mirror of
https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
synced 2026-09-12 22:34:18 +08:00
richardandClaude
e9f5c606b6
自选页新增批量导入：支持 CSV/TXT 文件与粘贴证券代码两种来源，解析出 候选并在弹窗内确认后按 M:N 分组一次性写入（目标分组可多选或就地新建， 默认只勾新增标的，已在自选的可并入所选分组）。 Co-Authored-By: Claude <noreply@anthropic.com>
444 lines
15 KiB
Python
444 lines
15 KiB
Python
"""自选股与分组服务。
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自选存储于 ``data/user_data/watchlist.parquet``，分组定义存储于同目录的
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``watchlist_groups.json``。
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成员关系为多值 (M:N): 每条自选带 ``group_ids: list[str]``, 同一标的可同时
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属于多个分组; 移出分组只摘标签(标的仍在自选), 移出自选才删除实体。
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旧 schema (单值 ``group_id`` 列) 读取时自动迁移为 ``[group_id]``, 首次写回
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新 schema 前留一份 ``watchlist.parquet.bak`` 备份。
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"""
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from __future__ import annotations
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import json
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import logging
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import os
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import shutil
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import threading
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import uuid
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from concurrent.futures import ThreadPoolExecutor
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from concurrent.futures import TimeoutError as FuturesTimeout
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from datetime import datetime
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from pathlib import Path
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import polars as pl
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from app.config import settings
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from app.tickflow.capabilities import Cap, CapabilitySet
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from app.tickflow.client import get_client
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from app.tickflow.rate_limits import chunked, resolve_limit
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logger = logging.getLogger(__name__)
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_LOCK = threading.RLock()
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# 数据版本号: 每次写盘 +1 (在 _LOCK 内递增, 读取免锁)。供监控引擎等进程内
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# 消费方做缓存失效判断 —— 版本没变就不必重读文件, 版本一变立即拿到新成员。
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_REVISION = 0
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def revision() -> int:
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"""自选/分组数据版本号, 每次写操作递增。"""
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return _REVISION
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_MAX_GROUP_NAME_LENGTH = 24
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DEFAULT_GROUP_COLOR = "sky"
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GROUP_COLORS = frozenset({
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"sky",
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"blue",
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"indigo",
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"violet",
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"fuchsia",
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"rose",
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"orange",
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"amber",
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"lime",
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"emerald",
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"teal",
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"cyan",
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})
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_ENTRY_SCHEMA = {
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"symbol": pl.Utf8,
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"added_at": pl.Utf8,
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"note": pl.Utf8,
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"group_ids": pl.List(pl.Utf8),
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}
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def _path() -> Path:
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p = settings.data_dir / "user_data" / "watchlist.parquet"
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p.parent.mkdir(parents=True, exist_ok=True)
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return p
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def _groups_path() -> Path:
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p = settings.data_dir / "user_data" / "watchlist_groups.json"
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p.parent.mkdir(parents=True, exist_ok=True)
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return p
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def _empty_entries() -> pl.DataFrame:
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return pl.DataFrame(schema=_ENTRY_SCHEMA)
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def _read_entries() -> pl.DataFrame:
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p = _path()
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if not p.exists():
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return _empty_entries()
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df = pl.read_parquet(p)
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# 旧 schema 兼容: 单值 group_id → group_ids=[gid]; 两列都缺 → 空列表
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if "group_ids" not in df.columns:
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old = df["group_id"].to_list() if "group_id" in df.columns else [None] * df.height
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df = df.with_columns(
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pl.Series("group_ids", [[g] if g else [] for g in old], dtype=pl.List(pl.Utf8))
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).drop("group_id", strict=False)
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if "symbol" not in df.columns:
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df = df.with_columns(pl.lit("", dtype=pl.Utf8).alias("symbol"))
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if "added_at" not in df.columns:
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df = df.with_columns(pl.lit("", dtype=pl.Utf8).alias("added_at"))
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if "note" not in df.columns:
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df = df.with_columns(pl.lit(None, dtype=pl.Utf8).alias("note"))
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return df.select(list(_ENTRY_SCHEMA))
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def _write_entries(df: pl.DataFrame) -> None:
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global _REVISION
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p = _path()
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# 首次从旧 schema 迁移到 group_ids 前, 备份原文件(一次性)
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if p.exists():
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try:
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if "group_ids" not in pl.read_parquet_schema(p).names():
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shutil.copy(p, p.with_suffix(p.suffix + ".bak"))
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except OSError as e:
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logger.warning("watchlist backup before migration failed: %s", e)
