mirror of
https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
51 lines
1.9 KiB
Plaintext
51 lines
1.9 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("Arnaud Legoux Moving Average (ALMA)", "ALMA", overlay=true)
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//@function Calculates ALMA using Gaussian distribution weights
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//@param source Series to calculate ALMA from
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//@param period Lookback period - window size
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//@param offset Controls the Gaussian peak location (0 to 1)
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//@param sigma Controls the Gaussian distribution width/curve shape
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//@returns ALMA value, calculates from first bar using available data
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//@optimized Uses Gaussian weighting with O(n) complexity per bar due to lookback loop
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alma(series float source, simple int period, simple float offset=0.85, simple float sigma=6.0) =>
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if period <= 0
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runtime.error("Period must be greater than 0")
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if offset < 0.0 or offset > 1.0
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runtime.error("Offset must be between 0 and 1")
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if sigma <= 0.0
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runtime.error("Sigma must be greater than 0")
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int p = math.min(bar_index + 1, period)
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if p <= 1
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source
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else
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float m = (1.0 - offset) * (p - 1)
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float s = p / sigma
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float s2 = 2.0 * (s * s)
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float sum = 0.0
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float weight_sum = 0.0
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for i = 0 to p - 1
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float price = source[i]
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if not na(price)
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float diff = i - m
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float weight = math.exp(-(diff * diff) / s2)
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sum += price * weight
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weight_sum += weight
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nz(sum / weight_sum, source)
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(50, "Period", minval=1, tooltip="Number of bars used in the calculation")
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i_offset = input.float(0.85, "Offset", minval=0.0, maxval=1.0, step=0.01)
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i_sigma = input.float(6.0, "Sigma", minval=0.1, maxval=20.0, step=0.1)
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i_source = input.source(close, "Source")
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// Calculation
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alma_value = alma(i_source, i_period, i_offset, i_sigma)
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// Plot
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plot(alma_value, "ALMA", color=color.yellow, linewidth=2)
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