48. Smart Hedging Engine¶

services/hedging-center/
├── src/
│   ├── main.py
│   ├── analyzer/
│   │   ├── exposure_analyzer.py
│   │   ├── risk_calculator.py
│   │   └── correlation_analyzer.py
│   ├── generator/
│   │   ├── hedge_signal_generator.py
│   │   ├── hedge_optimizer.py
│   │   └── strategy_selector.py
│   ├── executor/
│   │   ├── hedge_executor.py
│   │   ├── order_manager.py
│   │   └── position_tracker.py
│   ├── monitor/
│   │   ├── hedge_monitor.py
│   │   ├── performance_tracker.py
│   │   └── rebalancer.py
│   ├── risk/
│   │   ├── hedge_risk_controller.py
│   │   ├── exposure_limiter.py
│   │   └── stress_tester.py
│   ├── api/
│   │   ├── hedging_api.py
│   ├── config.py
│   └── requirements.txt
├── Dockerfile
└── tests/

class ExposureAnalyzer:
    def __init__(self, portfolio_manager, market_data_provider):
        self.portfolio_manager = portfolio_manager
        self.market_data_provider = market_data_provider

    def analyze_exposure(self, account_id: str) -> Dict:
        """Analyze comprehensive risk exposure for an account"""

        holdings = self.portfolio_manager.get_holdings(account_id)
        positions = self.portfolio_manager.get_positions(account_id)

        # Calculate directional exposure
        directional_exposure = self.calculate_directional_exposure(positions)

        # Calculate sector exposure
        sector_exposure = self.calculate_sector_exposure(holdings)

        # Calculate currency exposure
        currency_exposure = self.calculate_currency_exposure(holdings)

        # Calculate beta exposure
        beta_exposure = self.calculate_beta_exposure(holdings)

        # Calculate correlation exposure
        correlation_exposure = self.calculate_correlation_exposure(holdings)

        return {
            "account_id": account_id,
            "timestamp": datetime.now().isoformat(),
            "directional_exposure": directional_exposure,
            "sector_exposure": sector_exposure,
            "currency_exposure": currency_exposure,
            "beta_exposure": beta_exposure,
            "correlation_exposure": correlation_exposure,
            "total_exposure_score": self.calculate_total_exposure_score(
                directional_exposure, sector_exposure, currency_exposure, beta_exposure
            )
        }

    def calculate_directional_exposure(self, positions: List[Dict]) -> Dict:
        """Calculate directional exposure by asset class"""

        exposure = {
            "equity_long": 0.0,
            "equity_short": 0.0,
            "crypto_long": 0.0,
            "crypto_short": 0.0,
            "fx_long": 0.0,
            "fx_short": 0.0,
            "commodity_long": 0.0,
            "commodity_short": 0.0
        }

        for position in positions:
            asset_type = position.get("asset_type", "equity")
            side = position.get("side", "long")
            value = position.get("market_value", 0.0)

            if asset_type == "equity":
                if side == "long":
                    exposure["equity_long"] += value
                else:
                    exposure["equity_short"] += value
            elif asset_type == "crypto":
                if side == "long":
                    exposure["crypto_long"] += value
                else:
                    exposure["crypto_short"] += value
            elif asset_type == "fx":
                if side == "long":
                    exposure["fx_long"] += value
                else:
                    exposure["fx_short"] += value
            elif asset_type == "commodity":
                if side == "long":
                    exposure["commodity_long"] += value
                else:
                    exposure["commodity_short"] += value

        # Calculate net exposure
        exposure["equity_net"] = exposure["equity_long"] - exposure["equity_short"]
        exposure["crypto_net"] = exposure["crypto_long"] - exposure["crypto_short"]
        exposure["fx_net"] = exposure["fx_long"] - exposure["fx_short"]
        exposure["commodity_net"] = exposure["commodity_long"] - exposure["commodity_short"]

        return exposure

    def calculate_sector_exposure(self, holdings: List[Dict]) -> Dict:
        """Calculate sector concentration exposure"""

        sector_exposure = {}
        total_value = 0.0

        for holding in holdings:
            sector = holding.get("sector", "unknown")
            value = holding.get("market_value", 0.0)

            sector_exposure[sector] = sector_exposure.get(sector, 0.0) + value
            total_value += value

