0
# Fundamental Analysis
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AKShare provides comprehensive fundamental analysis capabilities with 47 functions in the STOCK_FUNDAMENTAL category. This covers financial statements, financial ratios, earnings forecasts, analyst recommendations, and company valuation metrics essential for equity research and investment analysis.
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## Financial Statement Data
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### Income Statement Analysis
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#### Comprehensive Financial Indicators
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```python { .api }
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import akshare as ak
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def stock_financial_analysis_indicator_em(symbol: str) -> pd.DataFrame:
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    """
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    Comprehensive financial analysis indicators from East Money
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    Parameters:
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        symbol: Stock code (e.g., "000001")
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    Returns:
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        pd.DataFrame: Financial indicators with columns:
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            - 指标名称 (Indicator): Financial metric name
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            - 报告期 (Period): Reporting period
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            - 指标值 (Value): Metric value
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            - 同比增长率 (YoY_growth): Year-over-year growth rate
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            - 行业平均 (Industry_avg): Industry average
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            - 行业排名 (Industry_rank): Industry ranking
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    """
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# Get comprehensive financial analysis for Ping An Bank
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financial_indicators = ak.stock_financial_analysis_indicator_em(symbol="000001")
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print(financial_indicators.head())
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#    指标名称     报告期     指标值  同比增长率  行业平均  行业排名
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# 0   营业收入  2024Q3  1234.56    12.34   987.65      5
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# 1   净利润    2024Q3   456.78     8.76   321.09      3
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# 2   ROE      2024Q3    15.67     1.23    12.45      2
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# 3   ROA      2024Q3     1.89     0.34     1.45      4
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```
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#### Financial Abstract Data
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```python { .api }
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import akshare as ak
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def stock_financial_abstract(symbol: str = "000001") -> pd.DataFrame:
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    """
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    Financial statement abstract and key metrics
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Financial abstract with columns:
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            - 报告期 (Period): Reporting period
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            - 营业收入 (Revenue): Operating revenue
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            - 营业收入同比增长 (Revenue_growth): Revenue YoY growth
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            - 净利润 (Net_profit): Net profit
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            - 净利润同比增长 (Profit_growth): Net profit YoY growth
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            - 每股收益 (EPS): Earnings per share
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            - 净资产收益率 (ROE): Return on equity
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            - 每股净资产 (BVPS): Book value per share
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            - 每股经营现金流 (CFPS): Cash flow per share
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    """
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# Get financial abstract
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financial_abstract = ak.stock_financial_abstract(symbol="000001")
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print(financial_abstract.head())
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#     报告期     营业收入  营业收入同比增长    净利润  净利润同比增长   每股收益    净资产收益率  每股净资产  每股经营现金流
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# 0  2024Q3    1567.89        12.34   234.56        8.76    0.89       15.67     5.67        1.23
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# 1  2024Q2    1456.78        10.23   212.34        7.89    0.81       14.89     5.45        1.15
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```
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### Balance Sheet Data
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#### Balance Sheet by Report Period
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```python { .api }
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import akshare as ak
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def stock_balance_sheet_by_report_em(symbol: str = "000001") -> pd.DataFrame:
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    """
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    Balance sheet data by reporting period
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Balance sheet items with columns:
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            - 报告期 (Period): Reporting period
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            - 货币资金 (Cash): Cash and equivalents
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            - 总资产 (Total_assets): Total assets
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            - 总负债 (Total_liabilities): Total liabilities
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            - 股东权益合计 (Total_equity): Total shareholders' equity
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            - 资产负债率 (Debt_ratio): Debt-to-assets ratio
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            - 流动比率 (Current_ratio): Current ratio
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            - 速动比率 (Quick_ratio): Quick ratio
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    """
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def stock_balance_sheet_by_yearly_em(symbol: str = "000001") -> pd.DataFrame:
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    """
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    Annual balance sheet data
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Annual balance sheet data
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    """
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# Get quarterly balance sheet data
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balance_sheet_q = ak.stock_balance_sheet_by_report_em(symbol="000001")
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# Get annual balance sheet data
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balance_sheet_y = ak.stock_balance_sheet_by_yearly_em(symbol="000001")
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```
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### Cash Flow Statement
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#### Cash Flow Analysis
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```python { .api }
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import akshare as ak
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def stock_cash_flow_sheet_by_report_em(symbol: str = "000001") -> pd.DataFrame:
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    """
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    Cash flow statement by reporting period
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Cash flow data with columns:
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            - 报告期 (Period): Reporting period
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            - 经营活动现金流量净额 (Operating_cf): Operating cash flow
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            - 投资活动现金流量净额 (Investing_cf): Investing cash flow
