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feat(screen): v1.15.0 — 强势股排名 + 修复证券类型识别与名称分批查询
新增：强势股排名（screen strength） - 全市场按 5/20/60 日涨幅加权合成强势分，纯离线扫描 - 三种预设：steady(稳健)/breakout(妖股)/balanced(均衡) - CLI: easy-tdx screen strength --preset steady --top 50 --table - Web API: GET /api/v1/market/strength - 支持自定义权重、成交额过滤、并发扫描 修复： - _detect_security_type 代码段不全，ETF/基金/科创板/逆回购被误判为 A 股 - screen strength/rank 名称补齐超 80 只时末尾被丢弃（分批查询） 详见 CHANGELOG.md
This commit is contained in:
Justin Gu
@@ -30,6 +30,9 @@ def _detect_security_type(filename: str) -> str:
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"""从文件名推断证券类型。
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文件名格式: {exchange}{code}.day，如 sh600000.day、sz000001.day
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依据上交所/深交所《证券代码段分配指南》判定。无法识别的代码段
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返回 "UNKNOWN"（而非默认深市 A 股），避免把基金/ETF/债券误判为股票。
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"""
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base = Path(filename).name.lower()
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exchange = base[:2] # "sh" or "sz"
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@@ -44,21 +47,29 @@ def _detect_security_type(filename: str) -> str:
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return "SZ_INDEX"
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if code_head in ("15", "16"):
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return "SZ_FUND"
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if code_head in ("17", "18"): # 封闭式基金 / LOF / ETF
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return "SZ_FUND"
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if code_head in ("10", "11", "12", "13", "14"):
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return "SZ_BOND"
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elif exchange == "sh":
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if code_head == "60":
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return "SH_A_STOCK"
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if code_head == "68": # 科创板（688 开头）
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return "SH_A_STOCK"
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if code_head == "90":
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return "SH_B_STOCK"
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if code_head in ("00", "88", "99"):
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return "SH_INDEX"
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if code_head in ("50", "51"):
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if code_head in ("50", "51", "52", "53", "55", "56", "58"):
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# 501 LOF / 510-519 ETF / 520-529 ETF / 530-539 ETF
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# 550-556 货币ETF / 560-563 LOF / 588-589 科创板ETF
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return "SH_FUND"
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if code_head in ("01", "10", "11", "12", "13", "14"):
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return "SH_BOND"
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if code_head == "20": # 国债逆回购（204xxx）
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return "SH_BOND"
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return "SZ_A_STOCK" # 默认按 A 股处理
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return "UNKNOWN"
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def read_daily_bars(filepath: str | Path) -> list[SecurityBar]:
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@@ -4,6 +4,8 @@
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1. scan: 用策略扫描全市场，找出触发买入信号的股票（纯离线）
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2. rank: 对扫描结果做历史回测排名
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另外提供 strength: 全市场强势股排名（按 5/20/60 日涨幅加权排序）。
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用法::
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# Step 1: 信号扫描
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@@ -11,11 +13,22 @@
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# Step 2: 回测排名
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easy-tdx screen rank --from signals.json --sort sharpe --top 20 --table
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# 强势股排名
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easy-tdx screen strength --preset steady --top 50 --table
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"""
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from easy_tdx.screen.scanner import ScanResult, SignalScanner # noqa: F401
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from easy_tdx.screen.strength import ( # noqa: F401
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STRENGTH_PRESETS,
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StrengthRanker,
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StrengthResult,
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)
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__all__ = [
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"SignalScanner",
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"ScanResult",
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"StrengthRanker",
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"StrengthResult",
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"STRENGTH_PRESETS",
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]
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@@ -3,11 +3,13 @@
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子命令：
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scan — 纯离线扫描信号
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rank — 回测排名
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strength — 全市场强势股排名（5/20/60 日涨幅加权）
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"""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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import click
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@@ -219,6 +221,174 @@ def rank_cmd(
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click.echo(ranker.to_json(entries, strategy_name, sort_by))
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# ── strength 子命令 ──────────────────────────────────────────────────────────
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@screen.command("strength")
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@click.option(
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"--preset",
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default="steady",
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type=click.Choice(["steady", "breakout", "balanced"]),
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help="预设模式: steady(中长期稳健,默认) / breakout(近期妖股) / balanced(均衡)",
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)
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@click.option("--w5", default=None, type=float, help="自定义 5 日权重（覆盖预设）")
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@click.option("--w20", default=None, type=float, help="自定义 20 日权重（覆盖预设）")
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@click.option("--w60", default=None, type=float, help="自定义 60 日权重（覆盖预设）")
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@click.option(
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"--vol-adjusted/--no-vol-adjusted",
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default=None,
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help="是否波动率惩罚（覆盖预设）",
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)
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@click.option("--top", "top_n", default=50, type=int, help="返回前 N 名（默认 50）")
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@click.option("--universe", default="all", help="范围: all/sh/sz/<文件路径>")
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@click.option("--vipdoc", default=None, help="离线数据目录（默认自动检测）")
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@click.option("--min-listed-days", default=65, type=int, help="最小上市天数（默认 65）")
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@click.option(
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"--min-amount",
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default=0.0,
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type=float,
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help="最近 5 日日均成交额下限（元，默认不过滤）",
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)
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@click.option(
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"--workers",
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default=0,
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type=int,
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help="并发进程数: 0=串行（默认），4-8 推荐",
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)
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@click.option("--output", "output_file", default=None, help="输出 JSON 文件（默认 stdout）")
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@click.option("--table", "use_table", is_flag=True, help="表格输出")
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@click.option("--names/--no-names", default=False, help="在线查询股票名称（默认关闭）")
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def strength_cmd(
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preset: str,
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w5: float | None,
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w20: float | None,
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w60: float | None,
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vol_adjusted: bool | None,
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top_n: int,
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universe: str,
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vipdoc: str | None,
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min_listed_days: int,
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min_amount: float,
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workers: int,
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output_file: str | None,
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use_table: bool,
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names: bool,
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) -> None:
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"""全市场强势股排名 — 按 5/20/60 日涨幅加权排序。
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三种预设：
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steady — 中长期稳健（60日主导 + 波动率惩罚），选稳着涨的票
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breakout — 近期妖股爆发（5日主导，纯涨幅），选最猛的票
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balanced — 三周期均衡 + 波动率调整
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示例：
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easy-tdx screen strength --preset steady --top 50 --table
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easy-tdx screen strength --preset breakout --top 20 --names --table
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easy-tdx screen strength --w5 0.5 --w20 0.3 --w60 0.2 --top 30
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"""
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from .strength import StrengthRanker
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click.echo(f"模式: {preset}", err=True)
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click.echo(f"范围: {universe} | Top: {top_n}", err=True)
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if workers > 0:
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click.echo(f"并发: {workers} 进程", err=True)
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ranker = StrengthRanker(
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vipdoc_path=vipdoc,
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preset=preset,
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w5=w5,
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w20=w20,
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w60=w60,
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vol_adjusted=vol_adjusted,
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min_listed_days=min_listed_days,
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min_amount=min_amount,
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)
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def on_progress(current: int, total: int, name: str) -> None:
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if name == "done":
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click.echo(f"\r扫描完成: {total} 只", err=True)
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else:
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pct = current * 100 // total if total > 0 else 0
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click.echo(f"\r[{current}/{total}] {pct}% {name}", nl=False, err=True)
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results = ranker.rank(
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universe=universe,
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top_n=top_n,
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workers=workers,
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progress_callback=on_progress,
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)
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# 数据截止日期（取排名第一的 last_date）
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data_date = results[0].last_date if results else 0
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# 可选补齐名称
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if names and results:
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click.echo("\n获取股票名称...", err=True)
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results = _enrich_strength_names(results)
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if use_table:
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click.echo(ranker.to_table(results, preset, data_date))
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else:
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json_str = ranker.to_json(results, preset, data_date)
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if output_file:
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Path(output_file).write_text(json_str, encoding="utf-8")
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click.echo(f"排名: {len(results)} 只 → {output_file}")
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else:
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click.echo(json_str)
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def _enrich_strength_names(
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results: list[Any],
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) -> list[Any]:
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"""在线查询补齐股票名称（复用 ranker 的逻辑）。
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分批查询（每批最多 80 只），避免超出 MAC 协议单次报价上限导致末尾名字丢失。
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"""
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try:
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from easy_tdx.cli.parsers import parse_market
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from easy_tdx.mac.client import MacClient
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pairs = [(parse_market(r.market), r.code) for r in results]
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client = MacClient.from_best_host()
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try:
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client.connect()
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# 分批查询：MAC 协议单次最多 80 只，超出部分会被服务器丢弃
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import pandas as pd
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frames: list[pd.DataFrame] = []
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for i in range(0, len(pairs), 80):
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batch = pairs[i : i + 80]
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frames.append(client.get_stock_quotes(batch))
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quotes_df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
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finally:
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client.close()
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if quotes_df.empty or "name" not in quotes_df.columns:
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return results
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_market_map = {0: "SZ", 1: "SH"}
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name_map: dict[str, str] = {}
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for _, row in quotes_df.iterrows():
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mkt_int = row.get("market", -1)
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mkt_str = _market_map.get(mkt_int, str(mkt_int))
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key = f"{mkt_str}{row.get('code', '')}"
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name_map[key] = str(row.get("name", ""))
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for r in results:
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r.name = name_map.get(f"{r.market}{r.code}", "")
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except Exception:
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# 名称查询失败不影响主流程
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pass
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return results
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# ── 辅助函数 ──────────────────────────────────────────────────────────────────
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@@ -191,6 +191,8 @@ class SignalRanker:
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仅对排名中的股票查询，通常只有几十只。
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分批查询（每批最多 80 只），避免超出 MAC 协议单次报价上限导致末尾名字丢失。
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Args:
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entries: 排名列表
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@@ -210,7 +212,14 @@ class SignalRanker:
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client = MacClient.from_best_host()
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try:
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client.connect()
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quotes_df = client.get_stock_quotes(pairs)
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# 分批查询：MAC 协议单次最多 80 只，超出部分会被服务器丢弃
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import pandas as pd
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frames: list[pd.DataFrame] = []
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for i in range(0, len(pairs), 80):
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batch = pairs[i : i + 80]
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frames.append(client.get_stock_quotes(batch))
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quotes_df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame()
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finally:
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client.close()
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@@ -0,0 +1,489 @@
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"""强势股排名引擎 — 全市场多周期涨幅加权排序。
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核心流程：
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1. 扫描 vipdoc/{sh,sz}/lday/*.day 获取 A 股文件列表
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2. 每只股票：read_daily_bars() → 计算 ret_5/ret_20/ret_60/vol_20
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3. 按预设模式加权合成 strength 分数
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4. 排序输出
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三种预设：
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steady — 中长期稳健（w60 主导 + 波动率惩罚），选出稳着涨的票
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breakout — 近期妖股爆发（w5 主导，纯涨幅），选出短期最猛的票
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balanced — 三周期均衡（等权 + 波动率惩罚）
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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 numpy as np
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import pandas as pd
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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_STOCK_TYPES = frozenset({"SH_A_STOCK", "SZ_A_STOCK"})
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# ── 预设模式 ──────────────────────────────────────────────────────────────
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STRENGTH_PRESETS: dict[str, dict[str, Any]] = {
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"steady": {
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"w5": 0.2,
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"w20": 0.3,
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"w60": 0.5,
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"vol_adjusted": True,
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"desc": "中长期稳健强势：权重偏 60 日，波动率惩罚，选出稳着涨的票",
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},
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"breakout": {
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"w5": 0.6,
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"w20": 0.3,
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"w60": 0.1,
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"vol_adjusted": False,
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"desc": "近期妖股爆发：权重偏 5 日，无波动率惩罚，选出短期最猛的票",
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},
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"balanced": {
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"w5": 0.34,
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"w20": 0.33,
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"w60": 0.33,
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"vol_adjusted": True,
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"desc": "均衡强势：三周期等权，波动率调整",
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},
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}
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@dataclass
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class StrengthResult:
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"""单只股票的强势分结果。
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Attributes:
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rank: 排名（排序后赋值）
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code: 6 位股票代码
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market: 市场（SZ/SH）
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name: 股票名称（可选，需在线查询补齐）
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last_close: 最新收盘价
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last_date: 最新交易日（YYYYMMDD 整数）
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ret_5: 5 日涨幅
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ret_20: 20 日涨幅
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ret_60: 60 日涨幅
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vol_20: 20 日波动率（对数收益率标准差）
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strength: 强势综合分
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"""
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rank: int = 0
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code: str = ""
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market: str = ""
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name: str = ""
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last_close: float = 0.0
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last_date: int = 0
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ret_5: float = 0.0
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ret_20: float = 0.0
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ret_60: float = 0.0
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vol_20: float = 0.0
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strength: float = 0.0
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def compute_strength_metrics(
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closes: pd.Series,
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w5: float,
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w20: float,
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w60: float,
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vol_adjusted: bool,
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) -> dict[str, float] | None:
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"""纯计算函数：给定收盘价序列，返回强势指标字典。
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Args:
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closes: 收盘价 Series（按时间升序）
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w5/w20/w60: 三周期权重（自动归一化）
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vol_adjusted: 是否除以波动率
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Returns:
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{"ret_5", "ret_20", "ret_60", "vol_20", "strength"} 或 None（数据不足）
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"""
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n = len(closes)
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if n < 65: # 至少需要 61 日算 ret_60，留余量
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return None
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# 权重归一化
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w_sum = w5 + w20 + w60
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||||
if w_sum <= 0:
|
||||
return None
|
||||
w5, w20, w60 = w5 / w_sum, w20 / w_sum, w60 / w_sum
|
||||
|
||||
last = closes.iloc[-1]
|
||||
ret_5 = last / closes.iloc[-6] - 1
|
||||
ret_20 = last / closes.iloc[-21] - 1
|
||||
ret_60 = last / closes.iloc[-61] - 1
|
||||
|
||||
# 20 日波动率（对数收益率标准差）
|
||||
log_ret = np.log(closes / closes.shift(1))
|
||||
vol_20 = float(log_ret.rolling(20).std().iloc[-1])
|
||||
|
||||
if vol_20 <= 0 or np.isnan(vol_20):
|
||||
return None
|
||||
|
||||
raw = w5 * ret_5 + w20 * ret_20 + w60 * ret_60
|
||||
strength = raw / vol_20 if vol_adjusted else raw
|
||||
|
||||
if np.isnan(strength):
|
||||
return None
|
||||
|
||||
return {
|
||||
"ret_5": float(ret_5),
|
||||
"ret_20": float(ret_20),
|
||||
"ret_60": float(ret_60),
|
||||
"vol_20": vol_20,
|
||||
"strength": float(strength),
|
||||
}
|
||||
|
||||
|
||||
class StrengthRanker:
|
||||
"""全市场强势股排名器。
|
||||
|
||||
用法::
|
||||
|
||||
ranker = StrengthRanker(preset="steady")
|
||||
results = ranker.rank(top_n=50)
|
||||
for r in results[:5]:
|
||||
print(f"#{r.rank} {r.market}{r.code} strength={r.strength:.2f}")
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vipdoc_path: str | Path | None = None,
|
||||
preset: str = "steady",
|
||||
w5: float | None = None,
|
||||
w20: float | None = None,
|
||||
w60: float | None = None,
|
||||
vol_adjusted: bool | None = None,
|
||||
min_listed_days: int = 65,
|
||||
min_amount: float = 0.0,
|
||||
) -> None:
|
||||
"""初始化排名器。
|
||||
|
||||
Args:
|
||||
vipdoc_path: vipdoc 目录路径，None 则自动检测
|
||||
preset: 预设模式 steady/breakout/balanced
|
||||
w5/w20/w60: 自定义权重（非 None 时覆盖预设）
|
||||
vol_adjusted: 自定义波动率惩罚开关（非 None 时覆盖预设）
|
||||
min_listed_days: 最小上市天数（默认 65，保证能算 60 日涨幅）
|
||||
min_amount: 最近 5 日日均成交额下限（默认 0 不过滤，单位：元）
|
||||
"""
|
||||
if preset not in STRENGTH_PRESETS:
|
||||
raise ValueError(f"未知预设 '{preset}'，可选: {list(STRENGTH_PRESETS.keys())}")
|
||||
cfg = STRENGTH_PRESETS[preset]
|
||||
self._preset = preset
|
||||
self._w5 = w5 if w5 is not None else cfg["w5"]
|
||||
self._w20 = w20 if w20 is not None else cfg["w20"]
|
||||
self._w60 = w60 if w60 is not None else cfg["w60"]
|
||||
self._vol_adjusted = vol_adjusted if vol_adjusted is not None else cfg["vol_adjusted"]
|
||||
self._min_listed_days = min_listed_days
|
||||
self._min_amount = min_amount
|
||||
self._vipdoc = resolve_vipdoc(vipdoc_path)
|
||||
|
||||
@property
|
||||
def preset(self) -> str:
|
||||
"""当前预设名称。"""
|
||||
return self._preset
|
||||
|
||||
def rank(
|
||||
self,
|
||||
universe: str = "all",
|
||||
top_n: int = 50,
|
||||
workers: int = 0,
|
||||
progress_callback: Any = None,
|
||||
) -> list[StrengthResult]:
|
||||
"""扫描全市场并返回强势股排名。
|
||||
|
||||
Args:
|
||||
universe: all/sh/sz/<文件路径>
|
||||
top_n: 返回前 N 名，0=全部
|
||||
workers: 并发进程数（0=串行，4-8 推荐）
|
||||
progress_callback: 回调(current, total, name)
|
||||
|
||||
Returns:
|
||||
按 strength 降序排列的 StrengthResult 列表
|
||||
"""
|
||||
files = self._collect_files(universe)
|
||||
if not files:
|
||||
return []
|
||||
total = len(files)
|
||||
|
||||
if workers <= 0:
|
||||
results = self._rank_serial(files, total, progress_callback)
|
||||
else:
|
||||
results = self._rank_parallel(files, total, workers, progress_callback)
|
||||
|
||||
# 排序 + 赋名次
|
||||
results.sort(key=lambda r: r.strength, reverse=True)
|
||||
for i, r in enumerate(results):
|
||||
r.rank = i + 1
|
||||
|
||||
if top_n > 0:
|
||||
results = results[:top_n]
|
||||
return results
|
||||
|
||||
def _collect_files(self, universe: str) -> list[tuple[Path, str, str]]:
|
||||
"""收集 A 股 .day 文件列表（复用 scanner 的逻辑）。"""
|
||||
exchanges: list[str] = []
|
||||
if universe in ("all", "sz"):
|
||||
exchanges.append("sz")
|
||||
if universe in ("all", "sh"):
|
||||
exchanges.append("sh")
|
||||
|
||||
# 从文件列表模式读取
|
||||
if universe not in ("all", "sh", "sz"):
|
||||
return self._collect_from_file(universe)
|
||||
|
||||
files: list[tuple[Path, str, str]] = []
|
||||
for exchange in exchanges:
|
||||
