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python: drop the NumPy runtime dependency (zero third-party deps) (#317)

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Makes the Python binding truly dependency-free (Task 5b). `pip install wickra` now pulls **zero** third-party packages, and `import wickra` never imports NumPy (verified: `"numpy" not in sys.modules` after a batch call).

## What changed
- **Inputs** accept any sequence or buffer of numbers — `array.array`, `memoryview`, a NumPy `ndarray`, or a plain `list` — via `Vec` extraction (newtypes `Buf1`/`BufI64`). `PyBuffer` is unavailable under `abi3-py39`, and `unsafe_code = forbid` rules out a zero-copy slice, so inputs are copied once (negligible vs. compute).
- **Single-output `batch(...)`** returns a stdlib `array.array('d')` (native buffer protocol → `numpy.asarray` zero-copy).
- **Multi-output `batch(...)`** returns a buffer-protocol `Matrix` preserving `.shape`, integer-row and `[i, j]` access, and `.tolist()`.
- **NumPy** moves to an optional extra (`pip install wickra[numpy]`); it is never required.
- Streaming `update(...)` is unchanged; results are numerically identical.

## Implementation
- Trait `IntoPyData` + a `matrix()`/`f64_array()` helper collapse the ~400 batch call sites; `array.array` is built via `bytemuck` (Zlib/MIT/Apache — `cargo deny check licenses` ok).
- Tests migrated to `array.array`/`Matrix` (1-D `.shape`→`len()`, `.dtype`→`.typecode`; numeric comparisons normalize through a `_to_np` helper). The non-contiguous-input test now asserts acceptance instead of rejection.

## Verification (local)
- `pytest bindings/python/tests` — **1991 passed**.
- `cargo fmt --all`, `cargo clippy --workspace --all-targets --all-features -- -D warnings`, `cargo test --workspace --all-features`, `cargo deny check licenses` — all green.

**BREAKING for Python**: batch return types change from NumPy arrays to `array.array`/`Matrix`. Documented in CHANGELOG; ships with the data-layer release bundle (no separate tag).
This commit is contained in:
kingchenc
2026-06-17 03:19:50 +02:00
committed by GitHub
parent 677ea37402
commit 8228be7069
12 changed files with 1910 additions and 2713 deletions
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bindings/python/tests/test_known_values.py
+97 -88
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@@ -10,8 +10,17 @@ import pytest
import wickra as ta
def _to_np(x) -> np.ndarray:
"""Normalize a Wickra batch result (``array.array`` or ``Matrix``) to an ndarray."""
if isinstance(x, np.ndarray):
return x.astype(np.float64)
if hasattr(x, "tolist") and hasattr(x, "shape"):
return np.asarray(x.tolist(), dtype=np.float64)
return np.asarray(x, dtype=np.float64)
def test_sma_constant_series():
out = ta.SMA(5).batch(np.full(20, 42.0, dtype=np.float64))
out = _to_np(ta.SMA(5).batch(np.full(20, 42.0, dtype=np.float64)))
# First 4 are warmup -> NaN; rest equal 42.
assert np.all(np.isnan(out[:4]))
assert np.allclose(out[4:], 42.0)
@@ -19,37 +28,37 @@ def test_sma_constant_series():
def test_sma_known_window():
# SMA(3) of [2, 4, 6, 8, 10] -> [_, _, 4, 6, 8]
out = ta.SMA(3).batch(np.array([2.0, 4.0, 6.0, 8.0, 10.0]))
out = _to_np(ta.SMA(3).batch(np.array([2.0, 4.0, 6.0, 8.0, 10.0])))
assert math.isnan(out[0]) and math.isnan(out[1])
np.testing.assert_allclose(out[2:], [4.0, 6.0, 8.0])
def test_ema_seed_equals_simple_mean_of_first_window():
# EMA(5) seed = mean([10, 20, 30, 40, 50]) = 30
out = ta.EMA(5).batch(np.array([10.0, 20.0, 30.0, 40.0, 50.0]))
out = _to_np(ta.EMA(5).batch(np.array([10.0, 20.0, 30.0, 40.0, 50.0])))
assert math.isnan(out[0])
assert math.isclose(out[4], 30.0, abs_tol=1e-12)
def test_wma_known_window():
# WMA(4) of [1, 2, 3, 4] = (1*1 + 2*2 + 3*3 + 4*4)/10 = 3
out = ta.WMA(4).batch(np.array([1.0, 2.0, 3.0, 4.0]))
out = _to_np(ta.WMA(4).batch(np.array([1.0, 2.0, 3.0, 4.0])))
assert math.isnan(out[0]) and math.isnan(out[1]) and math.isnan(out[2])
assert math.isclose(out[3], 3.0, abs_tol=1e-12)
def test_rsi_pure_uptrend_is_100():
out = ta.RSI(14).batch(np.arange(1.0, 21.0, dtype=np.float64))
out = _to_np(ta.RSI(14).batch(np.arange(1.0, 21.0, dtype=np.float64)))
np.testing.assert_allclose(out[14:], 100.0)
def test_rsi_pure_downtrend_is_0():
out = ta.RSI(14).batch(np.arange(20.0, 0.0, -1.0))
out = _to_np(ta.RSI(14).batch(np.arange(20.0, 0.0, -1.0)))
np.testing.assert_allclose(out[14:], 0.0)
def test_rsi_flat_series_is_50():
out = ta.RSI(14).batch(np.full(30, 100.0))
out = _to_np(ta.RSI(14).batch(np.full(30, 100.0)))
np.testing.assert_allclose(out[14:], 50.0)
@@ -62,13 +71,13 @@ def test_rsi_wilder_textbook_first_value():
],
dtype=np.float64,
)
out = ta.RSI(14).batch(prices)
out = _to_np(ta.RSI(14).batch(prices))
assert math.isclose(out[14], 70.464, abs_tol=0.05)
def test_anchored_rsi_cumulative_reference():
"""Cumulative anchored RSI: 10 -> 11 (+1) -> 9 (-2) -> 12 (+3)."""
