# v0.2.17 # { "Depends": "py-genlayer:1jb45aa8ynh2a9c9xn3b7qqh8sm5q93hwfp7jqmwsfhh8jpz09h6" } from genlayer import * import json class StrategyLab(gl.Contract): simulations: TreeMap[str, str] # user -> latest simulation result admin: Address def __init__(self) -> None: self.admin = gl.message.sender_address self.simulations = TreeMap() @gl.public.view def get_admin(self) -> str: return str(self.admin) @gl.public.view def get_simulation(self, user: str) -> str: return self.simulations.get(user, "{}") @gl.public.write def simulate_strategy( self, user: str, vault_name: str, protocol: str, apy: str, leverage: str, lp_allocation: str, collateral_ratio: str, staking_duration: str, amount: str, risk_score: str, ) -> str: """ AI-powered strategy simulation. Evaluates risk parameters and produces an intelligent assessment of the strategy's sustainability. """ prompt = f""" You are SentraIQ's Strategy Lab intelligence. Evaluate this DeFi strategy for sustainability and risk. STRATEGY PARAMETERS: - Vault: {vault_name} ({protocol}) - Base APY: {apy}% - Leverage: {leverage}x - LP Allocation: {lp_allocation}% - Collateral Ratio: {collateral_ratio}% - Staking Duration: {staking_duration} days - Investment Amount: ${amount} - Vault Risk Score: {risk_score}/100 ANALYSIS REQUIREMENTS: 1. Calculate liquidation probability given the leverage and collateral ratio 2. Assess strategy sustainability over the staking duration 3. Evaluate total risk exposure 4. Project stability under market stress scenarios (-20%, -40%, -60% market moves) 5. Estimate realistic yield after fees and risks RESPOND ONLY with valid JSON: {{ "liquidation_probability": <0-100 integer>, "sustainability_score": <0-100 integer>, "risk_exposure": "Low|Medium|High|Critical", "projected_stability": "Stable|Moderate|Volatile|Highly Volatile", "estimated_yield": , "max_drawdown": <0-100 integer percent>, "recommendations": [ "", "", "" ], "stress_test": {{ "minus_20pct": "", "minus_40pct": "", "minus_60pct": "" }}, "summary": "<2-3 sentence overall assessment>" }} """ raw = gl.exec_prompt(prompt) try: parsed = json.loads(raw.strip()) except Exception: try: lev = float(leverage) except Exception: lev = 1.0 try: col = float(collateral_ratio) except Exception: col = 100.0 try: apy_value = float(apy) except Exception: apy_value = 0.0 try: lp_value = float(lp_allocation) except Exception: lp_value = 100.0 if col <= 0: col = 1.0 liq_prob = max(0, min(100, int((lev / col) * 100 * 0.8))) parsed = { "liquidation_probability": liq_prob, "sustainability_score": max(0, 100 - liq_prob), "risk_exposure": "High" if lev > 3 else "Medium", "projected_stability": "Volatile" if lev > 3 else "Moderate", "estimated_yield": apy_value * lp_value / 100, "max_drawdown": min(int(lev * 20), 100), "recommendations": [ "Reduce leverage to improve safety margin", "Consider shorter staking duration", "Diversify across multiple vaults", ], "stress_test": { "minus_20pct": "Position survives with margin call risk", "minus_40pct": "Liquidation likely at this level", "minus_60pct": "Full liquidation expected", }, "summary": f"Strategy with {leverage}x leverage carries significant liquidation risk. Proceed with caution.", } result_json = json.dumps(parsed) self.simulations[user] = result_json return result_json