mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
3c6ed99922
- 新建 app/services/index_const.py 单一权威: 展示层固定四只核心指数 (上证/深证成指/创业板/科创综指), quote_service/overview/ market_overview_builder/sector_monitor 四处重复定义全部收敛 - fuyao 插件接入指数快照端点 (/api/a-share-index/prices/snapshot): client 增加 index_snapshot (批量上限 627 码), provider 实现可选协议 get_realtime_indices — .BJ 后缀先行过滤 (未知代码整批 1002 连坐), volume 不做股转手; 修复 fuyao 路由下指数冻结在日K兜底的 bug - quote_service 自定义源分支鸭子类型调用 get_realtime_indices 补拉指数, 请求清单 = 核心四只 + 启用指数监控规则标的; 未实现的源走日K兜底 - TickFlow 分支指数固定按码显式拉取, 移除 CN_Index 全量 universe 与 mode core/all 分支; 指数落盘固定 merge 不截断 - 指数偏好全套下线: realtime_index_symbols / sidebar_index_symbols / indices_nav_pinned / realtime_pull_index / realtime_index_mode - /api/index/list 与 /search (全指数浏览搜索) 删除; daily/minute 保留 供指数详情页, sync_instruments/sync_daily 保留供数据页 - sector_monitor 指数标的恒可监控, catalog 签名不再依赖指数偏好 - 新增 7 个用例: 指数快照映射/.BJ 过滤/软失败 + 自定义源指数补充链路 (含监控规则并入/无协议源静默/指数失败软降级)
350 lines
14 KiB
Python
350 lines
14 KiB
Python
"""板块监控目标目录与实时聚合快照。"""
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from __future__ import annotations
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import hashlib
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import math
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import re
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from collections import deque
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from datetime import datetime
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from pathlib import Path
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from typing import Any
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import polars as pl
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from app.services import preferences
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from app.services.ext_data import ExtConfig, ExtConfigStore
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from app.services.index_const import CORE_INDEX_NAMES as CORE_INDICES
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SECTOR_KINDS = {"index", "concept", "industry"}
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_VALUE_SEP = re.compile(r"[\u3001,\uff0c;\uff1b|]+")
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_NULL_VALUES = {"nan", "none", "null", "<na>", "n/a", "-"}
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_HISTORY_SECONDS = 31 * 60
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_WINDOW_TOLERANCE_SECONDS = 90
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def _finite(value: Any) -> float | None:
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if value is None:
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return None
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try:
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number = float(value)
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except (TypeError, ValueError):
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return None
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return number if math.isfinite(number) else None
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def _dimension_kind(field_name: str, field_label: str) -> str | None:
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text = f"{field_name} {field_label}".lower()
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if any(word in text for word in ("概念", "题材", "concept", "theme")):
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return "concept"
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if any(word in text for word in ("行业", "申万", "中信", "industry", "sector")):
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return "industry"
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return None
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def _target_key(kind: str, source_id: str, field: str, value: str, level: int | None) -> str:
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raw = f"{kind}\0{source_id}\0{field}\0{level or 0}\0{value}"
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digest = hashlib.sha1(raw.encode("utf-8")).hexdigest()[:16]
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return f"{kind}:{digest}"
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class SectorMonitorService:
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"""缓存板块成员关系, 并按启用规则构建轻量实时快照。"""
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def __init__(self, repo) -> None:
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self._repo = repo
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self._data_dir: Path = repo.store.data_dir
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self._catalog_signature: tuple[tuple[str, int, int], ...] | None = None
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self._catalog: dict[str, list[dict]] = {kind: [] for kind in SECTOR_KINDS}
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self._targets_by_key: dict[str, dict] = {}
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self._members_by_key: dict[str, set[str]] = {}
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self._history: dict[str, deque[tuple[float, float]]] = {}
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self._history_day: str | None = None
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def list_targets(self) -> dict[str, list[dict]]:
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self._ensure_catalog()
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return {kind: [dict(item) for item in self._catalog[kind]] for kind in self._catalog}
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def missing_target_keys(self, targets: list[dict]) -> list[str]:
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self._ensure_catalog()
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return [str(target.get("key") or "") for target in targets if target.get("key") not in self._targets_by_key]
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def unavailable_target_keys(self, targets: list[dict]) -> list[str]:
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self._ensure_catalog()
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return [
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str(target.get("key") or "")
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for target in targets
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if target.get("key") in self._targets_by_key
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and not self._targets_by_key[target["key"]].get("available", True)
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]
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def build_snapshots(
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self,
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stock_df: pl.DataFrame,
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index_df: pl.DataFrame,
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targets: list[dict],
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windows: set[int],
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*,
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now: float,
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) -> dict[str, dict]:
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if not targets:
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return {}
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self._ensure_catalog()
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self._reset_history_for_day(now)
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stock_rows = self._row_map(stock_df, index_values_are_percent=False)
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index_rows = self._row_map(index_df, index_values_are_percent=True)
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snapshots: dict[str, dict] = {}
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for raw_target in targets:
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key = str(raw_target.get("key") or "")
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target = self._targets_by_key.get(key)
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if not target:
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continue
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if target["kind"] == "index":
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snapshot = self._index_snapshot(target, index_rows)
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else:
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snapshot = self._dimension_snapshot(target, stock_rows)
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if snapshot is None:
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continue
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change_pct = snapshot.get("change_pct")
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history = self._history.setdefault(key, deque())
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if snapshot["valid"] and change_pct is not None:
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history.append((now, float(change_pct)))
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while history and history[0][0] < now - _HISTORY_SECONDS:
