Analyzing stock data can be a powerful tool for investors, traders, or anyone interested in understanding the financial markets. Yahoo Finance is a popular platform that provides free access to a wealth of financial data, making it an excellent resource for beginners. In this article, we'll guide you through a simple analysis project using Python and Yahoo Finance. Even if you have no prior experience with programming, we'll break down the steps to help you get started.
Step project
Data
Summarize the Data Frames
Comparison of Stocks
Market Capitalization
Moving Averages
Volatility and Stability
Correlations
Generate Heatmaps
Import the Libraries and Data
import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snsimport yfinance as yffrom pandas.plotting import scatter_matrix We will be working with Microsoft, Adobe, SalesForce, and Oracle. The time period is from 1st January 2020 to 31st October 2033.
start = "2020-01-01"end = "2023-10-31"msft = yf.download("MSFT", start, end)crm = yf.download("CRM", start, end)orcl = yf.download("ORCL", start, end)adbe = yf.download("ADBE", start, end)
Summarize the Data Frames
# Obtain the necessary information for each stock def get_info(dataframe) : stock_info = pd.DataFrame({ 'Datatype' : dataframe.dtypes, # Data types of columns 'Total_Element': dataframe.count(), # Total elements in columns 'Null_Count': dataframe.isnull().sum(), # Total null values in columns 'Null_Percentage': dataframe.isnull().sum()/len(dataframe) * 100 # Percentage of null values }) return stock_info# Call the function for each stockget_info(msft)get_info(crm)get_info(orcl)get_info(adbe) 
# Describe the numerical data for each stockmsft.describe()crm.describe()orcl.describe()adbe.describe()
Comparison of Stocks
# Find the average of the volume of stocks traded over time for each stock.# Round off the value.msft_vol_avg = round(msft["Volume"].mean())crm_vol_avg = round(crm["Volume"].mean())orcl_vol_avg = round(orcl["Volume"].mean())adbe_vol_avg = round(adbe["Volume"].mean())# Run this code cell to convert the results into an arrayvol_avg = np.array([msft_vol_avg, crm_vol_avg, orcl_vol_avg, adbe_vol_avg])# Create the pie chartmylabels = ["Microsoft", "SalesForce", "Oracle", "Adobe"]myexplode = [0.2, 0, 0, 0] # To create an exploding wedgeplt.pie(vol_avg, labels = mylabels, explode = myexplode, shadow = True, autopct="%.2f")plt.show() Visualize the Trade Volume Over Time
# Perform the visualization in a single graphmsft['Volume'].plot(label = 'Microsoft', figsize = (16,4))crm['Volume'].plot(label = 'SalesFoce')orcl['Volume'].plot(label = 'Oracle')adbe['Volume'].plot(label = 'Adobe')plt.title('Volume of Stock traded')plt.legend() Microsoft is almost consistently traded the most, with plenty of variation in its trade volume throughout the years. Oracle and SaleForce come next, also with plenty of variation throughout the years, and, at times, going higher than Microsoft. Finally, we have Adobe with minimal variation in its trade volume, and located consistently below its 3 competitors.
Find the Highest Price of Stocks Traded with Time
msft['High'].plot(label = 'Microsoft', figsize = (16,8))crm['High'].plot(label = 'SalesFoce')orcl['High'].plot(label = 'Oracle')adbe['High'].plot(label = 'Adobe')plt.title('Highest Price Reached for Each Stock traded')plt.legend() Find the Lowest Prices of Stocks Traded with Time
msft['Low'].plot(label = 'Microsoft', figsize = (16,8))crm['Low'].plot(label = 'SalesFoce')orcl['Low'].plot(label = 'Oracle')adbe['Low'].plot(label = 'Adobe')plt.title('Lowest Price Reached for Each Stock traded')plt.legend() Find the Open Price of Stocks Traded with Time
msft['Open'].plot(label = 'Microsoft', figsize = (16,8))crm['Open'].plot(label = 'SalesFoce')orcl['Open'].plot(label = 'Oracle')adbe['Open'].plot(label = 'Adobe')plt.title('Open Price for Each Stock traded')plt.legend() Market Capitalization
Market capitalization is a simple but essential concept in finance that represents the total value of a publicly traded company's outstanding shares of stock. In other words, it is the total worth of a company as perceived by the stock market.
