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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)×Volume−Transaction_Costs−Risk_CostsProfit = (Ask_Price - Bid_Price) \times Volume - Transaction_Costs - Risk_CostsProfit=(Ask_Price−Bid_Price)×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:


Spread Determination: Optimal_Spread=2×σ2×TγOptimal_Spread = 2 \times \sqrt{\frac{\sigma^2 \times T}{\gamma}}Optimal_Spread=2×γσ2×T​​


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=−α×Inventory−TargetMax_InventoryBid_Skew = -\alpha \times \frac{Inventory - Target}{Max_Inventory}Bid_Skew=−α×Max_InventoryInventory−Target​ Ask_Skew=+α×Inventory−TargetMax_InventoryAsk_Skew = +\alpha \times \frac{Inventory - Target}{Max_Inventory}Ask_Skew=+α×Max_InventoryInventory−Target​


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=Pricecurrent−μσZ = \frac{Price_{current} - \mu}{\sigma}Z=σPricecurrent​−μ​


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=∑i=1n(Pricei×Volumei)∑i=1nVolumeiFair_Value = \frac{\sum_{i=1}^{n} (Price_i \times Volume_i)}{\sum_{i=1}^{n} Volume_i}Fair_Value=∑i=1n​Volumei​∑i=1n​(Pricei​×Volumei​)​


Arbitrage Opportunity: Profit=∣Priceexchange_A−Priceexchange_B∣−Fees−SlippageProfit = |Price_{exchange_A} - Price_{exchange_B}| - Fees - SlippageProfit=∣Priceexchange_A​−Priceexchange_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: σrealized=1n−1∑i=1n(ri−rˉ)2\sigma_{realized} = \sqrt{\frac{1}{n-1} \sum_{i=1}^{n} (r_i - \bar{r})^2}σrealized​=n−11​∑i=1n​(ri​−rˉ)2​


Where r = log returns


Spread Adjustment: Spreadadjusted=Spreadbase×(1+β×σcurrentσnormal)Spread_{adjusted} = Spread_{base} \times (1 + \beta \times \frac{\sigma_{current}}{\sigma_{normal}})Spreadadjusted​=Spreadbase​×(1+β×σnormal​σcurrent​​)


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×Percentage_Limit)Max_Position = \min(Absolute_Limit, Daily_Volume \times Percentage_Limit)Max_Position=min(Absolute_Limit,Daily_Volume×Percentage_Limit)


Stop-Loss Mechanisms


Individual Position Stop-Loss: Stop_Loss=Entry_Price×(1−Stop_Loss_Percentage)Stop_Loss = Entry_Price \times (1 - Stop_Loss_Percentage)Stop_Loss=Entry_Price×(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×0.05Daily_Loss_Limit = Trading_Capital \times 0.05Daily_Loss_Limit=Trading_Capital×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=−Δ×Position_SizeHedge_Size = -\Delta \times Position_SizeHedge_Size=−Δ×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=∑(Sell_Price−Buy_Price)×VolumeGross_Profit = \sum (Sell_Price - Buy_Price) \times VolumeGross_Profit=∑(Sell_Price−Buy_Price)×Volume


Net Profit: Net_Profit=Gross_Profit−Fees+Rebates−Slippage−Funding_CostsNet_Profit = Gross_Profit - Fees + Rebates - Slippage - Funding_CostsNet_Profit=Gross_Profit−Fees+Rebates−Slippage−Funding_Costs


Return on Capital: ROC=Net_ProfitCapital_Deployed×100%ROC = \frac{Net_Profit}{Capital_Deployed} \times 100\%ROC=Capital_DeployedNet_Profit​×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=Returnavg−Risk_Free_RateσreturnsSharpe = \frac{Return_{avg} - Risk_Free_Rate}{\sigma_{returns}}Sharpe=σreturns​Returnavg​−Risk_Free_Rate​


Target: >2.0 for professional market makers


Fill Rate: Fill_Rate=Orders_FilledOrders_Placed×100%Fill_Rate = \frac{Orders_Filled}{Orders_Placed} \times 100\%Fill_Rate=Orders_PlacedOrders_Filled​×100%


Target: >60% for competitive market making


Inventory Turnover: Turnover=Total_Volume_TradedAverage_InventoryTurnover = \frac{Total_Volume_Traded}{Average_Inventory}Turnover=Average_InventoryTotal_Volume_Traded​


Target: >10× daily for active market making


Risk Metrics


Value at Risk (VaR): VaR95%=μ−1.645×σVaR_{95\%} = \mu - 1.645 \times \sigmaVaR95%​=μ−1.645×σ


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=Peak_Value−Trough_ValuePeak_ValueMax_Drawdown = \frac{Peak_Value - Trough_Value}{Peak_Value}Max_Drawdown=Peak_ValuePeak_Value−Trough_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×min⁡(0.5,Actual_Volume−Target_VolumeTarget_Volume)Bonus = Base_Rebate \times \min(0.5, \frac{Actual_Volume - Target_Volume}{Target_Volume})Bonus=Base_Rebate×min(0.5,Target_VolumeActual_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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Updated on: 10/03/2026

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