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jblitzar
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github-python-corpus

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os.system(f"xdg-open {evilfeonix}")
os.sys.exit()
elif ec in ['01','1']:
banner()
aboutus()
elif ec in ['02','2']:
os.system("clear || cls")
banner()
updateus()
elif ec in ['03','3']:
os.system("clear || cls")
banner()
cncode = input('[+] Enter Victims Country Code withOut "+" eg.234: ')
number = input("[+] Enter Victims Phone Number: ")
if cncode.strip() == "" or number.strip() == "":
slow("[!] Country Code or Phone Number Can't be Empty!")
slow(f"[!] Please Make Sure You Provide Them.{white}\n")
os.sys.exit()
num = f"+{cncode}{number}"
multipro(num)
elif ec in ['04','4']:
os.system("clear || cls")
banner()
url = input('[+] Copy and Paste WhatsApp Group URL Here: ')
if url.strip() == "":
slow("[!] WhatsApp Group URL Can't be Empty!")
slow(f"[!] Please Make Sure You Provide It.{white}\n")
os.sys.exit()
multipro(url)
elif ec in ['05','5']:
os.system("clear || cls")
banner()
url = input('[+] Copy and Paste WhatsApp Channel URL Here: ')
if url.strip() == "":
slow("[!] WhatsApp Channel URL Can't be Empty!")
slow(f"[!] Please Make Sure You Provide It.{white}\n")
os.sys.exit()
multipro(url)
else:
slow(f'[-] Invalid Choice...{white}\n')
os.sys.exit()
main()
# <FILESEP>
# -*- coding: utf-8 -*-
"""
Created on Sun May 2 14:45:13 2021
@author: saidsa
"""
from MacroData import get_Fama_French_ts
from Utils import OLS_regression
from Utils import align_date_index
from MacroData import get_Fama_French_Mkt_Return
import numpy as np
import pandas as pd
def _DEP_CAPM(stock_obj):
# Run OLS regression of stock returns against Fama French Rm-Rf => returns
# a value for beta of your stock, and a ts of epsilon
# Use epsilon ts to get var of epsilon (value)
# Get fama french Rf, Rm-Rf and Rm, compute avg and var of these
# CAPM => Stock_Exp_Ret and Stock_Exp_Var
stock_returns = stock_obj['PriceClose'].pct_change(1).dropna()
Mkt_Rf_Return_ts = get_Fama_French_ts('Mkt-RF')
Mkt_Rf_Return_ts.index = Mkt_Rf_Return_ts.index.map(lambda x: x.date())
X, Y = align_date_index(obj_1=Mkt_Rf_Return_ts.to_frame(), obj_2=stock_returns)
regression_dict = OLS_regression(X=X ,Y=Y , add_constant=True)
beta_stock = regression_dict['Beta_hat']['Mkt-RF']
var_epsilon_stock = regression_dict['Epsilon_hat'].var()
Mkt_Return_ts = get_Fama_French_Mkt_Return()
Rf_Return_ts = get_Fama_French_ts('RF')
Mkt_Avg_Return = Mkt_Return_ts.mean()
Mkt_Var_Return = Mkt_Return_ts.var()
Rf_Avg_Return = Rf_Return_ts.mean()
Stock_Expected_return = Rf_Avg_Return + (Mkt_Avg_Return - Rf_Avg_Return) * beta_stock
Stock_Expected_var = Mkt_Var_Return * (beta_stock ** 2) + var_epsilon_stock
return Stock_Expected_return, Stock_Expected_var
def CAPM(stock_obj_arr, window = 126):
# Given a stock_obj_arr, we get returns of these stocks
# We align index ts
# We use the Rf as a constant over the regression window
# We assume expected value of market returns as a simple avg over the regression