feat(breadth): complete the Market Breadth family (14 indicators) (#157)
Completes expansion-roadmap block **A2 — Market Breadth**: the 14 indicators that remained after the `AdvanceDecline` bootstrap, all built on the existing `CrossSection` input. ## Indicators (all scalar `Indicator<Input = CrossSection, Output = f64>`) | Indicator | Reading | |-----------|---------| | `AdvanceDeclineRatio` | advancers / decliners | | `AdVolumeLine` | cumulative net advancing volume | | `McClellanOscillator` | 19/39 EMAs of ratio-adjusted net advances | | `McClellanSummationIndex` | running total of the oscillator | | `Trin` (Arms Index) | A/D ratio over up/down volume ratio | | `BreadthThrust` (Zweig) | SMA of the advancing-issues share | | `NewHighsNewLows` | new highs − new lows | | `HighLowIndex` | SMA of the record-high percent | | `PercentAboveMa` | % of the universe above its MA | | `UpDownVolumeRatio` | advancing / declining volume | | `BullishPercentIndex` | % on a point-and-figure buy signal | | `CumulativeVolumeIndex` | volume-normalised cumulative net advancing volume | | `AbsoluteBreadthIndex` | \|advancers − decliners\| | | `TickIndex` | instantaneous net advancers − decliners | ## Input model `AdVolumeLine` and `CumulativeVolumeIndex` are kept distinct (the latter normalises each tick's net advancing volume by total volume, so it stays comparable across volume regimes). `PercentAboveMa` and `BullishPercentIndex` need a per-symbol state signal that `Member` did not carry, so `Member` gains two additive flags (`above_ma`, `on_buy_signal`) via a new `Member::with_signals` constructor; the 4-arg `Member::new` leaves both cleared, so every existing caller and binding is unchanged. `CrossSection` gains volume / new-extreme / state aggregation helpers. ## Wiring Fully wired across the Rust core, the python/node/wasm bindings, the cross-section fuzz target, the README + docs indicator counters (325 → 339), and dedicated python/node streaming-vs-batch tests. `fmt` / `test --workspace --all-features` / `clippy --workspace -D warnings` / node build+test / pytest all green locally.
This commit is contained in:
kingchenc
@@ -352,6 +352,20 @@ from ._wickra import (
|
||||
TermStructureBasis,
|
||||
CalendarSpread,
|
||||
# Market Breadth
|
||||
TickIndex,
|
||||
AbsoluteBreadthIndex,
|
||||
CumulativeVolumeIndex,
|
||||
BullishPercentIndex,
|
||||
UpDownVolumeRatio,
|
||||
PercentAboveMa,
|
||||
HighLowIndex,
|
||||
NewHighsNewLows,
|
||||
BreadthThrust,
|
||||
Trin,
|
||||
McClellanSummationIndex,
|
||||
McClellanOscillator,
|
||||
AdVolumeLine,
|
||||
AdvanceDeclineRatio,
|
||||
AdvanceDecline,
|
||||
# Risk / Performance
|
||||
SharpeRatio,
|
||||
@@ -702,6 +716,20 @@ __all__ = [
|
||||
"TermStructureBasis",
|
||||
"CalendarSpread",
|
||||
# Market Breadth
|
||||
"TickIndex",
|
||||
"AbsoluteBreadthIndex",
|
||||
"CumulativeVolumeIndex",
|
||||
"BullishPercentIndex",
|
||||
"UpDownVolumeRatio",
|
||||
"PercentAboveMa",
|
||||
"HighLowIndex",
|
||||
"NewHighsNewLows",
|
||||
"BreadthThrust",
|
||||
"Trin",
|
||||
"McClellanSummationIndex",
|
||||
"McClellanOscillator",
|
||||
"AdVolumeLine",
|
||||
"AdvanceDeclineRatio",
|
||||
"AdvanceDecline",
|
||||
# Risk / Performance
|
||||
"SharpeRatio",
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
@@ -2783,6 +2783,190 @@ def test_advance_decline_rejects_ragged_universe():
|
||||
ad.update([1.0, -1.0], [10.0], [False, False], [False, False])
|
||||
|
||||
|
||||
def _breadth_streaming_equals_batch(indicator, change, volume, new_high, new_low):
|
||||
"""Assert a 4-array breadth indicator's batch matches its streaming output."""
|
||||
batch = indicator().batch(change, volume, new_high, new_low)
|
||||
streamer = indicator()
|
||||
streamed = np.array(
|
||||
[
|
||||
streamer.update(change[i], volume[i], new_high[i], new_low[i])
|
||||
for i in range(len(change))
|
||||
],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert batch.shape == (len(change),)
|
||||
assert _eq_nan(batch, streamed)
|
||||
return batch
|
||||
|
||||
|
||||
def test_advance_decline_ratio_breadth():
|
||||
change = [[1.0, 1.0, 1.0, -1.0], [1.0, 0.0, 0.0, 0.0], [-1.0, -1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 4 for _ in range(3)]
|
||||
flags = [[False] * 4 for _ in range(3)]
|
||||
batch = _breadth_streaming_equals_batch(ta.AdvanceDeclineRatio, change, volume, flags, flags)
