A deep Q-learning based algorithmic trading system for commodity futures markets

Massahi, Mahdi; Mahootchi, Masoud

doi:10.1016/j.eswa.2023.121711

Cited by 4 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The global transition to sustainable energy sources necessitates the development of mechanisms like green certificates (GCs) to incentivize renewable energy production. Scholars from China, Europe, America, and other regions have extensively researched and explored issues related to the market mechanisms and models of GCs [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] , technological innovations including blockchain and artificial intelligence platform technologies [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37] , policies and economic strategies and market changes 10,19,[38][39][40][41][42][43][44][45] .…”

Section: Literature Reviewmentioning

confidence: 99%

“…The application of reinforcement learning and Q-learning in financial market forecasting 33 , learning trading rules for specific financial assets 34 , and improving financial trading decisions 35 offers a new perspective for GC trading strategies. Particularly, deep Q-learning in the algorithmic trading system for the commodity futures market 36 and the design of a supply chain carbon allowance allocation auction based on multi-agent modeling and Q-learning 37 , 57 , 58 demonstrate the potential of AI technology in energy management and GC trading.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

He,

Wang,

Shi

et al. 2024

Sci Rep

View full text Add to dashboard Cite

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China’s green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.

show abstract