Stock Market Analysis with Pandas DataReader and Plotly for Beginners

Introduction

If you‘re interested in investing in the stock market, being able to analyze historical stock price data is an essential skill. While stock prices are notoriously difficult to predict, examining past price movements, trading volumes, and other metrics can provide insights into the current and future state of a company and the broader market.

Fortunately, the Python data science ecosystem provides powerful tools for importing, analyzing, and visualizing stock market data. In this tutorial, we‘ll walk through how to use the pandas datareader and plotly libraries to perform stock market analysis in Python, even if you‘re a relative beginner. By the end, you‘ll be able to pull in stock price data for any publicly traded company, examine trends and patterns, and create interactive visualizations to bring the data to life.

Overview of Libraries

Before we dive into the code, let‘s briefly go over the two main Python libraries we‘ll be using:

Pandas datareader: Pandas is the go-to data analysis library in Python. It provides data structures and functions for manipulating numerical tables and time series data. Pandas datareader is a sub-package that allows pulling data from various Internet sources into a pandas DataFrame. We‘ll use it to import stock price data from Yahoo Finance.

Plotly: Plotly is a graphing library for creating interactive, publication-quality graphs and charts. It provides a simple interface in Python for creating a wide variety of plots and integrates well with pandas DataFrames. We‘ll use plotly to create visualizations of our stock data like candlestick charts.

Importing Stock Data

The first step is pulling in some historical stock price data to analyze. Thanks to pandas datareader, this is extremely simple. All we need is a list of the stock ticker symbols we want and a date range.

Let‘s analyze stock data for some of the biggest tech companies over the last 5 years. Our list of stock tickers will be:

• AAPL (Apple)
• AMZN (Amazon)
• FB (Meta/Facebook)
• GOOG (Alphabet/Google)
• MSFT (Microsoft)

We set our date range from January 1st, 2017 to December 31st, 2022 to get the last 5 complete years of price history.

First, make sure you have pandas datareader installed. You can install it using pip:

!pip install pandas-datareader

Then we can import pandas datareader along with pandas itself:

import pandas as pd
import pandas_datareader as pdr

Next, let‘s set our parameters and pull in the data from Yahoo Finance:

# Set list of stock tickers and date range
tickers = [‘AAPL‘, ‘AMZN‘, ‘FB‘, ‘GOOG‘, ‘MSFT‘] 
start_date = ‘2017-01-01‘
end_date = ‘2022-12-31‘

# Import stock data from Yahoo Finance
stock_data = pdr.get_data_yahoo(tickers, start=start_date, end=end_date)

Let‘s take a look at the first few rows:

stock_data.head()

As you can see, we now have a pandas DataFrame containing daily stock prices for each company. The DataFrame has a multi-level column index, with the first level being the stock ticker and the second level being the price metric (open, high, low, close, volume, etc.).

Preparing the Data

Before we start analyzing the data, we‘ll likely need to do some cleaning and preparation first. Some common data cleaning steps:

• Handling missing values: Check if there are rows with missing stock prices and decide how to deal with them (dropping those rows, filling with previous day‘s price, etc.)
• Removing unnecessary columns: We may want to simplify our DataFrame by removing columns we don‘t need for our analysis, like stock split/dividend data.
• Normalizing prices: If we want to compare percent change between stocks with very different price ranges, we may want to normalize all the prices to a common starting value (like 100).

For brevity, we‘ll skip the data cleaning for this example, but it‘s an important step in real-world analysis.

Exploratory Data Analysis

Now that we have our data ready, we can start exploring it. Let‘s focus our analysis on the daily closing price for each stock.

To make the data easier to work with, we‘ll convert our DataFrame to a single-level column index, using the closing price for each stock ticker:

# Convert to single-level column index with close price 
close_prices = stock_data[[‘Adj Close‘]].reset_index().pivot(index=‘Date‘, columns=‘Symbols‘, values=‘Adj Close‘)

We can get a quick summary of the closing price data using pandas describe() function:

close_prices.describe()

This gives us an overview of the distribution of prices for each stock, including the count, mean, min, max, and quartile values.

We can also calculate the daily percent change in price for each stock:

daily_pct_change = close_prices.pct_change()
daily_pct_change.head()

This shows us the day-over-day relative change in price, which can be more insightful than looking at the absolute price movements.

Visualizing the Data

To really understand what‘s going on with our stock price data, visualization is key. We‘ll use the plotly library to create a few different interactive plots.

