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Python for Traders
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8 Modules
4 Projects
105 Lessons
248 Code Examples
34 Hours of Content



Module 1: Introduction
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

Module 2: Python Fundamentals for Finance
2.1. Python Installation and Setup
2.2. Running Python Code
2.3. Basic Python (26:34)
2.4. Intermediate Python (5:07)
2.5. Advanced Python
2.6. Data Science in Python
2.7. Key library: Pandas
2.8. Key library: NumPy
2.9. Key library: Matplotlib
2.10. Key library: Statsmodels
2.11. Key library: Scikit-learn

Module 3: Working with Financial Data
3.1. Introduction to Financial Data: Time Series and Cross-Sections
3.2. Data Acquisition and Cleaning (18:09)
3.3. Time Series Analysis (13:38)
3.4. Understanding Stationarity (11:55)
3.5. Time Series Forecasting
3.6. Exploratory Data Analysis
3.7. Section summary

Module 4: How to Code and Backtest a Trading Algorithm
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

Project 1: Research & Backtest a Realistic Trading Algorithm
Project Overview (6:57)
Step 1: Getting Started on QuantConnect (6:53)
Step 2: Formulate a Strategy
Solution: Formulate a Strategy
Step 3: Develop the Algorithm
Solution: Develop the Algorithm
Step 4: Run a Backtesting Analysis
Solution 4: Run a Backtesting Analysis
Project Summary

Module 5: Automated Data Collection, Cleaning, and Storage
5.1. Sourcing financial data (5:38)
5.2. Working with CSVs
5.3. Working with JSON
5.4. Scraping data from APIs (51:35)
5.5. Scraping data from websites
5.6. Persisting data: files and databases
5.7. Section summary

Module 6: Analyzing Fundamentals in Python
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

Module 7: Options & Derivatives Pricing Models
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)

Project 2: Volatility Surface Analysis Tool
Project Overview
Step 1: Fetching Options Data
Solution: Fetching Options Data
Step 2: Calculating Implied Volatilities
Solution: Calculating Implied Volatilities
Step 3: Plot a 3D Volatility Surface
Solution: Plot a 3D Volatility Surface
Project Summary

Module 8: Introduction to High-Frequency Trading
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 3: Design & Build a Limit Order Book
Project Overview
Step 1: Design the Data Structure
Solution: Design the Data Structure
Step 2: Add Functionality
Solution: Add Functionality
Step 3: Simulate Live Orders
Solution: Simulate Live Orders
Project Summary

Capstone Project: Coding a Simple HFT Market Making Bot
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