Update QuantumStablecoinAlgorithm.py

src/stabilizer/QuantumStablecoinAlgorithm.py

@@ -1,18 +1,63 @@
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestRegressor
+import blockchain_sdk  # Hypothetical SDK for blockchain interactions
+
 class QuantumStablecoinAlgorithm:
     def __init__(self):
         self.target_price = 314.159
         self.stabilization_parameters = {
             'expansion_contract_mechanism': True,
             'adaptive_monetary_policy': True,
-            'quantum_price_oracle_integration': True
+            'quantum_price_oracle_integration': True,
+            'decentralized_liquidity_provision': True  # New feature for DeFi integration
         }
-
+        self.price_history = self._fetch_price_history()
+        self.model = RandomForestRegressor()
+
+    def _fetch_price_history(self):
+        # Simulate fetching historical price data
+        return pd.DataFrame({
+            'timestamp': pd.date_range(start='2022-01-01', periods=100, freq='D'),
+            'price': np.random.uniform(300, 350, 100)  # Random price data
+        })
+
+    def _calculate_current_price(self):
+        # Use machine learning to predict the current price based on historical data
+        X = np.arange(len(self.price_history)).reshape(-1, 1)
+        y = self.price_history['price'].values
+        self.model.fit(X, y)
+        current_index = len(self.price_history)
+        predicted_price = self.model.predict([[current_index]])
+        return predicted_price[0]
+
     def maintain_precise_valuation(self):
+        current_price = self._calculate_current_price()
+        actions = self._determine_stabilization_actions(current_price)
+        self._execute_stabilization_actions(actions)
         return {
-            'current_price': self._calculate_current_price(),
-            'stabilization_actions': [
-                'mint_tokens_if_below_target',
-                'burn_tokens_if_above_target',
-                'liquidity_pool_rebalancing'
-            ]
+            'current_price': current_price,
+            'stabilization_actions': actions
         }
+
+    def _determine_stabilization_actions(self, current_price):
+        actions = []
+        if current_price < self.target_price:
+            actions.append('mint_tokens_if_below_target')
+        elif current_price > self.target_price:
+            actions.append('burn_tokens_if_above_target')
+        actions.append('liquidity_pool_rebalancing')
+        return actions
+
+    def _execute_stabilization_actions(self, actions):
+        for action in actions:
+            if action == 'mint_tokens_if_below_target':
+                blockchain_sdk.mint_tokens(amount=100)  # Hypothetical minting function
+            elif action == 'burn_tokens_if_above_target':
+                blockchain_sdk.burn_tokens(amount=100)  # Hypothetical burning function
+            # Additional actions can be implemented here
+
+# Example usage
+quantum_stablecoin_algorithm = QuantumStablecoinAlgorithm()
+results = quantum_stablecoin_algorithm.maintain_precise_valuation()
+print(results)