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The Science Of Algorithmic Trading And Portfolio Management

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The Science of Algorithmic Trading and Portfolio Management

The Science of Algorithmic Trading and Portfolio Management Book
Author : Robert Kissell
Publisher : Academic Press
Release : 2013-10-01
ISBN : 0124016936
Language : En, Es, Fr & De

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Book Description :

The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. Helps readers design systems to manage algorithmic risk and dark pool uncertainty. Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.

The Science of Algorithmic Trading and Portfolio Management

The Science of Algorithmic Trading and Portfolio Management Book
Author : Robert Kissell
Publisher : Academic Press
Release : 2013-07-01
ISBN : 9780124016897
Language : En, Es, Fr & De

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Book Description :

The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. Helps readers design systems to manage algorithmic risk and dark pool uncertainty. Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.

Algorithmic Trading Methods

Algorithmic Trading Methods Book
Author : Robert L. Kissell
Publisher : Academic Press
Release : 2020-09-08
ISBN : 0128156317
Language : En, Es, Fr & De

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Book Description :

Algorithmic Trading Methods: Applications using Advanced Statistics, Optimization, and Machine Learning Techniques, Second Edition, is a sequel to The Science of Algorithmic Trading and Portfolio Management. This edition includes new chapters on algorithmic trading, advanced trading analytics, regression analysis, optimization, and advanced statistical methods. Increasing its focus on trading strategies and models, this edition includes new insights into the ever-changing financial environment, pre-trade and post-trade analysis, liquidation cost & risk analysis, and compliance and regulatory reporting requirements. Highlighting new investment techniques, this book includes material to assist in the best execution process, model validation, quality and assurance testing, limit order modeling, and smart order routing analysis. Includes advanced modeling techniques using machine learning, predictive analytics, and neural networks. The text provides readers with a suite of transaction cost analysis functions packaged as a TCA library. These programming tools are accessible via numerous software applications and programming languages. Provides insight into all necessary components of algorithmic trading including: transaction cost analysis, market impact estimation, risk modeling and optimization, and advanced examination of trading algorithms and corresponding data requirements. Increased coverage of essential mathematics, probability and statistics, machine learning, predictive analytics, and neural networks, and applications to trading and finance. Advanced multiperiod trade schedule optimization and portfolio construction techniques. Techniques to decode broker-dealer and third-party vendor models. Methods to incorporate TCA into proprietary alpha models and portfolio optimizers. TCA library for numerous software applications and programming languages including: MATLAB, Excel Add-In, Python, Java, C/C++, .Net, Hadoop, and as standalone .EXE and .COM applications.

Quantitative Portfolio Management

Quantitative Portfolio Management Book
Author : Michael Isichenko
Publisher : John Wiley & Sons
Release : 2021-09-10
ISBN : 1119821215
Language : En, Es, Fr & De

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Book Description :

Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.

Statistical Arbitrage

Statistical Arbitrage Book
Author : Andrew Pole
Publisher : John Wiley & Sons
Release : 2011-07-07
ISBN : 1118160738
Language : En, Es, Fr & De

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Book Description :

While statistical arbitrage has faced some tough times?as markets experienced dramatic changes in dynamics beginning in 2000?new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole?s own research and experience running a statistical arbitrage hedge fund for eight years?in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading?this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.

Machine Learning for Algorithmic Trading

Machine Learning for Algorithmic Trading Book
Author : Stefan Jansen
Publisher : Packt Publishing Ltd
Release : 2020-07-31
ISBN : 1839216786
Language : En, Es, Fr & De

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Book Description :

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. 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.

Quantitative Trading

Quantitative Trading Book
Author : Xin Guo,Tze Leung Lai,Howard Shek,Samuel Po-Shing Wong
Publisher : CRC Press
Release : 2017-01-06
ISBN : 1315354357
Language : En, Es, Fr & De

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Book Description :

The first part of this book discusses institutions and mechanisms of algorithmic trading, market microstructure, high-frequency data and stylized facts, time and event aggregation, order book dynamics, trading strategies and algorithms, transaction costs, market impact and execution strategies, risk analysis, and management. The second part covers market impact models, network models, multi-asset trading, machine learning techniques, and nonlinear filtering. The third part discusses electronic market making, liquidity, systemic risk, recent developments and debates on the subject.

Financial Trading and Investing

Financial Trading and Investing Book
Author : John L. Teall
Publisher : Academic Press
Release : 2018-03-21
ISBN : 0128111178
Language : En, Es, Fr & De

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Book Description :

Financial Trading and Investing, Second Edition, delivers the most current information on trading and market microstructure for undergraduate and master’s students. Without demanding a background in econometrics, it explores alternative markets and highlights recent regulatory developments, implementations, institutions and debates. New explanations of controversial trading tactics (and blunders), such as high-frequency trading, dark liquidity pools, fat fingers, insider trading, and flash orders emphasize links between the history of financial regulation and events in financial markets. New sections on valuation and hedging techniques, particularly with respect to fixed income and derivatives markets, accompany updated regulatory information. In addition, new case studies and additional exercises are included on a website that has been revised, expanded and updated. Combining theory and application, the book provides the only up-to-date, practical beginner's introduction to today's investment tools and markets. Concentrates on trading, trading institutions, markets and the institutions that facilitate and regulate trading activities Introduces foundational topics relating to trading and securities markets, including auctions, market microstructure, the roles of information and inventories, behavioral finance, market efficiency, risk, arbitrage, trading technology, trading regulation and ECNs Covers market and technology advances and innovations, such as execution algo trading, Designated Market Makers (DMMs), Supplemental Liquidity Providers (SLPs), and the Super Display Book system (SDBK)

Artificial Intelligence in Asset Management

Artificial Intelligence in Asset Management Book
Author : Söhnke M. Bartram,Jürgen Branke,Mehrshad Motahari
Publisher : CFA Institute Research Foundation
Release : 2020-08-28
ISBN : 195292703X
Language : En, Es, Fr & De

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Book Description :

Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.

