docs(dolt-plan): drop workstream B and record why

B would have replaced Alpaca historical OHLCV with the DoltHub stocks repo. It
was scoped inside a plan whose goal was killing the quota-limited free-tier APIs
— which A6 achieved without it, since Alpaca was never one of them.

Its only concrete benefit was `corporate_actions` for the KLAC-class post-filing
split (TTM EPS pre-split against a post-split price). That needs split events,
not a 4.7 GB clone, and the Alpaca SDK already in the venv exposes them. Against
that: fundamentals are 20% of the composite and P/E one of three inputs, so the
wart is small and self-correcting at the next filing; and B would have made
symbol history mutable as a routine event, which the backtest/prod parity guard
exists to catch.

The design is kept as a record, struck through rather than deleted, with the
reasoning next to it so this isn't re-derived. Also corrects the doc's status
header, the never-written migration 027 (that number went to
weighted_avg_diluted_shares), and the note that the stocks repo's license was
never reviewed.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

.env.example

@@ -18,16 +18,9 @@ OPENAI_API_KEY=
 OPENAI_MODEL=gpt-4o-mini
 OPENAI_SENTIMENT_BATCH_SIZE=5
 
-# Fundamentals Provider — Financial Modeling Prep
+# Dolt bulk data — local clone of post-no-preference/earnings. Together with the
-FMP_API_KEY=
+# SEC EDGAR block below this is the ONLY fundamentals source; there is no
-
+# provider-API fallback.
-# Fundamentals Provider — Finnhub (optional fallback)
-FINNHUB_API_KEY=
-
-# Fundamentals Provider — Alpha Vantage (optional fallback)
-ALPHA_VANTAGE_API_KEY=
-
-# Dolt bulk data — local clone of post-no-preference/earnings (workstream A).
 # DOLT_BINARY: path to the dolt CLI (set the full path in dev if it's not on PATH,
 #   e.g. Windows: C:\Program Files\Dolt\bin\dolt.exe). DOLT_DATA_DIR holds the
 #   clones; in PRODUCTION it MUST be outside the deploy tree (deploy is
@@ -52,10 +45,6 @@ SEC_REQUEST_SPACING_SECONDS=0.2
 SEC_MAX_RETRIES=4
 SEC_REQUEST_TIMEOUT_SECONDS=30.0
 
-# A5 read-only parity report archive. In production keep this outside the
-# rsync deployment tree, e.g. /var/lib/signal-platform/reports/fundamentals-parity.
-FUNDAMENTALS_PARITY_REPORT_DIR=reports/fundamentals-parity
-
 # Regime Monitor — FRED (VIX + HY credit spreads). Free key: https://fred.stlouisfed.org/docs/api/api_key.html
 # Optional: without it the volatility (V1) and credit (C1) pillars show as n/a.
 FRED_API_KEY=
@@ -63,10 +52,7 @@ FRED_API_KEY=
 # Scheduled Jobs
 DATA_COLLECTOR_FREQUENCY=daily
 SENTIMENT_POLL_INTERVAL_MINUTES=30
-FUNDAMENTAL_FETCH_FREQUENCY=daily
 RR_SCAN_FREQUENCY=daily
-FUNDAMENTAL_RATE_LIMIT_RETRIES=3
-FUNDAMENTAL_RATE_LIMIT_BACKOFF_SECONDS=15
 
 # Scoring Defaults
 DEFAULT_WATCHLIST_AUTO_SIZE=10

README.md

@@ -155,7 +155,7 @@ Hourly mid-session (Mon–Fri ~10:00–15:00 ET): only **OHLCV → Outcome Eval*
 
 ### Other jobs
 
-Fundamentals (weekly, early Monday ET) · Backtest (weekly) · Ticker-universe sync (daily). Alerts auto-fire only via the near-close pipeline (still manually triggerable). Deep history backfill and event study are manual-only (Admin → Jobs).
+Dolt earnings import (daily 02:30 ET) · SEC fundamentals import (daily 04:00 ET, also refreshes the fundamentals cache scoring reads) · Backtest (weekly) · Ticker-universe sync (daily). Alerts auto-fire only via the near-close pipeline (still manually triggerable). Deep history backfill and event study are manual-only (Admin → Jobs).
 
 ### From score to "top pick"
 
@@ -255,18 +255,11 @@ A systematic single-variable sweep (offline prod snapshot, production gate/rank/
 | ATR trail multiple {1.5–4.0} | **Keep 3.0** | Return+Sharpe peak; ≤2.0 whipsaws out the momentum right tail; ≥2.5 is a plateau |
 | SPY 200d-MA regime overlay (block entries / go flat) | **Reject** | Halves return (315%→138%) with zero drawdown benefit — the ATR trail already manages downside, and the filter blocks the recovery-phase entries that make the money |
 | Momentum lookback: 6-1, 3-1, 12-7 (Novy-Marx), composites | **Keep residual 12-1** | 6-1/3-1 rank-IC ≈ 0; 12-7 IC 0.045 / t 1.58 — weaker than residual 12-1 (0.055 / t 1.98) |
-| Selection cutoff {70, 75, 85, 90} × book size {10, 15, 20} | **Keep cutoff 80; capacity reopened** | The older weekly replay favored 80 × 10, but its no-cap-pressure conclusion is superseded by 519 book-full rejections versus 472 trades under the current daily gate-reset control |
+| Selection cutoff {70, 75, 85, 90} × book size {10, 15, 20} | **Keep 80 × 10** | Monotonically worse in both directions from 80; the 10-slot cap never binds (<10 concurrent) |
 | Position sizing: equal-weight, inverse-vol, risk-% sweep | **Keep 1% fixed-fractional** | See the inverse-vol warning below |
 | Post-stop re-entry: immediate, fixed 2–5 sessions, gate resets, confirmation filters | **Keep normal gate reset for the 10-position production book** | Sharpe 1.77 vs 1.67 immediate and 1.47 cooldown 5; rerun before changing portfolio capacity |
 | FIP path-smoothness as an in-book tie-breaker/filter | **Reject** (but see the lead below) | Non-monotonic across FIP quintiles within the qualified set; either half of a median split underperforms the full book — thinning the entry stream costs more compounding than the tilt returns |
 
-> **Capacity correction (2026-08-05):** the table's older weekly conclusion
-> that the ten-slot cap never binds is superseded. Under the current daily
-> gate-reset Phase A control, 472 trades were admitted and 519 qualified entries
-> were rejected because the book was full (52.4% of admitted+blocked
-> opportunities). Cutoff 80 remains the signal setting; portfolio capacity is
-> reopened in the focused capacity-bracket study.
-
 Two findings future sessions must not re-litigate:
 
 - **The "inverse-vol sizing win" (July 2026) was mis-attributed — do not resurrect.** The diagnostic sized `notional = equity × 1% / vol_6m`, and the 20% notional cap bound on 95% of entries, so it actually measured "~5 positions × 20% notional each" — a concentration/risk-appetite bump economically equivalent to raising risk to 1.5%, not vol-managed sizing. Genuine inverse-vol sizing (risk budget × median-vol/vol) cuts max drawdown to −18.2% but costs ~58pp total return at flat Sharpe: a risk-preference trade, not edge.
@@ -308,13 +301,13 @@ Corollaries: never let an unvalidated score gate setups; the outcome evaluator m
 | Charts | Canvas 2D candlestick chart with S/R overlays |
 | Routing | React Router v6 (SPA) |
 | HTTP | Axios with JWT interceptor |
-| Data providers | Alpaca (OHLCV); OpenAI / Gemini / DeepSeek / xAI (sentiment, pluggable); Fundamentals chain: FMP → Finnhub → Alpha Vantage; FRED (regime); Telegram (alerts) |
+| Data providers | Alpaca (OHLCV); OpenAI / Gemini / DeepSeek / xAI (sentiment, pluggable); SEC EDGAR Company Facts + DoltHub earnings (fundamentals, bulk import); FRED (regime); Telegram (alerts) |
 
 ## Features
 
 ### Backend
 - Ticker registry with full cascade delete
-- Universe bootstrap for `sp500`, `nasdaq100`, `nasdaq_all` via admin endpoint
+- Universe bootstrap for `sp500`, `nasdaq100`, `nasdaq_all` via admin endpoint — free public sources (Wikipedia / NASDAQ Trader), then the cached snapshot, then a built-in seed list. The seeds are representative, not complete, so a *fresh* install bootstrapped while the public source is unreachable gets a partial universe; a warm instance falls through to its cache.
 - OHLCV price storage with upsert and validation
 - Technical indicators: ADX, EMA, RSI, ATR, Volume Profile, Pivot Points, EMA Cross
 - Structural Support/Resistance detection with rejection/recency strength, ATR-adaptive merging and a hard cap; persisted for charts and alerts
@@ -590,18 +583,12 @@ Configure in `.env` (copy from `.env.example`):
 | `OPENAI_API_KEY` | For sentiment (OpenAI path) | — | OpenAI API key |
 | `OPENAI_MODEL` | No | `gpt-4o-mini` | OpenAI model name |
 | `OPENAI_SENTIMENT_BATCH_SIZE` | No | `5` | Micro-batch size for sentiment collector |
-| `FMP_API_KEY` | Optional (fundamentals) | — | Financial Modeling Prep API key (first provider in chain) |
-| `FINNHUB_API_KEY` | Optional (fundamentals) | — | Finnhub API key (fallback provider) |
-| `ALPHA_VANTAGE_API_KEY` | Optional (fundamentals) | — | Alpha Vantage API key (fallback provider) |
 | `FRED_API_KEY` | Optional (regime) | — | FRED key for the regime monitor (VIX, credit spreads) |
 | `TELEGRAM_BOT_TOKEN` | Optional (alerts) | — | Telegram bot token for alerts (can also be set in Admin) |
 | `TELEGRAM_CHAT_ID` | Optional (alerts) | — | Telegram chat id for alerts |
 | `DATA_COLLECTOR_FREQUENCY` | No | `daily` | OHLCV collection schedule (legacy — see note below) |
 | `SENTIMENT_POLL_INTERVAL_MINUTES` | No | `30` | Sentiment polling interval |
-| `FUNDAMENTAL_FETCH_FREQUENCY` | No | `weekly` | Fundamentals fetch cadence |
 | `RR_SCAN_FREQUENCY` | No | `daily` | R:R scanner schedule |
-| `FUNDAMENTAL_RATE_LIMIT_RETRIES` | No | `3` | Retries per ticker on fundamentals rate-limit |
-| `FUNDAMENTAL_RATE_LIMIT_BACKOFF_SECONDS` | No | `15` | Base backoff seconds for fundamentals retry (exponential) |
 | `DEFAULT_WATCHLIST_AUTO_SIZE` | No | `10` | Auto-watchlist size |
 | `DEFAULT_RR_THRESHOLD` | No | `1.5` | Minimum R:R ratio for setups |
 | `DB_POOL_SIZE` | No | `5` | Database connection pool size |

alembic/versions/029_retire_legacy_fundamentals_settings.py

@@ -0,0 +1,96 @@
+"""Retire the legacy fundamentals settings (A6)
+
+Revision ID: 029
+Revises: 028
+Create Date: 2026-08-07 00:00:00.000000
+
+A6 removed the FMP/Finnhub/Alpha Vantage providers, the weekly
+``fundamental_collector`` job and the A5 parity report. Five SystemSetting rows
+are left over. They are NOT all deleted, because the deploy runs migrations
+before restarting the service: for a short window — and for the whole of any
+rollback — pre-A6 code is still live, and it reads absent rows permissively
+(cutover absent -> disabled; ``job_<name>_enabled`` absent -> enabled). Deleting
+both would hand a rolled-back process a re-armed legacy collector writing over
+the SEC/Dolt cache.
+
+So the two rows that carry behavior become tombstones pinned to the safe value,
+and only the inert ones are deleted. The tombstones are dropped in a later
+release once the rollback window has closed; ``SettingsForm`` hides them
+meanwhile.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+revision: str = "029"
+down_revision: Union[str, None] = "028"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+# Behavior-bearing under pre-A6 code -> pin to the safe value, keep the row.
+_TOMBSTONES: dict[str, str] = {
+    "fundamental_data_sec_dolt_cutover_enabled": "true",
+    "job_fundamental_collector_enabled": "false",
+}
+
+# Inert either way: an absent cron falls back to a default for a job that no
+# longer registers, and the parity report never wrote anything.
+_OBSOLETE: tuple[str, ...] = (
+    "schedule_fundamentals_cron",
+    "schedule_fundamentals_parity_cron",
+    "job_fundamentals_parity_report_enabled",
+)
+
+_settings = sa.table(
+    "system_settings",
+    sa.column("id", sa.Integer),
+    sa.column("key", sa.String),
+    sa.column("value", sa.Text),
+    sa.column("updated_at", sa.DateTime(timezone=True)),
+)
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    now = sa.func.now()
+
+    for key, pinned in _TOMBSTONES.items():
+        row = conn.execute(
+            sa.select(_settings.c.value).where(_settings.c.key == key)
+        ).fetchone()
+        old_value = row[0] if row is not None else None
+        print(f"a6_tombstone {key}: {old_value!r} -> {pinned!r}", flush=True)
+        if row is None:
+            conn.execute(
+                sa.insert(_settings).values(key=key, value=pinned, updated_at=now)
+            )
+        elif old_value != pinned:
+            conn.execute(
+                sa.update(_settings)
+                .where(_settings.c.key == key)
+                .values(value=pinned, updated_at=now)
+            )
+
+    # Print the value before deleting — a bare DELETE cannot be undone from the
+    # migration output.
+    for key in _OBSOLETE:
+        row = conn.execute(
+            sa.select(_settings.c.value).where(_settings.c.key == key)
+        ).fetchone()
+        if row is None:
+            print(f"a6_delete {key}: absent", flush=True)
+            continue
+        print(f"a6_delete {key}: {row[0]!r}", flush=True)
+        conn.execute(sa.delete(_settings).where(_settings.c.key == key))
+
+
+def downgrade() -> None:
+    """No-op.
+
+    The deleted rows configured jobs this revision's code no longer registers,
+    and the tombstones already hold the values pre-A6 code needs. Recreating
+    them would restore nothing useful; the printed values above cover recovery.
+    """

alembic/versions/030_drop_legacy_fundamentals_tombstones.py

@@ -0,0 +1,71 @@
+"""Drop the A6 rollback tombstones
+
+Revision ID: 030
+Revises: 029
+Create Date: 2026-08-07 00:00:00.000000
+
+Migration ``029`` kept two SystemSetting rows alive as rollback tombstones,
+pinned to the values a pre-A6 process needed to behave safely. A6 is deployed
+and healthy, and the provider keys are gone from the production ``.env`` — which
+makes the legacy collector inert regardless of any settings row — so the
+tombstones have no remaining job.
+
+Nothing in the current codebase reads either key.
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+revision: str = "030"
+down_revision: Union[str, None] = "029"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+# The safe values 029 pinned. Kept here so downgrade restores real protection
+# rather than leaving a rolled-back process reading absent rows permissively.
+_TOMBSTONES: dict[str, str] = {
+    "fundamental_data_sec_dolt_cutover_enabled": "true",
+    "job_fundamental_collector_enabled": "false",
+}
+
+_settings = sa.table(
+    "system_settings",
+    sa.column("id", sa.Integer),
+    sa.column("key", sa.String),
+    sa.column("value", sa.Text),
+    sa.column("updated_at", sa.DateTime(timezone=True)),
+)
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    for key in _TOMBSTONES:
+        row = conn.execute(
+            sa.select(_settings.c.value).where(_settings.c.key == key)
+        ).fetchone()
+        if row is None:
+            print(f"a6_tombstone_drop {key}: absent", flush=True)
+            continue
+        print(f"a6_tombstone_drop {key}: {row[0]!r}", flush=True)
+        conn.execute(sa.delete(_settings).where(_settings.c.key == key))
+
+
+def downgrade() -> None:
+    """Restore the tombstones at their safe values.
+
+    Unlike 029's no-op downgrade, this one is meaningful: going back past this
+    revision implies going back toward code that still reads these keys.
+    """
+    conn = op.get_bind()
+    now = sa.func.now()
+    for key, pinned in _TOMBSTONES.items():
+        exists = conn.execute(
+            sa.select(_settings.c.id).where(_settings.c.key == key)
+        ).fetchone()
+        if exists is None:
+            conn.execute(
+                sa.insert(_settings).values(key=key, value=pinned, updated_at=now)
+            )

app/config.py

@@ -28,15 +28,6 @@ class Settings(BaseSettings):
     deepseek_api_key: str = ""
     xai_api_key: str = ""
 
-    # Fundamentals Provider — Financial Modeling Prep
-    fmp_api_key: str = ""
-
-    # Fundamentals Provider — Finnhub (optional fallback)
-    finnhub_api_key: str = ""
-
-    # Fundamentals Provider — Alpha Vantage (optional fallback)
-    alpha_vantage_api_key: str = ""
-
     # Dolt bulk-data — local clone of post-no-preference/earnings (workstream A).
     # dolt_binary: full path when not on PATH (dev/Windows install). dolt_data_dir
     # holds the clones; in production it MUST be outside the deploy tree (deploy is
@@ -61,10 +52,6 @@ class Settings(BaseSettings):
     sec_max_retries: int = 4
     sec_request_timeout_seconds: float = 30.0
 
-    # A5 read-only comparison artifacts. Production must keep this outside the
-    # rsync deployment tree so the 5-7 day review window survives deploys.
-    fundamentals_parity_report_dir: str = "reports/fundamentals-parity"
-
     # Regime Monitor — FRED (VIX level + HY credit spreads). Optional: without it
     # the volatility (P5) and credit-spread (F2) signals are reported as n/a.
     fred_api_key: str = ""
@@ -86,15 +73,8 @@ class Settings(BaseSettings):
     # the score window is 7 days).
     sentiment_fresh_hours: int = 120
     sentiment_top_composite: int = 30
-    fundamental_fetch_frequency: str = "weekly"  # quarterly-ish data; weekly conserves API quota
     rr_scan_frequency: str = "daily"  # legacy label; qualifying scan is cron near-close
     # alerts_frequency removed: alerts fire only via morning + near-close pipelines
-    fundamental_rate_limit_retries: int = 3
-    fundamental_rate_limit_backoff_seconds: int = 15
-    # Pause between tickers in the bulk fundamentals job. Free tiers throttle
-    # hard (Finnhub ~60 calls/min, ~3 calls/ticker → ~3s/ticker); without
-    # spacing the job bursts straight into 429s. 0 disables.
-    fundamental_request_spacing_seconds: float = 3.0
 
     # Scoring Defaults
     default_watchlist_auto_size: int = 10

app/providers/fmp.py

@@ -1,174 +0,0 @@
-"""Financial Modeling Prep (FMP) fundamentals provider using httpx.
-
-Uses the stable API endpoints (https://financialmodelingprep.com/stable/)
-which replaced the legacy /api/v3/ endpoints deprecated in Aug 2025.
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-from datetime import datetime, timezone
-from pathlib import Path
-
-import httpx
-
-from app.exceptions import ProviderError, RateLimitError
-from app.providers.protocol import FundamentalData
-
-logger = logging.getLogger(__name__)
-
-_FMP_STABLE_URL = "https://financialmodelingprep.com/stable"
-
-# Resolve CA bundle for explicit httpx verify
-_CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
-if not _CA_BUNDLE or not Path(_CA_BUNDLE).exists():
-    _CA_BUNDLE_PATH: str | bool = True  # use system default
-else:
-    _CA_BUNDLE_PATH = _CA_BUNDLE
-
-
-class FMPFundamentalProvider:
-    """Fetches fundamental data from Financial Modeling Prep REST API."""
-
-    def __init__(self, api_key: str) -> None:
-        if not api_key:
-            raise ProviderError("FMP API key is required")
-        self._api_key = api_key
-
-    # Mapping from FMP endpoint name to the FundamentalData field it populates
-    _ENDPOINT_FIELD_MAP: dict[str, str] = {
-        "ratios-ttm": "pe_ratio",
-        "financial-growth": "revenue_growth",
-        "earnings": "earnings_surprise",
-    }
-
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        """Fetch P/E, revenue growth, earnings surprise, and market cap.
-
-        Fetches from multiple stable endpoints. If a supplementary endpoint
-        (ratios, growth, earnings) returns 402 (paid tier), we gracefully
-        degrade and return partial data rather than failing entirely, and
-        record the affected field in ``unavailable_fields``.
-        """
-        try:
-            endpoints_402: set[str] = set()
-
-            async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
-                params = {"symbol": ticker, "apikey": self._api_key}
-
-                # Profile is the primary source — must succeed
-                profile = await self._fetch_json(client, "profile", params, ticker)
-
-                # Supplementary sources — degrade gracefully on 402
-                ratios, was_402 = await self._fetch_json_optional(client, "ratios-ttm", params, ticker)
-                if was_402:
-                    endpoints_402.add("ratios-ttm")
-
-                growth, was_402 = await self._fetch_json_optional(client, "financial-growth", params, ticker)
-                if was_402:
-                    endpoints_402.add("financial-growth")
-
-                earnings, was_402 = await self._fetch_json_optional(client, "earnings", params, ticker)
-                if was_402:
-                    endpoints_402.add("earnings")
-
-            pe_ratio = self._safe_float(ratios.get("priceToEarningsRatioTTM"))
-            revenue_growth = self._safe_float(growth.get("revenueGrowth"))
-            market_cap = self._safe_float(profile.get("marketCap"))
-            earnings_surprise = self._compute_earnings_surprise(earnings)
-
-            # Build unavailable_fields from 402 endpoints
-            unavailable_fields: dict[str, str] = {
-                self._ENDPOINT_FIELD_MAP[ep]: "requires paid plan"
-                for ep in endpoints_402
-                if ep in self._ENDPOINT_FIELD_MAP
-            }
-
-            return FundamentalData(
-                ticker=ticker,
-                pe_ratio=pe_ratio,
-                revenue_growth=revenue_growth,
-                earnings_surprise=earnings_surprise,
-                market_cap=market_cap,
-                fetched_at=datetime.now(timezone.utc),
-                unavailable_fields=unavailable_fields,
-            )
-
-        except (ProviderError, RateLimitError):
-            raise
-        except Exception as exc:
-            logger.error("FMP provider error for %s: %s", ticker, exc)
-            raise ProviderError(f"FMP provider error for {ticker}: {exc}") from exc
-
-    async def _fetch_json(
-        self,
-        client: httpx.AsyncClient,
-        endpoint: str,
-        params: dict,
-        ticker: str,
-    ) -> dict:
-        """Fetch a stable endpoint and return the first item (or empty dict)."""
-        url = f"{_FMP_STABLE_URL}/{endpoint}"
-        resp = await client.get(url, params=params)
-        self._check_response(resp, ticker, endpoint)
-        data = resp.json()
-        if isinstance(data, list):
-            return data[0] if data else {}
-        return data if isinstance(data, dict) else {}
-
-    async def _fetch_json_optional(
-        self,
-        client: httpx.AsyncClient,
-        endpoint: str,
-        params: dict,
-        ticker: str,
-    ) -> tuple[dict, bool]:
-        """Fetch a stable endpoint, returning ``({}, True)`` on 402 (paid tier).
-
-        Returns a tuple of (data_dict, was_402) so callers can track which
-        endpoints required a paid plan.
-        """
-        url = f"{_FMP_STABLE_URL}/{endpoint}"
-        resp = await client.get(url, params=params)
-        if resp.status_code == 402:
-            logger.warning("FMP %s requires paid plan — skipping for %s", endpoint, ticker)
-            return {}, True
-        self._check_response(resp, ticker, endpoint)
-        data = resp.json()
-        if isinstance(data, list):
-            return (data[0] if data else {}, False)
-        return (data if isinstance(data, dict) else {}, False)
-
-    def _compute_earnings_surprise(self, earnings_data: dict) -> float | None:
-        """Compute earnings surprise % from the most recent actual vs estimated EPS."""
-        actual = self._safe_float(earnings_data.get("epsActual"))
-        estimated = self._safe_float(earnings_data.get("epsEstimated"))
-        if actual is None or estimated is None or estimated == 0:
-            return None
-        return ((actual - estimated) / abs(estimated)) * 100
-
-    def _check_response(
-        self, resp: httpx.Response, ticker: str, endpoint: str
-    ) -> None:
-        """Raise appropriate errors for non-200 responses."""
-        if resp.status_code == 429:
-            raise RateLimitError(f"FMP rate limit hit for {ticker} ({endpoint})")
-        if resp.status_code == 403:
-            raise ProviderError(
-                f"FMP {endpoint} access denied for {ticker}: HTTP 403 — check API key validity and plan tier"
-            )
-        if resp.status_code != 200:
-            raise ProviderError(
-                f"FMP {endpoint} error for {ticker}: HTTP {resp.status_code}"
-            )
-
-    @staticmethod
-    def _safe_float(value: object) -> float | None:
-        """Convert a value to float, returning None on failure."""
-        if value is None:
-            return None
-        try:
-            return float(value)
-        except (TypeError, ValueError):
-            return None

app/providers/fundamentals_chain.py

@@ -1,354 +0,0 @@
-"""Chained fundamentals provider with fallback adapters.
-
-Order:
-1) FMP (if configured)
-2) Finnhub (if configured)
-3) Alpha Vantage (if configured)
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-from datetime import date, datetime, timedelta, timezone
-from pathlib import Path
-
-import httpx
-
-from app.config import settings
-from app.exceptions import ProviderError, RateLimitError
-from app.providers.fmp import FMPFundamentalProvider
-from app.providers.protocol import FundamentalData, FundamentalProvider
-
-logger = logging.getLogger(__name__)
-
-_CA_BUNDLE = os.environ.get("SSL_CERT_FILE", "")
-if not _CA_BUNDLE or not Path(_CA_BUNDLE).exists():
-    _CA_BUNDLE_PATH: str | bool = True
-else:
-    _CA_BUNDLE_PATH = _CA_BUNDLE
-
-
-def _safe_float(value: object) -> float | None:
-    if value is None:
-        return None
-    try:
-        return float(value)
-    except (TypeError, ValueError):
-        return None
-
-
-def _to_api_symbol(symbol: str) -> str:
-    """Convert internal symbol format (BRK-B) to API format (BRK.B).
-
-    Finnhub and Alpha Vantage use dot-separated share class notation.
-    """
-    return symbol.replace("-", ".")
-
-
-class FinnhubFundamentalProvider:
-    """Fundamentals provider backed by Finnhub free endpoints."""
-
-    def __init__(self, api_key: str) -> None:
-        if not api_key:
-            raise ProviderError("Finnhub API key is required")
-        self._api_key = api_key
-        self._base_url = "https://finnhub.io/api/v1"
-
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        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",
-                params={"symbol": api_symbol, "token": self._api_key},
-            )
-            metric_resp = await client.get(
-                f"{self._base_url}/stock/metric",
-                params={"symbol": api_symbol, "metric": "all", "token": self._api_key},
-            )
-            earnings_resp = await client.get(
-                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})")
-            if resp.status_code in (401, 403):
-                raise ProviderError(f"Finnhub access denied for {ticker} ({endpoint}): HTTP {resp.status_code}")
-            if resp.status_code != 200:
-                raise ProviderError(f"Finnhub error for {ticker} ({endpoint}): HTTP {resp.status_code}")
-
-        profile_payload = profile_resp.json() if profile_resp.text else {}
-        metric_payload = metric_resp.json() if metric_resp.text else {}
-        earnings_payload = earnings_resp.json() if earnings_resp.text else []
-
-        metrics = metric_payload.get("metric", {}) if isinstance(metric_payload, dict) else {}
-        # Finnhub profile2 marketCapitalization is in millions of USD.
-        # Normalize to absolute dollars so cap bands / formatters match FMP & Alpha Vantage.
-        market_cap_millions = _safe_float((profile_payload or {}).get("marketCapitalization"))
-        market_cap = market_cap_millions * 1_000_000.0 if market_cap_millions is not None else None
-        pe_ratio = _safe_float(metrics.get("peTTM") or metrics.get("peNormalizedAnnual"))
-        revenue_growth = _safe_float(metrics.get("revenueGrowthTTMYoy") or metrics.get("revenueGrowth5Y"))
-
-        earnings_surprise = None
-        if isinstance(earnings_payload, list) and earnings_payload:
-            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:
-            unavailable["revenue_growth"] = "not available from provider payload"
-        if earnings_surprise is None:
-            unavailable["earnings_surprise"] = "not available from provider payload"
-        if market_cap is None:
-            unavailable["market_cap"] = "not available from provider payload"
-
-        return FundamentalData(
-            ticker=ticker,
-            pe_ratio=pe_ratio,
-            revenue_growth=revenue_growth,
-            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."""
-
-    def __init__(self, api_key: str) -> None:
-        if not api_key:
-            raise ProviderError("Alpha Vantage API key is required")
-        self._api_key = api_key
-        self._base_url = "https://www.alphavantage.co/query"
-
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        unavailable: dict[str, str] = {}
-        api_symbol = _to_api_symbol(ticker)
-
-        async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
-            overview_resp = await client.get(
-                self._base_url,
-                params={"function": "OVERVIEW", "symbol": api_symbol, "apikey": self._api_key},
-            )
-            earnings_resp = await client.get(
-                self._base_url,
-                params={"function": "EARNINGS", "symbol": api_symbol, "apikey": self._api_key},
-            )
-            income_resp = await client.get(
-                self._base_url,
-                params={"function": "INCOME_STATEMENT", "symbol": api_symbol, "apikey": self._api_key},
-            )
-
-        for resp, endpoint in (
-            (overview_resp, "OVERVIEW"),
-            (earnings_resp, "EARNINGS"),
-            (income_resp, "INCOME_STATEMENT"),
-        ):
-            if resp.status_code == 429:
-                raise RateLimitError(f"Alpha Vantage rate limit hit for {ticker} ({endpoint})")
-            if resp.status_code != 200:
-                raise ProviderError(f"Alpha Vantage error for {ticker} ({endpoint}): HTTP {resp.status_code}")
-
-        overview = overview_resp.json() if overview_resp.text else {}
-        earnings = earnings_resp.json() if earnings_resp.text else {}
-        income = income_resp.json() if income_resp.text else {}
-
-        if isinstance(overview, dict) and overview.get("Information"):
-            raise ProviderError(f"Alpha Vantage unavailable for {ticker}: {overview.get('Information')}")
-        if isinstance(overview, dict) and overview.get("Note"):
-            raise RateLimitError(f"Alpha Vantage rate limit for {ticker}: {overview.get('Note')}")
-
-        pe_ratio = _safe_float((overview or {}).get("PERatio"))
-        market_cap = _safe_float((overview or {}).get("MarketCapitalization"))
-
-        earnings_surprise = None
-        quarterly = earnings.get("quarterlyEarnings", []) if isinstance(earnings, dict) else []
-        if isinstance(quarterly, list) and quarterly:
-            first = quarterly[0] if isinstance(quarterly[0], dict) else {}
-            earnings_surprise = _safe_float(first.get("surprisePercentage"))
-
-        revenue_growth = None
-        annual = income.get("annualReports", []) if isinstance(income, dict) else []
-        if isinstance(annual, list) and len(annual) >= 2:
-            curr = _safe_float((annual[0] or {}).get("totalRevenue"))
-            prev = _safe_float((annual[1] or {}).get("totalRevenue"))
-            if curr is not None and prev not in (None, 0):
-                revenue_growth = ((curr - prev) / abs(prev)) * 100.0
-
-        if pe_ratio is None:
-            unavailable["pe_ratio"] = "not available from provider payload"
-        if revenue_growth is None:
-            unavailable["revenue_growth"] = "not available from provider payload"
-        if earnings_surprise is None:
-            unavailable["earnings_surprise"] = "not available from provider payload"
-        if market_cap is None:
-            unavailable["market_cap"] = "not available from provider payload"
-
-        return FundamentalData(
-            ticker=ticker,
-            pe_ratio=pe_ratio,
-            revenue_growth=revenue_growth,
-            earnings_surprise=earnings_surprise,
-            market_cap=market_cap,
-            fetched_at=datetime.now(timezone.utc),
-            unavailable_fields=unavailable,
-        )
-
-
-_FUNDAMENTAL_FIELDS = ("pe_ratio", "revenue_growth", "earnings_surprise", "market_cap")
-
-
-class ChainedFundamentalProvider:
-    """Merge fundamentals across providers, filling gaps from later sources.
-
-    A single provider rarely covers everything on free tiers — FMP's free plan,
-    for example, returns only market cap (the ratios/growth/earnings endpoints
-    402). Rather than stop at the first provider with *any* field, we take each
-    field from the first provider that supplies it, so FMP's market cap is
-    combined with Finnhub's P/E and earnings surprise.
-    """
-
-    def __init__(self, providers: list[tuple[str, FundamentalProvider]]) -> None:
-        if not providers:
-            raise ProviderError("No fundamental providers configured")
-        self._providers = providers
-
-    async def fetch_fundamentals(self, ticker: str, allow_partial: bool = False) -> FundamentalData:
-        """Merge fundamentals across providers.
-
-        ``allow_partial`` controls behaviour when a fallback provider is *rate
-        limited* and we end up with missing fields. By default we raise
-        RateLimitError so the caller (the bulk collector) can back off and retry
-        the ticker once the window frees — otherwise a transient 429 on Finnhub
-        would be silently stored as market-cap-only. Pass ``allow_partial=True``
-        (manual single fetches, or the collector's final give-up attempt) to
-        accept whatever was gathered instead of raising.
-        """
-        merged: dict[str, float | None] = {f: None for f in _FUNDAMENTAL_FIELDS}
-        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) and next_earnings_date:
-                break
-            try:
-                data = await provider.fetch_fundamentals(ticker)
-            except RateLimitError as exc:
-                rate_limited = True
-                errors.append(f"{provider_name}: RateLimitError: {exc}")
-                continue
-            except Exception as exc:
-                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)
-                    if value is not None:
-                        merged[field] = value
-                        field_source[field] = provider_name
-
-        missing = [f for f in _FUNDAMENTAL_FIELDS if merged[f] is None]
-
-        # A rate limit left data incomplete: signal it (unless partial is OK) so
-        # the collector backs off rather than persisting a degraded record.
-        if rate_limited and missing and not allow_partial:
-            attempts = "; ".join(errors[:6])
-            raise RateLimitError(
-                f"Fundamentals incomplete for {ticker} due to provider rate limits "
-                f"(missing {', '.join(missing)}). Attempts: {attempts}"
-            )
-
-        if all(merged[f] is None for f in _FUNDAMENTAL_FIELDS):
-            attempts = "; ".join(errors[:6]) if errors else "no usable metrics from any provider"
-            raise ProviderError(f"All fundamentals providers failed for {ticker}. Attempts: {attempts}")
-
-        unavailable: dict[str, str] = {
-            field: "not available from any configured provider"
-            for field in _FUNDAMENTAL_FIELDS
-            if merged[field] is None
-        }
-        # Record which provider supplied each field for transparency.
-        for field, src in field_source.items():
-            unavailable[f"source_{field}"] = src
-
-        return FundamentalData(
-            ticker=ticker,
-            pe_ratio=merged["pe_ratio"],
-            revenue_growth=merged["revenue_growth"],
-            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,
-        )
-
-
-def build_fundamental_provider_chain() -> FundamentalProvider:
-    providers: list[tuple[str, FundamentalProvider]] = []
-
-    if settings.fmp_api_key:
-        providers.append(("fmp", FMPFundamentalProvider(settings.fmp_api_key)))
-    if settings.finnhub_api_key:
-        providers.append(("finnhub", FinnhubFundamentalProvider(settings.finnhub_api_key)))
-    if settings.alpha_vantage_api_key:
-        providers.append(("alpha_vantage", AlphaVantageFundamentalProvider(settings.alpha_vantage_api_key)))
-
-    if not providers:
-        raise ProviderError(
-            "No fundamentals provider configured. Set one of FMP_API_KEY, FINNHUB_API_KEY, ALPHA_VANTAGE_API_KEY"
-        )
-
-    logger.info("Fundamentals provider chain configured: %s", [name for name, _ in providers])
-    return ChainedFundamentalProvider(providers)

app/providers/protocol.py

@@ -44,20 +44,6 @@ class SentimentData:
     recommendation: str | None = None  # "buy" | "hold" | "avoid" — actionable LLM view
 
 
-@dataclass(frozen=True, slots=True)
-class FundamentalData:
-    """Fundamental metrics returned by fundamental providers."""
-
-    ticker: str
-    pe_ratio: float | None
-    revenue_growth: float | None
-    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)
-
-
 # ---------------------------------------------------------------------------
 # Provider Protocols
 # ---------------------------------------------------------------------------
@@ -81,9 +67,5 @@ class SentimentProvider(Protocol):
         ...
 
 
-class FundamentalProvider(Protocol):
+# No fundamentals provider protocol: since A6 fundamentals come only from the
-    """Protocol for fundamental data providers."""
+# batch SEC/Dolt imports, never from a request-time provider call.
-
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        """Fetch fundamental data for a ticker."""
-        ...

app/routers/admin.py

@@ -13,7 +13,6 @@ from app.schemas.admin import (
     AlertConfigUpdate,
     CreateUserRequest,
     DataCleanupRequest,
-    FundamentalsCutoverConfigUpdate,
     JobTriggerRequest,
     JobToggle,
     RecommendationConfigUpdate,
@@ -138,27 +137,6 @@ async def list_settings(
     )
 
 
-@router.get("/admin/settings/fundamentals-cutover", response_model=APIEnvelope)
-async def get_fundamentals_cutover_settings(
-    _admin: User = Depends(require_admin),
-    db: AsyncSession = Depends(get_db),
-):
-    config = await admin_service.get_fundamentals_cutover_config(db)
-    return APIEnvelope(status="success", data=config)
-
-
-@router.put("/admin/settings/fundamentals-cutover", response_model=APIEnvelope)
-async def update_fundamentals_cutover_settings(
-    body: FundamentalsCutoverConfigUpdate,
-    _admin: User = Depends(require_admin),
-    db: AsyncSession = Depends(get_db),
-):
-    config = await admin_service.update_fundamentals_cutover_config(
-        db, body.enabled
-    )
-    return APIEnvelope(status="success", data=config)
-
-
 @router.get("/admin/settings/recommendations", response_model=APIEnvelope)
 async def get_recommendation_settings(
     _admin: User = Depends(require_admin),
@@ -475,36 +453,6 @@ async def toggle_job(
     )
 
 
-@router.get("/admin/fundamentals-parity", response_model=APIEnvelope)
-async def get_fundamentals_parity_report(
-    _admin: User = Depends(require_admin),
-):
-    """Latest read-only A5 source/score comparison, or null before first run."""
-    return APIEnvelope(
-        status="success", data=admin_service.get_fundamentals_parity_report()
-    )
-
-
-@router.get("/admin/fundamentals-parity/csv", response_model=APIEnvelope)
-async def get_fundamentals_parity_csv(
-    _admin: User = Depends(require_admin),
-):
-    """Latest flattened A5 report for an authenticated browser download."""
-    artifact = admin_service.get_fundamentals_parity_csv()
-    data = None if artifact is None else {"filename": artifact[0], "content": artifact[1]}
-    return APIEnvelope(status="success", data=data)
-
-
-@router.get("/admin/fundamentals-parity/json", response_model=APIEnvelope)
-async def get_fundamentals_parity_json(
-    _admin: User = Depends(require_admin),
-):
-    """Canonical A5 JSON artifact for an authenticated browser download."""
-    artifact = admin_service.get_fundamentals_parity_json()
-    data = None if artifact is None else {"filename": artifact[0], "content": artifact[1]}
-    return APIEnvelope(status="success", data=data)
-
-
 # ---------------------------------------------------------------------------
 # System events (operational warnings / errors)
 # ---------------------------------------------------------------------------

app/routers/ingestion.py

@@ -23,7 +23,6 @@ from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
 from app.models.user import User
 from app.providers.alpaca import AlpacaOHLCVProvider
-from app.providers.fundamentals_chain import build_fundamental_provider_chain
 from app.services.rr_scanner_service import (
     resolve_activation_ranks_for_symbol,
     scan_ticker,
@@ -31,7 +30,6 @@ from app.services.rr_scanner_service import (
 from app.services.sentiment_provider_service import build_sentiment_provider
 from app.schemas.common import APIEnvelope
 from app.services import (
-    fundamental_service,
     ingestion_service,
     scoring_service,
     sentiment_service,
@@ -185,34 +183,14 @@ async def fetch_symbol(
                 sources_out["sentiment"] = {"status": "error", "message": str(exc)}
 
     # --- Fundamentals ---
+    # No per-ticker fetch exists any more: fundamental_data is rebuilt for the
+    # whole universe by the nightly SEC + Dolt imports, from local PostgreSQL.
+    # The source key is still accepted so older clients get a truthful answer.
     if "fundamentals" in requested:
-        if settings.fmp_api_key or settings.finnhub_api_key or settings.alpha_vantage_api_key:
+        sources_out["fundamentals"] = {
-            try:
+            "status": "skipped",
-                fundamentals_provider = build_fundamental_provider_chain()
+            "message": "Fundamentals refresh nightly from the SEC + Dolt imports",
-                # Manual single fetch: take whatever we can get (a lone 429 on a
+        }
-                # fallback shouldn't fail the whole refresh).
-                fdata = await fundamentals_provider.fetch_fundamentals(
-                    symbol_upper, allow_partial=True
-                )
-                await fundamental_service.store_fundamental(
-                    db,
-                    symbol=symbol_upper,
-                    pe_ratio=fdata.pe_ratio,
-                    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}
-            except Exception as exc:
-                logger.error("Fundamentals fetch failed for %s: %s", symbol_upper, exc)
-                sources_out["fundamentals"] = {"status": "error", "message": str(exc)}
-        else:
-            sources_out["fundamentals"] = {
-                "status": "skipped",
-                "message": "No fundamentals provider key configured",
-            }
 
     # --- Derived pipeline: S/R levels (free, always) ---
     try:

app/scheduler.py

@@ -1,9 +1,9 @@
 """APScheduler job definitions and FastAPI lifespan integration.
 
