Economics of Market Making

A market maker's profit and loss can be expressed as:

Net P&L = Gross Revenue - Total Costs

Where:

Gross Revenue = (Volume × Gross Spread) + Rebates + Other Income

Total Costs = Adverse Selection Costs
+ Inventory Costs
+ Execution Costs
+ Operating Costs
+ Capital Costs
```

Let's expand each component:

DETAILED P&L EQUATION:

Net P&L = V × S                          [Spread Revenue]
        + V × R                          [Rebates]
        - V × AS                         [Adverse Selection]
        - V × EC                         [Execution Costs]
        - I × HC × T                     [Inventory Holding Cost]
        - OC                             [Operating Costs]
        - K × CC                         [Capital Costs]

Where:
V  = Volume (round-trips)
S  = Gross spread per round-trip
R  = Rebate per round-trip (can be negative if paying fees)
AS = Adverse selection cost per round-trip
EC = Execution costs (slippage, partial fills)
I  = Average inventory held
HC = Holding cost rate (opportunity cost + risk premium)
T  = Time period
OC = Operating costs (technology, personnel, overhead)
K  = Capital deployed
CC = Cost of capital rate

This looks complicated, but the key insight is simple: you must earn more from spreads than you lose to informed traders and operating costs.

Spread Revenue (V × S):

The primary revenue source. If you complete 1,000 round-trips per day at $0.01 spread, gross spread revenue is $10 per day, or approximately $3,650 annually.

But wait—that seems tiny for the capital required. Let's scale it properly:

Example: XRP Market Making

- Trading XRP/USD at $2.00 per XRP
- Spread: 0.10% ($0.002 per XRP)
- Position size: 50,000 XRP per side ($100,000 notional)
- Round-trips per day: 5

Daily Spread Revenue:
= 5 round-trips × 50,000 XRP × $0.002
= $500 per day
= ~$182,500 per year

Capital required: ~$200,000 (for positions + buffer)
Gross return on capital: ~91% annually

That looks attractive—but we haven't subtracted costs yet.

Rebates (V × R):

Many exchanges pay rebates to market makers (liquidity providers) and charge fees to takers (liquidity removers). This is the "maker-taker" model.

Typical Crypto Exchange Fee Structure:

Maker fee: -0.01% to +0.02% (negative = rebate)
Taker fee: 0.05% to 0.10%

- You post a limit order (maker): Receive 0.01% rebate
- You get filled: Rebate on both buy and sell
- Per round-trip rebate: 0.02%

On 50,000 XRP at $2.00:
Rebate = $100,000 × 0.02% × 2 = $40 per round-trip

With 5 round-trips/day:
Daily rebates = $200
Annual rebates = ~$73,000

Rebates can be significant—in this example, 40% of gross spread revenue. Some market makers are "rebate capture" specialists, making most of their money from rebates rather than spreads.

Other Income:

• Arbitrage profits (capturing price discrepancies across venues)
• Market maker incentive programs (some exchanges pay bonuses)
• Information revenue (insights from order flow—ethically complex)

For conservative modeling, assume zero other income unless you have specific programs.

Adverse Selection Costs (V × AS):

This is the big one. Adverse selection occurs when informed traders—those who know something you don't—trade against your quotes.

Adverse Selection Example:

- Bid: $2.00 for 10,000 XRP
- Ask: $2.02 for 10,000 XRP

An informed trader knows that in 30 seconds, news will break causing XRP to drop to $1.90. They sell you 10,000 XRP at $2.00.

Your position: Long 10,000 XRP at $2.00
Market moves to: $1.90
Your loss: 10,000 × ($2.00 - $1.90) = $1,000

You "earned" half the spread ($0.01 × 10,000 = $100) but lost $1,000.
Net: -$900

This single adverse selection event wiped out 90 round-trips worth of spread revenue.

Adverse selection is why market making is hard. A small percentage of your trades—those against informed counterparties—can dominate your P&L.

Execution Costs (V × EC):

• Partial fills leave you with unwanted inventory
• Slippage when you cross the spread to manage inventory
• Timing differences between legs of a round-trip

Execution Cost Example:

You want to sell 10,000 XRP to flatten inventory.
Best bid: $2.00 for 5,000 XRP
Next bid: $1.99 for 8,000 XRP

- 5,000 at $2.00 = $10,000
- 5,000 at $1.99 = $9,950
- Total: $19,950

Vs. ideal (all at $2.00): $20,000
Execution cost: $50 (0.25%)

Inventory Holding Costs (I × HC × T):

• Opportunity cost (capital tied up could earn returns elsewhere)
• Risk premium (price might move against you)
• Funding cost (if using borrowed capital)

Inventory Holding Cost Example:

Average inventory: 25,000 XRP ($50,000 at $2.00)
Holding period: 4 hours average
Annual holding cost rate: 15% (opportunity cost + risk premium)

Daily holding cost:
= $50,000 × 15% × (4/24) × (1/365)
= $3.42 per day

Or approximately $1,250 per year.

This seems small, but it compounds. And the "risk premium" component can spike during volatile periods.

Operating Costs (OC):

• Technology (servers, connectivity, software)
• Personnel (if any)
• Data feeds
• Legal/compliance
• Office/overhead

Operating Cost Examples:

- Cloud servers: $200/month
- Data feeds: $100/month
- Software tools: $100/month
- Total: $400/month = $4,800/year

- Co-located servers: $2,000/month
- Premium data: $500/month
- Proprietary software maintenance: $1,000/month
- Part-time developer: $3,000/month
- Legal/compliance: $500/month
- Total: $7,000/month = $84,000/year

These costs exist regardless of trading performance.

Capital Costs (K × CC):

The opportunity cost of capital deployed in the operation:

Capital Cost Example:

Capital deployed: $200,000
Alternative return (e.g., index fund): 8%
Capital cost: $200,000 × 8% = $16,000/year

This is the return you forgo by deploying capital in market making 
instead of passive investment.

Let's build a complete annual P&L for a hypothetical XRP market making operation:

XRP MARKET MAKING P&L MODEL

REVENUE:
Spread revenue: 5 × 365 × 50,000 × $0.002 = $182,500
Rebates: 5 × 365 × $100,000 × 0.02% = $36,500
Gross revenue: $219,000

COSTS:
Adverse selection: $182,500 × 35% = $63,875
Execution costs: $182,500 × 10% = $18,250
Inventory holding: $50,000 × 15% = $7,500
Operating costs: $4,800
Capital costs: $200,000 × 8% = $16,000
Total costs: $110,425

NET P&L: $219,000 - $110,425 = $108,575

Return on capital: 54.3%
```

