This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.
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How do you implement the z-score normalization in the pre-processing progress? #15
Thanks for your nice paper and codes.
I am a little confused about the z-score normalization in the pre-processiong progress. Did you implement it independently for each feature (40 features in total)? e.g. for feature "AskPrice5", you collect all "AskPrice5" in the past 5 days to calculate mean and std? If so, the original order of price levels may be destroyed after z-score normalization.
Thanks for your nice paper and codes. I am a little confused about the z-score normalization in the pre-processiong progress. Did you implement it independently for each feature (40 features in total)? e.g. for feature "AskPrice5", you collect all "AskPrice5" in the past 5 days to calculate mean and std? If so, the original order of price levels may be destroyed after z-score normalization.