24 Hour Volume Rankings (Exchange)

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Initially we were motivated to investigate trading volume when we noticed that high volume days daily trade volume to be associated with higher volatility. Looking deeper, daily trade volume saw the relationship between volume and value: Once we have each day sorted into a class, we daily trade volume use daily trade volume regression to predict whether today is likely to be high or low volume based on the first minute of trading. An easy way to cluster a 2-dimensional dataset like this is by using k-means clustering.

We can use the SciPy to perform k-means in Python. This accomplishes a similar objective to our first article but with the benefit of being easier to interpret intuitively.

The breakeven probability occurs at around 1. If anything over 1. Check out a good tutorial from JustGlowing on k-means with Python. PythonQuantitative Trading. Reblogged this on atoast2trading. You are commenting using your WordPress. You are commenting using your Twitter account.

You are commenting using your Facebook account. Notify me of new comments via email. January 28, 0. April 6, 1. April 4, 1. March 31, 1. March 22, 0. Python daily trade volume, Quantitative Trading Tagged as: Leave a Reply Cancel reply Enter your comment here Fill in your details below or click an icon to log in: Email required Address never made public. Post was not sent - check your email addresses!

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