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Beschreibung
Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures.
The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios.
By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.
What You Will Learn
Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management
Know the concepts of feature engineering, data visualization, and hyperparameter optimization
Design, build, and test supervised and unsupervised ML and DL models
Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Know the concepts of feature engineering, data visualization, and hyperparameter optimization
Design, build, and test supervised and unsupervised ML and DL models
Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Who This Book Is For
Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)
Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures.
The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios.
By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems.
What You Will Learn
Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management
Know the concepts of feature engineering, data visualization, and hyperparameter optimization
Design, build, and test supervised and unsupervised ML and DL models
Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Know the concepts of feature engineering, data visualization, and hyperparameter optimization
Design, build, and test supervised and unsupervised ML and DL models
Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices
Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk
Who This Book Is For
Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)
Über den Autor
Tshepo Chris Nokeri harnesses big data, advanced analytics, and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor's degree in information management. He then graduated with an honors degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize. He has authored the Apress book Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning.
Zusammenfassung
Bridges the gap between finance and data science by presenting a systematic method for structuring, analyzing, and optimizing an investment portfolio and its underlying asset classes
Covers supervised and unsupervised machine learning (ML) models and deep learning (DL) models, including techniques of testing, validating, and optimizing model performance
Presents a diverse range of machine learning libraries (such as statsmodels, scikit-learn, Auto ARIMA, and FB Prophet) and covers the Keras DL framework plus the Pyfolio package for portfolio risk analysis and performance analysis
Inhaltsverzeichnis
Chapter 1: Introduction to the Financial Markets and Algorithmic Trading
Foreign exchange market
- Exchange rate
- Exchange rates quotation
The Interbank market
The retail market
Brokerage
- Understanding leverage and margin
- Contract for difference trading
The share market
Raising capital
- Public listing
- Stock exchange
- Share trading
Speculative nature of foreign exchange market
Techniques for speculating market movement
Algorithmic trading
- Supervised machine learning
The parametric method
- The non-parametric method
Binary classification
Multiclass classification
- The ensemble method
- Unsupervised learning
- Deep learning - Dimension reduction
Chapter 2: Forecasting Using ARIMA, SARIMA and Additive Model
Time series in action
Split data into training and test data
Test for stationary
Test for white noise
Autocorrelation function
Partial autocorrelation function
The moving averages smoothing technique
The exponential smoothing technique
Rate of return
The ARIMA Model
ARIMA Hyperparameter Optimization
- Develop the ARIMA model
- Forecast prices using the ARIMA model
The SARIMA model
- Develop SARIMA model
- Forecast using the SARIMA model
Additive model
- Develop the additive model
- Forecast prices the additive model
- Seasonal decomposition
Conclusion
Chapter 3: Univariate Time Series using Recurrent Neural Nets
What is deep learning?
Activation function
Loss function
Optimize an artificial neural network
The sequential data problem
The recurrent net model The recurrent net problem
The LSTM model
Gates
Unfolded LSTM network
Stacked LSTM network
LSTM in action
- Split data into training, test and validation
- Normalize data
- Develop LSTM model
- Forecasting using the LSTM
- Model evaluation
- Training and validation loss across epochs
- Training and validation accuracy across epochs Conclusion
Chapter 4: Discover Market Regimes
HMM
HMM application in finance
- Develop GaussianHMM
Mean and variance
Expected returns and volumes
Conclusions
Chapter 5: Stock Clustering
Investment Portfolio Diversification
Stock market volatility
K-Means clustering
K-Means in practice
Conclusions
Chapter 6: Future Price Prediction using Linear Regression
Linear Regression in Practice
Detect missing values
Pearson correlation
Covariance
Pairwise scatter plot
Eigen matrix
Split data into training and test data.
Normalize data
Least squares model hyperparameter optimization
Step 1: Fit least squares model with default hyperparameters
Step 2: Determine the mean and standard deviation of the cross-validation scores
Step 3: Determine Hyper-parameters that yield the best score.
Develop least squares model
Find an intercept
Find the estimated coefficient
Test least squares model performance using SciKit-Learn
Plotting actual values and predicted values
Conclusion
Chapter 7: Stock Market Simulation
Understanding value at risk
Estimate VAR using the Variance-Covariance Method
Understanding Monte Carlo
Application of Monte Carlo simulation in finance
- Run Monte Carlo simulation
- Plot simulations
Conclusions
Chapter 8: Market Trend Classification using ML and DL
Classification in practice
Data preprocessing
Split Data into training and test data
Logistic regression
- Finalize a logistic classifier
- Evaluate a logistic classifier
- Learning curve
Multilayer layer perceptron
- Architecture
- Finalize model
- Training and validation loss across epochs
- Training and validation accuracy across epochs
Conclusions
Chapter 9: Investment Portfolio and Risk Analysis
Investment
Investment Analysis
Investment Risk Management
Investment Portfolio Management
Pyfolio in action Performance statistics
Drawback
Rate of returns
Annual rate of return
Rolling returns
- Monthly rate of returns
Conclusions
Details
| Erscheinungsjahr: | 2021 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 200 |
| Inhalt: |
xviii
182 S. 53 s/w Illustr. 182 p. 53 illus. |
| ISBN-13: | 9781484271094 |
| ISBN-10: | 1484271092 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Nokeri, Tshepo Chris |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 235 x 155 x 12 mm |
| Von/Mit: | Tshepo Chris Nokeri |
| Erscheinungsdatum: | 27.05.2021 |
| Gewicht: | 0,312 kg |
Über den Autor
Tshepo Chris Nokeri harnesses big data, advanced analytics, and artificial intelligence to foster innovation and optimize business performance. In his functional work, he has delivered complex solutions to companies in the mining, petroleum, and manufacturing industries. He initially completed a bachelor's degree in information management. He then graduated with an honors degree in business science at the University of the Witwatersrand on a TATA Prestigious Scholarship and a Wits Postgraduate Merit Award. They unanimously awarded him the Oxford University Press Prize. He has authored the Apress book Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning.
