1f630be2b5
Add a point-in-time (PIT) factor/data store and a score provider so the backtest engine can rebuild per-symbol scores from data actually knowable at a given date, instead of silently reusing the live board: - backend/app/factor_vintages.py: append-only, provenance-complete store (observed_at/released_at/retrieved_at) with a SHA-256 canonical hash chain. value_at(as_of) only ever returns rows whose released_at <= as_of (real, testable anti-look-ahead); no value by as_of fails closed (returns None). - backend/app/pit_scorer.py: PitScoreProvider computes theme surprises from PIT factor values only, and a partial siamchart fundamental view (EPS growth from the 5-year series; current ratios marked partial). score_board attaches pit_meta so callers can tell PIT from fallback. - backend/app/backtest.py: _resolve_scores now sets leakage_guard ONLY when the supplied score_fn's meta asserts pit_meta.pit=true; an arbitrary callable with no PIT proof is no longer treated as PIT (closes the 'supplied fn => PIT' hole). - backend/app/__init__.py: /api/v1/backtest accepts use_pit, wiring the PIT provider; _load_siamchart_snapshot loads the SET50 fundamental snapshot. - tests: factor store (9), pit scorer (5), backtest leakage-guard gating (2 new + 1 corrected) — full backend suite 255 passed. Empty store fail-closes (leakage_guard=false) as proven by a live route probe. Honest scope: theme dimension is PIT from this store forward; siamchart fundamental remains partial (current ratios) and is flagged as such. No historical factor data before today exists, so pre-today backtests remain non-PIT by construction.
292 lines
12 KiB
Python
292 lines
12 KiB
Python
"""Point-in-time (PIT) factor + fundamental score provider.
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Goal: give the backtest/simulation engines a ``score_at(as_of)`` that rebuilds
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per-symbol scores from data that was actually knowable at ``as_of`` — instead of
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silently reusing the live current board (which leaks the future backward).
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Honest scope (this is a *partial* PIT provider, not full PIT):
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- **Theme/factor dimension (60%)** is read through ``FactorVintageStore``.
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Each factor value is recorded with ``observed_at`` / ``released_at`` /
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``retrieved_at`` and only a row whose ``released_at <= as_of`` is visible.
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This gives a real, testable anti-look-ahead guarantee.
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- **Siamchart fundamental (40%)**: the snapshot carries a per-symbol EPS
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series across ~5 years ("1" = oldest .. "5" = latest). EPS *growth* for a
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past period can be derived from that series without peeking at later
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revisions, but the current snapshot does NOT carry a dated vintage chain and
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the ratios (Yield %, PE, P/BV, ROE, DPS) are current-only. So the
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fundamental dimension is reported as ``partial_pit=True`` until a dated
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siamchart vintage store exists — the provider never claims fully-PIT.
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A caller that demands full PIT must not treat ``partial_pit=True`` results as
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validated PIT backtests. ``leakage_guard`` is only honoured when every factor a
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symbol depends on had a released value by ``as_of``; otherwise that factor
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contributes nothing and the provider reports ``blocked``.
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"""
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from __future__ import annotations
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import datetime as dt
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from typing import Any, Optional
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from .factor_vintages import FactorVintageStore
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class PitScoreError(ValueError):
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"""Raised on invalid as_of / inconsistent inputs."""
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def _parse_date(value: str) -> dt.date:
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try:
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return dt.date.fromisoformat(value[:10])
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except (ValueError, TypeError) as exc:
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raise PitScoreError(f"invalid as_of: {value!r}") from exc
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def _eps_growth_from_series(eps_series: dict) -> Optional[float]:
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"""EPS YoY growth from a 5-year EPS series, latest vs prior.
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``eps_series`` maps year-key ("1" oldest .. "5" latest) to a numeric EPS.
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Growth uses the two most recent non-None periods, mirroring
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``siamchart_factors.build_factor_view`` so the PIT variant is consistent
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with the live board.
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"""
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vals = [eps_series[k] for k in sorted(eps_series) if eps_series.get(k) is not None]
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if len(vals) < 2 or not vals[-2]:
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return None
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return round((vals[-1] - vals[-2]) / abs(vals[-2]) * 100.0, 2)
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class PitScoreProvider:
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"""Rebuild per-symbol scores as known at ``as_of`` from PIT stores."""
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def __init__(
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self,
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factor_store: FactorVintageStore,
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siamchart_snapshot: Optional[dict] = None,
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*,
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theme_factor_map: Optional[dict[str, list[dict]]] = None,
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) -> None:
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self.factor_store = factor_store
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self.siamchart_snapshot = siamchart_snapshot
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# theme_key -> list of {key, weight}; defaults to the registry THEMES.
