Bitcoin Price Prediction with Ollama: Advanced Technical Analysis Tutorial

# Install the recommended models for Bitcoin analysis
ollama pull llama2:13b  # For complex market pattern recognition
ollama pull codellama   # For automated strategy generation
ollama pull mistral     # For fast real-time predictions

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

class BitcoinDataCollector:
    def __init__(self):
        self.base_urls = {
            'coinapi': 'https://rest.coinapi.io/v1/',
            'binance': 'https://api.binance.com/api/v3/',
            'coingecko': 'https://api.coingecko.com/api/v3/'
        }
    
    def fetch_ohlcv_data(self, symbol='BTCUSD', interval='1h', limit=1000):
        """
        Fetch OHLCV data from multiple sources
        Returns standardized DataFrame for analysis
        """
        # Implementation for data fetching
        url = f"{self.base_urls['binance']}klines"
        params = {
            'symbol': 'BTCUSDT',
            'interval': interval,
            'limit': limit
        }
        
        response = requests.get(url, params=params)
        data = response.json()
        
        # Convert to standardized format
        df = pd.DataFrame(data, columns=[
            'timestamp', 'open', 'high', 'low', 'close', 
            'volume', 'close_time', 'quote_volume', 
            'trades', 'taker_buy_base', 'taker_buy_quote', 'ignore'
        ])
        
        # Data type conversion and cleaning
        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
        numeric_cols = ['open', 'high', 'low', 'close', 'volume']
        df[numeric_cols] = df[numeric_cols].astype(float)
        
        return df[['timestamp', 'open', 'high', 'low', 'close', 'volume']]

import numpy as np
import talib

class AdvancedIndicators:
    @staticmethod
    def calculate_rsi_divergence(prices, rsi_period=14):
        """
        Calculate RSI with divergence detection
        Ollama uses this for trend reversal predictions
        """
        rsi = talib.RSI(prices, timeperiod=rsi_period)
        
        # Find peaks and troughs for divergence analysis
        peaks = []
        troughs = []
        
        for i in range(2, len(prices) - 2):
            if prices[i] > prices[i-1] and prices[i] > prices[i+1]:
                peaks.append((i, prices[i], rsi[i]))
            elif prices[i] < prices[i-1] and prices[i] < prices[i+1]:
                troughs.append((i, prices[i], rsi[i]))
        
        return rsi, peaks, troughs
    
    @staticmethod
    def bollinger_squeeze_detector(prices, period=20, std_dev=2):
        """
        Detect Bollinger Band squeeze patterns
        High probability setups for Ollama analysis
        """
        middle = talib.SMA(prices, timeperiod=period)
        std = talib.STDDEV(prices, timeperiod=period)
        
        upper_band = middle + (std * std_dev)
        lower_band = middle - (std * std_dev)
        
        # Calculate band width percentage
        band_width = ((upper_band - lower_band) / middle) * 100
        
        # Identify squeeze periods (band width below 20th percentile)
        squeeze_threshold = np.percentile(band_width, 20)
        squeeze_signals = band_width < squeeze_threshold
        
        return {
            'upper_band': upper_band,
            'lower_band': lower_band,
            'band_width': band_width,
            'squeeze_signals': squeeze_signals
        }

def analyze_market_structure(df):
    """
    Identify key support/resistance levels and trend structure
    Ollama processes this data for directional bias
    """
    
    # Calculate swing highs and lows
    swing_highs = []
    swing_lows = []
    
    for i in range(2, len(df) - 2):
        current_high = df.iloc[i]['high']
        current_low = df.iloc[i]['low']
        
        # Check for swing high
        if (current_high > df.iloc[i-1]['high'] and 
            current_high > df.iloc[i-2]['high'] and
            current_high > df.iloc[i+1]['high'] and
            current_high > df.iloc[i+2]['high']):
            swing_highs.append({
                'index': i,
                'price': current_high,
                'timestamp': df.iloc[i]['timestamp']
            })
        
        # Check for swing low
        if (current_low < df.iloc[i-1]['low'] and 
            current_low < df.iloc[i-2]['low'] and
            current_low < df.iloc[i+1]['low'] and
            current_low < df.iloc[i+2]['low']):
            swing_lows.append({
                'index': i,
                'price': current_low,
                'timestamp': df.iloc[i]['timestamp']
            })
    
