{"id":"W2068466712","doi":"10.1145/1830483.1830694","title":"Interday foreign exchange trading using linear genetic programming","year":2010,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Foreign exchange market; Profitability index; Currency; Genetic programming; Profit (economics); Foreign exchange; Genetic algorithm; Trading strategy; Algorithmic trading; Computer science; Value (mathematics); Econometrics; Mathematical optimization; Business; Economics; Monetary economics; Financial economics; Artificial intelligence; Microeconomics; Mathematics; Machine learning; Finance","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001095277,0.00007924769,0.00006513161,0.00005983726,0.0001797361,0.00008058947,0.0004492884,0.00004096911,0.00006202052],"category_scores_gemma":[0.00000679229,0.00007071102,0.00004201021,0.0002608145,0.00003382225,0.0002519251,0.0001356226,0.0001325753,0.00002356706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001385178,"about_ca_system_score_gemma":0.0000276057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003274361,"about_ca_topic_score_gemma":0.00001294683,"domain_scores_codex":[0.9993061,0.000009343643,0.0001326833,0.0002395569,0.0001075217,0.0002047677],"domain_scores_gemma":[0.9994945,0.00002457622,0.0000366723,0.0003250989,0.0000403143,0.00007885942],"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":[0.000001049077,0.0002069865,0.001646564,0.00002477533,0.00001870921,0.00001290565,0.000875034,0.0001314168,0.03038905,0.425226,0.0006928304,0.5407747],"study_design_scores_gemma":[0.00008863579,0.00002242447,0.0008657013,0.000005189688,0.000003068255,0.0000702961,0.00002557738,0.9784576,0.0012388,0.005728947,0.01336749,0.0001263119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05953167,0.00003192783,0.936725,0.0002613071,0.0001598764,0.0001527199,5.18048e-7,0.0001694619,0.002967574],"genre_scores_gemma":[0.4003291,0.00000125797,0.5992879,0.00006595281,0.0001495191,0.00001901225,7.018839e-7,0.000004660399,0.0001419496],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9783261,"threshold_uncertainty_score":0.2883512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283862661378646,"score_gpt":0.2750459180685451,"score_spread":0.2466596519306805,"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."}}