A MACD Implementation in Python From Scratch

A MACD Implementation in Python From Scratch

This post is for entertainment/educational purposes only. The content of this post is not meant to provide investment advice or to help with investment decision making.

This post is for entertainment/educational purposes only. The content of this post is not meant to provide investment advice or to help with investment decision making.

Moving Average Convergence Divergence (MACD [MAK-DEE]) is a trading algorithm that uses the price momentum of a security to define buying and selling opportunities. The algorithm works by monitoring the convergence/divergence of two different moving averages (MAs) of the security’s price (one long MA and one short MA) and uses a moving average of this convergence/divergence measurement (known as the signal line) to signal buying and selling opportunities. Typically, the long MA uses 26 periods, the short MA uses 12 periods and the signal line uses 9 periods where the MAs are computed as Exponential Moving Averages (EMAs) which give more weight to recent data points. Such a process is known as MACD(12, 26, 9). Although these numbers typically work well there’s nothing absolute about them, any of the EMAs can be modified to use a different number of periods to signal different trading opportunities.

When looking for implementations of this algorithm to gain insight on its inner workings I found almost no from-scratch implementations in Python. Most of the implementations in Python use libraries that already have the algorithm implemented so the logic boils down to, essentially, a few function calls. In this post, I will discuss the MACD crossover indicator and develop a Python implementation from scratch. This implementation will be wrapped in a Python class making it easy to use in other projects.

stocks finance algorithmic-trading python

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