Switzerland(SMI) stock market

What is the name of Switzerland(SMI) stock market?

The SMI is symbol of the Switzerland(SMI) stock market.

How to get Switzerland(SMI) stock market data ?

you get data from yahoo finance,Quanda,google etc.

How to install data in python?

Here install python lib.

import yfinance as yf

import numpy as np

import pandas as pd

import seaborn as sns

import scipy.stats as scs

import statsmodels.api as sm

import statsmodels.tsa.api as smt

import matplotlib.pyplot as plt

Some python code data collected from yahoo finance

df = yf.download('^SSMI',

start='1985-01-01',

end='2021-07-07',

progress=False)

How to get indivisual column SMI adj price?

df = df.loc[:, ['Adj Close']]

df.rename(columns={'Adj Close':'adj_close'}, inplace=True)

Simple return and Log return SSMI

df['simple_rtn'] = df.adj_close.pct_change()

df['log_rtn'] = np.log(df.adj_close/df.adj_close.shift(1))

df[['simple_rtn','log_rtn']].tail(20)

Simple and log return

How to get realised volatility SMI in python?

def realized_volatility(x):

 return np.sqrt(np.sum(x**2))

df_rv = df.groupby(pd.Grouper(freq='M')).apply(realized_volatility)

df_rv.rename(columns={'log_rtn': 'rv'}, inplace=True)

df_rv.rv = df_rv.rv * np.sqrt(12)

fig, ax = plt.subplots(2, 1, sharex=True)

ax[0].plot(df)

ax[1].plot(df_rv)

Plot Realised volatility SMI

[<matplotlib.lines.Line2D at 0x7f8af4356f50>,
 <matplotlib.lines.Line2D at 0x7f8af4366090>,
 <matplotlib.lines.Line2D at 0x7f8af4366250>]

Plot time series

fig, ax = plt.subplots(3, 1, figsize=(24, 20), sharex=True)

df.adj_close.plot(ax=ax[0])

ax[0].set(title = 'SSMI time series',

ylabel = 'Stock price ($)')

df.simple_rtn.plot(ax=ax[1])

ax[1].set(ylabel = 'Simple returns (%)')

df.log_rtn.plot(ax=ax[2])

ax[2].set(xlabel = 'Date',

ylabel = 'Log returns (%)')

Plot time series in python

Get mean and standard deviation

df_rolling = df[['simple_rtn']].rolling(window=21) \

.agg(['mean', 'std'])

df_rolling.columns = df_rolling.columns.droplevel()

df_outliers = df.join(df_rolling)

def indentify_outliers(row, n_sigmas=3):

   x = row['simple_rtn']

   mu = row['mean']

   sigma = row['std']

   if (x > mu + 3 * sigma) | (x < mu - 3 * sigma):

    return 1

   else:

    return 0

How to plot Outliers?

df_outliers['outlier'] = df_outliers.apply(indentify_outliers,

axis=1)

outliers = df_outliers.loc[df_outliers['outlier'] == 1,

['simple_rtn']]

fig, ax = plt.subplots()

ax.plot(df_outliers.index, df_outliers.simple_rtn,

color='blue', label='Normal')

ax.scatter(outliers.index, outliers.simple_rtn,

color='red', label='Anomaly')

ax.set_title("SSMI returns")

ax.legend(loc='lower right')

<matplotlib.legend.Legend at 0x7f8aef0d5690>

r_range = np.linspace(min(df.log_rtn), max(df.log_rtn), num=1000)

mu = df.log_rtn.mean()

sigma = df.log_rtn.std()

norm_pdf = scs.norm.pdf(r_range, loc=mu, scale=sigma)

fig, ax = plt.subplots(1, 2, figsize=(16, 8))

# histogram

sns.distplot(df.log_rtn, kde=False, norm_hist=True, ax=ax[0])

ax[0].set_title('Distribution of SSMI returns', fontsize=16)

ax[0].plot(r_range, norm_pdf, 'g', lw=2,

label=f'N({mu:.2f}, {sigma**2:.4f})')

ax[0].legend(loc='upper left');

Plot normal probability distribution function

df.log_rtn.plot(title='Daily SSMI returns')

<matplotlib.axes._subplots.AxesSubplot at 0x7f8aeeea6810>

How to compare SSMI and VIX?

df = yf.download(['^SSMI', '^VIX'],

start='1985-01-01',

end='2021-07-07',

progress=False)

Data ssmi and vix

ax = sns.regplot(x='log_rtn', y='vol_rtn', data=df,

line_kws={'color': 'red'})

ax.set(title=f'SSMI vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='SSMI log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'SSMI log returns'),
 Text(0.5, 1.0, 'SSMI vs. VIX ($\\rho$ = -0.38)')]

Click Here

NEW SSMI DATA ANALYSIS