{"product_id":"machine-learning-for-algorithmic-trading-second-edition","title":"Machine Learning for Algorithmic Trading - Second Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python","description":"\u003cp\u003e\u003cstrong\u003eLeverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format.\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eDesign, train, and evaluate machine learning algorithms that underpin automated trading strategies\u003c\/li\u003e\n\u003cli\u003eCreate a research and strategy development process to apply predictive modeling to trading decisions\u003c\/li\u003e\n\u003cli\u003eLeverage NLP and deep learning to extract tradeable signals from market and alternative data\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eThe explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models.\u003c\/p\u003e\u003cp\u003eThis book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research.\u003c\/p\u003e\u003cp\u003eThis edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples.\u003c\/p\u003e\u003cp\u003eBy the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eLeverage market, fundamental, and alternative text and image data\u003c\/li\u003e\n\u003cli\u003eResearch and evaluate alpha factors using statistics, Alphalens, and SHAP values\u003c\/li\u003e\n\u003cli\u003eImplement machine learning techniques to solve investment and trading problems\u003c\/li\u003e\n\u003cli\u003eBacktest and evaluate trading strategies based on machine learning using Zipline and Backtrader\u003c\/li\u003e\n\u003cli\u003eOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio\u003c\/li\u003e\n\u003cli\u003eCreate a pairs trading strategy based on cointegration for US equities and ETFs\u003c\/li\u003e\n\u003cli\u003eTrain a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eMachine Learning for Trading - From Idea to Execution\u003c\/li\u003e\n\u003cli\u003eMarket and Fundamental Data - Sources and Techniques\u003c\/li\u003e\n\u003cli\u003eAlternative Data for Finance - Categories and Use Cases\u003c\/li\u003e\n\u003cli\u003eFinancial Feature Engineering - How to Research Alpha Factors\u003c\/li\u003e\n\u003cli\u003ePortfolio Optimization and Performance Evaluation\u003c\/li\u003e\n\u003cli\u003eThe Machine Learning Process\u003c\/li\u003e\n\u003cli\u003eLinear Models - From Risk Factors to Return Forecasts\u003c\/li\u003e\n\u003cli\u003eThe ML4T Workflow - From Model to Strategy Backtesting\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e","brand":"None","offers":[{"title":"Couverture rigide","offer_id":46403344695506,"sku":"9781837027095","price":101.99,"currency_code":"CAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0655\/8980\/5233\/files\/1_9d646195-0af5-476e-ba1f-c73d05261f05.jpg?v=1762833110","url":"https:\/\/www.indigo.ca\/fr\/products\/machine-learning-for-algorithmic-trading-second-edition","provider":"Indigo","version":"1.0","type":"link"}