Files
qlib/backend/app/application/services/signal_service.py
T
Simon ba52edc2d6 feat(signal): M8.1 交易信号引擎(规则 + signal_event 落库 + /api/signals)
- SignalRules(买入 rank 阈值/趋势 MA/动量 + 卖出区间/破位警示)+ SignalEvent
  (BUY/WATCH/SELL,score/price/trigger_reason 可解释)+ SignalResult/Meta
- quant/signal.generate_signals:与选股同一评分引擎取全市场 rank,按规则分类输出
- signal_snapshot/signal_event 表(migration d8e0b2f3c4d5,MySQL 已应用)+ Repo
- SignalService + POST /api/signals(同步+落库)、GET 详情/列表
- tests/test_signals.py(引擎分类/排序/破位不 BUY、service、API 提交读回);全量 pytest 通过
2026-09-09 00:35:37 +08:00

40 lines
1.5 KiB
Python

"""信号用例(M8.1):基于选股评分排序 + 技术条件生成交易信号。
信号与回测买入逻辑同源(同一评分引擎、同一口径),保证「为什么 BUY/SELL」可解释。
"""
from __future__ import annotations
from datetime import date, timedelta
import pandas as pd
from app.domain.entities.selection import SelectionQuery
from app.domain.entities.signal import SignalResult, SignalRules
from app.domain.repositories.market import DailyBarRepository, StockRepository
from app.quant.selection import factor_columns
from app.quant.service import filter_stocks, load_daily_df
from app.quant.signal import generate_signals
class SignalService:
def __init__(self, stock_repo: StockRepository, daily_repo: DailyBarRepository) -> None:
self._stock_repo = stock_repo
self._daily_repo = daily_repo
def signal(self, query: SelectionQuery, rules: SignalRules) -> SignalResult:
as_of = query.as_of or date.today()
stocks = filter_stocks(self._stock_repo.list(), query.universe, as_of=as_of)
if not stocks:
return generate_signals(pd.DataFrame(), query, rules, as_of)
columns = sorted(factor_columns(query))
daily = load_daily_df(
self._daily_repo,
[s.symbol for s in stocks],
as_of - timedelta(days=query.warmup_days),
as_of,
columns,
adjust=query.price_adjustment,
)
return generate_signals(daily, query, rules, as_of)