273 lines
12 KiB
Python
273 lines
12 KiB
Python
import pandas as pd
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import numpy as np
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import os
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import json
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import asyncio
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import httpx
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from typing import Dict, List, Optional, Any, Generator, AsyncGenerator
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from dotenv import load_dotenv
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from logger import get_logger
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from utils.api_utils import APIUtils
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# 获取日志器
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logger = get_logger()
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class AIAnalyzer:
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"""
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异步AI分析服务
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负责调用AI API对股票数据进行分析
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"""
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def __init__(self, custom_api_url=None, custom_api_key=None, custom_api_model=None, custom_api_timeout=None):
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"""
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初始化AI分析服务
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Args:
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custom_api_url: 自定义API URL
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custom_api_key: 自定义API密钥
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custom_api_model: 自定义API模型
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custom_api_timeout: 自定义API超时时间
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"""
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# 加载环境变量
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load_dotenv()
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# 设置API配置
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self.API_URL = custom_api_url or os.getenv('API_URL')
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self.API_KEY = custom_api_key or os.getenv('API_KEY')
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self.API_MODEL = custom_api_model or os.getenv('API_MODEL', 'gpt-3.5-turbo')
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self.API_TIMEOUT = int(custom_api_timeout or os.getenv('API_TIMEOUT', 60))
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logger.debug(f"初始化AIAnalyzer: API_URL={self.API_URL}, API_MODEL={self.API_MODEL}, API_KEY={'已提供' if self.API_KEY else '未提供'}, API_TIMEOUT={self.API_TIMEOUT}")
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async def get_ai_analysis(self, df: pd.DataFrame, stock_code: str, market_type: str = 'A', stream: bool = False) -> AsyncGenerator[str, None]:
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"""
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对股票数据进行AI分析
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Args:
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df: 包含技术指标的DataFrame
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stock_code: 股票代码
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market_type: 市场类型，默认为'A'股
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stream: 是否使用流式响应
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Returns:
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异步生成器，生成分析结果字符串
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"""
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try:
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logger.info(f"开始AI分析 {stock_code}, 流式模式: {stream}")
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# AI 分析内容
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# 最近14天的股票数据记录
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recent_data = df.tail(14).to_dict('records')
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# 包含trend, volatility, volume_trend, rsi_level的字典
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technical_summary = {
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'trend': 'upward' if df.iloc[-1]['MA5'] > df.iloc[-1]['MA20'] else 'downward',
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'volatility': f"{df.iloc[-1]['Volatility']:.2f}%",
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'volume_trend': 'increasing' if df.iloc[-1]['Volume_Ratio'] > 1 else 'decreasing',
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'rsi_level': df.iloc[-1]['RSI']
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}
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# 根据市场类型调整分析提示
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if market_type in ['ETF', 'LOF']:
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prompt = f"""
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分析基金 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请分析该基金的技术面状况，包括：
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1. 趋势分析：判断基金当前的趋势方向
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2. 动量分析：基于RSI和交易量评估基金动量
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3. 支撑与阻力位：确定关键价格位
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4. 技术面总结
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5. 投资建议
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将分析结果格式化为JSON，像这样：
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{{
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"trend_analysis": "趋势分析结果...",
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"momentum_analysis": "动量分析结果...",
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"support_resistance": "支撑阻力位分析...",
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"technical_summary": "技术面总结...",
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"investment_advice": "投资建议..."
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}}
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"""
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else:
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prompt = f"""
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分析股票 {stock_code}：
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技术指标概要：
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{technical_summary}
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近14日交易数据：
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{recent_data}
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请分析该股票的技术面状况，包括：
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1. 趋势分析：当前趋势方向及强度
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2. 动量分析：基于MACD、RSI等指标
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3. 支撑与阻力位：关键价格位分析
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4. 成交量分析：交易量的变化及意义
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5. 波动性评估：ATR和波动率分析
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6. 技术面总结
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7. 投资建议：根据技术分析给出操作建议
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将分析结果格式化为JSON，像这样：
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{{
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"trend_analysis": "趋势分析结果...",
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"momentum_analysis": "动量分析结果...",
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"support_resistance": "支撑阻力位分析...",
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"volume_analysis": "成交量分析...",
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"volatility_assessment": "波动性评估...",
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"technical_summary": "技术面总结...",
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"investment_advice": "投资建议..."
