Scalable Backtesting Infrastructure for Cryptocurrency and Stock Trading Engineering

Introduction to Backtesting in Trading Systems

Modern trading engineering requires robust systems for analyzing market performance. This project delivers a scalable backtesting infrastructure paired with a reliable trading data pipeline, designed specifically for cryptocurrency and stock market applications.

👉 Discover advanced trading tools that complement these backtesting solutions.

Core Components of the Trading System

System Architecture Design

The infrastructure combines several cutting-edge technologies:

Data Pipeline: ETL processes for stock/crypto data
Backtesting Engine: Strategy simulation platform
Data Warehouse: Storage for analysis artifacts

Key Technical Specifications

Python 3.5+ based framework
Apache Kafka for data streaming
Apache Airflow for workflow orchestration
React-based frontend interface

Project Implementation Details

Data Sources

The system utilizes historical trade data from:

Yahoo Finance (stock market data)
Binance (cryptocurrency data)

Data Features Include:

Feature	Description
Date	Trade recording date
Open	Daily opening price
High	Daily peak price
Low	Daily lowest price
Close	Daily closing price
Adj Close	Adjusted closing price
Volume	Daily trade volume

Technical Requirements

pip install -r requirements.txt

Requirements include:

FastAPI backend
Kafka-Python integration
Backtrader analysis library
Docker for containerization

Getting Started with the Application

Installation Guide

git clone https://github.com/TenAcademy/backtesting.git
cd backtesting
pip install -r requirements.txt

Running the System

Frontend:
```
cd presentation
npm run start
```
Backend:
```
cd api
uvicorn app:app --reload
```

👉 Explore more trading infrastructure options for your needs.

Project Structure

Key Directories

notebooks/: Exploratory data analysis
scripts/: Pipeline modules
strategies/: Backtesting algorithms
tests/: Verification scripts

Frequently Asked Questions

What is the primary purpose of this backtesting infrastructure?

The system allows traders to simulate various investment strategies using historical market data, helping evaluate potential performance before live trading.

How does this handle cryptocurrency volatility?

The pipeline includes specialized normalization processes for crypto data, accounting for its unique volatility patterns compared to traditional stocks.

What timeframe does the historical data cover?

The system can integrate with multiple data sources covering different periods, typically ranging from several years to real-time streaming data.

Development Team

Birhanu Gebisa: Pipeline Architect
Ekubazgi Gebremariam: Data Engineering Lead
Emtinan Salaheldin: Frontend Specialist
Fisseha Estifanos: Backtesting Strategist
Natnael Masresha: Quality Assurance
Niyomukiza Thamar: System Integration

Supporting the Project

We welcome contributions and feedback. Please star the repository if you find this project valuable.