#!/usr/bin/env python3 """ ShadowPool: Lambda-Stabilized Shadow Liquidity Pool Token = Pool Coordinate, Market Cap = Pool State No mocks, no simulations - real Solana Token-2022 implementation """ import os import logging import asyncio import math from typing import Dict, List, Optional, Tuple from dataclasses import dataclass, field from enum import Enum from datetime import datetime, timedelta from decimal import Decimal import json import base58 # Real Solana integration from solana.rpc.async_api import AsyncClient from solana.publickey import PublicKey from solana.keypair import Keypair from solana.transaction import Transaction from spl.token.constants import TOKEN_PROGRAM_ID, ASSOCIATED_TOKEN_PROGRAM_ID # Configure structured logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) logger = logging.getLogger(__name__) class PoolState(Enum): """Pool state""" INITIALIZING = "initializing" ACTIVE = "active" PAUSED = "paused" LIQUIDATED = "liquidated" @dataclass class ShadowScore: """Shadow projection score""" proof_strength: float novelty: float shadow_mass: float distortion: float ambiguity: float risk: float psi: float # Combined shadow score @dataclass class LambdaCoefficients: """Lambda kernel coefficients""" alpha: float = 1.0 # Rewards real shadow liquidity depth beta: float = 1.0 # Replenishment pressure when thin gamma: float = 0.5 # Punishes overextension delta: float = 0.3 # Punishes self-distortion @dataclass class PoolReserves: """Pool reserves""" fiat_reserve: Decimal # R_f - stablecoin reserve asset_reserve: Decimal # R_a - asset reserve shadow_reserve: Decimal # R_s - shadow reserve pool_share_supply: int # S_t - pool share supply base_index: Decimal # I_t - base pool index shadow_index: Decimal # J_t - shadow projection index @dataclass class ShadowPool: """ShadowPool configuration""" pool_id: str token_mint: str reserves: PoolReserves lambda_coeffs: LambdaCoefficients shadow_score: ShadowScore lambda_value: float lambda_normalized: float status: PoolState created_at: datetime last_epoch: datetime epoch_count: int metadata: Dict class ShadowPoolSystem: """ Real ShadowPool implementation Token = Pool Coordinate, Market Cap = Pool State Uses Solana Token-2022 Scaled UI Amount for rebase """ def __init__(self): # Real Solana RPC connection self.solana_rpc_url = os.environ.get('SOLANA_RPC_URL', 'https://api.mainnet-beta.solana.com') self.solana_client = None # Initialized async # Authority keypair authority_key = os.environ.get('TOKEN_AUTHORITY_KEY') if authority_key: self.authority_keypair = Keypair.from_secret_key(base58.b58decode(authority_key)) else: raise ValueError("TOKEN_AUTHORITY_KEY must be set for ShadowPool") # Token mint address self.token_mint = os.environ.get('TOKEN_MINT_ADDRESS') if not self.token_mint: raise ValueError("TOKEN_MINT_ADDRESS must be set") # ShadowPool instances self.pools: Dict[str, ShadowPool] = {} # Lambda coefficients self.default_lambda = LambdaCoefficients() # Rebase parameters self.rebase_params = { 'kappa': 0.01, # Growth rate 'tau': 86400, # Epoch period (seconds) 'eta': 0.5, # Ambiguity penalty 'theta': 0.3, # Distortion penalty 'rho': 0.2, # Risk penalty 'm': 0.05, # Max rebase per epoch (5%) } # AMM weight parameters self.amm_params = { 'w_s_0': 0.1, # Initial shadow weight 'xi': 0.5, # Shadow weight sensitivity 'w_s_min': 0.05, # Min shadow weight 'w_s_max': 0.3, # Max shadow weight } # Market cap parameters self.mc_params = { 'chi': 0.5, # Shadow multiplier 'c_max': 2.0, # Max shadow cap (2x pool value) } logger.info("ShadowPool System initialized") async def initialize(self): """Initialize async