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tmp = p.with_suffix(p.suffix + ".tmp")
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df.select(list(_ENTRY_SCHEMA)).write_parquet(tmp)
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os.replace(tmp, p)
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_REVISION += 1
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def _read_groups() -> list[dict]:
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p = _groups_path()
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if not p.exists():
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return []
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try:
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raw = json.loads(p.read_text(encoding="utf-8"))
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except (OSError, json.JSONDecodeError) as exc:
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raise ValueError("自选分组配置损坏，请检查 watchlist_groups.json") from exc
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if not isinstance(raw, list):
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raise ValueError("自选分组配置格式不正确")
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groups = []
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for item in raw:
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if not isinstance(item, dict) or not item.get("id") or not item.get("name"):
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continue
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color = str(item.get("color", DEFAULT_GROUP_COLOR))
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groups.append({
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"id": str(item["id"]),
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"name": str(item["name"]),
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"color": color if color in GROUP_COLORS else DEFAULT_GROUP_COLOR,
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})
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return groups
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def _write_groups(groups: list[dict]) -> None:
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global _REVISION
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p = _groups_path()
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tmp = p.with_suffix(p.suffix + ".tmp")
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tmp.write_text(json.dumps(groups, ensure_ascii=False, indent=2), encoding="utf-8")
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os.replace(tmp, p)
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_REVISION += 1
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def _normalize_group_name(name: str) -> str:
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normalized = name.strip()
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if not normalized:
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raise ValueError("分组名称不能为空")
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if len(normalized) > _MAX_GROUP_NAME_LENGTH:
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raise ValueError(f"分组名称不能超过 {_MAX_GROUP_NAME_LENGTH} 个字符")
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return normalized
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def _normalize_group_color(color: str | None) -> str:
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normalized = (color or DEFAULT_GROUP_COLOR).strip().lower()
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if normalized not in GROUP_COLORS:
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raise ValueError("不支持的分组颜色")
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return normalized
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def _validate_group_id(group_id: str | None, groups: list[dict]) -> None:
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if group_id is not None and not any(group["id"] == group_id for group in groups):
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raise ValueError("自选分组不存在")
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def list_symbols() -> list[dict]:
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with _LOCK:
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df = _read_entries()
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return [] if df.is_empty() else df.to_dicts()
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def add(symbol: str, note: str = "", group_id: str | None = None) -> list[dict]:
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rows, _ = add_batch([symbol], note=note, group_id=group_id)
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return rows
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def add_batch(
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symbols: list[str],
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note: str = "",
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group_id: str | None = None,
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group_ids: list[str] | None = None,
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) -> tuple[list[dict], int]:
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"""批量添加并保持既有语义：每个新处理的标的移动到列表最前面。
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分组为可选的初始分组：``group_id`` 单组（如从某分组页添加）或 ``group_ids``
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多组（如批量导入同时并入多个分组）。重复添加的标的保留既有全部分组，
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仅把尚未属于的传入分组并入；二者可同时使用、内部去重。
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"""
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with _LOCK:
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groups = _read_groups()
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# 合并单/多组参数并去重；逐组校验存在性
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apply_ids: list[str] = []
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for gid in (group_ids or []) + ([group_id] if group_id is not None else []):
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if gid in apply_ids:
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continue
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_validate_group_id(gid, groups)
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apply_ids.append(gid)
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rows = _read_entries().to_dicts()
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added = 0
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for symbol in symbols:
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existing = next((row for row in rows if row["symbol"] == symbol), None)
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if existing is None:
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added += 1
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rows = [row for row in rows if row["symbol"] != symbol]
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gids = list((existing or {}).get("group_ids") or [])
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for gid in apply_ids:
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if gid not in gids:
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gids.append(gid)
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rows.insert(0, {
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"symbol": symbol,
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"added_at": datetime.utcnow().isoformat(timespec="seconds"),
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"note": note,
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"group_ids": gids,
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})
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out = pl.DataFrame(rows, schema=_ENTRY_SCHEMA) if rows else _empty_entries()
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_write_entries(out)
