Testing

Pandas TA Classic uses a multi-layered testing strategy to ensure indicator correctness, robustness, and reliability.

Unit Tests 

Why: Verify individual indicators produce correct values for known inputs.

Files: test_indicator_candle.py, test_indicator_cycles.py, test_indicator_momentum.py, test_indicator_overlap.py, test_indicator_performance.py, test_indicator_statistics.py, test_indicator_trend.py, test_indicator_volatility.py, test_indicator_volume.py, test_indicator_math.py.

Uses IndicatorSpec-based assertions (assert_indicator_standard) against real market data from SPY_D.csv.

Run: python -m unittest tests.test_indicator_momentum -v

Extension API Tests 

Why: Confirm indicators work correctly through the df.ta DataFrame accessor with append=True.

Files: test_ext_indicator_candle.py, test_ext_indicator_cycles.py, test_ext_indicator_momentum.py, test_ext_indicator_overlap_ext.py, test_ext_indicator_performance.py, test_ext_indicator_statistics.py, test_ext_indicator_trend.py, test_ext_indicator_volatility.py, test_ext_indicator_volume.py.

Run: python -m pytest tests/test_ext_indicator_momentum.py -v

Accessor API Tests 

Why: Validate DataFrame accessor metadata and utilities: prefix/suffix naming, indicators() discovery, ticker() data fetching, time range filtering, and constants().

Files: test_accessor_api.py, test_ext_assertions.py.

Run: python -m pytest tests/test_accessor_api.py -v

Oracle / Comparison Tests 

Why: Compare native (talib=False) implementations against TA-Lib (C library) and tulipy outputs to catch numerical divergence. Requires ta-lib and tulipy installed.

Files: test_oracle_talib.py, test_oracle_tulipy.py.

Run: python -m pytest tests/test_oracle_talib.py -v

Native Indicator Tests 

Why: Cover indicators that have no TA-Lib alternative, validating return type, non-NaN row count, value finiteness, and mathematical bounds.

Files: test_native_indicators.py.

Run: python -m pytest tests/test_native_indicators.py -v

Regression Tests 

Why: Prevent reintroduction of known bugs and catch silent value drift.

test_regression.py — Spot-checks indicator values at 5 fixed indices (50, 200, 500, 1500, 3000) against stored fixture data.
test_regression_bugfixes.py — Pins ~12 documented fixes from CHANGELOG.
test_indicator_values.py — Golden fixture tests: checks last non-NaN values and per-column NaN counts against snapshots in tests/fixtures/.

Run: python -m pytest tests/test_regression.py -v

Edge-Case Tests 

Why: Verify indicators don’t crash on degenerate inputs.

test_indicator_edge_cases.py — All-NaN series, constant-price series, ±Inf injection at mid-series positions, and mismatched OHLCV lengths.
test_nan_behaviour.py — NaN prefix warmup periods, minimum length requirements, boundary conditions.

Run: python -m pytest tests/test_indicator_edge_cases.py -v

Integration / E2E Tests 

Why: Exercise full workflows end-to-end.

Files: test_integration_e2e.py — Multi-indicator chaining, Strategy execution with df.ta.strategy(), plugin binding, and category-strategy runs.

Run: python -m pytest tests/test_integration_e2e.py -v

Fluent API Tests 

Why: Validate the df.ta.chain() fluent programming API.

Files: test_fluent_chaining.py — Chained indicator calls, auto-append behaviour, unchain().

Run: python -m pytest tests/test_fluent_chaining.py -v

Strategy Tests 

Why: Confirm the Strategy class executes correctly, including multi-core processing.

Files: test_strategy.py (runs separately from the main suite).

Run: python -m pytest tests/test_strategy.py -v

Custom / Plugin Tests 

Why: Verify the custom indicator registration system.

Files: test_custom.py — ta.custom.bind(), import_dir(), module loading, and custom indicator discovery.

Run: python -m pytest tests/test_custom.py -v

Property-Based Tests 

Why: Randomized input testing using Hypothesis to discover edge cases that deterministic tests miss — overflow conditions, NaN propagation bugs, boundary violations.

Files: test_property_based.py.

What’s tested:

Output invariants — Type correctness, length preservation, naming.
Mathematical invariants — Bollinger Band ordering, ATR/STDEV non-negativity, MOM/ROC relationship.
Core utilities — verify_series, apply_offset, apply_fill.
None-guard safety — Indicators return None for None input.
NaN propagation — All-NaN input → all-NaN output, no crash.
Idempotence — Same args twice → identical result.
Category discovery — Dynamic discovery stays consistent.
Boundedness — RSI, stochastic oscillator within expected ranges (where input assumptions hold).

Strategies used:

Random walks — Cumulative sum of normal increments.
OHLCV DataFrames — Derived OHLC with high ≥ low, close ∈ [low, high].
Constant series — Degenerate arithmetic testing.
Controlled NaN injection — Finite floats with proportionally sampled NaN.

Run:

python -m pytest tests/test_property_based.py -v
python -m pytest tests/test_property_based.py -v --hypothesis-show-statistics
python -m pytest tests/test_property_based.py -v --hypothesis-profile=ci

Adding property tests for a new indicator:

import hypothesis.strategies as st
from hypothesis import assume, given, settings

@given(price_series(min_size=30, max_size=200), st.integers(min_value=2, max_value=20))
@settings(max_examples=100)
def test_my_indicator_output_invariant(s, length):
    assume(len(s) >= length + 2)
    result = ta.my_indicator(s, length=length)
    assert isinstance(result, pd.Series)
    assert len(result) == len(s)
    assert str(length) in result.name

Utility Tests 

Files: test_utils.py (verify_series, apply_offset, apply_fill, cross detection), test_utils_metrics.py (Sharpe ratio, drawdown, CAGR, Jensen’s alpha), test_utils_data_alphavantage.py (AlphaVantage data fetching).

Run: python -m pytest tests/test_utils.py -v

Running All Tests 

# Full test suite (primary — matches CI, auto-regenerates fixture JSONs)
python -m unittest discover tests/ -v

# Regenerate fixtures then run all tests (recommended after indicator changes)
make test-all

# Regenerate fixture JSONs only (requires TA-Lib installed)
make fixtures

# pytest equivalent
python -m pytest tests/ -v

# With coverage
python -m pytest --cov=pandas_ta_classic --cov-report=html tests/

Fixture Files 

tests/fixtures/expected_values.json and tests/fixtures/regression_snapshots.json are generated files. They are rebuilt automatically when tests/ is imported (before any test runs) if TA-Lib is available. Manual regeneration:

python -m tests.fixtures.generate_fixtures
python -m tests.fixtures.generate_regression_snapshots

Both scripts can also be invoked directly (python tests/fixtures/generate_*.py) and require the project root to be on sys.path.