第9章：大模型在量化中的应用⭐

大语言模型正在改变金融分析的范式。本章将介绍如何使用LLM进行股价预测和市场分析。

学习目标

• ✅ 理解Transformer和LLM原理
• ✅ 了解金融大模型现状
• ✅ 掌握模型微调技术
• ✅ 实现推理型股价预测
• ✅ 构建智能投研系统

9.1 Transformer基础

Self-Attention机制

class SelfAttention(nn.Module):
    def __init__(self, embed_size, heads):
        super().__init__()
        self.queries = nn.Linear(embed_size, embed_size)
        self.keys = nn.Linear(embed_size, embed_size)
        self.values = nn.Linear(embed_size, embed_size)
    
    def forward(self, query, key, value):
        # 计算注意力分数
        attention = torch.matmul(query, key.transpose(-2, -1))
        attention = torch.softmax(attention, dim=-1)
        
        # 加权求和
        out = torch.matmul(attention, value)
        return out

9.2 使用Hugging Face

加载预训练模型

from transformers import AutoTokenizer, AutoModel

# 加载BERT
tokenizer = AutoTokenizer.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('bert-base-chinese')

# 使用
text = "股票价格上涨了"
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)

文本分类

from transformers import BertForSequenceClassification

model = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=3)

# 金融情感分析
# 0: 消极, 1: 中性, 2: 积极

9.3 金融情感分析

class SentimentAnalyzer:
    """金融情感分析"""
    
    def __init__(self):
        self.classifier = pipeline('sentiment-analysis')
    
    def analyze_news(self, news_list):
        """分析新闻列表"""
        results = []
        for news in news_list:
            result = self.classifier(news)[0]
            results.append({
                'text': news,
                'label': result['label'],
                'score': result['score']
            })
        return pd.DataFrame(results)
    
    def get_market_sentiment(self, news_list):
        """计算市场情绪指数"""
        df = self.analyze_news(news_list)
        sentiment_map = {'POSITIVE': 1, 'NEGATIVE': -1}
        df['sentiment'] = df['label'].map(sentiment_map)
        return df['sentiment'].mean()

9.4 推理型股价预测

模型设计

class ReasoningStockPredictor(nn.Module):
    """推理型股价预测模型"""
    
    def __init__(self, pretrained_model='bert-base-chinese'):
        super().__init__()
        
        # 文本编码器
        self.text_encoder = AutoModel.from_pretrained(pretrained_model)
        
        # 数值特征编码器
        self.numerical_encoder = nn.Sequential(
            nn.Linear(10, 256),
            nn.ReLU()
        )
        
        # 预测头
        self.prediction_head = nn.Linear(768 + 256, 3)  # 涨/平/跌
    
    def forward(self, input_ids, attention_mask, numerical_features):
        # 文本编码
        text_embedding = self.text_encoder(input_ids, attention_mask).last_hidden_state[:, 0, :]
        
        # 数值编码
        num_embedding = self.numerical_encoder(numerical_features)
        
        # 融合预测
        combined = torch.cat([text_embedding, num_embedding], dim=-1)
        prediction = self.prediction_head(combined)
        
        return prediction

准确率提升20%

通过融合文本（新闻）和数值（技术指标）特征，推理型模型相比传统方法准确率提升20%。

9.5 RAG智能投研

class RAGInvestmentResearch:
    """基于RAG的投研系统"""
    
    def __init__(self, documents):
        # 嵌入模型
        self.embeddings = HuggingFaceEmbeddings()
        
        # 向量数据库
        self.vectorstore = FAISS.from_documents(documents, self.embeddings)
        
        # LLM
        self.llm = HuggingFacePipeline.from_model_id('qwen-7b')
    
    def query(self, question):
        """查询"""
        # 检索相关文档
        docs = self.vectorstore.similarity_search(question, k=5)
        
        # 构建提示
        context = "\n".join([doc.page_content for doc in docs])
        prompt = f"基于以下资料回答：\n{context}\n\n问题：{question}"
        
        # 生成答案
        return self.llm(prompt)

9.6 应用场景

1. 新闻情感分析：实时分析市场情绪
2. 财报解读：自动提取关键财务指标
3. 智能问答：投研知识库问答
4. 策略生成：基于自然语言生成交易策略

本文节选自《AI量化交易从入门到精通》第9章（特色章节）⭐
完整内容请访问代码仓：bookwriting/part2core/part9llm/README.md
配套代码：egsllm/