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            - 筹资活动现金流量净额 (Financing_cf): Financing cash flow
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            - 现金及现金等价物净增加额 (Net_cf): Net change in cash
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            - 自由现金流 (Free_cf): Free cash flow
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    """
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def stock_cash_flow_sheet_by_yearly_em(symbol: str = "000001") -> pd.DataFrame:
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    """
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    Annual cash flow statement data
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Annual cash flow data
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    """
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# Get cash flow data
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cash_flow_q = ak.stock_cash_flow_sheet_by_report_em(symbol="000001")
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cash_flow_y = ak.stock_cash_flow_sheet_by_yearly_em(symbol="000001")
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```
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## Financial Ratio Analysis
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### Profitability Ratios
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#### Detailed Financial Ratios
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```python { .api }
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import akshare as ak
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def stock_financial_hk_report_em(symbol: str) -> pd.DataFrame:
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    """
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    Hong Kong listed stock financial ratios
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    Parameters:
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        symbol: HK stock code
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    Returns:
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        pd.DataFrame: Financial ratios and metrics
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    """
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def stock_roe_em(symbol: str = "000001") -> pd.DataFrame:
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    """
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    Return on Equity (ROE) analysis and components
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: ROE decomposition with columns:
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            - 报告期 (Period): Reporting period
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            - 净资产收益率 (ROE): Return on equity
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            - 净利润率 (Net_margin): Net profit margin
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            - 资产周转率 (Asset_turnover): Asset turnover ratio
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            - 权益乘数 (Equity_multiplier): Equity multiplier
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            - 杜邦分析 (DuPont): DuPont analysis components
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    """
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# Get ROE analysis
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roe_analysis = ak.stock_roe_em(symbol="000001")
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print(roe_analysis.head())
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#     报告期   净资产收益率  净利润率  资产周转率  权益乘数
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# 0  2024Q3      15.67     8.45      0.89     2.08
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# 1  2024Q2      14.89     8.12      0.87     2.11
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```
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### Valuation Metrics
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#### Market Valuation Indicators
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```python { .api }
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import akshare as ak
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def stock_a_pe_lg() -> pd.DataFrame:
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    """
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    A-share market PE ratio distribution
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    Returns:
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        pd.DataFrame: Market PE statistics and distribution
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    """
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def stock_a_indicator_lg() -> pd.DataFrame:
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    """
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    A-share market valuation indicators
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    Returns:
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        pd.DataFrame: Market-wide valuation metrics including:
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            - 平均市盈率 (Avg_PE): Average P/E ratio
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            - 平均市净率 (Avg_PB): Average P/B ratio
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            - 平均股息率 (Avg_dividend): Average dividend yield
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            - 总市值 (Total_mv): Total market cap
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            - 流通市值 (Float_mv): Floating market cap
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    """
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# Get market PE distribution
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market_pe = ak.stock_a_pe_lg()
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# Get market valuation indicators
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market_valuation = ak.stock_a_indicator_lg()
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```
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## Earnings and Forecasts
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### Earnings Forecast Data
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#### Analyst Earnings Forecasts
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```python { .api }
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import akshare as ak
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def stock_profit_forecast() -> pd.DataFrame:
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    """
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    Earnings forecast summary across all stocks
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    Returns:
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        pd.DataFrame: Earnings forecasts with columns:
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            - 代码 (Code): Stock code
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            - 名称 (Name): Stock name
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            - 2024预测每股收益 (EPS_2024F): 2024 forecast EPS
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            - 2025预测每股收益 (EPS_2025F): 2025 forecast EPS
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            - 2026预测每股收益 (EPS_2026F): 2026 forecast EPS
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            - 预测PE (Forecast_PE): Forward P/E ratio
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            - 预测收益增长率 (Growth_rate): Forecast earnings growth
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    """
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def stock_profit_forecast_ths(symbol: str) -> pd.DataFrame:
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    """
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    Individual stock earnings forecast from Tonghuashun
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Detailed earnings forecasts for individual stock
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    """
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# Get overall earnings forecasts
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earnings_forecasts = ak.stock_profit_forecast()
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print(earnings_forecasts.head())
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#     代码    名称   2024预测每股收益  2025预测每股收益  2026预测每股收益  预测PE  预测收益增长率
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# 0  000001  平安银行      1.45        1.67        1.89    7.2       15.2
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# 1  000002   万科A        0.89        1.02        1.15   8.7       14.6
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# Get individual stock forecast
