151 lines
4.7 KiB
Python
151 lines
4.7 KiB
Python
"""Extended sentiment factor implementations."""
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from __future__ import annotations
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from typing import Dict, Optional, Sequence
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import numpy as np
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from app.core.sentiment import (
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news_sentiment_momentum,
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news_impact_score,
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market_sentiment_index,
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industry_sentiment_divergence
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)
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from dataclasses import dataclass
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from datetime import datetime, timezone
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from app.utils.data_access import DataBroker
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from app.utils.db import db_session
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from app.utils.logging import get_logger
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LOGGER = get_logger(__name__)
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LOG_EXTRA = {"stage": "sentiment_factors"}
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class SentimentFactors:
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"""情绪因子计算实现类。
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实现了一组基于新闻、市场和行业情绪的因子：
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1. 新闻情感动量 (sent_momentum)
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2. 新闻影响力 (sent_impact)
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3. 市场情绪指数 (sent_market)
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4. 行业情绪背离度 (sent_divergence)
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使用示例:
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calculator = SentimentFactors()
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broker = DataBroker()
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factors = calculator.compute_stock_factors(
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broker,
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"000001.SZ",
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"20251001"
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)
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"""
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def __init__(self):
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"""初始化情绪因子计算器"""
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self.factor_specs = {
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"sent_momentum": 20, # 情感动量窗口
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"sent_impact": 0, # 新闻影响力
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"sent_market": 20, # 市场情绪窗口
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"sent_divergence": 0, # 情绪背离度
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}
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def compute_stock_factors(
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self,
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broker: DataBroker,
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ts_code: str,
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trade_date: str,
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) -> Dict[str, Optional[float]]:
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"""计算单个股票的情绪因子
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Args:
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broker: 数据访问器
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ts_code: 股票代码
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trade_date: 交易日期
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Returns:
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因子名称到因子值的映射字典
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"""
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LOGGER.debug(
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"新闻因子计算已禁用，返回空结果 code=%s date=%s",
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ts_code,
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trade_date,
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extra=LOG_EXTRA,
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)
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return {name: None for name in self.factor_specs}
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def compute_batch(
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self,
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broker: DataBroker,
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ts_codes: list[str],
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trade_date: str,
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batch_size: int = 100
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) -> None:
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"""批量计算多个股票的情绪因子并保存
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Args:
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broker: 数据访问器
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ts_codes: 股票代码列表
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trade_date: 交易日期
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batch_size: 批处理大小
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"""
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# 准备SQL语句
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columns = list(self.factor_specs.keys())
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insert_columns = ["ts_code", "trade_date", "updated_at"] + columns
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placeholders = ",".join("?" * len(insert_columns))
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update_clause = ", ".join(
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f"{column}=excluded.{column}"
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for column in ["updated_at"] + columns
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)
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sql = (
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f"INSERT INTO factors ({','.join(insert_columns)}) "
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f"VALUES ({placeholders}) "
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f"ON CONFLICT(ts_code, trade_date) DO UPDATE SET {update_clause}"
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)
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# 获取当前时间戳
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timestamp = datetime.now(timezone.utc).isoformat()
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# 分批处理
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total_processed = 0
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rows_to_persist = []
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for ts_code in ts_codes:
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# 计算因子
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values = self.compute_stock_factors(broker, ts_code, trade_date)
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# 准备数据
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if any(v is not None for v in values.values()):
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payload = [ts_code, trade_date, timestamp]
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payload.extend(values.get(col) for col in columns)
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rows_to_persist.append(payload)
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total_processed += 1
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if total_processed % batch_size == 0:
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LOGGER.info(
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"情绪因子计算进度: %d/%d (%.1f%%)",
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total_processed,
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len(ts_codes),
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(total_processed / len(ts_codes)) * 100,
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extra=LOG_EXTRA
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)
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# 执行批量写入
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if rows_to_persist:
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with db_session() as conn:
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try:
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conn.executemany(sql, rows_to_persist)
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LOGGER.info(
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"情绪因子持久化完成 total=%d",
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len(rows_to_persist),
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extra=LOG_EXTRA
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)
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except Exception as e:
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LOGGER.error(
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"情绪因子持久化失败 error=%s",
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str(e),
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exc_info=True,
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extra=LOG_EXTRA
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)
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