yfinance基本面分析：财务报表分析和估值模型

yfinance基本面分析：财务报表分析和估值模型

【免费下载链接】yfinance Download market data from Yahoo! Finance's API 项目地址: https://gitcode.com/GitHub_Trending/yf/yfinance

还在为获取上市公司财务数据而烦恼吗？yfinance作为Python生态中最强大的金融数据获取库，提供了完整的财务报表分析和估值建模能力。本文将深入解析如何使用yfinance进行专业的基本面分析，从财务报表提取到估值模型构建，一文解决你的投资分析需求。

读完本文你能得到

• ✅ 掌握yfinance财务报表数据获取的完整方法
• ✅ 学会三大财务报表（利润表、资产负债表、现金流量表）的分析技巧
• ✅ 构建专业的财务比率分析体系
• ✅ 实现基于财务数据的估值模型
• ✅ 获得完整的Python代码示例和实战案例

yfinance财务报表数据获取

基础配置与安装

首先确保安装yfinance库：

pip install yfinance pandas numpy matplotlib

获取完整财务报表数据

yfinance提供了丰富的财务报表接口，支持年度和季度数据：

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 创建股票对象
ticker = yf.Ticker("MSFT")  # 微软公司

# 获取三大财务报表
income_stmt = ticker.income_stmt          # 利润表
balance_sheet = ticker.balance_sheet      # 资产负债表  
cash_flow = ticker.cash_flow              # 现金流量表

# 获取季度数据
quarterly_income = ticker.quarterly_income_stmt
quarterly_balance = ticker.quarterly_balance_sheet
quarterly_cashflow = ticker.quarterly_cash_flow

print("利润表结构:")
print(income_stmt.head())
print("\n资产负债表结构:")
print(balance_sheet.head())
print("\n现金流量表结构:")
print(cash_flow.head())

财务报表深度分析

利润表关键指标分析

def analyze_income_statement(income_df):
    """利润表深度分析"""
    analysis = {}
    
    # 收入增长分析
    revenue = income_df.loc['Total Revenue']
    analysis['revenue_growth'] = revenue.pct_change().dropna() * 100
    
    # 盈利能力分析
    gross_profit = income_df.loc['Gross Profit']
    net_income = income_df.loc['Net Income']
    
    analysis['gross_margin'] = (gross_profit / revenue) * 100
    analysis['net_margin'] = (net_income / revenue) * 100
    
    # 运营效率分析
    operating_income = income_df.loc['Operating Income']
    analysis['operating_margin'] = (operating_income / revenue) * 100
    
    return pd.DataFrame(analysis)

# 执行利润表分析
income_analysis = analyze_income_statement(income_stmt)
print("利润表分析结果:")
print(income_analysis)

资产负债表健康度评估

def analyze_balance_sheet(balance_df):
    """资产负债表健康度分析"""
    analysis = {}
    
    total_assets = balance_df.loc['Total Assets']
    total_liabilities = balance_df.loc['Total Liabilities Net Minority Interest']
    total_equity = balance_df.loc['Total Equity Gross Minority Interest']
    
    # 财务杠杆比率
    analysis['debt_to_equity'] = (total_liabilities / total_equity) * 100
    analysis['debt_to_assets'] = (total_liabilities / total_assets) * 100
    
    # 流动性比率
    current_assets = balance_df.loc['Current Assets']
    current_liabilities = balance_df.loc['Current Liabilities']
    analysis['current_ratio'] = current_assets / current_liabilities
    
    # 营运资本
    analysis['working_capital'] = current_assets - current_liabilities
    
    return pd.DataFrame(analysis)

balance_analysis = analyze_balance_sheet(balance_sheet)
print("资产负债表分析结果:")
print(balance_analysis)

