Modular Filter (MODF)

// Licensed under the Apache License, Version 2.0
// © mihakralj
//@version=6
indicator("Modular Filter (MODF)", "MODF", overlay=true)

//@function Calculates Modular Filter (alexgrover)
//@param src Series to filter
//@param period Smoothing period for EMA alpha = 2/(period+1)
//@param beta Blend weight: 1.0 = smooth filter, 0.0 = trailing stop
//@param feedback Enable feedback loop (output fed back as input blend)
//@param fbWeight Feedback weighting: lower = smoother when feedback enabled
//@returns Modular Filter value
//@description Dual-path adaptive filter with upper/lower EMA bands and
//  conditional state selection. Beta controls blend between filter and
//  trailing stop behavior. Optional feedback loop for additional smoothing.
//  Author: alexgrover (TradingView, LuxAlgo CPO)
modf(series float src, simple int period, simple float beta, simple bool feedback, simple float fbWeight) =>
    float alpha = 2.0 / (period + 1)
    float oneMinusAlpha = 1.0 - alpha

    var float b = 0.0
    var float c = 0.0
    var float os = 0.0
    var float ts = 0.0

    // Input: optionally blend source with previous output (feedback)
    float a = feedback ? fbWeight * src + (1.0 - fbWeight) * nz(ts, src) : src

    // Upper band: EMA that snaps up to 'a' when a exceeds EMA
    float ema_b = alpha * a + oneMinusAlpha * nz(b, a)
    b := a > ema_b ? a : ema_b

    // Lower band: EMA that snaps down to 'a' when a falls below EMA
    float ema_c = alpha * a + oneMinusAlpha * nz(c, a)
    c := a < ema_c ? a : ema_c

    // Oscillator state: 1 = upper (bullish), 0 = lower (bearish)
    os := a == b ? 1.0 : a == c ? 0.0 : os

    // Beta-weighted band combinations
    float upper = beta * b + (1.0 - beta) * c
    float lower = beta * c + (1.0 - beta) * b

    // Final output: state-selected weighted band
    ts := os * upper + (1.0 - os) * lower
    ts

// ---------- Main loop ----------

// Inputs
i_period   = input.int(14, "Period", minval=2)
i_beta     = input.float(0.8, "Beta", minval=0.0, maxval=1.0, step=0.1)
i_feedback = input.bool(false, "Feedback")
i_fbWeight = input.float(0.5, "Feedback Weight", minval=0.01, maxval=1.0, step=0.05)
i_source   = input.source(close, "Source")

// Calculation
result = modf(i_source, i_period, i_beta, i_feedback, i_fbWeight)

// Plot
plot(result, "MODF", color=color.yellow, linewidth=2)