This looks great—54% return! But notice how sensitive this is to assumptions. Let's stress test it.

---

Adverse selection is the most important variable. Let's see how P&L changes:

ADVERSE SELECTION SENSITIVITY

Base case: 35% of gross spread lost to adverse selection

AS Rate    AS Cost    Net P&L    ROC
20%        $36,500    $135,950   68.0%
25%        $45,625    $126,825   63.4%
30%        $54,750    $117,700   58.9%
35%        $63,875    $108,575   54.3%  ← Base case
40%        $73,000    $99,450    49.7%
45%        $82,125    $90,325    45.2%
50%        $91,250    $81,200    40.6%
60%        $109,500   $62,950    31.5%
70%        $127,750   $44,700    22.4%
80%        $146,000   $26,450    13.2%
90%        $164,250   $8,200     4.1%
100%       $182,500   -$10,050   -5.0%

At 100% adverse selection—meaning every trade is against an informed counterparty—you lose money despite earning the gross spread. This is the "winner's curse" in market making: if someone is eagerly trading with you, ask why.

• Highly liquid, well-arbitraged markets: 60-80%
• Moderately liquid markets: 40-60%
• Less efficient markets: 20-40%
• Truly inefficient markets: 10-30%

The more efficient the market, the higher the adverse selection because the only traders who bother to trade are informed ones.

Volume directly multiplies both revenue and variable costs:

VOLUME SENSITIVITY

Base case: 5 round-trips per day

Daily RT    Annual Vol    Gross Rev    Net P&L    ROC
2           730          $87,600      $27,315    13.7%
3           1,095        $131,400     $57,698    28.8%
4           1,460        $175,200     $88,080    44.0%
5           1,825        $219,000     $108,575   54.3%  ← Base
7           2,555        $306,600     $159,318   79.7%
10          3,650        $438,000     $230,225   115.1%

- Operating costs and capital costs are fixed
- Variable costs scale with volume
- Returns to scale are positive (fixed costs spread over more volume)

This shows why volume matters so much. The jump from 2 to 5 daily round-trips nearly quadruples net P&L because fixed costs are spread over more revenue.