lday_dir = self._vipdoc / exchange / "lday"
|
||||
if not lday_dir.is_dir():
|
||||
continue
|
||||
for filepath in sorted(lday_dir.glob("*.day")):
|
||||
if _detect_security_type(filepath.name) not in _A_STOCK_TYPES:
|
||||
continue
|
||||
code = filepath.name.lower()[2:8]
|
||||
files.append((filepath, exchange.upper(), code))
|
||||
return files
|
||||
|
||||
def _collect_from_file(self, filepath: str) -> list[tuple[Path, str, str]]:
|
||||
"""从文件读取股票列表（每行 "市场 代码"）。"""
|
||||
path = Path(filepath)
|
||||
if not path.is_file():
|
||||
raise FileNotFoundError(f"股票列表文件不存在: {filepath}")
|
||||
|
||||
files: list[tuple[Path, str, str]] = []
|
||||
with open(path, encoding="utf-8") as f:
|
||||
for line in f:
|
||||
line = line.strip()
|
||||
if not line or line.startswith("#"):
|
||||
continue
|
||||
parts = line.split()
|
||||
if len(parts) >= 2:
|
||||
market_str = parts[0].upper()
|
||||
code = parts[1]
|
||||
else:
|
||||
continue
|
||||
exchange = market_str.lower()
|
||||
day_file = self._vipdoc / exchange / "lday" / f"{exchange}{code}.day"
|
||||
if day_file.is_file():
|
||||
files.append((day_file, market_str, code))
|
||||
return files
|
||||
|
||||
def _rank_serial(
|
||||
self,
|
||||
files: list[tuple[Path, str, str]],
|
||||
total: int,
|
||||
progress_callback: Any,
|
||||
) -> list[StrengthResult]:
|
||||
"""串行扫描。"""
|
||||
results: list[StrengthResult] = []
|
||||
for idx, (filepath, market, code) in enumerate(files):
|
||||
if progress_callback:
|
||||
progress_callback(idx, total, filepath.name)
|
||||
try:
|
||||
r = self._compute_one(filepath, market, code)
|
||||
if r is not None:
|
||||
results.append(r)
|
||||
except Exception:
|
||||
continue
|
||||
if progress_callback:
|
||||
progress_callback(total, total, "done")
|
||||
return results
|
||||
|
||||
def _rank_parallel(
|
||||
self,
|
||||
files: list[tuple[Path, str, str]],
|
||||
total: int,
|
||||
workers: int,
|
||||
progress_callback: Any,
|
||||
) -> list[StrengthResult]:
|
||||
"""并发扫描（ProcessPoolExecutor）。"""
|
||||
import concurrent.futures
|
||||
|
||||
tasks = [
|
||||
(
|
||||
str(fp),
|
||||
mkt,
|
||||
code,
|
||||
self._w5,
|
||||
self._w20,
|
||||
self._w60,
|
||||
self._vol_adjusted,
|
||||
self._min_listed_days,
|
||||
self._min_amount,
|
||||
)
|
||||
for fp, mkt, code in files
|
||||
]
|
||||
|
||||
results: list[StrengthResult] = []
|
||||
with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as ex:
|
||||
future_map = {ex.submit(_compute_strength_one, *t): i for i, t in enumerate(tasks)}
|
||||
done = 0
|
||||
for fut in concurrent.futures.as_completed(future_map):
|
||||
done += 1
|
||||
idx = future_map[fut]
|
||||
if progress_callback:
|
||||
progress_callback(done, total, files[idx][0].name)
|
||||
try:
|
||||
r = fut.result()
|
||||
if r is not None:
|
||||
results.append(r)
|
||||
except Exception:
|
||||
continue
|
||||
if progress_callback:
|
||||
progress_callback(total, total, "done")
|
||||
return results
|
||||
|
||||
def _compute_one(self, filepath: Path, market: str, code: str) -> StrengthResult | None:
|
||||
"""计算单只股票的强势分。"""
|
||||
bars = read_daily_bars(filepath)
|
||||
if len(bars) < self._min_listed_days:
|
||||
return None
|
||||
|
||||
closes = pd.Series([b.close for b in bars])
|
||||
|
||||
# 成交额过滤（最近 5 日平均值）
|
||||
if self._min_amount > 0:
|
||||
recent_amount = float(np.mean([b.amount for b in bars[-5:]]))
|
||||
if recent_amount < self._min_amount:
|
||||
return None
|
||||
|
||||
metrics = compute_strength_metrics(
|
||||
closes, self._w5, self._w20, self._w60, self._vol_adjusted
|
||||
)
|
||||
if metrics is None:
|
||||
return None
|
||||
|
||||
last_bar = bars[-1]
|
||||
return StrengthResult(
|
||||
code=code,
|
||||
market=market,
|
||||
last_close=last_bar.close,
|
||||
last_date=last_bar.year * 10000 + last_bar.month * 100 + last_bar.day,
|
||||
ret_5=metrics["ret_5"],
|
||||
ret_20=metrics["ret_20"],
|
||||
ret_60=metrics["ret_60"],
|
||||
vol_20=metrics["vol_20"],
|
||||
strength=metrics["strength"],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def to_json(results: list[StrengthResult], preset: str, data_date: int) -> str:
|
||||
"""将排名结果序列化为 JSON 字符串。"""
|
||||
data = {
|
||||
"scan_time": datetime.now().isoformat(timespec="seconds"),
|
||||
"preset": preset,
|
||||
"preset_desc": STRENGTH_PRESETS.get(preset, {}).get("desc", ""),
|
||||
"data_date": data_date,
|
||||
"total_ranked": len(results),
|
||||
"ranking": [
|
||||
{
|
||||
"rank": r.rank,
|
||||
"code": r.code,
|
||||
"market": r.market,
|
||||
"name": r.name,
|
||||
"last_close": r.last_close,
|
||||
"last_date": r.last_date,
|
||||
"ret_5": r.ret_5,
|
||||
"ret_20": r.ret_20,
|
||||
"ret_60": r.ret_60,
|
||||
"vol_20": r.vol_20,
|
||||
"strength": r.strength,
|
||||
}
|
||||
for r in results
|
||||
],
|
||||
}
|
||||
return json.dumps(data, ensure_ascii=False, indent=2, default=_json_default)
|
||||
|
||||
@staticmethod
|
||||
def to_table(results: list[StrengthResult], preset: str, data_date: int) -> str:
|
||||
"""将排名结果格式化为表格字符串。"""
|
||||
if not results:
|
||||
return "无有效排名结果"
|
||||
|
||||
desc = STRENGTH_PRESETS.get(preset, {}).get("desc", "")
|
||||
lines = [
|
||||
f"[*] 强势股排名 [{preset}] 共 {len(results)} 只",
|
||||
f" 数据截止: {_fmt_date(data_date)} | {desc}",
|
||||
"═" * 96,
|
||||
f"{'排名':>4} {'代码':<10} {'名称':<8} {'现价':>10} "
|
||||
f"{'5日':>8} {'20日':>8} {'60日':>8} {'波动率':>8} {'强势分':>8}",
|
||||
"─" * 96,
|
||||
]
|
||||
|
||||
for r in results:
|
||||
medal = (
|
||||
" *1*"
|
||||
if r.rank == 1
|
||||
else " *2*"
|
||||
if r.rank == 2
|
||||
else " *3*"
|
||||
if r.rank == 3
|
||||
else " "
|
||||
)
|
||||
name = r.name[:6] if r.name else ""
|
||||
lines.append(
|
||||
f"{medal}{r.rank:>2} {r.market}{r.code:<9} {name:<8} "
|
||||
f"{r.last_close:>9.2f} {r.ret_5:>7.2%} {r.ret_20:>7.2%} "
|
||||
f"{r.ret_60:>7.2%} {r.vol_20:>7.4f} {r.strength:>8.2f}"
|
||||
)
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def _compute_strength_one(
|
||||
filepath: str,
|
||||
market: str,
|
||||
code: str,
|
||||
w5: float,
|
||||
w20: float,
|
||||
w60: float,
|
||||
vol_adjusted: bool,
|
||||
min_listed_days: int,
|
||||
min_amount: float,
|
||||
) -> StrengthResult | None:
|
||||
"""顶层函数（供 ProcessPoolExecutor 调用）。"""
|
||||
bars = read_daily_bars(filepath)
|
||||
if len(bars) < min_listed_days:
|
||||
return None
|
||||
|
||||
closes = pd.Series([b.close for b in bars])
|
||||
|
||||
if min_amount > 0:
|
||||
recent = float(np.mean([b.amount for b in bars[-5:]]))
|
||||
if recent < min_amount:
|
||||
return None
|
||||
|
||||
metrics = compute_strength_metrics(closes, w5, w20, w60, vol_adjusted)
|
||||
if metrics is None:
|
||||
return None
|
||||
|
||||
last = bars[-1]
|
||||
return StrengthResult(
|
||||
code=code,
|
||||
market=market,
|
||||
last_close=last.close,
|
||||
last_date=last.year * 10000 + last.month * 100 + last.day,
|
||||
ret_5=metrics["ret_5"],
|
||||
ret_20=metrics["ret_20"],
|
||||
ret_60=metrics["ret_60"],
|
||||
vol_20=metrics["vol_20"],
|
||||
strength=metrics["strength"],
|
||||
)
|
||||
|
||||
|
||||
def _fmt_date(d: int) -> str:
|
||||
"""YYYYMMDD 整数 → YYYY-MM-DD 字符串。"""
|
||||
s = str(d)
|
||||
return f"{s[:4]}-{s[4:6]}-{s[6:]}" if len(s) == 8 else str(d)
|
||||
|
||||
|
||||
def _json_default(obj: Any) -> Any:
|
||||
"""JSON 序列化辅助（numpy 标量等）。"""
|
||||
if hasattr(obj, "item"):
|
||||
return obj.item()
|
||||
raise TypeError(f"无法序列化 {type(obj)}")
|
||||
@@ -98,3 +98,71 @@ async def history_fund_flow(
|
||||
"""获取个股历史日线资金流向。"""
|
||||
df = await client.get_history_fund_flow(market_from_str(market), code, start, count)
|
||||
return _df_response(df)
|
||||
|
||||
|
||||
@router.get("/market/strength", response_model=DataFrameResponse)
|
||||
async def market_strength(
|
||||
preset: str = Query(
|
||||
"steady",
|
||||
description="预设模式: steady(中长期稳健) / breakout(近期妖股) / balanced(均衡)",
|
||||
),
|
||||
w5: float | None = Query(None, description="自定义 5 日权重（覆盖预设）"),
|
||||
w20: float | None = Query(None, description="自定义 20 日权重（覆盖预设）"),
|
||||
w60: float | None = Query(None, description="自定义 60 日权重（覆盖预设）"),
|
||||
vol_adjusted: bool | None = Query(None, description="波动率惩罚开关（覆盖预设）"),
|
||||
top_n: int = Query(50, ge=1, le=5000, description="返回前 N 名"),
|
||||
universe: str = Query("all", description="范围: all/sh/sz"),
|
||||
min_listed_days: int = Query(65, ge=30, description="最小上市天数"),
|
||||
min_amount: float = Query(0.0, ge=0, description="最近 5 日日均成交额下限（元）"),
|
||||
vipdoc: str | None = Query(None, description="离线数据目录（默认自动检测）"),
|
||||
) -> DataFrameResponse:
|
||||
"""全市场强势股排名（基于本地通达信 .day 日线文件）。
|
||||
|
||||
按 5/20/60 日涨幅加权合成强势分。三种预设：
|
||||
|
||||
- **steady**: 中长期稳健（60日主导 + 波动率惩罚），选出稳着涨的票
|
||||
- **breakout**: 近期妖股爆发（5日主导，纯涨幅），选出短期最猛的票
|
||||
- **balanced**: 三周期均衡 + 波动率调整
|
||||
|
||||
注意：需要本地 vipdoc 数据，扫描 ~5000 只约 30-60 秒。
|
||||
"""
|
||||
import asyncio
|
||||
|
||||
from easy_tdx.screen.strength import StrengthRanker
|
||||
|
||||
ranker = StrengthRanker(
|
||||
vipdoc_path=vipdoc,
|
||||
preset=preset,
|
||||
w5=w5,
|
||||
w20=w20,
|
||||
w60=w60,
|
||||
vol_adjusted=vol_adjusted,
|
||||
min_listed_days=min_listed_days,
|
||||
min_amount=min_amount,
|
||||
)
|
||||
|
||||
# Web 端用线程池执行，避免阻塞事件循环（扫描全市场耗时较长）
|
||||
# 注：在协程内用 get_running_loop() 而非 get_event_loop()，
|
||||
# 后者在 Python 3.12+ 已弃用。
|
||||
loop = asyncio.get_running_loop()
|
||||
results = await loop.run_in_executor(
|
||||
None, lambda: ranker.rank(universe=universe, top_n=top_n)
|
||||
)
|
||||
|
||||
records = [
|
||||
{
|
||||
"rank": r.rank,
|
||||
"code": r.code,
|
||||
"market": r.market,
|
||||
"name": r.name,
|
||||
"last_close": r.last_close,
|
||||
"last_date": r.last_date,
|
||||
"ret_5": r.ret_5,
|
||||
"ret_20": r.ret_20,
|
||||
"ret_60": r.ret_60,
|
||||
"vol_20": r.vol_20,
|
||||
"strength": r.strength,
|
||||
}
|
||||
for r in results
|
||||
]
|
||||
return DataFrameResponse(data=records, count=len(records))
|
||||
|
||||
Reference in New Issue
Block a user