IB Analytics MCP Server

#!/usr/bin/env python3 """ Interest Rate Scenario Analysis for Treasury STRIP Multiple scenarios for rate changes and their impact on bond value """ from datetime import datetime import math def calculate_bond_price(face_value, years_to_maturity, yield_rate): """Calculate zero-coupon bond price given yield""" return face_value / math.pow(1 + yield_rate, years_to_maturity) def analyze_rate_scenarios(): """Analyze multiple interest rate scenarios""" print("=" * 80) print("INTEREST RATE SCENARIO ANALYSIS") print("US Treasury STRIP - S 0 11/15/40") print("=" * 80) print() # Bond details face_value = 50000 current_price = 24650.96 purchase_price = 23457 purchase_date = datetime(2025, 7, 10) maturity_date = datetime(2040, 11, 15) current_date = datetime(2025, 10, 5) years_to_maturity = (maturity_date - current_date).days / 365.25 # Calculate current implied yield current_yield = math.pow(face_value / current_price, 1 / years_to_maturity) - 1 print("CURRENT POSITION") print("-" * 80) print(f"Security: S 0 11/15/40 (Treasury STRIP)") print(f"Face Value: ${face_value:,.2f}") print(f"Current Price: ${current_price:,.2f}") print(f"Purchase Price: ${purchase_price:,.2f}") print(f"Years to Maturity: {years_to_maturity:.2f} years") print(f"Current Yield (YTM): {current_yield * 100:.3f}%") print(f"Unrealized P&L: ${current_price - purchase_price:,.2f}") print() print("=" * 80) print("UNDERSTANDING INTEREST RATE RISK") print("=" * 80) print() print("Zero-Coupon Bond Mechanics:") print("-" * 80) print("• Zero-coupon bonds are MOST sensitive to interest rate changes") print("• Longer maturity = Higher sensitivity (yours: 15 years)") print("• When rates ↑ → Bond prices ↓ (inverse relationship)") print("• When rates ↓ → Bond prices ↑") print() print("Duration Concept:") print("-" * 80) print(f"• Your bond's duration: ~{years_to_maturity:.1f} years") print(f"• Rule of thumb: 1% rate change → ~{years_to_maturity:.1f}% price change") print(f"• Example: Rates ↑1% → Price ↓~{years_to_maturity:.1f}% (${current_price * years_to_maturity / 100:,.2f})") print() print("=" * 80) print("SCENARIO ANALYSIS: INTEREST RATE CHANGES") print("=" * 80) print() # Define scenarios scenarios = [ {'name': 'Severe Rate Hike', 'change': 0.03, 'probability': '5%', 'likelihood': 'Low'}, {'name': 'Significant Rate Hike', 'change': 0.02, 'probability': '15%', 'likelihood': 'Moderate'}, {'name': 'Moderate Rate Hike', 'change': 0.01, 'probability': '25%', 'likelihood': 'Likely'}, {'name': 'Minor Rate Hike', 'change': 0.005, 'probability': '20%', 'likelihood': 'Likely'}, {'name': 'No Change (Base Case)', 'change': 0.00, 'probability': '10%', 'likelihood': 'Possible'}, {'name': 'Minor Rate Cut', 'change': -0.005, 'probability': '10%', 'likelihood': 'Possible'}, {'name': 'Moderate Rate Cut', 'change': -0.01, 'probability': '10%', 'likelihood': 'Possible'}, {'name': 'Significant Rate Cut', 'change': -0.02, 'probability': '4%', 'likelihood': 'Low'}, {'name': 'Severe Rate Cut', 'change': -0.03, 'probability': '1%', 'likelihood': 'Very Low'}, ] print(f"{'Scenario':<30} {'Rate Change':<15} {'New Yield':<12} {'New Price':<15} " f"{'P&L Change':<15} {'Total P&L':<15}") print("-" * 80) scenario_results = [] for scenario in scenarios: rate_change = scenario['change'] new_yield = current_yield + rate_change new_price = calculate_bond_price(face_value, years_to_maturity, new_yield) pnl_change = new_price - current_price total_pnl = new_price - purchase_price scenario_results.append({ 'name': scenario['name'], 'rate_change': rate_change, 'new_yield': new_yield, 'new_price': new_price, 'pnl_change': pnl_change, 'total_pnl': total_pnl, 'probability': scenario['probability'], 'likelihood': scenario['likelihood'] }) # Format rate change rate_str = f"+{rate_change*100:.1f}%" if rate_change > 0 else f"{rate_change*100:.1f}%" print(f"{scenario['name']:<30} {rate_str:<15} {new_yield*100:<11.3f}% " f"${new_price:<14,.2f} ${pnl_change:<14,.2f} ${total_pnl:<14,.2f}") print() print("=" * 80) print("DETAILED SCENARIO BREAKDOWN") print("=" * 80) print() # Group scenarios by direction rate_hikes = [s for s in scenario_results if s['rate_change'] > 0] rate_cuts = [s for s in scenario_results if s['rate_change'] < 0] base_case = [s for s in scenario_results if s['rate_change'] == 0] print("📈 RATE HIKE SCENARIOS (Unfavorable)") print("-" * 80) for scenario in rate_hikes: print() print(f"SCENARIO: {scenario['name']}") print(f" Rate Change: {scenario['rate_change']*100:+.1f}%") print(f" New Yield: {scenario['new_yield']*100:.3f}%") print(f" New Bond Price: ${scenario['new_price']:,.2f}") print(f" Price Change: ${scenario['pnl_change']:,.2f} ({scenario['pnl_change']/current_price*100:.1f}%)") print(f" Total P&L: ${scenario['total_pnl']:,.2f}") print(f" Probability: {scenario['probability']}") print(f" Likelihood: {scenario['likelihood']}") # Decision guidance if scenario['total_pnl'] < 0: print(f" ⚠️ WARNING: Would result in net loss") elif scenario['pnl_change'] < -1000: print(f" ⚠️ CAUTION: Significant unrealized loss") print() print() print("➡️ BASE CASE (Current Rates)") print("-" * 80) for scenario in base_case: print() print(f"SCENARIO: {scenario['name']}") print(f" Rate Change: {scenario['rate_change']*100:+.1f}%") print(f" Current Yield: {scenario['new_yield']*100:.3f}%") print(f" Current Price: ${scenario['new_price']:,.2f}") print(f" Total P&L: ${scenario['total_pnl']:,.2f}") print() print() print("📉 RATE CUT SCENARIOS (Favorable)") print("-" * 80) for scenario in rate_cuts: print() print(f"SCENARIO: {scenario['name']}") print(f" Rate Change: {scenario['rate_change']*100:+.1f}%") print(f" New Yield: {scenario['new_yield']*100:.3f}%") print(f" New Bond Price: ${scenario['new_price']:,.2f}") print(f" Price Change: ${scenario['pnl_change']:,.2f} ({scenario['pnl_change']/current_price*100:+.1f}%)") print(f" Total P&L: ${scenario['total_pnl']:,.2f}") print(f" Probability: {scenario['probability']}") print(f" Likelihood: {scenario['likelihood']}") # Upside potential if scenario['pnl_change'] > 2000: print(f" ✅ OPPORTUNITY: Significant price appreciation") print() print("=" * 80) print("RISK/REWARD ANALYSIS") print("=" * 80) print() # Calculate weighted average outcome total_prob = sum(float(s['probability'].rstrip('%')) for s in scenario_results) weighted_price = sum( s['new_price'] * float(s['probability'].rstrip('%')) / 100 for s in scenario_results ) weighted_pnl = weighted_price - purchase_price print("EXPECTED VALUE ANALYSIS") print("-" * 80) print(f"Weighted Average Price: ${weighted_price:,.2f}") print(f"Weighted Average P&L: ${weighted_pnl:,.2f}") print(f"Current P&L: ${current_price - purchase_price:,.2f}") print() # Calculate upside/downside