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本文目录

1. 同花顺炒股软件AI分时顶底指标好用吗
2. 美股AI板块是什么
3. 如何建立自己的算法交易
4. 假期余额不到半天!8天大假发生的财经大事都在这里

同花顺炒股软件AI分时顶底指标好用吗

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美股AI板块是什么

浜哄伐鏅鸿兘銆傚緱鐩婁簬鍥惧舰澶勭悊鍣ㄧ殑鍙戝睍锛屽紑婧愯蒋浠剁殑澧為暱浠ュ強鏁版嵁鐨勭垎鍙戯紝浜哄伐鏅鸿兘涔熷氨鏄疉I鏉垮潡鎶€鏈紑濮嬪疄琛屽湪浜嗙編鑲＄殑鍚勪釜棰嗗煙鍐咃紝浠ｈ〃鐫€浜哄伐鏅鸿兘鐨勮偂绁ㄤ篃閫愭笎鍑虹幇鍦ㄨ偂绁ㄥ競鍦恒€?

如何建立自己的算法交易

涓€銆佷紶缁熸柟娉?/p>

鍦ㄦ煇浜涘亣璁句笅鐨勬樉寮忔渶浼樼瓥鐣?/p>

銆怋ertsimas,Dimitris,andAndrewW.Lo."Optimalcontrolofexecutioncosts."JournalofFinancialMarkets1.1(1998):1-50.銆戣繖閲屽亣璁句簡涓嶅悓鐨勪环鏍煎啿鍑诲嚱鏁帮紝鐒跺悗姹傝В寰楀埌鏈€浼樼殑浜ゆ槗鎵ц鏂规銆傛牴鎹弬鏁扮殑涓嶅悓锛屾渶浼樼殑绛栫暐瑕佷箞鏄叏閮ㄥ紑澶村崠鎺夈€佸潎鍖€鍑忎粨銆佹垨鑰呭叏閮ㄦ渶鍚庡崠鎺夈€?nbsp;https://stuff.mit.edu/afs/athena.mit.edu/user/d/b/dbertsim/www/papers/Finance/Optimal%20control%20of%20execution%20costs.pdf

銆怉lmgren,Robert,andNeilChriss."Optimalexecutionofportfoliotransactions."JournalofRisk3(2001):5-40.銆戣繖绡囨枃绔犳垜浠笓鏍忓墠闈㈡湁璁茶繃锛屽緢钁楀悕鐨?Almgren-Chriss妯″瀷銆?nbsp;https://www.smallake.kr/wp-content/uploads/2016/03/optliq.pdf寮犳鐝╋細銆愪氦鏄撴墽琛屻€慉lmgren-ChrissModel

銆怗u茅antO,LehalleCA,Fernandez-TapiaJ.Optimalportfolioliquidationwithlimitorders[J].SIAMJournalonFinancialMathematics,2012,3(1):740-764.銆戣繖绡囨枃绔犳垜浠笓鏍忓墠闈篃鏈夎杩囷紱鍓嶉潰鐨?Almgren-Chriss鍏跺疄鑰冭檻鐨勬槸浣跨敤甯備环鍗曪紝鑰岃繖閲岃€冭檻浣跨敤闄愪环鍗曡繘琛屼氦鏄撱€?nbsp;https://arxiv.org/pdf/1106.3279.pdf寮犳鐝╋細銆愪氦鏄撴墽琛屻€戦檺浠峰崟浜ゆ槗鎵ц

銆怗u茅ant,Olivier,andCharles鈥怉lbertLehalle."Generalintensityshapesinoptimalliquidation."MathematicalFinance25.3(2015):457-495.銆戣繖閲屼篃鏄€冭檻闄愪环鍗曡繘琛屼氦鏄擄紝浣嗘槸涓庡墠闈笉鍚岀殑鏄細鍓嶄竴涓亣璁鹃檺浠峰崟鑰冭檻鐨勬垚浜ゆ鐜囬殢鐫€浠锋牸鎸囨暟琛板噺锛岃€岃繖閲岃€冭檻浜嗕竴涓洿鍔犱竴鑸殑褰㈠紡銆?nbsp;https://arxiv.org/pdf/1204.0148.pdf

銆怌arteaA,JaimungalS.Optimalexecutionwithlimitandmarketorders[J].QuantitativeFinance,2015,15(8):1279-1291.銆戣繖閲岃€冭檻鍚屾椂浣跨敤闄愪环鍗曞拰甯備环鍗曡繘琛屼氦鏄擄紝浠庤€岃兘澶熷畬鎴?Almgren-Chriss妯″瀷鎵€瑙勫畾鐨勬柟妗堬紝鎴栬€呮壘鍒颁竴涓洿鏈夌殑浜ゆ槗鏂规銆?nbsp;https://sci-hub.se//https://www.tandfonline.com/doi/abs/10.1080/14697688.2015.1032543

