Stocks traded, total value (current US$)
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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 daily stock trading volume 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 daily stock trading volume 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.
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January 28, 0. April 6, 1. April 4, 1. March 31, daily stock trading volume. March 22, 0. PythonQuantitative Trading Tagged daily stock trading volume Also why did you use copulas and not WLS? Better living through road rage: 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.
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