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Artificial Intelligence in Finance: A Python-Based Guide [Pehme köide]

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  • Formaat: Paperback / softback, 474 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 23-Oct-2020
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1492055433
  • ISBN-13: 9781492055433
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Artificial Intelligence in Finance: A Python-Based GuideSuurem pilt
  • Formaat: Paperback / softback, 474 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 23-Oct-2020
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1492055433
  • ISBN-13: 9781492055433
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781492055433.html
Märksõnad:

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
Preface ix
Part I Machine Intelligence
1 Artificial Intelligence
3(28)
Algorithms
3(6)
Types of Data
4(1)
Types of Learning
4(4)
Types of Tasks
8(1)
Types of Approaches
8(1)
Neural Networks
9(13)
OLS Regression
9(4)
Estimation with Neural Networks
13(7)
Classification with Neural Networks
20(2)
Importance of Data
22(7)
Small Data Set
23(3)
Larger Data Set
26(2)
Big Data
28(1)
Conclusions
29(1)
References
30(1)
2 Superintelligence
31(30)
Success Stories
32(10)
Atari
32(6)
Go
38(2)
Chess
40(2)
Importance of Hardware
42(2)
Forms of Intelligence
44(1)
Paths to Superintelligence
45(5)
Networks and Organizations
46(1)
Biological Enhancements
46(1)
Brain-Machine Hybrids
47(1)
Whole Brain Emulation
48(1)
Artificial Intelligence
49(1)
Intelligence Explosion
50(1)
Goals and Control
50(4)
Superintelligence and Goals
51(2)
Superintelligence and Control
53(1)
Potential Outcomes
54(2)
Conclusions
56(1)
References
56(5)
Part II Finance and Machine Learning
3 Normative Finance
61(38)
Uncertainty and Risk
62(4)
Definitions
62(1)
Numerical Example
63(3)
Expected Utility Theory
66(6)
Assumptions and Results
66(3)
Numerical Example
69(3)
Mean-Variance Portfolio Theory
72(10)
Assumptions and Results
72(3)
Numerical Example
75(7)
Capital Asset Pricing Model
82(8)
Assumptions and Results
83(2)
Numerical Example
85(5)
Arbitrage Pricing Theory
90(5)
Assumptions and Results
91(2)
Numerical Example
93(2)
Conclusions
95(1)
References
96(3)
4 Data-Driven Finance
99(62)
Scientific Method
100(1)
Financial Econometrics and Regression
101(3)
Data Availability
104(13)
Programmatic APIs
105(1)
Structured Historical Data
105(3)
Structured Streaming Data
108(2)
Unstructured Historical Data
110(2)
Unstructured Streaming Data
112(1)
Alternative Data
113(4)
Normative Theories Revisited
117(26)
Expected Utility and Reality
118(5)
Mean-Variance Portfolio Theory
123(7)
Capital Asset Pricing Model
130(4)
Arbitrage Pricing Theory
134(9)
Debunking Central Assumptions
143(12)
Normally Distributed Returns
143(10)
Linear Relationships
153(2)
Conclusions
155(1)
References
156(1)
Python Code
156(5)
5 Machine Learning
161(24)
Learning
162(1)
Data
162(3)
Success
165(4)
Capacity
169(3)
Evaluation
172(6)
Bias and Variance
178(2)
Cross-Validation
180(3)
Conclusions
183(1)
References
183(2)
6 Al-First Finance
185(26)
Efficient Markets
186(6)
Market Prediction Based on Returns Data
192(7)
Market Prediction with More Features
199(5)
Market Prediction Intraday
204(1)
Conclusions
205(2)
References
207(4)
Part III Statistical Inefficiencies
7 Dense Neural Networks
211(18)
The Data
212(2)
Baseline Prediction
214(4)
Normalization
218(2)
Dropout
220(2)
Regularization
222(3)
Bagging
225(2)
Optimizers
227(1)
Conclusions
228(1)
References
228(1)
8 Recurrent Neural Networks
229(20)
First Example
230(4)
Second Example
234(3)
Financial Price Series
237(3)
Financial Return Series
240(2)
Financial Features
242(4)
Estimation
243(1)
Classification
244(1)
Deep RNNs
245(1)
Conclusions
246(1)
References
247(2)
9 Reinforcement Learning
249(32)
Fundamental Notions
250(1)
OpenAI Gym
251(4)
Monte Carlo Agent
255(2)
Neural Network Agent
257(3)
DQL Agent
260(4)
Simple Finance Gym
264(4)
Better Finance Gym
268(3)
FQL Agent
271(6)
Conclusions
277(1)
References
278(3)
Part IV Algorithmic Trading
10 Vectorized Backtesting
281(22)
Backtesting an SMA-Based Strategy
282(7)
Backtesting a Daily DNN-Based Strategy
289(6)
Backtesting an Intraday DNN-Based Strategy
295(6)
Conclusions
301(1)
References
301(2)
11 Risk Management
303(42)
Trading Bot
304(4)
Vectorized Backtesting
308(3)
Event-Based Backtesting
311(7)
Assessing Risk
318(4)
Backtesting Risk Measures
322(10)
Stop Loss
324(2)
Trailing Stop Loss
326(2)
Take Profit
328(4)
Conclusions
332(1)
References
332(1)
Python Code
333(12)
Finance Environment
333(2)
Trading Bot
335(4)
Backtesting Base Class
339(3)
Backtesting Class
342(3)
12 Execution and Deployment
345(34)
Oanda Account
346(1)
Data Retrieval
347(4)
Order Execution
351(6)
Trading Bot
357(7)
Deployment
364(4)
Conclusions
368(1)
References
369(1)
Python Code
369(10)
Oanda Environment
369(3)
Vectorized Backtesting
372(1)
Oanda Trading Bot
373(6)
Part V Outlook
13 Al-Based Competition
379(16)
AI and Finance
380(2)
Lack of Standardization
382(1)
Education and Training
383(2)
Fight for Resources
385(1)
Market Impact
386(1)
Competitive Scenarios
387(1)
Risks, Regulation, and Oversight
388(3)
Conclusions
391(1)
References
392(3)
14 Financial Singularity
395(12)
Notions and Definitions
396(1)
What Is at Stake?
396(4)
Paths to Financial Singularity
400(1)
Orthogonal Skills and Resources
401(1)
Scenarios Before and After
402(1)
Star Trek or Star Wars
403(1)
Conclusions
404(1)
References
404(3)
Part VI Appendixes
A Interactive Neural Networks
407(18)
B Neural Network Classes
425(14)
C Convolutional Neural Networks
439(8)
Index 447
Dr. Yves J. Hilpisch is founder and managing partner of The Python Quants (http: //tpq.io), a group that focuses on the use of open source technologies for financial data science, algorithmic trading and computational finance. He is the author of the books Python for Finance (O'Reilly, 2014), Derivatives Analytics with Python (Wiley, 2015) and Listed Volatility and Variance Derivatives (Wiley, 2017). Yves lectures on computational finance at the CQF Program (http: //cqf.com), on data science at htw saar University of Applied Sciences (http: //htwsaar.de), and is the director for the online training program leading to the first Python for Finance University Certificate (awarded by htw saar).