Time Series Analysis & Forecasting for Python Hackers

Stock Market Hacking with Pandas

4.40 (35 reviews)

Udemy

platform

English

language

Investing & Trading

What you will learn

Time Series Analysis

Anaconda IDE

Time Series Forecasting

Jupyter IDE

Pandas Library

Matplotlib

Data Visualization

Machine Learning

Web Scraping

Stock Market Analysis

Why take this course?

Time Series Analysis & Forecasting | Stock Market Hacking with Pandas is a course for those interested in Time Series Analysis & Forecasting, or Stock Market Hacking with Pandas. You will learn how to procure data from the cloud, scraping it from the web and saving it for local hacking. You will gain general knowledge of the S&P 500 and how it works. You will learn how to hack and analyze data in a given period or time series to make future predictions. You will learn how to use machine learning algorithms to make predictions of your time series model. Here is an additional summary of what is taught in this course, to see a complete course outline please view curriculum.

WEB SCRAPING STOCK DATA
SAVING STOCK DATA FOR LOCAL HACKING
HACK FORTUNE 500 COMPANY STOCK DATA
ANALYZE TIME SERIES BETWEEN COMPANIES
HOW TO VISUALIZE TRADING STRATEGIES
HOW TO FORMULATE TRADING STRATEGIES
ANALYZING TRADING STRATEGIES WITH A TIME SERIES

Reviews

Sangeeta

October 3, 2018

I wished he had talked about how the DataReader library works, and how it exactly fetches the stock data from Yahoo finance without the user/coder specifying the yahoo finance web link.

Rayner

July 2, 2018

There are many moments that commands are inserted without any explanation. The course lacks didactic.

Archana

June 25, 2018

Needed more in depth explanation of the trading strategies. No mention of stationarity. Could have been better.

Emilio

June 4, 2018

it has cleared a lot of concepts but seems that since some parameters are no longer working -due to causes outside of the author- it seems that we need an update

Aquil

January 11, 2018

The course was good and informative, but I was hoping to see a little bit more on dealing autocorrelation and autoregressive models. However, I do realize that those subjects were not described in the course description. I also think that the lecturer should be more clear about why volatility is important and why risk associated with the twitter stock, in his example is a safer investment.

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