backend/app/ai/simulation.py

from sqlalchemy.orm import Session
from app.database.models import Account, Goal, Investment, Subscription
from app.ai.forecasting import get_cashflow_metrics

def simulate_purchase_impact(db: Session, user_id: str, amount: float, category: str, merchant: str):
    """
    Simulates buying a large asset or item (e.g. a car) and assesses risk.
    """
    accounts = db.query(Account).filter(Account.user_id == user_id).all()
    total_balance = sum(acc.balance for acc in accounts)
    checking_acc = next((a for a in accounts if a.type.lower() == "checking"), None)
    
    # Target emergency fund amount
    goals = db.query(Goal).filter(Goal.user_id == user_id).all()
    emergency_goal = next((g for g in goals if "emergency" in g.title.lower()), None)
    emergency_threshold = emergency_goal.target_amount if emergency_goal else 3000.0
    
    new_balance = total_balance - amount
    
    # Cashflow metrics
    _, daily_income, daily_spending = get_cashflow_metrics(db, user_id)
    monthly_net = (daily_income - daily_spending) * 30.4
    
    # Risk Analysis
    risk_level = "low"
    reasons = []
    
    if amount > total_balance:
        risk_level = "critical"
        reasons.append("Purchase exceeds your total available balance, requiring debt.")
    elif new_balance < emergency_threshold:
        risk_level = "high"
        reasons.append(f"This purchase depletes your emergency buffer (threshold of ${emergency_threshold:,.2f}).")
    elif amount > total_balance * 0.3:
        risk_level = "medium"
        reasons.append("Single purchase consumes more than 30% of your total liquid cash.")
        
    if monthly_net < 0 and amount > 500:
        risk_level = "high"
        reasons.append("You have a negative monthly cashflow; making large purchases increases financial strain.")
        
    # Recommendations
    recommendation = ""
    if risk_level == "critical":
        recommendation = "❌ Strongly advise against this purchase. Consider financing options, delaying, or establishing a dedicated goal."
    elif risk_level == "high":
        recommendation = "⚠️ Refrain from this purchase if possible. Rebuilding your emergency fund should be prioritized."
    elif risk_level == "medium":
        recommendation = "💡 Proceed with caution. Consider trimming discretionary expenses next month to offset the cost."
    else:
        recommendation = "✅ Purchase is safe. It fits within your financial profile without impacting key safety buffers."
        
    return {
        "purchase_amount": amount,
        "merchant": merchant,
        "category": category,
        "current_balance": round(total_balance, 2),
        "projected_balance": round(max(0.0, new_balance), 2),
        "savings_impact": {
            "immediate_reduction": round(amount, 2),
            "emergency_buffer_breached": new_balance < emergency_threshold,
            "emergency_threshold": round(emergency_threshold, 2)
        },
        "risk_analysis": {
            "risk_level": risk_level,
            "reasons": reasons
        },
        "recommendation": recommendation
    }

def simulate_investment_impact(db: Session, user_id: str, monthly_sip: float, asset_type: str, lump_sum: float = 0.0):
    """
    Simulates investment growth and evaluates opportunity cost.
    """
    # Expected annual returns based on asset type
    returns_map = {
        "stock": 0.10,        # 10%
        "crypto": 0.20,       # 20%
        "mutual_fund": 0.08,  # 8%
        "fd": 0.05,           # 5%
        "bond": 0.04          # 4%
    }
    apr = returns_map.get(asset_type.lower(), 0.07)
    
    # Calculate current balance
    accounts = db.query(Account).filter(Account.user_id == user_id).all()
    total_balance = sum(acc.balance for acc in accounts)
    
    # Cashflow metrics
    _, daily_income, daily_spending = get_cashflow_metrics(db, user_id)
    monthly_net = (daily_income - daily_spending) * 30.4
    
    # Check if SIP is affordable
    is_affordable = monthly_net >= monthly_sip
    
    growth_projection = []
    current_value = lump_sum
    total_invested = lump_sum
    
