mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
697c27bb02
- 市场环境: 新增情绪周期6阶段(冰点/启动/主升/高潮/退潮/修复, 连板梯队驱动, EMA平滑+2日确认+弱档否决, 平均段长9.7天)与概念/行业主线排名(涨停梯队聚合, 可配置宽基/风格标签过滤); 市场环境页重构, regime 透明加列, 与5档state并存 - 挖掘: 因子与策略挖掘全链路(API/worker/进程锁/候选库/前端工作台/文档), 周度调度默认关闭且永不自动发布 - 回测: 财务快照因子(点时口径), 批量回测预计算共享下期收益, 信号路径矩阵列依赖展开修复(consecutive_limit_ups 缺列报错) - 数据/性能: enriched 生成与预热治理, 重任务限流, 行情/K线缓存复用, 时区修复 - 测试: 后端全量 914 通过; GUI 黑盒验证截图存证 gui-test-screenshots/
249 lines
9.6 KiB
Python
249 lines
9.6 KiB
Python
"""市场主线(板块/概念)识别 — 基于涨停梯队的历史聚合。
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用户判据的量化: 主升阶段的主线 = 同一概念内涨停家数多、最高连板高、
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梯队档位填得满(2 板到最高板之间不断层)。对每个交易日按概念聚合涨停梯队,
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截面 rank 归一后加权成主线分, 持久化为日频时序, 供市场环境页展示
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"什么阶段走什么主升"。
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口径限制(重要): 概念成分来自 ext_gn_ths 快照(本地自 2026-07 起留存, 无历史
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版本)。历史主线是把"今天的成分"回看历史 — 早年存在归属漂移(新概念不会
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出现在旧时段、成分调整会错归属)。MEMBERSHIP_NOTE 随 API 返回给前端展示。
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性能: 全量回填只窄扫 enriched 的 4 列并先过滤连板 >=1(全历史 ~10 万行),
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join 概念映射后 group_by, 峰值内存 <100MB。
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"""
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from __future__ import annotations
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import logging
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from datetime import date
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from pathlib import Path
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import polars as pl
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from app.services.rps_rotation import _load_concept_map_df
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logger = logging.getLogger(__name__)
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MEMBERSHIP_NOTE = (
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"概念成分为当前快照回看历史(本地自 2026-07 起留存, 无历史版本), "
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"早年主线存在归属漂移, 越近越准"
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)
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MAINLINE_DIR = "mainline_history"
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_TOP_PER_DAY = 30 # 每日持久化的主线数(按分数截断)
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_INDUSTRY_LEVEL = 2 # 行业主线取前两级(如 计算机-软件开发)
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_MIN_LIMIT_UP = 3 # 单概念当日最少涨停家数(低于此不参与排名)
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# 主线分权重: 概念内涨停家数 / 最高连板 / 梯队档位数 / 二板以上家数
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_SCORE_WEIGHTS = {
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"limit_up_count": 0.35,
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"max_boards": 0.25,
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"rungs_filled": 0.25,
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"ge2_count": 0.15,
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}
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def _resolve_filter_config(filter_cfg: dict | None) -> dict:
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"""解析过滤配置; None 时读用户偏好(宽基/风格标签过滤, 见 preferences 文档)。"""
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if filter_cfg is not None:
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return {
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"min_members": int(filter_cfg.get("min_members", 4)),
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"max_members": int(filter_cfg.get("max_members", 600)),
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"blacklist": {str(x) for x in filter_cfg.get("blacklist") or []},
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}
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try:
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from app.services import preferences
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cfg = preferences.get_mainline_filter_config()
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return {
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"min_members": int(cfg["min_members"]),
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"max_members": int(cfg["max_members"]),
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"blacklist": set(cfg["blacklist"]),
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}
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except Exception:
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return {"min_members": 4, "max_members": 600, "blacklist": set()}
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def mainline_path(data_dir: Path) -> Path:
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return data_dir / MAINLINE_DIR / "part.parquet"
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def load_mainline_history(data_dir: Path, kind: str = "concept") -> pl.DataFrame:
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"""读取主线时序(全部 kind), 不存在返回空 DataFrame。"""
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p = mainline_path(data_dir)
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if not p.exists():
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return pl.DataFrame()
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try:
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df = pl.read_parquet(p)
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except Exception as e:
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logger.warning("load_mainline_history failed: %s", e)
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return pl.DataFrame()
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if df.is_empty() or "kind" not in df.columns:
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return df
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return df.filter(pl.col("kind") == kind)
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def _industry_member(member: str, kind: str) -> str:
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"""行业维度取前 _INDUSTRY_LEVEL 级; 概念原样返回。"""
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if kind != "industry":
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return member
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return "-".join(member.split("-")[:_INDUSTRY_LEVEL])
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def compute_mainline_range(repo, data_dir: Path, start: date, end: date,
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kind: str = "concept",
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filter_cfg: dict | None = None) -> pl.DataFrame:
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"""计算 [start, end] 每日主线排行(按 _SCORE_WEIGHTS 加权截面分)。
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filter_cfg: {"min_members", "max_members", "blacklist"}; None 时读用户偏好。
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宽基/风格标签(融资融券/沪深股通等数千成分)按成员数上限过滤,
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用户黑名单按名称过滤(不论大小)。修改配置后重算主线生效。
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返回列: date, kind, member, limit_up_count, ge2_count, max_boards,
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boards_sum, rungs_filled, leader_symbol, score, rank。空数据返回空表。
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"""
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if start > end:
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return pl.DataFrame()
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enriched_dir = repo.store.data_dir / "kline_daily_enriched"
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if not enriched_dir.exists():
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return pl.DataFrame()
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map_df, _ = _load_concept_map_df(repo, kind)
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if map_df.is_empty():
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return pl.DataFrame()
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cfg = _resolve_filter_config(filter_cfg)
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if cfg["min_members"] > 1 or cfg["max_members"] < 5000 or cfg["blacklist"]:
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member_counts = map_df.group_by(kind).len().rename({"len": "_members"})
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member_counts = member_counts.filter(
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pl.col("_members").ge(cfg["min_members"])
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& pl.col("_members").le(cfg["max_members"])
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& ~pl.col(kind).is_in(sorted(cfg["blacklist"]))
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)
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allowed = member_counts.select(kind)
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map_df = map_df.join(allowed, on=kind, how="semi")
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if map_df.is_empty():
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return pl.DataFrame()
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limit_rows = (
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pl.scan_parquet(enriched_dir / "**" / "*.parquet")
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.select(["date", "symbol", "consecutive_limit_ups", "amount"])
