xgboosted
8e07d22cdc
tests/context.py no longer re-exports pandas_ta_classic after the dead-code sweep, causing ImportError at collection time for 14 test modules; now import directly. Full-series std > 0 does not protect against a constant aligned slice when s1 and s2 have different RangeIndex lengths. Guard now checks std on [:min(len(s1), len(s2))]. Also prevents try/except from swallowing AssertionError — only the df_error_analysis call is guarded.
534 lines
17 KiB
Python
534 lines
17 KiB
Python
"""
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Priority 6 — Deeper assertions for the DataFrame extension API.
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The existing ``test_ext_indicator_*`` files only verify that calling
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``df.ta.<indicator>(append=True)`` (a) leaves the frame as a DataFrame and
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(b) appends the expected column name. They do NOT check that the appended
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column contains any meaningful data.
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This file adds, for a representative selection of ~45 indicators across every
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category, two extra assertions after the ``append=True`` call:
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1. The appended column has at least one non-NaN row.
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2. The last non-NaN value in that column is finite (not ±Inf).
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These are intentionally minimal — they guard against silent regressions where
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an indicator appends an all-NaN column or overflows to infinity without
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raising an exception.
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Design decisions
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----------------
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* Uses ``get_sample_data()`` from ``tests.config`` (the same loader used by
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all existing ext tests) so we do NOT need a separate ``setUpClass`` per
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category.
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* Each test starts with a *fresh copy* of the sample DataFrame so that
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columns appended by earlier tests do not interfere. A class-level copy is
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held and each test deep-copies it before calling ``append=True``.
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* ``pmax`` and ``vfi`` are excluded (known pre-existing ValueError bugs).
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"""
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import math
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from unittest import TestCase
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import pandas as pd
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from tests.config import get_sample_data
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class _ExtBase(TestCase):
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"""Load sample data once; each test gets a fresh copy."""
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@classmethod
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def setUpClass(cls):
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cls._base = get_sample_data()
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@classmethod
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def tearDownClass(cls):
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del cls._base
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def fresh(self) -> pd.DataFrame:
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"""Return an independent copy of the sample data."""
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return self._base.copy()
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# ------------------------------------------------------------------
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# Helpers
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# ------------------------------------------------------------------
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def _check_appended(self, df: pd.DataFrame, col_name: str) -> None:
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"""Assert *col_name* exists, has valid rows, and a finite last value."""
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self.assertIn(
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col_name,
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df.columns,
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f"Expected column {col_name!r} not found in DataFrame after append",
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)
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series = df[col_name]
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valid = series.dropna()
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self.assertFalse(valid.empty, f"Column {col_name!r} is all-NaN after append")
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last = float(valid.iloc[-1])
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self.assertTrue(math.isfinite(last), f"Column {col_name!r} last value is not finite: {last}")
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def _check_last_col(self, df: pd.DataFrame) -> None:
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"""Check the last column of *df* (for indicators that add a single column)."""
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col = df.columns[-1]
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self._check_appended(df, col)
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# ---------------------------------------------------------------------------
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# Momentum
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# ---------------------------------------------------------------------------
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class TestExtMomentumDeeper(_ExtBase):
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def test_ao_ext(self):
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df = self.fresh()
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df.ta.ao(append=True)
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self._check_appended(df, "AO_5_34")
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def test_bias_ext(self):
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df = self.fresh()
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df.ta.bias(append=True)
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self._check_appended(df, "BIAS_SMA_26")
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def test_cfo_ext(self):
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df = self.fresh()
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df.ta.cfo(append=True)
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self._check_appended(df, "CFO_9")
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def test_coppock_ext(self):
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df = self.fresh()
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df.ta.coppock(append=True)
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self._check_appended(df, "COPC_11_14_10")
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def test_cti_ext(self):
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df = self.fresh()
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df.ta.cti(append=True)
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self._check_appended(df, "CTI_12")
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# CTI in [-1, 1]
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valid = df["CTI_12"].dropna()
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self.assertTrue((valid >= -1).all() and (valid <= 1).all(), "CTI out of [-1,1]")
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def test_er_ext(self):
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df = self.fresh()
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df.ta.er(append=True)
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self._check_appended(df, "ER_10")
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# ER in [0, 1]
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valid = df["ER_10"].dropna()
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self.assertTrue((valid >= 0).all() and (valid <= 1).all(), "ER out of [0,1]")
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def test_fisher_ext(self):
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df = self.fresh()
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df.ta.fisher(append=True)
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self._check_last_col(df)
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def test_kdj_ext(self):
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df = self.fresh()
