308 lines
13 KiB
Python
308 lines
13 KiB
Python
import json
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import os
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import re
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from openai import OpenAI
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class AIAnalysisService:
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def __init__(self):
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self.model = "ep-20250111143839-vn8l8" # endpoint ID
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self.client = OpenAI(
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api_key = "cf4edd4d-55cd-4e0f-82f6-49072660bdaf", # 直接使用API Key
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base_url = "https://ark.cn-beijing.volces.com/api/v3"
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)
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def analyze_value_investment(self, analysis_data):
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"""
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对股票进行价值投资分析
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:param analysis_data: 包含各项财务指标的字典
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:return: AI分析结果
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"""
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try:
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# 打印输入数据用于调试
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print(f"输入的分析数据: {json.dumps(analysis_data, ensure_ascii=False, indent=2)}")
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# 构建提示词
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prompt = self._build_analysis_prompt(analysis_data)
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# 打印提示词用于调试
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print(f"AI分析提示词: {prompt}")
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# 调用API
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": prompt
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}
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]
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}
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]
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)
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# 获取分析结果
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analysis_text = response.choices[0].message.content
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print(f"AI原始返回结果: {analysis_text}")
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try:
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# 尝试解析JSON
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analysis_result = json.loads(analysis_text)
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print(f"解析后的JSON结果: {json.dumps(analysis_result, ensure_ascii=False, indent=2)}")
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# 构建完整的返回结果
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result = analysis_result
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print(f"最终返回结果: {json.dumps(result, ensure_ascii=False, indent=2)}")
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return result
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except json.JSONDecodeError as e:
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print(f"JSON解析失败: {str(e)}")
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# 如果JSON解析失败，返回错误信息
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return {
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'stock_info': analysis_data.get('stock_info', {}),
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'valuation': analysis_data.get('valuation', {}),
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'profitability': analysis_data.get('profitability', {}),
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'growth': analysis_data.get('growth', {}),
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'operation': analysis_data.get('operation', {}),
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'solvency': analysis_data.get('solvency', {}),
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'cash_flow': analysis_data.get('cash_flow', {}),
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'per_share': analysis_data.get('per_share', {}),
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'analysis_result': {
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"error": "AI返回的结果不是有效的JSON格式",
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"raw_text": analysis_text
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}
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}
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except Exception as e:
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print(f"AI分析失败: {str(e)}")
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print(f"错误详情: {e.__class__.__name__}")
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import traceback
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print(f"错误堆栈: {traceback.format_exc()}")
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return {"error": f"AI分析失败: {str(e)}"}
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def _parse_analysis_result(self, analysis_text, current_price):
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"""
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解析AI返回的分析文本，提取结构化信息
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"""
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try:
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print(f"开始解析分析文本...")
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# 提取投资建议
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suggestion_pattern = r"投资建议[：:]([\s\S]*?)(?=\n\n|$)"
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suggestion_match = re.search(suggestion_pattern, analysis_text, re.MULTILINE | re.DOTALL)
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investment_suggestion = suggestion_match.group(1).strip() if suggestion_match else ""
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print(f"提取到的投资建议: {investment_suggestion}")
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# 提取合理价格区间
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price_pattern = r"合理股价区间[：:]\s*(\d+\.?\d*)\s*[元-]\s*(\d+\.?\d*)[元]"
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price_match = re.search(price_pattern, analysis_text)
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if price_match:
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price_min = float(price_match.group(1))
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price_max = float(price_match.group(2))
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else:
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price_min = current_price * 0.8
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price_max = current_price * 1.2
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print(f"提取到的价格区间: {price_min}-{price_max}")
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# 提取目标市值区间（单位：亿元）
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market_value_pattern = r"目标市值区间[：:]\s*(\d+\.?\d*)\s*[亿-]\s*(\d+\.?\d*)[亿]"
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market_value_match = re.search(market_value_pattern, analysis_text)
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if market_value_match:
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market_value_min = float(market_value_match.group(1))
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market_value_max = float(market_value_match.group(2))
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else:
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# 尝试从文本中提取计算得出的市值
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calc_pattern = r"最低市值[=≈约]*(\d+\.?\d*)[亿].*最高市值[=≈约]*(\d+\.?\d*)[亿]"
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calc_match = re.search(calc_pattern, analysis_text)
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if calc_match:
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market_value_min = float(calc_match.group(1))
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market_value_max = float(calc_match.group(2))
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else:
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market_value_min = 0
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market_value_max = 0
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print(f"提取到的市值区间: {market_value_min}-{market_value_max}")
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# 提取各个分析维度的内容
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analysis_patterns = {
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"valuation_analysis": r"估值分析([\s\S]*?)(?=###\s*财务状况分析|###\s*成长性分析|$)",
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"financial_health": r"财务状况分析([\s\S]*?)(?=###\s*成长性分析|###\s*风险评估|$)",
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"growth_potential": r"成长性分析([\s\S]*?)(?=###\s*风险评估|###\s*投资建议|$)",
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"risk_assessment": r"风险评估([\s\S]*?)(?=###\s*投资建议|$)"
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}
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analysis_results = {}
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for key, pattern in analysis_patterns.items():
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match = re.search(pattern, analysis_text, re.MULTILINE | re.DOTALL)
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content = match.group(1).strip() if match else ""
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# 移除markdown标记和多余的空白字符
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content = re.sub(r'[#\-*]', '', content).strip()
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analysis_results[key] = content
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print(f"提取到的{key}: {content[:100]}...")