銆怋ulthuis,Brian,etal."Optimalexecutionoflimitandmarketorderswithtradedirector,speedlimiter,andfilluncertainty."InternationalJournalofFinancialEngineering4.02n03(2017):1750020.銆戜篃鏄€冭檻浣跨敤闄愪环鍗曞拰甯備环鍗曚竴璧蜂氦鏄撱€?nbsp;https://arxiv.org/pdf/1604.04963.pdf寮犳鐝╋細銆愪氦鏄撴墽琛屻€戝競浠峰崟+闄愪环鍗?鏈€浼樻墽琛?/p>

銆怌arteaA,JaimungalS.Incorporatingorder-flowintooptimalexecution[J].MathematicsandFinancialEconomics,2016,10(3):339-364.銆戣繖閲岃€冭檻甯傚満鎵€鏈変氦鏄撹€呯殑璁㈠崟閮戒細浜х敓绾挎€х殑鐭湡/闀挎湡甯傚満鍐插嚮锛屽洜姝ゅ彲浠ヤ及璁℃湭鏉ヤ竴娈垫椂闂寸殑璁㈠崟娴佸悜锛堜拱鍗曟€婚噺鍜屽崠鍗曟€婚噺鐨勫樊锛夛紝浠庤€岃兘澶熷湪 Almgren-Chriss妯″瀷鐨勫熀纭€涓婅繘琛屼竴瀹氱殑璋冩暣锛屼娇寰楃瓥鐣ユ洿浼樸€?nbsp;https://sci-hub.se//https://link.springer.com/content/pdf/10.1007/s11579-016-0162-z.pdf

鍥句功

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銆怗u茅antO.TheFinancialMathematicsofMarketLiquidity:Fromoptimalexecutiontomarketmaking[M].CRCPress,2016.銆戜粠Almgren-Chriss妯″瀷寮€濮嬭锛屼竴鐩村埌鐩稿簲鐨勬嫇灞曞拰瀹為檯鐨勯棶棰橈紝鍗佸垎鎺ㄨ崘銆?/p>

铻嶅悎瀵逛簬甯傚満鐜闅愬彉閲忕殑浼拌

銆怌asgrainP,JaimungalS.Tradingalgorithmswithlearninginlatentalphamodels[J].MathematicalFinance,2019,29(3):735-772.銆戝競鍦轰氦鏄撹€呬細鏍规嵁涓嶅悓鐨勫競鍦烘寕鍗曞拰浠锋牸璧板娍鑰岄噰鍙栦笉鍚岀殑鍙嶆槧锛屽洜姝ゆ垜浠篃鍙互鏍规嵁鍘嗗彶鏁版嵁瀛︿範鍒板悇绉嶆儏鍐典笅鐨勪环鏍煎悗楠屽垎甯冿紝浠庤€屾洿濂藉湴甯姪鎴戜滑杩涜浜ゆ槗鎵ц鎴栬€呭鍒┿€傛渶鍚庣殑缁撴灉鍙互鐪嬪仛鍦?Almgren-Chriss妯″瀷鐨勫熀纭€涓婂鍔犱簡涓€涓皟鎺ч」锛屽弽鏄犳垜浠浜庢湭鏉ョ殑棰勬湡銆?nbsp;https://arxiv.org/pdf/1806.04472.pdf

濡備綍瀹炵幇浠ユ寜閲忓姞鏉冨钩鍧囦环鏍硷紙VWAP锛変氦鏄?/p>

銆怟akade,ShamM.,etal."CompetitivealgorithmsforVWAPandlimitordertrading."Proceedingsofthe5thACMconferenceonElectroniccommerce.2004.銆戜粠鍦ㄧ嚎瀛︿範鐨勮搴︽彁鍑轰簡鍑犱釜鐢ㄤ簬浣垮緱鎴戜滑浜ゆ槗鍒?VWAP浠锋牸鐨勬ā鍨嬨€備负浠€涔堜細鍏虫敞VWAP鐨勪氦鏄撴墽琛岋紵褰撳ぇ鐨勬祦閫氳偂鑲′笢闇€瑕佸噺鎸佺殑鏃跺€欙紝涓轰簡閬垮厤鐩存帴鍑哄敭寮曡捣鐨勪环鏍兼尝鍔紝涓€鑸槸鎶婇渶瑕佸噺鎸佺殑鑲＄エ鍗栫粰鍒稿晢锛岀劧鍚庣敱鍒稿晢鏉ユ媶鍗曞嚭鍞紝鑰屼氦鏄撲环鏍间竴鑸负鏈潵涓€娈垫椂闂寸殑VWAP锛屽洜姝ゅ埜鍟嗛渶瑕佸敖閲忎互VWAP鏉ヤ氦鏄撴墽琛屻€?nbsp;https://sci-hub.se//https://dl.acm.org/doi/abs/10.1145/988772.988801

