AI-Powered Risk Management: Real-time Portfolio Risk Monitoring

1import asyncio
2from dataclasses import dataclass
3from typing import Dict, List, Optional
4import numpy as np
5import torch
6import torch.nn as nn
7
8@dataclass
9class PortfolioPosition:
10    """Representation of a portfolio position"""
11    symbol: str
12    quantity: float
13    current_price: float
14    entry_price: float
15    position_value: float
16    unrealized_pnl: float
17    
18    # Risk metrics
19    delta: float  # Price sensitivity
20    gamma: float  # Delta sensitivity
21    vega: float  # Volatility sensitivity
22    theta: float  # Time decay
23    
24    # Liquidity
25    avg_daily_volume: float
26    days_to_liquidate: float
27
28@dataclass
29class RiskMetrics:
30    """Portfolio-level risk metrics"""
31    
32    # Value at Risk
33    var_95: float  # 95% confidence 1-day VaR
34    var_99: float  # 99% confidence 1-day VaR
35    cvar_95: float  # Expected shortfall at 95%
36    
37    # Volatility
38    portfolio_volatility: float
39    vol_contribution: Dict[str, float]  # Per-position vol contribution
40    
41    # Concentration
42    largest_position_pct: float
43    herfindahl_index: float
44    sector_concentration: Dict[str, float]
45    
46    # Liquidity
47    liquidity_score: float
48    days_to_liquidate_portfolio: float
49    
50    # Stress tests
51    stress_test_results: Dict[str, float]
52    
53    # Regime
54    current_regime: str
55    regime_probability: Dict[str, float]
56
57class RiskEngine:
58    """
59    AI-powered real-time risk management engine.
60    
61    Combines traditional risk models with ML for:
62    - Adaptive volatility forecasting
63    - Regime detection
64    - Correlation breakdown prediction
65    - Stress scenario generation
66    """
67    
68    def __init__(self, config: dict):
69        self.config = config
70        
71        # ML models
72        self.volatility_model = VolatilityForecaster()
73        self.regime_detector = RegimeClassifier()
74        self.correlation_model = DynamicCorrelationNetwork()
75        self.stress_generator = StressScenarioGAN()
76        self.liquidity_model = LiquidityPredictor()
77        
78        # Traditional models
79        self.historical_var = HistoricalVaR()
80        self.parametric_var = ParametricVaR()
81        
82        # Risk limits
83        self.limits = config['risk_limits']
84        
85        # Alert system
86        self.alert_manager = AlertManager()
87    
88    async def assess_portfolio_risk(
89        self,
90        positions: List[PortfolioPosition],
91        market_data: dict
92    ) -> RiskMetrics:
93        """
94        Comprehensive real-time risk assessment.
95        """
96        # Run analyses in parallel
97        results = await asyncio.gather(
98            self._calculate_var(positions, market_data),
99            self._assess_concentration(positions),
100            self._evaluate_liquidity(positions, market_data),
101            self._run_stress_tests(positions, market_data),
102            self._detect_regime(market_data)
103        )
104        
105        var_metrics, concentration, liquidity, stress_results, regime = results
106        
107        # Construct comprehensive risk metrics
108        metrics = RiskMetrics(
109            var_95=var_metrics['var_95'],
110            var_99=var_metrics['var_99'],
111            cvar_95=var_metrics['cvar_95'],
112            portfolio_volatility=var_metrics['volatility'],
113            vol_contribution=var_metrics['vol_contribution'],
114            largest_position_pct=concentration['largest_pct'],
115            herfindahl_index=concentration['herfindahl'],
116            sector_concentration=concentration['sectors'],
117            liquidity_score=liquidity['score'],
118            days_to_liquidate_portfolio=liquidity['days_to_liquidate'],
119            stress_test_results=stress_results,
120            current_regime=regime['regime'],
121            regime_probability=regime['probabilities']
122        )
123        
124        # Check limits and alert if breached
125        await self._check_limits(metrics)
126        
127        return metrics
128    
129    async def _calculate_var(
130        self,
131        positions: List[PortfolioPosition],
132        market_data: dict
133    ) -> dict:
134        """