        # Calculate sector weights
        if total_value > 0:
            sector_weights = {
                sector: value / total_value 
                for sector, value in sector_exposure.items()
            }
        else:
            sector_weights = {}

        # Calculate concentration risk
        concentration_risk = self.calculate_concentration_risk(sector_weights)

        return {
            "sector_exposure": sector_exposure,
            "sector_weights": sector_weights,
            "concentration_risk": concentration_risk,
            "top_sectors": sorted(sector_weights.items(), key=lambda x: x[1], reverse=True)[:5]
        }

    def calculate_currency_exposure(self, holdings: List[Dict]) -> Dict:
        """Calculate currency exposure"""

        currency_exposure = {}

        for holding in holdings:
            currency = holding.get("currency", "USD")
            value_usd = holding.get("value_usd", 0.0)

            currency_exposure[currency] = currency_exposure.get(currency, 0.0) + value_usd

        # Calculate currency risk
        currency_risk = self.calculate_currency_risk(currency_exposure)

        return {
            "currency_exposure": currency_exposure,
            "currency_risk": currency_risk,
            "primary_currency": max(currency_exposure.items(), key=lambda x: x[1])[0] if currency_exposure else "USD"
        }

    def calculate_beta_exposure(self, holdings: List[Dict]) -> Dict:
        """Calculate beta exposure to market indices"""

        beta_exposure = {
            "sp500_beta": 0.0,
            "nasdaq_beta": 0.0,
            "dow_beta": 0.0,
            "btc_beta": 0.0,
            "total_beta": 0.0
        }

        total_value = 0.0

        for holding in holdings:
            value = holding.get("market_value", 0.0)
            betas = holding.get("betas", {})

            total_value += value

            # Weighted beta calculation
            for index, beta in betas.items():
                if index in beta_exposure:
                    beta_exposure[index] += beta * value

        # Normalize by total value
        if total_value > 0:
            for index in beta_exposure:
                beta_exposure[index] /= total_value

        # Calculate total beta (weighted average)
        beta_exposure["total_beta"] = (
            beta_exposure["sp500_beta"] * 0.4 +
            beta_exposure["nasdaq_beta"] * 0.3 +
            beta_exposure["dow_beta"] * 0.2 +
            beta_exposure["btc_beta"] * 0.1
        )

        return beta_exposure

    def calculate_concentration_risk(self, sector_weights: Dict) -> float:
        """Calculate Herfindahl-Hirschman Index for concentration risk"""

        if not sector_weights:
            return 0.0

        hhi = sum(weight ** 2 for weight in sector_weights.values())

        # Normalize to 0-1 scale
        normalized_hhi = hhi / len(sector_weights) if len(sector_weights) > 0 else 0

        return normalized_hhi

    def calculate_currency_risk(self, currency_exposure: Dict) -> float:
        """Calculate currency risk based on exposure distribution"""

        if not currency_exposure:
            return 0.0

        total_exposure = sum(currency_exposure.values())
        if total_exposure == 0:
            return 0.0

        # Calculate currency concentration
        currency_weights = {
            currency: value / total_exposure 
            for currency, value in currency_exposure.items()
        }

        # Risk increases with concentration in non-USD currencies
        usd_weight = currency_weights.get("USD", 0.0)
        currency_risk = 1.0 - usd_weight

        return currency_risk

class HedgeSignalGenerator:
    def __init__(self, exposure_analyzer, market_data_provider):
        self.exposure_analyzer = exposure_analyzer
        self.market_data_provider = market_data_provider

    def generate_hedge_plan(self, account_id: str) -> Dict:
        """Generate comprehensive hedging plan"""

        # Analyze current exposure
        exposure = self.exposure_analyzer.analyze_exposure(account_id)

        # Generate different types of hedging signals
        directional_hedges = self.generate_directional_hedges(exposure)
        sector_hedges = self.generate_sector_hedges(exposure)
        beta_hedges = self.generate_beta_hedges(exposure)
        currency_hedges = self.generate_currency_hedges(exposure)