out = ta.AnchoredRSI().batch(np.array([10.0, 11.0, 9.0, 12.0]))
out = _to_np(ta.AnchoredRSI().batch(np.array([10.0, 11.0, 9.0, 12.0])))
assert math.isclose(out[1], 100.0, abs_tol=1e-9)
assert math.isclose(out[2], 100.0 - 100.0 / 1.5, abs_tol=1e-6)
assert math.isclose(out[3], 100.0 - 100.0 / 3.0, abs_tol=1e-6)
@@ -79,9 +88,9 @@ def test_inertia_constant_rvi_passes_through_linreg():
# RVI = (c-o) / (h-l) = 0.5 / 2 = 0.25 every bar. LinReg of a constant
# series equals that constant after warmup.
n = 60
out = ta.Inertia(3, 4).batch(
out = _to_np(ta.Inertia(3, 4).batch(
np.full(n, 10.0), np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.5)
)
))
# warmup_period = 3 + 4 - 1 = 6.
np.testing.assert_allclose(out[5:], 0.25, atol=1e-12)
@@ -90,7 +99,7 @@ def test_connors_rsi_output_is_bounded():
# CRSI is the average of three [0, 100] components, so the aggregate must
# also sit in [0, 100] after warmup.
prices = 100.0 + 20.0 * np.sin(np.linspace(0, 30, 250))
out = ta.ConnorsRSI(3, 2, 100).batch(prices.astype(np.float64))
out = _to_np(ta.ConnorsRSI(3, 2, 100).batch(prices.astype(np.float64)))
ready = out[~np.isnan(out)]
assert ready.size > 0
assert ready.min() >= 0.0
@@ -101,7 +110,7 @@ def test_laguerre_rsi_constant_series_stays_at_mid_band():
# All four Laguerre stages seed to the first input, so subsequent flat
# inputs keep them equal and the up/down accumulator is 0 — Wickra maps
# that to the neutral 50.
out = ta.LaguerreRSI(0.5).batch(np.full(40, 42.0, dtype=np.float64))
out = _to_np(ta.LaguerreRSI(0.5).batch(np.full(40, 42.0, dtype=np.float64)))
np.testing.assert_allclose(out, 50.0, atol=1e-12)
@@ -109,7 +118,7 @@ def test_smi_close_at_centre_yields_zero():
# Close at the midpoint of a flat high/low range -> displacement is
# always zero -> SMI converges to 0.
n = 60
out = ta.SMI(5, 3, 3).batch(np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.0))
out = _to_np(ta.SMI(5, 3, 3).batch(np.full(n, 11.0), np.full(n, 9.0), np.full(n, 10.0)))
# warmup_period = 5 + 3 + 3 - 2 = 9.
np.testing.assert_allclose(out[8:], 0.0, atol=1e-12)
@@ -118,7 +127,7 @@ def test_kst_constant_series_yields_zero():
# ROC is zero on a flat input, so every RCMA is zero, so KST and its
# signal SMA are both zero after warmup.
kst = ta.KST(10, 15, 20, 30, 10, 10, 10, 15, 9)
out = kst.batch(np.full(80, 42.0, dtype=np.float64))
out = _to_np(kst.batch(np.full(80, 42.0, dtype=np.float64)))
warmup = kst.warmup_period()
# Use NaN-safe comparison on the post-warmup tail.
tail = out[warmup - 1 :]
@@ -133,7 +142,7 @@ def test_pgo_flat_close_yields_zero():
high = np.full(n, 11.0)
low = np.full(n, 9.0)
close = np.full(n, 10.0)
out = ta.PGO(5).batch(high, low, close)
out = _to_np(ta.PGO(5).batch(high, low, close))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
@@ -141,12 +150,12 @@ def test_pgo_flat_close_yields_zero():
def test_rvi_reference_value_period_2():
# Two bars: (open, high, low, close) = (10, 11, 9, 10.5), (10.5, 11.5, 10, 11).
# num = (0.5 + 0.5) = 1.0; den = (2.0 + 1.5) = 3.5; RVI = 1 / 3.5.
out = ta.RVI(2).batch(
out = _to_np(ta.RVI(2).batch(
np.array([10.0, 10.5]),
np.array([11.0, 11.5]),
np.array([9.0, 10.0]),
np.array([10.5, 11.0]),
)
))
assert math.isnan(out[0])
assert math.isclose(out[1], 1.0 / 3.5, abs_tol=1e-12)
@@ -154,7 +163,7 @@ def test_rvi_reference_value_period_2():
def test_alma_constant_series_yields_the_constant():
# ALMA's Gaussian weights are normalised, so any constant series is
# reproduced exactly after warmup.
out = ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64))
out = _to_np(ta.ALMA(9, 0.85, 6.0).batch(np.full(30, 42.0, dtype=np.float64)))
assert np.all(np.isnan(out[:8]))
np.testing.assert_allclose(out[8:], 42.0, atol=1e-12)
@@ -162,7 +171,7 @@ def test_alma_constant_series_yields_the_constant():
def test_alma_reference_value_period_3():