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return out.to_dicts(), added
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def remove(symbol: str) -> list[dict]:
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with _LOCK:
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df = _read_entries().filter(pl.col("symbol") != symbol)
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_write_entries(df)
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return df.to_dicts()
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def move_to_top(symbol: str) -> list[dict]:
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with _LOCK:
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df = _read_entries()
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if df.is_empty() or symbol not in df["symbol"].to_list():
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return df.to_dicts()
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target = df.filter(pl.col("symbol") == symbol)
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rest = df.filter(pl.col("symbol") != symbol)
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out = pl.concat([target, rest], how="diagonal_relaxed")
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_write_entries(out)
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return out.to_dicts()
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def clear() -> int:
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"""清空自选列表。返回移除的数量。"""
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with _LOCK:
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df = _read_entries()
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count = df.height
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if count > 0:
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_write_entries(_empty_entries())
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return count
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def list_groups() -> list[dict]:
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with _LOCK:
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return _read_groups()
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def create_group(name: str, color: str | None = None) -> tuple[list[dict], dict]:
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with _LOCK:
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normalized = _normalize_group_name(name)
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normalized_color = _normalize_group_color(color)
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groups = _read_groups()
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if any(group["name"].casefold() == normalized.casefold() for group in groups):
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raise ValueError("分组名称已存在")
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group = {
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"id": uuid.uuid4().hex,
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"name": normalized,
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"color": normalized_color,
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}
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groups.append(group)
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_write_groups(groups)
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return groups, group
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def rename_group(group_id: str, name: str, color: str | None = None) -> list[dict]:
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with _LOCK:
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normalized = _normalize_group_name(name)
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groups = _read_groups()
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target = next((group for group in groups if group["id"] == group_id), None)
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if target is None:
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raise KeyError(group_id)
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if any(
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group["id"] != group_id and group["name"].casefold() == normalized.casefold()
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for group in groups
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):
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raise ValueError("分组名称已存在")
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target["name"] = normalized
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if color is not None:
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target["color"] = _normalize_group_color(color)
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_write_groups(groups)
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return groups
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def reorder_groups(ordered_ids: list[str]) -> list[dict]:
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"""按给定 id 顺序重排分组 (json 数组顺序即定义顺序)。"""
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with _LOCK:
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groups = _read_groups()
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by_id = {group["id"]: group for group in groups}
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if len(ordered_ids) != len(groups) or set(ordered_ids) != set(by_id):
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raise ValueError("分组顺序与现有分组不一致")
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reordered = [by_id[group_id] for group_id in ordered_ids]
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_write_groups(reordered)
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return reordered
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def delete_group(group_id: str) -> tuple[list[dict], list[dict]]:
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"""删除分组定义，原分组内的自选保留并转为未分组(仅摘掉该组标签)。"""
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with _LOCK:
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groups = _read_groups()
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if not any(group["id"] == group_id for group in groups):
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raise KeyError(group_id)
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df = _strip_group(_read_entries(), group_id)
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remaining = [group for group in groups if group["id"] != group_id]
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_write_entries(df)
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_write_groups(remaining)
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return remaining, df.to_dicts()
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def set_group(symbol: str, group_id: str | None) -> list[dict]:
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"""互斥设定: 该标的只保留这一个分组(group_id=None 即全部移出, 变未分组)。
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多组模型的日常操作走 add_to_group / remove_from_group; 本函数服务于
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「仅保留此组」的显式场景。
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"""
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with _LOCK:
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groups = _read_groups()
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_validate_group_id(group_id, groups)
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rows = _read_entries().to_dicts()
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if not any(row["symbol"] == symbol for row in rows):
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raise KeyError(symbol)
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for row in rows:
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if row["symbol"] == symbol:
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row["group_ids"] = [group_id] if group_id is not None else []
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out = pl.DataFrame(rows, schema=_ENTRY_SCHEMA)
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_write_entries(out)
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return out.to_dicts()
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def add_to_group(symbol: str, group_id: str) -> list[dict]:
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"""把标的加入一个分组(多组成员关系: 不影响已属于的其他分组)。"""
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with _LOCK:
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groups = _read_groups()
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_validate_group_id(group_id, groups)
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rows = _read_entries().to_dicts()
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if not any(row["symbol"] == symbol for row in rows):
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raise KeyError(symbol)
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for row in rows:
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if row["symbol"] == symbol:
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gids = row["group_ids"] or []
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if group_id not in gids:
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gids.append(group_id)
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row["group_ids"] = gids
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out = pl.DataFrame(rows, schema=_ENTRY_SCHEMA)
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_write_entries(out)
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return out.to_dicts()
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def remove_from_group(symbol: str, group_id: str) -> list[dict]:
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"""把标的移出一个分组(仅摘本组标签; 标的仍在自选, 可能落入未分组)。"""
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with _LOCK:
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groups = _read_groups()
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_validate_group_id(group_id, groups)
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rows = _read_entries().to_dicts()
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if not any(row["symbol"] == symbol for row in rows):
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raise KeyError(symbol)
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for row in rows:
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if row["symbol"] == symbol:
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row["group_ids"] = [g for g in (row["group_ids"] or []) if g != group_id]
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out = pl.DataFrame(rows, schema=_ENTRY_SCHEMA)
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_write_entries(out)
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return out.to_dicts()
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def _strip_group(df: pl.DataFrame, group_id: str) -> pl.DataFrame:
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"""从所有条目的 group_ids 中摘掉指定分组(删除分组/清空分组共用)。"""
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rows = df.to_dicts()
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for row in rows:
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gids = row.get("group_ids") or []
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if group_id in gids:
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row["group_ids"] = [g for g in gids if g != group_id]
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return pl.DataFrame(rows, schema=_ENTRY_SCHEMA) if rows else _empty_entries()
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def clear_group(group_id: str) -> list[dict]:
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"""清空分组成员:把该分组标签从所有条目摘掉(变未分组),保留分组定义。"""
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with _LOCK:
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groups = _read_groups()
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if not any(group["id"] == group_id for group in groups):
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raise KeyError(group_id)
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df = _strip_group(_read_entries(), group_id)
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_write_entries(df)
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return df.to_dicts()
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def fetch_quotes(symbols: list[str], capset: CapabilitySet, timeout_s: float = 8.0) -> list[dict]:
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"""拉取实时行情。
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优先用 quote.batch;否则降级为 quote.by_symbol 单股请求。
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timeout_s: 单批次请求超时(秒)，防止 API 卡死阻塞整个请求。
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"""
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if not symbols:
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return []
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tf = get_client()
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quotes: list[dict] = []
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# 走 batch
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if capset.has(Cap.QUOTE_BATCH):
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batch_size = resolve_limit(capset, Cap.QUOTE_BATCH, default_batch=50).batch
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elif capset.has(Cap.QUOTE_BY_SYMBOL):
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batch_size = resolve_limit(capset, Cap.QUOTE_BY_SYMBOL, default_batch=5).batch
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else:
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# 无任何实时行情能力(none/free 档走 free-api 服务器,不提供实时行情)
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# 提前返回空,避免发起注定失败的请求
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return []
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chunks = chunked(symbols, batch_size)
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# 用线程池为每个批次加超时保护
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pool = ThreadPoolExecutor(max_workers=1)
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for chunk in chunks:
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try:
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future = pool.submit(tf.quotes.get, symbols=chunk, as_dataframe=True)
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raw = future.result(timeout=timeout_s)
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if raw is None or len(raw) == 0:
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continue
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df = pl.from_pandas(raw)
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rename_map = {
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"last_price": "price",
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"ext.change_pct": "pct",
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"ext.name": "name",
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}
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df = df.rename({k: v for k, v in rename_map.items() if k in df.columns})
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quotes.extend(df.to_dicts())
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except FuturesTimeout:
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logger.warning("quote fetch timeout (%.1fs) for %d symbols", timeout_s, len(chunk))
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break # 超时后不再尝试后续批次
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except Exception as e: # noqa: BLE001
|
|
logger.warning("quote fetch failed for %d symbols: %s", len(chunk), e)
|
|
pool.shutdown(wait=False)
|
|
|
|
return quotes
|