endpoints

from fintools import endpoints

# end_points prices (in this case, monthly)
end_points = endpoints(period='M', trading_days=prices_d.index)
prices_m = prices_d.loc[end_points]
prices_m[:2]

hist_data.py

import pandas as pd
from datetime import datetime, date
import yfinance as yf
 
assets = ['AAPL',       # Apple
          'KO',         # Coca-Cola
          'DIS',        # Disney
          'XOM',        # Exxon Mobil
          'JPM',        # JPMorgan Chase
          'MCD',        # McDonald's
          'WMT']         # Walmart
 
# download historical data from yfinance
hist_data = {}
for asset in assets:
    data = yf.download(asset, start='2015-01-01', end='2017-12-31')
    hist_data[asset] = data['Adj Close']
hist_data = pd.concat(hist_data, axis=1)

historical_SP500_data

# S&P500 Historical Data
import yfinance as yf
from datetime import datetime
import pytz
import pandas as pd

start = str(datetime(1900, 1, 1, 0, 0, 0, 0, pytz.utc).date())
end = str(datetime.today().date())
df = yf.download( ['^GSPC'])

df_d = df['Close'].to_frame()
df_daily = df_d.copy()
df_10 = df_d.resample('10B').first().copy()
df_21 = df_d.resample('21B').first().copy()
df_m = df_d.resample('M').last().copy()
df_sm = df_d.resample('SM').last().copy()
df_ms = df_d.resample('M').first().copy()

load_historical_data

#*****************************************************************
# Load historical data
#****************************************************************** 

import pandas as pd
from datetime import datetime
import pytz
import yfinance as yf

cash_proxy = 'CASHX'
risk_free = 'BIL'

rs_lookback = 12
risk_lookback = 12
n_top = 7

# these are the ETFs used by Systematic Investor (2005 - today)
symbols = ['SPY','EFA','EWJ','EEM','IYR','RWX','IEF','TLT','DBC','GLD']

# get data
tickers = symbols.copy()
if cash_proxy != 'CASHX' :
    tickers = list(set(tickers + [cash_proxy]))
if isinstance(risk_free, str) :
    tickers = list(set(tickers + [risk_free]))

data_path = '/home/scubamut/MEGAsync/10_DATA/'

start = datetime(2000, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2014, 1, 1, 0, 0, 0, 0, pytz.utc)
# end = datetime.today().replace(tzinfo=timezone.utc)

# data as a DatFrame of DataFrames, one for each symbol
data = yf.download(tickers, start, end).copy()
data1 = data.copy()
data.columns = data.columns.swaplevel(1,0)

# for one symbol
# print(data.SPY[:2])

# if required
inception_dates = pd.DataFrame([data[ticker].first_valid_index().date() for ticker in tickers], index=tickers, columns=['inception'])
# print (inception_dates)

# daily prices
prices_d = data1['Adj Close'].copy().dropna()

# month-end prices
prices_m = prices_d.resample('M').last()
prices_m[:2]

# Alias	Description
# --------------------
# B    business day frequency
# C    custom business day frequency
# D    calendar day frequency
# W    weekly frequency
# M    month end frequency
# SM    semi-month end frequency (15th and end of month)
# BM    business month end frequency
# CBM    custom business month end frequency
# MS    month start frequency
# SMS    semi-month start frequency (1st and 15th)
# BMS      business month start frequency
# CBMS    custom business month start frequency
# Q    quarter end frequency
# BQ    business quarter end frequency
# QS    quarter start frequency
# BQS    business quarter start frequency
# A,Y          year end frequency
# BA,BY      business year end frequency
# AS,YS       year start frequency
# BAS,BYS  business year start frequency

monthly_returns.py

import yfinance as yf
import pandas as pd
from datetime import datetime
# NOTE: RETURNS MATCH WITH PV

from fintools import *
 
start = "1990-01-01"
end = datetime.today().strftime('%Y-%m-%d')
 
symbols = ['SPY','XLY','XLK','XLI','XLB','XLE','XLP','XLV','XLU','XLF'])
daily_prices =  yf.download(symbols, start, end)['Adj Close']
 
symbol = 'SPY'
 
df = monthly_return_table(daily_prices[symbol])
df = pd.DataFrame(df.values, index=df.index, columns=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep',
                                                               'Oct','Nov','Dec','Annual Return'])