Simon
92627f5b6b
- 后端业务 API：GET /api/stocks(搜索/分页)、GET /api/factors（因子目录）、POST /api/factor-tests 与 /api/backtests（Research Spec 驱动同步执行）、GET /api/backtests/last；Annotated 依赖注入 + CORS(dev) - Repository 批量查询 get_range_many（研究装配一次查询，避免逐只拉取） - 前端 frontend/web：Next.js 15(TS) + ECharts —— 总览 / 股票池 / 因子研究(IC·RankIC·分层展示) / 回测（净值·回撤·月度·持仓·未建模标注） - 前端只消费业务 API 与标准化 BacktestResult，无 Qlib/SQL 概念泄漏 - 真实数据：同步 20 只权重股 2023-2024 日线（9680 根）支撑截面研究 - 验证：API 集成测试 8 项（DTO 校验/装配/引擎/标准结果，内存 repo 全链路）+ 全量 pytest 68 passed；前端 tsc + next build 通过；无头浏览器端到端（factors/backtest 页面渲染后端数据） - ruff clean
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
"""研究服务：把 Research Specification 编排为数据获取 + 引擎执行。
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本层是业务入口：API / Agent 只能调用这里的用例（AGENT.md §16/§17），
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禁止直接拼接引擎配置。数据一律经 Repository 获取（防未来函数由查询层保证）。
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"""
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from __future__ import annotations
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from datetime import date, timedelta
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import pandas as pd
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from app.domain.entities.market import Stock
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from app.domain.entities.research import (
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BacktestResult,
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FactorTestReport,
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ResearchSpec,
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UniverseSpec,
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)
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from app.domain.repositories.market import (
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DailyBarRepository,
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StockRepository,
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)
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from app.quant.engine import QuantEngine
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def filter_stocks(stocks: list[Stock], universe: UniverseSpec, as_of: date) -> list[Stock]:
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"""按股票池口径过滤（名称含 ST 判定 —— 名称快照为当日口径，属历史可追溯数据）。"""
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out: list[Stock] = []
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for s in stocks:
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if s.delist_date is not None and s.delist_date < as_of:
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continue
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if universe.exclude_st and s.name and "ST" in s.name.upper():
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continue
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if (
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universe.min_listing_days
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and s.list_date
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and (as_of - s.list_date).days < universe.min_listing_days
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):
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continue
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out.append(s)
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return out
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def bars_to_daily_df(bars) -> pd.DataFrame:
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"""DailyBar 列表 → 引擎长表 DataFrame（symbol/trade_date/ohlc/volume/amount）。
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领域实体中的 Decimal 在此转 float，供 pandas 数值运算（保持 DataFrame 全数值列）。
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"""
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df = pd.DataFrame([b.model_dump() for b in bars])
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if not df.empty:
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for col in ("open", "high", "low", "close", "volume", "amount"):
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if col in df.columns:
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df[col] = df[col].astype(float)
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return df
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class ResearchService:
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"""研究用例入口（因子测试 / 回测）。依赖注入 Repository 与引擎。"""
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def __init__(
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self,
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stock_repo: StockRepository,
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daily_repo: DailyBarRepository,
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engine: QuantEngine,
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) -> None:
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self._stock_repo = stock_repo
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self._daily_repo = daily_repo
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self._engine = engine
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def run_factor_test(self, spec: ResearchSpec, horizon_days: int = 21) -> FactorTestReport:
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if spec.type != "factor_test":
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raise ValueError("factor_test 用例需要 spec.type=factor_test")
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daily = self._load_daily(spec)
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return self._engine.run_factor_test(daily, spec, horizon_days=horizon_days)
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def run_backtest(self, spec: ResearchSpec) -> BacktestResult:
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if spec.type != "backtest":
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raise ValueError("backtest 用例需要 spec.type=backtest")
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daily = self._load_daily(spec)
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return self._engine.run_backtest(daily, spec)
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# ---- 数据装配 ----
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def _load_daily(self, spec: ResearchSpec) -> pd.DataFrame:
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start, end = spec.period
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# 回测前预留因子 warmup（lookback≤120 交易日，取 300 自然日余量）
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data_start = start - timedelta(days=300)
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stocks = filter_stocks(self._stock_repo.list(), spec.universe, as_of=start)
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if not stocks:
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return pd.DataFrame()
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get_many = getattr(self._daily_repo, "get_range_many", None)
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if get_many is not None:
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bars = list(get_many([s.symbol for s in stocks], data_start, end))
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else: # 兜底：逐只查询
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bars = []
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for s in stocks:
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bars.extend(self._daily_repo.get_range(s.symbol, data_start, end))
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return bars_to_daily_df(bars)
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