Big refactoring

tests/property/test_recommendation_properties.py

@@ -0,0 +1,104 @@
+from __future__ import annotations
+
+from hypothesis import given, settings, strategies as st
+
+from app.services.recommendation_service import direction_analyzer, probability_estimator
+
+
+@settings(max_examples=100, deadline=None)
+@given(
+    technical=st.floats(min_value=0, max_value=100),
+    momentum=st.floats(min_value=0, max_value=100),
+    fundamental=st.floats(min_value=0, max_value=100),
+    sentiment=st.sampled_from(["bearish", "neutral", "bullish", None]),
+)
+def test_property_confidence_bounds(technical, momentum, fundamental, sentiment):
+    """Feature: intelligent-trade-recommendations, Property 3: Confidence Score Bounds."""
+    scores = {
+        "technical": technical,
+        "momentum": momentum,
+        "fundamental": fundamental,
+    }
+
+    long_conf = direction_analyzer.calculate_confidence("long", scores, sentiment, conflicts=[])
+    short_conf = direction_analyzer.calculate_confidence("short", scores, sentiment, conflicts=[])
+
+    assert 0 <= long_conf <= 100
+    assert 0 <= short_conf <= 100
+
+
+@settings(max_examples=100, deadline=None)
+@given(
+    strength_low=st.floats(min_value=0, max_value=50),
+    strength_high=st.floats(min_value=50, max_value=100),
+)
+def test_property_strength_monotonic_probability(strength_low, strength_high):
+    """Feature: intelligent-trade-recommendations, Property 11: S/R Strength Monotonicity."""
+    config = {
+        "recommendation_signal_alignment_weight": 0.15,
+        "recommendation_sr_strength_weight": 0.20,
+        "recommendation_distance_penalty_factor": 0.10,
+    }
+    scores = {"technical": 65.0, "momentum": 65.0}
+
+    base_target = {
+        "classification": "Moderate",
+        "distance_atr_multiple": 3.0,
+    }
+
+    low = probability_estimator.estimate_probability(
+        {**base_target, "sr_strength": strength_low},
+        scores,
+        "bullish",
+        "long",
+        config,
+    )
+    high = probability_estimator.estimate_probability(
+        {**base_target, "sr_strength": strength_high},
+        scores,
+        "bullish",
+        "long",
+        config,
+    )
+
+    assert high >= low
+
+
+@settings(max_examples=100, deadline=None)
+@given(
+    near_distance=st.floats(min_value=1.0, max_value=3.0),
+    far_distance=st.floats(min_value=3.1, max_value=8.0),
+)
+def test_property_distance_probability_relationship(near_distance, far_distance):
+    """Feature: intelligent-trade-recommendations, Property 12: Distance Probability Relationship."""
+    config = {
+        "recommendation_signal_alignment_weight": 0.15,
+        "recommendation_sr_strength_weight": 0.20,
+        "recommendation_distance_penalty_factor": 0.10,
+    }
+    scores = {"technical": 65.0, "momentum": 65.0}
+
+    near_prob = probability_estimator.estimate_probability(
+        {
+            "classification": "Conservative",
+            "sr_strength": 60,
+            "distance_atr_multiple": near_distance,
+        },
+        scores,
+        "bullish",
+        "long",
+        config,
+    )
+    far_prob = probability_estimator.estimate_probability(
+        {
+            "classification": "Aggressive",
+            "sr_strength": 60,
+            "distance_atr_multiple": far_distance,
+        },
+        scores,
+        "bullish",
+        "long",
+        config,
+    )
+
+    assert near_prob >= far_prob

