Learn from a master of quantitative finance the rules that made him a success. The UnRules presents the dynamic rules for success in the age of exponential information. Written by Igor Tulchinsky, the trader behind global quantitative investment management firm WorldQuant, this book is more than just another Big Data guide for financial wonks — it’s a prescriptive, inspirational book for everyone navigating the tidal waves of the information age. Data is everywhere, coming at us in a never-ceasing, ever-rising river that threatens to overwhelm us. Tulchinsky shows us, however, how natural patterns underlie that data — patterns that may dictate life or death, success or failure. The marriage of man and machines has allowed scientists to explore increasingly complex worlds, to predict outcomes and eventualities. This book demonstrates how to exercise real intelligence by discerning the patterns that surround us every day and how to leverage this information into success in the workplace and beyond. Igor Tulchinsky has spent his career discerning meaningful patterns in information. For decades, Tulchinsky has been at the forefront of developing predictive trading algorithms known as alphas — a quest that has led Tulchinsky to explore the nature of markets, the fundamentals of risk and reward, and the science behind complex nonlinear systems. Tulchinsky explains what we know of these systems, both natural and man-made, in accessible and personal terms, and he shares how alphas have driven his success as an investor and shaped his central “UnRule,” which is that no rule applies in every case. As markets evolve, even the most effective trading algorithms weaken over time. Decades of creating successful alphas — and learning how to effectively transform them into strategies — have taught Tulchinsky about the need to combine flexibility and focus, discipline and creativity when building complex models. At a time when data and computing power are exploding exponentially, The UnRules provides an expert introduction to our increasingly quantitative world.

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.

Leverage 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. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The 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. This 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. This 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. By 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. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If 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.

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.

"The decisions that investment professionals and fund managers make have a direct impact on investor return. Unfortunately, the best implementation methodologies are not widely disseminated throughout the professional community, compromising the best interests of funds, their managers, and ultimately the individual investor. But now there is a strategy that lets professionals make better decisions. This valuable reference answers crucial questions such as: * How do I compare strategies? * Should I trade aggressively or passively? * How do I estimate trading costs, ""slice"" an order, and measure performance? and dozens more. Optimal Trading Strategies is the first book to give professionals the methodology and framework they need to make educated implementation decisions based on the objectives and goals of the funds they manage and the clients they serve."

Aswath Damodaran, distinguished author, Professor of Finance, and David Margolis, Teaching Fellow at the NYU Stern School of Business, has delivered the newest edition of Applied Corporate Finance. This readable text provides the practical advice students and practitioners need rather than a sole concentration on debate theory, assumptions, or models. Like no other text of its kind, Applied Corporate Finance, 4th Edition applies corporate finance to real companies. It now contains six real-world core companies to study and follow. Business decisions are classified for students into three groups: investment, financing, and dividend decisions.

I’m an alpha. Top of the food chain. What I want, I take, simple as. And I want her. I don’t care she’s a beta, not an omega. She’s mine. I’ve wanted Neve for years, now she works for us. The problem is, I can’t keep my hands off her. And neither can Dante. I’m good with sharing, as long as I can call her mine. Beta or not, She’s the hottest woman I’ve ever seen. She’s lucky she isn’t an omega. If she was, she’d already be wearing my mark and we’d never let her out of her nest. Then her ex starts sniffing around, and he really doesn’t know what he’s gotten himself into. We might wear suits now, but we haven’t forgotten the monsters within. Now they’re coming out to play… Please be aware: This is a much darker and grittier world than my other series, so readers are advised to proceed with caution. A Continuing Story: Please be aware this is a serialized romance in three parts. Keywords: paranormal romance ebooks, shifter romance kindle, alpha male, romance reads, paranormal romance, paranormal romance books for adults, Fated mates romance, shifter romance books, shifter books, omega verse book, omega verse romance, omegaverse alpha, omegaverse dystopian, omegaverse erotica, omegaverse kindle, omegaverse knot, omegaverse knotting heat, omegaverse paranormal romance