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individual_forecast = ak.stock_profit_forecast_ths(symbol="000001")
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```
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#### Earnings Surprise Analysis
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```python { .api }
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import akshare as ak
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def stock_profit_forecast_em(symbol: str) -> pd.DataFrame:
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    """
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    East Money earnings forecast and consensus
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Consensus earnings forecasts with:
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            - 机构数量 (Num_analysts): Number of analysts
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            - 预测均值 (Consensus): Consensus estimate
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            - 预测最高 (High_estimate): Highest estimate
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            - 预测最低 (Low_estimate): Lowest estimate
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            - 标准差 (Std_dev): Standard deviation
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    """
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# Get consensus earnings data
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consensus_earnings = ak.stock_profit_forecast_em(symbol="000001")
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```
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### Revenue Forecasts
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#### Revenue Growth Projections
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```python { .api }
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import akshare as ak
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def stock_revenue_forecast_em(symbol: str) -> pd.DataFrame:
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    """
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    Revenue forecast data from East Money
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Revenue projections and growth rates
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    """
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# Get revenue forecasts
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revenue_forecasts = ak.stock_revenue_forecast_em(symbol="000001")
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```
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## Analyst Coverage and Recommendations
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### Analyst Recommendations
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#### Stock Recommendations
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```python { .api }
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import akshare as ak
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def stock_recommend() -> pd.DataFrame:
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    """
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    Stock analyst recommendations summary
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    Returns:
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        pd.DataFrame: Recommendation data with columns:
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            - 代码 (Code): Stock code
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            - 名称 (Name): Stock name
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            - 最新评级 (Latest_rating): Latest rating
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            - 上次评级 (Previous_rating): Previous rating
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            - 目标价 (Target_price): Price target
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            - 机构名称 (Institution): Research institution
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            - 分析师 (Analyst): Analyst name
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            - 评级日期 (Rating_date): Rating date
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    """
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def stock_recommend_detail(symbol: str) -> pd.DataFrame:
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    """
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    Detailed analyst recommendations for individual stock
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Detailed recommendation history
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    """
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# Get overall recommendations
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recommendations = ak.stock_recommend()
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print(recommendations.head())
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#     代码    名称  最新评级  上次评级   目标价     机构名称    分析师    评级日期
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# 0  000001  平安银行   买入     增持   12.50   中金公司    张三   2024-12-01
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# 1  000002   万科A    增持     买入    9.80   国泰君安    李四   2024-11-30
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# Get individual stock recommendations
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stock_recommendations = ak.stock_recommend_detail(symbol="000001")
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```
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#### Rating Distribution Analysis
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```python { .api }
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import akshare as ak
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def stock_rating_all() -> pd.DataFrame:
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    """
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    Comprehensive stock rating distribution
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    Returns:
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        pd.DataFrame: Rating statistics across all stocks
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    """
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def stock_rating_change() -> pd.DataFrame:
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    """
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    Recent rating changes and upgrades/downgrades
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    Returns:
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        pd.DataFrame: Rating change activity
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    """
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# Get rating distribution
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rating_distribution = ak.stock_rating_all()
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# Get recent rating changes
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rating_changes = ak.stock_rating_change()
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```
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## Company Quality Analysis
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### Dividend Analysis
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#### Dividend History and Yield
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```python { .api }
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import akshare as ak
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def stock_dividend_detail(symbol: str) -> pd.DataFrame:
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    """
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    Detailed dividend history for individual stock
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Dividend history with columns:
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            - 分红年度 (Year): Dividend year
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            - 送股比例 (Bonus_ratio): Bonus share ratio
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            - 转增比例 (Conversion_ratio): Capital reserve conversion
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            - 派息比例 (Dividend_ratio): Cash dividend ratio
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            - 除权除息日 (Ex_date): Ex-dividend date
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            - 股息率 (Yield): Dividend yield
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    """
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# Get dividend history
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dividend_history = ak.stock_dividend_detail(symbol="000001")
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```
422

