feat: Family 06 Trend-Strength - 5 new directional/random-walk indicators (#44)

* feat(adxr): add Wilder Average Directional Movement Index Rating ADXR is the trend-strength smoother Wilder published alongside ADX in *New Concepts in Technical Trading Systems* (1978): ADXR_t = (ADX_t + ADX_{t - (period - 1)}) / 2 The lookback length is the same period that feeds the underlying ADX. Because the older ADX is period - 1 bars stale, ADXR responds more slowly than ADX and is the canonical metric for comparing trend-strength across instruments. Implementation reuses the existing wickra_core::Adx engine plus a period-length ring of past ADX values; warmup is 3 * period - 1 (41 for period = 14). Bindings: Python PyAdxr (PyArray1 batch), Node AdxrNode (number scalar), WASM WasmAdxr. Fuzz target covers the candle-input path. Python + Node streaming-vs-batch tests parametrised, plus a pure-uptrend reference value (ADXR == 100 when ADX saturates at 100). Criterion bench added under crates/ wickra/benches/indicators.rs. README family table and indicator counter updated (71 -> 72). * feat(rwi): add Mike Poulos Random Walk Index RWI compares actual price displacement to what a random walk would produce over the same horizon: for each lookback i in [2, period], RWI_High_t(i) = (high_t - low_{t-i+1}) / (ATR_i(t) * sqrt(i)) RWI_Low_t(i) = (high_{t-i+1} - low_t) / (ATR_i(t) * sqrt(i)) Per-bar output is the maximum across lookbacks for each direction; a reading > 1 means the trend beats random-walk noise, > 2 is the typical strong-trend threshold. Multi-output (high, low). period must be >= 2 (the shortest meaningful lookback); period < 2 returns InvalidPeriod. Warmup = period (e.g. 14 for the standard default). Bindings: Python PyRwi (PyArray2 shape (n, 2)), Node RwiNode + RwiValue struct, WASM WasmRwi (Object/Reflect for update, Float64Array interleaved for batch). Fuzz target adds the candle input case. Python parametric streaming-vs-batch test and pure uptrend reference test (RWI_High dominates RWI_Low and exceeds 1). Node parametric streaming-vs-interleaved-batch test. Criterion bench under crates/wickra/benches/indicators.rs. README family table and indicator counter updated (72 -> 73). * feat(tii): add M.H. Pee Trend Intensity Index TII is a [0, 100] oscillator that asks 'what fraction of the recent SMA deviations are positive?'. The construction is dev_t = close_t - SMA(close, sma_period)_t SD_pos = sum of positive dev_t over the last dev_period bars SD_neg = sum of |negative dev_t| over the last dev_period bars TII = 100 * SD_pos / (SD_pos + SD_neg) Saturates at 100 on a pure uptrend (every close above the lagging SMA), at 0 on a pure downtrend, and returns the neutral mid-point 50 on a perfectly flat window. The output is clamped to [0, 100] as the rolling-sum subtraction loop can accumulate a few ULP of error on long histories. Canonical Pee parameters (sma_period=60, dev_period=30) wired as Python defaults; warmup is sma_period + dev_period - 1 (89 for the defaults). Bindings: Python PyTii (PyArray1 batch), Node TiiNode (scalar update + batch), WASM WasmTii via the two-arg wasm_scalar_indicator! macro. Fuzz target adds the scalar path. Python parametric streaming-vs-batch test plus pure-uptrend (TII == 100) and flat-market (TII == 50) reference tests. Node parametric streaming-vs-batch test. Criterion bench under crates/wickra/ benches/indicators.rs. README family table and indicator counter updated (73 -> 74). * feat(kst): add Pring Know Sure Thing oscillator KST is Martin Pring's long-horizon momentum gauge: four smoothed rate-of-change components combined with fixed weights (1, 2, 3, 4), plus an SMA signal line. RCMA_i = SMA(ROC(close, roc_i), sma_i) for i in 1..=4 KST = 1*RCMA_1 + 2*RCMA_2 + 3*RCMA_3 + 4*RCMA_4 Signal = SMA(KST, signal_period) Kst::classic() exposes Pring's recommended parameter set (roc = (10, 15, 20, 30), sma = (10, 10, 10, 15), signal = 9); warmup = max(roc_i + sma_i) + signal_period - 1 (53 for the classic parameters). All four parallel branches are fed unconditionally so they warm in lock-step. Bindings: Python PyKst (PyArray2 shape (n, 2)) with a KST.classic() staticmethod, Node KstNode + KstValue with a KST.classic() factory, WASM WasmKst with both new(...) and classic() constructors plus Object/Reflect for update and Float64Array for batch. Fuzz target adds the scalar multi-output path. Python tests gain a new MULTI_SCALAR section parametric over scalar-input/multi-output indicators, plus a classic-on-constant-series reference test. Node tests gain a KST entry in the multi-output section. Criterion benchmark added under crates/wickra/benches/indicators.rs. README family table and indicator counter updated (74 -> 75). * feat(wave-trend): add LazyBear Wave Trend Oscillator Two-line mean-reverting momentum gauge built from the typical price and three cascaded EMAs: ap = (high + low + close) / 3 esa = EMA(ap, channel_period) d = EMA(|ap - esa|, channel_period) ci = (ap - esa) / (0.015 * d) wt1 = EMA(ci, average_period) wt2 = SMA(wt1, signal_period) WaveTrend::classic() exposes LazyBear's defaults (channel = 10, average = 21, signal = 4); warmup is 2 * channel_period + average_period + signal_period - 3 (42 for the classic defaults). On a perfectly flat market the SMA-seeded EMA introduces a single-ULP drift between ap and esa, which on a tiny d would make the ratio explode to -1/0.015 = -66.67; a price-scaled flat-tolerance guard (d <= 16 * EPSILON * max(|esa|, 1)) collapses the channel index to 0 in that regime so both lines remain at zero. Bindings: Python PyWaveTrend (PyArray2 shape (n, 2)) with a WaveTrend.classic() staticmethod, Node WaveTrendNode + WaveTrendValue with a WaveTrend.classic() factory, WASM WasmWaveTrend with both new(...) and classic() constructors. Fuzz target adds the candle multi-output path (sorted alphabetically). Python parametric streaming-vs-batch test plus a flat-market reference test. Node parametric streaming-vs-interleaved-batch test. Criterion bench under crates/wickra/benches/indicators.rs. README family table and indicator counter updated (75 -> 76). * fix(family-06): re-add KST::classic() factory + drop dup fuzz block Family-06 PR's tests call ta.KST.classic() / wickra.KST.classic() — main's KST binding shipped without the static factory. Add classic() in Python (staticmethod) and Node (napi factory); WASM already had it. Also drop the duplicate Kst::classic().unwrap() block in fuzz/indicator_update.rs that the merge left behind (main's API no longer returns Result). * test(rwi): drop dead count==0 guard The loop `for i in 2..=period` makes `count = tr_end - tr_start = i - 1` which is always >= 1, so the `if count == 0 { continue; }` branch was unreachable defensive code that codecov flagged on the family-06 PR.
2026-05-25 19:00:13 +02:00
parent 54194a4ff8
commit 6287bd48c1
18 changed files with 2141 additions and 85 deletions
@@ -19,8 +19,8 @@ use wickra_core::{
    ElderImpulse, Ema, Frama, HistoricalVolatility, Hma, Indicator, Jma, Kama, Kst, LaguerreRsi,
    LinRegAngle, LinRegChannel, LinRegSlope, LinearRegression, MaEnvelope, MacdIndicator,
    McGinleyDynamic, Mom, Pmo, Ppo, Roc, Rsi, RviVolatility, Sma, Smma, StandardErrorBands, Stc,
-    StdDev, StochRsi, T3, Tema, Trima, Trix, Tsi, UlcerIndex, VerticalHorizontalFilter, Vidya, Wma,
-    ZScore, ZeroLagMacd, Zlema,
+    StdDev, StochRsi, T3, Tema, Tii, Trima, Trix, Tsi, UlcerIndex, VerticalHorizontalFilter, Vidya,
+    Wma, ZScore, ZeroLagMacd, Zlema,
 };

