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cd7d602b8f
* refactor quantitative for v5 * add factors endpoint and add a few charts --------- Co-authored-by: deeleeramone <>
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
"""Integration-test fixtures for openbb-quantitative."""
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import threading
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import time
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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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_SEED = 20240517
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_N_ROWS = 500
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@pytest.fixture(scope="session", autouse=True)
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def api_server(pytestconfig):
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"""Run the OpenBB REST API on 0.0.0.0:8000 for the integration session."""
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if pytestconfig.getoption("markexpr") == "not integration":
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yield None
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return
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import uvicorn
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from openbb_core.api.rest_api import app
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config = uvicorn.Config(app, host="0.0.0.0", port=8000, log_level="warning") # noqa: S104
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server = uvicorn.Server(config)
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thread = threading.Thread(target=server.run, daemon=True)
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thread.start()
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deadline = time.time() + 60
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while time.time() < deadline:
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if server.started:
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break
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time.sleep(0.25)
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else: # pragma: no cover
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raise RuntimeError("The OpenBB REST API did not start within 60s.")
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yield server
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server.should_exit = True
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thread.join(timeout=15)
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def _timeseries_records() -> list[dict]:
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"""Build a deterministic OHLCV time series as JSON-ready records."""
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rng = np.random.default_rng(_SEED)
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t = np.arange(_N_ROWS)
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base = 100 + 0.1 * t + rng.normal(0, 1, _N_ROWS).cumsum() * 0.3
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close = np.abs(base + rng.normal(0, 1, _N_ROWS)) + 1.0
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open_ = close + rng.normal(0, 0.5, _N_ROWS)
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high = np.maximum(open_, close) + np.abs(rng.normal(1, 0.5, _N_ROWS))
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low = np.minimum(open_, close) - np.abs(rng.normal(1, 0.5, _N_ROWS))
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volume = 1_000_000 + rng.normal(0, 100_000, _N_ROWS) + 5_000 * t
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dates = pd.date_range("2021-01-04", periods=_N_ROWS, freq="B")
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return [
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{
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"date": day.strftime("%Y-%m-%d"),
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"open": round(float(o), 4),
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"high": round(float(h), 4),
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"low": round(float(lo), 4),
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"close": round(float(c), 4),
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"volume": round(float(v), 2),
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}
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for day, o, h, lo, c, v in zip(dates, open_, high, low, close, volume)
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]
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@pytest.fixture(scope="session")
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def prices_data() -> list[dict]:
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"""Deterministic OHLCV records for the quantitative command tests."""
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return _timeseries_records()
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