Introduction to Time Series with Pandas
A free video tutorial from Jose Portilla
Head of Data Science at Pierian Training
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3,632,179 students
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Python for Financial Analysis and Algorithmic Trading
Learn numpy , pandas , matplotlib , quantopian , finance , and more for algorithmic trading with Python!
16:35:31 of on-demand video • Updated December 2020
Use NumPy to quickly work with Numerical Data
Use Pandas for Analyze and Visualize Data
Use Matplotlib to create custom plots
Learn how to use statsmodels for Time Series Analysis
Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc..
Use Exponentially Weighted Moving Averages
Use ARIMA models on Time Series Data
Calculate the Sharpe Ratio
Optimize Portfolio Allocations
Understand the Capital Asset Pricing Model
Learn about the Efficient Market Hypothesis
Conduct algorithmic Trading on Quantopian
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Welcome, everyone to the section on working with Pandas with Time series data. Now that we have an understanding of how to work with Pandas for general Data, let's go over a few key points of working with Time series data with the Pandas Library. The majority of our datasets will be in the form of a time series. That is, it has some sort of date time index and some sort of corresponding value per that date time index, for example, some stock price for a particular date on the index. What we're going to be doing in this section is learning how to use Pandas special time series features to work with this sort of data. This section will cover the following. We'll talk about date time indices. Talk about time resampling how to work with time shifts, and then we'll also discuss pandas built in rolling and expanding methods. Okay, let's get started. I'll see you at the next lecture.