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history.popleft()
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snapshot["window_changes"] = {
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window: self._window_change(history, now, window, change_pct)
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for window in windows
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}
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snapshots[key] = snapshot
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return snapshots
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def _ensure_catalog(self) -> None:
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signature = self._data_signature()
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if signature == self._catalog_signature:
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return
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catalog = {kind: [] for kind in SECTOR_KINDS}
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targets_by_key: dict[str, dict] = {}
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members_by_key: dict[str, set[str]] = {}
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index_names = dict(CORE_INDICES)
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try:
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indices = self._repo.get_index_instruments()
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if not indices.is_empty() and "symbol" in indices.columns:
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for row in indices.to_dicts():
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if row.get("asset_type") == "etf":
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continue
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symbol = str(row.get("symbol") or "").strip().upper()
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if symbol:
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index_names[symbol] = str(row.get("name") or symbol)
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except Exception:
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pass
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# 指数标的恒可实时监控: quote_service 对核心四只 + 启用规则标的显式拉取
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for symbol, name in sorted(index_names.items()):
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target = {
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"key": f"index:{symbol}",
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"kind": "index",
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"name": name,
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"symbol": symbol,
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"available": True,
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"member_count": 1,
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}
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catalog["index"].append(target)
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targets_by_key[target["key"]] = target
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catalog["index"].sort(key=lambda item: (not item["available"], item["symbol"]))
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for config in ExtConfigStore(self._data_dir).load_all():
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df = self._read_ext_dataframe(config)
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if df.is_empty():
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continue
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symbol_col = self._symbol_column(config, df)
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if not symbol_col:
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continue
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for field in config.fields:
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kind = _dimension_kind(field.name, field.label)
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if kind is None or field.name not in df.columns:
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continue
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for row in df.select([symbol_col, field.name]).iter_rows(named=True):
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symbol = str(row.get(symbol_col) or "").strip().upper()
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if not symbol:
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continue
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for raw_value in self._dimension_values(row.get(field.name)):
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paths = self._industry_paths(raw_value) if kind == "industry" else [(raw_value, None, raw_value)]
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for value, level, name in paths:
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key = _target_key(kind, config.id, field.name, value, level)
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members_by_key.setdefault(key, set()).add(symbol)
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if key not in targets_by_key:
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target = {
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"key": key,
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"kind": kind,
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"name": name,
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"source_id": config.id,
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"field": field.name,
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"source_field": f"{config.id}.{field.name}",
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"value": value,
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"level": level,
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"available": True,
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}
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targets_by_key[key] = target
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catalog[kind].append(target)
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for kind in ("concept", "industry"):
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for target in catalog[kind]:
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target["member_count"] = len(members_by_key.get(target["key"], set()))
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catalog[kind].sort(key=lambda item: (item.get("level") or 0, item["name"], item["value"]))
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self._catalog_signature = signature
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self._catalog = catalog
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self._targets_by_key = targets_by_key
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self._members_by_key = members_by_key
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self._history.clear()
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def _data_signature(self) -> tuple[tuple[str, int, int], ...]:
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base = self._data_dir / "ext_data"
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paths: list[Path] = []
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for config in ExtConfigStore(self._data_dir).load_all():
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if not any(_dimension_kind(field.name, field.label) for field in config.fields):
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continue
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config_dir = base / config.id
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paths.extend(config_dir.rglob("config.json"))
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paths.extend(config_dir.rglob("*.parquet"))
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signature = [
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(str(path), path.stat().st_mtime_ns, path.stat().st_size)
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for path in sorted(paths)
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if path.is_file()
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]
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return tuple(signature)
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def _read_ext_dataframe(self, config: ExtConfig) -> pl.DataFrame:
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base = self._data_dir / "ext_data" / config.id
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if config.mode == "timeseries":
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files = sorted((base / "timeseries").rglob("*.parquet"))
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files = files[-1:] if files else []
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else:
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files = sorted(base.glob("*.parquet"))
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if not files:
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return pl.DataFrame()
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try:
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return pl.read_parquet(files)
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except Exception:
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return pl.DataFrame()