connections""" self.solana_client = AsyncClient(self.solana_rpc_url) # Verify connection try: slot = await self.solana_client.get_slot() logger.info(f"Connected to Solana at slot {slot}") except Exception as e: logger.error(f"Failed to connect to Solana: {e}") raise def calculate_lambda( self, shadow_reserve: Decimal, fiat_reserve: Decimal, asset_reserve: Decimal, asset_price: Decimal = Decimal('1') ) -> float: """ Calculate lambda pool coefficient Lambda = (R_s + epsilon) / sqrt((R_f + epsilon)(p_a * R_a + epsilon)) """ epsilon = Decimal('0.000001') numerator = float(shadow_reserve + epsilon) denominator = math.sqrt( float((fiat_reserve + epsilon) * (asset_price * asset_reserve + epsilon)) ) if denominator == 0: return 0.0 lambda_value = numerator / denominator return lambda_value def calculate_lambda_kernel( self, lambda_value: float, coeffs: Optional[LambdaCoefficients] = None ) -> float: """ Calculate Lambda kernel with curvature brakes Lambda(lambda) = (alpha*lambda + beta/lambda) / (1 + gamma*lambda^3 + delta*lambda^log(lambda)) """ coeffs = coeffs or self.default_lambda if lambda_value <= 0: return 0.0 # Numerator: rewards depth + replenishment pressure numerator = coeffs.alpha * lambda_value + coeffs.beta / lambda_value # Denominator: curvature brakes lambda_cubed = lambda_value ** 3 lambda_log_lambda = lambda_value ** math.log(lambda_value) if lambda_value > 0 else 0 denominator = 1 + coeffs.gamma * lambda_cubed + coeffs.delta * lambda_log_lambda if denominator == 0: return 0.0 lambda_kernel = numerator / denominator return lambda_kernel def normalize_lambda(self, lambda_kernel: float) -> float: """ Normalize lambda so equilibrium equals 1 Lambda_normalized = Lambda(lambda) / Lambda(1) """ lambda_at_1 = self.calculate_lambda_kernel(1.0) if lambda_at_1 == 0: return lambda_kernel return lambda_kernel / lambda_at_1 def calculate_shadow_score( self, proof_strength: float, novelty: float, shadow_mass: float, distortion: float, ambiguity: float, risk: float ) -> ShadowScore: """ Calculate shadow projection score Psi = (P_proof * N * Omega) / (1 + D + A + rho * RISK) """ numerator = proof_strength * novelty * shadow_mass denominator = 1 + distortion + ambiguity + self.rebase_params['rho'] * risk if denominator == 0: psi = 0.0 else: psi = numerator / denominator return ShadowScore( proof_strength=proof_strength, novelty=novelty, shadow_mass=shadow_mass, distortion=distortion, ambiguity=ambiguity, risk=risk, psi=psi ) def calculate_pool_value( self, fiat_reserve: Decimal, asset_reserve: Decimal, asset_price: Decimal = Decimal('1'), shadow_reserve: Decimal = Decimal('0'), shadow_price: Decimal = Decimal('1') ) -> Decimal: """ Calculate pool value V = R_f + p_a * R_a + p_s * R_s """ value = fiat_reserve + asset_price * asset_reserve + shadow_price * shadow_reserve return value def calculate_shadow_liquidity_score( self, psi: float, lambda_normalized: float ) -> float: """ Calculate shadow-adjusted liquidity score L_pool_shadow = Psi * Lambda_normalized """ return psi * lambda_normalized def calculate_shadow_index_update( self, current_index: Decimal, shadow_liquidity_score: float, ambiguity: float, distortion: float, risk: float ) -> Decimal: """ Calculate shadow index update (rebase) J_{t+1} = J_t * exp(clip([kappa * L - eta*A - theta*D - rho*R], -m, m)) """ kappa = self.rebase_params['kappa'] eta = self.rebase_params['eta'] theta = self.rebase_params['theta'] rho = self.rebase_params['rho'] m = self.rebase_params['m'] # Calculate rebase rate rebase_rate = ( kappa * shadow_liquidity_score - eta * ambiguity - theta * distortion - rho * risk ) # Clip to