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# pi_coin_stabilizer.py | ||
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||
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 | ||
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# Scientific & Numerical Libraries | ||
import numpy as np | ||
import pandas as pd | ||
import scipy.stats as stats | ||
import sympy as sp | ||
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# Blockchain Technologies | ||
from web3 import Web3 | ||
from eth_account import Account | ||
from eth_account.messages import encode_defunct | ||
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# 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 | ||
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# Distributed Computing | ||
import ray | ||
import dask.distributed | ||
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# Cryptography | ||
from cryptography.fernet import Fernet | ||
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# Logging Configuration | ||
logging.basicConfig( | ||
level=logging.INFO, | ||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' | ||
) | ||
logger = logging.getLogger(__name__) | ||
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@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 | ||
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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 | ||
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# Security Initialization | ||
self.encryption_manager = Fernet(Fernet.generate_key()) | ||
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# Distributed Computing Initialization | ||
try: | ||
ray.init(num_cpus=8, ignore_reinit_error=True) | ||
except Exception as e: | ||
logger.error(f"Ray Initialization Failed: {e}") | ||
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# System Initialization Sequence | ||
self._initialize_blockchain_infrastructure() | ||
self._initialize_economic_models() | ||
self._initialize_ml_stabilization_models() | ||
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logger.info("Pi Coin Stabilizer Initialized Successfully") | ||
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def _initialize_blockchain_infrastructure(self): | ||
""" | ||
Advanced Blockchain Infrastructure Setup | ||
""" | ||
try: | ||
# Ethereum-compatible Blockchain Account | ||
self.blockchain_account = Account.create() | ||
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# Web3 Provider Configuration | ||
self.w3 = Web3(Web3.HTTPProvider( | ||
'https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID' | ||
)) | ||
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# Cryptographic Parameters | ||
self.signing_key = self.blockchain_account.privateKey | ||
self.public_address = self.blockchain_account.address | ||
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logger.info(f"Blockchain Infrastructure Initialized: {self.public_address}") | ||
except Exception as e: | ||
logger.error(f"Blockchain Infrastructure Setup Failed: {e}") | ||
raise | ||
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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) | ||
) | ||
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# Advanced Economic Simulation Parameters | ||
self.economic_parameters = { | ||
'market_liquidity': 0.75, | ||
'price_sensitivity': 0.5, | ||
'volatility_threshold': 0.2 | ||
} | ||
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logger.info("Economic Models Initialized Successfully") | ||
except Exception as e: | ||
logger.error(f"Economic Models Initialization Failed: {e}") | ||
raise | ||
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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' | ||
) | ||
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# 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) | ||
) | ||
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def forward(self, x): | ||
return self.layers(x) | ||
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self.torch_stability_model = EconomicStabilityNetwork() | ||
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logger.info("Machine Learning Models Initialized") | ||
except Exception as e: | ||
logger.error(f"ML Models Initialization Failed: {e}") | ||
raise |