Position limits, hedging strategies, risk management, and inventory optimization for LX market makers

Inventory Management

Effective inventory management is critical for market maker profitability and risk control. This guide covers position limits, hedging strategies, risk management tools, and best practices for LX market makers.

Position Limits

Maximum Position by Tier

┌─────────────────────────────────────────────────────────────────────┐
│                    POSITION LIMITS BY TIER                           │
├─────────────────────────────────────────────────────────────────────┤
│                                                                      │
│  Tier   │ Per-Market Limit │ Total Exposure │ Concentration Limit   │
│  ────────┼──────────────────┼────────────────┼─────────────────────  │
│  Tier 1  │ $500K            │ $2M            │ 50% single market    │
│  Tier 2  │ $2M              │ $10M           │ 40% single market    │
│  Tier 3  │ $10M             │ $50M           │ 30% single market    │
│  Tier 4  │ $50M             │ $250M          │ 25% single market    │
│                                                                      │
└─────────────────────────────────────────────────────────────────────┘

Position Limit Calculation

# Position limit enforcement
class PositionLimits:
    def __init__(self, tier: int):
        self.limits = {
            1: {"per_market": 500_000, "total": 2_000_000, "concentration": 0.50},
            2: {"per_market": 2_000_000, "total": 10_000_000, "concentration": 0.40},
            3: {"per_market": 10_000_000, "total": 50_000_000, "concentration": 0.30},
            4: {"per_market": 50_000_000, "total": 250_000_000, "concentration": 0.25},
        }[tier]

    def check_order(self, order, current_positions):
        # Check per-market limit
        market_position = current_positions.get(order.symbol, 0)
        new_position = market_position + order.notional_value
        if abs(new_position) > self.limits["per_market"]:
            return False, "Per-market limit exceeded"

        # Check total exposure
        total_exposure = sum(abs(p) for p in current_positions.values())
        total_exposure += order.notional_value
        if total_exposure > self.limits["total"]:
            return False, "Total exposure limit exceeded"

        # Check concentration
        if abs(new_position) / total_exposure > self.limits["concentration"]:
            return False, "Concentration limit exceeded"

        return True, "OK"

Dynamic Position Limits

Position limits may be adjusted based on:

Factor	Impact	Example
Market Volatility	-20% to -50%	30-day vol > 100% reduces limits
Liquidity Conditions	-10% to -30%	Thin markets reduce limits
Account Equity	+/- proportional	Higher equity = higher limits
Track Record	+10% to +25%	12+ months good standing
Collateral Quality	+/- 20%	Stablecoin vs volatile collateral

Collateral Management

Accepted Collateral

Asset	Haircut	Max Weight	Notes
USDC	0%	100%	Primary collateral
USDT	2%	80%	Secondary stablecoin
DAI	3%	60%	Decentralized stable
BTC	15%	50%	Volatile, liquid
ETH	18%	50%	Volatile, liquid
LUX	20%	40%	Native token
Other	25-50%	30%	Case by case

Margin Calculation

Portfolio Margin Model:

1. Mark-to-Market Valuation
   - All positions valued at mid-price
   - Unrealized P&L included in equity

2. Initial Margin (IM)
   - Base requirement: 10% of notional
   - Volatility adjustment: +/- 5%
   - Correlation offset: Up to -30%

3. Maintenance Margin (MM)
   - Standard: 80% of IM
   - Reduced for hedged positions
   - Minimum: 5% of notional

4. Margin Call Trigger
   - Account equity < MM
   - 2-hour cure period
   - Auto-liquidation at 50% of MM

Cross-Margin Example

// Example: Cross-margin calculation for hedged position
const positions = [
  { symbol: 'BTC-USD', size: 10, notional: 500000, side: 'long' },
  { symbol: 'BTC-PERP', size: -9.5, notional: 475000, side: 'short' },
];

// Without cross-margin:
// IM = (500000 + 475000) * 0.10 = $97,500

// With cross-margin (90% correlation offset):
// Net exposure = 500000 - 475000 = $25,000
// Hedged portion = 475000 * 2 = $950,000
// IM = 25000 * 0.10 + 950000 * 0.10 * (1 - 0.90) = $2,500 + $9,500 = $12,000

// Savings: 87% margin reduction for hedged position

Hedging Strategies

On-Platform Hedging

LX provides native hedging capabilities:

1. Perpetual Futures Hedge

Spot-Perp Basis Trade:

