Altcoin Season Timing with Ollama: 60% Correction Recovery Strategy

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

# Pull required models for crypto analysis
ollama pull llama2:7b
ollama pull codellama:7b

# Verify installation
ollama list

# crypto_analyzer.py
import requests
import json
import subprocess
from datetime import datetime, timedelta

class OllamasCryptoAnalyzer:
    def __init__(self):
        self.base_url = "http://localhost:11434"
        self.model = "llama2:7b"
    
    def analyze_market_sentiment(self, market_data):
        """Analyze current market conditions using Ollama"""
        prompt = f"""
        Analyze this crypto market data and provide altcoin season probability:
        
        Bitcoin Dominance: {market_data['btc_dominance']}%
        Market Cap Change 24h: {market_data['market_cap_change']}%
        Fear & Greed Index: {market_data['fear_greed']}
        Top 10 Altcoin Average Change: {market_data['alt_performance']}%
        
        Rate altcoin season probability (1-10) and explain reasoning.
        """
        
        return self._query_ollama(prompt)

def confirm_correction(self, price_data):
    """Confirm if current price action represents a major correction"""
    
    # Calculate drawdown from recent high
    recent_high = max(price_data['prices'][-90:])  # 90-day high
    current_price = price_data['prices'][-1]
    drawdown = ((recent_high - current_price) / recent_high) * 100
    
    # Ollama analysis prompt
    analysis_prompt = f"""
    Current drawdown: {drawdown:.2f}%
    Volume profile: {price_data['volume_analysis']}
    Time since high: {price_data['days_since_high']} days
    
    Is this a major correction suitable for altcoin accumulation?
    Provide specific entry criteria and risk factors.
    """
    
    return self._query_ollama(analysis_prompt), drawdown

def rank_altcoins(self, altcoin_data):
    """Rank altcoins based on recovery potential"""
    
    ranking_prompt = f"""
    Rank these altcoins for recovery potential after 60% correction:
    
    {json.dumps(altcoin_data, indent=2)}
    
    Consider:
    - Technology fundamentals
    - Developer activity
    - Market position
    - Previous recovery patterns
    
    Provide top 5 picks with reasoning.
    """
    
    return self._query_ollama(ranking_prompt)

def calculate_entry_points(self, asset_data):
    """Determine optimal entry points for selected altcoins"""
    
    entry_analysis = f"""
    Asset: {asset_data['symbol']}
    Current Price: ${asset_data['price']}
    Support Levels: {asset_data['support_levels']}
    RSI: {asset_data['rsi']}
    Volume: {asset_data['volume_ratio']}
    
    Suggest 3 entry points with:
    - Price targets
    - Position sizing
    - Stop loss levels
    """
    
    return self._query_ollama(entry_analysis)

# market_check.sh
#!/bin/bash

# Fetch market data
python3 crypto_analyzer.py --mode="market_check"

# Generate Ollama analysis
python3 crypto_analyzer.py --mode="sentiment_analysis"

# Output results to dashboard
python3 crypto_analyzer.py --mode="generate_report"

def automated_scoring(self):
    """Generate comprehensive altcoin scores"""
    
    # Fetch data for top 100 altcoins
    altcoins = self.fetch_altcoin_data(limit=100)
    
    scores = {}
    for coin in altcoins:
        # Technical analysis
        technical_score = self.analyze_technicals(coin)
        
        # Fundamental analysis via Ollama
        fundamental_prompt = f"""
        Analyze {coin['name']} fundamentals:
        - Market cap: ${coin['market_cap']:,}
        - Volume 24h: ${coin['volume']:,}
        - GitHub commits: {coin['dev_activity']}
        - Social sentiment: {coin['social_score']}
        
        Score 1-100 for recovery potential.
        """
        
        fundamental_score = self._query_ollama(fundamental_prompt)
        scores[coin['symbol']] = {
            'technical': technical_score,
            'fundamental': fundamental_score,
            'combined': (technical_score + int(fundamental_score)) / 2
        }
    
    return sorted(scores.items(), key=lambda x: x[1]['combined'], reverse=True)

class PositionManager:
    def __init__(self, total_capital, max_risk_per_trade=0.02):
        self.capital = total_capital
        self.max_risk = max_risk_per_trade
        self.positions = {}
    
    def calculate_position_size(self, entry_price, stop_loss, conviction_score):
        """Calculate position size based on risk and conviction"""
        
        risk_per_share = entry_price - stop_loss
        max_position_value = self.capital * self.max_risk
        base_shares = max_position_value / risk_per_share
        
        # Adjust based on Ollama conviction score
        conviction_multiplier = conviction_score / 100
        final_shares = base_shares * conviction_multiplier
        
        return min(final_shares, self.capital * 0.1 / entry_price)  # Max 10% per position

def consensus_analysis(self, market_data):
    """Get consensus from multiple Ollama models"""
    
    models = ['llama2:7b', 'codellama:7b', 'llama2:13b']
    analyses = []
    
    for model in models:
        self.model = model
        analysis = self.analyze_market_sentiment(market_data)
        analyses.append(analysis)
    