To calculate market capitalization, you multiply the current market price of one share of a company's stock by the total number of outstanding shares. It's a straightforward equation:
Market Capitalization = Current Stock Price per Share x Total Outstanding Shares
Market capitalization provides a quick and easy way to gauge the size of a company and its relative importance in the financial markets. It is often used to categorize companies into various size classes:
1. Large-Cap: Companies with a market capitalization typically exceeding $10 billion.
2. Mid-Cap: Companies with a market capitalization between $2 billion and $10 billion.
3. Small-Cap: Companies with a market capitalization below $2 billion.
msft['M_Cap'] = msft['Open'] * msft['Volume']crm['M_Cap'] = crm['Open'] * crm['Volume']orcl['M_Cap'] = orcl['Open'] * orcl['Volume']adbe['M_Cap'] = adbe['Open'] * adbe['Volume']msft['M_Cap'].plot(label = 'Microsoft', figsize = (15,7))crm['M_Cap'].plot(label = 'SalesForce')orcl['M_Cap'].plot(label = 'Oracle')adbe['M_Cap'].plot(label = 'Adobe')plt.title('Market Cap')plt.legend() For the given data, we will find that Microsoft and SalesForce are traded more compared to Adobe and Oracle.
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Thus, we can conclude that investing in Microsoft and SalesForce would provide a lower risk, with greater returns over time.
Moving Averages
Moving averages are a fundamental tool in finance used to smooth out price data and identify trends. They provide a simple and effective way to analyze the historical performance of a stock, currency, or any financial asset.
In essence, a moving average calculates the average price of an asset over a specific period of time and continually updates this average as new data becomes available. This "moving" average helps reduce noise and fluctuations in price data, making it easier to spot trends and patterns.
There are two main types of moving averages:
1. Simple Moving Average (SMA): This type calculates the average price over a fixed number of periods. For example, a 50-day SMA would add up the closing prices of the last 50 days and divide by 50 to get the average.
2. Exponential Moving Average (EMA): The EMA gives more weight to recent prices, making it more responsive to recent changes. It's often favored when traders want to react quickly to market movements.
Moving averages can be used for various purposes, such as identifying trends, determining support and resistance levels, and generating buy or sell signals. When the current price crosses above or below a moving average, it may signal a change in the asset's trend.
msft['MA50'] = msft['Open'].rolling(50).mean()msft['MA200'] = msft['Open'].rolling(200).mean()crm['MA50'] = crm['Open'].rolling(50).mean()crm['MA200'] = crm['Open'].rolling(200).mean()orcl['MA50'] = orcl['Open'].rolling(50).mean()orcl['MA200'] = orcl['Open'].rolling(200).mean()adbe['MA50'] = adbe['Open'].rolling(50).mean()adbe['MA200'] = adbe['Open'].rolling(200).mean()# Plot them together to compare themfigure, axes = plt.subplots(2, 2, figsize = (15, 10))figure.suptitle('Moving Averages for Microsoft, SalesForce, Oracle, and Adobe')axes[0,0].set_title('Microsoft')axes[0,1].set_title('SalesForce,')axes[1,0].set_title('Oracle')axes[1,1].set_title('Adobe')msft['MA50'].plot(ax=axes[0, 0])msft['MA200'].plot(ax=axes[0, 0])msft['Open'].plot(ax=axes[0, 0])crm['MA50'].plot(ax=axes[0, 1])crm['MA200'].plot(ax=axes[0, 1])crm['Open'].plot(ax=axes[0, 1])adbe['MA50'].plot(ax=axes[1, 0])adbe['MA200'].plot(ax=axes[1, 0])adbe['Open'].plot(ax=axes[1, 0])orcl['MA50'].plot(ax=axes[1, 1])orcl['MA200'].plot(ax=axes[1, 1])orcl['Open'].plot(ax=axes[1, 1]) Volatility and Stability
Volatility and stability are two contrasting concepts in the world of finance that describe the degree of uncertainty or risk associated with an asset's price or the financial system as a whole.
Volatility:
- Volatility refers to the degree of price fluctuations in a financial asset, such as a stock, currency, or commodity.
- High volatility implies that the price of the asset can change rapidly and dramatically over a short period.
- Low volatility suggests that the price changes are relatively steady and minimal.
- Volatility is often measured using indicators like the standard deviation of price returns or the VIX (Volatility Index).
Stability:
- Stability, on the other hand, describes a situation where prices or the financial system remain relatively constant and predictable over time.
- It implies a lower level of risk and uncertainty.
- Stability is usually preferred by long-term investors and is often associated with more established and less speculative assets.