-Defines four scheduled jobs:
+Defines the scheduled jobs, among them:
   - Data Collector (OHLCV fetch for all tickers)
   - Sentiment Collector (sentiment for all tickers)
-  - Fundamental Collector (fundamentals for all tickers)
+  - Dolt Earnings / SEC Fundamentals imports (bulk fundamentals sources)
   - R:R Scanner (trade setup scan for all tickers)
 
 Each job processes tickers independently, logs errors as structured JSON,
@@ -25,22 +25,18 @@ from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.config import settings
 from app.database import async_session_factory
-from app.models.fundamental import FundamentalData
 from app.models.ohlcv import OHLCVRecord
 from app.models.sentiment import SentimentScore
 from app.models.ticker import Ticker
 from app.exceptions import ProviderError
 from app.providers.alpaca import AlpacaOHLCVProvider
-from app.providers.fundamentals_chain import build_fundamental_provider_chain
 from app.providers.protocol import SentimentData
 from app.services import (
-    fundamental_service,
     ingestion_service,
     pipeline_run,
     sentiment_service,
     settings_store,
     shadow_book_service,
-    fundamentals_parity_service,
     fundamental_data_refresh_service,
 )
 from app.services.data_import import (
@@ -93,7 +89,6 @@ _last_successful: dict[str, str | None] = {
     "data_collector": None,
     "data_backfill": None,
     "sentiment_collector": None,
-    "fundamental_collector": None,
 }
 
 # Jobs whose per-run progress is surfaced to Admin → Jobs. (outcome_evaluator is
@@ -102,10 +97,8 @@ _JOB_NAMES = [
     "data_collector",
     "data_backfill",
     "sentiment_collector",
-    "fundamental_collector",
     "dolt_earnings_import",
     "sec_fundamentals_import",
-    "fundamentals_parity_report",
     "rr_scanner",
     "ticker_universe_sync",
     "alerts",
@@ -261,7 +254,14 @@ def _runtime_finish(
     processed: int,
     total: int | None,
     message: str | None = None,
+    emit_event: bool = True,
 ) -> None:
+    """Finalize a job's runtime row, optionally raising a durable event.
+
+    ``emit_event=False`` is for a *re-finalize* that only rewords an outcome an
+    earlier call already reported. The dedup key includes the message, so a
+    reworded error would otherwise land in Admin → System Events twice.
+    """
     runtime = _job_runtime.get(job_name, {})
     runtime.update({
         "running": False,
@@ -275,7 +275,7 @@ def _runtime_finish(
     })
     _job_runtime[job_name] = runtime
     # Durable event for error / rate-limit finishes (badge + Admin → Jobs panel).
-    if status in ("error", "rate_limited"):
+    if emit_event and status in ("error", "rate_limited"):
         severity = "error" if status == "error" else "warning"
         try:
             loop = asyncio.get_running_loop()
@@ -466,23 +466,6 @@ async def _get_sentiment_priority_tickers(db: AsyncSession) -> list[str]:
     return priority_syms + filler_syms
 
 
-async def _get_fundamental_priority_tickers(db: AsyncSession) -> list[str]:
-    """Return symbols prioritized for fundamentals refresh.
-
-    Priority:
-      1) Tickers with no fundamentals snapshot yet
-      2) Tickers with existing fundamentals, oldest fetched_at first
-      3) Alphabetical tiebreaker
-    """
-    missing_first = case((FundamentalData.fetched_at.is_(None), 0), else_=1)
-    result = await db.execute(
-        select(Ticker.symbol)
-        .outerjoin(FundamentalData, FundamentalData.ticker_id == Ticker.id)
-        .order_by(missing_first.asc(), FundamentalData.fetched_at.asc(), Ticker.symbol.asc())
-    )
-    return list(result.scalars().all())
-
-
 def _resume_tickers(symbols: list[str], job_name: str) -> list[str]:
     """Reorder tickers to resume after the last successful one (rate-limit resume).
 
@@ -816,149 +799,16 @@ async def collect_sentiment() -> None:
 
 
 # ---------------------------------------------------------------------------
-# Job: Fundamental Collector
+# Jobs: bulk fundamentals source imports
 # ---------------------------------------------------------------------------
 
 
-async def collect_fundamentals() -> None:
+async def _run_source_import(job_name: str, importer: SourceImporter) -> bool:
-    """Fetch fundamentals for all tracked tickers via FMP.
-
-    Processes each ticker independently. On rate limit, records last
-    successful ticker for resume.
-    """
-    job_name = "fundamental_collector"
-    _log_event(logging.INFO, "job_start", job=job_name)
-    _runtime_start(job_name)
-    processed = 0
-    total: int | None = None
-
-    try:
-        async with async_session_factory() as db:
-            if not await _is_job_enabled(db, job_name):
-                _log_event(logging.INFO, "job_skipped", job=job_name, reason="disabled")
-                _runtime_finish(job_name, "skipped", processed=0, total=0, message="Disabled")
-                return
-            if await fundamental_data_refresh_service.is_enabled(db):
-                message = "SEC + Dolt fundamentals cutover is active"
-                _log_event(
-                    logging.INFO,
-                    "job_skipped",
-                    job=job_name,
-                    reason="sec_dolt_cutover_active",
-                )
-                _runtime_finish(
-                    job_name,
-                    "skipped",
-                    processed=0,
-                    total=0,
-                    message=message,
-                )
-                return
-
-            symbols = await _get_fundamental_priority_tickers(db)
-            if not symbols:
-                _log_event(logging.INFO, "job_complete", job=job_name, tickers=0)
-                _runtime_finish(job_name, "completed", processed=0, total=0, message="No tickers")
-                return
-
-        total = len(symbols)
-        _runtime_progress(job_name, processed=0, total=total)
-
-        if not (settings.fmp_api_key or settings.finnhub_api_key or settings.alpha_vantage_api_key):
-            _log_event(logging.WARNING, "job_skipped", job=job_name, reason="no fundamentals provider keys configured")
-            _runtime_finish(job_name, "skipped", processed=0, total=total, message="No fundamentals provider keys configured")
-            return
-
-        try:
-            provider = build_fundamental_provider_chain()
-        except Exception as exc:
-            _log_event(logging.ERROR, "job_error", job=job_name, error_type=type(exc).__name__, message=str(exc))
-            _runtime_finish(job_name, "error", processed=0, total=total, message=str(exc))
-            return
-
-        max_retries = max(0, settings.fundamental_rate_limit_retries)
-        base_backoff = max(1, settings.fundamental_rate_limit_backoff_seconds)
-        spacing = max(0.0, settings.fundamental_request_spacing_seconds)
-
-        async def _store(symbol: str, data) -> None:
-            async with async_session_factory() as db:
-                await fundamental_service.store_fundamental(
-                    db,
-                    symbol=symbol,
-                    pe_ratio=data.pe_ratio,
-                    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,
-                )
-
-        for symbol in symbols:
-            _runtime_progress(job_name, processed=processed, total=total, current_ticker=symbol)
-            attempt = 0
-            while True:
-                try:
-                    data = await provider.fetch_fundamentals(symbol)
-                    await _store(symbol, data)
-                    _last_successful[job_name] = symbol
-                    processed += 1
-                    _runtime_progress(job_name, processed=processed, total=total, current_ticker=symbol)
-                    _log_event(logging.INFO, "ticker_collected", job=job_name, ticker=symbol)
-                    break
-                except Exception as exc:
-                    msg = str(exc).lower()
-                    if "rate" in msg or "429" in msg:
-                        if attempt < max_retries:
-                            wait_seconds = base_backoff * (2 ** attempt)
-                            attempt += 1
-                            _log_event(logging.WARNING, "rate_limited_retry", job=job_name, ticker=symbol, attempt=attempt, max_retries=max_retries, wait_seconds=wait_seconds, processed=processed)
-                            _runtime_progress(
-                                job_name,
-                                processed=processed,
-                                total=total,
-                                current_ticker=symbol,
-                                message=f"Rate-limited at {symbol}; retry {attempt}/{max_retries} in {wait_seconds}s",
-                            )
-                            await asyncio.sleep(wait_seconds)
-                            continue
-
-                        # Retries exhausted: store whatever partial data we can
-                        # still get (e.g. FMP market cap) and move on, rather than
-                        # aborting the whole run and leaving every later ticker
-                        # untouched.
-                        _log_event(logging.WARNING, "rate_limited_partial", job=job_name, ticker=symbol, processed=processed)
-                        try:
-                            data = await provider.fetch_fundamentals(symbol, allow_partial=True)
-                            await _store(symbol, data)
-                            processed += 1
-                        except Exception as exc2:
-                            _log_job_error(job_name, symbol, exc2)
-                        break
-                    _log_job_error(job_name, symbol, exc)
-                    break
-
-            if spacing:
-                await asyncio.sleep(spacing)
-
-        _last_successful[job_name] = None
-        _log_event(logging.INFO, "job_complete", job=job_name, tickers=processed)
-        _runtime_finish(job_name, "completed", processed=processed, total=total, message=f"Processed {processed} tickers")
-    except Exception as exc:
-        _log_event(logging.ERROR, "job_error", job=job_name, error_type=type(exc).__name__, message=str(exc))
-        _runtime_finish(job_name, "error", processed=processed, total=total, message=str(exc))
-
-
-# ---------------------------------------------------------------------------
-# Jobs: shadow fundamentals sources
-# ---------------------------------------------------------------------------
-
-
-async def _run_shadow_import(job_name: str, importer: SourceImporter) -> bool:
     """Run an importer and return whether its scheduled job was enabled.
 
-    The SEC wrapper uses the return value to run its activated local cache step
+    The SEC wrapper uses the return value only to word its runtime message: its
-    after deferred, failed, no-op, promoted, or source-locked attempts while honoring
+    local cache step runs after deferred, failed, no-op, promoted, source-locked
-    the job-level disable switch.
+    and disabled attempts alike.
     """
     _log_event(logging.INFO, "job_start", job=job_name)
     _runtime_start(job_name, total=1)
@@ -968,7 +818,7 @@ async def _run_shadow_import(job_name: str, importer: SourceImporter) -> bool:
             if not await _is_job_enabled(db, job_name):
                 _log_event(logging.INFO, "job_skipped", job=job_name, reason="disabled")
                 _runtime_finish(job_name, "skipped", processed=0, total=1, message="Disabled")
-                return
+                return False
 
         run = await run_import(importer)
         if run is None:
@@ -1015,25 +865,26 @@ async def _run_shadow_import(job_name: str, importer: SourceImporter) -> bool:
 
 
 async def run_dolt_earnings_import() -> None:
-    """Pull and import the Dolt earnings calendar/results feed in shadow."""
+    """Pull and import the Dolt earnings calendar/results feed."""
-    await _run_shadow_import("dolt_earnings_import", DoltEarningsImporter())
+    await _run_source_import("dolt_earnings_import", DoltEarningsImporter())
 
 
 async def run_sec_fundamentals_import() -> None:
-    """Import SEC facts, then run the activated local compat-cache refresh.
+    """Import SEC facts, then refresh the local compat cache.
 
-    The refresh is deliberately separate from the network import result. Once
+    The refresh is deliberately independent of the network import: it reads only
-    activated it therefore still runs from stored snapshots/earnings/prices when
+    stored snapshots, earnings events and closes, so it runs identically when SEC
-    SEC is unavailable, unchanged, or another SEC import owns the source lock.
+    is unavailable, unchanged, or owned by another import — and also when the
+    job's ingestion is switched off in Admin → Jobs. Disabling the job stops
+    SEC network access, not the cache; prices and earnings move daily even when
+    no filing does, and `fundamental_data` feeds scoring.
     """
     job_name = "sec_fundamentals_import"
-    job_enabled = await _run_shadow_import(job_name, SecFundamentalsImporter())
+    import_ran = await _run_source_import(job_name, SecFundamentalsImporter())
-    if not job_enabled:
-        return
 
     try:
         async with async_session_factory() as db:
-            summary = await fundamental_data_refresh_service.refresh_if_enabled(db)
+            summary = await fundamental_data_refresh_service.refresh(db)
     except asyncio.CancelledError:
         _runtime_finish(
             job_name, "error", processed=0, total=1, message="Cancelled"
@@ -1051,79 +902,38 @@ async def run_sec_fundamentals_import() -> None:
         _runtime_finish(job_name, "error", processed=0, total=1, message=message)
         return
 
-    if not summary["enabled"]:
-        _log_event(
-            logging.INFO,
-            "fundamental_data_refresh_skipped",
-            job=job_name,
-            reason="cutover_disabled",
-            setting=fundamental_data_refresh_service.ACTIVATION_KEY,
-        )
-        return
-
     _log_event(
         logging.INFO,
         "fundamental_data_refresh_complete",
         job=job_name,
         **summary,
     )
+    cache_message = (
+        f"cache {summary['refreshed']} · "
+        f"{summary['score_inputs_changed']} score inputs changed"
+    )
+    # Every outcome carries the cache summary — including deferred, failed and
+    # source-locked ones. The import status is what varies; the refresh always
+    # happened, and Admin → Jobs is the only place an operator sees that.
+    #
+    # This only rewords what _run_source_import already finalized, so it must not
+    # emit a second durable event: the dedup key includes the message, and a
+    # failure would otherwise show up twice in Admin → System Events.
     runtime = get_job_runtime_snapshot(job_name)
-    if runtime.get("status") == "completed":
+    if import_ran:
+        status = str(runtime.get("status") or "completed")
         import_message = runtime.get("message") or "import completed"
-        cache_message = (
+        processed = 1 if status == "completed" else 0
-            f"cache {summary['refreshed']} · "
+    else:
-            f"{summary['score_inputs_changed']} score inputs changed"
+        status, import_message, processed = "completed", "Import disabled", 1
-        )
+    _runtime_finish(
-        _runtime_finish(
+        job_name,
-            job_name,
+        status,
-            "completed",
+        processed=processed,
-            processed=1,
+        total=1,
-            total=1,
+        message=f"{import_message} · {cache_message}",
-            message=f"{import_message} · {cache_message}",
+        emit_event=False,
-        )
+    )
-
-
-async def run_fundamentals_parity_report() -> None:
-    """Generate the A5 comparison bundle without mutating live fundamentals/scores."""
-    job_name = "fundamentals_parity_report"
-    _log_event(logging.INFO, "job_start", job=job_name)
-    _runtime_start(job_name, total=1)
-    try:
-        async with async_session_factory() as db:
-            if not await _is_job_enabled(db, job_name):
-                _runtime_finish(
-                    job_name, "skipped", processed=0, total=1, message="Disabled"
-                )
-                return
-            report, artifacts = await fundamentals_parity_service.generate_and_store(
-                db, settings.fundamentals_parity_report_dir
-            )
-        summary = report["summary"]
-        message = (
-            f"{summary['universe_count']} tickers · "
-            f"{summary['fundamental_score_material_changes']} material score changes"
-        )
-        _runtime_finish(job_name, "completed", processed=1, total=1, message=message)
-        _log_event(
-            logging.INFO,
-            "job_complete",
-            job=job_name,
-            generated_at=report["generated_at"],
-            json_path=artifacts["json"],
-            csv_path=artifacts["csv"],
-        )
-    except asyncio.CancelledError:
-        _runtime_finish(job_name, "error", processed=0, total=1, message="Cancelled")
-        raise
-    except Exception as exc:
-        _runtime_finish(job_name, "error", processed=0, total=1, message=str(exc))
-        _log_event(
-            logging.ERROR,
-            "job_error",
-            job=job_name,
-            error_type=type(exc).__name__,
-            message=str(exc),
-        )
 
 
 # ---------------------------------------------------------------------------
@@ -1666,19 +1476,16 @@ SCHEDULE_DEFAULTS: dict[str, str] = {
     "schedule_timezone": "America/New_York",
     # Morning data/display refresh (no qualifying R:R scan).
     "schedule_daily_pipeline_cron": "0 2 * * *",
-    # Bulk source imports. The SEC job writes the legacy compat cache only after
+    # Bulk source imports. The SEC job also refreshes the fundamental_data compat
-    # the explicit, default-off A5 cutover setting is enabled.
+    # cache that scoring reads — locally, from stored snapshots/earnings/closes.
     "schedule_dolt_earnings_cron": "30 2 * * *",
     "schedule_sec_fundamentals_cron": "0 4 * * *",
-    "schedule_fundamentals_parity_cron": "30 5 * * *",
     # Fetch in-progress bars → scan → Telegram (manual MOC window).
     "schedule_near_close_pipeline_cron": "30 15 * * mon-fri",
     # Fetch final bars → outcome eval (must not run on the partial near-close bar).
     "schedule_after_close_pipeline_cron": "45 16 * * mon-fri",
     # Hourly mid-session price + outcome (10:00–15:00 ET Mon–Fri).
     "schedule_intraday_pipeline_cron": "0 10-15 * * mon-fri",
-    # Weekly fundamentals early Monday NY.
-    "schedule_fundamentals_cron": "0 1 * * mon",
 }
 
 # job id -> schedule setting key
@@ -1686,11 +1493,9 @@ _CRON_JOBS: dict[str, str] = {
     "daily_pipeline": "schedule_daily_pipeline_cron",
     "dolt_earnings_import": "schedule_dolt_earnings_cron",
     "sec_fundamentals_import": "schedule_sec_fundamentals_cron",
-    "fundamentals_parity_report": "schedule_fundamentals_parity_cron",
     "near_close_pipeline": "schedule_near_close_pipeline_cron",
     "after_close_pipeline": "schedule_after_close_pipeline_cron",
     "intraday_pipeline": "schedule_intraday_pipeline_cron",
-    "fundamental_collector": "schedule_fundamentals_cron",
 }
 
 
@@ -1779,7 +1584,7 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
             "schedule_dolt_earnings_cron",
         ),
         id="dolt_earnings_import",
-        name="Dolt Earnings Import (shadow)",
+        name="Dolt Earnings Import",
         replace_existing=True,
     )
     scheduler.add_job(
@@ -1793,17 +1598,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
         name="SEC Fundamentals Import",
         replace_existing=True,
     )
-    scheduler.add_job(
-        run_fundamentals_parity_report,
-        _cron_trigger(
-            cfg["schedule_fundamentals_parity_cron"],
-            tz,
-            "schedule_fundamentals_parity_cron",
-        ),
-        id="fundamentals_parity_report",
-        name="Fundamentals Parity Report (read-only)",
-        replace_existing=True,
-    )
     scheduler.add_job(
         run_near_close_pipeline,
         _cron_trigger(
@@ -1831,13 +1625,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
         _cron_trigger(cfg["schedule_intraday_pipeline_cron"], tz, "schedule_intraday_pipeline_cron"),
         id="intraday_pipeline", name="Intraday Pipeline", replace_existing=True,
     )
-    # Fundamentals — quarterly-ish data; weekly by default (conserves API quota).
-    # Its own early cron so the slow, rate-limited fetch finishes before the day.
-    scheduler.add_job(
-        collect_fundamentals,
-        _cron_trigger(cfg["schedule_fundamentals_cron"], tz, "schedule_fundamentals_cron"),
-        id="fundamental_collector", name="Fundamental Collector", replace_existing=True,
-    )
 
     # Independent interval jobs (own cadence, no ordering dependency)
     scheduler.add_job(
@@ -1879,9 +1666,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
         },
         dolt_earnings_import={"cron": cfg["schedule_dolt_earnings_cron"]},
         sec_fundamentals_import={"cron": cfg["schedule_sec_fundamentals_cron"]},
-        fundamentals_parity_report={
-            "cron": cfg["schedule_fundamentals_parity_cron"]
-        },
         near_close_pipeline={
             "cron": cfg["schedule_near_close_pipeline_cron"],
             "steps": [name for name, _ in _NEAR_CLOSE_PIPELINE_STEPS],
@@ -1894,7 +1678,6 @@ def configure_scheduler(schedule_config: dict[str, str] | None = None) -> None:
             "cron": cfg["schedule_intraday_pipeline_cron"],
             "steps": [name for name, _ in _INTRADAY_PIPELINE_STEPS],
         },
-        fundamental_collector={"cron": cfg["schedule_fundamentals_cron"]},
         independent=["ticker_universe_sync", "backtest"],
         manual_only=["alerts", "data_backfill", "event_study"],
     )

app/schemas/admin.py

@@ -73,11 +73,6 @@ class ActivationConfigUpdate(BaseModel):
     exclude_neutral: bool | None = None
 
 
-class FundamentalsCutoverConfigUpdate(BaseModel):
-    """Switch the legacy fundamentals cache from quota APIs to SEC/Dolt."""
-    enabled: bool
-
-
 class ScheduleConfigUpdate(BaseModel):
     """Cron schedule for the pipelines + fundamentals. Crons are 5-field
     (min hour dom month dow); timezone is an IANA name (e.g. America/New_York)."""
@@ -85,11 +80,9 @@ class ScheduleConfigUpdate(BaseModel):
     schedule_daily_pipeline_cron: str | None = Field(default=None, max_length=120)
     schedule_dolt_earnings_cron: str | None = Field(default=None, max_length=120)
     schedule_sec_fundamentals_cron: str | None = Field(default=None, max_length=120)
-    schedule_fundamentals_parity_cron: str | None = Field(default=None, max_length=120)
     schedule_near_close_pipeline_cron: str | None = Field(default=None, max_length=120)
     schedule_after_close_pipeline_cron: str | None = Field(default=None, max_length=120)
     schedule_intraday_pipeline_cron: str | None = Field(default=None, max_length=120)
-    schedule_fundamentals_cron: str | None = Field(default=None, max_length=120)
 
 
 class PerformanceConfigUpdate(BaseModel):

app/services/admin_service.py

@@ -17,7 +17,7 @@ from app.models.settings import SystemSetting
 from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
 from app.models.user import User
-from app.services import fundamental_data_refresh_service, settings_store
+from app.services import settings_store
 
 logger = logging.getLogger(__name__)
 
@@ -159,28 +159,6 @@ async def update_setting(db: AsyncSession, key: str, value: str) -> SystemSettin
     return setting
 
 
-# ---------------------------------------------------------------------------
-# Fundamentals source cutover
-# ---------------------------------------------------------------------------
-
-async def get_fundamentals_cutover_config(db: AsyncSession) -> dict[str, bool]:
-    """Return the explicit A5 cache-cutover switch (default off)."""
-    return {"enabled": await fundamental_data_refresh_service.is_enabled(db)}
-
-
-async def update_fundamentals_cutover_config(
-    db: AsyncSession, enabled: bool
-) -> dict[str, bool]:
-    """Activate or pause SEC/Dolt writes to the legacy fundamentals cache."""
-    await settings_store.upsert_setting(
-        db,
-        fundamental_data_refresh_service.ACTIVATION_KEY,
-        "true" if enabled else "false",
-    )
-    await db.commit()
-    return await get_fundamentals_cutover_config(db)
-
-
 # ---------------------------------------------------------------------------
 # Activation thresholds
 # ---------------------------------------------------------------------------
@@ -633,10 +611,8 @@ VALID_JOB_NAMES = {
     "data_backfill",
     "benchmark_collector",
     "sentiment_collector",
-    "fundamental_collector",
     "dolt_earnings_import",
     "sec_fundamentals_import",
-    "fundamentals_parity_report",
     "rr_scanner",
     "ticker_universe_sync",
     "outcome_evaluator",
@@ -657,10 +633,8 @@ JOB_LABELS = {
     "data_backfill": "Data Backfill (deep history)",
     "benchmark_collector": "Benchmark Collector",
     "sentiment_collector": "Sentiment Collector",
-    "fundamental_collector": "Fundamental Collector",
+    "dolt_earnings_import": "Dolt Earnings Import",
-    "dolt_earnings_import": "Dolt Earnings Import (shadow)",
     "sec_fundamentals_import": "SEC Fundamentals Import",
-    "fundamentals_parity_report": "Fundamentals Parity Report (read-only)",
     "rr_scanner": "R:R Scanner",
     "ticker_universe_sync": "Ticker Universe Sync",
     "outcome_evaluator": "Outcome Evaluator",
@@ -799,30 +773,3 @@ async def toggle_job(db: AsyncSession, job_name: str, enabled: bool) -> SystemSe
 
     key = f"job_{job_name}_enabled"
     return await update_setting(db, key, str(enabled).lower())
-
-
-def get_fundamentals_parity_report() -> dict | None:
-    """Return the latest compact A5 summary, if the job has run."""
-    from app.config import settings
-    from app.services.fundamentals_parity_service import load_latest
-
-    report = load_latest(settings.fundamentals_parity_report_dir)
-    if report is not None:
-        report.pop("rows", None)  # full per-ticker data is download-only
-    return report
-
-
-def get_fundamentals_parity_csv() -> tuple[str, str] | None:
-    """Return the latest A5 CSV filename and content for authenticated download."""
-    from app.config import settings
-    from app.services.fundamentals_parity_service import load_latest_csv
-
-    return load_latest_csv(settings.fundamentals_parity_report_dir)
-
-
-def get_fundamentals_parity_json() -> tuple[str, str] | None:
-    """Return the canonical A5 JSON artifact for authenticated download."""
-    from app.config import settings
-    from app.services.fundamentals_parity_service import load_latest_json
-
-    return load_latest_json(settings.fundamentals_parity_report_dir)

app/services/backtest_service.py

@@ -1320,7 +1320,6 @@ def _replay_candidates_for_period(
     cadence: str = DEFAULT_BACKTEST_CADENCE,
     include_short_candidates: bool = False,
     include_universe_rank_observations: bool = False,
-    outcome_horizon_sessions: int = HORIZON,
 ) -> list[dict]:
     """Slim picklable replay used by local event studies.
 
@@ -1344,13 +1343,10 @@ def _replay_candidates_for_period(
         )
     ]
     cadence = validate_backtest_cadence(cadence)
-    replay_horizon = int(outcome_horizon_sessions)
-    if replay_horizon < 0:
-        raise ValueError('outcome_horizon_sessions must be non-negative')
     candidates: list[dict] = []
     for i in range(
         MIN_LOOKBACK - 1,
-        len(bars) - replay_horizon,
+        len(bars) - HORIZON,
         backtest_step_sessions(cadence),
     ):
         if bars[i].date < start_date:
@@ -1705,7 +1701,14 @@ def _gate_ablation(candidates: list[dict], activation: dict, threshold: float) -
 # the QUALIFIED setups at their detection close, best momentum first while
 # slots and cash allow.
 SIM_STARTING_CAPITAL = 10_000.0
-SIM_MAX_POSITIONS = 10
+# Headroom, not a target: the count cap should never bind. The capacity study
+# (reports/portfolio-construction-prod505-capacity-bracket-daily-v1) showed a book
+# that never hits the count cap earns +1.1pp CAGR over the old 10 (51 cohorts of 175
+# better, 2 worse) at unchanged drawdown, because the blocked entries were as good as
+# the taken ones — capacity costs trade COUNT, not trade quality. The real ceiling is
+# cash plus SIM_NOTIONAL_CAP, which saturates the book near 12 positions, so 15/20/None
+# are the same experiment. Judge any future change here on CAGR, never on EV per trade.
+SIM_MAX_POSITIONS = 15
 SIM_RISK_PER_TRADE = 0.01  # fraction of equity risked per position (entry→stop)
 SIM_NOTIONAL_CAP = 0.20    # max fraction of equity per position (no margin)
 _EULER_MASCHERONI = 0.5772156649015329
@@ -1946,7 +1949,6 @@ def _make_gate_reset_reentry_fn(
     cadence: str,
     qualified_fn: Callable[[dict], bool] | None = None,
     ranking_key: str = PRODUCTION_PERCENTILE_KEY,
-    evaluation_horizon_sessions: int = HORIZON,
 ) -> Callable[[str, int, dict, Any], dict | None]:
     """Build the production post-stop gate-reset callback.
 
@@ -1964,18 +1966,11 @@ def _make_gate_reset_reentry_fn(
 
     evaluation_ords: dict[str, set[int]] = {}
     step_sessions = backtest_step_sessions(cadence)
-    evaluation_horizon = int(evaluation_horizon_sessions)
-    if evaluation_horizon < 0:
-        raise ValueError('evaluation_horizon_sessions must be non-negative')
     for symbol, columns in prices.items():
         ordinals = columns[0]
         evaluation_ords[symbol] = {
             int(ordinals[index])
-            for index in range(
+            for index in range(MIN_LOOKBACK - 1, len(ordinals) - HORIZON, step_sessions)
-                MIN_LOOKBACK - 1,
-                len(ordinals) - evaluation_horizon,
-                step_sessions,
-            )
         }
 
     qualified_by_symbol_date: dict[tuple[str, int], dict] = {}
@@ -2022,7 +2017,7 @@ def _simulate_portfolio(
     *,
     qualified_fn: Callable[[dict], bool] | None = None,
     ranking_key: str = PRODUCTION_PERCENTILE_KEY,
-    max_positions: int | None = SIM_MAX_POSITIONS,
+    max_positions: int = SIM_MAX_POSITIONS,
     risk_per_trade: float = SIM_RISK_PER_TRADE,
     atr_trail_multiplier: float = ATR_TRAIL_MULTIPLIER,
     cost_per_side: float = COST_PER_SIDE,
@@ -2046,12 +2041,6 @@ def _simulate_portfolio(
     corr_lookback: int = 120,
     corr_action: str = "skip",
     corr_min_overlap: int = 60,
-    min_initial_risk_fraction: float | None = None,
-    weekly_top_n_rebalance: bool = False,
-    daily_rank_map: dict[tuple[str, str], dict[str, float | None]] | None = None,
-    measurement_start_date: date | None = None,
-    hard_end_date: date | None = None,
-    include_capacity_diagnostics: bool = False,
 ) -> dict | None:
     """Replay the qualified setups as ONE capital-constrained book and report
     portfolio economics from the daily equity curve (return, CAGR, drawdown,
@@ -2101,20 +2090,6 @@ def _simulate_portfolio(
         raise ValueError("corr_action must be 'skip' or 'half_size'")
     if vol_target is not None and vol_target <= 0:
         raise ValueError("vol_target must be positive when set")
-    if max_positions is not None and int(max_positions) <= 0:
-        raise ValueError("max_positions must be positive or None")
-    if min_initial_risk_fraction is not None and not (
-        0.0 < float(min_initial_risk_fraction) < 1.0
-    ):
-        raise ValueError("min_initial_risk_fraction must be between 0 and 1")
-    if weekly_top_n_rebalance and (
-        max_positions is None or daily_rank_map is None
-    ):
-        raise ValueError(
-            "weekly_top_n_rebalance requires max_positions and daily_rank_map"
-        )
-    if weekly_top_n_rebalance and fill_mode != FILL_MODE_CLOSE:
-        raise ValueError("weekly_top_n_rebalance requires fill_mode=close")
     clamp_lo, clamp_hi = float(vol_clamp[0]), float(vol_clamp[1])
     if clamp_lo <= 0 or clamp_hi < clamp_lo:
         raise ValueError("vol_clamp must satisfy 0 < lo <= hi")
@@ -2126,26 +2101,8 @@ def _simulate_portfolio(
 
     entries_by_ord: dict[int, list[dict]] = defaultdict(list)
     start_ord = start_date.toordinal() if start_date is not None else None
-    measurement_start_ord = (
-        measurement_start_date.toordinal()
-        if measurement_start_date is not None
-        else start_ord
-    )
-    hard_end_ord = hard_end_date.toordinal() if hard_end_date is not None else None
     # Explicit simulator/holdout end dates are exclusive split boundaries.
     end_ord = end_date.toordinal() if end_date is not None else None
-    if (
-        start_ord is not None
-        and measurement_start_ord is not None
-        and measurement_start_ord < start_ord
-    ):
-        raise ValueError("measurement_start_date cannot precede start_date")
-    if (
-        hard_end_ord is not None
-        and measurement_start_ord is not None
-        and hard_end_ord <= measurement_start_ord
-    ):
-        raise ValueError("hard_end_date must follow measurement_start_date")
     for c in candidates:
         if not qualified_fn(c) or c.get("direction") != "long":
             continue
@@ -2154,8 +2111,6 @@ def _simulate_portfolio(
             continue
         if end_ord is not None and entry_ord >= end_ord:
             continue  # holdout/validation: entries strictly before the split
-        if hard_end_ord is not None and entry_ord >= hard_end_ord:
-            continue
         if not c.get("entry") or not c.get("stop"):
             continue
         entries_by_ord[entry_ord].append(c)
@@ -2168,12 +2123,7 @@ def _simulate_portfolio(
     }
 
     first_ord = start_ord if start_ord is not None else min(entries_by_ord)
-    full_calendar = sorted({o for cols in prices.values() for o in cols[0]})
+    calendar = sorted({o for cols in prices.values() for o in cols[0] if o >= first_ord})
-    calendar = [
-        o
-        for o in full_calendar
-        if o >= first_ord and (hard_end_ord is None or o < hard_end_ord)
-    ]
     if not calendar:
         return None
 
@@ -2181,39 +2131,20 @@ def _simulate_portfolio(
     # fill lag). Prevents trailing flat-cash after the last resolvable entry —
     # the clear-air train-window bug — for train, validation, and full-period
     # books alike (including max-hold sweeps out to 90 days).
-    if hard_end_ord is None:
+    last_signal_ord = max(entries_by_ord)
-        last_signal_ord = max(entries_by_ord)
+    resolve_pad = hold_days + (1 if fill_mode in DELAYED_FILL_MODES else 0)
-        resolve_pad = hold_days + (1 if fill_mode in DELAYED_FILL_MODES else 0)
+    cut = bisect.bisect_left(calendar, last_signal_ord) + resolve_pad + 1
-        cut = bisect.bisect_left(calendar, last_signal_ord) + resolve_pad + 1
+    calendar = calendar[:cut]
-        calendar = calendar[:cut]
     if not calendar:
         return None
 
-    weekly_rebalance_ords: set[int] = set()
-    for index, session_ord in enumerate(full_calendar):
-        session_date = date.fromordinal(session_ord)
-        iso = session_date.isocalendar()
-        if index + 1 < len(full_calendar):
-            next_iso = date.fromordinal(full_calendar[index + 1]).isocalendar()
-            if (iso.year, iso.week) != (next_iso.year, next_iso.week):
-                weekly_rebalance_ords.add(session_ord)
-        elif session_date.weekday() == 4:
-            weekly_rebalance_ords.add(session_ord)
-
     cash = SIM_STARTING_CAPITAL
     positions: dict[str, dict] = {}
     curve: list[tuple[int, float]] = []
     trades: list[dict] = []
     skipped_full = 0
-    measurement_skipped_full = 0
     skipped_cooldown = 0
     skipped_corr = 0
-    skipped_min_initial_risk = 0
-    measurement_skipped_min_initial_risk = 0
-    opened_positions = 0
-    measurement_opened_positions = 0
-    weekly_rank_rejected_entries = 0
-    measurement_weekly_rank_rejected_entries = 0
     skipped_missing_fill = 0
     skipped_gap_cap = 0
     cooldown_until_index: dict[str, int] = {}
@@ -2228,12 +2159,6 @@ def _simulate_portfolio(
     vol_scalars: list[float] = []
     overnight_slippage_pct: list[float] = []
     pending_delayed: list[dict] = []
-    measurement_start_equity: float | None = None
-    measurement_start_position_count: int | None = None
-    capacity_samples: list[dict[str, float | int]] = []
-    weekly_rebalance_events: list[dict] = []
-    rebalance_exit_index: dict[str, tuple[int, int]] = {}
-    rebalance_reentry_events: list[dict] = []
 
     def _bar(sym: str, o: int):
         idx = index_of.get(sym, {}).get(o)
@@ -2303,13 +2228,6 @@ def _simulate_portfolio(
         cost = proceeds * cost_rate
         cash += proceeds - cost
         risk = pos["entry"] - pos["initial_stop"]
-        initial_risk_dollars = pos["shares"] * risk
-        net_pnl = (
-            proceeds
-            - pos["shares"] * pos["entry"]
-            - cost
-            - pos["entry_cost"]
-        )
         trades.append({
             "symbol": sym,
             "entry_ord": pos["entry_ord"],
@@ -2318,13 +2236,8 @@ def _simulate_portfolio(
             "initial_stop": pos["initial_stop"],
             "active_stop": pos["stop"],
             "fill": fill,
-            "shares": pos["shares"],
+            "pnl": proceeds - pos["shares"] * pos["entry"] - cost - pos["entry_cost"],
-            "initial_risk_dollars": initial_risk_dollars,
-            "pnl": net_pnl,
             "r": (fill - pos["entry"]) / risk if risk > 0 else 0.0,
-            "net_r": net_pnl / initial_risk_dollars
-            if initial_risk_dollars > 0
-            else 0.0,
             "hold": pos["bars_held"],
             "reason": reason,
             "stop_refreshes": pos["stop_refreshes"],
@@ -2339,13 +2252,6 @@ def _simulate_portfolio(
 
     cooldown_sessions = max(0, int(reentry_cooldown_sessions))
     for calendar_index, o in enumerate(calendar):
-        in_measurement = (
-            measurement_start_ord is None or o >= measurement_start_ord
-        )
-        if in_measurement and measurement_start_equity is None:
-            measurement_start_equity = _marked_equity()
-            measurement_start_position_count = len(positions)
-
         # 1) exits on today's bars (stop intraday, target intraday, time at close)
         for sym in list(positions):
             pos = positions[sym]
@@ -2459,82 +2365,6 @@ def _simulate_portfolio(
             reverse=True,
         )
 
-        weekly_selected_entries: list[dict] | None = None
-        if weekly_top_n_rebalance and o in weekly_rebalance_ords:
-            assert max_positions is not None
-            assert daily_rank_map is not None
-            asof = date.fromordinal(o).isoformat()
-            protected: set[str] = set()
-            ranked_pool: list[tuple[float, int, str, dict | None]] = []
-            for sym in positions:
-                rank_row = daily_rank_map.get((sym, asof))
-                current_rank = (
-                    rank_row.get("strategy_rank") if rank_row is not None else None
-                )
-                if current_rank is None or _bar(sym, o) is None:
-                    protected.add(sym)
-                    continue
-                ranked_pool.append((float(current_rank), 0, sym, None))
-
-            entrants_by_symbol: dict[str, dict] = {}
-            for candidate in signal_todays:
-                sym = str(candidate["symbol"])
-                if sym in positions or sym in entrants_by_symbol:
-                    continue
-                entrants_by_symbol[sym] = candidate
-            eligible_entrants = 0
-            for sym, candidate in entrants_by_symbol.items():
-                rank_row = daily_rank_map.get((sym, asof))
-                current_rank = (
-                    rank_row.get("strategy_rank") if rank_row is not None else None
-                )
-                if current_rank is None:
-                    continue
-                eligible_entrants += 1
-                ranked_pool.append((float(current_rank), 1, sym, candidate))
-
-            available_slots = max(0, int(max_positions) - len(protected))
-            ranked_pool.sort(key=lambda row: (-row[0], row[1], row[2]))
-            selected = ranked_pool[:available_slots]
-            selected_holding_symbols = {
-                sym for _rank, kind, sym, _candidate in selected if kind == 0
-            }
-            weekly_selected_entries = [
-                candidate
-                for _rank, kind, _sym, candidate in selected
-                if kind == 1 and candidate is not None
-            ]
-            selected_entrant_symbols = {
-                str(candidate["symbol"]) for candidate in weekly_selected_entries
-            }
-            rejected_now = max(0, eligible_entrants - len(selected_entrant_symbols))
-            weekly_rank_rejected_entries += rejected_now
-            if in_measurement:
-                measurement_weekly_rank_rejected_entries += rejected_now
-
-            exited_symbols: list[str] = []
-            for sym in list(positions):
-                if sym in protected or sym in selected_holding_symbols:
-                    continue
-                bar = _bar(sym, o)
-                if bar is None:
-                    continue
-                _close_trade(sym, float(bar.close), "weekly_rebalance")
-                rebalance_exit_index[sym] = (calendar_index, o)
-                exited_symbols.append(sym)
-
-            weekly_rebalance_events.append({
-                "ord": o,
-                "fresh_entrant_pool": len(entrants_by_symbol),
-                "rank_eligible_entrant_pool": eligible_entrants,
-                "selected_entrants": len(selected_entrant_symbols),
-                "replacements": len(exited_symbols),
-                "exited_symbols": sorted(exited_symbols),
-                "selected_entrant_symbols": sorted(selected_entrant_symbols),
-                "measurement": in_measurement,
-            })
-            equity = _marked_equity()
-
         if fill_mode in DELAYED_FILL_MODES:
             fill_candidates = sorted(
                 pending_delayed,
@@ -2543,11 +2373,7 @@ def _simulate_portfolio(
             )
             pending_delayed = []
         else:
-            fill_candidates = (
+            fill_candidates = signal_todays
-                weekly_selected_entries
-                if weekly_selected_entries is not None
-                else signal_todays
-            )
 
         def _corr_scale_for(sym: str, asof_idx: int) -> float | None:
             """1.0 ok, 0.5 half-size, None = skip. Missing history → uncorrelated."""
@@ -2592,21 +2418,15 @@ def _simulate_portfolio(
             corr_scale: float,
             fill_bar: Any | None,
         ) -> None:
-            nonlocal cash, equity, skipped_full, measurement_skipped_full
+            nonlocal cash, equity, skipped_full, skipped_cooldown, post_stop_events
-            nonlocal skipped_cooldown, post_stop_events
-            nonlocal skipped_min_initial_risk
-            nonlocal measurement_skipped_min_initial_risk
-            nonlocal opened_positions, measurement_opened_positions
             sym = c["symbol"]
             if sym in positions:
                 return
             if calendar_index < cooldown_until_index.get(sym, -1):
                 skipped_cooldown += 1
                 return
-            if max_positions is not None and len(positions) >= max_positions:
+            if len(positions) >= max_positions:
                 skipped_full += 1
-                if in_measurement:
-                    measurement_skipped_full += 1
                 return
             risk_ps = entry - stop
             if risk_ps <= 0 or entry <= 0:
@@ -2623,16 +2443,6 @@ def _simulate_portfolio(
                 (equity * SIM_NOTIONAL_CAP) / entry,
                 max(cash, 0.0) / (entry * (1.0 + cost_rate)),
             )
-            initial_risk_dollars = shares * risk_ps
-            if (
-                min_initial_risk_fraction is not None
-                and initial_risk_dollars
-                < equity * float(min_initial_risk_fraction)
-            ):
-                skipped_min_initial_risk += 1
-                if in_measurement:
-                    measurement_skipped_min_initial_risk += 1
-                return
             if shares * entry < 1.0:
                 return
             entry_cost = shares * entry * cost_rate
@@ -2672,21 +2482,6 @@ def _simulate_portfolio(
                 "vol_scalar": scalar,
                 "corr_scale": corr_scale,
             }
-            opened_positions += 1
-            if in_measurement:
-                measurement_opened_positions += 1
-            prior_rebalance_exit = rebalance_exit_index.pop(sym, None)
-            if prior_rebalance_exit is not None:
-                prior_exit_index, prior_exit_ord = prior_rebalance_exit
-                rebalance_reentry_events.append({
-                    "symbol": sym,
-                    "exit_ord": prior_exit_ord,
-                    "exit_calendar_index": prior_exit_index,
-                    "reentry_calendar_index": calendar_index,
-                    "wait_sessions": calendar_index - prior_exit_index,
-                    "reentry_ord": entry_ord,
-                    "measurement": in_measurement,
-                })
             # next_open only: fill is at the open, so the rest of the bar can stop out.
             # stale_close fills at the close — same-day stop after entry does not apply.
             # bars_held stays 0 on the fill day (matches close-fill cadence).
@@ -2788,25 +2583,7 @@ def _simulate_portfolio(
             # Queue today's signals for the next session's fill.
             pending_delayed.extend(signal_todays)
 
