Case Study
Algorithmic Equities Swing Trader
Quantitative swing trading machine processing live market feeds and executing automated trades on low-latency cloud infrastructure.
- Python
- Alpaca API
- SQLModel
- Pandas
- trading
- finance
- python
- machine-learning
The Business Case
Succeeding in volatile financial markets requires emotionless execution, continuous data processing, and highly responsive risk limits. This project builds a production-grade algorithmic swing trading machine. Operating on a low-latency cloud server, the system ingests live market data, feeds indicators into custom-trained machine learning forecasting models, and manages orders, position-sizing, and stop-loss boundaries automatically via broker APIs.
Engineering Highlights
1. High-Performance Data Processing
The pipeline ingests real-time pricing data and order-book snapshots via WebSocket connections. The system uses Pandas and NumPy to quickly calculate technical indicators (moving averages, MACD, and RSI) on sliding time-series windows without introducing latency.
2. Multi-Stage Order Lifecycle
To manage risk, the execution engine tracks trade state machines. It handles bracket orders, dynamic trailing stops, and profit-taking targets, continuously polling the Alpaca API to update order boundaries.
3. VPS Backtesting & Simulation
Features the Belfort Loop paper-trading framework. It runs on a lightweight VPS, using Ollama to evaluate financial news feeds and sentiment signals alongside technical indicators to simulate trading performance before deploying real capital.