# ALMA(period=3, offset=0.85, sigma=6) on [10, 20, 30].
# m = 0.85 * 2 = 1.7; s = 3 / 6 = 0.5; 2*s^2 = 0.5.
out = ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0]))
out = _to_np(ta.ALMA(3, 0.85, 6.0).batch(np.array([10.0, 20.0, 30.0])))
assert math.isnan(out[0]) and math.isnan(out[1])
# Independently compute the expected Gaussian-weighted sum.
w = np.exp(-((np.arange(3, dtype=np.float64) - 1.7) ** 2) / 0.5)
@@ -175,7 +184,7 @@ def test_alma_reference_value_period_3():
def test_mcginley_dynamic_constant_series_yields_the_constant():
# ratio = 1, so the recurrence collapses to MD + 0 / divisor = MD.
out = ta.McGinleyDynamic(5).batch(np.full(30, 42.0, dtype=np.float64))
out = _to_np(ta.McGinleyDynamic(5).batch(np.full(30, 42.0, dtype=np.float64)))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
@@ -183,7 +192,7 @@ def test_mcginley_dynamic_constant_series_yields_the_constant():
def test_mcginley_dynamic_reference_value():
# Period 3, seed = SMA([10, 20, 30]) = 20.0. Next price 40.0:
# ratio = 2; divisor = 0.6 * 3 * 16 = 28.8; next = 20 + 20/28.8.
out = ta.McGinleyDynamic(3).batch(np.array([10.0, 20.0, 30.0, 40.0]))
out = _to_np(ta.McGinleyDynamic(3).batch(np.array([10.0, 20.0, 30.0, 40.0])))
assert math.isnan(out[0]) and math.isnan(out[1])
assert math.isclose(out[2], 20.0, abs_tol=1e-12)
expected = 20.0 + 20.0 / (0.6 * 3.0 * 16.0)
@@ -193,21 +202,21 @@ def test_mcginley_dynamic_reference_value():
def test_frama_constant_series_yields_the_constant():
# Flat input -> degenerate ranges -> alpha clamps to 0.01 and the EMA
# recurrence holds the seed value.
out = ta.FRAMA(4).batch(np.full(20, 42.0, dtype=np.float64))
out = _to_np(ta.FRAMA(4).batch(np.full(20, 42.0, dtype=np.float64)))
assert np.all(np.isnan(out[:3]))
np.testing.assert_allclose(out[3:], 42.0, atol=1e-12)
def test_frama_pure_uptrend_hugs_latest():
# Monotonic uptrend -> alpha pushed toward 1.0, FRAMA tracks close.
out = ta.FRAMA(4).batch(np.arange(1.0, 9.0, dtype=np.float64))
out = _to_np(ta.FRAMA(4).batch(np.arange(1.0, 9.0, dtype=np.float64)))
assert math.isclose(out[-1], 8.0, abs_tol=0.05)
def test_jma_constant_series_yields_the_constant():
# JMA seeds e0 and the output to the first input, so a constant series
# is reproduced exactly from the first sample.
out = ta.JMA(14, 0.0, 2).batch(np.full(30, 42.0, dtype=np.float64))
out = _to_np(ta.JMA(14, 0.0, 2).batch(np.full(30, 42.0, dtype=np.float64)))
np.testing.assert_allclose(out, 42.0, atol=1e-12)
@@ -215,7 +224,7 @@ def test_evwma_reference_value_period_2():
# EVWMA(2). Bars: (close, volume) = (10, 1), (20, 3), (30, 1).
# Bar 2: sum_v = 4, seeded prev = 20, EVWMA = (1*20 + 3*20)/4 = 20.
# Bar 3: sum_v = 4 (drops 1, gains 1), EVWMA = (3*20 + 1*30)/4 = 22.5.
out = ta.EVWMA(2).batch(np.array([10.0, 20.0, 30.0]), np.array([1.0, 3.0, 1.0]))
out = _to_np(ta.EVWMA(2).batch(np.array([10.0, 20.0, 30.0]), np.array([1.0, 3.0, 1.0])))
assert math.isnan(out[0])
assert math.isclose(out[1], 20.0, abs_tol=1e-12)
assert math.isclose(out[2], 22.5, abs_tol=1e-12)
@@ -227,7 +236,7 @@ def test_alligator_constant_series_holds_at_median_price():
n = 30
high = np.full(n, 11.0)
low = np.full(n, 9.0)
out = ta.Alligator(13, 8, 5).batch(high, low)
out = _to_np(ta.Alligator(13, 8, 5).batch(high, low))
assert out.shape == (n, 3)
for row in out[12:]:
assert math.isclose(row[0], 10.0, abs_tol=1e-12)
@@ -237,7 +246,7 @@ def test_alligator_constant_series_holds_at_median_price():
def test_vidya_constant_series_holds_seed():
# CMO = 0 on a flat series -> alpha = 0 -> VIDYA holds its seed value.
out = ta.VIDYA(14, 4).batch(np.full(20, 42.0, dtype=np.float64))
out = _to_np(ta.VIDYA(14, 4).batch(np.full(20, 42.0, dtype=np.float64)))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 42.0, atol=1e-12)
@@ -245,7 +254,7 @@ def test_vidya_constant_series_holds_seed():
def test_zero_lag_macd_constant_series_converges_to_zero():
