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 Python's integer and float data types, perform basic arithmetic, understand when division yields a float, and convert between int, float, and string using int, float, and string type functions.

Explore Python strings for financial market analytics, learning concatenation, type casting, and formatted output to present tickers, prices, and tables clearly.

Explore Python lists for financial markets: create lists with square brackets, mix data types, convert strings to lists, use zero-based indexing, and apply append, insert, count, remove, sort, and reverse.

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

Learn how to use Python for loops to iterate lists and dictionaries, display stock prices, and format key-value pairs with items() for financial market analysis.

Learn to use the if condition inside a for loop to filter numbers, print evens, and manage branches with else, elif, break, and pass.

Learn how to define and call functions in Python to structure code, pass parameters, and return results, with examples like a mean calculator and a simple square root function.

Explore interactive financial charts with Plotly and cufflinks in Python, learn to install cufflinks, load stock data, and create normalized multi-series charts with zoom and hover.

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.

Create spread charts to compare two securities over time, visualizing price differentials with Plotly cufflinks, including normalization and handling positive/negative spreads.

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.

Explore candlestick charts that depict open, high, low, and close prices to reveal market emotion; use green candles for up moves and red for down moves.

Learn to build and apply technical indicators on stock data using a config object, plotting SMA, RSI, and Bollinger Bands with customizable colors and crossovers.

Explore interactive addition of technical indicators, including MACD with its 20 and 26 day parameters and the signal line, and add volume data to charts for trading signals.

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.

Learn to build an interactive candlestick chart with open-high-low-close data using cufflinks and plotly, adding moving averages, RSI, Bollinger bands, and MACD indicators for hover-enabled analysis.

Explore how pandas index powers time series analysis, enabling precise data frame access and operations like reset index and cross tab.

Explore how to handle missing data in time series using pandas, leveraging date-time indices to fill gaps, inspect stock close prices, and build complete data frames.

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.

Apply bfill and ffill to fill missing data in a time series, using the next value for backfill or the previous value for forward fill, with in-place updates.

Resample time series in pandas to annual intervals, using aggregate to compute first and last points, max, min, mean, standard deviation for open, high, low, close, and yearly percentage change.

Explore time zone handling in intraday time series, convert between utc and local zones such as America/New_York and Asia/Singapore, and align side-by-side data frames for cross time zone analysis.

Shift dates in financial time series with pandas date offsets. Add or subtract days, weeks, months, and years in a data frame for forecasting and analysis.

Learn to find the largest and smallest values in a dataframe using pandas, including top 10 days by volume or returns and computing traded value.

Explore the pandas profiling library to generate a profile report that summarizes a dataframe with descriptive statistics, correlations, and missing values for time series data.

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.

Construct multiple boolean conditions in Python to filter a data frame by combining and, or, and not operators. Demonstrate applying conditions to open, close, and daily returns to select rows.

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.

Calculate the rate of return for a single security and a portfolio using simple and log returns, then compute average daily and annualized returns and explore standard deviation and correlation.

Compute log returns from adjusted close prices using the log function in the Nampai package, and compare annualized log returns with simple returns through plotting and mean calculations.

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.

Calculate annualized rate of returns for major indices, using Yahoo Finance data for Dow Jones, S&P 500, Nasdaq and Russell; normalize prices, compute daily returns, and derive annualized performance.

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.

Construct a price weighted index by summing stock prices and normalizing to a base of 100. The method weights higher priced stocks more and enables interactive comparison with individual stocks.

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

Create an equal weighted index by computing daily returns, averaging them, and building a cumulative index with a base of 100 using cumulative products; compare with price-weighted indices and visualize.

Demonstrates creating point and figure charts in Python with the MPO Finance library, comparing Renko charts and line charts using five years of Apple stock data.

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.

Visualize stock relationships with a heatmap of correlations and scatter plots, then assess risk and return via averages and standard deviation.

Learn to compute monthly best and worst returns for financial instruments using Python in Google Colab, calculating daily returns from yfinance data and visualizing with a bar chart.