财务报表介绍

OpenBB 平台数据扩展提供对季度或年度财务报表的访问。

还有用于比率和其他常见非 GAAP 指标的端点。

大多数数据提供商需要订阅才能访问所有数据，请参考特定提供商的网站了解权限和覆盖范围的详细信息。

财务报表函数归类在 obb.equity.fundamental 模块下。

信息

首先，将 OpenBB 平台导入 Python 会话：

from openbb import obb

财务报表

典型的财务报表包含三个端点：

• 资产负债表：obb.equity.fundamental.balance()
• 损益表：obb.equity.fundamental.income()
• 现金流量表：obb.equity.fundamental.cash()

主要参数包括：

• symbol：公司的股票代码
• period：'annual'（年度）或 'quarter'（季度）。默认为 'annual'
• limit：限制返回的结果数量，从最新开始。默认为 5。从历史角度看，150 可以追溯到 1985 年。历史记录的数量因提供商而异

字段名称

信息

• 每个数据提供商都有自己解析和组织三大财务报表的方式
• 每个报表中的项目因数据源和报告公司类型而异
• 行项目的名称因数据源而异
• "日期"值可能不同，因为它们来自期间开始/结束或报告日期

此示例突出显示了不同提供商对公司事实的不同标签。

import pandas as pd

# 比较不同提供商的资产负债表数据
df = pd.DataFrame()

# 获取 yfinance 提供商的数据
yfinance_data = obb.equity.fundamental.balance("TGT", provider="yfinance", limit=3)
df["yfinance"] = yfinance_data.to_dataframe().columns.tolist()

# 获取其他提供商的数据进行比较
try:
    fmp_data = obb.equity.fundamental.balance("TGT", provider="fmp", limit=3)
    df["fmp"] = fmp_data.to_dataframe().columns.tolist()
except:
    print("FMP 提供商不可用")

print("不同提供商的字段名称对比:")
print(df.head(10))

基本财务报表获取

资产负债表

# 获取年度资产负债表
balance_sheet = obb.equity.fundamental.balance(
    symbol="AAPL", 
    period="annual", 
    limit=5,
    provider="yfinance"
)

df_balance = balance_sheet.to_dataframe()
print("苹果公司资产负债表:")
print(df_balance.head())

# 查看主要资产项目
if 'total_assets' in df_balance.columns:
    print(f"\n总资产趋势:")
    print(df_balance[['period_ending', 'total_assets']].head())

损益表

# 获取季度损益表
income_statement = obb.equity.fundamental.income(
    symbol="MSFT",
    period="quarter",
    limit=8,  # 获取最近8个季度
    provider="yfinance"
)

df_income = income_statement.to_dataframe()
print("微软公司损益表:")
print(df_income.head())

# 分析收入趋势
if 'total_revenue' in df_income.columns:
    print(f"\n收入趋势:")
    print(df_income[['period_ending', 'total_revenue']].head())

现金流量表

# 获取现金流量表
cash_flow = obb.equity.fundamental.cash(
    symbol="GOOGL",
    period="annual",
    limit=5,
    provider="yfinance"
)

df_cash = cash_flow.to_dataframe()
print("谷歌现金流量表:")
print(df_cash.head())

# 查看经营现金流
if 'operating_cash_flow' in df_cash.columns:
    print(f"\n经营现金流趋势:")
    print(df_cash[['period_ending', 'operating_cash_flow']].head())

财务比率分析

def analyze_financial_ratios(symbol, provider="yfinance"):
    """
    分析公司的财务比率
    """
    try:
        # 获取最新的财务数据
        balance = obb.equity.fundamental.balance(
            symbol=symbol, 
            period="annual", 
            limit=1, 
            provider=provider
        ).to_dataframe()
        
        income = obb.equity.fundamental.income(
            symbol=symbol, 
            period="annual", 
            limit=1, 
            provider=provider
        ).to_dataframe()
        
        if balance.empty or income.empty:
            print(f"无法获取 {symbol} 的财务数据")
            return None
        
