Hey guys! Want to dive into the Philippine Stock Exchange Index (PSEi) using Python and Yahoo Finance? You've come to the right place! This guide will walk you through everything you need to know to get started, from setting up your environment to pulling and analyzing PSEi data. Let's get started!

Setting Up Your Python Environment

Before we dive into the code, you'll need to set up your Python environment. This involves installing Python and the necessary libraries. If you haven't already, download and install the latest version of Python from the official Python website. Once Python is installed, you can use pip, Python's package installer, to install the libraries we'll be using.

Installing Required Libraries

We'll primarily use the yfinance library to fetch data from Yahoo Finance. To install it, open your terminal or command prompt and run the following command:

pip install yfinance

In addition to yfinance, we'll also use pandas for data manipulation and matplotlib for data visualization. Install them using pip as well:

pip install pandas matplotlib

With these libraries installed, you're ready to start coding!

Importing Libraries in Your Script

To use the installed libraries, import them into your Python script. Here’s how:

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

Here, we've aliased yfinance as yf and pandas as pd for brevity. This is a common practice and makes your code cleaner and easier to read. Now that you have your environment set up, let's move on to fetching the PSEi data.

Fetching PSEi Data from Yahoo Finance

Yahoo Finance is a great source for historical stock market data. The yfinance library makes it incredibly easy to access this data using Python. To fetch PSEi data, you need to know the ticker symbol for the PSEi. However, PSEi itself is an index and not a stock, getting direct data can be tricky. Instead, let's fetch data from a PSEi Exchange Traded Fund (ETF) or a major component stock as a proxy.

Finding a Suitable Ticker Symbol

Since PSEi is an index, we can use a related ETF or a major component stock as a proxy. For example, you might use a local ETF that tracks the PSEi or a large, publicly traded company listed on the Philippine Stock Exchange. Common examples include stocks like BDO, SM, or TEL. For this example, let’s assume we're using a hypothetical ticker symbol PSE.PS.

Using yfinance to Get the Data

Here’s how you can use yfinance to fetch historical data for the PSEi (using our hypothetical ticker):

# Define the ticker symbol
ticker_symbol = "PSE.PS"

# Create a Ticker object
ticker = yf.Ticker(ticker_symbol)

# Fetch historical data
data = ticker.history(period="1y")  # You can change the period as needed (e.g., '1mo', '5y', 'max')

# Print the first few rows of the data
print(data.head())

In this code:

• We define the ticker symbol as PSE.PS.
• We create a Ticker object using yf.Ticker(). This object allows us to access various data related to the stock.
• We use the history() method to fetch historical data. The period parameter specifies the time frame for which we want the data. In this case, we're fetching data for the past year (1y). You can adjust this to other values like 1mo for one month, 5y for five years, or max for the maximum available data.
• Finally, we print the first few rows of the data using data.head() to see what we've fetched. The data is returned as a Pandas DataFrame, which is highly versatile for data manipulation and analysis.

Handling Potential Errors

Sometimes, you might encounter errors when fetching data. This could be due to network issues, incorrect ticker symbols, or temporary problems with the Yahoo Finance API. To handle these potential errors, it's a good idea to wrap your code in a try-except block:

try:
    ticker_symbol = "PSE.PS"
    ticker = yf.Ticker(ticker_symbol)
    data = ticker.history(period="1y")
    print(data.head())
except Exception as e:
    print(f"An error occurred: {e}")

This will catch any exceptions that occur during the data fetching process and print an error message, preventing your script from crashing. Error handling is crucial for robust and reliable code.

Analyzing the Data with Pandas

Once you have the data, you can use Pandas to analyze it. Pandas provides powerful tools for data manipulation, filtering, and aggregation. Let's look at some common analysis tasks.

Calculating Daily Returns

One common task is to calculate the daily returns of the stock. This can be done using the pct_change() method:

data['Daily Return'] = data['Close'].pct_change()
print(data.head())

This adds a new column called Daily Return to your DataFrame, which contains the percentage change in the closing price from one day to the next. This is a fundamental metric for assessing the stock's performance.

Calculating Moving Averages

Moving averages are used to smooth out price data and identify trends. You can calculate moving averages using the rolling() method:

data['50-day MA'] = data['Close'].rolling(window=50).mean()
data['200-day MA'] = data['Close'].rolling(window=200).mean()
print(data.tail())

This calculates the 50-day and 200-day moving averages of the closing price. Moving averages can help you identify potential support and resistance levels, as well as overall trends in the stock's price.

Identifying Key Statistics

Pandas makes it easy to calculate key statistics such as mean, median, and standard deviation:

print(f"Mean Daily Return: {data['Daily Return'].mean()}")
print(f"Median Daily Return: {data['Daily Return'].median()}")
print(f"Standard Deviation of Daily Returns: {data['Daily Return'].std()}")

These statistics provide insights into the stock's volatility and typical performance. The standard deviation, in particular, is a measure of how much the daily returns deviate from the mean.

Visualizing the Data with Matplotlib

Visualizing your data can provide valuable insights that might not be apparent from looking at raw numbers. Matplotlib is a popular Python library for creating charts and graphs. Let's explore some common visualizations.

Plotting the Closing Price

To plot the closing price of the stock over time, you can use the plot() method:

plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Closing Price')
plt.title('PSE.PS Closing Price Over Time')
plt.xlabel('Date')
plt.ylabel('Price (PHP)')
plt.legend()
plt.grid(True)
plt.show()

This creates a line chart of the closing price over the specified time period. The figsize parameter sets the size of the chart, and the title, xlabel, and ylabel methods add labels to the chart. The legend() method displays the label for the line, and the grid(True) method adds a grid to the chart for easier reading.

Plotting Moving Averages

You can also plot moving averages along with the closing price to visualize trends:

plt.figure(figsize=(12, 6))
plt.plot(data['Close'], label='Closing Price')
plt.plot(data['50-day MA'], label='50-day Moving Average')
plt.plot(data['200-day MA'], label='200-day Moving Average')
plt.title('PSE.PS Closing Price with Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price (PHP)')
plt.legend()
plt.grid(True)
plt.show()

This adds the 50-day and 200-day moving averages to the chart, allowing you to see how they relate to the closing price. This can help you identify potential buy and sell signals.

Creating Histograms of Daily Returns

Histograms can be used to visualize the distribution of daily returns:

plt.figure(figsize=(10, 6))
plt.hist(data['Daily Return'].dropna(), bins=50, label='Daily Returns')
plt.title('Distribution of PSE.PS Daily Returns')
plt.xlabel('Daily Return')
plt.ylabel('Frequency')
plt.legend()
plt.grid(True)
plt.show()

This creates a histogram of the daily returns, showing the frequency of different return values. The dropna() method removes any missing values from the Daily Return column before plotting.

Conclusion

Alright, guys, that's it! You've now learned how to fetch, analyze, and visualize PSEi data using Python and Yahoo Finance. With these skills, you can start exploring the Philippine stock market and making data-driven investment decisions. Remember to always do your own research and consult with a financial professional before making any investment decisions. Happy coding, and happy investing!