{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008542557,0.0005230398,0.0006266133,0.0006274837,0.0003854407,0.001368582,0.0007271303,0.0008907532,0.001474074],"category_scores_gemma":[0.0024832,0.0003860031,0.0005133986,0.0005865101,0.0004426445,0.000674553,0.0005245123,0.0005843524,0.0001813911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065073,"about_ca_system_score_gemma":0.0008208894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01183143,"about_ca_topic_score_gemma":0.009512627,"domain_scores_codex":[0.9996967,0.0001463379,0.00001038692,0.00004713813,0.00006461954,0.00003488809],"domain_scores_gemma":[0.9989849,0.000732688,0.0001081196,0.00003290239,0.000111569,0.00002985478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001895883,0.00002109593,0.0004792217,0.000008031041,0.00001724833,0.00001866379,0.0000152363,0.9846538,0.0003851444,0.001165665,0.00008854908,0.01312849],"study_design_scores_gemma":[0.000003374958,0.00001164012,0.00006731943,0.000001298179,0.00000323548,0.00000352952,0.000003187142,0.9991624,0.0001215706,0.000560352,0.00006011975,0.000001863989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2984452,0.0003473769,0.6877146,0.0005748594,0.00004947794,0.000108553,0.00008666069,0.0009287126,0.0117445],"genre_scores_gemma":[0.8826097,0.0001459163,0.1135148,0.00008911595,0.00001980227,0.0001052955,0.00007563757,0.00005205713,0.003387748],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01183143,"threshold_uncertainty_score":0.02352512,"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."}}