Treasury ALM Risk Controller

def calculate_fx_counterparty_risk(cash_positions: str, fx_rates: Dict[str, float], counterparty_limits: Dict[str, float]) -> str:
    """
    Calculates FX exposure and counterparty risk for treasury operations.

    Parameters:
    - cash_positions: JSON string — list of objects with keys Date(str), Currency(str),
                      Counterparty(str), Amount(float).
                      Example: '[{"Date":"2024-01-01","Currency":"USD","Counterparty":"BankA","Amount":1000000}]'
    - fx_rates: Dictionary with currency codes as keys and FX rate to base currency as values.
                Example: {"USD": 1.08, "GBP": 1.25, "EUR": 1.0}
    - counterparty_limits: Dictionary with counterparty names as keys and maximum allowed exposure as values.
                           Example: {"BankA": 5000000, "BankB": 3000000}

    Returns:
    - JSON string with calculated exposures, FX-adjusted amounts, and limit breaches.
    """
    try:
        data = json.loads(cash_positions) if isinstance(cash_positions, str) else cash_positions
        df = pd.DataFrame(data)

        df['FX_Amount'] = df.apply(lambda x: x['Amount'] * fx_rates.get(x['Currency'], 1), axis=1)
        df['Limit'] = df['Counterparty'].apply(lambda c: counterparty_limits.get(c, float('inf')))
        df['LimitBreach'] = df['FX_Amount'] > df['Limit']

        result = df[['Date', 'Currency', 'Counterparty', 'Amount', 'FX_Amount', 'Limit', 'LimitBreach']]
        return result.to_json(orient='records', indent=2)

    except Exception as e:
        return json.dumps({'error': str(e)})

def calculate_alm_metrics(balance_sheet: str, interest_rates: Dict[str, float]) -> str:
    """
    Calculates key ALM metrics including liquidity ratios, interest rate gaps, and capital adequacy.

    Parameters:
    - balance_sheet: JSON string — list of objects with keys Type('Asset' or 'Liability'),
                     Amount(float), Maturity(int, days), Currency(str).
                     Example: '[{"Type":"Asset","Amount":5000000,"Maturity":365,"Currency":"EUR"}]'
    - interest_rates: Dictionary with currency as key and current interest rate as value.
                      Example: {"EUR": 0.03, "USD": 0.05}

    Returns:
    - JSON string with TotalAssets, TotalLiabilities, LiquidityRatio, InterestRateGap, CapitalAdequacyRatio.
    """
    try:
        data = json.loads(balance_sheet) if isinstance(balance_sheet, str) else balance_sheet
        df = pd.DataFrame(data)

        total_assets = float(df[df['Type'] == 'Asset']['Amount'].sum())
        total_liabilities = float(df[df['Type'] == 'Liability']['Amount'].sum())
        liquidity_ratio = total_assets / max(total_liabilities, 1)

        def weighted_ir(row: Any) -> float:
            rate = interest_rates.get(row['Currency'], 0)
            return row['Amount'] * rate * (1 if row['Type'] == 'Asset' else -1)

        interest_rate_gap = float(df.apply(weighted_ir, axis=1).sum())
        capital = total_assets - total_liabilities
        capital_adequacy_ratio = capital / max(total_assets, 1)

        return json.dumps({
            'TotalAssets': round(total_assets, 2),
            'TotalLiabilities': round(total_liabilities, 2),
            'LiquidityRatio': round(liquidity_ratio, 4),
            'InterestRateGap': round(interest_rate_gap, 2),
            'CapitalAdequacyRatio': round(capital_adequacy_ratio, 4)
        }, indent=2)

    except Exception as e:
        return json.dumps({'error': str(e)})