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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
90 lines
2.8 KiB
Python
90 lines
2.8 KiB
Python
"""Shared fixtures for the quantitative test suite."""
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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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from openbb_core.app.utils import df_to_basemodel
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SEED = 42
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N_ROWS = 250
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FACTOR_N_ROWS = 800
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@pytest.fixture(scope="session")
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def prices_df() -> pd.DataFrame:
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"""Build a deterministic OHLCV price series with a date column."""
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rng = np.random.default_rng(SEED)
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t = np.arange(N_ROWS)
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close = 100 + 0.1 * t + rng.normal(0, 1, N_ROWS).cumsum() * 0.5
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dates = pd.date_range("2022-01-03", periods=N_ROWS, freq="B")
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return pd.DataFrame(
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{
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"date": dates,
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"open": close + rng.normal(0, 0.3, N_ROWS),
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"high": close + np.abs(rng.normal(0.8, 0.3, N_ROWS)),
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"low": close - np.abs(rng.normal(0.8, 0.3, N_ROWS)),
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"close": close,
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"volume": (1e6 + rng.normal(0, 5e4, N_ROWS)).astype(float),
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}
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)
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@pytest.fixture(scope="session")
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def prices_data(prices_df) -> list:
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"""Expose the price DataFrame as a ``list[Data]``."""
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return df_to_basemodel(prices_df)
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@pytest.fixture(scope="session")
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def factor_dates() -> pd.DatetimeIndex:
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"""Daily business-day index long enough for rolling 252-day windows."""
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return pd.date_range("2018-01-01", periods=FACTOR_N_ROWS, freq="B")
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@pytest.fixture(scope="session")
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def factor_matrix_df(factor_dates) -> pd.DataFrame:
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"""Two synthetic factors plus a constant risk-free column.
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Used together with ``target_returns_df`` so the true betas are known
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(`y = 0.7*f1 - 0.3*f2 + rf + noise`).
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"""
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rng = np.random.default_rng(SEED)
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n = len(factor_dates)
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return pd.DataFrame(
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{
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"date": factor_dates,
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"f1": rng.normal(0, 0.01, n),
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"f2": rng.normal(0, 0.01, n),
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# rf varies slightly so statsmodels keeps it as its own regressor
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# instead of folding it into the intercept term.
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"rf": 0.0001 + rng.normal(0, 1e-6, n),
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}
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)
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@pytest.fixture(scope="session")
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def target_returns_df(factor_dates, factor_matrix_df) -> pd.DataFrame:
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"""Target series whose returns are a known linear combo of the factors."""
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rng = np.random.default_rng(SEED + 1)
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n = len(factor_dates)
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y = (
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0.7 * factor_matrix_df["f1"].to_numpy()
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- 0.3 * factor_matrix_df["f2"].to_numpy()
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+ factor_matrix_df["rf"].to_numpy()
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+ rng.normal(0, 0.002, n)
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)
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return pd.DataFrame({"date": factor_dates, "close": y})
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@pytest.fixture(scope="session")
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def factor_matrix_data(factor_matrix_df) -> list:
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"""Factor matrix as ``list[Data]``."""
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return df_to_basemodel(factor_matrix_df)
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@pytest.fixture(scope="session")
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def target_returns_data(target_returns_df) -> list:
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"""Target returns as ``list[Data]``."""
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return df_to_basemodel(target_returns_df)
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