Crypto Trading Engineering: Scalable Backtesting Infrastructure

Overview

This project aims to design and build a reliable, large-scale trading data pipeline for a startup called Mela, which wants to make it simple for everyone to enter the world of cryptocurrencies. The pipeline will enable investors to run backtests that simulate current and past particular situations as well as their trend over time.

Business Need

The startup wants to provide a reliable source of investment while lowering the risk associated with trading cryptocurrencies. To achieve this, a robust data pipeline is needed to run various backtests and store useful artifacts in a robust data warehouse system.

Data

The project will use candlestick data from Yahoo Finance and Binance. Candlestick data is a type of financial data that represents the price action of a security over time.

Expected Outcomes

• A robust data pipeline that can run various backtests
• Storage of useful artifacts in a robust data warehouse system
• Skills in technical analysis, backtesting, trading, and data engineering
• Knowledge of financial prediction, movement prediction, and enterprise-grade data engineering

Installation

To get started with this project, clone the repository and install the required dependencies.

git clone https://github.com/group1Goldreich/Scalable-Backtesting-Infrastructure.git
cd Scalable-Backtesting-Infrastructure
pip install -r requirements.txt

Ensure you have the necessary environment setup, including Python 3.8+ and any other dependencies specified in the requirements.txt file.

Usage

1. Build and run the Docker containers:

docker-compose build
docker-compose up -d

2. Execute the backtest:

python3 backtest/main.py

3. Start the backend: Then run the backend

fastapi run backend/main.py

4. Launch the frontend: Then The forntend

npm install
nvm start

Contributors

For a list of contributors, see the CONTRIBUTORS.md file.