Algorithmic Trading Prototype & API Integration

Overview

This project was a personal learning prototype designed to explore the fundamentals of algorithmic trading, market data analysis, and real-time API integration.

Rather than aiming for production trading or profit optimisation, the focus was on understanding how trading systems are structured, how strategies are encoded, and how software can react to continuously changing external data sources.

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Core Objectives

The project was guided by a small set of exploratory goals:

• integrate with a real-world exchange API
• retrieve and process live market data
• model simple, rule-based trading strategies
• evaluate signals under realistic conditions
• build a system suitable for experimentation and iteration

The emphasis was on learning and correctness, not automation at scale.

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Trading Logic & Strategy Exploration

I implemented several basic strategy patterns, such as:

• threshold-based decision rules
• simple trend or momentum indicators
• signal filtering to reduce noise

Strategies were deliberately kept simple to ensure their behaviour was understandable and debuggable.

This made it easier to reason about why a trade signal appeared, rather than treating outcomes as opaque.

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API Integration & System Design

The application interfaced with a public exchange API to:

• retrieve market prices and historical data
• structure data for analysis
• simulate decision-making based on incoming information

Care was taken to isolate API interaction from strategy logic, allowing strategies to be modified or replaced without changing the data ingestion layer.

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Project Status

The project was paused intentionally due to competing professional commitments, not technical blockers.

Despite its prototype nature, it successfully fulfilled its learning objectives and provided a practical foundation for understanding:

• financial data flows
• API-driven systems
• time-sensitive decision logic
• risk-aware system design

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Skills Demonstrated

This project highlights:

• rapid self-directed learning
• practical API integration
• translating abstract concepts into working code
• designing systems for experimentation
• knowing when to stop and reassess scope responsibly

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Why This Project Matters

While simple by design, this project reflects an important professional habit:

using software to explore and validate new domains methodically.

It demonstrates curiosity, restraint, and an engineering mindset that values understanding over premature optimisation.

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Final Note

This was a personal, non-commercial project created solely for educational purposes.

No automated trading was deployed with real capital, and the system was never used for live financial decision-making.

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