feat: TA-Lib parity — 19 standalone indicators (DM components, price transforms, ROC/LinReg/MACD/SAR variants, Hilbert outputs) (#148)
Closes the remaining TA-Lib function-name gap by shipping each missing or bundled-only function as a real, standalone, fully-covered indicator. 19 new indicators across 5 families; mod-count 295 -> 314. ### Trend & Directional — Directional Movement components - `PlusDm` (`PLUS_DM`), `MinusDm` (`MINUS_DM`) — Wilder-smoothed ±DM. - `PlusDi` (`PLUS_DI`), `MinusDi` (`MINUS_DI`) — `100·smoothed(±DM)/ATR`. - `Dx` (`DX`) — `100·|+DI−−DI|/(+DI+−DI)`. ### Price Statistics - `AvgPrice` (`AVGPRICE`) — `(O+H+L+C)/4`. - `MidPoint` (`MIDPOINT`) — `(max+min)/2` of a scalar series over N. - `MidPrice` (`MIDPRICE`) — `(highestHigh+lowestLow)/2` over N. - `LinRegIntercept` (`LINEARREG_INTERCEPT`) — OLS intercept. - `Tsf` (`TSF`) — time series forecast `a + b·period`. ### Momentum Oscillators - `Rocp` (`ROCP`), `Rocr` (`ROCR`), `Rocr100` (`ROCR100`) — ROC ratio forms. ### Trailing Stops - `SarExt` (`SAREXT`) — Parabolic SAR with start value, reversal offset, separate long/short acceleration, signed output. ### Trend & Directional — MACD variants - `MacdFix` (`MACDFIX`) — MACD fixed 12/26. - `MacdExt` (`MACDEXT`) — MACD with a selectable moving-average type per line (new public `MaType` enum: SMA/EMA/WMA/DEMA/TEMA/TRIMA). ### Ehlers / Cycle (DSP) — Hilbert transform outputs - `HtPhasor` (`HT_PHASOR`) — in-phase / quadrature components. - `HtDcPhase` (`HT_DCPHASE`) — dominant-cycle phase (degrees). - `HtTrendMode` (`HT_TRENDMODE`) — trend (1) vs cycle (0) classification. Each indicator ships the full chain: core + every-branch unit tests, Python / Node / WASM bindings, fuzz coverage, README counter + family rows, CHANGELOG. `cargo test`, doctests, `clippy -D warnings`, `npm test` and pytest all green locally; mod-count == lib-block == README counter (314), FAMILIES total 309.
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kingchenc
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//! Time Series Forecast (TSF).
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use std::collections::VecDeque;
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use crate::error::{Error, Result};
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use crate::traits::Indicator;
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/// Time Series Forecast (`TSF`): the rolling least-squares line projected one bar
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/// past the window.
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///
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/// Over the last `period` inputs, indexed `x = 0, 1, …, period − 1`, it fits
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/// `y = a + b·x` by ordinary least squares and reports the line's value at
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/// `x = period` (one step beyond the most recent point):
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///
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/// ```text
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/// b (slope) = (n·Σxy − Σx·Σy) / (n·Σxx − (Σx)²)
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/// a (intercept) = (Σy − b·Σx) / n
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/// TSF = a + b·period
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/// ```
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///
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/// Where [`LinearRegression`](crate::LinearRegression) evaluates the fit at the
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/// current bar (`a + b·(period − 1)`), `TSF` advances it one further bar, giving a
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/// trend-following one-step-ahead forecast. Each update is O(1).
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///
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/// # Example
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///
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/// ```
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/// use wickra_core::{Indicator, Tsf};
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///
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/// let mut indicator = Tsf::new(14).unwrap();
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/// let mut last = None;
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/// for i in 0..80 {
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/// last = indicator.update(f64::from(i));
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/// }
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/// assert!(last.is_some());
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/// ```
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#[derive(Debug, Clone)]
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pub struct Tsf {
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period: usize,
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window: VecDeque<f64>,
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sum_x: f64,
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denom: f64,
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sum_y: f64,
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sum_xy: f64,
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}
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impl Tsf {
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/// Construct a new rolling time-series forecast over `period` inputs.