best_case = max(scenario_results, key=lambda x: x['new_price']) worst_case = min(scenario_results, key=lambda x: x['new_price']) print("BEST/WORST CASE SCENARIOS") print("-" * 80) print(f"Best Case ({best_case['name']}):") print(f" Price: ${best_case['new_price']:,.2f}") print(f" Upside: ${best_case['new_price'] - current_price:,.2f} ({(best_case['new_price']/current_price - 1)*100:+.1f}%)") print() print(f"Worst Case ({worst_case['name']}):") print(f" Price: ${worst_case['new_price']:,.2f}") print(f" Downside: ${worst_case['new_price'] - current_price:,.2f} ({(worst_case['new_price']/current_price - 1)*100:.1f}%)") print() # Risk/Reward Ratio upside_potential = best_case['new_price'] - current_price downside_risk = current_price - worst_case['new_price'] risk_reward_ratio = upside_potential / downside_risk if downside_risk > 0 else 0 print(f"Risk/Reward Ratio: {risk_reward_ratio:.2f}:1") print() print("=" * 80) print("PROBABILITY-WEIGHTED OUTCOMES") print("=" * 80) print() # Group by outcome type loss_scenarios = [s for s in scenario_results if s['total_pnl'] < 0] small_gain_scenarios = [s for s in scenario_results if 0 <= s['total_pnl'] < 2000] large_gain_scenarios = [s for s in scenario_results if s['total_pnl'] >= 2000] loss_prob = sum(float(s['probability'].rstrip('%')) for s in loss_scenarios) small_gain_prob = sum(float(s['probability'].rstrip('%')) for s in small_gain_scenarios) large_gain_prob = sum(float(s['probability'].rstrip('%')) for s in large_gain_scenarios) print(f"{'Outcome Category':<25} {'Probability':<15} {'Scenarios'}") print("-" * 80) print(f"{'Net Loss':<25} {loss_prob:<14.0f}% {len(loss_scenarios)} scenarios") print(f"{'Small Gain ($0-2K)':<25} {small_gain_prob:<14.0f}% {len(small_gain_scenarios)} scenarios") print(f"{'Large Gain (>$2K)':<25} {large_gain_prob:<14.0f}% {len(large_gain_scenarios)} scenarios") print() print("=" * 80) print("DECISION FRAMEWORK") print("=" * 80) print() print("RISK TOLERANCE ASSESSMENT") print("-" * 80) print() # Conservative investor print("FOR CONSERVATIVE INVESTORS:") print(" Concerned about: Potential loss scenarios ({:.0f}% probability)".format(loss_prob)) print(f" Downside risk: ${downside_risk:,.2f} (worst case)") print(f" Recommendation: Consider selling if rate hikes expected") print() # Moderate investor print("FOR MODERATE INVESTORS:") print(" Focus on: Balanced risk/reward") print(f" Expected value: ${weighted_pnl:,.2f}") print(f" Recommendation: Hold if comfortable with volatility") print() # Aggressive investor print("FOR AGGRESSIVE INVESTORS:") print(" Opportunity: Large gains ({:.0f}% probability)".format(large_gain_prob)) print(f" Upside potential: ${upside_potential:,.2f}") print(f" Recommendation: Hold or buy more if rate cuts expected") print() print("=" * 80) print("HEDGING STRATEGIES") print("=" * 80) print() strategies = [ { 'name': 'Do Nothing (Hold)', 'action': 'Keep current position', 'pros': 'No transaction costs, full upside potential', 'cons': 'Full downside exposure', 'best_for': 'Confident in stable/falling rates', }, { 'name': 'Sell Entire Position', 'action': 'Exit now at current price', 'pros': 'Lock in current gain, eliminate rate risk', 'cons': 'Forfeit future gains, $5 commission', 'best_for': 'Expecting significant rate hikes', }, { 'name': 'Partial Sale (50%)', 'action': 'Sell half, keep half', 'pros': 