-        marked_equity = _marked_equity()
+        curve.append((o, _marked_equity()))
-        if in_measurement and include_capacity_diagnostics:
-            gross_notional = sum(
-                pos["shares"] * pos["last_close"] for pos in positions.values()
-            )
-            capacity_samples.append({
-                "positions": len(positions),
-                "cash_pct": cash / marked_equity * 100.0
-                if marked_equity > 0
-                else 0.0,
-                "gross_exposure_pct": gross_notional / marked_equity * 100.0
-                if marked_equity > 0
-                else 0.0,
-                "at_capacity": int(
-                    max_positions is not None
-                    and len(positions) >= max_positions
-                ),
-            })
-        curve.append((o, marked_equity))
 
     # Close whatever is still open at its last mark so final equity is realized.
     for sym in list(positions):
@@ -2814,57 +2591,32 @@ def _simulate_portfolio(
     final_equity = cash
     curve[-1] = (calendar[-1], final_equity)
 
-    metric_start_ord = (
+    total_return_pct = (final_equity / SIM_STARTING_CAPITAL - 1.0) * 100.0
-        measurement_start_ord if measurement_start_ord is not None else calendar[0]
+    years = (calendar[-1] - calendar[0]) / 365.25
-    )
-    metric_curve = [(day_ord, eq) for day_ord, eq in curve if day_ord >= metric_start_ord]
-    if not metric_curve:
-        return None
-    metric_base_equity = (
-        measurement_start_equity
-        if measurement_start_date is not None and measurement_start_equity is not None
-        else SIM_STARTING_CAPITAL
-    )
-    total_return_pct = (final_equity / metric_base_equity - 1.0) * 100.0
-    years = (calendar[-1] - metric_start_ord) / 365.25
     cagr_pct = (
-        ((final_equity / metric_base_equity) ** (1.0 / years) - 1.0) * 100.0
+        ((final_equity / SIM_STARTING_CAPITAL) ** (1.0 / years) - 1.0) * 100.0
         if years > 0.25 and final_equity > 0
         else None
     )
 
     peak = float("-inf")
     max_dd = 0.0
-    drawdown_equities = (
+    for _, eq in curve:
-        [metric_base_equity, *(eq for _, eq in metric_curve)]
-        if measurement_start_date is not None
-        else [eq for _, eq in metric_curve]
-    )
-    for eq in drawdown_equities:
         peak = max(peak, eq)
         if peak > 0:
             max_dd = max(max_dd, (peak - eq) / peak)
 
-    return_equities = (
+    rets = [b / a - 1.0 for (_, a), (_, b) in zip(curve, curve[1:]) if a > 0]
-        [metric_base_equity, *(eq for _, eq in metric_curve)]
-        if measurement_start_date is not None
-        else [eq for _, eq in metric_curve]
-    )
-    rets = [
-        b / a - 1.0
-        for a, b in zip(return_equities, return_equities[1:])
-        if a > 0
-    ]
     diag = sharpe_diagnostics(rets)
     sharpe = diag["sharpe"]
 
     # Per-calendar-year returns off the equity curve — shows whether every year
     # contributed or one exceptional stretch carried the result.
     yearly: list[dict] = []
-    year_start_eq = metric_base_equity
+    year_start_eq = curve[0][1]
-    cur_year = date.fromordinal(metric_start_ord).year
+    cur_year = date.fromordinal(curve[0][0]).year
-    last_eq = metric_base_equity
+    last_eq = curve[0][1]
-    for o, eq in metric_curve:
+    for o, eq in curve:
         y = date.fromordinal(o).year
         if y != cur_year:
             yearly.append({
@@ -2883,29 +2635,24 @@ def _simulate_portfolio(
         ),
     })
 
-    metric_trades = [
+    pnls = [t["pnl"] for t in trades]
-        trade for trade in trades if trade["entry_ord"] >= metric_start_ord
-    ]
-    pnls = [t["pnl"] for t in metric_trades]
     wins = sum(1 for p in pnls if p > 0)
     reason_counts = {
-        reason: sum(1 for t in metric_trades if t["reason"] == reason)
+        reason: sum(1 for t in trades if t["reason"] == reason)
-        for reason in sorted({t["reason"] for t in metric_trades})
+        for reason in sorted({t["reason"] for t in trades})
     }
     spy_pct = None
     if spy_closes:
         from app.services.benchmark_service import benchmark_return_pct
 
         spy_pct = benchmark_return_pct(
-            spy_closes,
+            spy_closes, date.fromordinal(calendar[0]), date.fromordinal(calendar[-1])
-            date.fromordinal(metric_start_ord),
-            date.fromordinal(calendar[-1]),
         )
 
     curve_payload: list[dict] | None = None
     benchmark_payload: list[dict] | None = None
     if include_curve:
-        curve_base = metric_base_equity
+        curve_base = curve[0][1] if curve else SIM_STARTING_CAPITAL
         curve_payload = [
             {
                 "date": date.fromordinal(o).isoformat(),
@@ -2914,12 +2661,12 @@ def _simulate_portfolio(
                 if curve_base > 0
                 else None,
             }
-            for o, eq in metric_curve
+            for o, eq in curve
         ]
         if spy_closes:
             benchmark_payload = []
             base_spy = None
-            for o, _ in metric_curve:
+            for o, _ in curve:
                 d = date.fromordinal(o)
                 close = spy_closes.get(d)
                 if close is None or close <= 0:
@@ -2938,8 +2685,6 @@ def _simulate_portfolio(
         calmar = float(cagr_pct) / max_dd_pct
     result = {
         "starting_capital": SIM_STARTING_CAPITAL,
-        "measurement_start_equity": round(metric_base_equity, 2),
-        "measurement_start_positions": measurement_start_position_count or 0,
         "cost_per_side_pct": round(cost_rate * 100.0, 3),
         "fill_mode": fill_mode,
         "final_equity": round(final_equity, 2),
@@ -2953,161 +2698,23 @@ def _simulate_portfolio(
         "n_returns": diag["n_returns"],
         "return_skew": diag["return_skew"],
         "return_kurtosis": diag["return_kurtosis"],
-        "trades": len(metric_trades),
+        "trades": len(trades),
-        "win_rate": (
+        "win_rate": round(wins / len(trades) * 100.0, 1) if trades else None,
-            round(wins / len(metric_trades) * 100.0, 1)
-            if metric_trades
-            else None
-        ),
         "avg_trade_pnl": round(sum(pnls) / len(pnls), 2) if pnls else None,
-        "best_trade_r": (
+        "best_trade_r": round(max(t["r"] for t in trades), 2) if trades else None,
-            round(max(t["r"] for t in metric_trades), 2)
+        "worst_trade_r": round(min(t["r"] for t in trades), 2) if trades else None,
-            if metric_trades
-            else None
-        ),
-        "worst_trade_r": (
-            round(min(t["r"] for t in metric_trades), 2)
-            if metric_trades
-            else None
-        ),
         "best_trade_pnl": round(max(pnls), 2) if pnls else None,
         "worst_trade_pnl": round(min(pnls), 2) if pnls else None,
         "avg_hold_days": (
-            round(
+            round(sum(t["hold"] for t in trades) / len(trades), 1) if trades else None
-                sum(t["hold"] for t in metric_trades) / len(metric_trades),
-                1,
-            )
-            if metric_trades
-            else None
         ),
         "exit_reasons": reason_counts,
         "skipped_book_full": skipped_full,
         "spy_return_pct": round(spy_pct, 1) if spy_pct is not None else None,
         "yearly_returns": yearly,
-        "start_date": date.fromordinal(metric_start_ord).isoformat(),
+        "start_date": date.fromordinal(calendar[0]).isoformat(),
         "end_date": date.fromordinal(calendar[-1]).isoformat(),
     }
-    if measurement_start_date is not None:
-        result["simulation_start_date"] = date.fromordinal(calendar[0]).isoformat()
-    if hard_end_date is not None:
-        result["hard_end_date_exclusive"] = hard_end_date.isoformat()
-    if measurement_start_date is not None:
-        result["measurement_skipped_book_full"] = measurement_skipped_full
-        result["measurement_opened_positions"] = measurement_opened_positions
-    if min_initial_risk_fraction is not None:
-        result["min_initial_risk_fraction"] = float(min_initial_risk_fraction)
-        result["skipped_min_initial_risk"] = skipped_min_initial_risk
-        result["measurement_skipped_min_initial_risk"] = (
-            measurement_skipped_min_initial_risk
-        )
-    if include_capacity_diagnostics:
-        measured_opened = (
-            measurement_opened_positions
-            if measurement_start_date is not None
-            else opened_positions
-        )
-        measured_full = (
-            measurement_skipped_full
-            if measurement_start_date is not None
-            else skipped_full
-        )
-        capacity_opportunities = measured_opened + measured_full
-        result["opened_positions"] = measured_opened
-        result["capacity_opportunities"] = capacity_opportunities
-        result["blocked_fraction"] = (
-            round(measured_full / capacity_opportunities, 6)
-            if capacity_opportunities
-            else 0.0
-        )
-        result["avg_positions"] = (
-            round(
-                sum(float(sample["positions"]) for sample in capacity_samples)
-                / len(capacity_samples),
-                4,
-            )
-            if capacity_samples
-            else 0.0
-        )
-        result["peak_positions"] = (
-            max(int(sample["positions"]) for sample in capacity_samples)
-            if capacity_samples
-            else 0
-        )
-        result["sessions_at_capacity"] = sum(
-            int(sample["at_capacity"]) for sample in capacity_samples
-        )
-        result["sessions_measured"] = len(capacity_samples)
-        result["avg_cash_pct"] = (
-            round(
-                sum(float(sample["cash_pct"]) for sample in capacity_samples)
-                / len(capacity_samples),
-                4,
-            )
-            if capacity_samples
-            else None
-        )
-        result["avg_gross_exposure_pct"] = (
-            round(
-                sum(
-                    float(sample["gross_exposure_pct"])
-                    for sample in capacity_samples
-                )
-                / len(capacity_samples),
-                4,
-            )
-            if capacity_samples
-            else None
-        )
-    if weekly_top_n_rebalance:
-        measured_events = [
-            event for event in weekly_rebalance_events if event["measurement"]
-        ]
-        measured_reentries = [
-            event for event in rebalance_reentry_events if event["measurement"]
-        ]
-        result["weekly_rank_rejected_entries"] = (
-            measurement_weekly_rank_rejected_entries
-            if measurement_start_date is not None
-            else weekly_rank_rejected_entries
-        )
-        result["weekly_rebalance_events"] = [
-            {
-                **{
-                    key: value
-                    for key, value in event.items()
-                    if key not in {"ord", "measurement"}
-                },
-                "date": date.fromordinal(event["ord"]).isoformat(),
-            }
-            for event in measured_events
-        ]
-        result["rebalance_reentry_events"] = [
-            {
-                **{
-                    key: value
-                    for key, value in event.items()
-                    if key
-                    not in {
-                        "exit_ord",
-                        "reentry_ord",
-                        "measurement",
-                        "exit_calendar_index",
-                        "reentry_calendar_index",
-                    }
-                },
-                "exit_date": date.fromordinal(event["exit_ord"]).isoformat(),
-                "reentry_date": date.fromordinal(
-                    event["reentry_ord"]
-                ).isoformat(),
-            }
-            for event in measured_reentries
-        ]
-        for session_limit in (5, 10, 20):
-            result[f"rebalance_reentries_within_{session_limit}_sessions"] = sum(
-                1
-                for event in measured_reentries
-                if int(event["wait_sessions"]) <= session_limit
-            )
     if vol_target is not None:
         result["vol_target"] = vol_target
         result["vol_lookback"] = int(vol_lookback)
@@ -3182,7 +2789,7 @@ def _simulate_portfolio(
                 "entry_date": date.fromordinal(trade["entry_ord"]).isoformat(),
                 "exit_date": date.fromordinal(trade["exit_ord"]).isoformat(),
             }
-            for trade in metric_trades
+            for trade in trades
         ]
     return result
 

app/services/fundamental_data_refresh_service.py

@@ -1,4 +1,7 @@
-"""A5 activation: refresh the legacy fundamentals cache from local bulk data."""
+"""Refresh the fundamentals compat cache from local SEC/Dolt bulk data.
+
+``fundamental_data`` is the table scoring reads. This is its only writer.
+"""
 
 from __future__ import annotations
 
@@ -12,38 +15,11 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from app.database import insert_for_session
 from app.models.fundamental import FundamentalData
 from app.models.score import CompositeScore, DimensionScore
-from app.services import fundamentals_candidate_service, settings_store
+from app.services import fundamentals_candidate_service
 
-
-# Absence is deliberately false. Production activation therefore requires one
-# explicit, durable SystemSetting change after the A5 evidence is approved.
-ACTIVATION_KEY = "fundamental_data_sec_dolt_cutover_enabled"
 _SCORE_FIELDS = ("pe_ratio", "revenue_growth", "earnings_surprise")
 
 
-async def is_enabled(db: AsyncSession) -> bool:
-    raw = await settings_store.get_value(db, ACTIVATION_KEY, "false")
-    return str(raw).strip().lower() == "true"
-
-
-async def refresh_if_enabled(
-    db: AsyncSession,
-    *,
-    now: datetime | None = None,
-    today: date | None = None,
-) -> dict[str, Any]:
-    """Refresh atomically when activated; otherwise perform no writes."""
-    if not await is_enabled(db):
-        return {
-            "enabled": False,
-            "refreshed": 0,
-            "score_inputs_changed": 0,
-            "dimension_scores_staled": 0,
-            "composite_scores_staled": 0,
-        }
-    return await refresh(db, now=now, today=today)
-
-
 async def refresh(
     db: AsyncSession,
     *,
@@ -117,7 +93,6 @@ async def refresh(
 
     await db.commit()
     return {
-        "enabled": True,
         "refreshed": len(candidates),
         "score_inputs_changed": len(changed_ids),
         "dimension_scores_staled": len(dimension_ids),

app/services/fundamental_service.py

@@ -1,22 +1,19 @@
-"""Fundamental data service.
+"""Fundamental data read access.
 
-Stores fundamental data (P/E, revenue growth, earnings surprise, market cap)
+``fundamental_data`` is the compat cache scoring reads. It is written solely by
-and marks the fundamental dimension score as stale on new data.
+``fundamental_data_refresh_service`` from SEC snapshots, Dolt earnings events and
+stored closes; nothing fetches it per ticker.
 """
 
 from __future__ import annotations
 
-import json
 import logging
-from datetime import datetime, timezone
 
-from sqlalchemy import select, update
+from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
-from app.database import insert_for_session
 from app.exceptions import NotFoundError
 from app.models.fundamental import FundamentalData
-from app.models.score import DimensionScore
 from app.models.ticker import Ticker
 
 logger = logging.getLogger(__name__)
@@ -32,65 +29,6 @@ async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
     return ticker
 
 
-async def store_fundamental(
-    db: AsyncSession,
-    symbol: str,
-    pe_ratio: float | None = None,
-    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.
-
-    Keeps a single latest snapshot per ticker. On new data, marks the
-    fundamental dimension score as stale (if one exists).
-    """
-    ticker = await _get_ticker(db, symbol)
-
-    now = datetime.now(timezone.utc)
-    unavailable_fields_json = json.dumps(unavailable_fields or {})
-
-    stmt = insert_for_session(db, FundamentalData).values(
-        ticker_id=ticker.id,
-        pe_ratio=pe_ratio,
-        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,
-    )
-    stmt = stmt.on_conflict_do_update(
-        index_elements=["ticker_id"],
-        set_={
-            "pe_ratio": stmt.excluded.pe_ratio,
-            "revenue_growth": stmt.excluded.revenue_growth,
-            "earnings_surprise": stmt.excluded.earnings_surprise,
-            "market_cap": stmt.excluded.market_cap,
-            "next_earnings_date": stmt.excluded.next_earnings_date,
-            "fetched_at": stmt.excluded.fetched_at,
-            "unavailable_fields_json": stmt.excluded.unavailable_fields_json,
-        },
-    ).returning(FundamentalData)
-    record = (await db.execute(stmt)).scalar_one()
-
-    # Mark fundamental dimension score as stale if it exists
-    # TODO: Use DimensionScore service when built
-    await db.execute(
-        update(DimensionScore)
-        .where(
-            DimensionScore.ticker_id == ticker.id,
-            DimensionScore.dimension == "fundamental",
-        )
-        .values(is_stale=True)
-    )
-
-    await db.commit()
-    return record
-
-
 async def get_fundamental(
     db: AsyncSession,
     symbol: str,

app/services/fundamentals_candidate_service.py

@@ -1,9 +1,8 @@
-"""Local SEC/Dolt candidate values for the legacy fundamentals cache.
+"""Local SEC/Dolt candidate values for the fundamentals compat cache.
 
-This is the single read path shared by the A5 parity report and the activated
+This is the read path behind the ``fundamental_data`` refresh. It never contacts
-``fundamental_data`` refresh.  It never contacts SEC or Dolt: every input comes
+SEC or Dolt: every input comes from PostgreSQL, so price- and earnings-driven
-from PostgreSQL, so price- and earnings-driven values can still refresh when an
+values can still refresh when an upstream import is unchanged or unavailable.
-upstream import is unchanged or unavailable.
 """
 
 from __future__ import annotations

app/services/fundamentals_parity_service.py

@@ -1,498 +0,0 @@
-"""Read-only A5 comparison of legacy and SEC/Dolt fundamental inputs.
-
-The report deliberately does not write ``fundamental_data`` or score tables.
-It reconstructs the current legacy and candidate fundamental scores, projects
-their composite-score/rank effect with the active weights, and archives a
-timestamped JSON + CSV bundle for explicit human approval.
-"""
-
-from __future__ import annotations
-
-import csv
-import io
-import json
-import math
-import os
-import statistics
-from datetime import date, datetime, timezone
-from pathlib import Path
-from typing import Any, Iterable
-from zoneinfo import ZoneInfo
-
-from sqlalchemy import select, text
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.models.data_import_run import DataImportRun
-from app.models.fundamental import FundamentalData
-from app.services import fundamentals_candidate_service as candidate_service
-
-REPORT_VERSION = 1
-APPROVAL_STATUS = "pending_explicit_approval"
-FIELD_KEYS = ("pe_ratio", "revenue_growth", "earnings_surprise")
-MIN_SCORE_METRICS = 2
-
-# Materiality is a review aid, never an automatic cutover verdict. Definition
-# changes remain visible even when a delta falls inside these bands.
-FIELD_TOLERANCES = {
-    "pe_ratio": {"absolute": 1.0, "relative_pct": 10.0},
-    "revenue_growth": {"absolute": 2.0, "relative_pct": None},
-    "earnings_surprise": {"absolute": 2.0, "relative_pct": None},
-}
-DEFINITION_NOTES = {
-    "pe_ratio": (
-        "Legacy provider P/E convention versus latest close divided by "
-        "SEC-derived TTM diluted EPS."
-    ),
-    "revenue_growth": (
-        "Legacy provider growth convention versus SEC-derived TTM revenue YoY."
-    ),
-    "earnings_surprise": (
-        "Legacy provider latest surprise versus latest completed Dolt earnings "
-        "event with actual and estimate."
-    ),
-}
-
-
-def fundamental_score(
-    pe_ratio: float | None,
-    revenue_growth: float | None,
-    earnings_surprise: float | None,
-) -> float | None:
-    """Match the production fundamental-dimension formula without persistence."""
-    scores: list[float] = []
-    if _finite(pe_ratio) and pe_ratio > 0:
-        scores.append(max(0.0, min(100.0, 100.0 - (pe_ratio - 15.0) * (100.0 / 30.0))))
-    if _finite(revenue_growth):
-        scores.append(max(0.0, min(100.0, 50.0 + revenue_growth * 2.5)))
-    if _finite(earnings_surprise):
-        scores.append(max(0.0, min(100.0, 50.0 + earnings_surprise * 5.0)))
-    return sum(scores) / len(scores) if len(scores) >= MIN_SCORE_METRICS else None
-
-
-async def build_report(
-    db: AsyncSession,
-    *,
-    generated_at: datetime | None = None,
-    today: date | None = None,
-) -> dict[str, Any]:
-    """Build a point-in-time parity report from one database session."""
-    generated_at = generated_at or datetime.now(timezone.utc)
-    today = today or datetime.now(ZoneInfo("America/New_York")).date()
-
-    # A report must not mix rows from before and after a concurrent import
-    # promotion. The scheduled job provides a fresh session, so establish the
-    # production snapshot before its first query and have Postgres enforce the
-    # no-write contract as well. SQLite tests retain their normal transaction.
-    if db.get_bind().dialect.name == "postgresql":
-        connection = await db.connection(
-            execution_options={"isolation_level": "REPEATABLE READ"}
-        )
-        await connection.execute(text("SET TRANSACTION READ ONLY"))
-
-    candidates = await candidate_service.build_candidates(db, today=today)
-    ticker_ids = [candidate.ticker_id for candidate in candidates]
-    legacy_by_ticker = await _legacy_values(db, ticker_ids)
-    source_runs = await _source_runs(db)
-
-    rows: list[dict[str, Any]] = []
-    for candidate in candidates:
-        legacy = legacy_by_ticker.get(candidate.ticker_id)
-        candidate_values = {
-            "pe_ratio": candidate.pe_ratio,
-            "revenue_growth": candidate.revenue_growth,
-            "earnings_surprise": candidate.earnings_surprise,
-        }
-        legacy_values = {
-            "pe_ratio": legacy.pe_ratio if legacy else None,
-            "revenue_growth": legacy.revenue_growth if legacy else None,
-            "earnings_surprise": legacy.earnings_surprise if legacy else None,
-        }
-        fields = {
-            key: _field_comparison(key, legacy_values[key], candidate_values[key])
-            for key in FIELD_KEYS
-        }
-        legacy_score = fundamental_score(**legacy_values)
-        candidate_score = fundamental_score(**candidate_values)
-        rows.append(
-            {
-                "symbol": candidate.symbol,
-                "cik": candidate.cik,
-                "legacy_fetched_at": _iso(legacy.fetched_at) if legacy else None,
-                "price_date": _iso(candidate.price_date),
-                "fields": fields,
-                "scores": {
-                    "legacy_fundamental": _round(legacy_score),
-                    "candidate_fundamental": _round(candidate_score),
-                    "fundamental_delta": _delta(legacy_score, candidate_score),
-                    "legacy_fundamental_rank": None,
-                    "candidate_fundamental_rank": None,
-                    "fundamental_rank_change": None,
-                },
-            }
-        )
-
-    _attach_ranks(rows, "legacy_fundamental", "legacy_fundamental_rank")
-    _attach_ranks(rows, "candidate_fundamental", "candidate_fundamental_rank")
-    for row in rows:
-        scores = row["scores"]
-        scores["fundamental_rank_change"] = _rank_change(
-            scores["legacy_fundamental_rank"], scores["candidate_fundamental_rank"]
-        )
-
-    return {
-        "report_version": REPORT_VERSION,
-        "generated_at": generated_at.isoformat(),
-        "as_of_date": today.isoformat(),
-        "approval_status": APPROVAL_STATUS,
-        "read_only": True,
-        "fundamental_score_formula": (
-            "Equal-weighted mean of 2+ available sub-scores: P/E = "
-            "clamp(100-(pe-15)*(100/30)); revenue growth = "
-            "clamp(50+growth*2.5); earnings surprise = "
-            "clamp(50+surprise*5)."
-        ),
-        "source_runs": source_runs,
-        "definition_notes": DEFINITION_NOTES,
-        "materiality_notes": {
-            "fields": FIELD_TOLERANCES,
-            "fundamental_score_absolute": 5.0,
-            "automatic_cutover": False,
-        },
-        "summary": _summary(rows),
-        "rows": rows,
-    }
-
-
-def store_report(report: dict[str, Any], report_dir: str | Path) -> dict[str, str]:
-    """Atomically archive JSON/CSV artifacts and update the latest manifest."""
-    directory = Path(report_dir).expanduser().resolve()
-    directory.mkdir(parents=True, exist_ok=True)
-    stamp = _artifact_stamp(report["generated_at"])
-    json_name = f"fundamentals-parity-{stamp}.json"
-    csv_name = f"fundamentals-parity-{stamp}.csv"
-    json_path = directory / json_name
-    csv_path = directory / csv_name
-
-    _atomic_write(json_path, json.dumps(report, indent=2, sort_keys=True) + "\n")
-    _atomic_write(csv_path, report_csv(report))
-    manifest = {
-        "generated_at": report["generated_at"],
-        "json_file": json_name,
-        "csv_file": csv_name,
-    }
-    _atomic_write(
-        directory / "latest.json",
-        json.dumps(manifest, indent=2, sort_keys=True) + "\n",
-    )
-    return {
-        "json": str(json_path),
-        "csv": str(csv_path),
-        "manifest": str(directory / "latest.json"),
-    }
-
-
-async def generate_and_store(
-    db: AsyncSession,
-    report_dir: str | Path,
-    *,
-    generated_at: datetime | None = None,
-    today: date | None = None,
-) -> tuple[dict[str, Any], dict[str, str]]:
-    report = await build_report(db, generated_at=generated_at, today=today)
-    return report, store_report(report, report_dir)
-
-
-def load_latest(report_dir: str | Path) -> dict[str, Any] | None:
-    manifest = _load_manifest(report_dir)
-    if manifest is None:
-        return None
-    try:
-        path = _manifest_artifact(report_dir, manifest, "json_file")
-        loaded = json.loads(path.read_text(encoding="utf-8"))
-    except (OSError, json.JSONDecodeError, TypeError, ValueError):
-        return None
-    return loaded if isinstance(loaded, dict) else None
-
-
-def load_latest_csv(report_dir: str | Path) -> tuple[str, str] | None:
-    return _load_latest_text_artifact(report_dir, "csv_file")
-
-
-def load_latest_json(report_dir: str | Path) -> tuple[str, str] | None:
-    return _load_latest_text_artifact(report_dir, "json_file")
-
-
-def _load_latest_text_artifact(
-    report_dir: str | Path, manifest_key: str
-) -> tuple[str, str] | None:
-    manifest = _load_manifest(report_dir)
-    if manifest is None:
-        return None
-    try:
-        path = _manifest_artifact(report_dir, manifest, manifest_key)
-        return path.name, path.read_text(encoding="utf-8")
-    except (OSError, TypeError, ValueError):
-        return None
-
-
-def report_csv(report: dict[str, Any]) -> str:
-    output = io.StringIO(newline="")
-    columns = [
-        "symbol",
-        "cik",
-        "legacy_fetched_at",
-        "price_date",
-        *(
-            f"{field}_{suffix}"
-            for field in FIELD_KEYS
-            for suffix in ("legacy", "candidate", "absolute_delta", "relative_delta_pct", "material")
-        ),
-        "legacy_fundamental",
-        "candidate_fundamental",
-        "fundamental_delta",
-        "legacy_fundamental_rank",
-        "candidate_fundamental_rank",
-        "fundamental_rank_change",
-    ]
-    writer = csv.DictWriter(output, fieldnames=columns)
-    writer.writeheader()
-    for row in report.get("rows", []):
-        flat = {
-            "symbol": row["symbol"],
-            "cik": row.get("cik"),
-            "legacy_fetched_at": row.get("legacy_fetched_at"),
-            "price_date": row.get("price_date"),
-            **row["scores"],
-        }
-        for field in FIELD_KEYS:
-            comparison = row["fields"][field]
-            for suffix in (
-                "legacy",
-                "candidate",
-                "absolute_delta",
-                "relative_delta_pct",
-                "material",
-            ):
-                flat[f"{field}_{suffix}"] = comparison.get(suffix)
-        writer.writerow(flat)
-    return output.getvalue()
-
-
-async def _legacy_values(
-    db: AsyncSession, ticker_ids: list[int]
-) -> dict[int, FundamentalData]:
-    if not ticker_ids:
-        return {}
-    rows = (
-        await db.execute(
-            select(FundamentalData).where(FundamentalData.ticker_id.in_(ticker_ids))
-        )
-    ).scalars()
-    return {row.ticker_id: row for row in rows}
-
-
-async def _source_runs(db: AsyncSession) -> dict[str, dict[str, Any] | None]:
-    sources = ("sec_facts", "dolt_earnings")
-    rows = (
-        await db.execute(
-            select(DataImportRun)
-            .where(
-                DataImportRun.source.in_(sources),
-                DataImportRun.status.in_(("promoted", "no_op")),
-            )
-            .order_by(DataImportRun.id.desc())
-        )
-    ).scalars()
-    latest: dict[str, dict[str, Any] | None] = {source: None for source in sources}
-    for row in rows:
-        if latest[row.source] is None:
-            latest[row.source] = {
-                "run_id": row.id,
-                "status": row.status,
-                "revision": row.revision,
-                "source_max_date": _iso(row.source_max_date),
-                "completed_at": _iso(row.completed_at),
-            }
-    return latest
-
-
-def _field_comparison(
-    key: str, legacy: float | None, candidate: float | None
-) -> dict[str, Any]:
-    legacy = float(legacy) if _finite(legacy) else None
-    candidate = float(candidate) if _finite(candidate) else None
-    absolute = _delta(legacy, candidate)
-    relative = (
-        None
-        if absolute is None or legacy in (None, 0)
-        else round(absolute / abs(legacy) * 100.0, 4)
-    )
-    tolerance = FIELD_TOLERANCES[key]
-    material = False
-    if absolute is not None:
-        material = abs(absolute) > tolerance["absolute"]
-        relative_limit = tolerance["relative_pct"]
-        if relative_limit is not None:
-            material = material and relative is not None and abs(relative) > relative_limit
-    return {
-        "legacy": _round(legacy),
-        "candidate": _round(candidate),
-        "absolute_delta": absolute,
-        "relative_delta_pct": relative,
-        "material": material,
-        "definition_changed": True,
-    }
-
-
-def _attach_ranks(rows: list[dict[str, Any]], value_key: str, rank_key: str) -> None:
-    values = [
-        row["scores"][value_key]
-        for row in rows
-        if _finite(row["scores"][value_key])
-    ]
-    for row in rows:
-        value = row["scores"][value_key]
-        row["scores"][rank_key] = (
-            1 + sum(other > value for other in values) if _finite(value) else None
-        )
-
-
-def _summary(rows: list[dict[str, Any]]) -> dict[str, Any]:
-    field_stats = {}
-    for key in FIELD_KEYS:
-        comparisons = [row["fields"][key] for row in rows]
-        deltas = [
-            abs(item["absolute_delta"])
-            for item in comparisons
-            if item["absolute_delta"] is not None
-        ]
-        field_stats[key] = {
-            "legacy_available": sum(item["legacy"] is not None for item in comparisons),
-            "candidate_available": sum(
-                item["candidate"] is not None for item in comparisons
-            ),
-            "both_available": len(deltas),
-            "material_differences": sum(item["material"] for item in comparisons),
-            "median_absolute_delta": _round(statistics.median(deltas) if deltas else None),
-            "p95_absolute_delta": _round(_percentile(deltas, 0.95)),
-            "max_absolute_delta": _round(max(deltas) if deltas else None),
-        }
-
-    fundamental_deltas = _score_deltas(rows, "fundamental_delta")
-    changed_rows = sorted(
-        (
-            {
-                "symbol": row["symbol"],
-                "fundamental_delta": row["scores"]["fundamental_delta"],
-                "fundamental_rank_change": row["scores"]["fundamental_rank_change"],
-            }
-            for row in rows
-            if row["scores"]["fundamental_delta"] is not None
-        ),
-        key=lambda item: (
-            abs(item["fundamental_delta"] or 0),
-        ),
-        reverse=True,
-    )[:20]
-    return {
-        "universe_count": len(rows),
-        "legacy_fundamental_score_available": _count_score(
-            rows, "legacy_fundamental"
-        ),
-        "candidate_fundamental_score_available": _count_score(
-            rows, "candidate_fundamental"
-        ),
-        "fundamental_scores_compared": len(fundamental_deltas),
-        "fundamental_score_material_changes": sum(
-            abs(delta) > 5.0 for delta in fundamental_deltas
-        ),
-        "fundamental_rank_changes": _rank_change_count(
-            rows, "fundamental_rank_change"
-        ),
-        "field_stats": field_stats,
-        "largest_changes": changed_rows,
-    }
-
-
-def _score_deltas(rows: Iterable[dict[str, Any]], key: str) -> list[float]:
-    return [
-        row["scores"][key]
-        for row in rows
-        if row["scores"][key] is not None
-    ]
-
-
-def _count_score(rows: Iterable[dict[str, Any]], key: str) -> int:
-    return sum(row["scores"][key] is not None for row in rows)
-
-
-def _rank_change_count(rows: Iterable[dict[str, Any]], key: str) -> int:
-    return sum(
-        row["scores"][key] not in (None, 0)
-        for row in rows
-    )
-
-
-def _rank_change(legacy: int | None, candidate: int | None) -> int | None:
-    # Positive means the candidate improved its rank.
-    return legacy - candidate if legacy is not None and candidate is not None else None
-
-
-def _delta(legacy: float | None, candidate: float | None) -> float | None:
-    if not _finite(legacy) or not _finite(candidate):
-        return None
-    return round(candidate - legacy, 4)
-
-
-def _round(value: float | None, digits: int = 4) -> float | None:
-    return round(float(value), digits) if _finite(value) else None
-
-
-def _percentile(values: list[float], quantile: float) -> float | None:
-    if not values:
-        return None
-    ordered = sorted(values)
-    index = max(0, math.ceil(quantile * len(ordered)) - 1)
-    return ordered[index]
-
-
-def _finite(value: Any) -> bool:
-    return (
-        isinstance(value, (int, float))
-        and not isinstance(value, bool)
-        and math.isfinite(value)
-    )
-
-
-def _iso(value: Any) -> str | None:
-    return value.isoformat() if value is not None else None
-
-
-def _artifact_stamp(raw: str) -> str:
-    parsed = datetime.fromisoformat(raw.replace("Z", "+00:00"))
-    return parsed.astimezone(timezone.utc).strftime("%Y%m%dT%H%M%S%fZ")
-
-
-def _atomic_write(path: Path, content: str) -> None:
-    temp = path.with_name(f".{path.name}.{os.getpid()}.tmp")
-    temp.write_text(content, encoding="utf-8", newline="")
-    os.replace(temp, path)
-
-
-def _load_manifest(report_dir: str | Path) -> dict[str, Any] | None:
-    path = Path(report_dir).expanduser().resolve() / "latest.json"
-    try:
-        loaded = json.loads(path.read_text(encoding="utf-8"))
-    except (OSError, json.JSONDecodeError, TypeError, ValueError):
-        return None
-    return loaded if isinstance(loaded, dict) else None
-
-
-def _manifest_artifact(
-    report_dir: str | Path, manifest: dict[str, Any], key: str
-) -> Path:
-    directory = Path(report_dir).expanduser().resolve()
-    name = Path(str(manifest.get(key, ""))).name
-    if not name:
-        raise ValueError(f"Latest parity manifest has no {key}")
-    return directory / name

app/services/fundamentals_quality_service.py

@@ -12,7 +12,6 @@ from app.models.data_import_run import DataImportRun
 from app.models.fundamental_snapshot import FundamentalSnapshot
 from app.models.sec_filing_gap import SecFilingGap
 from app.models.ticker import Ticker
-from app.services import fundamental_data_refresh_service
 
 _SEC_FORMS = ("10-K", "10-Q", "10-K/A", "10-Q/A")
 
@@ -78,8 +77,6 @@ async def blocked_reasons_by_cik(
     ciks: set[str] | None = None,
 ) -> dict[str, str]:
     """Current SEC blocker code by CIK; no historical audit scan."""
-    if not await fundamental_data_refresh_service.is_enabled(db):
-        return {}
     if ciks is not None and not ciks:
         return {}
 

app/services/scoring_service.py

@@ -497,8 +497,8 @@ async def _compute_fundamental_score(
             "reason": "Earnings surprise data not available",
         })
 
-    # Require at least two real metrics — a single available metric (e.g. only
+    # Require at least two real metrics — a single available metric (e.g. an
-    # market cap is free on FMP) does not make a meaningful fundamental score.
+    # issuer with only a market cap) does not make a meaningful fundamental score.
     MIN_METRICS = 2
     if len(scores) < MIN_METRICS:
         unavailable.append({

app/services/sec_client.py

@@ -39,7 +39,7 @@ logger = logging.getLogger(__name__)
 _WWW = "https://www.sec.gov"
 _DATA = "https://data.sec.gov"
 
-# Resolve CA bundle for explicit httpx verify (matches app/providers/fmp.py).
+# Resolve CA bundle for explicit httpx verify (matches app/providers/alpaca.py).
 _CA = os.environ.get("SSL_CERT_FILE", "")
 _CA_VERIFY: str | bool = _CA if _CA and Path(_CA).exists() else True
 

app/services/shadow_book_service.py

@@ -40,10 +40,12 @@ KEY_CAPACITY = "shadow_book_capacity"
 KEY_RISK_PCT = "shadow_book_risk_pct"
 KEY_START_EQUITY = "shadow_book_start_equity"
 
-# Matches the validated configuration: 10-position book, 1% fixed-fractional
+# Matches the validated configuration: 1% fixed-fractional risk, and a count cap
-# risk. Start equity is only a sizing base — comparisons are drawn in percent
+# set as headroom rather than a target — see backtest_service.SIM_MAX_POSITIONS,
-# and R-multiples, never in raw currency.
+# which this must track. NOTIONAL_CAP below saturates the book near 12 positions,
-DEFAULT_CAPACITY = 10
+# so the count cap should simply never bind. Start equity is only a sizing base —
+# comparisons are drawn in percent and R-multiples, never in raw currency.
+DEFAULT_CAPACITY = 15
 DEFAULT_RISK_PCT = 1.0
 DEFAULT_START_EQUITY = 100_000.0
 

app/services/ticker_universe_service.py

@@ -113,116 +113,6 @@ def _normalise_symbols(symbols: Iterable[str]) -> list[str]:
     return sorted(deduped)
 
