feat: complete heaven readings and screener publication
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leefer
@@ -15,6 +15,11 @@ from backend.features.screener.compiler import (
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from backend.features.screener.catalog import FACTOR_FIELDS, FACTOR_GROUPS, REGIMES
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from backend.features.screener.data_sync import FactorDataService
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from backend.features.screener.formula import compile_local_strategy
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from backend.features.screener.publication import resolve_published_batch
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from backend.features.screener.signals import (
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attach_strategy_validity,
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build_candidate_archive,
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)
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SCREENER_LIBRARY_VERSION = 8
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@@ -91,26 +96,71 @@ class ScreenerServiceMixin:
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factor_health = self.screener.factor_health(normalized_date)
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strategies = self.database.list_screener_strategies(self.current_user_id)
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for strategy in strategies:
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attach_strategy_validity(strategy)
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missing = self._strategy_missing_data(strategy, factor_dates, factor_health)
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strategy["data_ready"] = not missing
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strategy["missing_data"] = missing
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automatic_results = self.database.screener_runs_for_date(0, normalized_date)
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personal_results = self.database.screener_runs_for_date(
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self.current_user_id, normalized_date
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batch_markers = self.database.list_screener_batch_markers(normalized_date, 120)
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complete_markers = [
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item for item in batch_markers if item.get("status") == "complete"
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][:30]
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legacy_results = []
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legacy_date = ""
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if not batch_markers:
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legacy_results = self.database.screener_runs_for_date(0, normalized_date)
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if legacy_results:
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legacy_date = normalized_date
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published_marker, automatic_status, published_batch = resolve_published_batch(
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batch_markers, normalized_date, legacy_date
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)
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published_date = str((published_batch or {}).get("trade_date") or "")
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automatic_results = (
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legacy_results
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if legacy_results and published_date == normalized_date
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else self.database.screener_runs_for_date(0, published_date)
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if published_date
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else []
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)
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personal_results = self.database.recent_screener_runs(
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self.current_user_id, normalized_date, "quant", 40
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)
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recent_results = [
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*[item for item in automatic_results if item.get("meta", {}).get("mode") in {"smart", "curated"}],
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*[item for item in personal_results if item.get("meta", {}).get("mode") == "quant"],
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*personal_results,
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]
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latest_results: dict[str, dict[str, Any]] = {}
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for result in reversed(recent_results):
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mode = str(result.get("meta", {}).get("mode") or "smart")
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latest_results[mode] = result
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automatic_status = self.database.get_data_snapshot(
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"screener_auto_v1", normalized_date
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) or {}
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published_dates = [
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str(item.get("trade_date") or "") for item in complete_markers
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if item.get("trade_date")
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]
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if legacy_date and legacy_date not in published_dates:
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published_dates.append(legacy_date)
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archive_runs = self.database.screener_runs_for_dates(0, published_dates, 1800)
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archive_runs.extend(personal_results)
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archive_as_of_date = published_date or (factor_dates[-1] if factor_dates else "")
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marker_regime = (published_marker or {}).get("regime") or {}
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archive_regime = str(
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(marker_regime.get("id") if isinstance(marker_regime, dict) else marker_regime)
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or regime.get("id") or "repair"
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)
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active_signals, candidate_history = build_candidate_archive(
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archive_runs,
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strategies,
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factor_dates,
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archive_as_of_date,
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archive_regime,
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)
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self._attach_published_strategy_status(
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strategies, automatic_results, published_marker, published_date
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)
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return {
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"trade_date": normalized_date,
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"requested_trade_date": normalized_date,
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"regime": regime,
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"regimes": [{"id": key, "label": value} for key, value in REGIMES.items()],
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"strategies": strategies,
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@@ -141,10 +191,49 @@ class ScreenerServiceMixin:
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"latest_results": latest_results,
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"recent_results": recent_results,
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"automatic_status": automatic_status,
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"published_batch": published_batch,
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"published_status": published_marker or {},
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"active_signals": active_signals,
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"candidate_history": candidate_history,
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# Kept during the client transition for compatibility with older frontends.
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"latest_result": latest_results.get("smart"),
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}
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@staticmethod
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def _attach_published_strategy_status(
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strategies: list[dict[str, Any]],
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automatic_results: list[dict[str, Any]],
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marker: dict[str, Any] | None,
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published_date: str,
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) -> None:
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results_by_name = {
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str((item.get("meta") or {}).get("strategy_name") or ""): item
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for item in automatic_results
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}
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skipped_by_name = {
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str(item.get("name") or ""): item
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for item in (marker or {}).get("skipped") or []
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}
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for strategy in strategies:
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name = str(strategy.get("name") or "")
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result = results_by_name.get(name)
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skipped = skipped_by_name.get(name)
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if result is not None:
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candidates = result.get("candidates") or []
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status = "ready" if candidates else "no_signal"
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detail = f"{len(candidates)} 只候选" if candidates else "数据完整，暂无符合条件个股"
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elif skipped is not None:
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status = "missing_data"
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detail = str(skipped.get("reason") or "缺少策略必需数据")
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else:
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status = "not_run"
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detail = "该成功批次未运行此策略"
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strategy["published_run"] = {
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"trade_date": published_date,
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"status": status,
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"detail": detail,
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}
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def screener_tracking(self, limit: int = 12) -> dict[str, Any]:
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return self.strategy_tracking.list_tracking(self.current_user_id, limit)
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