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///
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/// # Errors
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/// Returns [`Error::InvalidPeriod`] if `period < 2` — a regression line is
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/// undefined for fewer than two points.
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pub fn new(period: usize) -> Result<Self> {
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if period < 2 {
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return Err(Error::InvalidPeriod {
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message: "time series forecast needs period >= 2",
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});
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}
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let n = period as f64;
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let sum_x = n * (n - 1.0) / 2.0;
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let sum_xx = (n - 1.0) * n * (2.0 * n - 1.0) / 6.0;
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Ok(Self {
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period,
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window: VecDeque::with_capacity(period),
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sum_x,
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denom: n * sum_xx - sum_x * sum_x,
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sum_y: 0.0,
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sum_xy: 0.0,
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})
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}
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/// Configured period.
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pub const fn period(&self) -> usize {
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self.period
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}
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}
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impl Indicator for Tsf {
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type Input = f64;
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type Output = f64;
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fn update(&mut self, value: f64) -> Option<f64> {
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if self.window.len() == self.period {
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let y0 = self.window.pop_front().expect("non-empty");
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self.sum_xy = self.sum_xy - self.sum_y + y0;
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self.sum_y -= y0;
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}
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let k = self.window.len() as f64;
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self.window.push_back(value);
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self.sum_y += value;
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self.sum_xy += k * value;
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if self.window.len() < self.period {
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return None;
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}
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let n = self.period as f64;
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let slope = (n * self.sum_xy - self.sum_x * self.sum_y) / self.denom;
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let intercept = (self.sum_y - slope * self.sum_x) / n;
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Some(intercept + slope * n)
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}
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fn reset(&mut self) {
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self.window.clear();
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self.sum_y = 0.0;
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self.sum_xy = 0.0;
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}
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fn warmup_period(&self) -> usize {
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self.period
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}
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fn is_ready(&self) -> bool {
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self.window.len() == self.period
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}
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fn name(&self) -> &'static str {
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"TSF"
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use crate::traits::BatchExt;
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use approx::assert_relative_eq;
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#[test]
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fn rejects_short_period() {
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assert!(matches!(Tsf::new(1), Err(Error::InvalidPeriod { .. })));
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}
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#[test]
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fn accessors_report_config() {
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let tsf = Tsf::new(5).unwrap();
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assert_eq!(tsf.period(), 5);
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assert_eq!(tsf.name(), "TSF");
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assert_eq!(tsf.warmup_period(), 5);
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assert!(!tsf.is_ready());
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}
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#[test]
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fn reference_value() {
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// period 3 over [1, 2, 9]: fit y = 0 + 4x, forecast at x = 3 is 12.
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let mut tsf = Tsf::new(3).unwrap();
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let out: Vec<Option<f64>> = tsf.batch(&[1.0, 2.0, 9.0]);
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assert!(out[0].is_none());
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assert!(out[1].is_none());
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assert_relative_eq!(out[2].unwrap(), 12.0, epsilon = 1e-9);
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assert!(tsf.is_ready());
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}
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#[test]
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fn forecasts_a_clean_line_one_step_ahead() {
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// Window [10, 12, 14]: y = 10 + 2x, forecast at x = 3 is 16.
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let mut tsf = Tsf::new(3).unwrap();
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let out: Vec<Option<f64>> = tsf.batch(&[1.0, 10.0, 12.0, 14.0]);
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assert_relative_eq!(out[3].unwrap(), 16.0, epsilon = 1e-9);
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}
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#[test]
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fn reset_clears_state() {
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let mut tsf = Tsf::new(3).unwrap();
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let _ = tsf.batch(&[1.0, 2.0, 9.0]);
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assert!(tsf.is_ready());
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tsf.reset();
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assert!(!tsf.is_ready());
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assert_eq!(tsf.update(1.0), None);
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}
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}
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