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### Corporate Governance
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#### Shareholding Structure
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```python { .api }
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import akshare as ak
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def stock_holder_em(symbol: str) -> pd.DataFrame:
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    """
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    Shareholding structure and major shareholders
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    Parameters:
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        symbol: Stock code
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    Returns:
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        pd.DataFrame: Shareholder information with columns:
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            - 股东名称 (Shareholder): Shareholder name
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            - 持股数量 (Shares): Number of shares held
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            - 持股比例 (Percentage): Ownership percentage
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            - 股东性质 (Type): Shareholder type
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            - 报告期 (Period): Reporting period
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    """
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# Get shareholding structure
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shareholders = ak.stock_holder_em(symbol="000001")
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```
448

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## Fundamental Analysis Workflows
450

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### Comprehensive Company Analysis
452
```python { .api }
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import akshare as ak
454
import pandas as pd
455

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def comprehensive_fundamental_analysis(symbol: str) -> dict:
457
    """
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    Complete fundamental analysis for a stock
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460
    Parameters:
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        symbol: Stock code
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463
    Returns:
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        dict: Comprehensive fundamental data
465
    """
466
    
467
    analysis = {}
468
    
469
    try:
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        # Financial statements and ratios
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        analysis['financial_indicators'] = ak.stock_financial_analysis_indicator_em(symbol)
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        analysis['financial_abstract'] = ak.stock_financial_abstract(symbol)
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        analysis['balance_sheet'] = ak.stock_balance_sheet_by_report_em(symbol)
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        analysis['cash_flow'] = ak.stock_cash_flow_sheet_by_report_em(symbol)
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        analysis['roe_analysis'] = ak.stock_roe_em(symbol)
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        # Forward-looking data
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        analysis['earnings_forecast'] = ak.stock_profit_forecast_ths(symbol)
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        analysis['consensus_estimates'] = ak.stock_profit_forecast_em(symbol)
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        analysis['revenue_forecast'] = ak.stock_revenue_forecast_em(symbol)
481
        
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        # Analyst coverage
483
        analysis['recommendations'] = ak.stock_recommend_detail(symbol)
484
        
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        # Corporate information
486
        analysis['dividend_history'] = ak.stock_dividend_detail(symbol)
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        analysis['shareholders'] = ak.stock_holder_em(symbol)
488
        
489
    except Exception as e:
490
        print(f"Error retrieving data for {symbol}: {e}")
491
    
492
    return analysis
493

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def sector_fundamental_comparison(sector_stocks: list) -> pd.DataFrame:
495
    """
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    Compare fundamental metrics across sector stocks
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    Parameters:
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        sector_stocks: List of stock codes in the same sector
500
        
501
    Returns:
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        pd.DataFrame: Comparative fundamental analysis
503
    """
504
    
505
    comparison_data = []
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507
    for symbol in sector_stocks:
508
        try:
509
            # Get key metrics
510
            indicators = ak.stock_financial_analysis_indicator_em(symbol)
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            abstract = ak.stock_financial_abstract(symbol)
512
            
513
            # Extract key metrics for comparison
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            stock_data = {
515
                'symbol': symbol,
516
                'revenue_growth': None,  # Extract from indicators
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                'profit_growth': None,   # Extract from abstract
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                'roe': None,            # Extract from indicators
519
                'pe_ratio': None,       # Calculate or extract
520
                'debt_ratio': None      # Extract from balance sheet
521
            }
522
            
523
            comparison_data.append(stock_data)
524
            
525
        except Exception as e:
526
            print(f"Error processing {symbol}: {e}")
527
    
528
    return pd.DataFrame(comparison_data)
529

530
# Usage examples
531
# comprehensive_data = comprehensive_fundamental_analysis("000001")
532
# sector_comparison = sector_fundamental_comparison(["000001", "000002", "600036"])
533
```
534

