claude强化功能
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390
backend/smart_selector.py
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390
backend/smart_selector.py
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"""智能选股引擎 — 可视化选股器、策略保存、回测验证。
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功能：
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1. 多条件组合选股
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2. 选股策略保存/加载
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3. 选股结果历史回测
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4. 选股结果对比（新入选/退出）
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5. 条件预警
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"""
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import datetime as dt
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import json
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from typing import Dict, List, Any, Optional
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from sqlalchemy import select, and_, or_, func
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import numpy as np
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from db import get_session
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from models import StockMetric, DailyQuote, Security
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class Condition:
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"""选股条件"""
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def __init__(self, field: str, operator: str, value: Any):
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self.field = field
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self.operator = operator
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self.value = value
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def to_dict(self):
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return {
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"field": self.field,
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"operator": self.operator,
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"value": self.value
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}
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@classmethod
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def from_dict(cls, data: Dict):
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return cls(data["field"], data["operator"], data["value"])
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def to_sql(self, model):
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"""转换为 SQLAlchemy 查询条件"""
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field = getattr(model, self.field, None)
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if field is None:
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return None
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op = self.operator
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val = self.value
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if op == "==":
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return field == val
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elif op == ">":
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return field > val
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elif op == ">=":
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return field >= val
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elif op == "<":
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return field < val
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elif op == "<=":
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return field <= val
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elif op == "between":
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return and_(field >= val[0], field <= val[1])
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elif op == "in":
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return field.in_(val)
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else:
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return None
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class Strategy:
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"""选股策略"""
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def __init__(self, name: str, description: str = ""):
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self.name = name
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self.description = description
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self.conditions: List[Condition] = []
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self.logic = "and" # and / or
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def add_condition(self, field: str, operator: str, value: Any):
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"""添加条件"""
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self.conditions.append(Condition(field, operator, value))
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def to_dict(self):
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return {
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"name": self.name,
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"description": self.description,
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"logic": self.logic,
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"conditions": [c.to_dict() for c in self.conditions]
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}
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@classmethod
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def from_dict(cls, data: Dict):
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strategy = cls(data["name"], data.get("description", ""))
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strategy.logic = data.get("logic", "and")
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for cond in data.get("conditions", []):
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strategy.conditions.append(Condition.from_dict(cond))
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return strategy
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def to_json(self) -> str:
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"""序列化为JSON"""
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return json.dumps(self.to_dict(), ensure_ascii=False)
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@classmethod
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def from_json(cls, json_str: str):
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"""从JSON反序列化"""
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return cls.from_dict(json.loads(json_str))
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def run_selector(strategy: Strategy, date: Optional[dt.date] = None) -> Dict[str, Any]:
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"""执行选股
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Args:
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strategy: 选股策略
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date: 选股日期，None表示最新
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Returns:
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选股结果
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"""
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with get_session() as s:
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# 确定日期
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if date is None:
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date = s.execute(select(func.max(StockMetric.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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# 构建查询
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query = select(StockMetric).where(StockMetric.date == date)
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# 应用条件
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sql_conditions = []
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for cond in strategy.conditions:
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sql_cond = cond.to_sql(StockMetric)
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if sql_cond is not None:
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sql_conditions.append(sql_cond)
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if sql_conditions:
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if strategy.logic == "and":
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query = query.where(and_(*sql_conditions))
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else: # or
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query = query.where(or_(*sql_conditions))
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# 执行查询
