┌─────────────────────────────────────────┐
│         WEALTH BUILDING                 │
│    (Investing, Retirement, Legacy)      │
├─────────────────────────────────────────┤
│         PROTECTION                      │
│    (Insurance, Estate Planning)         │
├─────────────────────────────────────────┤
│         DEBT ELIMINATION                │
│    (High-interest → Low-interest)       │
├─────────────────────────────────────────┤
│         EMERGENCY FUND                  │
│    (3-6 months expenses)                │
├─────────────────────────────────────────┤
│         FOUNDATION                      │
│    (Budget, Cash Flow, Basic Savings)   │
└─────────────────────────────────────────┘

from dataclasses import dataclass
from typing import Dict, List, Optional
from enum import Enum
import pandas as pd

class BudgetMethod(Enum):
    FIFTY_THIRTY_TWENTY = "50/30/20"
    ZERO_BASED = "zero_based"
    ENVELOPE = "envelope"
    PAY_YOURSELF_FIRST = "pay_yourself_first"

@dataclass
class Income:
    """Monthly income sources."""
    salary: float
    side_income: float = 0
    investment_income: float = 0
    other: float = 0

    @property
    def total_gross(self) -> float:
        return self.salary + self.side_income + self.investment_income + self.other

    def after_tax(self, tax_rate: float = 0.25) -> float:
        """Calculate after-tax income."""
        return self.total_gross * (1 - tax_rate)

@dataclass
class Expense:
    """Expense item with category."""
    name: str
    amount: float
    category: str  # 'need', 'want', 'savings'
    is_fixed: bool = True

class BudgetPlanner:
    """Personal budget planning and analysis."""

    def __init__(self, income: Income, tax_rate: float = 0.25):
        self.income = income
        self.tax_rate = tax_rate
        self.expenses: List[Expense] = []

    def add_expense(self, expense: Expense) -> None:
        """Add an expense to the budget."""
        self.expenses.append(expense)

    @property
    def monthly_income(self) -> float:
        """After-tax monthly income."""
        return self.income.after_tax(self.tax_rate)

    # ==================== 50/30/20 RULE ====================

    def fifty_thirty_twenty(self) -> Dict[str, float]:
        """
        50/30/20 Budget Rule.

        - 50% Needs: Housing, utilities, groceries, insurance, minimum debt payments
        - 30% Wants: Entertainment, dining out, hobbies, subscriptions
        - 20% Savings: Emergency fund, retirement, investments, extra debt payments
        """
        income = self.monthly_income
        return {
            'needs_budget': income * 0.50,
            'wants_budget': income * 0.30,
            'savings_budget': income * 0.20,
            'needs_actual': sum(e.amount for e in self.expenses if e.category == 'need'),
            'wants_actual': sum(e.amount for e in self.expenses if e.category == 'want'),
            'savings_actual': sum(e.amount for e in self.expenses if e.category == 'savings'),
        }

    def analyze_fifty_thirty_twenty(self) -> Dict[str, any]:
        """Analyze budget against 50/30/20 targets."""
        budget = self.fifty_thirty_twenty()

        return {
            'needs': {
                'budget': budget['needs_budget'],
                'actual': budget['needs_actual'],
                'variance': budget['needs_budget'] - budget['needs_actual'],
                'on_track': budget['needs_actual'] <= budget['needs_budget']
            },
            'wants': {
                'budget': budget['wants_budget'],
                'actual': budget['wants_actual'],
                'variance': budget['wants_budget'] - budget['wants_actual'],
                'on_track': budget['wants_actual'] <= budget['wants_budget']
            },
            'savings': {
                'budget': budget['savings_budget'],
                'actual': budget['savings_actual'],
                'variance': budget['savings_actual'] - budget['savings_budget'],
                'on_track': budget['savings_actual'] >= budget['savings_budget']
            }
        }

    # ==================== ZERO-BASED BUDGETING ====================

    def zero_based_budget(self) -> Dict[str, float]:
        """
        Zero-Based Budgeting.