        # Combine and optimize hedge plan
        hedge_plan = self.optimize_hedge_plan(
            directional_hedges + sector_hedges + beta_hedges + currency_hedges
        )

        return {
            "account_id": account_id,
            "timestamp": datetime.now().isoformat(),
            "exposure_summary": exposure,
            "hedge_plan": hedge_plan,
            "hedge_effectiveness": self.calculate_hedge_effectiveness(hedge_plan, exposure),
            "implementation_cost": self.calculate_implementation_cost(hedge_plan)
        }

    def generate_directional_hedges(self, exposure: Dict) -> List[Dict]:
        """Generate directional hedging signals"""

        hedges = []
        directional_exposure = exposure.get("directional_exposure", {})

        # Equity directional hedge
        equity_net = directional_exposure.get("equity_net", 0.0)
        if abs(equity_net) > 10000:  # Threshold for hedging
            hedge_ratio = min(0.8, abs(equity_net) / 50000)  # Scale hedge ratio
            hedge_quantity = equity_net * hedge_ratio

            if equity_net > 0:  # Long exposure - need short hedge
                hedges.append({
                    "type": "directional",
                    "subtype": "equity",
                    "symbol": "SPY",
                    "side": "sell",
                    "quantity": abs(hedge_quantity),
                    "reason": "equity_long_exposure",
                    "priority": "high"
                })
            else:  # Short exposure - need long hedge
                hedges.append({
                    "type": "directional",
                    "subtype": "equity",
                    "symbol": "SPY",
                    "side": "buy",
                    "quantity": abs(hedge_quantity),
                    "reason": "equity_short_exposure",
                    "priority": "high"
                })

        # Crypto directional hedge
        crypto_net = directional_exposure.get("crypto_net", 0.0)
        if abs(crypto_net) > 5000:
            hedge_ratio = min(0.9, abs(crypto_net) / 20000)
            hedge_quantity = crypto_net * hedge_ratio

            if crypto_net > 0:
                hedges.append({
                    "type": "directional",
                    "subtype": "crypto",
                    "symbol": "BTC-PERP",
                    "side": "sell",
                    "quantity": abs(hedge_quantity),
                    "reason": "crypto_long_exposure",
                    "priority": "medium"
                })
            else:
                hedges.append({
                    "type": "directional",
                    "subtype": "crypto",
                    "symbol": "BTC-PERP",
                    "side": "buy",
                    "quantity": abs(hedge_quantity),
                    "reason": "crypto_short_exposure",
                    "priority": "medium"
                })

        return hedges

    def generate_sector_hedges(self, exposure: Dict) -> List[Dict]:
        """Generate sector hedging signals"""

        hedges = []
        sector_exposure = exposure.get("sector_exposure", {})

        # Check for sector concentration
        concentration_risk = sector_exposure.get("concentration_risk", 0.0)
        top_sectors = sector_exposure.get("top_sectors", [])

        if concentration_risk > 0.3 and top_sectors:  # High concentration
            # Hedge the largest sector exposure
            largest_sector, weight = top_sectors[0]

            if weight > 0.4:  # More than 40% in one sector
                hedge_quantity = weight * 0.5  # Hedge 50% of exposure

                # Map sector to ETF
                sector_etf = self.get_sector_etf(largest_sector)
                if sector_etf:
                    hedges.append({
                        "type": "sector",
                        "subtype": "concentration",
                        "symbol": sector_etf,
                        "side": "sell",
                        "quantity": hedge_quantity,
                        "reason": f"sector_concentration_{largest_sector}",
                        "priority": "medium"
                    })

        return hedges

    def generate_beta_hedges(self, exposure: Dict) -> List[Dict]:
        """Generate beta hedging signals"""

        hedges = []
        beta_exposure = exposure.get("beta_exposure", {})

        total_beta = beta_exposure.get("total_beta", 0.0)

