{"id":"https://openalex.org/W4385567872","doi":"https://doi.org/10.1145/3580305.3599813","title":"Efficient Continuous Space Policy Optimization for High-frequency Trading","display_name":"Efficient Continuous Space Policy Optimization for High-frequency Trading","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567872","doi":"https://doi.org/10.1145/3580305.3599813"},"language":"en","primary_location":{"id":"doi:10.1145/3580305.3599813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101566937","display_name":"Li Han","orcid":"https://orcid.org/0000-0001-5797-2554"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Li Han","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101558274","display_name":"Nan Ding","orcid":"https://orcid.org/0000-0003-0579-966X"},"institutions":[{"id":"https://openalex.org/I66867065","display_name":"East China Normal University","ror":"https://ror.org/02n96ep67","country_code":"CN","type":"education","lineage":["https://openalex.org/I66867065"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Ding","raw_affiliation_strings":["East China Normal University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"East China Normal University, Shanghai, China","institution_ids":["https://openalex.org/I66867065"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008295417","display_name":"Guoxuan Wang","orcid":"https://orcid.org/0000-0002-9178-2137"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoxuan Wang","raw_affiliation_strings":["Tongji University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tongji University, Shanghai, China","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069869295","display_name":"Dawei Cheng","orcid":"https://orcid.org/0000-0002-5877-7387"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]},{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dawei Cheng","raw_affiliation_strings":["Tongji University &amp; Shanghai Artificial Intelligence Laboratory, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Tongji University &amp; Shanghai Artificial Intelligence Laboratory, Shanghai, China","institution_ids":["https://openalex.org/I116953780","https://openalex.org/I4391012619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101508038","display_name":"Yuqi Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yuqi Liang","raw_affiliation_strings":["Emoney Inc., Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Emoney Inc., Shanghai, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101566937"],"corresponding_institution_ids":["https://openalex.org/I66867065"],"apc_list":null,"apc_paid":null,"fwci":4.4702,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.94781894,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4112","last_page":"4122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9993000030517578,"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":0.9993000030517578,"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/T10047","display_name":"Financial Markets and Investment Strategies","score":0.9988999962806702,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9958000183105469,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6966000199317932},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6791815757751465},{"id":"https://openalex.org/keywords/portfolio-optimization","display_name":"Portfolio optimization","score":0.664584755897522},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.6117287874221802},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.527267336845398},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5082444548606873},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.5061168074607849},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.48938173055648804},{"id":"https://openalex.org/keywords/portfolio","display_name":"Portfolio","score":0.4488089084625244},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.43502843379974365},{"id":"https://openalex.org/keywords/dynamic-programming","display_name":"Dynamic programming","score":0.4165806174278259},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.4163490831851959},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.278991162776947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2716655433177948},{"id":"https://openalex.org/keywords/finance","display_name":"Finance","score":0.20768165588378906},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1637609899044037},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12975627183914185},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.10460236668586731}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6966000199317932},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6791815757751465},{"id":"https://openalex.org/C202655437","wikidata":"https://www.wikidata.org/wiki/Q7231728","display_name":"Portfolio optimization","level":3,"score":0.664584755897522},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.6117287874221802},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.527267336845398},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5082444548606873},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.5061168074607849},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.48938173055648804},{"id":"https://openalex.org/C2780821815","wikidata":"https://www.wikidata.org/wiki/Q5340806","display_name":"Portfolio","level":2,"score":0.4488089084625244},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.43502843379974365},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.4165806174278259},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.4163490831851959},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.278991162776947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2716655433177948