    # Determine trend direction
    recent_highs = swing_highs[-3:] if len(swing_highs) >= 3 else swing_highs
    recent_lows = swing_lows[-3:] if len(swing_lows) >= 3 else swing_lows
    
    trend = "sideways"
    if len(recent_highs) >= 2 and len(recent_lows) >= 2:
        higher_highs = all(recent_highs[i]['price'] > recent_highs[i-1]['price'] 
                          for i in range(1, len(recent_highs)))
        higher_lows = all(recent_lows[i]['price'] > recent_lows[i-1]['price'] 
                         for i in range(1, len(recent_lows)))
        
        if higher_highs and higher_lows:
            trend = "uptrend"
        elif not higher_highs and not higher_lows:
            trend = "downtrend"
    
    return {
        'swing_highs': swing_highs,
        'swing_lows': swing_lows,
        'trend': trend,
        'support_levels': [level['price'] for level in recent_lows],
        'resistance_levels': [level['price'] for level in recent_highs]
    }

def create_market_analysis_prompt(market_data, indicators, structure):
    """
    Generate comprehensive prompts for Ollama Bitcoin analysis
    Returns structured prompt for consistent predictions
    """
    
    current_price = market_data['close'].iloc[-1]
    price_change_24h = ((current_price - market_data['close'].iloc[-25]) / 
                       market_data['close'].iloc[-25]) * 100
    
    prompt = f"""
Analyze Bitcoin market conditions for price prediction:

CURRENT MARKET STATUS:
- Price: ${current_price:,.2f}
- 24h Change: {price_change_24h:+.2f}%
- Trend: {structure['trend']}
- Volume: {market_data['volume'].iloc[-1]:,.0f} BTC

TECHNICAL INDICATORS:
- RSI (14): {indicators['rsi']:.2f}
- Bollinger Position: {indicators['bb_position']:.1f}%
- Support Levels: {structure['support_levels']}
- Resistance Levels: {structure['resistance_levels']}

MARKET STRUCTURE:
- Recent Swing Highs: {len(structure['swing_highs'][-5:])} in last 5 periods
- Recent Swing Lows: {len(structure['swing_lows'][-5:])} in last 5 periods
- Trend Strength: {structure['trend_strength']}/10

ANALYSIS REQUIREMENTS:
1. Probability assessment for next 24 hours (bullish/bearish/neutral)
2. Key price targets (support and resistance)
3. Risk factors and catalysts
4. Confidence level (1-10) with reasoning
5. Recommended position size (% of portfolio)

Provide analysis in JSON format with specific price levels and percentages.
    """
    
    return prompt

import json
import re

class PredictionProcessor:
    def __init__(self):
        self.confidence_threshold = 7
        self.risk_tolerance = 0.02  # 2% max risk per trade
        
    def parse_ollama_response(self, response_text):
        """
        Extract structured data from Ollama's analysis
        Returns dictionary with trading signals
        """
        try:
            # Extract JSON from response
            json_match = re.search(r'\{.*\}', response_text, re.DOTALL)
            if json_match:
                analysis = json.loads(json_match.group())
            else:
                # Fallback to text parsing
                analysis = self.parse_text_response(response_text)
            
            # Validate and structure the analysis
            structured_analysis = {
                'direction': analysis.get('direction', 'neutral'),
                'confidence': analysis.get('confidence', 5),
                'target_price': analysis.get('target_price', None),
                'stop_loss': analysis.get('stop_loss', None),
                'time_horizon': analysis.get('time_horizon', '24h'),
                'risk_reward_ratio': analysis.get('risk_reward', 1.0),
                'reasoning': analysis.get('reasoning', '')
            }
            
            return structured_analysis
            
        except Exception as e:
            return {
                'direction': 'neutral',
                'confidence': 1,
                'error': str(e)
            }
    
    def generate_trading_signal(self, analysis, current_price):
        """
        Convert analysis into executable trading signals
        Only generates signals above confidence threshold
        """
        if analysis['confidence'] < self.confidence_threshold:
            return {'action': 'wait', 'reason': 'Low confidence'}
        
        signal = {
            'timestamp': datetime.now(),
            'action': analysis['direction'],
            'entry_price': current_price,
            'target_price': analysis.get('target_price'),
            'stop_loss': analysis.get('stop_loss'),
            'position_size': self.calculate_position_size(
                current_price, 
                analysis.get('stop_loss', current_price * 0.95)
            ),
            'confidence': analysis['confidence']
        }
        
        return signal
    
    def calculate_position_size(self, entry_price, stop_loss_price):
        """
        Calculate appropriate position size based on risk management
        Returns percentage of portfolio to risk
        """
        risk_per_share = abs(entry_price - stop_loss_price)
        risk_percentage = risk_per_share / entry_price
        
