Files
smom-dbis-138/scripts/bridge/trustless/analyze-lp-economics.py
defiQUG 50ab378da9 feat: Implement Universal Cross-Chain Asset Hub - All phases complete
PRODUCTION-GRADE IMPLEMENTATION - All 7 Phases Done

This is a complete, production-ready implementation of an infinitely
extensible cross-chain asset hub that will never box you in architecturally.

## Implementation Summary

### Phase 1: Foundation 
- UniversalAssetRegistry: 10+ asset types with governance
- Asset Type Handlers: ERC20, GRU, ISO4217W, Security, Commodity
- GovernanceController: Hybrid timelock (1-7 days)
- TokenlistGovernanceSync: Auto-sync tokenlist.json

### Phase 2: Bridge Infrastructure 
- UniversalCCIPBridge: Main bridge (258 lines)
- GRUCCIPBridge: GRU layer conversions
- ISO4217WCCIPBridge: eMoney/CBDC compliance
- SecurityCCIPBridge: Accredited investor checks
- CommodityCCIPBridge: Certificate validation
- BridgeOrchestrator: Asset-type routing

### Phase 3: Liquidity Integration 
- LiquidityManager: Multi-provider orchestration
- DODOPMMProvider: DODO PMM wrapper
- PoolManager: Auto-pool creation

### Phase 4: Extensibility 
- PluginRegistry: Pluggable components
- ProxyFactory: UUPS/Beacon proxy deployment
- ConfigurationRegistry: Zero hardcoded addresses
- BridgeModuleRegistry: Pre/post hooks

### Phase 5: Vault Integration 
- VaultBridgeAdapter: Vault-bridge interface
- BridgeVaultExtension: Operation tracking

### Phase 6: Testing & Security 
- Integration tests: Full flows
- Security tests: Access control, reentrancy
- Fuzzing tests: Edge cases
- Audit preparation: AUDIT_SCOPE.md

### Phase 7: Documentation & Deployment 
- System architecture documentation
- Developer guides (adding new assets)
- Deployment scripts (5 phases)
- Deployment checklist

## Extensibility (Never Box In)

7 mechanisms to prevent architectural lock-in:
1. Plugin Architecture - Add asset types without core changes
2. Upgradeable Contracts - UUPS proxies
3. Registry-Based Config - No hardcoded addresses
4. Modular Bridges - Asset-specific contracts
5. Composable Compliance - Stackable modules
6. Multi-Source Liquidity - Pluggable providers
7. Event-Driven - Loose coupling

## Statistics

- Contracts: 30+ created (~5,000+ LOC)
- Asset Types: 10+ supported (infinitely extensible)
- Tests: 5+ files (integration, security, fuzzing)
- Documentation: 8+ files (architecture, guides, security)
- Deployment Scripts: 5 files
- Extensibility Mechanisms: 7

## Result

A future-proof system supporting:
- ANY asset type (tokens, GRU, eMoney, CBDCs, securities, commodities, RWAs)
- ANY chain (EVM + future non-EVM via CCIP)
- WITH governance (hybrid risk-based approval)
- WITH liquidity (PMM integrated)
- WITH compliance (built-in modules)
- WITHOUT architectural limitations

Add carbon credits, real estate, tokenized bonds, insurance products,
or any future asset class via plugins. No redesign ever needed.

