stock_sharpe.py

# .npz    - past 5 years
# (1).npz - past year
# (2).npz - past 6 months
# (3).npz - past 3 months
# (4).npz - past month

# ROLLING SHARPE RATIO
import pickle
import datetime
import requests
import bs4 as bs
import numpy as np 
import pandas as pd
import matplotlib.pyplot as plt
from pandas_datareader import DataReader
import seaborn as sns


start_date = datetime.datetime(2019,1,15)
end_date = datetime.date.today()

'''
# save_sp500_tickers()
def save_spx_tickers():
    resp = requests.get('https://en.wikipedia.org/wiki/List_of_S%26P_500_companies')
    soup = bs.BeautifulSoup(resp.text, 'lxml')
    table = soup.find('table', {'class':'wikitable sortable'})
    tickers = []
    for row in table.findAll('tr')[1:]:
        ticker = row.find_all('td') [0].text.strip()
        tickers.append(ticker)
        
    with open('spxTickers.pickle', 'wb') as f:
            pickle.dump(tickers, f)       
    return tickers
        
tickers = save_spx_tickers()
tickers = [item.replace(".", "-") for item in tickers]
'''

tickers = pd.read_csv('nasdaq.csv')
tickers = tickers['Symbol']
tickers = tickers.drop(['ABAC'])
sharpe_ratios = []

for ticker in tickers:
    df = DataReader(ticker, 'yahoo', start_date, end_date) 
    x = 5000
    
    y = (x)
    
    stock_df = df
    stock_df['Norm return'] = stock_df['Adj Close'] / stock_df.iloc[0]['Adj Close']
    
    allocation = float(x/y)
    stock_df['Allocation'] = stock_df['Norm return'] * allocation
    
    stock_df['Position'] = stock_df['Allocation'] * x
    pos = [df['Position']]
    val = pd.concat(pos, axis=1)
    val.columns = ['WMT Pos']
    val['Total Pos'] = val.sum(axis=1)
    
    val.tail(1)
    
    val['Daily Return'] = val['Total Pos'].pct_change(1)
    
    Sharpe_Ratio = val['Daily Return'].mean() / val['Daily Return'].std()
    
    A_Sharpe_Ratio = (252**0.5) * Sharpe_Ratio
    
    print('---------------------------------------------------------------')
    print ('{} has an average annualized sharpe ratio of {}'.format(ticker, A_Sharpe_Ratio))
    
    sharpe_ratios.append(A_Sharpe_Ratio)

np.savez("nasdaq_sharpe_ratios(1).npz", sharpe_ratios)

'''
all_sharpe_ratios = np.load("nasdaq_sharpe_ratios(1).npz")
all_sharpe_ratios = all_sharpe_ratios['arr_0'] 
all_sharpe_ratios = all_sharpe_ratios.tolist()

# Create a dataframe with each company and their corressponding beta/alpha values
dataframe = pd.DataFrame(list(zip(tickers, all_sharpe_ratios)), columns =['Company', 'Sharpe_Ratio']) 

pd.set_option('display.max_rows', None)
pd.set_option('display.min_rows', None)
# Sorting the dataframe from highest beta values to lowest
sort_by_sharpe = dataframe.sort_values('Sharpe_Ratio', ascending = True)
print(sort_by_sharpe)
'''