銆怋iałkowski,Jędrzej,SergeDarolles,andGaëlleLeFol."ImprovingVWAPstrategies:Adynamicvolumeapproach."JournalofBanking&Finance32.9(2008):1709-1722.銆戞敼杩涘浜庝氦鏄撻噺鐨勫缓妯★紝浠庤€屽緱鍒版洿濂界殑 VWAP浜ゆ槗绠楁硶銆傛妸浜ゆ槗閲忔媶鍒嗕负涓や釜閮ㄥ垎锛屼竴閮ㄥ垎鏄競鍦烘暣浣撶殑浜ゆ槗閲忓彉鍔紝鍙︿竴閮ㄥ垎鏄壒瀹氳偂绁ㄤ笂鐨勪氦鏄撻噺妯″紡銆?nbsp;https://sci-hub.se//https://www.sciencedirect.com/science/article/pii/S0378426607003226

浠ユ寜鏃堕棿鍔犳潈骞冲潎浠锋牸锛圱WAP锛変氦鏄?/p>

涓轰簡瀵圭О锛屽彲浠ヤ粙缁嶄竴涓嬪彟涓€绉嶅姞鏉冨钩鍧囩殑鎯呭舰 TWAP锛岃繖绉嶆儏褰㈠疄鐜拌捣鏉ョ浉瀵规瘮杈冪畝鍗曪紱濡傛灉涓嶈€冭檻甯傚満鍐插嚮锛屽氨鎷嗗垎鍒版瘡涓椂闂存涓婂潎鍖€鍑哄敭鍗冲彲瀹炵幇銆?/p>

鍙互璇佹槑TWAP浜ゆ槗鍦ㄤ互涓嬩袱绉嶆儏褰笅鏈€浼橈細甯傚満浠锋牸涓哄竷鏈楄繍鍔ㄥ苟涓斾环鏍煎啿鍑讳负甯告暟锛涘浜庢櫄浜ゆ槗娌℃湁鎯╃綒锛堝叾瀹炴洿鏅氫氦鏄撴剰鍛崇潃闈复鏇村ぇ鐨勯闄╋級锛屼絾鏄浜庢渶鍚庢湭瀹屾垚浜ゆ槗鐨勬儵缃氳緝澶с€?/p>

浜屻€佸己鍖栧涔犳柟娉?/p>

鍩轰簬浼犵粺妯″瀷鐨勫己鍖栧涔犳柟娉?/p>

銆怘endricksD,WilcoxD.AreinforcementlearningextensiontotheAlmgren-Chrissframeworkforoptimaltradeexecution[C]//2014IEEEConferenceonComputationalIntelligenceforFinancialEngineering&Economics(CIFEr).IEEE,2014:457-464.銆戞湰涓撴爮鏈夎銆?nbsp;https://arxiv.org/pdf/1403.2229.pdf

寮哄寲瀛︿範+浜ゆ槗鎵ц锛圥aper/Article锛?/p>

銆怤evmyvakaY,FengY,KearnsM.Reinforcementlearningforoptimizedtradeexecution[C]//Proceedingsofthe23rdinternationalconferenceonMachinelearning.2006:673-680.銆戞瘮杈冪粡鍏哥殑涓€绡囷紝鍙戝湪 ICML涓婏紝鏈笓鏍忓墠闈㈡湁璁层€備娇鐢?DQN鏂规硶锛屽疄鐜板舰寮忔帴杩?DP銆?nbsp;http://smallake.kr/wp-content/uploads/2019/01/rlexec.pdf

銆怐ab茅riusK,GranatE,KarlssonP.DeepExecution-ValueandPolicyBasedReinforcementLearningforTradingandBeatingMarketBenchmarks[J].AvailableatSSRN3374766,2019.銆戜娇鐢ㄤ簡DDQN鍜?PPO鏂规硶锛屽熀浜庣敓鎴愮殑浠锋牸搴忓垪鏉ヨ繘琛屽疄楠岋紝浣跨敤鐗瑰畾鐨勬ā鍨嬭€冭檻鐭湡鍜岄暱鏈熷競鍦哄啿鍑汇€?nbsp;https://sci-hub.se//https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3374766

