TradeStation: March 2025
In “Removing Moving Average Lag” in this issue, John Ehlers introduces a projected moving average (PMA) designed to remove the lag inherent in moving averages. He does this by adding the slope times half the length of the average to the average itself. A function labeled $PMA is provided for the calculations. A sample chart displaying the PMA, the PMA slope, and its prediction, as discussed in Ehlers’ article.
Code: Select all
Function: $PMA
{
TASC MAR 2025
Projected Moving Average ($PMA) Function
(C) 2024 John F. Ehlers
}
inputs:
Price( numericseries ),
Length( numericsimple ),
PMA( numericref ),
Slope( numericref ),
SMA( numericref );
variables:
Count( 0 ),
Sx( 0 ),
Sy( 0 ),
Sxx( 0 ),
Syy( 0 ),
Sxy( 0 );
Sx = 0;
Sy = 0;
Sxx = 0;
Syy = 0;
Sxy = 0;
for Count = 1 to Length
begin
Sx = Sx + Count;
Sy = Sy + Price[Count - 1];
Sxx = Sxx + Count * Count;
Syy = Syy + Price[Count - 1] * Price[Count - 1];
Sxy = Sxy + count*Price[Count - 1];
end;
Slope = -(Length * Sxy - Sx * Sy) / (Length * Sxx - Sx * Sx);
SMA = Sy / Length;
PMA = SMA + Slope * Length / 2;
//Function Return Value
$PMA = 1;
Indicator: Projected Moving Average (PMA)
{
TASC MAR 2025
Projected Moving Average (PMA)
(C) 2024 John F. Ehlers
}
inputs:
Length( 20 );
variables:
ReturnValue( 0 ),
PMA( 0 ),
Slope( 0 ),
SMA( 0 ),
Predict( 0 );
ReturnValue = $PMA(Close, Length, PMA, Slope, SMA);
Predict = PMA + .5 * (Slope - Slope[2])*Length;
Plot1( PMA, "PMA" );
Plot2( Predict, "Predict" );
//Plot3( SMA, "SMA" )
Indicator: PMA Slope and Prediction
{
TASC MAR 2025
PMA Slope and Its Prediction
(C) 2024 John F. Ehlers
}
inputs:
Length( 20 );
variables:
ReturnValue( 0 ),
PMA( 0 ),
Slope( 0 ),
SMA( 0 ),
Predict( 0 );
ReturnValue = $PMA(Close, Length, PMA, Slope, SMA);
Predict = 1.5 * Slope - .5 * Slope[4];
Plot1( Slope, "Slope" );
Plot2( 0, "Zero Line" );
Plot3( Predict, "Predict" );