Zusammenfassung
Bridges the gap between finance and data science by presenting a systematic method for structuring, analyzing, and optimizing an investment portfolio and its underlying asset classes
Covers supervised and unsupervised machine learning (ML) models and deep learning (DL) models, including techniques of testing, validating, and optimizing model performance
Presents a diverse range of machine learning libraries (such as statsmodels, scikit-learn, Auto ARIMA, and FB Prophet) and covers the Keras DL framework plus the Pyfolio package for portfolio risk analysis and performance analysis
Inhaltsverzeichnis
Chapter 1: Introduction to the Financial Markets and Algorithmic Trading
Foreign exchange market
- Exchange rate
- Exchange rates quotation
The Interbank market
The retail market
Brokerage
- Understanding leverage and margin
- Contract for difference trading
The share market
Raising capital
- Public listing
- Stock exchange
- Share trading
Speculative nature of foreign exchange market
Techniques for speculating market movement
Algorithmic trading
- Supervised machine learning
The parametric method
- The non-parametric method
Binary classification
Multiclass classification
- The ensemble method
- Unsupervised learning
- Deep learning - Dimension reduction
Chapter 2: Forecasting Using ARIMA, SARIMA and Additive Model
Time series in action
Split data into training and test data
Test for stationary
Test for white noise
Autocorrelation function
Partial autocorrelation function
The moving averages smoothing technique
The exponential smoothing technique
Rate of return
The ARIMA Model
ARIMA Hyperparameter Optimization
- Develop the ARIMA model
- Forecast prices using the ARIMA model
The SARIMA model
- Develop SARIMA model
- Forecast using the SARIMA model
Additive model
- Develop the additive model
- Forecast prices the additive model
- Seasonal decomposition
Conclusion
Chapter 3: Univariate Time Series using Recurrent Neural Nets
What is deep learning?
Activation function
Loss function
Optimize an artificial neural network
The sequential data problem
The recurrent net model The recurrent net problem
The LSTM model
Gates
Unfolded LSTM network
Stacked LSTM network
LSTM in action
- Split data into training, test and validation
- Normalize data
- Develop LSTM model
- Forecasting using the LSTM
- Model evaluation
- Training and validation loss across epochs
- Training and validation accuracy across epochs Conclusion
Chapter 4: Discover Market Regimes
HMM
HMM application in finance
- Develop GaussianHMM
Mean and variance
Expected returns and volumes
Conclusions
Chapter 5: Stock Clustering
Investment Portfolio Diversification
Stock market volatility
K-Means clustering
K-Means in practice
Conclusions
Chapter 6: Future Price Prediction using Linear Regression
Linear Regression in Practice
Detect missing values
Pearson correlation
Covariance
Pairwise scatter plot
Eigen matrix
Split data into training and test data.
Normalize data
Least squares model hyperparameter optimization
Step 1: Fit least squares model with default hyperparameters
Step 2: Determine the mean and standard deviation of the cross-validation scores
Step 3: Determine Hyper-parameters that yield the best score.
Develop least squares model
Find an intercept
Find the estimated coefficient
Test least squares model performance using SciKit-Learn
Plotting actual values and predicted values
Conclusion
Chapter 7: Stock Market Simulation
Understanding value at risk
Estimate VAR using the Variance-Covariance Method
Understanding Monte Carlo
Application of Monte Carlo simulation in finance
- Run Monte Carlo simulation
- Plot simulations
Conclusions
Chapter 8: Market Trend Classification using ML and DL
Classification in practice
Data preprocessing
Split Data into training and test data
Logistic regression
- Finalize a logistic classifier
- Evaluate a logistic classifier
- Learning curve
Multilayer layer perceptron
- Architecture
- Finalize model
- Training and validation loss across epochs
- Training and validation accuracy across epochs
Conclusions
Chapter 9: Investment Portfolio and Risk Analysis
Investment
Investment Analysis
Investment Risk Management
Investment Portfolio Management
Pyfolio in action Performance statistics
Drawback
Rate of returns
Annual rate of return
Rolling returns
- Monthly rate of returns
Conclusions
Details
| Erscheinungsjahr: | 2021 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 200 |
| Inhalt: |
xviii
182 S. 53 s/w Illustr. 182 p. 53 illus. |
| ISBN-13: | 9781484271094 |
| ISBN-10: | 1484271092 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Nokeri, Tshepo Chris |
| Auflage: | 1st ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 235 x 155 x 12 mm |
| Von/Mit: | Tshepo Chris Nokeri |
| Erscheinungsdatum: | 27.05.2021 |
| Gewicht: | 0,312 kg |
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