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Time Series Analysis in Python
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  • Time Series Analysis in Python

    Welcome to your webinar workspace! You can follow along as we go through an introduction to time series analysis in Python.

    This first cell imports some of the main packages we will be using, as well as sets the visualization theme we will be using.

    import pandas as pd
    import numpy as np
    import plotly.express as px
    import plotly.graph_objects as go
    import plotly.io as pio
    from datetime import datetime
    
    # Set colors
    dc_colors = ["#2B3A64", "#96aae3", "#C3681D", "#EFBD95", "#E73F74", "#80BA5A", "#E68310", "#008695", "#CF1C90", "#f97b72", "#4b4b8f", "#A5AA99"]
    
    # Set template
    pio.templates["dc"] = go.layout.Template(
        layout=dict(
        	font={"family": "Poppins, Sans-serif", "color": "#505050"},
            title={"font": {"family": "Poppins, Sans-serif", "color": "black"}, "yanchor": "top", "y": 0.92, "xanchor": "left", "x": 0.025},
        	plot_bgcolor="white",
        	paper_bgcolor="white",
        	hoverlabel=dict(bgcolor="white"),
        	margin=dict(l=100, r=50, t=75, b=70),
            colorway=dc_colors,
            xaxis=dict(showgrid=False),
            yaxis=dict(showgrid=True, 
                       gridwidth=0.1, 
                       gridcolor='lightgrey', 
                       showline=True,
                       nticks=10,
                       linewidth=1, 
                       linecolor='black', 
                       rangemode="tozero")
        )
    ) 

    Loading and Inspecting the Data

    The first thing we will do is use the yfinance package to download market data from the Yahoo! Finance API.

    We will define the date range that we want to use, as well as the ticker we want to download.

    # Import yfinance
    
    
    # Set the date range
    
    
    
    # Set the ticker we want to use (GameStop)
    
    
    # Get the data for the ticker GME
    
    
    # Preview DataFrame
    

    We can also use the .describe() method to get a sense of the data over the period.

    # Get a numeric summary of the data
    

    Visualizing the data

    Next, we can use a Plotly line plot to examine the data over time.

    # Create a Plotly figure
    
    
    # Show the plot
    

    Let's add an annotation to make it clear when key events happened. We will cover three key events in the timeline:

    • The date that the new board was announced, and r/wallstreetbets began hyping the stock.
    • The date when the trading app RobinHood restricted trading for GameStop (and some other stocks).
    • An late February surge fueld by more activity on r/wallstreetbets.

    Note: due to a bug with Plotly, we need to use strptime() to convert the dates to milliseconds to enable our annotations.

    # Create a filtered DataFrame for early 2021
    
    
    # Create a Plotly figure
    
    
    # Define three key events
    
    
    # Add these as lines
    
    
    # Show the plot
    

    Alternatively, we can use a candlestick chart to get a good sense of price action.

    # Define the candlestick data
    
    
    # Create a candlestick figure   
    
    
    # Show the plot
    

    Rolling averages

    The data is quite noisy. We can also use a window function to calculate the rolling mean over a certain number of periods. In our case, we'll use the past 28 days of data.

    This also smooths out the line, and still gives day-by-day performance.

    # Calculate the 28 day rolling mean price
    
    
    # Plot the rolling average
    
    
    # Show the plot
    

    Comparing to a benchmark

    It would be nice to be able to compare the performance of GameStop against a stock market index such as the S&P 500 (an index tracking the performance of 500 large US companies).