587 lines
30 KiB
Python
587 lines
30 KiB
Python
import json
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import os
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from datetime import datetime
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import pandas as pd
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from app import pro
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from app.config import Config
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import numpy as np
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class StockService:
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def __init__(self):
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self.watchlist = {}
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self.cache_file = os.path.join(Config.BASE_DIR, "stock_cache.json")
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self.load_watchlist()
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self.load_cache()
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def load_watchlist(self):
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try:
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if os.path.exists(Config.CONFIG_FILE):
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with open(Config.CONFIG_FILE, 'r', encoding='utf-8') as f:
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data = json.load(f)
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self.watchlist = data.get('watchlist', {})
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except Exception as e:
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print(f"Error loading watchlist: {str(e)}")
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self.watchlist = {}
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def _save_watchlist(self):
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try:
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with open(Config.CONFIG_FILE, 'w', encoding='utf-8') as f:
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json.dump({'watchlist': self.watchlist}, f, ensure_ascii=False, indent=4)
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except Exception as e:
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print(f"Error saving watchlist: {str(e)}")
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def load_cache(self):
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try:
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if os.path.exists(self.cache_file):
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with open(self.cache_file, 'r', encoding='utf-8') as f:
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self.cache_data = json.load(f)
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else:
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self.cache_data = {}
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except Exception as e:
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print(f"Error loading cache: {str(e)}")
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self.cache_data = {}
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def save_cache(self, stock_code, data):
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try:
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self.cache_data[stock_code] = {
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'data': data,
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'timestamp': datetime.now().strftime('%Y-%m-%d')
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}
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with open(self.cache_file, 'w', encoding='utf-8') as f:
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json.dump(self.cache_data, f, ensure_ascii=False, indent=4)
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except Exception as e:
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print(f"Error saving cache: {str(e)}")
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def get_stock_info(self, stock_code: str, force_refresh: bool = False):
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try:
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# 检查缓存
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today = datetime.now().strftime('%Y-%m-%d')
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if not force_refresh and stock_code in self.cache_data and self.cache_data[stock_code]['timestamp'] == today:
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print(f"从缓存获取股票 {stock_code} 的数据")
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cached_data = self.cache_data[stock_code]['data']
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cached_data['stock_info']['from_cache'] = True
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return cached_data
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# 如果强制刷新或缓存不存在或已过期，从API获取数据
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print(f"从API获取股票 {stock_code} 的数据...")
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# 处理股票代码格式
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if len(stock_code) != 6:
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return {"error": "股票代码格式错误"}
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# 确定交易所
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if stock_code.startswith('6'):
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ts_code = f"{stock_code}.SH"
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elif stock_code.startswith(('0', '3')):
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ts_code = f"{stock_code}.SZ"
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else:
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return {"error": "不支持的股票代码"}
