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.
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@@ -217,3 +217,17 @@ def test_orderbook_streaming_matches_batch():
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streamer = ta.Microprice()
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streamed = np.array([streamer.update(*snap) for snap in snaps], dtype=np.float64)
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assert _equal_with_nan(batch, streamed)
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def test_tradeflow_streaming_matches_batch():
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n = 30
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price = np.full(n, 100.0)
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size = np.array([1.0 + (i % 4) for i in range(n)], dtype=np.float64)
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is_buy = [i % 3 != 0 for i in range(n)]
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batch = ta.CumulativeVolumeDelta().batch(price, size, is_buy)
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streamer = ta.CumulativeVolumeDelta()
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streamed = np.array(
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[streamer.update(price[i], size[i], is_buy[i]) for i in range(n)],
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dtype=np.float64,
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)
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assert _equal_with_nan(batch, streamed)
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