        # Target beta-neutral position
        if abs(total_beta) > 0.2:  # Significant beta exposure
            target_beta = 0.0  # Beta neutral
            beta_adjustment = target_beta - total_beta

            if beta_adjustment > 0:  # Need to increase beta
                hedges.append({
                    "type": "beta",
                    "subtype": "market",
                    "symbol": "SPY",
                    "side": "buy",
                    "quantity": abs(beta_adjustment),
                    "reason": "beta_neutralization",
                    "priority": "high"
                })
            else:  # Need to decrease beta
                hedges.append({
                    "type": "beta",
                    "subtype": "market",
                    "symbol": "SPY",
                    "side": "sell",
                    "quantity": abs(beta_adjustment),
                    "reason": "beta_neutralization",
                    "priority": "high"
                })

        return hedges

    def generate_currency_hedges(self, exposure: Dict) -> List[Dict]:
        """Generate currency hedging signals"""

        hedges = []
        currency_exposure = exposure.get("currency_exposure", {})

        currency_risk = currency_exposure.get("currency_risk", 0.0)

        if currency_risk > 0.3:  # Significant currency risk
            # Hedge non-USD exposures
            for currency, value in currency_exposure.get("currency_exposure", {}).items():
                if currency != "USD" and value > 10000:
                    hedge_ratio = min(0.7, value / 50000)
                    hedge_quantity = value * hedge_ratio

                    # Use currency futures or ETFs
                    currency_symbol = self.get_currency_hedge_symbol(currency)
                    if currency_symbol:
                        hedges.append({
                            "type": "currency",
                            "subtype": "fx",
                            "symbol": currency_symbol,
                            "side": "sell",
                            "quantity": hedge_quantity,
                            "reason": f"currency_exposure_{currency}",
                            "priority": "medium"
                        })

        return hedges

    def optimize_hedge_plan(self, hedges: List[Dict]) -> List[Dict]:
        """Optimize hedge plan by removing redundancies and conflicts"""

        if not hedges:
            return []

        # Group by symbol and side
        hedge_groups = {}
        for hedge in hedges:
            key = (hedge["symbol"], hedge["side"])
            if key not in hedge_groups:
                hedge_groups[key] = []
            hedge_groups[key].append(hedge)

        # Consolidate hedges
        optimized_hedges = []
        for (symbol, side), hedge_list in hedge_groups.items():
            # Sum quantities and use highest priority reason
            total_quantity = sum(hedge["quantity"] for hedge in hedge_list)
            highest_priority = max(hedge["priority"] for hedge in hedge_list)
            primary_reason = hedge_list[0]["reason"]

            optimized_hedges.append({
                "symbol": symbol,
                "side": side,
                "quantity": total_quantity,
                "reason": primary_reason,
                "priority": highest_priority,
                "consolidated_from": len(hedge_list)
            })

        return optimized_hedges

    def get_sector_etf(self, sector: str) -> str:
        """Map sector to corresponding ETF"""

        sector_etf_map = {
            "technology": "XLK",
            "healthcare": "XLV",
            "financial": "XLF",
            "energy": "XLE",
            "consumer_discretionary": "XLY",
            "consumer_staples": "XLP",
            "industrials": "XLI",
            "materials": "XLB",
            "utilities": "XLU",
            "real_estate": "XLRE"
        }

        return sector_etf_map.get(sector.lower(), None)

    def get_currency_hedge_symbol(self, currency: str) -> str:
        """Map currency to hedge symbol"""

        currency_symbol_map = {
            "EUR": "FXE",
            "JPY": "FXY",
            "GBP": "FXB",
            "CAD": "FXC",
            "AUD": "FXA",
            "CHF": "FXF"
        }

        return currency_symbol_map.get(currency, None)

class HedgeExecutor:
    def __init__(self, order_manager, position_tracker):
        self.order_manager = order_manager
        self.position_tracker = position_tracker

    async def execute_hedge_plan(self, account_id: str, hedge_plan: List[Dict]) -> Dict:
        """Execute hedging plan"""

        execution_results = []
        total_cost = 0.0

        for hedge in hedge_plan:
            try:
                # Execute hedge order
                order_result = await self.execute_hedge_order(account_id, hedge)
                execution_results.append(order_result)

                if order_result["status"] == "success":
                    total_cost += order_result.get("cost", 0.0)

            except Exception as e:
                execution_results.append({
                    "hedge": hedge,
                    "status": "failed",
                    "error": str(e)
                })

        return {
            "account_id": account_id,
            "timestamp": datetime.now().isoformat(),
            "execution_results": execution_results,
            "total_cost": total_cost,
            "success_rate": len([r for r in execution_results if r["status"] == "success"]) / len(execution_results)
        }

    async def execute_hedge_order(self, account_id: str, hedge: Dict) -> Dict:
        """Execute individual hedge order"""

        symbol = hedge["symbol"]
        side = hedge["side"]
        quantity = hedge["quantity"]