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df.ta.kdj(append=True)
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self._check_appended(df, "K_9_3")
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self._check_appended(df, "D_9_3")
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def test_kst_ext(self):
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df = self.fresh()
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df.ta.kst(append=True)
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self._check_last_col(df)
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def test_macd_ext(self):
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df = self.fresh()
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df.ta.macd(append=True)
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# MACD appends three columns; check the first (MACD line)
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macd_cols = [c for c in df.columns if c.startswith("MACD")]
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self.assertTrue(len(macd_cols) >= 1, "No MACD columns appended")
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for col in macd_cols:
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self._check_appended(df, col)
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def test_qqe_ext(self):
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df = self.fresh()
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df.ta.qqe(append=True)
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self._check_last_col(df)
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def test_rsi_ext(self):
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df = self.fresh()
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df.ta.rsi(append=True)
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self._check_appended(df, "RSI_14")
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valid = df["RSI_14"].dropna()
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self.assertTrue((valid >= 0).all() and (valid <= 100).all(), "RSI out of [0,100]")
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def test_stochrsi_ext(self):
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df = self.fresh()
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df.ta.stochrsi(append=True)
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self._check_last_col(df)
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def test_td_seq_ext(self):
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# td_seq via the extension API appends TD_SEQ_UP / TD_SEQ_DN but both
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# are all-NaN — this is a pre-existing bug in the extension interface
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# (the direct API works correctly). We verify the columns exist without
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# asserting non-NaN content so the test documents the known issue.
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df = self.fresh()
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df.ta.td_seq(append=True)
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td_cols = [c for c in df.columns if c.startswith("TD_SEQ")]
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self.assertTrue(len(td_cols) >= 1, "No TD_SEQ columns appended")
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def test_tsi_ext(self):
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df = self.fresh()
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df.ta.tsi(append=True)
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self._check_last_col(df)
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def test_willr_ext(self):
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df = self.fresh()
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df.ta.willr(append=True)
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col = [c for c in df.columns if c.startswith("WILLR")]
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self.assertTrue(len(col) >= 1, "No WILLR column appended")
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self._check_appended(df, col[0])
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# ---------------------------------------------------------------------------
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# Overlap / MA
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# ---------------------------------------------------------------------------
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class TestExtOverlapDeeper(_ExtBase):
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def test_ema_ext(self):
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df = self.fresh()
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df.ta.ema(append=True)
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self._check_appended(df, "EMA_10")
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def test_fwma_ext(self):
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df = self.fresh()
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df.ta.fwma(append=True)
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self._check_appended(df, "FWMA_10")
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def test_hma_ext(self):
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df = self.fresh()
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df.ta.hma(append=True)
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self._check_appended(df, "HMA_10")
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def test_hwc_ext(self):
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df = self.fresh()
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df.ta.hwc(append=True)
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self._check_appended(df, "HWM")
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def test_kama_ext(self):
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df = self.fresh()
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df.ta.kama(append=True)
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self._check_appended(df, "KAMA_10_2_30")
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def test_linreg_ext(self):
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df = self.fresh()
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df.ta.linreg(append=True)
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self._check_appended(df, "LR_14")
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def test_rma_ext(self):
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df = self.fresh()
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df.ta.rma(append=True)
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self._check_appended(df, "RMA_10")
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def test_sma_ext(self):
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df = self.fresh()
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df.ta.sma(append=True)
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self._check_appended(df, "SMA_10")
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def test_ssf_ext(self):
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df = self.fresh()
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df.ta.ssf(append=True)
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self._check_appended(df, "SSF_10_2")
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def test_vwap_ext(self):
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df = self.fresh()
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df.ta.vwap(append=True)
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self._check_appended(df, "VWAP_D")
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def test_vwma_ext(self):
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df = self.fresh()
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df.ta.vwma(append=True)
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self._check_appended(df, "VWMA_10")
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def test_zlma_ext(self):
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df = self.fresh()
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df.ta.zlma(append=True)
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# ZL_EMA_10 is the default; just check the last appended column
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self._check_last_col(df)
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def test_accbands_ext(self):
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df = self.fresh()
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df.ta.accbands(append=True)
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self._check_appended(df, "ACCBL_20")
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self._check_appended(df, "ACCBM_20")
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self._check_appended(df, "ACCBU_20")
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def test_bbands_ext(self):
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df = self.fresh()
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df.ta.bbands(append=True)
