Algorithmic & Options Trading with Python

Set up a Python development environment with Python from python.org, Visual Studio Code, and Jupyter notebooks; create and activate a virtual environment, install libraries, and run Python files.

Define variables in Python and display values, such as price, with print. Identify data types (integer, float, string, boolean), use type(), and note case sensitivity and flow of execution.

Explore Python lists as a versatile datatype that stores multiple values using square brackets. Learn common list methods like append and index, accessed via dot notation.

Create a pandas data frame quickly by passing a dictionary of column names and values, then add a person column and view the two-column result with number and person.

Use pandas loc to select data by label, choosing rows and columns by names like zero and stock, with ranges for time-series filtering. Compare label-based loc with ilog for access.

Explore how to use the diff function to compute row differences, dropNa values with the dropNa function, and apply the rolling function to calculate a 10‑day moving average.

Explore how axis (axis=0 vs axis=1) and the access argument drive row-wise versus column-wise operations, demonstrating dropNa and shift effects on a data frame with daily changes.

Learn to use Pandas nlargest and nsmallest to extract top or bottom values from a close series or dataframe, returning matching dates or full rows.

Learn how to combine time series data with pandas pd.concat on axis 0 or 1, handle overlapping dates with drop_duplicates, and compare Apple and Tesla close prices from 2015 onward.

Iterate over a data frame with iterose to extract trading signals and track the highest high in the S&P 500 since 2022, using a max variable and index.

Vectorization with pandas operations dramatically speeds calculations, outperforming iterations on 24k-row S&P 500 data by ~40x in execution time using the time library; avoid loops and pursue vectorized backtests.

Plot the yearly returns of the S&P 500 since 2010 using resampling; learn which libraries and the ticker symbol to use, and set the starting date.

Learn vectorized backtesting by building a full S&P 500 trading strategy using a 100-day moving average, boolean indexing, and first-buy logic, removing loops and enabling mtm profit evaluation.

Explore time-series momentum trading in Python by computing log returns, rolling sums with a window, and a position strategy across stocks, cryptos, and forex, including intraday configurations.

Use puts for protection, calls to speculate, and selling options to generate income via the premium, including the covered call strategy.

Learn how intrinsic value and extrinsic value (time value) compose an option's price, with calls and puts, strike price, stock price, time to expiration, volatility, and supply and demand.

Explore implied volatility, how it differs from historical volatility, and how market price and Black-Scholes pricing reveal forward-looking expectations for option pricing.

Apply option theory to practical strategies by combining stock with options through a covered call and a protective put, learning how income is earned and downside risk is protected.

Master vertical spreads: two-leg options with the same expiration, buy one and sell another. Use bull call spreads to target moderate upside; bear put spreads to target moderate downside.

Explore the option Greeks as practical tools that reveal how time decay, volatility, and price moves affect strategies like covered calls and iron condors, guiding risk and timing.

Connect to Binance vanilla options websocket to stream real-time bitcoin option prices, save to a csv, and build a data pipeline for estimating implied volatility and volatility smiles with Python.

Learn how to compute mean, variance, standard deviation, and covariance, compare sample versus population variance, and derive correlation between two data series.

Compute portfolio expected return and portfolio risk in Python using pandas, deriving means, variances, and covariance from a covariance matrix, and apply the 0.5 and 0.5 weights to illustrate formula.

Apply the dot product to compute portfolio expected return from weights and returns, then derive variance via the covariance matrix and weights; implement in Python with NumPy for scalable portfolios.

Explore a standard sharp optimization in Python, comparing unconstrained and 30% cap portfolios, and assess weight stability through rolling windows and stress testing.

Build your own finance database of stock prices with python, pandas, SQLAlchemy, and yfinance, importing data, setting date as a column, and storing in SQLite per market for easy querying.

Create an index performance dashboard in Streamlit that shows the top and bottom S&P 500 performers over a chosen horizon, with selectable winners and losers time series.