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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
24e86d762a
- Updated BBWN, BBWP, CCV, CV, CVI, EWMA, GKV, HLV, HV, Jvolty, JVOLTYN, MASSI, NATR, RSV, RV, RVI, TR, UI, VOV, VR, YZV indicators with documentation links. - Added documentation links for Aberration, Acceleration Bands, Andrews' Pitchfork, Adaptive Price Zone, ATR Bands, Bollinger Bands, Center of Gravity, Donchian Channels, Decay Min-Max Channel, Detrended Synthetic Price, EACP, EBSW, HOMOD, Jurik Volatility Bands, Keltner Channel, MA Envelope, Min-Max Channel, Price Channel, Regression Channels, Standard Deviation Channel, Stoller Average Range Channel, Super Trend Bands, Ultimate Bands, Ultimate Channel, VWAP Bands, and VWAP with Standard Deviation Bands.
47 lines
1.5 KiB
Plaintext
47 lines
1.5 KiB
Plaintext
// The MIT License (MIT)
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// © mihakralj
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//@version=6
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indicator("Bias (BIAS)", "BIAS", overlay=false)
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//@function Calculates the deviation of a signal from its moving average (BIAS).
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//@param src The source series.
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//@param len The lookback period for the SMA. Must be > 0.
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//@returns The BIAS value.
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//@optimized for performance and dirty data
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bias(series float src, int len) =>
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if len <= 0
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runtime.error("BIAS length must be greater than 0")
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var float sma_sum = 0.0
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var float[] sma_buffer = array.new_float(len, na)
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var int sma_head = 0
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var int sma_validCount = 0
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float sma_oldestVal = array.get(sma_buffer, sma_head)
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if not na(sma_oldestVal)
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sma_sum -= sma_oldestVal
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else if not na(src)
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sma_validCount +=1
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sma_sum += nz(src, 0.0)
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array.set(sma_buffer, sma_head, src)
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sma_head := (sma_head + 1) % len
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float movingAverage = na
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if sma_validCount >= len or bar_index + 1 >= len
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movingAverage := sma_sum / math.max(sma_validCount, 1)
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if bar_index < len -1
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movingAverage := sma_sum / math.max(bar_index + 1, 1)
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float bias_result = na
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if not na(movingAverage) and movingAverage != 0
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bias_result := (src - movingAverage) / movingAverage
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bias_result
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// ---------- Main loop ----------
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// Inputs
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i_source = input.source(close, "Source")
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i_length = input.int(20, "Length", minval=1)
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// Calculation
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biasValue = bias(i_source, i_length)
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// Plot
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plot(biasValue, "BIAS", color=color.yellow, linewidth=2)
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