        # Adjust position size to maintain consistent risk
        max_position_size = self.risk_tolerance / risk_percentage
        return min(max_position_size, 0.10)  # Never risk more than 10%

import asyncio
import logging
from datetime import datetime

class BitcoinPredictionPipeline:
    def __init__(self, ollama_client, data_collector):
        self.ollama = ollama_client
        self.data_collector = data_collector
        self.processor = PredictionProcessor()
        self.indicators = AdvancedIndicators()
        self.running = False
        
        # Set up logging
        logging.basicConfig(level=logging.INFO)
        self.logger = logging.getLogger(__name__)
    
    async def run_prediction_cycle(self):
        """
        Execute complete prediction cycle
        Returns structured prediction results
        """
        try:
            # 1. Collect latest market data
            market_data = self.data_collector.fetch_ohlcv_data(limit=200)
            
            # 2. Calculate technical indicators
            indicators = self.calculate_all_indicators(market_data)
            
            # 3. Analyze market structure
            structure = analyze_market_structure(market_data)
            
            # 4. Generate Ollama prompt
            prompt = create_market_analysis_prompt(market_data, indicators, structure)
            
            # 5. Get Ollama prediction
            response = await self.ollama.generate(
                model='llama2:13b',
                prompt=prompt,
                options={
                    'temperature': 0.3,  # Lower temperature for consistent analysis
                    'top_p': 0.9,
                    'max_tokens': 1000
                }
            )
            
            # 6. Process response
            analysis = self.processor.parse_ollama_response(response['response'])
            
            # 7. Generate trading signal
            current_price = market_data['close'].iloc[-1]
            signal = self.processor.generate_trading_signal(analysis, current_price)
            
            # 8. Log results
            self.logger.info(f"Prediction completed: {signal['action']} at ${current_price:.2f}")
            
            return {
                'timestamp': datetime.now(),
                'market_data': market_data.tail(5).to_dict(),
                'indicators': indicators,
                'analysis': analysis,
                'signal': signal
            }
            
        except Exception as e:
            self.logger.error(f"Prediction cycle error: {str(e)}")
            return {'error': str(e)}
    
    def calculate_all_indicators(self, df):
        """Calculate comprehensive technical indicator suite"""
        prices = df['close'].values
        
        return {
            'rsi': talib.RSI(prices)[-1],
            'macd': talib.MACD(prices)[0][-1],
            'bb_position': self.calculate_bb_position(prices),
            'volume_sma_ratio': df['volume'].iloc[-1] / talib.SMA(df['volume'].values, 20)[-1],
            'atr': talib.ATR(df['high'].values, df['low'].values, prices, timeperiod=14)[-1]
        }
    
    async def start_monitoring(self, interval_minutes=15):
        """
        Start continuous Bitcoin monitoring
        Runs prediction cycles at specified intervals
        """
        self.running = True
        self.logger.info(f"Starting Bitcoin prediction monitoring (interval: {interval_minutes}m)")
        
        while self.running:
            try:
                result = await self.run_prediction_cycle()
                
                # Store results (implement your storage solution)
                await self.store_prediction_result(result)
                
                # Wait for next cycle
                await asyncio.sleep(interval_minutes * 60)
                
            except KeyboardInterrupt:
                self.logger.info("Monitoring stopped by user")
                break
            except Exception as e:
                self.logger.error(f"Monitoring error: {str(e)}")
                await asyncio.sleep(60)  # Wait 1 minute before retry
    
    async def store_prediction_result(self, result):
        """Store prediction results for backtesting and analysis"""
        # Implement database storage or file logging
        pass

class PredictionTracker:
    def __init__(self):
        self.predictions = []
        self.performance_metrics = {}
    
    def record_prediction(self, prediction, actual_outcome):
        """
        Record prediction results for accuracy tracking
        Calculates performance metrics automatically
        """
        record = {
            'timestamp': prediction['timestamp'],
            'predicted_direction': prediction['signal']['action'],
            'confidence': prediction['analysis']['confidence'],
            'entry_price': prediction['signal']['entry_price'],
            'target_price': prediction['signal'].get('target_price'),
            'actual_price_24h': actual_outcome['price_24h_later'],
            'actual_direction': actual_outcome['direction'],
            'profit_loss': actual_outcome['profit_loss_percentage']
        }
        
        self.predictions.append(record)
        self.update_performance_metrics()
    
    def update_performance_metrics(self):
        """Calculate comprehensive performance statistics"""
        if len(self.predictions) < 10:
            return
        
        df = pd.DataFrame(self.predictions)
        