|
||||
# 3/1 = 3 ; 1/max(0,1) = 1 ; 0/3 = 0.
|
||||
assert list(batch) == [3.0, 1.0, 0.0]
|
||||
|
||||
|
||||
def test_ad_volume_line_breadth():
|
||||
change = [[1.0, -1.0], [1.0, -1.0], [1.0, 0.0]]
|
||||
volume = [[150.0, 50.0], [60.0, 60.0], [30.0, 0.0]]
|
||||
flags = [[False] * 2 for _ in range(3)]
|
||||
batch = _breadth_streaming_equals_batch(ta.AdVolumeLine, change, volume, flags, flags)
|
||||
# net +100 -> 100 ; net 0 -> 100 ; net +30 -> 130.
|
||||
assert list(batch) == [100.0, 100.0, 130.0]
|
||||
|
||||
|
||||
def test_mcclellan_oscillator_breadth():
|
||||
change = [[1.0, 1.0, 1.0, -1.0], [-1.0, -1.0, -1.0, 1.0], [1.0, 1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 4 for _ in range(3)]
|
||||
flags = [[False] * 4 for _ in range(3)]
|
||||
batch = _breadth_streaming_equals_batch(ta.McClellanOscillator, change, volume, flags, flags)
|
||||
# seed 0 ; -50 ; -67.5.
|
||||
assert abs(batch[0]) < 1e-9
|
||||
assert abs(batch[1] - (-50.0)) < 1e-9
|
||||
assert abs(batch[2] - (-67.5)) < 1e-9
|
||||
|
||||
|
||||
def test_mcclellan_summation_index_breadth():
|
||||
change = [[1.0, 1.0, 1.0, -1.0], [-1.0, -1.0, -1.0, 1.0], [1.0, 1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 4 for _ in range(3)]
|
||||
flags = [[False] * 4 for _ in range(3)]
|
||||
batch = _breadth_streaming_equals_batch(ta.McClellanSummationIndex, change, volume, flags, flags)
|
||||
# 0 ; -50 ; -117.5.
|
||||
assert abs(batch[0]) < 1e-9
|
||||
assert abs(batch[1] - (-50.0)) < 1e-9
|
||||
assert abs(batch[2] - (-117.5)) < 1e-9
|
||||
|
||||
|
||||
def test_trin_breadth():
|
||||
change = [[1.0, 1.0, 1.0, -1.0], [1.0, 1.0, -1.0, -1.0]]
|
||||
volume = [[50.0, 50.0, 50.0, 50.0], [10.0, 10.0, 40.0, 40.0]]
|
||||
flags = [[False] * 4 for _ in range(2)]
|
||||
batch = _breadth_streaming_equals_batch(ta.Trin, change, volume, flags, flags)
|
||||
# (3/1)/(150/50) = 1 ; (2/2)/(20/80) = 4.
|
||||
assert abs(batch[0] - 1.0) < 1e-9
|
||||
assert abs(batch[1] - 4.0) < 1e-9
|
||||
|
||||
|
||||
def test_breadth_thrust_breadth():
|
||||
change = [[1.0] * 8 + [-1.0] * 2, [1.0] * 6 + [-1.0] * 4]
|
||||
volume = [[10.0] * 10 for _ in range(2)]
|
||||
flags = [[False] * 10 for _ in range(2)]
|
||||
batch = ta.BreadthThrust(2).batch(change, volume, flags, flags)
|
||||
streamer = ta.BreadthThrust(2)
|
||||
streamed = np.array(
|
||||
[streamer.update(change[i], volume[i], flags[i], flags[i]) for i in range(2)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert _eq_nan(batch, streamed)
|
||||
# 0.8 (warmup -> NaN) ; SMA(2) of [0.8, 0.6] = 0.7.
|
||||
assert math.isnan(batch[0])
|
||||
assert abs(batch[1] - 0.7) < 1e-9
|
||||
|
||||
|
||||
def test_new_highs_new_lows_breadth():
|
||||
change = [[1.0, 1.0, -1.0], [1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 3 for _ in range(2)]
|
||||
new_high = [[True, True, False], [True, False, False]]
|
||||
new_low = [[False, False, True], [False, True, True]]
|
||||
batch = _breadth_streaming_equals_batch(ta.NewHighsNewLows, change, volume, new_high, new_low)
|
||||
# 2 - 1 = 1 ; 1 - 2 = -1.
|
||||
assert list(batch) == [1.0, -1.0]
|
||||
|
||||
|
||||
def test_high_low_index_breadth():
|
||||
change = [[1.0] * 10, [1.0] * 10]
|
||||
volume = [[10.0] * 10 for _ in range(2)]
|
||||
new_high = [[True] * 8 + [False] * 2, [True] * 6 + [False] * 4]
|
||||
new_low = [[False] * 8 + [True] * 2, [False] * 6 + [True] * 4]
|
||||
batch = ta.HighLowIndex(2).batch(change, volume, new_high, new_low)
|
||||
streamer = ta.HighLowIndex(2)
|
||||
streamed = np.array(
|
||||
[streamer.update(change[i], volume[i], new_high[i], new_low[i]) for i in range(2)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert _eq_nan(batch, streamed)
