Quant Stack Project

This repository contains a quantitative analysis environment setup for data science and machine learning projects. The project includes configuration files for creating a consistent development environment and running Jupyter Lab. This stack is used for courses of https://quantscience.io/.

Prerequisites

Docker

Make (optional, for using the provided makefile commands)

Quick Start

Clone this repository:

git clone https://github.com/pi-2r/Quant-Science.git
cd Quant-Science

Build the Docker image:

make build

Run the container:

make run

By default, the Jupyter password is set to "password". You can customize it by running:

make run JUPYTER_PASSWORD=your_custom_password

Access JupyterLab in your browser at: http://localhost:8888

Environment Details

This environment is built on Anaconda with Python 3.9.13 and includes the following key libraries:

Core Libraries

NumPy 1.23.4

Pandas

SciPy

StatsModels

scikit-learn

Financial Libraries

OpenBB

QuantLib

riskfolio-lib

vectorbt

ta-lib

zipline-reloaded

pyfolio-reloaded

alphalens-reloaded

quantstats

Interactive Brokers API (ibapi)

Machine Learning

LightGBM 3.3.5

CatBoost 1.1.1

XGBoost 1.7.4

Optimization

CVXPY 1.2.2

Available Commands

The makefile provides several convenient commands:

make build: Build the Docker image

make run: Start the container with JupyterLab

make stop: Stop and remove the running container

make clean: Remove the Docker image

make logs: Display container logs

make shell: Open a shell inside the container

make help: Display available commands

Customization

You can customize the environment by modifying:

quant_environment.yml: Add or modify conda and pip packages

Dockerfile: Change the base image or add system dependencies

Troubleshooting

If you encounter dependency conflicts during the build process, try:

Updating the charset-normalizer version to >=3.4.0 in the quant_environment.yml file

Ensuring compatible versions between packages