How I Built A Trading Neural Network The Tnn White Paper
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How I Built A Trading Neural Network The Tnn White Paper
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Author : Kenneth Kam
language : en
Publisher: POVPublish.com
Release Date : 2025-03-31
How I Built A Trading Neural Network The Tnn White Paper written by Kenneth Kam and has been published by POVPublish.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-31 with Business & Economics categories.
How I Built a Trading Neural Network: The TNN White Paper This is not just another trading book. It’s the official white paper of the Trading Neural Network (TNN) — a complete, sustainable, and scalable system designed to merge AI-based prediction, disciplined execution, and long-term profitability into one operational framework. After laying the foundation in Books 1–5, this special volume pulls back the curtain on how the TNN system truly works — not only at the level of trading logic, but across commercial, legal, and global deployment structures. Built on thousands of hours of field-testing, engineering, and behavioral insight, The TNN White Paper delivers a deep, yet practical blueprint for traders, firms, and educators seeking a clear path forward in a chaotic financial landscape. You’ll explore: • Chapter 1: Understanding Trading Neural Networks Demystify what trading neural networks are, why they matter, and how they’re changing the future of decision-making in markets. • Chapter 2: Origins of the TNN Model Discover how the system was conceived — the mistakes, breakthroughs, and real-world needs that shaped its modular, teachable design. • Chapter 3: Foundations – AI, Trading, and the FT Philosophy Understand the core principles of Fractional Trading (FT), risk control, and how AI signals are converted into sustainable trading behavior. • Chapter 4: Transformer-Based Prediction Engine Dive deep into the transformer model powering TNN’s signal generation, how it forecasts market behavior, and why it beats traditional strategies. • Chapter 5: The Role of Fractional Trading in TNN Learn how the FT logic enforces discipline and protects capital — the real reason TNN works over time and in all market conditions. • Chapter 6: Real-Time Dashboard, Execution and Logic Get a walkthrough of the TNN Dashboard: how signals are filtered, trades executed, and rules enforced in real-time with zero guesswork. • Chapter 7: Licensing, IP, and Commercial Framework See how TNN is protected and deployed through structured licenses, including profit-sharing, customization tiers, and legal safeguards. • Chapter 8: TNN Tiers and Path to Profitability Explore the 3-tier licensing system that allows everyone—from individuals to institutions—to grow with the system at their own pace. • Chapter 9: TNN Certification Pathway Discover the training roadmap designed to help users master the system, become certified educators, and even build signal rooms and trade hubs. • Chapter 10: The Vision Ahead – From Signal Rooms to Global Nodes A bold look into the future of TNN: a decentralized global network of trusted operators, white-label partners, and AI-driven trade ecosystems. • Chapter 11: Appendix – Reference Charts, Parameters, and Resources A complete snapshot of system defaults, licensing models, risk tables, URLs, and reference docs for operational rollout. Why This Book Matters In a world where trading is increasingly algorithmic, emotional, and inconsistent, The TNN White Paper provides clarity, control, and confidence. It’s not about hype. It’s about structure. Not about gambling. But compounding. If you’re looking to grow as a trader, educator, or fintech builder — this is the systems-level thinking you’ve been looking for. Who This Book Is For Retail and professional traders Trading educators and coaches Prop trading firms and signal rooms Fintech developers and white-label partners Anyone interested in AI + finance, with discipline at the core BONUS: Includes links to all foundational documents, FT Manifesto, licensing terms, certification pathway, and global tax compliance strategy. Whether you’re just starting your trading journey or scaling a fintech enterprise, this book is your gateway to understanding, operating, and growing within the TNN ecosystem — with discipline, integrity, and scalability.
Neural Network Design
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Author : Martin T. Hagan
language : en
Publisher:
Release Date : 2003
Neural Network Design written by Martin T. Hagan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Neural networks (Computer science) categories.
Government Reports Announcements Index
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Author :
language : en
Publisher:
Release Date : 1993
Government Reports Announcements Index written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Science categories.
Icann99
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Author :
language : en
Publisher:
Release Date : 1999
Icann99 written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Neural networks (Computer science) categories.
Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Graph Representation Learning written by William L. Hamilton and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Computers categories.
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Information Systems For Global Financial Markets Emerging Developments And Effects
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Author : Yap, Alexander Y.
language : en
Publisher: IGI Global
Release Date : 2011-11-30
Information Systems For Global Financial Markets Emerging Developments And Effects written by Yap, Alexander Y. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-30 with Computers categories.
"This book offers focused research on the systems and technologies that provide intelligence and expertise to traders and investors and facilitate the agile ordering processes, networking, and regulation of global financial electronic markets"--Provided by publisher.
An Introduction To Neural Networks
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Author : Kevin Gurney
language : en
Publisher: CRC Press
Release Date : 2018-10-08
An Introduction To Neural Networks written by Kevin Gurney and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Computers categories.
Though mathematical ideas underpin the study of neural networks, the author presents the fundamentals without the full mathematical apparatus. All aspects of the field are tackled, including artificial neurons as models of their real counterparts; the geometry of network action in pattern space; gradient descent methods, including back-propagation; associative memory and Hopfield nets; and self-organization and feature maps. The traditionally difficult topic of adaptive resonance theory is clarified within a hierarchical description of its operation. The book also includes several real-world examples to provide a concrete focus. This should enhance its appeal to those involved in the design, construction and management of networks in commercial environments and who wish to improve their understanding of network simulator packages. As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, psychology, computer science and electrical engineering.
Knowledge Science Engineering And Management
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Author : Gang Li
language : en
Publisher: Springer Nature
Release Date : 2020-08-20
Knowledge Science Engineering And Management written by Gang Li and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-20 with Computers categories.
This two-volume set of LNAI 12274 and LNAI 12275 constitutes the refereed proceedings of the 13th International Conference on Knowledge Science, Engineering and Management, KSEM 2020, held in Hangzhou, China, in August 2020.* The 58 revised full papers and 27 short papers were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: knowledge graph; knowledge representation; knowledge management for education; knowledge-based systems; and data processing and mining. The papers of the second volume are organized in the following topical sections: machine learning; recommendation algorithms and systems; social knowledge analysis and management; text mining and document analysis; and deep learning. *The conference was held virtually due to the COVID-19 pandemic.
151 Trading Strategies
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Author : Zura Kakushadze
language : en
Publisher: Palgrave Macmillan
Release Date : 2018-12-29
151 Trading Strategies written by Zura Kakushadze and has been published by Palgrave Macmillan this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-29 with Business & Economics categories.
The book provides detailed descriptions, including more than 550 mathematical formulas, for more than 150 trading strategies across a host of asset classes and trading styles. These include stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility, real estate, distressed assets, cash, cryptocurrencies, weather, energy, inflation, global macro, infrastructure, and tax arbitrage. Some strategies are based on machine learning algorithms such as artificial neural networks, Bayes, and k-nearest neighbors. The book also includes source code for illustrating out-of-sample backtesting, around 2,000 bibliographic references, and more than 900 glossary, acronym and math definitions. The presentation is intended to be descriptive and pedagogical and of particular interest to finance practitioners, traders, researchers, academics, and business school and finance program students.
Mathematics For Machine Learning
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Author : Marc Peter Deisenroth
language : en
Publisher: Cambridge University Press
Release Date : 2020-04-23
Mathematics For Machine Learning written by Marc Peter Deisenroth and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-23 with Computers categories.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.