FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205372916> ?p ?o ?g. }

• W4205372916 endingPage "108543" @default.
• W4205372916 startingPage "108543" @default.
• W4205372916 abstract "In this paper, we propose an online model optimization algorithm based on reinforcement learning for quantitative trading. The combination of prediction model and trading policy is the most commonly used framework in practical quantitative trading. Integrated with machine learning methods, this framework brings huge profits to quantified companies. In the framework, the prediction model is used to predict future trading price trend, and the trading policy is used to determine the price and number of orders. Even though, the shortcomings of machine learning models are obvious, mainly are, (1) Slow prediction speed. Huge human-craft features and model computing cost much time, which is ten times of pure trading policy without model. (2) Poor generalization. This kind of models can hardly adapt to market data in each period, because market traders will change time to time at micro level, thus the distribution of market data will change. But current model is trained on a long period dataset, it achieves best effect at average, but can not adapt to different market at each period. To address this problem, we propose a novel online model optimization algorithm. A light model library will be constructed. Each light model in this library corresponds to a different market distribution. By devising the appropriate reward function via inverse reinforcement learning algorithm, the algorithm can accurately estimate the profits of each model. Then the model can be selected automatically in real-time trading, so that the trading policies can automatically adapt to changes in trading market, overcoming previous shortcoming of manually updating model and slow prediction speed. Experimental results show that the proposed algorithm achieves state-of-the-art performance on China Commodity Futures Market Data." @default.
• W4205372916 created "2022-01-25" @default.
• W4205372916 creator A5014567045 @default.
• W4205372916 creator A5019708391 @default.
• W4205372916 creator A5054418515 @default.
• W4205372916 creator A5056339174 @default.
• W4205372916 creator A5087158377 @default.
• W4205372916 date "2022-05-01" @default.
• W4205372916 modified "2023-10-16" @default.
• W4205372916 title "Auto uning of price prediction models for high-frequency trading via reinforcement learning" @default.
• W4205372916 cites W2000080679 @default.
• W4205372916 cites W2020009149 @default.
• W4205372916 cites W2074500080 @default.
• W4205372916 cites W2103359683 @default.
• W4205372916 cites W2107726111 @default.
• W4205372916 cites W2126357802 @default.
• W4205372916 cites W2145339207 @default.
• W4205372916 cites W2161742217 @default.
• W4205372916 cites W2164655924 @default.
• W4205372916 cites W2168405694 @default.
• W4205372916 cites W2257979135 @default.
• W4205372916 cites W2344786740 @default.
• W4205372916 cites W2478611617 @default.
• W4205372916 cites W3015645915 @default.
• W4205372916 cites W3123085760 @default.
• W4205372916 cites W3124244238 @default.
• W4205372916 cites W3127969248 @default.
• W4205372916 cites W3128776646 @default.
• W4205372916 cites W3173765301 @default.
• W4205372916 cites W3174671941 @default.
• W4205372916 cites W3179886952 @default.
• W4205372916 cites W3186061320 @default.
• W4205372916 cites W3190469032 @default.
• W4205372916 cites W3198493423 @default.
• W4205372916 doi "https://doi.org/10.1016/j.patcog.2022.108543" @default.
• W4205372916 hasPublicationYear "2022" @default.
• W4205372916 type Work @default.
• W4205372916 citedByCount "6" @default.
• W4205372916 countsByYear W42053729162023 @default.
• W4205372916 crossrefType "journal-article" @default.
• W4205372916 hasAuthorship W4205372916A5014567045 @default.
• W4205372916 hasAuthorship W4205372916A5019708391 @default.
• W4205372916 hasAuthorship W4205372916A5054418515 @default.
• W4205372916 hasAuthorship W4205372916A5056339174 @default.
• W4205372916 hasAuthorship W4205372916A5087158377 @default.
• W4205372916 hasConcept C10138342 @default.
• W4205372916 hasConcept C119857082 @default.
• W4205372916 hasConcept C131562839 @default.
• W4205372916 hasConcept C134306372 @default.
• W4205372916 hasConcept C14036430 @default.
• W4205372916 hasConcept C149782125 @default.
• W4205372916 hasConcept C154945302 @default.
• W4205372916 hasConcept C162324750 @default.
• W4205372916 hasConcept C177148314 @default.
• W4205372916 hasConcept C33923547 @default.
• W4205372916 hasConcept C41008148 @default.
• W4205372916 hasConcept C78458016 @default.
• W4205372916 hasConcept C78508483 @default.
• W4205372916 hasConcept C86803240 @default.
• W4205372916 hasConcept C97541855 @default.
• W4205372916 hasConceptScore W4205372916C10138342 @default.
• W4205372916 hasConceptScore W4205372916C119857082 @default.
• W4205372916 hasConceptScore W4205372916C131562839 @default.
• W4205372916 hasConceptScore W4205372916C134306372 @default.
• W4205372916 hasConceptScore W4205372916C14036430 @default.
• W4205372916 hasConceptScore W4205372916C149782125 @default.
• W4205372916 hasConceptScore W4205372916C154945302 @default.
• W4205372916 hasConceptScore W4205372916C162324750 @default.
• W4205372916 hasConceptScore W4205372916C177148314 @default.
• W4205372916 hasConceptScore W4205372916C33923547 @default.
• W4205372916 hasConceptScore W4205372916C41008148 @default.
• W4205372916 hasConceptScore W4205372916C78458016 @default.
• W4205372916 hasConceptScore W4205372916C78508483 @default.
• W4205372916 hasConceptScore W4205372916C86803240 @default.
• W4205372916 hasConceptScore W4205372916C97541855 @default.
• W4205372916 hasFunder F4320321001 @default.
• W4205372916 hasFunder F4320321885 @default.
• W4205372916 hasLocation W42053729161 @default.
• W4205372916 hasOpenAccess W4205372916 @default.
• W4205372916 hasPrimaryLocation W42053729161 @default.
• W4205372916 hasRelatedWork W3016228233 @default.
• W4205372916 hasRelatedWork W3035574064 @default.
• W4205372916 hasRelatedWork W3122425648 @default.
• W4205372916 hasRelatedWork W3126577088 @default.
• W4205372916 hasRelatedWork W3175255688 @default.
• W4205372916 hasRelatedWork W3176836227 @default.
• W4205372916 hasRelatedWork W4311038708 @default.
• W4205372916 hasRelatedWork W4313641676 @default.
• W4205372916 hasRelatedWork W4319083788 @default.
• W4205372916 hasRelatedWork W4365600551 @default.
• W4205372916 hasVolume "125" @default.
• W4205372916 isParatext "false" @default.
• W4205372916 isRetracted "false" @default.
• W4205372916 workType "article" @default.