import numpy as np
import pandas as pd
df = pd.read_csv('../../../raw_data/FDX.csv', index_col = 0, parse_dates = True)
returns = df['adjClose'].pct_change().dropna()Computing Value at Risk (VaR) using Python.
VaR is the maximum amount loss that can happen within a confidence interval over a pre-dertermined interval. If the 1-day 95% confidence Interval Var is $100; this means that 95% of the time, under ‘normal’ market conditions, we do not expect to loose more than $100 per day for our portfolio.
print(df)
df
returns open high low close adjClose \
date
2023-03-09 211.080000 213.310000 207.300000 207.830000 207.830002
2023-03-08 205.900000 209.970000 205.390000 209.540000 209.539993
2023-03-07 209.720000 209.720000 206.160000 206.320000 206.320007
2023-03-06 209.600000 211.890000 209.350000 210.130000 210.130005
2023-03-03 208.460000 210.100000 207.130000 209.080000 209.080002
... ... ... ... ... ...
2001-01-08 36.671723 37.013253 36.432653 36.825411 36.835415
2001-01-05 38.165914 38.165914 36.722953 37.406011 37.416172
2001-01-04 36.714414 38.422061 36.458267 37.790232 37.800480
2001-01-03 32.624599 35.228761 32.445296 35.057997 35.067524
2001-01-02 33.683341 34.127329 32.616061 32.616061 32.624928
volume unadjustedVolume change changePercent vwap \
date
2023-03-09 1953078 1918911 -3.250000 -1.54000 210.08
2023-03-08 1073890 1073900 3.640000 1.77000 208.29
2023-03-07 1527138 1526900 -3.400000 -1.62000 207.00
2023-03-06 2148072 2148000 0.530000 0.25286 210.52
2023-03-03 1612507 1612300 0.620000 0.29742 208.87
... ... ... ... ... ...
2001-01-08 1120700 1120700 0.153688 0.41909 36.76
2001-01-05 1485800 1485800 -0.759903 -1.99000 37.43
2001-01-04 3410600 3410600 1.075820 2.93000 37.56
2001-01-03 2203000 2203000 2.433400 7.46000 34.24
2001-01-02 936600 936600 -1.067280 -3.17000 33.12
label changeOverTime
date
2023-03-09 March 09, 23 -0.015400
2023-03-08 March 08, 23 0.017700
2023-03-07 March 07, 23 -0.016200
2023-03-06 March 06, 23 0.002529
2023-03-03 March 03, 23 0.002974
... ... ...
2001-01-08 January 08, 01 0.004191
2001-01-05 January 05, 01 -0.019900
2001-01-04 January 04, 01 0.029300
2001-01-03 January 03, 01 0.074600
2001-01-02 January 02, 01 -0.031700
[5581 rows x 12 columns]date
2023-03-08 0.008228
2023-03-07 -0.015367
2023-03-06 0.018466
2023-03-03 -0.004997
2023-03-02 -0.010283
...
2001-01-08 0.020345
2001-01-05 0.015766
2001-01-04 0.010271
2001-01-03 -0.072300
2001-01-02 -0.069654
Name: adjClose, Length: 5580, dtype: float64