Hey guys! Ever wondered how you could use Python to analyze financial data, specifically data from the Philippine Stock Exchange Index (PSEi)? Well, you're in the right place! This tutorial will guide you through using Python, along with some powerful libraries, to explore and understand financial data. We'll be focusing on practical examples, so you can start applying these techniques right away. Get ready to dive into the exciting world of quantitative finance with Python!

Setting Up Your Environment

Before we jump into the code, let's make sure your environment is set up correctly. You'll need Python installed on your machine, along with a few key libraries. If you don't have Python yet, head over to the official Python website and download the latest version. Once Python is installed, you can use pip, Python's package installer, to install the necessary libraries. Open your terminal or command prompt and run the following commands:

pip install pandas
pip install numpy
pip install matplotlib
pip install yfinance

• Pandas is a powerful library for data manipulation and analysis. It provides data structures like DataFrames that make it easy to work with tabular data.
• NumPy is the fundamental package for numerical computing in Python. It provides support for arrays and mathematical functions.
• Matplotlib is a plotting library that allows you to create visualizations of your data. We'll use it to generate charts and graphs.
• yfinance is a library that allows you to download historical stock data from Yahoo Finance. This will be our primary source of PSEi data.

With these libraries installed, you're ready to start coding! Let's move on to downloading and exploring PSEi data.

Downloading PSEi Data with yfinance

Now that we have our environment set up, let's download some PSEi data. We'll use the yfinance library to fetch historical stock prices. First, you need to know the ticker symbol for the PSEi. Unfortunately, yfinance doesn't directly provide PSEi data. Instead, we can download data for individual stocks that are part of the PSEi or use a relevant ETF (Exchange Traded Fund) if available. For demonstration purposes, let’s assume we want to analyze the stock data of a specific company listed on the PSE, such as BDO Unibank (BDO.PS).

Here's how you can download the data:

import yfinance as yf
import pandas as pd

# Define the ticker symbol
ticker_symbol = "BDO.PS"  # BDO Unibank

# Download data for the past year
data = yf.download(ticker_symbol, period="1y")

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

# Save the data to a CSV file
data.to_csv("BDO_data.csv")

In this code snippet:

1. We import the yfinance library and alias it as yf for convenience.
2. We define the ticker_symbol as "BDO.PS".
3. We use the yf.download() function to download historical data for the specified ticker symbol. The period parameter specifies the time range for the data. In this case, we're downloading data for the past year ("1y").
4. We print the first few rows of the data using data.head() to get a quick look at the data.
5. We save the data to a CSV file named "BDO_data.csv" using data.to_csv(). This allows us to easily load the data later.

Now that you've downloaded the data, let's explore it using Pandas.

Exploring the Data with Pandas

Pandas is your best friend when it comes to data analysis in Python. It provides a DataFrame object that makes it easy to manipulate and analyze tabular data. Let's load the CSV file we created earlier and explore the data.

import pandas as pd

# Load the data from the CSV file
data = pd.read_csv("BDO_data.csv", index_col="Date", parse_dates=True)

# Print some information about the data
print(data.info())
print(data.describe())

# Plot the closing prices
import matplotlib.pyplot as plt

plt.figure(figsize=(12, 6))
plt.plot(data["Close"])
plt.xlabel("Date")
plt.ylabel("Closing Price")
plt.title("BDO Unibank Closing Prices")
plt.grid(True)
plt.show()

In this code:

1. We load the data from the "BDO_data.csv" file using pd.read_csv(). We specify index_col="Date" to use the "Date" column as the index of the DataFrame, and parse_dates=True to parse the dates correctly.
2. We print some information about the data using data.info(). This shows us the data types of each column and the number of non-null values.
3. We print descriptive statistics using data.describe(). This gives us summary statistics like mean, standard deviation, minimum, and maximum values for each column.
4. We plot the closing prices using Matplotlib. We create a figure and axes, plot the "Close" column, add labels and a title, and display the plot using plt.show().

By exploring the data with Pandas, you can gain insights into the stock's behavior over time. You can calculate moving averages, analyze trends, and identify potential trading opportunities. Pandas is incredibly versatile; you can filter data, group it, and perform all sorts of calculations with ease.

Calculating Moving Averages

Moving averages are a common tool in technical analysis. They smooth out price fluctuations and help identify trends. Let's calculate the 50-day and 200-day moving averages for BDO Unibank.

import pandas as pd
import matplotlib.pyplot as plt

# Load the data
data = pd.read_csv("BDO_data.csv", index_col="Date", parse_dates=True)

# Calculate the 50-day moving average
data["MA50"] = data["Close"].rolling(window=50).mean()

# Calculate the 200-day moving average
data["MA200"] = data["Close"].rolling(window=200).mean()

# Plot the closing prices and moving averages
plt.figure(figsize=(12, 6))
plt.plot(data["Close"], label="Closing Price")
plt.plot(data["MA50"], label="50-day MA")
plt.plot(data["MA200"], label="200-day MA")
plt.xlabel("Date")
plt.ylabel("Price")
plt.title("BDO Unibank Closing Prices with Moving Averages")
plt.legend()
plt.grid(True)
plt.show()

In this code:

1. We load the data as before.
2. We calculate the 50-day moving average using data["Close"].rolling(window=50).mean(). The rolling() function creates a rolling window of 50 days, and the mean() function calculates the average price within that window. This average is then assigned to a new column named "MA50".
3. We do the same for the 200-day moving average, creating a column named "MA200".
4. We plot the closing prices and the two moving averages. The plot now includes the original closing prices, the 50-day moving average, and the 200-day moving average, allowing for visual analysis of trends.

By plotting the moving averages along with the closing prices, you can easily identify potential buy and sell signals. For example, when the 50-day moving average crosses above the 200-day moving average, it's often considered a bullish signal.

Calculating Daily Returns

Another important metric in finance is the daily return, which measures the percentage change in price from one day to the next. Let's calculate the daily returns for BDO Unibank.

import pandas as pd
import matplotlib.pyplot as plt

# Load the data
data = pd.read_csv("BDO_data.csv", index_col="Date", parse_dates=True)

# Calculate the daily returns
data["Daily Return"] = data["Close"].pct_change()

# Print the first few rows of the daily returns
print(data["Daily Return"].head())

# Plot the daily returns
plt.figure(figsize=(12, 6))
plt.plot(data["Daily Return"])
plt.xlabel("Date")
plt.ylabel("Daily Return")
plt.title("BDO Unibank Daily Returns")
plt.grid(True)
plt.show()

In this code:

1. We load the data as before.
2. We calculate the daily returns using data["Close"].pct_change(). The pct_change() function calculates the percentage change between the current and previous elements in the "Close" column. This is then assigned to a new column called "Daily Return".
3. We print the first few rows of the daily returns to see the calculated values.
4. We plot the daily returns to visualize the volatility of the stock.

Analyzing daily returns can help you understand the risk associated with a particular stock. You can calculate the standard deviation of the daily returns to measure volatility, and you can use the daily returns to calculate other risk metrics like Sharpe ratio.

Conclusion

So there you have it! A basic tutorial on using Python for finance, specifically with PSEi data (or, more accurately, data from a company listed on the PSE). We covered setting up your environment, downloading data with yfinance, exploring data with Pandas, calculating moving averages, and calculating daily returns. This is just the tip of the iceberg, but hopefully, it gives you a good starting point for your own financial analysis projects. Remember to always do your own research and consult with a financial professional before making any investment decisions.

The possibilities are endless! You can explore other technical indicators, build trading strategies, and even create your own financial models. Keep practicing, keep learning, and have fun exploring the world of finance with Python!