165 lines
7.4 KiB
Python
165 lines
7.4 KiB
Python
"""候选卡的取数层：把各条证据线从三处库读成"按前缀码索引的字典"（全部只读）。
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## 为什么要有它
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候选卡的规则在 card.py，是纯函数；证据从哪来、怎么对齐代码格式、缺了怎么办，
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全部收在这里，plan.py 只做装配。三处来源：
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基座 PG v_factor_transmission_moved 已启动成员及其所在环节的传导证据（只认 Segment 目标）
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v_factor_stock_daily 数据日涨幅、主力净额异常值、热度变化
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153 代理 strategy_daily_results 决策系统昨夜结论：信号、支撑压力位、吸筹块
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（吸筹的评分、状态、评分日三项一次读齐；基座落库的吸筹版本没有评分日，所以不从基座取）
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平台 MySQL gp_day_data 交易日历（算评分日龄用；取一只长期存在的票的日期序列）
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代码格式：基座是点后缀式 600000.SH，决策系统与桥是前缀式 SH600000，进出都过 common.to_prefix。
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读失败的语义：每一路读不到都返回空字典并打印一行原因，候选卡按"缺失"处理（进关注或仅展示），
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不让计划断产——与 pool.py 的安全边界一致。
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"""
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from __future__ import annotations
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import datetime as dt
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import json
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import pandas as pd
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import common
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import db
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def moved_members(ds: str) -> dict[str, dict]:
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"""数据日 ds 被传导指向的环节里，已启动（不在未动名单）的成员。
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同一票挂在多个被指向环节上时，取源数最多、其次链符最高的那条作卡上的证据。"""
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try:
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df = db.read_pg(
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"SELECT ts_code, target, n_sources, chain_fit, members_total, moved, "
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"moved_ratio, mkt_trade_date FROM v_factor_transmission_moved "
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"WHERE scan_date = %s", (ds,))
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except Exception as e: # noqa: BLE001
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print(f" （已动成员视图读取失败，候选卡的传导门槛整体缺席: {e!r}）")
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return {}
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if df.empty:
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return {}
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df["k"] = df["ts_code"].map(lambda s: common.to_prefix(str(s).strip()))
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df["n_sources"] = pd.to_numeric(df["n_sources"], errors="coerce").fillna(0)
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df["chain_fit"] = pd.to_numeric(df["chain_fit"], errors="coerce").fillna(0)
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df = df.sort_values(["n_sources", "chain_fit"], ascending=False).drop_duplicates("k")
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out = {}
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for r in df.itertuples():
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out[r.k] = {"theme": str(r.target), "n_sources": int(r.n_sources),
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"chain_fit": float(r.chain_fit),
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"members_total": None if pd.isna(r.members_total) else int(r.members_total),
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"moved": None if pd.isna(r.moved) else int(r.moved),
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"mkt_trade_date": None if pd.isna(r.mkt_trade_date) else str(r.mkt_trade_date)}
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return out
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def stock_daily(ds: str) -> dict[str, dict]:
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"""数据日 ds 的个股行情三列：涨幅（百分数）、主力净额异常值、热度五日变化。"""
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try:
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df = db.read_pg(
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"SELECT ts_code, pct_change, net_z, heat_chg FROM v_factor_stock_daily "
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"WHERE trade_date = %s", (ds,))
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except Exception as e: # noqa: BLE001
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print(f" （个股日行情视图读取失败，候选卡的已启动门槛整体缺席: {e!r}）")
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return {}
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out = {}
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for r in df.itertuples():
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k = common.to_prefix(str(r.ts_code).strip())
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out[k] = {"pct0": _f(r.pct_change), "net_z": _f(r.net_z), "heat_chg": _f(r.heat_chg)}
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return out
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def trading_days(upto: str, back_days: int = 90) -> list[str]:
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"""交易日历：取一只长期存在的票在 gp_day_data 里的日期序列（表有一千四百万行，
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全表 DISTINCT 太慢；按 symbol 走索引）。返回升序 ISO 日期串。"""
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start = (dt.date.fromisoformat(upto) - dt.timedelta(days=back_days)).isoformat()
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try:
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df = db.read_mysql(
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"factor", "SELECT DISTINCT DATE(`timestamp`) d FROM gp_day_data "
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"WHERE symbol = %s AND `timestamp` >= %s AND `timestamp` <= %s "
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"ORDER BY d", ("SH600519", start, upto))
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return [pd.Timestamp(x).date().isoformat() for x in df["d"].tolist()]
