Simon
73d191b43a
- 新增 finance/tests/ 6 个测试套件（agents/backtest/dao_upsert/factors/features/fundamental_lookahead） - 数据层: data_manager / dao 优化，新增 upsert 逻辑 - 因子层: 基本面因子抽象定位 _mapping、ROE/PE/PB 重构 - 回测层: vectorbt/engine 大改动（251 行），report 增强 - ML 层: features/backtest_integration 特征工程与回测优化 - CLI: agent_cli 重构 - config/settings 扩充配置项
212 lines
7.4 KiB
Python
212 lines
7.4 KiB
Python
"""
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SelectionAgent — 多因子股票打分。
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综合技术因子 + 基本面因子 + ML 预测值 → 股票综合得分排序。
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"""
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import numpy as np
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import pandas as pd
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from agents.base import BaseAgent
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from config.settings import (
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SELECTION_CORE_FACTORS, SELECTION_UNIVERSE_SIZE,
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SELECTION_SCORE_LIMIT, SELECTION_WINSORIZE_ZSCORE,
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)
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class SelectionAgent(BaseAgent):
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"""股票打分 Agent。"""
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name = "Selection"
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description = "多因子股票打分与推荐"
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def execute(
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self,
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date: str | None = None,
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ts_codes: list[str] | None = None,
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top_n: int = 15,
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weighting: str = "equal",
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) -> dict:
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"""
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对股票池打分排序。
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参数:
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date: 目标日期，None=最新
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ts_codes: 股票池，None=指数成分股
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top_n: 返回 Top N
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weighting: 'equal' | 'ic_weighted' | 'ml'
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返回:
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{"date": ..., "top_picks": [...], "score_df": DataFrame}
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"""
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from factors.registry import get_factor, list_factors
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# 确定股票池
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if ts_codes is None:
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if self.sent:
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ts_codes = self.sent.get_scope_stocks()
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else:
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stocks = self.dm.get_stock_list()
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ts_codes = list(stocks.index[:SELECTION_UNIVERSE_SIZE])
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if not ts_codes:
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return {"date": date or self._today(), "top_picks": [], "score_df": pd.DataFrame()}
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# 选择核心因子（覆盖多个维度，减少计算量；参数来自配置中心）
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core_factors = list(SELECTION_CORE_FACTORS)
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factor_objects = [get_factor(n) for n in core_factors]
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self.log("打分 {} 只股票 (权重={})".format(len(ts_codes), weighting))
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# 1. 筛选有 DB 缓存的股票（Orchestrator 已在 Step1 同步了全量范围）
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from database.dao import get_latest_trade_date
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available = []
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for ts_code in sorted(ts_codes):
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if get_latest_trade_date(ts_code):
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available.append(ts_code)
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self.log("缓存命中: {}/{} ({:.1f}%)".format(
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len(available), len(ts_codes),
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len(available) / len(ts_codes) * 100 if ts_codes else 0))
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# 2. 打分（限制上限防止单次太慢）
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score_limit = min(len(available), SELECTION_SCORE_LIMIT)
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scores = {}
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valid_count = 0
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for i, ts_code in enumerate(available[:score_limit]):
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try:
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score = self._score_stock(ts_code, factor_objects, date, weighting)
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if score is not None:
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scores[ts_code] = score
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valid_count += 1
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except Exception:
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continue
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if (i + 1) % 50 == 0:
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self.log(" 进度: {}/{}".format(i + 1, score_limit))
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self.log("有效评分: {}/{}".format(len(scores), score_limit))
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if not scores:
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return {"date": date or self._today(), "top_picks": [], "score_df": pd.DataFrame()}
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# 排序
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sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
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top = sorted_scores[:top_n]
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# 取股票名称（ts_code 是 index）
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stock_list = self.dm.get_stock_list() if self.dm else pd.DataFrame()
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name_map = {}
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if not stock_list.empty and "name" in stock_list.columns:
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name_map = dict(zip(stock_list.index, stock_list["name"]))
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top_picks = []
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for ts_code, score in top:
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# 尝试多种格式匹配名称
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code_no_suffix = ts_code.replace(".SZ", "").replace(".SH", "").replace(".BJ", "")
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name = name_map.get(ts_code, name_map.get(code_no_suffix, ts_code))
