Time Series Analysis & Forecasting for Python Hackers

Learn how to set up an Anaconda environment, launch a Jupyter notebook, install pandas_datareader, and scrape Tesla stock data from Yahoo Finance for a time series analysis and forecasting workflow.

Save web-scraped data to csv, set the date as the index, and plot time series visuals with pandas, exploring volume, adjusted close, open, and close data.

Learn to compute and visualize moving averages in a time series using a rolling window (e.g., 100 days), plot adjusted close, moving average, and volume with subplots.

Plot a two-year time series from Yahoo Finance, compute the moving average on close prices with pandas rolling mean, and visualize the result.

Learn how volatility measures stock price swings using standard deviation, compare Facebook and Twitter daily changes, compute weekly medians, and apply Python to visualize and compare time series volatility.

Study time series volatility in Python by fetching stock data with data frames, and visualizing open, high, low, close and adjusted prices with candlestick charts and moving averages.

Explore time series trading strategies using moving average crossovers, backtesting, and risk management in portfolios, including bullish/bearish regimes, exits, and stop losses.

Explore how stock splits and dividends affect time series trading, adjust data with adjusted closes, and back test a portfolio using stop-loss rules and moving averages (20/50/200 days).

Work with a time series dataset in a Jupyter notebook using pandas, plotting adjusted close and moving averages for stocks, and prepare for forecasting with machine learning.

Apply time series forecasting with machine learning to predict Apple stock prices and returns, using adjusted close data, volume, high-low percentage, and percentage change.