feat(dolt): migration 026 + models — fundamentals/earnings schema (A1 schema)

First reviewable slice of workstream A: schema only, no importers, no data.

- data_import_runs: lean batch-import audit (source/revision/status,
  row_counts_json + validation_json as Text-holding-JSON per repo convention).
- fundamental_snapshots: CIK-keyed, one immutable row per accession; stores
  per-period raw facts (duration = cumulative YTD/FY, balance-sheet =
  period-end) plus period_start/period_end/fiscal_year/fiscal_period so
  discrete quarters, Q4, TTM and YoY are derived at read time.
- earnings_events: Dolt-sourced calendar + surprise history, unique
  (ticker_id, announce_date).
- tickers: nullable cik/sic/sic_description — the ticker<->issuer join point.

fundamental_data is left untouched (cutover gated separately at A5). Models
registered in app/models/__init__.py; Ticker gains an earnings_events
relationship. Verified: create_all builds the tables, mappers configure, and
migration 026 renders valid Postgres DDL up and down.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/models/__init__.py

@@ -3,6 +3,9 @@ from app.models.ohlcv import OHLCVRecord
 from app.models.user import User
 from app.models.sentiment import SentimentScore
 from app.models.fundamental import FundamentalData
+from app.models.fundamental_snapshot import FundamentalSnapshot
+from app.models.earnings_event import EarningsEvent
+from app.models.data_import_run import DataImportRun
 from app.models.score import DimensionScore, CompositeScore
 from app.models.sr_level import SRLevel
 from app.models.trade_setup import TradeSetup
@@ -21,6 +24,9 @@ __all__ = [
     "User",
     "SentimentScore",
     "FundamentalData",
+    "FundamentalSnapshot",
+    "EarningsEvent",
+    "DataImportRun",
     "DimensionScore",
     "CompositeScore",
     "SRLevel",

app/models/data_import_run.py

@@ -0,0 +1,42 @@
+from datetime import date, datetime
+
+from sqlalchemy import Date, DateTime, Index, String, Text
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.database import Base
+
+
+class DataImportRun(Base):
+    """One row per bulk-import attempt (SEC facts / Dolt earnings / Dolt stocks).
+
+    Lean audit record for the batch import framework: every attempt is logged,
+    whether it promoted, was a ``no_op`` (unchanged revision), or ``failed``.
+    ``row_counts`` and ``validation`` hold JSON strings (repo convention — see
+    ``fundamental_data.unavailable_fields_json``), not JSONB; the validation
+    blob carries reconciliation/discrepancy summaries so no separate conflicts
+    table is needed. One run per source at a time is enforced at write time by a
+    Postgres advisory lock keyed by ``source``.
+    """
+
+    __tablename__ = "data_import_runs"
+    __table_args__ = (
+        Index("ix_data_import_runs_source_started", "source", "started_at"),
+    )
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    # sec_facts | dolt_earnings | dolt_stocks
+    source: Mapped[str] = mapped_column(String(32), nullable=False)
+    # Dolt commit hash, or SEC archive SHA-256. Null until known.
+    revision: Mapped[str | None] = mapped_column(String(64), nullable=True)
+    # running | validated | promoted | no_op | failed
+    status: Mapped[str] = mapped_column(String(16), nullable=False)
+    source_max_date: Mapped[date | None] = mapped_column(Date, nullable=True)
+    row_counts_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    validation_json: Mapped[str | None] = mapped_column(Text, nullable=True)
+    started_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.utcnow, nullable=False
+    )
+    completed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+    error_details: Mapped[str | None] = mapped_column(Text, nullable=True)

app/models/earnings_event.py

@@ -0,0 +1,42 @@
+from datetime import date, datetime
+
+from sqlalchemy import Date, DateTime, Float, ForeignKey, Index, String, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column, relationship
+
+from app.database import Base
+
+
+class EarningsEvent(Base):
+    """Earnings calendar + surprise history, sourced from the DoltHub earnings repo.
+
+    Forward rows (``announce_date`` > today) are the calendar; past rows are
+    results. Rescheduling is handled in the importer's promotion transaction:
+    this source's future-dated rows are deleted and re-inserted from the new
+    snapshot so moved/cancelled dates never linger; past rows are never deleted.
+    """
+
+    __tablename__ = "earnings_events"
+    __table_args__ = (
+        UniqueConstraint("ticker_id", "announce_date", name="uq_earnings_ticker_announce"),
+        Index("ix_earnings_events_announce_date", "announce_date"),
+    )
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    ticker_id: Mapped[int] = mapped_column(
+        ForeignKey("tickers.id", ondelete="CASCADE"), nullable=False
+    )
+    announce_date: Mapped[date] = mapped_column(Date, nullable=False)
+    # bmo | amc | unknown (source coverage is partial)
+    session: Mapped[str] = mapped_column(String(10), nullable=False, default="unknown")
+    period_end: Mapped[date | None] = mapped_column(Date, nullable=True)
+    eps_estimate: Mapped[float | None] = mapped_column(Float, nullable=True)
+    eps_actual: Mapped[float | None] = mapped_column(Float, nullable=True)
+    source: Mapped[str] = mapped_column(String(32), nullable=False)
+    import_run_id: Mapped[int | None] = mapped_column(
+        ForeignKey("data_import_runs.id", ondelete="SET NULL"), nullable=True
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.utcnow, nullable=False
+    )
+
+    ticker = relationship("Ticker", back_populates="earnings_events")

