dennisthiessen
80b4113280
Backtest report now includes research-only hold-to-horizon portfolio variants comparing raw vs residual 12-1 momentum, cutoff 80 vs 90, max 10 vs 15 positions, and SPY-200 risk scaling. A dynamic research recommendation panel flags residual momentum, cutoff 90, or regime scaling only when transparent promotion rules pass. Adds signal_context_snapshots with migration 016 and captures one point-in-time context row per newly generated TradeSetup: setup fields, composite/dimensions, latest sentiment, latest fundamentals, and strategy_version=momentum_12_1_rr_time_v1. This is forward-only; no historical sentiment/fundamental backfill is attempted. No live gate, paper-trade exit, or production ranking behavior changes. Verification: 458 backend tests pass, ruff check app/ clean, frontend npm run build clean. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
"""Tests for point-in-time signal context snapshots."""
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from __future__ import annotations
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import json
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from datetime import date, datetime, timezone
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import pytest
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from app.models.fundamental import FundamentalData
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from app.models.score import CompositeScore, DimensionScore
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from app.models.sentiment import SentimentScore
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from app.models.signal_context_snapshot import SignalContextSnapshot
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from app.models.ticker import Ticker
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from app.models.trade_setup import TradeSetup
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from app.services import rr_scanner_service as rr
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from tests.conftest import _test_session_factory # type: ignore
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@pytest.fixture
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async def session():
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async with _test_session_factory() as s:
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yield s
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async def test_create_signal_context_snapshot_captures_latest_context(session):
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now = datetime(2026, 7, 2, 12, tzinfo=timezone.utc)
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ticker = Ticker(symbol="CTX")
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session.add(ticker)
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await session.flush()
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session.add_all([
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DimensionScore(
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ticker_id=ticker.id,
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dimension="technical",
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score=71.0,
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is_stale=False,
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computed_at=now,
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),
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DimensionScore(
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ticker_id=ticker.id,
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dimension="momentum",
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score=82.0,
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is_stale=False,
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computed_at=now,
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),
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CompositeScore(
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ticker_id=ticker.id,
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score=76.5,
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is_stale=False,
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weights_json='{"technical": 0.25}',
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computed_at=now,
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),
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SentimentScore(
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ticker_id=ticker.id,
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classification="BULLISH",
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confidence=78,
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source="test",
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timestamp=now,
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reasoning="",
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citations_json="[]",
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recommendation="BUY",
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),
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FundamentalData(
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ticker_id=ticker.id,
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pe_ratio=25.0,
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revenue_growth=0.18,
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earnings_surprise=0.05,
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market_cap=1_000_000_000.0,
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next_earnings_date=date(2026, 8, 1),
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fetched_at=now,
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unavailable_fields_json="{}",
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),
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])
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setup = TradeSetup(
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ticker_id=ticker.id,
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direction="long",
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entry_price=100.0,
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stop_loss=95.0,
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target=120.0,
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rr_ratio=4.0,
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composite_score=76.5,
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detected_at=now,
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confidence_score=64.0,
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momentum_percentile=88.0,
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recommended_action="LONG_HIGH",
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risk_level="Low",
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)
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session.add(setup)
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await session.flush()
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await rr._create_signal_context_snapshots(session, [setup])
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await session.commit()
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row = (await session.get(SignalContextSnapshot, 1))
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assert row is not None
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assert row.trade_setup_id == setup.id
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assert row.strategy_version == rr.STRATEGY_VERSION
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assert row.momentum_percentile == 88.0
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score = json.loads(row.score_context_json)
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sentiment = json.loads(row.sentiment_context_json)
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fundamental = json.loads(row.fundamental_context_json)
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assert score["composite_score"] == 76.5
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assert score["dimensions"]["technical"]["score"] == 71.0
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assert sentiment["classification"] == "BULLISH"
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assert sentiment["confidence"] == 78
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assert fundamental["pe_ratio"] == 25.0
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assert fundamental["next_earnings_date"] == "2026-08-01"
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