add earnings-date guard — warn when a report falls in the target horizon
Deploy / lint (push) Successful in 5s
Deploy / test (push) Successful in 36s
Deploy / deploy (push) Successful in 25s

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>
This commit is contained in:
2026-06-15 12:44:08 +02:00
parent c4f2673799
commit f0b92a9718
13 changed files with 136 additions and 6 deletions
@@ -0,0 +1,29 @@
"""add next_earnings_date to fundamental_data
Revision ID: 006
Revises: 005
Create Date: 2026-06-15 00:00:00.000000
"""
from typing import Sequence, Union
from alembic import op
import sqlalchemy as sa
# revision identifiers, used by Alembic.
revision: str = "006"
down_revision: Union[str, None] = "005"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.add_column(
"fundamental_data",
sa.Column("next_earnings_date", sa.Date(), nullable=True),
)
def downgrade() -> None:
op.drop_column("fundamental_data", "next_earnings_date")
+3 -2
View File
@@ -1,6 +1,6 @@
from datetime import datetime
from datetime import date, datetime
from sqlalchemy import DateTime, Float, ForeignKey, Text
from sqlalchemy import Date, DateTime, Float, ForeignKey, Text
from sqlalchemy.orm import Mapped, mapped_column, relationship
from app.database import Base
@@ -17,6 +17,7 @@ class FundamentalData(Base):
revenue_growth: Mapped[float | None] = mapped_column(Float, nullable=True)
earnings_surprise: Mapped[float | None] = mapped_column(Float, nullable=True)
market_cap: Mapped[float | None] = mapped_column(Float, nullable=True)
next_earnings_date: Mapped[date | None] = mapped_column(Date, nullable=True)
fetched_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), nullable=False
)
+43 -2
View File
@@ -10,7 +10,7 @@ from __future__ import annotations
import logging
import os
from datetime import datetime, timezone
from datetime import date, datetime, timedelta, timezone
from pathlib import Path
import httpx
@@ -59,6 +59,7 @@ class FinnhubFundamentalProvider:
unavailable: dict[str, str] = {}
api_symbol = _to_api_symbol(ticker)
today = date.today()
async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
profile_resp = await client.get(
f"{self._base_url}/stock/profile2",
@@ -72,11 +73,21 @@ class FinnhubFundamentalProvider:
f"{self._base_url}/stock/earnings",
params={"symbol": api_symbol, "limit": 1, "token": self._api_key},
)
calendar_resp = await client.get(
f"{self._base_url}/calendar/earnings",
params={
"symbol": api_symbol,
"from": today.isoformat(),
"to": (today + timedelta(days=120)).isoformat(),
"token": self._api_key,
},
)
for resp, endpoint in (
(profile_resp, "profile2"),
(metric_resp, "stock/metric"),
(earnings_resp, "stock/earnings"),
(calendar_resp, "calendar/earnings"),
):
if resp.status_code == 429:
raise RateLimitError(f"Finnhub rate limit hit for {ticker} ({endpoint})")
@@ -99,6 +110,8 @@ class FinnhubFundamentalProvider:
first = earnings_payload[0] if isinstance(earnings_payload[0], dict) else {}
earnings_surprise = _safe_float(first.get("surprisePercent"))
next_earnings_date = self._next_earnings(calendar_resp)
if pe_ratio is None:
unavailable["pe_ratio"] = "not available from provider payload"
if revenue_growth is None:
@@ -115,9 +128,32 @@ class FinnhubFundamentalProvider:
earnings_surprise=earnings_surprise,
market_cap=market_cap,
fetched_at=datetime.now(timezone.utc),
next_earnings_date=next_earnings_date,
unavailable_fields=unavailable,
)
@staticmethod
def _next_earnings(resp: httpx.Response) -> date | None:
"""Earliest upcoming earnings date from Finnhub's calendar payload."""
try:
payload = resp.json() if resp.text else {}
except ValueError:
return None
entries = payload.get("earningsCalendar", []) if isinstance(payload, dict) else []
dates: list[date] = []
today = date.today()
for entry in entries if isinstance(entries, list) else []:
raw = entry.get("date") if isinstance(entry, dict) else None
if not raw:
continue
try:
parsed = date.fromisoformat(raw)
except ValueError:
continue
if parsed >= today:
dates.append(parsed)
return min(dates) if dates else None
class AlphaVantageFundamentalProvider:
"""Fundamentals provider backed by Alpha Vantage free endpoints."""