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rows = s.execute(query).scalars().all()
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# 格式化结果
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results = [{
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"code": r.code,
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"name": r.name,
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"close": round(r.close, 2),
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"pct": round(r.pct, 2),
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"ret5": round(r.ret5, 2),
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"ret20": round(r.ret20, 2),
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"vol_ratio": round(r.vol_ratio, 2),
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"rsi14": round(r.rsi14, 2),
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"pos60": round(r.pos60 * 100, 1),
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"amount": round(r.amount, 2),
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"ma_bull": r.ma_bull,
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"macd_gold": r.macd_gold
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} for r in rows]
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return {
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"ok": True,
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"date": date.isoformat(),
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"strategy": strategy.name,
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"count": len(results),
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"results": results
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}
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def backtest_selector(strategy: Strategy, days: int = 60) -> Dict[str, Any]:
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"""选股策略回测
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Args:
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strategy: 选股策略
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days: 回测天数
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Returns:
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回测结果
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"""
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with get_session() as s:
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# 获取最近N个交易日
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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": "暂无数据"}
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start_date = latest_date - dt.timedelta(days=days)
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# 获取这段时间内的所有交易日
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dates = s.execute(
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select(StockMetric.date)
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.where(StockMetric.date >= start_date)
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.group_by(StockMetric.date)
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.order_by(StockMetric.date)
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).scalars().all()
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if len(dates) < 5:
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return {"ok": False, "msg": "数据不足"}
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# 逐日选股并统计后续N日收益
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daily_results = []
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for i, date in enumerate(dates[:-5]): # 至少保留5日用于计算收益
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# 执行选股
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result = run_selector(strategy, date)
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if not result["ok"] or not result["results"]:
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continue
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selected_codes = [r["code"] for r in result["results"]]
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# 查询5日后的收益
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future_date = dates[min(i + 5, len(dates) - 1)]
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with get_session() as s:
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# 获取选中股票5日后的表现
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future_rows = s.execute(
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select(DailyQuote.code, DailyQuote.close)
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.where(
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and_(
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DailyQuote.code.in_(selected_codes),
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DailyQuote.date == future_date
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)
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)
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).all()
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current_rows = s.execute(
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select(DailyQuote.code, DailyQuote.close)
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.where(
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and_(
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DailyQuote.code.in_(selected_codes),
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DailyQuote.date == date
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)
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)
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).all()
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# 计算收益
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current_prices = {r.code: float(r.close) for r in current_rows}
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future_prices = {r.code: float(r.close) for r in future_rows}
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returns = []
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for code in selected_codes:
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if code in current_prices and code in future_prices:
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ret = (future_prices[code] / current_prices[code] - 1) * 100
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returns.append(ret)
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if returns:
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daily_results.append({
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"date": date.isoformat(),
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"count": len(selected_codes),
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"avg_return": round(np.mean(returns), 2),
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"median_return": round(np.median(returns), 2),
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"win_rate": round(sum(1 for r in returns if r > 0) / len(returns) * 100, 1),
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"max_return": round(max(returns), 2),
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"min_return": round(min(returns), 2)
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})
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if not daily_results:
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return {"ok": False, "msg": "回测数据不足"}
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# 汇总统计
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avg_returns = [r["avg_return"] for r in daily_results]
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win_rates = [r["win_rate"] for r in daily_results]
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summary = {
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"total_days": len(daily_results),
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"avg_return": round(np.mean(avg_returns), 2),
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"avg_win_rate": round(np.mean(win_rates), 1),
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"best_day": max(daily_results, key=lambda x: x["avg_return"]),
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"worst_day": min(daily_results, key=lambda x: x["avg_return"])
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}
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return {
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"ok": True,
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"strategy": strategy.name,
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"days": days,
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"summary": summary,
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"daily": daily_results
