feat: Family 01 Moving Averages — ALMA / McGinley / FRAMA / VIDYA / JMA / Alligator / EVWMA (#39)

* feat(alma): add Arnaud Legoux Moving Average

Gaussian-weighted moving average with configurable centre (offset in
[0, 1]) and kernel width (sigma > 0). Pre-computes normalised weights
at construction so each update is a single rolling window dot product.

Reference: Arnaud Legoux and Dimitrios Kouzis-Loukas, 2009.

Touchpoints:
- crates/wickra-core: alma.rs + mod.rs + lib.rs re-export
- bindings/python: PyAlma + __init__.py + test_new_indicators +
  test_known_values reference
- bindings/node: AlmaNode + index.d.ts/index.js + indicators.test.js
  factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers ALMA(9, 0.85, 6.0)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* feat(mcginley): add McGinley Dynamic moving average

John McGinley's self-adjusting moving average with the recurrence
MD + (price - MD) / (0.6 * period * (price / MD)^4). Speeds up when
price falls below the indicator and damps when price runs above the
indicator. Seeded with the simple average of the first period inputs.

Reference: McGinley, Technical Analysis of Stocks & Commodities, 1990.

Touchpoints:
- crates/wickra-core: mcginley_dynamic.rs + mod.rs + lib.rs re-export
- bindings/python: PyMcGinleyDynamic + __init__.py + test_new_indicators
  + test_known_values reference
- bindings/node: McGinleyDynamicNode (scalar macro) + index.d.ts/index.js
  + indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers McGinleyDynamic(10)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* feat(frama): add Fractal Adaptive Moving Average

Ehlers' FRAMA adapts its smoothing constant to the fractal dimension of
the recent window: tight tracking in trends, heavy smoothing in chop.
Uses the close-only variant where max/min over each window half drive
the dimension estimate. Period must be even (default 16).

Reference: Ehlers, Fractal Adaptive Moving Average, 2005.

Touchpoints:
- crates/wickra-core: frama.rs + mod.rs + lib.rs re-export
- bindings/python: PyFrama + __init__.py + test_new_indicators +
  test_known_values reference (constant series + uptrend tracking)
- bindings/node: FramaNode (scalar macro) + index.d.ts/index.js +
  indicators.test.js factory + reference value
- bindings/wasm: wasm_scalar_indicator! macro
- fuzz: indicator_update target covers Frama(16)
- crates/wickra/benches: bench_scalar entry
- README + CHANGELOG: Moving Averages row + Unreleased entry

* feat(vidya): add Variable Index Dynamic Average

Chande's VIDYA — an EMA whose alpha scales with |CMO(cmo_period)| / 100.
Strong directional momentum lifts the smoothing constant toward the
EMA-of-period rate; flat or choppy windows shrink it toward zero so
VIDYA coasts on its previous value. Two parameters: period (14) and
cmo_period (9). Reuses the existing wickra-core Cmo internally.

Reference: Chande, Stocks & Commodities, 1992.

Also fixes a silent gap from d37fbd1 (feat(frama)): the PyFrama Python
class wrapper and its add_class registration were dropped because the
two edits hit "File has not been read yet" errors that scrolled past
in a batch. Adds them here alongside VIDYA's bindings.

Touchpoints (VIDYA): vidya.rs + mod.rs + lib.rs re-export, PyVidya +
__init__.py + test_new_indicators + test_known_values reference,
VidyaNode (manual two-param binding) + index.d.ts/index.js +
indicators.test.js factory + reference, wasm_scalar_indicator! macro,
fuzz target, bench, README + CHANGELOG.

* feat(jma): add Jurik Moving Average

Three-stage filter reconstruction of Mark Jurik's adaptive MA (the
algorithm is proprietary; this is the form used by most open-source
ports since the 1999 TASC article). Parameters: period (14), phase in
[-100, 100] (0), power in 1..=4 (2). State is seeded by setting
e0 = JMA = first input so a constant input stream is reproduced exactly.

