions to upload and clean data
├── [utils.py](utils.py)                                        # Various utils functions
├── [data_preprocessing.py](data_preprocessing.py)              # Functions to further clean the data for pre-processing
├── [market_state_strategy.py](market_state_strategy.py)        # Functions to implement the clustering and the strategies
├── [data_cleaning.ipynb](data_cleaning.ipynb)                  # Notebook to clean the data
├── [data_preprocessing.ipynb](data_preprocessing.ipynb)        # Notebook of the preprocessing of the data
├── [data_observation.ipynb](data_observation.ipynb)            # Notebook to observe the clean and processed data and to compute statistics 
├── [strateg_test.ipynb](strateg_test.ipynb)                    # Notebook containing all the strategies and their performance
├── ResearchNotebooks/                                          # Folder with deprecated notebooks used for exploration and various tests
|
├── [requirements.txt](requirements.txt)                        # Requirement files
|
└── README.md 

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Last edited: 2024-02-01

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