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Learning ValueAtRisk(VAR).py
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Learning ValueAtRisk(VAR).py
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#!/usr/bin/env python
# coding: utf-8
# In[39]:
import pandas as pd
from pandas_datareader import data as pdr
import yfinance as yf
import numpy as np
import datetime as dt
import matplotlib.pyplot as plt
# In[117]:
tickers = ['HD','DIS','WMT','VZ']
# In[118]:
weights = np.array([.25, .3, .15, .3])
# In[119]:
initial_investment = 1000
# In[120]:
start = dt.datetime(2020,1,1)
end = dt.datetime(2020,12,31)
# In[121]:
data = pdr.get_data_yahoo(tickers, start, end=dt.date.today())['Close']
# In[122]:
returns = data.pct_change()
# In[123]:
returns.tail()
# In[124]:
cov_matrix = returns.cov()
cov_matrix
# ### Calculating Means
# In[125]:
avg_rets = returns.mean()
# In[126]:
port_mean = avg_rets.dot(weights)
# Calculate portfolio standard deviation
port_stdev = np.sqrt(weights.T.dot(cov_matrix).dot(weights))
# Calculate mean of investment
mean_investment = (1+port_mean) * initial_investment
# Calculate standard deviation of investmnet
stdev_investment = initial_investment * port_stdev
# ### Confidence Level
# In[142]:
# Select our confidence interval (I'll choose 95% here)
conf_level1 = 0.05
from scipy.stats import norm
cutoff1 = norm.ppf(conf_level1, mean_investment, stdev_investment)
# ### 95% Confidence
# In[143]:
#Finally, we can calculate the VaR at our confidence interval
var_1d1 = initial_investment - cutoff1
var_1d1
# In[140]:
# Calculate n Day VaR
var_array = []
num_days = int(15)
for x in range(1, num_days+1):
var_array.append(np.round(var_1d1 * np.sqrt(x),2))
print(str(x) + " day VaR @ 95% confidence: " + str(np.round(var_1d1 * np.sqrt(x),2)))
# In[139]:
plt.xlabel("Day #")
plt.ylabel("Max portfolio loss (USD)")
plt.title("Max portfolio loss (VaR) over 15-day period")
plt.plot(var_array, "r")
# In[130]:
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
from scipy.stats import norm
from scipy import stats
import scipy as sp
# In[138]:
returns['HD'].hist(bins=40, histtype="stepfilled",alpha=0.5)
x = np.linspace(port_mean - 3*port_stdev, port_mean+3*port_stdev,100)
plt.plot(x, sp.stats.norm.pdf(x, port_mean, port_stdev), "r")
plt.title("HD returns (binned) vs. normal distribution")
plt.show()
# In[137]:
returns['DIS'].hist(bins=40, histtype="stepfilled",alpha=0.5)
x = np.linspace(port_mean - 3*port_stdev, port_mean+3*port_stdev,100)
plt.plot(x, sp.stats.norm.pdf(x, port_mean, port_stdev), "r")
plt.title("DIS returns (binned) vs. normal distribution")
plt.show()
# In[136]:
returns['WMT'].hist(bins=40, histtype="stepfilled",alpha=0.5)
x = np.linspace(port_mean - 3*port_stdev, port_mean+3*port_stdev,100)
plt.plot(x, sp.stats.norm.pdf(x, port_mean, port_stdev), "r")
plt.title("WMT returns (binned) vs. normal distribution")
plt.show()
# In[135]:
returns['VZ'].hist(bins=40, histtype="stepfilled",alpha=0.5)
x = np.linspace(port_mean - 3*port_stdev, port_mean+3*port_stdev,100)
plt.plot(x, sp.stats.norm.pdf(x, port_mean, port_stdev), "r")
plt.title("VZ returns (binned) vs. normal distribution")
plt.show()
# ### Source
# #### https://www.interviewqs.com/blog/value_at_risk
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