Financial Statements

class Finance:
    """
    Financial data adapter for corporate financial statements and ratios.
    
    Supported sources: VCI, TCBS
    """
    
    def __init__(self, source: str, symbol: str, period: str = "quarter", get_all: bool = True, show_log: bool = False):
        """
        Initialize Finance data adapter.
        
        Args:
            source (str): Data source ("vci", "tcbs")
            symbol (str): Company symbol
            period (str): Reporting period ("quarter", "year"), defaults to "quarter"
            get_all (bool): Get all available periods, defaults to True
            show_log (bool): Enable logging, defaults to False
        """

def balance_sheet(self, *args, **kwargs) -> pd.DataFrame:
    """
    Get balance sheet data with assets, liabilities, and equity information.
    
    Common parameters (vary by source):
        symbol (str): Company symbol
        period (str): Reporting period ("quarter", "year")
        lang (str): Language ("vi", "en"), defaults to "vi"
        dropna (bool): Drop rows with all NaN values, defaults to True
        show_log (bool): Enable detailed logging, defaults to False
        
    Returns:
        pd.DataFrame: Balance sheet data including:
            - total_assets: Total assets
            - current_assets: Current assets
            - cash_and_equivalents: Cash and cash equivalents
            - short_term_investments: Short-term investments
            - accounts_receivable: Accounts receivable
            - inventory: Inventory
            - prepaid_expenses: Prepaid expenses
            - other_current_assets: Other current assets
            - non_current_assets: Non-current assets
            - property_plant_equipment: Property, plant & equipment
            - intangible_assets: Intangible assets
            - long_term_investments: Long-term investments
            - other_non_current_assets: Other non-current assets
            - total_liabilities: Total liabilities
            - current_liabilities: Current liabilities
            - accounts_payable: Accounts payable
            - short_term_debt: Short-term debt
            - accrued_expenses: Accrued expenses
            - other_current_liabilities: Other current liabilities
            - non_current_liabilities: Non-current liabilities
            - long_term_debt: Long-term debt
            - other_non_current_liabilities: Other non-current liabilities
            - total_equity: Total shareholders' equity
            - share_capital: Share capital
            - retained_earnings: Retained earnings
            - other_equity: Other equity components
    """

from vnstock import Finance

# Initialize Finance adapter for quarterly data
finance = Finance(source="vci", symbol="TCB", period="quarter")

# Get quarterly balance sheet
quarterly_bs = finance.balance_sheet(
    symbol="TCB",
    period="quarter",
    lang="vi"
)

# Get annual balance sheet
annual_bs = finance.balance_sheet(
    symbol="VCB",
    period="year",
    lang="en"
)

# Get balance sheet without NaN cleanup
raw_bs = finance.balance_sheet(
    symbol="HPG",
    period="quarter",
    dropna=False
)

def income_statement(self, *args, **kwargs) -> pd.DataFrame:
    """
    Get income statement data with revenue, expenses, and profit information.
    
    Common parameters (vary by source):
        symbol (str): Company symbol
        period (str): Reporting period ("quarter", "year")
        lang (str): Language ("vi", "en"), defaults to "vi"
        dropna (bool): Drop rows with all NaN values, defaults to True
        
    Returns:
        pd.DataFrame: Income statement data including:
            - total_revenue: Total revenue/net sales
            - cost_of_goods_sold: Cost of goods sold
            - gross_profit: Gross profit
            - operating_expenses: Total operating expenses
            - selling_expenses: Selling and marketing expenses
            - administrative_expenses: General and administrative expenses
            - research_development: Research and development expenses
            - other_operating_expenses: Other operating expenses
            - operating_income: Operating income/EBIT
            - financial_income: Financial income
            - financial_expenses: Financial expenses
            - other_income: Other non-operating income
            - other_expenses: Other non-operating expenses
            - pre_tax_income: Income before tax
            - tax_expense: Income tax expense
            - net_income: Net income after tax
            - earnings_per_share: Earnings per share (EPS)
            - diluted_eps: Diluted earnings per share
    """

# Get quarterly income statement
quarterly_is = finance.income_statement(
    symbol="TCB",
    period="quarter",
    lang="vi"
)

# Get annual income statement
annual_is = finance.income_statement(
    symbol="VCB", 
    period="year"
)

# Multi-company income statement comparison
symbols = ["TCB", "VCB", "BID"]
income_data = {}

for symbol in symbols:
    company_finance = Finance(source="vci", symbol=symbol, period="year")
    income_data[symbol] = company_finance.income_statement(symbol=symbol, period="year")

def cash_flow(self, *args, **kwargs) -> pd.DataFrame:
    """
    Get cash flow statement data with operating, investing, and financing activities.
    