 
-def _extract_symbols_from_fmp_payload(payload: object) -> list[str]:
-    if not isinstance(payload, list):
-        return []
-
-    symbols: list[str] = []
-    for item in payload:
-        if not isinstance(item, dict):
-            continue
-        candidate = item.get("symbol") or item.get("ticker")
-        if isinstance(candidate, str):
-            symbols.append(candidate)
-    return symbols
-
-
-async def _try_fmp_urls(
-    client: httpx.AsyncClient,
-    urls: list[str],
-) -> tuple[list[str], list[str]]:
-    failures: list[str] = []
-    for url in urls:
-        endpoint = url.split("?")[0]
-        try:
-            response = await client.get(url)
-        except httpx.HTTPError as exc:
-            failures.append(f"{endpoint}: network error ({type(exc).__name__}: {exc})")
-            continue
-
-        if response.status_code != 200:
-            failures.append(f"{endpoint}: HTTP {response.status_code}")
-            continue
-
-        try:
-            payload = response.json()
-        except ValueError:
-            failures.append(f"{endpoint}: invalid JSON payload")
-            continue
-
-        symbols = _extract_symbols_from_fmp_payload(payload)
-        if symbols:
-            return symbols, failures
-
-        failures.append(f"{endpoint}: empty/unsupported payload")
-
-    return [], failures
-
-
-async def _fetch_universe_symbols_from_fmp(universe: str) -> list[str]:
-    if not settings.fmp_api_key:
-        raise ValidationError(
-            "FMP API key is required for universe bootstrap (set FMP_API_KEY)"
-        )
-
-    api_key = settings.fmp_api_key
-    stable_base = "https://financialmodelingprep.com/stable"
-    legacy_base = "https://financialmodelingprep.com/api/v3"
-
-    stable_candidates: dict[str, list[str]] = {
-        "sp500": [
-            f"{stable_base}/sp500-constituent?apikey={api_key}",
-            f"{stable_base}/sp500-constituents?apikey={api_key}",
-        ],
-        "nasdaq100": [
-            f"{stable_base}/nasdaq-100-constituent?apikey={api_key}",
-            f"{stable_base}/nasdaq100-constituent?apikey={api_key}",
-            f"{stable_base}/nasdaq-100-constituents?apikey={api_key}",
-        ],
-        "nasdaq_all": [
-            f"{stable_base}/stock-screener?exchange=NASDAQ&isEtf=false&limit=10000&apikey={api_key}",
-            f"{stable_base}/available-traded/list?apikey={api_key}",
-        ],
-    }
-
-    legacy_candidates: dict[str, list[str]] = {
-        "sp500": [
-            f"{legacy_base}/sp500_constituent?apikey={api_key}",
-            f"{legacy_base}/sp500_constituent",
-        ],
-        "nasdaq100": [
-            f"{legacy_base}/nasdaq_constituent?apikey={api_key}",
-            f"{legacy_base}/nasdaq_constituent",
-        ],
-        "nasdaq_all": [
-            f"{legacy_base}/stock-screener?exchange=NASDAQ&isEtf=false&limit=10000&apikey={api_key}",
-        ],
-    }
-
-    failures: list[str] = []
-    async with httpx.AsyncClient(timeout=30.0, verify=_CA_BUNDLE_PATH) as client:
-        stable_symbols, stable_failures = await _try_fmp_urls(client, stable_candidates[universe])
-        failures.extend(stable_failures)
-
-        if stable_symbols:
-            return stable_symbols
-
-        legacy_symbols, legacy_failures = await _try_fmp_urls(client, legacy_candidates[universe])
-        failures.extend(legacy_failures)
-
-        if legacy_symbols:
-            return legacy_symbols
-
-    if failures:
-        reason = "; ".join(failures[:6])
-        logger.warning("FMP universe fetch failed for %s: %s", universe, reason)
-        raise ProviderError(
-            f"Failed to fetch universe symbols from FMP for '{universe}'. Attempts: {reason}"
-        )
-
-    raise ProviderError(f"Failed to fetch universe symbols from FMP for '{universe}'")
-
-
 async def _fetch_wiki_constituent_symbols(
     client: httpx.AsyncClient,
     url: str,
@@ -351,13 +241,16 @@ async def fetch_universe_symbols(
 
     Fallback order:
     1) Free public sources (Wikipedia/NASDAQ trader)
-    2) FMP endpoints (if available)
+    2) Cached snapshot in SystemSetting
-    3) Cached snapshot in SystemSetting
+    3) Built-in seed symbols
-    4) Built-in seed symbols
 
     Returns ``(symbols, source_label)`` so bootstrap UI can show where the
-    list came from (important when Wikipedia/FMP fail and a stale cache still
+    list came from (important when the public source fails and a stale cache
-    lists BK instead of BNY).
+    still lists BK instead of BNY).
+
+    The seeds are representative, not complete, so a *fresh* install whose
+    public source is down bootstraps a partial universe. A warm instance is
+    unaffected — it falls through to its cached snapshot.
     """
     normalised_universe = _validate_universe(universe)
     failures: list[str] = []
@@ -369,15 +262,6 @@ async def fetch_universe_symbols(
         await _write_cached_symbols(db, normalised_universe, cleaned_public, public_source or "public")
         return cleaned_public, public_source or "public"
 
-    try:
-        fmp_symbols = await _fetch_universe_symbols_from_fmp(normalised_universe)
-        cleaned_fmp = _normalise_symbols(fmp_symbols)
-        if cleaned_fmp:
-            await _write_cached_symbols(db, normalised_universe, cleaned_fmp, "fmp")
-            return cleaned_fmp, "fmp"
-    except (ProviderError, ValidationError) as exc:
-        failures.append(str(exc))
-
     cached_symbols = await _read_cached_symbols(db, normalised_universe)
     if cached_symbols:
         logger.warning(

deploy/provision_fundamentals.sh

@@ -15,7 +15,6 @@ MIN_FREE_GB="${DOLT_MIN_FREE_DISK_GB:-5}"
 EARNINGS_DIR="${DOLT_DATA_DIR}/${DOLT_EARNINGS_SUBDIR}"
 DOLT_IDENTITY_NAME="${DOLT_IDENTITY_NAME:-Signal Platform}"
 DOLT_IDENTITY_EMAIL="${DOLT_IDENTITY_EMAIL:-signal-platform@localhost}"
-FUNDAMENTALS_PARITY_REPORT_DIR="${FUNDAMENTALS_PARITY_REPORT_DIR:-/var/lib/signal-platform/reports/fundamentals-parity}"
 
 fail() {
   echo "ERROR: $*" >&2
@@ -80,8 +79,6 @@ check_env() {
     || fail "set DOLT_EARNINGS_SUBDIR=$DOLT_EARNINGS_SUBDIR in $ENV_FILE"
   grep -Eq '^SEC_USER_AGENT=.*@.*' "$ENV_FILE" \
     || fail "SEC_USER_AGENT in $ENV_FILE must contain a real contact email"
-  grep -Fqx "FUNDAMENTALS_PARITY_REPORT_DIR=$FUNDAMENTALS_PARITY_REPORT_DIR" "$ENV_FILE" \
-    || fail "set FUNDAMENTALS_PARITY_REPORT_DIR=$FUNDAMENTALS_PARITY_REPORT_DIR in $ENV_FILE"
 }
 
 check_all() {
@@ -104,15 +101,6 @@ check_all() {
   identity_email="$(repo_config_value user.email 2>/dev/null || true)"
   [[ -n "$identity_name" ]] || fail "missing Dolt user.name for $EARNINGS_DIR"
   [[ -n "$identity_email" ]] || fail "missing Dolt user.email for $EARNINGS_DIR"
-  [[ -d "$FUNDAMENTALS_PARITY_REPORT_DIR" ]] \
-    || fail "missing parity report directory: $FUNDAMENTALS_PARITY_REPORT_DIR"
-  if [[ "$(id -un)" == "$APP_USER" ]]; then
-    [[ -w "$FUNDAMENTALS_PARITY_REPORT_DIR" ]] \
-      || fail "parity report directory is not writable by $APP_USER"
-  else
-    runuser -u "$APP_USER" -- test -w "$FUNDAMENTALS_PARITY_REPORT_DIR" \
-      || fail "parity report directory is not writable by $APP_USER"
-  fi
   check_free_space
   check_env
   echo "OK: Dolt $DOLT_VERSION and earnings clone are provisioned"
@@ -139,7 +127,6 @@ fi
 version_ok || fail "Dolt $DOLT_VERSION installation failed"
 
 install -d -o "$APP_USER" -g "$APP_GROUP" -m 0750 "$DOLT_DATA_DIR"
-install -d -o "$APP_USER" -g "$APP_GROUP" -m 0750 "$FUNDAMENTALS_PARITY_REPORT_DIR"
 check_free_space
 
 if [[ ! -d "$EARNINGS_DIR/.dolt" ]]; then

docs/dolt-integration-plan.md

@@ -1,15 +1,18 @@
 # Dolt bulk-data integration — implementation plan
 
-Status: approved 2026-07-21, revised through four review rounds; direction: KISS
+Status: **workstream A complete and deployed** (A0–A6, last step 2026-08-07);
-backend, UI value first. Hand-off document for the implementing agent;
+**workstream B dropped 2026-08-07** — see § Why B was dropped. Approved 2026-07-21,
-self-contained.
+revised through five review rounds; direction: KISS backend, UI value first.
+Originally a hand-off document for the implementing agent; now the design record.
+Current operations live in `docs/fundamentals-deployment.md`.
 
 ## Objective
 
 Replace the free-tier fundamentals APIs (FMP, Finnhub, Alpha Vantage) with bulk
-data: SEC Company Facts for fundamentals, the DoltHub earnings repo for the
+data: SEC Company Facts for fundamentals and the DoltHub earnings repo for the
-earnings calendar/history, and — later, independently — the DoltHub stocks repo for
+earnings calendar/history. PostgreSQL stays the production system of record.
-historical OHLCV. PostgreSQL stays the production system of record.
+(A third source — the DoltHub stocks repo for historical OHLCV — was planned as
+workstream B and dropped; Alpaca remains the price source.)
 
 **Delivery order: two independent workstreams.**
 
@@ -17,9 +20,9 @@ historical OHLCV. PostgreSQL stays the production system of record.
   FundamentalsPanel + decommission FMP/Finnhub/Alpha Vantage. Valuation uses the
   existing Alpaca closes already in `ohlcv_records`. This alone achieves the goal
   (killing the quota-limited APIs) and delivers all the UI value.
-- **Workstream B (later, optional until needed):** replace historical OHLCV with
+- **Workstream B — DROPPED 2026-08-07, see below.** Would have replaced historical
-  the Dolt stocks repo. The most complex machinery (4.7 GB clone, split
+  OHLCV with the Dolt stocks repo. Its design is retained further down as a record,
-  adjustment, source-bar table, reconciliation) lives here and blocks nothing in A.
+  not as a backlog item.
 
 **Guiding principle: KISS.** Plain daily importers with staging and atomic
 promotion — no forensic replay, no permanent archive store, no conflict tables, no
@@ -70,8 +73,9 @@ notes (retain a CC BY-SA 4.0 reference + attribution to `post-no-preference/earn
 and a note of the transformations applied — e.g. in a repo `NOTICE`/attribution file
 and the importer module); **no public API, bulk export, or redistribution** of the
 data; re-review licensing before any public or commercial access. The
-`post-no-preference/stocks` repo (workstream B) is **not** covered here and will be
+`post-no-preference/stocks` repo (workstream B) is **not** covered here. B was
-reviewed separately if B begins.
+dropped before any licensing review, so that repo has never been assessed — any
+future use of it starts that review from scratch.
 
 ## Schema
 
@@ -119,7 +123,9 @@ reviewed separately if B begins.
   cache, repopulated by the daily SEC job — but only after the phase-A5 parity
   gate.
 
-**Migration 027 (workstream B, written when B starts):**
+**Migration 027 (workstream B — NEVER WRITTEN; B was dropped, and `027` was
+subsequently used for `fundamental_snapshots.weighted_avg_diluted_shares`). The
+design below is a record only:**
 
 - `ohlcv_source_bars` — source-truth bar table, required because `ohlcv_records`
   allows one row per (ticker_id, date) (`app/models/ohlcv.py:12`) and Alpaca
@@ -219,7 +225,7 @@ Workstream A:
   **The new API valuation object is not stored anywhere** — it is computed at
   request time (below). No valuation cache or table exists.
 
-Workstream B:
+Workstream B (dropped — never built):
 
 - Dolt OHLCV+splits pull/import: `0 2 * * tue-sat` ET. If source_max_date is not
   fresh, retry hourly until ~06:00, then give up quietly. After a successful
@@ -430,16 +436,55 @@ workstream B — Alpaca remains the price source throughout.
   approval** — see the handoff section below. Step (c) is implemented behind the
   default-off `fundamental_data_sec_dolt_cutover_enabled` SystemSetting; the
   remaining production action is flipping that switch on and observing it.
-- A6. Remove FMP/Finnhub/Alpha Vantage; keep monitoring + manual fallback.
+- A6. **DONE 2026-08-07.** FMP/Finnhub/Alpha Vantage removed, along with the
+  weekly `fundamental_collector` job, the A5 cutover toggle (SEC+Dolt is now the
+  unconditional path) and the parity report. Migration `029` tombstoned the two
+  behavior-bearing settings rows for the rollback window and `030` dropped them
+  once the deploy was confirmed healthy; the archived parity bundles stay as the
+  A5 evidence trail.
 
-**Workstream B (independent, start when wanted):**
+**Workstream B — DROPPED 2026-08-07.** The phases below are recorded for anyone
+who revisits the decision; none of them are scheduled work.
 
-- B0. Stocks clone (~4.7 GB) provisioned; migration 027.
+- ~~B0. Stocks clone (~4.7 GB) provisioned; migration 027.~~
-- B1. OHLCV + split adjustment in shadow (writes `ohlcv_source_bars` only; Alpaca
+- ~~B1. OHLCV + split adjustment in shadow (writes `ohlcv_source_bars` only; Alpaca
-  keeps owning `ohlcv_records`); historical backfill.
+  keeps owning `ohlcv_records`); historical backfill.~~
-- B2. Reconciliation window (≥ 2 weeks) vs Alpaca; review validation summaries.
+- ~~B2. Reconciliation window (≥ 2 weeks) vs Alpaca; review validation summaries.~~
-- B3. Promote Dolt as historical OHLCV source (canonical rebuilt from raw source
+- ~~B3. Promote Dolt as historical OHLCV source (canonical rebuilt from raw source
-  bars + splits); morning pipeline → 03:00.
+  bars + splits); morning pipeline → 03:00.~~
+
+### Why B was dropped
+
+Reviewed after A6 shipped. Four reasons, in order of weight:
+
+1. **Its motivation no longer exists.** B was scoped inside a plan whose goal was
+   killing the quota-limited free-tier APIs. Alpaca was never one of them, and the
+   plan always said so (§ Decommissioning: "Alpaca remains the price source
+   throughout"). A6 achieved the goal. What remained was swapping one working
+   price source for another.
+2. **Its only concrete benefit is reachable far more cheaply.** The prize was
+   `corporate_actions`, the documented fix for the KLAC-class post-filing split
+   (TTM EPS pre-split vs a post-split price → P/E 6.19 instead of ~13, invisible to
+   snapshots). That needs *split events*, not 4.7 GB of bars — and the Alpaca SDK
+   already in the venv exposes them via
+   `alpaca.data.historical.corporate_actions.CorporateActionsClient.get_corporate_actions`
+   with `CorporateActionsRequest` / `CorporateActionsType`. See the follow-up below.
+3. **The benefit is small.** Fundamentals carry 20% of the composite, P/E is one of
+   three fundamental inputs, and only names that split between their last 10-Q and
+   today are affected — a handful at a time, self-correcting at the next filing.
+4. **B would add a risk the current setup does not carry.** By design a newly
+   published split rewrites a symbol's entire adjusted history. A backtest↔prod
+   parity guard exists precisely because changed history invalidates comparisons;
+   B makes history mutable as a routine event. It also needs its own license
+   review — the A0 CC BY-SA decision covers only `post-no-preference/earnings`.
+
+**Optional follow-up, not scheduled:** a small `corporate_actions` table populated
+from Alpaca, used to null or correct P/E when a split post-dates the newest
+snapshot. Roughly a day's work; captures essentially all of B's value with no
+clone, no `ohlcv_source_bars`, no split-adjustment pipeline and no reconciliation
+window. Worth doing only if the wart starts costing something — it has been visible
+and harmless since July 2026. Note that migration numbering has moved on: head is
+`030`, so any such table would be `031+`, not the `027` named below.
 
 ## Test plan
 
@@ -464,8 +509,8 @@ workstream B — Alpaca remains the price source throughout.
   falls back from P/E to FCF yield for the valuation segment when P/E is null.
 - Peer comparison disappears below 5 peer issuers; favorable-percentile direction
   correct for both polarities.
-- Workstream B: split-adjusted OHLCV matches Alpaca on representative normal /
+- ~~Workstream B: split-adjusted OHLCV matches Alpaca on representative normal /
-  split / reverse-split symbols.
+  split / reverse-split symbols.~~ (dropped)
 - UI states: positive, adverse, neutral, insufficient history, insufficient
   peers; mobile layout; non-color accessibility.
 - Unit, integration, scheduler and frontend suites pass.
@@ -492,30 +537,32 @@ Post-fix: candidate scores 504 of 511 vs legacy's 507 (gap = PSKY/Q new registra
 FITB, all explained); revenue-growth agreement 0.0038 median abs delta where both exist.
 Dennis reviewed the evidence 2026-07-24 and directed proceeding to cutover.
 
-**Task 1 — A5 activation (IMPLEMENTED 2026-07-24; production switch remains).** The
+**Task 1 — A5 activation: DONE.** Implemented 2026-07-24, switched on and observed
-post-activation local refresh of `fundamental_data` derives `pe_ratio` and
+in production, and made unconditional by A6 (2026-08-07) — there is no longer a
-`market_cap` from newest valid snapshots × latest PostgreSQL close, `revenue_growth`
+switch, an Admin card, or a weekly legacy collector to skip. The local refresh of
-from snapshots, `earnings_surprise`/`next_earnings_date` from `earnings_events`; mark
+`fundamental_data` derives `pe_ratio` and `market_cap` from newest valid snapshots ×
-affected cached fundamental scores stale; must run identically when SEC is unreachable.
+latest PostgreSQL close, `revenue_growth` from snapshots, and
+`earnings_surprise`/`next_earnings_date` from `earnings_events`; it marks affected
+cached fundamental scores stale and runs identically when SEC is unreachable.
 It consumes `fundamentals_derivation.derive()` outputs, NOT raw snapshot fields —
-that path carries the split guard (`ttm_diluted_eps`
+that path carries the split guard (`ttm_diluted_eps` nulls when contaminated, with
-nulls when contaminated, with `ttm_diluted_eps_caveat`) and the multi-class share
+`ttm_diluted_eps_caveat`) and the multi-class share fallback (`shares_outstanding` +
-fallback (`shares_outstanding` + `shares_outstanding_estimated`). Parity and activation
+`shares_outstanding_estimated`). See `docs/fundamentals-deployment.md` for current
-share the same candidate builder. Activation is the explicit
+operations and rollback.
-`fundamental_data_sec_dolt_cutover_enabled` SystemSetting and defaults off. It is
-managed by the **Fundamentals data source** card in Admin → Settings; while active,
-the weekly legacy collector skips itself so it cannot overwrite the SEC/Dolt cache.
-See `docs/fundamentals-deployment.md` for the production flip and rollback procedure.
 
-**Task 2 — A6 decommissioning.** After a short observation window: remove
+**Task 2 — A6 decommissioning: DONE 2026-08-07.** The cutover ran on and was
-FMP/Finnhub/Alpha Vantage providers, config and env keys; keep monitoring + manual
+observed in production, so the legacy providers, their config/env keys, the weekly
-fallback. Gated by the acceptance criteria above — especially forward-calendar
+collector job and the parity report were all removed. Two consequences to carry:
-timeliness from `dolt_earnings` (its `source_max_date` ran ~5 weeks ahead as of
+(1) `fundamental_data` now has no provider fallback — recovery is restore-from-backup;
-2026-07-23, which passes).
+(2) disabling **SEC Fundamentals Import** stops the SEC fetch only, because the local
+cache refresh was deliberately moved outside the job-enable check. No follow-ups
+remain: migration `030` dropped the tombstone rows after the deploy was verified.
 
 **Known caveats to carry (documented in the findings report, not bugs to fix):**
 - KLAC-class post-filing splits: P/E wrong until the next 10-Q; undetectable from
-  snapshots. Workstream B's `corporate_actions` table is the natural future fix.
+  snapshots. Still open and still harmless. The fix, if ever wanted, is a small
+  `corporate_actions` table fed from Alpaca — **not** workstream B, which was
+  dropped; see § Why B was dropped.
 - BRK-B: no share count exists anywhere in companyfacts → no market cap, correctly.
 - FITB: unscored (split guard + no taggable revenue) — the one name that lost its
   score relative to legacy; composite renormalises.
@@ -528,7 +575,7 @@ timeliness from `dolt_earnings` (its `source_max_date` ran ~5 weeks ahead as of
 
 ## Deferred (explicitly, until a concrete need appears)
 
-- Workstream B itself is deferred relative to A and blocks nothing in A.
+- Workstream B: **dropped** 2026-08-07, not deferred — see § Why B was dropped.
 - Exact byte-level source replay of historical imports; permanent archive store.
 - Point-in-time backtest enforcement (`accepted_at` is stored now; derivation and
   backtest visibility rules are built only when fundamentals enter

docs/fundamentals-deployment.md

@@ -1,17 +1,21 @@
 # Fundamentals production deployment
 
 This is the one-time production setup for the Dolt earnings and SEC fundamentals
-imports. The A5 scoring cutover was approved on 2026-07-24; the compat-cache write
+imports. Since A6 (2026-08) these are the *only* fundamentals sources — the
-path is still default-off until the explicit production switch below is set. Do
+FMP/Finnhub/Alpha Vantage providers, the weekly legacy collector and the A5 parity
-not add OS cron entries: the application scheduler owns both jobs.
+report are gone, and the cache write path is unconditional. Do not add OS cron
+entries: the application scheduler owns both jobs.
 
 ## What the deployment adds
 
-- `Dolt Earnings Import (shadow)` runs daily at 02:30 America/New_York.
+- `Dolt Earnings Import` runs daily at 02:30 America/New_York.
-- `SEC Fundamentals Import` runs daily at 04:00 America/New_York. Its local
+- `SEC Fundamentals Import` runs daily at 04:00 America/New_York, then refreshes
-  `fundamental_data` refresh runs only when the A5 switch is enabled.
+  `fundamental_data` — the compat cache scoring reads — from stored snapshots,
-- `Fundamentals Parity Report (read-only)` runs daily at 05:30 America/New_York.
+  earnings events and closes.
 - Both jobs are visible, toggleable, and manually triggerable in Admin → Jobs.
+  **Disabling the SEC job stops its SEC network fetch only**; the local cache
+  refresh still runs, because prices and earnings move daily even when no filing
+  does.
 - Cron expressions are editable in Admin → Schedule.
 - Every attempt is recorded in `data_import_runs`; failures also create a system
   event. A failed validation does not promote partial data.
@@ -36,14 +40,16 @@ DOLT_EARNINGS_SUBDIR=earnings
 DOLT_MIN_FREE_DISK_GB=5.0
 SEC_USER_AGENT=signal-platform/1.0 (contact: real-address@example.com)
 SEC_REQUEST_SPACING_SECONDS=0.2
-FUNDAMENTALS_PARITY_REPORT_DIR=/var/lib/signal-platform/reports/fundamentals-parity
 ```
 
 Use a real monitored contact address. Keep at least 5 GB free at the Dolt data
 path; 8–10 GB gives comfortable growth headroom. The data directory must stay
 outside `/opt/signalplatform`, because deployments use `rsync --delete` there.
-The parity-report directory is also persistent and owned by the service user;
+
-its small timestamped JSON/CSV bundles form the temporary A5 review trail.
+`FMP_API_KEY`, `FINNHUB_API_KEY` and `ALPHA_VANTAGE_API_KEY` must be **removed**
+from this file. Nothing reads them any more, and leaving them installed is the
+one thing that would let a rolled-back pre-A6 process resume the legacy
+collector and overwrite the SEC/Dolt cache.
 
 ## One-time provisioning
 
@@ -80,7 +86,7 @@ a reviewed change to `DOLT_VERSION`, followed by the same provision/check flow.
 
 In Admin → Jobs, wait until no other job is running, then:
 
-1. Trigger **Dolt Earnings Import (shadow)**. Expect `completed` with import
+1. Trigger **Dolt Earnings Import**. Expect `completed` with import
    status `promoted`; a repeat without an upstream change should report `no_op`.
 2. Trigger **SEC Fundamentals Import**. The first run performs the
    tracked-universe history backfill and can take materially longer than a daily
@@ -91,27 +97,7 @@ In Admin → Jobs, wait until no other job is running, then:
    data and still handles partial/missing issuers cleanly. A ticker held by the
    quality gate should show **New setups paused** with the specific SEC reason.
 
-## A5 parity observation window
+## Verification
-
-After both shadow imports are healthy, trigger **Fundamentals Parity Report
-(read-only)** once in Admin → Jobs. The **A5 Fundamentals Parity** card above
-the jobs shows the latest coverage/delta summary and provides authenticated JSON
-and CSV downloads. The canonical server-side bundles are archived at:
-
-```text
-/var/lib/signal-platform/reports/fundamentals-parity/
-```
-
-The scheduler then generates one report daily at 05:30 New York time, after the
-02:30 Dolt and 04:00 SEC jobs. Review 5–7 consecutive reports before making the
-cutover decision. A report never writes `fundamental_data`, dimension/composite
-scores, rankings, qualification state, or an approval flag. Materiality bands
-only highlight rows for review; A5 still requires explicit approval.
-
-Each bundle contains legacy and candidate P/E, revenue growth, and earnings
-surprise; definition notes; source revisions and price dates; recomputed legacy
-and candidate fundamental scores; and per-universe fundamental-rank changes.
-Definition changes remain explicit even when numeric deltas are small.
 
 Optional database verification:
 
@@ -162,45 +148,24 @@ Expect `OK: source lock is busy`. This is the remaining live-PostgreSQL
 mutual-exclusion check; SQLite unit tests cannot exercise PostgreSQL advisory
 locks. A second Admin trigger should independently report the job as busy.
 
-## A5 production activation (approved 2026-07-24)
+## The fundamentals cache
 
-The write path is controlled by the SystemSetting
+`fundamental_data` is the compat cache scoring reads. The SEC Fundamentals
-`fundamental_data_sec_dolt_cutover_enabled`. An absent value, `false`, or any
+Import rebuilds it every run from data already in PostgreSQL: newest valid
-value other than `true` leaves `fundamental_data` untouched. Before enabling it,
+snapshots x latest close for `pe_ratio` and `market_cap`, snapshots alone for
-confirm the normal PostgreSQL backup containing `fundamental_data` is current.
+`revenue_growth`, and `earnings_events` for `earnings_surprise` and
+`next_earnings_date`. It therefore also runs after an SEC network/validation
+failure, a `no_op`, a source-lock skip, or with the job disabled — no network
+access is involved. The job message appends the cache row count and the changed
+score-input count.
 
-In **Admin → Settings → Fundamentals data source**:
+A refresh marks affected fundamental and composite score caches stale. The
+normal 15:30 near-close scanner recomputes them before using the rankings; until
+then, reads truthfully expose the stale state.
 
-1. Turn on **Use SEC + Dolt for scoring inputs** and accept the confirmation.
+Verify the refreshed rows:
-2. Click **Run refresh now**. The SEC import may be `promoted` or `no_op`; either
-   result runs the local cache refresh.
-
-The weekly legacy collector is automatically skipped while the switch is on, so
-it cannot overwrite the activated cache. The switch remains visible even before
-its SystemSetting row exists because the safe default is off.
-
-If the Admin UI is unavailable, enable the cutover directly in PostgreSQL:
 
