dennisthiessen
6511a1020b
A NEUTRAL ("No Clear Setup") recommendation means the engine found no clear
directional trade, yet such setups could still qualify and even be crowned the
top pick purely on momentum rank (e.g. an extended momentum leader with a far,
5%-probability target). A NEUTRAL signal isn't actionable, so it shouldn't
qualify.
New `exclude_neutral` activation flag (default on): setup_qualifies drops setups
whose recommended_action is NEUTRAL. It lives in the shared gate, so it flows
through the dashboard's qualified/top-pick selection, the track record's
qualified stats, and the backtest (which computes recommended_action and gates on
meets_core). Toggleable in Admin → Settings → Activation; the frontend mirror and
activationSummary ("directional") match.
Re-run the backtest after enabling to confirm it holds/improves expectancy.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
653 lines
24 KiB
Python
653 lines
24 KiB
Python
"""Admin service: user management, system settings, data cleanup, job control."""
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import logging
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from datetime import datetime, timedelta, timezone
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from passlib.hash import bcrypt
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from sqlalchemy import delete, func, select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.exceptions import DuplicateError, NotFoundError, ValidationError
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from app.models.fundamental import FundamentalData
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from app.models.ohlcv import OHLCVRecord
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from app.models.score import CompositeScore, DimensionScore
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from app.models.sentiment import SentimentScore
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from app.models.sr_level import SRLevel
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from app.models.settings import SystemSetting
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from app.models.ticker import Ticker
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from app.models.trade_setup import TradeSetup
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from app.models.user import User
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from app.services import settings_store
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logger = logging.getLogger(__name__)
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RECOMMENDATION_CONFIG_DEFAULTS: dict[str, float] = {
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"recommendation_high_confidence_threshold": 70.0,
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"recommendation_moderate_confidence_threshold": 50.0,
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"recommendation_confidence_diff_threshold": 20.0,
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"recommendation_signal_alignment_weight": 0.15,
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"recommendation_sr_strength_weight": 0.20,
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"recommendation_momentum_technical_divergence_threshold": 30.0,
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"recommendation_fundamental_technical_divergence_threshold": 40.0,
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}
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DEFAULT_TICKER_UNIVERSE = "sp500"
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SUPPORTED_TICKER_UNIVERSES = {"sp500", "nasdaq100", "nasdaq_all"}
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# Activation gate: what counts as a signal worth acting on. Used by the
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# Dashboard's "Qualified" metric, the Signals "Qualified only" view, and the
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# Track Record's qualified stats. The outcome evaluator deliberately ignores
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# these — every setup is evaluated so the gate itself can be validated.
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#
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# The core test is expected value (in R): probability-weighted asymmetry, so a
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# fat-but-improbable target and a likely-but-thin one are both rejected. R:R and
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# confidence are floors; high-conviction / clean-read / target-probability are
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# optional tighteners (off by default — turn on to be more selective).
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_ACTIVATION_FLOAT_KEYS: dict[str, str] = {
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"min_momentum_percentile": "activation_min_momentum_percentile",
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"min_rr": "activation_min_rr",
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"min_confidence": "activation_min_confidence",
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}
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_ACTIVATION_BOOL_KEYS: dict[str, str] = {
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"require_high_conviction": "activation_require_high_conviction",
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"exclude_conflicts": "activation_exclude_conflicts",
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"exclude_neutral": "activation_exclude_neutral",
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}
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ACTIVATION_DEFAULTS: dict[str, float | bool] = {
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"min_momentum_percentile": 80.0,
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"min_rr": 1.2,
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"min_confidence": 55.0,
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"require_high_conviction": False,
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"exclude_conflicts": False,
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# On by default: a NEUTRAL ("no clear setup") recommendation isn't an
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# actionable signal, so it shouldn't qualify or be crowned a top pick.
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"exclude_neutral": True,
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}
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# ---------------------------------------------------------------------------
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# User management
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# ---------------------------------------------------------------------------
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async def list_users(db: AsyncSession) -> list[User]:
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"""Return all users ordered by id."""
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result = await db.execute(select(User).order_by(User.id))
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return list(result.scalars().all())
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async def create_user(
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db: AsyncSession,
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username: str,
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password: str,
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role: str = "user",
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has_access: bool = False,
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) -> User:
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"""Create a new user account (admin action)."""
