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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

Random walk III

In this exercise, you'll complete your random walk simulation using Facebook stock returns over the last five years. You'll start off with a random sample of returns like the one you've generated during the last exercise and use it to create a random stock price path.

Instructions

100 XP

We have already imported pandas as pd, choice and seed from numpy.random, and matplotlib.pyplot as plt. We have loaded the Facebook price as a pd.DataFrame in the variable fb and a random sample of daily FB returns as pd.Series in the variable random_walk.

  • Select the first Facebook price by applying .first('D') to fb.price, and assign to start.
  • Add 1 to random_walk and reassign it to itself, then .append() random_walk to start and assign this to random_price.
  • Apply .cumprod() to random_price and reassign it to itself.
  • Insert random_price as new column labeled random into fb and plot the result.