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top_picks.append({
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"ts_code": ts_code,
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"name": name,
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"score": round(score, 4),
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})
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score_df = pd.DataFrame(
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{"ts_code": list(scores.keys()), "score": list(scores.values())}
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).sort_values("score", ascending=False).reset_index(drop=True)
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return {
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"date": date or self._today(),
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"top_picks": top_picks,
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"score_df": score_df,
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"universe_size": len(ts_codes),
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"valid_scores": valid_count,
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}
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def _filter_cached_stocks(self, ts_codes: list[str], limit: int = 100) -> list[str]:
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"""筛选有 DB 日线缓存的股票，避免逐个调用 AkShare。"""
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from database.dao import get_latest_trade_date
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cached = []
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for ts_code in ts_codes[:limit]:
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latest = get_latest_trade_date(ts_code)
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if latest:
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cached.append(ts_code)
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return cached
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def _score_stock(
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self,
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ts_code: str,
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factors: list,
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date: str | None,
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weighting: str,
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) -> float | None:
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"""对单只股票打分。"""
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daily = self.dm.get_daily(ts_code)
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if daily is None or daily.empty:
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return None
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daily = daily.set_index("trade_date").sort_index()
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factor_df = self.fe.compute(ts_code, factors)
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if factor_df is None or factor_df.empty:
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return None
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if date and date in factor_df.index:
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row = factor_df.loc[date]
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else:
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row = factor_df.iloc[-1] # 最新一天
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if row.isna().all():
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return None
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if weighting == "ml" and self.ml_models:
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return self._score_ml(ts_code, factor_df, date)
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# 等权打分：标准化因子值后求和
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row_clean = row.dropna()
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if len(row_clean) < 3:
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return None
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# z-score 标准化（用历史均值/标准差），可选的去极值避免单股离群主导 Top
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cols = row_clean.index
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mu = factor_df[cols].mean()
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std = factor_df[cols].std().replace(0, 1)
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z = (row_clean - mu) / std
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if SELECTION_WINSORIZE_ZSCORE:
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z = z.clip(-3, 3)
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return float(z.mean())
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def _score_ml(self, ts_code: str, factor_df: pd.DataFrame, date: str | None) -> float | None:
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"""ML 模型打分。
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要求注入名为 feature_engine 的、已用训练集 fit 过的 FeatureEngine，
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以及 ml_models（已训练模型）。两者缺一时明确退出，而不是沿用旧的
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未 fit 引擎静默失败。
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"""
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fe = self.feature_engine
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if fe is None:
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self.log("ML 打分需要注入 feature_engine（已 fit），当前未提供，跳过 ML 打分")
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return None
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if not self.ml_models:
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self.log("ML 打分需要 ml_models（已训练），当前为空，跳过 ML 打分")
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return None
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daily = self.dm.get_daily(ts_code) if self.dm else None
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if daily is None or daily.empty:
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return None
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daily = daily.set_index("trade_date")
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try:
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X, _ = fe.build(factor_df, daily, fit=False)
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if X is None or X.empty:
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return None
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if date and date in X.index:
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X = X.loc[[date]]
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else:
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X = X.iloc[[-1]]
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model = self.ml_models.get("lightgbm") or list(self.ml_models.values())[0]
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pred = model.predict(X)
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return float(pred.iloc[0]) if len(pred) > 0 else None
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except Exception as e:
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# 不再静默返回 None：记录原因，便于定位预测路径问题
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self.log("[WARN] ML 打分失败 ({}): {}".format(ts_code, e))
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return None
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