121 lines
3.5 KiB
Python
121 lines
3.5 KiB
Python
"""Market sentiment indicators."""
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from __future__ import annotations
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from typing import Dict, List, Optional, Sequence
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import numpy as np
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from scipy import stats
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def news_sentiment_momentum(
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sentiment_series: Sequence[float],
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window: int = 20
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) -> Optional[float]:
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"""计算新闻情感动量指标
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Args:
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sentiment_series: 新闻情感得分序列，从新到旧排序
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window: 计算窗口
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Returns:
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情感动量得分 (-1到1)，或 None（数据不足时）
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"""
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if len(sentiment_series) < window:
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return None
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# 计算情感趋势
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sentiment_series = np.array(sentiment_series[:window])
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slope, _, r_value, _, _ = stats.linregress(
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np.arange(len(sentiment_series)),
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sentiment_series
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)
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# 结合斜率和拟合度
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trend = np.tanh(slope * 10) # 归一化斜率
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quality = abs(r_value) # 趋势显著性
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return float(trend * quality)
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def news_impact_score(
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sentiment: float,
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heat: float,
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entity_count: int
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) -> float:
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"""计算新闻影响力得分
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Args:
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sentiment: 情感得分 (-1到1)
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heat: 热度得分 (0到1)
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entity_count: 涉及实体数量
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Returns:
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影响力得分 (0到1)
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"""
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# 新闻影响力 = 情感强度 * 热度 * 实体覆盖度
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sentiment_strength = abs(sentiment)
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entity_coverage = min(entity_count / 5, 1.0) # 标准化实体数量
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return sentiment_strength * heat * (0.7 + 0.3 * entity_coverage)
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def market_sentiment_index(
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sentiment_scores: Sequence[float],
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heat_scores: Sequence[float],
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volume_ratios: Sequence[float],
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window: int = 20
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) -> Optional[float]:
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"""计算综合市场情绪指数
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Args:
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sentiment_scores: 个股情感得分序列
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heat_scores: 个股热度得分序列
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volume_ratios: 个股成交量比序列
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window: 计算窗口
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Returns:
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市场情绪指数 (-1到1)，或 None（数据不足时）
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"""
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if len(sentiment_scores) < window or \
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len(heat_scores) < window or \
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len(volume_ratios) < window:
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return None
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# 截取窗口数据
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sentiment_scores = np.array(sentiment_scores[:window])
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heat_scores = np.array(heat_scores[:window])
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volume_ratios = np.array(volume_ratios[:window])
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# 计算带量化权重的情感得分
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volume_weights = volume_ratios / np.mean(volume_ratios)
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weighted_sentiment = sentiment_scores * volume_weights
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# 计算热度加权平均
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heat_weights = heat_scores / np.sum(heat_scores)
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market_mood = np.sum(weighted_sentiment * heat_weights)
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return float(np.tanh(market_mood)) # 压缩到[-1,1]区间
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def industry_sentiment_divergence(
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industry_sentiment: float,
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peer_sentiments: Sequence[float]
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) -> Optional[float]:
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"""计算行业情绪背离度
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Args:
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industry_sentiment: 行业整体情感得分
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peer_sentiments: 成分股情感得分序列
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Returns:
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情绪背离度 (-1到1)，或 None（数据不足时）
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"""
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if not peer_sentiments:
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return None
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peer_sentiments = np.array(peer_sentiments)
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peer_mean = np.mean(peer_sentiments)
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peer_std = np.std(peer_sentiments)
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if peer_std == 0:
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return 0.0
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# 计算Z分数衡量背离程度
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z_score = (industry_sentiment - peer_mean) / peer_std
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return float(np.tanh(z_score)) # 压缩到[-1,1]区间
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