Live trading – where Python faces its limits

Thus said, trading applications written in pure Python are not suitable for any live trading activity that assumes the minimization of time from the moment market data is received to the moment an order is sent. Therefore, traditional arbitrage and many high-frequency trading activities (which sometimes suggest sending thousands of orders per second) are definitely not for Python.

Besides that, there is another risk even for slow trading strategies that derive from automated memory management. We already know that trading strategies rely on price time series and the amount of processed market data may be quite large. Although both native Python and third-party libraries such as pandas offer data structures that ensure data persistence, it may become problematic to update data on the fly, especially in trading environments with high throughputs.

There are different ways to speed up Python to some extent. There are static compilers...