Jansen Machine Learning for Algorithmic Trading

Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
2. Auflage 2020
ISBN: 978-1-83921-678-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 820 Seiten

No detailed description available for "Machine Learning for Algorithmic Trading".

Autoren/Hrsg.

Jansen, Stefan

Fachgebiete

• Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftsprognose

Inhaltsverzeichnis

Table of Contents - Machine Learning for Trading
- Market and Fundamental Data
- Alternative Data for Finance
- Financial Feature Engineering
- Portfolio Optimization and Performance Evaluation
- The Machine Learning Process
- Linear Models
- The ML4T Workflow
- Time-Series Models for Volatility Forecasts and Statistical Arbitrage
- Bayesian ML
- Random Forests
- Boosting Your Trading Strategy
- Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning
- Text Data for Trading
- Topic Modeling
- Word Embeddings for Earnings Calls and SEC Filings
- Deep Learning for Trading
- CNNs for Financial Time Series and Satellite Images
- RNNs for Multivariate Time Series and Sentiment Analysis
- Autoencoders for Conditional Risk Factors and Asset Pricing
- Generative Adversarial Networks for Synthetic Time-Series Data
- Deep Reinforcement Learning
- Conclusions and Next Steps
- Appendix

Über Autor(innen)

Jansen Stefan:
Stefan is the founder and CEO of Applied AI. He advises Fortune 500 companies, investment firms, and startups across industries on data & AI strategy, building data science teams, and developing end-to-end machine learning solutions for a broad range of business problems. Before his current venture, he was a partner and managing director at an international investment firm, where he built the predictive analytics and investment research practice. He was also a senior executive at a global fintech company with operations in 15 markets, advised Central Banks in emerging markets, and consulted for the World Bank. He holds Master's degrees in Computer Science from Georgia Tech and in Economics from Harvard and Free University Berlin, and a CFA Charter. He has worked in six languages across Europe, Asia, and the Americas and taught data science at Datacamp and General Assembly.