Quantitative Finance: Complete Guide In Python

Install python and anaconda on mac, set up vscode or pycharm, create and activate a conda environment named course with python 3.12.7, and run a jupyter notebook with ipykernel.

Explore time value of money by comparing simple and compound interest, calculating present and future values, and applying a bond valuation example to show how interest rates affect investments.

Master the time value of money by calculating future value and present value with compounding and discount rates. Apply the FV and PV concepts with rate and time.

Value a bond by computing the present value of coupons and the face value at the current market rate, then compare to price to judge undervaluation or overvaluation.

Explore measuring risk in investment returns with variance and standard deviation. Learn to compute mean return, squared deviations, and dispersion to assess how far returns spread from the mean.

Explore covariance between two asset returns, see how positive, negative, and zero covariance reflect the relationship, and learn the covariance formula with means, observations, and n minus 1 denominator.

Learn to fetch three years of stock closing prices from Yahoo Finance and plot them with Plotly, comparing multiple tickers in a Python notebook.

Compare ETF performance over three years by calculating daily and cumulative returns for equity, bond, commodity, and real estate ETFs, and visualize the results with a four-panel Plotly plot.

Visualize real estate investment trusts over three years with price returns and dividend yields. Compute daily and cumulative returns and create line and bar charts using Python, yfinance, and plotly.

Learn to analyze risk and return by computing log returns, visualizing return distributions, and plotting rolling 30-day volatility with annualized standard deviation using Python.

Define a portfolio as a mix of assets—stocks, bonds, commodities, real estate, cash equivalents, and funds—designed for diversification and balanced by return, risk, and the Sharpe ratio.

Explore how indifference curves capture risk-return preferences and how investors combine risk-free assets with an optimal risky portfolio along the capital allocation line.

explains the capital market line and market portfolio, showing how the CML is tangent to the market efficient frontier at the risk-free rate and reflects the market portfolio's Sharpe ratio.

Learn to maximize the Sharpe ratio by optimizing a five-asset portfolio using SciPy's minimize, including negative Sharpe implementation, bounds, and the path to the global minimum variance portfolio.

Generate the efficient frontier by computing minimum-risk portfolios for specified target returns using mean-variance optimization, covariance matrices, and constraint handling.

Apply the capital asset pricing model to estimate an asset's expected return using the risk-free rate, beta, and the market risk premium, noting CAPM compensates only for systematic risk.

Identify the security market line as CAPM's graphical representation that links beta to expected returns using the risk-free rate and market premium, guiding whether securities are undervalued or overpriced.

Calculate beta for selected stocks using the covariance method relative to the S&P 500 market index by computing log returns, covariance, and market variance.

Compute beta using linear regression, with stock price as the dependent variable and market price as the independent variable; beta equals the slope, alpha the intercept, validating against covariance-based results.