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# 获取基本信息和总市值
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basic_info = pro.daily_basic(ts_code=ts_code, fields='ts_code,total_mv', limit=1)
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if basic_info.empty:
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return {"error": "股票代码不存在"}
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# 获取股票名称
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stock_name = pro.stock_basic(ts_code=ts_code, fields='name').iloc[0]['name']
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# 获取最新财务指标
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fina_indicator = pro.fina_indicator(ts_code=ts_code, period=datetime.now().strftime('%Y%m%d'), fields='roe,grossprofit_margin,netprofit_margin,debt_to_assets,op_income_yoy,netprofit_yoy,bps,ocfps')
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if fina_indicator.empty:
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fina_indicator = pro.fina_indicator(ts_code=ts_code, limit=1)
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# 获取实时行情
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today = datetime.now().strftime('%Y%m%d')
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daily_data = pro.daily(ts_code=basic_info['ts_code'].iloc[0], start_date=today, end_date=today)
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if daily_data.empty:
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daily_data = pro.daily(ts_code=basic_info['ts_code'].iloc[0], limit=1)
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if daily_data.empty:
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return {"error": "无法获取股票行情数据"}
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# 获取市值信息（用于其他指标）
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daily_basic = pro.daily_basic(ts_code=basic_info['ts_code'].iloc[0],
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fields='ts_code,trade_date,pe,pb,ps,dv_ratio',
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limit=1)
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if daily_basic.empty:
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return {"error": "无法获取股票基础数据"}
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latest_basic = daily_basic.iloc[0]
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latest_fina = fina_indicator.iloc[0] if not fina_indicator.empty else pd.Series()
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# 计算实时总市值（单位：亿元）
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current_price = float(daily_data['close'].iloc[0])
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market_value = float(basic_info['total_mv'].iloc[0]) / 10000 # 转换为亿元
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print(f"市值计算: 当前价格={current_price}, 总市值={market_value}亿元")
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# 处理股息率：tushare返回的是百分比值，需要转换为小数
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dv_ratio = float(latest_basic['dv_ratio']) if pd.notna(latest_basic['dv_ratio']) else 0
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dividend_yield = round(dv_ratio / 100, 4) # 转换为小数
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# 处理财务指标，确保所有值都有默认值0，转换为小数
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roe = round(float(latest_fina['roe']) / 100, 4) if pd.notna(latest_fina.get('roe')) else 0
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gross_profit_margin = round(float(latest_fina['grossprofit_margin']) / 100, 4) if pd.notna(latest_fina.get('grossprofit_margin')) else 0
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net_profit_margin = round(float(latest_fina['netprofit_margin']) / 100, 4) if pd.notna(latest_fina.get('netprofit_margin')) else 0
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debt_to_assets = round(float(latest_fina['debt_to_assets']) / 100, 4) if pd.notna(latest_fina.get('debt_to_assets')) else 0
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revenue_yoy = round(float(latest_fina['op_income_yoy']) / 100, 4) if pd.notna(latest_fina.get('op_income_yoy')) else 0
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net_profit_yoy = round(float(latest_fina['netprofit_yoy']) / 100, 4) if pd.notna(latest_fina.get('netprofit_yoy')) else 0
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bps = round(float(latest_fina['bps']), 3) if pd.notna(latest_fina.get('bps')) else 0 # 保留3位小数
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ocfps = round(float(latest_fina['ocfps']), 3) if pd.notna(latest_fina.get('ocfps')) else 0 # 保留3位小数
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stock_info = {
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"code": stock_code,
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"name": stock_name,
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"market_value": round(market_value, 2), # 总市值（亿元）
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"pe_ratio": round(float(latest_basic['pe']), 2) if pd.notna(latest_basic['pe']) else 0, # 市盈率
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"pb_ratio": round(float(latest_basic['pb']), 2) if pd.notna(latest_basic['pb']) else 0, # 市净率
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"ps_ratio": round(float(latest_basic['ps']), 2) if pd.notna(latest_basic['ps']) else 0, # 市销率
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"dividend_yield": dividend_yield, # 股息率（小数）
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"price": round(current_price, 2), # 股价保留2位小数
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"change_percent": round(float(daily_data['pct_chg'].iloc[0]) / 100, 4), # 涨跌幅转换为小数