tests/unit/test_fundamentals_chain_provider.py

@@ -0,0 +1,72 @@
+"""Unit tests for chained fundamentals provider fallback behavior."""
+
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import pytest
+
+from app.exceptions import ProviderError
+from app.providers.fundamentals_chain import ChainedFundamentalProvider
+from app.providers.protocol import FundamentalData
+
+
+class _FailProvider:
+    def __init__(self, message: str) -> None:
+        self._message = message
+
+    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
+        raise ProviderError(f"{self._message} ({ticker})")
+
+
+class _DataProvider:
+    def __init__(self, data: FundamentalData) -> None:
+        self._data = data
+
+    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
+        return FundamentalData(
+            ticker=ticker,
+            pe_ratio=self._data.pe_ratio,
+            revenue_growth=self._data.revenue_growth,
+            earnings_surprise=self._data.earnings_surprise,
+            market_cap=self._data.market_cap,
+            fetched_at=self._data.fetched_at,
+            unavailable_fields=self._data.unavailable_fields,
+        )
+
+
+@pytest.mark.asyncio
+async def test_chained_provider_uses_fallback_provider_on_primary_failure():
+    fallback_data = FundamentalData(
+        ticker="AAPL",
+        pe_ratio=25.0,
+        revenue_growth=None,
+        earnings_surprise=None,
+        market_cap=1_000_000.0,
+        fetched_at=datetime.now(timezone.utc),
+        unavailable_fields={},
+    )
+
+    provider = ChainedFundamentalProvider([
+        ("primary", _FailProvider("primary down")),
+        ("fallback", _DataProvider(fallback_data)),
+    ])
+
+    result = await provider.fetch_fundamentals("AAPL")
+
+    assert result.pe_ratio == 25.0
+    assert result.market_cap == 1_000_000.0
+    assert result.unavailable_fields.get("provider") == "fallback"
+
+
+@pytest.mark.asyncio
+async def test_chained_provider_raises_when_all_providers_fail():
+    provider = ChainedFundamentalProvider([
+        ("p1", _FailProvider("p1 failed")),
+        ("p2", _FailProvider("p2 failed")),
+    ])
+
+    with pytest.raises(ProviderError) as exc:
+        await provider.fetch_fundamentals("MSFT")
+
+    assert "All fundamentals providers failed" in str(exc.value)

tests/unit/test_openai_sentiment_provider.py

@@ -8,6 +8,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from app.exceptions import ProviderError
 from app.providers.openai_sentiment import OpenAISentimentProvider
 
 
@@ -160,3 +161,42 @@ class TestCitationsExtraction:
 
         assert result.citations == []
         assert result.reasoning == "Quiet day"
+
+
+class TestBatchSentiment:
+    @pytest.mark.asyncio
+    async def test_batch_sentiment_parses_multiple_tickers(self, provider):
+        json_text = (
+            '[{"ticker":"AAPL","classification":"bullish","confidence":81,"reasoning":"Positive earnings"},'
+            '{"ticker":"MSFT","classification":"neutral","confidence":52,"reasoning":"Mixed guidance"}]'
+        )
+        mock_response = _build_response(json_text)
+        provider._client.responses.create = AsyncMock(return_value=mock_response)
+
+        result = await provider.fetch_sentiment_batch(["AAPL", "MSFT"])
+
+        assert set(result.keys()) == {"AAPL", "MSFT"}
+        assert result["AAPL"].classification == "bullish"
+        assert result["MSFT"].classification == "neutral"
+
+    @pytest.mark.asyncio
+    async def test_batch_sentiment_skips_invalid_rows(self, provider):
+        json_text = (
+            '[{"ticker":"AAPL","classification":"bullish","confidence":81,"reasoning":"Positive earnings"},'
+            '{"ticker":"TSLA","classification":"invalid","confidence":95,"reasoning":"Bad shape"}]'
+        )
+        mock_response = _build_response(json_text)
+        provider._client.responses.create = AsyncMock(return_value=mock_response)
+
+        result = await provider.fetch_sentiment_batch(["AAPL", "MSFT"])
+
+        assert set(result.keys()) == {"AAPL"}
+
+    @pytest.mark.asyncio
+    async def test_batch_sentiment_requires_array_json(self, provider):
+        json_text = '{"ticker":"AAPL","classification":"bullish","confidence":81,"reasoning":"Positive earnings"}'
+        mock_response = _build_response(json_text)
+        provider._client.responses.create = AsyncMock(return_value=mock_response)
+
+        with pytest.raises(ProviderError):
+            await provider.fetch_sentiment_batch(["AAPL", "MSFT"])

