feat: add Market Breadth family with CrossSection input (#153)
## What Adds a new indicator input type and family for **market-breadth** analysis — indicators that aggregate the state of an entire universe of symbols at each tick, rather than a single instrument's price. This is the last open input-type on the expansion roadmap (S10) and unblocks the remaining breadth indicators (McClellan, TRIN, High-Low Index, ...). ## Core - **`CrossSection` input type** (`crates/wickra-core/src/cross_section.rs`) — one tick carrying the per-symbol state of the whole universe as a `Vec<Member>` + `timestamp`. Each `Member` precomputes a signed `change` (sign classifies advancing / declining / unchanged), a `volume`, and `new_high` / `new_low` extreme flags, so the breadth indicators stay stateless per tick. Both `Member` and `CrossSection` are `#[non_exhaustive]` for additive field growth. `CrossSection::new` validates the universe (non-empty, finite changes, finite non-negative volumes); `new_unchecked` skips validation for hot paths. `advancers()` / `decliners()` count by sign. - **`Error::InvalidCrossSection`** variant for the validation failures. - **`AdvanceDecline`** (`advance_decline.rs`) — the Advance/Decline Line: the running cumulative sum of net advancing-minus-declining issues. `Input = CrossSection`, `Output = f64`, ready after the first tick. - New **"Market Breadth"** `FAMILIES` group; indicator count **314 → 315**, family count nineteen → twenty. ## Bindings All custom (CrossSection is non-scalar, so no macros apply). The universe crosses each boundary as parallel arrays (`change`, `volume`, `new_high`, `new_low`): - **Python / Node** expose `update` + `batch` (one array group per tick). Node satisfies the completeness contract (`update`/`batch`/`reset`/`isReady`/`warmupPeriod`). - **WASM** exposes only `update` (the universe is ragged across ticks, matching the other multi-input wasm indicators) with numeric high/low flags. - Python `map_err` gains the new error arm; `__init__.py` gets a `# Market Breadth` section in both the import and `__all__` blocks. `index.d.ts` / `index.js` regenerated. ## Tests / Fuzz - Dedicated **streaming-vs-batch + reference-value + ragged-rejection** tests in Python (`test_new_indicators.py`) and Node (`indicators.test.js`) — kept out of the scalar/candle parametrize lists. - Rust unit tests cover every reject branch (empty / non-finite change / negative & non-finite volume) and every indicator branch. - New fuzz target `indicator_update_crosssection` drives `AdvanceDecline` over bounded ragged universes built with `new_unchecked`. ## Verify - `cargo fmt --all` clean - `cargo test -p wickra-core --lib` → 2593 passed; `--doc` → 298 passed - `cargo clippy --workspace --all-targets --all-features -- -D warnings` clean - `cd bindings/node && npm run build && npm test` → 398 passed - `maturin develop --release` + `pytest bindings/python/tests` → all passed - counter check: mod-count 315 == lib-block 315
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@@ -2659,6 +2659,38 @@ def test_funding_indicators_streaming_equals_batch():
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assert _eq_nan(batch, streamed)
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def test_advance_decline_streaming_equals_batch():
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# Three ticks over a universe of four symbols; the sign of `change`
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# classifies each symbol as advancing / declining / unchanged.
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change = [
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[1.0, 0.5, 2.0, -1.0], # 3 up, 1 down -> net +2
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[-1.0, -0.5, -2.0, 1.0], # 1 up, 3 down -> net -2
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[0.0, 0.0, 1.0, -1.0], # 1 up, 1 down -> net 0
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]
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volume = [[10.0] * 4 for _ in range(3)]
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new_high = [[False] * 4 for _ in range(3)]
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new_low = [[False] * 4 for _ in range(3)]
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batch = ta.AdvanceDecline().batch(change, volume, new_high, new_low)
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streamer = ta.AdvanceDecline()
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streamed = np.array(
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[
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streamer.update(change[i], volume[i], new_high[i], new_low[i])
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for i in range(3)
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],
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dtype=np.float64,
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)
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assert batch.shape == (3,)
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assert _eq_nan(batch, streamed)
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# Cumulative line: +2 -> 0 -> 0.
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assert list(batch) == [2.0, 0.0, 0.0]
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def test_advance_decline_rejects_ragged_universe():
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ad = ta.AdvanceDecline()
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with pytest.raises(ValueError):
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ad.update([1.0, -1.0], [10.0], [False, False], [False, False])
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def test_funding_basis_streaming_equals_batch():
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n = 40
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index = np.array([100.0 + 0.5 * math.sin(i * 0.2) for i in range(n)], dtype=np.float64)
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