Exchange Simulator

Exchange Simulator is an advanced simulation platform designed to model core functionalities of a financial exchange. It includes a matching engine and portfolio management components, making it a valuable tool for quantitative researchers, algorithmic traders, and developers interested in exchange infrastructure.

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Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
  • PostgreSQL Setup
  • Docker Compose Usage
• Usage
• Project Structure
• Documentation
• Contributing
• License

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Features

• 🚀 Matching Engine — Simulates real-time order matching for efficient trade execution.
• 📊 Portfolio Management — Tracks and manages simulated user portfolios based on executed trades.
• 🔄 Exchange Data Integration — Utilizes realistic or synthetic exchange data for accurate testing.
• 🧩 Modular Design — Easily extend or integrate new components for custom workflows.
• 🧪 Research & Testing Friendly — Suitable for development, backtesting, and academic research.
• 🛢️ PostgreSQL Support — Store exchange and portfolio data persistently using PostgreSQL.

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Getting Started

Prerequisites

• Python 3.8 or higher
• PostgreSQL 13 or higher (local or via Docker)
• (Optional) pipenv or venv for virtual environments
• (Optional) Docker and Docker Compose for containerized deployment

Installation

1. Clone the repository:

   git clone https://github.com/fifisayad/ExchangeSimulator.git
   cd ExchangeSimulator

2. Install dependencies:

   pip install -r requirements.txt

   The following dependencies are required to connect to and use PostgreSQL:

   psycopg2-binary
   sqlalchemy
   

   These should be listed in your requirements.txt. If not, add them:

   pip install psycopg2-binary sqlalchemy

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PostgreSQL Setup

1. Local PostgreSQL installation:
   Install PostgreSQL by following the official instructions, create a database (e.g., exchange_simulator), and update your project’s configuration to include:

   • DB_HOST
   • DB_PORT
   • DB_USER
   • DB_PASSWORD
   • DB_NAME

2. Or, use Docker Compose as described below.

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Usage

1. Configure simulation parameters and database connection in your config files or environment.

2. Run the main simulation script (locally):

   python main.py

   Or inside Docker Compose:

   docker-compose up

3. Review the simulation outputs for exchange activities and portfolio analytics.

For detailed usage guides and tutorial notebooks, see the Documentation section below.

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Project Structure

ExchangeSimulator/
├── data/               # Exchange data samples and synthetic generators
├── engine/             # Core matching engine code
├── portfolio/          # Portfolio management logic
├── tests/              # Unit tests and simulation scenarios
├── utils/              # Utility functions and helpers
├── requirements.txt    # Python dependencies (includes psycopg2-binary, sqlalchemy)
├── Dockerfile          # Container build definition (if present)
├── docker-compose.yml  # Sample compose file for Dockerized workflow
└── main.py             # Entry point for the simulator

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Documentation

Comprehensive documentation including architecture details, component walkthroughs, and setup instructions can be found here:

👉 Exchange Simulator — Full Documentation (Notion)

Please consult this for design rationale, API reference, and advanced usage.

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Contributing

Contributions are welcome! Please open issues or submit pull requests for feature requests, bug fixes, or improvements.

1. Fork this repo
2. Create your feature branch (git checkout -b feature/my-feature)
3. Commit your changes (git commit -am 'Add awesome feature')
4. Push to the branch (git push origin feature/my-feature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.