# Each inner ZLEMA reproduces a constant, so macd, signal and histogram
# are all 0 once the slowest branch warms up.
out = ta.ZeroLagMACD(3, 5, 3).batch(np.full(60, 42.0, dtype=np.float64))
out = _to_np(ta.ZeroLagMACD(3, 5, 3).batch(np.full(60, 42.0, dtype=np.float64)))
# Take the last row and verify all three columns are 0.
last = out[-1]
assert math.isclose(last[0], 0.0, abs_tol=1e-12)
@@ -265,7 +274,7 @@ def test_awesome_oscillator_histogram_flat_series_converges_to_zero():
def test_stc_constant_series_yields_zero():
# Flat input collapses both stochastic stages to zero -> STC stays at 0.
out = ta.STC(3, 5, 4, 0.5).batch(np.full(60, 42.0, dtype=np.float64))
out = _to_np(ta.STC(3, 5, 4, 0.5).batch(np.full(60, 42.0, dtype=np.float64)))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_array_equal(ready[-5:], np.zeros(5))
@@ -273,7 +282,7 @@ def test_stc_constant_series_yields_zero():
def test_elder_impulse_constant_series_is_neutral():
# Flat input -> neither EMA nor MACD histogram moves -> Impulse stays at 0.
out = ta.ElderImpulse(13, 12, 26, 9).batch(np.full(120, 42.0, dtype=np.float64))
out = _to_np(ta.ElderImpulse(13, 12, 26, 9).batch(np.full(120, 42.0, dtype=np.float64)))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_array_equal(ready, np.zeros_like(ready))
@@ -281,19 +290,19 @@ def test_elder_impulse_constant_series_is_neutral():
def test_cfo_perfect_linear_series_yields_zero():
# LinReg of a perfectly linear series fits exactly, so CFO = 0 after warmup.
out = ta.CFO(5).batch(np.arange(1.0, 21.0, dtype=np.float64) * 2.0)
out = _to_np(ta.CFO(5).batch(np.arange(1.0, 21.0, dtype=np.float64) * 2.0))
np.testing.assert_allclose(out[4:], 0.0, atol=1e-9)
def test_apo_constant_series_converges_to_zero():
# Both EMAs reproduce a constant exactly, so APO = 0 after warmup.
out = ta.APO(3, 5).batch(np.full(30, 42.0, dtype=np.float64))
out = _to_np(ta.APO(3, 5).batch(np.full(30, 42.0, dtype=np.float64)))
assert np.all(np.isnan(out[:4]))
np.testing.assert_allclose(out[4:], 0.0, atol=1e-12)
def test_macd_constant_series_converges_to_zero():
out = ta.MACD().batch(np.full(200, 100.0))
out = _to_np(ta.MACD().batch(np.full(200, 100.0)))
# Last row's MACD and signal must be ~0.
last = out[-1]
assert math.isclose(last[0], 0.0, abs_tol=1e-9)
@@ -302,13 +311,13 @@ def test_macd_constant_series_converges_to_zero():
def test_bollinger_constant_series_zero_width():
out = ta.BollingerBands(20, 2.0).batch(np.full(50, 100.0))
out = _to_np(ta.BollingerBands(20, 2.0).batch(np.full(50, 100.0)))
row = out[-1]
np.testing.assert_allclose(row, [100.0, 100.0, 100.0, 0.0], atol=1e-12)
def test_bollinger_upper_middle_lower_ordering():
out = ta.BollingerBands(20, 2.0).batch(np.linspace(50.0, 150.0, 100))
out = _to_np(ta.BollingerBands(20, 2.0).batch(np.linspace(50.0, 150.0, 100)))
ready = out[~np.isnan(out[:, 0])]
assert np.all(ready[:, 0] >= ready[:, 1])
assert np.all(ready[:, 1] >= ready[:, 2])
@@ -319,7 +328,7 @@ def test_atr_constant_range_constant_output():
high = np.full(30, 11.0)
low = np.full(30, 9.0)
close = np.full(30, 10.0)
out = ta.ATR(14).batch(high, low, close)
out = _to_np(ta.ATR(14).batch(high, low, close))
# Once seeded, ATR equals the constant TR of 2.
np.testing.assert_allclose(out[13:], 2.0, atol=1e-12)
@@ -329,14 +338,14 @@ def test_stochastic_extremes():
high = np.array([10.0, 11.0, 12.0])
low = np.array([8.0, 9.0, 10.0])
close = np.array([9.0, 10.0, 12.0])
out = ta.Stochastic(3, 1).batch(high, low, close)
out = _to_np(ta.Stochastic(3, 1).batch(high, low, close))
assert math.isclose(out[2, 0], 100.0, abs_tol=1e-12)
def test_obv_cumulative_known_sequence():
close = np.array([10.0, 11.0, 10.5, 10.5, 12.0])
volume = np.array([100.0, 20.0, 30.0, 40.0, 10.0])
out = ta.OBV().batch(close, volume)
out = _to_np(ta.OBV().batch(close, volume))
np.testing.assert_allclose(out, [0.0, 20.0, -10.0, -10.0, 0.0])
@@ -346,7 +355,7 @@ def test_obv_cumulative_known_sequence():
def test_sharpe_ratio_known_window():
