How to Analyze DEX Trading Volume with Ollama: Uniswap and PancakeSwap Data

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Verify installation
ollama --version

# Pull the recommended model for Data Analysis
ollama pull llama2:13b

# Test the installation
ollama run llama2:13b "Hello, I'm ready to analyze DEX data!"

# requirements.txt
import subprocess
import sys

# Install required packages
packages = [
    "web3==6.8.0",
    "requests==2.31.0", 
    "pandas==2.0.3",
    "numpy==1.24.3",
    "matplotlib==3.7.1",
    "seaborn==0.12.2",
    "python-dotenv==1.0.0"
]

for package in packages:
    subprocess.check_call([sys.executable, "-m", "pip", "install", package])

from web3 import Web3
import json
import os
from dotenv import load_dotenv

load_dotenv()

# Initialize Web3 connection
class UniswapDataConnector:
    def __init__(self):
        self.w3 = Web3(Web3.HTTPProvider(os.getenv('ETHEREUM_RPC_URL')))
        self.uniswap_v3_factory = "0x1F98431c8aD98523631AE4a59f267346ea31F984"
        
    def get_pool_data(self, token0, token1, fee_tier):
        """
        Fetch pool data for specific token pair
        Fee tiers: 500 (0.05%), 3000 (0.30%), 10000 (1.00%)
        """
        pool_address = self.compute_pool_address(token0, token1, fee_tier)
        
        # Pool ABI for essential functions
        pool_abi = json.loads('''[
            {
                "inputs": [],
                "name": "liquidity",
                "outputs": [{"type": "uint128"}],
                "stateMutability": "view",
                "type": "function"
            },
            {
                "inputs": [],
                "name": "slot0",
                "outputs": [
                    {"type": "uint160", "name": "sqrtPriceX96"},
                    {"type": "int24", "name": "tick"},
                    {"type": "uint16", "name": "observationIndex"},
                    {"type": "uint16", "name": "observationCardinality"},
                    {"type": "uint16", "name": "observationCardinalityNext"},
                    {"type": "uint8", "name": "feeProtocol"},
                    {"type": "bool", "name": "unlocked"}
                ],
                "stateMutability": "view",
                "type": "function"
            }
        ]''')
        
        pool_contract = self.w3.eth.contract(
            address=pool_address,
            abi=pool_abi
        )
        
        return {
            'address': pool_address,
            'liquidity': pool_contract.functions.liquidity().call(),
            'slot0': pool_contract.functions.slot0().call()
        }

import requests
import pandas as pd
from datetime import datetime, timedelta

class UniswapAnalytics:
    def __init__(self):
        self.graph_url = "https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3"
        
    def fetch_daily_volume(self, pool_id, days=30):
        """
        Fetch daily trading volume for specific pool
        """
        end_date = datetime.now()
        start_date = end_date - timedelta(days=days)
        
        query = f"""
        {{
            poolDayDatas(
                where: {{
                    pool: "{pool_id}",
                    date_gte: {int(start_date.timestamp())},
                    date_lte: {int(end_date.timestamp())}
                }},
                orderBy: date,
                orderDirection: desc
            ) {{
                id
                date
                volumeUSD
                tvlUSD
                feesUSD
                token0Price
                token1Price
                txCount
            }}
        }}
        """
        
        response = requests.post(self.graph_url, json={'query': query})
        data = response.json()
        
        if 'data' in data:
            return pd.DataFrame(data['data']['poolDayDatas'])
        return pd.DataFrame()
    
    def get_top_pools_by_volume(self, limit=10):
        """
        Get top performing pools by 24h volume
        """
        query = f"""
        {{
            pools(
                first: {limit},
                orderBy: volumeUSD,
                orderDirection: desc
            ) {{
                id
                token0 {{
                    symbol
                    name
                }}
                token1 {{
                    symbol
                    name
                }}
                volumeUSD
                tvlUSD
                feeTier
            }}
        }}
        """
        
        response = requests.post(self.graph_url, json={'query': query})
        data = response.json()
        
        if 'data' in data:
            return data['data']['pools']
        return []

class PancakeSwapAnalyzer:
    def __init__(self):
        # BSC RPC endpoints
        self.bsc_rpc = "https://bsc-dataseed.binance.org/"
        self.w3 = Web3(Web3.HTTPProvider(self.bsc_rpc))
        
        # PancakeSwap V2 Factory
        self.pancake_factory = "0xcA143Ce32Fe78f1f7019d7d551a6402fC5350c73"
        
        # PancakeSwap subgraph
        self.graph_url = "https://api.thegraph.com/subgraphs/name/pancakeswap/exchange"
        
    def fetch_pancake_pairs(self, min_volume_usd=10000):
        """
        Fetch active PancakeSwap pairs with minimum volume
        """
        query = f"""
        {{
            pairs(
                first: 100,
                where: {{
                    volumeUSD_gt: "{min_volume_usd}"
                }},
                orderBy: volumeUSD,
                orderDirection: desc
            ) {{
                id
                token0 {{
                    symbol
                    name
                }}
                token1 {{
                    symbol
                    name
                }}
                volumeUSD
                reserveUSD
                totalSupply
                txCount
            }}
        }}
        """
        
