Researchers propose OrderFlowDiT diffusion transformer for predicting limit order supply
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Jung Hun Phee and Tucker Balch have published a research paper proposing OrderFlowDiT, a conditional diffusion transformer model trained with an unbalanced optimal transport (UOT) auxiliary loss, used to predict the conditional distribution of limit order submissions over the next five minutes based on the current order book and recent order flow. Under 1,950 validation conditions covering 30 US stocks, adding the UOT auxiliary loss reduced the full-field transport cost by 45.8% and the transport energy score by 26.1% compared to models trained using only mean squared error, with improvements in both metrics for every stock.
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- arXiv q-finOrderFlowDiT: A Transport-Regularized Diffusion Transformer for Probabilistic Limit Order Supply Prediction
Researchers propose OrderFlowDiT, a conditional diffusion Transformer trained with an unbalanced optimal transport (UOT) auxiliary loss, to predict the conditional distribution of limit order submissions over the next five minutes. Across 1,950 validation conditions covering 30 U.S. stocks, adding UOT reduces the full-field transport cost by 45.8% and the transport energy score by 26.1% compared with training using only mean squared error, with improvements in both metrics for every stock.
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