# returns [0.01, 0.02, 0.03, 0.04], rf = 0; mean = 0.025;
# sample-var = 0.000166...; Sharpe = 0.025 / sqrt(var).
out = ta.SharpeRatio(4, 0.0).batch(np.array([0.01, 0.02, 0.03, 0.04]))
out = _to_np(ta.SharpeRatio(4, 0.0).batch(np.array([0.01, 0.02, 0.03, 0.04])))
expected = 0.025 / math.sqrt(0.000_166_666_666_666_666_67)
assert math.isclose(out[3], expected, rel_tol=1e-9)
@@ -354,49 +363,49 @@ def test_sharpe_ratio_known_window():
def test_sortino_ratio_known_window():
# returns [-0.02, 0.01, -0.01, 0.03], mar = 0; mean = 0.0025;
# downside_sq = 0.0005; dd = sqrt(0.0005/4); Sortino = 0.0025/dd.
out = ta.SortinoRatio(4, 0.0).batch(np.array([-0.02, 0.01, -0.01, 0.03]))
out = _to_np(ta.SortinoRatio(4, 0.0).batch(np.array([-0.02, 0.01, -0.01, 0.03])))
expected = 0.0025 / math.sqrt(0.000_125)
assert math.isclose(out[3], expected, rel_tol=1e-9)
def test_max_drawdown_known_window():
# window [100, 120, 90] -> peak 120, trough 90 -> 25% drawdown.
out = ta.MaxDrawdown(3).batch(np.array([100.0, 120.0, 90.0]))
out = _to_np(ta.MaxDrawdown(3).batch(np.array([100.0, 120.0, 90.0])))
assert math.isclose(out[2], 0.25, abs_tol=1e-12)
def test_pain_index_known_window():
# dd[0..2] = 0, 0, 0.25; mean = 0.25/3.
out = ta.PainIndex(3).batch(np.array([100.0, 120.0, 90.0]))
out = _to_np(ta.PainIndex(3).batch(np.array([100.0, 120.0, 90.0])))
assert math.isclose(out[2], 0.25 / 3.0, abs_tol=1e-12)
def test_profit_factor_known_window():
# gains 0.05, losses 0.03 -> PF = 5/3.
out = ta.ProfitFactor(4).batch(np.array([0.02, -0.01, 0.03, -0.02]))
out = _to_np(ta.ProfitFactor(4).batch(np.array([0.02, -0.01, 0.03, -0.02])))
assert math.isclose(out[3], 5.0 / 3.0, rel_tol=1e-9)
def test_gain_loss_ratio_known_window():
# avg_win 0.03, avg_loss 0.02 -> GLR = 1.5.
out = ta.GainLossRatio(4).batch(np.array([0.02, -0.01, 0.04, -0.03]))
out = _to_np(ta.GainLossRatio(4).batch(np.array([0.02, -0.01, 0.04, -0.03])))
assert math.isclose(out[3], 1.5, rel_tol=1e-9)
def test_omega_ratio_known_window():
# gains 0.04, losses 0.03 -> Omega = 4/3.
out = ta.OmegaRatio(4, 0.0).batch(np.array([-0.02, 0.01, -0.01, 0.03]))
out = _to_np(ta.OmegaRatio(4, 0.0).batch(np.array([-0.02, 0.01, -0.01, 0.03])))
assert math.isclose(out[3], 4.0 / 3.0, rel_tol=1e-9)
def test_kelly_criterion_known_window():
# n_win=n_loss=2, payoff=2 -> Kelly = 0.5 - 0.5/2 = 0.25.
out = ta.KellyCriterion(4).batch(np.array([0.02, 0.04, -0.01, -0.02]))
out = _to_np(ta.KellyCriterion(4).batch(np.array([0.02, 0.04, -0.01, -0.02])))
assert math.isclose(out[3], 0.25, rel_tol=1e-9)
def test_drawdown_duration_under_water_counter():
out = ta.DrawdownDuration().batch(np.array([100.0, 95.0, 90.0, 85.0]))
out = _to_np(ta.DrawdownDuration().batch(np.array([100.0, 95.0, 90.0, 85.0])))
np.testing.assert_allclose(out, [0.0, 1.0, 2.0, 3.0])
@@ -404,35 +413,35 @@ def test_recovery_factor_known_path():
# Start 100, peak 110, trough 88 -> max_dd = 0.20; end 130 ->
# net_return = 0.30 -> Recovery = 1.5.
prices = np.array([100.0, 110.0, 105.0, 95.0, 88.0, 100.0, 120.0, 130.0])
out = ta.RecoveryFactor().batch(prices)
out = _to_np(ta.RecoveryFactor().batch(prices))
assert math.isclose(out[-1], 1.5, rel_tol=1e-9)
def test_alpha_perfect_capm_fit_yields_zero():
bench = np.array([0.01 * i for i in range(1, 21)])
asset = 2.0 * bench
out = ta.Alpha(20, 0.0).batch(asset, bench)
out = _to_np(ta.Alpha(20, 0.0).batch(asset, bench))
assert math.isclose(out[-1], 0.0, abs_tol=1e-12)
def test_alpha_additive_offset_recovered():
bench = np.array([0.01 * i for i in range(1, 21)])
asset = bench + 0.005
out = ta.Alpha(20, 0.0).batch(asset, bench)
out = _to_np(ta.Alpha(20, 0.0).batch(asset, bench))
assert math.isclose(out[-1], 0.005, rel_tol=1e-9)
def test_treynor_ratio_known_window():
bench = np.array([0.01 * i for i in range(1, 21)])
asset = 2.0 * bench
out = ta.TreynorRatio(20, 0.0).batch(asset, bench)
out = _to_np(ta.TreynorRatio(20, 0.0).batch(asset, bench))
assert math.isclose(out[-1], bench.mean(), rel_tol=1e-9)
def test_information_ratio_known_window():
asset = np.array([0.02, 0.04, 0.06, 0.08])
bench = np.array([0.01, 0.02, 0.03, 0.04])
out = ta.InformationRatio(4).batch(asset, bench)
out = _to_np(ta.InformationRatio(4).batch(asset, bench))
expected = 0.025 / math.sqrt(0.000_166_666_666_666_666_67)
assert math.isclose(out[-1], expected, rel_tol=1e-9)
@@ -443,7 +452,7 @@ def test_pairwise_beta_squared_price_is_two():