        response = requests.post(self.graph_url, json={'query': query})
        return response.json()
    
    def analyze_token_performance(self, token_address, timeframe='1d'):
        """
        Analyze specific token performance across pools
        """
        query = f"""
        {{
            tokenDayDatas(
                where: {{
                    token: "{token_address}"
                }},
                first: 30,
                orderBy: date,
                orderDirection: desc
            ) {{
                id
                date
                priceUSD
                volumeUSD
                totalLiquidityUSD
                txCount
            }}
        }}
        """
        
        response = requests.post(self.graph_url, json={'query': query})
        data = response.json()
        
        if 'data' in data:
            return pd.DataFrame(data['data']['tokenDayDatas'])
        return pd.DataFrame()

import ollama
import json
from typing import Dict, List, Any

class OllamaAnalyzer:
    def __init__(self, model_name="llama2:13b"):
        self.model_name = model_name
        self.client = ollama.Client()
        
    def analyze_volume_patterns(self, data: pd.DataFrame) -> Dict[str, Any]:
        """
        Use Ollama to analyze volume patterns and trends
        """
        # Prepare data summary for AI analysis
        data_summary = {
            'total_volume': data['volumeUSD'].sum(),
            'avg_daily_volume': data['volumeUSD'].mean(),
            'volume_trend': self._calculate_trend(data['volumeUSD']),
            'volatility': data['volumeUSD'].std(),
            'peak_volume_date': data.loc[data['volumeUSD'].idxmax(), 'date'],
            'recent_performance': data.tail(7)['volumeUSD'].tolist()
        }
        
        prompt = f"""
        Analyze this DEX trading volume data and provide insights:
        
        Data Summary:
        - Total Volume: ${data_summary['total_volume']:,.2f}
        - Average Daily Volume: ${data_summary['avg_daily_volume']:,.2f}
        - Volume Trend: {data_summary['volume_trend']}
        - Volatility: {data_summary['volatility']:,.2f}
        - Peak Volume Date: {data_summary['peak_volume_date']}
        - Recent 7-day Performance: {data_summary['recent_performance']}
        
        Please provide:
        1. Market trend analysis
        2. Volume pattern identification
        3. Potential trading opportunities
        4. Risk assessment
        5. Recommendations for further analysis
        
        Format the response as structured insights.
        """
        
        response = self.client.generate(
            model=self.model_name,
            prompt=prompt
        )
        
        return {
            'raw_response': response['response'],
            'data_summary': data_summary,
            'analysis_timestamp': datetime.now().isoformat()
        }
    
    def compare_dex_performance(self, uniswap_data: pd.DataFrame, 
                               pancake_data: pd.DataFrame) -> Dict[str, Any]:
        """
        Compare performance between Uniswap and PancakeSwap
        """
        comparison = {
            'uniswap_metrics': self._calculate_metrics(uniswap_data),
            'pancakeswap_metrics': self._calculate_metrics(pancake_data)
        }
        
        prompt = f"""
        Compare these DEX trading metrics:
        
        Uniswap Metrics:
        - Total Volume: ${comparison['uniswap_metrics']['total_volume']:,.2f}
        - Average Daily Volume: ${comparison['uniswap_metrics']['avg_volume']:,.2f}
        - Transaction Count: {comparison['uniswap_metrics']['tx_count']}
        - Liquidity: ${comparison['uniswap_metrics']['tvl']:,.2f}
        
        PancakeSwap Metrics:
        - Total Volume: ${comparison['pancakeswap_metrics']['total_volume']:,.2f}
        - Average Daily Volume: ${comparison['pancakeswap_metrics']['avg_volume']:,.2f}
        - Transaction Count: {comparison['pancakeswap_metrics']['tx_count']}
        - Liquidity: ${comparison['pancakeswap_metrics']['tvl']:,.2f}
        
        Provide a comprehensive comparison including:
        1. Performance differences
        2. Market positioning
        3. User behavior patterns
        4. Strategic advantages of each platform
        5. Investment implications
        """
        
        response = self.client.generate(
            model=self.model_name,
            prompt=prompt
        )
        
        return {
            'comparison_analysis': response['response'],
            'metrics': comparison,
            'timestamp': datetime.now().isoformat()
        }
    
    def _calculate_trend(self, series: pd.Series) -> str:
        """Calculate trend direction"""
        if len(series) < 2:
            return "insufficient_data"
        
        recent_avg = series.tail(5).mean()
        earlier_avg = series.head(5).mean()
        
        if recent_avg > earlier_avg * 1.1:
            return "upward"
        elif recent_avg < earlier_avg * 0.9:
            return "downward"
        else:
            return "sideways"
    
    def _calculate_metrics(self, data: pd.DataFrame) -> Dict[str, float]:
        """Calculate key metrics from DEX data"""
        return {
            'total_volume': data['volumeUSD'].sum(),
            'avg_volume': data['volumeUSD'].mean(),
            'tx_count': data['txCount'].sum() if 'txCount' in data else 0,
            'tvl': data['tvlUSD'].iloc[-1] if 'tvlUSD' in data else 0
        }