 ```sql
-INSERT INTO system_settings (key, value, updated_at)
-VALUES ('fundamental_data_sec_dolt_cutover_enabled', 'true', now())
-ON CONFLICT (key) DO UPDATE
-SET value = EXCLUDED.value, updated_at = now();
-```
-
-Then trigger **SEC Fundamentals Import** once in Admin → Jobs. Once enabled, the
-same refresh also runs after an SEC network/validation failure or a source-lock
-skip, because it reads only PostgreSQL snapshots, earnings events, and closes.
-The job message appends the cache row count and changed score-input count when
-the import itself completed successfully.
-
-Verify the switch and refreshed rows:
-
-```sql
-SELECT key, value, updated_at
-FROM system_settings
-WHERE key = 'fundamental_data_sec_dolt_cutover_enabled';
-
 SELECT count(*) AS rows,
        max(fetched_at) AS refreshed_at,
        count(pe_ratio) AS pe_available,
@@ -213,28 +178,26 @@ SELECT dimension, is_stale, count(*)
 FROM dimension_scores
 WHERE dimension = 'fundamental'
 GROUP BY dimension, is_stale;
-
-SELECT is_stale, count(*)
-FROM composite_scores
-GROUP BY is_stale;
 ```
 
-The first refresh intentionally marks affected fundamental and composite score
-caches stale. The normal 15:30 near-close scanner recomputes them before using
-the rankings; until then, reads truthfully expose the stale state. Observe at
-least several scheduled cycles before A6 removes the legacy providers.
-
 ## Failure and rollback
 
-- To stop the A5 cache writes without stopping SEC snapshot ingestion, turn off
+- **There is no provider fallback any more, and no Admin switch that freezes the
-  **Use SEC + Dolt for scoring inputs** in Admin → Settings. If the UI is
+  cache.** Disabling **SEC Fundamentals Import** stops SEC network access only;
-  unavailable, set `fundamental_data_sec_dolt_cutover_enabled` back to `false`
+  the 04:00 job still rebuilds `fundamental_data` from the stored snapshots,
-  with the SQL above (changing only the value). This prevents the next local
+  earnings events and closes.
-  refresh but does not restore rows already replaced. Restore `fundamental_data`
+- Restoring `fundamental_data` from the PostgreSQL backup is therefore a
-  from the pre-cutover database backup, or—before A6—manually run the legacy
+  *temporary* fix on its own: if the bad values come from the snapshots or from
-  Fundamental Collector if its provider keys and quota are still available.
+  the derivation code, the next scheduled run reproduces them. Fix the cause —
-- Disable a failing source-import job in Admin → Jobs only when ingestion itself
+  restore or repair `fundamental_snapshots` / `earnings_events`, or revert the
-  must stop. Existing promoted snapshots/events remain available.
+  parser change and re-run `scripts/reparse_fundamentals.py --apply`.
+- To genuinely freeze the cache while you work, stop the service
+  (`sudo systemctl stop signalplatform.service`) — that stops the scheduler with
+  it. There is no finer-grained control, by design: a silently frozen scoring
+  input is worse than an obvious outage.
+- Disable a failing source-import job in Admin → Jobs when SEC network access
+  itself must stop. Existing promoted snapshots and events remain available, and
+  the job's runtime message still reports the cache result.
 - Inspect the job runtime, latest `data_import_runs.validation_json`, service
   logs, and Admin → System Events before retrying.
 - `unresolved_filing` is emitted once when a filing enters automatic retry. It
@@ -250,8 +213,3 @@ least several scheduled cycles before A6 removes the legacy providers.
 - The Dolt clone is a reproducible cache and does not need a bespoke backup.
   PostgreSQL (including `earnings_events`, `fundamental_snapshots`, and import
   audit rows) must remain covered by the normal production database backup.
-- Do not proceed to A6 until the activated cache has completed the observation
-  window and the forward earnings calendar remains timely.
-- If report generation fails, inspect Admin → System Events and verify
-  `FUNDAMENTALS_PARITY_REPORT_DIR` exists and is writable by `deploy`. Existing
-  reports and all live data remain untouched.

docs/research/README.md

@@ -25,7 +25,7 @@ score, Structural S/R, the Gate Target Ladder, sentiment, fundamentals) is
 | 1.5× ATR initial stop | Real exit | Cuts losers fast |
 | 3× ATR trailing stop, 30-day max hold | Real exit | Best Sharpe of every exit tested |
 | Post-stop normal gate reset | Re-entry policy | Stop always closes; a later gate failure and subsequent fresh qualification define the next signal episode. The selected study arm reached Sharpe 1.77 / CAGR 48.3% at capacity 10; live scan-before-outcome timing is stricter (Sharpe 1.68 / CAGR 44.8% analogue). [Full study](post-stop-reentry.md) |
-| Max 10 concurrent positions, 1% risk per trade | Sizing | The cap binds by signal count, but the focused bracket found negligible opportunity cost: cap 15 admitted every blocked setup and added only 0.0018 R/trade in affected paths. [Findings](portfolio-capacity-bracket-findings.md) |
+| Max **15** concurrent positions, 1% risk per trade | Sizing | Raised from 10 (2026-08-05) so the count cap never binds: +1.075pp CAGR paired, 51 paths better / 2 worse, drawdown unchanged. Cash plus the 20% notional cap saturates the book near 12. [Findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens) |
 | Structural S/R | Human-facing product context | Clean, capped zones for charts and alerts; not read by the scanner |
 | Gate Target Ladder | Screening machinery | Volume-free transient proposals preserve the production candidate set exactly; never an exit |
 
@@ -61,7 +61,7 @@ invites overfitting.
 |---|---|
 | ATR trail multiple {1.5–4.0} | **Keep 3.0** — ≤2.0 whipsaws out the right tail; ≥2.5 is a plateau |
 | Momentum lookback (6-1, 3-1, 12-7 Novy-Marx, composites) | **Keep residual 12-1** — the others have IC ≈ 0 or weaker t-stats |
-| Selection cutoff {70…90} × book size {10, 15, 20} | **Keep 80 × 10** — the focused daily bracket found no meaningful gain from cap 15, while weekly rank replacement hurt. [Findings](portfolio-capacity-bracket-findings.md) |
+| Selection cutoff {70…90} × book size {10, 15, 20} | **Keep cutoff 80; book size now 15** — the focused daily bracket found cap 15 worth +1.075pp CAGR (the weekly replay's contrary reading was EV-per-trade). Weekly rank replacement hurt. [Findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens) |
 | Position sizing (equal-weight, inverse-vol, risk-% sweep) | **Keep 1% fixed-fractional** |
 | Primary-target probability floor | **Keep 20%** — pruned lottery targets, 1,428 → 1,089 qualified, lifted Sharpe |
 | Primary-target R:R selector | **Keep 1.5** — target choice is intentionally independent of the later 2.0 activation floor |
@@ -146,7 +146,7 @@ knobs.
 | **Broader universe** | Composition changes factor signs (fip tug-of-war); vol-tilt on breadth is only a **directional hypothesis** (auth. −0.048 / t −1.36) | Any prod broaden must re-validate 80/20 tilt; offline research only; research.sqlite requires completion manifest |
 | **Forward paper-trade record** | The only true out-of-sample evidence the snapshot cannot give | Time; mark entries at actual near-close fill once ops ships |
 | **Better target model for clear-air names** | The return is demonstrably there (#2 wins on raw CAGR in *both* train and test); it's the *flat* 3× ATR target that makes it too expensive in risk | Needs a per-name model, not a constant k×ATR |
-| **Minimum effective-risk floor** | In cap-never-bound paths, the confounded 0.5% floor arm removed about 8% of fills while EV rose from 0.328 to 0.399 R and PF from 1.60 to 1.75, with exposure nearly unchanged | Run the frozen single-variable cap-10 A/B. [Specification](effective-risk-floor-ab.md) / [capacity findings](portfolio-capacity-bracket-findings.md) |
+| **Minimum effective-risk floor** | ⛔ CLOSED NEGATIVE, not run. The floor lifts EV/trade (+0.032) and PF (+0.073) *by deleting trades* — 11.4 fewer per path, never one more — and costs **−0.753pp CAGR**, −0.047 Sharpe, −0.051 Calmar | Do not run the A/B; its EV-based pass rule would have shipped it. [Withdrawn specification](effective-risk-floor-ab.md) / [findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens) |
 
 ---
 
@@ -198,15 +198,19 @@ qualification. The [daily re-entry matrix](post-stop-reentry.md) supports this
 for the current 10-position book, but not as a universal rule for other
 portfolio capacities.
 
-Capacity is now closed as a negative result. The current daily Phase A control
+Capacity is closed **positive**: the count cap was raised 10 → 15 so it no longer
-does reject 519 qualified entries because the ten-slot book is full versus 472
+binds, worth **+1.075pp CAGR** paired across 175 paths (51 better, 2 worse) at
-admitted trades, so the older weekly “cap never binds” claim was stale. But the
+unchanged drawdown. Fifteen is headroom, not a target — cap15 peaked at 12 with
-clean cap-15 arm admitted every opportunity the strategy requested and added
+zero full-book skips, so cash plus the 20% notional cap is the real ceiling.
-only 0.0018 R/trade in paths where cap 10 bound. Weekly current-rank replacement
+
-reduced mean EV and created substantial churn. Keep cap 10 and do not build the
+An earlier reading of this run concluded "keep cap 10, added only 0.0018 R/trade."
-replacement policy. See the [frozen specification](portfolio-capacity-bracket.md)
+That was **EV per trade**, which is the wrong metric for a treatment that changes
-and the separate [capacity findings](portfolio-capacity-bracket-findings.md).
+trade *count*: flat EV/trade means the blocked entries were as good as the taken
-The only open follow-up from that run is the
+ones, so refusing them cost their whole contribution to return. Weekly
-[frozen confound-free 0.5% minimum effective-risk-floor A/B](effective-risk-floor-ab.md).
+current-rank replacement remains rejected (−0.043 EV R, 24% churn). The 0.5%
+effective-risk-floor A/B is **closed negative** without being run — it costs
+0.75pp of CAGR while raising EV/trade, and its frozen pass rule would have shipped
+it. See the [frozen specification](portfolio-capacity-bracket.md) and the
+[capacity findings](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens).
 
 The next real evidence is **forward**, not backward: the live paper-trade record.

docs/research/effective-risk-floor-ab.md

@@ -1,8 +1,27 @@
 # Effective initial-risk floor A/B - frozen specification
 
+> ## ⛔ CLOSED 2026-08-05 — NEGATIVE. DO NOT RUN.
+>
+> This A/B was never executed because the capacity-bracket run already contains
+> it. `cap15_incumbent` (peak 12, zero blocked, no floor) and `cash_unbounded`
+> (peak 12, floor) have the same effective capacity and differ essentially only
+> by `min_initial_risk_fraction`. Paired over 175 paths, the 0.5% floor gives
+> **EV/trade +0.032 and profit factor +0.073, but CAGR −0.753pp, total return
+> −0.765pp, Sharpe −0.047, Calmar −0.051**, and it removes 11.4 trades per path
+> while never adding one (174 worse / 0 better).
+>
+> **The pass rule below is unsafe.** It promotes on paired EV, and the floor
+> raises EV per trade *precisely by deleting trades* that were net positive
+> contributors — so this specification would have shipped a change costing
+> 0.75pp of CAGR. Any successor study must decide on CAGR/total return and treat
+> EV per trade as a diagnostic.
+>
+> See [portfolio-capacity-bracket-findings.md](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens).
+> Retained as a record of what was specified and why it was withdrawn.
+
 Date frozen: 2026-08-05
-Branch: research/portfolio-capacity-rebalancing
+Branch: research/portfolio-capacity-rebalancing (deleted; tag `research/portfolio-capacity-final`)
-Runner: scripts/run_portfolio_construction_matrix.py
+Runner: scripts/run_portfolio_construction_matrix.py (not on main; see tag)
 Study ID: risk-floor-ab
 
 ## Question

docs/research/phase-a-matrix.md

@@ -32,8 +32,13 @@ Mechanics guards confirmed before reading results: calendar truncation asserted
 skipped_book_full = 519 versus 472 admitted trades, so the ten-slot book
 refuses 52.4% of admitted+blocked qualified opportunities. The older weekly
 claim that the cap never bound is stale and does not apply to this daily
-gate-reset configuration. Capacity is now isolated in the
+gate-reset configuration. Capacity was isolated in the
-[focused bracket study](portfolio-capacity-bracket.md).
+[focused bracket study](portfolio-capacity-bracket.md) and **resolved: the count
+cap was raised 10 → 15 so it no longer binds (+1.075pp CAGR paired, 51 paths
+better / 2 worse, drawdown unchanged).** Note that the blocked *count* was a poor
+guide in both directions — one path had 244 blocked entries and relieving all of
+them moved CAGR by −0.1pp. See the
+[findings correction](portfolio-capacity-bracket-findings.md#correction-2026-08-05-ev-per-trade-was-the-wrong-lens).
 
 Validation SE ≈ 0.72 — almost no arm clears a 1-SE delta.
 

docs/research/portfolio-capacity-bracket-findings.md

@@ -2,8 +2,16 @@
 
 Date interpreted: 2026-08-05
 
-Status: **capacity and weekly replacement closed as negative results; the
+Status: **SUPERSEDED IN PART — see [Correction](#correction-2026-08-05-ev-per-trade-was-the-wrong-lens)
-minimum effective-risk floor remains an open single-variable follow-up.**
+at the foot of this document before acting on anything here.** Weekly replacement
+is closed as a negative result and that still holds. The capacity decision below
+("keep cap 10") and the recommendation to run the effective-risk-floor A/B were
+both reached on EV per trade and are **reversed** by the correction: the count cap
+was raised so it no longer binds, and the floor A/B is closed as negative.
+
+> The runner (`scripts/run_portfolio_construction_matrix.py`), the research
+> simulator hooks, and the study's unit tests were deliberately not merged to
+> main. They live at tag `research/portfolio-capacity-final`.
 
 This document interprets the frozen v2 run without modifying its generated
 outputs:
@@ -116,9 +124,96 @@ start-date evidence, but they necessarily mix initialization with market regime.
 
 ## Final decisions
 
-1. Keep cap 10; its measured opportunity cost is negligible.
+1. ~~Keep cap 10; its measured opportunity cost is negligible.~~ **REVERSED —
-2. Reject weekly rank replacement.
+   see the correction below.**
+2. Reject weekly rank replacement. *(Stands.)*
 3. Do not interpret the `cash_unbounded` improvement as a capacity effect.
-4. Run only the focused cap-10 effective-risk-floor A/B next.
+   *(Stands — and it is not a floor effect worth having either; see below.)*
+4. ~~Run only the focused cap-10 effective-risk-floor A/B next.~~ **REVERSED —
+   that A/B is answered and negative; do not run it.**
 5. Report means, inert fractions, and absolute dispersion beside medians and
-   ratios in future sparse-treatment studies.
+   ratios in future sparse-treatment studies. *(Stands, and see below — the
+   metric itself matters as much as the summary statistic.)*
+
+## Correction 2026-08-05: EV per trade was the wrong lens
+
+Everything above judged the arms on **mean paired net EV per trade**. That is the
+wrong metric for any treatment that changes how many trades the book takes.
+Capacity does not change trade *quality*; it changes trade *count*. A flat EV/trade
+delta therefore does not mean "no benefit" — it means the blocked entries were
+**just as good** as the taken ones, so refusing them cost their entire
+contribution to return. Re-running the same paired comparison on CAGR inverts two
+conclusions.
+
+### Capacity: raise the cap (reverses decision 1)
+
+`cap15_incumbent` versus `cap10_incumbent`, paired, all 175 paths, 0.10% per fill:
+
+| Metric | Mean Δ | Worse / better |
+|---|---:|---:|
+| Trades | +1.00 | **0 / 76** (never fewer) |
+| **CAGR pp** | **+1.075** | 2 / 51 |
+| Total return pp | +1.079 | 1 / 51 |
+| Max drawdown pp | +0.007 | 1 / 2 |
+| Calmar | +0.062 | **1 / 51** |
+| Sharpe | +0.022 | 10 / 28 |
+| Net EV R/trade | +0.001 | 47 / 29 |
+
+Restricted to the 105 paths where the cap actually bound: **+1.791pp CAGR**.
+
+The honest tail: exactly one path was materially hurt — `empty-2023-04`, CAGR
+87.2 → 81.2 (−6.0pp), drawdown 13.0 → 14.3, from two extra trades. Second-worst
+was −0.1pp. The best paths (+6.6/+6.7/+6.9pp) came with *identical* drawdown. Best
+and worst magnitudes are symmetric at roughly ±6pp, but the frequency is 51:1.
+
+Blocked count is not lost value in either direction: `empty-2021-05` had **244**
+blocked entries under cap 10, and relieving every one of them moved CAGR by
+−0.1pp.
+
+**Shipped:** `SIM_MAX_POSITIONS` and `shadow_book_service.DEFAULT_CAPACITY` raised
+10 → 15. Fifteen is headroom, not a target — cap15 peaked at 12 with zero
+full-book skips, so cash plus the 20% notional cap is the real ceiling and
+15/20/None are the same experiment.
+
+### Effective-risk floor: closed negative (reverses decision 4)
+
+The floor A/B does not need running — this study already contains it.
+`cap15_incumbent` (peak 12, zero blocked, no floor) and `cash_unbounded` (peak 12,
+floor) have the same effective capacity and differ essentially only by
+`min_initial_risk_fraction`. Paired, n=175, 0.10% per fill, floor minus no-floor:
+
+| Metric | Mean Δ | Worse / better |
+|---|---:|---:|
+| Net EV R/trade | **+0.032** | 53 / 121 |
+| Profit factor | **+0.073** | 46 / 128 |
+| Trades | **−11.4** | **174 / 0** (never adds one) |
+| **CAGR pp** | **−0.753** | 105 / 68 |
+| Total return pp | −0.765 | 105 / 68 |
+| Sharpe | −0.047 | 108 / 65 |
+| Calmar | −0.051 | 103 / 71 |
+| Max drawdown pp | +0.333 (worse) | — |
+
+The same trap, mirrored: the floor raises per-trade quality *precisely by deleting
+trades*, and the deleted trades were net positive contributors. The frozen
+specification in [effective-risk-floor-ab.md](effective-risk-floor-ab.md) would
+have passed it on paired EV and shipped a change costing 0.75pp of CAGR.
+
+Genuinely open, low priority: 0.005 clearly over-cuts, but the sizing code's real
+floor is a **$1** minimum, which is no floor at all. Whether something near 0.001
+strips true dust without cutting real trades is untested, and only worth revisiting
+if live broker order minimums force it.
+
+### Start-date sensitivity is real but not a capacity artifact
+
+Within-year spread of EV across monthly start dates is ~0.672 R and is
+*identical* for `cap10` (0.672), `cap15` (0.672) and `cash_unbounded` (0.677). It
+is small-sample noise — roughly 84 trades per 252-session window drawn from a
+fat-tailed R distribution gives an EV standard error near 0.15–0.25 R — not a
+queueing artifact. No construction policy reduces it.
+
+### Rule for future studies
+
+Choose the metric from the treatment's mechanism before reading any table. If a
+treatment changes trade count, CAGR and total return are the decision metrics and
+EV per trade is a diagnostic. The generated report's headline tables lead with
+ΔEV net R, which is what made this error easy to make twice.

frontend/src/api/admin.ts

@@ -4,7 +4,6 @@ import type {
   AdminUser,
   AlertConfig,
   AlertTestResult,
-  FundamentalsCutoverConfig,
   PipelineReadiness,
   RecommendationConfig,
   ScheduleConfig,
@@ -57,18 +56,6 @@ export function updateSetting(key: string, value: string) {
     .then((r) => r.data);
 }
 
-export function getFundamentalsCutoverSettings() {
-  return apiClient
-    .get<FundamentalsCutoverConfig>('admin/settings/fundamentals-cutover')
-    .then((r) => r.data);
-}
-
-export function updateFundamentalsCutoverSettings(enabled: boolean) {
-  return apiClient
-    .put<FundamentalsCutoverConfig>('admin/settings/fundamentals-cutover', { enabled })
-    .then((r) => r.data);
-}
-
 export function getRecommendationSettings() {
   return apiClient
     .get<RecommendationConfig>('admin/settings/recommendations')
@@ -246,40 +233,6 @@ export interface TriggerJobResponse {
   cadence?: BacktestCadence;
 }
 
-export interface ParityFieldStats {
-  legacy_available: number;
-  candidate_available: number;
-  both_available: number;
-  material_differences: number;
-  median_absolute_delta: number | null;
-  p95_absolute_delta: number | null;
-  max_absolute_delta: number | null;
-}
-
-export interface FundamentalsParityReport {
-  report_version: number;
-  generated_at: string;
-  as_of_date: string;
-  approval_status: string;
-  read_only: boolean;
-  summary: {
-    universe_count: number;
-    legacy_fundamental_score_available: number;
-    candidate_fundamental_score_available: number;
-    fundamental_scores_compared: number;
-    fundamental_score_material_changes: number;
-    fundamental_rank_changes: number;
-    field_stats: Record<string, ParityFieldStats>;
-  };
-  source_runs: Record<string, {
-    run_id: number;
-    status: string;
-    revision: string | null;
-    source_max_date: string | null;
-    completed_at: string | null;
-  } | null>;
-}
-
 export type BacktestTargetModel = 'production_gtl' | 'structural_sr';
 export type BacktestCadence = 'weekly' | 'daily';
 
@@ -306,24 +259,6 @@ export function triggerJob(
     .then((r) => r.data);
 }
 
-export function getFundamentalsParityReport() {
-  return apiClient
-    .get<FundamentalsParityReport | null>('admin/fundamentals-parity')
-    .then((r) => r.data);
-}
-
-export function getFundamentalsParityCsv() {
-  return apiClient
-    .get<{ filename: string; content: string } | null>('admin/fundamentals-parity/csv')
-    .then((r) => r.data);
-}
-
-export function getFundamentalsParityJson() {
-  return apiClient
-    .get<{ filename: string; content: string } | null>('admin/fundamentals-parity/json')
-    .then((r) => r.data);
-}
-
 // System events (operational warnings / errors)
 export interface SystemEvent {
   id: number;

frontend/src/api/ingestion.ts

@@ -14,7 +14,7 @@ export interface FetchDataResult {
 }
 
 /** Provider sources that cost an API call/quota. */
-export type FetchSource = 'ohlcv' | 'sentiment' | 'fundamentals';
+export type FetchSource = 'ohlcv' | 'sentiment';
 /** Source selector: omit → fetch all; array → those providers; 'recompute' → derived only (free). */
 export type FetchSelector = FetchSource[] | 'recompute';
 

frontend/src/components/admin/FundamentalsCutoverSettings.tsx

@@ -1,176 +0,0 @@
-import {
-  useFundamentalsCutoverSettings,
-  useJobs,
-  useTriggerJob,
-  useUpdateFundamentalsCutoverSettings,
-} from '../../hooks/useAdmin';
-import { SkeletonCard } from '../ui/Skeleton';
-
-const SEC_JOB = 'sec_fundamentals_import';
-
-function formatRun(iso: string | null | undefined): string {
-  if (!iso) return 'not run in this process';
-  const minutes = Math.floor((Date.now() - new Date(iso).getTime()) / 60_000);
-  if (minutes < 1) return 'just now';
-  if (minutes < 60) return `${minutes}m ago`;
-  const hours = Math.floor(minutes / 60);
-  return hours < 24 ? `${hours}h ago` : `${Math.floor(hours / 24)}d ago`;
-}
-
-export function FundamentalsCutoverSettings() {
-  const cutover = useFundamentalsCutoverSettings();
-  const update = useUpdateFundamentalsCutoverSettings();
-  const trigger = useTriggerJob();
-  const { data: jobs } = useJobs();
-
-  if (cutover.isLoading) return <SkeletonCard />;
-  if (cutover.isError || !cutover.data) {
-    return (
-      <p className="text-sm text-red-400">
-        {(cutover.error as Error)?.message || 'Failed to load fundamentals data source'}
-      </p>
-    );
-  }
-
-  const enabled = cutover.data.enabled;
-  const secJob = jobs?.find((job) => job.name === SEC_JOB);
-  const runningJob = jobs?.find((job) => job.running);
-  const refreshBlocked = Boolean(runningJob && runningJob.name !== SEC_JOB);
-
-  const changeSource = () => {
-    const next = !enabled;
-    const confirmed = window.confirm(
-      next
-        ? 'Activate SEC + Dolt fundamentals? The next SEC import will replace the legacy cache and mark affected scores stale.'
-        : 'Pause SEC + Dolt cache refreshes? Existing cache values will stay in place; legacy values are not restored automatically.',
-    );
-    if (confirmed) update.mutate(next);
-  };
-
-  return (
-    <section className="glass overflow-hidden" aria-labelledby="fundamentals-source-title">
-      <div className={`h-0.5 ${enabled ? 'bg-gradient-to-r from-sky-500 via-cyan-300 to-emerald-400' : 'bg-white/[0.06]'}`} />
-      <div className="space-y-5 p-5">
-        <div className="flex flex-wrap items-start justify-between gap-3">
-          <div>
-            <div className="flex items-center gap-2">
-              <h3 id="fundamentals-source-title" className="text-sm font-semibold text-gray-200">
-                Fundamentals data source
-              </h3>
-              <span
-                className={`rounded-full border px-2 py-0.5 text-[10px] font-semibold uppercase tracking-[0.14em] ${
-                  enabled
-                    ? 'border-cyan-400/25 bg-cyan-400/10 text-cyan-300'
-                    : 'border-white/10 bg-white/[0.04] text-gray-500'
-                }`}
-              >
-                {enabled ? 'SEC + Dolt active' : 'Legacy cache'}
-              </span>
-            </div>
-            <p className="mt-1 max-w-3xl text-xs leading-relaxed text-gray-500">
-              Controls what repopulates <span className="num text-gray-400">fundamental_data</span>, the
-              compatibility cache used by scoring. SEC filings supply P/E, growth and estimated market
-              cap; Dolt supplies earnings dates and surprises. Everything is derived locally from PostgreSQL.
-            </p>
-          </div>
-        </div>
-
-        <div className="grid grid-cols-[minmax(0,1fr)_5rem_minmax(0,1fr)] items-center gap-3 rounded-xl border border-white/[0.06] bg-black/10 px-4 py-3">
-          <div className={enabled ? 'text-gray-600' : 'text-amber-200/90'}>
-            <div className="num text-[10px] uppercase tracking-[0.16em]">Legacy APIs</div>
-            <div className="mt-0.5 text-[11px]">FMP / Finnhub / Alpha Vantage</div>
-          </div>
-          <div className="relative h-px bg-white/10" aria-hidden="true">
-            <span
-              className={`absolute top-1/2 h-2.5 w-2.5 -translate-y-1/2 rounded-full border-2 border-[#0e120f] transition-all duration-300 ${
-                enabled
-                  ? 'right-0 bg-cyan-300 shadow-[0_0_12px_rgba(103,232,249,0.55)]'
-                  : 'left-0 bg-amber-300'
-              }`}
-            />
-          </div>
-          <div className={`text-right ${enabled ? 'text-cyan-200' : 'text-gray-600'}`}>
-            <div className="num text-[10px] uppercase tracking-[0.16em]">SEC + Dolt</div>
-            <div className="mt-0.5 text-[11px]">Bulk imports → PostgreSQL cache</div>
-          </div>
-        </div>
-
-        <div className="grid gap-4 border-t border-white/[0.06] pt-4 md:grid-cols-2">
-          <div className="flex items-start justify-between gap-4 rounded-xl bg-white/[0.025] p-3.5">
-            <div>
-              <div className="num text-[10px] uppercase tracking-[0.14em] text-gray-600">1 · Source</div>
-              <div className="mt-1 text-sm text-gray-200">Use SEC + Dolt for scoring inputs</div>
-              <p className="mt-1 text-[11px] leading-relaxed text-gray-500">
-                While active, the weekly legacy collector is skipped so it cannot overwrite the new cache.
-              </p>
-            </div>
-            <button
-              type="button"
-              role="switch"
-              aria-checked={enabled}
-              aria-label="Use SEC and Dolt fundamentals"
-              onClick={changeSource}
-              disabled={update.isPending}
-              className={`relative mt-1 inline-flex h-6 w-11 shrink-0 rounded-full border-2 border-transparent transition-colors focus:outline-none focus:ring-2 focus:ring-cyan-400/70 focus:ring-offset-2 focus:ring-offset-[#0e120f] disabled:cursor-wait disabled:opacity-50 ${
-                enabled ? 'bg-gradient-to-r from-sky-500 to-cyan-400' : 'bg-white/10'
-              }`}
-            >
-              <span
-                className={`pointer-events-none inline-block h-5 w-5 rounded-full bg-white shadow transition-transform ${
-                  enabled ? 'translate-x-5' : 'translate-x-0'
-                }`}
-              />
-            </button>
-          </div>
-
-          <div className="rounded-xl bg-white/[0.025] p-3.5">
-            <div className="num text-[10px] uppercase tracking-[0.14em] text-gray-600">2 · Refresh</div>
-            <div className="mt-1 flex flex-wrap items-center justify-between gap-3">
-              <div>
-                <div className="text-sm text-gray-200">Apply the source now</div>
-                <p className="mt-1 text-[11px] text-gray-500">
-                  {secJob?.running
-                    ? 'SEC import and cache refresh are running.'
-                    : secJob?.runtime_message || `Last SEC run: ${formatRun(secJob?.runtime_finished_at)}`}
-                </p>
-              </div>
-              <button
-                type="button"
-                onClick={() => trigger.mutate(SEC_JOB)}
-                disabled={
-                  !enabled ||
-                  trigger.isPending ||
-                  Boolean(secJob?.running) ||
-                  refreshBlocked ||
-                  secJob?.enabled === false
-                }
-                className="btn-primary px-3 py-2 text-xs disabled:cursor-not-allowed disabled:opacity-40"
-              >
-                <span>
-                  {secJob?.running
-                    ? 'Refreshing…'
-                    : trigger.isPending
-                      ? 'Starting…'
-                      : refreshBlocked
-                        ? 'Another job is running'
-                        : 'Run refresh now'}
-                </span>
-              </button>
-            </div>
-            {!enabled && (
-              <p className="mt-2 text-[11px] text-amber-300/70">Activate the source before running the refresh.</p>
-            )}
-            {enabled && secJob?.enabled === false && (
-              <p className="mt-2 text-[11px] text-amber-300/70">Enable the SEC Fundamentals job on the Jobs tab first.</p>
-            )}
-          </div>
-        </div>
-
-        <p className="text-[11px] leading-relaxed text-gray-600">
-          Rollback pauses future writes only. To restore pre-cutover values, use the database backup or
-          pause this source and manually run the legacy collector while its provider keys remain installed.
-        </p>
-      </div>
-    </section>
-  );
-}

frontend/src/components/admin/FundamentalsParityPanel.tsx

@@ -1,156 +0,0 @@
-import { useState } from 'react';
-import {
-  getFundamentalsParityCsv,
-  getFundamentalsParityJson,
-} from '../../api/admin';
-import { useFundamentalsParityReport } from '../../hooks/useAdmin';
-import { SkeletonTable } from '../ui/Skeleton';
-
-const FIELD_LABELS: Record<string, string> = {
-  pe_ratio: 'P/E',
-  revenue_growth: 'Revenue growth',
-  earnings_surprise: 'Earnings surprise',
-};
-
-function downloadText(filename: string, content: string, type: string) {
-  const blob = new Blob([content], { type });
-  const url = URL.createObjectURL(blob);
-  const anchor = document.createElement('a');
-  anchor.href = url;
-  anchor.download = filename;
-  anchor.click();
-  URL.revokeObjectURL(url);
-}
-
-export function FundamentalsParityPanel() {
-  const { data: report, isLoading, isError, error } = useFundamentalsParityReport();
-  const [downloading, setDownloading] = useState(false);
-
-  if (isLoading) return <SkeletonTable rows={2} cols={4} />;
-  if (isError) {
-    return <p className="text-sm text-red-400">{(error as Error).message}</p>;
-  }
-
-  if (!report) {
-    return (
-      <div className="glass p-5">
-        <h3 className="text-sm font-semibold text-gray-200">A5 Fundamentals Parity</h3>
-        <p className="mt-1 text-xs text-gray-500">
-          No report yet. Trigger “Fundamentals Parity Report (read-only)” below.
-        </p>
-      </div>
-    );
-  }
-
-  const summary = report.summary;
-  const generated = new Date(report.generated_at).toLocaleString();
-
-  async function downloadCsv() {
-    setDownloading(true);
-    try {
-      const artifact = await getFundamentalsParityCsv();
-      if (artifact) downloadText(artifact.filename, artifact.content, 'text/csv;charset=utf-8');
-    } finally {
-      setDownloading(false);
-    }
-  }
-
-  async function downloadJson() {
-    setDownloading(true);
-    try {
-      const artifact = await getFundamentalsParityJson();
-      if (artifact) downloadText(artifact.filename, artifact.content, 'application/json;charset=utf-8');
-    } finally {
-      setDownloading(false);
-    }
-  }
-
-  return (
-    <div className="glass p-5 space-y-4">
-      <div className="flex flex-wrap items-start justify-between gap-3">
-        <div>
-          <div className="flex flex-wrap items-center gap-2">
-            <h3 className="text-sm font-semibold text-gray-200">A5 Fundamentals Parity</h3>
-            <span className="rounded-full border border-amber-400/20 bg-amber-400/10 px-2 py-0.5 text-[10px] uppercase tracking-wide text-amber-300">
-              approval pending
-            </span>
-            <span className="rounded-full border border-cyan-400/20 bg-cyan-400/10 px-2 py-0.5 text-[10px] uppercase tracking-wide text-cyan-300">
-              read-only
-            </span>
-          </div>
-          <p className="mt-1 text-xs text-gray-500">
-            Generated {generated} · as of {report.as_of_date} · {summary.universe_count} tracked tickers
-          </p>
-        </div>
-        <div className="flex gap-2">
-          <button
-            type="button"
-            className="rounded border border-white/10 px-3 py-1.5 text-xs text-gray-300 hover:text-white"
-            onClick={downloadJson}
-            disabled={downloading}
-          >
-            Download JSON
-          </button>
-          <button
-            type="button"
-            className="rounded border border-white/10 px-3 py-1.5 text-xs text-gray-300 hover:text-white disabled:opacity-50"
-            onClick={downloadCsv}
-            disabled={downloading}
-          >
-            {downloading ? 'Preparing…' : 'Download CSV'}
-          </button>
-        </div>
-      </div>
-
-      <div className="grid gap-3 sm:grid-cols-2 lg:grid-cols-4">
-        <Summary label="Candidate score coverage" value={`${summary.candidate_fundamental_score_available}/${summary.universe_count}`} />
-        <Summary label="Scores compared" value={summary.fundamental_scores_compared} />
-        <Summary label="Material score moves" value={summary.fundamental_score_material_changes} />
-        <Summary label="Fundamental rank moves" value={summary.fundamental_rank_changes} />
-      </div>
-
-      <div className="overflow-x-auto">
-        <table className="w-full text-left text-xs">
-          <thead className="text-[10px] uppercase tracking-wider text-gray-500">
-            <tr>
-              <th className="pb-2 pr-4 font-medium">Field</th>
-              <th className="pb-2 px-3 font-medium">Legacy</th>
-              <th className="pb-2 px-3 font-medium">Candidate</th>
-              <th className="pb-2 px-3 font-medium">Compared</th>
-              <th className="pb-2 px-3 font-medium">Material</th>
-              <th className="pb-2 pl-3 font-medium">Median |Δ|</th>
-            </tr>
-          </thead>
-          <tbody className="divide-y divide-white/[0.06] text-gray-300">
-            {Object.entries(summary.field_stats).map(([key, stats]) => (
-              <tr key={key}>
-                <td className="py-2.5 pr-4">{FIELD_LABELS[key] ?? key}</td>
-                <td className="py-2.5 px-3 num">{stats.legacy_available}</td>
-                <td className="py-2.5 px-3 num">{stats.candidate_available}</td>
-                <td className="py-2.5 px-3 num">{stats.both_available}</td>
-                <td className="py-2.5 px-3 num">{stats.material_differences}</td>
-                <td className="py-2.5 pl-3 num">
-                  {stats.median_absolute_delta == null ? 'n/a' : stats.median_absolute_delta.toFixed(2)}
-                </td>
-              </tr>
-            ))}
-          </tbody>
-        </table>
-      </div>
-
-      <p className="text-[11px] leading-relaxed text-gray-500">
-        Materiality bands highlight review candidates only. They do not approve a cutover or write fundamentals,
-        scores, rankings, or qualification state.
-      </p>
-    </div>
-  );
-}
-
-function Summary({ label, value }: { label: string; value: string | number }) {
-  return (
-    <div className="rounded-lg border border-white/[0.07] bg-white/[0.025] px-3 py-2.5">
-      <div className="text-[10px] uppercase tracking-wider text-gray-500">{label}</div>
-      <div className="mt-1 num text-lg text-gray-200">{value}</div>
-    </div>
-  );
-}

frontend/src/components/admin/ScheduleSettings.tsx

@@ -8,11 +8,9 @@ const DEFAULTS: ScheduleConfig = {
   schedule_daily_pipeline_cron: '0 2 * * *',
   schedule_dolt_earnings_cron: '30 2 * * *',
   schedule_sec_fundamentals_cron: '0 4 * * *',
-  schedule_fundamentals_parity_cron: '30 5 * * *',
   schedule_near_close_pipeline_cron: '30 15 * * mon-fri',
   schedule_after_close_pipeline_cron: '45 16 * * mon-fri',
   schedule_intraday_pipeline_cron: '0 10-15 * * mon-fri',
-  schedule_fundamentals_cron: '0 1 * * mon',
 };
 
 const FIELDS: { key: keyof ScheduleConfig; label: string; hint: string; mono?: boolean }[] = [
@@ -30,19 +28,13 @@ const FIELDS: { key: keyof ScheduleConfig; label: string; hint: string; mono?: b
   {
     key: 'schedule_dolt_earnings_cron',
     label: 'Dolt earnings',
-    hint: 'Pull and import earnings dates/results daily at 02:30 ET. The activated cache refresh uses these local events.',
+    hint: 'Pull and import earnings dates/results daily at 02:30 ET. The fundamentals cache refresh uses these local events.',
     mono: true,
   },
   {
     key: 'schedule_sec_fundamentals_cron',
     label: 'SEC fundamentals',
-    hint: 'Import tracked-universe SEC facts daily at 04:00 ET and refresh the scoring cache when the cutover is active.',
+    hint: 'Import tracked-universe SEC facts daily at 04:00 ET, then refresh the fundamentals cache scoring reads. Disabling the job stops the SEC fetch only — the local cache refresh still runs.',
-    mono: true,
-  },
-  {
-    key: 'schedule_fundamentals_parity_cron',
-    label: 'Fundamentals parity report',
-    hint: 'Read-only legacy vs SEC/Dolt comparison daily at 05:30 ET, after the bulk imports.',
     mono: true,
   },
   {
@@ -63,12 +55,6 @@ const FIELDS: { key: keyof ScheduleConfig; label: string; hint: string; mono?: b
     hint: 'Refresh prices + resolve outcomes mid-session. Default hourly 10:00–15:00 ET weekdays.',
     mono: true,
   },
-  {
-    key: 'schedule_fundamentals_cron',
-    label: 'Legacy fundamentals (weekly)',
-    hint: 'Fallback provider chain. Automatically skipped while the SEC + Dolt cutover is active.',
-    mono: true,
-  },
 ];
 
 export function ScheduleSettings() {

frontend/src/components/admin/SettingsForm.tsx

@@ -3,8 +3,6 @@ import { useSettings, useUpdateSetting } from '../../hooks/useAdmin';
 import { SkeletonTable } from '../ui/Skeleton';
 import type { SystemSetting } from '../../lib/types';
 
-const MANAGED_SETTINGS = new Set(['fundamental_data_sec_dolt_cutover_enabled']);
-
 export function SettingsForm() {
   const { data: settings, isLoading, isError, error } = useSettings();
   const updateSetting = useUpdateSetting();
@@ -34,11 +32,10 @@ export function SettingsForm() {
   if (isLoading) return <SkeletonTable rows={4} cols={2} />;
   if (isError) return <p className="text-sm text-red-400">{(error as Error)?.message || 'Failed to load settings'}</p>;
   if (!settings || settings.length === 0) return <p className="text-sm text-gray-500">No settings found.</p>;
-  const visibleSettings = settings.filter((setting) => !MANAGED_SETTINGS.has(setting.key));
 
   return (
     <div className="space-y-4">
-      {visibleSettings.map((setting) => (
+      {settings.map((setting) => (
         <div key={setting.key} className="glass p-4 flex flex-wrap items-center gap-3 glass-hover">
           <label className="min-w-[140px] text-sm font-medium text-gray-300">{setting.key}</label>
           {setting.key === 'registration' ? (

frontend/src/hooks/useAdmin.ts

@@ -90,36 +90,6 @@ export function useUpdateSetting() {
   });
 }
 
-export function useFundamentalsCutoverSettings() {
-  return useQuery({
-    queryKey: ['admin', 'fundamentals-cutover'],
-    queryFn: () => adminApi.getFundamentalsCutoverSettings(),
-  });
-}
-
-export function useUpdateFundamentalsCutoverSettings() {
-  const qc = useQueryClient();
-  const { addToast } = useToast();
-
-  return useMutation({
-    mutationFn: (enabled: boolean) =>
-      adminApi.updateFundamentalsCutoverSettings(enabled),
-    onSuccess: (config) => {
-      qc.setQueryData(['admin', 'fundamentals-cutover'], config);
-      qc.invalidateQueries({ queryKey: ['admin', 'settings'] });
-      addToast(
-        config.enabled ? 'success' : 'info',
-        config.enabled
-          ? 'SEC + Dolt fundamentals activated'
-          : 'SEC + Dolt cache refresh paused',
-      );
-    },
-    onError: (error: Error) => {
-      addToast('error', error.message || 'Failed to update fundamentals data source');
-    },
-  });
-}
-
 export function useRecommendationSettings() {
   return useQuery({
     queryKey: ['admin', 'recommendation-settings'],
@@ -346,14 +316,6 @@ export function useJobs() {
   });
 }
 
-export function useFundamentalsParityReport() {
-  return useQuery({
-    queryKey: ['admin', 'fundamentals-parity'],
-    queryFn: () => adminApi.getFundamentalsParityReport(),
-    refetchInterval: 15_000,
-  });
-}
-
 export function usePipelineReadiness() {
   return useQuery({
     queryKey: ['admin', 'pipeline-readiness'],

frontend/src/lib/types.ts

@@ -187,21 +187,15 @@ export interface ActivationConfig {
   exclude_neutral: boolean;
 }
 
-export interface FundamentalsCutoverConfig {
-  enabled: boolean;
-}
-
 // Cron schedule for morning / near-close / after-close / intraday + fundamentals
 export interface ScheduleConfig {
   schedule_timezone: string;
   schedule_daily_pipeline_cron: string;
   schedule_dolt_earnings_cron: string;
   schedule_sec_fundamentals_cron: string;
-  schedule_fundamentals_parity_cron: string;
   schedule_near_close_pipeline_cron: string;
   schedule_after_close_pipeline_cron: string;
   schedule_intraday_pipeline_cron: string;
-  schedule_fundamentals_cron: string;
 }
 
 // Runtime sentiment LLM configuration
@@ -892,7 +886,7 @@ export interface TickerUniverseSetting {
 
 export interface TickerUniverseBootstrapResult {
   universe: TickerUniverse;
-  /** Where the member list came from: wikipedia_sp500 | fmp | cache | seed | … */
+  /** Where the member list came from: wikipedia_sp500 | nasdaq_trader | cache | seed | … */
   source?: string;
   total_universe_symbols: number;
   added: number;

frontend/src/pages/AdminPage.tsx

@@ -5,8 +5,6 @@ import { AlertSettings } from '../components/admin/AlertSettings';
 import { SentimentProviderSettings } from '../components/admin/SentimentProviderSettings';
 import { DataCleanup } from '../components/admin/DataCleanup';
 import { JobControls } from '../components/admin/JobControls';
-import { FundamentalsParityPanel } from '../components/admin/FundamentalsParityPanel';
-import { FundamentalsCutoverSettings } from '../components/admin/FundamentalsCutoverSettings';
 import { PerformanceSettings } from '../components/admin/PerformanceSettings';
 import { PipelineReadinessPanel } from '../components/admin/PipelineReadinessPanel';
 import { SystemEventsPanel } from '../components/admin/SystemEventsPanel';
@@ -37,7 +35,6 @@ export default function AdminPage() {
         {activeTab === 'Tickers' && <TickerManagement />}
         {activeTab === 'Settings' && (
           <div className="space-y-4">
-            <FundamentalsCutoverSettings />
             <ActivationSettings />
             <ExitPolicySettings />
             <PerformanceSettings />
@@ -51,7 +48,6 @@ export default function AdminPage() {
         {activeTab === 'Jobs' && (
           <div className="space-y-4">
             <ScheduleSettings />
-            <FundamentalsParityPanel />
             <JobControls />
             <PipelineReadinessPanel />
           </div>

frontend/src/pages/TickerDetailPage.tsx

@@ -102,7 +102,7 @@ interface DataStatusItem {
   available: boolean;
   timestamp?: string | null;
   timestampLabel?: string | null;
-  selector: FetchSelector;   // what a refresh of this row fetches
+  selector?: FetchSelector;  // what a refresh fetches; omit for rows with no manual refresh
   paid?: boolean;            // provider call that may cost money/quota
 }
 
@@ -138,14 +138,16 @@ function DataFreshnessBar({
           ) : !item.available ? (
             <span className="text-[10px] text-gray-600">no data</span>
           ) : null}
-          <button
+          {item.selector && (
-            onClick={() => onRefresh(item)}
+            <button
-            disabled={busy}
+              onClick={() => onRefresh(item)}
-            title={item.paid ? `Fetch ${item.label} (uses provider quota)` : `Recompute ${item.label}`}
+              disabled={busy}
-            className="ml-0.5 text-gray-500 hover:text-blue-300 disabled:opacity-40 transition-colors"
