Liquidity Interest Rate Risk Assessor

def calculate_nii_sensitivity(asset_cf: str, liability_cf: str, delta_rate: float) -> str:
    """
    Calculates the change in Net Interest Income(NII) for the next year.

    Parameters:
    - asset_cf: JSON string — list of objects with keys Amount(float), Rate(float).
                Example: '[{"Amount":500000,"Rate":0.03}]'
    - liability_cf: JSON string — same format as asset_cf.
    - delta_rate: Shock in decimal(e.g. 0.01 for +100bps).

    Returns:
    - JSON string with NII change value.
    """
    try:
        a_data = json.loads(asset_cf) if isinstance(asset_cf, str) else asset_cf
        l_data = json.loads(liability_cf) if isinstance(liability_cf, str) else liability_cf

        a_df = pd.DataFrame(a_data)
        l_df = pd.DataFrame(l_data)

        asset_impact = float((a_df['Amount'] * (a_df['Rate'] + delta_rate)).sum())
        liability_impact = float((l_df['Amount'] * (l_df['Rate'] + delta_rate)).sum())
        nii_change = asset_impact - liability_impact

        return json.dumps({'nii_change': round(nii_change, 2), 'delta_rate': delta_rate})

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

def calculate_eve_sensitivity(asset_cf: str, liability_cf: str, delta_rate: float) -> str:
    """
    Calculates the change in Economic Value of Equity(EVE) due to interest rate shock.

    Parameters:
    - asset_cf: JSON string — list of objects with keys Maturity(int, days), Amount(float), Rate(float).
                Example: '[{"Maturity":30,"Amount":500000,"Rate":0.03}]'
    - liability_cf: JSON string — same format as asset_cf.
    - delta_rate: Shock in decimal(e.g. 0.01 for +100bps).

    Returns:
    - JSON string with EVE change value.
    """
    try:
        a_data = json.loads(asset_cf) if isinstance(asset_cf, str) else asset_cf
        l_data = json.loads(liability_cf) if isinstance(liability_cf, str) else liability_cf

        a_df = pd.DataFrame(a_data)
        l_df = pd.DataFrame(l_data)

        asset_change = float((a_df['Amount'] * delta_rate * a_df['Maturity'] / 365).sum())
        liability_change = float((l_df['Amount'] * delta_rate * l_df['Maturity'] / 365).sum())
        eve_change = asset_change - liability_change

        return json.dumps({'eve_change': round(eve_change, 2), 'delta_rate': delta_rate})

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

def calculate_liquidity_gaps(balance_sheet: str, time_buckets: List[int]) -> str:
    """
    Calculates cumulative liquidity gaps across different time buckets.

    Args:
        balance_sheet: JSON string — list of objects with keys Category('Assets' or 'Liabilities'), Amount(float), Maturity(int, days).
                       Example: '[{"Category":"Assets","Amount":500,"Maturity":150}, {"Category":"Liabilities","Amount":2500,"Maturity":7}]'
        time_buckets: List of day boundaries defining the buckets, e.g. [0, 30, 180, 365].

    Returns:
        JSON string with liquidity gap per bucket.
    """
    try:
        data = json.loads(balance_sheet) if isinstance(balance_sheet, str) else balance_sheet
        df = pd.DataFrame(data)

        gaps = []
        for i in range(len(time_buckets) - 1):
            start, end = time_buckets[i], time_buckets[i + 1]
            inflows = df[(df['Category'] == 'Assets') &
                         (df['Maturity'] >= start) &
                         (df['Maturity'] < end)]['Amount'].sum()
            outflows = df[(df['Category'] == 'Liabilities') &
                          (df['Maturity'] >= start) &
                          (df['Maturity'] < end)]['Amount'].sum()
            gap = inflows - outflows
            gaps.append({'Bucket': f'{start}-{end} days', 'Inflows': round(float(inflows), 2),
                         'Outflows': round(float(outflows), 2), 'LiquidityGap': round(float(gap), 2)})

        return json.dumps(gaps, indent=2)

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