Quantitative Finance: Advanced Analysis with Python: A Comprehensive Guide for 2024
Unlock the potential of Quantitative Finance with Python and elevate your professional trading strategies. 'Advance
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English Pages 879 Year 2024
Table of contents :
Title Page
Dedication
Contents
Chapter 1: Introduction to Algorithmic Trading
1.1 Definition of Algorithmic Trading
1.2 Key Benefits of Algorithmic Trading
1.3 Fundamentals of Algorithm Design
1.4 Regulatory and Ethical Considerations
Chapter 2: Understanding Financial Markets
2.1 Market Structure and Microstructure
2.2 Asset Classes and Instruments
2.3 Fundamental and Technical Analysis
2.4 Trading Economics
Chapter 3: Python for Finance
3.1 Basics of Python Programming
3.2 Data Handling and Manipulation
3.3 API Integration for Market Data
3.4 Performance and Scalability
Chapter 4: Quantitative Analysis and Modeling
4.1 Statistical Foundations
4.2 Portfolio Theory
4.3 Value at Risk (VaR)
4.4 Algorithm Evaluation Metrics
Chapter 5: Strategy Identification and Hypothesis
5.1 Identifying Market Opportunities
5.2 Strategy Hypothesis Formulation
5.3 Data Requirements and Sources
5.4 Tools for Strategy Development
Chapter 6: Building and Backtesting Strategies
6.1 Strategy Coding in Python
6.2 Backtesting Frameworks
6.3 Performance Analysis
6.4 Optimization Techniques
Chapter 7: Advanced Trading Strategies
7.1 Machine Learning for Predictive Modeling
7.2 High-Frequency Trading Algorithms
7.3 Sentiment Analysis Strategies
7.4 Multi-Asset and Cross-Asset Trading
Chapter 8: Real-Time Back testing and Paper Trading
8.1 Simulating Live Market Conditions
8.2 Refinement and Iteration
8.3 Robustness and Stability
8.4 Compliance and Reporting in Algorithmic Trading
Chapter 9: Machine Learning and AI
9.1 Deep Learning and Neural Networks
9.2 Reinforcement Learning for Trading
9.3 Natural Language Processing (NLP)
9.4 NLP Integration in Market Prediction Models
Chapter 10 : Blockchain and Cryptocurrency Markets
10.1 Fundamentals of Blockchain Technology
10.2 Trading Cryptocurrencies
10.3 Tokenization and Asset Representation
10.4 Decentralized Finance (DeFi)
Chapter 11: Quantum Computing in Finance
11.1 Quantum Computing Fundamentals
11.2 Quantum Algorithms for Optimization
11.3 Quantum Computing for Risk Analysis
11.4 Future Prospects of Quantum Computing in Trading
Epilogue
Additional Resources
Title Page
Dedication
Contents
Chapter 1: Introduction to Algorithmic Trading
1.1 Definition of Algorithmic Trading
1.2 Key Benefits of Algorithmic Trading
1.3 Fundamentals of Algorithm Design
1.4 Regulatory and Ethical Considerations
Chapter 2: Understanding Financial Markets
2.1 Market Structure and Microstructure
2.2 Asset Classes and Instruments
2.3 Fundamental and Technical Analysis
2.4 Trading Economics
Chapter 3: Python for Finance
3.1 Basics of Python Programming
3.2 Data Handling and Manipulation
3.3 API Integration for Market Data
3.4 Performance and Scalability
Chapter 4: Quantitative Analysis and Modeling
4.1 Statistical Foundations
4.2 Portfolio Theory
4.3 Value at Risk (VaR)
4.4 Algorithm Evaluation Metrics
Chapter 5: Strategy Identification and Hypothesis
5.1 Identifying Market Opportunities
5.2 Strategy Hypothesis Formulation
5.3 Data Requirements and Sources
5.4 Tools for Strategy Development
Chapter 6: Building and Backtesting Strategies
6.1 Strategy Coding in Python
6.2 Backtesting Frameworks
6.3 Performance Analysis
6.4 Optimization Techniques
Chapter 7: Advanced Trading Strategies
7.1 Machine Learning for Predictive Modeling
7.2 High-Frequency Trading Algorithms
7.3 Sentiment Analysis Strategies
7.4 Multi-Asset and Cross-Asset Trading
Chapter 8: Real-Time Back testing and Paper Trading
8.1 Simulating Live Market Conditions
8.2 Refinement and Iteration
8.3 Robustness and Stability
8.4 Compliance and Reporting in Algorithmic Trading
Chapter 9: Machine Learning and AI
9.1 Deep Learning and Neural Networks
9.2 Reinforcement Learning for Trading
9.3 Natural Language Processing (NLP)
9.4 NLP Integration in Market Prediction Models
Chapter 10 : Blockchain and Cryptocurrency Markets
10.1 Fundamentals of Blockchain Technology
10.2 Trading Cryptocurrencies
10.3 Tokenization and Asset Representation
10.4 Decentralized Finance (DeFi)
Chapter 11: Quantum Computing in Finance
11.1 Quantum Computing Fundamentals
11.2 Quantum Algorithms for Optimization
11.3 Quantum Computing for Risk Analysis
11.4 Future Prospects of Quantum Computing in Trading
Epilogue
Additional Resources
- Author / Uploaded
- Van Der Post
- Hayden
- Similar Topics
- Computers
- Software: Systems: scientific computing
