{"id":"W4389483481","doi":"10.1016/j.procs.2023.10.433","title":"Deep Learning based Currency Trend Classification Trained on Technical Indicators based Generated Dataset","year":2023,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Market Dynamics and Volatility","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Computer science; Currency; Technical analysis; Foreign exchange market; Convergence (economics); Deep learning; Divergence (linguistics); Artificial intelligence; Index (typography); Moving average; Machine learning; Econometrics; Finance; Economics; Macroeconomics","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":[0.001836286,0.0001625843,0.0002225707,0.0009905926,0.0003223011,0.0001884708,0.0006579641,0.00008036964,0.00007285843],"category_scores_gemma":[0.0003200806,0.0001740018,0.00005429537,0.003115271,0.0002200332,0.0002318215,0.00009544653,0.0002581652,0.000114277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001118237,"about_ca_system_score_gemma":0.0001177315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006641394,"about_ca_topic_score_gemma":0.000009725809,"domain_scores_codex":[0.9981302,0.00002565786,0.0004552429,0.0008516014,0.0001438881,0.0003934147],"domain_scores_gemma":[0.9989743,0.0001359265,0.000256535,0.0004320615,0.000035307,0.0001658476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001514043,0.00127342,0.6494145,0.0003324088,0.00003232705,0.00002414779,0.0006208454,0.03890449,0.0009093523,0.06941145,0.01003987,0.2288858],"study_design_scores_gemma":[0.0002666657,0.00008058257,0.1270903,0.000009362716,0.000001523666,4.103996e-7,0.000001827145,0.8676705,0.00002595345,0.0008299854,0.003841863,0.0001810013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2623879,0.00007086839,0.7298908,0.001679143,0.001181589,0.0006224886,0.0008680329,0.0006416927,0.002657495],"genre_scores_gemma":[0.9919347,0.000007405245,0.00668649,0.0002653702,0.00007790061,0.00004190039,0.0009570869,0.00001317303,0.00001599596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.828766,"threshold_uncertainty_score":0.709559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04395655796247519,"score_gpt":0.2618296579063581,"score_spread":0.217873099943883,"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."}}