+              title={item.paid ? `Fetch ${item.label} (uses provider quota)` : `Recompute ${item.label}`}
-          >
+              className="ml-0.5 text-gray-500 hover:text-blue-300 disabled:opacity-40 transition-colors"
-            <RefreshIcon spinning={pendingLabel === item.label} />
+            >
-          </button>
+              <RefreshIcon spinning={pendingLabel === item.label} />
+            </button>
+          )}
           {item.paid && <span className="text-[9px] text-amber-500/70" title="Uses a paid/quota provider call">$</span>}
         </div>
       ))}
@@ -226,11 +228,11 @@ export default function TickerDetailPage() {
       paid: true,
     },
     {
+      // Rebuilt for the whole universe by the nightly SEC + Dolt imports —
+      // there is no per-ticker fetch to offer here.
       label: 'Fundamentals',
       available: !!fundamentals.data && fundamentals.data.fetched_at !== null,
       timestamp: fundamentals.data?.fetched_at,
-      selector: ['fundamentals'] as FetchSelector,
-      paid: true,
     },
     {
       label: 'S/R Levels',
@@ -247,6 +249,7 @@ export default function TickerDetailPage() {
   ], [ohlcv.data, sentiment.data, fundamentals.data, srLevels.data, scores.data]);
 
   const handleRefresh = (item: DataStatusItem) => {
+    if (!item.selector) return;
     setRefreshingLabel(item.label);
     ingestion.mutate(
       { symbol, sources: item.selector },

scripts/backfill_earnings_events.py

@@ -1,515 +0,0 @@
-"""Bulk-only historical earnings backfill for a local SQLite snapshot.
-
-The job uses FMP's date-range earnings-calendar endpoint. One request covers all
-symbols in a date window; per-symbol endpoints are intentionally not available
-in this task runner. Successful windows are committed independently so a later
-run resumes after a daily quota boundary without repeating completed windows.
-
-Example:
-  python scripts/backfill_earnings_events.py --snapshot backtest_snapshots/prod.sqlite \
-      --from-date 2012-01-01 --window-days 30 --limit 250
-"""
-
-from __future__ import annotations
-
-import argparse
-import asyncio
-import json
-import math
-import sys
-from datetime import date, datetime, timedelta, timezone
-from pathlib import Path
-from typing import Any
-
-import httpx
-from sqlalchemy import create_engine, text
-
-ROOT = Path(__file__).resolve().parents[1]
-if str(ROOT) not in sys.path:
-    sys.path.insert(0, str(ROOT))
-
-from app.ssl_bootstrap import bootstrap_ssl  # noqa: E402
-
-bootstrap_ssl()
-
-FMP_STABLE = "https://financialmodelingprep.com/stable"
-EVENTS_DDL = """
-CREATE TABLE IF NOT EXISTS earnings_events (
-    id INTEGER PRIMARY KEY,
-    symbol TEXT NOT NULL,
-    announce_date TEXT NOT NULL,
-    announce_time TEXT,
-    eps_estimate REAL,
-    eps_actual REAL,
-    revenue_estimate REAL,
-    revenue_actual REAL,
-    source TEXT NOT NULL,
-    fetched_at TEXT NOT NULL,
-    UNIQUE(symbol, announce_date)
-)
-"""
-META_DDL = """
-CREATE TABLE IF NOT EXISTS earnings_backfill_meta (
-    symbol TEXT PRIMARY KEY,
-    status TEXT NOT NULL,
-    n_events INTEGER NOT NULL DEFAULT 0,
-    updated_at TEXT NOT NULL,
-    note TEXT
-)
-"""
-WINDOW_DDL = """
-CREATE TABLE IF NOT EXISTS earnings_backfill_windows (
-    from_date TEXT NOT NULL,
-    to_date TEXT NOT NULL,
-    status TEXT NOT NULL,
-    requests INTEGER NOT NULL DEFAULT 0,
-    rows_raw INTEGER NOT NULL DEFAULT 0,
-    rows_universe INTEGER NOT NULL DEFAULT 0,
-    duplicate_rows INTEGER NOT NULL DEFAULT 0,
-    restated_rows INTEGER NOT NULL DEFAULT 0,
-    updated_at TEXT NOT NULL,
-    note TEXT,
-    PRIMARY KEY(from_date, to_date)
-)
-"""
-
-
-def _parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--snapshot", default="backtest_snapshots/prod.sqlite")
-    parser.add_argument("--from-date", default="2012-01-01")
-    parser.add_argument("--to-date", default=None)
-    parser.add_argument("--window-days", type=int, default=30)
-    parser.add_argument("--limit", type=int, default=250)
-    parser.add_argument("--sleep", type=float, default=0.35)
-    parser.add_argument(
-        "--refetch-windows",
-        action="store_true",
-        help="Re-fetch date windows already logged as done.",
-    )
-    return parser.parse_args()
-
-
-def _ensure_tables(engine) -> None:
-    with engine.begin() as conn:
-        conn.execute(text(EVENTS_DDL))
-        conn.execute(text(META_DDL))
-        conn.execute(text(WINDOW_DDL))
-
-
-def _number(value: Any) -> float | None:
-    if value is None or value == "":
-        return None
-    try:
-        result = float(value)
-    except (TypeError, ValueError):
-        return None
-    return result if math.isfinite(result) else None
-
-
-def _normalise_session(value: Any) -> str | None:
-    if value is None:
-        return None
-    cleaned = str(value).strip().lower().replace("_", " ").replace("-", " ")
-    aliases = {
-        "bmo": "bmo",
-        "before market open": "bmo",
-        "before open": "bmo",
-        "amc": "amc",
-        "after market close": "amc",
-        "after close": "amc",
-        "during market hours": "during",
-        "dmh": "during",
-    }
-    return aliases.get(cleaned, cleaned or None)
-
-
-def _parse_bulk_item(item: dict) -> dict | None:
-    symbol = str(item.get("symbol") or "").strip().upper().replace(".", "-")
-    raw_date = item.get("date") or item.get("earningsDate")
-    if not symbol or not raw_date:
-        return None
-    return {
-        "symbol": symbol,
-        "announce_date": str(raw_date)[:10],
-        "announce_time": _normalise_session(
-            item.get("time") or item.get("announceTime")
-        ),
-        "eps_estimate": _number(
-            item.get("epsEstimated")
-            if item.get("epsEstimated") is not None
-            else item.get("estimatedEarning")
-        ),
-        "eps_actual": _number(
-            item.get("epsActual")
-            if item.get("epsActual") is not None
-            else item.get("eps")
-        ),
-        "revenue_estimate": _number(item.get("revenueEstimated")),
-        "revenue_actual": _number(item.get("revenueActual")),
-    }
-
-
-def _windows(start: date, end: date, window_days: int) -> list[tuple[date, date]]:
-    if window_days < 1:
-        raise ValueError("window_days must be positive")
-    result: list[tuple[date, date]] = []
-    cursor = start
-    while cursor <= end:
-        window_end = min(end, cursor + timedelta(days=window_days - 1))
-        result.append((cursor, window_end))
-        cursor = window_end + timedelta(days=1)
-    return result
-
-
-def _dedupe_bulk_rows(rows: list[dict]) -> tuple[list[dict], int, int]:
-    """Prefer the most complete duplicate; use the later row as the tie-break."""
-    fields = (
-        "announce_time",
-        "eps_estimate",
-        "eps_actual",
-        "revenue_estimate",
-        "revenue_actual",
-    )
-    chosen: dict[tuple[str, str], dict] = {}
-    duplicate_extras = 0
-    restated = 0
-    for row in rows:
-        key = (str(row["symbol"]), str(row["announce_date"]))
-        previous = chosen.get(key)
-        if previous is None:
-            chosen[key] = row
-            continue
-        duplicate_extras += 1
-        if any(
-            previous.get(field) is not None
-            and row.get(field) is not None
-            and previous.get(field) != row.get(field)
-            for field in fields
-        ):
-            restated += 1
-        previous_score = sum(previous.get(field) is not None for field in fields)
-        new_score = sum(row.get(field) is not None for field in fields)
-        if new_score >= previous_score:
-            chosen[key] = row
-    return list(chosen.values()), duplicate_extras, restated
-
-
-def _upsert_events(conn, rows: list[dict]) -> int:
-    if not rows:
-        return 0
-    fetched_at = datetime.now(timezone.utc).isoformat()
-    statement = text(
-        """
-        INSERT INTO earnings_events (
-            symbol, announce_date, announce_time, eps_estimate, eps_actual,
-            revenue_estimate, revenue_actual, source, fetched_at
-        ) VALUES (
-            :symbol, :announce_date, :announce_time, :eps_estimate, :eps_actual,
-            :revenue_estimate, :revenue_actual, 'fmp_earnings_calendar', :fetched_at
-        )
-        ON CONFLICT(symbol, announce_date) DO UPDATE SET
-            announce_time=COALESCE(excluded.announce_time, earnings_events.announce_time),
-            eps_estimate=COALESCE(excluded.eps_estimate, earnings_events.eps_estimate),
-            eps_actual=COALESCE(excluded.eps_actual, earnings_events.eps_actual),
-            revenue_estimate=COALESCE(excluded.revenue_estimate, earnings_events.revenue_estimate),
-            revenue_actual=COALESCE(excluded.revenue_actual, earnings_events.revenue_actual),
-            source=excluded.source,
-            fetched_at=excluded.fetched_at
-        """
-    )
-    conn.execute(statement, [{**row, "fetched_at": fetched_at} for row in rows])
-    return len(rows)
-
-
-async def _fetch_bulk_window(
-    client: httpx.AsyncClient, api_key: str, start: date, end: date
-) -> tuple[list[dict], int, str | None]:
-    response = await client.get(
-        f"{FMP_STABLE}/earnings-calendar",
-        params={"from": start.isoformat(), "to": end.isoformat(), "apikey": api_key},
-    )
-    if response.status_code in (402, 403):
-        return [], response.status_code, "bulk_endpoint_unavailable"
-    if response.status_code == 429:
-        return [], response.status_code, "daily_limit_reached"
-    response.raise_for_status()
-    payload = response.json()
-    if not isinstance(payload, list):
-        return [], response.status_code, f"unexpected_payload:{type(payload).__name__}"
-    rows = []
-    for item in payload:
-        if isinstance(item, dict):
-            parsed = _parse_bulk_item(item)
-            if parsed:
-                rows.append(parsed)
-    return rows, response.status_code, None
-
-
-def _write_window_status(
-    engine,
-    *,
-    start: date,
-    end: date,
-    status: str,
-    raw_n: int = 0,
-    universe_n: int = 0,
-    duplicate_n: int = 0,
-    restated_n: int = 0,
-    note: str | None = None,
-) -> None:
-    with engine.begin() as conn:
-        conn.execute(
-            text(
-                """
-                INSERT INTO earnings_backfill_windows(
-                    from_date, to_date, status, requests, rows_raw, rows_universe,
-                    duplicate_rows, restated_rows, updated_at, note
-                ) VALUES (:a, :b, :status, 1, :raw, :uni, :dup, :rest, :now, :note)
-                ON CONFLICT(from_date, to_date) DO UPDATE SET
-                    status=excluded.status,
-                    requests=earnings_backfill_windows.requests + 1,
-                    rows_raw=excluded.rows_raw,
-                    rows_universe=excluded.rows_universe,
-                    duplicate_rows=excluded.duplicate_rows,
-                    restated_rows=excluded.restated_rows,
-                    updated_at=excluded.updated_at,
-                    note=excluded.note
-                """
-            ),
-            {
-                "a": start.isoformat(),
-                "b": end.isoformat(),
-                "status": status,
-                "raw": raw_n,
-                "uni": universe_n,
-                "dup": duplicate_n,
-                "rest": restated_n,
-                "now": datetime.now(timezone.utc).isoformat(),
-                "note": note,
-            },
-        )
-
-
-async def _main() -> None:
-    args = _parse_args()
-    snapshot = Path(args.snapshot)
-    if not snapshot.exists():
-        raise SystemExit(f"Snapshot not found: {snapshot}")
-
-    from app.config import settings
-
-    if not settings.fmp_api_key:
-        raise SystemExit("FMP_API_KEY required")
-    start = date.fromisoformat(args.from_date)
-    end = date.fromisoformat(args.to_date) if args.to_date else date.today()
-    if start > end:
-        raise SystemExit("--from-date must not be after --to-date")
-
-    engine = create_engine(f"sqlite:///{snapshot.resolve().as_posix()}", future=True)
-    _ensure_tables(engine)
-    all_windows = _windows(start, end, int(args.window_days))
-    with engine.connect() as conn:
-        symbols = [
-            str(row[0]).upper().replace(".", "-")
-            for row in conn.execute(text("SELECT symbol FROM tickers ORDER BY symbol"))
-        ]
-        completed = {
-            (str(row[0]), str(row[1]))
-            for row in conn.execute(
-                text(
-                    "SELECT from_date, to_date FROM earnings_backfill_windows "
-                    "WHERE status='done'"
-                )
-            )
-        }
-    pending = [
-        window
-        for window in all_windows
-        if args.refetch_windows
-        or (window[0].isoformat(), window[1].isoformat()) not in completed
-    ]
-    universe = set(symbols)
-    print(f"Snapshot: {snapshot}")
-    print(f"Universe: {len(symbols)} symbols")
-    print(f"Window: {start} -> {end}")
-    print(
-        f"Bulk windows: {len(all_windows)} total; "
-        f"{len(all_windows) - len(pending)} done; {len(pending)} pending"
-    )
-    print("Provider: FMP bulk earnings-calendar only")
-
-    requests_this_run = 0
-    rows_upserted = 0
-    duplicate_rows = 0
-    restated_rows = 0
-    stop_note: str | None = None
-    async with httpx.AsyncClient(timeout=60.0) as client:
-        for index, (window_start, window_end) in enumerate(pending, 1):
-            if requests_this_run >= int(args.limit):
-                stop_note = "request_budget_exhausted"
-                break
-            try:
-                raw_rows, status_code, error = await _fetch_bulk_window(
-                    client, settings.fmp_api_key, window_start, window_end
-                )
-            except Exception as exc:
-                raw_rows, status_code = [], 0
-                error = f"request_error:{type(exc).__name__}:{exc}"
-            requests_this_run += 1
-            if error:
-                _write_window_status(
-                    engine,
-                    start=window_start,
-                    end=window_end,
-                    status="error",
-                    note=f"http={status_code} {error}"[:300],
-                )
-                stop_note = error
-                print(
-                    f"STOP {window_start}..{window_end}: {error} "
-                    f"(http={status_code}, request={requests_this_run})"
-                )
-                break
-
-            in_universe = [row for row in raw_rows if row["symbol"] in universe]
-            deduped, duplicate_n, restated_n = _dedupe_bulk_rows(in_universe)
-            with engine.begin() as conn:
-                rows_upserted += _upsert_events(conn, deduped)
-            _write_window_status(
-                engine,
-                start=window_start,
-                end=window_end,
-                status="done",
-                raw_n=len(raw_rows),
-                universe_n=len(deduped),
-                duplicate_n=duplicate_n,
-                restated_n=restated_n,
-                note="bulk",
-            )
-            duplicate_rows += duplicate_n
-            restated_rows += restated_n
-            if index == 1 or index % 10 == 0 or index == len(pending):
-                print(
-                    f"progress windows={index}/{len(pending)} "
-                    f"requests={requests_this_run}/{args.limit} "
-                    f"last={window_start}..{window_end} rows={len(deduped)}"
-                )
-            if args.sleep > 0:
-                await asyncio.sleep(float(args.sleep))
-
-    with engine.begin() as conn:
-        windows_done = int(
-            conn.execute(
-                text(
-                    "SELECT COUNT(*) FROM earnings_backfill_windows "
-                    "WHERE status='done' AND from_date >= :a AND to_date <= :b"
-                ),
-                {"a": start.isoformat(), "b": end.isoformat()},
-            ).scalar_one()
-        )
-        complete = windows_done >= len(all_windows)
-        if complete:
-            now = datetime.now(timezone.utc).isoformat()
-            for symbol in symbols:
-                count = int(
-                    conn.execute(
-                        text(
-                            "SELECT COUNT(*) FROM earnings_events "
-                            "WHERE symbol=:symbol AND announce_date BETWEEN :a AND :b"
-                        ),
-                        {"symbol": symbol, "a": start.isoformat(), "b": end.isoformat()},
-                    ).scalar_one()
-                )
-                conn.execute(
-                    text(
-                        """
-                        INSERT INTO earnings_backfill_meta(symbol, status, n_events, updated_at, note)
-                        VALUES (:symbol, 'done', :count, :now, 'bulk_complete')
-                        ON CONFLICT(symbol) DO UPDATE SET
-                            status='done', n_events=excluded.n_events,
-                            updated_at=excluded.updated_at, note=excluded.note
-                        """
-                    ),
-                    {"symbol": symbol, "count": count, "now": now},
-                )
-        params = {"a": start.isoformat(), "b": end.isoformat()}
-        total_events = int(
-            conn.execute(
-                text(
-                    "SELECT COUNT(*) FROM earnings_events "
-                    "WHERE symbol IN (SELECT symbol FROM tickers) "
-                    "AND announce_date BETWEEN :a AND :b"
-                ),
-                params,
-            ).scalar_one()
-        )
-        paired_events = int(
-            conn.execute(
-                text(
-                    "SELECT COUNT(*) FROM earnings_events "
-                    "WHERE symbol IN (SELECT symbol FROM tickers) "
-                    "AND announce_date BETWEEN :a AND :b "
-                    "AND eps_actual IS NOT NULL AND eps_estimate IS NOT NULL"
-                ),
-                params,
-            ).scalar_one()
-        )
-        date_range = conn.execute(
-            text(
-                "SELECT MIN(announce_date), MAX(announce_date) FROM earnings_events "
-                "WHERE symbol IN (SELECT symbol FROM tickers) "
-                "AND announce_date BETWEEN :a AND :b"
-            ),
-            params,
-        ).fetchone()
-        done_symbols = int(
-            conn.execute(
-                text("SELECT COUNT(*) FROM earnings_backfill_meta WHERE status='done'")
-            ).scalar_one()
-        )
-        totals = conn.execute(
-            text(
-                "SELECT COALESCE(SUM(requests),0), COALESCE(SUM(duplicate_rows),0), "
-                "COALESCE(SUM(restated_rows),0) FROM earnings_backfill_windows "
-                "WHERE from_date >= :a AND to_date <= :b"
-            ),
-            params,
-        ).fetchone()
-
-    summary = {
-        "mode": "fmp_bulk_date_range_only",
-        "window": {"from": start.isoformat(), "to": end.isoformat()},
-        "window_days": int(args.window_days),
-        "bulk_windows_total": len(all_windows),
-        "bulk_windows_done": windows_done,
-        "bulk_requests_this_run": requests_this_run,
-        "bulk_requests_logged_total": int(totals[0]),
-        "rows_upserted_this_run": rows_upserted,
-        "duplicate_rows_this_run": duplicate_rows,
-        "restated_rows_this_run": restated_rows,
-        "duplicate_rows_logged_total": int(totals[1]),
-        "restated_rows_logged_total": int(totals[2]),
-        "dedupe_policy": (
-            "UNIQUE(symbol, announce_date); prefer more non-null fields, then "
-            "the provider's later occurrence; non-null bulk fields replace prior "
-            "values while null bulk fields retain existing values"
-        ),
-        "events_in_window": total_events,
-        "events_with_actual_and_estimate": paired_events,
-        "symbols_done": done_symbols,
-        "symbols_universe": len(symbols),
-        "announce_date_range": {"min": date_range[0], "max": date_range[1]},
-        "request_budget": int(args.limit),
-        "stop_note": stop_note,
-        "complete": complete,
-    }
-    output = Path("reports/earnings-backfill-status.json")
-    output.parent.mkdir(parents=True, exist_ok=True)
-    output.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8")
-    print(json.dumps(summary, indent=2))
-    print(f"Wrote {output}")
-
-
-if __name__ == "__main__":
-    asyncio.run(_main())

scripts/extend_snapshot_universe.py

@@ -128,11 +128,10 @@ async def _resolve_pool() -> tuple[list[str], dict[str, str]]:
     """Return sorted unique symbols and source labels.
 
     Offline-safe: does **not** use production Postgres or SystemSetting cache
-    (those require a schema). Public sources first, then FMP, then seeds.
+    (those require a schema). Public sources first, then seeds.
     """
     from app.services.ticker_universe_service import (
         _SEED_UNIVERSES,
-        _fetch_universe_symbols_from_fmp,
         _fetch_universe_symbols_from_public,
         _normalise_symbols,
     )
@@ -150,19 +149,11 @@ async def _resolve_pool() -> tuple[list[str], dict[str, str]]:
         cleaned = _normalise_symbols(public_symbols)
         if cleaned:
             src = public_source or "public"
-        else:
+        elif public_failures:
-            if public_failures:
+            print(
-                print(
+                f"  WARNING: public fetch {universe}: "
-                    f"  WARNING: public fetch {universe}: "
+                f"{'; '.join(public_failures[:3])}"
-                    f"{'; '.join(public_failures[:3])}"
+            )
-                )
-            try:
-                fmp_symbols = await _fetch_universe_symbols_from_fmp(universe)
-                cleaned = _normalise_symbols(fmp_symbols)
-                if cleaned:
-                    src = "fmp"
-            except Exception as exc:
-                print(f"  WARNING: FMP fetch {universe}: {exc}")
 
         if not cleaned:
             cleaned = _normalise_symbols(_SEED_UNIVERSES.get(universe, []))

scripts/portfolio_capacity_research.py

@@ -1,764 +0,0 @@
-'''Pure helpers for the focused daily portfolio-capacity research matrix.'''
-
-from __future__ import annotations
-
-import hashlib
-import math
-import random
-import statistics
-from collections import defaultdict
-from datetime import date, timedelta
-from typing import Any, Iterable
-
-
-ARMS: tuple[dict[str, Any], ...] = (
-    {
-        'id': 'cap10_incumbent',
-        'label': 'Cap 10, arrival-order incumbents',
-        'max_positions': 10,
-        'min_initial_risk_fraction': None,
-        'weekly_top_n_rebalance': False,
-    },
-    {
-        'id': 'cash_unbounded',
-        'label': 'Cash-constrained, no count cap',
-        'max_positions': None,
-        'min_initial_risk_fraction': 0.005,
-        'weekly_top_n_rebalance': False,
-    },
-    {
-        'id': 'cap10_weekly_top10',
-        'label': 'Cap 10, weekly current-rank top 10',
-        'max_positions': 10,
-        'min_initial_risk_fraction': None,
-        'weekly_top_n_rebalance': True,
-    },
-    {
-        'id': 'cap15_incumbent',
-        'label': 'Cap 15, arrival-order incumbents',
-        'max_positions': 15,
-        'min_initial_risk_fraction': None,
-        'weekly_top_n_rebalance': False,
-    },
-)
-
-ARM_BY_ID = {arm['id']: arm for arm in ARMS}
-RISK_FLOOR_ARMS: tuple[dict[str, Any], ...] = (
-    ARMS[0],
-    {
-        'id': 'cap10_min_risk_005',
-        'label': 'Cap 10, 0.5% minimum effective initial risk',
-        'max_positions': 10,
-        'min_initial_risk_fraction': 0.005,
-        'weekly_top_n_rebalance': False,
-    },
-)
-COSTS_PER_SIDE_PCT = (0.1, 0.2)
-ANCHOR_YEARS = tuple(range(2019, 2026))
-SCORING_SESSIONS = 504
-MEASUREMENT_SESSIONS = 252
-RESIDUAL_BENCHMARK_SESSIONS = 252
-WARM_SEED_MIN_OFFSET = 63
-WARM_SEED_MAX_OFFSET = 126
-BOOTSTRAP_REPLICATES = 10_000
-BOOTSTRAP_SEED = 20260805
-PRIMARY_METRICS = (
-    'ev_net_r',
-    'calmar',
-    'profit_factor',
-    'gain_to_pain',
-    'sortino',
-)
-PAIRED_METRICS = (
-    *PRIMARY_METRICS,
-    'cagr_pct',
-    'max_drawdown_pct',
-    'total_return_pct',
-    'sharpe',
-)
-
-
-def _end_exclusive(
-    sessions: list[date], start_index: int, count: int
-) -> date:
-    end_index = start_index + count
-    if end_index < len(sessions):
-        return sessions[end_index]
-    return sessions[-1] + timedelta(days=1)
-
-
-def build_cohort_manifest(session_dates: Iterable[date]) -> dict[str, Any]:
-    sessions = sorted(set(session_dates))
-    minimum = RESIDUAL_BENCHMARK_SESSIONS + SCORING_SESSIONS
-    if len(sessions) <= minimum + MEASUREMENT_SESSIONS:
-        raise ValueError('Snapshot is too short for the frozen cohort design')
-
-    index_of = {session: index for index, session in enumerate(sessions)}
-    first_eligible_index = RESIDUAL_BENCHMARK_SESSIONS - 1 + SCORING_SESSIONS
-    last_eligible_index = len(sessions) - MEASUREMENT_SESSIONS
-
-    first_by_month: dict[tuple[int, int], date] = {}
-    for session in sessions:
-        first_by_month.setdefault((session.year, session.month), session)
-
-    empty: list[dict[str, Any]] = []
-    for (year, month), session in sorted(first_by_month.items()):
-        index = index_of[session]
-        if year not in ANCHOR_YEARS:
-            continue
-        if index < first_eligible_index or index > last_eligible_index:
-            continue
-        empty.append({
-            'protocol': 'empty_book',
-            'path_id': f'empty-{year:04d}-{month:02d}',
-            'cluster': year,
-            'simulation_start': session.isoformat(),
-            'measurement_start': session.isoformat(),
-            'hard_end_exclusive': _end_exclusive(
-                sessions, index, MEASUREMENT_SESSIONS
-            ).isoformat(),
-        })
-
-    first_by_year: dict[int, date] = {}
-    for session in sessions:
-        first_by_year.setdefault(session.year, session)
-
-    warm: list[dict[str, Any]] = []
-    warm_seed_counts: dict[str, int] = {}
-    for year in ANCHOR_YEARS:
-        anchor = first_by_year.get(year)
-        if anchor is None:
-            continue
-        anchor_index = index_of[anchor]
-        if (
-            anchor_index < WARM_SEED_MAX_OFFSET
-            or anchor_index > last_eligible_index
-        ):
-            continue
-        seed_window = sessions[
-            anchor_index - WARM_SEED_MAX_OFFSET:
-            anchor_index - WARM_SEED_MIN_OFFSET + 1
-        ]
-        first_by_iso_week: dict[tuple[int, int], date] = {}
-        for session in seed_window:
-            iso = session.isocalendar()
-            first_by_iso_week.setdefault((iso.year, iso.week), session)
-        seeds = sorted(first_by_iso_week.values())
-        warm_seed_counts[str(year)] = len(seeds)
-        for seed_index, seed in enumerate(seeds, 1):
-            warm.append({
-                'protocol': 'warm_book',
-                'path_id': f'warm-{year}-seed-{seed_index:02d}',
-                'cluster': year,
-                'simulation_start': seed.isoformat(),
-                'measurement_start': anchor.isoformat(),
-                'hard_end_exclusive': _end_exclusive(
-                    sessions, anchor_index, MEASUREMENT_SESSIONS
-                ).isoformat(),
-                'seed_offset_sessions': anchor_index - index_of[seed],
-            })
-
-    return {
-        'snapshot_first_session': sessions[0].isoformat(),
-        'snapshot_last_session': sessions[-1].isoformat(),
-        'session_count': len(sessions),
-        'expected_clusters': list(ANCHOR_YEARS),
-        'empty_book': empty,
-        'warm_book': warm,
-        'empty_cluster_counts': dict(
-            sorted(
-                (
-                    str(year),
-                    sum(1 for row in empty if row['cluster'] == year),
-                )
-                for year in {row['cluster'] for row in empty}
-            )
-        ),
-        'warm_seed_counts': warm_seed_counts,
-        'empty_cluster_count': len({row['cluster'] for row in empty}),
-        'warm_cluster_count': len({row['cluster'] for row in warm}),
-    }
-
-
-def validate_cohort_manifest(manifest: dict[str, Any]) -> list[str]:
-    errors: list[str] = []
-    expected = set(ANCHOR_YEARS)
-    empty_clusters = {row['cluster'] for row in manifest['empty_book']}
-    warm_clusters = {row['cluster'] for row in manifest['warm_book']}
-    if empty_clusters != expected:
-        errors.append(
-            f'empty-book clusters {sorted(empty_clusters)} != {sorted(expected)}'
-        )
-    if warm_clusters != expected:
-        errors.append(
-            f'warm-book clusters {sorted(warm_clusters)} != {sorted(expected)}'
-        )
-    for year in ANCHOR_YEARS:
-        seed_count = int(manifest['warm_seed_counts'].get(str(year), 0))
-        if seed_count < 12:
-            errors.append(f'warm anchor {year} has only {seed_count} seeds')
-    return errors
-
-
-def build_cells(
-    manifest: dict[str, Any],
-    *,
-    arms: tuple[dict[str, Any], ...] = ARMS,
-    protocols: tuple[str, ...] = ('empty_book', 'warm_book'),
-    costs: tuple[float, ...] = COSTS_PER_SIDE_PCT,
-) -> list[dict[str, Any]]:
-    paths = [
-        path
-        for protocol in protocols
-        for path in manifest[protocol]
-    ]
-    cells: list[dict[str, Any]] = []
-    for cost in costs:
-        for path in paths:
-            for arm in arms:
-                cell_id = (
-                    f'{arm["id"]}|{path["protocol"]}|{path["path_id"]}'
-                    f'|cost={cost:.1f}'
-                )
-                cells.append({
-                    **path,
-                    'cell_id': cell_id,
-                    'arm_id': arm['id'],
-                    'cost_per_side_pct': cost,
-                })
-    return cells
-
-
-def percentile(values: Iterable[float], probability: float) -> float | None:
-    ordered = sorted(float(value) for value in values if value is not None)
-    if not ordered:
-        return None
-    if len(ordered) == 1:
-        return ordered[0]
-    location = (len(ordered) - 1) * probability
-    lower = math.floor(location)
-    upper = math.ceil(location)
-    if lower == upper:
-        return ordered[lower]
-    weight = location - lower
-    return ordered[lower] * (1.0 - weight) + ordered[upper] * weight
-
-
-def iqr(values: Iterable[float]) -> float | None:
-    clean: list[float] = []
-    for value in values:
-        if value is None:
-            continue
-        parsed = float(value)
-        if math.isfinite(parsed):
-            clean.append(parsed)
-    q25 = percentile(clean, 0.25)
-    q75 = percentile(clean, 0.75)
-    if q25 is None or q75 is None:
-        return None
-    return q75 - q25
-
-
-def median(values: Iterable[float | None]) -> float | None:
-    clean = [float(value) for value in values if value is not None]
-    return statistics.median(clean) if clean else None
-
-
-def _safe_ratio(numerator: float | None, denominator: float | None) -> float | None:
-    if numerator is None or denominator is None:
-        return None
-    if abs(denominator) <= 1e-12:
-        return 1.0 if abs(numerator) <= 1e-12 else None
-    return numerator / denominator
-
-
-def _stable_seed(*parts: object) -> int:
-    digest = hashlib.sha256('|'.join(map(str, parts)).encode('utf-8')).digest()
-    return BOOTSTRAP_SEED + int.from_bytes(digest[:4], 'big')
-
-
-def bootstrap_median_interval(
-    values: Iterable[float | None],
-    *,
-    seed_parts: tuple[object, ...],
-    replicates: int = BOOTSTRAP_REPLICATES,
-) -> dict[str, float | int | None]:
-    clean = [float(value) for value in values if value is not None]
-    if not clean:
-        return {'n': 0, 'point': None, 'p05': None, 'p95': None}
-    rng = random.Random(_stable_seed(*seed_parts))
-    draws = [
-        statistics.median(rng.choices(clean, k=len(clean)))
-        for _ in range(replicates)
-    ]
-    return {
-        'n': len(clean),
-        'replicates': replicates,
-        'point': statistics.median(clean),
-        'p05': percentile(draws, 0.05),
-        'p95': percentile(draws, 0.95),
-    }
-
-
-def _monthly_returns(
-    equity_curve: list[dict[str, Any]], base_equity: float
-) -> list[float]:
-    month_ends: dict[tuple[int, int], float] = {}
-    for point in equity_curve:
-        point_date = date.fromisoformat(str(point['date']))
-        month_ends[(point_date.year, point_date.month)] = float(point['equity'])
-    previous = float(base_equity)
-    returns: list[float] = []
-    for month in sorted(month_ends):
-        equity = month_ends[month]
-        if previous > 0:
-            returns.append(equity / previous - 1.0)
-        previous = equity
-    return returns
-
-
-def _time_underwater(equities: list[float]) -> tuple[int, float]:
-    peak = float('-inf')
-    current = 0
-    longest = 0
-    underwater = 0
-    for equity in equities:
-        peak = max(peak, equity)
-        if peak > 0 and equity < peak - 1e-9:
-            current += 1
-            underwater += 1
-            longest = max(longest, current)
-        else:
-            current = 0
-    percentage = underwater / len(equities) * 100.0 if equities else 0.0
-    return longest, percentage
-
-
-def summarize_simulation(sim: dict[str, Any]) -> dict[str, Any]:
-    trades = list(sim.get('trade_details') or [])
-    equity_curve = list(sim.get('equity_curve') or [])
-    net_rs = [float(trade['net_r']) for trade in trades]
-    positive_rs = [value for value in net_rs if value > 0]
-    negative_rs = [value for value in net_rs if value < 0]
-    ev_net_r = statistics.fmean(net_rs) if net_rs else None
-    profit_factor = (
-        sum(positive_rs) / abs(sum(negative_rs))
-        if negative_rs
-        else None
-    )
-
-    base_equity = float(
-        sim.get('measurement_start_equity') or sim.get('starting_capital') or 0.0
-    )
-    curve_equities = [float(point['equity']) for point in equity_curve]
-    daily_equities = [base_equity, *curve_equities]
-    daily_returns = [
-        current / previous - 1.0
-        for previous, current in zip(daily_equities, daily_equities[1:])
-        if previous > 0
-    ]
-    downside_deviation = (
-        math.sqrt(
-            statistics.fmean(min(value, 0.0) ** 2 for value in daily_returns)
-        )
-        if daily_returns
-        else None
-    )
-    sortino = (
-        statistics.fmean(daily_returns) / downside_deviation * math.sqrt(252.0)
-        if downside_deviation is not None and downside_deviation > 0
-        else None
-    )
-    monthly_returns = _monthly_returns(equity_curve, base_equity)
-    negative_monthly = sum(value for value in monthly_returns if value < 0)
-    gain_to_pain = (
-        sum(monthly_returns) / abs(negative_monthly)
-        if negative_monthly < 0
-        else None
-    )
-    longest_underwater, underwater_pct = _time_underwater(daily_equities)
-
-    transaction_cost = sum(
-        float(trade.get('transaction_cost') or 0.0) for trade in trades
-    )
-    traded_notional = sum(
-        float(trade.get('shares') or 0.0)
-        * (float(trade.get('entry') or 0.0) + float(trade.get('fill') or 0.0))
-        for trade in trades
-    )
-    turnover_multiple = (
-        traded_notional / base_equity if base_equity > 0 else None
-    )
-
-    ordered_rs = sorted(net_rs, reverse=True)
-    ev_without_best: dict[str, float | None] = {}
-    for count in (1, 5, 10):
-        remaining = ordered_rs[count:]
-        ev_without_best[str(count)] = (
-            statistics.fmean(remaining) if remaining else None
-        )
-
-    events = list(sim.get('weekly_rebalance_events') or [])
-    entrant_sizes = [int(event['fresh_entrant_pool']) for event in events]
-    eligible_sizes = [
-        int(event['rank_eligible_entrant_pool']) for event in events
-    ]
-    replacements = [int(event['replacements']) for event in events]
-
-    capacity_skips = int(
-        sim.get('measurement_skipped_book_full', sim.get('skipped_book_full', 0))
-    )
-    opened = int(sim.get('opened_positions', sim.get('trades', 0)))
-    capacity_opportunities = opened + capacity_skips
-
-    result = {
-        'start_date': sim.get('start_date'),
-        'end_date': sim.get('end_date'),
-        'simulation_start_date': sim.get('simulation_start_date'),
-        'measurement_start_equity': base_equity,
-        'measurement_start_positions': sim.get('measurement_start_positions', 0),
-        'trades': len(trades),
-        'ev_net_r': ev_net_r,
-        'profit_factor': profit_factor,
-        'gain_to_pain': gain_to_pain,
-        'sortino': sortino,
-        'ev_without_best': ev_without_best,
-        'total_return_pct': sim.get('total_return_pct'),
-        'cagr_pct': sim.get('cagr_pct'),
-        'max_drawdown_pct': sim.get('max_drawdown_pct'),
-        'calmar': sim.get('calmar'),
-        'sharpe': sim.get('sharpe'),
-        'win_rate': sim.get('win_rate'),
-        'avg_hold_days': sim.get('avg_hold_days'),
-        'longest_underwater_sessions': longest_underwater,
-        'underwater_pct': underwater_pct,
-        'transaction_cost': transaction_cost,
-        'turnover_multiple': turnover_multiple,
-        'skipped_book_full': capacity_skips,
-        'opened_positions': opened,
-        'capacity_opportunities': capacity_opportunities,
-        'blocked_fraction': (
-            capacity_skips / capacity_opportunities
-            if capacity_opportunities
-            else 0.0
-        ),
-        'skipped_min_initial_risk': int(
-            sim.get('measurement_skipped_min_initial_risk', 0)
-        ),
-        'avg_positions': sim.get('avg_positions'),
-        'peak_positions': sim.get('peak_positions'),
-        'sessions_at_capacity': sim.get('sessions_at_capacity'),
-        'sessions_measured': sim.get('sessions_measured'),
-        'avg_cash_pct': sim.get('avg_cash_pct'),
-        'avg_gross_exposure_pct': sim.get('avg_gross_exposure_pct'),
-        'exit_reasons': sim.get('exit_reasons'),
-    }
-    if events:
-        result['weekly_rebalance'] = {
-            'events': len(events),
-            'zero_entrant_fraction': (
-                sum(1 for value in entrant_sizes if value == 0) / len(events)
-            ),
-            'entrant_pool_mean': statistics.fmean(entrant_sizes),
-            'entrant_pool_median': statistics.median(entrant_sizes),
-            'entrant_pool_p90': percentile(entrant_sizes, 0.9),
-            'eligible_pool_mean': statistics.fmean(eligible_sizes),
-            'replacements': sum(replacements),
-            'weekly_rank_rejected_entries': int(
-                sim.get('weekly_rank_rejected_entries', 0)
-            ),
-            'reentries_within_5_sessions': int(
-                sim.get('rebalance_reentries_within_5_sessions', 0)
-            ),
-            'reentries_within_10_sessions': int(
-                sim.get('rebalance_reentries_within_10_sessions', 0)
-            ),
-            'reentries_within_20_sessions': int(
-                sim.get('rebalance_reentries_within_20_sessions', 0)
-            ),
-        }
-    return result
-
-
-def _cluster_rows(
-    cells: list[dict[str, Any]],
-    *,
-    arm_id: str,
-    protocol: str,
-    cost: float,
-) -> list[dict[str, Any]]:
-    treatment = {
-        row['path_id']: row
-        for row in cells
-        if row['arm_id'] == arm_id
-        and row['protocol'] == protocol
-        and float(row['cost_per_side_pct']) == cost
-    }
-    control = {
-        row['path_id']: row
-        for row in cells
-        if row['arm_id'] == 'cap10_incumbent'
-        and row['protocol'] == protocol
-        and float(row['cost_per_side_pct']) == cost
-    }
-    shared_paths = sorted(set(treatment) & set(control))
-    by_cluster: dict[int, list[tuple[dict, dict]]] = defaultdict(list)
-    for path_id in shared_paths:
-        row = treatment[path_id]
-        by_cluster[int(row['cluster'])].append((row, control[path_id]))
-
-    summaries: list[dict[str, Any]] = []
-    for cluster, pairs in sorted(by_cluster.items()):
-        metrics: dict[str, Any] = {}
-        for metric in PAIRED_METRICS:
-            arm_values = [
-                pair[0]['metrics'].get(metric)
-                for pair in pairs
-                if pair[0]['metrics'].get(metric) is not None
-                and math.isfinite(float(pair[0]['metrics'][metric]))
-            ]
-            control_values = [
-                pair[1]['metrics'].get(metric)
-                for pair in pairs
-                if pair[1]['metrics'].get(metric) is not None
-                and math.isfinite(float(pair[1]['metrics'][metric]))
-            ]
-            deltas = [
-                float(arm['metrics'][metric])
-                - float(base['metrics'][metric])
-                for arm, base in pairs
-                if arm['metrics'].get(metric) is not None
-                and base['metrics'].get(metric) is not None
-                and math.isfinite(float(arm['metrics'][metric]))
-                and math.isfinite(float(base['metrics'][metric]))
-            ]
-            arm_median = median(arm_values)
-            control_median = median(control_values)
-            metrics[metric] = {
-                'arm_median': arm_median,
-                'control_median': control_median,
-                'paired_delta_median': median(deltas),
-                'arm_control_ratio': _safe_ratio(
-                    arm_median, control_median
-                ),
-                'paired_paths': len(deltas),
-            }
-        summaries.append({
-            'cluster': cluster,
-            'paths': len(pairs),
-            'metrics': metrics,
-        })
-    return summaries
-
-
-def aggregate_results(
-    cells: list[dict[str, Any]],
-    *,
-    arms: tuple[dict[str, Any], ...] = ARMS,
-    protocols: tuple[str, ...] = ('empty_book', 'warm_book'),
-    costs: tuple[float, ...] = COSTS_PER_SIDE_PCT,
-    include_warm_dispersion: bool = True,
-) -> dict[str, Any]:
-    paired: list[dict[str, Any]] = []
-    path_distributions: list[dict[str, Any]] = []
-    for cost in costs:
-        for protocol in protocols:
-            control_by_path = {
-                row['path_id']: row
-                for row in cells
-                if row['arm_id'] == 'cap10_incumbent'
-                and row['protocol'] == protocol
-                and float(row['cost_per_side_pct']) == float(cost)
-            }
-            for arm in arms:
-                arm_id = str(arm['id'])
-                clusters = _cluster_rows(
-                    cells,
-                    arm_id=arm_id,
-                    protocol=protocol,
-                    cost=float(cost),
-                )
-                headline: dict[str, Any] = {}
-                for metric in PAIRED_METRICS:
-                    deltas = [
-                        cluster['metrics'][metric]['paired_delta_median']
-                        for cluster in clusters
-                    ]
-                    arm_levels = [
-                        cluster['metrics'][metric]['arm_median']
-                        for cluster in clusters
-                    ]
-                    control_levels = [
-                        cluster['metrics'][metric]['control_median']
-                        for cluster in clusters
-                    ]
-                    arm_level = median(arm_levels)
-                    control_level = median(control_levels)
-                    metric_summary: dict[str, Any] = {
-                        'paired_delta_median': median(deltas),
-                        'arm_median': arm_level,
-                        'control_median': control_level,
-                        'arm_control_ratio': _safe_ratio(
-                            arm_level, control_level
-                        ),
-                    }
-                    if metric in ('ev_net_r', 'calmar'):
-                        metric_summary['bootstrap_90'] = (
-                            bootstrap_median_interval(
-                                deltas,
-                                seed_parts=(
-                                    arm_id,
-                                    protocol,
-                                    cost,
-                                    metric,
-                                    'paired-delta',
-                                ),
-                            )
-                        )
-                    headline[metric] = metric_summary
-                paired.append({
-                    'arm_id': arm_id,
-                    'protocol': protocol,
-                    'cost_per_side_pct': cost,
-                    'clusters': clusters,
-                    'headline': headline,
-                })
-                treatment_by_path = {
-                    row['path_id']: row
-                    for row in cells
-                    if row['arm_id'] == arm_id
-                    and row['protocol'] == protocol
-                    and float(row['cost_per_side_pct']) == float(cost)
-                }
-                shared_paths = sorted(
-                    set(treatment_by_path) & set(control_by_path)
-                )
-                path_metrics: dict[str, Any] = {}
-                for metric in PAIRED_METRICS:
-                    deltas = [