现金流量表质量分析

def analyze_cash_flow(cashflow_df):
    """现金流量表质量分析"""
    analysis = {}
    
    operating_cashflow = cashflow_df.loc['Operating Cash Flow']
    investing_cashflow = cashflow_df.loc['Investing Cash Flow']  
    financing_cashflow = cashflow_df.loc['Financing Cash Flow']
    free_cashflow = cashflow_df.loc['Free Cash Flow']
    
    # 现金流质量指标
    analysis['cash_flow_margin'] = (operating_cashflow / income_stmt.loc['Total Revenue']) * 100
    analysis['fcf_to_revenue'] = (free_cashflow / income_stmt.loc['Total Revenue']) * 100
    
    # 现金流结构分析
    total_cashflow = operating_cashflow + investing_cashflow + financing_cashflow
    analysis['operating_cash_ratio'] = (operating_cashflow / total_cashflow.abs()) * 100
    
    return pd.DataFrame(analysis)

cashflow_analysis = analyze_cash_flow(cash_flow)
print("现金流量表分析结果:")
print(cashflow_analysis)

财务比率分析体系

盈利能力比率

def profitability_ratios(income_df, balance_df):
    """盈利能力比率分析"""
    ratios = {}
    
    revenue = income_df.loc['Total Revenue']
    net_income = income_df.loc['Net Income']
    total_assets = balance_df.loc['Total Assets']
    total_equity = balance_df.loc['Total Equity Gross Minority Interest']
    
    # 核心盈利能力指标
    ratios['roa'] = (net_income / total_assets) * 100          # 总资产收益率
    ratios['roe'] = (net_income / total_equity) * 100          # 净资产收益率
    ratios['gross_margin'] = (income_df.loc['Gross Profit'] / revenue) * 100
    ratios['operating_margin'] = (income_df.loc['Operating Income'] / revenue) * 100
    ratios['net_margin'] = (net_income / revenue) * 100
    
    return pd.DataFrame(ratios)

profit_ratios = profitability_ratios(income_stmt, balance_sheet)

偿债能力比率

def solvency_ratios(balance_df, income_df):
    """偿债能力比率分析"""
    ratios = {}
    
    total_debt = balance_df.loc['Total Debt']
    total_equity = balance_df.loc['Total Equity Gross Minority Interest']
    ebit = income_df.loc['Operating Income']  # 息税前利润
    
    # 杠杆比率
    ratios['debt_to_equity'] = (total_debt / total_equity) * 100
    ratios['debt_to_assets'] = (total_debt / balance_df.loc['Total Assets']) * 100
    ratios['interest_coverage'] = ebit / income_df.loc['Interest Expense']
    
    return pd.DataFrame(ratios)

solvency_ratios = solvency_ratios(balance_sheet, income_stmt)

营运能力比率

def efficiency_ratios(income_df, balance_df):
    """营运能力比率分析"""
    ratios = {}
    
    revenue = income_df.loc['Total Revenue']
    cogs = income_df.loc['Cost Of Revenue']
    inventory = balance_df.loc['Inventory']
    receivables = balance_df.loc['Accounts Receivable']
    
    # 营运效率指标
    ratios['asset_turnover'] = revenue / balance_df.loc['Total Assets']
    ratios['inventory_turnover'] = cogs / inventory
    ratios['receivables_turnover'] = revenue / receivables
    ratios['days_sales_outstanding'] = 365 / ratios['receivables_turnover']
    
    return pd.DataFrame(ratios)

efficiency_ratios = efficiency_ratios(income_stmt, balance_sheet)

估值模型构建

相对估值模型

def relative_valuation(ticker_obj, peers):
    """相对估值模型"""
    valuation = {}
    
    # 获取当前股票信息
    info = ticker_obj.info
    current_pe = info.get('trailingPE')
    current_pb = info.get('priceToBook')
    
    # 获取同行业公司估值
    peer_valuations = []
    for peer in peers:
        peer_ticker = yf.Ticker(peer)
        peer_info = peer_ticker.info
        peer_valuations.append({
            'ticker': peer,
            'pe': peer_info.get('trailingPE'),
            'pb': peer_info.get('priceToBook')
        })
    
    peer_df = pd.DataFrame(peer_valuations)
    
