{"id":"W4410155420","doi":"10.1080/14697688.2026.2665153","title":"ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books","year":2025,"lang":"en","type":"preprint","venue":"Quantitative Finance","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Limit (mathematics); Cluster analysis; Order (exchange); Order book; Computer science; Business; Mathematics; Artificial intelligence; Finance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001439966,0.001359608,0.001220287,0.003778028,0.0007570415,0.001662178,0.001649994,0.0009158961,0.0009741283],"category_scores_gemma":[0.005801759,0.0004383056,0.0009341505,0.002797417,0.0007093178,0.001773639,0.001514158,0.001191544,0.001148371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009935583,"about_ca_system_score_gemma":0.0016664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01668304,"about_ca_topic_score_gemma":0.02849795,"domain_scores_codex":[0.9992111,0.0001214013,0.0000612268,0.0003031659,0.000193283,0.0001097818],"domain_scores_gemma":[0.9983006,0.0004455605,0.000277687,0.0003836632,0.0004443142,0.0001482272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001435272,0.001227163,0.1038132,0.0004340566,0.000659526,0.0002540905,0.001336224,0.2076204,0.02409672,0.005838191,0.02886057,0.6244246],"study_design_scores_gemma":[0.00007961251,0.0001580087,0.0124659,0.00003112326,0.00005367207,0.0001064631,0.0003238402,0.9681404,0.00655593,0.006355692,0.00567071,0.00005881634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4178815,0.001816778,0.5531256,0.0006032738,0.0001955359,0.0005715201,0.004671247,0.01699705,0.004137538],"genre_scores_gemma":[0.5931566,0.0003646639,0.3910992,0.0002677734,0.0001572297,0.0002526984,0.01138699,0.0007514877,0.002563278],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01668304,"threshold_uncertainty_score":0.03317189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04748618657569227,"score_gpt":0.2741858639966652,"score_spread":0.226699677420973,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}