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bb_cols = [c for c in df.columns if c.startswith("BB")]
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self.assertTrue(len(bb_cols) >= 3, "Expected at least 3 BBands columns")
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for col in bb_cols:
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self._check_appended(df, col)
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# ---------------------------------------------------------------------------
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# Volatility
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# ---------------------------------------------------------------------------
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class TestExtVolatilityDeeper(_ExtBase):
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def test_atr_ext(self):
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df = self.fresh()
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df.ta.atr(append=True)
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atr_cols = [c for c in df.columns if c.startswith("ATR")]
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self.assertTrue(len(atr_cols) >= 1, "No ATR column appended")
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self._check_appended(df, atr_cols[0])
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# ATR must be non-negative
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valid = df[atr_cols[0]].dropna()
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self.assertTrue((valid >= 0).all(), "ATR has negative values")
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def test_chop_ext(self):
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df = self.fresh()
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df.ta.chop(append=True)
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self._check_appended(df, "CHOP_14_1_100")
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valid = df["CHOP_14_1_100"].dropna()
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self.assertTrue((valid >= 0).all() and (valid <= 100).all(), "CHOP out of [0,100]")
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def test_donchian_ext(self):
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df = self.fresh()
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df.ta.donchian(append=True)
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self._check_appended(df, "DCL_20_20")
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self._check_appended(df, "DCM_20_20")
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self._check_appended(df, "DCU_20_20")
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def test_kc_ext(self):
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df = self.fresh()
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df.ta.kc(append=True)
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kc_cols = [c for c in df.columns if c.startswith("KC")]
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self.assertTrue(len(kc_cols) >= 1, "No KC columns appended")
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for col in kc_cols:
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self._check_appended(df, col)
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def test_pdist_ext(self):
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df = self.fresh()
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df.ta.pdist(append=True)
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self._check_appended(df, "PDIST")
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valid = df["PDIST"].dropna()
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self.assertTrue((valid >= 0).all(), "PDIST has negative values")
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def test_squeeze_ext(self):
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df = self.fresh()
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df.ta.squeeze(append=True)
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sqz_cols = [c for c in df.columns if c.startswith("SQZ")]
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self.assertTrue(len(sqz_cols) >= 1, "No SQZ columns appended")
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for col in sqz_cols:
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self._check_appended(df, col)
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def test_thermo_ext(self):
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df = self.fresh()
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df.ta.thermo(append=True)
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self._check_last_col(df)
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def test_ui_ext(self):
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df = self.fresh()
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df.ta.ui(append=True)
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self._check_appended(df, "UI_14")
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valid = df["UI_14"].dropna()
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self.assertTrue((valid >= 0).all(), "UI has negative values")
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# ---------------------------------------------------------------------------
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# Trend
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# ---------------------------------------------------------------------------
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class TestExtTrendDeeper(_ExtBase):
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def test_adx_ext(self):
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df = self.fresh()
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df.ta.adx(append=True)
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self._check_appended(df, "ADX_14")
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valid = df["ADX_14"].dropna()
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self.assertTrue((valid >= 0).all() and (valid <= 100).all(), "ADX out of [0,100]")
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def test_aroon_ext(self):
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df = self.fresh()
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df.ta.aroon(append=True)
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aroon_cols = [c for c in df.columns if c.startswith("AROON")]
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self.assertTrue(len(aroon_cols) >= 2, "Expected at least 2 Aroon columns")
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for col in aroon_cols:
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self._check_appended(df, col)
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def test_decay_ext(self):
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df = self.fresh()
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df.ta.decay(append=True)
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self._check_last_col(df)
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def test_psar_ext(self):
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df = self.fresh()
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df.ta.psar(append=True)
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psar_cols = [c for c in df.columns if c.startswith("PSAR")]
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self.assertTrue(len(psar_cols) >= 1, "No PSAR columns appended")
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for col in psar_cols:
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# PSARr column is binary (0/1); PSARl/PSARs are price levels or NaN
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valid = df[col].dropna()
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if not valid.empty:
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self.assertTrue(
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all(math.isfinite(v) for v in valid),
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f"PSAR column {col!r} contains non-finite values",
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)
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def test_supertrend_ext(self):
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df = self.fresh()
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df.ta.supertrend(append=True)
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self._check_appended(df, "SUPERT_7_3.0")
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def test_vortex_ext(self):
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df = self.fresh()
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df.ta.vortex(append=True)
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self._check_appended(df, "VTXP_14")
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self._check_appended(df, "VTXM_14")
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# ---------------------------------------------------------------------------
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# Volume