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# 财务指标（全部转换为小数）
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"roe": roe, # ROE（小数）
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"gross_profit_margin": gross_profit_margin, # 毛利率（小数）
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"net_profit_margin": net_profit_margin, # 净利率（小数）
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"debt_to_assets": debt_to_assets, # 资产负债率（小数）
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"revenue_yoy": revenue_yoy, # 营收增长率（小数）
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"net_profit_yoy": net_profit_yoy, # 净利润增长率（小数）
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"bps": bps, # 每股净资产
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"ocfps": ocfps, # 每股经营现金流
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"from_cache": False
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}
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# 获取目标值
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targets = self.watchlist.get(stock_code, {})
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result = {
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"stock_info": stock_info,
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"targets": targets
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}
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# 保存到缓存
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self.save_cache(stock_code, result)
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return result
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except Exception as e:
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print(f"Error fetching stock info: {str(e)}")
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import traceback
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print(f"详细错误: {traceback.format_exc()}")
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return {"error": f"获取股票数据失败: {str(e)}"}
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def get_watchlist(self):
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result = []
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for stock_code, targets in self.watchlist.items():
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try:
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# 从缓存获取数据
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today = datetime.now().strftime('%Y-%m-%d')
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if stock_code in self.cache_data and self.cache_data[stock_code]['timestamp'] == today:
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result.append(self.cache_data[stock_code]['data'])
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continue
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# 如果没有缓存，只获取基本信息
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if stock_code.startswith('6'):
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ts_code = f"{stock_code}.SH"
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elif stock_code.startswith(('0', '3')):
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ts_code = f"{stock_code}.SZ"
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else:
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print(f"不支持的股票代码: {stock_code}")
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continue
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# 获取股票名称
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stock_name = pro.stock_basic(ts_code=ts_code, fields='name').iloc[0]['name']
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result.append({
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"stock_info": {
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"code": stock_code,
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"name": stock_name
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},
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"targets": targets
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})
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except Exception as e:
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print(f"Error getting watchlist info for {stock_code}: {str(e)}")
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continue
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return result
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def add_watch(self, stock_code: str, target_market_value_min: float = None, target_market_value_max: float = None):
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self.watchlist[stock_code] = {
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"target_market_value": {
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"min": target_market_value_min,
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"max": target_market_value_max
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}
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}
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self._save_watchlist()
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return {"status": "success"}
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def remove_watch(self, stock_code: str):
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if stock_code in self.watchlist:
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del self.watchlist[stock_code]
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# 同时删除缓存
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if stock_code in self.cache_data:
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del self.cache_data[stock_code]
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try:
