The course is designed to be highly practical. You’ll learn through hands-on projects and real-world examples, enabling you to apply Python skills directly to trading scenarios. Each module includes practical exercises to reinforce the concepts taught.

• 8 Modules
• 4 Projects
• 105 Lessons
• 248 Code Examples
• 34 Hours of Content

ONE Program to Take You from Total Amateur to Algo Trader

What You’ll Learn In The Python for Traders Masterclass

• Python Fundamentals for Finance
  Starting with basic Python, you’ll progress to advanced concepts and dive into data science. Learn essential tools like pandas, numpy, matplotlib, statsmodels, and scikit-learn, key for data analysis and machine learning in finance. This course is your streamlined path to mastering Python in the financial industry.
• Working with Financial Data in Python
  You’ll learn about various financial data types, how to clean and acquire data, and dive into time series analysis. Understand stationarity, practice time series forecasting, and conduct exploratory data analysis to uncover insights.
• Trading Algorithm Design Principles
  You’ll learn what trading algorithms are and their core design principles. Explore modules on data management, signal generation, risk and trade execution, and portfolio management. Then, dive into backtesting, including basics, software, and advanced techniques, and finish with optimization and parameter tuning for enhancing your trading strategies.
• Automation & Analysis
  You’ll learn how to source financial data effectively. This includes working with common formats like CSVs and JSON. You’ll also gain skills in scraping data from APIs and websites, followed by techniques for persisting data using files and databases. The section concludes with a summary that reinforces these key data collection methods.
• Analyzing Fundamentals
  You’ll learn about fundamental data in finance, including its types and how to gather and clean it. The section covers automated methods for screening and filtering this data, techniques for statistical analysis, and using natural language processing to analyze annual reports.
• Options & Derivatives Pricing
  You’ll learn about options and derivatives, basic option pricing, and delve into models like Binomial and Black-Scholes-Merton. Explore Monte Carlo simulations, exotic options, interest rate derivatives, and finite difference methods for pricing. The section also covers volatility concepts, including implied volatility, and offers advanced topics for further exploration.
• HFT and Market Making
  You’ll explore ‘High Frequency Trading (HFT)’ and understand how to handle high-frequency tick data. Learn about latency measurement and simulation, the strategies behind HFT market making, and the concept of statistical arbitrage with high-frequency data. Dive into signal processing specific to HFT and real-time news processing.

About Author

James is a quant trader and software engineer with years of experience in the world of algorithmic trading. With past experience at a major research lab and top tech company, he’s been independently trading equities and crypto using automated strategies since 2018. His passion for teaching and firsthand experience with the struggles that traders face when learning to code for the first time motivated him to create Python for Traders, knowing that there must be a better way to help his fellow traders turn better technology into better profits.