feat: add 19 indicators for external feature-extractor coverage (377 -> 396) (#175)

Adds 19 streaming indicators so an external trading-bot feature extractor can replace its hand-built features with native, batch/streaming-equivalent ones. Each is a real gap (verified against the existing catalogue), production-only, with full Python/Node/WASM bindings, fuzz drivers, and tests. Five commits, one per family group; counter 377 -> 396.

## What's added

**Price Statistics (6)** — `LogReturn`, `RealizedVolatility` (raw quadratic variation, the un-annualised counterpart to `HistoricalVolatility`), `RollingQuantile`, `RollingIqr`, `RollingPercentileRank`, `SpreadAr1Coefficient` (pairwise AR(1) rho of the spread; complements `OuHalfLife`).

**Price Action (4)** — `CloseVsOpen`, `BodySizePct`, `WickRatio`, `HighLowRange` (stateless per-bar OHLC transforms).

**Regime / Trend / Jump labels (3)** — `TrendLabel` (sign of the rolling OLS slope), `JumpIndicator` (return outliers vs trailing volatility, measured as deviation from the trailing mean so steady drift is not flagged), `RegimeLabel` (volatility-quantile regime split).

**Risk / Performance (2)** — `WinRate`, `Expectancy` (R-multiple).

**Microstructure (4)** — `OrderFlowImbalance` (Cont-Kukanov-Stoikov OFI), `Vpin`, `AmihudIlliquidity`, `RollMeasure`. These reuse the existing `OrderBook` / `Trade` inputs (no new input type).

## Intentionally NOT added (already present, would be duplicates)

- **Population skew / kurtosis** — `skewness.rs` / `kurtosis.rs` are already population moments (divisor n).
- **Hurst R/S** — `hurst_exponent.rs` already uses rescaled-range (R/S) analysis.
- **Queue Imbalance** — exactly `OrderBookImbalanceTop1` ((bidSize - askSize) / (bidSize + askSize)).

## Verification

`cargo test -p wickra-core` (lib 3187 + doc 354), `cargo clippy --workspace --all-targets --all-features -D warnings` clean, node `npm run build && npm test` (471), python `pytest` (784). Counter consistent across `mod.rs`, lib block, README, and docs/README at 396.

bindings/python/python/wickra/__init__.py

@@ -25,6 +25,20 @@ from __future__ import annotations
 
 from ._wickra import (
     __version__,
+    Expectancy,
+    WinRate,
+    RegimeLabel,
+    JumpIndicator,
+    TrendLabel,
+    HighLowRange,
+    WickRatio,
+    BodySizePct,
+    CloseVsOpen,
+    RollingQuantile,
+    RollingPercentileRank,
+    RollingIqr,
+    RealizedVolatility,
+    LogReturn,
     TSF,
     LINEARREG_INTERCEPT,
     ROCR100,
@@ -189,6 +203,7 @@ from ._wickra import (
     PearsonCorrelation,
     Beta,
     PairwiseBeta,
+    SpreadAr1Coefficient,
     PairSpreadZScore,
     LeadLagCrossCorrelation,
     Cointegration,
@@ -351,6 +366,7 @@ from ._wickra import (
     FibExtension,
     FibRetracement,
     # Microstructure: order book
+    OrderFlowImbalance,
     OrderBookImbalanceTop1,
     OrderBookImbalanceTopN,
     OrderBookImbalanceFull,
@@ -358,6 +374,9 @@ from ._wickra import (
     QuotedSpread,
     DepthSlope,
     # Microstructure: trade flow
+    RollMeasure,
+    AmihudIlliquidity,
+    Vpin,
     SignedVolume,
     CumulativeVolumeDelta,
     TradeImbalance,
@@ -430,6 +449,20 @@ from ._wickra import (
 )
 
 __all__ = [
+    "Expectancy",
+    "WinRate",
+    "RegimeLabel",
+    "JumpIndicator",
+    "TrendLabel",
+    "HighLowRange",
+    "WickRatio",
+    "BodySizePct",
+    "CloseVsOpen",
+    "RollingQuantile",
+    "RollingPercentileRank",
+    "RollingIqr",
+    "RealizedVolatility",
+    "LogReturn",
     "TSF",
     "LINEARREG_INTERCEPT",
     "ROCR100",
@@ -595,6 +628,7 @@ __all__ = [
     "PearsonCorrelation",
     "Beta",
     "PairwiseBeta",
+    "SpreadAr1Coefficient",
     "PairSpreadZScore",
     "LeadLagCrossCorrelation",
     "Cointegration",
@@ -757,6 +791,7 @@ __all__ = [
     "FibExtension",
     "FibRetracement",
     # Microstructure: order book
+    "OrderFlowImbalance",
     "OrderBookImbalanceTop1",
     "OrderBookImbalanceTopN",
     "OrderBookImbalanceFull",
@@ -764,6 +799,9 @@ __all__ = [
     "QuotedSpread",
     "DepthSlope",
     # Microstructure: trade flow
+    "RollMeasure",
+    "AmihudIlliquidity",
+    "Vpin",
     "SignedVolume",
     "CumulativeVolumeDelta",
     "TradeImbalance",