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with open(self.cache_file, 'w', encoding='utf-8') as f:
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json.dump(self.cache_data, f, ensure_ascii=False, indent=4)
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except Exception as e:
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print(f"Error saving cache after removal: {str(e)}")
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self._save_watchlist()
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return {"status": "success"}
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def update_target(self, stock_code: str, target_market_value_min: float = None, target_market_value_max: float = None):
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"""更新股票的目标市值"""
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if stock_code not in self.watchlist:
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return {"error": "股票不在监控列表中"}
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self.watchlist[stock_code] = {
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"target_market_value": {
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"min": target_market_value_min,
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"max": target_market_value_max
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}
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}
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self._save_watchlist()
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return {"status": "success"}
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def get_index_info(self):
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"""获取主要指数数据"""
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try:
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# 主要指数代码列表
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index_codes = {
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'000001.SH': '上证指数',
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'399001.SZ': '深证成指',
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'399006.SZ': '创业板指',
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'000016.SH': '上证50',
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'000300.SH': '沪深300',
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'000905.SH': '中证500',
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'000852.SH': '中证1000',
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'899050.BJ': '北证50',
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}
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result = []
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for ts_code, name in index_codes.items():
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try:
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# 获取指数基本信息
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df = pro.index_daily(ts_code=ts_code, limit=1)
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if not df.empty:
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data = df.iloc[0]
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# 获取K线数据(最近20天)
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kline_df = pro.index_daily(ts_code=ts_code, limit=20)
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kline_data = []
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if not kline_df.empty:
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for _, row in kline_df.iterrows():
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kline_data.append({
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'date': row['trade_date'],
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'open': float(row['open']),
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'close': float(row['close']),
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'high': float(row['high']),
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'low': float(row['low']),
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'vol': float(row['vol'])
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})
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result.append({
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'code': ts_code,
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'name': name,
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'price': float(data['close']),
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'change': float(data['pct_chg']),
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'kline_data': kline_data
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})
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except Exception as e:
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print(f"获取指数 {ts_code} 数据失败: {str(e)}")
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continue
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return result
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except Exception as e:
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print(f"获取指数数据失败: {str(e)}")
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return []
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def get_company_detail(self, stock_code: str):
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try:
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print(f"开始获取公司详情: {stock_code}")
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# 处理股票代码格式
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if stock_code.startswith('6'):
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ts_code = f"{stock_code}.SH"
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elif stock_code.startswith(('0', '3')):
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ts_code = f"{stock_code}.SZ"