Touchpoints: jma.rs + mod.rs + lib.rs re-export, PyJma + __init__.py +
test_new_indicators + test_known_values reference, JmaNode (manual
three-param binding) + index.d.ts/index.js + indicators.test.js factory
+ reference, wasm_scalar_indicator! macro, fuzz target, bench, README +
CHANGELOG.

* feat(alligator): add Bill Williams Alligator

Three SMMA lines (Jaw / Teeth / Lips) over the median price
(high + low) / 2 with default periods 13 / 8 / 5. Multi-output
indicator returning AlligatorOutput { jaw, teeth, lips }. The
original chart variant shifts each line forward for display; we
publish the unshifted SMMA values and leave the visual shift to
the consumer.

Reference: Bill Williams, Trading Chaos, 1995.

Touchpoints: alligator.rs + mod.rs + lib.rs re-export, PyAlligator
(Candle input, returns 3-tuple, ndarray (n, 3) batch) + __init__.py
+ test_new_indicators + test_known_values reference, AlligatorNode +
AlligatorValue + index.d.ts/index.js + indicators.test.js multi
factory + reference, WasmAlligator (manual JsValue object) +
candle-fuzz target + README + CHANGELOG.

* feat(evwma): add Elastic Volume-Weighted Moving Average

Christian P. Fries' elastic recurrence where the smoothing weight is the
bar's volume relative to the running window total:

  V_sum_t = sum of volumes over the last period candles
  EVWMA_t = ((V_sum_t - v_t) * EVWMA_{t-1} + v_t * close_t) / V_sum_t

A bar whose volume is small barely moves the average; a bar that
dominates the window pulls it strongly toward that bar's close. Seeded
with the close of the first full window; holds its previous value if
the entire window has zero volume.

Reference: Fries, Wilmott Magazine, 2001.

Touchpoints: evwma.rs + mod.rs + lib.rs re-export, PyEvwma (close +
volume batch) + __init__.py + test_new_indicators CANDLE_SCALAR +
test_known_values reference, EvwmaNode + index.d.ts/index.js +
indicators.test.js candleScalar factory + reference, WasmEvwma,
candle-fuzz target + README + CHANGELOG.

* ci: Force local wheel install in Python jobs

Use --no-index --no-deps so the Python matrix installs the freshly
built wheel from dist/ and never falls back to PyPI. Previously pip
sometimes picked the released 0.2.x wheel on macOS / Windows when its
platform tag was a wider match than the local build, which made the
job test the released package and miss any new symbols added in the
PR (e.g. AttributeError: module 'wickra' has no attribute 'ALMA').
numpy is already installed by the preceding pip step, so --no-deps
is safe.

bindings/python/tests/test_known_values.py

@@ -66,6 +66,97 @@ def test_rsi_wilder_textbook_first_value():
     assert math.isclose(out[14], 70.464, abs_tol=0.05)
 
