Algorithmic & Options Trading with Python | Udemy [Update 03/2026]
English | Size: 6.67 GB
Genre: eLearning[/center]

Learn to build trading strategies with Python: data analysis, backtesting, options pricing, and portfolio optimization

What you'll learn
Build and backtest complete algorithmic trading strategies in Python using real market data
Design trading signals (momentum, volatility, VIX-based) and evaluate their performance over time
Price options, compute implied volatility, and construct multi-leg strategies such as spreads and condors
Create fast, vectorized backtests and analyze results using professional performance metrics
Optimize portfolios using risk/return concepts such as the Sharpe Ratio and efficient frontier
Have an outstanding technical skillset to apply for a quant job
Apply machine learning models to financial data for strategy analysis and improvement
Visualize your data in interactive Dashboards
Learn about best practices and relevant practice advice working with financial data
Understand the difference between Log returns and returns
Optimize weights by using the concept of the Efficient Frontier
Leverage Algebra concepts to do powerful calculations
Solve real-world problems using Python
Learn to use the powerful intersection of Pandas & SQL to build, maintain and leverage Databases
Understand how you can leverage Algebra to make powerful computations
Work with real financial time series data using Pandas and structure reusable research workflows

Build algorithmic trading and options strategies with Python - from raw market data to fully backtested systems.

This course teaches you how to design, implement, and evaluate real trading strategies using Python.

You will not learn isolated concepts. Instead, you will follow a complete workflow used in quantitative finance:

Data → Signal → Strategy → Backtest → Optimization

From stock strategies to options pricing and portfolio construction, everything is built step by step using real data.

What you will learn

By the end of this course, you will be able to:

• Build and backtest momentum and volatility-based trading strategies
• Work with real financial time series data using Pandas
• Create fast, fully vectorized backtests
• Understand and implement options pricing (Black-Scholes, Monte Carlo)
• Understand and calculate implied Volatility on Options
• Build and analyze options trading strategies (spreads, condors, etc.)
• Apply machine learning models to financial data
• Optimize portfolios using risk/return techniques
• Build dashboards to analyze performance
• Store and manage financial data using SQL

What makes this course different?

Most courses either focus on trading strategies, options, or Python basics.

This course combines all of them into one consistent framework.

Every project follows the same structure:

Data → Signal → Strategy → Backtest → Optimization

You will not just learn individual tools. You will learn how to build complete trading systems.

Projects you will build

• Cross-sectional and time-series momentum strategies
• A VIX-based trading strategy inspired by institutional research
• Options pricing models using Black-Scholes and Monte Carlo
• Multi-leg options strategies such as covered calls, spreads, and condors
• A machine learning model for market prediction
• Portfolio optimization using the Sharpe Ratio
• A Streamlit dashboard for market analysis
• A financial database using Python and SQL

Who this course is for

• People with basic Python knowledge who want to apply it to trading
• Aspiring quants, analysts, and data-driven traders
• Anyone who wants to build and test trading strategies properly

This is not a theory-heavy course.

Everything is implemented in Python using real market data.

No filler content and no toy examples.

This course focuses on practical implementation and real workflows used in quantitative finance.

Start building your own algorithmic trading and options strategies with Python.

Who this course is for:
Aspiring quants, analysts, and data-driven investors
Traders who want to move beyond intuition and properly backtest their ideas
Business and Finance students who look for an opportunity to attain a high in demand skillset
People who are interested in applied Financial Analysis
People who are interested in Finance, Data Science and Analytics
People who want to build a highly valuable skillset
People who want to get a better understanding of there own portfolio
People who want to understand the statistics and Algebra behind Portfolio Analysis
Developers interested in applying data science techniques to financial markets

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