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else:
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print(f"不支持的股票代码格式: {stock_code}")
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return {"error": "不支持的股票代码"}
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print(f"转换后的ts_code: {ts_code}")
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# 获取公司基本信息
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basic = pro.stock_basic(ts_code=ts_code, fields='name,industry,area,list_date')
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if basic.empty:
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print(f"无法获取公司基本信息: {ts_code}")
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return {"error": "无法获取公司信息"}
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company_info = basic.iloc[0]
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print(f"获取到的公司基本信息: {company_info.to_dict()}")
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# 获取公司详细信息
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try:
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company_detail = pro.stock_company(ts_code=ts_code)
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if not company_detail.empty:
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detail_info = company_detail.iloc[0]
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company_detail_dict = {
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"com_name": str(detail_info.get('com_name', '')),
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"chairman": str(detail_info.get('chairman', '')),
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"manager": str(detail_info.get('manager', '')),
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"secretary": str(detail_info.get('secretary', '')),
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"reg_capital": float(detail_info.get('reg_capital', 0)) if pd.notna(detail_info.get('reg_capital')) else 0,
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"setup_date": str(detail_info.get('setup_date', '')),
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"province": str(detail_info.get('province', '')),
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"city": str(detail_info.get('city', '')),
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"introduction": str(detail_info.get('introduction', '')),
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"website": f"http://{str(detail_info.get('website', '')).strip('http://').strip('https://')}" if detail_info.get('website') else "",
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"email": str(detail_info.get('email', '')),
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"office": str(detail_info.get('office', '')),
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"employees": int(detail_info.get('employees', 0)) if pd.notna(detail_info.get('employees')) else 0,
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"main_business": str(detail_info.get('main_business', '')),
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"business_scope": str(detail_info.get('business_scope', ''))
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}
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else:
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company_detail_dict = {
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"com_name": "", "chairman": "", "manager": "", "secretary": "",
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"reg_capital": 0, "setup_date": "", "province": "", "city": "",
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"introduction": "", "website": "", "email": "", "office": "",
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"employees": 0, "main_business": "", "business_scope": ""
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}
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except Exception as e:
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print(f"获取公司详细信息失败: {str(e)}")
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company_detail_dict = {
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"com_name": "", "chairman": "", "manager": "", "secretary": "",
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"reg_capital": 0, "setup_date": "", "province": "", "city": "",
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"introduction": "", "website": "", "email": "", "office": "",
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"employees": 0, "main_business": "", "business_scope": ""
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}
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# 获取最新财务指标
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try:
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fina = pro.fina_indicator(ts_code=ts_code, period=datetime.now().strftime('%Y%m%d'))
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if fina.empty:
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print("当前期间无财务数据，尝试获取最新一期数据")
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fina = pro.fina_indicator(ts_code=ts_code, limit=1)
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if fina.empty:
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print(f"无法获取财务指标数据: {ts_code}")
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return {"error": "无法获取财务数据"}
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fina_info = fina.iloc[0]
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print(f"获取到的财务指标: {fina_info.to_dict()}")
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except Exception as e:
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print(f"获取财务指标失败: {str(e)}")
