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Time Series Analysis in Python
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  • 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
    import yfinance as yf
    
    # Set the date range
    start = "2020-01-01"
    stop = "2023-02-01"
    
    # Set the ticker we want to use (GameStop)
    ticker = "GME"
    
    # Get the data for the ticker GME
    gme = yf.download(ticker, start, stop)
    
    # Preview DataFrame
    gme
    # Get Bitcoin data for the past month
    btc = yf.download("BTC-USD", period="1mo", interval="1d")
    
    # Print the Bitcoin data
    print(btc)
    

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

    # Get a numeric summary of the data
    gme.describe()

    Visualizing the data

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

    # Create a Plotly figure
    fig = px.line(gme,
                  x=gme.index,
                  y="Close",
                  template="dc",
                  title="GameStop Closing Price (daily)"
                 )
    
    # Show the plot
    fig.show()

    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
    gme_2021 = gme["2021-01": "2021-03"]
    
    # Create a Plotly figure
    fig = px.line(gme_2021,
                  x=gme_2021.index,
                  y="Close",
                  template="dc",
                  title="GameStop Closing Price (early 2021)"
                 )
    
    # Define three key events
    short = datetime.strptime("2021-01-11", "%Y-%m-%d").timestamp() * 1000
    robin = datetime.strptime("2021-01-28", "%Y-%m-%d").timestamp() * 1000
    late_feb = datetime.strptime("2021-02-23", "%Y-%m-%d").timestamp() * 1000
    
    # Add these as lines
    fig.add_vline(x=short, line_width=0.5, annotation_text="r/wallstreetbets")
    fig.add_vline(x=robin, line_width=0.5, annotation_text="Robinhood")
    fig.add_vline(x=late_feb, line_width=0.5, annotation_text="Memes")
    
    # Show the plot
    fig.show()

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

    # Define the candlestick data
    candlestick = go.Candlestick(
        x=gme.index,
        open=gme["Open"],
        high=gme["High"],
        low=gme["Low"],
        close=gme["Close"]
    )
    
    # Create a candlestick figure   
    fig = go.Figure(data=candlestick)
    fig.update_layout(title="GME Prices (Candlestick)", template="dc")
    
    # Show the plot
    fig.show()

    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
    gme_rolling = gme.rolling("28D").mean()
    
    # Create a Plotly figure
    fig = px.line(gme_rolling,
                  x=gme_rolling.index,
                  y="Close",
                  template="dc",
                  title="GameStop Closing Price (rolling 28 day mean)"
                 )
    
    
    # Show the plot
    fig.show()