app/models/fundamental_snapshot.py

@@ -0,0 +1,73 @@
+from datetime import date, datetime
+
+from sqlalchemy import Date, DateTime, Float, ForeignKey, Index, String, UniqueConstraint
+from sqlalchemy.orm import Mapped, mapped_column
+
+from app.database import Base
+
+
+class FundamentalSnapshot(Base):
+    """CIK-keyed, one immutable row per SEC accession.
+
+    Keyed by issuer (CIK), not ticker — multi-class issuers (GOOG/GOOGL) share
+    one CIK and one set of fundamentals; the ``tickers.cik`` column is the only
+    join point. Amendments are retained: every accession is a distinct immutable
+    row, and readers pick the newest valid ``accepted_at`` per
+    (cik, fiscal_year, fiscal_period) at read time — no flags, no mutation.
+
+    **Facts are stored as the filing reports them, never as derived quarters.**
+    Duration facts (revenue, net_income, operating_income, diluted_eps, cfo,
+    capex, depreciation_amortization) hold the filing's normalized **cumulative
+    YTD/FY** value over (period_start -> period_end). Balance-sheet facts
+    (cash_and_st_investments, total_debt, shares_outstanding) are **period-end**
+    values. ``shares_outstanding`` is a point-in-time count
+    (``dei:EntityCommonStockSharesOutstanding``, summed across share classes for
+    a multi-class issuer) — deliberately not the weighted-average diluted share
+    count, since both consumers (estimated market cap, YoY dilution read) want a
+    point-in-time value. Discrete quarters (10-Q YTD deltas, Q4 = FY - Q1..Q3), TTM, YoY and
+    the quarter tape are all derived at read time — so non-calendar fiscal years
+    resolve correctly and a later amendment never leaves a stale frozen quarter.
+    """
+
+    __tablename__ = "fundamental_snapshots"
+    __table_args__ = (
+        UniqueConstraint("accession", name="uq_fundamental_snapshots_accession"),
+        Index("ix_fundamental_snapshots_cik_period", "cik", "fiscal_year", "fiscal_period"),
+        Index("ix_fundamental_snapshots_cik_period_end", "cik", "period_end"),
+    )
+
+    id: Mapped[int] = mapped_column(primary_key=True)
+    cik: Mapped[str] = mapped_column(String(10), nullable=False)
+    accession: Mapped[str] = mapped_column(String(25), nullable=False)
+    form: Mapped[str] = mapped_column(String(12), nullable=False)  # 10-Q, 10-K, 10-K/A ...
+    filed_date: Mapped[date] = mapped_column(Date, nullable=False)
+    # Kept although PIT enforcement is deferred (one timestamp now vs painful retrofit).
+    accepted_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), nullable=False)
+
+    # Period identity — required to align non-calendar fiscal years and to derive
+    # discrete quarters from cumulative facts.
+    period_start: Mapped[date | None] = mapped_column(Date, nullable=True)
+    period_end: Mapped[date] = mapped_column(Date, nullable=False)
+    fiscal_year: Mapped[int] = mapped_column(nullable=False)
+    fiscal_period: Mapped[str] = mapped_column(String(4), nullable=False)  # Q1|Q2|Q3|Q4|FY
+
+    # Duration facts — cumulative YTD/FY over (period_start -> period_end).
+    revenue: Mapped[float | None] = mapped_column(Float, nullable=True)
+    net_income: Mapped[float | None] = mapped_column(Float, nullable=True)
+    operating_income: Mapped[float | None] = mapped_column(Float, nullable=True)
+    diluted_eps: Mapped[float | None] = mapped_column(Float, nullable=True)
+    cfo: Mapped[float | None] = mapped_column(Float, nullable=True)  # cash flow from operations
+    capex: Mapped[float | None] = mapped_column(Float, nullable=True)
+    depreciation_amortization: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    # Balance-sheet facts — period-end values.
+    cash_and_st_investments: Mapped[float | None] = mapped_column(Float, nullable=True)
+    total_debt: Mapped[float | None] = mapped_column(Float, nullable=True)
+    shares_outstanding: Mapped[float | None] = mapped_column(Float, nullable=True)
+
+    import_run_id: Mapped[int | None] = mapped_column(
+        ForeignKey("data_import_runs.id", ondelete="SET NULL"), nullable=True
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), default=datetime.utcnow, nullable=False
+    )

app/models/ticker.py

@@ -14,6 +14,13 @@ class Ticker(Base):
     # Company name (e.g. "Biogen Inc."); backfilled from Alpaca, nullable for
     # symbols Alpaca doesn't know.
     name: Mapped[str | None] = mapped_column(String(120), nullable=True)
+    # SEC issuer identity, refreshed by the SEC fundamentals import from
+    # company_tickers.json / submissions. The only ticker<->issuer join point;
+    # multi-class tickers (GOOG/GOOGL) share these values. Nullable: not every
+    # symbol resolves to a CIK (e.g. ADRs, foreign issuers not in SEC data).
+    cik: Mapped[str | None] = mapped_column(String(10), nullable=True)
+    sic: Mapped[str | None] = mapped_column(String(4), nullable=True)
+    sic_description: Mapped[str | None] = mapped_column(String(160), nullable=True)
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), default=datetime.utcnow, nullable=False
     )
@@ -28,3 +35,4 @@ class Ticker(Base):
     trade_setups = relationship("TradeSetup", back_populates="ticker", cascade="all, delete-orphan")
     watchlist_entries = relationship("WatchlistEntry", back_populates="ticker", cascade="all, delete-orphan")
     ingestion_progress = relationship("IngestionProgress", back_populates="ticker", cascade="all, delete-orphan", uselist=False)
+    earnings_events = relationship("EarningsEvent", back_populates="ticker", cascade="all, delete-orphan")