mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 20:24:19 +08:00
Justin Gu
a3d6d93122
- Add 'screen' CLI command group with 'scan' and 'rank' subcommands
- scan: offline signal scanning from local .day files, zero network IO
- rank: backtest ranking of scanned signals by sharpe/drawdown/etc
- Two-step workflow: scan outputs JSON, rank reads JSON and evaluates
- Support --universe (all/sh/sz/custom file), --sort, --names
- Support pipe mode: scan ... | rank --from - --table
- New module: src/easy_tdx/screen/{scanner,ranker,cli}.py
- 20 unit tests (offline, no network required)
321 lines
9.1 KiB
Python
321 lines
9.1 KiB
Python
"""信号扫描引擎 — 纯离线，从本地 .day 文件提取策略信号。
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核心流程：
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1. 扫描 vipdoc/{sh,sz}/lday/*.day 获取文件列表
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2. 按 universe 过滤（all/sh/sz/文件列表）
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3. 过滤掉非 A 股（指数、基金、债券）
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4. 每个文件：read_daily_bars() → DataFrame → extract_factor_signals() → 检查 buy_mask[-1]
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5. 输出触发信号的股票列表
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"""
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from __future__ import annotations
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import json
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from dataclasses import dataclass
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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 pandas as pd
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from easy_tdx.backtest.combo import extract_factor_signals
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from easy_tdx.backtest.strategy import Strategy
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from easy_tdx.offline.daily_bar import _detect_security_type, read_daily_bars
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from easy_tdx.offline.paths import resolve_vipdoc
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# A 股类型白名单
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_A_STOCK_TYPES = frozenset(
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{
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"SH_A_STOCK",
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"SZ_A_STOCK",
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}
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)
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@dataclass
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class ScanResult:
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"""单只股票的扫描结果。
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Attributes:
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code: 6 位股票代码
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market: 市场（SZ/SH）
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signal_date: 信号日期（YYYYMMDD 整数）
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last_close: 最后收盘价
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"""
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code: str
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market: str
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signal_date: int
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last_close: float
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class SignalScanner:
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"""策略信号扫描器。
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用法::
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scanner = SignalScanner(
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strategy_cls=RSIStrategy,
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vipdoc_path="C:\\new_jyplug\\vipdoc",
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)
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results = scanner.scan(universe="all")
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for r in results:
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print(f"{r.market}{r.code} 触发买入信号 @ {r.signal_date}")
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"""
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def __init__(
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self,
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strategy_cls: type[Strategy],
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vipdoc_path: str | Path | None = None,
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cash: float = 100_000.0,
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commission: float = 0.0003,
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) -> None:
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"""初始化扫描器。
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Args:
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strategy_cls: 策略类（Strategy 子类）
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vipdoc_path: vipdoc 目录路径，None 则自动检测
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cash: 初始资金（影响全仓信号判断）
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commission: 佣金率
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"""
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self._strategy_cls = strategy_cls
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self._vipdoc = resolve_vipdoc(vipdoc_path)
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self._cash = cash
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self._commission = commission
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def scan(
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self,
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universe: str = "all",
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progress_callback: Any = None,
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) -> list[ScanResult]:
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"""扫描全市场，返回触发买入信号的股票列表。
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Args:
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universe: 股票范围
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- "all": 沪深全部 A 股（默认）
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- "sh": 仅上海
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- "sz": 仅深圳
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- 文件路径: 每行一个 "市场 代码"（如 "SZ 000001"）
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progress_callback: 进度回调函数(current, total, filename)
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Returns:
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触发买入信号的 ScanResult 列表
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"""
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# 1. 收集文件列表
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files = self._collect_files(universe)
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if not files:
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return []
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results: list[ScanResult] = []
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total = len(files)
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for idx, (filepath, market, code) in enumerate(files):
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if progress_callback:
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progress_callback(idx, total, filepath.name)
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try:
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result = self._scan_one(filepath, market, code)
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if result is not None:
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results.append(result)
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except Exception:
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# 单个文件出错不中断整体扫描
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continue
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if progress_callback:
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progress_callback(total, total, "done")
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return results
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def _collect_files(self, universe: str) -> list[tuple[Path, str, str]]:
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"""收集需要扫描的 .day 文件列表。
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Args:
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universe: 股票范围
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Returns:
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[(filepath, market_str, code), ...] 列表
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"""
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# 确定要扫描的交易所目录
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exchanges: list[str] = []
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if universe in ("all", "sz"):
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exchanges.append("sz")
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if universe in ("all", "sh"):
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exchanges.append("sh")
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# 从文件列表模式读取
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if universe not in ("all", "sh", "sz"):
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return self._collect_from_file(universe)
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# 扫描目录
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files: list[tuple[Path, str, str]] = []
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for exchange in exchanges:
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lday_dir = self._vipdoc / exchange / "lday"
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if not lday_dir.is_dir():
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continue
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for filepath in sorted(lday_dir.glob("*.day")):