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return {"error": "获取财务指标失败"}
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# 获取市值信息（用于PE、PB等指标）
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try:
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daily_basic = pro.daily_basic(ts_code=ts_code, fields='pe,pb,ps,dv_ratio', limit=1)
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if not daily_basic.empty:
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latest_basic = daily_basic.iloc[0]
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else:
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print("无法获取PE/PB数据")
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latest_basic = pd.Series({'pe': 0, 'pb': 0, 'ps': 0, 'dv_ratio': 0})
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except Exception as e:
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print(f"获取PE/PB失败: {str(e)}")
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latest_basic = pd.Series({'pe': 0, 'pb': 0, 'ps': 0, 'dv_ratio': 0})
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result = {
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"basic_info": {
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"name": str(company_info['name']),
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"industry": str(company_info['industry']),
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"list_date": str(company_info['list_date']),
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"area": str(company_info['area']),
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**company_detail_dict
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},
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"financial_info": {
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# 估值指标
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"pe_ratio": float(latest_basic['pe']) if pd.notna(latest_basic['pe']) else 0,
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"pb_ratio": float(latest_basic['pb']) if pd.notna(latest_basic['pb']) else 0,
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"ps_ratio": float(latest_basic['ps']) if pd.notna(latest_basic['ps']) else 0,
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"dividend_yield": float(latest_basic['dv_ratio'])/100 if pd.notna(latest_basic['dv_ratio']) else 0,
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# 盈利能力
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"roe": float(fina_info['roe']) if pd.notna(fina_info.get('roe')) else 0,
|
||
"roe_dt": float(fina_info['roe_dt']) if pd.notna(fina_info.get('roe_dt')) else 0,
|
||
"roa": float(fina_info['roa']) if pd.notna(fina_info.get('roa')) else 0,
|
||
"grossprofit_margin": float(fina_info['grossprofit_margin']) if pd.notna(fina_info.get('grossprofit_margin')) else 0,
|
||
"netprofit_margin": float(fina_info['netprofit_margin']) if pd.notna(fina_info.get('netprofit_margin')) else 0,
|
||
|
||
# 成长能力
|
||
"netprofit_yoy": float(fina_info['netprofit_yoy']) if pd.notna(fina_info.get('netprofit_yoy')) else 0,
|
||
"dt_netprofit_yoy": float(fina_info['dt_netprofit_yoy']) if pd.notna(fina_info.get('dt_netprofit_yoy')) else 0,
|
||
"tr_yoy": float(fina_info['tr_yoy']) if pd.notna(fina_info.get('tr_yoy')) else 0,
|
||
"or_yoy": float(fina_info['or_yoy']) if pd.notna(fina_info.get('or_yoy')) else 0,
|
||
|
||
# 营运能力
|
||
"assets_turn": float(fina_info['assets_turn']) if pd.notna(fina_info.get('assets_turn')) else 0,
|
||
"inv_turn": float(fina_info['inv_turn']) if pd.notna(fina_info.get('inv_turn')) else 0,
|
||
"ar_turn": float(fina_info['ar_turn']) if pd.notna(fina_info.get('ar_turn')) else 0,
|
||
"ca_turn": float(fina_info['ca_turn']) if pd.notna(fina_info.get('ca_turn')) else 0,
|
||
|
||
# 偿债能力
|
||
"current_ratio": float(fina_info['current_ratio']) if pd.notna(fina_info.get('current_ratio')) else 0,
|
||
"quick_ratio": float(fina_info['quick_ratio']) if pd.notna(fina_info.get('quick_ratio')) else 0,
|
||
"debt_to_assets": float(fina_info['debt_to_assets']) if pd.notna(fina_info.get('debt_to_assets')) else 0,
|
||
"debt_to_eqt": float(fina_info['debt_to_eqt']) if pd.notna(fina_info.get('debt_to_eqt')) else 0,
|
||
|
||
# 现金流
|
||
"ocf_to_or": float(fina_info['ocf_to_or']) if pd.notna(fina_info.get('ocf_to_or')) else 0,
|
||
"ocf_to_opincome": float(fina_info['ocf_to_opincome']) if pd.notna(fina_info.get('ocf_to_opincome')) else 0,
|
||
"ocf_yoy": float(fina_info['ocf_yoy']) if pd.notna(fina_info.get('ocf_yoy')) else 0,
|
||
|
||
# 每股指标
|
||
"eps": float(fina_info['eps']) if pd.notna(fina_info.get('eps')) else 0,
|
||
"dt_eps": float(fina_info['dt_eps']) if pd.notna(fina_info.get('dt_eps')) else 0,
|
||
"bps": float(fina_info['bps']) if pd.notna(fina_info.get('bps')) else 0,
|
||
"ocfps": float(fina_info['ocfps']) if pd.notna(fina_info.get('ocfps')) else 0,
|
||
"retainedps": float(fina_info['retainedps']) if pd.notna(fina_info.get('retainedps')) else 0,
|
||
"cfps": float(fina_info['cfps']) if pd.notna(fina_info.get('cfps')) else 0,
|
||
"ebit_ps": float(fina_info['ebit_ps']) if pd.notna(fina_info.get('ebit_ps')) else 0,
|
||
"fcff_ps": float(fina_info['fcff_ps']) if pd.notna(fina_info.get('fcff_ps')) else 0,
|
||
"fcfe_ps": float(fina_info['fcfe_ps']) if pd.notna(fina_info.get('fcfe_ps')) else 0
|
||
}
|
||
}
|
||
|
||
print(f"返回结果: {result}")
|
||
return result
|
||
|
||
except Exception as e:
|
||
print(f"Error getting company detail: {str(e)}")
|
||
import traceback
|
||
print(f"详细错误: {traceback.format_exc()}")
|
||
return {"error": f"获取公司详情失败: {str(e)}"}
|
||
|
||
def get_top_holders(self, stock_code: str):
|
||
"""获取前十大股东数据"""
|
||
try:
|
||
# 处理股票代码格式
|
||
if stock_code.startswith('6'):