'Reduce risk while maintaining upside', 'cons': 'Partial commission, half exposure', 'best_for': 'Uncertain about rate direction', }, { 'name': 'Stop-Loss Order', 'action': 'Set automatic sell if price drops X%', 'pros': 'Limits downside automatically', 'cons': 'May trigger on temporary volatility', 'best_for': 'Want protection with minimal effort', }, { 'name': 'Ladder Strategy', 'action': 'Sell and buy multiple maturities', 'pros': 'Diversifies rate risk across time', 'cons': 'Multiple commissions, complexity', 'best_for': 'Long-term conservative approach', }, ] for i, strategy in enumerate(strategies, 1): print(f"STRATEGY {i}: {strategy['name']}") print("-" * 80) print(f"Action: {strategy['action']}") print(f"Pros: {strategy['pros']}") print(f"Cons: {strategy['cons']}") print(f"Best For: {strategy['best_for']}") print() print("=" * 80) print("MARKET CONTEXT & OUTLOOK") print("=" * 80) print() print("CURRENT RATE ENVIRONMENT (October 2025)") print("-" * 80) print("• Fed policy stance: [Consult current market data]") print("• Inflation trends: [Check latest CPI data]") print("• Economic growth: [Review GDP forecasts]") print("• Market expectations: [See Fed funds futures]") print() print("FACTORS SUPPORTING RATE CUTS:") print(" ✓ Economic slowdown") print(" ✓ Inflation returning to target") print(" ✓ Recession concerns") print(" → FAVORABLE for bond prices ↑") print() print("FACTORS SUPPORTING RATE HIKES:") print(" ✗ Persistent inflation") print(" ✗ Strong economic growth") print(" ✗ Wage growth acceleration") print(" → UNFAVORABLE for bond prices ↓") print() print("=" * 80) print("RECOMMENDATIONS BY SCENARIO") print("=" * 80) print() print("IF YOU EXPECT RATES TO RISE:") print("-" * 80) print("1. Consider selling now to avoid losses") print("2. Wait for better entry points (lower prices)") print("3. Switch to shorter-duration bonds") print("4. Diversify into inflation-protected securities") print() print("IF YOU EXPECT RATES TO FALL:") print("-" * 80) print("1. HOLD current position for capital gains") print("2. Consider buying more at current levels") print("3. Extend duration for greater sensitivity") print("4. Ride the price appreciation") print() print("IF YOU'RE UNCERTAIN:") print("-" * 80) print("1. Partial sale (50%) to reduce risk") print("2. Set stop-loss at -5% or -10%") print("3. Build ladder with multiple maturities") print("4. Monitor Fed communications closely") print() print("=" * 80) print("SUMMARY & ACTION ITEMS") print("=" * 80) print() print(f"Current Position: ${current_price:,.2f} (${current_price - purchase_price:,.2f} unrealized gain)") print(f"Rate Risk: ~{years_to_maturity:.1f}% price change per 1% rate move") print(f"Best Case: ${best_case['new_price']:,.2f} (+${best_case['new_price'] - current_price:,.2f})") print(f"Worst Case: ${worst_case['new_price']:,.2f} (-${current_price - worst_case['new_price']:,.2f})") print(f"Expected Value: ${weighted_price:,.2f}") print() print("NEXT STEPS:") print("1. ✓ Monitor Federal Reserve policy announcements") print("2. ✓ Track 10-year Treasury yields as benchmark") print("3. ✓ Review inflation and economic data monthly") print("4. ✓ Reassess position after major rate changes") print("5. ✓ Consider your personal risk tolerance and time horizon") print() print("=" * 80) def main(): analyze_rate_scenarios() if __name__ == '__main__': main()