},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.20768165588378906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1637609899044037},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12975627183914185},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.10460236668586731},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3580305.3599813","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3580305.3599813","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1121271761","display_name":null,"funder_award_id":"Program","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1272656946","display_name":null,"funder_award_id":"62202168","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5722224901","display_name":null,"funder_award_id":"62102287","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1965766592","https://openalex.org/W2069936503","https://openalex.org/W2098486486","https://openalex.org/W2162288466","https://openalex.org/W2169015875","https://openalex.org/W2238750598","https://openalex.org/W2293279554","https://openalex.org/W2586702902","https://openalex.org/W2613328025","https://openalex.org/W2796220501","https://openalex.org/W2885054548","https://openalex.org/W2899295964","https://openalex.org/W2944090041","https://openalex.org/W2952277250","https://openalex.org/W2997874851","https://openalex.org/W2998034590","https://openalex.org/W3011631586","https://openalex.org/W3012223895","https://openalex.org/W3016693931","https://openalex.org/W3032048943","https://openalex.org/W3035336738","https://openalex.org/W3081156433","https://openalex.org/W3088545074","https://openalex.org/W3100789280","https://openalex.org/W3119653011","https://openalex.org/W3128663285","https://openalex.org/W3156518543","https://openalex.org/W3172807453","https://openalex.org/W3175835345","https://openalex.org/W3182350536","https://openalex.org/W3182455279","https://openalex.org/W3190469032","https://openalex.org/W3198493423","https://openalex.org/W3207999419","https://openalex.org/W4205372916","https://openalex.org/W4225120457","https://openalex.org/W4225140907","https://openalex.org/W4285600170","https://openalex.org/W4300672946","https://openalex.org/W4302307947","https://openalex.org/W4306316902","https://openalex.org/W4306317752","https://openalex.org/W4313908847"],"related_works":["https://openalex.org/W4385342861","https://openalex.org/W1515117609","https://openalex.org/W1536296381","https://openalex.org/W3102039646","https://openalex.org/W3198564127","https://openalex.org/W2996383434","https://openalex.org/W4287990848","https://openalex.org/W1511927616","https://openalex.org/W2991384368","https://openalex.org/W2156992384"],"abstract_inverted_index":{"High-frequency":[0],"trading":[1],"is":[2,9,172],"an":[3,81,143,185],"extraordinarily":[4],"intricate":[5],"financial":[6],"task,":[7],"which":[8,153],"normally":[10],"treated":[11],"as":[12],"a":[13,63],"near":[14],"real-time":[15],"sequential":[16],"decision":[17],"problem.":[18],"Compared":[19],"with":[20,46,69,99],"the":[21,95,106,110,128,131,166,173,178,193,200,224,230],"traditional":[22],"two-phase":[23],"approach,":[24],"forecasting":[25],"equity's":[26],"trend":[27],"and":[28,135,207,227],"then":[29],"weighting":[30],"them":[31],"by":[32,103,114,183],"combinatorial":[33],"optimization,":[34],"deep":[35],"reinforcement":[36],"learning":[37],"(DRL)":[38],"methods":[39,52],"have":[40],"shown":[41],"advances":[42],"in":[43,109,192],"reward":[44,145],"chasing":[45],"optimal":[47],"policies.":[48],"However,":[49],"existing":[50],"DRL-based":[51,83],"either":[53],"leverage":[54],"portfolio":[55,96,137,180],"optimization":[56,85,181,190],"on":[57,199],"low-frequency":[58],"scenarios":[59],"or":[60],"only":[61],"support":[62],"very":[64],"limited":[65],"number":[66],"of":[67,168,229],"assets":[68],"discrete":[70],"action":[71,111],"space,":[72],"facing":[73],"significant":[74],"computing":[75],"efficiency":[76],"challenges.":[77],"Therefore,":[78],"we":[79,93,126,141,210],"propose":[80],"efficient":[82,144,186],"policy":[84,189],"(DRPO)":[86],"method":[87],"for":[88],"high-frequency":[89,179],"trading.":[90],"In":[91],"particular,":[92],"model":[94],"management":[97],"task":[98],"Markov":[100],"Decision":[101],"Process":[102],"directly":[104,157],"inferring":[105],"equity":[107],"weights":[108],"space":[112,188],"guided":[113],"maximum":[115],"accumulated":[116],"returns.":[117],"To":[118,165],"reduce":[119],"agents'":[120],"interaction":[121],"complexity":[122],"without":[123],"reducing":[124],"interpretation,":[125],"detach":[127],"environment":[129],"into":[130],"\"static''":[132],"market":[133],"states":[134],"\"dynamic''":[136],"weight":[138],"states.":[139],"Then,":[140],"design":[142],"expectation":[146],"calculation":[147],"algorithm":[148,191],"via":[149],"probabilistic":[150],"dynamic":[151],"programming,":[152],"enables":[154],"our":[155,169,213],"agents":[156],"collect":[158],"feedback":[159],"away":[160],"from":[161,203],"trajectory":[162],"sampling-based":[163],"morass.":[164],"best":[167],"knowledge,":[170],"this":[171],"first":[174],"work":[175],"that":[176,212],"solves":[177],"problem":[182],"devising":[184],"continuous":[187],"DRL":[194],"framework.":[195],"Through":[196],"extensive":[197],"experiments":[198],"real-world":[201],"data":[202],"Dow":[204],"Jones,":[205],"Coinbase":[206],"SSE":[208],"exchanges,":[209],"show":[211],"proposed":[214,231],"DRPO":[215],"significantly":[216],"outperforms":[217],"state-of-the-art":[218],"benchmark":[219],"methods.":[220],"The":[221],"results":[222],"demonstrate":[223],"practical":[225],"applicability":[226],"effectiveness":[228],"method.":[232]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":9}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
