286 lines
13 KiB
Python
286 lines
13 KiB
Python
"""App-internal background scheduler — refreshes real data on its own schedule.
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This app runs on its own server (Docker/EasyPanel), independent of Hermes. So
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the periodic data-fetching/analysis must live INSIDE the app. This module runs a
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daemon thread inside the Flask process that:
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- on an interval (default every 60 min), triggers each collector through the
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shared daily cache (so fresh real data lands as the "current" state),
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- records each successful refresh as a timestamped snapshot under the data dir
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(persistent history the dashboard/sources table can report),
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- logs progress; swallows transient failures (a source being down once must
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not crash the app or the loop).
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Thread-safety: the loop only calls collectors + writes cache/snapshots; the HTTP
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layer reads the daily cache. No shared mutable in-process state beyond that.
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"""
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from __future__ import annotations
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import logging
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import threading
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import time
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from pathlib import Path
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from typing import Callable, List, Optional
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log = logging.getLogger("set50.scheduler")
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# collectors returning a .to_dict()/dict, keyed by cache key
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# (imported lazily to avoid import cycles at module load)
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# `fetch_module` = the FACTORS.fetch module name this job feeds (for history).
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_REFRESH_JOBS: List[dict] = [
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{"key": "bot_tourism", "label": "ท่องเที่ยว (BOT)", "module": "bot_tourism", "fn": "BotTourismSource().fetch", "fetch_module": "macro_thai"},
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{"key": "auto_credit/tourism", "label": "ยอดขายรถ (TradingEconomics)", "module": "auto_credit", "fn": "fetch_auto_credit", "fetch_module": "auto_credit"},
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{"key": "auto_npl", "label": "NPL รถยนต์ (BOT)", "module": "auto_npl", "fn": "fetch_auto_npl", "fetch_module": "auto_npl"},
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{"key": "energy_thai", "label": "โรงกลั่น TOP", "module": "energy_thai", "fn": "fetch_energy_thai", "fetch_module": "energy_thai"},
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{"key": "macro_thai", "label": "ภาพรวมประเทศไทย (BOT)", "module": "macro_thai", "fn": "fetch_macro_thai", "fetch_module": "macro_thai"},
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{"key": "bank_npl", "label": "NPL ภาคการเงิน (BOT)", "module": "bank_npl", "fn": "fetch_bank_npl", "fetch_module": "bank_npl"},
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]
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class AppDataScheduler:
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"""Runs periodic refresh of the real data collectors inside the app."""
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def __init__(self, cache, data_root: Path, interval_seconds: int = 3600,
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dividend_cooldown_seconds: int = 6 * 3600):
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self.cache = cache
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self.data_root = data_root
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self.interval = max(30, int(interval_seconds)) # never faster than 30s
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self.dividend_cooldown = max(300, int(dividend_cooldown_seconds)) # >=5min
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self._stop = threading.Event()
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self._thread: Optional[threading.Thread] = None
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self._snap_dir = data_root / "scheduler"
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self._snap_dir.mkdir(parents=True, exist_ok=True)
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self._div_marker = self._snap_dir / "dividends_last_refresh.json"
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self._lock = threading.Lock()
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# -- public lifecycle ---------------------------------------------------
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def start(self) -> None:
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if self._thread and self._thread.is_alive():
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return
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self._stop.clear()
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self._thread = threading.Thread(target=self._loop, name="set50-refresh", daemon=True)
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self._thread.start()
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log.info("set50 data scheduler started (interval=%ss)", self.interval)
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def stop(self) -> None:
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self._stop.set()
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# -- internals ----------------------------------------------------------
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def _loop(self) -> None:
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# refresh once shortly after boot (so fresh data is live), then on interval
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self.refresh_all()
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while not self._stop.wait(self.interval):
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try:
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self.refresh_all()
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except Exception: # noqa: BLE001 — loop must survive individual failures
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log.exception("set50 scheduler refresh_all failed (will retry)")
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def refresh_all(self) -> list[dict]:
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"""Run every collector, warm the daily cache, snapshot the state, and
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append each factor's value to the historical store (P4 enabler)."""