# and an undefined slope, which the indicator reports as 0).
b = np.array([100.0 + 10.0 * math.sin(i * 0.5) for i in range(20)])
a = b**2
out = ta.PairwiseBeta(5).batch(a, b)
out = _to_np(ta.PairwiseBeta(5).batch(a, b))
assert math.isclose(out[-1], 2.0, rel_tol=1e-9)
@@ -451,7 +460,7 @@ def test_pairwise_beta_inverse_price_is_minus_one():
# a = 1/b ⇒ a's log-returns are −1× b's ⇒ pairwise beta = −1.
b = np.array([100.0 + 10.0 * math.sin(i * 0.5) for i in range(20)])
a = 1.0 / b
out = ta.PairwiseBeta(5).batch(a, b)
out = _to_np(ta.PairwiseBeta(5).batch(a, b))
assert math.isclose(out[-1], -1.0, rel_tol=1e-9)
@@ -460,7 +469,7 @@ def test_pair_spread_zscore_flat_benchmark_sign():
# collapses to the sign of the last move: rising a ⇒ +1, falling a ⇒ −1.
a = np.array([100.0, 100.0, 110.0, 105.0, 130.0])
b = np.full_like(a, 100.0)
out = ta.PairSpreadZScore(2, 2).batch(a, b)
out = _to_np(ta.PairSpreadZScore(2, 2).batch(a, b))
assert math.isclose(out[-1], 1.0, abs_tol=1e-9)
assert math.isclose(out[-2], -1.0, abs_tol=1e-9)
@@ -473,7 +482,7 @@ def test_lead_lag_cross_correlation_negative_lead():
n = 60
a = np.array([sig(t - 2) for t in range(n)])
b = np.array([sig(t) for t in range(n)])
out = ta.LeadLagCrossCorrelation(12, 5).batch(a, b)
out = _to_np(ta.LeadLagCrossCorrelation(12, 5).batch(a, b))
assert int(out[-1, 0]) == -2
assert out[-1, 1] > 0.99
@@ -482,7 +491,7 @@ def test_cointegration_perfect_pair():
# a = 2*b + 5 exactly ⇒ hedge ratio 2, zero spread, degenerate ADF ⇒ 0.
b = np.array([100.0 + t for t in range(40)])
a = 2.0 * b + 5.0
out = ta.Cointegration(20, 1).batch(a, b)
out = _to_np(ta.Cointegration(20, 1).batch(a, b))
assert math.isclose(out[-1, 0], 2.0, rel_tol=1e-9)
assert math.isclose(out[-1, 1], 0.0, abs_tol=1e-6)
assert math.isclose(out[-1, 2], 0.0, abs_tol=1e-12)
@@ -493,7 +502,7 @@ def test_relative_strength_rising_ratio_is_overbought():
n = 20
a = np.array([100.0 + 2.0 * t for t in range(n)])
b = np.full(n, 100.0)
out = ta.RelativeStrengthAB(5, 5).batch(a, b)
out = _to_np(ta.RelativeStrengthAB(5, 5).batch(a, b))
assert out[-1, 0] > 1.0
assert math.isclose(out[-1, 2], 100.0, abs_tol=1e-9)
@@ -501,21 +510,21 @@ def test_relative_strength_rising_ratio_is_overbought():
def test_value_at_risk_known_window():
# returns -5..4 *0.01; q=0.05*9=0.45 -> -0.0455; VaR = 0.0455.
returns = np.array([i * 0.01 for i in range(-5, 5)])
out = ta.ValueAtRisk(10, 0.95).batch(returns)
out = _to_np(ta.ValueAtRisk(10, 0.95).batch(returns))
assert math.isclose(out[-1], 0.0455, rel_tol=1e-9)
def test_conditional_value_at_risk_known_window():
# tail = {-0.10}; CVaR = 0.10.
returns = np.array([i * 0.01 for i in range(-10, 10)])
out = ta.ConditionalValueAtRisk(20, 0.95).batch(returns)
out = _to_np(ta.ConditionalValueAtRisk(20, 0.95).batch(returns))
assert math.isclose(out[-1], 0.10, rel_tol=1e-9)
def test_calmar_ratio_known_path():
# returns [0.10, -0.20, 0.05]; equity 1.0->1.10->0.88->0.924;
# mdd = 0.20; mean = -0.01666...; Calmar = mean / 0.20.
out = ta.CalmarRatio(3).batch(np.array([0.10, -0.20, 0.05]))
out = _to_np(ta.CalmarRatio(3).batch(np.array([0.10, -0.20, 0.05])))
expected = ((0.10 - 0.20 + 0.05) / 3.0) / 0.20
assert math.isclose(out[-1], expected, rel_tol=1e-9)
@@ -523,7 +532,7 @@ def test_calmar_ratio_known_path():
def test_average_drawdown_known_window():