    # 计算相对估值
    valuation['pe_vs_peer_avg'] = current_pe / peer_df['pe'].mean()
    valuation['pb_vs_peer_avg'] = current_pb / peer_df['pb'].mean()
    
    return valuation, peer_df

# 科技股同行业公司
tech_peers = ['AAPL', 'GOOGL', 'AMZN', 'META', 'NVDA']
relative_val, peer_data = relative_valuation(ticker, tech_peers)

绝对估值模型（现金流折现模型）

def dcf_valuation(ticker_obj, discount_rate=0.1, growth_rate=0.03, terminal_growth=0.02):
    """现金流折现估值模型"""
    
    # 获取财务数据
    cashflow = ticker_obj.cash_flow.loc['Free Cash Flow']
    latest_fcf = cashflow.iloc[0]
    
    # 估算未来5年自由现金流
    forecast_years = 5
    forecast_cashflows = []
    
    for year in range(1, forecast_years + 1):
        forecast_fcf = latest_fcf * (1 + growth_rate) ** year
        discounted_fcf = forecast_fcf / (1 + discount_rate) ** year
        forecast_cashflows.append(discounted_fcf)
    
    # 计算终值
    terminal_fcf = forecast_cashflows[-1] * (1 + terminal_growth)
    terminal_value = terminal_fcf / (discount_rate - terminal_growth)
    discounted_terminal = terminal_value / (1 + discount_rate) ** forecast_years
    
    # 计算企业价值
    enterprise_value = sum(forecast_cashflows) + discounted_terminal
    
    # 获取当前信息
    info = ticker_obj.info
    cash = info.get('totalCash', 0)
    debt = info.get('totalDebt', 0)
    
    # 计算股权价值
    equity_value = enterprise_value - debt + cash
    shares_outstanding = info.get('sharesOutstanding', 1)
    
    # 计算每股价值
    intrinsic_value = equity_value / shares_outstanding
    current_price = info.get('currentPrice', 0)
    
    return {
        'intrinsic_value': intrinsic_value,
        'current_price': current_price,
        'margin_of_safety': (intrinsic_value - current_price) / intrinsic_value * 100,
        'enterprise_value': enterprise_value,
        'equity_value': equity_value
    }

dcf_result = dcf_valuation(ticker)
print("DCF估值结果:")
print(dcf_result)

财务数据可视化分析

财务趋势图表

def plot_financial_trends(ticker_obj):
    """财务趋势可视化"""
    fig, axes = plt.subplots(2, 2, figsize=(15, 10))
    
    # 收入与利润趋势
    income = ticker_obj.income_stmt
    revenue = income.loc['Total Revenue']
    net_income = income.loc['Net Income']
    
    axes[0, 0].plot(revenue.index, revenue.values, 'b-', label='Revenue')
    axes[0, 0].plot(net_income.index, net_income.values, 'r-', label='Net Income')
    axes[0, 0].set_title('Revenue vs Net Income Trend')
    axes[0, 0].legend()
    axes[0, 0].grid(True)
    
    # 利润率趋势
    gross_margin = (income.loc['Gross Profit'] / revenue) * 100
    net_margin = (net_income / revenue) * 100
    
    axes[0, 1].plot(gross_margin.index, gross_margin.values, 'g-', label='Gross Margin')
    axes[0, 1].plot(net_margin.index, net_margin.values, 'orange', label='Net Margin')
    axes[0, 1].set_title('Profit Margin Trend (%)')
    axes[0, 1].legend()
    axes[0, 1].grid(True)
    