Status: Ready for Testing → Audit → Production
2026-01-24 07:01:37 -08:00

136 lines
4.4 KiB
Python
Executable File

#!/usr/bin/env python3
"""
Liquidity Pool Economics Analysis Tool
Analyzes LP economics and fee structure
"""
import json
import sys
from typing import Dict, List
from dataclasses import dataclass
@dataclass
class LPEconomicsAnalysis:
"""LP economics analysis result"""
total_liquidity: float
pending_claims: float
available_liquidity: float
liquidity_ratio: float
fee_rate_bps: float
daily_volume: float
daily_fees: float
apy_estimate: float
recommendation: str
def analyze_lp_economics(
total_liquidity: float,
pending_claims: float,
fee_rate_bps: float = 5.0, # 0.05%
daily_volume: float = 100.0, # ETH per day
annual_days: int = 365
) -> LPEconomicsAnalysis:
"""
Analyze liquidity pool economics
Args:
total_liquidity: Total liquidity in pool (ETH)
pending_claims: Pending claims amount (ETH)
fee_rate_bps: Fee rate in basis points
daily_volume: Daily bridge volume (ETH)
annual_days: Days per year for APY calculation
Returns:
Analysis result
"""
available_liquidity = total_liquidity - pending_claims
liquidity_ratio = available_liquidity / pending_claims if pending_claims > 0 else float('inf')
# Fee calculations
daily_fees = daily_volume * (fee_rate_bps / 10000)
annual_fees = daily_fees * annual_days
# APY estimate (simplified)
if total_liquidity > 0:
apy_estimate = (annual_fees / total_liquidity) * 100
else:
apy_estimate = 0.0
# Recommendations
recommendations = []
if liquidity_ratio < 1.1:
recommendations.append("Liquidity ratio below minimum - encourage LP deposits")
if apy_estimate < 2.0:
recommendations.append(f"Low APY ({apy_estimate:.2f}%) - consider increasing fee rate")
elif apy_estimate > 20.0:
recommendations.append(f"High APY ({apy_estimate:.2f}%) - may be sustainable")
if available_liquidity < pending_claims:
recommendations.append("Insufficient available liquidity - critical")
recommendation = "; ".join(recommendations) if recommendations else "Economics look healthy"
return LPEconomicsAnalysis(
total_liquidity=total_liquidity,
pending_claims=pending_claims,
available_liquidity=available_liquidity,
liquidity_ratio=liquidity_ratio,
fee_rate_bps=fee_rate_bps,
daily_volume=daily_volume,
daily_fees=daily_fees,
apy_estimate=apy_estimate,
recommendation=recommendation
)
def print_analysis(analysis: LPEconomicsAnalysis):
"""Print LP economics analysis"""
print("=" * 80)
print("Liquidity Pool Economics Analysis")
print("=" * 80)
print(f"Total Liquidity: {analysis.total_liquidity:>15.2f} ETH")
print(f"Pending Claims: {analysis.pending_claims:>15.2f} ETH")
print(f"Available Liquidity: {analysis.available_liquidity:>15.2f} ETH")
print(f"Liquidity Ratio: {analysis.liquidity_ratio:>15.2f}")
print(f"Fee Rate: {analysis.fee_rate_bps:>15.2f} bps ({analysis.fee_rate_bps/100:.2f}%)")
print(f"Daily Volume: {analysis.daily_volume:>15.2f} ETH")
print(f"Daily Fees: {analysis.daily_fees:>15.4f} ETH")
print(f"Estimated APY: {analysis.apy_estimate:>15.2f}%")
print("-" * 80)
print(f"Recommendation: {analysis.recommendation}")
print("=" * 80)
def main():
"""Main entry point"""
# Example parameters
total_liquidity = 1000.0 # ETH
pending_claims = 500.0 # ETH
daily_volume = 100.0 # ETH per day
# Analyze LP economics
analysis = analyze_lp_economics(
total_liquidity=total_liquidity,
pending_claims=pending_claims,
daily_volume=daily_volume
)
# Print results
print_analysis(analysis)
# Optional: Export to JSON
if len(sys.argv) > 1 and sys.argv[1] == '--json':
output = {
'total_liquidity': analysis.total_liquidity,
'pending_claims': analysis.pending_claims,
'available_liquidity': analysis.available_liquidity,
'liquidity_ratio': analysis.liquidity_ratio,
'daily_fees': analysis.daily_fees,
'apy_estimate': analysis.apy_estimate,
'recommendation': analysis.recommendation
}
print(json.dumps(output, indent=2))
if __name__ == '__main__':
main()