SMB Bitcoin Treasury Guide: Ollama Small Business Cryptocurrency Strategy

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

# Pull a financial analysis model
ollama pull llama2:13b

# Verify installation
ollama list

# bitcoin_analysis.py
import subprocess
import json

def analyze_bitcoin_allocation(cash_reserves, risk_tolerance):
    prompt = f"""
    Analyze Bitcoin treasury allocation for SMB with:
    - Cash reserves: ${cash_reserves:,}
    - Risk tolerance: {risk_tolerance}/10
    
    Provide:
    1. Recommended allocation percentage
    2. Dollar cost averaging schedule
    3. Risk mitigation strategies
    4. Exit criteria
    
    Focus on conservative business practices.
    """
    
    # Run Ollama analysis
    result = subprocess.run(
        ['ollama', 'run', 'llama2:13b', prompt],
        capture_output=True,
        text=True
    )
    
    return result.stdout

# Example usage
allocation_advice = analyze_bitcoin_allocation(500000, 6)
print(allocation_advice)

# Generate secure Bitcoin wallet using Bitcoin Core
bitcoin-cli createwallet "company_treasury" false false "" false false true

# Create multi-sig address for enhanced security
bitcoin-cli createmultisig 2 '["pubkey1", "pubkey2", "pubkey3"]'

# treasury_strategy.py
def create_allocation_model(company_profile):
    analysis_prompt = f"""
    Create Bitcoin allocation strategy for:
    - Industry: {company_profile['industry']}
    - Revenue: ${company_profile['annual_revenue']:,}
    - Cash flow: {company_profile['cash_flow_stability']}
    - Investment horizon: {company_profile['timeline']} years
    
    Recommend:
    - Initial allocation percentage (1-10%)
    - DCA schedule and amounts
    - Rebalancing triggers
    - Performance metrics
    """
    
    # Process with Ollama
    strategy = subprocess.run(
        ['ollama', 'run', 'llama2:13b', analysis_prompt],
        capture_output=True, text=True
    ).stdout
    
    return strategy

# Company profile example
profile = {
    'industry': 'Software Development',
    'annual_revenue': 2000000,
    'cash_flow_stability': 'High',
    'timeline': 5
}

strategy = create_allocation_model(profile)

# price_monitor.py
import requests
import time

def monitor_bitcoin_treasury(allocation_amount, alert_threshold=0.15):
    """Monitor Bitcoin price for treasury management alerts"""
    
    while True:
        # Fetch current Bitcoin price
        response = requests.get('https://api.coindesk.com/v1/bpi/currentprice.json')
        current_price = response.json()['bpi']['USD']['rate_float']
        
        # Calculate portfolio impact
        portfolio_value = allocation_amount * current_price / 100000000  # satoshis to BTC
        
        # Check for significant changes
        if abs(portfolio_value - allocation_amount) / allocation_amount > alert_threshold:
            send_treasury_alert(portfolio_value, allocation_amount)
        
        time.sleep(3600)  # Check hourly

def send_treasury_alert(current_value, original_allocation):
    change_percent = ((current_value - original_allocation) / original_allocation) * 100
    print(f"Treasury Alert: Bitcoin allocation changed {change_percent:.2f}%")

# dca_execution.py
def execute_dca_purchase(amount_usd, exchange_api):
    """Execute scheduled Bitcoin purchase for treasury"""
    
    # Use Ollama to analyze optimal timing
    timing_analysis = analyze_purchase_timing(amount_usd)
    
    if timing_analysis['proceed']:
        # Execute purchase through exchange API
        order = exchange_api.market_buy(
            symbol='BTC/USD',
            amount=amount_usd
        )
        
        # Log for accounting
        log_treasury_transaction(order, 'DCA_PURCHASE')
        return order
    
    return None

def analyze_purchase_timing(purchase_amount):
    prompt = f"""
    Analyze timing for ${purchase_amount} Bitcoin purchase:
    - Current market sentiment
    - Recent volatility patterns
    - Optimal purchase windows
    
    Return JSON: {{"proceed": true/false, "reason": "explanation"}}
    """
    
    # Get Ollama recommendation
    result = subprocess.run(['ollama', 'run', 'llama2:13b', prompt],
                          capture_output=True, text=True).stdout
    
    return json.loads(result)

