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my_functions.py
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my_functions.py
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# +
#Importing Required Libraries
import pandas as pd
import numpy as np
import yfinance as yf
import matplotlib.pyplot as plt
import cufflinks as cf
from pandas_datareader import data
from pandas.tseries.frequencies import to_offset
import csv
from datetime import datetime
import time
import matplotlib.pyplot as plt
from scipy.optimize import brute
plt.style.use("seaborn")
# -
def data_agreggation_yahoo(start,end,a):
df = pd.DataFrame()
data=[]
for ticker in a:
data = yf.download(ticker, start = start, end = end)
a=ticker+"_returns"
df[a] = np.log(data.Open / data.Close)*100
return df
def data_agreggation_csv(start,end,name_csv,dates):
week_13_treasury = pd.read_csv(name_csv)
mask = (week_13_treasury["Date"]>=start) & (week_13_treasury["Date"]<=end)
week_13_treasury = week_13_treasury.loc[mask]
week_13_treasury["yield_returns"] = np.log(week_13_treasury.Open / week_13_treasury.Close)*100
dates_treasury = list(week_13_treasury["Date"])
dates_treasury_return = list(week_13_treasury["yield_returns"])
data=pd.DataFrame(columns = ['Date','Yield_returns'])
for i in dates:
for j in range(len(dates_treasury_return)):
if dates_treasury[j] in str(i):
a=[i,dates_treasury_return[j]]
data.loc[len(data.index)] = a
data.index = data["Date"]
return data