Measure based on individual related values vs Calculated Column vs Power Query

// The Spend Data 1, 2, 3... should be
// one table as they have the same
// layout. Just use PQ to union them.
// You can add another column to this
// union that will tell you which table
// the data comes from.

// Based on this assumption:

[Gross Amount] =
// This measure could be slow as it
// needs to iterate the fact table. If you
// want to have a blasingly fast one, you need
// to precalculate the gross spend in
// Power Query. It'll then be the fastest
// since it will only have to sum up
// a column with the gross spend. YOu can't
// have a measure faster than that.
SUMX(
	FactTable,
	var Fee1 = RELATED( CRM[Fee % 1] )
	var Fee2 = RELATED( CRM[Fee % 2] )
	var EndDate = RELATED( CRM[End Date] )
	var Date_ = FactTable[Date]
	var Fee2IsBlank = ISBLANK( Fee2 )
	// TheFee codes the following logic:
	// IF Fee % 2 is BLANK, then Fee % 1, else
	// IF Date (from "UnionTable") is less or equal 
	// to Fee % 1 end Date then Fee % 1, else Fee % 2.
	var TheFee =
		var ChooseFee1 = (Date_ <= EndDate)
		var ChooseFee2 = 1 - ChooseFee1
		return
			Fee2IsBlank * Fee1
			+ (1 - Fee2IsBlank)
			* (
				Fee1 * ChooseFee1
				+ 
				Fee2 * ChooseFee2
			)
	var TheNetSpend = FactTable[NetSpend]
	var TheGrossSpend =
		DIVIDE( TheNetSpend, 1 - TheFee )
	return
		TheGrossSpend
)

[Gross Amount] =
SUMX(
	SUMMARIZE(
		FactTable,
		FactTable[Date],
		CRM[Fee % 1],
		CRM[Fee % 2],
		CRM[End Date]
	),
	var Fee1 = CRM[Fee % 1]
	var Fee2 = CRM[Fee % 2]
	var EndDate = CRM[End Date]
	var Date_ = FactTable[Date]
	var Fee2IsBlank = ISBLANK( Fee2 )
	var TheFee =
		var ChooseFee1 = (Date_ <= EndDate)
		var ChooseFee2 = 1 - ChooseFee1
		return
			Fee2IsBlank * Fee1
			+ (1 - Fee2IsBlank)
			* (
				Fee1 * ChooseFee1
				+ 
				Fee2 * ChooseFee2
			)
	var TheNetSpend = 
		CALCULATE( SUM( FactTable[NetSpend] ) )
	var TheGrossSpend = 
		DIVIDE( TheNetSpend,  1 - TheFee )
	return
		TheGrossSpend
)

You could try the version above... might be faster but it depends on the data in your tables.