        Every dollar has a job. Income - All Expenses = $0
        """
        total_expenses = sum(e.amount for e in self.expenses)
        unallocated = self.monthly_income - total_expenses

        return {
            'income': self.monthly_income,
            'total_allocated': total_expenses,
            'unallocated': unallocated,
            'is_balanced': abs(unallocated) < 1.0,  # Within $1
            'expenses_by_category': self._group_expenses_by_category()
        }

    def _group_expenses_by_category(self) -> Dict[str, float]:
        """Group expenses by category."""
        categories = {}
        for expense in self.expenses:
            if expense.category not in categories:
                categories[expense.category] = 0
            categories[expense.category] += expense.amount
        return categories

    # ==================== ENVELOPE SYSTEM ====================

    def envelope_budget(self, envelopes: Dict[str, float]) -> Dict[str, Dict]:
        """
        Envelope Budgeting System.

        Allocate cash to specific spending categories.
        When envelope is empty, stop spending.
        """
        result = {}
        for category, budget in envelopes.items():
            spent = sum(e.amount for e in self.expenses if e.category == category)
            result[category] = {
                'budget': budget,
                'spent': spent,
                'remaining': budget - spent,
                'percent_used': (spent / budget * 100) if budget > 0 else 0
            }
        return result

    # ==================== PAY YOURSELF FIRST ====================

    def pay_yourself_first(self, savings_target_percent: float = 0.20) -> Dict[str, float]:
        """
        Pay Yourself First Strategy.

        Save first, then budget the rest for expenses.
        """
        savings_amount = self.monthly_income * savings_target_percent
        spending_budget = self.monthly_income - savings_amount
        actual_spending = sum(e.amount for e in self.expenses if e.category != 'savings')

        return {
            'target_savings': savings_amount,
            'spending_budget': spending_budget,
            'actual_spending': actual_spending,
            'spending_variance': spending_budget - actual_spending,
            'on_track': actual_spending <= spending_budget
        }

    # ==================== SAVINGS RATE ====================

    def calculate_savings_rate(self) -> float:
        """Calculate monthly savings rate."""
        savings = sum(e.amount for e in self.expenses if e.category == 'savings')
        return savings / self.income.total_gross if self.income.total_gross > 0 else 0

from dataclasses import dataclass
from typing import List, Dict
import numpy as np

@dataclass
class Debt:
    """Individual debt account."""
    name: str
    balance: float
    interest_rate: float  # Annual rate as decimal
    minimum_payment: float

    def monthly_interest(self) -> float:
        """Calculate monthly interest charge."""
        return self.balance * (self.interest_rate / 12)

class DebtPayoffCalculator:
    """Debt payoff strategy calculator."""

    def __init__(self, debts: List[Debt], extra_payment: float = 0):
        self.debts = debts
        self.extra_payment = extra_payment

    @property
    def total_debt(self) -> float:
        return sum(d.balance for d in self.debts)

    @property
    def total_minimum_payments(self) -> float:
        return sum(d.minimum_payment for d in self.debts)

    @property
    def weighted_avg_rate(self) -> float:
        """Calculate weighted average interest rate."""
        if self.total_debt == 0:
            return 0
        return sum(d.balance * d.interest_rate for d in self.debts) / self.total_debt

    # ==================== DEBT SNOWBALL ====================

    def debt_snowball(self) -> Dict:
        """
        Debt Snowball Method.

        Pay minimum on all debts, put extra toward SMALLEST balance first.
        Psychological wins build momentum.
        """
        # Sort by balance (smallest first)
        sorted_debts = sorted(self.debts, key=lambda d: d.balance)
        return self._simulate_payoff(sorted_debts, "snowball")

    # ==================== DEBT AVALANCHE ====================

    def debt_avalanche(self) -> Dict:
        """
        Debt Avalanche Method.

