import pandas as pd import numpy as np import yfinance as yf import matplotlib.pyplot as plt import datetime as dt # Get the stock ticker tickers ='^GSPC' start=dt.datetime(2000,1,1) #end= dt.datetime.now() end = dt.datetime.now() assets=yf.download(tickers,start,end) #['Adj Close'] assets.rename(columns={'Adj Close': 'Adj_Close'}, inplace=True) # compute daily returns using pandas pct_change() assets['daily_returns'] = assets['Adj_Close'].pct_change() # skip first row with NA #assets = assets['daily_returns'][1:] assets = assets[1:] plt.plot(assets['daily_returns']) first_half = assets[assets.index.month.isin([1,2,3,4,11,12])] second_half = assets[assets.index.month.isin([5,6,7,8,9,10])] # Calculate the cumulative daily returns first_half['FH_Culm_Return'] = (1 + first_half['daily_returns']).cumprod() - 1 # Calculate the cumulative daily returns second_half['SH_Culm_Return'] = (1 + second_half['daily_returns']).cumprod() - 1 s1 = first_half['FH_Culm_Return'] s2 = second_half['SH_Culm_Return'] s1.fillna(method='ffill', inplace=True) s2.fillna(method='ffill', inplace=True) out = pd.concat([s1, s2], axis=1) out.fillna(method ='ffill', inplace=True) out.tail() plt.figure(figsize=(12,5)) plt.title(tickers+" Seasonal Performance") plt.xlabel('Year') plt.ylabel('Culmulative Perf') ax1 = out.FH_Culm_Return.plot(color='black', grid=True, label='Nov-Apr') ax2 = out.SH_Culm_Return.plot(color='red', label='May-Oct') ax1.legend(loc=1) ax2.legend(loc=2) plt.show()

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