000			03927 a2200241 4500
005			20251022201132.0
008			251022b |||||||| |||| 00| 0 eng d
020			_a9781394268436
082			_a332.6420285 _bPIC
100			_a Pik, Jiri _924992
245			_aHands-on AI trading with python, quantconnect and AWS
260			_aNew Jersey _bWiley _c2025
300			_axxvi, 381p.
365			_aUSD _b54.95
500			_aBiographies xiii Preface: QuantConnect xv Introduction xxiii Part I Foundations of Capital Markets and Quantitative Trading 1 Chapter 1 Foundations of Capital Markets 3 Market Mechanics 3 Market Participants 4 Trading Is the “Play” 4 The Stage and Basic Rules of Trading—The Limit Order Book 4 Actors—Liquidity Trader, Market Maker, and Informed Trader 5 Liquidity Trader 5 Market Maker 5 Informed Trader 6 AI Actors Wanted! 7 Data and Data Feeds 7 Custom and Alternative Data 9 Brokerages and Transaction Costs 10 Transaction Costs 11 Security Identifiers 13 Assets and Derivatives 15 US Equities 15 US Equity Options 19 Index Options 21 US Futures 21 Cryptocurrency 23 Chapter 2 Foundations of Quantitative Trading 25 Research Process 25
520			_aMaster 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. (https://www.wiley.com/en-us/Hands-On+AI+Trading+with+Python%2C+QuantConnect%2C+and+AWS-p-9781394268436)
650			_aArtificial Intelligence
700			_aChan, Ernest P. _925740
700			_aBroad, Jared _925741
700			_aSun, Philip _925742
700			_aSingh ,Vivek _925743
942			_cBK _2ddc
999			_c10401 _d10401