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except Exception as e: # noqa: BLE001
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print(f" （交易日历读取失败，评分日龄按自然日×5/7 近似: {e!r}）")
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return []
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def night_conclusions(codes, ds: str) -> dict[str, dict]:
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"""决策系统昨夜结论（每票最新一行）：信号、支撑压力位、吸筹块与评分日龄。
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评分日龄 = 结论行的 trade_date 到数据日 ds 之间的交易日数（含头不含尾）。
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该表会被盘中补扫就地改写、没有落库时刻列，日龄以行的 trade_date 为准，
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这是已知局限（方案 2.2 第三项）。"""
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codes = sorted({common.to_prefix(str(c).strip()) for c in codes if c})
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if not codes:
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return {}
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try:
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marks = ",".join(["%s"] * len(codes))
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df = db.read_mysql(
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"pms",
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f"SELECT stock_code, trade_date, signal_type, support_level, pressure_level, "
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f"raw_logic_json FROM strategy_daily_results WHERE stock_code IN ({marks})",
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tuple(codes))
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except Exception as e: # noqa: BLE001
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print(f" （决策系统结论表读取失败，吸筹确认线与坏信号风险整体缺席: {e!r}）")
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return {}
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if df.empty:
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return {}
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df = df.sort_values("trade_date").drop_duplicates("stock_code", keep="last")
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cal = trading_days(ds)
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cal_index = {d: i for i, d in enumerate(cal)}
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out = {}
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for r in df.itertuples():
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k = common.to_prefix(str(r.stock_code).strip())
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tdate = _ymd(r.trade_date)
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age = _age(tdate, ds, cal_index)
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ff = {}
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try:
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raw = json.loads(r.raw_logic_json) if isinstance(r.raw_logic_json, str) else (r.raw_logic_json or {})
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ff = (raw or {}).get("fund_flow") or {}
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except Exception: # noqa: BLE001 —— 坏 JSON 当无吸筹块
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ff = {}
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out[k] = {
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"signal": str(r.signal_type or "").strip().upper() or None,
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"support": _f(r.support_level), "pressure": _f(r.pressure_level),
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"conclusion_date": tdate,
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"accum_state": str(ff.get("state") or "") or None,
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"accum_score": _f(ff.get("score")),
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"accum_structure": ff.get("structure"), "accum_pos_tag": ff.get("pos_tag"),
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"accum_age": age,
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}
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return out
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def _ymd(v) -> str | None:
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"""strategy_daily_results.trade_date 是整数 YYYYMMDD（也可能是日期），统一成 ISO 串。"""
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if v is None or (isinstance(v, float) and pd.isna(v)):
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return None
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s = str(v).strip()
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if len(s) == 8 and s.isdigit():
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return f"{s[:4]}-{s[4:6]}-{s[6:]}"
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try:
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return pd.Timestamp(s).date().isoformat()
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except Exception: # noqa: BLE001
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return None
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def _age(tdate: str | None, ds: str, cal_index: dict) -> int | None:
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if not tdate:
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return None
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if tdate in cal_index and ds in cal_index:
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return cal_index[ds] - cal_index[tdate]
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try: # 日历缺失或日期在日历之外：自然日 × 5/7 近似
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nat = (dt.date.fromisoformat(ds) - dt.date.fromisoformat(tdate)).days
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return max(0, round(nat * 5 / 7))
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except ValueError:
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return None
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def _f(v):
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try:
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x = float(v)
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except (TypeError, ValueError):
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return None
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return None if x != x else x
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