        Pay minimum on all debts, put extra toward HIGHEST interest rate first.
        Mathematically optimal - minimizes total interest paid.
        """
        # Sort by interest rate (highest first)
        sorted_debts = sorted(self.debts, key=lambda d: d.interest_rate, reverse=True)
        return self._simulate_payoff(sorted_debts, "avalanche")

    def _simulate_payoff(self, sorted_debts: List[Debt], method: str) -> Dict:
        """Simulate debt payoff over time."""
        # Create working copies
        balances = {d.name: d.balance for d in sorted_debts}
        rates = {d.name: d.interest_rate for d in sorted_debts}
        minimums = {d.name: d.minimum_payment for d in sorted_debts}

        total_paid = 0
        total_interest = 0
        months = 0
        payoff_order = []
        monthly_schedule = []

        while any(b > 0 for b in balances.values()):
            months += 1
            if months > 360:  # 30 year cap
                break

            month_interest = 0
            month_principal = 0
            extra_remaining = self.extra_payment

            # Apply interest to all debts
            for name in balances:
                if balances[name] > 0:
                    interest = balances[name] * (rates[name] / 12)
                    balances[name] += interest
                    month_interest += interest

            # Pay minimums on all debts
            for name in balances:
                if balances[name] > 0:
                    payment = min(minimums[name], balances[name])
                    balances[name] -= payment
                    month_principal += payment

                    if balances[name] <= 0:
                        balances[name] = 0
                        payoff_order.append({'name': name, 'month': months})
                        extra_remaining += minimums[name]  # Freed up payment

            # Apply extra payment to target debt
            for debt in sorted_debts:
                if balances[debt.name] > 0 and extra_remaining > 0:
                    payment = min(extra_remaining, balances[debt.name])
                    balances[debt.name] -= payment
                    month_principal += payment
                    extra_remaining -= payment

                    if balances[debt.name] <= 0:
                        balances[debt.name] = 0
                        payoff_order.append({'name': debt.name, 'month': months})

            total_interest += month_interest
            total_paid += month_interest + month_principal

            monthly_schedule.append({
                'month': months,
                'interest_paid': month_interest,
                'principal_paid': month_principal,
                'remaining_balance': sum(balances.values())
            })

        return {
            'method': method,
            'months_to_payoff': months,
            'years_to_payoff': months / 12,
            'total_paid': total_paid,
            'total_interest': total_interest,
            'original_balance': self.total_debt,
            'payoff_order': payoff_order,
            'monthly_schedule': monthly_schedule
        }

    def compare_methods(self) -> Dict:
        """Compare snowball vs avalanche methods."""
        snowball = self.debt_snowball()
        avalanche = self.debt_avalanche()

        return {
            'snowball': snowball,
            'avalanche': avalanche,
            'interest_savings': snowball['total_interest'] - avalanche['total_interest'],
            'time_difference_months': snowball['months_to_payoff'] - avalanche['months_to_payoff'],
            'recommended': 'avalanche' if avalanche['total_interest'] < snowball['total_interest'] else 'snowball'
        }

@dataclass
class MonthlyExpenses:
    """Monthly essential expenses."""
    housing: float  # Rent/mortgage
    utilities: float
    groceries: float
    transportation: float
    insurance: float
    minimum_debt_payments: float
    other_essentials: float = 0

    @property
    def total(self) -> float:
        return (
            self.housing + self.utilities + self.groceries +
            self.transportation + self.insurance +
            self.minimum_debt_payments + self.other_essentials
        )

class EmergencyFundPlanner:
    """Emergency fund planning and tracking."""

    def __init__(
        self,
        monthly_expenses: MonthlyExpenses,
        current_savings: float = 0,
        job_stability: str = "stable"  # stable, moderate, unstable
    ):
        self.expenses = monthly_expenses
        self.current_savings = current_savings
        self.job_stability = job_stability

    def recommended_months(self) -> int:
        """Get recommended emergency fund months based on job stability."""
        recommendations = {
            "stable": 3,       # Stable job, dual income
            "moderate": 6,    # Single income, some variability
            "unstable": 9,    # Freelance, commission-based, volatile industry
            "self_employed": 12  # Business owners
        }
        return recommendations.get(self.job_stability, 6)

    def target_amount(self) -> float:
        """Calculate target emergency fund amount."""
        return self.expenses.total * self.recommended_months()

    def current_coverage(self) -> float:
        """Calculate current months of coverage."""
        return self.current_savings / self.expenses.total if self.expenses.total > 0 else 0

