Artificial Intelligence in Finance (ebook)(audiobook)(audiobook)

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

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