Python or MQL for the execution layer: where each wins

[Amanhasnoname]

Choosing the language for an automated scalping system involves trade-offs. MQL runs inside the terminal with direct access to ticks and orders and low overhead. Python offers a rich ecosystem for analysis and modeling, but communicates with the platform through an API.

For simple, latency-sensitive logic, MQL has an advantage: no inter-process communication, and tick events are handled directly. For complex models, data handling, and machine learning, Python is far more convenient.

A hybrid design separates concerns: MQL handles execution and risk controls close to the market, while Python performs analysis and sends parameters or signals. Communication through files, sockets, or shared memory adds latency, which must be measured.

Maintenance matters too. A system split across languages needs careful versioning and monitoring.

Also consider the available talent and tools. A language the developer knows well produces fewer bugs than a theoretically faster one used poorly.

Measure the end-to-end latency of the design and test its robustness under load.

How is the execution layer split between languages, and what is the measured latency of the interface?