        # 计算基本比率
        ratios = {}
        
        # 流动比率 = 流动资产 / 流动负债
        if 'current_assets' in balance.columns and 'current_liabilities' in balance.columns:
            current_assets = balance['current_assets'].iloc[0]
            current_liabilities = balance['current_liabilities'].iloc[0]
            if current_liabilities != 0:
                ratios['流动比率'] = current_assets / current_liabilities
        
        # 资产负债率 = 总负债 / 总资产
        if 'total_liabilities' in balance.columns and 'total_assets' in balance.columns:
            total_liabilities = balance['total_liabilities'].iloc[0]
            total_assets = balance['total_assets'].iloc[0]
            if total_assets != 0:
                ratios['资产负债率'] = total_liabilities / total_assets
        
        # 净利润率 = 净利润 / 总收入
        if 'net_income' in income.columns and 'total_revenue' in income.columns:
            net_income = income['net_income'].iloc[0]
            total_revenue = income['total_revenue'].iloc[0]
            if total_revenue != 0:
                ratios['净利润率'] = net_income / total_revenue
        
        return ratios
        
    except Exception as e:
        print(f"分析 {symbol} 财务比率时出错: {e}")
        return None

# 使用示例
companies = ['AAPL', 'MSFT', 'GOOGL', 'AMZN']
for company in companies:
    print(f"\n{company} 财务比率分析:")
    ratios = analyze_financial_ratios(company)
    if ratios:
        for ratio_name, value in ratios.items():
            print(f"  {ratio_name}: {value:.4f}")

多期间财务分析

def multi_period_analysis(symbol, periods=5):
    """
    多期间财务分析
    """
    try:
        # 获取多年度数据
        income_data = obb.equity.fundamental.income(
            symbol=symbol,
            period="annual",
            limit=periods,
            provider="yfinance"
        ).to_dataframe()
        
        if income_data.empty:
            print(f"无法获取 {symbol} 的收入数据")
            return None
        
        # 按日期排序
        income_data = income_data.sort_values('period_ending')
        
        analysis = {
            'symbol': symbol,
            'periods_analyzed': len(income_data)
        }
        
        # 收入增长分析
        if 'total_revenue' in income_data.columns:
            revenues = income_data['total_revenue'].tolist()
            if len(revenues) > 1:
                revenue_growth = []
                for i in range(1, len(revenues)):
                    if revenues[i-1] != 0:
                        growth = (revenues[i] - revenues[i-1]) / revenues[i-1] * 100
                        revenue_growth.append(growth)
                
                analysis['平均收入增长率'] = sum(revenue_growth) / len(revenue_growth) if revenue_growth else 0
                analysis['最新收入'] = revenues[-1]
        
        # 净利润分析
        if 'net_income' in income_data.columns:
            net_incomes = income_data['net_income'].tolist()
            if len(net_incomes) > 1:
                profit_growth = []
                for i in range(1, len(net_incomes)):
                    if net_incomes[i-1] != 0:
                        growth = (net_incomes[i] - net_incomes[i-1]) / net_incomes[i-1] * 100
                        profit_growth.append(growth)
                
                analysis['平均净利润增长率'] = sum(profit_growth) / len(profit_growth) if profit_growth else 0
                analysis['最新净利润'] = net_incomes[-1]
        
        return analysis
        
    except Exception as e:
        print(f"多期间分析 {symbol} 时出错: {e}")
        return None

# 分析多家公司
tech_companies = ['AAPL', 'MSFT', 'GOOGL', 'META']
analysis_results = []

for company in tech_companies:
    result = multi_period_analysis(company, periods=5)
    if result:
        analysis_results.append(result)

# 显示结果
print("科技公司财务分析结果:")
for result in analysis_results:
    print(f"\n{result['symbol']}:")
    for key, value in result.items():
        if key != 'symbol':
            if isinstance(value, float):
                print(f"  {key}: {value:.2f}")
            else:
                print(f"  {key}: {value}")

