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Create pi_coin_stabilizer.py
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KOSASIH authored Dec 7, 2024
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170 changes: 170 additions & 0 deletions src/stabilizer/pi_coin_stabilizer.py
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# 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

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