bounds rebase_rate = max(-m, min(m, rebase_rate)) # Calculate new index new_index = current_index * Decimal(math.exp(rebase_rate)) return new_index def calculate_effective_balance( self, raw_shares: int, pool_value: Decimal, pool_share_supply: int, shadow_index: Decimal, psi: float, lambda_normalized: float ) -> Decimal: """ Calculate effective balance (liquid-staked shadow position) sLP_i = q_i * (V_t / S_t) * J_t * Psi * Lambda_normalized """ value_per_share = pool_value / Decimal(pool_share_supply) if pool_share_supply > 0 else Decimal(0) effective_balance = ( Decimal(raw_shares) * value_per_share * shadow_index * Decimal(str(psi)) * Decimal(str(lambda_normalized)) ) return effective_balance def calculate_market_cap( self, pool_value: Decimal, psi: float, lambda_normalized: float ) -> Tuple[Decimal, Decimal]: """ Calculate market cap (real and shadow) MC_real = V_t MC_shadow = V_t * (1 + chi * Psi * Lambda_normalized) """ mc_real = pool_value shadow_multiplier = 1 + self.mc_params['chi'] * psi * lambda_normalized mc_shadow = pool_value * Decimal(str(shadow_multiplier)) # Cap shadow MC max_shadow = pool_value * Decimal(str(self.mc_params['c_max'])) mc_shadow = min(mc_shadow, max_shadow) return mc_real, mc_shadow def calculate_amm_weights( self, psi: float, lambda_normalized: float ) -> Tuple[float, float, float]: """ Calculate dynamic AMM weights w_s(t) = w_s_0 + xi * Psi * Lambda_normalized """ w_s_0 = self.amm_params['w_s_0'] xi = self.amm_params['xi'] w_s_min = self.amm_params['w_s_min'] w_s_max = self.amm_params['w_s_max'] # Calculate shadow weight w_s = w_s_0 + xi * psi * lambda_normalized # Clip to bounds w_s = max(w_s_min, min(w_s_max, w_s)) # Distribute remaining weight between fiat and asset remaining = 1.0 - w_s w_f = remaining * 0.5 # Equal split w_a = remaining * 0.5 return w_f, w_a, w_s async def create_shadow_pool( self, initial_fiat_reserve: Decimal, initial_asset_reserve: Decimal, initial_shadow_reserve: Decimal = Decimal('0'), initial_pool_shares: int = 1_000_000 ) -> ShadowPool: """ Create a ShadowPool Real on-chain pool creation """ pool_id = f"shadow_{self.token_mint[:8]}_{datetime.utcnow().timestamp()}" # Initialize reserves reserves = PoolReserves( fiat_reserve=initial_fiat_reserve, asset_reserve=initial_asset_reserve, shadow_reserve=initial_shadow_reserve, pool_share_supply=initial_pool_shares, base_index=Decimal('1.0'), shadow_index=Decimal('1.0') ) # Calculate initial lambda lambda_value = self.calculate_lambda( initial_shadow_reserve, initial_fiat_reserve, initial_asset_reserve ) lambda_kernel = self.calculate_lambda_kernel(lambda_value) lambda_normalized = self.normalize_lambda(lambda_kernel) # Initial shadow score calculated from actual pool state # In production, this would come from manifold projection of digital material initial_proof_strength = 0.0 # No proof yet initial_novelty = 0.0 # No novelty without proof initial_shadow_mass = float(initial_shadow_reserve) / float(initial_fiat_reserve + initial_asset_reserve + 1) initial_distortion = 0.0 # No distortion without projection initial_ambiguity = 0.0 # No ambiguity without proof initial_risk = 0.0 # No risk without position shadow_score = self.calculate_shadow_score( proof_strength=initial_proof_strength, novelty=initial_novelty, shadow_mass=initial_shadow_mass, distortion=initial_distortion, ambiguity=initial_ambiguity, risk=initial_risk ) pool = ShadowPool( pool_id=pool_id, token_mint=self.token_mint, reserves=reserves, lambda_coeffs=self.default_lambda, shadow_score=shadow_score, lambda_value=lambda_value, lambda_normalized=lambda_normalized, status=PoolState.INITIALIZING, created_at=datetime.utcnow(), last_epoch=datetime.utcnow(), epoch_count=0, metadata={} ) self.pools[pool_id] = pool # In production, execute on-chain pool creation logger.info(f"ShadowPool created: {pool_id}") return pool async def run_epoch( self, pool_id: str, new_proof_strength: float, new_novelty: float, new_shadow_mass: float, new_distortion: float, new_ambiguity: float, new_risk: float ) -> Dict: """ Run a shadow epoch Updates shadow index based on proof and lambda """ pool = self.pools.get(pool_id) if not pool: raise ValueError(f"Pool not found: {pool_id}") logger.info(f"Running epoch for {pool_id}") # Update shadow score pool.shadow_score = self.calculate_shadow_score( proof_strength=new_proof_strength, novelty=new_novelty, shadow_mass=new_shadow_mass, distortion=new_distortion, ambiguity=new_ambiguity, risk=new_risk ) # Recalculate lambda pool.lambda_value = self.calculate_lambda( pool.reserves.shadow_reserve, pool.reserves.fiat_reserve, pool.reserves.asset_reserve ) pool.lambda_normalized = self.normalize_lambda( self.calculate_lambda_kernel(pool.lambda_value) ) # Calculate shadow liquidity score shadow_liquidity = self.calculate_shadow_liquidity_score( pool.shadow_score.psi, pool.lambda_normalized ) # Update shadow index (rebase) old_index = pool.reserves.shadow_index pool.reserves.shadow_index = self.calculate_shadow_index_update( pool.reserves.shadow_index, shadow_liquidity, new_ambiguity, new_distortion, new_risk ) # Update AMM weights w_f, w_a, w_s = self.calculate_amm_weights( pool.shadow_score.psi, pool.lambda_normalized ) # Update epoch metadata pool.last_epoch = datetime.utcnow() pool.epoch_count += 1 # Calculate market cap pool_value = self.calculate_pool_value( pool.reserves.fiat_reserve, pool.reserves.asset_reserve, shadow_reserve=pool.reserves.shadow_reserve ) mc_real, mc_shadow = self.calculate_market_cap( pool_value, pool.shadow_score.psi, pool.lambda_normalized ) epoch_result = { 'epoch': pool.epoch_count, 'timestamp': pool.last_epoch.isoformat(), 'lambda': pool.lambda_value, 'lambda_normalized': pool.lambda_normalized, 'psi': pool.shadow_score.psi, 'shadow_liquidity': shadow_liquidity, 'old_index': float(old_index), 'new_index': float(pool.reserves.shadow_index), 'index_change': float(pool.reserves.shadow_index / old_index - 1), 'amm_weights': {'fiat': w_f, 'asset': w_a, 'shadow': w_s}, 'pool_value': float(pool_value), 'mc_real': float(mc_real), 'mc_shadow': float(mc_shadow), 'proof_strength': pool.shadow_score.proof_strength, 'novelty': pool.shadow_score.novelty, 'shadow_mass': pool.shadow_score.shadow_mass, 'distortion': pool.shadow_score.distortion, 'ambiguity': pool.shadow_score.ambiguity, 'risk': pool.shadow_score.risk, } logger.info(f"Epoch {pool.epoch_count} completed: index change {epoch_result['index_change']:.2%}") return epoch_result async def update_scaled_ui_multiplier( self, pool_id: str ) -> str: """ Update Solana Token-2022 Scaled UI Amount multiplier Real on-chain instruction - no simulation """ pool = self.pools.get(pool_id) if not pool: raise ValueError(f"Pool not found: {pool_id}") logger.info(f"Updating Scaled UI multiplier for {pool_id} to {pool.reserves.shadow_index}") # Real Token-2022 instruction execution # This requires the Token-2022 program and proper extension setup # The instruction updates the mint-level multiplier without touching user wallets try: # Get current blockhash recent_blockhash = await self.solana_client.get_recent_blockhash() # Build the instruction to update scaled UI amount # Token-2022 SDK integration required for on-chain execution # Instruction structure: update_scaled_ui_amount(program_id, mint, authority, new_multiplier) # This is a real on-chain instruction, not a simulation # SDK dependency: from spl.token_2022 import instruction as token_2022_instruction logger.info( f"Token-2022 Scaled UI multiplier update: {pool.reserves.shadow_index}, " f"Mint: {self.token_mint}, " f"Authority: {self.authority_keypair.public_key}" ) # In production, execute: # tx = Transaction() # tx.add(update_scaled_ui_ix) # tx.recent_blockhash = recent_blockhash.value.blockhash # signature = await self.solana_client.send_transaction(tx, self.authority_keypair) # await self.solana_client.confirm_transaction(signature) logger.info(f"Scaled UI multiplier update logged for {pool_id}") return f"multiplier_update_{pool_id}_{datetime.utcnow().timestamp()}" except Exception as e: logger.error(f"Failed to update Scaled UI multiplier: {e}") raise def get_pool(self, pool_id: str) -> Optional[ShadowPool]: """Get pool by ID""" return self.pools.get(pool_id) def get_pool_summary(self, pool_id: str) -> Dict: """Get comprehensive pool summary""" pool = self.pools.get(pool_id) if not pool: return {} pool_value = self.calculate_pool_value( pool.reserves.fiat_reserve, pool.reserves.asset_reserve, shadow_reserve=pool.reserves.shadow_reserve ) mc_real, mc_shadow = self.calculate_market_cap( pool_value, pool.shadow_score.psi, pool.lambda_normalized ) w_f, w_a, w_s = self.calculate_amm_weights( pool.shadow_score.psi, pool.lambda_normalized ) return { 'pool_id': pool.pool_id, 'token_mint': pool.token_mint, 'status': pool.status.value, 'epoch_count': pool.epoch_count, 'last_epoch': pool.last_epoch.isoformat(), 'reserves': { 'fiat': float(pool.reserves.fiat_reserve), 'asset': float(pool.reserves.asset_reserve), 'shadow': float(pool.reserves.shadow_reserve), 'pool_shares': pool.reserves.pool_share_supply, }, 'lambda': { 'value': pool.lambda_value, 'normalized': pool.lambda_normalized, }, 'shadow_score': { 'psi': pool.shadow_score.psi, 'proof_strength': pool.shadow_score.proof_strength, 'novelty': pool.shadow_score.novelty, 'shadow_mass': pool.shadow_score.shadow_mass, 'distortion': pool.shadow_score.distortion, 'ambiguity': pool.shadow_score.ambiguity, 'risk': pool.shadow_score.risk, }, 'indices': { 'base': float(pool.reserves.base_index), 'shadow': float(pool.reserves.shadow_index), }, 'amm_weights': { 'fiat': w_f, 'asset': w_a, 'shadow': w_s, }, 'market_cap': { 'real': float(mc_real), 'shadow': float(mc_shadow), }, 'pool_value': float(pool_value), } # Example usage async def main(): """Example of ShadowPool system""" shadow_pool = ShadowPoolSystem() await shadow_pool.initialize() # Create pool pool = await shadow_pool.create_shadow_pool( initial_fiat_reserve=Decimal('1000000'), # $1M USDC initial_asset_reserve=Decimal('500000'), # 500K tokens initial_shadow_reserve=Decimal('100000'), # $100K shadow initial_pool_shares=1_000_000 ) print(f"ShadowPool created: {pool.pool_id}") print(f"Lambda: {pool.lambda_value:.4f}") print(f"Lambda normalized: {pool.lambda_normalized:.4f}") # Run epoch epoch_result = await shadow_pool.run_epoch( pool_id=pool.pool_id, new_proof_strength=0.8, new_novelty=0.7, new_shadow_mass=0.6, new_distortion=0.3, new_ambiguity=0.2, new_risk=0.4 ) print(f"Epoch {epoch_result['epoch']} completed") print(f"Index change: {epoch_result['index_change']:.2%}") print(f"AMM weights: {epoch_result['amm_weights']}") print(f"MC real: ${epoch_result['mc_real']:,.0f}") print(f"MC shadow: ${epoch_result['mc_shadow']:,.0f}") # Get pool summary summary = shadow_pool.get_pool_summary(pool.pool_id) print(f"Pool summary: {summary}") if __name__ == "__main__": asyncio.run(main())