    # Consensus prompt
    consensus_prompt = f"""
    Three AI models analyzed the market:
    
    Model 1: {analyses[0]}
    Model 2: {analyses[1]}
    Model 3: {analyses[2]}
    
    Provide consensus altcoin season probability and strategy.
    """
    
    return self._query_ollama(consensus_prompt)

def adaptive_strategy(self, current_positions, market_update):
    """Adjust strategy based on new market data"""
    
    adjustment_prompt = f"""
    Current portfolio:
    {json.dumps(current_positions, indent=2)}
    
    Market update:
    {json.dumps(market_update, indent=2)}
    
    Suggest portfolio adjustments:
    - Position sizing changes
    - New entry opportunities
    - Exit strategies
    - Risk adjustments
    """
    
    return self._query_ollama(adjustment_prompt)

def intelligent_alerts(self, portfolio_data):
    """Generate smart alerts based on portfolio performance"""
    
    alert_prompt = f"""
    Portfolio performance update:
    - Total return: {portfolio_data['total_return']}%
    - Largest drawdown: {portfolio_data['max_drawdown']}%
    - Win rate: {portfolio_data['win_rate']}%
    - Sharpe ratio: {portfolio_data['sharpe']}
    
    Current market phase: {portfolio_data['market_phase']}
    
    Generate specific action items and risk warnings.
    """
    
    return self._query_ollama(alert_prompt)

def performance_analysis(self, historical_trades):
    """Analyze strategy performance and suggest improvements"""
    
    performance_prompt = f"""
    Strategy performance analysis:
    
    Trades: {len(historical_trades)}
    Winners: {sum(1 for trade in historical_trades if trade['pnl'] > 0)}
    Average win: {np.mean([t['pnl'] for t in historical_trades if t['pnl'] > 0]):.2f}%
    Average loss: {np.mean([t['pnl'] for t in historical_trades if t['pnl'] < 0]):.2f}%
    
    Identify strategy weaknesses and improvement opportunities.
    """
    
    return self._query_ollama(performance_prompt)

def emotion_check(self, proposed_action, market_sentiment):
    """Check if proposed action is emotionally driven"""
    
    emotion_prompt = f"""
    Proposed action: {proposed_action}
    Current market sentiment: {market_sentiment}
    Fear & Greed Index: {self.get_fear_greed_index()}
    
    Is this decision driven by emotion or logic?
    Provide objective assessment and alternative actions.
    """
    
    return self._query_ollama(emotion_prompt)

def entry_confirmation(self, asset_data, market_data):
    """Confirm optimal entry timing"""
    
    confirmation_prompt = f"""
    Entry analysis for {asset_data['symbol']}:
    
    Technical signals:
    - RSI: {asset_data['rsi']} (oversold < 30)
    - Volume: {asset_data['volume_spike']} (spike confirmation)
    - Support: {asset_data['support_test']} (holding/breaking)
    
    Market context:
    - VIX equivalent: {market_data['volatility']}
    - Correlation breakdown: {market_data['correlation']}
    - Institutional flows: {market_data['institutional']}
    
    Confirm entry timing (Yes/No) with reasoning.
    """
    
    return self._query_ollama(confirmation_prompt)

def sector_rotation_strategy(self, correction_phase):
    """Implement sector-based altcoin selection"""
    
    sector_prompt = f"""
    Correction phase: {correction_phase}
    (Early: 20-40%, Mid: 40-65%, Late: 65%+)
    
    Rank altcoin sectors for current phase:
    - DeFi protocols
    - Layer 1 blockchains
    - Gaming/NFT tokens
    - Infrastructure tokens
    - Meme coins
    
    Provide sector allocation percentages and rationale.
    """
    
    return self._query_ollama(sector_prompt)

def intelligent_dca(self, dca_schedule, market_conditions):
    """Adjust DCA timing based on market analysis"""
    
    dca_prompt = f"""
    Standard DCA schedule: {dca_schedule}
    Market conditions: {market_conditions}
    
    Suggest DCA timing adjustments:
    - Accelerate purchases during extreme fear
    - Pause during relief rallies
    - Double down on technical confirmations
    
    Provide specific timing and sizing recommendations.
    """
    
    return self._query_ollama(dca_prompt)

import ccxt

class TradingExecutor:
    def __init__(self, exchange_config):
        self.exchange = ccxt.binance(exchange_config)
        self.analyzer = OllamasCryptoAnalyzer()
    
    def execute_ollama_signals(self):
        """Execute trades based on Ollama analysis"""
        
        # Get current market data
        market_data = self.fetch_market_data()
        
        # Generate Ollama recommendations
        recommendations = self.analyzer.generate_recommendations(market_data)
        
        # Execute trades with risk management
        for rec in recommendations:
            if rec['confidence'] > 0.7:  # High confidence threshold
                self.place_order(rec)

def automated_rebalancing(self, target_allocation, current_portfolio):
    """Rebalance portfolio using Ollama guidance"""
    
    rebalance_prompt = f"""
    Target allocation: {target_allocation}
    Current portfolio: {current_portfolio}
    Market phase: {self.get_market_phase()}
    
    Calculate optimal rebalancing trades:
    - Minimize transaction costs
    - Consider tax implications
    - Account for market impact
    
    Provide specific buy/sell orders with reasoning.
    """
    
    return self._query_ollama(rebalance_prompt)