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result = await db.execute(select(User).where(User.username == username))
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if result.scalar_one_or_none() is not None:
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raise DuplicateError(f"Username already exists: {username}")
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user = User(
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username=username,
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password_hash=bcrypt.hash(password),
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role=role,
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has_access=has_access,
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)
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db.add(user)
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await db.commit()
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await db.refresh(user)
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return user
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async def set_user_access(db: AsyncSession, user_id: int, has_access: bool) -> User:
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"""Grant or revoke API access for a user."""
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result = await db.execute(select(User).where(User.id == user_id))
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user = result.scalar_one_or_none()
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if user is None:
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raise NotFoundError(f"User not found: {user_id}")
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user.has_access = has_access
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await db.commit()
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await db.refresh(user)
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return user
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async def reset_password(db: AsyncSession, user_id: int, new_password: str) -> User:
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"""Reset a user's password."""
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result = await db.execute(select(User).where(User.id == user_id))
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user = result.scalar_one_or_none()
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if user is None:
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raise NotFoundError(f"User not found: {user_id}")
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user.password_hash = bcrypt.hash(new_password)
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await db.commit()
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await db.refresh(user)
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return user
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# ---------------------------------------------------------------------------
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# Registration toggle
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# ---------------------------------------------------------------------------
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async def toggle_registration(db: AsyncSession, enabled: bool) -> SystemSetting:
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"""Enable or disable user registration via SystemSetting."""
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setting = await settings_store.upsert_setting(db, "registration_enabled", str(enabled).lower())
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await db.commit()
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await db.refresh(setting)
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return setting
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# ---------------------------------------------------------------------------
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# System settings CRUD
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# ---------------------------------------------------------------------------
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async def list_settings(db: AsyncSession) -> list[SystemSetting]:
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"""Return all system settings."""
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result = await db.execute(select(SystemSetting).order_by(SystemSetting.key))
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return list(result.scalars().all())
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async def update_setting(db: AsyncSession, key: str, value: str) -> SystemSetting:
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"""Create or update a system setting."""
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setting = await settings_store.upsert_setting(db, key, value)
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await db.commit()
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await db.refresh(setting)
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return setting
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# ---------------------------------------------------------------------------
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# Activation thresholds
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# ---------------------------------------------------------------------------
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async def get_activation_config(db: AsyncSession) -> dict[str, float | bool]:
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"""Return the activation gate config with public keys."""
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result = await db.execute(
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select(SystemSetting).where(SystemSetting.key.like("activation_%"))
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)
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stored = {s.key: s.value for s in result.scalars().all()}
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config: dict[str, float | bool] = dict(ACTIVATION_DEFAULTS)
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for public_key, storage_key in _ACTIVATION_FLOAT_KEYS.items():
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if storage_key in stored:
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try:
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config[public_key] = float(stored[storage_key])
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except (TypeError, ValueError):
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pass
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for public_key, storage_key in _ACTIVATION_BOOL_KEYS.items():
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if storage_key in stored:
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config[public_key] = str(stored[storage_key]).strip().lower() == "true"
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return config
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async def update_activation_config(
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db: AsyncSession, updates: dict[str, float | bool]
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) -> dict[str, float | bool]:
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"""Update the activation gate. Accepts public keys; only supplied keys change."""
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if "min_momentum_percentile" in updates and not 0 <= updates["min_momentum_percentile"] <= 100:
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raise ValidationError("min_momentum_percentile must be between 0 and 100")
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if "min_rr" in updates and updates["min_rr"] < 0:
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raise ValidationError("min_rr must be >= 0")
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if "min_confidence" in updates and not 0 <= updates["min_confidence"] <= 100:
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raise ValidationError("min_confidence must be between 0 and 100")
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for public_key, storage_key in _ACTIVATION_FLOAT_KEYS.items():
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if public_key in updates and updates[public_key] is not None:
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await update_setting(db, storage_key, str(float(updates[public_key])))
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for public_key, storage_key in _ACTIVATION_BOOL_KEYS.items():
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if public_key in updates and updates[public_key] is not None:
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await update_setting(db, storage_key, "true" if updates[public_key] else "false")
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return await get_activation_config(db)
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# ---------------------------------------------------------------------------
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# Pipeline schedule (cron)
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# ---------------------------------------------------------------------------
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async def get_schedule_config(db: AsyncSession) -> dict[str, str]:
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"""Cron schedule for the daily/intraday pipelines and fundamentals."""
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from app.scheduler import load_schedule_config
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return await load_schedule_config(db)
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async def update_schedule_config(
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db: AsyncSession, updates: dict[str, str]
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) -> dict[str, str]:
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"""Validate, persist, and apply cron schedule changes to the running scheduler."""
