Improving the speed of Python code in HFT
The critical components we defined during the previous chapters must run at high speed. Using any of the tools we described previously will help you create C/C++-like code and create performant Python code using libraries. It is essential to begin constructing a new algorithm in Python utilizing NumPy and SciPy while avoiding looping code by leveraging the vectorized idioms of both libraries. In reality, this means attempting to replace any nested for loops with similar calls to NumPy array functions. The purpose is to prevent the CPU from wasting time on the Python interpreter instead of crunching numbers for trading strategies.
However, there are situations when an algorithm cannot be efficiently expressed in simple vectorized NumPy code. The following is the recommended method in this case:
- Find the primary bottleneck in the Python implementation and isolate it in a dedicated module-level function.
- If there is a small but...
