feat: order-book microstructure indicators (part 1 of 4) (#112)

* feat(core): add microstructure input types (OrderBook, Trade, TradeQuote)

New non-OHLCV value types for the order-book / trade-flow indicator family:
Level, OrderBook (sorted, uncrossed depth snapshot), Side, Trade (with
aggressor side), and TradeQuote (trade paired with prevailing mid). Each has a
validating constructor plus a new_unchecked hot-path constructor, with full
unit coverage. Adds InvalidOrderBook / InvalidTrade error variants.

* feat(core): add 5 order-book microstructure indicators

OrderBookImbalanceTop1/TopN/Full (signed depth imbalance), Microprice
(size-weighted fair value), and QuotedSpread (top-of-book spread in bps). All
consume the OrderBook snapshot type, emit f64, are stateless and ready after
the first snapshot, with full unit coverage. Registers a new Microstructure
family in the taxonomy.

* feat(bindings): expose order-book microstructure indicators

Python, Node, and WASM bindings for OrderBookImbalanceTop1/TopN/Full,
Microprice and QuotedSpread. Each takes a depth snapshot via four equal-length
(bid_px, bid_sz, ask_px, ask_sz) arrays. Python and Node expose a batch over a
list of snapshots; WASM exposes per-snapshot update (the streaming model that
fits a browser book feed). Regenerates node index.d.ts/.js and registers the
new InvalidOrderBook/InvalidTrade arms in the Python error mapping.

* test(bindings,fuzz): cover order-book microstructure indicators

Python: smoke, reference values, streaming-vs-batch, lifecycle/repr and input
validation (mismatched lengths, crossed book, misordered levels, zero levels)
for all five order-book indicators. Node: reference values, streaming-vs-batch,
and rejection cases. Adds an indicator_update_orderbook fuzz target driving
every order-book indicator over arbitrary (incl. degenerate) snapshots.

* bench(microstructure): synthetic order-book benchmarks

Add a bench_orderbook_input harness and synthesise a five-level book around
each candle close (no order-book dataset ships with the repo). Benches the
cheapest (top-of-book imbalance) and most-expensive (full-depth imbalance) plus
microprice, matching the curated cheapest/expensive-per-family approach.

* docs: add Microstructure family + bump indicator counter to 224

README gains the Microstructure family row (order-book imbalance, microprice,
quoted spread) and the indicator counter goes 219 -> 224 across seventeen
families; CHANGELOG records the new order-book indicators and value types.

bindings/python/tests/test_new_indicators.py

@@ -1863,3 +1863,38 @@ def test_new_indicators_expose_lifecycle():
         assert ind.warmup_period() >= 1
         ind.reset()
         assert ind.is_ready() is False
+
+
+def _orderbook_snapshots(n: int) -> list:
+    """A deterministic varying sequence of order-book snapshots."""
+    snaps = []
+    for i in range(n):
+        bid_sz = 1.0 + (i % 5)
+        ask_sz = 1.0 + ((i + 2) % 4)
+        snaps.append(
+            (
+                [100.0, 99.0],
+                [bid_sz, 1.0],
+                [101.0, 102.0],
+                [ask_sz, 1.0],
+            )
+        )
+    return snaps
+
+
+def test_orderbook_indicators_streaming_equals_batch():
+    snaps = _orderbook_snapshots(40)
+    for make in (
+        ta.OrderBookImbalanceTop1,
+        lambda: ta.OrderBookImbalanceTopN(2),
+        ta.OrderBookImbalanceFull,
+        ta.Microprice,
+        ta.QuotedSpread,
+    ):
+        batch = make().batch(snaps)
+        streamer = make()
+        streamed = np.array(
+            [streamer.update(*snap) for snap in snaps], dtype=np.float64
+        )
+        assert batch.shape == (len(snaps),)
+        assert _eq_nan(batch, streamed)