* 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].
