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Name: Vimal Dhama |
Primary: Quant Finance, Algo Trading |
Sharpe Ratio: 2.1 |
CFA Level I (In Progress) |
Mode: ALPHA GENERATION |
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Bridging decision‑making under uncertainty, scalable optimization, and physics‑informed ML — with a dash of quantum.
| Topic | Focus | Tools / Frameworks |
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| 🤖 Reinforcement Learning (RL) | Policy optimization, exploration–exploitation, model‑based & safe/offline RL | PyTorch, Gymnasium, RLlib |
| 📐 Measure Theory & Advanced Probability | Foundations for stochastic processes, convergence, filtrations | Billingsley, Kallenberg (texts), custom notes |
| 🌊 Fourier Neural Operators (FNOs) | Operator learning for PDEs, generalization across domains | PyTorch, FNO implementations, NVIDIA Modulus |
| ⚛️ Quantum Computing | Finance, optimization, and simulation algorithms (VQE/QAOA) | Qiskit, Qiskit Finance |
| 🎯 Bayesian & Probabilistic ML | Posterior inference, UQ, hierarchical modeling (VI vs MCMC) | PyMC, NumPyro, Pyro |
| 🧩 Mixed‑Integer Programming (MIP) | Clustering, scheduling, portfolio optimization; decomposition & cuts | Pyomo, OR‑Tools, Gurobi/CPLEX |
| 🚀 High‑Performance Computing (HPC) | Parallel simulation/optimization, memory‑aware acceleration | MPI, Dask, CUDA, JAX |
| 🧮 Numerical Linear Algebra | Krylov methods, preconditioning, low‑rank structure | SciPy, PETSc, cuBLAS |
| 🧭 Convex & Non‑Convex Optimization | Convex relaxations, first‑order methods, metaheuristics | CVXPY, NLopt, Optuna |
| 📈 Stochastic Calculus | SDEs, Itô calculus, pricing/risk for quant finance | SDE solvers, Monte Carlo (GBM/Heston) |
Quick notes
- RL: from value‑based to policy‑gradient; emphasis on safe/offline RL for real constraints
- Measure theory: martingales, stopping times, stochastic integration
- FNOs: fast PDE surrogates via operator learning
- Quantum: encode finance/optimization as Ising problems; variational solvers
- Bayesian ML: priors, calibration, VI–MCMC trade‑offs
- MIP: cutting planes + hybrid MILP/heuristics for scale
- HPC: profiling, GPU/TPU acceleration for large workloads
- LinAlg: CG/GMRES, spectral properties, preconditioners
- Optimization: relaxations for non‑convexity; metaheuristics for combinatorics
- Stochastic calculus: risk‑neutral pricing, Greeks, variance reduction
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Advanced Algorithmic Trading Platform impact:
automation_gain: "40% ↓ manual trades"
sharpe_improvement: "7-10% ↑ baseline"
alpha_generation: "15%+ consistent"
risk_management: "Real-time monitoring"
features:
- Multi-strategy deployment
- Advanced risk assessment
- TradingView integration
- Portfolio optimization
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AI-Powered Financial Oracle achievements:
accuracy_boost: "+12% vs traditional"
ai_integration: "GPT-4 + Custom Models"
processing: "Real-time analysis"
capabilities: "Multi-modal insights"
features:
- Sentiment analysis engine
- Predictive market modeling
- Risk-adjusted allocation
- Economic indicator integration
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DeFi Analytics Powerhouse capabilities:
defi_protocols: "50+ supported"
yield_optimization: "Smart routing"
risk_scoring: "ML-powered analysis"
liquidity_analysis: "Cross-chain insights"
features:
- Yield farming optimization
- Smart contract auditing
- Token economics modeling
- Cross-chain analytics
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Advanced Development Environment development_stack:
environment: "GitHub Codespaces Cloud"
ai_assistant: "GitHub Copilot"
capabilities: "Advanced code generation"
integration: "DevOps + CI/CD pipelines"
dev_skills:
- AI-assisted programming workflows
- Cloud-native development practices
- Collaborative coding environments
- Advanced Git and automation
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Next-Gen AI Solutions Platform highlights:
ai_models: "Cutting-edge architectures"
deployment: "Fast, scalable, secure"
applications: "Finance, NLP, Vision"
impact: "Human-centric intelligence"
features:
- Real-time analytics
- Custom AI model training
- Multi-domain support
- API integrations
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Advanced Trading Intelligence Platform capabilities:
indicators: "15+ technical indicators"
strategies: "Algorithmic trading systems"
intelligence: "Real-time market analysis"
deployment: "Streamlit web interface"
features:
- Comprehensive financial analysis
- Multi-indicator strategies
- Live market intelligence
- Interactive visualizations
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Medical AI iOS Application innovation:
domain: "Healthcare + AI"
platform: "iOS Native"
ml_models: "Deep learning detection"
impact: "Life-saving diagnostics"
features:
- Real-time ECG analysis
- Arrhythmia classification
- Mobile edge computing
- Clinical-grade accuracy
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Advanced Portfolio Optimization methodology:
approach: "Cardinality constraints"
optimization: "Mixed-integer programming"
objective: "Risk-adjusted returns"
constraints: "Real-world limitations"
features:
- Integer programming solvers
- Efficient frontier analysis
- Transaction cost modeling
- Asset allocation strategies
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📊 CODE COMMITS |
⭐ REPOSITORY STARS |
🍴 PROJECT FORKS |
🔥 CODING STREAK |
📈 PULL REQUESTS |
🎯 ISSUES RESOLVED |
🔗 Professional Network |
💻 Code Repository |
📧 Direct Contact |
📋 On Request |
"In quantitative finance, alpha emerges not from avoiding risk, but from understanding, modeling, and systematically exploiting market inefficiencies through advanced analytics and AI-driven insights."
⚡ SYSTEM STATUS: ONLINE AND OPTIMIZED FOR COLLABORATION ⚡
🚀 NEURAL NETWORKS ACTIVE - BUILDING THE FUTURE OF QUANTITATIVE FINANCE 🚀
