order-matching-engine

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🚀 Intraday Trade Matching & Slippage Analysis

A Python application for simulating real-time order matching and slippage analysis in equity trading. Designed for speed, clarity, and robust analytics.

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📦 App Structure

order-matching-engine/
├── README.md
├── requirements.txt  # All runtime & dev dependencies
├── intraday_trade_matching.md
├── engine/           # Core matching engine logic
│   ├── __init__.py
│   └── matcher.py    # Matching algorithm implementation
├── tests/            # Unit and integration tests
│   └── test_matcher.py
└── scripts/          # CLI and utility scripts
	└── run_engine.py

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🛠️ Features

• Fast in-memory matching of orders and market trades
• FIFO priority for equal prices
• Partial fills supported
• Real-time queries:
  • get_open_orders(symbol)
  • get_filled(order_id)
  • get_slippage(order_id)
• Efficient: O(log N) matching per trade

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⚙️ Deployment

1. Clone the repository:

   git clone https://github.com/git-abdoul/order-matching-engine.git
   cd order-matching-engine

2. Set up a Python environment and install dependencies:

   python3 -m venv venv
   source venv/bin/activate
   pip install --upgrade pip
   pip install -r requirements.txt

   This will install all required libraries: numpy, pandas, pytest, flake8, black.
3. Run the engine:

   python scripts/run_engine.py

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🧑‍💻 Development Setup

1. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate

2. Install all dependencies (runtime & dev):

   pip install -r requirements.txt

   This single requirements file includes all libraries for running, testing, and linting the app.
3. Run tests:

   pytest tests/

4. Lint and format code:

   flake8 engine/
   black engine/

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📚 How to Use

• Place orders and stream market trades using the provided API or CLI.
• Query open orders, filled quantities, and slippage in real time.
• See intraday_trade_matching.md for scenario details and requirements.

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💡 Example

• Order: BUY 500 AAPL @190.25
• Tape prints:
  • AAPL 190.10 x100 → fills 100 (ok, <= 190.25)
  • AAPL 190.20 x250 → fills 250 (ok, <= 190.25)
  • AAPL 190.40 x200 → ignored (price too high)

Open qty = 150 left.
Filled qty = 350.
Avg fill price = (100×190.10 + 250×190.20)/350 = 190.17.
Slippage = 190.17 − 190.25 = −0.08 (better fill).

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⚡️ Build, test, and deploy your matching engine for high-performance trading analytics!