dennisthiessenandClaude Opus 4.8
b3dcf356a6
1. Multi-class subject is priced by the REQUESTED ticker: the peer group's representative for the subject CIK is overridden to the requested ticker_id (other issuers pick a deterministic-by-symbol rep), so GOOGL's P/E uses GOOGL's price, not GOOG's. Differing-price GOOG/GOOGL test added. 2. reads matches the selected contract: header is null when there is no read; by_key is a fixed map over every metric key plus pe and fcf_yield, null when unavailable (was a sparse dict). 3. Earnings use the New York calendar date; same-day is UPCOMING (days_until 0), recent is strictly earlier. 4. Valuation is null when there is no usable price (> 0 required for P/E and market cap); when present, price_date is non-null. Added a real router/API-envelope test with a seeded legacy record (the endpoint, not just the schema merge). 6 tests pass. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
93 lines
2.6 KiB
Python
93 lines
2.6 KiB
Python
"""Pydantic schemas for fundamental data endpoints."""
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from __future__ import annotations
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from datetime import date, datetime
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from pydantic import BaseModel
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class MetricIndustry(BaseModel):
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label: str
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median: float
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favorable_percentile: int # 0-100, polarity-aware (higher = more favorable)
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peer_count: int
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class MetricHistoryPoint(BaseModel):
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period_end: str # YYYY-MM-DD
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value: float | None
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class MetricItem(BaseModel):
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key: str
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value: float | None = None
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history: list[MetricHistoryPoint] = []
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industry: MetricIndustry | None = None
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period_end: str | None = None
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filed_date: str | None = None
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source: str = "sec"
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class EarningsNext(BaseModel):
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date: str
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session: str
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days_until: int
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class EarningsRecent(BaseModel):
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announce_date: str
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period_end: str | None = None
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eps_estimate: float | None = None
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eps_actual: float | None = None
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surprise_pct: float | None = None
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class EarningsObject(BaseModel):
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next: EarningsNext | None = None
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recent: list[EarningsRecent] = []
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class Valuation(BaseModel):
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pe: float | None = None
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fcf_yield: float | None = None
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market_cap_est: float | None = None
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pe_industry: MetricIndustry | None = None
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fcf_yield_industry: MetricIndustry | None = None
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price_date: str | None = None
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class FundamentalsReads(BaseModel):
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"""Deterministic text outputs, separate from the numeric metrics.
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``by_key`` is a fixed map over every metric key plus ``pe`` and ``fcf_yield``,
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each a read string or null. ``header`` is null when there is no read at all."""
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header: str | None = None
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by_key: dict[str, str | None] = {}
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class FundamentalResponse(BaseModel):
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"""Envelope-ready fundamental data response.
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Legacy fields are preserved unchanged (they come from ``fundamental_data`` /
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the legacy providers). The additive v1 objects — earnings, metrics, valuation,
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reads — are SEC/Dolt-derived and independent; a null legacy field is never
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mapped onto the new SEC metrics and vice-versa.
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"""
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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: date | None = None
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fetched_at: datetime | None = None
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unavailable_fields: dict[str, str] = {}
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# --- additive v1 (always present; empty/null when unavailable) ---
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earnings: EarningsObject | None = None
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metrics: list[MetricItem] | None = None
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valuation: Valuation | None = None
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reads: FundamentalsReads | None = None
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