Crypto Trading and Exchanges Flashcards

A trader on a centralized exchange notices that the order book shows 500 BTC in buy walls at $60,000, but when the price approaches that level, the walls consistently disappear before being filled. What trading manipulation technique does this most likely represent?

Answer: Spoofing (layering)

Spoofing (also called layering) involves placing large orders with no intent to execute them — purely to create a false impression of supply or demand. When price approaches, the spoofer cancels the order. This differs from wash trading (self-dealing to inflate volume), front-running (using advance knowledge of pending orders), and painting the tape (coordinated trades to simulate activity).

On a decentralized exchange using an Automated Market Maker (AMM) model, a liquidity provider deposits equal value of ETH and USDC into a pool. If ETH's price doubles externally, what financial phenomenon does the LP experience compared to simply holding the assets?

Answer: Impermanent loss due to pool rebalancing

Impermanent loss (IL) occurs because the AMM's constant-product formula (x*y=k) automatically rebalances the pool as arbitrageurs exploit price discrepancies. When ETH doubles, arbitrageurs buy ETH from the pool until its ratio reflects the new price, leaving the LP with less ETH and more USDC than if they had simply held. The loss is 'impermanent' because it reverses if price returns to the original ratio.

A crypto exchange operates under a Proof-of-Reserves (PoR) audit using a Merkle tree structure. Which critical limitation does this audit method still fail to address, even when conducted correctly?

Answer: It does not prove the exchange has no undisclosed liabilities or loans against those reserves

A Merkle tree PoR proves that the exchange holds at least as many assets as it claims users have deposited. However, it says nothing about liabilities — the exchange could have borrowed or pledged those same reserves as collateral elsewhere, effectively making them unavailable. This is the key vulnerability exposed after the FTX collapse: PoR without proof-of-liabilities (PoL) is incomplete solvency verification.

A sophisticated arbitrageur executes a 'triangular arbitrage' trade across three cryptocurrency pairs on the same exchange: BTC/USD → ETH/BTC → ETH/USD. For this strategy to be profitable after fees, which condition must hold?

Answer: The implied cross-rate must differ from the direct rate by more than the cumulative trading fees

Triangular arbitrage profits from a mispricing between the implied cross-rate (derived from multiplying/dividing two pairs) and the direct quoted rate. If BTC/USD × ETH/BTC implies 1 ETH = $2,010 but ETH/USD quotes $2,000, a ~$10 spread exists. Profitability requires this spread to exceed the sum of all three legs' trading fees. High liquidity (not low) is preferred to minimize slippage, and 24-hour highs or fee discounts are irrelevant to the core arbitrage condition.

When a cryptocurrency exchange implements a 'maker-taker' fee model, which of the following trader behaviors would result in earning a rebate (negative fee) on some professional-grade platforms?

Answer: Submitting a limit order that rests in the order book and adds liquidity before being filled

In maker-taker models, 'makers' post limit orders that rest in the book and add liquidity — they are compensated (sometimes with negative fees/rebates) because they improve the exchange's order book depth. 'Takers' consume existing liquidity with market orders or marketable limit orders and pay higher fees. Stop-loss and OCO take-profit orders that execute at market are taker orders. The rebate structure applies specifically to limit orders that provide, not consume, liquidity.

A trader wants to execute a large BTC purchase worth $10 million on an exchange. Instead of a single market order, they use a TWAP (Time-Weighted Average Price) algorithm splitting execution over 4 hours. What is the PRIMARY risk this strategy fails to mitigate compared to a VWAP strategy?

Answer: The algorithm executing orders during low-liquidity overnight hours regardless of volume

TWAP splits orders equally across time intervals, ignoring trading volume patterns. This means it executes as aggressively during low-volume periods (overnight, weekends) as during high-volume periods — resulting in higher slippage when market depth is thin. VWAP, by contrast, weights execution toward high-volume periods where liquidity is deeper and slippage is lower. Both strategies face directional risk equally. Fees are comparable, and slippage per child order is generally reduced versus a block trade in both models.