feat: trade-flow microstructure indicators (part 2 of 4) (#113)

* feat(core): add 3 trade-flow microstructure indicators

SignedVolume (per-trade size signed by aggressor), CumulativeVolumeDelta
(running signed-volume total), and TradeImbalance (rolling buy/sell volume
imbalance over a trade window). All consume the Trade type, with full unit
coverage. Extends the Microstructure family.

* feat(bindings): expose trade-flow microstructure indicators

Python, Node and WASM bindings for SignedVolume, CumulativeVolumeDelta and
TradeImbalance. Each takes a trade via update(price, size, is_buy); Python and
Node expose a batch over three parallel arrays, WASM exposes per-trade update.
Regenerates node index.d.ts/.js.

* test(bindings,fuzz,bench): cover trade-flow microstructure indicators

Python and Node: reference values, streaming-vs-batch, lifecycle/repr and input
validation (zero window, negative size, non-positive price, mismatched batch
lengths). New indicator_update_trade fuzz target. Synthetic trade-tape benches
(signed_volume cheapest, trade_imbalance windowed/expensive).

* docs: add trade-flow indicators + bump counter to 227

README Microstructure family row gains signed volume / CVD / trade imbalance and
the counter goes 224 -> 227; CHANGELOG records the trade-flow indicators.
This commit is contained in:
kingchenc
2026-06-01 16:38:48 +02:00
committed by GitHub
parent 2be21df803
commit 5867f71450
23 changed files with 1141 additions and 33 deletions
@@ -217,3 +217,17 @@ def test_orderbook_streaming_matches_batch():
streamer = ta.Microprice()
streamed = np.array([streamer.update(*snap) for snap in snaps], dtype=np.float64)
assert _equal_with_nan(batch, streamed)
def test_tradeflow_streaming_matches_batch():
n = 30
price = np.full(n, 100.0)
size = np.array([1.0 + (i % 4) for i in range(n)], dtype=np.float64)
is_buy = [i % 3 != 0 for i in range(n)]
batch = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
streamer = ta.CumulativeVolumeDelta()
streamed = np.array(
[streamer.update(price[i], size[i], is_buy[i]) for i in range(n)],
dtype=np.float64,
)
assert _equal_with_nan(batch, streamed)