Giorgio Gilestro
6f9a710726
A 6h gap meant weekend visitors could see the feed sit on the same 1pm batch through to dinner. Tightening to 2h gives roughly 12 ingests/weekend-day at a fraction of the active-window load (which stays at 20-min cadence). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
93 lines
4 KiB
Python
93 lines
4 KiB
Python
"""When should expensive AI jobs fire?
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Markets matter. The scheduler wakes every hour, but there's no point spending
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OpenRouter tokens at 03:00 UTC on a Sunday when nothing has moved. This module
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encodes a single policy: weekday active hours (LSE open through NYSE close,
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roughly 07:00-21:00 UTC) get the full hourly cadence; off-hours and weekends
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get throttled.
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Used by ai_log_job and indicator_summary_job to decide whether to run NOW or
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skip until enough time has passed since the last successful run. Market /
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news / portfolio ingestion jobs keep running hourly — they're cheap.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import datetime, timezone
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@dataclass(frozen=True)
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class CadencePolicy:
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# Active trading windows in UTC. A timestamp is "active" if its hour
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# falls in ANY listed window. Add or remove tuples to change coverage.
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#
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# LSE opens 07:00 BST → 07:00 UTC summer / 08:00 UTC winter.
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# NYSE closes 16:00 ET → 21:00 UTC summer / 21:00 UTC winter.
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# Tokyo trades 09:00-15:00 JST → 00:00-06:00 UTC.
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# HK/Shanghai trade 09:30-16:00 local → 01:30-08:00 UTC.
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active_windows: tuple[tuple[int, int], ...] = (
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(7, 21), # EU/US (LSE open through NYSE close)
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# (0, 8), # Asia (Tokyo + HK/Shanghai) — uncomment to add
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)
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# Minimum gap between successful runs DURING the active window. The
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# cron may fire more frequently than this — we just skip until enough
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# time has passed since the last success. Default 0 means "run on
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# every cron fire" (the original AI-job behaviour).
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active_gap_h: float = 0.0
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# Minimum gap between successful runs outside the active window.
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off_hours_gap_h: float = 4.0
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weekend_gap_h: float = 12.0
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def is_active_window(self, now: datetime | None = None) -> bool:
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now = now or datetime.now(timezone.utc)
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if now.weekday() >= 5: # Saturday / Sunday
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return False
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return any(start <= now.hour < end for start, end in self.active_windows)
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def min_gap_hours(self, now: datetime | None = None) -> float:
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now = now or datetime.now(timezone.utc)
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if now.weekday() >= 5:
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return self.weekend_gap_h
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if self.is_active_window(now):
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return self.active_gap_h
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return self.off_hours_gap_h
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def should_run(
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self,
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last_success_at: datetime | None,
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now: datetime | None = None,
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) -> tuple[bool, str]:
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"""Returns (should_run, reason). The reason is human-readable for logs
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and the job_runs.error column when a run is skipped."""
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now = now or datetime.now(timezone.utc)
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min_gap = self.min_gap_hours(now)
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if last_success_at is None:
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return True, "no prior successful run"
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# Normalise tz; DB returns naive but we treat it as UTC.
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if last_success_at.tzinfo is None:
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last_success_at = last_success_at.replace(tzinfo=timezone.utc)
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age_h = (now - last_success_at).total_seconds() / 3600.0
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if min_gap <= 0 and self.is_active_window(now):
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return True, "active window"
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if age_h >= min_gap:
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band = "active" if self.is_active_window(now) else (
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"weekend" if now.weekday() >= 5 else "off-hours"
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)
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return True, f"{band}: last run {age_h:.2f}h ago (≥ {min_gap:.2f}h)"
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band = "active" if self.is_active_window(now) else (
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"weekend" if now.weekday() >= 5 else "off-hours"
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)
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return False, f"{band} throttled — last run {age_h:.2f}h ago (< {min_gap:.2f}h)"
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# AI jobs: run hot during the active window, throttle off-hours.
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DEFAULT_POLICY = CadencePolicy()
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# News + tagging: 3 runs/hour during the active window (20-min gap),
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# every 3h off-hours, every 2h on weekends. Cron fires every 20 min;
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# the policy gates whether each fire actually does work.
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NEWS_POLICY = CadencePolicy(
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active_gap_h=1.0 / 3.0, # 20 minutes
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off_hours_gap_h=3.0,
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weekend_gap_h=2.0,
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)
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