增加更多的历史记录，修正部份API数据获取错误，增加备用API (#5)
* 增加更多的历史记录，修正部份数据获取错误 * 增加更多的历史记录，修正部份API数据获取错误，增加备用API --------- Co-authored-by: bathfire <>
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Eikwang
+90
-75
@@ -125,7 +125,7 @@ class StockDataFetcher:
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print(f"[Akshare] 获取个股详细信息失败: {e}")
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# 如果akshare失败，尝试从tushare获取
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if self.data_source_manager.tushare_available and info['name'] == '未知':
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print(f"[Tushare] 尝试获取基本信息（备用数据源）...")
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print(f"[Tushare] 尝试获取基本信息（tushare）...")
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try:
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ts_code = self.data_source_manager._convert_to_ts_code(symbol)
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df = self.data_source_manager.tushare_api.daily_basic(
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@@ -141,65 +141,65 @@ class StockDataFetcher:
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except Exception as te:
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print(f"[Tushare] ❌ 获取失败: {te}")
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# 方法2: 尝试获取实时价格和涨跌幅（如果网络允许）
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try:
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# 使用更简单的接口获取实时价格
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real_time_data = ak.stock_zh_a_spot_em()
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if real_time_data is not None and not real_time_data.empty:
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stock_real_time = real_time_data[real_time_data['代码'] == symbol]
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if not stock_real_time.empty:
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row = stock_real_time.iloc[0]
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info['current_price'] = row.get('最新价', 'N/A')
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info['change_percent'] = row.get('涨跌幅', 'N/A')
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if info['name'] == '未知':
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info['name'] = row.get('名称', '未知')
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# 方法2: 尝试获取历史价格和涨跌幅（如果网络允许）
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# try:
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# # 使用更简单的接口获取实时价格
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# real_time_data = ak.stock_zh_a_spot_em()
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# if real_time_data is not None and not real_time_data.empty:
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# stock_real_time = real_time_data[real_time_data['代码'] == symbol]
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# if not stock_real_time.empty:
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# row = stock_real_time.iloc[0]
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# info['current_price'] = row.get('最新价', 'N/A')
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# info['change_percent'] = row.get('涨跌幅', 'N/A')
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# if info['name'] == '未知':
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# info['name'] = row.get('名称', '未知')
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# 如果实时数据中有市盈率和市净率，优先使用
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if '市盈率-动态' in row and info['pe_ratio'] == 'N/A':
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try:
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pe_val = row['市盈率-动态']
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if pe_val and pe_val != '-':
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pe_val = float(pe_val)
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if 0 < pe_val <= 1000:
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info['pe_ratio'] = pe_val
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except:
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pass
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# # 如果实时数据中有市盈率和市净率，优先使用
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# if '市盈率-动态' in row and info['pe_ratio'] == 'N/A':
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# try:
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# pe_val = row['市盈率-动态']
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# if pe_val and pe_val != '-':
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# pe_val = float(pe_val)
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# if 0 < pe_val <= 1000:
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# info['pe_ratio'] = pe_val
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# except:
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# pass
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if '市净率' in row and info['pb_ratio'] == 'N/A':
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try:
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pb_val = row['市净率']
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if pb_val and pb_val != '-':
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pb_val = float(pb_val)
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if 0 < pb_val <= 100:
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info['pb_ratio'] = pb_val
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except:
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pass
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# if '市净率' in row and info['pb_ratio'] == 'N/A':
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# try:
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# pb_val = row['市净率']
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# if pb_val and pb_val != '-':
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# pb_val = float(pb_val)
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# if 0 < pb_val <= 100:
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# info['pb_ratio'] = pb_val
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# except:
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# pass
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except Exception as e:
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print(f"[Akshare] 获取实时数据失败: {e}")
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# 如果实时数据获取失败，尝试使用数据源管理器获取历史数据（支持tushare备用）
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try:
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print(f"[数据源管理器] 尝试获取最近交易数据...")
