Quantitative Finance Path

Welcome!

This repository is my personal roadmap and collection of projects in quantitative finance, financial engineering, and machine learning in finance.

I created this space to organize resources, document hands-on projects, and explore advanced topics in the quant space. If you're someone interested in finance, machine learning, or building trading models – I hope you find something useful here!

📂 Repo Structure

Quantitative-Finance-Path/
├── 00_Resources/                     # Books, Research Papers, Cheat Sheets
│   ├── Books/                        # Key books on finance and statistics
│   ├── Research_Papers/              # Relevant papers and PDFs
│   └── Other_Materials/              # Supplementary learning resources
├── 01_QuantFundamentals/             # Core foundational topics
│   ├── Probability_Statistics/       
│   ├── StochasticCalculus/
│   ├── TimeSeriesAnalysis/
│   └── LinearAlgebra/
├── 02_QuantProjects/                 # Hands-on projects
│   ├── PortfolioOptimization/        # Projects on portfolio strategies
│   ├── OptionPricing/                # Option pricing models and methods
│   ├── RiskModeling/                 # Credit risk, market risk, and VaR models
│   └── MachineLearningInFinance/     # ML/DL models applied to finance
├── 03_RiskManagement/                # Risk assessment and modeling
│   ├── VaR_ES_models/                # Value at Risk and Expected Shortfall models
│   ├── CreditRisk/                   # Credit risk modeling
│   └── LiquidityModels/              # Liquidity risk assessment
├── 04_AdvancedTopics/                # Cutting-edge topics in quant finance
│   ├── DerivativesModeling/
│   ├── AlgorithmicTrading/           # High-frequency trading, market making
│   └── DeepLearningForFinance/       # DL models for asset pricing and forecasting
└── README.md                         # This file

What's Inside?

• Resources – Books, papers, and cheat sheets including the math and strategies behind finance and investing.
• Projects – Real-world projects covering portfolio optimization, option pricing, and machine learning.
• Advanced Trading Algorithms – Explore algorithmic trading strategies, market-making models, and backtesting systems.
• Risk Management – Value at risk (VaR), credit risk modeling, and liquidity risk assessments.
• AI for Finance – Understand how AI can be applied to forecasting, asset pricing, and time series analysis.

How to Use This Repo

• Beginners – Start with the 01_QuantFundamentals section to build a solid understanding of core concepts.
• Advanced Users – Jump to 02_QuantProjects to explore portfolio analysis, risk management, and machine learning models in finance. In addition, the section 04_AdvancedTopics includes advanced algorithmic trading strategies.
• AI/ML in Finance – The MachineLearningInFinance and DeepLearningInFinance folder is for those interested in applying AI techniques to trading, pricing, and forecasting.

Why I Built This Repo

This repo helps me stay organized while learning, and I hope it can serve as a valuable resource for others interested in the field.

Want to Collaborate?

I'm open to collaboration! If you have ideas for new projects or resources, feel free to fork the repo, open an issue, or submit a pull request.