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Thank you by sharing this valuable dataset to quantitative researchers around the globe.
I have questions about these two lines in your paper:
Volumes are normalized by dividing by the top 10% volume quantile level, but limited to 1
Number of orders are normalized similar to volumes, dividing by the 10% order number
quantile level, but limited to 1.
It seems you are dividing the column with its top 10% quantile. The frequency, however, does not seem to match what you have described in the paper
>>>Counter(data[:, 11]) #this is the 12th column, which is bid volume at level 1Counter({1.0: 234366,
0.01: 1230,
0.02: 913,
0.03: 847,
....
}
>>>m=Counter(data[:, 11])
>>>sum(m[k] forkinmifk!=1.0)/len(data)
0.15361085731001334
Other columns with normalization show the same problem to me. I am probably wrong somewhere. Can you please share a separate raw volume data that could help clear my confusions?
Thanks
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Thank you by sharing this valuable dataset to quantitative researchers around the globe.
I have questions about these two lines in your paper:
It seems you are dividing the column with its top 10% quantile. The frequency, however, does not seem to match what you have described in the paper
Other columns with normalization show the same problem to me. I am probably wrong somewhere. Can you please share a separate raw volume data that could help clear my confusions?
Thanks
The text was updated successfully, but these errors were encountered: