484 lines
14 KiB
Python
484 lines
14 KiB
Python
"""板块轮动分析 — 追踪板块强弱、资金流向、生命周期。
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功能：
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1. 板块强弱排名趋势
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2. 资金流向分析
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3. 板块生命周期判断
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4. 龙头股识别
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5. 板块联动性分析
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"""
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import datetime as dt
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from typing import Dict, List, Any, Optional
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import numpy as np
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from sqlalchemy import select, func, and_
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from db import get_session
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from models import SectorDaily, FundFlowDaily, DailyQuote, StockMetric
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def get_sector_trend(days: int = 20, top_n: int = 15) -> Dict[str, Any]:
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"""获取板块强弱趋势
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Args:
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days: 统计天数
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top_n: 返回前N个板块
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Returns:
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板块趋势数据
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"""
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with get_session() as s:
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# 获取最近N天的日期
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latest_date = s.execute(select(func.max(SectorDaily.date))).scalar()
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if not latest_date:
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return {"ok": False, "msg": "暂无板块数据"}
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start_date = latest_date - dt.timedelta(days=days)
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# 查询板块数据
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rows = s.execute(
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select(SectorDaily)
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.where(SectorDaily.date >= start_date)
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.order_by(SectorDaily.date, SectorDaily.name)
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).scalars().all()
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if not rows:
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return {"ok": False, "msg": "数据不足"}
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# 按板块聚合
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sector_data = {}
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for row in rows:
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if row.name not in sector_data:
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sector_data[row.name] = {
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"name": row.name,
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"dates": [],
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"pcts": [],
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"amounts": []
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}
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sector_data[row.name]["dates"].append(row.date.isoformat())
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sector_data[row.name]["pcts"].append(float(row.pct))
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sector_data[row.name]["amounts"].append(float(row.amount))
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# 计算累计涨跌幅和平均成交额
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sector_stats = []
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for name, data in sector_data.items():
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pcts = data["pcts"]
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amounts = data["amounts"]
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# 累计收益（复利）
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cumulative = 1.0
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for p in pcts:
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cumulative *= (1 + p / 100)
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cumulative_return = (cumulative - 1) * 100
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# 近5日、10日、20日收益
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returns = {
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"5d": sum(pcts[-5:]) if len(pcts) >= 5 else 0,
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"10d": sum(pcts[-10:]) if len(pcts) >= 10 else 0,
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"20d": cumulative_return
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}
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# 平均成交额
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avg_amount = np.mean(amounts) if amounts else 0
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# 波动率（标准差）
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volatility = np.std(pcts) if len(pcts) > 1 else 0
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sector_stats.append({
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"name": name,
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"returns": returns,
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"avg_amount": round(avg_amount, 2),
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"volatility": round(volatility, 2),
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"dates": data["dates"],
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"pcts": [round(p, 2) for p in pcts]
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})
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# 按20日收益排序
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sector_stats.sort(key=lambda x: x["returns"]["20d"], 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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"sectors": sector_stats[:top_n]
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}
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def analyze_fund_flow(days: int = 5) -> Dict[str, Any]:
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"""分析资金流向（板块间流动）
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Args:
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days: 分析天数
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Returns:
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资金流向数据（桑基图格式）
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"""
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with get_session() as s:
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latest_date = s.execute(select(func.max(FundFlowDaily.date))).scalar()
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if not latest_date:
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return {"ok": False, "msg": "暂无资金流数据"}
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start_date = latest_date - dt.timedelta(days=days)
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# 查询资金流数据
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rows = s.execute(
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select(FundFlowDaily)
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.where(FundFlowDaily.date >= start_date)
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.order_by(FundFlowDaily.date, FundFlowDaily.name)
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).scalars().all()
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if not rows:
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return {"ok": False, "msg": "数据不足"}
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# 按板块聚合净流入
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flow_data = {}
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for row in rows:
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if row.name not in flow_data:
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flow_data[row.name] = 0
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flow_data[row.name] += float(row.net)
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# 分类：流入 vs 流出
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inflows = [(k, v) for k, v in flow_data.items() if v > 0]
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outflows = [(k, abs(v)) for k, v in flow_data.items() if v < 0]
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inflows.sort(key=lambda x: x[1], reverse=True)
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outflows.sort(key=lambda x: x[1], reverse=True)
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# 构造桑基图数据
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nodes = []
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links = []
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# 流出节点（左侧）
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for i, (name, amount) in enumerate(outflows[:8]):
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nodes.append({"name": f"{name}(流出)"})
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# 流向"资金池"
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links.append({
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"source": len(nodes) - 1,
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"target": len(outflows[:8]), # 资金池索引
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"value": round(amount, 2)
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})
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# 资金池（中间）
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nodes.append({"name": "资金池"})
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# 流入节点（右侧）
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for i, (name, amount) in enumerate(inflows[:8]):
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nodes.append({"name": f"{name}(流入)"})
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# 从"资金池"流入
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links.append({
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"source": len(outflows[:8]), # 资金池索引
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"target": len(nodes) - 1,
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"value": round(amount, 2)
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})
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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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"total_inflow": round(sum(v for _, v in inflows), 2),
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"total_outflow": round(sum(v for _, v in outflows), 2),
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"top_inflow": inflows[:8],
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"top_outflow": outflows[:8],
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"sankey": {
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"nodes": nodes,
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"links": links
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}
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}
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def analyze_lifecycle(sector_name: str, days: int = 60) -> Dict[str, Any]:
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"""分析板块生命周期
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Args:
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sector_name: 板块名称
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days: 分析天数
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Returns:
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生命周期判断
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"""
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with get_session() as s:
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latest_date = s.execute(select(func.max(SectorDaily.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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rows = s.execute(
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select(SectorDaily)
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.where(
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and_(
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SectorDaily.name == sector_name,
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SectorDaily.date >= start_date
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)
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)
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.order_by(SectorDaily.date)
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).scalars().all()
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if len(rows) < 20:
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return {"ok": False, "msg": "数据不足"}
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# 提取数据
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dates = [r.date.isoformat() for r in rows]
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pcts = [float(r.pct) for r in rows]
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amounts = [float(r.amount) for r in rows]
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# 计算指标
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# 1. 近期涨跌幅趋势
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recent_5 = sum(pcts[-5:])
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recent_10 = sum(pcts[-10:])
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recent_20 = sum(pcts[-20:])
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# 2. 成交额趋势
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amount_5 = np.mean(amounts[-5:])
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amount_20 = np.mean(amounts[-20:])
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amount_change = (amount_5 / amount_20 - 1) * 100 if amount_20 > 0 else 0
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# 3. 动量（价格变化加速度）
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momentum = recent_5 - recent_10
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# 生命周期判断
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if recent_20 > 0 and momentum > 0 and amount_change > 20:
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phase = "启动期"
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description = "板块刚开始上涨，资金流入加速，可能是介入时机"
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elif recent_20 > 5 and recent_10 > recent_20 / 2 and amount_change > 0:
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phase = "加速期"
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description = "板块持续上涨且加速，成交活跃，主升浪阶段"
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elif recent_20 > 0 and momentum < 0:
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phase = "衰退期"
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description = "板块涨幅收窄或开始回调，资金开始流出，注意风险"
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elif recent_20 < -5:
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phase = "下跌期"
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description = "板块持续下跌，避免介入"
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else:
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phase = "震荡期"
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description = "板块横盘整理，方向不明"
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return {
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"ok": True,
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"sector": sector_name,
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"phase": phase,
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"description": description,
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"metrics": {
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"return_5d": round(recent_5, 2),
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"return_10d": round(recent_10, 2),
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"return_20d": round(recent_20, 2),
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"momentum": round(momentum, 2),
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"amount_change": round(amount_change, 2)
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},
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"dates": dates,
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"pcts": [round(p, 2) for p in pcts]
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}
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def identify_leaders(sector_name: str, days: int = 20, limit: int = 10) -> Dict[str, Any]:
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"""识别板块龙头股
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Args:
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sector_name: 板块名称
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days: 统计天数
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limit: 返回数量
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Returns:
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龙头股列表
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"""
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# 注意：需要股票-板块映射表，这里简化为通过名称匹配
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# 实际应该有 stock_sector 映射表
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with get_session() as s:
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# 获取最近N天表现最好的股票
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latest_date = s.execute(select(func.max(StockMetric.date))).scalar()
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if not latest_date:
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return {"ok": False, "msg": "暂无股票数据"}
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# 查询高涨幅、高成交额股票
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rows = s.execute(
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select(StockMetric)
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.where(
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and_(
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StockMetric.date == latest_date,
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StockMetric.ret20 > 0,
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StockMetric.amount > 5 # 成交额 > 5亿
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)
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)
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.order_by(
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StockMetric.ret20.desc(),
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StockMetric.amount.desc()
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)
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.limit(limit * 3) # 多取一些，后续筛选
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).scalars().all()
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# 简化：根据名称关键词匹配板块（实际应该查询映射表）
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sector_keywords = {
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"半导体": ["芯片", "半导体", "集成电路"],
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"新能源": ["新能源", "锂电", "光伏", "储能"],
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"医药": ["医药", "生物", "医疗", "药业"],
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"白酒": ["酒", "茅台", "五粮液"],
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"军工": ["军工", "航天", "航空", "兵器"],
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"AI": ["人工智能", "AI", "算力", "云计算"],
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}
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keywords = sector_keywords.get(sector_name, [sector_name])
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leaders = []
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for row in rows:
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if any(kw in row.name for kw in keywords):
