SMA Trading Strategy Using Python

This strategy works on the basic logic that whenever the 30 Day Simple Moving Average is greater than the closing price of the stock it gives a Buy signal and whenever the 30 Day SMA is smaller than the closing price then it gives a sell signal. This way we get a clear understanding of when to place a buy order and when to exit our position.

I've built this strategy using python and here is the program and logic for the strategy.

#import libraries

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

plt.style.use('fivethirtyeight')

#Load data

from google.colab import files

files.upload()

#Store the data

df = pd.read_csv('HDFC.NS.csv')

#set date as index

df = df.set_index(pd.DatetimeIndex(df['Date'].values))

#show data

df

#visualize the close price

plt.figure(figsize=(16,8))

plt.title('Close Price')

plt.plot(df['Close'])

plt.xlabel('Date')

plt.ylabel('Close Price')

def SMA(data, period=30, column='Close'):

  return data[column].rolling(window=period).mean()

df['SMA30'] = SMA(df)

#Show the data

df 

def strategy(df):

  buy = []

  sell = []

  flag = 0

  buy_price = 0

  for i in range(0, len(df)):

    if df['SMA30'][i] > df['Close'][i] and flag == 0:

      buy.append(df['Close'][i])

      sell.append(np.nan)

      buy_price = df['Close'][i]

      flag = 1

    elif df['SMA30'][i] < df['Close'][i] and flag == 1 and buy_price < df['Close'][i]:

      sell.append(df['Close'][i])

      buy.append(np.nan)

      buy_price = 0

      flag = 0

    else: 

      sell.append(np.nan)

      buy.append(np.nan)

  return (buy, sell)

Here in this function, the logic is whenever the SMA30 is greater than the Close price of the dataset and flag is equal to 0 then it will generate a buy signal and append the Buy list and change the flag to 1, and after that whenever the SMA30 is smaller than the Close price of the dataset and flag is already 1 then it will generate sell signal and append the sell list this way the flag will again be set back to 0 and this process will happen on repeat whenever this condition will ever happen again.

#get the buy and sell list

start = strategy(df)

df['Buy'] = start[0]

df['sell'] = start[1]

#visualize the close price and buy sell signal

plt.figure(figsize=(16,8))

plt.title('Close price buy sell signal')

plt.plot(df['Close'], alpha = 0.5, label = 'Close')

plt.plot(df['SMA30'], alpha = 0.5, label = 'SMA30')

plt.scatter(df.index, df['Buy'], color='green', label='Buy Signal', marker= '^', alpha = 1)

plt.scatter(df.index, df['sell'], color='red', label='sell Signal', marker= 'v', alpha = 1)

plt.xlabel('Date')

plt.ylabel('Close Price')

plt.show()

Now here you can see we are getting consecutive buy & sell signals that are giving good opportunities for booking profit.