{"id":"W3047809254","doi":"10.3390/jrfm13080178","title":"Cryptocurrency Trading Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Markets and Investment Strategies","field":"Economics, Econometrics and Finance","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cryptocurrency; Downside risk; Portfolio; Computer science; Trading strategy; Transaction cost; Reinforcement learning; Relation (database); Database transaction; Econometrics; Artificial intelligence; Machine learning; Financial economics; Economics; Microeconomics; Data mining; Computer security; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004253276,0.0001366113,0.0003811184,0.00017469,0.0001740095,0.00008006776,0.0001365953,0.00004791337,0.00007996245],"category_scores_gemma":[0.0001615216,0.0001371794,0.0001211106,0.0002254549,0.00004009625,0.0003002062,0.00006005575,0.0002920198,0.00001020052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003155943,"about_ca_system_score_gemma":0.00001204464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004665007,"about_ca_topic_score_gemma":0.000002105852,"domain_scores_codex":[0.998908,0.00001995932,0.0006467691,0.0001798082,0.00005531287,0.0001901131],"domain_scores_gemma":[0.9991343,0.00002080676,0.000655392,0.00005938323,0.00002300038,0.0001071269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002274152,0.0001260869,0.2028728,0.0002315828,0.00007789539,0.0001273087,0.002506174,0.000463422,0.00003141919,0.7236968,0.001034694,0.06860445],"study_design_scores_gemma":[0.002642431,0.001001822,0.1309095,0.0001526179,0.0001158151,0.00002492988,0.0004947926,0.02155843,0.00003362545,0.1393253,0.7030888,0.0006519926],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8298981,0.02091498,0.1302898,0.0006859846,0.001127263,0.0002897131,0.00005776399,0.00003248222,0.01670397],"genre_scores_gemma":[0.9882602,0.006316337,0.004770181,0.0002518585,0.000353795,0.000001191393,0.000001077763,0.0000136311,0.00003170273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.702054,"threshold_uncertainty_score":0.5594015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03310636941977436,"score_gpt":0.2118035633309431,"score_spread":0.1786971939111688,"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."}}