@@ -235,9 +271,10 @@ class ChainedFundamentalProvider:
field_source: dict[str, str] = {}
errors: list[str] = []
rate_limited = False
next_earnings_date = None
for provider_name, provider in self._providers:
if all(merged[f] is not None for f in _FUNDAMENTAL_FIELDS):
if all(merged[f] is not None for f in _FUNDAMENTAL_FIELDS) and next_earnings_date:
break
try:
data = await provider.fetch_fundamentals(ticker)
@@ -249,6 +286,9 @@ class ChainedFundamentalProvider:
errors.append(f"{provider_name}: {type(exc).__name__}: {exc}")
continue
if next_earnings_date is None and data.next_earnings_date is not None:
next_earnings_date = data.next_earnings_date
for field in _FUNDAMENTAL_FIELDS:
if merged[field] is None:
value = getattr(data, field)
@@ -287,6 +327,7 @@ class ChainedFundamentalProvider:
earnings_surprise=merged["earnings_surprise"],
market_cap=merged["market_cap"],
fetched_at=datetime.now(timezone.utc),
next_earnings_date=next_earnings_date,
unavailable_fields=unavailable,
)
+1
View File
@@ -53,6 +53,7 @@ class FundamentalData:
earnings_surprise: float | None
market_cap: float | None
fetched_at: datetime
next_earnings_date: date | None = None
unavailable_fields: dict[str, str] = field(default_factory=dict)
+1
View File
@@ -42,6 +42,7 @@ async def read_fundamentals(
revenue_growth=record.revenue_growth,
earnings_surprise=record.earnings_surprise,
market_cap=record.market_cap,
next_earnings_date=record.next_earnings_date,
fetched_at=record.fetched_at,
unavailable_fields=_parse_unavailable_fields(record.unavailable_fields_json),
)
+1
View File
@@ -171,6 +171,7 @@ async def fetch_symbol(
revenue_growth=fdata.revenue_growth,
earnings_surprise=fdata.earnings_surprise,
market_cap=fdata.market_cap,
next_earnings_date=fdata.next_earnings_date,
unavailable_fields=fdata.unavailable_fields,
)
sources_out["fundamentals"] = {"status": "ok", "message": None}
+1
View File
@@ -599,6 +599,7 @@ async def collect_fundamentals() -> None:
revenue_growth=data.revenue_growth,
earnings_surprise=data.earnings_surprise,
market_cap=data.market_cap,
next_earnings_date=data.next_earnings_date,
unavailable_fields=data.unavailable_fields,
)
+2 -1
View File
@@ -2,7 +2,7 @@
from __future__ import annotations
from datetime import datetime
from datetime import date, datetime
from pydantic import BaseModel
@@ -15,5 +15,6 @@ class FundamentalResponse(BaseModel):
revenue_growth: float | None = None
earnings_surprise: float | None = None
market_cap: float | None = None
next_earnings_date: date | None = None
fetched_at: datetime | None = None
unavailable_fields: dict[str, str] = {}
+3
View File
@@ -38,6 +38,7 @@ async def store_fundamental(
revenue_growth: float | None = None,
earnings_surprise: float | None = None,
market_cap: float | None = None,
next_earnings_date=None,
unavailable_fields: dict[str, str] | None = None,
) -> FundamentalData:
"""Store or update fundamental data for a ticker.
