719 lines
30 KiB
Python
719 lines
30 KiB
Python
import json
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import os
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from openai import OpenAI
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from app.config import Config
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class AIAnalysisService:
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def __init__(self):
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# 配置OpenAI客户端连接到Volces API
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self.model = "ep-20251113170010-6qdcp" # Volces 模型接入点ID
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self.client = OpenAI(
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api_key = "ec3ebae6-e131-4b1e-a5ae-30f70468e165", # 豆包大模型APIkey
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base_url = "https://ark.cn-beijing.volces.com/api/v3"
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)
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# 创建AI分析结果缓存目录
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self.cache_dir = os.path.join(Config.BASE_DIR, "ai_stock_analysis")
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self.dao_cache_dir = os.path.join(Config.BASE_DIR, "dao_analysis")
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self.daka_cache_dir = os.path.join(Config.BASE_DIR, "daka_analysis")
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# 确保所有缓存目录存在
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for directory in [self.cache_dir, self.dao_cache_dir, self.daka_cache_dir]:
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if not os.path.exists(directory):
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os.makedirs(directory)
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def get_cache_path(self, stock_code: str) -> str:
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"""获取缓存文件路径"""
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return os.path.join(self.cache_dir, f"{stock_code}.json")
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def get_dao_cache_path(self, stock_code: str) -> str:
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"""获取道德经分析缓存文件路径"""
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return os.path.join(self.dao_cache_dir, f"{stock_code}.json")
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def get_daka_cache_path(self, stock_code: str) -> str:
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"""获取大咖分析缓存文件路径"""
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return os.path.join(self.daka_cache_dir, f"{stock_code}.json")
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def load_cache(self, stock_code: str):
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"""加载缓存的AI分析结果"""
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cache_path = self.get_cache_path(stock_code)
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if os.path.exists(cache_path):
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try:
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with open(cache_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except Exception as e:
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print(f"读取AI分析缓存失败: {str(e)}")
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return None
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def save_cache(self, stock_code: str, analysis_result: dict):
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"""保存AI分析结果到缓存"""
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cache_path = self.get_cache_path(stock_code)
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try:
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with open(cache_path, 'w', encoding='utf-8') as f:
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json.dump(analysis_result, f, ensure_ascii=False, indent=4)
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except Exception as e:
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print(f"保存AI分析缓存失败: {str(e)}")
