{"id":"https://openalex.org/W4415428038","doi":"https://doi.org/10.3233/faia251249","title":"TRADES: Generating Realistic Market Simulations with Diffusion Models","display_name":"TRADES: Generating Realistic Market Simulations with Diffusion Models","publication_year":2025,"publication_date":"2025-10-21","ids":{"openalex":"https://openalex.org/W4415428038","doi":"https://doi.org/10.3233/faia251249"},"language":null,"primary_location":{"id":"doi:10.3233/faia251249","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251249","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.3233/faia251249","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047828984","display_name":"Leonardo Berti","orcid":null},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Leonardo Berti","raw_affiliation_strings":["Sapienza University of Rome"],"affiliations":[{"raw_affiliation_string":"Sapienza University of Rome","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017702643","display_name":"Bardh Prenkaj","orcid":"https://orcid.org/0000-0002-2991-2279"},"institutions":[{"id":"https://openalex.org/I62916508","display_name":"Technical University of Munich","ror":"https://ror.org/02kkvpp62","country_code":"DE","type":"education","lineage":["https://openalex.org/I62916508"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Bardh Prenkaj","raw_affiliation_strings":["Technical University of Munich"],"affiliations":[{"raw_affiliation_string":"Technical University of Munich","institution_ids":["https://openalex.org/I62916508"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070270772","display_name":"Paola Velardi","orcid":"https://orcid.org/0000-0003-0884-1499"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Paola Velardi","raw_affiliation_strings":["Sapienza University of Rome"],"affiliations":[{"raw_affiliation_string":"Sapienza University of Rome","institution_ids":["https://openalex.org/I861853513"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5047828984"],"corresponding_institution_ids":["https://openalex.org/I861853513"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.63215808,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.8758999705314636,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.8758999705314636,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10047","display_name":"Financial Markets and Investment Strategies","score":0.7924000024795532,"subfield":{"id":"https://openalex.org/subfields/2003","display_name":"Finance"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12659","display_name":"Innovation Diffusion and Forecasting","score":0.7483999729156494,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/market-data","display_name":"Market data","score":0.5753999948501587},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.554099977016449},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.5205000042915344},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.5091000199317932},{"id":"https://openalex.org/keywords/market-microstructure","display_name":"Market microstructure","score":0.477400004863739},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4426000118255615},{"id":"https://openalex.org/keywords/order-book","display_name":"Order book","score":0.43639999628067017},{"id":"https://openalex.org/keywords/trading-strategy","display_name":"Trading strategy","score":0.4277999997138977},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.40290001034736633},{"id":"https://openalex.org/keywords/stock-market","display_name":"Stock market","score":0.3961000144481659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6561999917030334},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.5753999948501587},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.554099977016449},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.5205000042915344},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.5091000199317932},{"id":"https://openalex.org/C51926234","wikidata":"https://www.wikidata.org/wiki/Q3312426","display_name":"Market microstructure","level":3,"score":0.477400004863739},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4426000118255615},{"id":"https://openalex.org/C2779309563","wikidata":"https://www.wikidata.org/wiki/Q649206","display_name":"Order book","level":3,"score":0.43639999628067017},{"id":"https://openalex.org/C131562839","wikidata":"https://www.wikidata.org/wiki/Q1574928","display_name":"Trading strategy","level":2,"score":0.4277999997138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41519999504089355},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C2780299701","wikidata":"https://www.wikidata.org/wiki/Q475000","display_name":"Stock market","level":3,"score":0.3961000144481659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37209999561309814},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3693999946117401},{"id":"https://openalex.org/C127288500","wikidata":"https://www.wikidata.org/wiki/Q1241055","display_name":"Market impact","level":4,"score":0.3653999865055084},{"id":"https://openalex.org/C19244329","wikidata":"https://www.wikidata.org/wiki/Q208697","display_name":"Financial market","level":2,"score":0.36079999804496765},{"id":"https://openalex.org/C2780021719","wikidata":"https://www.wikidata.org/wiki/Q282283","display_name":"Prediction