The Python for Traders Masterclass
8 Modules
4 Projects
105 Lessons
248 Code Examples
34 Hours of Content
ONE Program to Take You from Total Amateur to Algo Trader
Preview some of our FREE lessons below:
- 1.1. Welcome to the Python for Traders Masterclass (2:14)
- 1.2. Why learn to code as a trader? (7:15)
- 1.3. Why should traders learn Python? (4:23)
- 1.4. What will I gain from this course?
- 1.5. What topics will be covered?
- 1.6. Who is the intended audience for this course?
- 1.7. How much finance knowledge do I need? (1:40)
- 1.8. How much coding knowledge do I need? (1:37)
- 1.9. Placement Quiz: Am I a good fit for this course?
- 1.10. Module Quiz
- 2.1. Python Installation and Setup
- 2.2. Running Python Code
- 2.3. Basic Python (26:34)
- 2.4. Intermediate Python (38:47)
- 2.5. Advanced Python (24:27)
- 2.6. Data Science in Python
- 2.7. Key library: Pandas (10:48)
- 2.8. Key library: NumPy (3:22)
- 2.9. Key library: Matplotlib (5:22)
- 2.10. Key library: Statsmodels (15:26)
- 2.11. Key library: Scikit-learn (4:40)
- 2.12. Module Summary
- 4.1. So what is a trading algorithm?
- 4.2. Algorithm Design Principles
- 4.3. Data Management Module (15:12)
- 4.4. Signal Generation Module (15:12)
- 4.5. Risk Management Module (10:58)
- 4.6. Trade Execution Module (10:27)
- 4.7. Portfolio Management Module (11:05)
- 4.8. Backtesting Basics
- 4.9. Backtesting Software
- 4.10. Advanced Backtesting Techniques
- 4.11. Optimization and Parameter Tuning
- 6.1. Structured vs. Unstructured Data
- 6.2. Types of Fundamental Data
- 6.3. Gathering & Cleaning Fundamental Data
- 6.4. Automated Screening & Filtering
- 6.5. Statistical Analysis of Fundamental Data
- 6.6. Natural Language Processing on News Articles
- 6.7. Natural Language Processing on Annual Reports
- 6.8. Using LLMs for Natural Language Processing
- 7.1. Introduction to Options & Derivatives
- 7.2. Basics of Option Pricing
- 7.3. The Binomial Options Pricing Model
- 7.4. The Black-Scholes-Merton Model
- 7.5. Monte Carlo Simulation for Option Pricing
- 7.6. Introduction to Exotic Options
- 7.7. Interest Rate Derivatives and Term Structure
- 7.8. Implementing Finite Difference Methods for Option Pricing
- 7.9. Volatility and Implied Volatility
- 7.10. Advanced Topics and Modern Developments (Optional)
- 8.1. What is High Frequency Trading (HFT)?
- 8.2. Handling High-Frequency Tick Data
- 8.3. Latency Measurement and Simulation
- 8.4. Understanding the HFT Market Making Strategy
- 8.5. Understanding Statistical Arbitrage with High-Frequency Data
- 8.6. Signal Processing for HFT
- 8.7. Real-time News Processing
- 8.8. Section summary
- Project Overview
- Step 1: Define a System and Class Architecture
- Solution: Define a System and Class Architecture
- Step 2: Define the Event Loop
- Solution: Define the Event Loop
- Step 3: Implement the Data Feeds
- Solution: Implement the Data Feeds
- Step 4: Implement the Order Manager
- Solution: Implement the Order Manager
- Step 5: Add Alpha to the Pricing Strategy
- Solution: Add Alpha to the Pricing Strategy
- Project Summary
What You'll Learn
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.
Hands-On Learning with Applied Projects
Build & Backtest a Real Trading Algorithm
This project teaches practical skills in coding and understanding financial markets. By the end, students will have their own working trading algorithm that they've tested and can use in real trading.
Analyze Options & Plot a 3D Volatility Surface
A key part of the course is learning to plot a 3D volatility surface. This is a visual tool that shows how market uncertainty changes with different option prices and expiration dates. By the end of the project, students will be able to analyze options effectively and visualize market volatility in 3D, helping them make better trading decisions.
Design & Implement a Limit Order Book
The course will teach them step by step how to create their own book, focusing on how stock buy and sell orders are arranged. Students will also learn to make their Limit Order Book work using simple programming. By the end, they'll know how to set up and use a Limit Order Book, helping them understand stock trading better and make smart trading choices.
Capstone Project
Build a Market Making Bot
In this final project, students will create their own Market Making Bot, a program used in trading stocks. Students will learn how these bots provide liquidity to markets by buying and selling stocks. They'll then use their programming skills to build a bot that can make quick trading decisions. The project focuses on teaching how to balance risks and rewards, and how to make the bot respond to changing market conditions. By the end, students will have a working Market Making Bot that they've built themselves, giving them practical experience in finance and coding.
Meet Your Instructor
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.
FAQs
What prior knowledge do I need before taking this course?
Ideally, you should have a basic understanding of Python programming and a fundamental knowledge of trading concepts. However, beginners in both areas can still benefit, as the course starts with foundational concepts before advancing to more complex topics.
Is this course theoretical or practical?
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.
How long is the course, and what is the time commitment?
The course is entirely self-paced and can be completed at whatever rate you choose. We estimate there are over 30 hours of content, including the projects, which can vary in terms of time commitments. This includes video lessons, assignments, and additional reading materials.
Can I use the skills learned in this course for cryptocurrency trading?
Absolutely. The principles and Python skills you learn in this course are applicable to various markets, including cryptocurrencies. We cover data analysis and algorithmic strategies that can be tailored to different trading instruments.
Do I get a certificate upon completing the course?
Yes, upon successful completion of the course and all assessments, you will receive a certificate that you can add to your professional profile.
Are there any additional resources provided with the course?
The course includes additional resources like cheat sheets, code repositories, and recommended reading lists to enhance your learning experience and provide further knowledge on Python for trading.
What makes this course different from other Python trading courses?
Our course is uniquely tailored to combine practical trading knowledge with Python programming. It's taught by industry professionals and includes up-to-date content, real-world trading scenarios, and continuous updates to course materials.