Japan Stock Price Nikkei 225 Analysis in Python

What is Nikkei 225?

Nikkei 225 is a stock market index for the Tokyo stock exchange.

How to get  Nikkei 225 stock price?

We can get data from yahoo finance,quandai,wiki etc.

You can get data through python code.

we need to downloaded some python package.

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

Code in python

df = yf.download('^N225',

start='1985-01-01',

end='2021-08-04',

progress=False)

How to convert Adj Close price?

df = df.loc[:, ['Adj Close']]

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

Getting simple and log return

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_rtn	log_rtn
Date
2021-07-06	0.001574	0.001573
2021-07-07	-0.009645	-0.009692
2021-07-08	-0.008775	-0.008814
2021-07-09	-0.006317	-0.006337
2021-07-12	0.022498	0.022249
2021-07-13	0.005223	0.005210
2021-07-14	-0.003822	-0.003829
2021-07-15	-0.011514	-0.011581
2021-07-16	-0.009760	-0.009808
2021-07-19	-0.012511	-0.012590
2021-07-20	-0.009568	-0.009614
2021-07-21	0.005836	0.005819
2021-07-26	0.010356	0.010303
2021-07-27	0.004920	0.004908
2021-07-28	-0.013892	-0.013989
2021-07-29	0.007279	0.007252
2021-07-30	-0.017955	-0.018118
2021-08-02	0.018232	0.018068
2021-08-03	-0.005010	-0.005023
2021-08-04	-0.002089	-0.002091

How to get realised volatility 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)

[<matplotlib.lines.Line2D at 0x7f7559778410>,
 <matplotlib.lines.Line2D at 0x7f753b5b9dd0>,
 <matplotlib.lines.Line2D at 0x7f753b5b9fd0>]

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

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

ax[0].set(title = 'Nikkei225 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 (%)')

Simple and log return graph

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 outlier in python?

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("Nikkei225 returns")

ax.legend(loc='lower right')

<matplotlib.legend.Legend at 0x7f75364b5e50>

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 Nikkei225 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');

Distribution of Nikkei225 return

How to plot Nikkei225 and vix in python?

df = yf.download(['^N225', '^VIX'],

start='1985-01-01',

end='2021-07-28',

progress=False)

df = df[['Adj Close']]

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^N225': 'Nikkei225', '^VIX': 'vix'})

df['log_rtn'] = np.log(df.Nikkei225 / df.Nikkei225.shift(1))

df['vol_rtn'] = np.log(df.vix / df.vix.shift(1))

df.dropna(how='any', axis=0, inplace=True)

corr_coeff = df.log_rtn.corr(df.vol_rtn)

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

line_kws={'color': 'red'})

ax.set(title=f'N225 vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='Nikkei225 log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'Nikkei225 log returns'),
 Text(0.5, 1.0, 'N225 vs. VIX ($\\rho$ = -0.11)')]

More new analysis Nikkei 225