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  3. Manipulating Time Series Data in Python
  • 1

    Working with Time Series in Pandas

    Free

    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

Exercise

Cumulative return on $1,000 invested in google vs apple II

Apple outperformed Google over the entire period, but this may have been different over various 1-year sub periods, so that switching between the two stocks might have yielded an even better result.

To analyze this, calculate that cumulative return for rolling 1-year periods, and then plot the returns to see when each stock was superior.

Instructions

100 XP

We have already imported pandas as pd and matplotlib.pyplot as plt. We have also loaded the GOOG and AAPL close prices from the last exercise into data.

  • Define a multi_period_return() function that returns the cumulative return from an array of period returns.
  • Calculate daily_returns by applying .pct_change() to data.
  • Create a '360D' .rolling() window on daily_returns, and .apply() multi_period_returns. Assign the result to rolling_annual_returns.
  • Plot rolling_annual_returns after multiplying it by 100.