bindings/python/tests/test_new_indicators.py

@@ -45,6 +45,16 @@ def ohlcv() -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
 # --- Scalar (f64 -> f64) indicators ---------------------------------------
 
 SCALAR = [
+    (ta.Expectancy, (20,)),
+    (ta.WinRate, (20,)),
+    (ta.RegimeLabel, (5, 20)),
+    (ta.JumpIndicator, (20, 3.0)),
+    (ta.TrendLabel, (10,)),
+    (ta.RollingQuantile, (20, 0.5)),
+    (ta.RollingPercentileRank, (14,)),
+    (ta.RollingIqr, (14,)),
+    (ta.RealizedVolatility, (20,)),
+    (ta.LogReturn, (1,)),
     (ta.TSF, (14,)),
     (ta.LINEARREG_INTERCEPT, (14,)),
     (ta.ROCR100, (10,)),
@@ -167,6 +177,7 @@ def test_scalar_streaming_matches_batch(cls, args, sine_prices):
 # --- Two-series (asset, benchmark) indicators -----------------------------
 
 PAIR = [
+    (ta.SpreadAr1Coefficient, (40,)),
     (ta.GrangerCausality, (60, 1)),
     (ta.VarianceRatio, (60, 2)),
     (ta.BetaNeutralSpread, (20,)),
@@ -330,6 +341,12 @@ def test_relative_strength_streaming_matches_batch():
 # 6-tuple candle; the batch helper takes only the columns it needs.
 
 CANDLE_SCALAR = {
+    # Per-bar OHLC transforms (open matters). The streaming harness feeds
+    # open == close, so batch passes the close column in for open to match.
+    "HighLowRange": (lambda: ta.HighLowRange(), lambda ind, h, l, c, v: ind.batch(c, h, l, c)),
+    "WickRatio": (lambda: ta.WickRatio(), lambda ind, h, l, c, v: ind.batch(c, h, l, c)),
+    "BodySizePct": (lambda: ta.BodySizePct(), lambda ind, h, l, c, v: ind.batch(c, h, l, c)),
+    "CloseVsOpen": (lambda: ta.CloseVsOpen(), lambda ind, h, l, c, v: ind.batch(c, h, l, c)),
     "ThreeDrives": (
         lambda: ta.ThreeDrives(),
         lambda ind, h, l, c, v: ind.batch(c, h, l, c),
@@ -2707,6 +2724,16 @@ def test_fib_time_zones_reference():
     assert t.update((151.0, 155.0, 151.0, 151.0, 1.0, 4)) == pytest.approx((0.0, 1.0))
     assert t.update((151.0, 155.0, 151.0, 151.0, 1.0, 5)) == pytest.approx((1.0, 3.0))
 
+
+def test_spread_ar1_coefficient_reference():
+    t = ta.SpreadAr1Coefficient(20)
+    assert t.update(1.0, 1.0) is None
+    # Spread a - b grows by exactly 1 each bar (unit root) => rho == 1.
+    a = np.array([2.0 * i for i in range(40)])
+    b = np.array([float(i) for i in range(40)])
+    out = ta.SpreadAr1Coefficient(20).batch(a, b)
+    assert math.isclose(out[-1], 1.0, abs_tol=1e-9)
+
 # --- Lifecycle ------------------------------------------------------------
 
 
@@ -3024,6 +3051,7 @@ def test_orderbook_indicators_streaming_equals_batch():
         ta.Microprice,
         ta.QuotedSpread,
         ta.DepthSlope,
+        lambda: ta.OrderFlowImbalance(10),
     ):
         batch = make().batch(snaps)
         streamer = make()
@@ -3043,6 +3071,9 @@ def test_tradeflow_indicators_streaming_equals_batch():
         ta.SignedVolume,
         ta.CumulativeVolumeDelta,
         lambda: ta.TradeImbalance(5),
+        lambda: ta.Vpin(8.0, 5),
+        lambda: ta.AmihudIlliquidity(14),
+        lambda: ta.RollMeasure(14),
     ):
         batch = make().batch(price, size, is_buy)
         streamer = make()