In　recent　years,　the　application　of　deep　reinforcement　learning　in　the　field　of　finance　has　received　a　lot　of　attention　from　researchers.　Due　to　the　non-stationary　characteristic　and　noisy　environment　in　the　financial　market,　single-scale　features　are　difficult　to　effectively　characterize　the　market　environment.　In　this　paper,　we　extract　multi-scale　volume-price　features　and　trend　features　from　financial　time　series　by　multi-scale　processing　and　propose　a　deep　reinforcement　learning　model　named　MSDDPG-R,　which　is　based　on　the　Deep　Deterministic　Policy　Gradient　(DDPG)　algorithm.　Specifically,　we　consider　the　trading　problem　as　a　Markov　Decision　Process　(MDP),　where　the　state　space　considering　both　single-scale　and　multi-scale　features　is　built　and　the　reward　function　combining　multi-scale　trend　features　is　used.　We　test　the　MSDDPG-R　model　on　the　datasets　of　SH000001,　SH000300,　SZ399905　and　S&P　500.　The　results　show　that　MSDDPG-R　model　performs　better　in　terms　of　return　and　risk　than　other　models　that　excludes　the　partial　components,　which　illustrates　the　validity　of　the　multi-scale　features　and　the　trend　reward　function.　©　2023　Copyright　held　by　the　owner/author(s).