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R 611.00
Hands-On AI Trading with Python, QuantConnect, and AWS
Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.Predict market volatility regimes and allocate funds accordingly.Predict daily returns of tech stocks using classifiers.Forecast Forex pairs' future prices using Support Vector Machines and wavelets.Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.
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Introduction to Python Programming for Scientists and Engineers
Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
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Python for Scientific Computing and Artificial Intelligence
Python for Scientific Computing and Artificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features:No prior experience of programming is requiredOnline GitHub repository available with codes for readers to practiceCovers applications and examples from biology, chemistry, computer science, data science, electrical and mechanical engineering, economics, mathematics, physics, statistics and binary oscillator computingFull solutions to exercises are available as Jupyter notebooks on the WebSupport MaterialGitHub Repository of Python Files and Notebooks: https://github.com/proflynch/CRC-Press/Solutions to All Exercises:Section 1: An Introduction to Python: https://drstephenlynch.github.io/webpages/SolutionsSection1.htmlSection 2: Python for Scientific Computing: https://drstephenlynch.github.io/webpages/SolutionsSection2.htmlSection 3: Artificial Intelligence: https://drstephenlynch.github.io/webpages/SolutionsSection3.html
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Python Using GPT-5 and Gemini
Python Using GPT-5 and Gemini provides a practical, up-to-date guide that combines solid Python programming skills with real-world integration of today’s leading large language models. The book teaches essential Python techniques while showing readers exactly how to connect and use GPT-5, Google Gemini, Claude, Grok, Perplexity, and other major models in their daily work.Readers will gain a strong foundation in Python data structures, comprehensions, generators, regular expressions, and object-oriented programming. The book then moves into detailed coverage of GPT-5 (including GPT-5.4 and GPT-5.3 Instant), Gemini 3.1, Claude Mythos and Opus 4.6, along with practical Python code samples for chatbots, tool calling, data analysis, graphics generation, and integration with PowerPoint and Excel. Additional chapters explore LLM benchmarks, deceptive behaviors such as alignment faking and scheming, military AI applications, and comparisons between the major models, giving readers clear insight into their relative strengths and limitations.This book is written for intermediate Python developers, data scientists, technical professionals, and anyone who wants to move beyond basic prompting and build production-ready applications that effectively combine Python with the latest generation of large language models.
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Introduction to Python Programming and Data Structures, Global Edition
For introductory courses in Python Programming and Data Structures. A fundamentals first approach to programming helps students create efficient, elegant code. Introduction to Python Programming and Data Structures introduces students to basic programming concepts using a fundamentals-first approach that prepares students to learn object-oriented programming and advanced Python programming. This approach presents programming concepts and techniques that include control statements, loops, functions, and arrays before designing custom classes. Students learn basic logic and programming concepts prior to moving into object-oriented and GUI programming. The content incorporates a wide variety of problems with various levels of difficulty and covers many application areas to engage and motivate students.
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