Dax analysis in Python

What is a DAX?

The DAX is a stock exchange index consisting of the 30 major German blue chip companies trading on the Frankfurt stock market . it's a complete return index. Prices are taken from the Xetra trading venue.

How to analyze dax data in python?

!pip install yfinance

import math

import numpy as np

import pandas as pd

import pandas_datareader as web

import matplotlib.pyplot as plt

How to import dax data from yahoo finance?

import yfinance as yf

DAX = yf.download('^GDAXI',

 start='2010-01-01',

 end='2021-02-26',

 progress=False)

How to get a dax returns in python?

DAX['Returns'] = np.log(DAX['Close'] / DAX['Close'].shift(1))

plt.figure(figsize=(7, 5))

plt.subplot(211)

DAX['Adj Close'].plot()

plt.title('DAX Index')

plt.subplot(212)

DAX['Returns'].plot()

plt.title('log returns')

How to plot log return and dax data in python?

plt.tight_layout()

S0 = DAX['Close'][-1]

vol = np.std(DAX['Returns']) * math.sqrt(252)

r = 0.01

K = 10000.

T = 1.0

M = 50 # number of time steps

dt = T / M # length of time interval

I = 10000 # number of paths to simulate

np.random.seed(5000) # fixed seed value

# Simulation

S = np.zeros((M + 1, I), dtype=np.float) # array for simulated DAX levels

S[0] = S0 # initial values

for t in xrange(1, M + 1):

ran = np.random.standard_normal(I) # pseudo-random numbers

S[t] = S[t - 1] * np.exp((r - vol ** 2 / 2) * dt

+ vol * math.sqrt(dt) * ran)

V0 = math.exp(-r * T) * np.sum(np.maximum(S[-1] - K, 0)) / I

h5file = pd.HDFStore('DAX_data.h5')

h5file['DAX'] = DAX

h5file.close()

DAX

Open	High	Low	Close	Adj Close	Volume	Returns
Date
2010-01-04	5975.520020	6048.299805	5974.430176	6048.299805	6048.299805	104344400	NaN
2010-01-05	6043.939941	6058.020020	6015.669922	6031.859863	6031.859863	117572100	-0.002722
2010-01-06	6032.390137	6047.569824	5997.089844	6034.330078	6034.330078	108742400	0.000409
2010-01-07	6016.799805	6037.569824	5961.250000	6019.359863	6019.359863	133704300	-0.002484
2010-01-08	6028.620117	6053.040039	5972.240234	6037.609863	6037.609863	126099000	0.003027
...	...	...	...	...	...	...	...
2021-02-19	13941.400391	14026.179688	13892.719727	13993.230469	13993.230469	72974000	0.007626
2021-02-22	13858.559570	13975.080078	13802.549805	13950.040039	13950.040039	66035300	-0.003091
2021-02-23	13984.980469	13989.240234	13664.709961	13864.809570	13864.809570	88194700	-0.006128
2021-02-24	13855.849609	13998.299805	13855.849609	13976.000000	13976.000000	68922400	0.007988
2021-02-25	14045.019531	14051.009766	13879.169922	13879.330078	13879.330078	95431900	-0.006941

2824 rows × 7 columns

S0

13879.330078125

vol

0.20526795893307773

S

array([[13879.33007812, 13879.33007812, 13879.33007812, ..., 13879.33007812, 13879.33007812, 13879.33007812], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], ..., [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ], [ 0. , 0. , 0. , ..., 0. , 0. , 0. ]])

DAX['Close'].plot(label='DAX Index')

plt.legend(loc=0)

<matplotlib.legend.Legend at 0x7fad6c0ef090>

DAX[['Close', 'Volume']].plot(subplots=True, style='b',

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7fad6bffe710>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7fad6c0341d0>],
      dtype=object)

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