Spread affects revenue directly but also affects volume (tighter spreads attract more flow):

SPREAD SENSITIVITY (assuming constant volume)

Spread Gross Rev Net P&L ROC
0.05% $109,500 $27,338 13.7%
0.08% $175,200 $78,168 39.1%
0.10% $219,000 $108,575 54.3% ← Base
0.12% $262,800 $138,982 69.5%
0.15% $328,500 $184,593 92.3%
0.20% $438,000 $255,800 127.9%
```

Spread compression is the existential threat to market making. If spreads fall from 0.10% to 0.05%, profitability drops by 75% (assuming volume doesn't increase proportionally).

What's the minimum volume needed to break even?

BREAK-EVEN CALCULATION

Fixed costs = Operating costs + Capital costs
= $4,800 + $16,000
= $20,800

Variable profit per round-trip:
= Spread revenue + Rebates - Adverse selection - Execution costs - Inventory costs
= ($100,000 × 0.10%) + ($100,000 × 0.02%) - ($100 × 35%) - ($100 × 10%) - $4.11*
= $100 + $20 - $35 - $10 - $4.11
= $70.89

*Inventory cost per round-trip = $7,500 annual / 1,825 round-trips

Break-even round-trips = $20,800 / $70.89 = 293 per year

Break-even daily volume = 293 / 365 = 0.8 round-trips per day
```

This operation breaks even with less than 1 round-trip per day—seemingly easy. But remember: our assumptions are optimistic. Let's stress test with worse assumptions:

STRESSED BREAK-EVEN

Fixed costs = $12,000 + $16,000 = $28,000

Variable profit per round-trip:
= ($100,000 × 0.08%) + ($100,000 × 0.02%) - ($80 × 60%) - ($80 × 20%) - $6.00
= $80 + $20 - $48 - $16 - $6
= $30

Break-even = $28,000 / $30 = 933 round-trips/year = 2.6 per day

With pessimistic assumptions, you need 3x more volume to break even.
```

Professional market makers think in terms of margin of safety:

MARGIN OF SAFETY FRAMEWORK

Question: If my assumptions are wrong, how wrong can they be before I lose money?

- Adverse selection: 35%
- Volume: 5 RT/day
- Spread: 0.10%
- Operating costs: $4,800

How much can each deteriorate independently before break-even?

Adverse selection: Can rise to 95% before break-even
Volume: Can fall to 0.8 RT/day before break-even
Spread: Can fall to 0.02% before break-even (with volume constant)
Operating costs: Can rise to $113,375 before break-even

- AS: 35% → 45.5%
- Volume: 5 → 3.5 RT/day
- Spread: 0.10% → 0.07%
- OpCosts: $4,800 → $6,240

Result: Net P&L drops from $108,575 to $23,847 (78% decline)
Still profitable, but barely.

This is why you need conservative assumptions and stress testing before committing capital.

---

Adverse selection deserves special attention because it's the primary determinant of market making success or failure.

The math is stark:

ADVERSE SELECTION ASYMMETRY

- They buy 10,000 XRP at your $2.01 ask
- Price doesn't move on information (they traded for liquidity reasons)
- You sell at $2.01, they're filled, market continues
- Your profit: Half the spread = $50

- They sell 10,000 XRP at your $2.00 bid
- They know price is about to drop to $1.90
- Price drops; you're stuck with losing position
- Your loss: 10,000 × ($2.00 - $1.90) = $1,000

One informed trade wipes out 20 uninformed trades.

Even if only 5% of your counterparties are informed, they can dominate your P&L if their information is good.

How do you know your adverse selection rate? You measure it.

ADVERSE SELECTION MEASUREMENT

Method 1: Post-Trade Price Movement

Example data:
Trade 1: Bought at $2.00, mid-price after 5 min: $2.01 → Favorable (+$0.01)
Trade 2: Bought at $2.00, mid-price after 5 min: $1.98 → Adverse (-$0.02)
Trade 3: Sold at $2.01, mid-price after 5 min: $2.00 → Favorable (+$0.01)
Trade 4: Sold at $2.01, mid-price after 5 min: $2.03 → Adverse (-$0.02)

Average adverse movement = sum of adverse / number of trades
Adverse selection cost = average adverse movement per trade
```

Method 2: Realized vs. Quoted Spread

Realized spread = Actual sell price - Actual buy price (for matched trades)
Quoted spread = Your offer - Your bid at time of first trade

AS rate = 1 - (Realized spread / Quoted spread)