class PatternRecognizer:
    def __init__(self, ollama_analyzer: OllamaAnalyzer):
        self.analyzer = ollama_analyzer
        
    def detect_unusual_activity(self, volume_data: pd.DataFrame, 
                              threshold_multiplier: float = 2.0) -> Dict[str, Any]:
        """
        Detect unusual trading activity using AI analysis
        """
        # Calculate baseline metrics
        baseline_volume = volume_data['volumeUSD'].rolling(window=7).mean()
        volume_spikes = volume_data[
            volume_data['volumeUSD'] > baseline_volume * threshold_multiplier
        ]
        
        if volume_spikes.empty:
            return {'unusual_activity': False, 'analysis': 'No unusual activity detected'}
        
        spike_analysis = {
            'spike_count': len(volume_spikes),
            'spike_dates': volume_spikes['date'].tolist(),
            'spike_volumes': volume_spikes['volumeUSD'].tolist(),
            'average_spike_volume': volume_spikes['volumeUSD'].mean(),
            'max_spike_volume': volume_spikes['volumeUSD'].max()
        }
        
        prompt = f"""
        Unusual trading activity detected in DEX data:
        
        Spike Analysis:
        - Number of volume spikes: {spike_analysis['spike_count']}
        - Spike dates: {spike_analysis['spike_dates']}
        - Spike volumes: {spike_analysis['spike_volumes']}
        - Average spike volume: ${spike_analysis['average_spike_volume']:,.2f}
        - Maximum spike volume: ${spike_analysis['max_spike_volume']:,.2f}
        
        Analyze this unusual activity and provide:
        1. Potential causes for volume spikes
        2. Market implications
        3. Trading strategy recommendations
        4. Risk assessment
        5. Monitoring suggestions
        """
        
        response = self.analyzer.client.generate(
            model=self.analyzer.model_name,
            prompt=prompt
        )
        
        return {
            'unusual_activity': True,
            'spike_analysis': spike_analysis,
            'ai_insights': response['response'],
            'timestamp': datetime.now().isoformat()
        }
    
    def identify_correlation_patterns(self, token_data: Dict[str, pd.DataFrame]) -> Dict[str, Any]:
        """
        Identify correlations between different token pairs
        """
        correlations = {}
        tokens = list(token_data.keys())
        
        for i, token1 in enumerate(tokens):
            for token2 in tokens[i+1:]:
                if len(token_data[token1]) > 0 and len(token_data[token2]) > 0:
                    correlation = token_data[token1]['volumeUSD'].corr(
                        token_data[token2]['volumeUSD']
                    )
                    correlations[f"{token1}_{token2}"] = correlation
        
        # Find strong correlations (>0.7 or <-0.7)
        strong_correlations = {
            pair: corr for pair, corr in correlations.items() 
            if abs(corr) > 0.7
        }
        
        prompt = f"""
        Token correlation analysis results:
        
        Strong Correlations Found:
        {json.dumps(strong_correlations, indent=2)}
        
        All Correlations:
        {json.dumps(correlations, indent=2)}
        
        Analyze these correlations and provide:
        1. Explanation of strong correlations
        2. Market dynamics interpretation
        3. Trading pair recommendations
        4. Risk diversification insights
        5. Portfolio allocation suggestions
        """
        
        response = self.analyzer.client.generate(
            model=self.analyzer.model_name,
            prompt=prompt
        )
        
        return {
            'correlations': correlations,
            'strong_correlations': strong_correlations,
            'analysis': response['response'],
            'timestamp': datetime.now().isoformat()
        }

class AutomatedReporter:
    def __init__(self, ollama_analyzer: OllamaAnalyzer):
        self.analyzer = ollama_analyzer
        self.uniswap = UniswapAnalytics()
        self.pancake = PancakeSwapAnalyzer()
        
    def generate_daily_report(self, top_pools: int = 5) -> Dict[str, Any]:
        """
        Generate comprehensive daily DEX analysis report
        """
        # Fetch data from both DEXs
        uniswap_pools = self.uniswap.get_top_pools_by_volume(top_pools)
        pancake_pairs = self.pancake.fetch_pancake_pairs()
        
        # Analyze top performers
        report_data = {
            'date': datetime.now().strftime('%Y-%m-%d'),
            'uniswap_analysis': self._analyze_uniswap_pools(uniswap_pools),
            'pancakeswap_analysis': self._analyze_pancake_pairs(pancake_pairs),
            'market_summary': self._generate_market_summary(uniswap_pools, pancake_pairs)
        }
        