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return {
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"investment_suggestion": investment_suggestion,
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"analysis": analysis_results,
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"price_analysis": {
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"reasonable_price_range": {
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"min": price_min,
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"max": price_max
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},
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"target_market_value": {
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"min": market_value_min,
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"max": market_value_max
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}
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}
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}
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except Exception as e:
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print(f"解析分析结果失败: {str(e)}")
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print(f"错误详情: {e.__class__.__name__}")
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import traceback
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print(f"错误堆栈: {traceback.format_exc()}")
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return {
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"investment_suggestion": "分析结果解析失败",
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"analysis": {
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"valuation_analysis": "解析失败",
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"financial_health": "解析失败",
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"growth_potential": "解析失败",
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"risk_assessment": "解析失败"
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},
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"price_analysis": {
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"reasonable_price_range": {
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"min": current_price * 0.8,
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"max": current_price * 1.2
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},
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"target_market_value": {
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"min": 0,
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"max": 0
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}
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}
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}
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def _build_analysis_prompt(self, data):
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"""
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构建AI分析提示词
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"""
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stock_info = data.get('stock_info', {})
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valuation = data.get('valuation', {})
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profitability = data.get('profitability', {})
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growth = data.get('growth', {})
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operation = data.get('operation', {})
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solvency = data.get('solvency', {})
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cash_flow = data.get('cash_flow', {})
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per_share = data.get('per_share', {})
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# 格式化数值，保留4位小数
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def format_number(value):
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try:
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if value is None:
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return "0.0000"
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if isinstance(value, (int, float)):
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if abs(value) < 0.0001: # 对于非常小的数值
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return "0.0000"
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return f"{value:.4f}"
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if isinstance(value, str):
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try:
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value = float(value)
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if abs(value) < 0.0001:
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return "0.0000"
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return f"{value:.4f}"
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except:
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pass
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return str(value)
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except:
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return "0.0000"
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# 格式化百分比，保留2位小数
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def format_percent(value):
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try:
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if value is None:
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return "0.00%"
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if isinstance(value, (int, float)):
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# 如果值已经是小数形式（如0.5代表50%），则乘以100
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if abs(value) <= 1:
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value = value * 100
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return f"{value:.2f}%"
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if isinstance(value, str):
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try:
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value = float(value)
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if abs(value) <= 1:
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value = value * 100
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return f"{value:.2f}%"
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except:
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pass
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return "0.00%"
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except:
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return "0.00%"
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# 构建数据部分
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data_section = f"""请作为一位专业的价值投资分析师，对{stock_info.get('name', '')}({stock_info.get('code', '')})进行深入的价值投资分析。
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当前市场信息：
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- 市盈率(PE)：{format_number(valuation.get('pe_ratio'))}
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- 市净率(PB)：{format_number(valuation.get('pb_ratio'))}
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- 市销率(PS)：{format_number(valuation.get('ps_ratio'))}
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- 股息率：{format_percent(valuation.get('dividend_yield'))}
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- 总市值(亿元)：{format_number(valuation.get('total_market_value'))}
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- 当前股价：{format_number(stock_info.get('current_price'))}元
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盈利能力指标：
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- ROE：{format_percent(profitability.get('roe'))}
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- 毛利率：{format_percent(profitability.get('gross_margin'))}
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- 净利率：{format_percent(profitability.get('net_margin'))}
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成长能力指标：
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- 净利润增长率：{format_percent(growth.get('net_profit_growth'))}
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- 扣非净利润增长率：{format_percent(growth.get('deducted_net_profit_growth'))}
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- 营收增长率：{format_percent(growth.get('revenue_growth'))}
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运营能力指标：
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- 总资产周转率：{format_number(operation.get('asset_turnover'))}次/年
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- 存货周转率：{format_number(operation.get('inventory_turnover'))}次/年
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- 应收账款周转率：{format_number(operation.get('receivables_turnover'))}次/年
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偿债能力指标：
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- 流动比率：{format_number(solvency.get('current_ratio'))}
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- 速动比率：{format_number(solvency.get('quick_ratio'))}
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- 资产负债率：{format_percent(solvency.get('debt_to_assets'))}
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现金流指标：
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- 经营现金流/营收比：{format_percent(cash_flow.get('ocf_to_revenue'))}
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- 经营现金流同比增长：{format_percent(cash_flow.get('ocf_growth'))}
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每股指标：
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- 每股收益(EPS)：{format_number(per_share.get('eps'))}元
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- 每股净资产(BPS)：{format_number(per_share.get('bps'))}元
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- 每股现金流(CFPS)：{format_number(per_share.get('cfps'))}元
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- 每股经营现金流(OCFPS)：{format_number(per_share.get('ocfps'))}元
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- 每股未分配利润：{format_number(per_share.get('retained_eps'))}元"""
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# 构建分析要求部分
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analysis_requirements = """
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请基于以上数据，从价值投资的角度进行分析。请特别注意：
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1. 结合行业特点、公司竞争力、成长性等因素，给出合理的估值区间
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2. 存货周转率为0可能表示数据缺失，分析时需要谨慎对待
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3. 考虑当前市场环境和行业整体估值水平
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在给出估值区间时，请充分考虑：
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1. 公司所处行业特点和竞争格局
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2. 公司的竞争优势和市场地位
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3. 当前的盈利能力和成长性
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4. 财务健康状况和风险因素
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5. 宏观经济环境和行业周期
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6. 可比公司的估值水平
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请以JSON格式返回分析结果，包含以下内容：
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1. investment_suggestion: 投资建议，包含summary(总体建议)、action(具体操作建议)和key_points(关注重点)
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2. analysis: 详细分析，包含估值分析、财务健康状况、成长潜力和风险评估
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3. price_analysis: 价格分析，包含合理价格区间和目标市值区间
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请确保返回的是一个有效的JSON格式，数值使用数字而不是字符串（价格、市值等），文本分析使用字符串。分析要客观、专业、详细。"""
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# 组合完整的提示词
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prompt = data_section + analysis_requirements
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return prompt |