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from app.scheduler import (
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SCHEDULE_DEFAULTS,
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load_schedule_config,
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reschedule_jobs,
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validate_cron,
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)
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current = await load_schedule_config(db)
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tz = (updates.get("schedule_timezone") or current["schedule_timezone"]).strip()
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for key, value in updates.items():
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if key not in SCHEDULE_DEFAULTS:
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raise ValidationError(f"Unknown schedule key: {key}")
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if key == "schedule_timezone":
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# Validate the timezone against an existing cron expression.
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try:
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validate_cron(current["schedule_daily_pipeline_cron"], value)
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except Exception as exc:
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raise ValidationError(f"Invalid timezone: {value}") from exc
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else:
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try:
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validate_cron(value, tz)
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except Exception as exc:
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raise ValidationError(f"Invalid cron for {key}: {value!r}") from exc
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for key, value in updates.items():
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await update_setting(db, key, str(value).strip())
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new_config = await load_schedule_config(db)
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try:
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reschedule_jobs(new_config)
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except Exception:
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# Scheduler may not be running (e.g. unit tests) — the config is saved
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# regardless and applied on next startup.
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logger.warning("Could not reschedule jobs after config update", exc_info=True)
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return new_config
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def _recommendation_public_to_storage_key(key: str) -> str:
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return f"recommendation_{key}"
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async def get_recommendation_config(db: AsyncSession) -> dict[str, float]:
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result = await db.execute(
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select(SystemSetting).where(SystemSetting.key.like("recommendation_%"))
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)
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rows = result.scalars().all()
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config = dict(RECOMMENDATION_CONFIG_DEFAULTS)
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for row in rows:
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try:
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config[row.key] = float(row.value)
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except (TypeError, ValueError):
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continue
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return {
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"high_confidence_threshold": config["recommendation_high_confidence_threshold"],
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"moderate_confidence_threshold": config["recommendation_moderate_confidence_threshold"],
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"confidence_diff_threshold": config["recommendation_confidence_diff_threshold"],
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"signal_alignment_weight": config["recommendation_signal_alignment_weight"],
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"sr_strength_weight": config["recommendation_sr_strength_weight"],
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"momentum_technical_divergence_threshold": config["recommendation_momentum_technical_divergence_threshold"],
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"fundamental_technical_divergence_threshold": config["recommendation_fundamental_technical_divergence_threshold"],
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}
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async def update_recommendation_config(
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db: AsyncSession,
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payload: dict[str, float],
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) -> dict[str, float]:
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for public_key, public_value in payload.items():
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storage_key = _recommendation_public_to_storage_key(public_key)
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await update_setting(db, storage_key, str(public_value))
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return await get_recommendation_config(db)
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async def get_ticker_universe_default(db: AsyncSession) -> dict[str, str]:
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setting = await settings_store.get_setting(db, "ticker_universe_default")
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universe = setting.value if setting else DEFAULT_TICKER_UNIVERSE
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if universe not in SUPPORTED_TICKER_UNIVERSES:
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universe = DEFAULT_TICKER_UNIVERSE
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return {"universe": universe}
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async def update_ticker_universe_default(db: AsyncSession, universe: str) -> dict[str, str]:
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normalised = universe.strip().lower()
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if normalised not in SUPPORTED_TICKER_UNIVERSES:
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supported = ", ".join(sorted(SUPPORTED_TICKER_UNIVERSES))
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raise ValidationError(f"Unsupported ticker universe '{universe}'. Supported: {supported}")
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await update_setting(db, "ticker_universe_default", normalised)
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return {"universe": normalised}
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# ---------------------------------------------------------------------------
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# Data cleanup
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# ---------------------------------------------------------------------------
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async def cleanup_data(db: AsyncSession, older_than_days: int) -> dict[str, int]:
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"""Delete OHLCV, sentiment, and fundamental records older than N days.
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Preserves tickers, users, and latest scores.
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Returns a dict with counts of deleted records per table.
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"""
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cutoff = datetime.now(timezone.utc) - timedelta(days=older_than_days)
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counts: dict[str, int] = {}
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# OHLCV — date column is a date, compare with cutoff date
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result = await db.execute(
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delete(OHLCVRecord).where(OHLCVRecord.date < cutoff.date())
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)
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counts["ohlcv"] = result.rowcount # type: ignore[assignment]
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# Sentiment — timestamp is datetime
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result = await db.execute(
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delete(SentimentScore).where(SentimentScore.timestamp < cutoff)
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)
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counts["sentiment"] = result.rowcount # type: ignore[assignment]
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# Fundamentals — fetched_at is datetime
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result = await db.execute(
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delete(FundamentalData).where(FundamentalData.fetched_at < cutoff)
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)
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counts["fundamentals"] = result.rowcount # type: ignore[assignment]
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await db.commit()
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return counts
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async def reset_trade_setups(db: AsyncSession) -> dict[str, int]:
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"""Delete all trade setups, wiping the track record for a fresh start.
