# Importing required modules import pandas as pd import numpy as np import matplotlib.pyplot as plt # Settings to produce nice plots in a Jupyter notebook plt.style.use('fivethirtyeight') %matplotlib inline # Reading in the data stock_data = pd.read_csv('/work/stock_data.csv', parse_dates = True, index_col = ['Date']).dropna() benchmark_data = pd.read_csv('/work/benchmark_data.csv', parse_dates = True, index_col = ['Date']).dropna()

# Display summary for stock_data print('Stocks\n') stock_data.info() print(stock_data.head()) # Display summary for benchmark_data print('\nBenchmarks\n') benchmark_data.info() print(benchmark_data.head())

# visualize the stock_data stock_data.plot(subplots = True, title = 'Stock Data') # summarize the stock_data stock_data.describe()

# plot the benchmark_data benchmark_data.plot(title = 'S&P 500') # summarize the benchmark_data benchmark_data.describe()

# calculate daily stock_data returns stock_returns = stock_data.pct_change() # plot the daily returns stock_returns.plot() # summarize the daily returns stock_returns.describe()

# calculate daily benchmark_data returns sp_returns = benchmark_data['S&P 500'].pct_change() # plot the daily returns sp_returns.plot() # summarize the daily returns sp_returns.describe()

# calculate the difference in daily returns excess_returns = stock_returns.sub(sp_returns, axis = 0) # plot the excess_returns excess_returns.plot(); # summarize the excess_returns excess_returns.describe()

# calculate the mean of excess_returns avg_excess_return = excess_returns.mean() # plot avg_excess_returns avg_excess_return.plot.bar(title = 'Mean of the Return Difference');

# calculate the standard deviations sd_excess_return = excess_returns.std() # plot the standard deviations sd_excess_return.plot.bar(title = 'Standard Deviation of the Return Difference')

# calculate the daily sharpe ratio daily_sharpe_ratio = avg_excess_return.div(sd_excess_return) # annualize the sharpe ratio annual_factor = np.sqrt(252) annual_sharpe_ratio = daily_sharpe_ratio.mul(annual_factor) # plot the annualized sharpe ratio annual_sharpe_ratio.plot.bar(title = 'Annualized Sharpe Ratio: Stocks vs S&P 500')

# Uncomment your choice. buy_amazon = True # buy_facebook = True