-                        float(treatment_by_path[path_id]['metrics'][metric])
-                        - float(control_by_path[path_id]['metrics'][metric])
-                        for path_id in shared_paths
-                        if treatment_by_path[path_id]['metrics'].get(metric)
-                        is not None
-                        and control_by_path[path_id]['metrics'].get(metric)
-                        is not None
-                        and math.isfinite(
-                            float(treatment_by_path[path_id]['metrics'][metric])
-                        )
-                        and math.isfinite(
-                            float(control_by_path[path_id]['metrics'][metric])
-                        )
-                    ]
-                    path_metrics[metric] = {
-                        'paired_paths': len(deltas),
-                        'paired_delta_mean': (
-                            statistics.fmean(deltas) if deltas else None
-                        ),
-                        'paired_delta_median': median(deltas),
-                        'paired_delta_p25': percentile(deltas, 0.25),
-                        'paired_delta_p75': percentile(deltas, 0.75),
-                        'positive_fraction': (
-                            sum(delta > 0.0 for delta in deltas) / len(deltas)
-                            if deltas
-                            else None
-                        ),
-                        'identical_fraction': (
-                            sum(abs(delta) <= 1e-12 for delta in deltas)
-                            / len(deltas)
-                            if deltas
-                            else None
-                        ),
-                    }
-                path_distributions.append({
-                    'arm_id': arm_id,
-                    'protocol': protocol,
-                    'cost_per_side_pct': cost,
-                    'metrics': path_metrics,
-                })
-
-    warm_rows = [
-        row for row in cells if row['protocol'] == 'warm_book'
-    ]
-    warm_dispersion: list[dict[str, Any]] = []
-    for cost in costs:
-        for arm in arms:
-            arm_id = str(arm['id'])
-            anchor_rows: list[dict[str, Any]] = []
-            for cluster in ANCHOR_YEARS:
-                arm_paths = [
-                    row
-                    for row in warm_rows
-                    if row['arm_id'] == arm_id
-                    and int(row['cluster']) == cluster
-                    and float(row['cost_per_side_pct']) == float(cost)
-                ]
-                control_by_path = {
-                    row['path_id']: row
-                    for row in warm_rows
-                    if row['arm_id'] == 'cap10_incumbent'
-                    and int(row['cluster']) == cluster
-                    and float(row['cost_per_side_pct']) == float(cost)
-                }
-                metric_rows: dict[str, Any] = {}
-                for metric in ('ev_net_r', 'calmar'):
-                    arm_spread = iqr(
-                        row['metrics'].get(metric) for row in arm_paths
-                    )
-                    control_spread = iqr(
-                        control_by_path[row['path_id']]['metrics'].get(metric)
-                        for row in arm_paths
-                        if row['path_id'] in control_by_path
-                    )
-                    metric_rows[metric] = {
-                        'arm_iqr': arm_spread,
-                        'control_iqr': control_spread,
-                        'iqr_ratio': _safe_ratio(
-                            arm_spread, control_spread
-                        ),
-                    }
-                anchor_rows.append({
-                    'cluster': cluster,
-                    'seeds': len(arm_paths),
-                    'metrics': metric_rows,
-                })
-
-            headline: dict[str, Any] = {}
-            for metric in ('ev_net_r', 'calmar'):
-                ratios = [
-                    row['metrics'][metric]['iqr_ratio']
-                    for row in anchor_rows
-                ]
-                headline[metric] = {
-                    'median_iqr_ratio': median(ratios),
-                    'bootstrap_90': bootstrap_median_interval(
-                        ratios,
-                        seed_parts=(
-                            arm_id,
-                            cost,
-                            metric,
-                            'warm-iqr-ratio',
-                        ),
-                    ),
-                }
-            warm_dispersion.append({
-                'arm_id': arm_id,
-                'cost_per_side_pct': cost,
-                'anchors': anchor_rows,
-                'headline': headline,
-            })
-
-    if not include_warm_dispersion:
-        warm_dispersion = []
-
-    return {
-        'paired_per_year': paired,
-        'paired_path_distributions': path_distributions,
-        'warm_seed_dispersion': warm_dispersion,
-        'bootstrap': {
-            'replicates': BOOTSTRAP_REPLICATES,
-            'seed': BOOTSTRAP_SEED,
-            'interval': 'central 90% percentile, context only',
-            'resampling_unit': 'seven annual paired summaries',
-        },
-    }

scripts/reparse_fundamentals.py

@@ -15,10 +15,10 @@ Cost: a reparse cannot be served from the database -- the facts a fixed parser n
 accepts were never stored -- so it refetches Company Facts for every tracked issuer
 under the SEC fair-access throttle. Expect a long run and a lot of network.
 
-Scope note: this rewrites ``fundamental_snapshots`` only. As of the A5 gate those
+Scope note: this rewrites ``fundamental_snapshots`` only. Those rows now feed both
-rows feed the fundamentals API/UI and the parity report; scoring still reads the
+the fundamentals API/UI *and* — through the nightly ``fundamental_data`` refresh —
-legacy ``fundamental_data`` table, so a reparse does not move composite scores or
+the fundamental dimension of the composite score, so a reparse does move scores
-backtests until the cutover happens.
+and backtests. Run it deliberately.
 
 Examples
 --------

scripts/run_tier1_macbook.sh

@@ -3,7 +3,6 @@
 #
 # Kept after Tier-1 cleanup:
 #   --ssl-check          diagnose corporate CA / proxy
-#   --earnings-only      resume FMP earnings backfill + 2a/2b (parked)
 #   --prod-book-matrix   re-run 505 vs liquid universe × horizon book matrix
 #
 # Prerequisites: git checkout research branch, .env, deep research.sqlite for
@@ -21,8 +20,6 @@ cd "$ROOT"
 RESEARCH_SNAP="${RESEARCH_SNAP:-backtest_snapshots/research.sqlite}"
 PROD_SNAP="${PROD_SNAP:-backtest_snapshots/prod.sqlite}"
 WORKERS="${WORKERS:-8}"
-FMP_LIMIT="${FMP_LIMIT:-250}"
-FMP_SLEEP="${FMP_SLEEP:-0.35}"
 PYTHON="${PYTHON:-python3}"
 USE_CORP_PROXY="${USE_CORP_PROXY:-0}"
 PHASE=""
@@ -35,7 +32,6 @@ usage() {
 while [[ $# -gt 0 ]]; do
   case "$1" in
     --ssl-check) PHASE=ssl; shift ;;
-    --earnings-only) PHASE=earnings; shift ;;
     --prod-book-matrix) PHASE=prod_book; shift ;;
     --corp-proxy) USE_CORP_PROXY=1; shift ;;
     --workers) WORKERS="$2"; shift 2 ;;
@@ -46,7 +42,7 @@ while [[ $# -gt 0 ]]; do
 done
 
 if [[ -z "$PHASE" ]]; then
-  echo "Pick a phase: --ssl-check | --earnings-only | --prod-book-matrix" >&2
+  echo "Pick a phase: --ssl-check | --prod-book-matrix" >&2
   usage 1
 fi
 
@@ -104,7 +100,6 @@ print(json.dumps(ssl_status(), indent=2))
 print("bootstrap ->", bootstrap_ssl())
 for url in (
     "https://data.alpaca.markets/v2/stocks/SPY/bars?timeframe=1Day&limit=1",
-    "https://financialmodelingprep.com/stable/profile?symbol=AAPL",
 ):
     try:
         req = urllib.request.Request(url, headers={"User-Agent": "ssl-check"})
@@ -118,17 +113,6 @@ PY
 setup_ssl
 case "$PHASE" in
   ssl) ssl_check ;;
-  earnings)
-    need_file "$PROD_SNAP"
-    need_file "$RESEARCH_SNAP"
-    log "Earnings Task 2 bulk backfill + registered 2a/2b closeout"
-    "$PYTHON" scripts/backfill_earnings_events.py \
-      --snapshot "$PROD_SNAP" --from-date 2016-01-04 --window-days 30 \
-      --limit "$FMP_LIMIT" --sleep "$FMP_SLEEP"
-    "$PYTHON" scripts/run_earnings_research.py \
-      --snapshot "$RESEARCH_SNAP" --universe-snapshot "$PROD_SNAP" \
-      --earnings-snapshot "$PROD_SNAP" --workers "$WORKERS" --allow-spawn
-    ;;
   prod_book)
     need_file "$RESEARCH_SNAP"
     log "Production book universe × horizon matrix"

tests/unit/test_activation_settings.py

@@ -8,9 +8,7 @@ from sqlalchemy.ext.asyncio import AsyncSession
 from app.exceptions import ValidationError
 from app.services.admin_service import (
     get_activation_config,
-    get_fundamentals_cutover_config,
     update_activation_config,
-    update_fundamentals_cutover_config,
 )
 
 
@@ -78,18 +76,3 @@ class TestActivationConfig:
     async def test_rejects_out_of_range_confidence(self, session: AsyncSession):
         with pytest.raises(ValidationError):
             await update_activation_config(session, {"min_confidence": 120.0})
-
-
-class TestFundamentalsCutoverConfig:
-    async def test_defaults_off_when_unset(self, session: AsyncSession):
-        assert await get_fundamentals_cutover_config(session) == {"enabled": False}
-
-    async def test_round_trips_explicit_switch(self, session: AsyncSession):
-        assert await update_fundamentals_cutover_config(session, True) == {
-            "enabled": True
-        }
-        assert await get_fundamentals_cutover_config(session) == {"enabled": True}
-
-        assert await update_fundamentals_cutover_config(session, False) == {
-            "enabled": False
-        }

tests/unit/test_earnings_research.py

@@ -1,6 +1,5 @@
 from datetime import date, timedelta
 
-from scripts.backfill_earnings_events import _dedupe_bulk_rows, _windows
 from scripts.import_dolthub_earnings import _align_symbol
 from scripts.run_earnings_research import (
     _analyse_2a_trades,
@@ -41,42 +40,6 @@ def test_dolthub_alignment_allows_fiscal_period_label_after_announcement() -> No
     assert matches == [(0, 0), (1, 1)]
 
 
-def test_bulk_windows_cover_range_without_overlap() -> None:
-    result = _windows(date(2020, 1, 1), date(2020, 1, 10), 4)
-    assert result == [
-        (date(2020, 1, 1), date(2020, 1, 4)),
-        (date(2020, 1, 5), date(2020, 1, 8)),
-        (date(2020, 1, 9), date(2020, 1, 10)),
-    ]
-
-
-def test_bulk_dedupe_prefers_more_complete_and_counts_restatement() -> None:
-    rows = [
-        {
-            "symbol": "AAPL",
-            "announce_date": "2024-01-01",
-            "announce_time": None,
-            "eps_estimate": 1.0,
-            "eps_actual": 1.1,
-            "revenue_estimate": None,
-            "revenue_actual": None,
-        },
-        {
-            "symbol": "AAPL",
-            "announce_date": "2024-01-01",
-            "announce_time": "amc",
-            "eps_estimate": 1.0,
-            "eps_actual": 1.2,
-            "revenue_estimate": 10.0,
-            "revenue_actual": 11.0,
-        },
-    ]
-    deduped, duplicates, restated = _dedupe_bulk_rows(rows)
-    assert duplicates == 1
-    assert restated == 1
-    assert deduped == [rows[1]]
-
-
 def test_2a_uses_net_r_strict_hold_and_next_session_stop() -> None:
     calendar = [
         date(2024, 1, 2),

tests/unit/test_finnhub_provider.py

@@ -1,92 +0,0 @@
-"""Unit tests for FinnhubFundamentalProvider unit conversions."""
-
-from __future__ import annotations
-
-from unittest.mock import AsyncMock, patch
-
-import httpx
-import pytest
-
-from app.providers.fundamentals_chain import FinnhubFundamentalProvider
-
-
-def _mock_response(status_code: int, json_data: object = None) -> httpx.Response:
-    return httpx.Response(
-        status_code=status_code,
-        json=json_data if json_data is not None else {},
-        request=httpx.Request("GET", "https://example.com"),
-    )
-
-
-@pytest.fixture
-def provider() -> FinnhubFundamentalProvider:
-    return FinnhubFundamentalProvider(api_key="test-key")
-
-
-@pytest.mark.asyncio
-async def test_finnhub_market_cap_converted_from_millions_to_dollars(provider):
-    """Finnhub marketCapitalization is in millions — store absolute USD.
-
-    SPCX-scale example: ~$1.8T → Finnhub reports 1_800_000 (millions).
-    Without conversion the UI showed 1.8M / micro cap.
-    """
-    profile = {"marketCapitalization": 1_800_000}  # millions → $1.8T
-    metrics = {"metric": {"peTTM": 40.0, "revenueGrowthTTMYoy": 25.0}}
-    earnings = [{"surprisePercent": 2.5}]
-    calendar = {"earningsCalendar": []}
-
-    async def mock_get(url, params=None):
-        if "profile2" in url:
-            return _mock_response(200, profile)
-        if "stock/metric" in url:
-            return _mock_response(200, metrics)
-        if "stock/earnings" in url:
-            return _mock_response(200, earnings)
-        if "calendar/earnings" in url:
-            return _mock_response(200, calendar)
-        return _mock_response(200, {})
-
-    with patch("app.providers.fundamentals_chain.httpx.AsyncClient") as MockClient:
-        instance = AsyncMock()
-        instance.get.side_effect = mock_get
-        instance.__aenter__ = AsyncMock(return_value=instance)
-        instance.__aexit__ = AsyncMock(return_value=False)
-        MockClient.return_value = instance
-
-        result = await provider.fetch_fundamentals("SPCX")
-
-    assert result.market_cap == 1_800_000 * 1_000_000  # $1.8T
-    assert result.pe_ratio == 40.0
-    assert result.revenue_growth == 25.0
-    assert result.earnings_surprise == 2.5
-
-
-@pytest.mark.asyncio
-async def test_finnhub_market_cap_none_when_missing(provider):
-    profile: dict = {}
-    metrics = {"metric": {}}
-    earnings: list = []
-    calendar = {"earningsCalendar": []}
-
-    async def mock_get(url, params=None):
-        if "profile2" in url:
-            return _mock_response(200, profile)
-        if "stock/metric" in url:
-            return _mock_response(200, metrics)
-        if "stock/earnings" in url:
-            return _mock_response(200, earnings)
-        if "calendar/earnings" in url:
-            return _mock_response(200, calendar)
-        return _mock_response(200, {})
-
-    with patch("app.providers.fundamentals_chain.httpx.AsyncClient") as MockClient:
-        instance = AsyncMock()
-        instance.get.side_effect = mock_get
-        instance.__aenter__ = AsyncMock(return_value=instance)
-        instance.__aexit__ = AsyncMock(return_value=False)
-        MockClient.return_value = instance
-
-        result = await provider.fetch_fundamentals("XYZ")
-
-    assert result.market_cap is None
-    assert "market_cap" in result.unavailable_fields

tests/unit/test_fmp_provider.py

@@ -1,156 +0,0 @@
-"""Unit tests for FMPFundamentalProvider 402 reason recording."""
-
-from __future__ import annotations
-
-from unittest.mock import AsyncMock, patch
-
-import httpx
-import pytest
-
-from app.providers.fmp import FMPFundamentalProvider
-
-
-def _mock_response(status_code: int, json_data: object = None) -> httpx.Response:
-    """Build a fake httpx.Response."""
-    resp = httpx.Response(
-        status_code=status_code,
-        json=json_data if json_data is not None else {},
-        request=httpx.Request("GET", "https://example.com"),
-    )
-    return resp
-
-
-@pytest.fixture
-def provider() -> FMPFundamentalProvider:
-    return FMPFundamentalProvider(api_key="test-key")
-
-
-class TestFetchJsonOptional402Tracking:
-    """_fetch_json_optional returns (data, was_402) tuple."""
-
-    @pytest.mark.asyncio
-    async def test_returns_empty_dict_and_true_on_402(self, provider):
-        mock_client = AsyncMock()
-        mock_client.get.return_value = _mock_response(402)
-
-        data, was_402 = await provider._fetch_json_optional(
-            mock_client, "ratios-ttm", {}, "AAPL"
-        )
-
-        assert data == {}
-        assert was_402 is True
-
-    @pytest.mark.asyncio
-    async def test_returns_data_and_false_on_200(self, provider):
-        mock_client = AsyncMock()
-        mock_client.get.return_value = _mock_response(
-            200, [{"priceToEarningsRatioTTM": 25.5}]
-        )
-
-        data, was_402 = await provider._fetch_json_optional(
-            mock_client, "ratios-ttm", {}, "AAPL"
-        )
-
-        assert data == {"priceToEarningsRatioTTM": 25.5}
-        assert was_402 is False
-
-
-class TestFetchFundamentals402Recording:
-    """fetch_fundamentals records 402 endpoints in unavailable_fields."""
-
-    @pytest.mark.asyncio
-    async def test_all_402_records_all_fields(self, provider):
-        """When all supplementary endpoints return 402, all three fields are recorded."""
-        profile_resp = _mock_response(200, [{"marketCap": 1_000_000}])
-        ratios_resp = _mock_response(402)
-        growth_resp = _mock_response(402)
-        earnings_resp = _mock_response(402)
-
-        async def mock_get(url, params=None):
-            if "profile" in url:
-                return profile_resp
-            if "ratios-ttm" in url:
-                return ratios_resp
-            if "financial-growth" in url:
-                return growth_resp
-            if "earnings" in url:
-                return earnings_resp
-            return _mock_response(200, [{}])
-
-        with patch("app.providers.fmp.httpx.AsyncClient") as MockClient:
-            instance = AsyncMock()
-            instance.get.side_effect = mock_get
-            instance.__aenter__ = AsyncMock(return_value=instance)
-            instance.__aexit__ = AsyncMock(return_value=False)
-            MockClient.return_value = instance
-
-            result = await provider.fetch_fundamentals("AAPL")
-
-        assert result.unavailable_fields == {
-            "pe_ratio": "requires paid plan",
-            "revenue_growth": "requires paid plan",
-            "earnings_surprise": "requires paid plan",
-        }
-
-    @pytest.mark.asyncio
-    async def test_mixed_200_402_records_only_402_fields(self, provider):
-        """When only ratios-ttm returns 402, only pe_ratio is recorded."""
-        profile_resp = _mock_response(200, [{"marketCap": 2_000_000}])
-        ratios_resp = _mock_response(402)
-        growth_resp = _mock_response(200, [{"revenueGrowth": 0.15}])
-        earnings_resp = _mock_response(200, [{"epsActual": 3.0, "epsEstimated": 2.5}])
-
-        async def mock_get(url, params=None):
-            if "profile" in url:
-                return profile_resp
-            if "ratios-ttm" in url:
-                return ratios_resp
-            if "financial-growth" in url:
-                return growth_resp
-            if "earnings" in url:
-                return earnings_resp
-            return _mock_response(200, [{}])
-
-        with patch("app.providers.fmp.httpx.AsyncClient") as MockClient:
-            instance = AsyncMock()
-            instance.get.side_effect = mock_get
-            instance.__aenter__ = AsyncMock(return_value=instance)
-            instance.__aexit__ = AsyncMock(return_value=False)
-            MockClient.return_value = instance
-
-            result = await provider.fetch_fundamentals("AAPL")
-
-        assert result.unavailable_fields == {"pe_ratio": "requires paid plan"}
-        assert result.revenue_growth == 0.15
-        assert result.earnings_surprise is not None
-
-    @pytest.mark.asyncio
-    async def test_no_402_empty_unavailable_fields(self, provider):
-        """When all endpoints succeed, unavailable_fields is empty."""
-        profile_resp = _mock_response(200, [{"marketCap": 3_000_000}])
-        ratios_resp = _mock_response(200, [{"priceToEarningsRatioTTM": 20.0}])
-        growth_resp = _mock_response(200, [{"revenueGrowth": 0.10}])
-        earnings_resp = _mock_response(200, [{"epsActual": 2.0, "epsEstimated": 1.8}])
-
-        async def mock_get(url, params=None):
-            if "profile" in url:
-                return profile_resp
-            if "ratios-ttm" in url:
-                return ratios_resp
-            if "financial-growth" in url:
-                return growth_resp
-            if "earnings" in url:
-                return earnings_resp
-            return _mock_response(200, [{}])
-
-        with patch("app.providers.fmp.httpx.AsyncClient") as MockClient:
-            instance = AsyncMock()
-            instance.get.side_effect = mock_get
-            instance.__aenter__ = AsyncMock(return_value=instance)
-            instance.__aexit__ = AsyncMock(return_value=False)
-            MockClient.return_value = instance
-
-            result = await provider.fetch_fundamentals("AAPL")
-
-        assert result.unavailable_fields == {}
-        assert result.pe_ratio == 20.0

tests/unit/test_fundamental_data_refresh.py

@@ -15,7 +15,6 @@ from app.models.fundamental import FundamentalData
 from app.models.fundamental_snapshot import FundamentalSnapshot
 from app.models.ohlcv import OHLCVRecord
 from app.models.score import CompositeScore, DimensionScore
-from app.models.settings import SystemSetting
 from app.models.ticker import Ticker
 from app.services import fundamentals_candidate_service as candidates
 from app.services import fundamentals_derivation as deriv
@@ -76,49 +75,9 @@ def _snapshot_rows(cik: str) -> list[FundamentalSnapshot]:
     return rows
 
 
-async def test_default_off_performs_no_candidate_read_or_write(
+async def test_refresh_updates_all_fields_and_invalidates_scores(
-    session: AsyncSession, monkeypatch
-):
-    ticker = Ticker(symbol="AAA")
-    session.add(ticker)
-    await session.flush()
-    session.add(
-        FundamentalData(
-            ticker_id=ticker.id,
-            pe_ratio=12,
-            revenue_growth=3,
-            earnings_surprise=1,
-            market_cap=100,
-            fetched_at=NOW,
-        )
-    )
-    await session.commit()
-
-    async def should_not_read(*args, **kwargs):
-        raise AssertionError("default-off refresh derived candidates")
-
-    monkeypatch.setattr(candidates, "build_candidates", should_not_read)
-    summary = await refresh_service.refresh_if_enabled(session, today=TODAY)
-
-    stored = await session.scalar(
-        select(FundamentalData).where(FundamentalData.ticker_id == ticker.id)
-    )
-    assert summary == {
-        "enabled": False,
-        "refreshed": 0,
-        "score_inputs_changed": 0,
-        "dimension_scores_staled": 0,
-        "composite_scores_staled": 0,
-    }
-    assert stored.pe_ratio == 12
-
-
-async def test_activated_refresh_updates_all_fields_and_invalidates_scores(
     session: AsyncSession,
 ):
-    session.add(
-        SystemSetting(key=refresh_service.ACTIVATION_KEY, value="true")
-    )
     first = Ticker(symbol="AAA", cik="0000000001")
     second = Ticker(symbol="AAB", cik="0000000001")
     session.add_all([first, second])
@@ -191,7 +150,7 @@ async def test_activated_refresh_updates_all_fields_and_invalidates_scores(
         )
     await session.commit()
 
-    summary = await refresh_service.refresh_if_enabled(
+    summary = await refresh_service.refresh(
         session, now=NOW, today=TODAY
     )
 
@@ -225,7 +184,7 @@ async def test_activated_refresh_updates_all_fields_and_invalidates_scores(
     for row in (*dimensions, *composites):
         row.is_stale = False
     await session.commit()
-    unchanged = await refresh_service.refresh_if_enabled(
+    unchanged = await refresh_service.refresh(
         session, now=NOW + timedelta(hours=1), today=TODAY
     )
     assert unchanged["score_inputs_changed"] == 0

tests/unit/test_fundamental_service.py

@@ -1,13 +1,20 @@
-"""Unit tests for fundamental_service — unavailable_fields persistence."""
+"""Unit tests for fundamental_service — the surviving read path.
+
+Writes to ``fundamental_data`` are covered by test_fundamental_data_refresh.py;
+this file only guards the lookup used by the router and scoring.
+"""
 
 from __future__ import annotations
 
 import json
+from datetime import datetime, timezone
 
 import pytest
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 
 from app.database import Base
+from app.exceptions import NotFoundError
+from app.models.fundamental import FundamentalData
 from app.models.ticker import Ticker
 from app.services import fundamental_service
 
@@ -43,57 +50,36 @@ async def ticker(session: AsyncSession) -> Ticker:
 
 
 @pytest.mark.asyncio
-async def test_store_fundamental_persists_unavailable_fields(
+async def test_get_fundamental_returns_the_cached_row(
     session: AsyncSession, ticker: Ticker
 ):
-    """unavailable_fields dict is serialized to JSON and stored."""
+    fields = {"pe_ratio": "split guard applied"}
-    fields = {"pe_ratio": "requires paid plan", "revenue_growth": "requires paid plan"}
+    session.add(
-
+        FundamentalData(
-    record = await fundamental_service.store_fundamental(
+            ticker_id=ticker.id,
-        session,
+            pe_ratio=None,
-        symbol="AAPL",
+            market_cap=1_000_000.0,
-        pe_ratio=None,
+            fetched_at=datetime.now(timezone.utc),
-        revenue_growth=None,
+            unavailable_fields_json=json.dumps(fields),
-        market_cap=1_000_000.0,
+        )
-        unavailable_fields=fields,
     )
+    await session.commit()
 
+    record = await fundamental_service.get_fundamental(session, symbol="aapl")
+
+    assert record is not None
+    assert record.market_cap == 1_000_000.0
     assert json.loads(record.unavailable_fields_json) == fields
 
 
 @pytest.mark.asyncio
-async def test_store_fundamental_defaults_to_empty_dict(
+async def test_get_fundamental_returns_none_without_a_cached_row(
     session: AsyncSession, ticker: Ticker
 ):
-    """When unavailable_fields is not provided, column defaults to '{}'."""
+    assert await fundamental_service.get_fundamental(session, symbol="AAPL") is None
-    record = await fundamental_service.store_fundamental(
-        session,
-        symbol="AAPL",
-        pe_ratio=25.0,
-    )
-
-    assert json.loads(record.unavailable_fields_json) == {}
 
 
 @pytest.mark.asyncio
-async def test_store_fundamental_updates_unavailable_fields(
+async def test_get_fundamental_rejects_an_unknown_symbol(session: AsyncSession):
-    session: AsyncSession, ticker: Ticker
+    with pytest.raises(NotFoundError):
-):
+        await fundamental_service.get_fundamental(session, symbol="NOPE")
-    """Updating an existing record also updates unavailable_fields_json."""
-    # First store
-    await fundamental_service.store_fundamental(
-        session,
-        symbol="AAPL",
-        pe_ratio=None,
-        unavailable_fields={"pe_ratio": "requires paid plan"},
-    )
-
-    # Second store — fields now available
-    record = await fundamental_service.store_fundamental(
-        session,
-        symbol="AAPL",
-        pe_ratio=25.0,
-        unavailable_fields={},
-    )
-
-    assert json.loads(record.unavailable_fields_json) == {}

tests/unit/test_fundamentals_chain_provider.py

@@ -1,181 +0,0 @@
-"""Unit tests for chained fundamentals provider fallback behavior."""
-
-from __future__ import annotations
-
-from datetime import datetime, timezone
-
-import pytest
-
-from app.exceptions import ProviderError, RateLimitError
-from app.providers.fundamentals_chain import ChainedFundamentalProvider
-from app.providers.protocol import FundamentalData
-
-
-class _FailProvider:
-    def __init__(self, message: str) -> None:
-        self._message = message
-
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        raise ProviderError(f"{self._message} ({ticker})")
-
-
-class _RateLimitedProvider:
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        raise RateLimitError(f"rate limit hit for {ticker}")
-
-
-class _DataProvider:
-    def __init__(self, data: FundamentalData) -> None:
-        self._data = data
-
-    async def fetch_fundamentals(self, ticker: str) -> FundamentalData:
-        return FundamentalData(
-            ticker=ticker,
-            pe_ratio=self._data.pe_ratio,
-            revenue_growth=self._data.revenue_growth,
-            earnings_surprise=self._data.earnings_surprise,
-            market_cap=self._data.market_cap,
-            fetched_at=self._data.fetched_at,
-            unavailable_fields=self._data.unavailable_fields,
-        )
-
-
-@pytest.mark.asyncio
-async def test_chained_provider_uses_fallback_provider_on_primary_failure():
-    fallback_data = FundamentalData(
-        ticker="AAPL",
-        pe_ratio=25.0,
-        revenue_growth=None,
-        earnings_surprise=None,
-        market_cap=1_000_000.0,
-        fetched_at=datetime.now(timezone.utc),
-        unavailable_fields={},
-    )
-
-    provider = ChainedFundamentalProvider([
-        ("primary", _FailProvider("primary down")),
-        ("fallback", _DataProvider(fallback_data)),
-    ])
-
-    result = await provider.fetch_fundamentals("AAPL")
-
-    assert result.pe_ratio == 25.0
-    assert result.market_cap == 1_000_000.0
-    assert result.unavailable_fields.get("source_pe_ratio") == "fallback"
-
-
-@pytest.mark.asyncio
-async def test_chained_provider_merges_fields_across_providers():
-    """Primary supplies only market cap; fallback fills P/E and earnings."""
-    primary_data = FundamentalData(
-        ticker="AAPL", pe_ratio=None, revenue_growth=None, earnings_surprise=None,
-        market_cap=2_000_000.0, fetched_at=datetime.now(timezone.utc), unavailable_fields={},
-    )
-    fallback_data = FundamentalData(
-        ticker="AAPL", pe_ratio=18.0, revenue_growth=12.0, earnings_surprise=4.0,
-        market_cap=999.0, fetched_at=datetime.now(timezone.utc), unavailable_fields={},
-    )
-
-    provider = ChainedFundamentalProvider([
-        ("fmp", _DataProvider(primary_data)),
-        ("finnhub", _DataProvider(fallback_data)),
-    ])
-
-    result = await provider.fetch_fundamentals("AAPL")
-
-    # market cap from primary (first to supply it), the rest from fallback
-    assert result.market_cap == 2_000_000.0
-    assert result.pe_ratio == 18.0
-    assert result.revenue_growth == 12.0
-    assert result.earnings_surprise == 4.0
-    assert result.unavailable_fields.get("source_market_cap") == "fmp"
-    assert result.unavailable_fields.get("source_pe_ratio") == "finnhub"
-
-
-@pytest.mark.asyncio
-async def test_chained_provider_raises_when_all_providers_fail():
-    provider = ChainedFundamentalProvider([
-        ("p1", _FailProvider("p1 failed")),
-        ("p2", _FailProvider("p2 failed")),
-    ])
-
-    with pytest.raises(ProviderError) as exc:
-        await provider.fetch_fundamentals("MSFT")
-
-    assert "All fundamentals providers failed" in str(exc.value)
-
-
-@pytest.mark.asyncio
-async def test_rate_limited_fallback_raises_when_incomplete():
-    """FMP gives market cap; the fallback is rate-limited → chain signals it so
-    the collector can back off instead of storing a degraded record."""
-    primary_data = FundamentalData(
-        ticker="AAPL", pe_ratio=None, revenue_growth=None, earnings_surprise=None,
-        market_cap=2_000_000.0, fetched_at=datetime.now(timezone.utc), unavailable_fields={},
-    )
-    provider = ChainedFundamentalProvider([
-        ("fmp", _DataProvider(primary_data)),
-        ("finnhub", _RateLimitedProvider()),
-    ])
-
-    with pytest.raises(RateLimitError):
-        await provider.fetch_fundamentals("AAPL")
-
-
-@pytest.mark.asyncio
-async def test_rate_limited_fallback_allows_partial():
-    """With allow_partial=True the chain returns the market cap it did get."""
-    primary_data = FundamentalData(
-        ticker="AAPL", pe_ratio=None, revenue_growth=None, earnings_surprise=None,
-        market_cap=2_000_000.0, fetched_at=datetime.now(timezone.utc), unavailable_fields={},
-    )
-    provider = ChainedFundamentalProvider([
-        ("fmp", _DataProvider(primary_data)),
-        ("finnhub", _RateLimitedProvider()),
-    ])
-
-    result = await provider.fetch_fundamentals("AAPL", allow_partial=True)
-    assert result.market_cap == 2_000_000.0
-    assert result.pe_ratio is None
-
-
-@pytest.mark.asyncio
-async def test_rate_limited_but_complete_does_not_raise():
-    """If every field is filled, a rate limit on a later (unused) provider is moot."""
-    full = FundamentalData(
-        ticker="AAPL", pe_ratio=20.0, revenue_growth=10.0, earnings_surprise=2.0,
-        market_cap=5.0, fetched_at=datetime.now(timezone.utc), unavailable_fields={},
-    )
-    provider = ChainedFundamentalProvider([
-        ("fmp", _DataProvider(full)),
-        ("finnhub", _RateLimitedProvider()),
-    ])
-
-    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)

tests/unit/test_fundamentals_parity.py

@@ -1,209 +0,0 @@
-"""A5 fundamentals parity report: read-only comparison + artifact archive."""
-
-from __future__ import annotations
-
-from datetime import date, datetime, timezone
-
-import pytest
-from sqlalchemy import func, select
-
-from app.models.data_import_run import DataImportRun
-from app.models.earnings_event import EarningsEvent
-from app.models.fundamental import FundamentalData
-from app.models.fundamental_snapshot import FundamentalSnapshot
-from app.models.ohlcv import OHLCVRecord
-from app.models.ticker import Ticker
-from app.services.fundamentals_parity_service import (
-    build_report,
-    fundamental_score,
-    load_latest,
-    load_latest_csv,
-    load_latest_json,
-    store_report,
-)
-
-UTC = timezone.utc
-GENERATED = datetime(2026, 7, 23, 10, 30, tzinfo=UTC)
-
-
-def _snapshot_rows(cik: str) -> list[FundamentalSnapshot]:
-    rows = []
-    periods = ("Q1", "Q2", "Q3", "FY")
-    months = (3, 6, 9, 12)
-    for fy, multiplier in ((2025, 1.0), (2026, 1.1)):
-        revenues = [100 * multiplier, 110 * multiplier, 120 * multiplier, 130 * multiplier]
-        eps = [1.0 * multiplier, 1.1 * multiplier, 1.2 * multiplier, 1.3 * multiplier]
-        for index, period in enumerate(periods):
-            period_end = date(fy, months[index], 28)
-            rows.append(
-                FundamentalSnapshot(
-                    cik=cik,
-                    accession=f"{cik}-{fy}-{period}",
-                    form="10-K" if period == "FY" else "10-Q",
-                    filed_date=period_end,
-                    accepted_at=datetime(fy, months[index], 28, tzinfo=UTC),
-                    period_end=period_end,
-                    fiscal_year=fy,
-                    fiscal_period=period,
-                    revenue=sum(revenues[: index + 1]),
-                    operating_income=sum(revenues[: index + 1]) * 0.2,
-                    diluted_eps=sum(eps[: index + 1]),
-                    cfo=sum(revenues[: index + 1]) * 0.25,
-                    capex=sum(revenues[: index + 1]) * 0.05,
-                    depreciation_amortization=sum(revenues[: index + 1]) * 0.05,
-                    cash_and_st_investments=40,
-                    total_debt=100,
-                    shares_outstanding=1000,
-                )
-            )
-    return rows
-
-
-async def _seed(db_session):
-    first = Ticker(symbol="AAA", cik="0000000001", sic="3571")
-    second = Ticker(symbol="BBB", cik=None, sic=None)
-    db_session.add_all([first, second])
-    await db_session.flush()
-    db_session.add_all(_snapshot_rows(first.cik))
-    db_session.add_all(
-        [
-            FundamentalData(
-                ticker_id=first.id,
-                pe_ratio=25,
-                revenue_growth=5,
-                earnings_surprise=0,
-                fetched_at=GENERATED,
-            ),
-            FundamentalData(
-                ticker_id=second.id,
-                pe_ratio=12,
-                revenue_growth=3,
-                earnings_surprise=None,
-                fetched_at=GENERATED,
-            ),
-            OHLCVRecord(
-                ticker_id=first.id,
-                date=date(2026, 7, 22),
-                open=100,
-                high=100,
-                low=100,
-                close=100,
-                volume=100,
-            ),
-            EarningsEvent(
-                ticker_id=first.id,
-                announce_date=date(2026, 7, 1),
-                session="amc",
-                eps_estimate=2,
-                eps_actual=2.2,
-                source="dolt_earnings",
-            ),
-            DataImportRun(
-                source="sec_facts",
-                revision="sec-rev",
-                status="promoted",
-                source_max_date=date(2026, 7, 22),
-                started_at=GENERATED,
-                completed_at=GENERATED,
-            ),
-            DataImportRun(
-                source="dolt_earnings",
-                revision="dolt-rev",
-                status="no_op",
-                source_max_date=date(2026, 7, 22),
-                started_at=GENERATED,
-                completed_at=GENERATED,
-            ),
-        ]
-    )
-    await db_session.flush()
-
-
-def test_score_formula_matches_production_rules():
-    score = fundamental_score(pe_ratio=15, revenue_growth=0, earnings_surprise=0)
-    assert score == pytest.approx((100 + 50 + 50) / 3)
-    assert fundamental_score(pe_ratio=15, revenue_growth=None, earnings_surprise=None) is None
-
-async def test_report_compares_sources_and_leaves_database_untouched(db_session):
-    await _seed(db_session)
-    before = await db_session.scalar(select(func.count()).select_from(FundamentalData))
-
-    report = await build_report(
-        db_session,
-        generated_at=GENERATED,
-        today=date(2026, 7, 23),
-    )
-
-    after = await db_session.scalar(select(func.count()).select_from(FundamentalData))
-    assert before == after == 2
-    assert not db_session.new and not db_session.dirty and not db_session.deleted
-    assert report["read_only"] is True
-    assert report["approval_status"] == "pending_explicit_approval"
-    assert report["source_runs"]["sec_facts"]["revision"] == "sec-rev"
-    assert report["source_runs"]["dolt_earnings"]["revision"] == "dolt-rev"
-
-    first = next(row for row in report["rows"] if row["symbol"] == "AAA")
-    assert first["fields"]["pe_ratio"]["candidate"] == pytest.approx(
-        100 / 5.06, abs=1e-4
-    )
-    assert first["fields"]["revenue_growth"]["candidate"] == pytest.approx(10)
-    assert first["fields"]["earnings_surprise"]["candidate"] == pytest.approx(10)
-    assert first["scores"]["candidate_fundamental"] is not None
-    assert report["summary"]["universe_count"] == 2
-    assert report["summary"]["field_stats"]["pe_ratio"]["both_available"] == 1
-
-
-async def test_artifacts_archive_and_latest_manifest(db_session, tmp_path):
-    await _seed(db_session)
-    report = await build_report(
-        db_session,
-        generated_at=GENERATED,
-        today=date(2026, 7, 23),
-    )
-
-    paths = store_report(report, tmp_path)
-
-    assert tmp_path.joinpath("latest.json").exists()
-    assert paths["json"].endswith(".json") and paths["csv"].endswith(".csv")
-    assert load_latest(tmp_path)["generated_at"] == GENERATED.isoformat()
-    csv_artifact = load_latest_csv(tmp_path)
-    assert csv_artifact is not None
-    assert csv_artifact[0].endswith(".csv")
-    assert "legacy_fundamental,candidate_fundamental" in csv_artifact[1]
-    assert "AAA" in csv_artifact[1]
-    json_artifact = load_latest_json(tmp_path)
-    assert json_artifact is not None and '"rows"' in json_artifact[1]
-
-
-async def test_admin_endpoints_return_compact_summary_and_downloads(
-    client, db_session, tmp_path, monkeypatch
-):
-    from app.config import settings
-    from app.dependencies import require_admin
-    from app.main import app
-
-    await _seed(db_session)
-    report = await build_report(
-        db_session,
-        generated_at=GENERATED,
-        today=date(2026, 7, 23),
-    )
-    store_report(report, tmp_path)
-    monkeypatch.setattr(settings, "fundamentals_parity_report_dir", str(tmp_path))
-    app.dependency_overrides[require_admin] = lambda: None
-    try:
-        summary_response = await client.get("/api/v1/admin/fundamentals-parity")
-        assert summary_response.status_code == 200
-        summary = summary_response.json()["data"]
-        assert summary["summary"]["universe_count"] == 2
-        assert "rows" not in summary
-
-        csv_response = await client.get("/api/v1/admin/fundamentals-parity/csv")
-        assert csv_response.status_code == 200
-        assert "AAA" in csv_response.json()["data"]["content"]
-
-        json_response = await client.get("/api/v1/admin/fundamentals-parity/json")
-        assert json_response.status_code == 200
-        assert '"rows"' in json_response.json()["data"]["content"]
-    finally:
-        app.dependency_overrides.pop(require_admin, None)

tests/unit/test_fundamentals_quality_service.py

@@ -6,7 +6,6 @@ from datetime import date, datetime, timezone
 from app.models.data_import_run import DataImportRun
 from app.models.fundamental_snapshot import FundamentalSnapshot
 from app.models.sec_filing_gap import SecFilingGap
-from app.models.settings import SystemSetting
 from app.models.ticker import Ticker
 from app.services import fundamentals_quality_service
 
@@ -19,12 +18,6 @@ async def test_latest_sec_validation_blocks_deferred_and_no_history_ciks(
     healthy = Ticker(symbol="HEALTHY", cik="0000000003")
     db_session.add_all([missing, no_history, healthy])
     await db_session.flush()
-    db_session.add(
-        SystemSetting(
-            key="fundamental_data_sec_dolt_cutover_enabled",
-            value="true",
-        )
-    )
     db_session.add(
         DataImportRun(
             source="sec_facts",
@@ -44,38 +37,11 @@ async def test_latest_sec_validation_blocks_deferred_and_no_history_ciks(
     }
 
 
-async def test_sec_quality_gate_is_inactive_before_cutover(db_session):
-    ticker = Ticker(symbol="SHADOW", cik="0000000042")
-    db_session.add(ticker)
-    await db_session.flush()
-    now = datetime.now(timezone.utc)
-    db_session.add(
-        SecFilingGap(
-            cik=ticker.cik,
-            accession="SHADOW-Q",
-            form="10-Q",
-            index_date=date.today(),
-            reason="not_in_companyfacts",
-            first_seen_at=now,
-            last_attempted_at=now,
-        )
-    )
-    await db_session.flush()
-
-    assert await fundamentals_quality_service.blocked_ticker_ids(db_session) == set()
-
-
-
-
 async def test_active_gap_is_blocked_until_a_later_filing_supersedes_it(db_session):
     ticker = Ticker(symbol="HIST", cik="0000000043")
     now = datetime.now(timezone.utc)
     db_session.add_all([
         ticker,
-        SystemSetting(
-            key="fundamental_data_sec_dolt_cutover_enabled",
-            value="true",
-        ),
         SecFilingGap(
             cik=ticker.cik,
             accession="HIST-Q",
@@ -116,10 +82,6 @@ async def test_gap_without_index_date_uses_first_seen_date_for_supersession(
     first_seen = datetime(2026, 5, 1, 12, tzinfo=timezone.utc)
     db_session.add_all([
         ticker,
-        SystemSetting(
-            key="fundamental_data_sec_dolt_cutover_enabled",
-            value="true",
-        ),
         SecFilingGap(
             cik=ticker.cik,
             accession="DATELESS-Q",
@@ -157,10 +119,6 @@ async def test_ticker_quality_explains_no_xbrl_block(db_session):
     ticker = Ticker(symbol="NEWREG", cik="0000000044")
     db_session.add_all([
         ticker,
-        SystemSetting(
-            key="fundamental_data_sec_dolt_cutover_enabled",
-            value="true",
-        ),
         DataImportRun(
             source="sec_facts",
             status="promoted",

tests/unit/test_ingestion_fundamentals_source.py

@@ -0,0 +1,38 @@
+"""A6: `sources=fundamentals` is accepted but never fetches from a provider.
+
+`fundamental_data` is rebuilt for the whole universe by the nightly SEC + Dolt
+imports, so there is no per-ticker fetch left. The source key stays valid so an
+older client gets a truthful `skipped` instead of a silent omission.
+"""
+
+from __future__ import annotations
+
+from app.models.ticker import Ticker
+
+
+async def test_fundamentals_source_reports_skipped(client, db_session):
+    from app.dependencies import require_access
+    from app.main import app
+
+    app.dependency_overrides[require_access] = lambda: None
+    try:
+        db_session.add(Ticker(symbol="AAPL"))
+        await db_session.flush()
+
+        resp = await client.post(
+            "/api/v1/ingestion/fetch/AAPL", params={"sources": "fundamentals"}
+        )
+        assert resp.status_code == 200
+        source = resp.json()["data"]["sources"]["fundamentals"]
+        assert source["status"] == "skipped"
+        assert "SEC + Dolt" in source["message"]
+    finally:
+        app.dependency_overrides.pop(require_access, None)
+
+
+def test_fundamentals_remains_a_recognised_source_key():
+    """Older clients keep getting an entry for it rather than a missing key."""
+    from app.routers.ingestion import _parse_requested_sources
+
+    assert "fundamentals" in _parse_requested_sources("fundamentals")
+    assert "fundamentals" in _parse_requested_sources(None)  # None => all sources

tests/unit/test_portfolio_capacity_research.py

@@ -1,905 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import pickle
-import sqlite3
-from datetime import date, timedelta
-
-import pytest
-
-from app.services import backtest_service as bt
-from scripts.portfolio_capacity_research import (
-    ANCHOR_YEARS,
-    RISK_FLOOR_ARMS,
-    aggregate_results,
-    bootstrap_median_interval,
-    build_cells,
-    build_cohort_manifest,
-    iqr,
-    summarize_simulation,
-    validate_cohort_manifest,
-)