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Stats are derived from evaluated trade setups, so this resets the Track
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Record to zero. Live setups regenerate on the next R:R scan. Used after
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material changes to scoring / setup generation, when historical outcomes no
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longer reflect current logic.
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"""
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result = await db.execute(delete(TradeSetup))
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await db.commit()
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return {"trade_setups": result.rowcount} # type: ignore[attr-defined]
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async def get_pipeline_readiness(db: AsyncSession) -> list[dict]:
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"""Return per-ticker readiness snapshot for ingestion/scoring/scanner pipeline."""
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tickers_result = await db.execute(select(Ticker).order_by(Ticker.symbol.asc()))
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tickers = list(tickers_result.scalars().all())
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if not tickers:
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return []
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ticker_ids = [ticker.id for ticker in tickers]
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ohlcv_stats_result = await db.execute(
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select(
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OHLCVRecord.ticker_id,
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func.count(OHLCVRecord.id),
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func.max(OHLCVRecord.date),
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)
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.where(OHLCVRecord.ticker_id.in_(ticker_ids))
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.group_by(OHLCVRecord.ticker_id)
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)
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ohlcv_stats = {
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ticker_id: {
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"bars": int(count or 0),
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"last_date": max_date.isoformat() if max_date else None,
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}
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for ticker_id, count, max_date in ohlcv_stats_result.all()
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}
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dim_rows_result = await db.execute(
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select(DimensionScore).where(DimensionScore.ticker_id.in_(ticker_ids))
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)
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dim_map_by_ticker: dict[int, dict[str, tuple[float | None, bool]]] = {}
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for row in dim_rows_result.scalars().all():
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dim_map_by_ticker.setdefault(row.ticker_id, {})[row.dimension] = (row.score, row.is_stale)
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sr_counts_result = await db.execute(
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select(SRLevel.ticker_id, func.count(SRLevel.id))
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.where(SRLevel.ticker_id.in_(ticker_ids))
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.group_by(SRLevel.ticker_id)
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)
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sr_counts = {ticker_id: int(count or 0) for ticker_id, count in sr_counts_result.all()}
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sentiment_stats_result = await db.execute(
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select(
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SentimentScore.ticker_id,
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func.count(SentimentScore.id),
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func.max(SentimentScore.timestamp),
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)
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.where(SentimentScore.ticker_id.in_(ticker_ids))
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.group_by(SentimentScore.ticker_id)
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)
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sentiment_stats = {
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ticker_id: {
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"count": int(count or 0),
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"last_at": max_ts.isoformat() if max_ts else None,
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}
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for ticker_id, count, max_ts in sentiment_stats_result.all()
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}
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fundamentals_result = await db.execute(
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select(FundamentalData.ticker_id, FundamentalData.fetched_at)
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.where(FundamentalData.ticker_id.in_(ticker_ids))
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)
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fundamentals_map = {
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ticker_id: fetched_at.isoformat() if fetched_at else None
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for ticker_id, fetched_at in fundamentals_result.all()
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}