    def gap_analysis(self) -> Dict:
        """Analyze emergency fund gap."""
        target = self.target_amount()
        gap = max(0, target - self.current_savings)

        return {
            'target_months': self.recommended_months(),
            'target_amount': target,
            'current_savings': self.current_savings,
            'current_months_coverage': self.current_coverage(),
            'gap_amount': gap,
            'percent_funded': (self.current_savings / target * 100) if target > 0 else 100,
            'is_fully_funded': self.current_savings >= target
        }

    def savings_plan(self, monthly_contribution: float) -> Dict:
        """Create plan to reach emergency fund target."""
        gap = self.gap_analysis()['gap_amount']

        if gap <= 0:
            return {'already_funded': True, 'months_to_goal': 0}

        months_to_goal = gap / monthly_contribution if monthly_contribution > 0 else float('inf')

        return {
            'already_funded': False,
            'gap_amount': gap,
            'monthly_contribution': monthly_contribution,
            'months_to_goal': months_to_goal,
            'years_to_goal': months_to_goal / 12,
            'target_date_months': int(np.ceil(months_to_goal))
        }

class RetirementPlanner:
    """Retirement savings and projection calculator."""

    def __init__(
        self,
        current_age: int,
        retirement_age: int,
        current_savings: float,
        annual_contribution: float,
        expected_return: float = 0.07,  # 7% average market return
        inflation_rate: float = 0.03
    ):
        self.current_age = current_age
        self.retirement_age = retirement_age
        self.current_savings = current_savings
        self.annual_contribution = annual_contribution
        self.expected_return = expected_return
        self.inflation_rate = inflation_rate

    @property
    def years_to_retirement(self) -> int:
        return max(0, self.retirement_age - self.current_age)

    def future_value(self) -> float:
        """
        Calculate future value of retirement savings.

        FV = PV × (1 + r)^n + PMT × [((1 + r)^n - 1) / r]
        """
        n = self.years_to_retirement
        r = self.expected_return

        # Future value of current savings
        fv_current = self.current_savings * ((1 + r) ** n)

        # Future value of annual contributions (annuity)
        if r > 0:
            fv_contributions = self.annual_contribution * (((1 + r) ** n - 1) / r)
        else:
            fv_contributions = self.annual_contribution * n

        return fv_current + fv_contributions

    def real_future_value(self) -> float:
        """Future value adjusted for inflation (today's dollars)."""
        nominal_fv = self.future_value()
        return nominal_fv / ((1 + self.inflation_rate) ** self.years_to_retirement)

    def retirement_income(self, withdrawal_rate: float = 0.04) -> Dict:
        """
        Calculate sustainable retirement income using 4% rule.

        The 4% rule: Withdraw 4% of portfolio in year 1, then adjust for inflation.
        """
        portfolio = self.future_value()
        real_portfolio = self.real_future_value()

        return {
            'portfolio_at_retirement': portfolio,
            'portfolio_real_value': real_portfolio,
            'annual_income_nominal': portfolio * withdrawal_rate,
            'annual_income_real': real_portfolio * withdrawal_rate,
            'monthly_income_nominal': portfolio * withdrawal_rate / 12,
            'monthly_income_real': real_portfolio * withdrawal_rate / 12
        }

    def required_savings_for_income(
        self,
        target_annual_income: float,
        withdrawal_rate: float = 0.04
    ) -> Dict:
        """Calculate required savings to achieve target retirement income."""
        required_portfolio = target_annual_income / withdrawal_rate

        # Solve for required annual contribution
        # FV = PV × (1 + r)^n + PMT × [((1 + r)^n - 1) / r]
        # PMT = (FV - PV × (1 + r)^n) / [((1 + r)^n - 1) / r]

        n = self.years_to_retirement
        r = self.expected_return

        fv_current = self.current_savings * ((1 + r) ** n)
        remaining_needed = required_portfolio - fv_current

        if r > 0 and n > 0:
            annuity_factor = ((1 + r) ** n - 1) / r
            required_annual = remaining_needed / annuity_factor
        else:
            required_annual = remaining_needed / max(n, 1)