Example:
Quoted spread: $0.01 (bid $2.00, ask $2.01)
Realized spread: $0.0055 (average across 100 round-trips)
AS rate = 1 - ($0.0055 / $0.01) = 45%
```

Understanding who adversely selects you helps you defend against it:

Arbitrageurs:
Cross-exchange arbitrageurs know when your price is stale relative to other venues. If Binance price moves to $2.05 and you're still quoting $2.01 ask, they'll buy from you instantly.

Defense: Monitor multiple venues; adjust quotes faster than arbitrageurs can execute.

News Traders:
Traders with faster news access know before you do. They hit your quotes in the milliseconds between news release and your quote update.

Defense: Widen spreads during news events; build faster news processing.

Statistical Arbitrageurs:
Sophisticated traders identify short-term predictability in price movements. They know, statistically, when prices are likely to move in a particular direction.

Defense: Study the same patterns; incorporate into your quoting.

Large Institutional Traders:
Institutions executing large orders often know more about supply/demand than you do. If a whale is selling $10M of XRP, they know price pressure is coming.

Defense: Recognize large order patterns; adjust inventory limits.

There's a deep relationship between market efficiency and adverse selection:

EFFICIENCY-ADVERSE SELECTION RELATIONSHIP

More efficient market:
→ Prices reflect information quickly
→ Only informed traders bother to trade (others use limit orders)
→ Higher adverse selection for market makers
→ Wider spreads required for profitability
→ But wide spreads attract arbitrageurs
→ Spreads compete down to minimum viable level

Less efficient market:
→ Information incorporated slowly
→ Mix of informed and uninformed traders
→ Lower adverse selection
→ Tighter spreads possible
→ But lower volume (less interest in inefficient markets)
→ May not have enough volume for profitability

This creates a paradox: the most attractive markets (high volume, efficient) have the highest adverse selection. The markets with lowest adverse selection often lack the volume to be profitable.

The sweet spot is markets with moderate efficiency—enough volume to be interesting, but enough inefficiency that not every counterparty is informed.

---

Even without adverse selection, holding inventory creates risk:

INVENTORY RISK EXAMPLE

You start the day flat (no position).
First trade: Buy 50,000 XRP at $2.00

You now have $100,000 of directional exposure.

- Position value: $102,000
- P&L: +$2,000 (plus any spread earned)

- Position value: $98,000
- P&L: -$2,000 (potentially wiping out many days of spread revenue)

With XRP's volatility often 3-5% daily, inventory risk can easily 
dominate spread revenue.

We can model inventory risk using volatility:

INVENTORY RISK MODEL

VaR (Value at Risk) approach:

Daily VaR = Position Size × Daily Volatility × Z-score

• Position Size = Dollar value of inventory

• Daily Volatility = Standard deviation of daily returns

• Z-score = Confidence level (1.65 for 95%, 2.33 for 99%)

• Position: $100,000 XRP

• Daily volatility: 4% (typical for XRP)

• 95% confidence VaR = $100,000 × 4% × 1.65 = $6,600

Interpretation: On 95% of days, you won't lose more than $6,600 from
inventory movement. On 5% of days (roughly monthly), you could lose more.
```

Compare this to daily spread revenue:

Daily spread revenue (from our model): $500
Daily inventory VaR (95%): $6,600

One bad day of inventory movement (which happens monthly) 
can wipe out 13 days of spread revenue.

This is why inventory management is critical.

Strategy 1: Tight Position Limits

Set hard limits on inventory accumulation:

• Max long: 50,000 XRP

• Max short: 50,000 XRP

• Stop quoting on the side that would increase position

• Aggressively quote to attract offsetting flow

• Consider crossing spread to flatten

Strategy 2: Skewed Quoting

Adjust quotes to attract offsetting flow:

• Bid: $2.00

• Ask: $2.01

• Bid: $1.99 (less attractive to sellers)

• Ask: $2.005 (more attractive to buyers)

The skew discourages adding to position and encourages offsetting trades.
```

Strategy 3: Hedging

Offset inventory risk in correlated markets:

• Long 50,000 XRP ($100,000)
• XRP has 0.7 correlation with BTC
• Short $70,000 of BTC futures as hedge

This reduces directional exposure while maintaining the XRP position.
Cost: Futures fees + imperfect hedge slippage
```

Strategy 4: Time-Based Flattening

Force inventory to zero at regular intervals:

• At 23:00 UTC, begin unwinding all positions
• Cross spread if necessary to flatten by midnight
• Start next day with clean slate

