Python for Financial Markets Analysis

Explore Python basics in a Jupyter notebook, learning that everything is an object, and master core data types—integers, floats, and strings—using type and print for numeric and open-high-low-close data.

Learn to define and manipulate variables in Python, using integers, floats, and strings, with naming conventions. Explore basic operations such as addition, division, and modulus with practical stock examples.

Explore how Python dictionaries store key-value pairs, access keys, values, and items, and add, update, or delete entries while checking their length.

Create beautifully customized financial market charts by adding titles, axis labels, grid, themes, and color schemes, and perform comparative analysis across multiple stocks using interactive plots.

Use histogram charts to examine the distribution of stock returns, with percentage change to analyze daily returns. Compare multiple securities and adjust histogram bins for clearer insights.

Learn to tell a data story by adding annotations to a BTC USD prices chart with Plotly Express, using text annotations and arrows to highlight peaks and troughs in 2021.

Create a new data frame from the index, extract day, day name, and quarter, reindex to include missing business dates, and handle missing data with backfill and forward fill.

Filter a five-year Apple time series using pandas to compute a boolean column where open is greater than close, then count how many times this condition holds.

Translate SQL where clauses by using pandas' query method to filter dataframes with readable, plain-English conditions, supporting and/or logic and Python variable references.

learn to compute the rate of return for a portfolio by normalizing prices to 100, calculating simple returns, and using numpy dot products with equal or weighted allocations.

Compute annualized returns from daily returns across multiple securities using pandas, then sort, visualize with a bar chart, and compare assets like Tesla, Google, Amazon, and Bitcoin.

Calculate security risk by comparing mean and volatility through daily returns and log returns, then annualize using 250 trading days in Python with NumPy and plotting.

Learn to prepare and normalize stock data for a weighted market index, save to a CSP file, and create interactive Apple-to-Apple comparisons with a base value of 100.

Calculate weights of constituents over time by normalizing stock prices and dividing by row sums to form a price-weighted index.

Analyze stock data with rolling means and 100-day moving averages to smooth prices and reveal trends, then compute returns and returns deviations to assess expected returns and price behavior.

Compare five-year adjusted close returns of Apple, Amazon, Facebook, Google, and Microsoft; analyze daily changes, correlations, scatter plots, and kernel density estimates.