        return {
            'target_annual_income': target_annual_income,
            'required_portfolio': required_portfolio,
            'current_trajectory': self.future_value(),
            'gap': max(0, required_portfolio - self.future_value()),
            'required_annual_contribution': max(0, required_annual),
            'required_monthly_contribution': max(0, required_annual / 12)
        }

    def compound_growth_schedule(self) -> List[Dict]:
        """Generate year-by-year growth projection."""
        schedule = []
        balance = self.current_savings

        for year in range(self.years_to_retirement + 1):
            age = self.current_age + year

            schedule.append({
                'year': year,
                'age': age,
                'starting_balance': balance,
                'contribution': self.annual_contribution if year > 0 else 0,
                'growth': balance * self.expected_return if year > 0 else 0,
                'ending_balance': balance
            })

            if year < self.years_to_retirement:
                balance = balance * (1 + self.expected_return) + self.annual_contribution

        # Update ending balances
        for i in range(len(schedule) - 1):
            schedule[i]['ending_balance'] = schedule[i + 1]['starting_balance']
        schedule[-1]['ending_balance'] = balance

        return schedule

    # ==================== RETIREMENT ACCOUNT OPTIMIZATION ====================

    def contribution_priority(
        self,
        has_employer_401k: bool = True,
        employer_match_percent: float = 0.03,
        has_hsa: bool = False
    ) -> List[str]:
        """
        Recommended contribution priority order.

        Standard priority:
        1. 401(k) up to employer match (free money)
        2. HSA (triple tax advantage)
        3. Max out Roth IRA
        4. Max out 401(k)
        5. Taxable brokerage
        """
        priority = []

        if has_employer_401k and employer_match_percent > 0:
            priority.append(f"1. 401(k) to employer match ({employer_match_percent*100:.0f}%)")

        if has_hsa:
            priority.append("2. Max HSA ($4,150 individual / $8,300 family 2024)")

        priority.extend([
            "3. Max Roth IRA ($7,000 / $8,000 if 50+ in 2024)",
            "4. Max 401(k) ($23,000 / $30,500 if 50+ in 2024)",
            "5. Taxable brokerage account"
        ])

        return priority

class PortfolioAllocator:
    """Personal investment portfolio allocation."""

    def __init__(self, age: int, risk_tolerance: str = "moderate"):
        self.age = age
        self.risk_tolerance = risk_tolerance  # conservative, moderate, aggressive

    def age_based_allocation(self) -> Dict[str, float]:
        """
        Age-based asset allocation rule.

        Traditional: Bonds = Age (e.g., 30 years old = 30% bonds)
        Modern: Bonds = Age - 20 (more aggressive for longer life expectancy)
        """
        # Modern rule (age - 20)
        bond_percent = max(0, min(80, self.age - 20)) / 100
        stock_percent = 1 - bond_percent

        return {
            'stocks': stock_percent,
            'bonds': bond_percent,
            'rationale': f"At age {self.age}, allocate {stock_percent*100:.0f}% stocks, {bond_percent*100:.0f}% bonds"
        }

    def risk_based_allocation(self) -> Dict[str, float]:
        """Allocation based on risk tolerance."""
        allocations = {
            "conservative": {
                'stocks': 0.30,
                'bonds': 0.50,
                'cash': 0.20
            },
            "moderate": {
                'stocks': 0.60,
                'bonds': 0.30,
                'cash': 0.10
            },
            "aggressive": {
                'stocks': 0.80,
                'bonds': 0.15,
                'cash': 0.05
            },
            "very_aggressive": {
                'stocks': 0.95,
                'bonds': 0.05,
                'cash': 0.00
            }
        }
        return allocations.get(self.risk_tolerance, allocations["moderate"])

    def three_fund_portfolio(self, international_percent: float = 0.30) -> Dict[str, float]:
        """
        Three-Fund Portfolio (Bogleheads approach).

