Role
- Python architecture
- Data pipeline design
- Strategy tooling
- Backtesting workflow
Stack
PythonMetaTrader 5PandasBacktestingAutomation
Project summary
BurrFx is a modular Python platform for experimenting with strategy execution, historical data and live monitoring in a MetaTrader 5 environment.
The problem
Trading experiments become difficult to manage when data collection, strategy execution, risk controls and monitoring are handled as disconnected scripts.
Requirements
- Separate broker connectivity from strategy logic.
- Handle historical data and live monitoring workflows.
- Support repeatable backtesting and experimentation.
- Keep risk and execution concerns visible in the architecture.
Architecture
01Strategy module
02Risk controls
03Market data services
04MetaTrader 5 bridge
05Execution monitor
06Backtesting reports
Your responsibilities
- Structured the Python modules.
- Designed data and monitoring flows.
- Built strategy execution helpers.
- Documented the experimental boundaries of the platform.
Technical implementation
- Python modules separate data ingestion, strategy logic, execution helpers and monitoring.
- Pandas supports historical data preparation and report-friendly transformations.
- The platform is treated as an engineering experiment rather than a guaranteed trading product.
Engineering challenges
Separating research from execution
Fast experiments can accidentally blend analysis code with live execution logic.
The project separates strategy, risk, data and execution modules so changes can be reasoned about independently.
Financial automation needs careful boundaries and explicit risk language.
Security and privacy
- Credentials and broker-specific configuration must be stored outside source code.
- No account identifiers or production trading details are published.
- The public case study avoids performance claims and unverified returns.
Outcome
- Created a modular platform for strategy experimentation and monitoring.
- Improved separation between data, execution and research concerns.
Lessons learned
- A trading platform should be presented as engineering capability, not as investment advice.