feat: signed candlestick directional ±1 encoding (Doji signed mode) (#111)

* feat(core): add signed dragonfly/gravestone encoding to Doji

Doji gains an opt-in `.signed()` mode that classifies a detected Doji by the
position of its body within the bar range: dragonfly (long lower shadow) emits
+1.0 (bullish), gravestone (long upper shadow) emits -1.0 (bearish), and a
long-legged/standard Doji emits 0.0. The default detection-flag behaviour
(+1.0/0.0) is unchanged, so existing callers are unaffected.

The other 14 candlestick patterns already emit the uniform +1 bull / -1 bear /
0 none convention; document that explicitly with a "Signed +-1 encoding"
section on each so the whole family is a consistent drop-in ML feature.

* feat(bindings): expose Doji signed mode in python, node, wasm

Hand-write the Doji binding in all three language bindings (instead of the
shared candle-pattern macro) so it accepts an opt-in `signed` flag and exposes
an `is_signed`/`isSigned` accessor:

- Python: `Doji(signed=False)` keyword argument
- Node: `new Doji(signed?)` optional constructor argument (index.d.ts/.js
  regenerated via napi build)
- WASM: `new Doji(signed?)` optional constructor argument

The default construction is unchanged, so existing callers keep the
direction-less +1/0 detection flag.

* test(bindings,fuzz): cover Doji signed dragonfly/gravestone encoding

- python: dragonfly(+1)/gravestone(-1)/neutral(0) and default-flag cases in
  test_known_values
- node: equivalent signed/default assertions in indicators.test.js
- fuzz: drive a signed Doji alongside the default in indicator_update_candle

* docs: document signed candlestick convention and Doji signed mode

README gains a candlestick sign-convention note; CHANGELOG records the new
opt-in Doji signed dragonfly/gravestone encoding under [Unreleased].

bindings/python/tests/test_known_values.py

@@ -823,3 +823,27 @@ def test_yang_zhang_zero_movement_yields_zero():
     ready = out[~np.isnan(out)]
     assert ready.size > 0
     np.testing.assert_allclose(ready, 0.0, atol=1e-12)
+
+
+def test_doji_default_is_directionless_flag():
+    # Default Doji is a direction-less detection flag: +1 on a doji, 0 else.
+    d = ta.Doji()
+    assert d.is_signed() is False
+    # body 0, range 2 -> doji.
+    assert d.update((10.0, 11.0, 9.0, 10.0, 1.0, 0)) == pytest.approx(1.0)
+    # body 2 == range -> not a doji.
+    assert d.update((10.0, 12.0, 10.0, 12.0, 1.0, 1)) == pytest.approx(0.0)
+
+
+def test_doji_signed_dragonfly_gravestone_neutral():
+    # Signed Doji classifies by body position within the range.
+    d = ta.Doji(signed=True)
+    assert d.is_signed() is True
+    # Dragonfly: body at the top, long lower shadow -> bullish +1.
+    assert d.update((10.0, 10.05, 6.0, 10.0, 1.0, 0)) == pytest.approx(1.0)
+    # Gravestone: body at the bottom, long upper shadow -> bearish -1.
+    assert d.update((10.0, 14.0, 9.95, 10.0, 1.0, 1)) == pytest.approx(-1.0)
+    # Long-legged: body centred, symmetric shadows -> neutral 0.
+    assert d.update((10.0, 12.0, 8.0, 10.0, 1.0, 2)) == pytest.approx(0.0)
+    # A large body is not a doji at all -> 0 regardless of position.
+    assert d.update((10.0, 12.0, 10.0, 12.0, 1.0, 3)) == pytest.approx(0.0)