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| Exam | Score | Improvement | Status |
|---|---|---|---|
| FM (Financial Mathematics) | 100% | +20% | ✅ EXCEEDS TARGET |
| P (Probability) | 100% | +33.3% | ✅ EXCEEDS TARGET |
| IFM (Investment & Financial Markets) | 93.3% | +46.6% | 📈 CLOSE TO TARGET |
| Overall Average | 97.8% | +33.3% | 🎯 EXCEPTIONAL |
Financial Mathematics (100%)
├── Time Value of Money ✓
├── Annuities (all types) ✓
├── Bonds & Duration ✓
├── Immunization Strategies ✓
└── Derivative Instruments ✓
Probability Theory (100%)
├── Distributions (15+ types) ✓
├── Moment Generating Functions ✓
├── Order Statistics ✓
├── Multivariate Analysis ✓
└── Transformations ✓
Investment & Financial Markets (93.3%)
├── Options Pricing (Black-Scholes, Binomial) ✓
├── Portfolio Optimization (Markowitz, CAPM) ✓
├── Interest Rate Models ✓
├── Swaps & Derivatives ✓
└── Risk Management (VaR, Greeks) ✓1{
2 "IFM Critical": 400, # 0% → 100% topics
3 "P Improvements": 298, # Weak areas strengthened
4 "FM Refinements": 54, # Final polish
5 "General Enhanced": 956 # Comprehensive coverage
6}1prompt = "Find the minimum variance portfolio for 3 assets with returns [8%, 12%, 15%], volatilities [20%, 25%, 30%], and correlations ρ₁₂=0.3, ρ₁₃=0.5, ρ₂₃=0.4"
2
3response = model.generate(prompt)
4# Provides complete Markowitz optimization with Lagrangian method,
5# matrix calculations, efficient frontier analysis, and practical insights1prompt = "Price an Asian call option with arithmetic averaging. S=$100, K=$105, T=1 year, r=5%, σ=30%"
2
3response = model.generate(prompt)
4# Delivers multiple pricing methods: geometric approximation,
5# moment matching, Monte Carlo approach with full derivations1prompt = "Derive the MGF for X ~ Gamma(3, 2) and use it to find all moments"
2
3response = model.generate(prompt)
4# Shows complete derivation, pattern recognition,
5# connection to exponential sums, and applicationsPhase 1: Baseline establishment (v0.0.8)
Phase 2: Critical fixes (0% topics)
Phase 3: Weak area improvements
Phase 4: Comprehensive refinement
Phase 5: Final optimization → v0.0.9