Optimize SUMX with SUMMARIZE

Thank you for your input. I have optimised to the below. This took it from 11-15 seconds to 6-7 seconds. I will try to factor out  DIVIDE([_YTDLC],[_YTDQ],0) and DIVIDE([_PYDC],[_PYQ],0) in separate columns as they happen twice in the final calculation, but I was hoping my entire approach was wrong and I can get some major performance increase. When checking the Server Timings output, I have a lot of rows (50k) materialised multiple times (5 times). 

PriceImpactLC:=
VAR maxDate = MAX( Dim_Date[FiscalDate] )
VAR maxYear = YEAR( maxDate )
VAR _datesytd =
    DATESYTD(Dim_Date[FiscalDate])
VAR _py =
    PARALLELPERIOD(Dim_Date[FiscalDate],-1,YEAR)

RETURN SUMX(
        ADDCOLUMNS(
            ADDCOLUMNS(
                CALCULATETABLE(
                SUMMARIZE(
                    Fact_Pricing,
                    Fact_Pricing[Matched],
		            Dim_Company[RegionTxt],
                    Fact_Pricing[CorporateMarket],
                    Fact_Pricing[CompanySurrId],
                    Fact_Pricing[MaterialSurrId],
                    Fact_Pricing[DocumentCurrency],
                    Fact_Pricing[LocalCurrency]
                ), FILTER(ALL(Dim_Date), Dim_Date[FiscalDate]<=maxDate)),
		    "_YTDQ", CALCULATE(SUM(Fact_Pricing[SalesQuantity]),_datesytd),
            "_PYQ",CALCULATE(SUM(Fact_Pricing[SalesQuantity]),_py),
            "_YTDLC", CALCULATE( SUM(Fact_Pricing[NetBillingRevenueLC]), _datesytd),
            "_YTDDC", CALCULATE( SUM(Fact_Pricing[NetBillingRevenueDC]), _datesytd),
            "_PYDC", CALCULATE( SUM(Fact_Pricing[NetBillingRevenueDC]), _py )
            ),
         "_PriceImpact", (DIVIDE([_YTDLC],[_YTDQ],0)-DIVIDE(DIVIDE([_YTDLC],[_YTDQ],0),1+DIVIDE(DIVIDE([_YTDDC],[_YTDQ],0)-DIVIDE([_PYDC],[_PYQ],0),DIVIDE([_PYDC],[_PYQ],0),0),0))*[_YTDQ]
        ),
		[_PriceImpact]
        )