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@staticmethod
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def _symbol_column(config: ExtConfig, df: pl.DataFrame) -> str | None:
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candidates = ["symbol", "code", "股票代码", "代码"]
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for mapping in (config.symbol_map, config.code_map):
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if isinstance(mapping, dict) and mapping.get("type") == "mapped":
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candidates.append(str(mapping.get("col") or ""))
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return next((column for column in candidates if column in df.columns), None)
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@staticmethod
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def _dimension_values(raw: Any) -> list[str]:
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if raw is None:
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return []
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return [
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value.strip()
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for value in _VALUE_SEP.split(str(raw))
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if value.strip() and value.strip().casefold() not in _NULL_VALUES
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]
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@staticmethod
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def _industry_paths(raw: str) -> list[tuple[str, int, str]]:
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parts = [part.strip() for part in raw.split("-") if part.strip()]
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if not parts:
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return []
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return [
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("-".join(parts[:level]), level, " / ".join(parts[:level]))
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for level in range(1, len(parts) + 1)
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]
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@staticmethod
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def _row_map(df: pl.DataFrame, *, index_values_are_percent: bool) -> dict[str, dict]:
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if df.is_empty() or "symbol" not in df.columns:
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return {}
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rows: dict[str, dict] = {}
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for row in df.to_dicts():
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symbol = str(row.get("symbol") or "").strip().upper()
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if not symbol:
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continue
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change_pct = _finite(row.get("change_pct"))
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if change_pct is not None and index_values_are_percent:
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change_pct /= 100
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rows[symbol] = {**row, "change_pct": change_pct}
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return rows
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@staticmethod
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def _index_snapshot(target: dict, index_rows: dict[str, dict]) -> dict | None:
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row = index_rows.get(str(target.get("symbol") or "").upper())
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if not row or row.get("change_pct") is None:
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return None
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return {
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**target,
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"valid": True,
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"change_pct": row["change_pct"],
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"price": _finite(row.get("close") or row.get("last_price")),
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"coverage_ratio": 1.0,
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"valid_count": 1,
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"total_count": 1,
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"up_count": int(row["change_pct"] > 0),
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"down_count": int(row["change_pct"] < 0),
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"leader": None,
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}
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def _dimension_snapshot(self, target: dict, stock_rows: dict[str, dict]) -> dict | None:
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members = self._members_by_key.get(target["key"], set())
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if not members:
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return None
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valid_rows = [
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stock_rows[symbol]
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for symbol in members
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if symbol in stock_rows and stock_rows[symbol].get("change_pct") is not None
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]
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total_count = len(members)
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valid_count = len(valid_rows)
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coverage_ratio = valid_count / total_count if total_count else 0.0
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valid = total_count >= 5 and coverage_ratio >= 0.8
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changes = [float(row["change_pct"]) for row in valid_rows]
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leader = max(valid_rows, key=lambda row: row["change_pct"]) if valid_rows else None
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return {
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**target,
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"valid": valid,
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"change_pct": sum(changes) / len(changes) if changes else None,
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"price": None,
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"coverage_ratio": coverage_ratio,
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"valid_count": valid_count,
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"total_count": total_count,
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"up_count": sum(value > 0 for value in changes),
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"down_count": sum(value < 0 for value in changes),
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"leader": {
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"symbol": leader.get("symbol"),
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"name": leader.get("name"),
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"change_pct": leader.get("change_pct"),
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} if leader else None,
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}
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@staticmethod
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def _window_change(
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history: deque[tuple[float, float]],
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now: float,
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window: int,
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current: float | None,
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) -> float | None:
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if current is None:
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return None
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cutoff = now - window * 60
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for timestamp, previous in reversed(history):
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if timestamp <= cutoff:
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if timestamp < cutoff - _WINDOW_TOLERANCE_SECONDS:
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return None
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return current - previous
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return None
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def _reset_history_for_day(self, now: float) -> None:
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day = datetime.fromtimestamp(now).date().isoformat()
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if self._history_day == day:
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return
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self._history_day = day
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self._history.clear()
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