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def load_dao_cache(self, stock_code: str):
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"""加载缓存的道德经分析结果"""
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cache_path = self.get_dao_cache_path(stock_code)
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if os.path.exists(cache_path):
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try:
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with open(cache_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except Exception as e:
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print(f"读取道德经分析缓存失败: {str(e)}")
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return None
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def save_dao_cache(self, stock_code: str, analysis_result: dict):
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"""保存道德经分析结果到缓存"""
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cache_path = self.get_dao_cache_path(stock_code)
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try:
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with open(cache_path, 'w', encoding='utf-8') as f:
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json.dump(analysis_result, f, ensure_ascii=False, indent=4)
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except Exception as e:
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print(f"保存道德经分析缓存失败: {str(e)}")
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def load_daka_cache(self, stock_code: str):
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"""加载缓存的大咖分析结果"""
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cache_path = self.get_daka_cache_path(stock_code)
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if os.path.exists(cache_path):
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try:
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with open(cache_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except Exception as e:
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print(f"读取大咖分析缓存失败: {str(e)}")
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return None
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def save_daka_cache(self, stock_code: str, analysis_result: dict):
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"""保存大咖分析结果到缓存"""
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cache_path = self.get_daka_cache_path(stock_code)
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try:
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with open(cache_path, 'w', encoding='utf-8') as f:
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json.dump(analysis_result, f, ensure_ascii=False, indent=4)
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except Exception as e:
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print(f"保存大咖分析缓存失败: {str(e)}")
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def analyze_value_investment(self, analysis_data: dict, force_refresh: bool = False):
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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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:param force_refresh: 是否强制刷新分析结果
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:return: AI分析结果
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"""
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try:
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stock_code = analysis_data["stock_info"]["code"]
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# 如果不是强制刷新，尝试从缓存加载
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if not force_refresh:
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cached_result = self.load_cache(stock_code)
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if cached_result:
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print(f"从缓存加载AI分析结果: {stock_code}")
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return cached_result