# window [100, 120, 90, 110]: one drawdown episode (peak 120, trough 90),
# never recovering -> depth (120-90)/120 = 0.25; one episode -> AvgDD = 0.25.
out = ta.AverageDrawdown(4).batch(np.array([100.0, 120.0, 90.0, 110.0]))
out = _to_np(ta.AverageDrawdown(4).batch(np.array([100.0, 120.0, 90.0, 110.0])))
expected = 0.25
assert math.isclose(out[-1], expected, rel_tol=1e-12)
@@ -536,7 +545,7 @@ def test_value_area_concentrated_volume_locates_poc():
high = np.array([100.5, 100.5, 100.5, 100.5, 110.5])
low = np.array([99.5, 99.5, 99.5, 99.5, 109.5])
volume = np.array([1.0, 1.0, 1.0, 1.0, 1000.0])
out = ta.ValueArea(5, 50, 0.70).batch(high, low, volume)
out = _to_np(ta.ValueArea(5, 50, 0.70).batch(high, low, volume))
poc = out[-1, 0]
assert 109.5 <= poc <= 110.5
# VAH >= POC >= VAL.
@@ -547,7 +556,7 @@ def test_initial_balance_locks_after_period():
# First two bars set IB = [99, 103]. Third bar (extreme) must be ignored.
high = np.array([102.0, 103.0, 200.0])
low = np.array([100.0, 99.0, 50.0])
out = ta.InitialBalance(2).batch(high, low)
out = _to_np(ta.InitialBalance(2).batch(high, low))
# Bar 0: IB = [100, 102]; Bar 1: IB locked at [99, 103]; Bar 2: unchanged.
np.testing.assert_allclose(out[0], [102.0, 100.0])
np.testing.assert_allclose(out[1], [103.0, 99.0])
@@ -560,7 +569,7 @@ def test_opening_range_breakout_distance_signed():
high = np.array([102.0, 103.0, 110.0, 110.0])
low = np.array([100.0, 101.0, 102.0, 90.0])
close = np.array([101.0, 102.0, 105.0, 95.0])
out = ta.OpeningRange(2).batch(high, low, close)
out = _to_np(ta.OpeningRange(2).batch(high, low, close))
assert math.isclose(out[2, 0], 103.0)
assert math.isclose(out[2, 1], 100.0)
assert math.isclose(out[2, 2], 105.0 - 101.5)
@@ -578,19 +587,19 @@ def test_inverse_fisher_saturates_for_large_input():
def test_super_smoother_constant_input_is_constant():
out = ta.SuperSmoother(20).batch(np.full(200, 50.0))
out = _to_np(ta.SuperSmoother(20).batch(np.full(200, 50.0)))
# Steady-state gain is 1, so a flat input stays flat.
np.testing.assert_allclose(out[-50:], 50.0, atol=1e-9)
def test_decycler_oscillator_flat_series_is_zero():
out = ta.DecyclerOscillator(10, 30).batch(np.full(80, 42.0))
out = _to_np(ta.DecyclerOscillator(10, 30).batch(np.full(80, 42.0)))
ready = out[~np.isnan(out)]
np.testing.assert_allclose(ready, 0.0, atol=1e-9)
def test_mama_constant_series_both_lines_converge_to_price():
out = ta.MAMA().batch(np.full(200, 100.0))
out = _to_np(ta.MAMA().batch(np.full(200, 100.0)))
last = out[-1]
# MAMA and FAMA both track price closely on a flat series.
assert abs(last[0] - 100.0) < 1.0
@@ -606,7 +615,7 @@ def test_td_setup_buy_setup_completes_at_minus_9_uptrend():
h = np.arange(2.0, 22.0)
l = h - 1.0
c = h - 0.5
out = ta.TDSetup(4, 9).batch(h, l, c)
out = _to_np(ta.TDSetup(4, 9).batch(h, l, c))
assert out[12] == pytest.approx(-9.0)
assert out[-1] == pytest.approx(-9.0)
@@ -615,7 +624,7 @@ def test_td_demarker_downtrend_pegs_at_zero():
n = 20
h = np.arange(30.0, 30.0 - n, -1.0)
l = h - 2.0
out = ta.TDDeMarker(5).batch(h, l)
out = _to_np(ta.TDDeMarker(5).batch(h, l))
assert out[-1] == pytest.approx(0.0)
@@ -626,7 +635,7 @@ def test_td_pressure_pure_bearish_yields_minus_100():
low = np.full(n, 9.0)
close = np.full(n, 9.0)
volume = np.full(n, 100.0)
out = ta.TDPressure(5).batch(open_, high, low, close, volume)
out = _to_np(ta.TDPressure(5).batch(open_, high, low, close, volume))
assert out[-1] == pytest.approx(-100.0)
@@ -638,7 +647,7 @@ def test_td_combo_uptrend_completes_to_minus_13():
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDCombo().batch(high, low, close)
out = _to_np(ta.TDCombo().batch(high, low, close))
assert out[-1] == pytest.approx(-13.0)
@@ -647,7 +656,7 @@ def test_td_countdown_uptrend_completes_to_minus_13():
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDCountdown().batch(high, low, close)
out = _to_np(ta.TDCountdown().batch(high, low, close))
assert out[-1] == pytest.approx(-13.0)
@@ -655,9 +664,9 @@ def test_td_range_projection_doji_reference():
