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Python for Traders Masterclass
Module 1: Introduction
1.1. Welcome to the Python for Traders Masterclass
1.2. Why Should Traders Learn to Code? (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?
1.8. How Much Coding Knowledge Do I Need?
1.9. Quiz: Is This Course For Me?
1.10. Module Summary
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 (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
Module 3: Working with Financial Data
3.1. Introduction to Financial Data: Time Series and Cross-Sections (11:07)
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. Module Summary
Module 4: Understanding Trading Algorithms
4.1. What Are Trading Algorithms?
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. Understanding and Avoiding Overfitting
4.11. Module Summary
Project 1: Research & Backtest a Realistic Trading Algorithm
Project 1 Overview (6:57)
Step 1: Get 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 Market Data (5:38)
5.2. Working with CSVs (0:31)
5.3. Working with JSON
5.4. Getting Data from APIs (51:35)
5.5. Getting Data from Websites (8:28)
5.6. Persisting Data in Files and Databases (11:49)
5.7. Automating Data Collection Jobs
5.8. Module Summary
Module 6: Analyzing Fundamentals in Python
6.1. Structured vs. Unstructured Data
6.2. Types of Fundamental Data (2:03)
6.3. Gathering & Cleaning Fundamental Data
6.4. Automated Screening & Filtering (11:33)
6.5. Factor Analysis of Fundamental Data
6.6. Natural Language Processing on News Articles (14:27)
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. Implementing The Binomial Options Pricing Model in Python
7.4. Implementing The Black-Scholes-Merton Model in Python
7.5. Monte Carlo Simulation for Option Pricing (4:56)
7.6. Pricing Exotic Options with Monte Carlo Simulations
7.7. Interest Rate Derivatives and Term Structure
7.8. Historical and Implied Volatility
7.9. Stochastic Volatility Models
7.10. Module Summary
Project 2: Volatility Surface Analysis Tool
Project 2 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
Module 8: Automated & 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. Strategy Breakdown: HFT Market Making
8.5. Strategy Breakdown: Statistical Arbitrage
8.6. Signal Processing for HFT
8.7. Real-Time News Processing
8.8. Module 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
Teach online with
Solution 4: Run a Backtesting Analysis
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