Python Algo Trading Developer

Move from a notebook to a maintainable system

Research notebooks are useful for exploration but often mix data loading, strategy rules and execution. I separate those responsibilities into modules with explicit inputs and outputs, so a strategy can be tested without placing an order or contacting a live broker.

Choose tools around the workload

Pandas and NumPy support data preparation and research; FastAPI or Django can expose a backend interface. PostgreSQL stores durable records, while Redis and Celery can support queued work where needed. The architecture should follow measured requirements rather than add infrastructure without a reason.

Make strategy conversion reviewable

When converting Pine Script, spreadsheet rules or an existing Python prototype, sample inputs and expected signals establish acceptance criteria. Indicator warm-up, timezone boundaries and candle-close timing are checked explicitly. Deployment documentation records dependencies and configuration so the system can be reproduced.

Potential project deliverables

• Modular Python strategy and data code
• Signal fixtures and reproducible checks
• Dependency and configuration documentation
• Backend API or scheduled-worker integration as scoped

The final scope, timeline and cost follow a review of your requirements.

Related work and next steps

Read a related project summary and its documentation status, or browse trading projects.

• Backtesting Developer
• Trading Bot Development

Trading-system engineering notes · Python algo trading course

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