135        Calculate Value at Risk using multiple methods.
136        Combines historical, parametric, and ML-based approaches.
137        """
138        # Get position returns
139        returns = self._get_position_returns(positions, market_data)
140        
141        # 1. Historical VaR
142        hist_var_95 = self.historical_var.calculate(returns, confidence=0.95)
143        hist_var_99 = self.historical_var.calculate(returns, confidence=0.99)
144        
145        # 2. ML-based volatility forecast
146        vol_forecast = await self.volatility_model.predict(
147            positions, market_data
148        )
149        
150        # 3. Dynamic correlation prediction
151        correlation = await self.correlation_model.predict(
152            positions, market_data
153        )
154        
155        # 4. Parametric VaR with ML forecasts
156        param_var = self.parametric_var.calculate(
157            positions,
158            volatility=vol_forecast,
159            correlation=correlation
160        )
161        
162        # 5. Regime-adjusted VaR
163        current_regime = await self.regime_detector.predict(market_data)
164        regime_adjustment = self._get_regime_adjustment(current_regime)
165        
166        # Combine methods with regime adjustment
167        var_95 = (0.4 * hist_var_95 + 0.6 * param_var['var_95']) * regime_adjustment
168        var_99 = (0.4 * hist_var_99 + 0.6 * param_var['var_99']) * regime_adjustment
169        
170        # Expected Shortfall (CVaR)
171        cvar_95 = self._calculate_cvar(returns, confidence=0.95)
172        
173        # Volatility contribution by position
174        vol_contribution = self._decompose_volatility(
175            positions, vol_forecast, correlation
176        )
177        
178        return {
179            'var_95': var_95,
180            'var_99': var_99,
181            'cvar_95': cvar_95,
182            'volatility': vol_forecast['portfolio_vol'],
183            'vol_contribution': vol_contribution
184        }
185

1class VolatilityForecaster(nn.Module):
2    """
3    LSTM-based volatility forecasting with regime awareness.
4    
5    Predicts:
6    - Individual asset volatilities
7    - Volatility regime (low/normal/high)
8    - Forecast uncertainty
9    """
10    
11    def __init__(
12        self,
13        n_assets: int,
14        feature_dim: int = 50,
15        hidden_dim: int = 128,
16        n_layers: int = 3
17    ):
18        super().__init__()
19        
20        # Feature embedding
21        self.feature_embed = nn.Linear(feature_dim, hidden_dim)
22        
23        # LSTM for temporal patterns
24        self.lstm = nn.LSTM(
25            input_size=hidden_dim,
26            hidden_size=hidden_dim,
27            num_layers=n_layers,
28            batch_first=True,
29            dropout=0.2
30        )
31        
32        # Volatility prediction head
33        self.vol_head = nn.Sequential(
34            nn.Linear(hidden_dim, hidden_dim // 2),
35            nn.ReLU(),
36            nn.Dropout(0.1),
37            nn.Linear(hidden_dim // 2, n_assets),
38            nn.Softplus()  # Ensure positive volatility
39        )
40        
41        # Regime classification head
42        self.regime_head = nn.Sequential(
43            nn.Linear(hidden_dim, 64),
44            nn.ReLU(),
45            nn.Linear(64, 3),  # Low/Normal/High volatility
46            nn.Softmax(dim=-1)
47        )
48        
49        # Uncertainty estimation
50        self.uncertainty_head = nn.Sequential(
51            nn.Linear(hidden_dim, n_assets),
52            nn.Softplus()
53        )
54    
55    def forward(self, features, hidden=None):
56        """
57        Args:
58            features: (batch, seq_len, feature_dim)
59            
60        Returns:
61            volatility: (batch, n_assets) - Predicted volatilities
62            regime: (batch, 3) - Regime probabilities
63            uncertainty: (batch, n_assets) - Prediction uncertainty
64        """
65        # Embed features
66        x = self.feature_embed(features)
67        
68        # LSTM processing
69        lstm_out, hidden = self.lstm(x, hidden)
70        
71        # Take last output
72        last_hidden = lstm_out[:, -1, :]
73        
74        # Predictions
75        volatility = self.vol_head(last_hidden)
76        regime = self.regime_head(last_hidden)
77        uncertainty = self.uncertainty_head(last_hidden)
78        
79        return {
80            'volatility': volatility,
81            'regime': regime,
82            'uncertainty': uncertainty,
83            'hidden': hidden
84        }
85    
86    async def predict(
87        self,
88        positions: List[PortfolioPosition],
89        market_data: dict
90    ) -> dict:
91        """
