From fb553c204383e189d6656f99cdd8e293138e318c Mon Sep 17 00:00:00 2001
From: KOSASIH <kosasihg88@gmail.com>
Date: Sat, 7 Dec 2024 15:58:24 +0700
Subject: [PATCH] Create pi_coin_stabilizer.py

---
 src/stabilizer/pi_coin_stabilizer.py | 170 +++++++++++++++++++++++++++
 1 file changed, 170 insertions(+)
 create mode 100644 src/stabilizer/pi_coin_stabilizer.py

diff --git a/src/stabilizer/pi_coin_stabilizer.py b/src/stabilizer/pi_coin_stabilizer.py
new file mode 100644
index 0000000..7b0759f
--- /dev/null
+++ b/src/stabilizer/pi_coin_stabilizer.py
@@ -0,0 +1,170 @@
+# pi_coin_stabilizer.py
+
+import asyncio
+import uuid
+import logging
+import json
+from typing import Dict, Any, List, Optional
+from dataclasses import dataclass, field
+from decimal import Decimal
+from datetime import datetime, timedelta
+
+# Scientific & Numerical Libraries
+import numpy as np
+import pandas as pd
+import scipy.stats as stats
+import sympy as sp
+
+# Blockchain Technologies
+from web3 import Web3
+from eth_account import Account
+from eth_account.messages import encode_defunct
+
+# Machine Learning Frameworks
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+import torch
+import torch.nn as nn
+
+# Distributed Computing
+import ray
+import dask.distributed
+
+# Cryptography
+from cryptography.fernet import Fernet
+
+# Logging Configuration
+logging.basicConfig(
+    level=logging.INFO, 
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+@dataclass
+class PiCoinStabilizationStrategy:
+    """
+    Advanced Pi Coin Stabilization Data Model
+    """
+    id: str = field(default_factory=lambda: str(uuid.uuid4()))
+    target_value: Decimal = Decimal('314.159')
+    current_value: Decimal = Decimal('0')
+    stabilization_timestamp: datetime = field(default_factory=datetime.utcnow)
+    volatility_index: float = 0.0
+    economic_entropy: float = 0.0
+    adaptive_parameters: Dict[str, Any] = field(default_factory=dict)
+    stabilization_proof: Optional[str] = None
+    encryption_key: Optional[bytes] = None
+
+class AdvancedPiCoinStabilizer:
+    def __init__(
+        self, 
+        initial_supply: Decimal = Decimal('1000000'),
+        target_price: Decimal = Decimal('314.159')
+    ):
+        # Core Stabilization Parameters
+        self.initial_supply = initial_supply
+        self.target_price = target_price
+        
+        # Security Initialization
+        self.encryption_manager = Fernet(Fernet.generate_key())
+        
+        # Distributed Computing Initialization
+        try:
+            ray.init(num_cpus=8, ignore_reinit_error=True)
+        except Exception as e:
+            logger.error(f"Ray Initialization Failed: {e}")
+        
+        # System Initialization Sequence
+        self._initialize_blockchain_infrastructure()
+        self._initialize_economic_models()
+        self._initialize_ml_stabilization_models()
+        
+        logger.info("Pi Coin Stabilizer Initialized Successfully")
+    
+    def _initialize_blockchain_infrastructure(self):
+        """
+        Advanced Blockchain Infrastructure Setup
+        """
+        try:
+            # Ethereum-compatible Blockchain Account
+            self.blockchain_account = Account.create()
+            
+            # Web3 Provider Configuration
+            self.w3 = Web3(Web3.HTTPProvider(
+                'https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID'
+            ))
+            
+            # Cryptographic Parameters
+            self.signing_key = self.blockchain_account.privateKey
+            self.public_address = self.blockchain_account.address
+            
+            logger.info(f"Blockchain Infrastructure Initialized: {self.public_address}")
+        except Exception as e:
+            logger.error(f"Blockchain Infrastructure Setup Failed: {e}")
+            raise
+    
+    def _initialize_economic_models(self):
+        """
+        Advanced Economic Stabilization Modeling
+        """
+        try:
+            # Symbolic Economic Equilibrium Modeling
+            x, y = sp.symbols('x y')
+            self.economic_equilibrium_equation = sp.Eq(
+                sp.diff(x**2 + y**2, x),
+                sp.diff(x**2 + y**2, y)
+            )
+            
+            # Advanced Economic Simulation Parameters
+            self.economic_parameters = {
+                'market_liquidity': 0.75,
+                'price_sensitivity': 0.5,
+                'volatility_threshold': 0.2
+            }
+            
+            logger.info("Economic Models Initialized Successfully")
+        except Exception as e:
+            logger.error(f"Economic Models Initialization Failed: {e}")
+            raise
+    
+    def _initialize_ml_stabilization_models(self):
+        """
+        Advanced Machine Learning Stabilization Networks
+        """
+        try:
+            # LSTM Price Prediction Network
+            self.price_prediction_model = Sequential([
+                LSTM(64, input_shape=(10, 5), return_sequences=True),
+                Dropout(0.3),
+                LSTM(32),
+                Dense(16, activation='relu'),
+                Dense(1, activation='linear')
+            ])
+            self.price_prediction_model.compile(
+                optimizer='adam', 
+                loss='mean_squared_error'
+            )
+            
+            # PyTorch Economic Stability Network
+            class EconomicStabilityNetwork(nn.Module):
+                def __init__(self):
+                    super().__init__()
+                    self.layers = nn.Sequential(
+                        nn.Linear(10, 64),
+                        nn.ReLU(),
+                        nn.Dropout(0.3),
+                        nn.Linear(64, 32),
+                        nn.ReLU(),
+                        nn.Linear(32, 1)
+                    )
+                
+                def forward(self, x):
+                    return self.layers(x)
+            
+            self.torch_stability_model = EconomicStabilityNetwork()
+            
+            logger.info("Machine Learning Models Initialized")
+        except Exception as e:
+            logger.error(f"ML Models Initialization Failed: {e}")
+            raise