import pandas as pd
import numpy as np
from scipy import stats

def calculate_herding_index(returns, window=20):
    """
    计算羊群效应指数（基于CSSD方法）
    CSSD越低，羊群效应越强
    """
    herding_index = []
    
    for date in returns.index[window:]:
        # 获取过去window天的收益率
        period_returns = returns.loc[:date].tail(window)
        
        # 计算横截面标准差（CSSD）
        cssd = period_returns.std(axis=1).mean()
        
        # 计算平均绝对偏差（CAD）
        market_return = period_returns.mean(axis=1)
        cad = np.abs(period_returns.sub(market_return, axis=0)).mean().mean()
        
        herding_index.append({
            'date': date,
            'CSSD': cssd,
            'CAD': cad,
            'herding_strength': -cssd  # 取负值，CSSD越低羊群效应越强
        })
    
    return pd.DataFrame(herding_index).set_index('date')

# 示例：计算A股市场的羊群效应
# stock_returns: DataFrame, columns=股票代码, index=日期
herding = calculate_herding_index(stock_returns, window=20)

# 生成交易信号：羊群效应极强时（CSSD < 历史10%分位数），预期反转
extreme_herding = herding['CSSD'] < herding['CSSD'].quantile(0.1)

def build_overreaction_portfolio(returns, window=60, top_n=50):
    """
    构建过度反应组合：过去表现最差的股票（输家）vs 最好的股票（赢家）
    基于De Bondt & Thaler (1985)的长期反转效应
    """
    portfolios = []
    
    for date in returns.index[window:]:
        # 计算过去window天的累计收益
        past_returns = returns.loc[:date].tail(window).sum()
        
        # 划分赢家/输家组合
        winners = past_returns.nlargest(top_n).index
        losers = past_returns.nsmallest(top_n).index
        
        portfolios.append({
            'date': date,
            'winners': list(winners),
            'losers': list(losers),
            'spread_return': past_returns[losers].mean() - past_returns[winners].mean()
        })
    
    return pd.DataFrame(portfolios).set_index('date')

# 示例：A股市场的长期反转策略
overreaction = build_overreaction_portfolio(stock_returns, window=60, top_n=50)

# 买入输家组合，做空赢家组合
spread_return = overreaction['spread_return']
cumulative_return = (1 + spread_return).cumprod()

from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
import jieba
from collections import Counter

def extract_behavioral_signals(texts):
    """
    从社交媒体文本中提取行为金融学信号
    信号包括：情绪极端化、注意力集中、羊群语言
    """
    analyzer = SentimentIntensityAnalyzer()
    signals = []
    
    for text in texts:
        # 1. 情绪强度（绝对值越大，情绪越极端）
        sentiment = analyzer.polarity_scores(text)
        emotion_extreme = abs(sentiment['compound'])
        
        # 2. 注意力集中度（关键词重复度）
        keywords = jieba.analyse.extract_tags(text, topK=10)
        attention_focus = len(set(keywords)) / len(keywords) if keywords else 0
        
        # 3. 羊群语言检测（"大家都"、"一致认为"等）
        herding_words = ['大家都', '一致认为', '跟风', '追涨', '杀跌']
        herding_score = sum([1 for word in herding_words if word in text])
        
        signals.append({
            'emotion_extreme': emotion_extreme,
            'attention_focus': attention_focus,
            'herding_language': herding_score
        })
    
    return pd.DataFrame(signals)

# 示例：分析股吧/雪球的帖子
# posts: List[str], 每个元素是一篇帖子
behavioral_signals = extract_behavioral_signals(posts)

# 当情绪极端化 + 羊群语言高发时，警惕反转
contrarian_signal = (behavioral_signals['emotion_extreme'] > 0.8) & \
                    (behavioral_signals['herding_language'] >= 2)

def construct_behavioral_factors(returns, sentiment_data, window=20):
    """
    构建行为金融多因子模型
    因子包括：羊群效应、过度反应、情绪极端化
    """
    factors = pd.DataFrame(index=returns.index[window:])
    
    # 因子1：羊群效应因子（反向）
    cssd = returns.rolling(window).std().mean(axis=1)
    factors['HERD'] = -cssd  # CSSD越低，羊群效应越强，预期反转
    
    # 因子2：长期反转因子（捕捉过度反应）
    past_returns = returns.rolling(60).sum()
    factors['REVR'] = -past_returns.rank(axis=1, pct=True)  # 过去表现越好，未来越差
    
    # 因子3：情绪极端化因子
    eei = sentiment_data['std'] / sentiment_data['mean'].rolling(20).mean()
    factors['SENT'] = -eei  # 情绪越极端，预期反转
    
    return factors.dropna()

# 构建多因子组合
factors = construct_behavioral_factors(stock_returns, sentiment_data)

# IC检验（信息系数）
ic_herd = []
ic_revr = []
ic_sent = []

for date in factors.index:
    # 计算因子值与实际收益的相关系数
    actual = stock_returns.loc[date]  # 下期收益
    ic_herd.append(actual.corr(factors.loc[date, 'HERD']))
    ic_revr.append(actual.corr(factors.loc[date, 'REVR']))
    ic_sent.append(actual.corr(factors.loc[date, 'SENT']))

print(f"HERD因子IC: {np.mean(ic_herd):.4f}")
print(f"REVR因子IC: {np.mean(ic_revr):.4f}")
print(f"SENT因子IC: {np.mean(ic_sent):.4f}")