Cryptocurrency and Stock Trading Engineering: Scalable Backtesting Infrastructure & Data Pipeline

In today’s fast-evolving financial landscape, the ability to simulate trading strategies using historical data has become a cornerstone of success for investors and developers alike. Whether you're exploring cryptocurrency markets or traditional stock trading, a robust backtesting infrastructure is essential for validating strategies before risking real capital. This article dives into the architecture of a scalable backtesting system designed to handle both crypto and stock market data at scale, powered by modern data engineering tools and pipelines.

The solution discussed here enables users to run multiple backtests across different assets using customizable parameters and strategy configurations—all supported by a reliable, large-scale data pipeline.

Why Backtesting Matters in Modern Trading

Backtesting allows traders and quantitative analysts to evaluate how a particular strategy would have performed historically. While past performance doesn’t guarantee future results, it provides valuable insights into risk exposure, drawdowns, profitability, and consistency.

For both retail and institutional investors, having access to an automated, repeatable, and accurate testing environment reduces emotional decision-making and increases confidence in strategy deployment.

👉 Discover how advanced trading tools can enhance your strategy development process.

Project Overview: Building a Robust Trading Data Pipeline

This project was developed to support Mela, a fintech startup aiming to simplify entry into cryptocurrency and stock market trading while minimizing investment risk. The core objective? To design and implement a scalable backtesting infrastructure integrated with a reliable, large-scale trading data pipeline.

The system processes historical market data from multiple sources, applies various trading strategies, runs simulations, and stores results in a structured data warehouse for analysis and visualization.

Key components include:

• Real-time data ingestion via Kafka
• Workflow orchestration with Apache Airflow
• RESTful API backend powered by FastAPI
• Interactive frontend built with React
• Strategy execution using Backtrader
• Containerization via Docker

This modular architecture ensures high scalability, fault tolerance, and ease of maintenance—critical for handling volatile financial datasets.

Core Components of the System Architecture

Data Sources & Structure

The backtesting engine relies on high-quality historical price data for both cryptocurrencies and stocks. Primary sources include:

• Yahoo Finance (for stock market data)
• Binance API (for cryptocurrency trading data)

Each dataset follows the standard OHLCV format:

• Date: Timestamp of the trading period
• Open: Opening price
• High: Highest price during the period
• Low: Lowest price
• Close: Closing price
• Adj Close: Adjusted closing price (accounting for splits and dividends)
• Volume: Number of shares or coins traded

This granular time-series data forms the foundation for technical analysis and strategy simulation.

Technology Stack

The system integrates several industry-standard tools:

• Python 3.5+ – Core programming language
• FastAPI – Backend API framework (supports async operations)
• Backtrader & yfinance – Strategy simulation and data fetching
• Apache Kafka & kafka-python – Real-time message streaming
• Zookeeper – Coordination service for Kafka clusters
• Apache Airflow – Orchestration of ETL workflows
• React (Node.js) – Frontend interface
• Docker & Docker Compose – Containerized deployment

These technologies work in harmony to ensure seamless data flow from ingestion to visualization.

How to Set Up the Backtesting Environment

To get started locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/TenAcademy/backtesting.git
   cd backtesting

2. Install dependencies in a virtual environment:

   pip install -r requirements.txt

3. Launch the frontend:

   cd presentation
   npm run start

4. Start the backend server:

   cd api
   uvicorn app:app --reload

Once both services are running, navigate to http://localhost:3000 in your browser to access the user interface.

Using the Application: Step-by-Step Guide

After launching the app:

1. Navigate to the sign-in page.
2. Create an account or log in if already registered.

3. Input desired trading parameters such as:

   • Asset type (crypto or stock)
   • Timeframe (e.g., daily, hourly)
   • Initial capital
   • Strategy selection (e.g., moving average crossover, RSI-based)
4. Click "Run Test" to initiate backtesting.

The system will process your inputs, execute the selected strategy against historical data, and return performance metrics including:

• Total return
• Sharpe ratio
• Maximum drawdown
• Win rate

These outputs help users refine their strategies before live deployment.

👉 Explore powerful platforms that support real-world strategy execution after testing.

Key Modules and Folder Structure

Understanding the codebase organization enhances usability and extensibility:

notebooks/

Contains Jupyter notebooks for:

• Exploratory Data Analysis (EDA)
• Data cleaning and preprocessing
• Machine learning model prototyping

scripts/

Houses utility scripts for:

• Kafka topic management
• Airflow DAG definitions
• Default configuration files

strategies/

Stores all backtesting algorithms such as:

• Mean reversion
• Momentum trading
• Breakout detection

New strategies can be added modularly following existing templates.

tests/

Includes unit and integration tests to ensure reliability and prevent regression.

presentation/ (Frontend)

Built with React, this module handles user interaction, form submission, and result visualization.

api/ (Backend)

Implements REST endpoints using FastAPI to manage user requests, trigger backtests, and serve results.

Frequently Asked Questions (FAQ)

What is backtesting in trading?

Backtesting is the process of applying a trading strategy to historical market data to assess its viability. It helps estimate potential profits, risks, and performance under varying market conditions without using real money.

Can this system handle both stocks and cryptocurrencies?

Yes. The pipeline supports both asset classes by integrating data from Yahoo Finance (stocks) and Binance (cryptocurrencies), enabling cross-market analysis and diversified strategy testing.

Is prior coding experience required to use this system?

While the system is developer-friendly, non-technical users can interact with it through the intuitive React-based frontend. However, customizing strategies or adding new features requires Python knowledge.

How does Apache Airflow improve the data pipeline?

Airflow automates scheduled tasks like data extraction, transformation, and loading (ETL), ensuring timely updates and consistency across datasets. It also provides monitoring and error alerts for pipeline failures.

Can I deploy this system in production?

Yes. With Docker and Docker Compose, the application can be containerized and deployed on cloud platforms like AWS, GCP, or Azure. Proper scaling and security measures should be applied for production use.

Where are backtest results stored?

Results are saved in a structured data warehouse format (e.g., PostgreSQL or Parquet files), making them queryable for further analysis or dashboarding.

Final Thoughts: The Future of Automated Trading Systems

As algorithmic trading becomes more accessible, tools that combine powerful data engineering with user-friendly interfaces will dominate the market. This backtesting infrastructure exemplifies how open-source collaboration can drive innovation in fintech.

By leveraging scalable technologies like Kafka, Airflow, and containerization, developers can build resilient systems capable of processing vast amounts of financial data efficiently.

Whether you're a quant developer refining strategies or an investor exploring algorithmic trading, mastering such systems gives you a significant edge.

👉 Take your trading strategies from concept to execution with cutting-edge platforms.