dennisthiessen
f0b92a9718
Finnhub's earnings calendar now supplies next_earnings_date through the fundamentals chain; persisted on fundamental_data (migration 006) and exposed in the fundamentals API. The recommendation panel warns when earnings fall within the ~30-day target horizon (a report can gap price through stop/target) and otherwise shows the next date. Informational only. Deploy: run alembic upgrade (new fundamental_data.next_earnings_date column). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
110 lines
3.4 KiB
Python
110 lines
3.4 KiB
Python
"""Fundamental data service.
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Stores fundamental data (P/E, revenue growth, earnings surprise, market cap)
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and marks the fundamental dimension score as stale on new data.
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"""
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from __future__ import annotations
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import json
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import logging
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from datetime import datetime, timezone
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.exceptions import NotFoundError
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from app.models.fundamental import FundamentalData
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from app.models.score import DimensionScore
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from app.models.ticker import Ticker
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logger = logging.getLogger(__name__)
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async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
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"""Look up a ticker by symbol."""
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normalised = symbol.strip().upper()
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result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
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ticker = result.scalar_one_or_none()
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if ticker is None:
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raise NotFoundError(f"Ticker not found: {normalised}")
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return ticker
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async def store_fundamental(
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db: AsyncSession,
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symbol: str,
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pe_ratio: float | None = None,
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revenue_growth: float | None = None,
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earnings_surprise: float | None = None,
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market_cap: float | None = None,
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next_earnings_date=None,
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unavailable_fields: dict[str, str] | None = None,
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) -> FundamentalData:
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"""Store or update fundamental data for a ticker.
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Keeps a single latest snapshot per ticker. On new data, marks the
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fundamental dimension score as stale (if one exists).
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"""
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ticker = await _get_ticker(db, symbol)
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# Check for existing record
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result = await db.execute(
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select(FundamentalData).where(FundamentalData.ticker_id == ticker.id)
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)
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existing = result.scalar_one_or_none()
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now = datetime.now(timezone.utc)
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unavailable_fields_json = json.dumps(unavailable_fields or {})
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if existing is not None:
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existing.pe_ratio = pe_ratio
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existing.revenue_growth = revenue_growth
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existing.earnings_surprise = earnings_surprise
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existing.market_cap = market_cap
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existing.next_earnings_date = next_earnings_date
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existing.fetched_at = now
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existing.unavailable_fields_json = unavailable_fields_json
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record = existing
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else:
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record = FundamentalData(
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ticker_id=ticker.id,
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pe_ratio=pe_ratio,
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revenue_growth=revenue_growth,
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earnings_surprise=earnings_surprise,
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market_cap=market_cap,
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next_earnings_date=next_earnings_date,
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fetched_at=now,
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unavailable_fields_json=unavailable_fields_json,
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)
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db.add(record)
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# Mark fundamental dimension score as stale if it exists
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# TODO: Use DimensionScore service when built
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dim_result = await db.execute(
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select(DimensionScore).where(
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DimensionScore.ticker_id == ticker.id,
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DimensionScore.dimension == "fundamental",
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)
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)
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dim_score = dim_result.scalar_one_or_none()
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if dim_score is not None:
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dim_score.is_stale = True
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await db.commit()
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await db.refresh(record)
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return record
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async def get_fundamental(
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db: AsyncSession,
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symbol: str,
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) -> FundamentalData | None:
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"""Get the latest fundamental data for a ticker."""
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ticker = await _get_ticker(db, symbol)
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result = await db.execute(
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select(FundamentalData).where(FundamentalData.ticker_id == ticker.id)
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)
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return result.scalar_one_or_none()
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