Australia Stock Market Index analysis python

How to get Australia Stock Market Index analysis 

forecast in python

df = yf.download('^AXJO',

start='1985-01-01',

end='2021-08-12',

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)

simple and log return

AXJO realized_volatility

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)

Australia Stock Market Index analysis charts

[<matplotlib.lines.Line2D at 0x7f6dfb74c450>,
 <matplotlib.lines.Line2D at 0x7f6dfb758810>,
 <matplotlib.lines.Line2D at 0x7f6dfb7589d0>]

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

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

ax[0].set(title = 'AXJO 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 (%)')

Australia Stock Market Index log return  graph analysis python

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

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

.agg(['mean', 'std'])

df_rolling.columns = df_rolling.columns.droplevel()

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("AXJO returns")

ax.legend(loc='lower right')

Current australia Stock Market Index outlier analysis python

<matplotlib.legend.Legend at 0x7f6df66b0790>

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

Current australia Stock Market Index analysis python

<matplotlib.axes._subplots.AxesSubplot at 0x7f447d0d1b90>

Current australia Stock Market Index and VIX analysis python

df = yf.download(['^AXJO', '^VIX'],

start='1985-01-01',

end='2021-07-10',

progress=False)

df.tail()

Data for AXJO and VIX

df = df[['Adj Close']]

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^AXJO': 'axjo', '^VIX': 'vix'})

df['log_rtn'] = np.log(df.axjo / df.axjo.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)

import pandas as pd

import numpy as np

import yfinance as yf

aus = yf.download('^AXJO',

start='1985-01-01',

end='2021-08-12',

progress=False)

import matplotlib.pyplot as plt

aus['Close'].plot(figsize=(12,8))

Current australia Stock Market Index Close and volume analysis 

<matplotlib.axes._subplots.AxesSubplot at 0x7f6df6073750>

aus['Volume'].plot(figsize=(12,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7f6df57b5950>

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

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6df56c0110>,
       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6df56603d0>],
      dtype=object)

aus.describe()

Mean, median, std of AXJO

aus['simple_rtn'] = aus.Close.pct_change()

aus['log_rtn'] = np.log(aus.Close/aus.Close.shift(1))

aus['log_rtn'].plot(subplots=True, style='b',

figsize=(12, 8))

array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6df5591f90>],
      dtype=object)

aus['log_rtn'].tail(12)

Date 2021-07-28 -0.007035 2021-07-29 0.005150 2021-07-30 -0.003349 2021-08-02 0.013276 2021-08-03 -0.002258 2021-08-04 0.003832 2021-08-05 0.001052 2021-08-06 0.003628 2021-08-09 0.000000 2021-08-10 0.003205 2021-08-11 0.002865 2021-08-12 0.000514 Name: log_rtn, dtype: float64

import cufflinks as cf

from plotly.offline import iplot, init_notebook_mode

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

.agg(['mean', 'std'])

df_rolling.columns = df_rolling.columns.droplevel()

df_outliers = aus.join(df_rolling)

df = yf.download(['^AXJO', '^VIX'],

start='1985-01-01',

end='2021-08-12',

progress=False)

df = df[['Adj Close']]

df.columns = df.columns.droplevel(0)

df = df.rename(columns={'^AXJO': 'axjo', '^VIX': 'vix'})

df['log_rtn'] = np.log(df.axjo / df.axjo.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)

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'AXJO vs. VIX ($\\rho$ = {corr_coeff:.2f})',

ylabel='VIX log returns',

xlabel='AXJO log returns')

[Text(0, 0.5, 'VIX log returns'),
 Text(0.5, 0, 'AXJO log returns'),
 Text(0.5, 1.0, 'AXJO vs. VIX ($\\rho$ = -0.12)')]

Current australia Stock Market Index and VIX correlation analysis python

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 AJOX 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');

# Q-Q plot

qq = sm.qqplot(df.log_rtn.values, line='s', ax=ax[1])

ax[1].set_title('Q-Q plot', fontsize = 16)

/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).

Text(0.5, 1.0, 'Q-Q plot')

Distributation and Q-Q plot of AJOX

acf = smt.graphics.plot_acf(df.log_rtn,

lags=N_LAGS,

alpha=SIGNIFICANCE_LEVEL

)

fig, ax = plt.subplots(2, 1, figsize=(12, 10))

smt.graphics.plot_acf(df.log_rtn ** 2, lags=N_LAGS,

alpha=SIGNIFICANCE_LEVEL, ax = ax[0])

ax[0].set(title='Autocorrelation Plots',

ylabel='Squared Returns')

smt.graphics.plot_acf(np.abs(df.log_rtn), lags=N_LAGS,

alpha=SIGNIFICANCE_LEVEL, ax = ax[1])

ax[1].set(ylabel='Absolute Returns',

xlabel='Lag')

[Text(0, 0.5, 'Absolute Returns'), Text(0.5, 0, 'Lag')]

Current australia Stock Market Index autocorrelation analysis python

df['moving_std_252'] = df[['log_rtn']].rolling(window=252).std()

df['moving_std_21'] = df[['log_rtn']].rolling(window=21).std()

fig, ax = plt.subplots(3, 1, figsize=(18, 15),

sharex=True)

df.plot(ax=ax[0])

ax[0].set(title='axjo time series',

ylabel='Stock price ($)')

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

ax[1].set(ylabel='Log returns (%)')

df.moving_std_252.plot(ax=ax[2], color='r',

label='Moving Volatility 252d')

df.moving_std_21.plot(ax=ax[2], color='g',

label='Moving Volatility 21d')

ax[2].set(ylabel='Moving Volatility',

xlabel='Date')

ax[2].legend()

<matplotlib.legend.Legend at 0x7f6dec802c50>

Current australia Stock Market Index  Moving volatility analysis python

NEW AXJO ANALYSIS