        Simple, low-cost, diversified portfolio:
        1. US Total Stock Market
        2. International Stock Market
        3. US Total Bond Market
        """
        base = self.age_based_allocation()
        stock_allocation = base['stocks']
        bond_allocation = base['bonds']

        return {
            'us_total_stock': stock_allocation * (1 - international_percent),
            'international_stock': stock_allocation * international_percent,
            'us_total_bond': bond_allocation,
            'total': 1.0,
            'example_funds': {
                'us_total_stock': 'VTI, VTSAX, FSKAX',
                'international_stock': 'VXUS, VTIAX, FTIHX',
                'us_total_bond': 'BND, VBTLX, FXNAX'
            }
        }

    def target_date_equivalent(self, retirement_year: int) -> Dict:
        """Calculate allocation similar to target-date funds."""
        years_to_retirement = retirement_year - 2024

        if years_to_retirement > 40:
            stocks = 0.90
        elif years_to_retirement > 25:
            stocks = 0.85
        elif years_to_retirement > 15:
            stocks = 0.75
        elif years_to_retirement > 5:
            stocks = 0.60
        elif years_to_retirement > 0:
            stocks = 0.50
        else:  # In retirement
            stocks = 0.40

        return {
            'retirement_year': retirement_year,
            'years_to_retirement': years_to_retirement,
            'stocks': stocks,
            'bonds': 1 - stocks,
            'glide_path': "Becomes more conservative as retirement approaches"
        }

@dataclass
class FinancialSnapshot:
    """Point-in-time financial snapshot."""
    date: str

    # Assets
    checking: float = 0
    savings: float = 0
    investments: float = 0  # Brokerage accounts
    retirement_401k: float = 0
    retirement_ira: float = 0
    home_value: float = 0
    vehicle_value: float = 0
    other_assets: float = 0

    # Liabilities
    mortgage: float = 0
    auto_loans: float = 0
    student_loans: float = 0
    credit_cards: float = 0
    other_debt: float = 0

    @property
    def total_assets(self) -> float:
        return (
            self.checking + self.savings + self.investments +
            self.retirement_401k + self.retirement_ira +
            self.home_value + self.vehicle_value + self.other_assets
        )

    @property
    def liquid_assets(self) -> float:
        return self.checking + self.savings + self.investments

    @property
    def retirement_assets(self) -> float:
        return self.retirement_401k + self.retirement_ira

    @property
    def total_liabilities(self) -> float:
        return (
            self.mortgage + self.auto_loans + self.student_loans +
            self.credit_cards + self.other_debt
        )

    @property
    def net_worth(self) -> float:
        return self.total_assets - self.total_liabilities

class NetWorthTracker:
    """Track net worth over time."""

    def __init__(self):
        self.snapshots: List[FinancialSnapshot] = []

    def add_snapshot(self, snapshot: FinancialSnapshot) -> None:
        """Add a financial snapshot."""
        self.snapshots.append(snapshot)
        self.snapshots.sort(key=lambda s: s.date)

    def current_net_worth(self) -> float:
        """Get most recent net worth."""
        return self.snapshots[-1].net_worth if self.snapshots else 0

    def net_worth_change(self, periods: int = 1) -> Dict:
        """Calculate net worth change over periods."""
        if len(self.snapshots) < 2:
            return {'change': 0, 'percent_change': 0}

        current = self.snapshots[-1].net_worth
        previous = self.snapshots[-min(periods + 1, len(self.snapshots))].net_worth
        change = current - previous

        return {
            'current': current,
            'previous': previous,
            'change': change,
            'percent_change': (change / previous * 100) if previous != 0 else 0
        }

    def financial_ratios(self) -> Dict[str, float]:
        """Calculate key financial health ratios."""
        if not self.snapshots:
            return {}

        latest = self.snapshots[-1]

        return {
            'liquidity_ratio': latest.liquid_assets / latest.total_liabilities if latest.total_liabilities > 0 else float('inf'),
            'debt_to_asset': latest.total_liabilities / latest.total_assets if latest.total_assets > 0 else 0,
            'emergency_fund_months': latest.liquid_assets / (latest.total_liabilities + 1),  # Rough estimate
            'retirement_progress': latest.retirement_assets / latest.total_assets if latest.total_assets > 0 else 0
        }

    def asset_allocation(self) -> Dict[str, float]:
        """Current asset allocation breakdown."""
        if not self.snapshots:
            return {}

