Algorithmic Trading Pro: Options Trading with Python: Learn to trade like a snake
In the high-stakes world of financial markets, "Algorithmic Trading Pro: Options Trading with Python" emerges
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English Pages 743 Year 2024
Table of contents :
Title Page
Contents
Chapter 1: Introduction to Options Markets
1.2. Options Basics
1.3. Options Pricing Models
1.4. Option Market Structure
1.5. Risk and Portfolio Management with Options
Chapter 2: Programming Fundamentals in Python
2.2. Object-Oriented Programming in Python
2.3. Essential Python Libraries
2.4 Advanced Python Features
2.5. Development Environment Setup
Chapter 3: Time Series Analysis for Financial Data
3.2. Time Series Descriptive Statistics
3.3. Time Series Visualization
3.4 Time Series Decomposition in Python
3.5. Time Series Forecasting Models
Chapter 4: Data Retrieval and Preparation for Options Trading
4.2. Data Cleaning and Preprocessing
4.3. Data Storage and Management
4.4 Options-Specific Data Challenges
4.5. Data Analysis and Feature Engineering
Chapter 5: Implementing Options Pricing Models in Python
5.2 The Binomial Tree Model for Option Valuation
5.3. Monte Carlo Simulation for Options Pricing
5.4. Volatility Modeling and the Greek
5.5 Numerical Methods and Optimization Techniques
Chapter 6: Statistical Analysis and Machine Learning for Options Trading
6.2. Regression Analysis for Option Pricing
6.3 Classification Algorithms for Trade Signals
6.4. Unsupervised Learning Techniques
6.5 Deep Learning for Options Strategies
Chapter 7: Quantitative Risk Management for Options
7.2. Credit and Counterparty Risk
7.3 Liquidity Risk and Management
7.4. Operational Risk Management
7.5. Integrating Risk with Portfolio Construction
Chapter 8: Option Trading Strategies and Profitability Analysis
8.2. Evaluating Option Strategies
8.3. Event-Driven Trading Strategies
8.4. Tail Risk Hedging with Options
8.5. Cost-Benefit Analysis of Tail Risk Hedges
Chapter 9: Real-Time Data Feed and Trade Execution
9.2. Building a Market Data Ticker Plant
9.3. Trade Execution Syms
9.5. Integrating with Brokers and Platforms
Chapter 10: Optimizing Trading Strategies with Artificial
10.2 Neural Network Optimization Techniques
10.3. Reinforcement Learning for Adaptive Trading
10.4. Ensemble Methods for Robust Trading Decision
10.5. Strategy Improvement with Natural Language Processing
Additional Resources
Title Page
Contents
Chapter 1: Introduction to Options Markets
1.2. Options Basics
1.3. Options Pricing Models
1.4. Option Market Structure
1.5. Risk and Portfolio Management with Options
Chapter 2: Programming Fundamentals in Python
2.2. Object-Oriented Programming in Python
2.3. Essential Python Libraries
2.4 Advanced Python Features
2.5. Development Environment Setup
Chapter 3: Time Series Analysis for Financial Data
3.2. Time Series Descriptive Statistics
3.3. Time Series Visualization
3.4 Time Series Decomposition in Python
3.5. Time Series Forecasting Models
Chapter 4: Data Retrieval and Preparation for Options Trading
4.2. Data Cleaning and Preprocessing
4.3. Data Storage and Management
4.4 Options-Specific Data Challenges
4.5. Data Analysis and Feature Engineering
Chapter 5: Implementing Options Pricing Models in Python
5.2 The Binomial Tree Model for Option Valuation
5.3. Monte Carlo Simulation for Options Pricing
5.4. Volatility Modeling and the Greek
5.5 Numerical Methods and Optimization Techniques
Chapter 6: Statistical Analysis and Machine Learning for Options Trading
6.2. Regression Analysis for Option Pricing
6.3 Classification Algorithms for Trade Signals
6.4. Unsupervised Learning Techniques
6.5 Deep Learning for Options Strategies
Chapter 7: Quantitative Risk Management for Options
7.2. Credit and Counterparty Risk
7.3 Liquidity Risk and Management
7.4. Operational Risk Management
7.5. Integrating Risk with Portfolio Construction
Chapter 8: Option Trading Strategies and Profitability Analysis
8.2. Evaluating Option Strategies
8.3. Event-Driven Trading Strategies
8.4. Tail Risk Hedging with Options
8.5. Cost-Benefit Analysis of Tail Risk Hedges
Chapter 9: Real-Time Data Feed and Trade Execution
9.2. Building a Market Data Ticker Plant
9.3. Trade Execution Syms
9.5. Integrating with Brokers and Platforms
Chapter 10: Optimizing Trading Strategies with Artificial
10.2 Neural Network Optimization Techniques
10.3. Reinforcement Learning for Adaptive Trading
10.4. Ensemble Methods for Robust Trading Decision
10.5. Strategy Improvement with Natural Language Processing
Additional Resources
- Author / Uploaded
- Van Der Post
- Hayden
- Similar Topics
- Computers
- Software: Systems: scientific computing