        # Generate AI insights
        insights = self.analyzer.analyze_volume_patterns(
            pd.DataFrame(report_data['market_summary'])
        )
        
        report_data['ai_insights'] = insights
        
        # Format report
        formatted_report = self._format_report(report_data)
        
        return {
            'report': formatted_report,
            'raw_data': report_data,
            'timestamp': datetime.now().isoformat()
        }
    
    def _analyze_uniswap_pools(self, pools: List[Dict]) -> Dict[str, Any]:
        """Analyze Uniswap pool performance"""
        if not pools:
            return {'error': 'No Uniswap data available'}
        
        total_volume = sum(float(pool['volumeUSD']) for pool in pools)
        total_tvl = sum(float(pool['tvlUSD']) for pool in pools)
        
        return {
            'total_volume_24h': total_volume,
            'total_tvl': total_tvl,
            'top_pool': pools[0] if pools else None,
            'pool_count': len(pools),
            'avg_volume_per_pool': total_volume / len(pools)
        }
    
    def _analyze_pancake_pairs(self, pairs_data: Dict) -> Dict[str, Any]:
        """Analyze PancakeSwap pair performance"""
        if 'data' not in pairs_data or 'pairs' not in pairs_data['data']:
            return {'error': 'No PancakeSwap data available'}
        
        pairs = pairs_data['data']['pairs']
        total_volume = sum(float(pair['volumeUSD']) for pair in pairs)
        total_reserve = sum(float(pair['reserveUSD']) for pair in pairs)
        
        return {
            'total_volume_24h': total_volume,
            'total_reserves': total_reserve,
            'top_pair': pairs[0] if pairs else None,
            'pair_count': len(pairs),
            'avg_volume_per_pair': total_volume / len(pairs) if pairs else 0
        }
    
    def _generate_market_summary(self, uniswap_pools: List, pancake_pairs: Dict) -> Dict[str, Any]:
        """Generate overall market summary"""
        uniswap_volume = sum(float(pool['volumeUSD']) for pool in uniswap_pools)
        
        pancake_volume = 0
        if 'data' in pancake_pairs and 'pairs' in pancake_pairs['data']:
            pancake_volume = sum(
                float(pair['volumeUSD']) for pair in pancake_pairs['data']['pairs']
            )
        
        return {
            'total_dex_volume': uniswap_volume + pancake_volume,
            'uniswap_dominance': uniswap_volume / (uniswap_volume + pancake_volume) if (uniswap_volume + pancake_volume) > 0 else 0,
            'pancakeswap_dominance': pancake_volume / (uniswap_volume + pancake_volume) if (uniswap_volume + pancake_volume) > 0 else 0,
            'market_activity': 'high' if (uniswap_volume + pancake_volume) > 1000000 else 'moderate'
        }
    
    def _format_report(self, data: Dict) -> str:
        """Format report for human consumption"""
        report = f"""
        # DEX Trading Analysis Report - {data['date']}
        
        ## Market Overview
        - Total DEX Volume: ${data['market_summary']['total_dex_volume']:,.2f}
        - Uniswap Market Share: {data['market_summary']['uniswap_dominance']*100:.1f}%
        - PancakeSwap Market Share: {data['market_summary']['pancakeswap_dominance']*100:.1f}%
        - Market Activity Level: {data['market_summary']['market_activity']}
        
        ## Uniswap Analysis
        - 24h Volume: ${data['uniswap_analysis']['total_volume_24h']:,.2f}
        - Total TVL: ${data['uniswap_analysis']['total_tvl']:,.2f}
        - Active Pools: {data['uniswap_analysis']['pool_count']}
        - Average Volume per Pool: ${data['uniswap_analysis']['avg_volume_per_pool']:,.2f}
        
        ## PancakeSwap Analysis
        - 24h Volume: ${data['pancakeswap_analysis']['total_volume_24h']:,.2f}
        - Total Reserves: ${data['pancakeswap_analysis']['total_reserves']:,.2f}
        - Active Pairs: {data['pancakeswap_analysis']['pair_count']}
        - Average Volume per Pair: ${data['pancakeswap_analysis']['avg_volume_per_pair']:,.2f}
        
        ## AI Insights
        {data['ai_insights']['raw_response']}
        
        ---
        Report generated on {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
        """
        
        return report

import time
import threading
from datetime import datetime, timedelta

class RealTimeMonitor:
    def __init__(self, ollama_analyzer: OllamaAnalyzer, 
                 check_interval: int = 300):  # 5 minutes
        self.analyzer = ollama_analyzer
        self.check_interval = check_interval
        self.monitoring = False
        self.alerts = []
        
    def start_monitoring(self, volume_threshold: float = 1000000):
        """
        Start real-time monitoring for significant volume changes
        """
        self.monitoring = True
        self.volume_threshold = volume_threshold
        
        monitor_thread = threading.Thread(target=self._monitor_loop)
        monitor_thread.daemon = True
        monitor_thread.start()
        
        print(f"Real-time monitoring started. Checking every {self.check_interval} seconds.")
    
    def stop_monitoring(self):
        """Stop monitoring"""
        self.monitoring = False
        print("Real-time monitoring stopped.")
    
    def _monitor_loop(self):
        """Main monitoring loop"""
        last_check = datetime.now()
        
        while self.monitoring:
            try:
                current_time = datetime.now()
                
