Australia Stock Market Index analysis python

How to get Australian Stock Market Index Analysis 

Analyzing Australian stock market indices, such as the S&P/ASX 200 (ASX 200), using Python involves collecting historical price data, performing data analysis, and creating visualizations to gain insights into the performance of the Australian stock market. Here's a step-by-step guide on how to conduct Australian stock market index analysis in Python:

Import Libraries:

Start by importing the necessary Python libraries for data manipulation, analysis, and visualization. Commonly used libraries include pandas, numpy, matplotlib, and yfinance to fetch historical data:

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import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

import yfinance as yf

Data Retrieval:

Use the yfinance library or other financial data sources to fetch historical data for the ASX 200 or other Australian indices. Specify the start and end dates for the data you want to analyze:

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asx200 = yf.download('^AXJO', start='2020-01-01', end='2021-12-31')

Data Exploration:

Explore the fetched data to understand its structure and contents. Use functions like head(), tail(), describe(), and info() to inspect the dataset:

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print(asx200.head())

Data Visualization:

Create visualizations to analyze the historical performance of the ASX 200 or other Australian indices. Common visualizations include line charts to visualize index price movements:

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plt.figure(figsize=(12, 6))

plt.plot(asx200['Adj Close'], label='ASX 200')

plt.title('ASX 200 Index Price')

plt.xlabel('Date')

plt.ylabel('Price')

plt.legend()

plt.show()

Technical Analysis (Optional):

Perform technical analysis by calculating and visualizing technical indicators like moving averages, relative strength index (RSI), and MACD. You can use libraries like ta-lib for these calculations.

Statistical Analysis (Optional):

Conduct statistical analysis to calculate summary statistics, volatility measures, and correlations with other assets. You can use Numpy and Pandas for these calculations.

Sentiment Analysis (Optional):

Consider incorporating sentiment analysis of news articles or social media data related to the Australian stock market to understand market sentiment's impact on the ASX 200.

Fundamental Analysis (Optional):

Analyze fundamental factors affecting the Australian economy, such as GDP growth, inflation rates, and interest rates, which can influence the ASX 200's performance.

Prediction and Forecasting (Optional):

You can use time series forecasting techniques like ARIMA or machine learning models to make predictions about future ASX 200 movements.

Risk Management and Decision Making:

Based on your analysis, formulate investment strategies, set risk management parameters, and make informed investment decisions regarding the ASX 200 or Australian stocks.

Regular Updates:

Keep your analysis up to date with the latest data to adapt to changing market conditions and make timely decisions.

Remember that investing in stock markets carries risks, and it's crucial to do thorough research, consider economic and geopolitical factors, and potentially consult with financial experts before making investment decisions based on your analysis of the ASX 200 or any other stock index.

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 Australian 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 Australian 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 Australian 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