92        Generate volatility forecast for current portfolio.
93        """
94        # Prepare features
95        features = self._prepare_features(positions, market_data)
96        
97        # Forward pass
98        with torch.no_grad():
99            output = self.forward(features)
100        
101        # Extract predictions
102        vol_forecast = output['volatility'].cpu().numpy()
103        regime_probs = output['regime'].cpu().numpy()
104        uncertainty = output['uncertainty'].cpu().numpy()
105        
106        # Calculate portfolio-level volatility
107        weights = self._get_position_weights(positions)
108        correlation = self._get_correlation_matrix(positions, market_data)
109        
110        portfolio_vol = np.sqrt(
111            weights @ np.diag(vol_forecast ** 2) @ correlation @ weights
112        )
113        
114        return {
115            'asset_volatilities': dict(zip(
116                [p.symbol for p in positions],
117                vol_forecast
118            )),
119            'portfolio_vol': portfolio_vol,
120            'regime': regime_probs,
121            'uncertainty': uncertainty
122        }
123

1import torch_geometric
2from torch_geometric.nn import GCNConv, GATConv
3
4class DynamicCorrelationNetwork(nn.Module):
5    """
6    Graph Neural Network for dynamic correlation prediction.
7    
8    Models assets as nodes in a graph, with edges representing
9    relationships. Learns how correlations evolve over time.
10    """
11    
12    def __init__(
13        self,
14        n_assets: int,
15        feature_dim: int = 64,
16        hidden_dim: int = 128,
17        n_layers: int = 3
18    ):
19        super().__init__()
20        
21        self.n_assets = n_assets
22        
23        # Node feature processing
24        self.node_encoder = nn.Sequential(
25            nn.Linear(feature_dim, hidden_dim),
26            nn.ReLU(),
27            nn.Linear(hidden_dim, hidden_dim)
28        )
29        
30        # Graph attention layers
31        self.gat_layers = nn.ModuleList([
32            GATConv(
33                hidden_dim,
34                hidden_dim,
35                heads=4,
36                concat=True,
37                dropout=0.1
38            ) for _ in range(n_layers)
39        ])
40        
41        # Output layer: predict correlation matrix
42        self.correlation_head = nn.Sequential(
43            nn.Linear(hidden_dim * 4, hidden_dim),
44            nn.ReLU(),
45            nn.Linear(hidden_dim, hidden_dim)
46        )
47    
48    def forward(self, node_features, edge_index):
49        """
50        Args:
51            node_features: (n_assets, feature_dim) - Features for each asset
52            edge_index: (2, n_edges) - Graph connectivity
53            
54        Returns:
55            correlation: (n_assets, n_assets) - Predicted correlation matrix
56        """
57        # Encode node features
58        x = self.node_encoder(node_features)
59        
60        # Apply graph attention layers
61        for gat in self.gat_layers:
62            x = gat(x, edge_index)
63            x = torch.relu(x)
64        
65        # Predict correlation head features
66        h = self.correlation_head(x)
67        
68        # Compute pairwise correlations
69        # correlation[i,j] = cosine similarity of h[i] and h[j]
70        correlation = self._compute_correlation(h)
71        
72        return correlation
73    
74    def _compute_correlation(self, embeddings):
75        """
