Market Making Framework

Market making is the cornerstone of exchange liquidity. use.com implements a sophisticated market making framework that combines traditional strategies with cutting-edge algorithms, creating a robust ecosystem that benefits both professional market makers and the broader trading community.

Market Making Fundamentals

What is Market Making?

Market Making is the practice of simultaneously providing buy (bid) and sell (ask) quotes to facilitate trading and earn the spread.

Core Principle: $Profit = (Ask\_Price - Bid\_Price) \times Volume - Transaction\_Costs - Risk\_Costs$

Example:

• Bid: $50,000 (buy 1 BTC)

• Ask: $50,050 (sell 1 BTC)

• Spread: $50 (0.1%)

• If both orders fill: Profit = $50 - fees

Market Maker Role

Benefits to Exchange:

• Provides continuous liquidity

• Reduces spreads for traders

• Enables price discovery

• Absorbs temporary imbalances

Benefits to Market Maker:

• Earns spread profits

• Receives fee rebates

• Gains market insights

• Builds trading infrastructure

Market Making Strategies

1. Pure Market Making

Strategy: Continuously quote both sides of the order book at competitive prices.

Algorithm:

mid_price = (best_bid + best_ask) / 2
bid_price = mid_price × (1 - spread/2)
ask_price = mid_price × (1 + spread/2)

Spread Determination: $Optimal\_Spread = 2 \times \sqrt{\frac{\sigma^2 \times T}{\gamma}}$

Where:

• σ = volatility

• T = time horizon

• γ = risk aversion parameter

Example:

• BTC volatility: 4% daily (σ = 0.04)

• Time horizon: 1 hour (T = 1/24)

• Risk aversion: γ = 0.1

• Optimal Spread: 2 × √((0.04² × 1/24) / 0.1) = 0.163%

2. Inventory-Based Market Making

Strategy: Adjust quotes based on current inventory to manage risk.

Inventory Skew Formula: $Bid\_Skew = -\alpha \times \frac{Inventory - Target}{Max\_Inventory}$ $Ask\_Skew = +\alpha \times \frac{Inventory - Target}{Max\_Inventory}$

Where α = skew intensity (typically 0.5-2.0)

Example:

• Target Inventory: 0 BTC (neutral)

• Current Inventory: +10 BTC (long)

• Max Inventory: 20 BTC

• Skew Intensity: α = 1.0

• Bid Skew: -1.0 × (10/20) = -0.5% (lower bids)

• Ask Skew: +1.0 × (10/20) = +0.5% (higher asks)

Result: Encourages selling to reduce long position.

3. Statistical Arbitrage

Strategy: Exploit mean reversion and correlation patterns.

Z-Score Calculation: $Z = \frac{Price_{current} - \mu}{\sigma}$

Trading Rules:

• Z > +2: Price too high → Sell

• Z < -2: Price too low → Buy

• |Z| < 1: Neutral → Provide liquidity

Example:

• BTC mean price (24h): $50,000

• Standard deviation: $500

• Current price: $51,200

• Z-Score: (51,200 - 50,000) / 500 = +2.4

• Action: Aggressive selling, wider ask spread

4. Cross-Exchange Arbitrage

Strategy: Maintain quotes based on prices across multiple exchanges.

Fair Value Calculation: $Fair\_Value = \frac{\sum_{i=1}^{n} (Price_i \times Volume_i)}{\sum_{i=1}^{n} Volume_i}$

Arbitrage Opportunity: $Profit = |Price_{exchange\_A} - Price_{exchange\_B}| - Fees - Slippage$

Example:

• Binance BTC: $50,000

• Coinbase BTC: $50,100

• use.com target: $50,050 (midpoint)

• Spread: ±0.05% ($25)

• Bid: $50,025, Ask: $50,075

5. Volatility-Adaptive Market Making

Strategy: Widen spreads during high volatility, tighten during calm periods.