行业比较分析

def industry_comparison(symbols, industry_name=""):
    """
    行业比较分析
    """
    comparison_data = []
    
    for symbol in symbols:
        try:
            # 获取最新财务数据
            balance = obb.equity.fundamental.balance(
                symbol=symbol, 
                limit=1, 
                provider="yfinance"
            ).to_dataframe()
            
            income = obb.equity.fundamental.income(
                symbol=symbol, 
                limit=1, 
                provider="yfinance"
            ).to_dataframe()
            
            if not balance.empty and not income.empty:
                company_data = {'symbol': symbol}
                
                # 提取关键指标
                if 'total_assets' in balance.columns:
                    company_data['总资产'] = balance['total_assets'].iloc[0]
                
                if 'total_revenue' in income.columns:
                    company_data['总收入'] = income['total_revenue'].iloc[0]
                
                if 'net_income' in income.columns:
                    company_data['净利润'] = income['net_income'].iloc[0]
                
                # 计算净利润率
                if '总收入' in company_data and '净利润' in company_data:
                    if company_data['总收入'] != 0:
                        company_data['净利润率'] = company_data['净利润'] / company_data['总收入'] * 100
                
                comparison_data.append(company_data)
                
        except Exception as e:
            print(f"获取 {symbol} 数据失败: {e}")
    
    if comparison_data:
        import pandas as pd
        df = pd.DataFrame(comparison_data)
        
        print(f"{industry_name}行业比较:")
        print(df.to_string(index=False))
        
        # 排名分析
        if '净利润率' in df.columns:
            df_sorted = df.sort_values('净利润率', ascending=False)
            print(f"\n按净利润率排名:")
            for i, row in df_sorted.iterrows():
                print(f"{row['symbol']}: {row['净利润率']:.2f}%")
    
    return comparison_data

# 银行业比较
bank_stocks = ['JPM', 'BAC', 'WFC', 'C']
bank_comparison = industry_comparison(bank_stocks, "银行")

# 科技股比较
tech_stocks = ['AAPL', 'MSFT', 'GOOGL', 'META']
tech_comparison = industry_comparison(tech_stocks, "科技")

财务健康度评估

def financial_health_score(symbol):
    """
    财务健康度评分
    """
    try:
        # 获取财务数据
        balance = obb.equity.fundamental.balance(
            symbol=symbol, 
            limit=1, 
            provider="yfinance"
        ).to_dataframe()
        
        income = obb.equity.fundamental.income(
            symbol=symbol, 
            limit=1, 
            provider="yfinance"
        ).to_dataframe()
        
        cash_flow = obb.equity.fundamental.cash(
            symbol=symbol, 
            limit=1, 
            provider="yfinance"
        ).to_dataframe()
        
        score = 0
        max_score = 0
        details = []
        
        # 盈利能力 (30分)
        max_score += 30
        if not income.empty and 'net_income' in income.columns:
            net_income = income['net_income'].iloc[0]
            if net_income > 0:
                score += 30
                details.append("✓ 盈利能力: 公司盈利 (+30分)")
            else:
                details.append("✗ 盈利能力: 公司亏损 (+0分)")
        
        # 流动性 (25分)
        max_score += 25
        if not balance.empty:
            if 'current_assets' in balance.columns and 'current_liabilities' in balance.columns:
                current_ratio = balance['current_assets'].iloc[0] / balance['current_liabilities'].iloc[0]
                if current_ratio > 2:
                    score += 25
                    details.append(f"✓ 流动性: 流动比率 {current_ratio:.2f} (+25分)")
                elif current_ratio > 1:
                    score += 15
                    details.append(f"△ 流动性: 流动比率 {current_ratio:.2f} (+15分)")
                else:
                    details.append(f"✗ 流动性: 流动比率 {current_ratio:.2f} (+0分)")
        
