社交媒体情绪分析：用大众心理捕捉市场先机

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense, Embedding, SpatialDropout1D
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

# 1. 数据预处理
tokenizer = Tokenizer(num_words=10000)
tokenizer.fit_on_texts(X_train)
X_train_seq = tokenizer.texts_to_sequences(X_train)
X_train_pad = pad_sequences(X_train_seq, maxlen=100)

# 2. 构建LSTM模型
model = Sequential([
    Embedding(10000, 128, input_length=100),
    SpatialDropout1D(0.2),
    LSTM(128, dropout=0.2, recurrent_dropout=0.2),
    Dense(3, activation='softmax')  # 3类：负面/中性/正面
])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train_pad, y_train, epochs=10, batch_size=32, validation_data=(X_val_pad, y_val))

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# 加载FinBERT模型（在金融文本上预训练）
tokenizer = AutoTokenizer.from_pretrained("ProsusAI/finbert")
model = AutoModelForSequenceClassification.from_pretrained("ProsusAI/finbert")

def predict_sentiment_finbert(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
    outputs = model(**inputs)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
    
    sentiment = torch.argmax(probabilities).item()
    # 0=负面, 1=中性, 2=正面
    return sentiment, probabilities.detach().numpy()[0]

# 示例
tweet = "Tesla's Q3 delivery numbers blew past expectations. Bullish!"
sentiment, probs = predict_sentiment_finbert(tweet)
print(f"Sentiment: {sentiment}, Probabilities: {probs}")

def calculate_sentiment_index(tweets, method='finbert'):
    """
    tweets: DataFrame, columns=['date', 'stock', 'text']
    method: 'lexicon' / 'lstm' / 'finbert'
    """
    sentiment_scores = []
    
    for _, row in tweets.iterrows():
        if method == 'finbert':
            score, _ = predict_sentiment_finbert(row['text'])
            # 将0/1/2映射为-1/0/1
            score = score - 1
        elif method == 'lexicon':
            score, _ = sentiment_lexicon(row['text'])
        
        sentiment_scores.append(score)
    
    # 按日期和股票分组，计算平均情绪
    tweets['sentiment'] = sentiment_scores
    sentiment_index = tweets.groupby(['date', 'stock'])['sentiment'].mean().unstack()
    
    return sentiment_index

def sentiment_contrarian_strategy(sentiment_index, price_data):
    signals = {}
    
    for stock in sentiment_index.columns:
        # 计算情绪的Z-Score（过去20天）
        sentiment_z = (sentiment_index[stock] - sentiment_index[stock].rolling(20).mean()) / sentiment_index[stock].rolling(20).std()
        
        # 生成信号：情绪Z-Score > 2（极度乐观）→ 做空
        if sentiment_z.iloc[-1] > 2:
            signals[stock] = -1  # 做空
        elif sentiment_z.iloc[-1] < -2:  # 情绪极度悲观 → 做多
            signals[stock] = 1
        else:
            signals[stock] = 0
    
    return signals

def hybrid_sentiment_technical(sentiment_index, price_data):
    signals = {}
    
    for stock in sentiment_index.columns:
        # 条件1：情绪为正
        sentiment_condition = sentiment_index[stock].iloc[-1] > 0.5
        
        # 条件2：价格突破20日均线
        ma20 = price_data[stock].rolling(20).mean()
        technical_condition = price_data[stock].iloc[-1] > ma20.iloc[-1]
        
        # 条件3：成交量放大（确认情绪不是虚假的）
        volume_condition = price_data[stock].volume.iloc[-1] > price_data[stock].volume.rolling(20).mean().iloc[-1]
        
        if sentiment_condition and technical_condition and volume_condition:
            signals[stock] = 1
        else:
            signals[stock] = 0
    
    return signals

# 爬取雪球帖子（需模拟登录）
import requests

def fetch_xueqiu_posts(stock_code):
    url = f"https://xueqiu.com/statuses/search.json?count=20&q={stock_code}"
    headers = {'User-Agent': 'Mozilla/5.0'}
    
    response = requests.get(url, headers=headers)
    posts = response.json()['list']
    
    return posts

# 对帖子进行情绪分析
def xueqiu_sentiment_index(stock_code, date):
    posts = fetch_xueqiu_posts(stock_code)
    
    sentiments = []
    for post in posts:
        text = post['text']
        sentiment, _ = predict_sentiment_finbert(text)
        sentiments.append(sentiment - 1)  # 映射为[-1, 1]
    
    return np.mean(sentiments)