Algorithmic Trading Platform

Building an Automated Machine-Learning Trading Platform

I independently conceived, architected, developed, and evolved the platform over approximately four years.

Business problem

Building a fully automated trading system required combining market-data processing, predictive modelling, trading logic, risk controls, and broker integration into one dependable platform. The system needed to move beyond research and generate and execute trades without manual intervention.

Architecture

flowchart LR
  Market[Market data] --> Features[Feature processing]
  Features --> ML[Three ML models]
  ML --> Signals[Trading signals]
  Signals --> Risk[Risk controls]
  Risk --> Exec[Automated trade execution]
  Exec --> Broker[Broker integration]

Technologies

Python
Machine-learning model integration
Automated trading workflows
Market-data processing
Broker integration
~10,000 lines of production-oriented code

Challenges

Unifying market data, three ML models, trading logic, risk controls, and execution in one system.
Moving from research-style modelling to dependable automated trade placement.
Evolving architecture and models over years based on live behaviour and results.
Owning the full stack independently—from concept through mature production operation.

Decisions

Designed the entire system architecture from the ground up.
Built approximately 10,000 lines of production-oriented code.
Developed and integrated three machine-learning models.
Created workflows for processing data, generating trading signals, and automatically placing trades.
Iteratively refined the platform based on model behaviour and trading results.
Integrated the analytical, decision-making, and execution components into a single automated system.

Results

Delivered a functioning platform capable of placing trades automatically.
Integrated three ML models into an end-to-end decision and execution pipeline.
Took the product from original concept to a mature system over four years.
Demonstrated strong ownership across software architecture, machine learning, automation, and financial-domain problem-solving.
Created a substantial independent software asset of approximately 10,000 lines of code.

Lessons learned

End-to-end ownership forces architectural clarity—research components must earn their place in an execution path.
Iterating models against live trading behaviour is as important as the initial architecture.
Automation only works when risk controls sit between signals and broker execution.