    # 现金流趋势
    cashflow = ticker_obj.cash_flow
    operating_cf = cashflow.loc['Operating Cash Flow']
    free_cf = cashflow.loc['Free Cash Flow']
    
    axes[1, 0].plot(operating_cf.index, operating_cf.values, 'purple', label='Operating CF')
    axes[1, 0].plot(free_cf.index, free_cf.values, 'cyan', label='Free CF')
    axes[1, 0].set_title('Cash Flow Trend')
    axes[1, 0].legend()
    axes[1, 0].grid(True)
    
    # ROE与ROA趋势
    balance = ticker_obj.balance_sheet
    roe = (net_income / balance.loc['Total Equity Gross Minority Interest']) * 100
    roa = (net_income / balance.loc['Total Assets']) * 100
    
    axes[1, 1].plot(roe.index, roe.values, 'brown', label='ROE')
    axes[1, 1].plot(roa.index, roa.values, 'pink', label='ROA')
    axes[1, 1].set_title('Return on Equity & Assets (%)')
    axes[1, 1].legend()
    axes[1, 1].grid(True)
    
    plt.tight_layout()
    plt.show()

plot_financial_trends(ticker)

财务比率雷达图

def plot_financial_radar(ratios_df):
    """财务比率雷达图"""
    categories = list(ratios_df.columns)
    values = ratios_df.iloc[0].values.tolist()
    values += values[:1]  # 闭合雷达图
    
    angles = np.linspace(0, 2*np.pi, len(categories), endpoint=False).tolist()
    angles += angles[:1]
    
    fig, ax = plt.subplots(figsize=(8, 8), subplot_kw=dict(polar=True))
    ax.plot(angles, values, 'o-', linewidth=2)
    ax.fill(angles, values, alpha=0.25)
    ax.set_thetagrids(np.degrees(angles[:-1]), categories)
    ax.set_title('Financial Ratios Radar Chart', size=16, y=1.1)
    ax.grid(True)
    
    plt.show()

# 选择关键比率绘制雷达图
key_ratios = profit_ratios[['roa', 'roe', 'net_margin']].join(
    solvency_ratios[['debt_to_equity']]).join(
    efficiency_ratios[['asset_turnover']])
plot_financial_radar(key_ratios)

完整的财务分析流水线

class FinancialAnalyzer:
    """完整的财务分析流水线"""
    
    def __init__(self, ticker_symbol):
        self.ticker = yf.Ticker(ticker_symbol)
        self.income_stmt = self.ticker.income_stmt
        self.balance_sheet = self.ticker.balance_sheet
        self.cash_flow = self.ticker.cash_flow
        self.info = self.ticker.info
    
    def comprehensive_analysis(self):
        """执行全面财务分析"""
        analysis = {}
        
        # 盈利能力分析
        analysis['profitability'] = profitability_ratios(self.income_stmt, self.balance_sheet)
        
        # 偿债能力分析
        analysis['solvency'] = solvency_ratios(self.balance_sheet, self.income_stmt)
        
        # 营运能力分析
        analysis['efficiency'] = efficiency_ratios(self.income_stmt, self.balance_sheet)
        
        # 现金流分析
        analysis['cashflow'] = analyze_cash_flow(self.cash_flow)
        
        # 估值分析
        analysis['dcf_valuation'] = dcf_valuation(self.ticker)
        
        return analysis
    
    def generate_report(self):
        """生成财务分析报告"""
        analysis = self.comprehensive_analysis()
        
        report = f"""
        Financial Analysis Report for {self.ticker.ticker}
        {'='*50}
        
        1. Profitability Analysis:
        {analysis['profitability'].to_string()}
        
        2. Solvency Analysis:
        {analysis['solvency'].to_string()}
        
        3. Efficiency Analysis:
        {analysis['efficiency'].to_string()}
        
        4. Cash Flow Analysis:
        {analysis['cashflow'].to_string()}
        
        5. DCF Valuation:

【免费下载链接】yfinance Download market data from Yahoo! Finance's API 项目地址: https://gitcode.com/GitHub_Trending/yf/yfinance