signal-platform/tests/conftest.py

"""Shared test fixtures and hypothesis strategies for the stock-data-backend test suite."""

from __future__ import annotations

import string
from datetime import date, datetime, timedelta, timezone
from typing import Any

import pytest
from httpx import ASGITransport, AsyncClient
from hypothesis import strategies as st
from sqlalchemy.ext.asyncio import (
    AsyncSession,
    async_sessionmaker,
    create_async_engine,
)

from sqlalchemy import delete

from app.database import Base
from app.providers.protocol import OHLCVData

# ---------------------------------------------------------------------------
# Test database (SQLite in-memory, async via aiosqlite)
# ---------------------------------------------------------------------------

TEST_DATABASE_URL = "sqlite+aiosqlite://"

_test_engine = create_async_engine(TEST_DATABASE_URL, echo=False)
_test_session_factory = async_sessionmaker(
    _test_engine,
    class_=AsyncSession,
    expire_on_commit=False,
)


_schema_created = False


@pytest.fixture(autouse=True)
async def _setup_db():
    """Hand every test an empty database.

    The schema is built once and then truncated per test rather than dropped and
    recreated. A create_all/drop_all cycle costs ~49ms against these 22 tables and
    ran for every test in the suite — including the many that never open a session
    — where deleting every row costs ~6ms for the same guarantee. No model sets
    ``sqlite_autoincrement``, so SQLite reuses rowids after a full delete and
    generated ids still restart at 1.
    """
    global _schema_created
    async with _test_engine.begin() as conn:
        if not _schema_created:
            await conn.run_sync(Base.metadata.create_all)
            _schema_created = True
        else:
            for table in reversed(Base.metadata.sorted_tables):
                await conn.execute(delete(table))
    yield


@pytest.fixture
async def db_session() -> AsyncSession:
    """Provide a transactional DB session that rolls back after the test."""
    async with _test_session_factory() as session:
        async with session.begin():
            yield session
            await session.rollback()


# ---------------------------------------------------------------------------
# FastAPI test client
# ---------------------------------------------------------------------------


@pytest.fixture
async def client(db_session: AsyncSession) -> AsyncClient:
    """Async HTTP test client wired to the FastAPI app with the test DB session."""
    from app.dependencies import get_db
    from app.main import app

    async def _override_get_db():
        yield db_session

    app.dependency_overrides[get_db] = _override_get_db
    transport = ASGITransport(app=app)
    async with AsyncClient(transport=transport, base_url="http://test") as ac:
        yield ac
    app.dependency_overrides.clear()


# ---------------------------------------------------------------------------
# Mock MarketDataProvider
# ---------------------------------------------------------------------------


class MockMarketDataProvider:
    """Configurable mock that satisfies the MarketDataProvider protocol."""

    def __init__(
        self,
        ohlcv_data: list[OHLCVData] | None = None,
        error: Exception | None = None,
    ) -> None:
        self.ohlcv_data = ohlcv_data or []
        self.error = error
        self.calls: list[dict[str, Any]] = []

    async def fetch_ohlcv(
        self, ticker: str, start_date: date, end_date: date
    ) -> list[OHLCVData]:
        self.calls.append(
            {"ticker": ticker, "start_date": start_date, "end_date": end_date}
        )
        if self.error is not None:
            raise self.error
        return [r for r in self.ohlcv_data if r.ticker == ticker]


@pytest.fixture
def mock_provider() -> MockMarketDataProvider:
    """Return a fresh MockMarketDataProvider instance."""
    return MockMarketDataProvider()


# ---------------------------------------------------------------------------
# Hypothesis custom strategies
# ---------------------------------------------------------------------------

_TICKER_ALPHABET = string.ascii_uppercase + string.digits


@st.composite
def valid_ticker_symbols(draw: st.DrawFn) -> str:
    """Generate uppercase alphanumeric ticker symbols (1-10 chars)."""
    return draw(
        st.text(alphabet=_TICKER_ALPHABET, min_size=1, max_size=10)
    )


@st.composite
def whitespace_strings(draw: st.DrawFn) -> str:
    """Generate strings composed entirely of whitespace (including empty)."""
    return draw(
        st.text(alphabet=" \t\n\r\x0b\x0c", min_size=0, max_size=20)
    )


@st.composite
def valid_ohlcv_records(draw: st.DrawFn) -> OHLCVData:
    """Generate valid OHLCV records (high >= low, prices >= 0, volume >= 0, date <= today)."""
    ticker = draw(valid_ticker_symbols())
    low = draw(st.floats(min_value=0.01, max_value=10000.0, allow_nan=False, allow_infinity=False))
    high = draw(st.floats(min_value=low, max_value=10000.0, allow_nan=False, allow_infinity=False))
    open_ = draw(st.floats(min_value=low, max_value=high, allow_nan=False, allow_infinity=False))
    close = draw(st.floats(min_value=low, max_value=high, allow_nan=False, allow_infinity=False))
    volume = draw(st.integers(min_value=0, max_value=10**12))
    record_date = draw(
        st.dates(min_value=date(2000, 1, 1), max_value=date.today())
    )
    return OHLCVData(
        ticker=ticker,
        date=record_date,
        open=open_,
        high=high,
        low=low,
        close=close,
        volume=volume,
    )


