With　the　development　of　artificial　intelligence,　more　and　more　financial　practitioners　apply　deep　reinforcement　learning　to　financial　trading　strategies.　However,　it　is　difficult　to　extract　accurate　features　due　to　the　characteristics　of　considerable　noise,　highly　non-stationary,　and　non-linearity　of　single-scale　time　series,　which　makes　it　hard　to　obtain　high　returns.　In　this　paper,　we　extract　a　multi-scale　feature　matrix　on　multiple　time　scales　of　financial　time　series,　according　to　the　classic　financial　theory-Chan　Theory,　and　put　forward　to　an　approach　of　multi-scale　stroke　deep　deterministic　policy　gradient　reinforcement　learning　model　(MSSDDPG)　to　search　for　the　optimal　trading　strategy.　We　carried　out　experiments　on　the　datasets　of　the　Dow　Jones,　S&P　500　of　U.S.　stocks,　and　China＇s　CSI　300　SSE　Composite,　evaluate　the　performance　of　our　approach　compared　with　turtle　trading　strategy,　Deep　Q-learning　(DQN)　reinforcement　learning　strategy,　and　deep　deterministic　policy　gradient　(DDPG)　reinforcement　learning　strategy.　The　result　shows　that　our　approach　gets　the　best　performance　in　China　CSI　300　SSE　Composite,　and　get　an　outstanding　result　in　Dow　Jones,　S&P　500　of　U.S.　©　2022　ACM.