asitdalai · April 28, 2021 18:29 · Oct 9, 2020
diff --git a/MA_crossover_strategy_script.py b/MA_crossover_strategy_script.py
@@ -0,0 +1,88 @@
+# 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'))