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)
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)
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