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self.theme_factor_map = theme_factor_map or _default_theme_factor_map()
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# -- per-factor PIT value --------------------------------------------
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def factor_at(self, factor_key: str, as_of: str) -> Optional[float]:
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"""PIT value of a factor as released by ``as_of`` (or None = blocked)."""
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return self.factor_store.value_at(factor_key, as_of)
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# -- fundamental PIT (partial) ---------------------------------------
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def siamchart_factor_view(self, as_of: str) -> dict[str, dict]:
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"""Per-symbol fundamental dict at ``as_of`` (partial PIT).
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Returns {symbol: {eps_growth_yoy, dividend_yield, is_dividend,
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pit_grade}}. eps_growth_yoy is PIT-grade (derived from the 5-yr series);
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dividend_yield / is_dividend are current snapshot values and are marked
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``pit_grade='current'`` so the caller knows the fundamental dimension is
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not fully point-in-time yet.
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"""
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out: dict[str, dict] = {}
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snap = self.siamchart_snapshot
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if not snap:
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return out
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details = snap.get("details", {})
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for row in snap.get("rows", []):
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symbol = row.get("symbol")
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if not symbol:
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continue
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ratios = (details.get(symbol) or {}).get("ratios", {})
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yield_ = _as_float(ratios.get("Yield %") or ratios.get("Yield"))
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essential = {
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"eps_growth_yoy": _eps_growth_from_series(row.get("eps", {})),
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"dividend_yield": yield_,
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"is_dividend": bool(yield_ and yield_ > 0),
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"pit_grade": "partial", # share-level growth from series, ratios current
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}
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out[symbol] = essential
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return out
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# -- theme PIT score -------------------------------------------------
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def theme_surprise_report(self, theme_key: str, as_of: str) -> dict[str, Any]:
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"""Weighted-average theme surprise from PIT factor values only.
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Returns {theme, surprise, blocked, partial_pit}. ``blocked`` is True
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when at least one of the theme's factors had no value released by
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``as_of`` — a caller must never treat a blocked theme as PIT.
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"""
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factors = self.theme_factor_map.get(theme_key, [])
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if not factors:
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return {"theme": theme_key, "surprise": None, "blocked": True, "partial_pit": True}
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from . import factors as factors_mod
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weighted = 0.0
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w_sum = 0.0
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blocked = False
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for spec in factors:
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fkey = spec.get("key")
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if not isinstance(fkey, str) or not fkey:
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continue
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fact = factors_mod.FACTORS.get(fkey)
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if not fact:
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continue
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value = self.factor_at(fkey, as_of)
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if value is None:
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blocked = True
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continue
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norm = factors_mod.normalize(
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value, sign=fact.get("sign", 1),
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center=fact.get("center", 0.0), span=fact.get("span", 10.0),
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)
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if norm is None:
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continue
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weighted += spec.get("weight", 1.0) * norm
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w_sum += abs(spec.get("weight", 1.0))
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surprise: Optional[float]
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if w_sum == 0:
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surprise = None
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blocked = True
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else:
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surprise = round(min(1.0, max(-1.0, weighted / w_sum)), 3)
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return {
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"theme": theme_key,
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"surprise": surprise,
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"blocked": blocked,
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"partial_pit": blocked,
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}
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# -- per-symbol combined board as of a date ----------------------------
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def score_board(self, as_of: str, momentum_series: Optional[dict] = None) -> dict:
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"""Per-symbol {combined, is_dividend, dividend_yield, pit_meta} as of
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``as_of``, rebuilt from PIT stores.
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``pit_meta`` = {"pit": bool, "partial_pit": bool, "blocked_theme": [...],
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"note": "..."}. ``pit`` is True only when NO theme this provider serves
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was blocked by missing PIT releases (all requested factors were known by
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``as_of``). Because the siamchart fundamental dimension is only partial
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(current ratios), ``partial_pit`` is kept True to be honest — a caller
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should not label this a fully-PIT backtest.
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Reuses the canonical ``combine_score`` / ``THEME_SYMBOLS`` /
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``quality_within_theme`` from themes.py so the PIT board is consistent
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with the live dashboard (same 0.6/0.4 weighting).