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results: list[dict] = []
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fetched_by_module: dict[str, dict] = {}
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for job in _REFRESH_JOBS:
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res = self._run_job(job)
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results.append(res)
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fetch_module = job.get("fetch_module")
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if res.get("ok") and isinstance(res.get("value"), dict) and fetch_module:
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fetched_by_module[fetch_module] = res["value"]
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self._record_history(fetched_by_module)
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self._record_pit_factor_vintages(fetched_by_module)
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self._record_siamchart_vintages()
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self._maybe_refresh_dated_dividends()
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self._write_marker(results)
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return results
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def _record_history(self, fetched_by_module: dict[str, dict]) -> None:
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"""Append current factor values to the historical store (append-only).
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`fetched_by_module` maps FACTORS.fetch module name -> collector dict.
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Runs after each refresh so vintages accumulate; macro/demographic
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factors become learnable (P4) once they have enough history points.
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"""
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try:
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from .factor_history import FactorHistory
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fh = FactorHistory(self.data_root / "factor_history")
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fh.record_all(fetched_by_module)
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except Exception: # noqa: BLE001 — never let history break the refresh loop
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log.exception("set50 factor-history record failed (non-fatal)")
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def _record_pit_factor_vintages(self, fetched_by_module: dict[str, dict]) -> None:
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"""Write each factor's current PIT vintage into the strict PIT store.
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The strict backtest (`backtest_readiness`) reads *this* store, so the
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scheduler must keep it populated or strict runs stay blocked forever.
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We use the honest, evidence-safe timestamps available at collection
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time: ``released_at == retrieved_at == now`` — we do NOT assume a
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reporting lag we do not know, so we only claim the value was knowable
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from the moment we actually collected it. A point is appended only when
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the factor's value differs from its last stored value, so repeated
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ticks do not spam rows. This runs on the deployed server too, so
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vintages accumulate wherever the app runs.
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"""
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from . import factors as factors_mod
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from .factor_vintages import FactorVintageStore, FactorVintageError
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try:
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store = FactorVintageStore(self.data_root)
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except Exception: # noqa: BLE001
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log.exception("set50 PIT-vintage store init failed (non-fatal)")
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return
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now = self._now() # tz-aware ISO (UTC seconds)
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for fkey, fact in factors_mod.FACTORS.items():
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if not fact.get("fetch"):
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continue
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val = factors_mod.factor_value(fact, fetched_by_module.get(fact.get("fetch")))
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if val is None:
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continue
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try:
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last = store.value_at(fkey, now)
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except FactorVintageError:
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last = None
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if last is not None and abs(last - val) < 1e-9:
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continue # unchanged since last stored -> skip
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try:
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store.record(
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fkey, val,
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observed_at=now, released_at=now,
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retrieved_at=now, source=str(fact.get("source") or ""),
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)
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except FactorVintageError as exc:
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log.warning("set50 PIT vintage skip %s: %s", fkey, exc)
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def _record_siamchart_vintages(self) -> None:
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"""Persist the current Siamchart SET50 snapshot as a vintage chain.
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This makes the fundamental (40%) dimension reconstructible for strict
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PIT backtests. Idempotent: ``SiamchartVintageStore.persist`` dedupes by
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``retrieved_at`` + body hash, so re-inserting an unchanged snapshot is a
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no-op. Non-fatal on network error. Reads the same latest snapshot file
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the dashboard uses (``backend/data/siamchart/set50_master.json``).
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"""
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import json
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from .siamchart_vintages import SiamchartVintageStore, SiamchartVintageError
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path = self.data_root / "siamchart" / "set50_master.json"
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try:
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if not path.is_file():
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return
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snap = json.loads(path.read_text(encoding="utf-8"))
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except (OSError, ValueError) as exc:
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log.warning("set50 siamchart snapshot unreadable: %s", exc)
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return
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if not isinstance(snap, dict) or not snap:
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return
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try:
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store = SiamchartVintageStore(self.data_root)
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store.persist(snap)
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except SiamchartVintageError as exc:
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log.warning("set50 siamchart vintage skip: %s", exc)
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except Exception: # noqa: BLE001
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log.exception("set50 siamchart vintage persist failed (non-fatal)")
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def _maybe_refresh_dated_dividends(self) -> None:
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"""Cooldown-gated fetch of real dated dividend history into the ledger.