        # Accuracy metrics
        correct_predictions = df['predicted_direction'] == df['actual_direction']
        accuracy = correct_predictions.mean()
        
        # Confidence-weighted accuracy
        weighted_accuracy = (correct_predictions * df['confidence']).sum() / df['confidence'].sum()
        
        # Profit metrics
        profitable_trades = df['profit_loss'] > 0
        win_rate = profitable_trades.mean()
        average_win = df[profitable_trades]['profit_loss'].mean()
        average_loss = df[~profitable_trades]['profit_loss'].mean()
        
        self.performance_metrics = {
            'total_predictions': len(df),
            'accuracy': accuracy,
            'weighted_accuracy': weighted_accuracy,
            'win_rate': win_rate,
            'average_win': average_win,
            'average_loss': average_loss,
            'profit_factor': abs(average_win / average_loss) if average_loss != 0 else 0,
            'sharpe_ratio': self.calculate_sharpe_ratio(df['profit_loss'])
        }
    
    def calculate_sharpe_ratio(self, returns):
        """Calculate risk-adjusted returns"""
        return returns.mean() / returns.std() if returns.std() != 0 else 0
    
    def generate_performance_report(self):
        """Create detailed performance analysis"""
        if not self.performance_metrics:
            return "Insufficient data for performance analysis"
        
        return f"""
BITCOIN PREDICTION PERFORMANCE REPORT
=====================================

Total Predictions: {self.performance_metrics['total_predictions']}
Directional Accuracy: {self.performance_metrics['accuracy']:.1%}
Confidence-Weighted Accuracy: {self.performance_metrics['weighted_accuracy']:.1%}

PROFITABILITY METRICS:
Win Rate: {self.performance_metrics['win_rate']:.1%}
Average Win: {self.performance_metrics['average_win']:.2f}%
Average Loss: {self.performance_metrics['average_loss']:.2f}%
Profit Factor: {self.performance_metrics['profit_factor']:.2f}
Sharpe Ratio: {self.performance_metrics['sharpe_ratio']:.2f}

RECOMMENDATIONS:
{self.generate_improvement_suggestions()}
        """
    
    def generate_improvement_suggestions(self):
        """Provide suggestions based on performance data"""
        suggestions = []
        
        if self.performance_metrics['accuracy'] < 0.6:
            suggestions.append("- Consider adjusting confidence thresholds")
            suggestions.append("- Review technical indicator selection")
        
        if self.performance_metrics['win_rate'] < 0.5:
            suggestions.append("- Implement tighter stop-loss rules")
            suggestions.append("- Adjust position sizing strategy")
        
        if self.performance_metrics['profit_factor'] < 1.5:
            suggestions.append("- Focus on higher risk-reward setups")
            suggestions.append("- Filter signals by market volatility")
        
        return "\n".join(suggestions) if suggestions else "- Performance metrics look good!"

class EnsemblePredictionSystem:
    def __init__(self):
        self.models = {
            'llama2:13b': {'weight': 0.4, 'strength': 'pattern_recognition'},
            'mistral': {'weight': 0.3, 'strength': 'speed'},
            'codellama': {'weight': 0.3, 'strength': 'logic'}
        }
    
    async def get_ensemble_prediction(self, prompt):
        """
        Get predictions from multiple models and combine results
        Returns weighted consensus prediction
        """
        predictions = {}
        
        for model_name, config in self.models.items():
            try:
                response = await self.ollama.generate(
                    model=model_name,
                    prompt=prompt,
                    options={'temperature': 0.2}
                )
                
                analysis = self.processor.parse_ollama_response(response['response'])
                predictions[model_name] = analysis
                
            except Exception as e:
                logging.warning(f"Model {model_name} failed: {str(e)}")
        
        # Combine predictions using weighted voting
        ensemble_result = self.combine_predictions(predictions)
        return ensemble_result
    
    def combine_predictions(self, predictions):
        """Combine multiple model predictions using weighted consensus"""
        if not predictions:
            return {'direction': 'neutral', 'confidence': 1}
        
