Kalman Filter is a variety of recursive filters. To assess the system status as of the current operation tact, it needs a status assessment (as the system status assessment and the error assessment of defining this status) at the preceding operation tact, and measuring at the current tact. This property distinguishes it from packet filters requiring to know at the current operation tact the history of measurements and/or assessments.

More about Kalman Filter.

Calculations:

Kalman[i] = Error + Velocity[i]

where:

Error = Kalman[i-1] + Distance * ShK
Velocity[i] = Velocity[i-1] + Distance * K / 100
Distance = Price[i] - Kalman[i-1]
ShK = sqrt(Sharpness * K / 100)

Code:

//@version=5
indicator(‘Kalman Filter [Loxx]’, overlay = true, shorttitle=”KF [Loxx]”, timeframe=””, timeframe_gaps=true)

src = input(ohlc4)
Sharpness = input.float(1.0)
K = input.float(1.0)

greencolor = color.lime
redcolor = color.red

velocity = 0.0
kfilt = 0.0

Distance = src – nz(kfilt[1], src)
Error = nz(kfilt[1], src) + Distance * math.sqrt(Sharpness*K/ 100)
velocity := nz(velocity[1], 0) + Distance*K / 100
kfilt := Error + velocity

plot(kfilt, color=velocity > 0 ? greencolor : redcolor, linewidth = 2)