As　the　financialization　of　carbon　emission　right,　developing　effective　carbon　asset　trading　strategy　is　important　for　both　investors　and　regulators.　Traditional　trading　strategy　based　on　technical　analysis　is　hardly　profitable　due　to　high　complexity　inherent　in　carbon　markets.　In　order　to　increase　investment　returns　and　reduce　risk　in　carbon　market,　this　paper　introduces　PSO-VMD-DRL,　an　innovative　carbon　asset　trading　strategy　that　integrates　signal　decomposition　technology　and　deep　reinforcement　learning　(DRL).　Firstly,　the　carbon　price　is　decomposed　into　multiple　components　by　variational　mode　decomposition　(VMD)　optimized　through　particle　swarm　optimization　(PSO)　to　extract　frequency　features　and　reduce　noise.　Then　state　representation　of　the　environment　(state　space)　is　constructed　based　on　the　components　and　transaction　data.　Finally,　deep　reinforcement　learning　(PPO,　A2C,　DDPG)　are　performed　separately　based　on　state　space　to　learn　optimal　trading　strategy.　Empirical　studies　based　on　the　data　from　carbon　markets　of　Hubei　and　Guangzhou　validate　that　PSO-VMD-DRL　can　adapt　to　carbon　asset　with　the　better　profitability　and　risk　resistance.　The　PSO-VMD-DRL　outperform　the　other　comparison　strategies　with　superiority　in　all　evaluation　indices,　achieving　annual　return　of　21.39%,　17.67%,　25.50%　and　46.34%,　44.55%,　43.03%　in　carbon　market　of　Hubei　and　Guangzhou.　©　2023　Elsevier　Ltd