The Python for Traders Masterclass

Description

About Course:

The Python for Traders Masterclass

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)PREVIEW
• 1.2. Why learn to code as a trader?(7:15)PREVIEW
• 1.3. Why should traders learn Python?(4:23)PREVIEW
• 1.4. What will I gain from this course?PREVIEW
• 1.5. What topics will be covered?PREVIEW
• 1.6. Who is the intended audience for this course?PREVIEW
• 1.7. How much finance knowledge do I need?(1:40)PREVIEW
• 1.8. How much coding knowledge do I need?(1:37)PREVIEW
• 1.9. Placement Quiz: Am I a good fit for this course?PREVIEW
• 1.10. Module QuizSTART

Module 2: Python Fundamentals for Finance

• 2.1. Python Installation and SetupSTART
• 2.2. Running Python CodeSTART
• 2.3. Basic Python(26:34)START
• 2.4. Intermediate Python(5:07)START
• 2.5. Advanced PythonSTART
• 2.6. Data Science in PythonSTART
• 2.7. Key library: PandasSTART
• 2.8. Key library: NumPySTART
• 2.9. Key library: MatplotlibSTART
• 2.10. Key library: StatsmodelsSTART
• 2.11. Key library: Scikit-learnSTART

Module 3: Working with Financial Data

• 3.1. Introduction to Financial Data: Time Series and Cross-SectionsSTART
• 3.2. Data Acquisition and Cleaning(18:09)START
• 3.3. Time Series Analysis(13:38)START
• 3.4. Understanding Stationarity(11:55)START
• 3.5. Time Series ForecastingSTART
• 3.6. Exploratory Data AnalysisSTART
• 3.7. Section summarySTART

Module 4: How to Code and Backtest a Trading Algorithm

• 4.1. So what is a trading algorithm?START
• 4.2. Algorithm Design PrinciplesSTART
• 4.3. Data Management Module(15:12)START
• 4.4. Signal Generation Module(15:12)START
• 4.5. Risk Management Module(10:58)START
• 4.6. Trade Execution Module(10:27)START
• 4.7. Portfolio Management Module(11:05)START
• 4.8. Backtesting BasicsSTART
• 4.9. Backtesting SoftwareSTART
• 4.10. Advanced Backtesting TechniquesSTART
• 4.11. Optimization and Parameter TuningSTART

Project 1: Research & Backtest a Realistic Trading Algorithm

• Project Overview(6:57)START
• Step 1: Getting Started on QuantConnect(6:53)START
• Step 2: Formulate a StrategySTART
• Solution: Formulate a StrategySTART
• Step 3: Develop the AlgorithmSTART
• Solution: Develop the AlgorithmSTART
• Step 4: Run a Backtesting AnalysisSTART
• Solution 4: Run a Backtesting AnalysisSTART
• Project SummarySTART

Module 5: Automated Data Collection, Cleaning, and Storage

• 5.1. Sourcing financial data(5:38)START
• 5.2. Working with CSVsSTART
• 5.3. Working with JSONSTART
• 5.4. Scraping data from APIs(51:35)START
• 5.5. Scraping data from websitesSTART
• 5.6. Persisting data: files and databasesSTART
• 5.7. Section summarySTART

Module 6: Analyzing Fundamentals in Python

• 6.1. Structured vs. Unstructured DataSTART
• 6.2. Types of Fundamental DataSTART
• 6.3. Gathering & Cleaning Fundamental DataSTART
• 6.4. Automated Screening & FilteringSTART
• 6.5. Statistical Analysis of Fundamental DataSTART
• 6.6. Natural Language Processing on News ArticlesSTART
• 6.7. Natural Language Processing on Annual ReportsSTART
• 6.8. Using LLMs for Natural Language ProcessingSTART

Module 7: Options & Derivatives Pricing Models

• 7.1. Introduction to Options & DerivativesSTART
• 7.2. Basics of Option PricingSTART
• 7.3. The Binomial Options Pricing ModelSTART
• 7.4. The Black-Scholes-Merton ModelSTART
• 7.5. Monte Carlo Simulation for Option PricingSTART
• 7.6. Introduction to Exotic OptionsSTART
• 7.7. Interest Rate Derivatives and Term StructureSTART
• 7.8. Implementing Finite Difference Methods for Option PricingSTART
• 7.9. Volatility and Implied VolatilitySTART
• 7.10. Advanced Topics and Modern Developments (Optional)START

Project 2: Volatility Surface Analysis Tool

• Project OverviewSTART
• Step 1: Fetching Options DataSTART
• Solution: Fetching Options DataSTART
• Step 2: Calculating Implied VolatilitiesSTART
• Solution: Calculating Implied VolatilitiesSTART
• Step 3: Plot a 3D Volatility SurfaceSTART
• Solution: Plot a 3D Volatility SurfaceSTART
• Project SummarySTART

Module 8: Introduction to High-Frequency Trading

• 8.1. What is High Frequency Trading (HFT)?START
• 8.2. Handling High-Frequency Tick DataSTART
• 8.3. Latency Measurement and SimulationSTART
• 8.4. Understanding the HFT Market Making StrategySTART
• 8.5. Understanding Statistical Arbitrage with High-Frequency DataSTART
• 8.6. Signal Processing for HFTSTART
• 8.7. Real-time News ProcessingSTART
• 8.8. Section summarySTART

Project 3: Design & Build a Limit Order Book

• Project OverviewSTART
• Step 1: Design the Data StructureSTART
• Solution: Design the Data StructureSTART
• Step 2: Add FunctionalitySTART
• Solution: Add FunctionalitySTART
• Step 3: Simulate Live OrdersSTART
• Solution: Simulate Live OrdersSTART
• Project SummarySTART

Capstone Project: Coding a Simple HFT Market Making Bot

• Project OverviewSTART
• Step 1: Define a System and Class ArchitectureSTART
• Solution: Define a System and Class ArchitectureSTART
• Step 2: Define the Event LoopSTART
• Solution: Define the Event LoopSTART
• Step 3: Implement the Data FeedsSTART
• Solution: Implement the Data FeedsSTART
• Step 4: Implement the Order ManagerSTART
• Solution: Implement the Order ManagerSTART
• Step 5: Add Alpha to the Pricing StrategySTART
• Solution: Add Alpha to the Pricing StrategySTART
• Project SummarySTART