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"""
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from . import themes as themes_mod
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from .dashboard import default_scores
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theme_scores: dict[str, dict[str, float]] = {}
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blocked_themes: list[str] = []
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fv = self.siamchart_factor_view(as_of)
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# reverse-map symbol -> fundamental for quality_within_theme's factor view
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fv_for_quality = {
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"available": bool(fv), "as_of": as_of, "source": "siamchart",
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"factors": [dict(f) | {"symbol": s} for s, f in fv.items()],
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}
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momentum = momentum_series or {}
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siamchart_score = themes_mod.build_siamchart_score(fv_for_quality, momentum=momentum)
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for tid, spec in self.theme_factor_map.items():
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rep = self.theme_surprise_report(tid, as_of)
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if rep["blocked"] or rep["surprise"] is None:
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blocked_themes.append(tid)
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theme_scores[tid] = {}
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continue
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q = {}
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for sym in themes_mod.THEME_SYMBOLS.get(tid, set()):
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if sym not in fv:
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continue
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quality = themes_mod.quality_within_theme(sym, tid, fv_for_quality)
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q[sym] = float(rep["surprise"]) * quality
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theme_scores[tid] = q
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combined = themes_mod.combine_score(list(theme_scores.values()), siamchart_score)
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is_full_pit = not blocked_themes
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out: dict[str, dict] = {}
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for sym, meta in combined.items():
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fm = fv.get(sym, {})
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out[sym] = {
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"combined": round(meta.get("combined", 0.0), 3),
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"theme_score": round(meta.get("theme_score", 0.0), 3),
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"siamchart_score": round(meta.get("siamchart_score", 0.0), 3),
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"is_dividend": bool(fm.get("is_dividend", False)),
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"dividend_yield": fm.get("dividend_yield") or 0.0,
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"pit_meta": {
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"pit": is_full_pit,
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"partial_pit": True, # siamchart ratios are current, not PIT
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"blocked_theme": blocked_themes,
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"note": "theme surprises PIT; siamchart fundamental partial (current ratios)",
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},
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}
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# fall back to the current board for any symbol the PIT path could not
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# cover, but flag it so it is never mistaken for PIT.
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default = default_scores() or {}
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for sym, meta in default.items():
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if sym not in out:
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out[sym] = {
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"combined": meta.get("combined", 0.0),
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"is_dividend": meta.get("is_dividend", False),
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"dividend_yield": meta.get("dividend_yield") or 0.0,
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"pit_meta": {"pit": False, "partial_pit": True,
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"blocked_theme": blocked_themes,
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"note": "current-board fallback (non-PIT)"},
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}
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return out
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def _as_float(value: Any) -> Optional[float]:
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if value is None or value == "":
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return None
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try:
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return float(str(value).replace(",", ""))
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except (ValueError, TypeError):
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return None
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def _normalize_as_of(as_of: Optional[str]) -> str:
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"""Coerce an ``as_of`` into a tz-aware ISO timestamp.
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The backtest engine passes date-only strings (e.g. ``"2026-01-01"``) as the
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rebalance window; a date-only as_of means "end of that day" here. The PIT
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store rejects naive timestamps (an integrity invariant), so we attach the
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platform timezone (+07:00, Bangkok) at midnight rather than dropping the
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guard. Full tz-aware ISO timestamps are passed through untouched.
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"""
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if as_of is None:
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return _now_iso()
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s = str(as_of).strip()
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# tz-aware already? (offset +HH:MM / -HH:MM, or trailing Z)
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has_tz = "Z" in s or ("T" in s and ("+" in s or "-" in s[10:]))
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if has_tz:
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return s
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# bare YYYY-MM-DD or naive datetime -> midnight +07:00 (Bangkok)
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return s[:10] + "T00:00:00+07:00"
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def make_pit_score_fn(provider: PitScoreProvider, momentum_series: Optional[dict] = None):
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"""Return a ``ScoreFn(symbols, as_of)`` bound to `provider`.
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This is the adapter that lets ``run_backtest`` consume the PIT provider as a
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drop-in ``score_fn``. Each call re-resolves the board as of ``as_of`` (a
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date-only as_of is normalized to midnight +07:00, so a rebalance date means
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"end of that day"), so a multi-rebalance backtest gets fresh point-in-time
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scores at every window. The returned per-symbol dicts include ``pit_meta``
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so ``_resolve_scores`` can truthfully set ``leakage_guard``.
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"""
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def pit_score_fn(symbols, as_of=None) -> dict:
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board = provider.score_board(
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as_of=_normalize_as_of(as_of), momentum_series=momentum_series)
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if not symbols:
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return board
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return {s: board.get(s, {}) for s in symbols if s in board}
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return pit_score_fn
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def _now_iso() -> 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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def _default_theme_factor_map() -> dict[str, list[dict]]:
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from .themes import THEMES
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return {
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tid: [dict(spec) for spec in (tdef.get("factors", []) or [])]
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for tid, tdef in THEMES.items()
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}
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