mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-28 17:57:45 +00:00
Miha Kralj
81 lines
2.7 KiB
Python
81 lines
2.7 KiB
Python
from __future__ import annotations
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import numpy as np
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import pandas as pd
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import pandas_ta as ta
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import pytest
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from quantalib import indicators as q
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SEED = 42
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N = 10_000
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VERIFY_COUNT = 100
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def _generate_gbm(
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n: int,
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seed: int = SEED,
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start_price: float = 100.0,
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mu: float = 0.05,
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sigma: float = 0.2,
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dt: float = 1 / 252,
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) -> np.ndarray:
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rng = np.random.default_rng(seed)
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z = rng.standard_normal(n - 1)
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drift = (mu - 0.5 * sigma**2) * dt
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diffusion = sigma * np.sqrt(dt) * z
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log_returns = drift + diffusion
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prices = np.empty(n, dtype=np.float64)
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prices[0] = start_price
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np.cumsum(log_returns, out=prices[1:])
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prices[1:] += np.log(start_price)
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np.exp(prices[1:], out=prices[1:])
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prices[0] = start_price
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return prices
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CLOSE = _generate_gbm(N)
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SERIES = pd.Series(CLOSE, name="close")
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def _verify_last_n(
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qtl_arr: np.ndarray,
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pta_arr: np.ndarray,
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*,
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verify_count: int = VERIFY_COUNT,
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tolerance: float = 1e-6,
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label: str,
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) -> None:
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assert len(qtl_arr) == len(pta_arr), f"{label}: length mismatch"
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start = max(0, len(qtl_arr) - verify_count)
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q_tail = qtl_arr[start:]
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p_tail = pta_arr[start:]
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finite = np.isfinite(q_tail) & np.isfinite(p_tail)
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assert int(np.sum(finite)) > 0, f"{label}: no finite overlap in tail"
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diff = np.abs(q_tail[finite] - p_tail[finite])
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max_diff = float(np.max(diff))
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assert max_diff <= tolerance, f"{label}: max_diff={max_diff:.3e} > tol={tolerance:.1e}"
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@pytest.mark.parametrize(
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"name,qtl,pta,tol",
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[
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("rsi_14", q.rsi(CLOSE, length=14), ta.rsi(SERIES, length=14).to_numpy(), 1e-6),
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("mom_10", q.mom(CLOSE, length=10), ta.mom(SERIES, length=10).to_numpy(), 1e-9),
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("cmo_14", q.cmo(CLOSE, length=14), ta.cmo(SERIES, length=14, talib=False).to_numpy(), 1e-6),
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("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),
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("bias_26", q.bias(CLOSE, length=26), ta.bias(SERIES, length=26).to_numpy(), 1e-6),
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("cfo_14", q.cfo(CLOSE, length=14), (100.0 * (SERIES - ta.linreg(SERIES, length=14, tsf=False, talib=False)) / SERIES).to_numpy(), 1e-6),
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("dpo_20", q.dpo(CLOSE, length=20), ta.dpo(SERIES, length=20, centered=False).to_numpy(), 1e-6),
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("trix_18", q.trix(CLOSE, length=18), ta.trix(SERIES, length=18).iloc[:, 0].to_numpy(), 1e-6),
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("er_10", q.er(CLOSE, length=10), ta.er(SERIES, length=10).to_numpy(), 1e-6),
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("cti_12", q.cti(CLOSE, length=12), ta.cti(SERIES, length=12).to_numpy(), 1e-6),
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],
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)
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def test_pandas_ta_parity_batch_01(name: str, qtl: np.ndarray, pta: np.ndarray, tol: float) -> None:
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_verify_last_n(qtl, pta, tolerance=tol, label=name) |