Benchmarking Moving Average Calculation Speed in MQL4 and MQL5

Summary

The article compares the time required to calculate simple, exponential, smoothed, and linear weighted moving averages using several MQL4 and MQL5 methods. Its benchmark varies period, timeframe, and applied price across thousands of calculations on a 10,000 element price array. It distinguishes calls to the built-in indicator from library routines and includes the cost of retrieving indicator data in MQL5.

The reported results show substantial differences among calculation methods: selected MQL5 library approaches are much faster for some averages, while other methods are slower, especially for linear weighted averages as the period grows. Built-in indicator timings are more similar across average types. The article also reports a faster result for MQL5 than MQL4 under its test setup. These are cold-cache measurements from one environment, with limited timing precision; absolute performance can vary, so the figures should be treated as comparative rather than universal.

Key ideas

• Moving average calculation speed depends strongly on the implementation method.
• The benchmark varies averaging period, timeframe, and applied price to limit reuse of cached indicator calculations.
• Built-in indicator calls show more consistent timing across average types than some library implementations.
• Linear weighted moving average cost rises with period in the tested library method.
• The reported MQL4 and MQL5 comparison reflects one cold-start setup and should not be generalized as a universal speed ratio.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.