    Common parameters (vary by source):
        symbol (str): Company symbol  
        period (str): Reporting period ("quarter", "year")
        lang (str): Language ("vi", "en"), defaults to "vi"
        dropna (bool): Drop rows with all NaN values, defaults to True
        
    Returns:
        pd.DataFrame: Cash flow statement data including:
            - operating_cash_flow: Net cash from operating activities
            - net_income_adjusted: Net income adjusted for non-cash items
            - depreciation_amortization: Depreciation and amortization
            - working_capital_changes: Changes in working capital
            - accounts_receivable_changes: Changes in accounts receivable
            - inventory_changes: Changes in inventory
            - accounts_payable_changes: Changes in accounts payable
            - other_operating_changes: Other operating cash flow changes
            - investing_cash_flow: Net cash from investing activities
            - capital_expenditures: Capital expenditures
            - acquisitions: Acquisitions and investments
            - asset_sales: Proceeds from asset sales
            - other_investing_activities: Other investing activities
            - financing_cash_flow: Net cash from financing activities
            - debt_issuance: Debt issuance proceeds
            - debt_repayment: Debt repayments
            - equity_issuance: Equity issuance proceeds
            - dividend_payments: Dividend payments
            - share_repurchases: Share repurchases
            - other_financing_activities: Other financing activities
            - net_cash_flow: Net change in cash
            - beginning_cash: Beginning cash balance
            - ending_cash: Ending cash balance
    """

# Get quarterly cash flow statement
quarterly_cf = finance.cash_flow(
    symbol="TCB",
    period="quarter"
)

# Get annual cash flow with English labels
annual_cf = finance.cash_flow(
    symbol="VCB",
    period="year", 
    lang="en"
)

# Analyze cash flow trends
hpg_cf = finance.cash_flow(symbol="HPG", period="quarter")

def ratio(self, *args, **kwargs) -> pd.DataFrame:
    """
    Get financial ratios and performance metrics.
    
    Common parameters (vary by source):
        symbol (str): Company symbol
        period (str): Reporting period ("quarter", "year")
        flatten_columns (bool): Flatten multi-level column headers
        separator (str): Column name separator for flattened columns
        drop_levels (list): Column levels to drop in multi-index
        
    Returns:
        pd.DataFrame: Financial ratios including:
            # Profitability Ratios
            - gross_profit_margin: Gross profit margin (%)
            - operating_profit_margin: Operating profit margin (%)
            - net_profit_margin: Net profit margin (%)
            - return_on_assets: Return on assets (ROA) (%)
            - return_on_equity: Return on equity (ROE) (%)
            - return_on_invested_capital: Return on invested capital (ROIC) (%)
            
            # Liquidity Ratios
            - current_ratio: Current ratio
            - quick_ratio: Quick ratio (acid test)
            - cash_ratio: Cash ratio
            - working_capital: Working capital
            
            # Efficiency Ratios
            - asset_turnover: Asset turnover ratio
            - inventory_turnover: Inventory turnover ratio
            - receivables_turnover: Accounts receivable turnover
            - days_sales_outstanding: Days sales outstanding (DSO)
            - days_inventory_outstanding: Days inventory outstanding (DIO)
            - days_payable_outstanding: Days payable outstanding (DPO)
            - cash_conversion_cycle: Cash conversion cycle
            
            # Leverage Ratios
            - debt_to_equity: Debt-to-equity ratio
            - debt_to_assets: Debt-to-assets ratio
            - interest_coverage: Interest coverage ratio
            - debt_service_coverage: Debt service coverage ratio
            
            # Market Ratios
            - price_to_earnings: Price-to-earnings (P/E) ratio
            - price_to_book: Price-to-book (P/B) ratio
            - price_to_sales: Price-to-sales (P/S) ratio
            - enterprise_value_ebitda: EV/EBITDA ratio
            - dividend_yield: Dividend yield (%)
            - dividend_payout_ratio: Dividend payout ratio (%)
    """