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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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# 保存到缓存
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self.save_cache(stock_code, analysis_result)
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return analysis_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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error_result = {
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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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return error_result
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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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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'))}
|
||
|
||
成长能力指标：
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||
- 净利润增长率：{format_percent(growth.get('net_profit_growth'))}
|
||
- 扣非净利润增长率：{format_percent(growth.get('deducted_net_profit_growth'))}
|
||
- 营收增长率：{format_percent(growth.get('revenue_growth'))}
|
||
|
||
运营能力指标：
|
||
- 总资产周转率：{format_number(operation.get('asset_turnover'))}次/年
|
||
- 存货周转率：{format_number(operation.get('inventory_turnover'))}次/年
|
||
- 应收账款周转率：{format_number(operation.get('receivables_turnover'))}次/年
|
||
|
||
偿债能力指标：
|
||
- 流动比率：{format_number(solvency.get('current_ratio'))}
|
||
- 速动比率：{format_number(solvency.get('quick_ratio'))}
|
||
- 资产负债率：{format_percent(solvency.get('debt_to_assets'))}
|
||
|
||
现金流指标：
|
||
- 经营现金流/营收比：{format_percent(cash_flow.get('ocf_to_revenue'))}
|
||
- 经营现金流同比增长：{format_percent(cash_flow.get('ocf_growth'))}
|
||
|
||
每股指标：
|
||
- 每股收益(EPS)：{format_number(per_share.get('eps'))}元
|
||
- 每股净资产(BPS)：{format_number(per_share.get('bps'))}元
|
||
- 每股现金流(CFPS)：{format_number(per_share.get('cfps'))}元
|
||
- 每股经营现金流(OCFPS)：{format_number(per_share.get('ocfps'))}元
|
||
- 每股未分配利润：{format_number(per_share.get('retained_eps'))}元"""
|
||
|
||
# 构建分析要求部分
|
||
analysis_requirements = """
|
||
请基于以上数据，从价值投资的角度进行分析。请特别注意：
|
||
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
|
||
2. 某些数据可能缺失或异常，分析时需要谨慎对待，或者从东财choice获取
|
||
3. 考虑当前市场环境和行业整体估值水平
|
||
|
||
在给出估值区间时，请充分考虑：
|
||
1. 公司所处行业特点和竞争格局
|
||
2. 公司的竞争优势和市场地位
|
||
3. 当前的盈利能力和成长性
|
||
4. 财务健康状况和风险因素
|
||
5. 宏观经济环境和行业周期
|
||
6. 可比公司的估值水平
|
||
|
||
请以JSON格式返回分析结果，包含以下内容：
|
||
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)
|
||
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
|
||
3. price_analysis: 价格分析，包含合理价格区间和目标市值区间
|
||
实例：
|
||
"price_analysis": {
|
||
"合理价格区间": [
|
||
xxx,
|
||
xxx
|
||
],
|
||
"目标市值区间": [
|
||
xxx,
|
||
xxx
|
||
]
|
||
|
||
请确保返回的是一个有效的JSON格式,不要使用代码块格式，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。"""
|
||
|
||
# 组合完整的提示词
|
||
prompt = data_section + analysis_requirements
|
||
|
||
return prompt
|
||
|
||
def analyze_tao_philosophy(self, company_info: dict, force_refresh: bool = False):
|
||
"""
|
||
基于道德经理念分析公司
|
||
:param company_info: 公司信息
|
||
:param force_refresh: 是否强制刷新分析结果
|
||
:return: AI分析结果
|
||
"""
|
||
try:
|
||
stock_code = company_info.get('basic_info', {}).get('code')
|
||
|
||
# 如果不是强制刷新，尝试从缓存加载
|
||
if not force_refresh and stock_code:
|
||
cached_result = self.load_dao_cache(stock_code)
|
||
if cached_result:
|
||
print(f"从缓存加载道德经分析结果: {stock_code}")
|
||
return cached_result
|
||
|
||
# 构建提示词
|
||
prompt = self._build_tao_analysis_prompt(company_info)
|
||
|
||
# 调用API
|
||
response = self.client.chat.completions.create(
|
||
model=self.model,
|
||
messages=[
|
||
{
|
||
"role": "user",
|
||
"content": prompt
|
||
}
|
||
]
|
||
)
|
||
|
||
# 获取分析结果
|
||
analysis_text = response.choices[0].message.content
|
||
|
||
try:
|
||
# 解析JSON结果
|
||
analysis_result = json.loads(analysis_text)
|
||
|
||
# 保存到缓存
|
||
if stock_code:
|
||
self.save_dao_cache(stock_code, analysis_result)
|
||
|
||
return analysis_result
|
||
except json.JSONDecodeError as e:
|
||
print(f"道德经分析结果JSON解析失败: {str(e)}")
|
||
return {"error": "分析结果格式错误"}
|
||
|
||
except Exception as e:
|
||
print(f"道德经分析失败: {str(e)}")
|
||