                # Check Uniswap for significant volume
                uniswap_alerts = self._check_uniswap_volume()
                
                # Check PancakeSwap for significant volume
                pancake_alerts = self._check_pancake_volume()
                
                # Process alerts
                new_alerts = uniswap_alerts + pancake_alerts
                
                if new_alerts:
                    self._process_alerts(new_alerts)
                
                last_check = current_time
                time.sleep(self.check_interval)
                
            except Exception as e:
                print(f"Monitoring error: {e}")
                time.sleep(self.check_interval)
    
    def _check_uniswap_volume(self) -> List[Dict]:
        """Check Uniswap for unusual volume activity"""
        alerts = []
        
        try:
            uniswap = UniswapAnalytics()
            top_pools = uniswap.get_top_pools_by_volume(10)
            
            for pool in top_pools:
                volume = float(pool['volumeUSD'])
                if volume > self.volume_threshold:
                    alerts.append({
                        'platform': 'Uniswap',
                        'pool_id': pool['id'],
                        'token_pair': f"{pool['token0']['symbol']}/{pool['token1']['symbol']}",
                        'volume': volume,
                        'tvl': float(pool['tvlUSD']),
                        'timestamp': datetime.now().isoformat()
                    })
                    
        except Exception as e:
            print(f"Uniswap monitoring error: {e}")
            
        return alerts
    
    def _check_pancake_volume(self) -> List[Dict]:
        """Check PancakeSwap for unusual volume activity"""
        alerts = []
        
        try:
            pancake = PancakeSwapAnalyzer()
            pairs_data = pancake.fetch_pancake_pairs(self.volume_threshold)
            
            if 'data' in pairs_data and 'pairs' in pairs_data['data']:
                for pair in pairs_data['data']['pairs']:
                    volume = float(pair['volumeUSD'])
                    if volume > self.volume_threshold:
                        alerts.append({
                            'platform': 'PancakeSwap',
                            'pair_id': pair['id'],
                            'token_pair': f"{pair['token0']['symbol']}/{pair['token1']['symbol']}",
                            'volume': volume,
                            'reserves': float(pair['reserveUSD']),
                            'timestamp': datetime.now().isoformat()
                        })
                        
        except Exception as e:
            print(f"PancakeSwap monitoring error: {e}")
            
        return alerts
    
    def _process_alerts(self, alerts: List[Dict]):
        """Process and analyze alerts using Ollama"""
        for alert in alerts:
            # Generate AI analysis for each alert
            analysis = self.analyzer.analyze_volume_patterns(
                pd.DataFrame([alert])
            )
            
            alert['ai_analysis'] = analysis['raw_response']
            self.alerts.append(alert)
            
            # Print alert (in production, you might send to notification service)
            print(f"\n🚨 HIGH VOLUME ALERT - {alert['platform']}")
            print(f"Pair: {alert['token_pair']}")
            print(f"Volume: ${alert['volume']:,.2f}")
            print(f"Time: {alert['timestamp']}")
            print(f"AI Analysis: {alert['ai_analysis'][:200]}...")
            print("-" * 50)
    
    def get_recent_alerts(self, hours: int = 24) -> List[Dict]:
        """Get alerts from the last N hours"""
        cutoff_time = datetime.now() - timedelta(hours=hours)
        
        recent_alerts = [
            alert for alert in self.alerts 
            if datetime.fromisoformat(alert['timestamp']) > cutoff_time
        ]
        
        return recent_alerts

class MultiChainAnalyzer:
    def __init__(self, ollama_analyzer: OllamaAnalyzer):
        self.analyzer = ollama_analyzer
        self.chains = {
            'ethereum': UniswapAnalytics(),
            'bsc': PancakeSwapAnalyzer(),
            'polygon': self._setup_polygon_analyzer(),
            'arbitrum': self._setup_arbitrum_analyzer()
        }
    
    def compare_chain_performance(self, timeframe_days: int = 7) -> Dict[str, Any]:
        """
        Compare DEX performance across multiple chains
        """
        chain_data = {}
        
        for chain_name, analyzer in self.chains.items():
            try:
                if hasattr(analyzer, 'get_chain_volume'):
                    volume_data = analyzer.get_chain_volume(timeframe_days)
                    chain_data[chain_name] = {
                        'total_volume': volume_data['total_volume'],
                        'daily_average': volume_data['daily_average'],
                        'unique_traders': volume_data['unique_traders'],
                        'transaction_count': volume_data['transaction_count']
                    }
            except Exception as e:
                print(f"Error fetching {chain_name} data: {e}")
                chain_data[chain_name] = {'error': str(e)}
        
        # Generate AI comparison
        prompt = f"""
        Multi-chain DEX volume comparison for the last {timeframe_days} days:
        
        Chain Performance Data:
        {json.dumps(chain_data, indent=2)}
        
        Analyze this cross-chain data and provide:
        1. Chain dominance analysis
        2. User behavior patterns across chains
        3. Liquidity migration trends
        4. Gas fee impact on trading volume
        5. Strategic recommendations for each chain
        6. Future market predictions
        """
        
        response = self.analyzer.client.generate(
            model=self.analyzer.model_name,
            prompt=prompt
        )
        
        return {
            'chain_data': chain_data,
            'ai_analysis': response['response'],
            'comparison_timestamp': datetime.now().isoformat()
        }
    
    def _setup_polygon_analyzer(self):
        """Setup Polygon QuickSwap analyzer"""
        # Placeholder for Polygon/QuickSwap integration
        return None
    
    def _setup_arbitrum_analyzer(self):
        """Setup Arbitrum DEX analyzer"""
        # Placeholder for Arbitrum DEX integration
        return None

