289 lines
8.3 KiB
Go
289 lines
8.3 KiB
Go
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package talib
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// Beta - Beta: the slope of a least-squares linear regression of one series' percentage returns (y, from inReal1) against another's (x, from inReal0) over a rolling window.
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// Measures how much a security moves relative to a market index.
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// Beta = 1 moves with the index; < 1 less volatile, > 1 more volatile.
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//
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// @param inReal0: Series whose returns are the regression x (market/index)
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// @param inReal1: Series whose returns are the regression y (security)
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// @param optInTimePeriod: Rolling window length (number of returns) for the regression sums; default is 5 (1-100000)
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//
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// @return: regression slope of inReal1-returns on inReal0-returns
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func Beta(inReal0, inReal1 []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal0) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal0))
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)
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if retCode := beta(
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startIdx,
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endIdx,
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inReal0,
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inReal1,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// Correl - Pearson's correlation coefficient (r) between two input series over a rolling window of optInTimePeriod bars.
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// Measures how linearly the two series move together. r near +1: strong positive co-movement; near -1: strong inverse; near 0: no linear relationship.
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//
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// r = (sumXY - sumX*sumY/n) / sqrt((sumX2 - sumX^2/n) * (sumY2 - sumY^2/n)), n = optInTimePeriod, sums over the window
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//
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// Note: When the correlation is undefined for a window (for example a constant series), the output is 0 rather than an error or NaN.
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//
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// @param inReal0: First data series (X)
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// @param inReal1: Second data series (Y)
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// @param optInTimePeriod: Rolling window length (number of bars) for the correlation sums; default is 30 (2-100000)
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//
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// @return: Correlation coefficient r in [-1, 1]
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func Correl(inReal0, inReal1 []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal0) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal0))
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)
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if retCode := correl(
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startIdx,
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endIdx,
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inReal0,
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inReal1,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// LinearReg - Least-squares straight-line fit over the last optInTimePeriod bars, reported as the fitted line value at the window endpoint (b + m*(period-1)).
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//
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// @param inReal: Input series
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// @param optInTimePeriod: Number of bars (period) for the regression; default is 14 (2-100000)
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//
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// @return: Fitted line value at the window endpoint
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func LinearReg(inReal []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := linearreg(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// LinearRegAngle - The angle, in degrees, of the least-squares best-fit line over the last N points.
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// It is the LINEARREG_SLOPE value passed through atan and converted to degrees.
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// Positive angle = rising fit line, negative = falling; magnitude reflects steepness.
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//
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// m = (N·SumXY − SumX·SumY) / (SumX² − N·SumXSqr), with SumX=N(N−1)/2, SumXSqr=N(N−1)(2N−1)/6; angle = atan(m)·(180/π)
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func LinearRegAngle(inReal []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := linearreg_angle(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// LinearRegIntercept - Returns the y-intercept (b) of the least-squares regression line fitted over the last optInTimePeriod values.
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// Part of the linear-regression family (LINEARREG, SLOPE, ANGLE, TSF).
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//
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// Fit y = b + m·x over the window with x = bars-ago (x=0 is the current bar, x=period-1 the oldest).
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// With SumX = period(period-1)/2, SumXSqr = period(period-1)(2·period-1)/6, Divisor = SumX² − period·SumXSqr:
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// m = (period·SumXY − SumX·SumY) / Divisor
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// b = (SumY − m·SumX) / period ← output
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func LinearRegIntercept(inReal []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := linearreg_intercept(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// LinearRegSlope - Slope 'm' of the least-squares best-fit line (y = b + m*x) over the last optInTimePeriod bars.
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// Reports the per-bar rate of change of the fitted trend line.
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// Positive slope = rising trend, negative = falling; magnitude is price change per bar.
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//
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// m = (n·SumXY − SumX·SumY) / Divisor
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// SumX = n(n−1)/2, SumXSqr = n(n−1)(2n−1)/6, Divisor = SumX² − n·SumXSqr
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// SumXY = Σ i·y[today−i], SumY = Σ y[today−i], i=0..n−1, n=period, y=inReal
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func LinearRegSlope(inReal []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := linearreg_slope(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// StdDev - Rolling standard deviation of a series over a window, scaled by a deviations multiplier.
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// Delegates to VAR, then takes the square root.
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//
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// Note: Uses population variance (divides by the period, not period minus one), so results differ slightly from the sample standard deviation used by some tools.
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// @param inReal: Input series
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// @param optInTimePeriod: Number of bars (period) for the rolling window; default is 5 (2-100000)
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// @param optInNbDev: Multiplier for the standard deviation; default is 1.0 (0.0-500.0)
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//
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// @return: Rolling standard deviation scaled by optInNbDev
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func StdDev(inReal []float64, optInTimePeriod int, optInNbDev float64) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := stddev(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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optInNbDev,
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// TSF - Time Series Forecast: fits a least-squares linear regression line over the last N bars and projects it one x-step beyond talib.LinearReg.
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// Same regression as talib.LinearReg but evaluated at x=period instead of x=period-1.
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//
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// Fit y=b+mx over window (x=0..N-1): m = (NSumXY - SumXSumY)/(SumX^2 - NSumXSqr), b = (SumY - mSumX)/N; output = b + mN.
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// With SumX=N(N-1)/2, SumXSqr=N(N-1)(2N-1)/6.
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func TSF(inReal []float64, optInTimePeriod int) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := tsf(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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// Var - Rolling population variance of a real series over a given period.
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// Measures dispersion of values around their mean. Higher values indicate greater dispersion; 0 means constant input.
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//
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// Var = (SumX2 - SumX^2/n) / n, n = optInTimePeriod, sums over the window
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//
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// Note: Computes population variance (divides by the period), not the sample variance (n-1) used by some definitions.
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// The deviation-count parameter is accepted but has no effect on the result.
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func Var(inReal []float64, optInTimePeriod int, optInNbDev float64) []float64 {
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var (
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startIdx int32
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endIdx = int32(len(inReal) - 1)
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outBegIdx int32
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outNBElement int32
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outReal = make([]float64, len(inReal))
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)
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if retCode := variance(
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startIdx,
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endIdx,
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inReal,
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int32(optInTimePeriod),
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optInNbDev,
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&outBegIdx,
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&outNBElement,
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outReal,
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); retCode != 0 {
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return nil
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}
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return outReal
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}
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