return {"error": f"道德经分析失败: {str(e)}"}
|
||
|
||
def _build_tao_analysis_prompt(self, company_info: dict):
|
||
"""
|
||
构建道德经分析提示词
|
||
"""
|
||
basic_info = company_info.get('basic_info', {})
|
||
|
||
prompt = f"""请作为一位精通道德经的智者，运用道德经的智慧来分析{basic_info.get('name', '')}({basic_info.get('code', '')})这家公司。
|
||
|
||
公司基本信息：
|
||
- 公司名称：{basic_info.get('name', '')}
|
||
- 所属行业：{basic_info.get('industry', '')}
|
||
- 主营业务：{basic_info.get('main_business', '')}
|
||
- 经营范围：{basic_info.get('business_scope', '')}
|
||
- 公司简介：{basic_info.get('introduction', '')}
|
||
|
||
请从道德经的智慧角度，分析以下几个方面：
|
||
|
||
1. 道德经视角：
|
||
- 公司的经营理念是否符合"道法自然"的原则
|
||
- 企业的发展是否遵循"无为而治"的智慧
|
||
- 公司是否体现"上善若水"的品质
|
||
- 管理方式是否符合"柔弱胜刚强"的道理
|
||
|
||
2. 企业道德评估：
|
||
- 公司对待员工、客户、供应商的态度
|
||
- 企业的社会责任感和可持续发展理念
|
||
- 公司的价值观和企业文化
|
||
- 经营中的道德风险评估
|
||
|
||
3. 投资建议：
|
||
- 基于道德经智慧的投资建议
|
||
- 长期发展潜力分析
|
||
- 需要关注的风险点
|
||
- 持有建议
|
||
|
||
请以JSON格式返回分析结果，包含以下字段：
|
||
1. tao_philosophy: 道德经视角的分析
|
||
2. business_ethics: 企业道德评估
|
||
3. investment_advice: 投资建议
|
||
|
||
分析要客观、专业、深入，同时体现道德经的智慧。"""
|
||
|
||
return prompt
|
||
|
||
def analyze_by_masters(self, company_info: dict, value_analysis: dict, force_refresh: bool = False):
|
||
"""
|
||
基于各位价值投资大咖的理念分析公司
|
||
:param company_info: 公司信息
|
||
:param value_analysis: 价值分析数据
|
||
:param force_refresh: 是否强制刷新分析结果
|
||
:return: AI分析结果
|
||
"""
|
||
try:
|
||
stock_code = company_info.get('basic_info', {}).get('code')
|
||
|
||
# 如果不是强制刷新，尝试从缓存加载
|
||
if not force_refresh and stock_code:
|
||
cached_result = self.load_daka_cache(stock_code)
|
||
if cached_result:
|
||
print(f"从缓存加载大咖分析结果: {stock_code}")
|
||
return cached_result
|
||
|
||
# 打印输入数据用于调试
|
||
print(f"公司信息: {json.dumps(company_info, ensure_ascii=False, indent=2)}")
|
||
print(f"价值分析数据: {json.dumps(value_analysis, ensure_ascii=False, indent=2)}")
|
||
|
||
# 构建提示词
|
||
prompt = self._build_masters_analysis_prompt(company_info, value_analysis)
|
||
|
||
# 打印提示词用于调试
|
||
print(f"大咖分析提示词: {prompt}")
|
||
|
||
# 调用API
|
||
response = self.client.chat.completions.create(
|
||
model=self.model,
|
||
messages=[
|
||
{
|
||
"role": "user",
|
||
"content": prompt
|
||
}
|
||
]
|
||
)
|
||
|
||
# 获取分析结果
|
||
analysis_text = response.choices[0].message.content
|
||
print(f"AI原始返回结果: {analysis_text}")
|
||
|
||
try:
|
||
# 解析JSON结果
|
||
analysis_result = json.loads(analysis_text)
|
||
print(f"解析后的JSON结果: {json.dumps(analysis_result, ensure_ascii=False, indent=2)}")
|
||
|
||
# 保存到缓存
|
||
if stock_code:
|
||
self.save_daka_cache(stock_code, analysis_result)
|
||
|
||
return analysis_result
|
||
except json.JSONDecodeError as e:
|
||
print(f"大咖分析结果JSON解析失败: {str(e)}")
|
||
return {"error": "分析结果格式错误"}
|
||
|
||
except Exception as e:
|
||
print(f"价值投资大咖分析失败: {str(e)}")
|
||
return {"error": f"价值投资大咖分析失败: {str(e)}"}
|
||
|
||
def _build_masters_analysis_prompt(self, company_info: dict, value_analysis: dict):
|
||
"""
|
||
构建价值投资大咖分析提示词
|
||
"""
|
||
basic_info = company_info.get('basic_info', {})
|
||
|
||
# 从value_analysis中获取财务数据
|
||
valuation = value_analysis.get('valuation', {})
|
||
profitability = value_analysis.get('profitability', {})
|
||
growth = value_analysis.get('growth', {})
|
||
operation = value_analysis.get('operation', {})
|
||
solvency = value_analysis.get('solvency', {})
|
||
cash_flow = value_analysis.get('cash_flow', {})
|
||
per_share = value_analysis.get('per_share', {})
|
||
stock_info = value_analysis.get('stock_info', {})
|
||
|
||
# 格式化百分比
|
||
def format_percent(value):
|
||
if value is None:
|
||
return '-'
|
||
try:
|
||
if isinstance(value, str):
|
||
value = float(value)
|
||
if abs(value) <= 1:
|
||
value = value * 100
|
||
return f"{value:.2f}%"
|
||
except:
|
||
return '-'
|
||
|
||
# 格式化数字
|
||
def format_number(value):
|
||
if value is None:
|
||
return '-'
|
||
try:
|
||
if isinstance(value, str):
|
||
value = float(value)
|
||
return f"{value:.4f}"
|
||
except:
|
||
return '-'
|
||
|
||
prompt = f"""请分别以五位价值投资大咖的视角，分析{basic_info.get('name', '')}({basic_info.get('code', '')})这家公司。
|
||
