Capital efficiency study simulation

[RZhang05]

Whitepaper:

7.4 Market Stress Testing Scenarios

36-Hour Stress Test Validation:

• Duration: 2160 minutes (36 hours)
• Price Decline: $100,000 → $50,000 (50% decline)
• Agent Count: 120 High Tide agents

Parameters:

• Initial HF: 1.1, Rebalancing HF: 1.025, Target HF: 1.04

Metrics to verify

• Survival Rate = Agents with HF > 1.0 / Total Agents
  Expected: 85-95% (with rebalancing)
• Avg Final Health Factor: around target
• Total Slippage = Σ(Expected MOET - Actual MOET) for all rebalancing events
  Expected: $500-2,000 per agent over 36h
• Arbitrage Profit = Σ(Pool Price - True Price) × Amount Traded
  Expected: $1,000-5,000 total profit over 36h
• Max Deviation = max(|pool_price - true_price| / true_price × 10,000)
  Expected Max: 75-150 bps
• Avg Deviation = mean(|pool_price - true_price| / true_price × 10,000)
  Expected Avg: 15-35 bps
• ALM Rebalancer triggers at
  minute 720 (12h): First scheduled rebalance,
  minute 1440 (24h): Second scheduled rebalance
  minute 2160 (36h): Final scheduled rebalance
• Algo Rebalancer triggers:
  When |pool_price - true_price| / true_price × 10,000 ≥ 50 bps
  Expected frequency: 5-15 events over 36h simulation

Actual Python Simulation Results

Stat	Result
Survival Rate	100.0% (120/120)
Avg Slippage	0.59/agent
Pool Max Deviation	49.8 bps
Pool Avg Deviation	20.0 bps
Avg Final HF	1.035
Total Agent Rebalances	15,480
ALM Rebalances	2
Algo Rebalances	8

Differences

10 agents, 216 ticks (5-min intervals) vs 120 agents, 2,160 minutely ticks, 36h