        # Weight votes by model reliability
        direction_votes = {'bullish': 0, 'bearish': 0, 'neutral': 0}
        confidence_sum = 0
        total_weight = 0
        
        for model_name, prediction in predictions.items():
            weight = self.models[model_name]['weight']
            direction_votes[prediction['direction']] += weight
            confidence_sum += prediction['confidence'] * weight
            total_weight += weight
        
        # Determine consensus direction
        consensus_direction = max(direction_votes, key=direction_votes.get)
        consensus_confidence = confidence_sum / total_weight if total_weight > 0 else 1
        
        # Adjust confidence based on agreement level
        max_votes = max(direction_votes.values())
        agreement_ratio = max_votes / total_weight
        adjusted_confidence = consensus_confidence * agreement_ratio
        
        return {
            'direction': consensus_direction,
            'confidence': min(adjusted_confidence, 10),
            'model_agreement': agreement_ratio,
            'individual_predictions': predictions
        }

def detect_market_regime(df, lookback_period=50):
    """
    Identify current market regime for adaptive predictions
    Returns regime type and strength indicator
    """
    
    recent_data = df.tail(lookback_period)
    
    # Calculate volatility metrics
    returns = recent_data['close'].pct_change()
    volatility = returns.std() * np.sqrt(24)  # Annualized for hourly data
    
    # Calculate trend strength
    price_change = (recent_data['close'].iloc[-1] - recent_data['close'].iloc[0]) / recent_data['close'].iloc[0]
    trend_strength = abs(price_change)
    
    # Volume analysis
    avg_volume = recent_data['volume'].mean()
    recent_volume = recent_data['volume'].tail(10).mean()
    volume_ratio = recent_volume / avg_volume
    
    # Determine regime
    if volatility > 0.6:  # High volatility threshold
        if volume_ratio > 1.5:
            regime = "high_volatility_trending"
        else:
            regime = "high_volatility_ranging"
    elif trend_strength > 0.1:  # 10% trend threshold
        regime = "low_volatility_trending"
    else:
        regime = "low_volatility_ranging"
    
    return {
        'regime': regime,
        'volatility': volatility,
        'trend_strength': trend_strength,
        'volume_ratio': volume_ratio,
        'confidence_modifier': min(volume_ratio, 2.0)  # Cap at 2x
    }

def adapt_prediction_to_regime(base_prediction, market_regime):
    """
    Adjust prediction confidence and parameters based on market regime
    Different regimes require different prediction approaches
    """
    
    regime_adjustments = {
        'high_volatility_trending': {
            'confidence_multiplier': 1.2,
            'stop_loss_multiplier': 1.5,
            'target_multiplier': 1.3
        },
        'high_volatility_ranging': {
            'confidence_multiplier': 0.8,
            'stop_loss_multiplier': 1.2,
            'target_multiplier': 0.8
        },
        'low_volatility_trending': {
            'confidence_multiplier': 1.1,
            'stop_loss_multiplier': 0.8,
            'target_multiplier': 1.0
        },
        'low_volatility_ranging': {
            'confidence_multiplier': 0.9,
            'stop_loss_multiplier': 1.0,
            'target_multiplier': 0.7
        }
    }
    
    adjustments = regime_adjustments.get(market_regime['regime'], {
        'confidence_multiplier': 1.0,
        'stop_loss_multiplier': 1.0,
        'target_multiplier': 1.0
    })
    
    # Apply regime-specific adjustments
    adapted_prediction = base_prediction.copy()
    adapted_prediction['confidence'] *= adjustments['confidence_multiplier']
    adapted_prediction['confidence'] = min(adapted_prediction['confidence'], 10)
    
    # Adjust risk parameters
    if 'stop_loss' in adapted_prediction:
        stop_distance = abs(adapted_prediction['entry_price'] - adapted_prediction['stop_loss'])
        adapted_prediction['stop_loss'] = (adapted_prediction['entry_price'] - 
                                         stop_distance * adjustments['stop_loss_multiplier'])
    
    return adapted_prediction

# docker-compose.yml for production deployment
"""
version: '3.8'
services:
  bitcoin-predictor:
    build: .
    environment:
      - OLLAMA_HOST=ollama:11434
      - REDIS_URL=redis://redis:6379
      - DB_URL=postgresql://user:pass@postgres:5432/bitcoin_predictions
    depends_on:
      - ollama
      - redis
      - postgres
    restart: unless-stopped
    
  ollama:
    image: ollama/ollama:latest
    volumes:
      - ollama_data:/root/.ollama
    ports:
      - "11434:11434"
    restart: unless-stopped
    
  redis:
    image: redis:alpine
    restart: unless-stopped
    
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: bitcoin_predictions
      POSTGRES_USER: user
      POSTGRES_PASSWORD: pass
    volumes:
      - postgres_data:/var/lib/postgresql/data
    restart: unless-stopped

volumes:
  ollama_data:
  postgres_data:
"""

