This repository documents my 30-day journey of learning basic level quantitative finance. I have created this repository to keep track of my progress and share my learning experience with others who are interested in this field.
I embarked on this 30-day journey because I realized that I have been going through various tutorials in an unsystematic way, which hasn't been very effective. Through this journey, I aim to consolidate my knowledge by teaching and sharing my learning experience with others. My ultimate goal is to land in a permanent position in quantitative finance, and I believe that this journey will be a stepping stone towards achieving that.
Quantitative finance is a multidisciplinary field that combines finance, mathematics, statistics, and computer science to analyze financial markets and develop trading strategies. In this 30-day journey, I will be learning the basics of quantitative finance and exploring various topics such as statistical arbitrage, machine learning, algorithmic trading, options trading, futures trading, risk management, and portfolio optimization.
Here's a brief overview of the syllabus for my 30-day journey:
Day 1-5: Foundations of Quantitative Finance
Day 6-10: Financial Mathematics
Day 11-15: Risk Management and Portfolio Management
Day 16-20: Derivatives and Financial Modeling
Day 21-25: Time Series Analysis and Econometrics
Day 26-30: Programming and Data Analysis
- Familiarize with basic financial concepts such as time value of money, risk and return, and financial statements.
- Learn about different financial instruments such as stocks, bonds, options, and futures, and understand their characteristics.
- Study statistical concepts such as probability theory, regression analysis, and hypothesis testing, which are crucial in quantitative finance.
- Study the principles of financial mathematics, including compound interest, annuities, and present value calculations.
- Learn about basic mathematical tools used in quantitative finance such as calculus, linear algebra, and stochastic calculus.
- Practice solving problems related to financial mathematics and quantitative methods to build the skills.
- Understand the concept of risk management and different types of risk, including market risk, credit risk, and operational risk.
- Learn about portfolio management techniques such as diversification, asset allocation, and risk-adjusted performance measurement.
- Familiarize with risk management tools such as Value at Risk (VaR) and Conditional Value at Risk (CVaR).
- Study derivatives, including options, futures, and swaps, and learn about their pricing models and applications.
- Learn about financial modeling techniques such as Black-Scholes model for option pricing, binomial option pricing, and Monte Carlo simulations.
- Practice building financial models using spreadsheets or specialized financial modeling software.
- Study time series analysis techniques used in quantitative finance, including autoregressive integrated moving average (ARIMA) models and GARCH models.
- Learn about econometric methods such as regression analysis, panel data analysis, and hypothesis testing in the context of finance.
- Practice analyzing financial time series data and interpreting the results of econometric models.
- Learn a programming language commonly used in quantitative finance and understand basic programming concepts.
- Practice coding financial models, performing data analysis, and visualizing financial data using programming libraries and tools.
- Build a portfolio of quantitative finance projects to showcase the skills to potential employers.