Cost: Spread crossing and potential adverse timing
Benefit: No overnight gap risk, clear P&L attribution
```

Academic research (Avellaneda-Stoikov model) suggests optimal quoting depends on inventory:

SIMPLIFIED AVELLANEDA-STOIKOV INSIGHT

The math:
δ = γσ²(T-t) + (1/γ)ln(1 + γ/k)

Where:
δ = optimal half-spread
γ = risk aversion parameter
σ = volatility
T-t = time remaining
k = order arrival rate

Key insight: Your quotes should dynamically adjust based on your
inventory and market conditions, not be static.
```

---

Market making has significant operating leverage—fixed costs that exist regardless of revenue:

OPERATING LEVERAGE EXAMPLE

Fixed costs: $20,800/year (operating + capital costs)
Variable profit margin: $70.89 per round-trip

The 60% volume drop caused a 71% profit drop due to fixed cost leverage.
```

Does doubling capital double profits?

SCALING ANALYSIS

The key insight: scaling is limited by market depth. You can't make twice as many trades just by having twice as much capital—the market has finite liquidity. Larger positions also increase market impact and adverse selection.

Scaling is attractive when:

SCALING DECISION FRAMEWORK

- You're not hitting position limits
- Market impact is minimal at current size
- Spread capture rate is consistent as you grow
- Operating costs have economies of scale

- Current market is capacity-constrained
- Operating infrastructure supports multiple markets
- Correlation between markets provides diversification
- Skills transfer to new market

- Market depth is limited
- Adverse selection rises with size
- Operating complexity would increase faster than revenue
- You haven't proven profitability at current scale

---

Before entering a market making opportunity, evaluate:

MARKET MAKING VIABILITY CHECKLIST

□ SPREAD ANALYSIS
  - Current spread: ____%
  - Spread after your entry: ____%
  - Spread volatility: ____
  - Spread vs. historical: ____

□ VOLUME ANALYSIS
  - Current daily volume: $____
  - Your expected capture: ____%
  - Expected daily round-trips: ____
  - Volume trend: ____

□ ADVERSE SELECTION ESTIMATE
  - Market efficiency: High / Medium / Low
  - Arbitrageur presence: High / Medium / Low
  - Estimated AS rate: ____%
  - Comparable market AS rates: ____%

□ COST STRUCTURE
  - Exchange fees: ____%
  - Rebates available: ____%
  - Technology costs: $____/month
  - Personnel costs: $____/month
  - Other costs: $____/month

□ CAPITAL REQUIREMENTS
  - Position capital: $____
  - Buffer capital: $____
  - Total deployment: $____
  - Alternative return: ____%

□ BREAK-EVEN ANALYSIS
  - Break-even volume: ____ RT/day
  - Current vs. break-even: ____x
  - Margin of safety: ____%

□ STRESS TEST
  - P&L if AS +50%: $____
  - P&L if spread -30%: $____
  - P&L if volume -50%: $____
  - Combined stress P&L: $____

Warning signs that an opportunity isn't viable:

RED FLAGS

🚩 Spread < transaction costs + minimum adverse selection
   (Mathematically impossible to profit)

🚩 Break-even volume > 50% of your expected volume
   (Insufficient margin of safety)

🚩 Adverse selection estimate based on hope, not measurement
   (You're probably underestimating)

🚩 P&L highly sensitive to single variable
   (One thing goes wrong and you're underwater)

🚩 Required ROC > 100% to justify risk
   (You're taking equity-like risk for bond-like returns)

🚩 Comparable market makers exiting
   (They know something you don't)

🚩 Spread compressing faster than costs declining
   (Margin squeeze in progress)

Let's recalculate our model with conservative assumptions:

CONSERVATIVE XRP MARKET MAKING MODEL

- Adverse selection: 50% (not 35%)
- Volume: 3 RT/day (not 5)
- Spread: 0.08% (not 0.10%)
- Execution costs: 15% (not 10%)
- Operating costs: $9,600/year (not $4,800)

REVENUE:
Spread revenue: 3 × 365 × 50,000 × $0.0016 = $87,600
Rebates: 3 × 365 × $100,000 × 0.02% = $21,900
Gross revenue: $109,500

COSTS:
Adverse selection: $87,600 × 50% = $43,800
Execution costs: $87,600 × 15% = $13,140
Inventory holding: $50,000 × 15% = $7,500
Operating costs: $9,600
Capital costs: $200,000 × 8% = $16,000
Total costs: $90,040

NET P&L: $109,500 - $90,040 = $19,460

Return on capital: 9.7%

With conservative assumptions, the return drops from 54% to 10%. Still positive, but barely better than passive investing—and with much more effort and risk.

This is the honest reality of market making: the optimistic case looks great, but the realistic case is marginal. This is why most market makers fail—they plan for the optimistic case and can't survive when reality is less generous.

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✅ The P&L equation is real: Every component we've discussed (spread revenue, adverse selection, inventory costs, etc.) is measurable and documented in academic research and practitioner experience.

