Divergence Trading: Moving Average Convergence-Divergence Strategy

Introduction

The Moving Average Crossover Strategy was covered in a previous article, which served as an intro to some of the different strategies used in Quantitative Analysis. In this blog post, we will be looking at a slightly more complex strategy in Python named the Moving Average Convergence Divergence strategy.

Photo by Nick Chong on Unsplash

Getting Started

Install the required Libraries Before diving in, make sure to install the necessary libraries. We’ll be using pandas for data manipulation, yfinance for getting the historical stock data and matplotlib for the data visualization

import pandas as pd
import yfinance as yf
import matplotlib.pyplot as plt
import numpy as np

Fetching Historical Stock Data

Define a function to get historical stock data using the yfinance library

def get_stock_data(symbol, start_date, end_date):
  stock_data = yf.download(symbol, start=start_date, end=end_date)
  return stock_data

Moving Average Convergence Divergence Strategy

This strategy creates buy/sell signals based on the convergence and divergence of two Exponential Moving Averages (EMAs).