Volatility Measurement: $\sigma_{realized} = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (r_i - \bar{r})^2}$

Where r = log returns

Spread Adjustment: $Spread_{adjusted} = Spread_{base} \times (1 + \beta \times \frac{\sigma_{current}}{\sigma_{normal}})$

Where β = volatility sensitivity (typically 0.5-1.5)

Example:

• Base spread: 0.05%

• Normal volatility: 2% daily

• Current volatility: 6% daily

• β = 1.0

• Adjusted spread: 0.05% × (1 + 1.0 × 6%/2%) = 0.15%

Risk Management

Position Limits

Maximum Position Sizes:

Asset	Max Position (USD)	Max Position (% of Daily Volume)
BTC	$50M	5%
ETH	$30M	5%
Major Alts	$10M	10%
Long-tail	$1M	20%

Position Limit Formula: $Max\_Position = \min(Absolute\_Limit, Daily\_Volume \times Percentage\_Limit)$

Stop-Loss Mechanisms

Individual Position Stop-Loss: $Stop\_Loss = Entry\_Price \times (1 - Stop\_Loss\_Percentage)$

Typical Stop-Loss Levels:

• BTC/ETH: 2%

• Major Alts: 5%

• Long-tail: 10%

Portfolio Stop-Loss: $Daily\_Loss\_Limit = Trading\_Capital \times 0.05$

Example:

• Trading Capital: $10M

• Daily Loss Limit: $500K

• If losses reach $500K: Halt all trading, unwind positions

Hedging Strategies

Delta Hedging: $Hedge\_Size = -\Delta \times Position\_Size$

Example:

• Long 100 BTC on use.com

• Hedge: Short 100 BTC perpetual on another exchange

• Net exposure: 0 (market neutral)

• Profit from spread capture only

Cross-Asset Hedging:

• Long BTC, Short ETH (correlation ~0.8)

• Reduces directional risk

• Maintains spread capture opportunity

Performance Metrics

Profitability Metrics

Gross Profit: $Gross\_Profit = \sum (Sell\_Price - Buy\_Price) \times Volume$

Net Profit: $Net\_Profit = Gross\_Profit - Fees + Rebates - Slippage - Funding\_Costs$

Return on Capital: $ROC = \frac{Net\_Profit}{Capital\_Deployed} \times 100\%$

Example:

• Monthly Gross Profit: $500K

• Fees Paid: $100K

• Rebates Received: $150K

• Net Profit: $500K - $100K + $150K = $550K

• Capital Deployed: $10M

• Monthly ROC: 5.5%

• Annualized ROC: 66%

Efficiency Metrics

Sharpe Ratio: $Sharpe = \frac{Return_{avg} - Risk\_Free\_Rate}{\sigma_{returns}}$

Target: >2.0 for professional market makers

Fill Rate: $Fill\_Rate = \frac{Orders\_Filled}{Orders\_Placed} \times 100\%$

Target: >60% for competitive market making

Inventory Turnover: $Turnover = \frac{Total\_Volume\_Traded}{Average\_Inventory}$

Target: >10× daily for active market making

Risk Metrics

Value at Risk (VaR): $VaR_{95\%} = \mu - 1.645 \times \sigma$

Example:

• Daily return mean: +0.1%

• Daily return std dev: 2%

• 95% VaR: 0.1% - 1.645 × 2% = -3.19%

• On $10M capital: $319K maximum expected daily loss (95% confidence)

Maximum Drawdown: $Max\_Drawdown = \frac{Peak\_Value - Trough\_Value}{Peak\_Value}$

Target: <10% for professional operations

Market Maker Incentive Program

Tier Structure

Tier	Monthly Volume	Uptime	Avg Spread	Rebate Rate	Additional Benefits
Diamond	>$5B	>99.5%	<0.03%	0.020%	Dedicated support, co-location
Platinum	$1B-$5B	>99%	<0.05%	0.015%	Priority API, custom limits
Gold	$500M-$1B	>98%	<0.08%	0.012%	Enhanced API limits
Silver	$100M-$500M	>95%	<0.10%	0.010%	Standard benefits
Bronze	$50M-$100M	>90%	<0.15%	0.008%	Basic benefits

Performance Bonuses

Volume Bonus: $Bonus = Base\_Rebate \times \min(0.5, \frac{Actual\_Volume - Target\_Volume}{Target\_Volume})$

Example:

• Target Volume: $1B

• Actual Volume: $1.5B

• Excess: 50%

• Bonus: 0.015% × 0.5 = 0.0075%

• Total Rebate: 0.015% + 0.0075% = 0.0225%

Uptime Bonus:

• 99.9% uptime: +10% rebate

• 99.95% uptime: +15% rebate

• 99.99% uptime: +20% rebate

Penalty Structure

Spread Violations:

• Spread >2× target: -25% rebate for that hour

• Spread >3× target: -50% rebate for that hour

• Persistent violations: Tier downgrade

Uptime Penalties:

• <90% uptime: -50% monthly rebate

• <80% uptime: -75% monthly rebate

• <70% uptime: Program suspension

Technology Requirements

Infrastructure

Minimum Requirements:

• Latency: <10ms to exchange

• Order rate: 100+ orders/second

• Uptime: 99%+

• Redundancy: Hot failover systems

Recommended Setup:

• Co-location in exchange data center

• Dedicated 10Gbps connection

• Multi-region deployment

• Real-time risk monitoring

API Integration

REST API:

• Order placement

• Account management

• Market data queries

• Rate limit: 1,200 requests/minute

WebSocket API:

• Real-time order book updates

• Trade stream

• Account updates

• 10 concurrent connections

FIX Protocol:

• Available for institutional market makers

• Lower latency than REST

• Industry-standard messaging

Risk Controls

Pre-Trade Checks:

• Position limit validation

• Capital adequacy check

• Duplicate order prevention

• Price collar validation

Post-Trade Monitoring:

• Real-time P&L tracking

• Position monitoring

• Exposure analysis

• Automated alerts

Market Making Best Practices

1. Start Conservative

Initial Strategy:

• Wider spreads (0.15-0.20%)

• Smaller position sizes

• Limited pairs (5-10 major pairs)

• Gradual scaling

2. Monitor Continuously

Key Metrics to Watch:

• Real-time P&L

• Inventory levels

• Fill rates

• Spread competitiveness

• Market volatility

3. Adapt to Market Conditions

Bull Market:

• Tighter spreads

• Larger ask sizes

• Inventory skew toward long

Bear Market:

• Wider spreads

• Larger bid sizes

• Inventory skew toward short

High Volatility:

• Wider spreads

• Smaller position sizes

• More frequent rebalancing

4. Diversify Strategies

Portfolio Approach:

• 40% pure market making

• 30% statistical arbitrage

• 20% cross-exchange arbitrage

• 10% volatility trading

5. Continuous Optimization

A/B Testing:

• Test different spread levels

• Compare inventory management approaches

• Evaluate order placement strategies

• Measure performance differences

Case Studies

Case Study 1: High-Frequency Market Maker

Profile:

• Capital: $50M

• Strategy: Pure market making with inventory management

• Pairs: 20 major pairs

• Technology: Co-located servers, <5ms latency

Performance (Monthly):

• Volume: $2B

• Gross Profit: $800K (0.04% of volume)

• Rebates: $300K

• Net Profit: $1.1M

• ROC: 2.2% monthly, 26.4% annually

Case Study 2: Statistical Arbitrage Firm

Profile:

• Capital: $20M

• Strategy: Mean reversion + cross-exchange arbitrage

• Pairs: 50 pairs across 5 exchanges

• Technology: Cloud-based, ML-powered

Performance (Monthly):

• Volume: $500M

• Gross Profit: $400K (0.08% of volume)

• Rebates: $50K

• Net Profit: $450K

• ROC: 2.25% monthly, 27% annually

Case Study 3: Retail Market Maker

Profile:

• Capital: $100K

• Strategy: Simple market making on 3 pairs

• Technology: Standard API integration

Performance (Monthly):

• Volume: $5M

• Gross Profit: $2.5K (0.05% of volume)

• Rebates: $500

• Net Profit: $3K

• ROC: 3% monthly, 36% annually

Future Developments

Q2 2025: AI-powered market making tools Q3 2025: Automated strategy optimization Q4 2025: Cross-chain market making 2026: Decentralized market maker network

Conclusion

use.com's market making framework provides a comprehensive ecosystem for professional and retail market makers alike. Through competitive rebates, advanced technology infrastructure, and sophisticated risk management tools, we enable market makers to operate efficiently while providing deep liquidity for all traders.

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Related Sections:

• Fee Model

• Matching Engine & Order Book Design

• Risk Engine & Liquidation System