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.filter(
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(pl.col("date") >= start) & (pl.col("date") <= end)
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& (pl.col("consecutive_limit_ups") >= 1)
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)
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.collect()
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)
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if limit_rows.is_empty():
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return pl.DataFrame()
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limit_rows = limit_rows.with_columns(pl.col("symbol").str.to_uppercase().alias("_sym_up"))
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joined = limit_rows.join(map_df, on="_sym_up", how="inner")
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if joined.is_empty():
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return pl.DataFrame()
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joined = joined.with_columns(
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pl.col(kind).map_elements(
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lambda m: _industry_member(str(m), kind),
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return_dtype=pl.Utf8,
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).alias("member")
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)
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agg = (
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joined.group_by(["date", "member"])
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.agg(
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pl.len().alias("limit_up_count"),
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(pl.col("consecutive_limit_ups") >= 2).sum().alias("ge2_count"),
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pl.col("consecutive_limit_ups").max().alias("max_boards"),
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pl.col("consecutive_limit_ups").sum().alias("boards_sum"),
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pl.col("consecutive_limit_ups")
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.filter(pl.col("consecutive_limit_ups") >= 2)
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.n_unique()
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.alias("rungs_filled"),
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pl.col("symbol")
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.sort_by(
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pl.col("consecutive_limit_ups"), pl.col("amount"),
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descending=[True, True],
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)
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.first()
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.alias("leader_symbol"),
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)
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)
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# 截面 rank 归一(0-1) → 加权主线分(0-100)。分母 max(n-1,1) 保证单概念日不除零。
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agg = agg.filter(pl.col("limit_up_count") >= _MIN_LIMIT_UP)
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norm_exprs = []
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for col in _SCORE_WEIGHTS:
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norm_exprs.append(
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((pl.col(col).rank(method="average") - 1.0)
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/ pl.max_horizontal(pl.len().over("date") - 1, 1)).over("date").alias(f"_{col}_r")
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)
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agg = agg.with_columns(norm_exprs)
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agg = agg.with_columns(
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(
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100.0 * sum(
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_SCORE_WEIGHTS[col] * pl.col(f"_{col}_r") for col in _SCORE_WEIGHTS
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)
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).alias("score")
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)
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agg = agg.with_columns(
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pl.col("score").rank(method="ordinal", descending=True).over("date").alias("rank")
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)
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result = (
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agg.filter(pl.col("rank") <= _TOP_PER_DAY)
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.drop([f"_{col}_r" for col in _SCORE_WEIGHTS])
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.with_columns(pl.lit(kind).alias("kind"))
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.select([
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"date", "kind", "member", "limit_up_count", "ge2_count",
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"max_boards", "boards_sum", "rungs_filled", "leader_symbol",
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"score", "rank",
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])
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.sort(["date", "rank"])
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)
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return result
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def upsert_mainline_history(data_dir: Path, new_rows: pl.DataFrame) -> None:
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"""按 (date, kind) 整日覆盖 upsert; schema 以 new_rows 为权威(同 regime 模式)。"""
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if new_rows.is_empty() or "date" not in new_rows.columns:
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return
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p = mainline_path(data_dir)
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p.parent.mkdir(parents=True, exist_ok=True)
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old = pl.read_parquet(p) if p.exists() else pl.DataFrame()
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if old.is_empty():
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combined = new_rows
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else:
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# 按 (date, kind) 整日覆盖: anti-join 掉本次重算的 (日, 维度) 组合
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kept = old.join(
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new_rows.select(["date", "kind"]).unique(),
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on=["date", "kind"],
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how="anti",
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)
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target_cols = new_rows.columns
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keep_exprs = [
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pl.col(c) if c in kept.columns else pl.lit(None).alias(c)
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for c in target_cols
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]
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kept = kept.select(keep_exprs)
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combined = pl.concat([kept, new_rows.select(target_cols)], how="vertical_relaxed")
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combined = combined.sort(["date", "kind", "rank"])
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combined.write_parquet(p)
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def compute_mainline_incremental(repo, data_dir: Path, *, today: date | None = None,
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kind: str = "concept") -> pl.DataFrame:
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"""增量补算主线(供 daily_pipeline / 手动触发): 补 enriched 已有而主线缺失的日。"""
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today = today or date.today()
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from app.services.regime_builder import enriched_date_set
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enriched_dates = enriched_date_set(repo)
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existing = load_mainline_history(data_dir, kind)
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existing_dates = set(existing["date"].to_list()) if not existing.is_empty() else set()
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missing = sorted(d for d in enriched_dates if d not in existing_dates and d <= today)
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if not missing:
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return pl.DataFrame()
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logger.info("mainline incremental(%s): compute %d days", kind, len(missing))
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new_rows = compute_mainline_range(repo, data_dir, missing[0], missing[-1], kind=kind)
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if not new_rows.is_empty():
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upsert_mainline_history(data_dir, new_rows)
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return new_rows
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