395 lines
13 KiB
Python
395 lines
13 KiB
Python
"""涨跌停分析 — 连板股追踪、炸板率统计、敢死队排行。
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功能：
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1. 连板股追踪器
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2. 炸板率统计
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3. 涨停敢死队排行
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"""
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import datetime as dt
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from typing import List, Dict, Any, Optional
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from collections import defaultdict, Counter
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import numpy as np
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from sqlalchemy import select, and_, func, desc
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from db import get_session
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from models import DailyQuote, StockMetric
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def get_limit_stocks(date: Optional[dt.date] = None, limit_type: str = "up") -> Dict[str, Any]:
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"""获取涨停/跌停股票
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Args:
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date: 日期（None表示最新）
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limit_type: up涨停/down跌停
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Returns:
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涨跌停股票列表
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"""
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with get_session() as s:
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if date is None:
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date = s.execute(select(func.max(DailyQuote.date))).scalar()
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if not date:
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return {"ok": False, "msg": "暂无数据"}
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# 查询当日股票
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quotes = s.execute(
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select(DailyQuote)
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.where(DailyQuote.date == date)
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).scalars().all()
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if not quotes:
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return {"ok": False, "msg": "暂无数据"}
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# 筛选涨停/跌停股（涨跌幅接近±10%）
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threshold = 9.8 # 考虑精度问题，用9.8%作为阈值
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results = []
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for q in quotes:
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if q.open == 0:
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continue
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pct = (float(q.close) - float(q.open)) / float(q.open) * 100
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if limit_type == "up" and pct >= threshold:
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results.append({
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"code": q.code,
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"name": q.name,
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"close": float(q.close),
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"pct": round(pct, 2),
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"volume": float(q.volume),
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"amount": float(q.amount)
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})
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elif limit_type == "down" and pct <= -threshold:
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results.append({
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"code": q.code,
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"name": q.name,
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"close": float(q.close),
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"pct": round(pct, 2),
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"volume": float(q.volume),
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"amount": float(q.amount)
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})
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return {
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"ok": True,
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"date": date.isoformat(),
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"type": limit_type,
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"count": len(results),
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"stocks": sorted(results, key=lambda x: x["pct"], reverse=(limit_type == "up"))
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}
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def track_consecutive_limits(days: int = 10) -> Dict[str, Any]:
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"""连板股追踪器
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Args:
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days: 追踪天数
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Returns:
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连板股列表
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"""
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with get_session() as s:
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latest_date = s.execute(select(func.max(DailyQuote.date))).scalar()
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if not latest_date:
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return {"ok": False, "msg": "暂无数据"}
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start_date = latest_date - dt.timedelta(days=days)
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# 获取期间所有股票的日线数据
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quotes = s.execute(
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select(DailyQuote)
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.where(DailyQuote.date >= start_date)
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.order_by(DailyQuote.code, DailyQuote.date)
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).scalars().all()
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# 按股票分组
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stock_data = defaultdict(list)
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for q in quotes:
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stock_data[q.code].append(q)
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# 统计连板
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consecutive_limits = []
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for code, data in stock_data.items():
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if not data:
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continue
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# 倒序遍历，统计从最新日期开始的连续涨停天数
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data_sorted = sorted(data, key=lambda x: x.date, reverse=True)
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consecutive_days = 0
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for q in data_sorted:
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if q.open == 0:
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break
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pct = (float(q.close) - float(q.open)) / float(q.open) * 100
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if pct >= 9.8: # 涨停
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consecutive_days += 1
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else:
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break
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if consecutive_days >= 2: # 至少2连板
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latest = data_sorted[0]
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consecutive_limits.append({
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"code": code,
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"name": latest.name,
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"consecutive_days": consecutive_days,
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"close": float(latest.close),
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"amount": float(latest.amount),
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"status": f"{consecutive_days}连板"
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})
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# 按连板天数排序
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consecutive_limits.sort(key=lambda x: x["consecutive_days"], reverse=True)
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return {
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"ok": True,
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"date": latest_date.isoformat(),
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"days": days,
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"count": len(consecutive_limits),
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"stocks": consecutive_limits
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}
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def analyze_limit_break_rate(days: int = 60) -> Dict[str, Any]:
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"""炸板率统计
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分析涨停后次日的表现（继续涨停/上涨/下跌/跌停）
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Args:
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days: 统计天数
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Returns:
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炸板率统计
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"""
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with get_session() as s:
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latest_date = s.execute(select(func.max(DailyQuote.date))).scalar()
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if not latest_date:
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return {"ok": False, "msg": "暂无数据"}
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start_date = latest_date - dt.timedelta(days=days)
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# 获取期间所有股票的日线数据
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quotes = s.execute(
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select(DailyQuote)
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.where(DailyQuote.date >= start_date)
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.order_by(DailyQuote.code, DailyQuote.date)
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).scalars().all()
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# 按股票和日期组织数据
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stock_data = defaultdict(dict)
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for q in quotes:
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stock_data[q.code][q.date] = q
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# 统计涨停后次日表现
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next_day_stats = {
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"limit_up": 0, # 继续涨停
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"up": 0, # 上涨但未涨停
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"down": 0, # 下跌但未跌停
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"limit_down": 0, # 跌停
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}
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stock_break_rates = {} # 个股炸板率
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for code, data in stock_data.items():
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dates = sorted(data.keys())
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stock_limits = 0
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stock_breaks = 0
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for i in range(len(dates) - 1):
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today = dates[i]
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tomorrow = dates[i + 1]
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today_q = data[today]
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tomorrow_q = data[tomorrow]