Position:
- Long 10 BTC spot at $50,000
- Short 10 BTC-PERP at $50,100

Outcome:
- Locked in $1,000 basis
- Annualized yield: ~7.3%
- Delta neutral
- Funding P&L variable

2. Cross-Asset Hedge

BTC-ETH Correlation Hedge:

Position:
- Long 10 BTC ($500,000)
- Short 166 ETH ($500,000)

Hedge Ratio: ~0.8 (beta-adjusted)
Correlation: 85%
Residual Risk: 15% of position

External Hedging

Market makers may hedge on external venues:

Venue Type	Latency	Cost	Capacity
CEX Spot	10-50ms	Low	High
CEX Perps	10-50ms	Medium	High
OTC Desks	1-5s	Variable	Very High
Other DEXs	2-30s	High	Medium

Automated Hedging

class AutoHedger:
    def __init__(self, config):
        self.max_unhedged = config.max_unhedged_exposure
        self.hedge_threshold = config.hedge_threshold
        self.venues = config.hedge_venues

    def check_and_hedge(self, positions):
        for symbol, position in positions.items():
            # Calculate unhedged exposure
            unhedged = self.calculate_unhedged(symbol, position)

            if abs(unhedged) > self.hedge_threshold:
                # Select optimal venue
                venue = self.select_venue(symbol, unhedged)

                # Execute hedge
                self.execute_hedge(venue, symbol, -unhedged)

    def select_venue(self, symbol, size):
        # Score venues by cost, latency, and capacity
        scores = []
        for venue in self.venues:
            cost = venue.estimate_cost(symbol, size)
            latency = venue.expected_latency
            capacity = venue.available_capacity(symbol)

            score = self.score_venue(cost, latency, capacity)
            scores.append((venue, score))

        return max(scores, key=lambda x: x[1])[0]

Risk Management

Real-Time Risk Monitoring

┌─────────────────────────────────────────────────────────────────────┐
│                    RISK DASHBOARD                                    │
├─────────────────────────────────────────────────────────────────────┤
│                                                                      │
│  Account Health                                                     │
│  ┌────────────────────────────────────────────────────────────┐    │
│  │ Equity: $10,450,000    │  Used Margin: $2,100,000         │    │
│  │ Available: $8,350,000  │  Margin Ratio: 497%              │    │
│  └────────────────────────────────────────────────────────────┘    │
│                                                                      │
│  Position Limits                                                    │
│  ┌────────────────────────────────────────────────────────────┐    │
│  │ BTC-USD:  $4.2M / $10M  ████████░░░░░░░░░░░░  42%        │    │
│  │ ETH-USD:  $2.1M / $10M  ████░░░░░░░░░░░░░░░░  21%        │    │
│  │ Total:    $8.5M / $50M  █████████░░░░░░░░░░░  17%        │    │
│  └────────────────────────────────────────────────────────────┘    │
│                                                                      │
│  Risk Metrics                                                       │
│  ┌────────────────────────────────────────────────────────────┐    │
│  │ VaR (1d, 99%):    $125,000                                │    │
│  │ Greeks Delta:      +$50,000/1%                             │    │
│  │ Greeks Gamma:      +$5,000/1%^2                           │    │
│  │ Max Drawdown:      $85,000 (0.8%)                          │    │
│  └────────────────────────────────────────────────────────────┘    │
│                                                                      │
└─────────────────────────────────────────────────────────────────────┘

Risk Limits

Metric	Tier 1	Tier 2	Tier 3	Tier 4
Max Daily Loss	5% equity	3% equity	2% equity	1.5% equity
Max Weekly Loss	10% equity	7% equity	5% equity	4% equity
Max Drawdown	15% equity	12% equity	10% equity	8% equity
VaR Limit (99%)	3% equity	2% equity	1.5% equity	1% equity

Kill Switch

Market makers must implement kill switch functionality:

interface KillSwitch {
  // Trigger conditions
  triggers: {
    maxLoss: number;        // $ loss threshold
    maxDrawdown: number;    // % drawdown threshold
    marginRatio: number;    // Minimum margin ratio
    heartbeat: number;      // Max seconds without heartbeat
    manualTrigger: boolean; // Manual activation
  };

  // Actions on trigger
  actions: {
    cancelAllOrders: boolean;   // Cancel open orders
    closePositions: boolean;    // Close all positions
    notifyOps: boolean;         // Alert operations team
    pauseTrading: boolean;      // Prevent new orders
  };
}

// Example configuration
const killSwitch: KillSwitch = {
  triggers: {
    maxLoss: 500000,        // $500K loss
    maxDrawdown: 0.05,      // 5% drawdown
    marginRatio: 1.5,       // 150% margin ratio
    heartbeat: 30,          // 30 seconds
    manualTrigger: false,
  },
  actions: {
    cancelAllOrders: true,
    closePositions: false,  // Manual review preferred
    notifyOps: true,
    pauseTrading: true,
  },
};

Inventory Optimization

Target Inventory Model

class InventoryOptimizer:
    def __init__(self, config):
        self.target_inventory = config.target  # Usually 0 or small long bias
        self.max_inventory = config.max
        self.skew_factor = config.skew_factor

    def calculate_quote_skew(self, current_inventory):
        """
        Skew quotes based on inventory to mean-revert position.

