Earnings Prediction using Ollama: Automate Financial Statement Analysis in 2025

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

# Download Llama2 model for financial analysis
ollama pull llama2:13b

# Verify installation
ollama list

# financial_config.py
import ollama
import pandas as pd
import json

class FinancialAnalyzer:
    def __init__(self):
        self.client = ollama.Client()
        self.model = "llama2:13b"
        
    def load_financial_statements(self, file_path):
        """Load financial statements from various formats"""
        if file_path.endswith('.csv'):
            return pd.read_csv(file_path)
        elif file_path.endswith('.json'):
            with open(file_path, 'r') as f:
                return json.load(f)
        else:
            raise ValueError("Unsupported file format")
    
    def prepare_prompt(self, financial_data):
        """Create structured prompt for earnings prediction"""
        prompt = f"""
        Analyze these financial statements and predict next quarter earnings:
        
        Revenue Data: {financial_data['revenue']}
        Expenses Data: {financial_data['expenses']}
        Historical Growth: {financial_data['growth_rate']}
        
        Provide:
        1. Earnings prediction for next quarter
        2. Confidence level (1-10)
        3. Key factors influencing prediction
        """
        return prompt

# earnings_predictor.py
import ollama
import pandas as pd
from datetime import datetime, timedelta

class EarningsPredictor:
    def __init__(self):
        self.analyzer = FinancialAnalyzer()
        
    def extract_key_metrics(self, financial_data):
        """Extract critical financial metrics for analysis"""
        metrics = {
            'revenue_growth': self.calculate_growth_rate(financial_data['revenue']),
            'expense_ratio': financial_data['expenses'] / financial_data['revenue'],
            'profit_margin': (financial_data['revenue'] - financial_data['expenses']) / financial_data['revenue'],
            'seasonal_trends': self.identify_seasonal_patterns(financial_data)
        }
        return metrics
    
    def calculate_growth_rate(self, revenue_data):
        """Calculate quarter-over-quarter revenue growth"""
        if len(revenue_data) < 2:
            return 0
        current = revenue_data[-1]
        previous = revenue_data[-2]
        return ((current - previous) / previous) * 100
    
    def predict_earnings(self, company_data):
        """Generate earnings prediction using Ollama"""
        metrics = self.extract_key_metrics(company_data)
        
        prompt = f"""
        Financial Analysis for Earnings Prediction:
        
        Company: {company_data['name']}
        Revenue Growth Rate: {metrics['revenue_growth']:.2f}%
        Expense Ratio: {metrics['expense_ratio']:.2f}
        Profit Margin: {metrics['profit_margin']:.2f}
        
        Based on these metrics and historical patterns, predict:
        1. Next quarter revenue estimate
        2. Expected profit margin
        3. Earnings per share prediction
        4. Risk factors that could affect results
        
        Format response as JSON with specific numerical predictions.
        """
        
        response = ollama.chat(
            model='llama2:13b',
            messages=[{'role': 'user', 'content': prompt}]
        )
        
        return self.parse_prediction_response(response['message']['content'])
    
    def parse_prediction_response(self, response_text):
        """Parse Ollama response into structured prediction data"""
        # Implementation depends on response format
        # This example shows the structure
        return {
            'predicted_revenue': 0,
            'predicted_eps': 0,
            'confidence_score': 0,
            'risk_factors': []
        }

# pattern_analyzer.py
import numpy as np
from sklearn.preprocessing import StandardScaler

class PatternAnalyzer:
    def __init__(self):
        self.scaler = StandardScaler()
        
    def identify_seasonal_patterns(self, historical_data):
        """Detect seasonal trends in financial performance"""
        quarterly_data = self.group_by_quarter(historical_data)
        
        # Calculate seasonal indices for each quarter
        seasonal_indices = {}
        for quarter in ['Q1', 'Q2', 'Q3', 'Q4']:
            quarter_data = quarterly_data.get(quarter, [])
            if quarter_data:
                seasonal_indices[quarter] = np.mean(quarter_data) / np.mean(list(quarterly_data.values()))
        
        return seasonal_indices
    
    def detect_growth_trends(self, revenue_data):
        """Identify long-term growth patterns"""
        if len(revenue_data) < 8:  # Need at least 2 years of quarterly data
            return "Insufficient data"
        
