mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
108 lines
3.9 KiB
Plaintext
108 lines
3.9 KiB
Plaintext
// Licensed under the Apache License, Version 2.0
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// © mihakralj
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//@version=6
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indicator("Ehlers Correlation Cycle (CCOR)", "CCOR", overlay=false)
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//@function Computes Ehlers Correlation Cycle — extracts cycle phase via Pearson correlation of price
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// with cosine (Real) and negative-sine (Imag) reference waves of a presumed fixed period.
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// Converts to phasor angle with monotonic constraint, and derives market state.
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//@param source Series to analyze
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//@param period Presumed dominant cycle wavelength
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//@param threshold Angle rate-of-change threshold (degrees) for trend/cycle state detection
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//@returns [real, imag, angle, state] — correlation components, phasor angle, market state (+1/-1/0)
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//@reference John F. Ehlers, "Correlation As A Cycle Indicator" (Stocks & Commodities, TASC Jun 2020)
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//@optimized O(period) per bar for dual correlation loops; O(1) state variables
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ccor(series float source, simple int period, simple float threshold) =>
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if period <= 0
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runtime.error("Period must be greater than 0")
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if threshold <= 0
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runtime.error("Threshold must be greater than 0")
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var float prev_angle = 0.0
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float price = nz(source)
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// --- Correlate price with cosine wave (Real component) ---
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float sx_r = 0.0
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float sy_r = 0.0
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float sxx_r = 0.0
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float sxy_r = 0.0
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float syy_r = 0.0
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for count = 0 to period - 1
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float x = nz(source[count], price)
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float y = math.cos(2.0 * math.pi * count / float(period))
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sx_r += x
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sy_r += y
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sxx_r += x * x
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sxy_r += x * y
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syy_r += y * y
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float n = float(period)
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float denom_r = (n * sxx_r - sx_r * sx_r) * (n * syy_r - sy_r * sy_r)
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float real_val = denom_r > 0.0 ? (n * sxy_r - sx_r * sy_r) / math.sqrt(denom_r) : 0.0
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// --- Correlate price with negative sine wave (Imaginary component) ---
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float sx_i = 0.0
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float sy_i = 0.0
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float sxx_i = 0.0
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float sxy_i = 0.0
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float syy_i = 0.0
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for count = 0 to period - 1
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float x = nz(source[count], price)
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float y = -math.sin(2.0 * math.pi * count / float(period))
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sx_i += x
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sy_i += y
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sxx_i += x * x
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sxy_i += x * y
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syy_i += y * y
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float denom_i = (n * sxx_i - sx_i * sx_i) * (n * syy_i - sy_i * sy_i)
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float imag_val = denom_i > 0.0 ? (n * sxy_i - sx_i * sy_i) / math.sqrt(denom_i) : 0.0
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// --- Compute phasor angle (degrees) with quadrant resolution ---
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float angle = 0.0
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if imag_val != 0.0
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angle := 90.0 + math.todegrees(math.atan(real_val / imag_val))
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if imag_val > 0.0
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angle -= 180.0
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// --- Monotonic constraint: angle cannot go backward ---
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float saved_prev = prev_angle
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if angle < prev_angle
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angle := prev_angle
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prev_angle := angle
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// --- Market state detection ---
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// Small angle change → trending; large angle change → cycling
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float angle_change = math.abs(angle - saved_prev)
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int state = 0
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if angle_change < threshold and angle <= 0.0
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state := -1 // downtrend
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if angle_change < threshold and angle >= 0.0
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state := 1 // uptrend
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// state = 0 → cycling mode
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[real_val, imag_val, angle, state]
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(20, "Period", minval=2)
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i_threshold = input.float(9.0, "State Threshold (degrees)", minval=0.1, step=0.5)
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i_source = input.source(close, "Source")
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// Calculation
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[real_out, imag_out, angle_out, state_out] = ccor(i_source, i_period, i_threshold)
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// Scaled Real/Imag for display: map [-1,+1] → [-100,+100]
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float real_scaled = real_out * 100.0
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float imag_scaled = imag_out * 100.0
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// Colors based on Real vs Imag crossover
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color sig_color = real_scaled > imag_scaled ? color.new(color.green, 0) : color.new(color.red, 0)
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// Plots
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plot(real_scaled, "Real (×100)", color=sig_color, linewidth=2)
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plot(imag_scaled, "Imag (×100)", color=color.gray, linewidth=1)
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hline(0, "Zero", color=color.gray, linestyle=hline.style_dotted)
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