market","level":2,"score":0.3553999960422516},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3499999940395355},{"id":"https://openalex.org/C60777511","wikidata":"https://www.wikidata.org/wiki/Q3045002","display_name":"Concept drift","level":3,"score":0.33719998598098755},{"id":"https://openalex.org/C78508483","wikidata":"https://www.wikidata.org/wiki/Q139445","display_name":"Algorithmic trading","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C91602232","wikidata":"https://www.wikidata.org/wiki/Q756115","display_name":"Volatility (finance)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C33400823","wikidata":"https://www.wikidata.org/wiki/Q1419119","display_name":"Market depth","level":4,"score":0.3107999861240387},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.31040000915527344},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3037000000476837},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C64112148","wikidata":"https://www.wikidata.org/wiki/Q160151","display_name":"Market price","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C179036041","wikidata":"https://www.wikidata.org/wiki/Q1901028","display_name":"Market analysis","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2842000126838684},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C2780898871","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Performance metric","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C24683644","wikidata":"https://www.wikidata.org/wiki/Q138372","display_name":"High-frequency trading","level":3,"score":0.2581000030040741},{"id":"https://openalex.org/C168065819","wikidata":"https://www.wikidata.org/wiki/Q845566","display_name":"Debugging","level":2,"score":0.2581000030040741},{"id":"https://openalex.org/C125308379","wikidata":"https://www.wikidata.org/wiki/Q363057","display_name":"Market segmentation","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/faia251249","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251249","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.3233/faia251249","is_oa":true,"landing_page_url":"https://doi.org/10.3233/faia251249","pdf_url":null,"source":{"id":"https://openalex.org/S4210201731","display_name":"Frontiers in artificial intelligence and applications","issn_l":"0922-6389","issn":["0922-6389","1879-8314"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Artificial Intelligence and Applications","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Financial":[0],"markets":[1],"are":[2,38],"complex":[3],"systems":[4],"characterized":[5],"by":[6,133],"high":[7],"statistical":[8],"noise,":[9],"nonlinearity,":[10],"volatility,":[11],"and":[12,29,42,50,91,143,161,190,201,213,218],"constant":[13],"evolution.":[14],"Thus,":[15],"modeling":[16],"them":[17],"is":[18,99],"extremely":[19],"hard.":[20],"Here,":[21],"we":[22,119,173,184,226,244],"address":[23],"the":[24,78,81,89,113,121,168,197,224,229],"task":[25],"of":[26,80,94,103,215,251],"generating":[27,51],"realistic":[28,70],"responsive":[30],"Limit":[31],"Order":[32],"Book":[33],"(LOB)":[34],"market":[35,47,53,96,109,131,177,187,219,236],"simulations,":[36],"which":[37],"fundamental":[39],"for":[40,64,106,179,234],"calibrating":[41],"testing":[43,144],"trading":[44,216],"strategies,":[45],"performing":[46],"impact":[48,220],"experiments,":[49,225],"synthetic":[52,141,176,247],"data.":[54,97,148],"We":[55,149],"propose":[56],"a":[57,84,100,124,135,159,246],"novel":[58],"TRAnsformer-based":[59],"Denoising":[60],"Diffusion":[61],"Probabilistic":[62],"Engine":[63],"LOB":[65,235,248],"Simulations":[66],"(TRADES).":[67],"TRADES":[68,151],"generates":[69],"order":[71],"flows":[72],"as":[73,127],"time":[74],"series":[75],"conditioned":[76],"on":[77,140,146,155],"state":[79],"market,":[82],"leveraging":[83],"transformer-based":[85],"architecture":[86],"that":[87,172,193],"captures":[88],"temporal":[90],"spatial":[92],"characteristics":[93],"high-frequency":[95],"There":[98],"notable":[101],"absence":[102],"quantitative":[104],"metrics":[105],"evaluating":[107],"generative":[108],"simulation":[110,188,237],"models":[111],"in":[112],"literature.":[114],"To":[115,222],"tackle":[116],"this":[117],"problem,":[118],"adapt":[120],"predictive":[122,138,169],"score,":[123,170],"metric":[125],"measured":[126],"an":[128,205],"MAE,":[129],"to":[130,167,204,210],"data":[132,142,178,199],"training":[134],"stock":[136],"price":[137],"model":[139],"it":[145,194],"real":[147],"compare":[150],"with":[152,238],"previous":[153],"works":[154],"two":[156],"stocks,":[157],"reporting":[158],"\u00d73.27":[160],"\u00d73.48":[162],"improvement":[163],"over":[164],"SoTA":[165],"according":[166],"demonstrating":[171],"generate":[174],"useful":[175],"financial":[180],"downstream":[181],"tasks.":[182],"Furthermore,":[183],"assess":[185],"TRADES\u2019s":[186,252],"realism":[189],"responsiveness,":[191],"showing":[192],"effectively":[195],"learns":[196],"conditional":[198],"distribution":[200],"successfully":[202],"reacts":[203],"experimental":[206],"agent,":[207],"giving":[208],"sprout":[209],"possible":[211],"calibrations":[212],"evaluations":[214],"strategies":[217],"experiments.":[221],"perform":[223],"developed":[227],"DeepMarket,":[228],"first":[230],"open-source":[231],"Python":[232],"framework":[233],"deep":[239],"learning.":[240],"In":[241],"our":[242],"repository,":[243],"include":[245],"dataset":[249],"composed":[250],"generated":[253],"simulations.":[254]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-24T00:00:00"}