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composites_result = await db.execute(
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select(CompositeScore.ticker_id, CompositeScore.is_stale)
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.where(CompositeScore.ticker_id.in_(ticker_ids))
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)
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composites_map = {
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ticker_id: is_stale
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for ticker_id, is_stale in composites_result.all()
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}
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setup_counts_result = await db.execute(
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select(TradeSetup.ticker_id, func.count(TradeSetup.id))
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.where(TradeSetup.ticker_id.in_(ticker_ids))
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.group_by(TradeSetup.ticker_id)
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)
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setup_counts = {ticker_id: int(count or 0) for ticker_id, count in setup_counts_result.all()}
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readiness: list[dict] = []
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for ticker in tickers:
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ohlcv = ohlcv_stats.get(ticker.id, {"bars": 0, "last_date": None})
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ohlcv_bars = int(ohlcv["bars"])
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ohlcv_last_date = ohlcv["last_date"]
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dim_map = dim_map_by_ticker.get(ticker.id, {})
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sr_count = int(sr_counts.get(ticker.id, 0))
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sentiment = sentiment_stats.get(ticker.id, {"count": 0, "last_at": None})
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sentiment_count = int(sentiment["count"])
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sentiment_last_at = sentiment["last_at"]
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fundamentals_fetched_at = fundamentals_map.get(ticker.id)
|
|
has_fundamentals = ticker.id in fundamentals_map
|
|
|
|
has_composite = ticker.id in composites_map
|
|
composite_stale = composites_map.get(ticker.id)
|
|
|
|
setup_count = int(setup_counts.get(ticker.id, 0))
|
|
|
|
missing_reasons: list[str] = []
|
|
if ohlcv_bars < 30:
|
|
missing_reasons.append("insufficient_ohlcv_bars(<30)")
|
|
if "technical" not in dim_map or dim_map["technical"][0] is None:
|
|
missing_reasons.append("missing_technical")
|
|
if "momentum" not in dim_map or dim_map["momentum"][0] is None:
|
|
missing_reasons.append("missing_momentum")
|
|
if "sr_quality" not in dim_map or dim_map["sr_quality"][0] is None:
|
|
missing_reasons.append("missing_sr_quality")
|
|
if sentiment_count == 0:
|
|
missing_reasons.append("missing_sentiment")
|
|
if not has_fundamentals:
|
|
missing_reasons.append("missing_fundamentals")
|
|
if not has_composite:
|
|
missing_reasons.append("missing_composite")
|
|
if setup_count == 0:
|
|
missing_reasons.append("missing_trade_setup")
|
|
|
|
readiness.append(
|
|
{
|
|
"symbol": ticker.symbol,
|
|
"ohlcv_bars": ohlcv_bars,
|
|
"ohlcv_last_date": ohlcv_last_date,
|
|
"dimensions": {
|
|
"technical": dim_map.get("technical", (None, True))[0],
|
|
"sr_quality": dim_map.get("sr_quality", (None, True))[0],
|
|
"sentiment": dim_map.get("sentiment", (None, True))[0],
|
|
"fundamental": dim_map.get("fundamental", (None, True))[0],
|
|
"momentum": dim_map.get("momentum", (None, True))[0],
|
|
},
|
|
"sentiment_count": sentiment_count,
|
|
"sentiment_last_at": sentiment_last_at,
|
|
"has_fundamentals": has_fundamentals,
|
|
"fundamentals_fetched_at": fundamentals_fetched_at,
|
|
"sr_level_count": sr_count,
|
|
"has_composite": has_composite,
|
|
"composite_stale": composite_stale,
|
|
"trade_setup_count": setup_count,
|
|
"missing_reasons": missing_reasons,
|
|
"ready_for_scanner": ohlcv_bars >= 15 and sr_count > 0,
|
|
}
|
|
)
|
|
|
|
return readiness
|
|
|
|
|
|
# ---------------------------------------------------------------------------
|
|
# Job control (placeholder — scheduler is Task 12.1)
|
|
# ---------------------------------------------------------------------------
|
|
|
|
VALID_JOB_NAMES = {
|
|
"data_collector",
|
|
"data_backfill",
|
|
"benchmark_collector",
|
|
"sentiment_collector",
|
|
"fundamental_collector",
|
|
"rr_scanner",
|
|
"ticker_universe_sync",
|
|
"outcome_evaluator",
|
|
"alerts",
|
|
"market_regime",
|
|
"regime_monitor",
|
|
"event_study",
|
|
"backtest",
|
|
"daily_pipeline",
|
|
"intraday_pipeline",
|
|
}
|
|
|
|
JOB_LABELS = {
|
|
"data_collector": "Data Collector (OHLCV)",
|
|
"data_backfill": "Data Backfill (deep history)",
|
|
"benchmark_collector": "Benchmark Collector",
|
|
"sentiment_collector": "Sentiment Collector",
|
|
"fundamental_collector": "Fundamental Collector",
|
|
"rr_scanner": "R:R Scanner",
|
|
"ticker_universe_sync": "Ticker Universe Sync",
|
|
"outcome_evaluator": "Outcome Evaluator",
|
|
"alerts": "Alerts Dispatcher",
|
|
"market_regime": "Market Regime",
|
|
"regime_monitor": "Regime Monitor",
|
|
"event_study": "Event Study",
|
|
"backtest": "Backtest",
|
|
"daily_pipeline": "Daily Pipeline",
|
|
"intraday_pipeline": "Intraday Pipeline",
|
|
}
|
|
|
|
# Jobs driven by the daily_pipeline (in order) rather than their own timer.
|
|
PIPELINE_MEMBERS = {
|
|
"data_collector",
|
|
"benchmark_collector",
|
|
"sentiment_collector",
|
|
"rr_scanner",
|
|
"outcome_evaluator",
|
|
"market_regime",
|
|
"regime_monitor",
|
|
}
|
|
|
|
|
|
async def list_jobs(db: AsyncSession) -> list[dict]:
|
|
"""Return status of all scheduled jobs."""