tests/unit/test_recommendation_service.py

@@ -0,0 +1,130 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from app.services.recommendation_service import (
+    direction_analyzer,
+    probability_estimator,
+    signal_conflict_detector,
+    target_generator,
+)
+
+
+@dataclass
+class _SRLevelStub:
+    id: int
+    price_level: float
+    type: str
+    strength: int
+
+
+def test_high_confidence_long_example():
+    dimension_scores = {
+        "technical": 75.0,
+        "momentum": 68.0,
+        "fundamental": 55.0,
+    }
+
+    confidence = direction_analyzer.calculate_confidence(
+        direction="long",
+        dimension_scores=dimension_scores,
+        sentiment_classification="bullish",
+        conflicts=[],
+    )
+
+    assert confidence > 70.0
+
+
+def test_high_confidence_short_example():
+    dimension_scores = {
+        "technical": 30.0,
+        "momentum": 35.0,
+        "fundamental": 45.0,
+    }
+
+    confidence = direction_analyzer.calculate_confidence(
+        direction="short",
+        dimension_scores=dimension_scores,
+        sentiment_classification="bearish",
+        conflicts=[],
+    )
+
+    assert confidence > 70.0
+
+
+def test_detects_sentiment_technical_conflict():
+    conflicts = signal_conflict_detector.detect_conflicts(
+        dimension_scores={"technical": 72.0, "momentum": 55.0, "fundamental": 50.0},
+        sentiment_classification="bearish",
+    )
+
+    assert any("sentiment-technical" in conflict for conflict in conflicts)
+
+
+def test_generate_targets_respects_direction_and_order():
+    sr_levels = [
+        _SRLevelStub(id=1, price_level=110.0, type="resistance", strength=80),
+        _SRLevelStub(id=2, price_level=115.0, type="resistance", strength=70),
+        _SRLevelStub(id=3, price_level=120.0, type="resistance", strength=60),
+        _SRLevelStub(id=4, price_level=95.0, type="support", strength=75),
+    ]
+
+    targets = target_generator.generate_targets(
+        direction="long",
+        entry_price=100.0,
+        stop_loss=96.0,
+        sr_levels=sr_levels,  # type: ignore[arg-type]
+        atr_value=2.0,
+    )
+
+    assert len(targets) >= 1
+    assert all(target["price"] > 100.0 for target in targets)
+    distances = [target["distance_from_entry"] for target in targets]
+    assert distances == sorted(distances)
+
+
+def test_probability_ranges_by_classification():
+    config = {
+        "recommendation_signal_alignment_weight": 0.15,
+        "recommendation_sr_strength_weight": 0.20,
+        "recommendation_distance_penalty_factor": 0.10,
+    }
+    dimension_scores = {"technical": 70.0, "momentum": 70.0}
+
+    conservative = probability_estimator.estimate_probability(
+        {
+            "classification": "Conservative",
+            "sr_strength": 80,
+            "distance_atr_multiple": 1.5,
+        },
+        dimension_scores,
+        "bullish",
+        "long",
+        config,
+    )
+    moderate = probability_estimator.estimate_probability(
+        {
+            "classification": "Moderate",
+            "sr_strength": 60,
+            "distance_atr_multiple": 3.0,
+        },
+        dimension_scores,
+        "bullish",
+        "long",
+        config,
+    )
+    aggressive = probability_estimator.estimate_probability(
+        {
+            "classification": "Aggressive",
+            "sr_strength": 40,
+            "distance_atr_multiple": 6.0,
+        },
+        dimension_scores,
+        "bullish",
+        "long",
+        config,
+    )
+
+    assert conservative > 60
+    assert 40 <= moderate <= 70
+    assert aggressive < 50

tests/unit/test_rr_scanner_integration.py

@@ -228,23 +228,23 @@ async def test_scan_ticker_full_flow_quality_selection_and_persistence(
             )
 
     # -- Assert: database persistence --
-    # Old dummy setup should be gone, only the 2 new setups should exist
+    # History is preserved: old setup remains, 2 new setups are appended
     db_result = await scan_session.execute(
         select(TradeSetup).where(TradeSetup.ticker_id == ticker.id)
     )
     persisted = list(db_result.scalars().all())
-    assert len(persisted) == 2, (
-        f"Expected 2 persisted setups (old deleted), got {len(persisted)}"
+    assert len(persisted) == 3, (
+        f"Expected 3 persisted setups (1 old + 2 new), got {len(persisted)}"
     )
 
     persisted_directions = sorted(s.direction for s in persisted)
-    assert persisted_directions == ["long", "short"], (
-        f"Expected ['long', 'short'] persisted, got {persisted_directions}"
+    assert persisted_directions == ["long", "long", "short"], (
+        f"Expected ['long', 'long', 'short'] persisted, got {persisted_directions}"
     )
 
-    # Verify persisted records match returned setups
-    persisted_long = [s for s in persisted if s.direction == "long"][0]
-    persisted_short = [s for s in persisted if s.direction == "short"][0]
+    # Verify latest persisted records match returned setups
+    persisted_long = max((s for s in persisted if s.direction == "long"), key=lambda s: s.id)
+    persisted_short = max((s for s in persisted if s.direction == "short"), key=lambda s: s.id)
 
     assert persisted_long.target == long_setup.target
     assert persisted_long.rr_ratio == long_setup.rr_ratio

tests/unit/test_scheduler.py

@@ -68,7 +68,7 @@ class TestResumeTickers:
 
 
 class TestConfigureScheduler:
-    def test_configure_adds_four_jobs(self):
+    def test_configure_adds_five_jobs(self):
         # Remove any existing jobs first
         scheduler.remove_all_jobs()
         configure_scheduler()
@@ -79,6 +79,7 @@ class TestConfigureScheduler:
             "sentiment_collector",
             "fundamental_collector",
             "rr_scanner",
+            "ticker_universe_sync",
         }
 