76        Compute correlation matrix from embeddings.
77        Use cosine similarity to ensure values in [-1, 1].
78        """
79        # Normalize embeddings
80        embeddings_norm = torch.nn.functional.normalize(embeddings, p=2, dim=1)
81        
82        # Compute pairwise cosine similarity
83        correlation = embeddings_norm @ embeddings_norm.T
84        
85        # Ensure diagonal is 1
86        correlation = correlation * (1 - torch.eye(self.n_assets)) + torch.eye(self.n_assets)
87        
88        return correlation
89    
90    async def predict(
91        self,
92        positions: List[PortfolioPosition],
93        market_data: dict
94    ) -> np.ndarray:
95        """
96        Predict correlation matrix for current portfolio.
97        """
98        # Build graph
99        node_features, edge_index = self._build_graph(positions, market_data)
100        
101        # Forward pass
102        with torch.no_grad():
103            correlation = self.forward(node_features, edge_index)
104        
105        return correlation.cpu().numpy()
106    
107    def _build_graph(self, positions, market_data):
108        """
109        Construct graph representation of portfolio.
110        
111        Nodes: Assets
112        Edges: Potential relationships (sector, market cap, geography, etc.)
113        """
114        n_assets = len(positions)
115        
116        # Node features: combine technical and fundamental data
117        node_features = []
118        for pos in positions:
119            features = np.concatenate([
120                self._get_technical_features(pos, market_data),
121                self._get_fundamental_features(pos),
122                self._get_sector_encoding(pos)
123            ])
124            node_features.append(features)
125        
126        node_features = torch.tensor(node_features, dtype=torch.float32)
127        
128        # Edge construction: connect assets with potential relationships
129        edge_list = []
130        for i in range(n_assets):
131            for j in range(i + 1, n_assets):
132                # Connect if:
133                # 1. Same sector
134                # 2. Similar market cap
135                # 3. Geographic overlap
136                if self._should_connect(positions[i], positions[j]):
137                    edge_list.append([i, j])
138                    edge_list.append([j, i])  # Undirected graph
139        
140        edge_index = torch.tensor(edge_list, dtype=torch.long).T
141        
142        return node_features, edge_index
143

1class StressScenarioGAN:
2    """
3    Generate realistic stress scenarios using GANs.
4    
5    Learns from historical crises to generate plausible but
6    unseen stress scenarios for testing portfolio resilience.
7    """
8    
9    def __init__(self, n_assets: int, latent_dim: int = 100):
10        self.generator = self._build_generator(latent_dim, n_assets)
11        self.discriminator = self._build_discriminator(n_assets)
12        
13        # Load historical crisis data for training
14        self.crisis_scenarios = self._load_crisis_data()
15    
16    def _build_generator(self, latent_dim: int, n_assets: int):
17        """Generate stress scenarios from random noise"""
18        return nn.Sequential(
19            nn.Linear(latent_dim, 256),
20            nn.LeakyReLU(0.2),
21            nn.BatchNorm1d(256),
22            
23            nn.Linear(256, 512),
24            nn.LeakyReLU(0.2),
25            nn.BatchNorm1d(512),
26            
27            nn.Linear(512, n_assets),
28            nn.Tanh()  # Scenarios in [-1, 1] (normalized)
29        )
30    
31    def _build_discriminator(self, n_assets: int):
32        """Distinguish real crises from generated scenarios"""
33        return nn.Sequential(
34            nn.Linear(n_assets, 512),
35            nn.LeakyReLU(0.2),
36            nn.Dropout(0.3),
37            
38            nn.Linear(512, 256),
39            nn.LeakyReLU(0.2),
40            nn.Dropout(0.3),
41            
42            nn.Linear(256, 1),
43            nn.Sigmoid()
44        )
45    
46    def generate_scenarios(self, n_scenarios: int = 1000) -> np.ndarray:
47        """