# Get comprehensive financial ratios
ratios = finance.ratio(
    symbol="TCB",
    period="quarter"
)

# Get ratios with flattened column names
flat_ratios = finance.ratio(
    symbol="VCB",
    period="year",
    flatten_columns=True,
    separator="_"
)

# Get ratios with specific column levels
custom_ratios = finance.ratio(
    symbol="HPG",
    period="quarter",
    drop_levels=[0]  # Drop first level of multi-index
)

from vnstock import Finance
import pandas as pd

# Initialize finance adapter
finance = Finance(source="vci", symbol="TCB", period="quarter")

# Get multi-year quarterly data
symbol = "TCB"
quarterly_bs = finance.balance_sheet(symbol=symbol, period="quarter")
quarterly_is = finance.income_statement(symbol=symbol, period="quarter")
quarterly_cf = finance.cash_flow(symbol=symbol, period="quarter")
quarterly_ratios = finance.ratio(symbol=symbol, period="quarter")

# Combine for comprehensive analysis
financial_data = {
    'balance_sheet': quarterly_bs,
    'income_statement': quarterly_is, 
    'cash_flow': quarterly_cf,
    'ratios': quarterly_ratios
}

# Analyze trends
print("Revenue Growth (QoQ):")
revenue_growth = quarterly_is['total_revenue'].pct_change()
print(revenue_growth)

print("ROE Trend:")
roe_trend = quarterly_ratios['return_on_equity']
print(roe_trend)

# Compare financial performance across companies
symbols = ["TCB", "VCB", "BID"]
comparison_metrics = {}

for symbol in symbols:
    company_finance = Finance(source="vci", symbol=symbol, period="year")
    
    # Get latest annual data
    ratios = company_finance.ratio(symbol=symbol, period="year")
    income = company_finance.income_statement(symbol=symbol, period="year")
    
    # Extract key metrics (latest period)
    latest_ratios = ratios.iloc[:, -1]  # Last column (most recent)
    latest_income = income.iloc[:, -1]
    
    comparison_metrics[symbol] = {
        'roe': latest_ratios.get('return_on_equity', 0),
        'roa': latest_ratios.get('return_on_assets', 0),
        'net_margin': latest_ratios.get('net_profit_margin', 0),
        'revenue': latest_income.get('total_revenue', 0),
        'net_income': latest_income.get('net_income', 0)
    }

# Create comparison DataFrame
comparison_df = pd.DataFrame(comparison_metrics).T
print("Financial Comparison:")
print(comparison_df)

def assess_financial_health(symbol, source="vci"):
    """Assess company financial health using key ratios."""
    
    finance = Finance(source=source, symbol=symbol, period="quarter")
    
    # Get latest financial data
    ratios = finance.ratio(symbol=symbol, period="quarter")
    balance_sheet = finance.balance_sheet(symbol=symbol, period="quarter")
    
    # Extract latest period data
    latest_ratios = ratios.iloc[:, -1]
    latest_bs = balance_sheet.iloc[:, -1]
    
    # Calculate health score components
    health_indicators = {
        'liquidity': {
            'current_ratio': latest_ratios.get('current_ratio', 0),
            'quick_ratio': latest_ratios.get('quick_ratio', 0),
            'cash_ratio': latest_ratios.get('cash_ratio', 0)
        },
        'profitability': {
            'roe': latest_ratios.get('return_on_equity', 0),
            'roa': latest_ratios.get('return_on_assets', 0),
            'net_margin': latest_ratios.get('net_profit_margin', 0)
        },
        'leverage': {
            'debt_to_equity': latest_ratios.get('debt_to_equity', 0),
            'debt_to_assets': latest_ratios.get('debt_to_assets', 0),
            'interest_coverage': latest_ratios.get('interest_coverage', 0)
        }
    }
    
    return health_indicators

# Assess multiple companies
symbols = ["TCB", "VCB", "HPG"]
health_assessments = {}

for symbol in symbols:
    health_assessments[symbol] = assess_financial_health(symbol)
    
print("Financial Health Assessment:")
for symbol, health in health_assessments.items():
    print(f"\n{symbol}:")
    for category, metrics in health.items():
        print(f"  {category.title()}:")
        for metric, value in metrics.items():
            print(f"    {metric}: {value:.2f}")