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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 I

In the last video, you have seen how to generate a random walk of returns, and how to convert this random return series into a random stock price path.

In this exercise, you'll build your own random walk by drawing random numbers from the normal distribution with the help of numpy.

Instructions

100 XP

We have already imported pandas as pd, functions normal and seed from numpy.random, and matplotlib.pyplot as plt.

  • Set seed to 42.
  • Use normal to generate 2,500 random returns with the parameters loc=.001, scale=.01 and assign this to random_walk.
  • Convert random_walk to a pd.Series object and reassign it to random_walk.
  • Create random_prices by adding 1 to random_walk and calculating the cumulative product.
  • Multiply random_prices by 1,000 and plot the result for a price series starting at 1,000.