銆怤ingB,LinFHT,JaimungalS.Doubledeepq-learningforoptimalexecution[J].arXivpreprintarXiv:1812.06600,2018.銆慏DQN鐨勫己鍖栧涔犺В娉曪紝鍦ㄧ編鑲′笂瀹為獙銆?nbsp;https://arxiv.org/pdf/1812.06600.pdf

銆怢inS,BelingPA.AnEnd-to-EndOptimalTradeExecutionFrameworkbasedonProximalPolicyOptimization[C]//IJCAI.2020:4548-4554.銆戜娇鐢?PPO鐨勮В娉曪紝姣旇緝鏈夋剰鎬濈殑鏄繖閲岄潰鐨勫疄楠岀粨鏋滄樉绀猴紝浣跨敤LSTM鍜屾妸鍘嗗彶鏁版嵁鍏ㄩ儴鍫嗗彔璧锋潵鐢?MLP鏁堟灉宸窛涓嶅ぇ銆備篃鏄湪缇庤偂涓婂疄楠屻€?nbsp;https://www.ijcai.org/Proceedings/2020/0627.pdf

銆怓angY,RenK,LiuW,etal.UniversalTradingforOrderExecutionwithOraclePolicyDistillation[J].arXivpreprintarXiv:2103.10860,2021.銆戝湪浣跨敤寮哄寲瀛︿範鐨勫熀纭€涓婏紝寮曞叆浜嗕竴涓暀甯堢綉缁滐紝鏁欏笀缃戠粶瀛︿範涓€涓熀浜庢湭鏉ユ暟鎹殑绛栫暐锛屽苟涓旂敤浜庤缁冨鐢熺綉缁溿€傛湰涓撴爮鍓嶉潰鏈夎銆?nbsp;https://www.aaai.org/AAAI21Papers/AAAI-3650.FangY.pdf

銆怴yetrenkoS,XuS.Risk-sensitivecompactdecisiontreesforautonomousexecutioninpresenceofsimulatedmarketresponse[J].arXivpreprintarXiv:1906.02312,2019.銆慖CML-19鐨勬枃绔犮€傛瀯閫犱簡涓€涓彲浠ュ弽鏄犲競浠峰崟甯傚満鍐插嚮鐨勬ā鎷熷櫒锛涗娇鐢?tabularQ-learning鏉ュ涔犲熀浜庡喅绛栨爲鐨勬ā鍨嬶紱浣跨敤鐗瑰緛閫夋嫨鐨勬柟娉曟潵绛涢€夌壒寰併€傞€氳繃浠ヤ笂鏂瑰紡锛岃兘澶熷涔犲埌涓€涓ā鍨嬪府鍔╁喅绛栦粈涔堟椂鍊欏簲璇ヤ笅甯備环鍗曘€佷粈涔堟椂鍊欏簲璇ヤ笅闄愪环鍗曘€?nbsp;https://arxiv.org/pdf/1906.02312.pdf

銆怉kbarzadehN,TekinC,vanderSchaarM.Onlinelearninginlimitorderbooktradeexecution[J].IEEETransactionsonSignalProcessing,2018,66(17):4626-4641.銆戜粠onlinelearning鐨勮瑙掓潵瑙ｅ喅杩欎釜闂锛屼娇鐢?DP绫荤殑鏂规硶锛屽垎鏋?regret銆?nbsp;http://repository.bilkent.edu.tr/bitstream/handle/11693/50289/Bilkent-research-paper.pdf?sequence=1

銆怶eiH,WangY,ManguL,etal.Model-basedreinforcementlearningforpredictionsandcontrolforlimitorderbooks[J].arXivpreprintarXiv:1910.03743,2019.銆戜笓鏍忓垰鍒氳浜嗙殑涓€绡囨枃绔狅紝浣跨敤 model-based绫荤殑寮哄寲瀛︿範绠楁硶锛岀洿鎺ュ涔犱竴涓笘鐣屾ā鍨嬶紝鐒跺悗璁╁己鍖栧涔犵瓥鐣ラ€氳繃鍜屼笘鐣屾ā鍨嬬殑浜や簰杩涜瀛︿範銆?nbsp;https://arxiv.org/pdf/1910.03743.pdf

