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86fe32a682
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat> Co-authored-by: Warp <agent@warp.dev>
49 lines
1.6 KiB
Plaintext
49 lines
1.6 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Pascal Weighted Moving Average (PWMA)", "PWMA", overlay=true)
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//@function Calculates PWMA using Pascal's triangle coefficients as weights with compensator
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//@doc https://github.com/mihakralj/pinescript/blob/main/indicators/trends_FIR/pwma.md
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//@param source Series to calculate PWMA from
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//@param period Lookback period - FIR window size
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//@returns PWMA value, calculates from first bar using available data
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//@optimized Uses Pascal's triangle weighting with O(n) complexity per bar due to lookback loop
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pwma(series float source, simple int period) =>
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if period <= 0
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runtime.error("Period must be greater than 0")
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int p = math.min(bar_index + 1, period)
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var array<float> weights = array.new_float(1, 1.0)
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var int last_p = 1
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if last_p != p
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weights := array.new_float(p, 0.0)
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array.set(weights, 0, 1.0)
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if p > 1
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float prev_weight = 1.0
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for i = 1 to p - 1
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float curr_weight = prev_weight * (p - i) / i
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array.set(weights, i, curr_weight)
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prev_weight := curr_weight
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last_p := p
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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 w = array.get(weights, i)
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sum += price * w
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weight_sum += w
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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(10, "Period", minval=1)
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i_source = input.source(close, "Source")
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// Calculation
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pwma_value = pwma(i_source, i_period)
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// Plot
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plot(pwma_value, "PWMA", color=color.yellow, linewidth=2)
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