535
### Screening and Ranking
536
```python { .api }
537
import akshare as ak
538

539
def fundamental_screening(criteria: dict) -> pd.DataFrame:
540
    """
541
    Screen stocks based on fundamental criteria
542
    
543
    Parameters:
544
        criteria: Dictionary of screening criteria
545
        
546
    Returns:
547
        pd.DataFrame: Stocks meeting the criteria
548
    """
549
    
550
    # Get overall market data
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    earnings_forecasts = ak.stock_profit_forecast()
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    recommendations = ak.stock_recommend()
553
    
554
    # Apply screening logic
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    screened_stocks = []
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557
    for _, row in earnings_forecasts.iterrows():
558
        symbol = row['代码']
559
        
560
        # Apply criteria (example logic)
561
        if (criteria.get('min_growth', 0) <= row.get('预测收益增长率', 0) and
562
            criteria.get('max_pe', 100) >= row.get('预测PE', 0)):
563
            
564
            screened_stocks.append({
565
                'symbol': symbol,
566
                'name': row.get('名称'),
567
                'forecast_eps_growth': row.get('预测收益增长率'),
568
                'forward_pe': row.get('预测PE')
569
            })
570
    
571
    return pd.DataFrame(screened_stocks)
572

573
def create_fundamental_dashboard(symbols: list) -> dict:
574
    """
575
    Create fundamental analysis dashboard for multiple stocks
576
    
577
    Parameters:
578
        symbols: List of stock symbols to analyze
579
        
580
    Returns:
581
        dict: Dashboard data for fundamental analysis
582
    """
583
    
584
    dashboard = {
585
        'earnings_forecasts': {},
586
        'financial_health': {},
587
        'valuation_metrics': {},
588
        'analyst_coverage': {}
589
    }
590
    
591
    for symbol in symbols:
592
        try:
593
            dashboard['earnings_forecasts'][symbol] = ak.stock_profit_forecast_em(symbol)
594
            dashboard['financial_health'][symbol] = ak.stock_financial_analysis_indicator_em(symbol)
595
            dashboard['analyst_coverage'][symbol] = ak.stock_recommend_detail(symbol)
596
            
597
        except Exception as e:
598
            print(f"Error creating dashboard for {symbol}: {e}")
599
    
600
    return dashboard
601

602
# Usage examples
603
screening_criteria = {
604
    'min_growth': 10,  # Minimum 10% earnings growth
605
    'max_pe': 20       # Maximum P/E ratio of 20
606
}
607

608
# screened_results = fundamental_screening(screening_criteria)
609
# dashboard = create_fundamental_dashboard(["000001", "000002", "600036"])
610
```
611

612
## Data Quality and Coverage
613

614
### Data Characteristics
615
- **Reporting Frequency**: Quarterly and annual financial statements
616
- **Forecast Horizon**: Typically 1-3 years forward-looking estimates
617
- **Analyst Coverage**: Major research institutions and independent analysts
618
- **Historical Depth**: Multi-year financial statement history
619

620
### Update Schedule
621
- **Financial Statements**: Updated after quarterly earnings releases
622
- **Analyst Forecasts**: Updated continuously as analysts revise estimates
623
- **Recommendations**: Updated as analysts issue new research reports
624
- **Dividend Data**: Updated after dividend announcements
625

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### Common Applications
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1. **Equity Valuation**: DCF modeling and relative valuation analysis
628
2. **Investment Screening**: Identifying stocks meeting specific criteria
629
3. **Risk Assessment**: Financial health and leverage analysis
630
4. **Sector Analysis**: Comparative fundamental analysis within industries
631
5. **Earnings Analysis**: Tracking earnings trends and analyst expectations
632
6. **Dividend Investing**: Identifying sustainable dividend-paying stocks
633

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The fundamental analysis capabilities in AKShare provide comprehensive tools for equity research, company valuation, and investment decision-making based on financial statement analysis and forward-looking estimates.