{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001251285,0.0005895099,0.0008429921,0.0003858907,0.000381017,0.001276172,0.001334347,0.001389394,0.002967732],"category_scores_gemma":[0.004149868,0.0002925988,0.0004936318,0.0004429095,0.0009594653,0.001519178,0.0006467748,0.001487177,0.0004199743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001219281,"about_ca_system_score_gemma":0.0009036099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00718992,"about_ca_topic_score_gemma":0.005537255,"domain_scores_codex":[0.9995453,0.0002105154,0.00001841514,0.00008075841,0.00008027986,0.00006459927],"domain_scores_gemma":[0.9979134,0.001423482,0.0002166529,0.0001347977,0.0002153709,0.00009639664],"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":[0.00003181397,0.00004772099,0.0007503079,0.00001301726,0.00002108798,0.0000460116,0.00001615165,0.9747069,0.000256649,0.01753746,0.0003479213,0.006225031],"study_design_scores_gemma":[0.000003823598,0.000007240664,0.00003537348,0.00000114863,0.000001458051,0.000004085143,8.233023e-7,0.9968395,0.00002856256,0.003001358,0.00007491008,0.000001747645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1222291,0.00046139,0.8653066,0.001381758,0.0001071948,0.00008640125,0.0001546367,0.0004612588,0.009811745],"genre_scores_gemma":[0.9611198,0.0001772287,0.03321068,0.00010056,0.00004327452,0.00009753663,0.00007448519,0.00002358253,0.005152904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00718992,"threshold_uncertainty_score":0.01429617,"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."}}