HANG SENG INDEX (^HSI) is stock market Hong Kong

What does Hsi stand for in stock market in python?

HANG SENG INDEX (^HSI) is stock market Hong Kong.

What country is hsi?

Hong kong

Why is hsi droping?

We have described below through python

How to download hsi stock price in python?

!pip install yfinance

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

This is the stock price of hsi from 1985 to 2021

df = yf.download('^HSI',

start='1985-01-01',

end='2021-07-07',

progress=False)

df = df.loc[:, ['Adj Close']]

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

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)

Stock price of Hsi simple and log return

simple_rtn	log_rtn
Date
2021-06-07	-0.004524	-0.004534
2021-06-08	-0.000205	-0.000205
2021-06-09	-0.001346	-0.001347
2021-06-10	-0.000130	-0.000130
2021-06-11	0.003593	0.003586
2021-06-15	-0.007059	-0.007084
2021-06-16	-0.007043	-0.007068
2021-06-17	0.004281	0.004272
2021-06-18	0.008498	0.008462
2021-06-21	-0.010842	-0.010901
2021-06-22	-0.006292	-0.006311
2021-06-23	0.017920	0.017761
2021-06-24	0.002269	0.002267
2021-06-25	0.014049	0.013951
2021-06-28	-0.000680	-0.000680
2021-06-29	-0.009369	-0.009413
2021-06-30	-0.005730	-0.005747
2021-07-02	-0.017952	-0.018115
2021-07-05	-0.005896	-0.005914
2021-07-06	-0.002510	-0.002513

How to get realised volatily of hsi?

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 0x7f0d3d28ba10>,
 <matplotlib.lines.Line2D at 0x7f0d3d2a35d0>,
 <matplotlib.lines.Line2D at 0x7f0d3d2a37d0>]

How to plot realised volatility?

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

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

ax[0].set(title = 'HSI 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 (%)')

[Text(0, 0.5, 'Log returns (%)'), Text(0.5, 0, 'Date')]

How to plot hsi time series?

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

init_notebook_mode()

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

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("HSI returns")

ax.legend(loc='lower right')

HSI Outlier simple return

<matplotlib.legend.Legend at 0x7f0d36f254d0>

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 HSI 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');

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning:

`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).

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

<matplotlib.axes._subplots.AxesSubplot at 0x7f0d3811f850>

df = yf.download(['^HSI', '^VIX'],

start='1985-01-01',

end='2021-07-07',

progress=False)

df.tail()

HSI and VIX data

df = df[['Adj Close']]

df.tail()

Starting Price

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^HSI': 'hsi', '^VIX': 'vix'})

df['log_rtn'] = np.log(df.hsi / df.hsi.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'BVSP vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='HSI log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'HSI log returns'),
 Text(0.5, 1.0, 'HSI vs. VIX ($\\rho$ = -0.13)')]

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