# Production monitoring setup
class ProductionMonitor:
    def __init__(self):
        self.alerts = AlertingSystem()
        self.metrics = MetricsCollector()
    
    def monitor_system_health(self):
        """Monitor all system components for issues"""
        checks = [
            self.check_ollama_connectivity(),
            self.check_data_feed_status(),
            self.check_prediction_latency(),
            self.check_memory_usage(),
            self.check_disk_space()
        ]
        
        failed_checks = [check for check in checks if not check['status']]
        
        if failed_checks:
            self.alerts.send_alert(f"System health issues: {failed_checks}")
    
    def check_prediction_accuracy_drift(self):
        """Alert if prediction accuracy drops significantly"""
        recent_accuracy = self.calculate_recent_accuracy()
        baseline_accuracy = self.get_baseline_accuracy()
        
        if recent_accuracy < baseline_accuracy * 0.8:  # 20% drop threshold
            self.alerts.send_alert(
                f"Prediction accuracy drift detected: "
                f"{recent_accuracy:.1%} vs baseline {baseline_accuracy:.1%}"
            )

# Streamlit dashboard for real-time monitoring
import streamlit as st
import plotly.graph_objects as go
import plotly.express as px

def create_prediction_dashboard():
    """
    Create interactive dashboard for Bitcoin prediction monitoring
    Shows real-time predictions, performance metrics, and market analysis
    """
    
    st.set_page_config(page_title="Bitcoin Prediction Dashboard", layout="wide")
    
    # Header
    st.title("🚀 Bitcoin Price Prediction Dashboard")
    st.markdown("Real-time predictions powered by Ollama AI")
    
    # Metrics overview
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        st.metric("Current Price", "$45,230", "+2.3%")
    with col2:
        st.metric("24h Prediction", "Bullish", "Confidence: 8.2/10")
    with col3:
        st.metric("Accuracy (7d)", "73.5%", "+5.2%")
    with col4:
        st.metric("Active Signals", "3", "+1")
    
    # Main chart
    st.subheader("Price Chart with Predictions")
    
    # Create candlestick chart with prediction overlays
    fig = go.Figure()
    
    # Add candlestick data (placeholder)
    fig.add_trace(go.Candlestick(
        x=px.data.stocks()['date'],
        open=px.data.stocks()['GOOG.Open'],
        high=px.data.stocks()['GOOG.High'],
        low=px.data.stocks()['GOOG.Low'],
        close=px.data.stocks()['GOOG.Close'],
        name="Bitcoin OHLC"
    ))
    
    fig.update_layout(
        title="Bitcoin Price with AI Predictions",
        xaxis_title="Time",
        yaxis_title="Price (USD)",
        height=500
    )
    
    st.plotly_chart(fig, use_container_width=True)
    
    # Prediction details
    col1, col2 = st.columns(2)
    
    with col1:
        st.subheader("Latest Predictions")
        predictions_df = pd.DataFrame({
            'Time': ['10:30 AM', '10:15 AM', '10:00 AM'],
            'Direction': ['Bullish', 'Bearish', 'Neutral'],
            'Confidence': [8.2, 6.5, 5.1],
            'Target': ['$46,500', '$44,800', '-']
        })
        st.dataframe(predictions_df, use_container_width=True)
    
    with col2:
        st.subheader("Performance Metrics")
        performance_df = pd.DataFrame({
            'Metric': ['Accuracy', 'Win Rate', 'Profit Factor', 'Sharpe Ratio'],
            'Value': ['73.5%', '68.2%', '1.85', '2.3'],
            'Trend': ['↑', '↓', '↑', '↑']
        })
        st.dataframe(performance_df, use_container_width=True)

if __name__ == "__main__":
    create_prediction_dashboard()

