Advanced Quantitative Finance Techniques: A Comprehensive Analysis

Welcome to my Certification in Quantitative Finance (CQF) project repository. This repository is a culmination of rigorous work and dedication in applying advanced quantitative techniques to solve complex financial problems. Below is an overview of the key projects included in this repository:

1. Deep Learning for Asset Price Prediction

• Project Description: This project leverages Long Short-Term Memory (LSTM) networks to predict upward price movements in assets. The project covers the entire pipeline from data collection and preprocessing to model evaluation and backtesting.
• Highlights:
  • Data Collection & Preprocessing: Utilized Yahoo Finance and Bloomberg APIs for reliable data acquisition.
  • Technical Indicators & Financial Ratios: Implemented diverse indicators to enhance feature selection.
  • Dimensional Reduction: Applied techniques such as SOM, UMAP, and K-Means Clustering.
  • LSTM Model Development: Focused on hyperparameter optimization for improved model accuracy.
  • Performance Evaluation: Utilized TensorBoard for detailed model analysis and backtesting for strategy validation.

2. Portfolio Optimization and Value at Risk (VaR)

• Project Description: This project delves into portfolio optimization using mean-variance analysis, efficient frontier construction, and calculating Value at Risk (VaR) to assess portfolio risks.
• Highlights:
  • Optimization Techniques: Solved optimization problems with constraints to achieve optimal portfolio allocations.
  • Efficient Frontier Analysis: Constructed and visualized the efficient frontier to understand risk-return trade-offs.
  • VaR Calculations: Implemented both analytical and Monte Carlo methods to compute VaR, and performed backtesting to validate the results.
  • Risk Management: Assessed portfolio risks using Expected Shortfall (ES) and conducted scenario analysis.

3. Asian & Lookback Options

• Project Description: This notebook explores the pricing of exotic options such as Asian and Lookback options using Monte Carlo simulations and the Euler-Maruyama method.
• Highlights:
  • Monte Carlo Simulations: Generated numerous asset paths to accurately price path-dependent options.
  • Euler-Maruyama Method: Applied this numerical technique to approximate solutions for stochastic differential equations (SDEs).
  • Geometric & Arithmetic Averaging: Compared different averaging techniques for pricing Asian options.
  • Fixed & Floating Strike Options: Analyzed the implications of using fixed versus floating strike prices in Lookback options.

4. Machine Learning and Linear Regression Analysis

• Project Description: This notebook implements machine learning techniques, with a particular focus on linear regression, to model and predict financial outcomes.
• Highlights:
  • Data Preprocessing: Extensive data cleaning and feature engineering to prepare the dataset.
  • Linear Regression Implementation: Detailed analysis of the regression model, including assumptions testing and interpretation.
  • Model Evaluation: Employed various metrics to assess model performance, and explored regularization techniques to prevent overfitting.
  • Extensions: Discussed possible extensions into more advanced machine learning models.

Note: This repository is intended for educational purposes and to showcase my capabilities in quantitative finance. The code and materials provided here are not to be used for future CQF exams or assessments.

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I hope you find these projects insightful and reflective of my skills and dedication to quantitative finance. Each project is carefully documented and structured to provide a deep understanding of the methodologies used and the results obtained.