✅ Adverse selection is the dominant factor: Academic studies consistently find that adverse selection costs are 30-70% of gross spreads in equity markets and can be even higher in crypto.

✅ Operating leverage creates fragility: Fixed costs mean that small changes in revenue have magnified effects on profitability. This is mathematical fact.

✅ Scaling is limited by market depth: You cannot infinitely scale a market making operation. Market impact and adverse selection rise with size.

⚠️ Exact adverse selection rates in XRP markets: We've used estimates based on comparable markets, but actual rates require measurement in the specific market you're trading.

⚠️ Future spread trajectories: Spreads have compressed over time in most markets, but the pace and floor are unknown.

⚠️ Sustainability of rebate programs: Exchanges can change fee structures. Rebate-dependent strategies have business model risk.

⚠️ Your execution quality vs. competitors: Even with correct market-level estimates, your individual performance may vary based on technology, skill, and luck.

📌 Optimistic assumption bias: It's natural to assume favorable conditions when evaluating opportunities. Fight this tendency with explicit stress testing.

📌 Ignoring adverse selection: Many aspiring market makers simply don't include adverse selection in their models. This is the fastest path to losing money.

📌 Confusing gross and net returns: A 0.10% spread is not a 0.10% return per trade. After costs, the net return is often 0.02-0.04%—or negative.

📌 Scaling before proving profitability: Don't increase capital until you've demonstrated profitability at smaller scale with real (not paper) trading.

Market making economics are unforgiving. The gap between optimistic projections and realized returns is large, and that gap is where most market makers' capital goes. Before committing capital, you must build a detailed model with conservative assumptions, stress test thoroughly, and ensure adequate margin of safety. The math in this lesson isn't optional—it's survival.

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Assignment: Build a comprehensive spreadsheet model for evaluating XRP market making profitability, incorporating all concepts from this lesson.

Requirements:

Part 1: Input Section (15%)

• Capital deployed
• Position size
• Expected daily volume (round-trips)
• Gross spread (%)
• Maker/taker fees and rebates
• Adverse selection rate
• Execution cost rate
• Average inventory level
• Inventory holding cost rate
• Operating costs (itemized)
• Cost of capital

Part 2: P&L Calculation (25%)

• Annual spread revenue
• Annual rebate revenue
• Total gross revenue
• Adverse selection costs
• Execution costs
• Inventory holding costs
• Operating costs
• Capital costs
• Net P&L
• Return on capital

All calculations must reference input cells (no hardcoded numbers in formulas).

Part 3: Sensitivity Analysis (25%)

• Adverse selection rate (20% to 80% in 10% increments)
• Daily volume (1 to 10 round-trips)
• Spread (0.04% to 0.16% in 0.02% increments)
• Combined two-variable sensitivity (AS rate vs. Volume)

Include conditional formatting to highlight profitable vs. unprofitable scenarios.

Part 4: Break-Even Analysis (15%)

• Break-even daily volume
• Current volume as multiple of break-even
• Break-even spread (at current volume)
• Break-even adverse selection rate

Part 5: Stress Testing (20%)

• Base case: Your primary assumptions

• Stress case: Each variable moves adversely by 30%

• Disaster case: Each variable moves adversely by 50%

• Net P&L

• Return on capital

• Whether still profitable

• Excel or Google Sheets

• Clean layout with clear sections

• Input cells highlighted

• Formulas must be auditable (no circular references)

• Include brief documentation of assumptions

• Formula accuracy (25%)

• Completeness of model (25%)

• Sensitivity analysis quality (20%)

• Stress testing rigor (15%)

• Professional presentation (15%)

Time Investment: 4-5 hours

Value: This model becomes your primary tool for evaluating any market making opportunity. You'll use it (or an enhanced version) throughout this course and in real decision-making. Building it yourself ensures you understand every component and can modify it as needed.

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For Next Lesson:
Lesson 3 will examine XRP market microstructure specifically—how XRP markets work across centralized exchanges and the XRPL DEX, where volume concentrates, and what makes XRP markets unique. We'll use the economic framework from this lesson to evaluate specific XRP market making opportunities.

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End of Lesson 2

Total Words: ~7,450
Estimated Completion Time: 55 minutes reading + 4-5 hours for deliverable