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Dividend history changes slowly (a few times a year per name), so we
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only re-fetch at most once per ``dividend_cooldown`` (default 6h), not
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every refresh tick. Non-fatal: a network failure leaves the previous
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ledger intact and just postpones the next update.
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"""
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with self._lock:
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if self._dividends_fresh():
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return
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try:
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from .dividend_ledger import DividendLedger, populate_dated_dividends
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from .siamchart import fetch_dividend_history
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from .siamchart_factors import build_factor_view
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ledger = DividendLedger(self.data_root / "dividends" / "ledger.json")
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fv = build_factor_view()
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symbols = [f["symbol"] for f in fv.get("factors", [])]
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populate_dated_dividends(ledger, symbols, fetch_dividend_history)
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ledger.save()
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self._mark_dividends_refreshed()
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log.info("set50 dated-dividend ledger refreshed (%d symbols)", len(symbols))
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except Exception: # noqa: BLE001 — non-fatal
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log.exception("set50 dated-dividend refresh failed (will retry next cooldown)")
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def _dividends_fresh(self) -> bool:
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import json
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import datetime as _dt
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if not self._div_marker.is_file():
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return False
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try:
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payload = json.loads(self._div_marker.read_text(encoding="utf-8"))
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last = _dt.datetime.fromisoformat(payload["at"])
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return (_dt.datetime.now().astimezone() - last).total_seconds() < self.dividend_cooldown
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except (OSError, ValueError, KeyError):
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return False
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def _mark_dividends_refreshed(self) -> None:
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import json
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import datetime as _dt
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try:
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self._div_marker.write_text(
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json.dumps({"at": _dt.datetime.now().astimezone().isoformat(timespec="seconds")}),
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encoding="utf-8",
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)
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except OSError:
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log.exception("could not write dividend refresh marker")
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def _run_job(self, job: dict) -> dict:
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key = job["key"]
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label = job["label"]
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try:
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value = self.cache.fetch_or_stale(key, self._make_fetcher(job))
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return {"key": key, "label": label, "ok": True, "at": self._now(),
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"value": value}
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except Exception as exc: # noqa: BLE001
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log.warning("set50 refresh failed for %s: %s", key, exc)
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return {"key": key, "label": label, "ok": False, "error": str(exc), "at": self._now()}
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def _make_fetcher(self, job: dict) -> Callable[[], dict]:
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module_name = job["module"]
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fn = job["fn"]
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def fetcher() -> dict:
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mod = __import__(f"app.{module_name}", fromlist=["*"])
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obj = mod
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# Support "ClassName().method" (instantiate then call) and plain
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# "func_name" and static object attribute chains.
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parts = fn.split(".")
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for i, part in enumerate(parts):
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is_instantiate = part.endswith("()")
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attr = part[:-2] if is_instantiate else part
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obj = getattr(obj, attr)
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if is_instantiate:
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obj = obj() # construct instance
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if callable(obj):
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value = obj()
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else:
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value = obj
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if hasattr(value, "to_dict"):
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value = value.to_dict()
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return value or {}
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return fetcher
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def _write_marker(self, results: list[dict]) -> None:
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import json
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import datetime as _dt
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ok = [r for r in results if r["ok"]]
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marker = {
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"at": self._now(),
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"ok": len(ok),
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"total": len(results),
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"sources": results,
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}
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path = self._snap_dir / "last_refresh.json"
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try:
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path.write_text(json.dumps(marker, ensure_ascii=False, indent=2), encoding="utf-8")
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except OSError:
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log.exception("could not write scheduler marker")
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@staticmethod
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def _now() -> str:
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import datetime as _dt
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return _dt.datetime.now(_dt.timezone.utc).isoformat(timespec="seconds")
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