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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
35a6702b06
Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
41 lines
1.4 KiB
Plaintext
41 lines
1.4 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("Linear Trend Moving Average (LTMA)", "LTMA", overlay=true)
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//@function Calculates LTMA using dual cascaded EMAs with linear trend extrapolation
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//@param source Series to smooth
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//@param period Lookback period (determines alpha = 2/(period+1))
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//@returns LTMA value: lag-corrected EMA (equivalent to DEMA = 2·EMA1 − EMA2)
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//@description Removes the EMA lag by estimating the per-bar slope from the spread
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// between two cascaded EMAs and projecting forward by exactly one lag interval.
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// EMA1 lags by τ = (1−α)/α bars; EMA1−EMA2 ≈ slope·τ; result = EMA1 + (EMA1−EMA2).
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// Initializing both EMAs to source on bar 1 gives zero warmup bias with no compensator needed.
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ltma(series float source, simple int period) =>
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float alpha = 2.0 / (period + 1)
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float beta = 1.0 - alpha
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var float ema1 = na
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var float ema2 = na
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float src = nz(source)
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if na(ema1)
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ema1 := src
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ema2 := src
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else
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ema1 := alpha * src + beta * ema1
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ema2 := alpha * ema1 + beta * ema2
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// slope = EMA1 − EMA2 ≈ slope_per_bar × lag
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// result = EMA1 + slope × 1.0 → 2·EMA1 − EMA2
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ema1 + (ema1 - ema2)
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// ---------- Main loop ----------
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i_period = input.int(14, "Period", minval=1)
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i_source = input.source(close, "Source")
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ltma_value = ltma(i_source, period=i_period)
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plot(ltma_value, "LTMA", color=color.yellow, linewidth=2) |