    # 5-year monthly projection
    for month in range(0, 61):
        if month > 0:
            current_value = (current_value + monthly_sip) * (1 + apr / 12)
            total_invested += monthly_sip
            
        if month in [12, 36, 60]:  # Save 1, 3, 5 year markers
            growth_projection.append({
                "year": month // 12,
                "total_invested": round(total_invested, 2),
                "future_value": round(current_value, 2),
                "earnings": round(max(0.0, current_value - total_invested), 2)
            })
            
    risk_level = "low"
    if asset_type.lower() == "crypto":
        risk_level = "high"
    elif asset_type.lower() in ["stock", "mutual_fund"] and monthly_sip > monthly_net * 0.5:
        risk_level = "medium"
        
    recommendation = ""
    if not is_affordable:
        recommendation = f"⚠️ Your monthly net surplus (${monthly_net:,.2f}) is lower than the planned SIP (${monthly_sip:,.2f}). This may lead to checking overdrafts."
    else:
        recommendation = f"✅ Excellent choice. Investing ${monthly_sip:,.2f} monthly in {asset_type} is fully supported by your net cashflow."
        
    return {
        "asset_type": asset_type,
        "monthly_sip": round(monthly_sip, 2),
        "lump_sum": round(lump_sum, 2),
        "is_affordable": is_affordable,
        "growth_projection": growth_projection,
        "risk_analysis": {
            "risk_level": risk_level,
            "expected_annual_return": apr
        },
        "savings_impact": {
            "opportunity_cost_yearly": round(monthly_sip * 12, 2),
            "monthly_surplus_retaining": round(max(0.0, monthly_net - monthly_sip), 2)
        },
        "recommendation": recommendation
    }

def simulate_subscription_cancellation(db: Session, user_id: str, subscription_ids: list):
    """
    Simulates the financial benefit of cancelling one or more subscriptions.
    """
    subs = db.query(Subscription).filter(
        Subscription.user_id == user_id,
        Subscription.id.in_(subscription_ids)
    ).all()
    
    if not subs:
        return {"message": "No matching subscriptions found for cancellation simulation."}
        
    monthly_savings = 0.0
    yearly_savings = 0.0
    cancelled_details = []
    
    for sub in subs:
        cost = sub.amount
        is_monthly = sub.billing_cycle.lower() == "monthly"
        
        m_cost = cost if is_monthly else (cost / 12)
        y_cost = (cost * 12) if is_monthly else cost
        
        monthly_savings += m_cost
        yearly_savings += y_cost
        
        cancelled_details.append({
            "id": sub.id,
            "merchant": sub.merchant,
            "amount": sub.amount,
            "billing_cycle": sub.billing_cycle
        })
        
    # Relate savings to user's Goals
    goals = db.query(Goal).filter(Goal.user_id == user_id).all()
    first_goal = goals[0] if goals else None
    
    goal_impact = None
    if first_goal:
        months_saved = 0.0
        remaining_needed = max(0.0, first_goal.target_amount - first_goal.current_amount)
        if monthly_savings > 0:
            months_saved = remaining_needed / (remaining_needed / 12 if remaining_needed > 0 else 1) # simple logic
            # Let's say if they direct this money to goal, it reduces target time by:
            months_saved = (remaining_needed / monthly_savings) if remaining_needed > 0 else 0
            
        goal_impact = {
            "goal_title": first_goal.title,
            "target_amount": round(first_goal.target_amount, 2),
            "months_to_reach_with_savings": round(months_saved, 1) if monthly_savings > 0 else 0
        }
        
    # Recommendations
    recommendation = f"Cancelling these subscriptions yields ${monthly_savings:,.2f} per month (${yearly_savings:,.2f} annually). Reinvesting these funds into high-yield savings or mutual funds is recommended."
    
    return {
        "cancelled_subscriptions": cancelled_details,
        "monthly_savings": round(monthly_savings, 2),
        "yearly_savings": round(yearly_savings, 2),
        "goal_impact": goal_impact,
        "recommendation": recommendation
    }