Deus Ex Machina: Machine Learning for Finance: A Concise guide to Pythonic Machine Learning in Finance
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English Pages 521 Year 2024
Table of contents :
Title Page
Epigraph
Dedication
Contents
Introduction
Chapter 1: Building Blocks of Financial Machine Learning
1.1 The Evolution of Quantitative Finance
1.2 Key Financial Concepts for Data Scientists
1.3 Statistical Foundations
1.4 Essentials of Machine Learning Algorithms
1.5 Data Management in Finance
Chapter 2: Emerging Innovations: Machine Learning Tools and Technologies in Finance
2.1 Computational Environments for Financial Analysis
2.2 Data Exploration and Visualization Tools
2.3 Feature Selection and Model Building
2.4 Machine Learning Frameworks and Libraries
2.5 Model Deployment and Monitoring
Chapter 3: Exploring Deep Learning: Applications in Financial Analysis.
3.1 Neural Networks and Finance
3.2 Convolutional Neural Networks (CNNs)
3.3 Recurrent Neural Networks (RNNs) and LSTMs
3.4 Reinforcement Learning for Trading
3.5 Generative Models and Anomaly Detection
Chapter 4: Predictive Analytics: Time Series Analysis and Forecasting in Finance.
4.1 Fundamental Time Series Concepts
4.2 Advanced Time Series Methods
4.3 Machine Learning for Time Series Data
4.4 Forecasting for Financial Decision Making
4.5 Evaluation and Validation of Forecasting Models
Chapter 5: The New Frontier: Algorithmic Trading and High-Frequency Finance Strategies.
5.1 Introduction to Algorithmic Trading
5.2 Strategy Design and Backtesting
5.3 High-Frequency Trading Algorithms
Conclusion:
Epilogue: Navigating Future Frontiers from Berlin
Additional Resources
Sample Trading Programs – Step by Step Guide
Glossary of Terms
Afterword
Title Page
Epigraph
Dedication
Contents
Introduction
Chapter 1: Building Blocks of Financial Machine Learning
1.1 The Evolution of Quantitative Finance
1.2 Key Financial Concepts for Data Scientists
1.3 Statistical Foundations
1.4 Essentials of Machine Learning Algorithms
1.5 Data Management in Finance
Chapter 2: Emerging Innovations: Machine Learning Tools and Technologies in Finance
2.1 Computational Environments for Financial Analysis
2.2 Data Exploration and Visualization Tools
2.3 Feature Selection and Model Building
2.4 Machine Learning Frameworks and Libraries
2.5 Model Deployment and Monitoring
Chapter 3: Exploring Deep Learning: Applications in Financial Analysis.
3.1 Neural Networks and Finance
3.2 Convolutional Neural Networks (CNNs)
3.3 Recurrent Neural Networks (RNNs) and LSTMs
3.4 Reinforcement Learning for Trading
3.5 Generative Models and Anomaly Detection
Chapter 4: Predictive Analytics: Time Series Analysis and Forecasting in Finance.
4.1 Fundamental Time Series Concepts
4.2 Advanced Time Series Methods
4.3 Machine Learning for Time Series Data
4.4 Forecasting for Financial Decision Making
4.5 Evaluation and Validation of Forecasting Models
Chapter 5: The New Frontier: Algorithmic Trading and High-Frequency Finance Strategies.
5.1 Introduction to Algorithmic Trading
5.2 Strategy Design and Backtesting
5.3 High-Frequency Trading Algorithms
Conclusion:
Epilogue: Navigating Future Frontiers from Berlin
Additional Resources
Sample Trading Programs – Step by Step Guide
Glossary of Terms
Afterword
- Author / Uploaded
- Bisette
- Vincent; Strauss
- Johann; Van Der Post
- Hayden
- Similar Topics
- Computers
- Software: Systems: scientific computing
