Adam Anwar
  1. Work
  2. Stock Trader Simulation Engine
  3. Docs

Stock Trader Simulation Engine

Concept prototype for a Python paper-trading engine with an order book, portfolio and strategy hooks.

2 min read

Source on GitHub

A financial market simulation and paper-trading engine. Designed to model order book dynamics, quote feeds, position management, and automated trading strategies in a simulated risk environment.

Overview

Trading algorithm validation requires backtesting against simulated order flows before executing on live brokerage APIs. Stock Trader provides a sandbox framework for testing order placement logic, calculating portfolio exposure, tracking slippage, and verifying profit-and-loss metrics.

Key Concepts and Architecture

  • Order Book Mechanics: Models bid and ask queues with limit and market order execution.
  • Portfolio and Risk Management: Tracks cash balances, margin requirements, realized/unrealized P&L, and portfolio drawdowns.
  • Market Feed Ingestion: Ingests historical daily/minute OHLCV price series to replay past market conditions.
  • Strategy Interface: Provides hook methods (on_tick, on_bar) for programmatic strategy execution (moving average crossovers, mean reversion, momentum).

Planned Project Structure

Stock Trader/
├── engine/
│   ├── orderbook.py        # Order matching engine and queue structures
│   ├── portfolio.py        # Position tracking and capital management
│   └── simulator.py        # Market event loop and tick dispatcher
├── strategies/
│   ├── base.py             # Strategy abstract base class
│   └── sma_crossover.py    # Sample dual moving average strategy
├── tests/
│   └── test_matching.py    # Order matching unit assertions
└── README.md               # Architecture and roadmap documentation

Requirements

  • Python 3.10 or higher
  • Standard library dependencies or NumPy / Pandas for time series processing

Usage Example

from engine.simulator import MarketSimulator
from strategies.sma_crossover import SMACrossoverStrategy

# Initialize simulation session with $10,000 initial capital
sim = MarketSimulator(initial_balance=10000.0)
strategy = SMACrossoverStrategy(short_window=10, long_window=30)

sim.load_historical_data("AAPL", filepath="data/aapl_1d.csv")
results = sim.run(strategy)

print(f"Final Balance: ${results.final_equity:,.2f}")
print(f"Sharpe Ratio: {results.sharpe_ratio:.2f}")

Roadmap

  • Real-time WebSocket feed integration with Alpha Vantage or Yahoo Finance.
  • Multi-asset portfolio rebalancing across equities and crypto.
  • Execution latency simulation with random packet jitter and fill slippage.

Project Status

Conceptual prototype and algorithmic trading sandbox. Related live algorithmic trading systems are maintained in algo-trading-bot.

Authors and Acknowledgment

Conceived and designed by Adam Anwar.

License

MIT License.

Support

Submit issues, questions, or bug reports to the GitHub issue tracker: https://github.com/ADM1SH/stock-trader/issues

Contributing

Contributions are welcome.

  1. Fork the repository.
  2. Create a feature branch: git checkout -b feature/improvement.
  3. Commit your changes: git commit -m "feat: enhance functionality".
  4. Push to the branch: git push origin feature/improvement.
  5. Open a Pull Request.

Back to top