Python for Financial Analysis and Algorithmic Trading

Explore Python basics in this crash course part two, mastering comparison and logical operators, if-elif-else control flow, for and while loops, the range function, and an introduction to list comprehension.

Explore NumPy, the numerical Python library at the core of the Python for finance ecosystem, and learn the basics that underpin other related libraries.

Import numpy as np to work with arrays, including vectors and matrices, and create zeros, ones, linspace, identity, and random samples for two-dimensional financial data.

Explore Pandas data frames built from series with a shared index, then select, create, and drop columns and rows using loc and iloc for precise indexing.

Explore pandas multi-index dataframes: build a two-level index from tuples, name index levels, index from outside in with .loc, and use cross section for multi-level selection.

Group by in pandas aggregates rows by a chosen column and applies numeric functions such as mean, sum, and std, returning a per-group numeric result while ignoring non-numeric columns.

Explore matplotlib's object-oriented plotting with plt.subplots for multi-panel figures, control figure size and dpi, add titles and labels, save figures, and create informative legends.

Practice Matplotlib through a guided set of exercises, building figures, axes and subplots with X, Y and Z data, while reviewing inline plotting in a Jupyter notebook and previewing solutions.

Learn to visualize time series with pandas and matplotlib, plot adjusted close and volume, control axes with x and y limits, and create interactive plots in Jupyter notebooks.

Practice Pandas visualization in a Jupyter notebook by creating scatter plots, histograms, box plots, kde, and area charts, and styling with ggplot, sizing, and legends.

Learn two ways to fetch financial data with Python on your computer: pandas data reader and Quandl. Understand api access, data frames, and csv fallbacks for regional or firewall issues.

Shift data with pandas' shift method to align price data across time steps; illustrate forward and backward shifts, handling nulls and lost tail, with a date time index.

Learn how to apply pandas rolling and expanding to financial time series, create moving averages with windows like 7, 30, and visualize with plots, plus Bollinger bands.

Explore ETS theory by decomposing time series into error, trend, and seasonality with Statsmodels, visualizing observed values, trend, seasonality, and residuals from the airline passengers dataset.

Decompose a time series with ETS in Python using statsmodels to separate trend, seasonality, and residuals, compare additive and multiplicative models, and visualize results in a Jupyter notebook.

Implement seasonal ARIMA in statsmodels, fit the model, review residuals, and forecast future values, noting ARIMA's limits for financial data and Brownian motion.