{"id":"https://openalex.org/W7161679016","doi":"https://doi.org/10.48550/arxiv.2605.17937","title":"BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting","display_name":"BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161679016","doi":"https://doi.org/10.48550/arxiv.2605.17937"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17937","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.17937","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136473471","display_name":"Zhensheng Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Zhensheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019284302","display_name":"Wenmian Yang","orcid":"https://orcid.org/0000-0001-8493-4449"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Wenmian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136481943","display_name":"Qingtai Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Qingtai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136474251","display_name":"Lequan Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lequan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133601842","display_name":"Yiquan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yiquan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136456644","display_name":"Weijia Jia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia, Weijia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.19339999556541443,"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"}},"topics":[{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.19339999556541443,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.04960000142455101,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.03739999979734421,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.6758000254631042},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.6008999943733215},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5327000021934509},{"id":"https://openalex.org/keywords/python","display_name":"Python (programming language)","score":0.4814000129699707},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4250999987125397},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.34470000863075256},{"id":"https://openalex.org/keywords/flexibility","display_name":"Flexibility (engineering)","score":0.32710000872612},{"id":"https://openalex.org/keywords/compiler","display_name":"Compiler","score":0.3010999858379364}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8127999901771545},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6758000254631042},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.6008999943733215},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5327000021934509},{"id":"https://openalex.org/C519991488","wikidata":"https://www.wikidata.org/wiki/Q28865","display_name":"Python (programming language)","level":2,"score":0.4814000129699707},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4666999876499176},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4250999987125397},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4147999882698059},{"id":"https://openalex.org/C115903868","wikidata":"https://www.wikidata.org/wiki/Q80993","display_name":"Software engineering","level":1,"score":0.3741999864578247},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35530000925064087},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C169590947","wikidata":"https://www.wikidata.org/wiki/Q47506","display_name":"Compiler","level":2,"score":0.3010999858379364},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C66153294","wikidata":"https://www.wikidata.org/wiki/Q899291","display_name":"CASP","level":4,"score":0.28360000252723694},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2599000036716461},{"id":"https://openalex.org/C106306483","wikidata":"https://www.wikidata.org/wiki/Q183984","display_name":"Futures contract","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2781089630","wikidata":"https://www.wikidata.org/wiki/Q21856745","display_name":"Realization (probability)","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17937","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.17937","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17937","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Quantitative":[0],"backtesting":[1,146],"is":[2,45],"essential":[3],"for":[4,79,131,137,144],"evaluating":[5],"trading":[6],"strategies":[7,123],"but":[8],"remains":[9],"hampered":[10],"by":[11,48,127,155],"high":[12],"technical":[13],"barriers":[14],"and":[15,39,108,141,165,172],"limited":[16],"scalability.":[17],"While":[18],"Large":[19],"Language":[20],"Models":[21],"(LLMs)":[22],"offer":[23],"a":[24,53,115,129,135,142],"transformative":[25],"path":[26],"to":[27,57],"automate":[28],"this":[29,65,69],"complex,":[30],"interdisciplinary":[31],"workflow":[32],"through":[33],"advanced":[34],"code":[35],"generation,":[36,140],"tool":[37],"usage,":[38],"agentic":[40],"planning,":[41],"the":[42,49,75,167],"practical":[43],"realization":[44],"significantly":[46],"challenged":[47],"current":[50],"lack":[51],"of":[52,169],"large-scale":[54,77],"benchmark":[55,78],"dedicated":[56],"automated":[58,80],"quantitative":[59,81],"backtesting,":[60],"which":[61],"hinders":[62],"progress":[63],"in":[64],"field.":[66],"To":[67],"bridge":[68],"critical":[70],"gap,":[71],"we":[72],"introduce":[73],"BacktestBench,":[74],"first":[76],"backtesting.":[82],"Built":[83],"from":[84],"over":[85],"6":[86],"million":[87],"real":[88],"market":[89],"records,":[90],"it":[91],"comprises":[92],"18,246":[93],"meticulously":[94],"annotated":[95],"question-answering":[96],"pairs":[97],"across":[98],"four":[99],"task":[100],"categories:":[101],"metrics":[102],"calculation,":[103],"ticker":[104],"selection,":[105,107],"strategy":[106],"parameter":[109],"confirmation.":[110],"We":[111],"also":[112],"propose":[113],"AutoBacktest,":[114],"robust":[116],"multi-agent":[117],"baseline":[118],"that":[119,161],"translates":[120],"natural":[121],"language":[122],"into":[124],"reproducible":[125],"backtests":[126],"coordinating":[128],"Summarizer":[130],"semantic":[132],"factor":[133],"extraction,":[134],"Retriever":[136],"validated":[138],"SQL":[139],"Coder":[143],"Python":[145],"implementation.":[147],"Our":[148],"evaluation":[149],"on":[150],"23":[151],"mainstream":[152],"LLMs,":[153],"complemented":[154],"targeted":[156],"ablations,":[157],"identifies":[158],"key":[159],"factors":[160],"influence":[162],"end-to-end":[163],"performance":[164],"highlights":[166],"importance":[168],"grounded":[170],"verification":[171],"standardized":[173],"indicator":[174],"representations.":[175]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2026-05-20T00:00:00"}