        # Calculate year-over-year growth rates
        yoy_growth = []
        for i in range(4, len(revenue_data)):
            current_quarter = revenue_data[i]
            same_quarter_last_year = revenue_data[i-4]
            growth = ((current_quarter - same_quarter_last_year) / same_quarter_last_year) * 100
            yoy_growth.append(growth)
        
        # Determine trend direction
        if np.mean(yoy_growth) > 5:
            return "Strong Growth"
        elif np.mean(yoy_growth) > 0:
            return "Moderate Growth"
        else:
            return "Declining"
    
    def group_by_quarter(self, financial_data):
        """Group financial data by quarters for pattern analysis"""
        quarterly_groups = {'Q1': [], 'Q2': [], 'Q3': [], 'Q4': []}
        
        for entry in financial_data:
            quarter = self.determine_quarter(entry['date'])
            quarterly_groups[quarter].append(entry['revenue'])
        
        return quarterly_groups
    
    def determine_quarter(self, date_string):
        """Determine quarter from date string"""
        month = int(date_string.split('-')[1])
        if month <= 3:
            return 'Q1'
        elif month <= 6:
            return 'Q2'
        elif month <= 9:
            return 'Q3'
        else:
            return 'Q4'

# data_preparation.py
def prepare_financial_data(company_name, data_source):
    """
    Prepare financial data for Ollama analysis
    Expected outcome: Structured dataset ready for prediction
    """
    financial_data = {
        'name': company_name,
        'revenue': [],  # Quarterly revenue figures
        'expenses': [], # Operating expenses
        'dates': [],    # Quarter end dates
        'industry': '', # Industry classification
    }
    
    # Load data from your source (API, CSV, database)
    raw_data = load_from_source(data_source)
    
    # Clean and structure the data
    for quarter in raw_data:
        financial_data['revenue'].append(quarter['total_revenue'])
        financial_data['expenses'].append(quarter['operating_expenses'])
        financial_data['dates'].append(quarter['period_end'])
    
    return financial_data

# Usage example
company_data = prepare_financial_data("ACME Corp", "financial_statements.csv")
print(f"Loaded {len(company_data['revenue'])} quarters of data")

# run_prediction.py
def generate_earnings_forecast(company_data):
    """
    Generate comprehensive earnings forecast
    Expected outcome: Detailed prediction with confidence metrics
    """
    predictor = EarningsPredictor()
    pattern_analyzer = PatternAnalyzer()
    
    # Analyze historical patterns
    patterns = pattern_analyzer.identify_seasonal_patterns(company_data)
    growth_trend = pattern_analyzer.detect_growth_trends(company_data['revenue'])
    
    # Generate prediction
    prediction = predictor.predict_earnings(company_data)
    
    # Enhance prediction with pattern insights
    enhanced_prediction = {
        'company': company_data['name'],
        'predicted_revenue': prediction['predicted_revenue'],
        'predicted_eps': prediction['predicted_eps'],
        'confidence_score': prediction['confidence_score'],
        'seasonal_adjustment': patterns,
        'growth_trend': growth_trend,
        'prediction_date': datetime.now().isoformat(),
    }
    
    return enhanced_prediction

# Execute prediction
forecast = generate_earnings_forecast(company_data)
print(f"Predicted EPS: ${forecast['predicted_eps']:.2f}")
print(f"Confidence: {forecast['confidence_score']}/10")

# validation.py
class PredictionValidator:
    def __init__(self):
        self.predictions_log = []
    
    def log_prediction(self, prediction, actual_result=None):
        """
        Log predictions for accuracy tracking
        Expected outcome: Historical record of prediction performance
        """
        log_entry = {
            'prediction_date': prediction['prediction_date'],
            'company': prediction['company'],
            'predicted_eps': prediction['predicted_eps'],
            'actual_eps': actual_result,
            'accuracy': self.calculate_accuracy(prediction, actual_result) if actual_result else None
        }
        
        self.predictions_log.append(log_entry)
        return log_entry
    
    def calculate_accuracy(self, prediction, actual):
        """Calculate prediction accuracy percentage"""
        if actual == 0:
            return 0
        error_rate = abs(prediction['predicted_eps'] - actual) / abs(actual)
        accuracy = max(0, 100 - (error_rate * 100))
        return round(accuracy, 2)
    
    def get_accuracy_report(self):
        """Generate accuracy report for all predictions"""
        completed_predictions = [p for p in self.predictions_log if p['accuracy'] is not None]
        
        if not completed_predictions:
            return "No completed predictions available"
        
        avg_accuracy = np.mean([p['accuracy'] for p in completed_predictions])
        return f"Average prediction accuracy: {avg_accuracy:.2f}%"