        latest = self.snapshots[-1]
        total = latest.total_assets

        if total == 0:
            return {}

        return {
            'cash': (latest.checking + latest.savings) / total,
            'investments': latest.investments / total,
            'retirement': latest.retirement_assets / total,
            'real_estate': latest.home_value / total,
            'other': (latest.vehicle_value + latest.other_assets) / total
        }

from datetime import datetime, timedelta
from typing import List, Dict, Optional

@dataclass
class FinancialGoal:
    """SMART financial goal."""
    name: str
    target_amount: float
    target_date: str  # YYYY-MM-DD
    current_savings: float = 0
    priority: int = 1  # 1 = highest priority

    @property
    def remaining_amount(self) -> float:
        return max(0, self.target_amount - self.current_savings)

    @property
    def percent_complete(self) -> float:
        return (self.current_savings / self.target_amount * 100) if self.target_amount > 0 else 100

    @property
    def months_remaining(self) -> int:
        target = datetime.strptime(self.target_date, "%Y-%m-%d")
        today = datetime.now()
        delta = target - today
        return max(0, delta.days // 30)

class GoalPlanner:
    """Multi-goal financial planning."""

    def __init__(self, monthly_savings_budget: float):
        self.monthly_budget = monthly_savings_budget
        self.goals: List[FinancialGoal] = []

    def add_goal(self, goal: FinancialGoal) -> None:
        """Add a financial goal."""
        self.goals.append(goal)
        self.goals.sort(key=lambda g: g.priority)

    def analyze_goal(self, goal: FinancialGoal) -> Dict:
        """Analyze single goal feasibility."""
        months = goal.months_remaining
        required_monthly = goal.remaining_amount / months if months > 0 else goal.remaining_amount

        return {
            'goal': goal.name,
            'target_amount': goal.target_amount,
            'current_savings': goal.current_savings,
            'remaining': goal.remaining_amount,
            'percent_complete': goal.percent_complete,
            'months_remaining': months,
            'required_monthly': required_monthly,
            'is_achievable': required_monthly <= self.monthly_budget,
            'surplus_deficit': self.monthly_budget - required_monthly
        }

    def allocate_to_goals(self) -> Dict[str, float]:
        """Allocate monthly savings across goals by priority."""
        allocations = {}
        remaining_budget = self.monthly_budget

        for goal in self.goals:
            analysis = self.analyze_goal(goal)
            required = analysis['required_monthly']

            allocation = min(required, remaining_budget)
            allocations[goal.name] = {
                'allocated': allocation,
                'required': required,
                'shortfall': max(0, required - allocation)
            }
            remaining_budget -= allocation

        allocations['unallocated'] = remaining_budget
        return allocations

    def summary(self) -> Dict:
        """Generate goals summary."""
        total_needed = sum(g.remaining_amount for g in self.goals)
        total_monthly_required = sum(
            self.analyze_goal(g)['required_monthly'] for g in self.goals
        )

        return {
            'total_goals': len(self.goals),
            'total_amount_needed': total_needed,
            'total_monthly_required': total_monthly_required,
            'monthly_budget': self.monthly_budget,
            'budget_gap': total_monthly_required - self.monthly_budget,
            'can_achieve_all': total_monthly_required <= self.monthly_budget
        }

# Budgeting
FIFTY_THIRTY_TWENTY = "50% needs, 30% wants, 20% savings"
PAY_YOURSELF_FIRST = "Save before you spend"

# Emergency Fund
EMERGENCY_FUND = "3-6 months of essential expenses"
STABLE_JOB = 3  # months
UNSTABLE_JOB = 6  # months
SELF_EMPLOYED = 12  # months

# Debt
DEBT_TO_INCOME_LIMIT = 0.36  # 36% max
HOUSING_RATIO_LIMIT = 0.28  # 28% max

# Retirement
SAVINGS_RATE_TARGET = 0.15  # 15% of income minimum
RULE_OF_25 = "Save 25x annual expenses for retirement"
FOUR_PERCENT_RULE = "Withdraw 4% annually in retirement"

# Investing
STOCKS_ALLOCATION = lambda age: 100 - age  # or 110 - age (aggressive)