        # Get current market price
        market_price = await self.get_market_price(symbol)

        # Calculate order parameters
        order_params = {
            "account_id": account_id,
            "symbol": symbol,
            "side": side,
            "quantity": quantity,
            "order_type": "market",  # Use market orders for hedging
            "time_in_force": "IOC",  # Immediate or cancel
            "hedge_flag": True
        }

        # Place order
        order_result = await self.order_manager.place_order(order_params)

        # Calculate execution cost
        execution_cost = self.calculate_execution_cost(order_result, market_price)

        return {
            "hedge": hedge,
            "order_result": order_result,
            "status": "success" if order_result["status"] == "filled" else "failed",
            "cost": execution_cost,
            "execution_price": order_result.get("avg_price", market_price)
        }

    async def get_market_price(self, symbol: str) -> float:
        """Get current market price for symbol"""

        # This would integrate with market data provider
        # For now, return a placeholder
        return 100.0  # Placeholder

    def calculate_execution_cost(self, order_result: Dict, market_price: float) -> float:
        """Calculate execution cost including slippage and fees"""

        if order_result["status"] != "filled":
            return 0.0

        executed_quantity = order_result.get("filled_quantity", 0)
        avg_price = order_result.get("avg_price", market_price)

        # Calculate slippage
        slippage = abs(avg_price - market_price) * executed_quantity

        # Calculate fees (assuming 0.1% commission)
        fees = avg_price * executed_quantity * 0.001

        return slippage + fees

@router.get("/hedge/exposure/{account_id}")
async def get_exposure_analysis(account_id: str):
    """Get current risk exposure analysis"""
    exposure = exposure_analyzer.analyze_exposure(account_id)
    return {"account_id": account_id, "exposure": exposure}

@router.post("/hedge/generate/{account_id}")
async def generate_hedge_plan(account_id: str):
    """Generate hedging plan for account"""
    hedge_plan = hedge_signal_generator.generate_hedge_plan(account_id)
    return hedge_plan

@router.post("/hedge/execute/{account_id}")
async def execute_hedge_plan(account_id: str, hedge_plan: List[Dict]):
    """Execute hedging plan"""
    result = await hedge_executor.execute_hedge_plan(account_id, hedge_plan)
    return result

@router.get("/hedge/status/{account_id}")
async def get_hedge_status(account_id: str):
    """Get current hedging status"""
    status = hedge_monitor.get_hedge_status(account_id)
    return {"account_id": account_id, "status": status}

@router.post("/hedge/rebalance/{account_id}")
async def rebalance_hedges(account_id: str):
    """Trigger hedge rebalancing"""
    result = await hedge_monitor.rebalance_hedges(account_id)
    return result

const HedgingDashboardView: React.FC = () => {
  const [exposureData, setExposureData] = useState<ExposureData | null>(null);
  const [hedgeStatus, setHedgeStatus] = useState<HedgeStatus | null>(null);
  const [hedgeHistory, setHedgeHistory] = useState<HedgeHistory[]>([]);

  return (
    <div className="hedging-dashboard">
      {/* Exposure Analysis */}
      <ExposureAnalysisPanel 
        exposureData={exposureData}
        onRefresh={refreshExposureData}
      />

      {/* Hedge Status */}
      <HedgeStatusPanel 
        hedgeStatus={hedgeStatus}
        onRebalance={triggerRebalance}
      />

      {/* Hedge History */}
      <HedgeHistoryPanel 
        hedgeHistory={hedgeHistory}
      />

      {/* Risk Metrics */}
      <RiskMetricsPanel 
        exposureData={exposureData}
        hedgeStatus={hedgeStatus}
      />

      {/* Hedge Performance */}
      <HedgePerformancePanel 
        hedgeHistory={hedgeHistory}
      />
    </div>
  );
};