銆怟arpeM,FangJ,MaZ,etal.Multi-agentreinforcementlearninginarealisticlimitorderbookmarketsimulation[J].arXivpreprintarXiv:2006.05574,2020.銆戣繖閲岀殑澶氭櫤鑳戒綋浼间箮閫傜敤浜庣粨鍚堝巻鍙叉暟鎹敓鎴愬叾浠栧競鍦哄弬涓庤€呯殑鍔ㄤ綔锛岃€屾渶浼樼瓥鐣ョ殑瀛︿範浠嶇劧鏄娇鐢ㄥ崟鏅鸿兘浣?DDQN鏂规硶鏉ュ仛銆備粬浠紑婧愪簡涓€涓€冭檻澶氭櫤鑳戒綋鐨勬ā鎷熺幆澧?ABIDES銆?nbsp;https://arxiv.org/pdf/2006.05574.pdf

銆怱chnaubeltM.Deepreinforcementlearningfortheoptimalplacementofcryptocurrencylimitorders[J].EuropeanJournalofOperationalResearch,2022,296(3):993-1006.銆戠爺绌舵暟瀛楄揣甯佷笂濡備綍涓嬮檺浠峰崟銆傚姣斾簡PPO鍜?DDQN锛屽彂鐜?PPO鏇村ソ銆傛帰绱㈠嚭浜嗕竴浜涢噸瑕佺殑鍥犲瓙锛屾瘮濡?currentliquiditycost锛宷ueueimbalance绛夈€?nbsp;https://www.econstor.eu/bitstream/10419/216206/1/1696077540.pdf

寮哄寲瀛︿範+浜ゆ槗鎵ц锛圱hesis锛?/p>

銆怘uR.OptimalOrderExecutionusingStochasticControlandReinforcementLearning[J].2016.銆慘TH锛堢憺鍏革級宸ョ▼瀛﹂櫌纭曞＋璁烘枃銆傜畻娉曠洿鎺ユ槸鍩轰簬浠峰€煎嚱鏁扮殑鍔ㄦ€佽鍒掋€備笉杩囨彁渚涗簡姣旇緝璇︾粏鐨勬ā鎷熺幆澧冨拰绠楁硶浼唬鐮併€?nbsp;https://www.diva-portal.org/smash/get/diva2:963057/FULLTEXT01.pdf

銆怰ockwellB.OptimalOrderExecutionwithDeepReinforcementLearning[J].2019.銆戝姞鎷垮ぇ钂欑壒鍒╁皵楂樼瓑鍟嗗闄㈢澹鏂囥€備娇鐢?TD3鍜?DDPG绠楁硶锛屼笉杩囧疄楠屾槸鍩轰簬浜哄伐鐢熸垚鐨勬暟鎹殑锛坰kew-normalBrownianmotion锛夈€?nbsp;https://biblos.hec.ca/biblio/memoires/m2019a628776.pdf

銆怰eiterMB.AnApplicationofDeepReinforcementLearningforOrderExecution[D].SchoolofEngineeringScience,OsakaUniversity,2020.銆戝浼﹀澶у鏈姣曚笟璁烘枃銆傚湪浣跨敤A3C绠楁硶鐨勫熀纭€涓婏紝鑰冭檻浜嗕娇鐢ㄦ暀甯堝鐢熺綉缁滅殑鏂瑰紡杩涜杩佺Щ瀛︿範锛屽苟涓旇€冭檻浜嗙煭鏈熷競鍦哄啿鍑汇€?nbsp;https://mbreiter.github.io/doc/thesis.pdf

寮哄寲瀛︿範+椋庨櫓鍋忓ソ

RobustRisk-SensitiveReinforcementLearningAgentsforTradingMarkets

Deepequalriskpricingoffinancialderivativeswithnon-translationinvariantriskmeasures

寮哄寲瀛︿範+鍋氬競绛栫暐

OptimalMarketMakingbyReinforcementLearning

OptimizingMarketMakingusingMulti-AgentReinforcementLearning

DeepReinforcementLearningforMarketMaking

DeepRecurrentQ-NetworksforMarketMaking

RobustMarketMakingviaAdversarialReinforcementLearning

Marketmakingviareinforcementlearning

寮哄寲瀛︿範+璧勪骇缁勫悎

DeepStockTrading:AHierarchicalReinforcementLearningFrameworkforPortfolioOptimizationandOrderExecution

Robo-Advising:EnhancingInvestmentwithInverseOptimizationandDeepReinforcementLearning

LargeScaleContinuous-TimeMean-VariancePortfolioAllocationviaReinforcementLearning

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好了，关于美股al线和美股alb的问题到这里结束啦，希望可以解决您的问题哈！