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}}
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"""
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# 格式化API URL
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api_url = APIUtils.format_api_url(self.API_URL)
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# 准备请求数据
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request_data = {
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"model": self.API_MODEL,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": 0.7,
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"stream": stream
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}
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# 准备请求头
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {self.API_KEY}"
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}
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# 异步请求API
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async with httpx.AsyncClient(timeout=self.API_TIMEOUT) as client:
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# 记录请求
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logger.debug(f"发送AI请求: URL={api_url}, MODEL={self.API_MODEL}, STREAM={stream}")
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if stream:
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# 流式响应处理
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async with client.stream("POST", api_url, json=request_data, headers=headers) as response:
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if response.status_code != 200:
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error_text = await response.aread()
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error_data = json.loads(error_text)
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error_message = error_data.get('error', {}).get('message', '未知错误')
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logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
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yield json.dumps({"error": f"API请求失败: {error_message}"})
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return
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# 处理流式响应
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buffer = ""
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collected_messages = []
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async for chunk in response.aiter_text():
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if chunk:
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chunk_str = chunk.strip()
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if chunk_str.startswith("data: "):
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chunk_str = chunk_str[6:] # 去除"data: "前缀
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if chunk_str == "[DONE]":
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continue
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try:
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# 解析数据块
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chunk_data = json.loads(chunk_str)
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delta = chunk_data.get("choices", [{}])[0].get("delta", {})
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content = delta.get("content", "")
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if content:
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buffer += content
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# 尝试提取完整的JSON
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if buffer.strip().startswith("{") and buffer.strip().endswith("}"):
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try:
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result_json = json.loads(buffer)
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yield json.dumps({
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"stock_code": stock_code,
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"analysis": result_json
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})
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buffer = "" # 重置缓冲区
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except json.JSONDecodeError:
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# JSON不完整，继续收集
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pass
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# 达到一定长度就输出
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if len(buffer) > 100:
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yield json.dumps({
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"stock_code": stock_code,
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"ai_analysis_chunk": buffer
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})
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collected_messages.append(buffer)
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buffer = ""
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except json.JSONDecodeError:
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# 忽略无法解析的块
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continue
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# 处理最后的缓冲区
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if buffer:
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yield json.dumps({
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"stock_code": stock_code,
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"ai_analysis_chunk": buffer
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})
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collected_messages.append(buffer)
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# 尝试从整个内容中提取JSON
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full_content = "".join(collected_messages)
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# 如果没有成功解析JSON，返回原始内容
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if not full_content.strip().startswith("{"):
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yield json.dumps({
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"stock_code": stock_code,
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"raw_analysis": full_content
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})
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else:
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# 非流式响应处理
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response = await client.post(api_url, json=request_data, headers=headers)
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if response.status_code != 200:
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error_data = response.json()
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error_message = error_data.get('error', {}).get('message', '未知错误')
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logger.error(f"AI API请求失败: {response.status_code} - {error_message}")
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yield json.dumps({"error": f"API请求失败: {error_message}"})
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return
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response_data = response.json()
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analysis_text = response_data.get("choices", [{}])[0].get("message", {}).get("content", "")
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try:
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# 尝试解析JSON
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analysis_json = json.loads(analysis_text)
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yield json.dumps({
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"stock_code": stock_code,
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"analysis": analysis_json
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})
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except json.JSONDecodeError:
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# 返回原始文本
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yield json.dumps({
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"stock_code": stock_code,
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"raw_analysis": analysis_text
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})
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logger.info(f"完成对 {stock_code} 的AI分析")
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except Exception as e:
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logger.error(f"AI分析 {stock_code} 时出错: {str(e)}")
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logger.exception(e)
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yield json.dumps({"error": f"分析出错: {str(e)}"})
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def _truncate_json_for_logging(self, json_obj, max_length=500):
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"""
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截断JSON对象以便记录日志
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Args:
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json_obj: JSON对象
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max_length: 最大长度
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Returns:
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截断后的字符串
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"""
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json_str = json.dumps(json_obj, ensure_ascii=False)
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if len(json_str) <= max_length:
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return json_str
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return json_str[:max_length] + "..." |