        # 负债水平 (25分)
        max_score += 25
        if not balance.empty:
            if 'total_liabilities' in balance.columns and 'total_assets' in balance.columns:
                debt_ratio = balance['total_liabilities'].iloc[0] / balance['total_assets'].iloc[0]
                if debt_ratio < 0.3:
                    score += 25
                    details.append(f"✓ 负债水平: 资产负债率 {debt_ratio:.2f} (+25分)")
                elif debt_ratio < 0.6:
                    score += 15
                    details.append(f"△ 负债水平: 资产负债率 {debt_ratio:.2f} (+15分)")
                else:
                    details.append(f"✗ 负债水平: 资产负债率 {debt_ratio:.2f} (+0分)")
        
        # 现金流 (20分)
        max_score += 20
        if not cash_flow.empty and 'operating_cash_flow' in cash_flow.columns:
            operating_cf = cash_flow['operating_cash_flow'].iloc[0]
            if operating_cf > 0:
                score += 20
                details.append("✓ 现金流: 经营现金流为正 (+20分)")
            else:
                details.append("✗ 现金流: 经营现金流为负 (+0分)")
        
        # 计算最终评分
        final_score = (score / max_score) * 100 if max_score > 0 else 0
        
        # 评级
        if final_score >= 80:
            rating = "优秀"
        elif final_score >= 60:
            rating = "良好"
        elif final_score >= 40:
            rating = "一般"
        else:
            rating = "较差"
        
        return {
            'symbol': symbol,
            'score': final_score,
            'rating': rating,
            'details': details
        }
        
    except Exception as e:
        print(f"评估 {symbol} 财务健康度时出错: {e}")
        return None

# 评估多家公司
companies_to_evaluate = ['AAPL', 'TSLA', 'AMZN', 'NFLX']

print("财务健康度评估结果:")
print("=" * 50)

for company in companies_to_evaluate:
    result = financial_health_score(company)
    if result:
        print(f"\n{result['symbol']} - 评分: {result['score']:.1f}/100 ({result['rating']})")
        for detail in result['details']:
            print(f"  {detail}")

最佳实践

1. 数据验证：始终检查数据的完整性和准确性
2. 多源对比：使用多个数据提供商验证关键数据
3. 时间序列分析：分析多个时期的趋势而非单点数据
4. 行业比较：将公司数据与同行业公司进行比较
5. 比率分析：使用财务比率进行深入分析

# 完整的财务分析工作流
def comprehensive_financial_analysis(symbol):
    """
    综合财务分析
    """
    print(f"开始分析 {symbol} 的财务状况...")
    print("=" * 60)
    
    # 1. 基本财务数据
    print("1. 获取基本财务数据...")
    try:
        balance = obb.equity.fundamental.balance(symbol=symbol, limit=3, provider="yfinance")
        income = obb.equity.fundamental.income(symbol=symbol, limit=3, provider="yfinance")
        cash_flow = obb.equity.fundamental.cash(symbol=symbol, limit=3, provider="yfinance")
        print("   ✓ 财务数据获取成功")
    except Exception as e:
        print(f"   ✗ 财务数据获取失败: {e}")
        return
    
    # 2. 财务比率分析
    print("2. 财务比率分析...")
    ratios = analyze_financial_ratios(symbol)
    if ratios:
        for ratio_name, value in ratios.items():
            print(f"   {ratio_name}: {value:.4f}")
    
    # 3. 多期间趋势分析
    print("3. 多期间趋势分析...")
    trend_analysis = multi_period_analysis(symbol, periods=5)
    if trend_analysis:
        for key, value in trend_analysis.items():
            if key != 'symbol' and isinstance(value, (int, float)):
                print(f"   {key}: {value:.2f}")
    
    # 4. 财务健康度评估
    print("4. 财务健康度评估...")
    health_score = financial_health_score(symbol)
    if health_score:
        print(f"   综合评分: {health_score['score']:.1f}/100 ({health_score['rating']})")
    
    print(f"\n{symbol} 财务分析完成!")

# 使用示例
comprehensive_financial_analysis("AAPL")