# open=close=10, high=12, low=9 -> doji branch.
# pivot_sum = 12 + 9 + 2*10 = 41; half = 20.5.
# projHigh = 20.5 - 9 = 11.5; projLow = 20.5 - 12 = 8.5.
out = ta.TDRangeProjection().batch(
out = _to_np(ta.TDRangeProjection().batch(
np.array([10.0]), np.array([12.0]), np.array([9.0]), np.array([10.0])
)
))
assert out[0, 0] == pytest.approx(11.5)
assert out[0, 1] == pytest.approx(8.5)
@@ -685,7 +694,7 @@ def test_td_lines_uptrend_support_reference():
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDLines().batch(high, low, close)
out = _to_np(ta.TDLines().batch(high, low, close))
assert math.isnan(out[-1, 0])
assert out[-1, 1] == pytest.approx(4.5)
@@ -699,7 +708,7 @@ def test_td_risk_level_uptrend_sell_risk_reference():
high = np.arange(1.0, 1.0 + n) + 0.5
low = high - 1.0
close = high - 0.5
out = ta.TDRiskLevel().batch(high, low, close)
out = _to_np(ta.TDRiskLevel().batch(high, low, close))
assert math.isnan(out[12, 0])
assert out[12, 1] == pytest.approx(15.0)
@@ -729,7 +738,7 @@ def test_donchian_stop_window_extremes():
# 5-bar window of highs 1..5 and lows 0..4.
high = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
low = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
out = ta.DonchianStop(5).batch(high, low)
out = _to_np(ta.DonchianStop(5).batch(high, low))
# First 4 rows NaN, fifth row: stop_long = 0, stop_short = 5.
for i in range(4):
assert math.isnan(out[i, 0])
@@ -744,7 +753,7 @@ def test_hilo_activator_flat_market_holds_low_sma():
h = np.full(15, 11.0)
l = np.full(15, 9.0)
c = np.full(15, 10.0)
out = ta.HiLoActivator(3).batch(h, l, c)
out = _to_np(ta.HiLoActivator(3).batch(h, l, c))
# warmup_period == period + 1 == 4, so indices 0..2 are NaN; index 3 onwards is 9.
for i in range(3):
assert math.isnan(out[i])
@@ -757,7 +766,7 @@ def test_volty_stop_flat_market_constant_level():
h = np.full(20, 11.0)
l = np.full(20, 9.0)
c = np.full(20, 10.0)
out = ta.VoltyStop(5, 2.0).batch(h, l, c)
out = _to_np(ta.VoltyStop(5, 2.0).batch(h, l, c))
for i in range(4):
assert math.isnan(out[i])
for i in range(4, 20):
@@ -769,7 +778,7 @@ def test_yoyo_exit_flat_market_constant_level():
h = np.full(20, 11.0)
l = np.full(20, 9.0)
c = np.full(20, 10.0)
out = ta.YoyoExit(5, 2.0).batch(h, l, c)
out = _to_np(ta.YoyoExit(5, 2.0).batch(h, l, c))
for i in range(4):
assert math.isnan(out[i])
for i in range(4, 20):
@@ -781,7 +790,7 @@ def test_rvi_volatility_pure_uptrend_saturates_at_one_hundred():
# RVIVolatility saturates at 100. Renamed from the original ta.RVI in
# PR 42 to disambiguate from Family 02's Relative Vigor Index, which
# now owns the short ta.RVI name (candle input).
out = ta.RVIVolatility(5).batch(np.arange(1.0, 41.0, dtype=np.float64))
out = _to_np(ta.RVIVolatility(5).batch(np.arange(1.0, 41.0, dtype=np.float64)))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready[-10:], 100.0, atol=1e-9)
@@ -791,7 +800,7 @@ def test_parkinson_volatility_zero_range_yields_zero():
# H == L every bar -> ln(H/L) = 0 -> Parkinson sigma is zero.
h = np.full(30, 10.0)
l = np.full(30, 10.0)
out = ta.ParkinsonVolatility(14, 252).batch(h, l)
out = _to_np(ta.ParkinsonVolatility(14, 252).batch(h, l))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
@@ -803,7 +812,7 @@ def test_garman_klass_zero_movement_yields_zero():
h = np.full(30, 10.0)
l = np.full(30, 10.0)
c = np.full(30, 10.0)
out = ta.GarmanKlassVolatility(14, 252).batch(o, h, l, c)
out = _to_np(ta.GarmanKlassVolatility(14, 252).batch(o, h, l, c))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
@@ -814,7 +823,7 @@ def test_rogers_satchell_zero_movement_yields_zero():
h = np.full(30, 10.0)
l = np.full(30, 10.0)
c = np.full(30, 10.0)
out = ta.RogersSatchellVolatility(14, 252).batch(o, h, l, c)
out = _to_np(ta.RogersSatchellVolatility(14, 252).batch(o, h, l, c))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
@@ -827,7 +836,7 @@ def test_yang_zhang_zero_movement_yields_zero():
h = np.full(30, 10.0)
l = np.full(30, 10.0)
c = np.full(30, 10.0)
out = ta.YangZhangVolatility(14, 252).batch(o, h, l, c)
out = _to_np(ta.YangZhangVolatility(14, 252).batch(o, h, l, c))
ready = out[~np.isnan(out)]
assert ready.size > 0
np.testing.assert_allclose(ready, 0.0, atol=1e-12)
@@ -952,7 +961,7 @@ def test_kyles_lambda_recovers_constant_impact():
size.append(sz)
is_buy.append(buy)
mids.append(mid)
out = ta.KylesLambda(6).batch(price, size, is_buy, mids)
out = _to_np(ta.KylesLambda(6).batch(price, size, is_buy, mids))
assert out[-1] == pytest.approx(0.5, abs=1e-9)