|
||||
# 80% (warmup) ; SMA(2) of [80, 60] = 70.
|
||||
assert math.isnan(batch[0])
|
||||
assert abs(batch[1] - 70.0) < 1e-9
|
||||
|
||||
|
||||
def test_percent_above_ma_breadth():
|
||||
change = [[1.0, 1.0, 1.0, -1.0], [1.0, 1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 4 for _ in range(2)]
|
||||
flags = [[False] * 4 for _ in range(2)]
|
||||
above_ma = [[True, True, True, False], [True, False, False, False]]
|
||||
batch = ta.PercentAboveMa().batch(change, volume, flags, flags, above_ma)
|
||||
streamer = ta.PercentAboveMa()
|
||||
streamed = np.array(
|
||||
[streamer.update(change[i], volume[i], flags[i], flags[i], above_ma[i]) for i in range(2)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert _eq_nan(batch, streamed)
|
||||
# 3/4 -> 75 ; 1/4 -> 25.
|
||||
assert list(batch) == [75.0, 25.0]
|
||||
|
||||
|
||||
def test_up_down_volume_ratio_breadth():
|
||||
change = [[1.0, -1.0], [1.0, 0.0]]
|
||||
volume = [[150.0, 50.0], [100.0, 0.0]]
|
||||
flags = [[False] * 2 for _ in range(2)]
|
||||
batch = _breadth_streaming_equals_batch(ta.UpDownVolumeRatio, change, volume, flags, flags)
|
||||
# 150/50 = 3 ; 100/max(0,1) = 100.
|
||||
assert list(batch) == [3.0, 100.0]
|
||||
|
||||
|
||||
def test_bullish_percent_index_breadth():
|
||||
change = [[1.0, 1.0, -1.0, -1.0], [1.0, 1.0, 1.0, 1.0]]
|
||||
volume = [[10.0] * 4 for _ in range(2)]
|
||||
flags = [[False] * 4 for _ in range(2)]
|
||||
on_buy = [[True, True, False, False], [True, True, True, True]]
|
||||
batch = ta.BullishPercentIndex().batch(change, volume, flags, flags, on_buy)
|
||||
streamer = ta.BullishPercentIndex()
|
||||
streamed = np.array(
|
||||
[streamer.update(change[i], volume[i], flags[i], flags[i], on_buy[i]) for i in range(2)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert _eq_nan(batch, streamed)
|
||||
# 2/4 -> 50 ; 4/4 -> 100.
|
||||
assert list(batch) == [50.0, 100.0]
|
||||
|
||||
|
||||
def test_cumulative_volume_index_breadth():
|
||||
change = [[1.0, -1.0], [1.0, -1.0], [0.0]]
|
||||
volume = [[150.0, 50.0], [60.0, 60.0], [0.0]]
|
||||
new_high = [[False, False], [False, False], [False]]
|
||||
new_low = [[False, False], [False, False], [False]]
|
||||
batch = ta.CumulativeVolumeIndex().batch(change, volume, new_high, new_low)
|
||||
streamer = ta.CumulativeVolumeIndex()
|
||||
streamed = np.array(
|
||||
[streamer.update(change[i], volume[i], new_high[i], new_low[i]) for i in range(3)],
|
||||
dtype=np.float64,
|
||||
)
|
||||
assert _eq_nan(batch, streamed)
|
||||
# (100/200) -> 0.5 ; net 0 -> 0.5 ; zero-volume tick -> 0.5.
|
||||
assert list(batch) == [0.5, 0.5, 0.5]
|
||||
|
||||
|
||||
def test_absolute_breadth_index_breadth():
|
||||
change = [[1.0, 1.0, -1.0, -1.0, -1.0], [1.0, 1.0, 1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 5 for _ in range(2)]
|
||||
flags = [[False] * 5 for _ in range(2)]
|
||||
batch = _breadth_streaming_equals_batch(ta.AbsoluteBreadthIndex, change, volume, flags, flags)
|
||||
# |2 - 3| = 1 ; |3 - 2| = 1.
|
||||
assert list(batch) == [1.0, 1.0]
|
||||
|
||||
|
||||
def test_tick_index_breadth():
|
||||
change = [[1.0, 1.0, -1.0, -1.0, -1.0], [1.0, 1.0, 1.0, -1.0, -1.0]]
|
||||
volume = [[10.0] * 5 for _ in range(2)]
|
||||
flags = [[False] * 5 for _ in range(2)]
|
||||
batch = _breadth_streaming_equals_batch(ta.TickIndex, change, volume, flags, flags)
|
||||
# 2 - 3 = -1 ; 3 - 2 = 1.
|
||||
assert list(batch) == [-1.0, 1.0]
|
||||
|
||||
|
||||
def test_funding_basis_streaming_equals_batch():
|
||||
n = 40
|
||||
index = np.array([100.0 + 0.5 * math.sin(i * 0.2) for i in range(n)], dtype=np.float64)
|
||||
|
||||
Reference in New Issue
Block a user