|
|
from app.scheduler import get_job_runtime_snapshot, scheduler
|
|
|
|
jobs_out = []
|
|
for name in sorted(VALID_JOB_NAMES):
|
|
# Check enabled setting
|
|
setting = await settings_store.get_setting(db, f"job_{name}_enabled")
|
|
enabled = setting.value == "true" if setting else True # default enabled
|
|
|
|
# Get scheduler job info
|
|
job = scheduler.get_job(name)
|
|
next_run = None
|
|
if job and job.next_run_time:
|
|
next_run = job.next_run_time.isoformat()
|
|
|
|
runtime = get_job_runtime_snapshot(name)
|
|
|
|
jobs_out.append({
|
|
"name": name,
|
|
"label": JOB_LABELS.get(name, name),
|
|
"enabled": enabled,
|
|
"next_run_at": next_run,
|
|
"via_pipeline": name in PIPELINE_MEMBERS,
|
|
"registered": job is not None,
|
|
"running": bool(runtime.get("running", False)),
|
|
"runtime_status": runtime.get("status"),
|
|
"runtime_processed": runtime.get("processed"),
|
|
"runtime_total": runtime.get("total"),
|
|
"runtime_progress_pct": runtime.get("progress_pct"),
|
|
"runtime_current_ticker": runtime.get("current_ticker"),
|
|
"runtime_started_at": runtime.get("started_at"),
|
|
"runtime_finished_at": runtime.get("finished_at"),
|
|
"runtime_message": runtime.get("message"),
|
|
})
|
|
|
|
return jobs_out
|
|
|
|
|
|
async def trigger_job(db: AsyncSession, job_name: str) -> dict[str, str]:
|
|
"""Trigger a manual job run via the scheduler.
|
|
|
|
Runs the job immediately (in addition to its regular schedule).
|
|
"""
|
|
if job_name not in VALID_JOB_NAMES:
|
|
raise ValidationError(f"Unknown job: {job_name}. Valid jobs: {', '.join(sorted(VALID_JOB_NAMES))}")
|
|
|
|
from app.scheduler import get_job_runtime_snapshot, scheduler
|
|
|
|
runtime_target = get_job_runtime_snapshot(job_name)
|
|
if runtime_target.get("running"):
|
|
return {
|
|
"job": job_name,
|
|
"status": "busy",
|
|
"message": f"Job '{job_name}' is already running",
|
|
}
|
|
|
|
all_runtime = get_job_runtime_snapshot()
|
|
for running_name, runtime in all_runtime.items():
|
|
if running_name == job_name:
|
|
continue
|
|
if runtime.get("running"):
|
|
return {
|
|
"job": job_name,
|
|
"status": "blocked",
|
|
"message": f"Cannot trigger '{job_name}' while '{running_name}' is running",
|
|
}
|
|
|
|
job = scheduler.get_job(job_name)
|
|
if job is None:
|
|
return {"job": job_name, "status": "not_found", "message": f"Job '{job_name}' is not registered in the scheduler"}
|
|
|
|
job.modify(next_run_time=None) # Reset, then trigger immediately
|
|
from datetime import datetime, timezone
|
|
job.modify(next_run_time=datetime.now(timezone.utc))
|
|
|
|
return {"job": job_name, "status": "triggered", "message": f"Job '{job_name}' triggered for immediate execution"}
|
|
|
|
|
|
async def toggle_job(db: AsyncSession, job_name: str, enabled: bool) -> SystemSetting:
|
|
"""Enable or disable a scheduled job by storing state in SystemSetting.
|
|
|
|
Actual scheduler integration happens in Task 12.1.
|
|
"""
|
|
if job_name not in VALID_JOB_NAMES:
|
|
raise ValidationError(f"Unknown job: {job_name}. Valid jobs: {', '.join(sorted(VALID_JOB_NAMES))}")
|
|
|
|
key = f"job_{job_name}_enabled"
|
|
return await update_setting(db, key, str(enabled).lower())
|