Quant Finance Self-Study

Documenting my journey of learning quantitative finance from scratch.

Topics Covered

Day 1: Law of Large Numbers & Central Limit Theorem
Day 2: Bayes Theorem, Monty Hall Problem & Probability Distributions
Day 3: Real Market Data Analysis — Nifty 50 stocks, Returns, Volatility, Sharpe Ratio
Day 4: Portfolio Optimization & Efficient Frontier
Day 5: Moving Average Crossover Strategy & Backtesting

First-year CSE (AI/ML) student at VIT Bhopal, exploring the intersection of mathematics, statistics, and financial markets.

Actively updating — one notebook per day

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
.ipynb_checkpoints		.ipynb_checkpoints
README.md		README.md
day1-probability-basics.ipynb		day1-probability-basics.ipynb
day2-bayes-conditional-probability.ipynb		day2-bayes-conditional-probability.ipynb
day3-market-data.ipynb		day3-market-data.ipynb
day4-portfolio-analysis.ipynb		day4-portfolio-analysis.ipynb
day5-trading-strategies.ipynb		day5-trading-strategies.ipynb
day6-rsi-indicator.ipynb		day6-rsi-indicator.ipynb