        Positive inventory -> lower bid, higher ask
        Negative inventory -> higher bid, lower ask
        """
        inventory_ratio = current_inventory / self.max_inventory

        # Linear skew model
        bid_skew = -self.skew_factor * inventory_ratio
        ask_skew = +self.skew_factor * inventory_ratio

        return bid_skew, ask_skew

    def example(self):
        # Example: $2M position limit, $500K current long
        # skew_factor = 2 bps per 10% inventory

        # inventory_ratio = 500K / 2M = 0.25 (25%)
        # bid_skew = -2 * 0.25 = -0.5 bps (lower bid)
        # ask_skew = +2 * 0.25 = +0.5 bps (higher ask)

        # Result: Slightly discourage buying, encourage selling
        pass

Inventory Decay Strategies

Strategy	Use Case	Parameters
Linear Decay	Gradual reduction	Rate: 10%/hour
Time-Weighted	EOD flat target	Window: Market close
Volatility-Adjusted	Risk reduction	Vol threshold: 50%
P&L Based	Lock in profits	Profit target: 5%

Rebalancing

Inventory Rebalancing Protocol:

1. Passive Rebalancing (Default)
   - Skew quotes to attract offsetting flow
   - Adjust over 1-4 hours
   - Minimal market impact

2. Active Rebalancing (Urgent)
   - Submit market/aggressive limit orders
   - Complete within 15-30 minutes
   - Accept some slippage

3. Block Trade (Large Positions)
   - Contact OTC desk
   - Negotiate block price
   - Single execution

Trigger Conditions:
- Inventory > 80% of limit
- VaR > 80% of limit
- Margin ratio < 200%
- Risk event detected

Reporting

Daily Inventory Report

┌─────────────────────────────────────────────────────────────────────┐
│  DAILY INVENTORY REPORT - 2024-12-10                                │
├─────────────────────────────────────────────────────────────────────┤
│                                                                      │
│  Position Summary                                                   │
│  ─────────────────────────────────────────────────────────────────  │
│  Symbol    │ Position   │ Notional   │ Avg Price  │ Unrealized P&L │
│  BTC-USD   │ +5.23      │ $261,500   │ $50,000    │ +$1,150       │
│  ETH-USD   │ -42.5      │ -$127,500  │ $3,000     │ -$425         │
│  LUX-USD   │ +10,000    │ $50,000    │ $5.00      │ +$500         │
│  ─────────────────────────────────────────────────────────────────  │
│  Total Net Exposure: $184,000                                       │
│  Total Gross Exposure: $439,000                                     │
│                                                                      │
│  Risk Metrics                                                       │
│  ─────────────────────────────────────────────────────────────────  │
│  Daily P&L:        +$1,225    │  MTD P&L:        +$15,340          │
│  Daily VaR:        $8,500     │  Max Drawdown:   $3,200            │
│  Turnover:         $2.5M      │  Trades:         847               │
│                                                                      │
└─────────────────────────────────────────────────────────────────────┘

Weekly Risk Summary

Generated every Monday covering:

Position limits utilization
VaR trend analysis
P&L attribution
Hedging effectiveness
Concentration analysis

Best Practices

Inventory Management Checklist

Set appropriate position limits per market
Configure inventory skew parameters
Implement kill switch with tested triggers
Establish hedging venues and protocols
Monitor real-time risk dashboard
Review daily inventory reports
Test rebalancing procedures monthly
Document all risk policies

Common Pitfalls

Pitfall	Impact	Prevention
Oversized positions	Margin call risk	Strict per-market limits
Concentration	Correlated losses	Diversification rules
Stale hedges	Basis risk	Regular hedge review
Manual overrides	Human error	Approval workflows
Ignoring correlation	Understated risk	Portfolio VaR models

Risk management guidelines are supplementary to individual firm policies. Last updated: December 2024

Inventory Management

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