Compare Volatility and Stability
Finding the volatility / stability for each of the stocks
msft['returns'] = (msft['Close']/msft['Close'].shift(1)) -1crm['returns'] = (crm['Close']/crm['Close'].shift(1)) -1orcl['returns'] = (orcl['Close']/orcl['Close'].shift(1)) -1adbe['returns'] = (adbe['Close']/adbe['Close'].shift(1)) -1# Visualize the results.figure, axes = plt.subplots(2, 2, figsize = (15, 10))figure.suptitle('Stability and Volatility for Microsoft, SalesForce, Oracle, and Adobe')axes[0,0].set_title('Microsoft')axes[0,1].set_title('SalesForce')axes[1,0].set_title('Oracle')axes[1,1].set_title('Adobe')msft['returns'].hist(bins = 100, label = 'Microsoft', alpha = 0.5, ax=axes[0, 0])crm['returns'].hist(bins = 100, label = 'SalesForce', alpha = 0.5, ax=axes[0, 1])orcl['returns'].hist(bins = 100, label = 'Oracle', alpha = 0.5, ax=axes[1, 0])adbe['returns'].hist(bins = 100, label = 'Adobe', alpha = 0.5, ax=axes[1, 1]) Comparing the volatility / stability for all the stocks
msft['returns'] = (msft['Close']/msft['Close'].shift(1)) -1crm['returns'] = (crm['Close']/crm['Close'].shift(1))-1orcl['returns'] = (orcl['Close']/orcl['Close'].shift(1)) - 1adbe['returns'] = (adbe['Close']/adbe['Close'].shift(1)) - 1msft['returns'].hist(bins = 100, label = 'Microsoft', alpha = 0.5, figsize = (15,7))crm['returns'].hist(bins = 100, label = 'SalesForce', alpha = 0.5)orcl['returns'].hist(bins = 100, label = 'Oracle', alpha = 0.5)adbe['returns'].hist(bins = 100, label = 'Adobe', alpha = 0.5)plt.suptitle('Comparing the volatility / stability for all the stocks')plt.legend()
Correlations
Correlations refer to the statistical relationship or connection between two or more variables. In finance and investing, correlations are often used to understand how different financial assets, such as stocks, bonds, or commodities, move in relation to each other.
Positive Correlation: When two variables have a positive correlation, it means that they tend to move in the same direction. If one variable goes up, the other is likely to go up as well. For example, if the stock market rises, the prices of many individual stocks also tend to rise, showing a positive correlation.
Negative Correlation: A negative correlation exists when two variables move in opposite directions. If one variable goes up, the other tends to go down. An example of negative correlation is when the price of a particular asset, like gold, rises when the value of the U.S. dollar falls.
Correlations are expressed as a value between -1 and 1
# Correlation table for Stock 1msft_corr = msft.corr()# Correlation table for Stock 2crm_corr = crm.corr()# Correlation table for Stock 3orcl_corr = orcl.corr()# Correlation table for Stock 4adbe_corr = adbe.corr()adbe_corr
Generate Heatmaps
# Correlation table for Stock 1msft_corr = msft.corr()# Correlation table for Stock 2crm_corr = crm.corr()# Correlation table for Stock 3orcl_corr = orcl.corr()# Correlation table for Stock 4adbe_corr = adbe.corr()adbe_corr # Create a 2x2 grid of subplotsfig, axes = plt.subplots(2, 2, figsize=(16, 16))# Plot heatmap for Stock 1sns.heatmap(msft_corr, annot=True, ax=axes[0, 0])axes[0, 0].set_title('Microsoft')# Plot heatmap for Stock 2sns.heatmap(crm_corr, annot=True, ax=axes[0, 1])axes[0, 1].set_title('SalesForce')# Plot heatmap for Stock 3sns.heatmap(orcl_corr, annot=True, ax=axes[1, 0])axes[1, 0].set_title('Oracle')# Plot heatmap for Stock 4sns.heatmap(adbe_corr, annot=True, ax=axes[1, 1])axes[1, 1].set_title('Adobe')# Adjust layoutplt.tight_layout()plt.show() Conclusion:
This beginner-friendly project demonstrates how to analyze stock data using Yahoo Finance and Python. You've learned how to fetch historical data, perform basic analyses, and create simple visualizations. As you become more comfortable with Python and data analysis, you can explore more advanced techniques and strategies for making informed investment decisions. The world of financial analysis is vast, so don't hesitate to explore further and expand your knowledge. Happy analyzing!
Example code 👇