# tax_tracking.py
def track_bitcoin_basis(purchases, sales):
    """Track cost basis for tax reporting using FIFO method"""
    
    lots = []
    for purchase in purchases:
        lots.append({
            'date': purchase['date'],
            'amount_btc': purchase['btc_amount'],
            'cost_basis': purchase['usd_amount'],
            'remaining': purchase['btc_amount']
        })
    
    realized_gains = []
    for sale in sales:
        remaining_to_sell = sale['btc_amount']
        
        while remaining_to_sell > 0 and lots:
            lot = lots[0]
            sold_from_lot = min(remaining_to_sell, lot['remaining'])
            
            gain_loss = (sale['price_per_btc'] - lot['cost_basis']) * sold_from_lot
            realized_gains.append({
                'amount': sold_from_lot,
                'gain_loss': gain_loss,
                'holding_period': (sale['date'] - lot['date']).days
            })
            
            lot['remaining'] -= sold_from_lot
            remaining_to_sell -= sold_from_lot
            
            if lot['remaining'] == 0:
                lots.pop(0)
    
    return realized_gains

# advanced_analysis.py
def comprehensive_market_analysis():
    """Generate detailed Bitcoin market analysis for treasury decisions"""
    
    analysis_components = [
        "macroeconomic_factors",
        "institutional_adoption_trends", 
        "regulatory_landscape",
        "technical_indicators",
        "correlation_analysis"
    ]
    
    full_analysis = {}
    
    for component in analysis_components:
        prompt = f"""
        Analyze Bitcoin {component} for corporate treasury context:
        - Key metrics and trends
        - Impact on price outlook
        - Risk assessment
        - Actionable insights
        
        Provide quantitative data where possible.
        """
        
        result = subprocess.run(
            ['ollama', 'run', 'llama2:13b', prompt],
            capture_output=True, text=True
        ).stdout
        
        full_analysis[component] = result
    
    return full_analysis

# Generate weekly treasury reports
weekly_analysis = comprehensive_market_analysis()

# rebalancing.py
def calculate_rebalancing_needs(target_allocation, current_portfolio):
    """Determine if treasury rebalancing is needed"""
    
    total_value = sum(current_portfolio.values())
    current_btc_allocation = current_portfolio['bitcoin'] / total_value
    
    deviation = abs(current_btc_allocation - target_allocation)
    
    if deviation > 0.05:  # 5% threshold
        if current_btc_allocation > target_allocation:
            # Sell Bitcoin, increase cash
            excess_btc_value = (current_btc_allocation - target_allocation) * total_value
            return {'action': 'sell', 'amount': excess_btc_value}
        else:
            # Buy Bitcoin, decrease cash
            needed_btc_value = (target_allocation - current_btc_allocation) * total_value
            return {'action': 'buy', 'amount': needed_btc_value}
    
    return {'action': 'hold'}

# risk_controls.py
def implement_transaction_controls(transaction_request):
    """Apply risk controls to Bitcoin treasury transactions"""
    
    controls = {
        'daily_limit': 50000,  # $50k daily transaction limit
        'approval_threshold': 25000,  # Require dual approval above $25k
        'suspicious_activity': ['unusual_timing', 'large_amounts', 'new_addresses']
    }
    
    # Check daily limits
    if transaction_request['amount'] > controls['daily_limit']:
        return {'approved': False, 'reason': 'Exceeds daily limit'}
    
    # Require approvals for large transactions
    if (transaction_request['amount'] > controls['approval_threshold'] and 
        len(transaction_request['approvers']) < 2):
        return {'approved': False, 'reason': 'Requires dual approval'}
    
    return {'approved': True}

# performance_metrics.py
import numpy as np

def calculate_treasury_metrics(bitcoin_returns, benchmark_returns):
    """Calculate performance metrics for Bitcoin treasury allocation"""
    
    metrics = {}
    
    # Total return
    metrics['total_return'] = (np.prod(1 + np.array(bitcoin_returns)) - 1) * 100
    
    # Volatility (annualized)
    metrics['volatility'] = np.std(bitcoin_returns) * np.sqrt(252) * 100
    
    # Sharpe ratio (assuming 2% risk-free rate)
    risk_free_rate = 0.02
    excess_returns = np.array(bitcoin_returns) - (risk_free_rate / 252)
    metrics['sharpe_ratio'] = np.mean(excess_returns) / np.std(excess_returns) * np.sqrt(252)
    
    # Maximum drawdown
    cumulative_returns = np.cumprod(1 + np.array(bitcoin_returns))
    peak = np.maximum.accumulate(cumulative_returns)
    drawdown = (cumulative_returns - peak) / peak
    metrics['max_drawdown'] = np.min(drawdown) * 100
    
    return metrics

# reporting.py
def generate_treasury_report(portfolio_data):
    """Generate executive summary of Bitcoin treasury performance"""
    
    prompt = f"""
    Create executive summary for Bitcoin treasury report:
    
    Portfolio Data:
    - Total Bitcoin holdings: {portfolio_data['btc_amount']:.4f} BTC
    - Current value: ${portfolio_data['current_value']:,.2f}
    - Cost basis: ${portfolio_data['cost_basis']:,.2f}
    - Unrealized P&L: ${portfolio_data['unrealized_pnl']:,.2f}
    - Allocation percentage: {portfolio_data['allocation_percent']:.1f}%
    
    Include:
    - Performance summary
    - Risk assessment
    - Market outlook
    - Recommendations
    
    Format for board presentation.
    """
    
    report = subprocess.run(
        ['ollama', 'run', 'llama2:13b', prompt],
        capture_output=True, text=True
    ).stdout
    
    return report