 /// Drive a single streaming + batch run through one scalar indicator. Marked
@@ -65,6 +65,7 @@ fuzz_target!(|data: Vec<f64>| {
    drive(|| Cmo::new(14).unwrap(), &data);
    drive(|| Tsi::new(25, 13).unwrap(), &data);
    drive(|| Pmo::new(35, 20).unwrap(), &data);
+    drive(|| Tii::new(60, 30).unwrap(), &data);
    drive(|| StochRsi::new(14, 14).unwrap(), &data);
    drive(|| Dpo::new(14).unwrap(), &data);
    drive(|| Ppo::new(12, 26).unwrap(), &data);
@@ -23,14 +23,14 @@

 use libfuzzer_sys::fuzz_target;
 use wickra_core::{
-    AccelerationBands, AcceleratorOscillator, Adl, Adx, Alligator, Aroon, AroonOscillator, Atr,
-    AtrBands, AtrTrailingStop, AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower,
+    AccelerationBands, AcceleratorOscillator, Adl, Adx, Adxr, Alligator, Aroon, AroonOscillator,
+    Atr, AtrBands, AtrTrailingStop, AwesomeOscillator, AwesomeOscillatorHistogram, BalanceOfPower,
    BatchExt, Candle, Cci, ChaikinMoneyFlow, ChaikinOscillator, ChaikinVolatility, ChandeKrollStop,
    ChandelierExit, ChoppinessIndex, Donchian, EaseOfMovement, Evwma, ForceIndex, FractalChaosBands,
    GarmanKlassVolatility, HurstChannel, Indicator, Inertia, Keltner, MassIndex, MedianPrice, Mfi,
-    Natr, Obv, ParkinsonVolatility, Pgo, Psar, RogersSatchellVolatility, RollingVwap, Rvi, Smi,
+    Natr, Obv, ParkinsonVolatility, Pgo, Psar, RogersSatchellVolatility, RollingVwap, Rvi, Rwi, Smi,
    StarcBands, Stochastic, SuperTrend, TrueRange, TtmSqueeze, TypicalPrice, UltimateOscillator,
-    VolumePriceTrend, Vortex, Vwap, VwapStdDevBands, Vwma, WeightedClose, WilliamsR,
+    VolumePriceTrend, Vortex, Vwap, VwapStdDevBands, Vwma, WaveTrend, WeightedClose, WilliamsR,
    YangZhangVolatility,
 };

@@ -94,10 +94,13 @@ fuzz_target!(|data: Vec<f64>| {

    // --- Trend & Directional ---
    drive(|| Adx::new(14).unwrap(), &candles);
+    drive(|| Adxr::new(14).unwrap(), &candles);
    drive(|| Aroon::new(14).unwrap(), &candles);
    drive(|| Alligator::new(13, 8, 5).unwrap(), &candles);
    drive(|| AroonOscillator::new(14).unwrap(), &candles);
    drive(|| Vortex::new(14).unwrap(), &candles);
+    drive(|| Rwi::new(14).unwrap(), &candles);
+    drive(|| WaveTrend::classic().unwrap(), &candles);
    drive(|| MassIndex::new(9, 25).unwrap(), &candles);
    drive(|| ChoppinessIndex::new(14).unwrap(), &candles);