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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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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>
203 lines
7.4 KiB
Python
203 lines
7.4 KiB
Python
"""自选股 CSV/TXT 与粘贴代码批量导入：解码 → 抽代码 → instruments 校验。
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国内行情软件（同花顺/东财/通达信）导出的自选多为 CSV/TXT，且常为 GBK 系编码
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（参见 ext_data.ensure_utf8_csv 的说明）。本模块把上传字节 / 粘贴文本解析为与截图
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OCR 一致的候选结构，前端复用同一套勾选确认流程；写入目标统一走自选分组语义
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（watchlist.add_batch 的 group_ids M:N 并入），本模块不落盘、不建标签。
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"""
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from __future__ import annotations
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import csv
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import io
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import re
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from pathlib import Path
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from typing import Any
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from app.services.watchlist_ocr.pipeline import (
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_CODE_RE,
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ImportCandidate,
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build_instrument_lookups,
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extract_codes,
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resolve_candidates,
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)
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_CJK_RE = re.compile(f"[{chr(0x4E00)}-{chr(0x9FFF)}]") # CJK 统一表意文字块
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# 编码回退链：UTF-8（含 BOM）→ GB18030（GB18030 是 GBK 超集，无需单独回退）
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_ENCODINGS = ("utf-8-sig", "gb18030")
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def _finalize(
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provider: str,
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text: str,
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codes: list[str],
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candidates: list[dict[str, Any]],
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) -> dict[str, Any]:
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"""组装与截图 OCR 一致的候选响应，统一 matched/unmatched 计数口径。"""
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matched_count = sum(1 for c in candidates if c["matched"])
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return {
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"provider": provider,
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"raw_text": text,
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"codes": codes,
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"candidates": candidates,
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"matched_count": matched_count,
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"unmatched_count": len(candidates) - matched_count,
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}
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def decode_csv_bytes(raw: bytes) -> str:
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"""把上传字节解码为文本，兼容 UTF-8 / GBK 系编码。"""
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if not raw:
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raise ValueError("空文件")
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last_err: Exception | None = None
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for enc in _ENCODINGS:
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try:
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return raw.decode(enc)
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except (UnicodeDecodeError, LookupError) as e:
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last_err = e
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raise ValueError("无法识别文件编码，请另存为 UTF-8 或 GBK 后重试") from last_err
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def _is_code_cell(cell: str) -> bool:
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# 调用方（parse_csv_rows）已 strip 过单元格
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return bool(_CODE_RE.fullmatch(cell))
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def _pick_name(cells: list[str]) -> str | None:
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"""取行内首个含 ≥2 个汉字且非六位代码的单元格作为名称候选。"""
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for cell in cells:
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if not cell or _is_code_cell(cell):
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continue
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if len(_CJK_RE.findall(cell)) >= 2:
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return cell
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return None
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def parse_csv_rows(text: str) -> list[tuple[list[str], str | None]]:
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"""解析 CSV/TXT 文本为 [(行内代码列表, 名称候选), ...]。
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- 自动识别逗号 / Tab 分隔（同花顺/通达信导出常见 Tab）。
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- 逐行取所有六位数字作为代码候选；无代码行保留给名称兜底（是否输出由
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import_watchlist_csv 决定：表头等名称命不中主数据的行会被忽略）。
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- 返回列表保持文件行序。
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"""
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if not text.strip():
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return []
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first = next((ln for ln in text.splitlines() if ln.strip()), "")
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delimiter = "\t" if first.count("\t") > first.count(",") else ","
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reader = csv.reader(io.StringIO(text), delimiter=delimiter)
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rows: list[tuple[list[str], str | None]] = []
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for raw_row in reader:
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cells = [c.strip() for c in raw_row if c is not None]
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if not cells:
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continue
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codes: list[str] = []
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for cell in cells:
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codes.extend(m.group(1) for m in _CODE_RE.finditer(cell))
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rows.append((codes, _pick_name(cells)))
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return rows
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def import_watchlist_csv(
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raw: bytes,
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data_dir: Path,
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*,