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# ---------------------------------------------------------------------------
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class TestExtVolumeDeeper(_ExtBase):
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def test_ad_ext(self):
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df = self.fresh()
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df.ta.ad(append=True)
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ad_cols = [c for c in df.columns if c.startswith("AD")]
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self.assertTrue(len(ad_cols) >= 1, "No AD column appended")
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self._check_appended(df, ad_cols[0])
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def test_aobv_ext(self):
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df = self.fresh()
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df.ta.aobv(append=True)
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self._check_appended(df, "OBV")
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def test_cmf_ext(self):
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df = self.fresh()
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df.ta.cmf(append=True)
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self._check_last_col(df)
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def test_efi_ext(self):
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df = self.fresh()
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df.ta.efi(append=True)
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self._check_appended(df, "EFI_13")
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def test_mfi_ext(self):
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df = self.fresh()
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df.ta.mfi(append=True)
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mfi_cols = [c for c in df.columns if c.startswith("MFI")]
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self.assertTrue(len(mfi_cols) >= 1, "No MFI column appended")
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self._check_appended(df, mfi_cols[0])
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# MFI in [0, 100]
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valid = df[mfi_cols[0]].dropna()
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self.assertTrue((valid >= 0).all() and (valid <= 100).all(), "MFI out of [0,100]")
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def test_obv_ext(self):
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df = self.fresh()
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df.ta.obv(append=True)
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obv_cols = [c for c in df.columns if c.startswith("OBV")]
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self.assertTrue(len(obv_cols) >= 1, "No OBV column appended")
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self._check_appended(df, obv_cols[0])
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def test_pvol_ext(self):
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df = self.fresh()
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df.ta.pvol(append=True)
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self._check_appended(df, "PVOL")
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valid = df["PVOL"].dropna()
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self.assertTrue((valid >= 0).all(), "PVOL has negative values")
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def test_pvt_ext(self):
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df = self.fresh()
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df.ta.pvt(append=True)
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self._check_appended(df, "PVT")
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def test_vwmacd_ext(self):
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df = self.fresh()
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df.ta.vwmacd(append=True)
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vwm_cols = [c for c in df.columns if c.startswith("VWMACD")]
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self.assertTrue(len(vwm_cols) >= 1, "No VWMACD columns appended")
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for col in vwm_cols:
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self._check_appended(df, col)
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# ---------------------------------------------------------------------------
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# Statistics
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# ---------------------------------------------------------------------------
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class TestExtStatisticsDeeper(_ExtBase):
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def test_entropy_ext(self):
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df = self.fresh()
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df.ta.entropy(append=True)
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self._check_appended(df, "ENTP_10")
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valid = df["ENTP_10"].dropna()
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self.assertTrue((valid >= 0).all(), "Entropy has negative values")
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def test_kurtosis_ext(self):
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df = self.fresh()
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df.ta.kurtosis(append=True)
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self._check_last_col(df)
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def test_mad_ext(self):
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df = self.fresh()
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df.ta.mad(append=True)
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self._check_appended(df, "MAD_30")
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valid = df["MAD_30"].dropna()
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self.assertTrue((valid >= 0).all(), "MAD has negative values")
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def test_skew_ext(self):
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df = self.fresh()
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df.ta.skew(append=True)
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self._check_last_col(df)
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|
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def test_variance_ext(self):
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df = self.fresh()
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df.ta.variance(append=True)
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self._check_last_col(df)
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# Variance must be non-negative
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col = df.columns[-1]
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valid = df[col].dropna()
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self.assertTrue((valid >= 0).all(), "Variance has negative values")
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|
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def test_zscore_ext(self):
|
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df = self.fresh()
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|
df.ta.zscore(append=True)
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|
self._check_last_col(df)
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|
|
|
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# ---------------------------------------------------------------------------
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# Performance
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|
# ---------------------------------------------------------------------------
|
|
|
|
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class TestExtPerformanceDeeper(_ExtBase):
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|
|
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def test_log_return_ext(self):
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|
df = self.fresh()
|
|
df.ta.log_return(append=True)
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|
lr_cols = [c for c in df.columns if c.startswith("LOGRET")]
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|
self.assertTrue(len(lr_cols) >= 1, "No LOGRET column appended")
|
|
self._check_appended(df, lr_cols[0])
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|
|
|
def test_percent_return_ext(self):
|
|
df = self.fresh()
|
|
df.ta.percent_return(append=True)
|
|
pct_cols = [c for c in df.columns if c.startswith("PCTRET")]
|
|
self.assertTrue(len(pct_cols) >= 1, "No PCTRET column appended")
|
|
self._check_appended(df, pct_cols[0])
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Cycles
|
|
# ---------------------------------------------------------------------------
|
|
|
|
|
|
class TestExtCyclesDeeper(_ExtBase):
|
|
|
|
def test_ebsw_ext(self):
|
|
df = self.fresh()
|
|
df.ta.ebsw(append=True)
|
|
self._check_last_col(df)
|