|
||
ts_code = f"{stock_code}.SH"
|
||
elif stock_code.startswith(('0', '3')):
|
||
ts_code = f"{stock_code}.SZ"
|
||
else:
|
||
return {"error": "不支持的股票代码"}
|
||
|
||
# 获取最新一期的股东数据
|
||
df = pro.top10_holders(ts_code=ts_code, limit=10)
|
||
if df.empty:
|
||
return {"error": "暂无股东数据"}
|
||
|
||
# 按持股比例降序排序
|
||
df = df.sort_values('hold_ratio', ascending=False)
|
||
|
||
# 获取最新的报告期
|
||
latest_end_date = df['end_date'].max()
|
||
latest_data = df[df['end_date'] == latest_end_date]
|
||
|
||
holders = []
|
||
for _, row in latest_data.iterrows():
|
||
holders.append({
|
||
"holder_name": str(row['holder_name']),
|
||
"hold_amount": float(row['hold_amount']) if pd.notna(row['hold_amount']) else 0,
|
||
"hold_ratio": float(row['hold_ratio']) if pd.notna(row['hold_ratio']) else 0,
|
||
"hold_change": float(row['hold_change']) if pd.notna(row['hold_change']) else 0,
|
||
"ann_date": str(row['ann_date']),
|
||
"end_date": str(row['end_date'])
|
||
})
|
||
|
||
result = {
|
||
"holders": holders,
|
||
"total_ratio": sum(holder['hold_ratio'] for holder in holders), # 合计持股比例
|
||
"report_date": str(latest_end_date) # 报告期
|
||
}
|
||
|
||
return result
|
||
|
||
except Exception as e:
|
||
print(f"获取股东数据失败: {str(e)}")
|
||
import traceback
|
||
print(f"详细错误: {traceback.format_exc()}")
|
||
return {"error": f"获取股东数据失败: {str(e)}"}
|
||
|
||
def get_value_analysis_data(self, stock_code: str):
|
||
"""获取价值投资分析所需的关键财务指标"""
|
||
try:
|
||
# 处理股票代码格式
|
||
if stock_code.startswith('6'):
|
||
ts_code = f"{stock_code}.SH"
|
||
elif stock_code.startswith(('0', '3')):
|
||
ts_code = f"{stock_code}.SZ"
|
||
else:
|
||
return {"error": "不支持的股票代码"}
|
||
|
||
# 获取最新每日指标（估值数据）
|
||
daily_basic = pro.daily_basic(ts_code=ts_code, fields='pe,pb,ps,dv_ratio,total_mv', limit=1)
|
||
if daily_basic.empty:
|
||
return {"error": "无法获取股票估值数据"}
|
||
|
||
# 获取最新财务指标
|
||
fina = pro.fina_indicator(ts_code=ts_code, fields='''roe,grossprofit_margin,netprofit_margin,
|
||
netprofit_yoy,dt_netprofit_yoy,tr_yoy,or_yoy,assets_turn,inv_turn,ar_turn,current_ratio,
|
||
quick_ratio,debt_to_assets,ocf_to_or,ocf_yoy,eps,bps,cfps,ocfps,retainedps''', limit=1)
|
||
if fina.empty:
|
||
return {"error": "无法获取财务指标数据"}
|
||
|
||
# 获取股票名称和当前价格
|
||
basic_info = pro.daily(ts_code=ts_code, fields='close,trade_date', limit=1)
|
||
stock_name = pro.stock_basic(ts_code=ts_code, fields='name').iloc[0]['name']
|
||
|
||
# 整合数据
|
||
latest_daily = daily_basic.iloc[0]
|
||
latest_fina = fina.iloc[0]
|
||
latest_price = basic_info.iloc[0]
|
||
|
||
analysis_data = {
|
||
"stock_info": {
|
||
"code": stock_code,
|
||
"name": stock_name,
|
||
"current_price": float(latest_price['close']),
|
||
"trade_date": str(latest_price['trade_date'])
|
||
},
|
||
"valuation": {
|
||
"pe_ratio": float(latest_daily['pe']) if pd.notna(latest_daily['pe']) else None,
|
||
"pb_ratio": float(latest_daily['pb']) if pd.notna(latest_daily['pb']) else None,
|
||
"ps_ratio": float(latest_daily['ps']) if pd.notna(latest_daily['ps']) else None,
|
||
"dividend_yield": float(latest_daily['dv_ratio'])/100 if pd.notna(latest_daily['dv_ratio']) else None,
|
||
"total_market_value": float(latest_daily['total_mv'])/10000 if pd.notna(latest_daily['total_mv']) else None # 转换为亿元
|
||
},
|
||
"profitability": {
|
||
"roe": float(latest_fina['roe'])/100 if pd.notna(latest_fina['roe']) else None,
|
||
"gross_margin": float(latest_fina['grossprofit_margin'])/100 if pd.notna(latest_fina['grossprofit_margin']) else None,
|
||
"net_margin": float(latest_fina['netprofit_margin'])/100 if pd.notna(latest_fina['netprofit_margin']) else None
|
||
},
|
||
"growth": {
|
||
"net_profit_growth": float(latest_fina['netprofit_yoy'])/100 if pd.notna(latest_fina['netprofit_yoy']) else None,
|
||
"deducted_net_profit_growth": float(latest_fina['dt_netprofit_yoy'])/100 if pd.notna(latest_fina['dt_netprofit_yoy']) else None,
|
||
"revenue_growth": float(latest_fina['tr_yoy'])/100 if pd.notna(latest_fina['tr_yoy']) else None,
|
||
"operating_revenue_growth": float(latest_fina['or_yoy'])/100 if pd.notna(latest_fina['or_yoy']) else None
|
||
},
|
||
"operation": {
|
||
"asset_turnover": float(latest_fina['assets_turn']) if pd.notna(latest_fina['assets_turn']) else None,
|
||
"inventory_turnover": float(latest_fina['inv_turn']) if pd.notna(latest_fina['inv_turn']) else None,
|
||
"receivables_turnover": float(latest_fina['ar_turn']) if pd.notna(latest_fina['ar_turn']) else None
|
||
},
|
||
"solvency": {
|
||
"current_ratio": float(latest_fina['current_ratio']) if pd.notna(latest_fina['current_ratio']) else None,
|
||
"quick_ratio": float(latest_fina['quick_ratio']) if pd.notna(latest_fina['quick_ratio']) else None,
|
||
"debt_to_assets": float(latest_fina['debt_to_assets'])/100 if pd.notna(latest_fina['debt_to_assets']) else None
|
||
},
|
||
"cash_flow": {
|
||
"ocf_to_revenue": float(latest_fina['ocf_to_or'])/100 if pd.notna(latest_fina['ocf_to_or']) else None,
|
||
"ocf_growth": float(latest_fina['ocf_yoy'])/100 if pd.notna(latest_fina['ocf_yoy']) else None
|
||
},
|
||
"per_share": {
|
||
"eps": float(latest_fina['eps']) if pd.notna(latest_fina['eps']) else None,
|
||
"bps": float(latest_fina['bps']) if pd.notna(latest_fina['bps']) else None,
|
||
"cfps": float(latest_fina['cfps']) if pd.notna(latest_fina['cfps']) else None,
|
||
"ocfps": float(latest_fina['ocfps']) if pd.notna(latest_fina['ocfps']) else None,
|
||
"retained_eps": float(latest_fina['retainedps']) if pd.notna(latest_fina['retainedps']) else None
|
||
}
|
||
}
|
||
|
||
return analysis_data
|
||
|
||
except Exception as e:
|
||
print(f"获取价值投资分析数据失败: {str(e)}")
|
||
import traceback
|
||
print(f"详细错误: {traceback.format_exc()}")
|
||
return {"error": f"获取价值投资分析数据失败: {str(e)}"} |