🕵️‍♂️ From Conspiracy to Forecast: Turn Wild Theories into Publishable Models

def synthetic_capital_flow(levers, time_horizon=12):
    """
    Model hidden capital flows with FinanCalc Pro.
    Detects shell company swaps and offshore funneling.
    """
    def calculate_flow_velocity(flows):
        return sum(f * (i + 1) for i, f in enumerate(flows)) / len(flows)
    
    def detect_circular_patterns(flows):
        variance = sum((f - sum(flows) / len(flows)) ** 2 for f in flows)
        return variance < 0.1
    
    def estimate_shell_count(risk_score):
        return int(risk_score / 100)
    
    base_flow = levers.get('delinquency_suppression', 0) * 1000000
    flows = [base_flow * (i + 1) * (1 + levers.get('blackrock_clone', 0)) for i in range(time_horizon)]
    velocity = calculate_flow_velocity(flows)
    is_circular = detect_circular_patterns(flows)
    risk_multiplier = 420 if levers.get('blackrock_clone', 0) > 0.8 else 69
    risk_score = velocity * risk_multiplier
    
    return {
        'monthly_flows': [f'${f:,.2f}' for f in flows],
        'flow_velocity': f'{velocity:.2f} truth units/month',
        'circular_pattern_detected': is_circular,
        'risk_score': f'{risk_score:.2f} truth units',
        'cat_audit': '😱 CALL THE FBI' if risk_score > 5000 else '😻 Expose Immediately' if risk_score > 1000 else '😿 Keep Tracking',
        'shell_companies_estimated': estimate_shell_count(risk_score),
        'offshore_probability': f'{min(100, risk_score / 50):.1f}%'
    }

def inflation_heatmap(levers, regions=['SF', 'NYC', 'LA', 'MIA', 'SEA']):
    """
    Generate multi-dimensional inflation heatmap.
    """
    def calculate_distortion_index(region, levers):
        base_inflation = levers.get('mortgage_pressure', 0) * 100
        media_amplifier = 1 + levers.get('media_noise', 0)
        concentration_factor = 1 + levers.get('blackrock_clone', 0) * 2
        regional_multipliers = {'SF': 2.5, 'NYC': 2.0, 'LA': 1.8, 'MIA': 1.6, 'SEA': 1.4}
        return base_inflation * media_amplifier * concentration_factor * regional_multipliers.get(region, 1.0)
    
    def predict_bubble_timing(distortion):
        if distortion > 200: return '3–6 months'
        if distortion > 100: return '6–12 months'
        if distortion > 50: return '12–24 months'
        return 'Sustainable (lol)'
    
    def generate_escape_routes(distortion):
        if distortion > 150: return ['🏃‍♂️ Exit ASAP', '🥫 Stock Canned Goods', '💰 Convert to Hard Assets']
        if distortion > 75: return ['📉 Sell High', '🏠 Consider Relocating', '💎 HODL Truth Coins']
        return ['📊 Monitor Closely', '🎯 Identify Entry Points']
    
    heatmap_data = {
        region: {
            'distortion_index': f'{calculate_distortion_index(region, levers):.1f}%',
            'bubble_timing': predict_bubble_timing(calculate_distortion_index(region, levers)),
            'escape_routes': generate_escape_routes(calculate_distortion_index(region, levers)),
            'heat_level': '🔥🔥🔥 MOLTEN' if calculate_distortion_index(region, levers) > 200 else '🔥🔥 BLAZING' if calculate_distortion_index(region, levers) > 100 else '🔥 HOT' if calculate_distortion_index(region, levers) > 50 else '❄️ Cool (sus)'
        } for region in regions
    }
    overall_risk = sum(float(data['distortion_index'].rstrip('%')) for data in heatmap_data.values()) / len(regions)
    
    return {
        'regional_data': heatmap_data,
        'national_average': f'{overall_risk:.1f}%',
        'cat_audit': '😱 EVACUATE CITIES' if overall_risk > 150 else '😻 Sound the Alarm' if overall_risk > 75 else '😿 Monitor Markets',
        'crash_probability': f'{min(100, overall_risk / 2):.1f}%'
    }

def shadow_bank_risk_engine(levers):
    """
    Model shadow banking risks and systemic vulnerabilities.
    """
    def calculate_leverage_ratio(levers):
        base_leverage = 50
        suppression_multiplier = 1 + levers.get('delinquency_suppression', 0)
        pump_amplifier = 1 + levers.get('snake_oil_pump', 0) * 2
        return base_leverage * suppression_multiplier * pump_amplifier
    
    def estimate_hidden_liabilities(leverage, levers):
        visible_assets = 25_000_000_000_000
        hidden_multiplier = leverage / 10
        concentration_factor = 1 + levers.get('blackrock_clone', 0) * 3
        return visible_assets * hidden_multiplier * concentration_factor
    
    def predict_liquidity_crisis(risk_score):
        if risk_score > 10000: return 'IMMINENT (days)'
        if risk_score > 5000: return 'High (weeks)'
        if risk_score > 2000: return 'Moderate (months)'
        return 'Low (years)'
    
    def generate_collapse_scenario(liabilities):
        scenarios = []
        if liabilities > 100_000_000_000_000: scenarios.append('🌍 Global Financial Reset')
        if liabilities > 50_000_000_000_000: scenarios.append('🏦 Major Bank Failures')
        if liabilities > 25_000_000_000_000: scenarios.append('💸 Currency Devaluation')
        return scenarios or ['📈 Manageable Risk (for now)']
    
    leverage = calculate_leverage_ratio(levers)
    hidden_liabilities = estimate_hidden_liabilities(leverage, levers)
    base_risk = levers.get('delinquency_suppression', 0) * 1000
    leverage_risk = leverage / 10
    pump_risk = levers.get('snake_oil_pump', 0) * 5000
    risk_score = base_risk + leverage_risk + pump_risk
    
    return {
        'leverage_ratio': f'{leverage:.1f}x',
        'hidden_liabilities': f'${hidden_liabilities:,.0f}',
        'risk_score': f'{risk_score:.2f} truth units',
        'liquidity_crisis_timing': predict_liquidity_crisis(risk_score),
        'collapse_scenarios': generate_collapse_scenario(hidden_liabilities),
        'cat_audit': '😱 MAYDAY MAYDAY' if risk_score > 10000 else '😻 Leak to WikiLeaks' if risk_score > 5000 else '😸 Build the Evidence' if risk_score > 1000 else '😿 Keep Investigating',
        'fed_intervention_probability': f'{min(100, risk_score / 100):.1f}%',
        'bailout_estimate': f'${hidden_liabilities / 10:,.0f} (minimum)'
    }