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Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python [Pehme köide]

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  • Formaat: Paperback / softback, 428 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 30-Nov-2020
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1492073059
  • ISBN-13: 9781492073055
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Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using PythonSuurem pilt
  • Formaat: Paperback / softback, 428 pages, kõrgus x laius: 233x178 mm
  • Ilmumisaeg: 30-Nov-2020
  • Kirjastus: O'Reilly Media
  • ISBN-10: 1492073059
  • ISBN-13: 9781492073055
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781492073055.html
Märksõnad:

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You&;ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP).

Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and robo-advisor and chatbot development. You&;ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples.

This book covers:

  • Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management
  • Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies
  • Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction
  • Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management
  • Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management
  • NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
Preface ix
Part I The Framework
1 Machine Learning in Finance: The Landscape
1(12)
Current and Future Machine Learning Applications in Finance
2(3)
Algorithmic Trading
2(1)
Portfolio Management and Robo-Advisors
2(1)
Fraud Detection
3(1)
Loans/Credit Card/Insurance Underwriting
3(1)
Automation and Chatbots
3(1)
Risk Management
4(1)
Asset Price Prediction
4(1)
Derivative Pricing
4(1)
Sentiment Analysis
5(1)
Trade Settlement
5(1)
Money Laundering
5(1)
Machine Learning, Deep Learning, Artificial Intelligence, and Data Science
5(2)
Machine Learning Types
7(3)
Supervised
7(1)
Unsupervised
8(1)
Reinforcement Learning
9(1)
Natural Language Processing
10(1)
Chapter Summary
11(2)
2 Developing a Machine Learning Model in Python
13(18)
Why Python?
13(1)
Python Packages for Machine Learning
14(1)
Python and Package Installation
15(1)
Steps for Model Development in Python Ecosystem
15(14)
Model Development Blueprint
16(13)
Chapter Summary
29(2)
3 Artificial Neural Networks
31(18)
ANNs: Architecture, Training, and Hyperparameters
32(8)
Architecture
32(2)
Training
34(2)
Hyperparameters
36(4)
Creating an Artificial Neural Network Model in Python
40(5)
Installing Keras and Machine Learning Packages
40(3)
Running an ANN Model Faster: GPU and Cloud Services
43(2)
Chapter Summary
45(4)
Part II Supervised Learning
4 Supervised Learning: Models and Concepts
49(34)
Supervised Learning Models: An Overview
51(22)
Linear Regression (Ordinary Least Squares)
52(3)
Regularized Regression
55(2)
Logistic Regression
57(1)
Support Vector Machine
58(2)
K-Nearest Neighbors
60(2)
Linear Discriminant Analysis
62(1)
Classification and Regression Trees
63(2)
Ensemble Models
65(6)
ANN-Based Models
71(2)
Model Performance
73(6)
Overfitting and Underfitting
73(1)
Cross Validation
74(1)
Evaluation Metrics
75(4)
Model Selection
79(3)
Factors for Model Selection
79(2)
Model Trade-off
81(1)
Chapter Summary
82(1)
5 Supervised Learning: Regression (Including Time Series Models)
83(68)
Time Series Models
86(9)
Time Series Breakdown
87(1)
Autocorrelation and Stationarity
88(2)
Traditional Time Series Models (Including the ARIMA Model)
90(2)
Deep Learning Approach to Time Series Modeling
92(3)
Modifying Time Series Data for Supervised Learning Models
95(1)
Case Study 1 Stock Price Prediction
95(19)
Blueprint for Using Supervised Learning Models to Predict a Stock Price
97(17)
Case Study 2 Derivative Pricing
114(11)
Blueprint for Developing a Machine Learning Model for Derivative Pricing
115(10)
Case Study 3 Investor Risk Tolerance and Robo-Advisors
125(16)
Blueprint for Modeling Investor Risk Tolerance and Enabling a Machine Learning-Based Robo-Advisor
127(14)
Case Study 4 Yield Curve Prediction
141(8)
Blueprint for Using Supervised Learning Models to Predict the Yield Curve
142(7)
Chapter Summary
149(1)
Exercises