@@ -61,6 +62,7 @@ async def store_fundamental(
existing.revenue_growth = revenue_growth
existing.earnings_surprise = earnings_surprise
existing.market_cap = market_cap
existing.next_earnings_date = next_earnings_date
existing.fetched_at = now
existing.unavailable_fields_json = unavailable_fields_json
record = existing
@@ -71,6 +73,7 @@ async def store_fundamental(
revenue_growth=revenue_growth,
earnings_surprise=earnings_surprise,
market_cap=market_cap,
next_earnings_date=next_earnings_date,
fetched_at=now,
unavailable_fields_json=unavailable_fields_json,
)
@@ -12,8 +12,19 @@ interface RecommendationPanelProps {
longSetup?: TradeSetup;
shortSetup?: TradeSetup;
currentPrice?: number;
nextEarningsDate?: string | null;
}
/** Whole days from today until an ISO date (negative if past). */
function daysUntil(iso: string): number | null {
const t = new Date(iso).getTime();
if (Number.isNaN(t)) return null;
return Math.ceil((t - Date.now()) / 86_400_000);
}
/** Earnings within the ~30-day target horizon can gap price through stop/target. */
const EARNINGS_HORIZON_DAYS = 30;
/**
* How far current price has drifted from the setup's entry. A setup whose
* entry is far from the live price (price already ran toward target, or fell
@@ -215,10 +226,11 @@ function RiskSettingsBar({ risk, update }: { risk: RiskSettings; update: (p: Par
);
}
export function RecommendationPanel({ symbol, longSetup, shortSetup, currentPrice }: RecommendationPanelProps) {
export function RecommendationPanel({ symbol, longSetup, shortSetup, currentPrice, nextEarningsDate }: RecommendationPanelProps) {
const { settings: risk, update: updateRisk } = useRiskSettings();
const regime = useMarketRegime().data;
const summary = longSetup?.recommendation_summary ?? shortSetup?.recommendation_summary;
const earningsDays = nextEarningsDate ? daysUntil(nextEarningsDate) : null;
const action = (summary?.action ?? 'NEUTRAL') as TradeSetup['recommended_action'];
const preferredDirection = recommendationActionDirection(action);
@@ -261,6 +273,17 @@ export function RecommendationPanel({ symbol, longSetup, shortSetup, currentPric
<RiskSettingsBar risk={risk} update={updateRisk} />
{earningsDays != null && earningsDays >= 0 && (
earningsDays <= EARNINGS_HORIZON_DAYS ? (
<p className="rounded border border-amber-500/30 bg-amber-500/10 px-3 py-2 text-[11px] text-amber-300">
Earnings in {earningsDays} day{earningsDays === 1 ? '' : 's'} ({nextEarningsDate}) inside the ~30-day
target horizon. A report can gap price through your stop or target; consider waiting or sizing down.
</p>
) : (
<p className="text-[11px] text-gray-500">Next earnings: {nextEarningsDate} ({earningsDays} days).</p>
)
)}
{preferredDirection !== 'neutral' && preferredSetup ? (
<div className="space-y-3">
<SetupCard setup={preferredSetup} action={action} currentPrice={currentPrice} risk={risk} regime={regime} />
+1
View File
@@ -285,6 +285,7 @@ export interface FundamentalResponse {
revenue_growth: number | null;
earnings_surprise: number | null;
market_cap: number | null;
next_earnings_date: string | null;
fetched_at: string | null;
unavailable_fields: Record<string, string>;
}
+1
View File
@@ -262,6 +262,7 @@ export default function TickerDetailPage() {
longSetup={longSetup}
shortSetup={shortSetup}
currentPrice={priceInfo?.price}
nextEarningsDate={fundamentals.data?.next_earnings_date}
/>
{/* Chart — always visible */}
@@ -153,3 +153,29 @@ async def test_rate_limited_but_complete_does_not_raise():
result = await provider.fetch_fundamentals("AAPL")
assert result.pe_ratio == 20.0
@pytest.mark.asyncio
async def test_chain_merges_next_earnings_date():
"""Earnings date is taken from the first provider that supplies it."""
from datetime import date as _date
primary = FundamentalData(
ticker="AAPL", pe_ratio=None, revenue_growth=None, earnings_surprise=None,
market_cap=100.0, fetched_at=datetime.now(timezone.utc),
)
class _EarningsProvider:
async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
return FundamentalData(
ticker=ticker, pe_ratio=10.0, revenue_growth=5.0, earnings_surprise=1.0,
market_cap=None, fetched_at=datetime.now(timezone.utc),
next_earnings_date=_date(2026, 7, 1),
)
provider = ChainedFundamentalProvider([
("fmp", _DataProvider(primary)),
("finnhub", _EarningsProvider()),
])
result = await provider.fetch_fundamentals("AAPL")
assert result.next_earnings_date == _date(2026, 7, 1)