@st.composite
def invalid_ohlcv_records(draw: st.DrawFn) -> OHLCVData:
    """Generate OHLCV records that violate at least one constraint."""
    ticker = draw(valid_ticker_symbols())
    violation = draw(st.sampled_from(["high_lt_low", "negative_price", "negative_volume", "future_date"]))

    if violation == "high_lt_low":
        high = draw(st.floats(min_value=0.01, max_value=100.0, allow_nan=False, allow_infinity=False))
        low = draw(st.floats(min_value=high + 0.01, max_value=200.0, allow_nan=False, allow_infinity=False))
        return OHLCVData(
            ticker=ticker, date=date.today(),
            open=high, high=high, low=low, close=high, volume=100,
        )
    elif violation == "negative_price":
        neg = draw(st.floats(min_value=-10000.0, max_value=-0.01, allow_nan=False, allow_infinity=False))
        return OHLCVData(
            ticker=ticker, date=date.today(),
            open=neg, high=abs(neg), low=abs(neg), close=abs(neg), volume=100,
        )
    elif violation == "negative_volume":
        price = draw(st.floats(min_value=0.01, max_value=100.0, allow_nan=False, allow_infinity=False))
        neg_vol = draw(st.integers(min_value=-10**9, max_value=-1))
        return OHLCVData(
            ticker=ticker, date=date.today(),
            open=price, high=price, low=price, close=price, volume=neg_vol,
        )
    else:  # future_date
        future = date.today() + timedelta(days=draw(st.integers(min_value=1, max_value=365)))
        price = draw(st.floats(min_value=0.01, max_value=100.0, allow_nan=False, allow_infinity=False))
        return OHLCVData(
            ticker=ticker, date=future,
            open=price, high=price, low=price, close=price, volume=100,
        )


_DIMENSIONS = ["technical", "sr_quality", "sentiment", "fundamental", "momentum"]


@st.composite
def dimension_scores(draw: st.DrawFn) -> float:
    """Generate float values in [0, 100] for dimension scores."""
    return draw(st.floats(min_value=0.0, max_value=100.0, allow_nan=False, allow_infinity=False))


@st.composite
def weight_configs(draw: st.DrawFn) -> dict[str, float]:
    """Generate dicts of dimension → positive float weight."""
    dims = draw(st.lists(st.sampled_from(_DIMENSIONS), min_size=1, max_size=5, unique=True))
    weights: dict[str, float] = {}
    for dim in dims:
        weights[dim] = draw(st.floats(min_value=0.01, max_value=10.0, allow_nan=False, allow_infinity=False))
    return weights


@st.composite
def sr_levels(draw: st.DrawFn) -> dict[str, Any]:
    """Generate SR level data (price, type, strength, detection_method)."""
    return {
        "price_level": draw(st.floats(min_value=0.01, max_value=10000.0, allow_nan=False, allow_infinity=False)),
        "type": draw(st.sampled_from(["support", "resistance"])),
        "strength": draw(st.integers(min_value=0, max_value=100)),
        "detection_method": draw(
            st.sampled_from(
                ["volume_profile", "pivot_point", "merged", "round_number"]
            )
        ),
    }


@st.composite
def sentiment_scores(draw: st.DrawFn) -> dict[str, Any]:
    """Generate sentiment data (classification, confidence, source, timestamp)."""
    naive_dt = draw(
        st.datetimes(
            min_value=datetime(2020, 1, 1),
            max_value=datetime.now(),
        )
    )
    return {
        "classification": draw(st.sampled_from(["bullish", "bearish", "neutral"])),
        "confidence": draw(st.integers(min_value=0, max_value=100)),
        "source": draw(st.text(alphabet=string.ascii_lowercase, min_size=3, max_size=20)),
        "timestamp": naive_dt.replace(tzinfo=timezone.utc),
    }


@st.composite
def trade_setups(draw: st.DrawFn) -> dict[str, Any]:
    """Generate trade setup data (direction, entry, stop, target, rr_ratio, composite_score)."""
    direction = draw(st.sampled_from(["long", "short"]))
    entry = draw(st.floats(min_value=1.0, max_value=10000.0, allow_nan=False, allow_infinity=False))
    atr_dist = draw(st.floats(min_value=0.01, max_value=entry * 0.2, allow_nan=False, allow_infinity=False))

    if direction == "long":
        stop = entry - atr_dist
        target = entry + atr_dist * draw(st.floats(min_value=3.0, max_value=10.0, allow_nan=False, allow_infinity=False))
    else:
        stop = entry + atr_dist
        target = entry - atr_dist * draw(st.floats(min_value=3.0, max_value=10.0, allow_nan=False, allow_infinity=False))

    rr_ratio = abs(target - entry) / abs(entry - stop) if abs(entry - stop) > 0 else 0.0

    return {
        "direction": direction,
        "entry_price": entry,
        "stop_loss": stop,
        "target": target,
        "rr_ratio": rr_ratio,
        "composite_score": draw(st.floats(min_value=0.0, max_value=100.0, allow_nan=False, allow_infinity=False)),
    }