solanaarbitrageanalyticsbackend-h7lz3t/app/services/arbitrage_detector.py

import json
from typing import List, Dict, Any, Optional, Set, Tuple
from decimal import Decimal

from loguru import logger
from sqlalchemy.orm import Session

from app.core.config import settings
from app.crud import block, transaction, dex, pool, arbitrage
from app.models.block import Block
from app.models.transaction import Transaction
from app.schemas.arbitrage import ArbitrageCreate, ArbitrageLegCreate


# Common DEX program IDs on Solana
DEX_PROGRAM_IDS = {
    "jup-ag": "JUP4Fb2cqiRUcaTHdrPC8h2gNsA8fvKXYbXUJJqRUrZP",  # Jupiter Aggregator
    "orca": "whirLbMiicVdio4qvUfM5KAg6Ct8VwpYzGff3uctyCc",     # Orca Whirlpools
    "raydium": "675kPX9MHTjS2zt1qfr1NYHuzeLXfQM9H24wFSUt1Mp8", # Raydium
    "serum": "9xQeWvG816bUx9EPjHmaT23yvVM2ZWbrrpZb9PusVFin",   # Serum v3
    "aldrin": "CURVGoZn8zycx6FXwwevgBTB2gVvdbGTEpvMJDbgs2t4",  # Aldrin
    "saber": "SSwpkEEcbUqx4vtoEByFjSkhKdCT862DNVb52nZg1UZ",    # Saber
    "mercurial": "MERLuDFBMmsHnsBPZw2sDQZHvXFMwp8EdjudcU2HKky", # Mercurial
    "lifinity": "EewxydAPCCVuNEyrVN68PuSYdQ7wKn27V9Gjeoi8dy3S", # Lifinity
    "crema": "6MLxLqiXaaSUpkgMnLbse4ConZQZnxLi3VPYSNznE9Ky",    # Crema Finance
    "meteora": "M2mx93ekt1fmXSVkTrUL9xVFHkmME8HTUi5Cyc5aF7K",   # Meteora
    "cykura": "cysPXAjehMpVKUapzbMCCnpFxUFFryEWEaLgnb9NrR8",    # Cykura
}


class ArbitrageDetector:
    """Service for detecting arbitrage opportunities in Solana transactions."""
    
    def __init__(self, db: Session):
        """Initialize arbitrage detector."""
        self.db = db
    
    def detect_arbitrages(self, num_blocks: Optional[int] = None) -> int:
        """
        Analyze unprocessed blocks to detect arbitrages.
        
        Returns the number of blocks processed.
        """
        # Get unprocessed blocks
        unprocessed_blocks = block.get_unprocessed_blocks(
            self.db, limit=num_blocks or 100
        )
        
        if not unprocessed_blocks:
            logger.info("No unprocessed blocks found")
            return 0
        
        logger.info(f"Analyzing {len(unprocessed_blocks)} blocks for arbitrages")
        
        processed_count = 0
        for blk in unprocessed_blocks:
            try:
                # Get the transactions for this block
                txs = transaction.get_by_block(self.db, block_id=blk.id)
                
                # Process each transaction
                for tx in txs:
                    self._analyze_transaction(tx)
                
                # Mark block as processed
                block.mark_as_processed(self.db, block_id=blk.id)
                processed_count += 1
            
            except Exception as e:
                logger.error(f"Error processing block {blk.block_height}: {str(e)}")
                continue
        
        logger.info(f"Processed {processed_count} blocks, detected arbitrages")
        return processed_count
    
    def _analyze_transaction(self, tx: Transaction) -> None:
        """
        Analyze a transaction for potential arbitrage patterns.
        
        This method looks for common arbitrage patterns:
        1. Same token in/out (circular trading)
        2. Jupiter/DEX aggregator interactions
        3. Multiple DEX interactions in the same transaction
        """
        # Skip failed transactions
        if not tx.success:
            return
        
        # Skip transactions with no program IDs
        if not tx.program_ids:
            return
        
        # Check if there are any DEX program IDs in this transaction
        has_dex_interaction = False
        dex_names = []
        
        for program_id in tx.program_ids:
            for dex_name, dex_program in DEX_PROGRAM_IDS.items():
                if program_id == dex_program:
                    has_dex_interaction = True
                    dex_names.append(dex_name)
                    break
        
        # Jupiter aggregator is a strong signal for arbitrage
        is_jupiter = any(p == DEX_PROGRAM_IDS["jup-ag"] for p in tx.program_ids)
        
        # Multiple different DEXes is another strong signal
        multiple_dexes = len(set(dex_names)) > 1
        
        if not (has_dex_interaction and (is_jupiter or multiple_dexes)):
            return
        
        # For simplicity in this demonstration, we'll create a simplified arbitrage record
        # In a real system, you would have logic to:
        # 1. Decode the transaction instructions to identify token swaps
        # 2. Track token transfers to identify the full arbitrage path
        # 3. Calculate exact profit amounts using token price and amount data
        
        # Create a simplified arbitrage record
        # In a real implementation, this would be based on actual analysis of instructions
        
        # Let's assume Jupiter transactions with multiple DEXes are arbitrages
        if is_jupiter and multiple_dexes:
            self._create_simplified_arbitrage(tx, dex_names)
    
    def _create_simplified_arbitrage(self, tx: Transaction, dex_names: List[str]) -> None:
        """
        Create a simplified arbitrage record for demonstration purposes.
        
        In a real implementation, this would be replaced with actual analysis of
        the transaction instructions to determine:
        - The exact tokens involved
        - The exact amount in/out
        - The specific pools and swap paths used
        """
        # For demonstration, we'll create a simulated arbitrage
        # with random values
        
        # Default values for demonstration
        initiator = tx.fee_payer
        success = True  # We only detect successful arbitrages in this demo
        start_token = "EPjFWdd5AufqSSqeM2qN1xzybapC8G4wEGGkZwyTDt1v"  # USDC token address
        start_token_symbol = "USDC"
        
        # Simulate profit values
        start_amount = 1000.0  # $1000 USDC
        profit_percentage = 0.5  # 0.5% profit
        end_amount = start_amount * (1 + profit_percentage / 100)
        profit_amount = end_amount - start_amount
        gas_cost = 0.01  # Solana gas cost in SOL
        net_profit = profit_amount - gas_cost
        
        # Create a description of the arbitrage path
        route_description = f"USDC → {'→'.join(dex_names)} → USDC"
        
        # Create the arbitrage
        arbitrage_in = ArbitrageCreate(
            transaction_id=tx.id,
            initiator_address=initiator,
            start_token_address=start_token,
            start_token_symbol=start_token_symbol,
            start_amount=start_amount,
            end_amount=end_amount,
            profit_amount=profit_amount,
            profit_percentage=profit_percentage,
            success=success,
            gas_cost=gas_cost,
            net_profit=net_profit,
            legs_count=len(dex_names),
            route_description=route_description,
            included_dexes=dex_names
        )
        
        # In a real implementation, we would create actual legs with real pool data
        # For now, we'll skip leg creation since we don't have real data
        legs = []
        
        # Create the arbitrage record
        arbitrage.create_with_legs(self.db, obj_in=arbitrage_in, legs=legs)