Market_Risk_VaR.py

import yfinance as yf
import pandas as pd
import datetime as dt
import numpy as np
from scipy.stats import norm


def load_raw_data(tickers, start_date, end_date):
    price = yf.download(tickers, start_date, end_date)['Adj Close']
    price = pd.DataFrame(price)
    price = price.reindex(columns=tickers)
    return price


def data_info(price):
    returns = np.log(price / price.shift(1)).dropna()

    returns_mean = returns.mean()
    returns_std = returns.std()
    weights = np.random.random(len(returns.columns))
    '''np.random.random(size) generates random floats in the half-open interval [0.0, 1.0). The argument inside the 
    parentheses len(returns.columns) is the number of stocks in your portfolio, i.e., the number of columns in your 
    returns DataFrame.'''
    weights /= np.sum(weights)
    '''normalizing these random numbers so that they sum to 1.0, making them proportions of the total.'''
    '''The whole process can be improve by portfolio optimization'''

    cov_var = returns.cov()
    port_std = np.sqrt(weights.T.dot(cov_var).dot(weights))
    port_ret = np.dot(returns_mean, weights)

    return returns, returns_mean, returns_std, port_std, port_ret


def port_VaR_parametric(port_ret, port_std):
    initial_investment = 1e6
    conf_level = 0.95
    alpha = norm.ppf(1 - conf_level, port_ret, port_std)
    port_VaR_param = (initial_investment - initial_investment * (1 + alpha))
    print("portfolio Parametric VaR result is {}".format(port_VaR_param))

    return port_VaR_param


def stock_VaR_parametric(returns_mean, returns_std, price):
    global stock_VaR_param
    initial_investment = 1e6
    conf_level = 0.95
    alpha = norm.ppf(1 - conf_level, returns_mean, returns_std)
    for i, j in zip(price.columns, range(len(price.columns))):
        stock_VaR_param = (initial_investment - initial_investment * (1 + alpha))[j]
        print("stock Parametric VaR result for {} is {}".format(i, stock_VaR_param))
    return stock_VaR_param


if __name__ == '__main__':
    tickers = ['NWSA', 'FOXA', 'CMCSA', '^GSPC', 'LYV', 'NKE', 'NFLX', 'SONY', 'ATVI', 'CHTR', 'MAR', 'LULU', 'LVS', 'DKNG', 'DIS']
    start_date = dt.datetime(2020, 1, 1)
    end_date = dt.datetime(2023, 7, 31)
    price_ = load_raw_data(tickers, start_date, end_date)
    returns_, returns_mean_, returns_std_, port_std_, port_ret_ = data_info(price_)
    port_VaR_param_ = port_VaR_parametric(port_ret_, port_std_)
    stock_VaR_param = stock_VaR_parametric(returns_mean_, returns_std_, price_)