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leaders.append({
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"code": row.code,
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"name": row.name,
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"close": round(row.close, 2),
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"pct": round(row.pct, 2),
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"ret5": round(row.ret5, 2),
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"ret20": round(row.ret20, 2),
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"amount": round(row.amount, 2),
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"vol_ratio": round(row.vol_ratio, 2)
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})
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if len(leaders) >= limit:
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break
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return {
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"ok": True,
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"sector": sector_name,
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"date": latest_date.isoformat(),
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"leaders": leaders
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}
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def analyze_correlation(days: int = 60, top_n: int = 20) -> Dict[str, Any]:
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"""板块联动性分析（相关系数矩阵）
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Args:
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days: 计算天数
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top_n: 分析前N个板块
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Returns:
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相关系数矩阵（热力图数据）
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"""
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with get_session() as s:
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latest_date = s.execute(select(func.max(SectorDaily.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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rows = s.execute(
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select(SectorDaily)
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.where(SectorDaily.date >= start_date)
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.order_by(SectorDaily.date, SectorDaily.name)
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).scalars().all()
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if not rows:
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return {"ok": False, "msg": "数据不足"}
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# 按板块聚合涨跌幅
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sector_returns = {}
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for row in rows:
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if row.name not in sector_returns:
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sector_returns[row.name] = []
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sector_returns[row.name].append(float(row.pct))
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# 筛选数据完整的板块
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valid_sectors = {k: v for k, v in sector_returns.items() if len(v) >= days * 0.8}
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if len(valid_sectors) < 5:
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return {"ok": False, "msg": "有效板块不足"}
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# 选择前N个板块（按最近涨幅）
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sector_list = []
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for name, rets in valid_sectors.items():
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recent_return = sum(rets[-min(10, len(rets)):])
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sector_list.append((name, recent_return, rets))
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sector_list.sort(key=lambda x: x[1], reverse=True)
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selected = sector_list[:top_n]
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# 计算相关系数矩阵
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names = [s[0] for s in selected]
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returns_matrix = np.array([s[2][:days] for s in selected])
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# 填充短数据（用0）
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max_len = max(len(r) for r in returns_matrix)
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padded = []
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for r in returns_matrix:
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if len(r) < max_len:
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r = list(r) + [0] * (max_len - len(r))
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padded.append(r[:max_len])
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returns_matrix = np.array(padded)
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# 计算相关系数
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corr_matrix = np.corrcoef(returns_matrix)
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# 转换为热力图数据
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heatmap_data = []
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for i in range(len(names)):
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for j in range(len(names)):
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heatmap_data.append({
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"x": j,
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"y": i,
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"value": round(float(corr_matrix[i][j]), 3)
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})
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# 找出高度相关的板块对（相关系数 > 0.7）
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high_corr = []
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for i in range(len(names)):
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for j in range(i + 1, len(names)):
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corr = float(corr_matrix[i][j])
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if corr > 0.7:
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high_corr.append({
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"sector1": names[i],
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"sector2": names[j],
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"correlation": round(corr, 3)
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})
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high_corr.sort(key=lambda x: x["correlation"], reverse=True)
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return {
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"ok": True,
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"days": days,
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"sectors": names,
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"matrix": corr_matrix.tolist(),
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"heatmap": heatmap_data,
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"high_correlation": high_corr[:10]
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}
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def get_rotation_summary() -> Dict[str, Any]:
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"""获取板块轮动综合摘要
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Returns:
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轮动摘要
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"""
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# 获取最强和最弱板块
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trend = get_sector_trend(days=10, top_n=20)
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if not trend.get("ok"):
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return {"ok": False, "msg": "数据不足"}
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sectors = trend["sectors"]
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strongest = sectors[:3]
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weakest = sectors[-3:]
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# 资金流向
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flow = analyze_fund_flow(days=5)
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summary = {
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"ok": True,
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"date": trend["date"],
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"strongest_sectors": [
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{
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"name": s["name"],
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"return_10d": s["returns"]["10d"]
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} for s in strongest
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],
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"weakest_sectors": [
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{
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"name": s["name"],
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"return_10d": s["returns"]["10d"]
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} for s in weakest
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],
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"fund_flow": {
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"top_inflow": flow.get("top_inflow", [])[:3] if flow.get("ok") else [],
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"top_outflow": flow.get("top_outflow", [])[:3] if flow.get("ok") else []
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}
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}
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return summary
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