First, let‘s import plotly and configure it for use in our Jupyter notebook:

import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

A basic but useful plot is a line chart showing the closing price over time. We can easily create this with plotly express:

fig = px.line(close_prices)
fig.show()

This plots the closing price history for all 5 stocks on a single chart. We can see the overall trend and compare the relative performance between stocks.

Another useful visualization for stock price data is the candlestick chart. Each candlestick represents the open, high, low, and closing price for a given period (usually one day). The candlestick is green if the closing price was higher than the open (gain), and red if the closing price was lower (loss).

To create a candlestick chart in plotly, we use the graph_objects API:

candlestick_fig = go.Figure(data=[go.Candlestick(x=stock_data.index,
                                                 open=stock_data[(‘AAPL‘, ‘Open‘)], 
                                                 high=stock_data[(‘AAPL‘, ‘High‘)],
                                                 low=stock_data[(‘AAPL‘, ‘Low‘)], 
                                                 close=stock_data[(‘AAPL‘, ‘Close‘)])])
candlestick_fig.show()

This shows the daily candlestick data for Apple stock over our 5 year period. The candlesticks help visualize the volatility and relative gains/losses over time.

We can plot candlestick charts for multiple stocks using plotly subplots:

multi_candlestick_fig = make_subplots(rows=2, cols=3)

for i, ticker in enumerate(tickers):

    if i < 3:
        row = 1
        col = i+1
    else:
        row = 2 
        col = i-2

    multi_candlestick_fig.add_trace(go.Candlestick(x=stock_data.index,
                                                  open=stock_data[(ticker, ‘Open‘)],
                                                  high=stock_data[(ticker, ‘High‘)], 
                                                  low=stock_data[(ticker, ‘Low‘)],
                                                  close=stock_data[(ticker, ‘Close‘)], name=ticker),
                                  row=row, col=col)

multi_candlestick_fig.show()

This creates a grid of candlestick charts, one for each stock. Having multiple charts side-by-side makes it easier to compare the price action between different stocks.

Analyzing the Visualizations

By creating these visualizations of the stock price history, we can start to identify trends and gain insights:

• All 5 tech stocks saw significant price appreciation over the 5-year period. This is indicative of the overall strength of the tech sector.

• There was a major dip in prices across the board in early 2020, coinciding with the start of the COVID-19 pandemic. However, prices recovered quickly and continued to climb.

• Amazon and Google saw the most growth, with prices roughly tripling over the 5 years.

• Apple and Microsoft had relatively steady growth, while Meta/Facebook was more volatile with some large price swings.

• By comparing the candlestick charts, we can see that all 5 stocks tend to move in the same direction on most days, but the magnitude of the moves differs between stocks. This is evidence of overall market correlation.

Of course, these are just basic observations. In reality stock analysis goes much deeper, examining more complex metrics and relationships.

Limitations of Technical Analysis

While analyzing price history can provide useful insights, it‘s important to understand the limitations of technical analysis:

• Historical performance does not guarantee future results. A stock that has gone up for the past 5 years may not continue to rise.

• Price movements alone don‘t tell the whole story. A company‘s underlying financials, industry conditions, and other external factors all influence the stock price as well.

• Short-term volatility is extremely difficult to predict. Focusing too much on daily price movements can lead to emotional decision making.

Fundamental analysis (examining a company‘s business model, market position, growth potential, etc.) should be used in conjunction with technical analysis to get a complete picture. Consulting other data sources besides just price history is essential.

Conclusion

Python libraries like pandas datareader and plotly make it easy to get started with downloading, analyzing, and visualizing stock market data. By walking through this example of pulling price history for 5 major tech stocks, creating visualizations, and interpreting the results, you‘ve taken your first steps toward mastering stock market analysis with Python.

Some key takeaways:

• Pandas datareader allows you to easily pull in data from finance APIs to a DataFrame
• Plotly provides interactive plotting tools well-suited for stock price data
• Examining metrics like closing price and percent change can reveal trends and allow comparison between stocks
• Price history is a useful input, but not sufficient on its own for making investment decisions

To further develop your skills, continue experimenting with acquiring and visualizing data for different stocks and time periods. Explore plotting other technical analysis metrics like moving averages and Bollinger Bands. And most importantly, augment your analysis by learning about financial statement analysis, valuation metrics, economic indicators, and other fundamental concepts.

The world of quantitative finance is a complex and fascinating one. Python is a powerful tool for navigating this world. Armed with pandas and plotly, you‘re well on your way to becoming a proficient stock market analyst.