Python Forum

Hello All!
I'm using historical stock prices for which I am using to calculate the SMA, but the SMA returns "None". Could you help figure out why my SMA is not returning a numerical value? Thank you for your help.

''' The Simple Moving Average (SMA) is calculated by adding the price of an instrument over a number of time periods and then dividing the sum by the number of time periods. The SMA is basically the average price of the given time period, with equal weighting given to the price of each period. Simple Moving Average SMA = ( Sum ( Price, n ) ) / n Where: n = Time Period ''' from tracemalloc import start from matplotlib import ticker import pandas_datareader as pdr import datetime as dt import statistics as stats ticker = "AAPL" start = dt. datetime(2021, 1, 14) end = dt.datetime(2021, 3, 18) data = pdr.get_data_yahoo(ticker, start, end) print(data["Adj Close"]) #================================================================================ close = data['Adj Close'] time_period = 20 # number of days over which to average history = [] # to track a history of prices sma_values = [] # to track simple moving average values for close_price in close: history.append(close_price) if len(history) > time_period: # we remove oldest price because we only average over last 'time_period' prices del (history[0]) sma_values.append(stats.mean(history)) quotes = sma_values.append(stats.mean(history)) print(quotes)

Error:
Date 2021-01-14 127.957146 2021-01-15 126.200218 2021-01-19 126.885124 2021-01-20 131.054077 2021-01-21 135.858307 2021-01-22 138.042068 2021-01-25 141.863586 2021-01-26 142.101807 2021-01-27 141.009964 2021-01-28 136.076660 2021-01-29 130.984619 2021-02-01 133.148483 2021-02-02 133.992203 2021-02-03 132.949982 2021-02-04 136.374451 2021-02-05 135.951965 2021-02-08 136.101074 2021-02-09 135.206390 2021-02-10 134.590073 2021-02-11 134.331604 2021-02-12 134.570175 2021-02-16 132.403061 2021-02-17 130.066925 2021-02-18 128.943619 2021-02-19 129.102676 2021-02-22 125.255547 2021-02-23 125.116371 2021-02-24 124.609383 2021-02-25 120.275139 2021-02-26 120.543556 2021-03-01 127.034966 2021-03-02 124.380745 2021-03-03 121.338821 2021-03-04 119.420227 2021-03-05 120.702599 2021-03-08 115.672493 2021-03-09 120.374542 2021-03-10 119.271118 2021-03-11 121.239410 2021-03-12 120.314903 2021-03-15 123.257416 2021-03-16 124.828079 2021-03-17 124.022865 2021-03-18 119.817863 Name: Adj Close, dtype: float64 None

list.append(value) appends value to the end of list and returns None.

quotes = sma_values.append(stats.mean(history)) #<- quotes = None

Hi,
See the code below.

import yfinance as yf ticker = "AAPL" date1 = '2010-01-01' # start and end date date2 = '2020-01-01' df=yf.download(ticker,date1, date2) def SMA(data, period=10): return data['Close'].rolling(window=period).mean() # df['SMA10']=SMA(df) df.head(11) Out[895]: Open High Low Close Adj Close Volume \ Date 2010-01-04 7.622500 7.660714 7.585000 7.643214 6.535085 493729600 2010-01-05 7.664286 7.699643 7.616071 7.656429 6.546385 601904800 2010-01-06 7.656429 7.686786 7.526786 7.534643 6.442255 552160000 2010-01-07 7.562500 7.571429 7.466071 7.520714 6.430344 477131200 2010-01-08 7.510714 7.571429 7.466429 7.570714 6.473095 447610800 2010-01-11 7.600000 7.607143 7.444643 7.503929 6.415992 462229600 2010-01-12 7.471071 7.491786 7.372143 7.418571 6.343011 594459600 2010-01-13 7.423929 7.533214 7.289286 7.523214 6.432483 605892000 2010-01-14 7.503929 7.516429 7.465000 7.479643 6.395229 432894000 2010-01-15 7.533214 7.557143 7.352500 7.354643 6.288351 594067600 2010-01-19 7.440357 7.685357 7.401429 7.680000 6.566539 730007600 SMA10 Date 2010-01-04 NaN 2010-01-05 NaN 2010-01-06 NaN 2010-01-07 NaN 2010-01-08 NaN 2010-01-11 NaN 2010-01-12 NaN 2010-01-13 NaN 2010-01-14 NaN 2010-01-15 7.520571 2010-01-19 7.524250 np.mean(df.Close[0:10]) Out[900]: 7.520571374893189