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

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.
This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.
This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.
By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

Autoren/Hrsg.

Jansen, Stefan

Fachgebiete

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

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.