Description

James – The Python for Traders Masterclass

James – The Python for Traders Masterclass . File Size – 491.8

What You Get :

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 Quiz START

Module 2: Python Fundamentals for Finance

• 2.1. Python Installation and Setup START
• 2.2. Running Python Code START
• 2.3. Basic Python(26:34)START
• 2.4. Intermediate Python(5:07)START
• 2.5. Advanced Python START
• 2.6. Data Science in Python START
• 2.7. Key library: Pandas START
• 2.8. Key library: NumPy START
• 2.9. Key library: Matplotlib START
• 2.10. Key library: Stats models START
• 2.11. Key library: Scikit-learn START

Module 3: Working with Financial Data

• 3.1. Introduction to Financial Data: Time Series and Cross-Sections START
• 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 Forecasting START
• 3.6. Exploratory Data Analysis START
• 3.7. Section summary START

Module 4: How to Code and Backtest a Trading Algorithm

• 4.1. So what is a trading algorithm ? START
• 4.2. Algorithm Design Principles START
• 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 Basics START
• 4.9. Backtesting Software START
• 4.10. Advanced Backtesting Techniques START
• 4.11. Optimization and Parameter Tuning START

Project 1: Research & Backtest a Realistic Trading Algorithm

• Project Overview(6:57)START
• Step 1: Getting Started on Quant Connect(6:53)START
• Step 2: Formulate a Strategy START
• Solution: Formulate a Strategy START
• Step 3: Develop the Algorithm START
• Solution: Develop the Algorithm START
• Step 4: Run a Backtesting Analysis START
• Solution 4: Run a Backtesting Analysis START
• Project Summary START

Module 5: Automated Data Collection, Cleaning, and Storage

• 5.1. Sourcing financial data(5:38)START
• 5.2. Working with CSVs START
• 5.3. Working with JSONSTART
• 5.4. Scraping data from APIs(51:35)START
• 5.5. Scraping data from websites START
• 5.6. Persisting data: files and databases START
• 5.7. Section summary START

Module 6: Analyzing Fundamentals in Python

• 6.1. Structured vs. Unstructured Data START
• 6.2. Types of Fundamental Data START
• 6.3. Gathering & Cleaning Fundamental Data START
• 6.4. Automated Screening & Filtering START
• 6.5. Statistical Analysis of Fundamental Data START
• 6.6. Natural Language Processing on News Articles START
• 6.7. Natural Language Processing on Annual Reports START
• 6.8. Using LLMs for Natural Language Processing START

Module 7: Options & Derivatives Pricing Models

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

Project 2: Volatility Surface Analysis Tool

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

Module 8: Introduction to High-Frequency Trading

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

Project 3: Design & Build a Limit Order Book

• Project Overview START
• Step 1: Design the Data Structure START
• Solution: Design the Data Structure START
• Step 2: Add Functionality START
• Solution: Add Functionality START
• Step 3: Simulate Live Orders START
• Solution: Simulate Live Orders START
• Project Summary START

Capstone Project: Coding a Simple HFT Market Making Bot

• Project Overview START
• Step 1: Define a System and Class Architecture START
• Solution: Define a System and Class Architecture START
• Step 2: Define the Event Loop START
• Solution: Define the Event Loop START
• Step 3: Implement the Data Feeds START
• Solution: Implement the Data Feeds START
• Step 4: Implement the Order Manager START
• Solution: Implement the Order Manager START
• Step 5: Add Alpha to the Pricing Strategy START
• Solution: Add Alpha to the Pricing Strategy START
• Project Summary START

• Please leave your email address for further Updates from Cultcourse
• Files will be delivered through MEGA Download Link
•  Permanent Stored Courses
• 100% Safe & Secure Payments