Discover the ins and outs of designing predictive trading models
Drawing on the expertise of WorldQuant’s global network, this new edition of Finding Alphas: A Quantitative Approach to Building Trading Strategies contains significant changes and updates to the original material, with new and updated data and examples.
Nine chapters have been added about alphas – models used to make predictions regarding the prices of financial instruments. The new chapters cover topics including alpha correlation, controlling biases, exchange-traded funds, event-driven investing, index alphas, intraday data in alpha research, intraday trading, machine learning, and the triple axis plan for identifying alphas.
• Provides more references to the academic literature
• Includes new, high-quality material
• Organizes content in a practical and easy-to-follow manner
• Adds new alpha examples with formulas and explanations
If you’re looking for the latest information on building trading strategies from a quantitative approach, this book has you covered.
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IGOR TULCHINSKY is the Founder, Chairman, and CEO of WorldQuant, a global quantitative asset management firm, based in Old Greenwich, Connecticut, that he established in 2007 following 12 years as a statistical arbitrage portfolio manager at Millennium Management. Before joining Millennium, Tulchinsky was a venture capitalist, scientist at AT&T Bell Laboratories, video game programmer, and author. He holds a master’s degree in Computer Science from the University of Texas, Austin, completed in a then-record nine months, and an MBA in Finance and Entrepreneurship from the Wharton School at the University of Pennsylvania. A strong believer in education, Tulchinsky is the founder of WorldQuant University, which offers an entirely free online MSc degree in financial engineering and an applied data science module.
THE TRADING PROFESSIONAL'S GUIDE TO DESIGNING QUANTITATIVE MATHEMATICAL MODELS, THOROUGHLY UPDATED AND REVISED
WorldQuant designs and develops "alphas" – quantitative mathematical models used to make predictions regarding the prices of financial instruments. A combination of mathematical expressions, computer source code, and configuration parameters, alpha algorithms convert data into positions or trades. An alpha is at the core of a predictive trading model, cutting through the noise of the market to identify and isolate a signal.
This fully revised edition of Finding Alphas provides substantially new and updated information that reflects the ever-increasing volume and variety of available market data, advances in computer technology, and cutting-edge techniques for designing and deploying alphas. In-depth chapters – written by WorldQuant Founder, Chairman, and CEO Igor Tulchinsky, along with current and former WorldQuant researchers, portfolio managers, and technologists – provide fresh insights on a broad range of topics, including machine learning, alpha correlation, intraday trading, exchange-traded funds, event-driven trading, and much more. Finding Alphas provides you with the necessary information to explore the universe of predictive signals.
For even the most experienced traders, designing the predictive mathematical models (alphas) at the core of quantitative trading is a complex, labor-intensive process that requires significant research and testing. Based on ever-changing data, quantitative strategies may have limited life spans; new models must be generated on a constant basis. Igor Tulchinsky, Founder, Chairman, and CEO of WorldQuant, and the team of researchers, portfolio managers, and technologists he has assembled at his global quantitative asset management firm have significant firsthand experience in this area. Finding Alphas: A Quantitative Approach to Building Trading Strategies draws on WorldQuant's expertise in developing quantitative mathematical models to help you identify potentially profitable opportunities and build your own quantitative trading strategies. Covering everything from basic theory to advanced design and analysis techniques, this one-stop resource:
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