# Solution: Adjust prompt engineering and data quality
def improve_confidence_scores():
    """
    Strategies to improve Ollama confidence in Bitcoin predictions
    """
    improvements = {
        'data_quality': [
            'Increase data collection frequency',
            'Add multiple exchange feeds',
            'Implement data validation checks'
        ],
        'prompt_optimization': [
            'Use more specific technical language',
            'Provide clearer context about market conditions',
            'Include historical pattern examples'
        ],
        'model_tuning': [
            'Adjust temperature settings (0.2-0.4 for analysis)',
            'Increase context window for more data',
            'Use ensemble predictions for consensus'
        ]
    }
    return improvements

def stabilize_predictions():
    """
    Reduce prediction variance through systematic approaches
    """
    stabilization_methods = {
        'input_normalization': 'Standardize all numeric inputs to 0-1 range',
        'prompt_templates': 'Use consistent prompt structure across calls',
        'model_versioning': 'Pin specific Ollama model versions',
        'result_validation': 'Implement sanity checks on outputs'
    }
    return stabilization_methods

class DataFeedMonitor:
    def __init__(self):
        self.feed_status = {}
        self.backup_sources = [
            'binance', 'coinbase', 'kraken', 'bitstamp'
        ]
    
    def check_feed_health(self, source):
        """Monitor data feed reliability and switch if needed"""
        try:
            # Test API connectivity
            response = requests.get(f"{source}/api/health", timeout=5)
            
            if response.status_code == 200:
                self.feed_status[source] = 'healthy'
                return True
            else:
                self.feed_status[source] = 'degraded'
                return False
                
        except Exception as e:
            self.feed_status[source] = 'failed'
            logging.error(f"Data feed {source} failed: {str(e)}")
            return False
    
    def get_best_data_source(self):
        """Select most reliable data source automatically"""
        for source in self.backup_sources:
            if self.check_feed_health(source):
                return source
        
        raise Exception("All data sources unavailable")

def optimize_memory_usage():
    """
    Implement memory management best practices
    """
    optimization_tips = {
        'data_management': [
            'Limit historical data retention (keep 30 days max)',
            'Use data compression for storage',
            'Implement rolling window calculations'
        ],
        'model_management': [
            'Load models on-demand',
            'Clear model cache periodically',
            'Use smaller models for frequent predictions'
        ],
        'system_optimization': [
            'Set appropriate Python garbage collection',
            'Monitor memory usage with logging',
            'Implement automatic restarts if memory exceeds limits'
        ]
    }
    return optimization_tips

class MultiTimeframePredictor:
    def __init__(self):
        self.timeframes = {
            '1h': {'weight': 0.2, 'horizon': '4-8 hours'},
            '4h': {'weight': 0.3, 'horizon': '1-2 days'},
            '1d': {'weight': 0.3, 'horizon': '1-2 weeks'},
            '1w': {'weight': 0.2, 'horizon': '1-3 months'}
        }
    
    async def get_multi_timeframe_prediction(self):
        """Generate predictions across multiple time horizons"""
        predictions = {}
        
        for timeframe, config in self.timeframes.items():
            # Get data for specific timeframe
            data = self.data_collector.fetch_ohlcv_data(
                interval=timeframe, 
                limit=200
            )
            
            # Generate timeframe-specific analysis
            analysis = await self.analyze_timeframe(data, timeframe)
            predictions[timeframe] = analysis
        
        # Combine into unified outlook
        unified_prediction = self.combine_timeframe_predictions(predictions)
        return unified_prediction

class PortfolioManager:
    def __init__(self, initial_balance=10000):
        self.balance = initial_balance
        self.positions = {}
        self.trade_history = []
    
    def execute_prediction_signal(self, signal, current_price):
        """
        Execute trades based on Ollama predictions
        Implements proper risk management
        """
        if signal['action'] == 'buy' and signal['confidence'] > 7:
            position_size = self.calculate_position_size(signal)
            self.open_position('long', current_price, position_size, signal)
            
        elif signal['action'] == 'sell' and signal['confidence'] > 7:
            self.close_all_positions(current_price)
    
    def calculate_portfolio_performance(self):
        """Track strategy performance vs. buy-and-hold"""
        total_return = (self.balance - 10000) / 10000
        bitcoin_return = self.calculate_bitcoin_return()
        
        return {
            'strategy_return': total_return,
            'bitcoin_return': bitcoin_return,
            'alpha': total_return - bitcoin_return,
            'trade_count': len(self.trade_history)
        }