-from scripts.run_portfolio_construction_matrix import (
-    CACHE_VERSION,
-    STUDIES,
-    _assert_clean_worktree,
-    _build_candidate_cache,
-    _checkpoint_state,
-    _construction_candidate_view,
-    _construction_universe_errors,
-    _json_hash,
-    _load_snapshot,
-    _markdown,
-    _operational_summary,
-    _risk_floor_markdown,
-    _worker_init,
-    _worker_run_cell,
-    _write_cell_checkpoint,
-)
-
-
-def _prices(ords: list[int], close: float = 100.0) -> tuple:
-    closes = [close] * len(ords)
-    return (
-        ords,
-        list(closes),
-        [value + 1.0 for value in closes],
-        [value - 1.0 for value in closes],
-        list(closes),
-        [1_000_000] * len(ords),
-    )
-
-
-def _candidate(
-    symbol: str,
-    day: date,
-    *,
-    entry: float = 100.0,
-    stop: float = 80.0,
-    rank: float = 90.0,
-) -> dict:
-    return {
-        'qualified': True,
-        'direction': 'long',
-        'symbol': symbol,
-        'date': day.isoformat(),
-        'entry': entry,
-        'stop': stop,
-        'target': entry + 100.0,
-        'momentum_percentile': rank,
-        'activation_momentum_percentile': rank,
-        'residual_high_vol_blend_80_20': rank,
-    }
-
-
-def _business_days(start: date, end: date) -> list[date]:
-    days: list[date] = []
-    current = start
-    while current <= end:
-        if current.weekday() < 5:
-            days.append(current)
-        current += timedelta(days=1)
-    return days
-
-
-def test_new_simulator_option_defaults_match_explicit_defaults():
-    start = date(2025, 1, 6)
-    ords = [start.toordinal() + offset for offset in range(8)]
-    prices = {'AAA': _prices(ords)}
-    candidates = [_candidate('AAA', start)]
-
-    legacy = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        3,
-        include_trades=True,
-    )
-    explicit = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        3,
-        max_positions=10,
-        min_initial_risk_fraction=None,
-        weekly_top_n_rebalance=False,
-        measurement_start_date=None,
-        hard_end_date=None,
-        include_capacity_diagnostics=False,
-        include_trades=True,
-    )
-
-    assert legacy == explicit
-
-
-def test_load_snapshot_accepts_pre_sec_ticker_schema(tmp_path, monkeypatch):
-    snapshot = tmp_path / 'legacy-research.sqlite'
-    with sqlite3.connect(snapshot) as connection:
-        connection.executescript(
-            '''
-            CREATE TABLE tickers (
-                id INTEGER PRIMARY KEY,
-                symbol VARCHAR(10) NOT NULL UNIQUE,
-                name VARCHAR(120),
-                created_at DATETIME NOT NULL
-            );
-            CREATE TABLE ohlcv_records (
-                id INTEGER PRIMARY KEY,
-                ticker_id INTEGER NOT NULL,
-                date DATE NOT NULL,
-                open FLOAT NOT NULL,
-                high FLOAT NOT NULL,
-                low FLOAT NOT NULL,
-                close FLOAT NOT NULL,
-                volume BIGINT NOT NULL,
-                created_at DATETIME NOT NULL
-            );
-            CREATE TABLE research_rank_only (
-                symbol VARCHAR(10) PRIMARY KEY
-            );
-            INSERT INTO tickers VALUES
-                (1, 'LEGACY', 'Legacy Co', '2024-01-01 00:00:00'),
-                (2, 'RANK', 'Rank Only Co', '2024-01-01 00:00:00');
-            INSERT INTO ohlcv_records VALUES
-                (1, 1, '2024-01-02', 100, 102, 99, 101, 1000000,
-                 '2024-01-02 00:00:00'),
-                (2, 2, '2024-01-02', 50, 51, 49, 50, 500000,
-                 '2024-01-02 00:00:00');
-            INSERT INTO research_rank_only VALUES ('RANK');
-            '''
-        )
-
-    async def recommendation_config(_db):
-        return {}
-
-    async def activation_config(_db):
-        return {'min_momentum_percentile': 80.0}
-
-    async def exit_policy(_db):
-        return {'mode': 'atr_trailing', 'hold_days': 30, 'atr_multiplier': 3.0}
-
-    async def benchmark_closes(_db, *, days, refresh):
-        assert days is None
-        assert refresh is False
-        return {date(2024, 1, 2): 100.0}
-
-    monkeypatch.setattr(
-        'app.services.recommendation_service.get_recommendation_config',
-        recommendation_config,
-    )
-    monkeypatch.setattr(
-        'app.services.admin_service.get_activation_config',
-        activation_config,
-    )
-    monkeypatch.setattr(
-        'app.services.paper_trade_service.get_exit_policy',
-        exit_policy,
-    )
-    monkeypatch.setattr(
-        'app.services.backtest_service._load_benchmark_closes_for_backtest',
-        benchmark_closes,
-    )
-
-    loaded = asyncio.run(_load_snapshot(snapshot, quiet=True))
-
-    assert loaded['symbols'] == ['LEGACY', 'RANK']
-    assert loaded['construction_symbols'] == {'LEGACY'}
-    assert loaded['prices']['LEGACY'] == (
-        [date(2024, 1, 2).toordinal()],
-        [100.0],
-        [102.0],
-        [99.0],
-        [101.0],
-        [1_000_000],
-    )
-    assert loaded['prices']['RANK'][4] == [50.0]
-    assert loaded['construction_universe_manifest'][
-        'construction_ticker_rows'
-    ] == 1
-    assert loaded['construction_universe_manifest']['rank_only_ticker_rows'] == 1
-    with sqlite3.connect(snapshot) as connection:
-        columns = {
-            row[1] for row in connection.execute('PRAGMA table_info(tickers)')
-        }
-    assert {'cik', 'sic', 'sic_description'}.isdisjoint(columns)
-
-
-def test_construction_view_filters_rank_only_rows_without_rebuilding_cache():
-    manifest = {
-        'ranking_ticker_rows': 506,
-        'ranking_symbols_with_prices': 506,
-        'construction_ticker_rows': 505,
-        'construction_symbols_with_prices': 505,
-        'rank_only_ticker_rows': 1,
-        'rank_only_symbols_with_prices': 1,
-        'rank_only_unknown_symbols': 0,
-    }
-    cached = {
-        'key': {'version': 'existing-broad-cache'},
-        'qualified_candidates': [
-            {'symbol': 'PROD', 'date': '2025-01-02'},
-            {'symbol': 'RANK', 'date': '2025-01-02'},
-        ],
-        'qualified_long_count': 2,
-        'daily_rank_map': {
-            ('RANK', '2025-01-02'): {'strategy_rank': 99.0},
-        },
-    }
-
-    view = _construction_candidate_view(
-        cached,
-        {
-            'construction_symbols': {'PROD'},
-            'construction_universe_manifest': manifest,
-        },
-    )
-
-    assert [row['symbol'] for row in view['qualified_candidates']] == ['PROD']
-    assert view['raw_full_universe_qualified_long_count'] == 2
-    assert view['filtered_rank_only_qualified_long_count'] == 1
-    assert view['qualified_long_count'] == 1
-    assert ('RANK', '2025-01-02') in view['daily_rank_map']
-    assert len(cached['qualified_candidates']) == 2
-
-
-def test_existing_broad_candidate_cache_key_remains_reusable(tmp_path, monkeypatch):
-    snapshot = tmp_path / 'research.sqlite'
-    snapshot.write_bytes(b'snapshot-placeholder')
-    cache_path = tmp_path / 'broad-cache.pkl'
-    snapshot_data = {
-        'recommendation_config': {'rr': 3.0},
-        'activation': {'min_momentum_percentile': 80.0},
-        'runtime_config': {'ranking_key': 'test'},
-        'universe_manifest': {
-            'ticker_rows': 4655,
-            'symbols_with_prices': 4654,
-            'symbols_sha256': 'symbols',
-        },
-    }
-    key = {
-        'version': CACHE_VERSION,
-        'snapshot': str(snapshot.resolve()),
-        'snapshot_sha256': 'snapshot-hash',
-        'cadence': 'daily',
-        'outcome_horizon_sessions': 0,
-        'recommendation_config_hash': _json_hash(
-            snapshot_data['recommendation_config']
-        ),
-        'activation_hash': _json_hash(snapshot_data['activation']),
-        'runtime_config': snapshot_data['runtime_config'],
-        'universe_manifest': snapshot_data['universe_manifest'],
-    }
-    cached = {'key': key, 'qualified_candidates': [{'symbol': 'PROD'}]}
-    cache_path.write_bytes(pickle.dumps(cached))
-    monkeypatch.setattr(
-        bt,
-        '_replay_candidates_for_period',
-        lambda *_args: pytest.fail('existing cache should avoid replay'),
-    )
-
-    loaded = _build_candidate_cache(
-        snapshot_data,
-        snapshot=snapshot,
-        snapshot_sha256='snapshot-hash',
-        cache_path=cache_path,
-        workers=1,
-        quiet=True,
-    )
-
-    assert loaded == cached
-
-
-def test_construction_universe_guard_rejects_leaked_broad_book():
-    valid = {
-        'ranking_ticker_rows': 4655,
-        'construction_ticker_rows': 506,
-        'construction_symbols_with_prices': 506,
-        'rank_only_ticker_rows': 4149,
-        'rank_only_unknown_symbols': 0,
-    }
-    assert _construction_universe_errors(valid) == []
-
-    leaked = {
-        **valid,
-        'construction_ticker_rows': 4655,
-        'construction_symbols_with_prices': 4654,
-        'rank_only_ticker_rows': 0,
-    }
-    errors = _construction_universe_errors(leaked)
-    assert any('450-600' in error for error in errors)
-
-
-def test_unbounded_count_and_effective_risk_floor():
-    start = date(2025, 1, 6)
-    ords = [start.toordinal() + offset for offset in range(4)]
-    symbols = [f'S{index}' for index in range(25)]
-    prices = {symbol: _prices(ords) for symbol in symbols}
-    candidates = [
-        _candidate(symbol, start, stop=80.0, rank=100.0 - index)
-        for index, symbol in enumerate(symbols)
-    ]
-
-    capped = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        30,
-        max_positions=1,
-        hard_end_date=start + timedelta(days=4),
-        measurement_start_date=start,
-        include_capacity_diagnostics=True,
-    )
-    unbounded = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        30,
-        max_positions=None,
-        min_initial_risk_fraction=0.005,
-        hard_end_date=start + timedelta(days=4),
-        measurement_start_date=start,
-        include_capacity_diagnostics=True,
-    )
-
-    assert capped is not None and unbounded is not None
-    assert capped['peak_positions'] == 1
-    assert capped['measurement_skipped_book_full'] == 24
-    assert unbounded['peak_positions'] > 1
-    assert unbounded['measurement_skipped_book_full'] == 0
-    assert unbounded['skipped_min_initial_risk'] > 0
-    assert unbounded['peak_positions'] == unbounded['trades']
-
-
-def test_measurement_window_carries_state_but_excludes_pre_anchor_trade_ev():
-    start = date(2025, 1, 6)
-    anchor = start + timedelta(days=2)
-    hard_end = start + timedelta(days=7)
-    ords = [
-        start.toordinal() + offset
-        for offset in range((hard_end - start).days)
-    ]
-    prices = {
-        'AAA': _prices(ords, 100.0),
-        'BBB': _prices(ords, 100.0),
-    }
-    candidates = [
-        _candidate('AAA', start),
-        _candidate('BBB', anchor + timedelta(days=1)),
-    ]
-
-    sim = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        30,
-        start_date=start,
-        end_date=hard_end,
-        measurement_start_date=anchor,
-        hard_end_date=hard_end,
-        include_curve=True,
-        include_trades=True,
-    )
-
-    assert sim is not None
-    assert sim['simulation_start_date'] == start.isoformat()
-    assert sim['start_date'] == anchor.isoformat()
-    assert sim['measurement_start_positions'] == 1
-    assert sim['trades'] == 1
-    assert [trade['symbol'] for trade in sim['trade_details']] == ['BBB']
-    assert sim['equity_curve'][0]['date'] == anchor.isoformat()
-
-
-def test_weekly_top10_uses_current_rank_for_both_sides_not_entry_rank():
-    monday = date(2025, 1, 6)
-    friday = date(2025, 1, 10)
-    sessions = _business_days(monday, friday)
-    ords = [session.toordinal() for session in sessions]
-    prices = {
-        'AAA': _prices(ords),
-        'BBB': _prices(ords),
-    }
-    candidates = [
-        _candidate('AAA', monday, rank=99.0),
-        _candidate('BBB', friday, rank=10.0),
-    ]
-    rank_map = {
-        ('AAA', friday.isoformat()): {'strategy_rank': 10.0},
-        ('BBB', friday.isoformat()): {'strategy_rank': 90.0},
-    }
-
-    sim = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        30,
-        max_positions=1,
-        weekly_top_n_rebalance=True,
-        daily_rank_map=rank_map,
-        measurement_start_date=monday,
-        hard_end_date=friday + timedelta(days=1),
-        include_trades=True,
-        include_capacity_diagnostics=True,
-    )
-
-    assert sim is not None
-    assert [trade['symbol'] for trade in sim['trade_details']] == ['AAA', 'BBB']
-    assert sim['trade_details'][0]['reason'] == 'weekly_rebalance'
-    event = sim['weekly_rebalance_events'][0]
-    assert event['exited_symbols'] == ['AAA']
-    assert event['selected_entrant_symbols'] == ['BBB']
-
-
-def test_weekly_top10_incumbent_wins_exact_current_rank_tie():
-    monday = date(2025, 1, 6)
-    friday = date(2025, 1, 10)
-    sessions = _business_days(monday, friday)
-    ords = [session.toordinal() for session in sessions]
-    prices = {
-        'AAA': _prices(ords),
-        'BBB': _prices(ords),
-    }
-    candidates = [
-        _candidate('AAA', monday, rank=10.0),
-        _candidate('BBB', friday, rank=99.0),
-    ]
-    rank_map = {
-        ('AAA', friday.isoformat()): {'strategy_rank': 80.0},
-        ('BBB', friday.isoformat()): {'strategy_rank': 80.0},
-    }
-
-    sim = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        30,
-        max_positions=1,
-        weekly_top_n_rebalance=True,
-        daily_rank_map=rank_map,
-        measurement_start_date=monday,
-        hard_end_date=friday + timedelta(days=1),
-        include_trades=True,
-    )
-
-    assert sim is not None
-    assert [trade['symbol'] for trade in sim['trade_details']] == ['AAA']
-    assert sim['trade_details'][0]['reason'] == 'open_at_end'
-    assert sim['weekly_rebalance_events'][0]['replacements'] == 0
-
-
-def test_weekly_rebalance_exit_bypasses_cooldown_and_churn_is_counted():
-    first_monday = date(2025, 1, 6)
-    friday = date(2025, 1, 10)
-    next_monday = date(2025, 1, 13)
-    sessions = _business_days(first_monday, next_monday)
-    ords = [session.toordinal() for session in sessions]
-    prices = {
-        'AAA': _prices(ords),
-        'BBB': (
-            ords,
-            [100.0] * len(ords),
-            [101.0] * len(ords),
-            [99.0] * (len(ords) - 1) + [70.0],
-            [100.0] * len(ords),
-            [1_000_000] * len(ords),
-        ),
-    }
-    candidates = [
-        _candidate('AAA', first_monday, rank=99.0),
-        _candidate('BBB', friday, rank=10.0),
-        _candidate('AAA', next_monday, rank=99.0),
-    ]
-    rank_map = {
-        ('AAA', friday.isoformat()): {'strategy_rank': 10.0},
-        ('BBB', friday.isoformat()): {'strategy_rank': 90.0},
-    }
-
-    sim = bt._simulate_portfolio(
-        candidates,
-        prices,
-        None,
-        'hold',
-        30,
-        max_positions=1,
-        reentry_cooldown_sessions=5,
-        weekly_top_n_rebalance=True,
-        daily_rank_map=rank_map,
-        measurement_start_date=first_monday,
-        hard_end_date=next_monday + timedelta(days=1),
-        include_trades=True,
-    )
-
-    assert sim is not None
-    assert [trade['symbol'] for trade in sim['trade_details']] == [
-        'AAA',
-        'BBB',
-        'AAA',
-    ]
-    assert sim['trade_details'][0]['reason'] == 'weekly_rebalance'
-    assert sim['rebalance_reentries_within_5_sessions'] == 1
-    assert sim['skipped_cooldown'] == 0
-
-
-def test_cohort_manifest_realizes_seven_frozen_clusters():
-    sessions = _business_days(date(2016, 1, 4), date(2026, 7, 17))
-    manifest = build_cohort_manifest(sessions)
-
-    assert validate_cohort_manifest(manifest) == []
-    assert manifest['empty_cluster_count'] == 7
-    assert manifest['warm_cluster_count'] == 7
-    assert set(map(int, manifest['empty_cluster_counts'])) == set(ANCHOR_YEARS)
-    assert all(
-        int(count) >= 12 for count in manifest['warm_seed_counts'].values()
-    )
-    cells = build_cells(manifest)
-    assert len(cells) == (
-        len(manifest['empty_book']) + len(manifest['warm_book'])
-    ) * 4 * 2
-    floor_cells = build_cells(manifest, arms=RISK_FLOOR_ARMS)
-    assert len(floor_cells) == (
-        len(manifest['empty_book']) + len(manifest['warm_book'])
-    ) * 2 * 2
-    assert {row['arm_id'] for row in floor_cells} == {
-        'cap10_incumbent',
-        'cap10_min_risk_005',
-    }
-
-
-def test_risk_floor_study_changes_only_the_effective_risk_floor():
-    control, treatment = RISK_FLOOR_ARMS
-
-    assert control['max_positions'] == treatment['max_positions'] == 10
-    assert (
-        control['weekly_top_n_rebalance']
-        == treatment['weekly_top_n_rebalance']
-        is False
-    )
-    assert control['min_initial_risk_fraction'] is None
-    assert treatment['min_initial_risk_fraction'] == 0.005
-    assert STUDIES['risk-floor-ab']['arms'] == RISK_FLOOR_ARMS
-    assert STUDIES['capacity-bracket']['arms'] != RISK_FLOOR_ARMS
-
-
-def test_zero_outcome_horizon_extends_rank_replay_to_last_session(monkeypatch):
-    monkeypatch.setattr(bt, '_window_setups', lambda *_args, **_kwargs: [])
-    count = bt.MIN_LOOKBACK + bt.HORIZON
-    start = date(2025, 1, 1)
-    ords = [start.toordinal() + offset for offset in range(count)]
-    columns = _prices(ords)
-
-    legacy = bt._replay_candidates_for_period(
-        'AAA',
-        columns,
-        {},
-        {},
-        None,
-        date.min,
-        'daily',
-        True,
-        True,
-    )
-    zero_horizon = bt._replay_candidates_for_period(
-        'AAA',
-        columns,
-        {},
-        {},
-        None,
-        date.min,
-        'daily',
-        True,
-        True,
-        0,
-    )
-
-    assert len(zero_horizon) == len(legacy) + bt.HORIZON
-    assert zero_horizon[-1]['date'] == date.fromordinal(ords[-1]).isoformat()
-
-
-def test_gain_to_pain_uses_all_monthly_returns_and_net_r():
-    sim = {
-        'measurement_start_equity': 100.0,
-        'trade_details': [
-            {
-                'net_r': 1.0,
-                'pnl': 10.0,
-                'shares': 1.0,
-                'entry': 100.0,
-                'fill': 110.0,
-                'transaction_cost': 0.0,
-            },
-            {
-                'net_r': -0.5,
-                'pnl': -5.0,
-                'shares': 1.0,
-                'entry': 100.0,
-                'fill': 95.0,
-                'transaction_cost': 0.0,
-            },
-        ],
-        'equity_curve': [
-            {'date': '2025-01-31', 'equity': 110.0},
-            {'date': '2025-02-28', 'equity': 99.0},
-        ],
-        'trades': 2,
-        'skipped_book_full': 0,
-    }
-
-    summary = summarize_simulation(sim)
-
-    assert summary['ev_net_r'] == pytest.approx(0.25)
-    assert summary['profit_factor'] == pytest.approx(2.0)
-    # Monthly returns are +10% and -10%; all-return numerator is zero.
-    assert summary['gain_to_pain'] == pytest.approx(0.0)
-
-
-def test_simple_cluster_bootstrap_is_deterministic_and_not_a_gate():
-    first = bootstrap_median_interval(
-        [1, 2, 3, 4, 5, 6, 7],
-        seed_parts=('determinism',),
-        replicates=500,
-    )
-    second = bootstrap_median_interval(
-        [1, 2, 3, 4, 5, 6, 7],
-        seed_parts=('determinism',),
-        replicates=500,
-    )
-
-    assert first == second
-    assert first['point'] == 4
-    assert first['p05'] <= first['point'] <= first['p95']
-
-
-def test_iqr_materializes_generator_before_both_quantiles():
-    assert iqr(value for value in (0.0, 1.0, 2.0, 3.0)) == pytest.approx(1.5)
-
-
-def test_aggregate_reports_paired_years_and_separate_warm_iqrs():
-    cells: list[dict] = []
-    for cost in (0.1, 0.2):
-        for cluster in ANCHOR_YEARS:
-            for seed in range(3):
-                path_id = f'warm-{cluster}-{seed}'
-                for arm_id, shift in (
-                    ('cap10_incumbent', 0.0),
-                    ('cash_unbounded', 0.2),
-                    ('cap10_weekly_top10', 0.1),
-                    ('cap15_incumbent', 0.05),
-                ):
-                    cells.append({
-                        'arm_id': arm_id,
-                        'protocol': 'warm_book',
-                        'path_id': path_id,
-                        'cluster': cluster,
-                        'cost_per_side_pct': cost,
-                        'metrics': {
-                            'ev_net_r': seed + shift,
-                            'calmar': 1.0 + seed * 0.1 + shift,
-                            'profit_factor': 1.5 + shift,
-                            'gain_to_pain': 2.0 + shift,
-                            'sortino': 1.0 + shift,
-                            'cagr_pct': 10.0 + shift,
-                            'max_drawdown_pct': 5.0,
-                            'total_return_pct': 10.0 + shift,
-                            'sharpe': 1.0 + shift,
-                        },
-                    })
-            for arm_id, shift in (
-                ('cap10_incumbent', 0.0),
-                ('cash_unbounded', 0.2),
-                ('cap10_weekly_top10', 0.1),
-                ('cap15_incumbent', 0.05),
-            ):
-                cells.append({
-                    'arm_id': arm_id,
-                    'protocol': 'empty_book',
-                    'path_id': f'empty-{cluster}',
-                    'cluster': cluster,
-                    'cost_per_side_pct': cost,
-                    'metrics': {
-                        'ev_net_r': 1.0 + shift,
-                        'calmar': 2.0 + shift,
-                        'profit_factor': 1.5 + shift,
-                        'gain_to_pain': 2.0 + shift,
-                        'sortino': 1.0 + shift,
-                        'cagr_pct': 10.0 + shift,
-                        'max_drawdown_pct': 5.0,
-                        'total_return_pct': 10.0 + shift,
-                        'sharpe': 1.0 + shift,
-                    },
-                })
-
-    report = aggregate_results(cells)
-
-    cash_empty = next(
-        row
-        for row in report['paired_per_year']
-        if row['arm_id'] == 'cash_unbounded'
-        and row['protocol'] == 'empty_book'
-        and row['cost_per_side_pct'] == 0.1
-    )
-    assert cash_empty['headline']['ev_net_r']['paired_delta_median'] == pytest.approx(
-        0.2
-    )
-    cash_paths = next(
-        row
-        for row in report['paired_path_distributions']
-        if row['arm_id'] == 'cash_unbounded'
-        and row['protocol'] == 'empty_book'
-        and row['cost_per_side_pct'] == 0.1
-    )
-    assert cash_paths['metrics']['ev_net_r']['paired_delta_mean'] == pytest.approx(
-        0.2
-    )
-    assert cash_paths['metrics']['ev_net_r']['positive_fraction'] == 1.0
-    assert cash_paths['metrics']['ev_net_r']['identical_fraction'] == 0.0
-    cash_warm = next(
-        row
-        for row in report['warm_seed_dispersion']
-        if row['arm_id'] == 'cash_unbounded'
-        and row['cost_per_side_pct'] == 0.1
-    )
-    assert set(cash_warm['headline']) == {'ev_net_r', 'calmar'}
-    assert 'D' not in cash_warm
-    assert cash_warm['headline']['ev_net_r']['median_iqr_ratio'] == pytest.approx(
-        1.0
-    )
-    assert cash_warm['headline']['calmar']['median_iqr_ratio'] == pytest.approx(
-        1.0
-    )
-    assert cash_warm['headline']['ev_net_r']['bootstrap_90']['n'] == 7
-    markdown = _markdown({
-        'generated_at': '2026-08-05T00:00:00Z',
-        'analysis': report,
-        'operational_summary': _operational_summary(cells),
-        'validation': {
-            'construction_universe_manifest': {
-                'construction_symbols_with_prices': 506,
-                'rank_only_symbols_with_prices': 4148,
-                'ranking_symbols_with_prices': 4654,
-            },
-            'candidate_rank_coverage': {
-                'construction_qualified_longs': 5000,
-                'filtered_rank_only_qualified_longs': 137000,
-            },
-        },
-    })
-    assert 'ΔGain-to-Pain' in markdown
-    assert '0.10% per fill' in markdown
-    assert '0.20% per fill' in markdown
-    assert 'Tradable setup symbols with prices: 506.' in markdown
-    assert 'Rank-only qualified rows removed: 137000.' in markdown
-    assert 'formal promotion gate' in markdown
-
-    focused_cells = [
-        row
-        for row in cells
-        if row['arm_id'] == 'cap10_incumbent'
-    ] + [
-        {
-            **row,
-            'arm_id': 'cap10_min_risk_005',
-        }
-        for row in cells
-        if row['arm_id'] == 'cash_unbounded'
-    ]
-    focused_analysis = aggregate_results(
-        focused_cells,
-        arms=RISK_FLOOR_ARMS,
-        include_warm_dispersion=False,
-    )
-    assert focused_analysis['warm_seed_dispersion'] == []
-    focused_markdown = _risk_floor_markdown({
-        'generated_at': '2026-08-05T00:00:00Z',
-        'arms': list(RISK_FLOOR_ARMS),
-        'protocols': ['empty_book', 'warm_book'],
-        'costs_per_side_pct': [0.1, 0.2],
-        'analysis': focused_analysis,
-        'operational_summary': _operational_summary(
-            focused_cells,
-            arms=RISK_FLOOR_ARMS,
-        ),
-    })
-    assert '# Effective initial-risk floor A/B' in focused_markdown
-    assert 'Mean dEV' in focused_markdown
-    assert 'Identical' in focused_markdown
-    assert 'Mean dGtP' in focused_markdown
-    assert 'Mean dCalmar/MAR' in focused_markdown
-    assert 'Floor rejects' in focused_markdown
-    assert 'not independent evidence' in focused_markdown
-
-
-def test_synthetic_worker_matrix_covers_four_arms_protocols_and_costs(monkeypatch):
-    monkeypatch.setenv('BACKTEST_SNAPSHOT_OFFLINE', '0')
-    monkeypatch.setenv('BACKTEST_ALLOW_SPAWN', '0')
-    start = date(2025, 1, 6)
-    sessions = _business_days(start, date(2025, 1, 17))
-    ords = [session.toordinal() for session in sessions]
-    symbols = [f'S{index}' for index in range(12)]
-    prices = {symbol: _prices(ords) for symbol in symbols}
-    candidates = [
-        _candidate(symbol, start, rank=99.0 - index)
-        for index, symbol in enumerate(symbols[:11])
-    ]
-    friday = date(2025, 1, 10)
-    candidates.append(_candidate('S11', friday, rank=99.0))
-    rank_map = {
-        (symbol, friday.isoformat()): {
-            'strategy_rank': 100.0 if symbol == 'S11' else float(index)
-        }
-        for index, symbol in enumerate(symbols)
-    }
-    _worker_init({
-        'qualified_candidates': candidates,
-        'daily_rank_map': rank_map,
-        'prices': prices,
-        'benchmark_closes': None,
-        'ranking_key': 'residual_high_vol_blend_80_20',
-        'exit_policy': 'hold',
-        'hold_days': 30,
-        'risk_per_trade': 0.01,
-        'atr_trail_multiplier': 3.0,
-    })
-
-    rows = []
-    for protocol, measurement_start in (
-        ('empty_book', start),
-        ('warm_book', date(2025, 1, 8)),
-    ):
-        for cost in (0.1, 0.2):
-            for arm_id in (
-                'cap10_incumbent',
-                'cash_unbounded',
-                'cap10_weekly_top10',
-                'cap15_incumbent',
-            ):
-                rows.append(_worker_run_cell({
-                    'cell_id': f'{arm_id}|{protocol}|{cost}',
-                    'arm_id': arm_id,
-                    'protocol': protocol,
-                    'path_id': f'{protocol}-synthetic',
-                    'cluster': 2025,
-                    'simulation_start': start.isoformat(),
-                    'measurement_start': measurement_start.isoformat(),
-                    'hard_end_exclusive': date(2025, 1, 14).isoformat(),
-                    'cost_per_side_pct': cost,
-                }))
-
-    assert len(rows) == 16
-    assert {row['arm_id'] for row in rows} == {
-        'cap10_incumbent',
-        'cash_unbounded',
-        'cap10_weekly_top10',
-        'cap15_incumbent',
-    }
-    assert {row['protocol'] for row in rows} == {'empty_book', 'warm_book'}
-    assert {row['cost_per_side_pct'] for row in rows} == {0.1, 0.2}
-    assert all('ev_net_r' in row['metrics'] for row in rows)
-
-
-def test_checkpoint_resume_rejects_fingerprint_mismatch(tmp_path):
-    checkpoint = tmp_path / 'checkpoint'
-    completed = _checkpoint_state(checkpoint, 'fingerprint-a', resume=False)
-    assert completed == {}
-    _write_cell_checkpoint(
-        checkpoint,
-        {'cell_id': 'one', 'metrics': {'ev_net_r': 1.0}},
-    )
-    resumed = _checkpoint_state(checkpoint, 'fingerprint-a', resume=True)
-    assert set(resumed) == {'one'}
-    with pytest.raises(SystemExit, match='fingerprint mismatch'):
-        _checkpoint_state(checkpoint, 'fingerprint-b', resume=True)
-
-
-def test_dirty_worktree_guard(monkeypatch):
-    monkeypatch.setattr(
-        'scripts.run_portfolio_construction_matrix._git_output',
-        lambda *_args: ' M changed.py',
-    )
-    with pytest.raises(SystemExit, match='dirty worktree'):
-        _assert_clean_worktree()

tests/unit/test_rr_scanner_preservation.py

@@ -24,7 +24,6 @@ from app.models.ohlcv import OHLCVRecord
 from app.models.paper_trade import PaperTrade
 from app.models.signal_context_snapshot import SignalContextSnapshot
 from app.models.sec_filing_gap import SecFilingGap
-from app.models.settings import SystemSetting
 from app.models.sr_level import SRLevel
 from app.models.ticker import Ticker
 from app.models.trade_setup import TradeSetup
@@ -524,10 +523,6 @@ async def test_get_trade_setups_hides_active_sec_filing_gap(
     db_session.add(ticker)
     await db_session.flush()
     db_session.add_all([
-        SystemSetting(
-            key="fundamental_data_sec_dolt_cutover_enabled",
-            value="true",
-        ),
         SecFilingGap(
             cik=ticker.cik,
             accession="0000000042-26-000001",

tests/unit/test_schedule_config.py

@@ -66,26 +66,11 @@ class TestTradingDayCrons:
         assert "Mon" in weekdays, f"{key} skips Mondays — numeric day-of-week?"
         assert {"Sat", "Sun"}.isdisjoint(weekdays), f"{key} fires on a weekend"
 
-    def test_fundamentals_runs_on_monday(self):
-        from datetime import datetime
-
-        from apscheduler.triggers.cron import CronTrigger
-
-        trigger = CronTrigger.from_crontab(
-            SCHEDULE_DEFAULTS["schedule_fundamentals_cron"],
-            timezone=SCHEDULE_DEFAULTS["schedule_timezone"],
-        )
-        fire = trigger.get_next_fire_time(
-            None, datetime(2026, 7, 19, tzinfo=trigger.timezone)
-        )
-        assert fire.strftime("%a") == "Mon"
-
     @pytest.mark.parametrize(
         ("key", "hour", "minute"),
         (
             ("schedule_dolt_earnings_cron", 2, 30),
             ("schedule_sec_fundamentals_cron", 4, 0),
-            ("schedule_fundamentals_parity_cron", 5, 30),
         ),
     )
     def test_shadow_imports_run_daily_at_expected_et_time(
@@ -122,7 +107,7 @@ class TestScheduleConfig:
 
     async def test_rejects_bad_cron(self, session: AsyncSession):
         with pytest.raises(ValidationError):
-            await update_schedule_config(session, {"schedule_fundamentals_cron": "every monday"})
+            await update_schedule_config(session, {"schedule_daily_pipeline_cron": "every monday"})
 
     async def test_rejects_bad_timezone(self, session: AsyncSession):
         with pytest.raises(ValidationError):

tests/unit/test_scheduler.py

@@ -1,5 +1,6 @@
 """Unit tests for app.scheduler module."""
 
+import asyncio
 from types import SimpleNamespace
 
 import pytest
@@ -12,9 +13,7 @@ from app.scheduler import (
     _parse_frequency,
     _resume_tickers,
     _last_successful,
-    _run_shadow_import,
+    _run_source_import,
-    collect_fundamentals,
-    run_fundamentals_parity_report,
     run_sec_fundamentals_import,
     configure_scheduler,
     get_job_runtime_snapshot,
@@ -123,10 +122,8 @@ class TestConfigureScheduler:
             "data_backfill",
             "benchmark_collector",
             "sentiment_collector",
-            "fundamental_collector",
             "dolt_earnings_import",
             "sec_fundamentals_import",
-            "fundamentals_parity_report",
             "rr_scanner",
             "shadow_book",
             "ticker_universe_sync",
@@ -157,10 +154,8 @@ class TestConfigureScheduler:
             "intraday_pipeline",
             "data_collector",
             "data_backfill",
-            "fundamental_collector",
             "dolt_earnings_import",
             "sec_fundamentals_import",
-            "fundamentals_parity_report",
             "market_regime",
             "near_close_pipeline",
             "regime_monitor",
@@ -181,42 +176,7 @@ class _SessionContext:
         return None
 
 
-class TestFundamentalCollector:
+class TestSourceImportJobs:
-    @staticmethod
-    def _session_factory():
-        return _SessionContext()
-
-    async def test_skips_legacy_provider_when_cutover_is_active(self, monkeypatch):
-        async def enabled(db, job_name):
-            return True
-
-        async def cutover_enabled(db):
-            return True
-
-        async def unexpected_ticker_lookup(db):
-            raise AssertionError("legacy ticker lookup must not run after cutover")
-
-        monkeypatch.setattr("app.scheduler.async_session_factory", self._session_factory)
-        monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
-        monkeypatch.setattr(
-            "app.scheduler.fundamental_data_refresh_service.is_enabled",
-            cutover_enabled,
-        )
-        monkeypatch.setattr(
-            "app.scheduler._get_fundamental_priority_tickers",
-            unexpected_ticker_lookup,
-        )
-
-        await collect_fundamentals()
-
-        runtime = get_job_runtime_snapshot("fundamental_collector")
-        assert runtime["status"] == "skipped"
-        assert runtime["processed"] == 0
-        assert runtime["total"] == 0
-        assert runtime["message"] == "SEC + Dolt fundamentals cutover is active"
-
-
-class TestShadowImportJobs:
     @staticmethod
     def _session_factory():
         return _SessionContext()
@@ -234,7 +194,7 @@ class TestShadowImportJobs:
         monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
         monkeypatch.setattr("app.scheduler.run_import", imported)
 
-        await _run_shadow_import("dolt_earnings_import", object())
+        await _run_source_import("dolt_earnings_import", object())
 
         runtime = get_job_runtime_snapshot("dolt_earnings_import")
         assert runtime["status"] == "completed"
@@ -254,7 +214,7 @@ class TestShadowImportJobs:
         monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
         monkeypatch.setattr("app.scheduler.run_import", imported)
 
-        await _run_shadow_import("sec_fundamentals_import", object())
+        await _run_source_import("sec_fundamentals_import", object())
 
         runtime = get_job_runtime_snapshot("sec_fundamentals_import")
         assert runtime["status"] == "error"
@@ -276,7 +236,7 @@ class TestShadowImportJobs:
         monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
         monkeypatch.setattr("app.scheduler.run_import", imported)
 
-        await _run_shadow_import("sec_fundamentals_import", object())
+        await _run_source_import("sec_fundamentals_import", object())
 
         runtime = get_job_runtime_snapshot("sec_fundamentals_import")
         assert runtime["status"] == STATUS_DEFERRED
@@ -294,7 +254,7 @@ class TestShadowImportJobs:
         monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
         monkeypatch.setattr("app.scheduler.run_import", imported)
 
-        await _run_shadow_import("dolt_earnings_import", object())
+        await _run_source_import("dolt_earnings_import", object())
 
         runtime = get_job_runtime_snapshot("dolt_earnings_import")
         assert runtime["status"] == "skipped"
@@ -311,14 +271,15 @@ class TestShadowImportJobs:
         monkeypatch.setattr("app.scheduler._is_job_enabled", disabled)
         monkeypatch.setattr("app.scheduler.run_import", should_not_run)
 
-        await _run_shadow_import("sec_fundamentals_import", object())
+        await _run_source_import("sec_fundamentals_import", object())
 
         runtime = get_job_runtime_snapshot("sec_fundamentals_import")
         assert runtime["status"] == "skipped"
         assert runtime["message"] == "Disabled"
 
-    async def test_sec_failure_still_runs_activated_local_refresh(self, monkeypatch):
+    async def test_sec_failure_still_runs_local_cache_refresh(self, monkeypatch):
         calls = []
+        events = []
 
         async def enabled(db, job_name):
             return True
@@ -329,29 +290,40 @@ class TestShadowImportJobs:
         async def refreshed(db):
             calls.append(db)
             return {
-                "enabled": True,
                 "refreshed": 511,
                 "score_inputs_changed": 2,
                 "dimension_scores_staled": 2,
                 "composite_scores_staled": 2,
             }
 
+        async def record(**kwargs):
+            events.append(kwargs)
+
         monkeypatch.setattr("app.scheduler.async_session_factory", self._session_factory)
         monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
         monkeypatch.setattr("app.scheduler.run_import", unavailable)
+        monkeypatch.setattr("app.scheduler._record_system_event", record)
         monkeypatch.setattr(
-            "app.scheduler.fundamental_data_refresh_service.refresh_if_enabled",
+            "app.scheduler.fundamental_data_refresh_service.refresh",
             refreshed,
         )
 
         await run_sec_fundamentals_import()
+        await asyncio.sleep(0)  # let the fire-and-forget event task run
 
         assert len(calls) == 1
         runtime = get_job_runtime_snapshot("sec_fundamentals_import")
         assert runtime["status"] == "error"
-        assert runtime["message"] == "SEC unavailable"
+        # the failure stays the headline, but the cache result is still visible
+        assert runtime["message"] == (
+            "SEC unavailable · cache 511 · 2 score inputs changed"
+        )
+        # Rewording the outcome must not duplicate the durable event: the dedup
+        # key includes the message, so a second finish would show up twice in
+        # Admin → System Events.
+        assert len(events) == 1, events
 
-    async def test_sec_success_surfaces_activated_refresh_summary(self, monkeypatch):
+    async def test_sec_success_surfaces_cache_refresh_summary(self, monkeypatch):
         async def enabled(db, job_name):
             return True
 
@@ -362,7 +334,6 @@ class TestShadowImportJobs:
 
         async def refreshed(db):
             return {
-                "enabled": True,
                 "refreshed": 511,
                 "score_inputs_changed": 2,
                 "dimension_scores_staled": 2,
@@ -373,7 +344,7 @@ class TestShadowImportJobs:
         monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
         monkeypatch.setattr("app.scheduler.run_import", imported)
         monkeypatch.setattr(
-            "app.scheduler.fundamental_data_refresh_service.refresh_if_enabled",
+            "app.scheduler.fundamental_data_refresh_service.refresh",
             refreshed,
         )
 
@@ -385,52 +356,79 @@ class TestShadowImportJobs:
             "no_op · abcdef123456 · cache 511 · 2 score inputs changed"
         )
 
-    async def test_disabled_sec_job_does_not_run_local_refresh(self, monkeypatch):
+    async def test_source_locked_sec_run_still_reports_the_cache_refresh(
-        async def disabled(db, job_name):
+        self, monkeypatch
-            return False
+    ):
+        """A skipped import keeps its skip status but shows the cache advanced."""
 
-        async def should_not_run(*args, **kwargs):
+        async def enabled(db, job_name):
-            raise AssertionError("disabled SEC job ran work")
+            return True
+
+        async def locked(importer):
+            return None  # another import owns the source lock
+
+        async def refreshed(db):
+            return {
+                "refreshed": 511,
+                "score_inputs_changed": 0,
+                "dimension_scores_staled": 0,
+                "composite_scores_staled": 0,
+            }
 
         monkeypatch.setattr("app.scheduler.async_session_factory", self._session_factory)
-        monkeypatch.setattr("app.scheduler._is_job_enabled", disabled)
+        monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
-        monkeypatch.setattr("app.scheduler.run_import", should_not_run)
+        monkeypatch.setattr("app.scheduler.run_import", locked)
         monkeypatch.setattr(
-            "app.scheduler.fundamental_data_refresh_service.refresh_if_enabled",
+            "app.scheduler.fundamental_data_refresh_service.refresh",
-            should_not_run,
+            refreshed,
         )
 
         await run_sec_fundamentals_import()
 
         runtime = get_job_runtime_snapshot("sec_fundamentals_import")
         assert runtime["status"] == "skipped"
-        assert runtime["message"] == "Disabled"
+        assert runtime["message"] == (
-
+            "Another import for this source is already running · "
-
+            "cache 511 · 0 score inputs changed"
-async def test_fundamentals_parity_job_surfaces_report_summary(monkeypatch):
-    async def enabled(db, job_name):
-        return True
-
-    async def generated(db, report_dir):
-        return (
-            {
-                "generated_at": "2026-07-23T10:30:00+00:00",
-                "summary": {
-                    "universe_count": 511,
-                    "fundamental_score_material_changes": 12,
-                },
-            },
-            {"json": "report.json", "csv": "report.csv"},
         )
 
-    monkeypatch.setattr("app.scheduler.async_session_factory", TestShadowImportJobs._session_factory)
+    async def test_disabled_sec_job_still_refreshes_local_cache(self, monkeypatch):
-    monkeypatch.setattr("app.scheduler._is_job_enabled", enabled)
+        """Disabling the job stops the SEC fetch, not the local cache.
-    monkeypatch.setattr(
-        "app.scheduler.fundamentals_parity_service.generate_and_store", generated
-    )
 
-    await run_fundamentals_parity_report()
+        The cache is derived from stored snapshots, earnings events and closes.
+        Prices and earnings move daily even when no filing does, and there is no
+        provider fallback since A6 — freezing it would silently stale scoring.
+        """
+        calls = []
 
-    runtime = get_job_runtime_snapshot("fundamentals_parity_report")
+        async def disabled(db, job_name):
-    assert runtime["status"] == "completed"
+            return False
-    assert runtime["message"] == "511 tickers · 12 material score changes"
+
+        async def should_not_run(*args, **kwargs):
+            raise AssertionError("disabled SEC job hit the network")
+
+        async def refreshed(db):
+            calls.append(db)
+            return {
+                "refreshed": 511,
+                "score_inputs_changed": 2,
+                "dimension_scores_staled": 2,
+                "composite_scores_staled": 2,
+            }
+
+        monkeypatch.setattr("app.scheduler.async_session_factory", self._session_factory)
+        monkeypatch.setattr("app.scheduler._is_job_enabled", disabled)
+        monkeypatch.setattr("app.scheduler.run_import", should_not_run)
+        monkeypatch.setattr(
+            "app.scheduler.fundamental_data_refresh_service.refresh",
+            refreshed,
+        )
+
+        await run_sec_fundamentals_import()
+
+        assert len(calls) == 1
+        runtime = get_job_runtime_snapshot("sec_fundamentals_import")
+        assert runtime["status"] == "completed"
+        assert runtime["message"] == (
+            "Import disabled · cache 511 · 2 score inputs changed"
+        )

tests/unit/test_ticker_universe_service.py

@@ -10,7 +10,6 @@ from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 
 from app.database import Base
-from app.exceptions import ProviderError
 from app.models.settings import SystemSetting
 from app.models.ticker import Ticker
 from app.services import ticker_universe_service
@@ -97,11 +96,7 @@ async def test_fetch_universe_symbols_uses_cached_snapshot_when_live_sources_fai
     async def _fake_public(_universe: str):
         return [], ["public failed"], None
 
-    async def _fake_fmp(_universe: str):
-        raise ProviderError("fmp failed")
-
     monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_public", _fake_public)
-    monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_fmp", _fake_fmp)
 
     symbols, source = await ticker_universe_service.fetch_universe_symbols(session, "sp500")
     assert symbols == ["AAPL", "MSFT"]
@@ -116,11 +111,7 @@ async def test_fetch_universe_symbols_uses_seed_when_live_and_cache_fail(
     async def _fake_public(_universe: str):
         return [], ["public failed"], None
 
-    async def _fake_fmp(_universe: str):
-        raise ProviderError("fmp failed")
-
     monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_public", _fake_public)
-    monkeypatch.setattr(ticker_universe_service, "_fetch_universe_symbols_from_fmp", _fake_fmp)
 
     symbols, source = await ticker_universe_service.fetch_universe_symbols(session, "sp500")
     assert "AAPL" in symbols