### Token Performance Tracking

Track specific token performance across multiple DEXs:

```python
class TokenPerformanceTracker:
    def __init__(self, ollama_analyzer: OllamaAnalyzer):
        self.analyzer = ollama_analyzer
        self.tracked_tokens = {}
        
    def add_token_to_track(self, token_address: str, symbol: str, chains: List[str]):
        """
        Add token to tracking list
        """
        self.tracked_tokens[token_address] = {
            'symbol': symbol,
            'chains': chains,
            'added_date': datetime.now().isoformat()
        }
    
    def analyze_token_across_chains(self, token_address: str, days: int = 30) -> Dict[str, Any]:
        """
        Analyze token performance across different chains
        """
        if token_address not in self.tracked_tokens:
            return {'error': 'Token not in tracking list'}
        
        token_info = self.tracked_tokens[token_address]
        cross_chain_data = {}
        
        # Collect data from each chain
        for chain in token_info['chains']:
            try:
                chain_data = self._get_token_chain_data(token_address, chain, days)
                cross_chain_data[chain] = chain_data
            except Exception as e:
                cross_chain_data[chain] = {'error': str(e)}
        
        # Generate AI analysis
        prompt = f"""
        Cross-chain token analysis for {token_info['symbol']} ({token_address}):
        
        Performance Data:
        {json.dumps(cross_chain_data, indent=2)}
        
        Provide comprehensive analysis including:
        1. Price consistency across chains
        2. Volume distribution analysis
        3. Liquidity comparison
        4. Arbitrage opportunities
        5. Market efficiency assessment
        6. Risk factors by chain
        7. Trading strategy recommendations
        """
        
        response = self.analyzer.client.generate(
            model=self.analyzer.model_name,
            prompt=prompt
        )
        
        return {
            'token_info': token_info,
            'cross_chain_data': cross_chain_data,
            'ai_analysis': response['response'],
            'analysis_timestamp': datetime.now().isoformat()
        }
    
    def _get_token_chain_data(self, token_address: str, chain: str, days: int) -> Dict[str, Any]:
        """Get token data for specific chain"""
        # Implementation depends on chain-specific APIs
        if chain == 'ethereum':
            return self._get_ethereum_token_data(token_address, days)
        elif chain == 'bsc':
            return self._get_bsc_token_data(token_address, days)
        else:
            return {'error': f'Chain {chain} not supported'}
    
    def _get_ethereum_token_data(self, token_address: str, days: int) -> Dict[str, Any]:
        """Get Ethereum token data from Uniswap"""
        uniswap = UniswapAnalytics()
        
        # Query for token data
        query = f"""
        {{
            tokenDayDatas(
                where: {{
                    token: "{token_address}"
                }},
                first: {days},
                orderBy: date,
                orderDirection: desc
            ) {{
                date
                priceUSD
                volumeUSD
                totalLiquidityUSD
                txCount
            }}
        }}
        """
        
        response = requests.post(uniswap.graph_url, json={'query': query})
        data = response.json()
        
        if 'data' in data and data['data']['tokenDayDatas']:
            token_data = data['data']['tokenDayDatas']
            return {
                'average_price': sum(float(d['priceUSD']) for d in token_data) / len(token_data),
                'total_volume': sum(float(d['volumeUSD']) for d in token_data),
                'current_liquidity': float(token_data[0]['totalLiquidityUSD']),
                'daily_tx_average': sum(int(d['txCount']) for d in token_data) / len(token_data),
                'price_data': [float(d['priceUSD']) for d in token_data]
            }
        
        return {'error': 'No data available'}
    
    def _get_bsc_token_data(self, token_address: str, days: int) -> Dict[str, Any]:
        """Get BSC token data from PancakeSwap"""
        pancake = PancakeSwapAnalyzer()
        
        # Similar implementation for PancakeSwap
        return pancake.analyze_token_performance(token_address, f'{days}d')

import pickle
import hashlib
from functools import wraps

class DataCache:
    def __init__(self, cache_duration: int = 300):  # 5 minutes default
        self.cache = {}
        self.cache_duration = cache_duration
    
    def cache_key(self, func_name: str, *args, **kwargs) -> str:
        """Generate cache key from function name and arguments"""
        key_data = f"{func_name}_{str(args)}_{str(sorted(kwargs.items()))}"
        return hashlib.md5(key_data.encode()).hexdigest()
    
    def get(self, key: str) -> Any:
        """Get cached data if still valid"""
        if key in self.cache:
            data, timestamp = self.cache[key]
            if (datetime.now() - timestamp).seconds < self.cache_duration:
                return data
            else:
                del self.cache[key]
        return None
    
    def set(self, key: str, data: Any):
        """Cache data with timestamp"""
        self.cache[key] = (data, datetime.now())
    
    def clear(self):
        """Clear all cached data"""
        self.cache.clear()

def cached_analysis(cache_duration: int = 300):
    """Decorator for caching analysis results"""
    def decorator(func):
        @wraps(func)
        def wrapper(self, *args, **kwargs):
            if not hasattr(self, '_cache'):
                self._cache = DataCache(cache_duration)
            
            cache_key = self._cache.cache_key(func.__name__, *args, **kwargs)
            cached_result = self._cache.get(cache_key)
            
            if cached_result is not None:
                return cached_result
            
            result = func(self, *args, **kwargs)
            self._cache.set(cache_key, result)
            return result
        return wrapper
    return decorator

# Apply caching to analysis methods
class OptimizedAnalyzer(OllamaAnalyzer):
    @cached_analysis(cache_duration=600)  # 10 minutes