48        Generate stress scenarios.
49        
50        Returns:
51            scenarios: (n_scenarios, n_assets) - Asset returns under stress
52        """
53        self.generator.eval()
54        
55        with torch.no_grad():
56            # Random noise
57            noise = torch.randn(n_scenarios, self.latent_dim)
58            
59            # Generate scenarios
60            scenarios = self.generator(noise)
61        
62        return scenarios.cpu().numpy()
63    
64    async def stress_test_portfolio(
65        self,
66        positions: List[PortfolioPosition],
67        n_scenarios: int = 1000
68    ) -> dict:
69        """
70        Run stress tests on portfolio.
71        
72        Returns worst-case P&L across generated scenarios.
73        """
74        # Generate stress scenarios
75        scenarios = self.generate_scenarios(n_scenarios)
76        
77        # Calculate portfolio returns under each scenario
78        weights = np.array([p.position_value for p in positions])
79        weights = weights / weights.sum()
80        
81        portfolio_returns = scenarios @ weights
82        
83        # Analyze results
84        results = {
85            'worst_case': np.min(portfolio_returns),
86            'worst_1pct': np.percentile(portfolio_returns, 1),
87            'worst_5pct': np.percentile(portfolio_returns, 5),
88            'mean_stress': np.mean(portfolio_returns),
89            'scenarios': scenarios[:10]  # Top 10 worst scenarios
90        }
91        
92        return results
93

1class RealTimeRiskMonitor:
2    """
3    Continuously monitor portfolio risk and trigger alerts.
4    """
5    
6    def __init__(self, risk_engine: RiskEngine, config: dict):
7        self.risk_engine = risk_engine
8        self.config = config
9        self.alert_manager = AlertManager()
10        
11        # Historical risk metrics for comparison
12        self.risk_history = []
13    
14    async def monitor_loop(self, position_stream, market_data_stream):
15        """
16        Main monitoring loop. Runs continuously in production.
17        """
18        while True:
19            try:
20                # Get latest data
21                positions = await position_stream.get_latest()
22                market_data = await market_data_stream.get_latest()
23                
24                # Assess risk
25                metrics = await self.risk_engine.assess_portfolio_risk(
26                    positions, market_data
27                )
28                
29                # Store history
30                self.risk_history.append({
31                    'timestamp': market_data['timestamp'],
32                    'metrics': metrics
33                })
34                
35                # Check for limit breaches
36                await self._check_and_alert(metrics)
37                
38                # Detect anomalies
39                await self._detect_anomalies(metrics)
40                
41                # Update dashboard
42                await self._update_dashboard(metrics)
43                
44                # Sleep briefly
45                await asyncio.sleep(1.0)  # 1Hz monitoring
46                
47            except Exception as e:
48                await self.alert_manager.send_critical_alert(
49                    f"Risk monitoring error: {str(e)}"
50                )
51    
52    async def _check_and_alert(self, metrics: RiskMetrics):
53        """Check if risk metrics breach limits"""
54        
55        # VaR limit
56        if metrics.var_95 > self.config['max_var_95']:
57            await self.alert_manager.send_alert(
58                severity='high',
59                message=f"VaR breach: {metrics.var_95:.2f} > {self.config['max_var_95']:.2f}"
60            )
61        
62        # Concentration limit
63        if metrics.largest_position_pct > self.config['max_single_position']:
64            await self.alert_manager.send_alert(
65                severity='medium',
66                message=f"Concentration risk: {metrics.largest_position_pct:.1%}"
67            )
68        
69        # Liquidity limit
70        if metrics.days_to_liquidate_portfolio > self.config['max_liquidation_days']:
71            await self.alert_manager.send_alert(
72                severity='medium',
73                message=f"Liquidity risk: {metrics.days_to_liquidate_portfolio:.1f} days"
74            )
75        
76        # Regime change
77        if metrics.current_regime == 'high_volatility':
78            await self.alert_manager.send_alert(
79                severity='high',
80                message=f"Regime shift detected: {metrics.current_regime}"
81            )
82