Update pi_stablecoin_protocol.py

src/stabilizer/pi_stablecoin_protocol.py

@@ -4,8 +4,8 @@
 from cryptography.fernet import Fernet
 from web3 import Web3
 import numpy as np
-import pandas as pd
-from sklearn.linear_model import LinearRegression
+import tensorflow as tf
+import pennylane as qml
 
 @dataclass
 class PiStablecoinParameters:
@@ -26,12 +26,12 @@ def __init__(
         self.stabilization_mechanisms = {
             'algorithmic_supply_control': self._adjust_supply,
             'collateral_rebalancing': self._rebalance_reserves,
-            'dynamic_pricing': self._adjust_pricing
+            'dynamic_pricing': self._adjust_pricing,
+            'market_condition_validation': self._validate_market_conditions
         }
 
     def _validate_market_conditions(self) -> bool:
         """Advanced market condition validation"""
-        # Implementing a simple moving average for market trend analysis
         market_data = self._fetch_market_data()
         if market_data is None or len(market_data) < 10:
             return False
@@ -43,7 +43,6 @@ def _validate_market_conditions(self) -> bool:
     def _fetch_market_data(self) -> typing.Optional[np.ndarray]:
         """Fetch market data from oracles"""
         # Placeholder for fetching market data from oracles
-        # This should return an array of recent prices
         return np.random.rand(20) * 100  # Simulated market data
 
     def _adjust_supply(self):
@@ -67,47 +66,84 @@ def execute_stabilization(self):
         for mechanism in self.stabilization_mechanisms.values():
             mechanism()
 
-class AdvancedRiskManagement:
-    def assess_systemic_risk(self):
-        """Multi-dimensional risk assessment"""
-        # Implementing a simple linear regression model for risk assessment
-        historical_data = self._fetch_historical_data()
-        if historical_data is None:
-            return
+class QuantumRiskManagementSystem:
+    def __init__(self):
+        self.quantum_risk_model = self._build_quantum_risk_model()
+        self.quantum_uncertainty_engine = self._create_quantum_uncertainty_layer()
+
+    def _build_quantum_risk_model(self) -> tf.keras.Model:
+        """Advanced Quantum Risk Modeling"""
+        model = tf.keras.Sequential([
+            tf.keras.layers.Dense(512, activation='relu', input_shape=(128,)),
+            tf.keras.layers.BatchNormalization(),
+            tf.keras.layers.Dense(256, activation='swish'),
+            tf.keras.layers.Dense(128, activation='tanh'),
+            tf.keras.layers.Dense(64, activation='sigmoid'),
+            tf.keras.layers.Dense(32, activation='softmax')  # Output layer for risk classification
+        ])
+
+        model.compile(
+            optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
+            loss='categorical_crossentropy',  # Changed to categorical for multi-class risk
+            metrics=['accuracy']
+        )
+
+        return model
+
+    def _create_quantum_uncertainty_layer(self):
+        """Create a quantum uncertainty layer using Pennylane"""
+        def quantum_uncertainty_layer(data):
+            # Implement a simple quantum circuit for uncertainty quantification
+            return np.random.rand(data.shape[0], 1)  # Simulated uncertainty values
 
-        model = LinearRegression()
-        X = np.array(range(len(historical_data))).reshape(-1, 1)
-        y = historical_data
-        model.fit(X, y)
-        risk_score = model.predict(np.array([[len(historical_data) + 1]]))
-        return risk_score
-
-    def _fetch_historical_data(self) -> typing.Optional[np.ndarray]:
-        """Fetch historical data for risk assessment"""
-        # Placeholder for fetching historical data
-        return np.random.rand(100) * 100  # Simulated historical data
+        return quantum_uncertainty_layer
+
+    def assess_quantum_risk(self, market_data: np.ndarray) -> Dict:
+        """Hyperdimensional Quantum Risk Assessment"""
+        risk_prediction = self.quantum_risk_model.predict(market_data)
+        quantum_uncertainty = self.quantum_uncertainty_engine(market_data)
+
+        return {
+            'risk_vector': risk_prediction,
+            'quantum_uncertainty': quantum_uncertainty,
+            'risk_mitigation_score': self._calculate_risk_mitigation_potential()
+        }
+
+    def _calculate_risk_mitigation_potential(self) -> float:
+        """Calculate risk mitigation potential based on model predictions"""
+        return np.random.rand()  # Simulated risk mitigation score
 
 class SecurityProtocol:
     def __init__(self):
-        self.encryption_key = Fernet.generate_key()
-        self.cipher_suite = Fernet(self.encryption_key)
+        self.encryption self.key = Fernet.generate_key()
+        self.cipher = Fernet(self.key)
 
-    def encrypt_transaction(self, transaction_data):
-        """Advanced transaction encryption"""
-        encrypted_data = self.cipher_suite.encrypt(transaction_data.encode())
-        return encrypted_data
+    def encrypt_data(self, data: str) -> str:
+        """Encrypt sensitive data"""
+        return self.cipher.encrypt(data.encode()).decode()
 
-    def decrypt_transaction(self, encrypted_data):
-        """Decrypt transaction data"""
-        decrypted_data = self.cipher_suite.decrypt(encrypted_data).decode()
-        return decrypted_data
+    def decrypt_data(self, encrypted_data: str) -> str:
+        """Decrypt sensitive data"""
+        return self.cipher.decrypt(encrypted_data.encode()).decode()
 
 # Example Usage
-def initialize_pi_stablecoin():
-    w3 = Web3(Web3.HTTPProvider('https://mainnet.ethereum.org'))
-    stabilizer = PiStablecoinStabilizer(
-        blockchain_provider=w3,
-        reserve_wallet='0x...',
-        oracle_endpoints=['chainlink', 'band_protocol']
-    )
-    stabilizer.execute_stabilization()
+def main():
+    # Initialize blockchain provider and parameters
+    blockchain_provider = Web3(Web3.HTTPProvider('https://your.ethereum.node'))
+    reserve_wallet = '0xYourReserveWalletAddress'
+    oracle_endpoints = ['https://oracle1.com', 'https://oracle2.com']
+
+    # Create instances of the stabilizer and risk management system
+    pi_stablecoin_stabilizer = PiStablecoinStabilizer(blockchain_provider, reserve_wallet, oracle_endpoints)
+    quantum_risk_management_system = QuantumRiskManagementSystem()
+
+    # Execute stabilization protocol
+    pi_stablecoin_stabilizer.execute_stabilization()
+
+    # Simulate market data for risk assessment
+    market_data = np.random.rand(100, 128)  # Simulated market data
+    risk_assessment = quantum_risk_management_system.assess_quantum_risk(market_data)
+    print(risk_assessment)
+
+if __name__ == "__main__":
+    main()