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# 从文件名提取代码
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name = filepath.name.lower()
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code = name[2:8]
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# 过滤非 A 股
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sec_type = _detect_security_type(filepath.name)
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if sec_type not in _A_STOCK_TYPES:
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continue
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market = exchange.upper()
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files.append((filepath, market, code))
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return files
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def _collect_from_file(self, filepath: str) -> list[tuple[Path, str, str]]:
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"""从文件读取股票列表。
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每行格式: "市场 代码"（如 "SZ 000001"）
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Args:
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filepath: 股票列表文件路径
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Returns:
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[(filepath, market_str, code), ...] 列表
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"""
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path = Path(filepath)
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if not path.is_file():
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raise FileNotFoundError(f"股票列表文件不存在: {filepath}")
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files: list[tuple[Path, str, str]] = []
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with open(path, encoding="utf-8") as f:
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for line in f:
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line = line.strip()
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if not line or line.startswith("#"):
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continue
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parts = line.split()
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if len(parts) >= 2:
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market_str = parts[0].upper()
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code = parts[1]
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else:
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continue
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# 定位 .day 文件
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exchange = market_str.lower()
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day_file = self._vipdoc / exchange / "lday" / f"{exchange}{code}.day"
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if day_file.is_file():
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files.append((day_file, market_str, code))
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return files
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def _scan_one(self, filepath: Path, market: str, code: str) -> ScanResult | None:
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"""扫描单只股票。
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Args:
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filepath: .day 文件路径
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market: 市场代码（SZ/SH）
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code: 6 位股票代码
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Returns:
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ScanResult 如果触发信号，否则 None
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"""
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bars = read_daily_bars(filepath)
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if len(bars) < 30:
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# 数据太少，无法计算有意义的指标
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return None
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df = _bars_to_df(bars)
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if df.empty:
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return None
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# 提取信号遮罩
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try:
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factor_signals = extract_factor_signals(
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self._strategy_cls,
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df,
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cash=self._cash,
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commission=self._commission,
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)
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except Exception:
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return None
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# 检查最后一根 bar 是否有买入信号
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if not factor_signals.buy_mask[-1]:
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return None
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# 获取最后收盘价和日期
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last_bar = bars[-1]
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signal_date = last_bar.year * 10000 + last_bar.month * 100 + last_bar.day
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last_close = last_bar.close
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return ScanResult(
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code=code,
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market=market,
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signal_date=signal_date,
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last_close=last_close,
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)
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def to_json(
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self,
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results: list[ScanResult],
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strategy_name: str,
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strategy_file: str,
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total_scanned: int,
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) -> str:
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"""将扫描结果序列化为 JSON 字符串。
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Args:
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results: 扫描结果列表
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strategy_name: 策略名称
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strategy_file: 策略文件路径
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total_scanned: 总扫描股票数
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Returns:
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JSON 字符串
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"""
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data = {
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"scan_time": datetime.now().isoformat(timespec="seconds"),
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"strategy": strategy_name,
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"strategy_file": strategy_file,
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"total_scanned": total_scanned,
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"total_signals": len(results),
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"signals": [
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{
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"code": r.code,
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"market": r.market,
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"signal_date": r.signal_date,
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"last_close": r.last_close,
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}
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for r in results
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],
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}
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return json.dumps(data, ensure_ascii=False, indent=2)
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def _bars_to_df(bars: list[Any]) -> pd.DataFrame:
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"""将 SecurityBar 列表转为策略所需的 DataFrame。
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Args:
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bars: SecurityBar 列表（按时间升序）
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Returns:
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DataFrame，包含 datetime, open, close, high, low, vol, amount 列
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"""
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if not bars:
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return pd.DataFrame()
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rows = []
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for b in bars:
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dt = pd.Timestamp(year=b.year, month=b.month, day=b.day)
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rows.append(
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{
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"datetime": dt,
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"open": b.open,
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"close": b.close,
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"high": b.high,
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"low": b.low,
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"vol": b.vol,
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"amount": b.amount,
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}
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)
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return pd.DataFrame(rows)
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