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existing_symbols: set[str] | None = None,
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) -> dict[str, Any]:
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"""解析 CSV/TXT 字节并返回候选列表（不写入自选）。返回结构与 OCR 一致。"""
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text = decode_csv_bytes(raw)
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return _resolve_rows(text, data_dir, existing_symbols=existing_symbols)
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def import_watchlist_codes(
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text: str,
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data_dir: Path,
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*,
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existing_symbols: set[str] | None = None,
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max_codes: int = 1000,
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) -> dict[str, Any]:
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"""解析粘贴的证券代码并返回候选列表（不写入自选）。
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与 CSV 行级解析不同：粘贴文本里的多个代码可能挤在同一行/同一段（逗号、空格、
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换行分隔），必须按 ``extract_codes`` 全量抽码、去重保序，逐码生成候选，
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否则会把同行多码压成单候选而静默丢码。仅与 CSV 路径共享 lookups/resolve。
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"""
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codes = extract_codes(text)
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if not codes:
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return _finalize("codes", text, [], [])
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if len(codes) > max_codes:
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raise ValueError(f"一次最多导入 {max_codes} 个股票代码，已识别 {len(codes)} 个")
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code_to_symbol, symbol_to_name = build_instrument_lookups(data_dir)
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candidates = resolve_candidates(codes, code_to_symbol, symbol_to_name, existing_symbols)
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return _finalize("codes", text, codes, [c.to_dict() for c in candidates])
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def _resolve_rows(
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text: str,
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data_dir: Path,
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*,
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existing_symbols: set[str] | None = None,
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) -> dict[str, Any]:
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"""逐行把文本解析为候选（CSV/TXT 用；行内多码取首个已匹配者）。"""
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rows = parse_csv_rows(text)
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code_to_symbol, symbol_to_name = build_instrument_lookups(data_dir)
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# 名称兜底反向表：CSV 可能只有名称列（无代码）
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name_to_symbol: dict[str, str] = {}
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for symbol, name in symbol_to_name.items():
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name_to_symbol.setdefault(name, symbol)
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existing = existing_symbols or set()
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# 全部唯一代码按出现顺序一次性构建候选（复用 OCR 的构造逻辑，单一来源）
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unique_codes: list[str] = []
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seen_codes: set[str] = set()
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for row_codes, _ in rows:
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for c in row_codes:
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if c not in seen_codes:
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seen_codes.add(c)
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unique_codes.append(c)
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cand_by_code = {
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c.code: c
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for c in resolve_candidates(unique_codes, code_to_symbol, symbol_to_name, existing)
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}
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candidates: list[dict[str, Any]] = []
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seen_symbols: set[str] = set() # 已发出的已匹配 symbol
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emitted_unmatched: set[str] = set() # 已发出的未匹配 code
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for row_codes, row_name in rows:
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# 行内代码优先：取第一个已匹配主数据的（避免价格/成交量数字误报）
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matched = next((cand_by_code[c] for c in row_codes if cand_by_code[c].matched), None)
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symbol = matched.symbol if matched else (name_to_symbol.get(row_name) if row_name else None)
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if symbol:
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if symbol in seen_symbols:
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continue
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seen_symbols.add(symbol)
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cand = matched or ImportCandidate(
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code=row_codes[0] if row_codes else "",
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symbol=symbol,
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name=symbol_to_name.get(symbol),
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matched=True,
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already_in_watchlist=symbol in existing,
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)
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else:
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# 名称兜底失败且无代码 → 表头/杂项行，忽略
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if not row_codes:
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continue
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code = row_codes[0]
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if code in emitted_unmatched:
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continue
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emitted_unmatched.add(code)
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cand = ImportCandidate(
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code=code,
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symbol=None,
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name=row_name,
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matched=False,
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already_in_watchlist=False,
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)
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candidates.append(cand.to_dict())
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return _finalize("csv", text, unique_codes, candidates)
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