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@pratiknabriya
Created October 9, 2020 15:46
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Generating trade signals using moving average(SMA/EMA) crossover strategy.
# import necessary libraries
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import datetime
from tabulate import tabulate
import warnings
warnings.filterwarnings('ignore')
import pandas_datareader.data as web
def MovingAverageCrossStrategy(stock_symbol = 'ULTRACEMCO.NS', start_date = '2018-01-01', end_date = '2020-01-01',
short_window = 20, long_window = 50, moving_avg = 'SMA', display_table = True):
'''
The function takes the stock symbol, time-duration of analysis,
look-back periods and the moving-average type(SMA or EMA) as input
and returns the respective MA Crossover chart along with the buy/sell signals for the given period.
'''
# stock_symbol - (str)stock ticker as on Yahoo finance. Eg: 'ULTRACEMCO.NS'
# start_date - (str)start analysis from this date (format: 'YYYY-MM-DD') Eg: '2018-01-01'
# end_date - (str)end analysis on this date (format: 'YYYY-MM-DD') Eg: '2020-01-01'
# short_window - (int)lookback period for short-term moving average. Eg: 5, 10, 20
# long_window - (int)lookback period for long-term moving average. Eg: 50, 100, 200
# moving_avg - (str)the type of moving average to use ('SMA' or 'EMA')
# display_table - (bool)whether to display the date and price table at buy/sell positions(True/False)
# import the closing price data of the stock for the aforementioned period of time in Pandas dataframe
start = datetime.datetime(*map(int, start_date.split('-')))
end = datetime.datetime(*map(int, end_date.split('-')))
stock_df = web.DataReader(stock_symbol, 'yahoo', start = start, end = end)['Close']
stock_df = pd.DataFrame(stock_df) # convert Series object to dataframe
stock_df.columns = {'Close Price'} # assign new colun name
stock_df.dropna(axis = 0, inplace = True) # remove any null rows
# column names for long and short moving average columns
short_window_col = str(short_window) + '_' + moving_avg
long_window_col = str(long_window) + '_' + moving_avg
if moving_avg == 'SMA':
# Create a short simple moving average column
stock_df[short_window_col] = stock_df['Close Price'].rolling(window = short_window, min_periods = 1).mean()
# Create a long simple moving average column
stock_df[long_window_col] = stock_df['Close Price'].rolling(window = long_window, min_periods = 1).mean()
elif moving_avg == 'EMA':
# Create short exponential moving average column
stock_df[short_window_col] = stock_df['Close Price'].ewm(span = short_window, adjust = False).mean()
# Create a long exponential moving average column
stock_df[long_window_col] = stock_df['Close Price'].ewm(span = long_window, adjust = False).mean()
# create a new column 'Signal' such that if faster moving average is greater than slower moving average
# then set Signal as 1 else 0.
stock_df['Signal'] = 0.0
stock_df['Signal'] = np.where(stock_df[short_window_col] > stock_df[long_window_col], 1.0, 0.0)
# create a new column 'Position' which is a day-to-day difference of the 'Signal' column.
stock_df['Position'] = stock_df['Signal'].diff()
# plot close price, short-term and long-term moving averages
plt.figure(figsize = (20,10))
plt.tick_params(axis = 'both', labelsize = 14)
stock_df['Close Price'].plot(color = 'k', lw = 1, label = 'Close Price')
stock_df[short_window_col].plot(color = 'b', lw = 1, label = short_window_col)
stock_df[long_window_col].plot(color = 'g', lw = 1, label = long_window_col)
# plot 'buy' signals
plt.plot(stock_df[stock_df['Position'] == 1].index,
stock_df[short_window_col][stock_df['Position'] == 1],
'^', markersize = 15, color = 'g', alpha = 0.7, label = 'buy')
# plot 'sell' signals
plt.plot(stock_df[stock_df['Position'] == -1].index,
stock_df[short_window_col][stock_df['Position'] == -1],
'v', markersize = 15, color = 'r', alpha = 0.7, label = 'sell')
plt.ylabel('Price in ₹', fontsize = 16 )
plt.xlabel('Date', fontsize = 16 )
plt.title(str(stock_symbol) + ' - ' + str(moving_avg) + ' Crossover', fontsize = 20)
plt.legend()
plt.grid()
plt.show()
if display_table == True:
df_pos = stock_df[(stock_df['Position'] == 1) | (stock_df['Position'] == -1)]
df_pos['Position'] = df_pos['Position'].apply(lambda x: 'Buy' if x == 1 else 'Sell')
print(tabulate(df_pos, headers = 'keys', tablefmt = 'psql'))
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