Artificial Intelligence in Finance - Helion

ebook

Autor: Yves Hilpisch
ISBN: 9781492055389
stron: 478, Format: ebook
Data wydania: 2020-10-14
Księgarnia: Helion

Cena książki: 245,65 zł (poprzednio: 285,64 zł)
Oszczędzasz: 14% (-39,99 zł)

Osoby, które kupiły tę książkę, wybierały także »

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading.

Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book.

In five parts, this guide helps you:

Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI)
Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice
Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets
Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies
Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about

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Spis treści

Artificial Intelligence in Finance eBook -- spis treści

Preface
- References
- Conventions Used in This Book
- Using Code Examples
- OReilly Online Learning
- How to Contact Us
- Acknowledgments
I. Machine Intelligence
1. Artificial Intelligence
- Algorithms
  - Types of Data
  - Types of Learning
    - Unsupervised Learning
    - Reinforcement learning
  - Types of Tasks
  - Types of Approaches
- Neural Networks
  - OLS Regression
  - Estimation with Neural Networks
    - Scikit-learn
    - Keras
  - Classification with Neural Networks
- Importance of Data
  - Small Data Set
  - Larger Data Set
  - Big Data
- Conclusions
- References
2. Superintelligence
- Success Stories
  - Atari
    - The story
    - An example
  - Go
  - Chess
- Importance of Hardware
- Forms of Intelligence
- Paths to Superintelligence
  - Networks and Organizations
  - Biological Enhancements
  - Brain-Machine Hybrids
  - Whole Brain Emulation
  - Artificial Intelligence
- Intelligence Explosion
- Goals and Control
  - Superintelligence and Goals
  - Superintelligence and Control
- Potential Outcomes
- Conclusions
- References
II. Finance and Machine Learning
3. Normative Finance
- Uncertainty and Risk
  - Definitions
  - Numerical Example
    - Traded assets
    - Arbitrage pricing
- Expected Utility Theory
  - Assumptions and Results
    - Axioms and normative theory
    - Preferences of an agent
    - Utility functions
    - Expected utility functions
    - Risk aversion
  - Numerical Example
- Mean-Variance Portfolio Theory
  - Assumptions and Results
    - Portfolio statistics
    - Sharpe ratio
  - Numerical Example
    - Portfolio statistics
    - Investment opportunity set
    - Minimum volatility and maximum Sharpe ratio
    - Efficient frontier
- Capital Asset Pricing Model
  - Assumptions and Results
  - Numerical Example
    - Capital market line
    - Optimal portfolio
    - Indifference curves
- Arbitrage Pricing Theory
  - Assumptions and Results
  - Numerical Example
- Conclusions
- References
4. Data-Driven Finance
- Scientific Method
- Financial Econometrics and Regression
- Data Availability
  - Programmatic APIs
  - Structured Historical Data
  - Structured Streaming Data
  - Unstructured Historical Data
  - Unstructured Streaming Data
  - Alternative Data
- Normative Theories Revisited
  - Expected Utility and Reality
  - Mean-Variance Portfolio Theory
  - Capital Asset Pricing Model
  - Arbitrage Pricing Theory
- Debunking Central Assumptions
  - Normally Distributed Returns
    - Sample data sets
    - Real financial returns
  - Linear Relationships
- Conclusions
- References
- Python Code
5. Machine Learning
- Learning
- Data
- Success
- Capacity
- Evaluation
- Bias and Variance
- Cross-Validation
- Conclusions
- References
6. AI-First Finance
- Efficient Markets
- Market Prediction Based on Returns Data
- Market Prediction with More Features
- Market Prediction Intraday
- Conclusions
- References
III. Statistical Inefficiencies
7. Dense Neural Networks
- The Data
- Baseline Prediction
- Normalization
- Dropout
- Regularization
- Bagging
- Optimizers
- Conclusions
- References
8. Recurrent Neural Networks
- First Example
- Second Example
- Financial Price Series
- Financial Return Series
- Financial Features
  - Estimation
  - Classification
  - Deep RNNs
- Conclusions
- References
9. Reinforcement Learning
- Fundamental Notions
- OpenAI Gym
- Monte Carlo Agent
- Neural Network Agent
- DQL Agent
- Simple Finance Gym
- Better Finance Gym
- FQL Agent
- Conclusions
- References
IV. Algorithmic Trading
10. Vectorized Backtesting
- Backtesting an SMA-Based Strategy
- Backtesting a Daily DNN-Based Strategy
- Backtesting an Intraday DNN-Based Strategy
- Conclusions
- References
11. Risk Management
- Trading Bot
- Vectorized Backtesting
- Event-Based Backtesting
- Assessing Risk
- Backtesting Risk Measures
  - Stop Loss
  - Trailing Stop Loss
  - Take Profit
- Conclusions
- References
- Python Code
  - Finance Environment
  - Trading Bot
  - Backtesting Base Class
  - Backtesting Class
12. Execution and Deployment
- Oanda Account
- Data Retrieval
- Order Execution
- Trading Bot
- Deployment
- Conclusions
- References
- Python Code
  - Oanda Environment
  - Vectorized Backtesting
  - Oanda Trading Bot
V. Outlook
13. AI-Based Competition
- AI and Finance
- Lack of Standardization
- Education and Training
- Fight for Resources
- Market Impact
- Competitive Scenarios
- Risks, Regulation, and Oversight
- Conclusions
- References
14. Financial Singularity
- Notions and Definitions
- What Is at Stake?
- Paths to Financial Singularity
- Orthogonal Skills and Resources
- Scenarios Before and After
- Star Trek or Star Wars
- Conclusions
- References
VI. Appendixes
A. Interactive Neural Networks
- Tensors and Tensor Operations
- Simple Neural Networks
  - Estimation
  - Classification
- Shallow Neural Networks
  - Estimation
  - Classification
- References
B. Neural Network Classes
- Activation Functions
- Simple Neural Networks
  - Estimation
  - Classification
- Shallow Neural Networks
  - Estimation
  - Classification
- Predicting Market Direction
C. Convolutional Neural Networks
- Features and Labels Data
- Training the Model
- Testing the Model
- Resources
Index