Module 1: Introduction
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1.1. Welcome to the Python for Traders Masterclass
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1.2. Why Should Traders Learn to Code? (7:15)
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1.3. Why Should Traders Learn Python? (4:23)
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1.4. What Will I Gain From This Course?
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1.5. What Topics Will Be Covered?
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1.6. Who is the Intended Audience for This Course?
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1.7. How Much Finance Knowledge Do I Need?
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1.8. How Much Coding Knowledge Do I Need?
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1.9. Quiz: Is This Course For Me?
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1.10. Module Summary
Module 2: Python Fundamentals for Finance
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2.1. Python Installation and Setup
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2.2. Running Python Code
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2.3. Basic Python (26:34)
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2.4. Intermediate Python (38:47)
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2.5. Advanced Python (24:27)
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2.6. Data Science in Python
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2.7. Key library: Pandas (10:48)
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2.8. Key library: NumPy (3:22)
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2.9. Key library: Matplotlib (5:22)
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2.10. Key library: Statsmodels (15:26)
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2.11. Key library: Scikit-learn (4:40)
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2.12. Module Summary
Module 3: Working with Financial Data
Module 4: Understanding Trading Algorithms
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4.1. What Are Trading Algorithms?
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4.2. Algorithm Design Principles
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4.3. Data Management Module (15:12)
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4.4. Signal Generation Module (15:12)
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4.5. Risk Management Module (10:58)
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4.6. Trade Execution Module (10:27)
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4.7. Portfolio Management Module (11:05)
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4.8. Backtesting Basics
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4.9. Backtesting Software
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4.10. Understanding and Avoiding Overfitting
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4.11. Module Summary
Project 1: Research & Backtest a Realistic Trading Algorithm
Module 5: Automated Data Collection, Cleaning, and Storage
Module 6: Analyzing Fundamentals in Python
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6.1. Structured vs. Unstructured Data
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6.2. Types of Fundamental Data (2:03)
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6.3. Gathering & Cleaning Fundamental Data
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6.4. Automated Screening & Filtering (11:33)
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6.5. Factor Analysis of Fundamental Data
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6.6. Natural Language Processing on News Articles (14:27)
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6.7. Natural Language Processing on Annual Reports
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6.8. Using LLMs for Natural Language Processing
Module 7: Options & Derivatives Pricing Models
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7.1. Introduction to Options & Derivatives
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7.2. Basics of Option Pricing
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7.3. Implementing The Binomial Options Pricing Model in Python
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7.4. Implementing The Black-Scholes-Merton Model in Python
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7.5. Monte Carlo Simulation for Option Pricing (4:56)
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7.6. Pricing Exotic Options with Monte Carlo Simulations
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7.7. Interest Rate Derivatives and Term Structure
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7.8. Historical and Implied Volatility
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7.9. Stochastic Volatility Models
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7.10. Module Summary
Project 2: Volatility Surface Analysis Tool
Module 8: Automated & High-Frequency Trading
Project 3: Design & Build a Limit Order Book
Capstone Project: Coding a Simple HFT Market Making Bot
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Project Overview
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Step 1: Define a System and Class Architecture
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Solution: Define a System and Class Architecture
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Step 2: Define the Event Loop
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Solution: Define the Event Loop
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Step 3: Implement the Data Feeds
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Solution: Implement the Data Feeds
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Step 4: Implement the Order Manager
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Solution: Implement the Order Manager
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Step 5: Add Alpha to the Pricing Strategy
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Solution: Add Alpha to the Pricing Strategy
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Project Summary