150(1)
6 Supervised Learning: Classification
151(44)
Case Study 1 Fraud Detection
153(13)
Blueprint for Using Classification Models to Determine Whether a Transaction Is Fraudulent
153(13)
Case Study 2 Loan Default Probability
166(13)
Blueprint for Creating a Machine Learning Model for Predicting Loan Default Probability
167(12)
Case Study 3 Bitcoin Trading Strategy
179(11)
Blueprint for Using Classification-Based Models to Predict Whether to Buy or Sell in the Bitcoin Market
180(10)
Chapter Summary
190(1)
Exercises
191(4)
Part III Unsupervised Learning
7 Unsupervised Learning: Dimensionality Reduction
195(42)
Dimensionality Reduction Techniques
197(5)
Principal Component Analysis
198(3)
Kernel Principal Component Analysis
201(1)
t-distributed Stochastic Neighbor Embedding
202(1)
Case Study 1 Portfolio Management: Finding an Eigen Portfolio
202(15)
Blueprint for Using Dimensionality Reduction for Asset Allocation
203(14)
Case Study 2 Yield Curve Construction and Interest Rate Modeling
217(10)
Blueprint for Using Dimensionality Reduction to Generate a Yield Curve
218(9)
Case Study 3 Bitcoin Trading: Enhancing Speed and Accuracy
227(9)
Blueprint for Using Dimensionality Reduction to Enhance a Trading Strategy
228(8)
Chapter Summary
236(1)
Exercises
236(1)
8 Unsupervised Learning: Clustering
237(44)
Clustering Techniques
239(4)
k-means Clustering
239(1)
Hierarchical Clustering
240(2)
Affinity Propagation Clustering
242(1)
Case Study 1 Clustering for Pairs Trading
243(16)
Blueprint for Using Clustering to Select Pairs
244(15)
Case Study 2 Portfolio Management: Clustering Investors
259(8)
Blueprint for Using Clustering for Grouping Investors
260(7)
Case Study 3 Hierarchical Risk Parity
267(10)
Blueprint for Using Clustering to Implement Hierarchical Risk Parity
268(9)
Chapter Summary
277(1)
Exercises
277(4)
Part IV Reinforcement Learning and Natural Language Processing
9 Reinforcement Learning
281(66)
Reinforcement Learning---Theory and Concepts
283(15)
RL Components
284(4)
RL Modeling Framework
288(5)
Reinforcement Learning Models
293(5)
Key Challenges in Reinforcement Learning
298(1)
Case Study 1 Reinforcement Learning-Based Trading Strategy
298(18)
Blueprint for Creating a Reinforcement Learning-Based Trading Strategy
300(16)
Case Study 2 Derivatives Hedging
316(18)
Blueprint for Implementing a Reinforcement Learning-Based Hedging Strategy
317(17)
Case Study 3 Portfolio Allocation
334(10)
Blueprint for Creating a Reinforcement Learning-Based Algorithm for Portfolio Allocation
335(9)
Chapter Summary
344(1)
Exercises
345(2)
10 Natural Language Processing
347(54)
Natural Language Processing: Python Packages
349(1)
NLTK
349(1)
TextBlob
349(1)
Spacy
350(1)
Natural Language Processing: Theory and Concepts
350(12)
1 Preprocessing
351(5)
2 Feature Representation
356(4)
3 Inference
360(2)
Case Study 1 NLP and Sentiment Analysis-Based Trading Strategies
362(21)
Blueprint for Building a Trading Strategy Based on Sentiment Analysis
363(20)
Case Study 2 Chatbot Digital Assistant
383(10)
Blueprint for Creating a Custom Chatbot Using NLP
385(8)
Case Study 3 Document Summarization
393(7)
Blueprint for Using NLP for Document Summarization
394(6)
Chapter Summary
400(1)
Exercises
400(1)
Index 401
Hariom Tatsat currently works as a Vice President in the Quantitative Analytics division of an investment bank in New York. Hariom has extensive experience as a Quant in the areas of predictive modelling, financial instrument pricing, and risk management in several global investment banks and financial organizations. He completed his MS at UC Berkeley and his BE at IIT Kharagpur (India). Hariom has also completed FRM (Financial Risk Manager), CQF (Certificate in Quantitative Finance) and is a candidate for CFA Level 3.

Sahil Puri works as a Quantitative Researcher in the Analytics Division at P.I.M.C.O. His work involves testing model assumptions and finding strategies for multiple asset classes. Sahil has applied multiple statistical and machine learning based techniques to a wide variety of problems; examples include: generating text features, labeling curve anomalies, non-linear risk factor detection, and time series prediction. He completed his MS at UC Berkeley and his BE at Delhi College of Engineering (India).