 
+def test_alma_constant_series_yields_the_constant():
+    # ALMA's Gaussian weights are normalised, so any constant series is
+    # reproduced exactly after warmup.
+    out = ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64))
+    assert np.all(np.isnan(out[:8]))
+    np.testing.assert_allclose(out[8:], 42.0, atol=1e-12)
+
+
+def test_alma_reference_value_period_3():
+    # ALMA(period=3, offset=0.85, sigma=6) on [10, 20, 30].
+    # m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
+    out = ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0]))
+    assert math.isnan(out[0]) and math.isnan(out[1])
+    # Independently compute the expected Gaussian-weighted sum.
+    w = np.exp(-((np.arange(3, dtype=np.float64) - 1.7) ** 2) / 0.5)
+    expected = float(np.dot([10.0, 20.0, 30.0], w) / w.sum())
+    assert math.isclose(out[2], expected, abs_tol=1e-12)
+    # Sanity: heavy offset toward the newest sample lifts the average above
+    # the simple mean of 20.
+    assert out[2] > 20.0
+
+
+def test_mcginley_dynamic_constant_series_yields_the_constant():
+    # ratio = 1, so the recurrence collapses to MD + 0 / divisor = MD.
+    out = ta.McGinleyDynamic(5).batch(np.full(30, 42.0, dtype=np.float64))
+    assert np.all(np.isnan(out[:4]))
+    np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
+
+
+def test_mcginley_dynamic_reference_value():
+    # Period 3, seed = SMA([10, 20, 30]) = 20.0. Next price 40.0:
+    # ratio   = 2; divisor = 0.6 * 3 * 16 = 28.8; next = 20 + 20/28.8.
+    out = ta.McGinleyDynamic(3).batch(np.array([10.0, 20.0, 30.0, 40.0]))
+    assert math.isnan(out[0]) and math.isnan(out[1])
+    assert math.isclose(out[2], 20.0, abs_tol=1e-12)
+    expected = 20.0 + 20.0 / (0.6 * 3.0 * 16.0)
+    assert math.isclose(out[3], expected, abs_tol=1e-12)
+
+
+def test_frama_constant_series_yields_the_constant():
+    # Flat input -> degenerate ranges -> alpha clamps to 0.01 and the EMA
+    # recurrence holds the seed value.
+    out = ta.FRAMA(4).batch(np.full(20, 42.0, dtype=np.float64))
+    assert np.all(np.isnan(out[:3]))
+    np.testing.assert_allclose(out[3:], 42.0, atol=1e-12)
+
+
+def test_frama_pure_uptrend_hugs_latest():
+    # Monotonic uptrend -> alpha pushed toward 1.0, FRAMA tracks close.
+    out = ta.FRAMA(4).batch(np.arange(1.0, 9.0, dtype=np.float64))
+    assert math.isclose(out[-1], 8.0, abs_tol=0.05)
+
+
+def test_jma_constant_series_yields_the_constant():
+    # JMA seeds e0 and the output to the first input, so a constant series
+    # is reproduced exactly from the first sample.
+    out = ta.JMA(14, 0.0, 2).batch(np.full(30, 42.0, dtype=np.float64))
+    np.testing.assert_allclose(out, 42.0, atol=1e-12)
+
+
+def test_evwma_reference_value_period_2():
+    # EVWMA(2). Bars: (close, volume) = (10, 1), (20, 3), (30, 1).
+    #   Bar 2: sum_v = 4, seeded prev = 20, EVWMA = (1*20 + 3*20)/4 = 20.
+    #   Bar 3: sum_v = 4 (drops 1, gains 1), EVWMA = (3*20 + 1*30)/4 = 22.5.
+    out = ta.EVWMA(2).batch(np.array([10.0, 20.0, 30.0]), np.array([1.0, 3.0, 1.0]))
+    assert math.isnan(out[0])
+    assert math.isclose(out[1], 20.0, abs_tol=1e-12)
+    assert math.isclose(out[2], 22.5, abs_tol=1e-12)
+
+
+def test_alligator_constant_series_holds_at_median_price():
+    # Median price = (11 + 9) / 2 = 10 on every candle, so all three SMMAs
+    # seed at 10 and stay there.
+    n = 30
+    high = np.full(n, 11.0)
+    low = np.full(n, 9.0)
+    out = ta.Alligator(13, 8, 5).batch(high, low)
+    assert out.shape == (n, 3)
+    for row in out[12:]:
+        assert math.isclose(row[0], 10.0, abs_tol=1e-12)
+        assert math.isclose(row[1], 10.0, abs_tol=1e-12)
+        assert math.isclose(row[2], 10.0, abs_tol=1e-12)
+
+
+def test_vidya_constant_series_holds_seed():
+    # CMO = 0 on a flat series -> alpha = 0 -> VIDYA holds its seed value.
+    out = ta.VIDYA(14, 4).batch(np.full(20, 42.0, dtype=np.float64))
+    assert np.all(np.isnan(out[:4]))
+    np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
+
+
 def test_macd_constant_series_converges_to_zero():
     out = ta.MACD().batch(np.full(200, 100.0))
     # Last row's MACD and signal must be ~0.