dennisthiessenandClaude Opus 5
083c9dbf7c
Groundwork for the Admin -> Jobs cleanup. Three sources of truth collapse into
app/job_catalog.py, which imports nothing from app so both the scheduler and
admin_service can import it at module level (admin_service otherwise has to
import the scheduler inside functions to dodge a cycle).
PIPELINE_MEMBERS is now DERIVED from the four pipeline step lists instead of
being a literal set in admin_service duplicating four lists in scheduler.py with
nothing asserting they agreed. A test pins that the derivation reproduces the
previous hand-maintained 9 names exactly, so this is behaviour-preserving.
Deletes the private _JOB_NAMES list, which held 16 of the 19 jobs:
benchmark_collector, outcome_evaluator and shadow_book had no runtime row, and
so no "last run" line in the panel, until their first run in a given process.
_job_runtime is now seeded from the catalog, and a test pins the invariant.
Next-run is decided by category rather than by reading a timestamp. A pipeline
step has no schedule of its own, so it reports its parent's ("next via Morning
Pipeline in 3h") instead of nothing; a manual job says manual_only rather than
rendering a date. This also fixes a real bug: triggering a paused job set
next_run_time=now, APScheduler re-armed the 520-week backstop behind it, and the
panel displayed "next run in ~87600h". Two independent guards -- the category
rule, plus _visible_next_run dropping anything past a year -- and an APScheduler
listener that re-pauses steps and manual jobs once their run finishes. The
listener is registered at module level because configure_scheduler is called
more than once and add_listener does not deduplicate.
Migrates backtest and ticker_universe_sync from interval to cron (Sun 03:00 ET
and 01:00 ET). configure_scheduler calls remove_all_jobs() on every startup, so
an interval countdown restarts each deploy -- a 168h backtest needed a week of
uninterrupted uptime to fire even once. The codebase already documented this
pitfall as the reason cron was adopted; these two were never migrated. Both are
now editable in Admin -> Schedule.
Also: list_jobs went from one settings query per job (19) to one for all of
them, and data_backfill is hidden from the listing while staying registered and
API-triggerable.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
134 lines
5.2 KiB
Python
134 lines
5.2 KiB
Python
"""Admin request/response schemas."""
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from typing import Literal
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from pydantic import BaseModel, Field
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class UserManagement(BaseModel):
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"""Schema for user access management."""
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has_access: bool
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class PasswordReset(BaseModel):
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"""Schema for resetting a user's password."""
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new_password: str = Field(..., min_length=6)
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class CreateUserRequest(BaseModel):
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"""Schema for admin-created user accounts."""
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username: str = Field(..., min_length=1)
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password: str = Field(..., min_length=6)
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role: str = Field(default="user", pattern=r"^(user|admin)$")
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has_access: bool = False
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class RegistrationToggle(BaseModel):
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"""Schema for toggling registration on/off."""
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enabled: bool
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class SystemSettingUpdate(BaseModel):
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"""Schema for updating a system setting."""
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value: str = Field(..., min_length=1)
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class DataCleanupRequest(BaseModel):
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"""Schema for data cleanup — delete records older than N days."""
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older_than_days: int = Field(..., gt=0)
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class JobToggle(BaseModel):
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"""Schema for enabling/disabling a scheduled job."""
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enabled: bool
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class JobTriggerRequest(BaseModel):
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"""Optional parameters for a one-time manual job run."""
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target_model: Literal["production_gtl", "structural_sr"] | None = None
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cadence: Literal["weekly", "daily"] | None = None
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class RecommendationConfigUpdate(BaseModel):
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high_confidence_threshold: float | None = Field(default=None, ge=0, le=100)
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moderate_confidence_threshold: float | None = Field(default=None, ge=0, le=100)
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confidence_diff_threshold: float | None = Field(default=None, ge=0, le=100)
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signal_alignment_weight: float | None = Field(default=None, ge=0, le=1)
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sr_strength_weight: float | None = Field(default=None, ge=0, le=1)
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momentum_technical_divergence_threshold: float | None = Field(default=None, ge=0, le=100)
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fundamental_technical_divergence_threshold: float | None = Field(default=None, ge=0, le=100)
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class TickerUniverseUpdate(BaseModel):
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universe: Literal["sp500", "nasdaq100", "nasdaq_all"]
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class ActivationConfigUpdate(BaseModel):
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"""Activation gate: what counts as an actionable signal."""
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min_momentum_percentile: float | None = Field(default=None, ge=0, le=100)
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min_rr: float | None = Field(default=None, ge=0)
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min_confidence: float | None = Field(default=None, ge=0, le=100)
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require_high_conviction: bool | None = None
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exclude_conflicts: bool | None = None
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exclude_neutral: bool | None = None
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class ScheduleConfigUpdate(BaseModel):
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"""Cron schedule for the pipelines + fundamentals. Crons are 5-field
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(min hour dom month dow); timezone is an IANA name (e.g. America/New_York)."""
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schedule_timezone: str | None = Field(default=None, max_length=64)
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schedule_daily_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_dolt_earnings_cron: str | None = Field(default=None, max_length=120)
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schedule_sec_fundamentals_cron: str | None = Field(default=None, max_length=120)
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schedule_near_close_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_after_close_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_intraday_pipeline_cron: str | None = Field(default=None, max_length=120)
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schedule_backtest_cron: str | None = Field(default=None, max_length=120)
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schedule_ticker_universe_cron: str | None = Field(default=None, max_length=120)
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class PerformanceConfigUpdate(BaseModel):
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"""Window for the Performance comparison.
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``start_date`` is an ISO date, or empty string to show all history. The
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strategy has been revised repeatedly; pinning a start keeps the shadow-vs-
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manual comparison inside one configuration instead of averaging across
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rules that no longer exist.
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"""
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start_date: str | None = Field(default=None, max_length=10)
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class ShadowBookConfigUpdate(BaseModel):
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"""Auto-traded shadow book: the validated strategy with no human input."""
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enabled: bool | None = None
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capacity: int | None = Field(default=None, ge=1, le=100)
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risk_pct: float | None = Field(default=None, gt=0, le=10)
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start_equity: float | None = Field(default=None, ge=1000)
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class SentimentConfigUpdate(BaseModel):
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"""Runtime sentiment LLM config. api_key is write-only; omit/empty to keep
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the stored key."""
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provider: Literal["openai", "gemini", "deepseek", "xai", "openai_compatible"] | None = None
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model: str | None = Field(default=None, max_length=100)
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api_key: str | None = Field(default=None, max_length=400)
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base_url: str | None = Field(default=None, max_length=300)
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class SentimentTestRequest(BaseModel):
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ticker: str = Field(default="AAPL", max_length=10)
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class AlertConfigUpdate(BaseModel):
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"""Telegram alert config. bot_token is write-only; omit/empty to keep the
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stored token."""
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enabled: bool | None = None
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bot_token: str | None = Field(default=None, max_length=200)
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telegram_chat_id: str | None = Field(default=None, max_length=64)
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qualified_enabled: bool | None = None
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sr_proximity_enabled: bool | None = None
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score_drop_enabled: bool | None = None
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digest_enabled: bool | None = None
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regime_quadrant_enabled: bool | None = None
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trade_closed_enabled: bool | None = None
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