     def test_configure_is_idempotent(self):
@@ -92,4 +93,5 @@ class TestConfigureScheduler:
             "fundamental_collector",
             "rr_scanner",
             "sentiment_collector",
+            "ticker_universe_sync",
         ])

tests/unit/test_ticker_universe_service.py

@@ -0,0 +1,123 @@
+"""Unit tests for ticker_universe_service bootstrap logic."""
+
+from __future__ import annotations
+
+import json
+from collections.abc import AsyncGenerator
+
+import pytest
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
+
+from app.database import Base
+from app.exceptions import ProviderError
+from app.models.settings import SystemSetting
+from app.models.ticker import Ticker
+from app.services import ticker_universe_service
+
+_engine = create_async_engine("sqlite+aiosqlite://", echo=False)
+_session_factory = async_sessionmaker(_engine, class_=AsyncSession, expire_on_commit=False)
+
+
+@pytest.fixture(autouse=True)
+async def _setup_tables() -> AsyncGenerator[None, None]:
+    async with _engine.begin() as conn:
+        await conn.run_sync(Base.metadata.create_all)
+    yield
+    async with _engine.begin() as conn:
+        await conn.run_sync(Base.metadata.drop_all)
+
+
+@pytest.fixture
+async def session() -> AsyncGenerator[AsyncSession, None]:
+    async with _session_factory() as s:
+        yield s
+
+
+@pytest.mark.asyncio
+async def test_bootstrap_universe_adds_missing_symbols(session: AsyncSession, monkeypatch: pytest.MonkeyPatch):
+    session.add(Ticker(symbol="AAPL"))
+    await session.commit()
+
+    async def _fake_fetch(_db: AsyncSession, _universe: str) -> list[str]:
+        return ["AAPL", "MSFT", "NVDA"]
+
+    monkeypatch.setattr(ticker_universe_service, "fetch_universe_symbols", _fake_fetch)
+
+    result = await ticker_universe_service.bootstrap_universe(session, "sp500")
+
+    assert result["added"] == 2
+    assert result["already_tracked"] == 1
+    assert result["deleted"] == 0
+
+    rows = await session.execute(select(Ticker.symbol).order_by(Ticker.symbol.asc()))
+    assert list(rows.scalars().all()) == ["AAPL", "MSFT", "NVDA"]
+
+
+@pytest.mark.asyncio
+async def test_bootstrap_universe_prunes_missing_symbols(session: AsyncSession, monkeypatch: pytest.MonkeyPatch):
+    session.add_all([Ticker(symbol="AAPL"), Ticker(symbol="MSFT"), Ticker(symbol="TSLA")])
+    await session.commit()
+
+    async def _fake_fetch(_db: AsyncSession, _universe: str) -> list[str]:
+        return ["AAPL", "MSFT"]
+
+    monkeypatch.setattr(ticker_universe_service, "fetch_universe_symbols", _fake_fetch)
+
+    result = await ticker_universe_service.bootstrap_universe(
+        session,
+        "sp500",
+        prune_missing=True,
+    )
+
+    assert result["added"] == 0
+    assert result["already_tracked"] == 2
+    assert result["deleted"] == 1
+
+    rows = await session.execute(select(Ticker.symbol).order_by(Ticker.symbol.asc()))
+    assert list(rows.scalars().all()) == ["AAPL", "MSFT"]
+
+
+@pytest.mark.asyncio
+async def test_fetch_universe_symbols_uses_cached_snapshot_when_live_sources_fail(
+    session: AsyncSession,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    session.add(
+        SystemSetting(
+            key="ticker_universe_cache_sp500",
+            value=json.dumps({"symbols": ["AAPL", "MSFT"], "source": "test"}),
+        )
+    )
+    await session.commit()
+
+    async def _fake_public(_universe: str):
+        return [], ["public failed"], None
+
+    async def _fake_fmp(_universe: str):
+        raise ProviderError("fmp failed")
+
+    monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_public", _fake_public)
+    monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_fmp", _fake_fmp)
+
+    symbols = await ticker_universe_service.fetch_universe_symbols(session, "sp500")
+    assert symbols == ["AAPL", "MSFT"]
+
+
+@pytest.mark.asyncio
+async def test_fetch_universe_symbols_uses_seed_when_live_and_cache_fail(
+    session: AsyncSession,
+    monkeypatch: pytest.MonkeyPatch,
+):
+    async def _fake_public(_universe: str):
+        return [], ["public failed"], None
+
+    async def _fake_fmp(_universe: str):
+        raise ProviderError("fmp failed")
+
+    monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_public", _fake_public)
+    monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_fmp", _fake_fmp)
+
+    symbols = await ticker_universe_service.fetch_universe_symbols(session, "sp500")
+    assert "AAPL" in symbols
+    assert len(symbols) > 10