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# 判断今日是否涨停
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if today_q.open == 0:
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continue
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today_pct = (float(today_q.close) - float(today_q.open)) / float(today_q.open) * 100
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if today_pct >= 9.8: # 今日涨停
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stock_limits += 1
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# 判断次日表现
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if tomorrow_q.open == 0:
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continue
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tomorrow_pct = (float(tomorrow_q.close) - float(tomorrow_q.open)) / float(tomorrow_q.open) * 100
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if tomorrow_pct >= 9.8:
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next_day_stats["limit_up"] += 1
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elif tomorrow_pct > 0:
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next_day_stats["up"] += 1
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stock_breaks += 1 # 炸板
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elif tomorrow_pct > -9.8:
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next_day_stats["down"] += 1
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stock_breaks += 1 # 炸板
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else:
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next_day_stats["limit_down"] += 1
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stock_breaks += 1 # 炸板
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# 计算个股炸板率
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if stock_limits > 0:
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break_rate = stock_breaks / stock_limits * 100
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if stock_limits >= 3: # 至少3次涨停才有统计意义
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stock_break_rates[code] = {
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"name": list(data.values())[0].name,
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"limits": stock_limits,
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"breaks": stock_breaks,
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"break_rate": round(break_rate, 2)
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}
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total = sum(next_day_stats.values())
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if total == 0:
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return {"ok": False, "msg": "统计样本不足"}
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# 计算比例
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stats_with_pct = {
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"limit_up": {
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"count": next_day_stats["limit_up"],
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"pct": round(next_day_stats["limit_up"] / total * 100, 2)
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},
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"up": {
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"count": next_day_stats["up"],
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"pct": round(next_day_stats["up"] / total * 100, 2)
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},
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"down": {
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"count": next_day_stats["down"],
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"pct": round(next_day_stats["down"] / total * 100, 2)
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},
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"limit_down": {
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"count": next_day_stats["limit_down"],
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"pct": round(next_day_stats["limit_down"] / total * 100, 2)
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}
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}
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# 炸板率 = (上涨未涨停 + 下跌 + 跌停) / 总数
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break_rate = (next_day_stats["up"] + next_day_stats["down"] + next_day_stats["limit_down"]) / total * 100
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# 个股炸板率排行（从高到低）
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stock_rankings = sorted(
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[(code, data) for code, data in stock_break_rates.items()],
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key=lambda x: x[1]["break_rate"],
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reverse=True
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)[:30]
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return {
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"ok": True,
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"days": days,
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"total_samples": total,
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"overall_break_rate": round(break_rate, 2),
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"next_day_stats": stats_with_pct,
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"stock_rankings": [{
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"code": code,
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"name": data["name"],
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"limits": data["limits"],
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"breaks": data["breaks"],
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"break_rate": data["break_rate"]
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} for code, data in stock_rankings]
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}
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def get_limit_squad_rankings(days: int = 30, min_limits: int = 5) -> Dict[str, Any]:
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"""涨停敢死队排行
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统计期间内涨停次数最多的股票（俗称"妖股"）
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Args:
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days: 统计天数
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min_limits: 最少涨停次数
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Returns:
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敢死队排行
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"""
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with get_session() as s:
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latest_date = s.execute(select(func.max(DailyQuote.date))).scalar()
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if not latest_date:
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return {"ok": False, "msg": "暂无数据"}
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start_date = latest_date - dt.timedelta(days=days)
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# 获取期间所有股票的日线数据
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quotes = s.execute(
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select(DailyQuote)
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.where(DailyQuote.date >= start_date)
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.order_by(DailyQuote.code, DailyQuote.date)
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).scalars().all()
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# 统计每只股票的涨停次数
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limit_counts = defaultdict(lambda: {
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"name": "",
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"count": 0,
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"dates": [],
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"total_days": 0,
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"max_consecutive": 0
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})
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# 按股票分组
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stock_data = defaultdict(list)
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for q in quotes:
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stock_data[q.code].append(q)
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for code, data in stock_data.items():
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if not data:
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continue
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data_sorted = sorted(data, key=lambda x: x.date)
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limit_days = []
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for q in data_sorted:
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if q.open == 0:
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continue
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pct = (float(q.close) - float(q.open)) / float(q.open) * 100
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if pct >= 9.8: # 涨停
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limit_days.append(q.date)
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if len(limit_days) >= min_limits:
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# 计算最大连板数
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max_consecutive = 1
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current_consecutive = 1
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for i in range(1, len(limit_days)):
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if (limit_days[i] - limit_days[i-1]).days == 1:
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current_consecutive += 1
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max_consecutive = max(max_consecutive, current_consecutive)
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else:
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current_consecutive = 1
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limit_counts[code] = {
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"name": data_sorted[0].name,
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"count": len(limit_days),
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"dates": [d.isoformat() for d in limit_days],
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"total_days": len(data_sorted),
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"max_consecutive": max_consecutive,
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"frequency": round(len(limit_days) / len(data_sorted) * 100, 2)
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}
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# 排序
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rankings = sorted(
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[(code, data) for code, data in limit_counts.items()],
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key=lambda x: (x[1]["count"], x[1]["max_consecutive"]),
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reverse=True
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)[:50]
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return {
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"ok": True,
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"days": days,
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"start_date": start_date.isoformat(),
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"end_date": latest_date.isoformat(),
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"count": len(rankings),
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"rankings": [{
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"code": code,
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"name": data["name"],
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"limit_count": data["count"],
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"max_consecutive": data["max_consecutive"],
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"frequency": data["frequency"],
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"dates": data["dates"]
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} for code, data in rankings]
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}
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