    def analyze_volume_patterns(self, data: pd.DataFrame) -> Dict[str, Any]:
        return super().analyze_volume_patterns(data)
    
    @cached_analysis(cache_duration=300)  # 5 minutes
    def compare_dex_performance(self, uniswap_data: pd.DataFrame, 
                               pancake_data: pd.DataFrame) -> Dict[str, Any]:
        return super().compare_dex_performance(uniswap_data, pancake_data)

class BatchProcessor:
    def __init__(self, ollama_analyzer: OllamaAnalyzer, batch_size: int = 100):
        self.analyzer = ollama_analyzer
        self.batch_size = batch_size
    
    def process_large_dataset(self, data: pd.DataFrame, analysis_type: str = 'volume') -> List[Dict]:
        """
        Process large datasets in batches to avoid memory issues
        """
        results = []
        total_batches = len(data) // self.batch_size + (1 if len(data) % self.batch_size else 0)
        
        for i in range(0, len(data), self.batch_size):
            batch = data.iloc[i:i + self.batch_size]
            batch_num = i // self.batch_size + 1
            
            print(f"Processing batch {batch_num}/{total_batches}")
            
            try:
                if analysis_type == 'volume':
                    batch_result = self.analyzer.analyze_volume_patterns(batch)
                elif analysis_type == 'patterns':
                    batch_result = self._analyze_batch_patterns(batch)
                else:
                    batch_result = {'error': f'Unknown analysis type: {analysis_type}'}
                
                batch_result['batch_number'] = batch_num
                batch_result['batch_size'] = len(batch)
                results.append(batch_result)
                
            except Exception as e:
                print(f"Error processing batch {batch_num}: {e}")
                results.append({
                    'batch_number': batch_num,
                    'error': str(e),
                    'batch_size': len(batch)
                })
        
        return results
    
    def _analyze_batch_patterns(self, batch: pd.DataFrame) -> Dict[str, Any]:
        """Analyze patterns in data batch"""
        pattern_recognizer = PatternRecognizer(self.analyzer)
        return pattern_recognizer.detect_unusual_activity(batch)
    
    def aggregate_batch_results(self, batch_results: List[Dict]) -> Dict[str, Any]:
        """
        Aggregate results from multiple batches
        """
        successful_batches = [r for r in batch_results if 'error' not in r]
        failed_batches = [r for r in batch_results if 'error' in r]
        
        if not successful_batches:
            return {'error': 'All batches failed processing'}
        
        # Combine insights from successful batches
        combined_insights = []
        for batch in successful_batches:
            if 'raw_response' in batch:
                combined_insights.append(batch['raw_response'])
        
        # Generate aggregated analysis
        prompt = f"""
        Analyze these batch processing results and provide consolidated insights:
        
        Successful Batches: {len(successful_batches)}
        Failed Batches: {len(failed_batches)}
        
        Batch Insights:
        {chr(10).join(combined_insights)}
        
        Provide:
        1. Overall trend analysis
        2. Key patterns identified
        3. Data quality assessment
        4. Consolidated recommendations
        5. Areas requiring further investigation
        """
        
        response = self.analyzer.client.generate(
            model=self.analyzer.model_name,
            prompt=prompt
        )
        
        return {
            'aggregated_analysis': response['response'],
            'successful_batches': len(successful_batches),
            'failed_batches': len(failed_batches),
            'total_batches': len(batch_results),
            'processing_timestamp': datetime.now().isoformat()
        }

import time
import random
from typing import Optional

class RobustDataFetcher:
    def __init__(self, max_retries: int = 3, base_delay: float = 1.0):
        self.max_retries = max_retries
        self.base_delay = base_delay
    
    def fetch_with_retry(self, fetch_function, *args, **kwargs) -> Optional[Any]:
        """
        Fetch data with exponential backoff retry logic
        """
        last_exception = None
        
        for attempt in range(self.max_retries):
            try:
                return fetch_function(*args, **kwargs)
                
            except requests.exceptions.RequestException as e:
                last_exception = e
                if attempt < self.max_retries - 1:
                    delay = self.base_delay * (2 ** attempt) + random.uniform(0, 1)
                    print(f"Request failed (attempt {attempt + 1}/{self.max_retries}). Retrying in {delay:.2f}s...")
                    time.sleep(delay)
                else:
                    print(f"All retry attempts failed. Last error: {e}")
            
            except Exception as e:
                print(f"Unexpected error in attempt {attempt + 1}: {e}")
                last_exception = e
                break
        
        return None
    
    def handle_rate_limit(self, response: requests.Response) -> bool:
        """
        Handle rate limit responses
        """
        if response.status_code == 429:
            retry_after = response.headers.get('Retry-After', '60')
            wait_time = int(retry_after)
            print(f"Rate limit hit. Waiting {wait_time} seconds...")
            time.sleep(wait_time)
            return True
        return False

# Enhanced analytics classes with robust error handling
class RobustUniswapAnalytics(UniswapAnalytics):
    def __init__(self):
        super().__init__()
        self.fetcher = RobustDataFetcher()
    
    def fetch_daily_volume(self, pool_id: str, days: int = 30) -> pd.DataFrame:
        """
        Fetch daily volume with robust error handling
        """
        def _fetch():
            return super().fetch_daily_volume(pool_id, days)
        
        result = self.fetcher.fetch_with_retry(_fetch)
        return result if result is not None else pd.DataFrame()

### Data Quality Validation

Implement comprehensive data validation:

```python
class DataValidator:
    def __init__(self):
        self.validation_rules = {
            'volume': self._validate_volume,
            'price': self._validate_price,
            'liquidity': self._validate_liquidity,
            'timestamps': self._validate_timestamps
        }
    
    def validate_dex_data(self, data: pd.DataFrame) -> Dict[str, Any]:
        """
        Validate DEX data quality
        """
        validation_results = {}
        issues = []
        
        for column, validator in self.validation_rules.items():
            if column in data.columns:
                result = validator(data[column])
                validation_results[column] = result
                if not result['valid']:
                    issues.extend(result['issues'])
        
        # Overall data quality score
        valid_columns = sum(1 for r in validation_results.values() if r['valid'])
        quality_score = valid_columns / len(validation_results) if validation_results else 0
        
        return {
            'overall_quality': quality_score,
            'validation_results': validation_results,
            'issues': issues,
            'data_rows': len(data),
            'validation_timestamp': datetime.now().isoformat()
        }
    
    def _validate_volume(self, volume_series: pd.Series) -> Dict[str, Any]:
        """Validate volume data"""
        issues = []
        
        # Check for negative values
        if (volume_series < 0).any():
            issues.append("Negative volume values detected")
        
        # Check for unrealistic values
        if (volume_series > 1e12).any():  # > 1 trillion
            issues.append("Unrealistically high volume values")
        
        # Check for missing values
        if volume_series.isna().any():
            issues.append(f"{volume_series.isna().sum()} missing volume values")
        
        # Check for zero values (suspicious)
        zero_count = (volume_series == 0).sum()
        if zero_count > len(volume_series) * 0.1:  # More than 10% zeros
            issues.append(f"High number of zero volume values: {zero_count}")
        
        return {
            'valid': len(issues) == 0,
            'issues': issues,
            'statistics': {
                'mean': volume_series.mean(),
                'median': volume_series.median(),
                'std': volume_series.std(),
                'min': volume_series.min(),
                'max': volume_series.max()
            }
        }
    
    def _validate_price(self, price_series: pd.Series) -> Dict[str, Any]:
        """Validate price data"""
        issues = []
        
        if (price_series <= 0).any():
            issues.append("Non-positive price values detected")
        
        # Check for price jumps (> 1000% change)
        price_changes = price_series.pct_change().abs()
        if (price_changes > 10).any():
            issues.append("Extreme price changes detected (>1000%)")
        
        return {
            'valid': len(issues) == 0,
            'issues': issues,
            'statistics': {
                'mean_price': price_series.mean(),
                'volatility': price_changes.std(),
                'max_change': price_changes.max()
            }
        }
    
    def _validate_liquidity(self, liquidity_series: pd.Series) -> Dict[str, Any]:
        """Validate liquidity data"""
        issues = []
        
        if (liquidity_series < 0).any():
            issues.append("Negative liquidity values detected")
        
        # Check for sudden liquidity drops
        liquidity_changes = liquidity_series.pct_change()
        if (liquidity_changes < -0.9).any():  # 90% drop
            issues.append("Sudden liquidity drops detected")
        
        return {
            'valid': len(issues) == 0,
            'issues': issues,
            'statistics': {
                'mean_liquidity': liquidity_series.mean(),
                'min_liquidity': liquidity_series.min(),
                'liquidity_stability': liquidity_changes.std()
            }
        }
    
    def _validate_timestamps(self, timestamp_series: pd.Series) -> Dict[str, Any]:
        """Validate timestamp data"""
        issues = []
        
        # Check for duplicate timestamps
        if timestamp_series.duplicated().any():
            issues.append("Duplicate timestamps detected")
        
        # Check for proper chronological order
        if not timestamp_series.is_monotonic_increasing:
            issues.append("Timestamps not in chronological order")
        
        return {
            'valid': len(issues) == 0,
            'issues': issues,
            'statistics': {
                'time_range': f"{timestamp_series.min()} to {timestamp_series.max()}",
                'data_points': len(timestamp_series)
            }
        }