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hist_data = self.data_source_manager.get_stock_hist_data(
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symbol=symbol,
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start_date=(datetime.now() - timedelta(days=5)).strftime('%Y%m%d'),
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end_date=datetime.now().strftime('%Y%m%d'),
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adjust='qfq'
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)
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if hist_data is not None and not hist_data.empty:
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# 标准化列名
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if 'close' in hist_data.columns:
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latest = hist_data.iloc[-1]
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info['current_price'] = latest['close']
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# 计算涨跌幅
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if len(hist_data) > 1:
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prev_close = hist_data.iloc[-2]['close']
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change_pct = ((latest['close'] - prev_close) / prev_close) * 100
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info['change_percent'] = round(change_pct, 2)
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print(f"[数据源管理器] ✅ 成功获取价格数据")
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except Exception as e2:
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print(f"获取历史数据也失败: {e2}")
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# except Exception as e:
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# print(f"[Akshare] 获取实时数据失败: {e}")
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# # 如果实时数据获取失败，尝试使用数据源管理器获取历史数据（支持tushare备用）
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try:
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print(f"[数据源管理器] 尝试获取最近交易数据...")
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hist_data = self.data_source_manager.get_stock_hist_data(
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symbol=symbol,
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start_date=(datetime.now() - timedelta(days=30)).strftime('%Y%m%d'),
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end_date=datetime.now().strftime('%Y%m%d'),
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adjust='qfq'
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)
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if hist_data is not None and not hist_data.empty:
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# 标准化列名
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if 'close' in hist_data.columns:
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latest = hist_data.iloc[-1]
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info['current_price'] = latest['close']
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# 计算涨跌幅
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if len(hist_data) > 1:
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prev_close = hist_data.iloc[-2]['close']
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change_pct = ((latest['close'] - prev_close) / prev_close) * 100
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info['change_percent'] = round(change_pct, 2)
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print(f"[数据源管理器] ✅ 成功获取价格数据")
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except Exception as e2:
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print(f"获取历史数据也失败: {e2}")
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# 方法3: 使用百度估值数据获取市盈率和市净率
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if info['pe_ratio'] == 'N/A':
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@@ -638,25 +638,40 @@ class StockDataFetcher:
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# 4. 获取主要财务指标
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try:
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financial_indicators = ak.stock_financial_analysis_indicator(symbol=symbol)
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if financial_indicators is not None and not financial_indicators.empty:
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latest_data = financial_indicators.iloc[0]
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financial_abstract = ak.stock_financial_abstract(symbol=symbol)
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if financial_abstract is not None and not financial_abstract.empty:
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# 提取关键财务指标
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key_indicators = [
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'净资产收益率(ROE)', '总资产报酬率(ROA)', '销售毛利率', '销售净利率',
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'资产负债率', '流动比率', '速动比率', '存货周转率', '应收账款周转率',
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'总资产周转率', '营业收入同比增长', '净利润同比增长'
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]
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financial_data["financial_ratios"] = {
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"报告期": latest_data.get('报告期', 'N/A'),
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"净资产收益率ROE": latest_data.get('净资产收益率', 'N/A'),
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"总资产收益率ROA": latest_data.get('总资产收益率', 'N/A'),
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"销售毛利率": latest_data.get('销售毛利率', 'N/A'),
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"销售净利率": latest_data.get('销售净利率', 'N/A'),
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"资产负债率": latest_data.get('资产负债率', 'N/A'),
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"流动比率": latest_data.get('流动比率', 'N/A'),
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"速动比率": latest_data.get('速动比率', 'N/A'),
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"存货周转率": latest_data.get('存货周转率', 'N/A'),
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"应收账款周转率": latest_data.get('应收账款周转率', 'N/A'),
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"总资产周转率": latest_data.get('总资产周转率', 'N/A'),
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"营业收入同比增长": latest_data.get('营业收入同比增长', 'N/A'),
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"净利润同比增长": latest_data.get('净利润同比增长', 'N/A'),
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}
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# 筛选出包含关键指标的行
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indicator_rows = financial_abstract[financial_abstract['指标'].isin(key_indicators)]
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if not indicator_rows.empty:
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# 获取最新的报告期数据（第一列日期）
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date_columns = [col for col in financial_abstract.columns if col not in ['选项', '指标']]
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if date_columns:
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latest_date = date_columns[0] # 最新日期列
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# 构建财务比率字典
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financial_ratios = {"报告期": latest_date}
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# 提取每个指标的最新值
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for _, row in indicator_rows.iterrows():
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indicator_name = row['指标']
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value = row.get(latest_date, 'N/A')
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if value is not None and not (isinstance(value, float) and pd.isna(value)):
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try:
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financial_ratios[indicator_name] = str(value)
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except:
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financial_ratios[indicator_name] = "N/A"
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else:
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financial_ratios[indicator_name] = "N/A"
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financial_data["financial_ratios"] = financial_ratios
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except Exception as e:
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print(f"获取财务指标失败: {e}")
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