In Technical Indicators Moving Average is Widely used Technical Indicators Moving Average is also known as Moving Mean(MM) or rolling mean it is basically used to analyse data sets by averages of it's required series of subsets of a full data set.It has many forms say basically Simple(SMA),Exponential(EMA) and Weighted(WMA) forms.It is Very much Responsive and Impulsive Filters which also can be used with Other Techncial Indicators

Moving Average Can be Calculated using Averages of 5day,15day,20day and 200day moving averages most of the time.

It is used to get the Smooth Signal it is mainly filter out the noise it mainly depend on the past prices and smoothly exceed out the data whenever fresh data sets are arrived.

It is Basic Building Blocks of the Other Techincal indicators

It is one of the "X" element we usually get whenever we include the days and get divided with the Exactly Choosen total no. of days in prior which we have considered to get the moving average of data sets.

Say Suppose if we take the 5-day data sets of Days in a Week it usually considered as Follows of First set of data SMA:(1+2+3+4+5)/5=3 then for the same if we consider Second set of Data SMA:(2+3+4+5+6)/5=4 so from this we get to conclusion that in a second data 1 element is removed so that 1 more element of fresh data is added to calculate it's of Averages of it's Days in a Week and Further Follows as Such.

We Can Follow as Such in Require of Finite Sequence of Numbers till we get the Moving Average of Data's we required to Calculate fo it.

The Below Given Python Code Will Show you How you can get Simple Moving Average Technical Indicators Value and Exactly Print,Visualize through Graph and Save it in your CSV Files for Further Analysis.

Please Follow the Video Tutorial for Proper Instructions Before Code Input

```
#import the required libraries for SMA Technical Indicators value
import pandas as pd
import quandl
import matplotlib.pyplot as plt
#API-Key Given From The Quandl
auth_token="YOUR API-KEY"
#define the Simple Moving Average such that it consider the Close price of the Given Stocks
```

`#and No. of days which can be defined further for Calculation of SMA def SMA(data,ndays): SMA=pd.Series(pd.rolling_mean(data['Close'],n),name='SMA') data=data.join(SMA) return data #retrieve the data from qandl add your own simble i have just added gold symbol from MCX data=quandl.get('MCX/GCQ2017',start='2016-08-12',end='2017-07-24') data=pd.DataFrame(data) #define the Number of days you need to calcuate the Moving Average n=20 SMG=SMA(data,n) SMA=SMG['SMA'] #call the function to print the SMA print(SMG) #Comment out Below if you need these Values in CSV Files. SMG.plot() #visualise the data plt.show() #save the File into CSV SMG.to_csv("FileName.csv")`

```
```

---End Of Code Input---

Further any Doubt Please Fell Free to Comment or Whatsapp to the Given Number in Website