Pratik Bhadane
102 lines
2.8 KiB
Python
102 lines
2.8 KiB
Python
"""
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Shared pytest fixtures for all test modules.
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This module provides session-scoped fixtures to avoid duplicated data setup
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across multiple test files. All fixtures use seeded RNG for reproducibility.
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"""
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from __future__ import annotations
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import pathlib
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import numpy as np
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import pandas as pd
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import pytest
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@pytest.fixture(scope="session")
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def ohlcv_500():
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"""500-bar seeded OHLCV data, always the same across all test files.
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Returns a dictionary with keys: open, high, low, close, volume.
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All arrays are numpy float64 arrays of length 500.
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Seeded with RNG seed=42 for reproducibility.
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"""
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rng = np.random.default_rng(42)
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n = 500
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# Generate realistic price movement
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close = 100.0 + np.cumsum(rng.standard_normal(n) * 0.5)
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high = close + rng.uniform(0.1, 1.5, n)
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low = close - rng.uniform(0.1, 1.5, n)
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open_ = close + rng.standard_normal(n) * 0.3
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volume = rng.uniform(500.0, 5000.0, n)
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return {
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"open": open_,
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"high": high,
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"low": low,
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"close": close,
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"volume": volume,
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}
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@pytest.fixture(scope="session")
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def ohlcv_real():
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"""Real data from tests/fixtures/ohlcv_daily.csv (252 bars).
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Returns a dictionary with keys: open, high, low, close, volume.
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All arrays are numpy float64 arrays of length 252.
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This is real market data for integration testing.
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"""
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fixture_path = pathlib.Path(__file__).parent / "fixtures" / "ohlcv_daily.csv"
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if not fixture_path.exists():
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pytest.skip(f"Fixture file not found: {fixture_path}")
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df = pd.read_csv(fixture_path)
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# Ensure required columns exist
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required_cols = ["open", "high", "low", "close", "volume"]
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for col in required_cols:
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if col not in df.columns:
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pytest.skip(f"Required column '{col}' not found in fixture")
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return {
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"open": df["open"].to_numpy(dtype=np.float64),
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"high": df["high"].to_numpy(dtype=np.float64),
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"low": df["low"].to_numpy(dtype=np.float64),
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"close": df["close"].to_numpy(dtype=np.float64),
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"volume": df["volume"].to_numpy(dtype=np.float64),
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}
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@pytest.fixture(scope="session")
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def ohlcv_100():
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"""100-bar seeded OHLCV data for quick tests.
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Returns a dictionary with keys: open, high, low, close, volume.
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All arrays are numpy float64 arrays of length 100.
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Seeded with RNG seed=42 for reproducibility.
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"""
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rng = np.random.default_rng(42)
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n = 100
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# Generate realistic price movement
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close = 44.0 + np.cumsum(rng.standard_normal(n) * 0.5)
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high = close + rng.uniform(0.1, 1.0, n)
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low = close - rng.uniform(0.1, 1.0, n)
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open_ = close + rng.standard_normal(n) * 0.2
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volume = rng.uniform(500.0, 2000.0, n)
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return {
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"open": open_,
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"high": high,
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"low": low,
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"close": close,
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"volume": volume,
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}
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