Stock exchange: trading volume of single stock options

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Its all well and good to know that high volume coincides with volatility and falling prices but how do we know we are going to be in a high volume period?

It would be nice if we could quickly identify which trading days are likely to be high volume in real-time. Again we will make use of the ambhas package for Python. We can create the copula with the following code:. Now suppose we observe that 1. The first value in the array is the mean for the prediction, i. The second value is the standard deviation of the prediction. The actual volume came in a little bit shy of 80 million, right at the lower bound of the IQR.

You can try it out for yourself by downloading the data here: PythonQuantitative Trading. PredictionStock MarketTrading Volume. Did you test the strategy that is short SPY if first minute volume is higher than a threshold and long otherwise? Indeed, Weighted Least Squares WLS can address the problem of heteroskedasticity, but you have a linear relationship and in a lot of implementations you are limited with Gaussian errors as well.

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. PythonQuantitative Trading Tagged as: Also why did you use copulas and rating brokers binary options and cfd WLS?

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