# Usage
validator = PredictionValidator()
validator.log_prediction(forecast)
print(validator.get_accuracy_report())

# comparative_analysis.py
def compare_industry_peers(companies_data):
    """
    Compare earnings predictions across industry competitors
    Expected outcome: Relative performance rankings and insights
    """
    predictor = EarningsPredictor()
    peer_analysis = []
    
    for company in companies_data:
        prediction = predictor.predict_earnings(company)
        peer_analysis.append({
            'company': company['name'],
            'predicted_growth': prediction['revenue_growth'],
            'risk_score': len(prediction['risk_factors']),
            'confidence': prediction['confidence_score']
        })
    
    # Rank by predicted performance
    peer_analysis.sort(key=lambda x: x['predicted_growth'], reverse=True)
    
    return peer_analysis

# Generate industry comparison
industry_comparison = compare_industry_peers([company_data, competitor_data])
print("Industry Rankings:")
for i, company in enumerate(industry_comparison, 1):
    print(f"{i}. {company['company']}: {company['predicted_growth']:.1f}% growth")

# risk_assessment.py
def assess_financial_risks(company_data, market_conditions):
    """
    Integrate risk factors into earnings predictions
    Expected outcome: Risk-adjusted earnings forecasts
    """
    risk_factors = {
        'market_volatility': market_conditions['volatility_index'],
        'debt_ratio': company_data['total_debt'] / company_data['total_assets'],
        'cash_flow_stability': calculate_cash_flow_variance(company_data),
        'industry_headwinds': assess_industry_risks(company_data['industry'])
    }
    
    # Calculate overall risk score (1-10 scale)
    risk_score = sum(risk_factors.values()) / len(risk_factors)
    
    # Apply risk adjustment to base prediction
    risk_adjustment = 1 - (risk_score * 0.1)  # Reduce prediction confidence by risk
    
    return {
        'risk_factors': risk_factors,
        'overall_risk_score': risk_score,
        'risk_adjustment_factor': risk_adjustment
    }

# automation_pipeline.py
import schedule
import time

def setup_automated_pipeline():
    """
    Configure automated earnings prediction pipeline
    Expected outcome: Hands-free prediction updates
    """
    def daily_data_update():
        # Fetch latest financial data
        updated_data = fetch_latest_financial_data()
        
        # Generate new predictions
        fresh_predictions = generate_earnings_forecast(updated_data)
        
        # Update database/dashboard
        save_predictions_to_database(fresh_predictions)
        
        print(f"Pipeline executed: {datetime.now()}")
    
    # Schedule daily updates
    schedule.every().day.at("06:00").do(daily_data_update)
    
    # Run scheduler
    while True:
        schedule.run_pending()
        time.sleep(3600)  # Check every hour

# Start automated pipeline
setup_automated_pipeline()

# performance_optimization.py
def optimize_ollama_performance():
    """
    Configure Ollama for optimal financial analysis performance
    Expected outcome: Faster prediction generation with maintained accuracy
    """
    # Use GPU acceleration if available
    import subprocess
    gpu_available = subprocess.run(['nvidia-smi'], capture_output=True).returncode == 0
    
    if gpu_available:
        # Configure Ollama for GPU usage
        subprocess.run(['ollama', 'serve', '--gpu'])
    
    # Batch process multiple companies
    def batch_predict_earnings(companies_batch):
        results = []
        predictor = EarningsPredictor()
        
        for company in companies_batch:
            prediction = predictor.predict_earnings(company)
            results.append(prediction)
        
        return results
    
    return batch_predict_earnings

# Usage for multiple companies
optimized_predictor = optimize_ollama_performance()
batch_results = optimized_predictor([company1_data, company2_data, company3_data])

# integration_examples.py
def integrate_with_bloomberg_api():
    """Example integration with Bloomberg Terminal API"""
    # Fetch real-time data from Bloomberg
    # Process through Ollama
    # Return enhanced predictions
    pass

def export_to_excel_dashboard():
    """Export predictions to Excel for traditional workflows"""
    # Format predictions for Excel consumption
    # Include charts and visualizations
    # Automate report generation
    pass