Moving Average

In statistics, a moving average is a calculation used to analyze data points by creating a series of averages of different subsets of the full data set. In finance, a moving average (MA) is a stock indicator that is commonly used in technical analysis.

Let find Moving Average Line followed by previous market stock prices.

Module Import

from urllib.request import *
from bs4 import *
import requests
import re
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

in previous analysis (ref. https://tomtomplace.blogspot.com/2022/05/web-crawling.html), we've already get object variable 'pList', which is closing market stock price of Samsung electronic, and now we used to its data, we are going to make moving average price using Queue ADT.

Queue ADT

Queue is a linear data structure in which the insertion and deletion operations are performed at two different ends. In a queue data structure, adding and removing elements are performed at two different positions. The insertion is performed at one end and deletion is performed at another end. (ref. btechsmartclass) We can find 5 days moving average price using following method :

numMA_5 = 5
  mList_5 = []
  Q_5 = []
  mSum_5 = pList[0] * numMA_5

  for i in range(numMA_5) :
      Q_5.append(pList[0])
  for M in pList :
      mSum_5 = mSum_5 - Q_5.pop(0)
      mSum_5 += M
      mList_5.append(mSum_5/numMA_5)
      Q_5.append(M)

numMA_5 is a number how much interval days we want to set, mList_5 is such container saving average price analyzed, and Q_5 is temporary container to make average price. As using similar method, we can make 20 days moving average price and its of 60 days to use following codes :

def makeMA(pList):
  numMA_5 = 5
  mList_5 = []
  Q_5 = []
  mSum_5 = pList[0] * numMA_5

  numMA_20 = 20
  mList_20 = []
  Q_20 = []
  mSum_20 = pList[0] * numMA_20

  numMA_60 = 60
  mList_60 = []
  Q_60 = []
  mSum_60 = pList[0] * numMA_60

  for i in range(numMA_5) :
      Q_5.append(pList[0])
  for i in range(numMA_20) :
      Q_20.append(pList[0])
  for i in range(numMA_60) :
      Q_60.append(pList[0])

  for M in pList :
      mSum_5 = mSum_5 - Q_5.pop(0)
      mSum_5 += M
      mList_5.append(mSum_5/numMA_5)
      Q_5.append(M)

      mSum_20 = mSum_20 - Q_20.pop(0)
      mSum_20 += M
      mList_20.append(mSum_20/numMA_20)
      Q_20.append(M)

      mSum_60 = mSum_60 - Q_60.pop(0)
      mSum_60 += M
      mList_60.append(mSum_60/numMA_60)
      Q_60.append(M)

  drawGraph(pList,mList_5,mList_20,mList_60)

  return mList_5, mList_20, mList_60

makeMA() is the function which makes moving average price whose interval is 5, 20, 60 days.

Visualization

To visualize our analyzed data, we need to make the function who draw a graph. From the above code, the function named drawGraph is chain-function which is make MA price graph.

def drawGraph(pList,mList_5,mList_20,mList_60):
  graph = pd.Series(pList, index=range(len(pList)))

  graph_m5 = pd.Series(mList_5, index=range(len(pList)))

  graph_m20 = pd.Series(mList_20, index=range(len(pList)))

  graph_m60 = pd.Series(mList_60, index=range(len(pList)))

  plt.style.use('seaborn')

  plt.plot(graph, label = 'stock price')

  plt.plot(graph_m5, label = '5MA')

  plt.plot(graph_m20, label = '20MA')

  plt.plot(graph_m60, label = '60MA')

  plt.legend(loc = 'upper left')

  plt.xlabel('Days')
  plt.ylabel('Price')
  plt.grid(True)
  plt.show()

The bottom image is following result.