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}
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def compare_results(date1: dt.date, date2: dt.date, strategy: Strategy) -> Dict[str, Any]:
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"""对比两个日期的选股结果
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Args:
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date1: 日期1（通常是昨日）
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date2: 日期2（通常是今日）
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strategy: 选股策略
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Returns:
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对比结果
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"""
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result1 = run_selector(strategy, date1)
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result2 = run_selector(strategy, date2)
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if not result1["ok"] or not result2["ok"]:
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return {"ok": False, "msg": "数据不足"}
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codes1 = set(r["code"] for r in result1["results"])
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codes2 = set(r["code"] for r in result2["results"])
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# 新入选
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new_in = codes2 - codes1
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# 退出
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dropped = codes1 - codes2
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# 持续入选
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continued = codes1 & codes2
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# 获取详细信息
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new_in_stocks = [r for r in result2["results"] if r["code"] in new_in]
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dropped_stocks = [r for r in result1["results"] if r["code"] in dropped]
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continued_stocks = [r for r in result2["results"] if r["code"] in continued]
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return {
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"ok": True,
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"date1": date1.isoformat(),
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"date2": date2.isoformat(),
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"count1": len(codes1),
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"count2": len(codes2),
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"new_in": {
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"count": len(new_in),
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"stocks": new_in_stocks
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},
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"dropped": {
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"count": len(dropped),
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"stocks": dropped_stocks
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},
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"continued": {
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"count": len(continued),
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"stocks": continued_stocks
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}
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}
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# 预设策略
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PRESET_STRATEGIES = {
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"momentum": Strategy("动量突破", "短期强势+放量"),
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"value": Strategy("价值洼地", "超跌低位+基本面支撑"),
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"growth": Strategy("成长加速", "持续上涨+量价齐升"),
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"reversal": Strategy("反转抄底", "超跌企稳+技术反转")
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}
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# 动量突破
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PRESET_STRATEGIES["momentum"].add_condition("ret5", ">", 10)
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PRESET_STRATEGIES["momentum"].add_condition("vol_ratio", ">", 2)
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PRESET_STRATEGIES["momentum"].add_condition("rsi14", "<", 80)
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# 价值洼地
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PRESET_STRATEGIES["value"].add_condition("pos60", "<", 0.3)
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PRESET_STRATEGIES["value"].add_condition("pct", ">", 0)
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PRESET_STRATEGIES["value"].add_condition("amount", ">", 5)
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# 成长加速
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PRESET_STRATEGIES["growth"].add_condition("ret20", ">", 15)
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PRESET_STRATEGIES["growth"].add_condition("ma_bull", "==", True)
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PRESET_STRATEGIES["growth"].add_condition("up_streak", ">=", 2)
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# 反转抄底
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PRESET_STRATEGIES["reversal"].add_condition("ret20", "<", -15)
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PRESET_STRATEGIES["reversal"].add_condition("rsi14", "<", 30)
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PRESET_STRATEGIES["reversal"].add_condition("pct", ">", 2)
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def get_preset_strategies() -> List[Dict[str, Any]]:
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"""获取预设策略列表"""
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return [
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{
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"id": key,
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"name": strategy.name,
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"description": strategy.description,
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"conditions_count": len(strategy.conditions)
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}
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for key, strategy in PRESET_STRATEGIES.items()
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]
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def get_available_fields() -> List[Dict[str, Any]]:
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"""获取可用的选股字段"""
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return [
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{"field": "close", "name": "现价", "type": "number", "unit": "元"},
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{"field": "pct", "name": "涨跌幅", "type": "number", "unit": "%"},
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{"field": "ret5", "name": "5日涨幅", "type": "number", "unit": "%"},
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{"field": "ret20", "name": "20日涨幅", "type": "number", "unit": "%"},
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{"field": "ret60", "name": "60日涨幅", "type": "number", "unit": "%"},
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{"field": "ma5", "name": "MA5", "type": "number", "unit": "元"},
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{"field": "ma10", "name": "MA10", "type": "number", "unit": "元"},
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{"field": "ma20", "name": "MA20", "type": "number", "unit": "元"},
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{"field": "ma60", "name": "MA60", "type": "number", "unit": "元"},
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{"field": "vol_ratio", "name": "量比", "type": "number", "unit": ""},
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{"field": "rsi14", "name": "RSI", "type": "number", "unit": ""},
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{"field": "pos60", "name": "60日分位", "type": "number", "unit": ""},
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{"field": "amount", "name": "成交额", "type": "number", "unit": "亿"},
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{"field": "up_streak", "name": "连涨天数", "type": "number", "unit": "天"},
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{"field": "ma_bull", "name": "均线多头", "type": "boolean", "unit": ""},
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{"field": "macd_gold", "name": "MACD金叉", "type": "boolean", "unit": ""}
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]
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