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