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Nº de artículo:
41674838
Statistically Sound Indicators For Financial Market Prediction: Algorithms in C++ (Paperback)
Nº de artículo:
41674838
GTQ 483
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Whether you compute a few indicators and trade by watching their plots on a computer screen, or do simple automated algorithmic trading, or employ sophisticated predictive models, this book provides tools that help you take your trading to a higher, more profitable level.
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What Stands Out
In-depth Algorithms
This book offers comprehensive insights into advanced algorithms specifically designed for financial market prediction, making it an essential resource for students and professionals seeking to enhance their quantitative analysis skills.
Practical C++ Use
Combining statistical theory with practical C++ coding, this title empowers readers to implement and fine-tune predictive models effectively, bridging the gap between academic concepts and real-world application.
Statistical Validity
Emphasizing statistically sound indicators, the book equips users with proven methodologies for making informed trading decisions, significantly enhancing the accuracy of market predictions and mitigating risks.
Detalles de producto
- Book of the Year award by The Technical Analyst
- Focus on the importance of indicator quality over trading algorithm
- Presentation of traditional and modern indicators with predictive information
- Discussion on indicator information-carrying capacity tests and transformations
- Identification of regions with maximum predictive power in indicators
- Introduction of powerful indicators such as Follow-Through Index and The JANUS Factor
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Business & Investing |
| Publication date | October, 2019 |
| Pages | 394 |
| Edition | Standard Edition |
| Publisher | Independently Published |
| Original languages | English |
| Language | English |
| Awards won | Book of the Year |
| Assembled product dimensions (l x w x h) | 9.00 x 6.00 x 1.50 in (22.9 x 15.2 x 3.8 cm) |
| Assembled product weight | 1 lb (450 grams) |
| Bisac subject heading | Business & Economics |
Who Should Buy?
-
Finance Students
Ideal for students studying finance or quantitative methods, seeking practical algorithms for market analysis in C++.
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Algorithm Developers
Developers focusing on financial applications will benefit from understanding and implementing the algorithms presented in this book.
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Data Analysts
Analysts interested in statistical methods for financial prediction will find robust techniques and coding examples applicable to their work.
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Beginner Programmers
Beginners may struggle with complex algorithms and C++ coding without prior programming knowledge or experience.
DESCRIPCIÓN DEL PRODUCTO
Statistically Sound Indicators For Financial Market Prediction: Algorithms in C++ (Paperback)
About This Item
Are you looking for a comprehensive and reliable resource to help you predict financial market trends? Look no further than "Statistically Sound Indicators For Financial Market Prediction: Algorithms in C++". This paperback book is your go-to guide for mastering the art of financial market forecasting using powerful algorithms. Inside this book, you'll discover a treasure trove of valuable insights and techniques to help you make informed investment decisions. The authors delve deep into the world of statistically solid indicators and show you how to leverage them to your advantage.
Whether you're a seasoned trader or just starting out in the world of finance, this book is packed with knowledge that will take your understanding of the market to new heights. One of the highlights of this book is its focus on C++ programming algorithms. By understanding and utilizing these algorithms effectively, you'll be able to uncover patterns, analyze data, and develop algorithmic trading strategies. The authors provide step-by-step instructions and real-world examples to ensure you fully grasp the concepts and can apply them confidently. This book goes beyond theoretical discussions and provides practical applications of statistical analysis in market trends.
The authors explore predictive analytics in finance, equipping you with the tools needed to make accurate predictions based on historical data and market patterns. Dive into statistical models for market prediction and discover how to interpret the results to gain a competitive edge. What sets this book apart is its emphasis on coded algorithms for financial analysis. You'll learn how to use these algorithms to extract valuable insights from large datasets, giving you a data-driven approach to financial market insights.
With code samples and explanations, you'll have everything you need to apply these algorithms effectively in real-world scenarios. If you're serious about becoming a successful trader or investor, "Statistically Sound Indicators For Financial Market Prediction: Algorithms in C++" is a must-have addition to your library. Gain a deeper understanding of market trends, develop algorithmic trading strategies, and elevate your financial analysis skills. Don't miss out on this invaluable resource for unlocking the secrets of financial market prediction.
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Preguntas y respuestas de los clientes
-
Pregunta:
What is the importance of indicators in trading?
Respuesta: Quality of indicators is vastly more important than the quality of the trading algorithm or predictive model. -
Pregunta:
What does the book provide?
Respuesta: The book provides numerous indicators that carry significant predictive information, techniques to measure and raise the information-carrying capacity of indicators, and tools to compute statistically sound probabilities for indicator performance. -
Pregunta:
Who devised the Follow-Through Index indicator?
Respuesta: The Follow-Through Index indicator was devised by Govinda Khalsa.
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ventajas
- In-depth analysis and insights
- Clear algorithm explanations
- Practical applications in C++
- Great for financial market enthusiasts
- Well-structured and easy to follow
Contras
- Some examples may be outdated.
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características y beneficios
- Quality of indicators is more important than the trading algorithm or predictive model.
- Indicators that carry significant predictive information.
- Methods to measure the potential information-carrying capacity of indicators.
- Techniques to raise the information-carrying capacity of indicators.
- How to identify the regions in an indicator's domain for maximum predictive power.
- Tools to compute statistically sound probabilities for indicator performance.
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