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  3. Manipulating Time Series Data in Python
  • 1

    Working with Time Series in Pandas

    Gratuito

    This chapter lays the foundations to leverage the powerful time series functionality made available by how Pandas represents dates, in particular by the DateTimeIndex. You will learn how to create and manipulate date information and time series, and how to do calculations with time-aware DataFrames to shift your data in time or create period-specific returns.

  • 2

    Basic Time Series Metrics & Resampling

    This chapter dives deeper into the essential time series functionality made available through the pandas DataTimeIndex. It introduces resampling and how to compare different time series by normalizing their start points.

  • 3

    Window Functions: Rolling & Expanding Metrics

    This chapter will show you how to use window function to calculate time series metrics for both rolling and expanding windows.

  • 4

    Putting it all together: Building a value-weighted index

    This chapter combines the previous concepts by teaching you how to create a value-weighted index. This index uses market-cap data contained in the stock exchange listings to calculate weights and 2016 stock price information. Index performance is then compared against benchmarks to evaluate the performance of the index you created.

Initializing

Ejercicio

Annual return correlations among several stocks

You have seen in the video how to calculate correlations, and visualize the result.

In this exercise, we have provided you with the historical stock prices for Apple (AAPL), Amazon (AMZN), IBM (IBM), WalMart (WMT), and Exxon Mobile (XOM) for the last 4,000 trading days from July 2001 until the end of May 2017.

You'll calculate the year-end returns, the pairwise correlations among all stocks, and visualize the result as an annotated heatmap.

Instrucciones

100 XP

We have already imported pandas as pd, seaborn as sns, and matplotlib.pyplot as plt. We have loaded the daily close price for the five stocks in a variable called data.

  • Inspect using .info().
  • Apply .resample() with year-end frequency (alias: 'A') to data and select the .last() price from each subperiod; assign this to annual_prices.
  • Calculate annual_returns by applying .pct_change() to annual_prices.
  • Calculate correlations by applying .corr() to annual_returns and print the result.
  • Visualize correlations as an annotated sns.heatmap().