|
||
公司基本信息：
|
||
- 公司名称：{basic_info.get('name', '')}
|
||
- 所属行业：{basic_info.get('industry', '')}
|
||
- 主营业务：{basic_info.get('main_business', '')}
|
||
- 经营范围：{basic_info.get('business_scope', '')}
|
||
- 公司简介：{basic_info.get('introduction', '')}
|
||
- 法人代表：{basic_info.get('chairman', '')}
|
||
- 总经理：{basic_info.get('manager', '')}
|
||
- 注册资本：{basic_info.get('reg_capital', '')}万元
|
||
- 员工人数：{basic_info.get('employees', '')}人
|
||
- 成立日期：{basic_info.get('setup_date', '')}
|
||
- 上市日期：{basic_info.get('list_date', '')}
|
||
|
||
当前市场信息：
|
||
- 当前股价：{format_number(stock_info.get('current_price'))}元
|
||
- 总市值：{format_number(valuation.get('total_market_value'))}亿元
|
||
- 流通市值：{format_number(valuation.get('circulating_market_value'))}亿元
|
||
- 流通比例：{format_percent(valuation.get('circulating_ratio'))}
|
||
- 换手率：{format_percent(stock_info.get('turnover_ratio'))}
|
||
|
||
估值指标：
|
||
- 市盈率(PE)：{format_number(valuation.get('pe_ratio'))}
|
||
- 市净率(PB)：{format_number(valuation.get('pb_ratio'))}
|
||
- 市销率(PS)：{format_number(valuation.get('ps_ratio'))}
|
||
- 股息率：{format_percent(valuation.get('dividend_yield'))}
|
||
|
||
盈利能力指标：
|
||
- ROE：{format_percent(profitability.get('roe'))}
|
||
- ROE(扣非)：{format_percent(profitability.get('deducted_roe'))}
|
||
- ROA：{format_percent(profitability.get('roa'))}
|
||
- 毛利率：{format_percent(profitability.get('gross_margin'))}
|
||
- 净利率：{format_percent(profitability.get('net_margin'))}
|
||
|
||
成长能力指标：
|
||
- 净利润增长率：{format_percent(growth.get('net_profit_growth'))}
|
||
- 扣非净利润增长率：{format_percent(growth.get('deducted_net_profit_growth'))}
|
||
- 营业总收入增长率：{format_percent(growth.get('revenue_growth'))}
|
||
- 营业收入增长率：{format_percent(growth.get('operating_revenue_growth'))}
|
||
|
||
运营能力指标：
|
||
- 总资产周转率：{format_number(operation.get('asset_turnover'))}
|
||
- 存货周转率：{format_number(operation.get('inventory_turnover'))}
|
||
- 应收账款周转率：{format_number(operation.get('receivables_turnover'))}
|
||
- 流动资产周转率：{format_number(operation.get('current_asset_turnover'))}
|
||
|
||
偿债能力指标：
|
||
- 流动比率：{format_number(solvency.get('current_ratio'))}
|
||
- 速动比率：{format_number(solvency.get('quick_ratio'))}
|
||
- 资产负债率：{format_percent(solvency.get('debt_to_assets'))}
|
||
- 产权比率：{format_number(solvency.get('equity_ratio'))}
|
||
|
||
现金流指标：
|
||
- 经营现金流/营收：{format_percent(cash_flow.get('ocf_to_revenue'))}
|
||
- 经营现金流/经营利润：{format_percent(cash_flow.get('ocf_to_operating_profit'))}
|
||
- 经营现金流同比增长：{format_percent(cash_flow.get('ocf_growth'))}
|
||
|
||
每股指标：
|
||
- 每股收益(EPS)：{format_number(per_share.get('eps'))}元
|
||
- 每股收益(扣非)：{format_number(per_share.get('deducted_eps'))}元
|
||
- 每股净资产：{format_number(per_share.get('bps'))}元
|
||
- 每股经营现金流：{format_number(per_share.get('ocfps'))}元
|
||
- 每股留存收益：{format_number(per_share.get('retained_eps'))}元
|
||
- 每股现金流量：{format_number(per_share.get('cfps'))}元
|
||
- 每股息税前利润：{format_number(per_share.get('ebit_ps'))}元
|
||
|
||
请分别从以下五位投资大师的视角进行分析：
|
||
|
||
1. 巴菲特视角：
|
||
- 是否具有护城河（品牌优势、规模效应、专利技术等）
|
||
- 管理层能力和诚信（从财务指标、现金流等反映的经营能力）
|
||
- 业务是否容易理解（商业模式的清晰度）
|
||
- 长期竞争优势（市场地位、核心竞争力）
|
||
- 是否是好生意（盈利能力、现金流状况）
|
||
- 以合理价格购买优秀企业的原则（估值分析）
|
||
|
||
2. 格雷厄姆视角：
|
||
- 安全边际分析（基于净资产、市盈率等）
|
||
- 内在价值计算（基于盈利能力和资产价值）
|
||
- 财务安全性（偿债能力、资产质量）
|
||
- 是否具有投资价值（基于定量分析）
|
||
- 基于定量分析的结论（综合财务指标评估）
|
||
|
||
3. 林园视角：
|
||
- 行业成长性（收入增长、利润增长）
|
||
- 公司治理结构（股权结构、管理层背景）
|
||
- 研发创新能力（技术优势、产品创新）
|
||
- 市场竞争格局（市场份额、竞争态势）
|
||
- 估值是否合理（相对估值和绝对估值）
|
||
|
||
4. 李大霄视角：
|
||
- 市场地位和品牌价值（行业地位、品牌影响力）
|
||
- 行业发展趋势（产业政策、市场空间）
|
||
- 政策影响分析（行业政策、监管环境）
|
||
- 投资时机把握（技术面和基本面）
|
||
- 投资建议（综合分析结论）
|
||
|
||
5. 段永平视角：
|
||
- 商业模式分析（盈利模式、竞争优势）
|
||
- 用户价值（产品力、客户粘性）
|
||
- 企业文化（管理理念、团队建设）
|
||
- 长期发展潜力（成长空间、持续经营能力）
|
||
- 是否值得长期持有（投资价值判断）
|
||
|
||
请以JSON格式返回分析结果，包含以下字段：
|
||
1. buffett_analysis: 巴菲特的分析观点
|
||
2. graham_analysis: 格雷厄姆的分析观点
|
||
3. lin_yuan_analysis: 林园的分析观点
|
||
4. li_daxiao_analysis: 李大霄的分析观点
|
||
5. duan_yongping_analysis: 段永平的分析观点
|
||
|
||
分析要客观、专业、深入，并体现每位投资大师的独特投资理念。请基于上述详细的财务数据进行分析（如果指标缺失或异常，请联网获取），尤其是定量指标的解读。"""
|
||
|
||
return prompt |