python wrapper

python/tests/test_pandas_ta_parity_batch_01.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+import pandas_ta as ta
+import pytest
+
+from quantalib import indicators as q
+
+
+SEED = 42
+N = 10_000
+VERIFY_COUNT = 100
+
+
+def _generate_gbm(
+    n: int,
+    seed: int = SEED,
+    start_price: float = 100.0,
+    mu: float = 0.05,
+    sigma: float = 0.2,
+    dt: float = 1 / 252,
+) -> np.ndarray:
+    rng = np.random.default_rng(seed)
+    z = rng.standard_normal(n - 1)
+    drift = (mu - 0.5 * sigma**2) * dt
+    diffusion = sigma * np.sqrt(dt) * z
+    log_returns = drift + diffusion
+
+    prices = np.empty(n, dtype=np.float64)
+    prices[0] = start_price
+    np.cumsum(log_returns, out=prices[1:])
+    prices[1:] += np.log(start_price)
+    np.exp(prices[1:], out=prices[1:])
+    prices[0] = start_price
+    return prices
+
+
+CLOSE = _generate_gbm(N)
+SERIES = pd.Series(CLOSE, name="close")
+
+
+def _verify_last_n(
+    qtl_arr: np.ndarray,
+    pta_arr: np.ndarray,
+    *,
+    verify_count: int = VERIFY_COUNT,
+    tolerance: float = 1e-6,
+    label: str,
+) -> None:
+    assert len(qtl_arr) == len(pta_arr), f"{label}: length mismatch"
+
+    start = max(0, len(qtl_arr) - verify_count)
+    q_tail = qtl_arr[start:]
+    p_tail = pta_arr[start:]
+
+    finite = np.isfinite(q_tail) & np.isfinite(p_tail)
+    assert int(np.sum(finite)) > 0, f"{label}: no finite overlap in tail"
+
+    diff = np.abs(q_tail[finite] - p_tail[finite])
+    max_diff = float(np.max(diff))
+    assert max_diff <= tolerance, f"{label}: max_diff={max_diff:.3e} > tol={tolerance:.1e}"
+
+
+@pytest.mark.parametrize(
+    "name,qtl,pta,tol",
+    [
+        ("rsi_14", q.rsi(CLOSE, length=14), ta.rsi(SERIES, length=14).to_numpy(), 1e-6),
+        ("mom_10", q.mom(CLOSE, length=10), ta.mom(SERIES, length=10).to_numpy(), 1e-9),
+        ("cmo_14", q.cmo(CLOSE, length=14), ta.cmo(SERIES, length=14, talib=False).to_numpy(), 1e-6),
+        ("apo_12_26", q.apo(CLOSE, fast=12, slow=26), ta.apo(SERIES, fast=12, slow=26, mamode="ema", talib=False).to_numpy(), 1e-6),
+        ("bias_26", q.bias(CLOSE, length=26), ta.bias(SERIES, length=26).to_numpy(), 1e-6),
+        ("cfo_14", q.cfo(CLOSE, length=14), (100.0 * (SERIES - ta.linreg(SERIES, length=14, tsf=False, talib=False)) / SERIES).to_numpy(), 1e-6),
+        ("dpo_20", q.dpo(CLOSE, length=20), ta.dpo(SERIES, length=20, centered=False).to_numpy(), 1e-6),
+        ("trix_18", q.trix(CLOSE, length=18), ta.trix(SERIES, length=18).iloc[:, 0].to_numpy(), 1e-6),
+        ("er_10", q.er(CLOSE, length=10), ta.er(SERIES, length=10).to_numpy(), 1e-6),
+        ("cti_12", q.cti(CLOSE, length=12), ta.cti(SERIES, length=12).to_numpy(), 1e-6),
+    ],
+)
+def test_pandas_ta_parity_batch_01(name: str, qtl: np.ndarray, pta: np.ndarray, tol: float) -> None:
+    _verify_last_n(qtl, pta, tolerance=tol, label=name)