Machine Learning for Asset Managers

Machine Learning for Asset Managers Book
Author : Marcos M. López de Prado
Publisher : Cambridge University Press
Release : 2020-04-22
ISBN : 1108879721
Language : En, Es, Fr & De

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Book Description :

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

Electronic and Algorithmic Trading Technology

Electronic and Algorithmic Trading Technology Book
Author : Kendall Kim
Publisher : Academic Press
Release : 2010-07-27
ISBN : 9780080548869
Language : En, Es, Fr & De

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Book Description :

Electronic and algorithmic trading has become part of a mainstream response to buy-side traders’ need to move large blocks of shares with minimum market impact in today’s complex institutional trading environment. This book illustrates an overview of key providers in the marketplace. With electronic trading platforms becoming increasingly sophisticated, more cost effective measures handling larger order flow is becoming a reality. The higher reliance on electronic trading has had profound implications for vendors and users of information and trading products. Broker dealers providing solutions through their products are facing changes in their business models such as: relationships with sellside customers, relationships with buyside customers, the importance of broker neutrality, the role of direct market access, and the relationship with prime brokers. Electronic and Algorithmic Trading Technology: The Complete Guide is the ultimate guide to managers, institutional investors, broker dealers, and software vendors to better understand innovative technologies that can cut transaction costs, eliminate human error, boost trading efficiency and supplement productivity. As economic and regulatory pressures are driving financial institutions to seek efficiency gains by improving the quality of software systems, firms are devoting increasing amounts of financial and human capital to maintaining their competitive edge. This book is written to aid the management and development of IT systems for financial institutions. Although the book focuses on the securities industry, its solution framework can be applied to satisfy complex automation requirements within very different sectors of financial services – from payments and cash management, to insurance and securities. Electronic and Algorithmic Trading: The Complete Guide is geared toward all levels of technology, investment management and the financial service professionals responsible for developing and implementing cutting-edge technology. It outlines a complete framework for successfully building a software system that provides the functionalities required by the business model. It is revolutionary as the first guide to cover everything from the technologies to how to evaluate tools to best practices for IT management. First book to address the hot topic of how systems can be designed to maximize the benefits of program and algorithmic trading Outlines a complete framework for developing a software system that meets the needs of the firm's business model Provides a robust system for making the build vs. buy decision based on business requirements

Python for Algorithmic Trading

Python for Algorithmic Trading Book
Author : Yves Hilpisch
Publisher : O'Reilly Media
Release : 2020-11-12
ISBN : 1492053325
Language : En, Es, Fr & De

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Book Description :

Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

Quantitative Trading

Quantitative Trading Book
Author : Ernest P. Chan
Publisher : Unknown
Release : 2009
ISBN : 9781119203377
Language : En, Es, Fr & De

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Book Description :

"While institutional traders continue to implement quantitative (or algorithmic) trading, many independent traders have wondered if they can still challenge powerful industry professionals at their own game? The answer is "yes," and in Quantitative Trading, Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you're an independent "retail" trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed"--Resource description page.

Algorithmic Trading

Algorithmic Trading Book
Author : Ernie Chan
Publisher : John Wiley & Sons
Release : 2013-05-28
ISBN : 1118460146
Language : En, Es, Fr & De

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Book Description :

Praise for Algorithmic Trading "Algorithmic Trading is an insightful book on quantitative trading written by a seasoned practitioner. What sets this book apart from many others in the space is the emphasis on real examples as opposed to just theory. Concepts are not only described, they are brought to life with actual trading strategies, which give the reader insight into how and why each strategy was developed, how it was implemented, and even how it was coded. This book is a valuable resource for anyone looking to create their own systematic trading strategies and those involved in manager selection, where the knowledge contained in this book will lead to a more informed and nuanced conversation with managers." —DAREN SMITH, CFA, CAIA, FSA, President and Chief Investment Officer, University of Toronto Asset Management "Using an excellent selection of mean reversion and momentum strategies, Ernie explains the rationale behind each one, shows how to test it, how to improve it, and discusses implementation issues. His book is a careful, detailed exposition of the scientific method applied to strategy development. For serious retail traders, I know of no other book that provides this range of examples and level of detail. His discussions of how regime changes affect strategies, and of risk management, are invaluable bonuses." —Roger Hunter, Mathematician and Algorithmic Trader

Applied Quantitative Methods for Trading and Investment

Applied Quantitative Methods for Trading and Investment Book
Author : Christian L. Dunis,Jason Laws,Patrick Naïm
Publisher : John Wiley & Sons
Release : 2004-01-09
ISBN : 0470871342
Language : En, Es, Fr & De

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Book Description :

This book provides a manual on quantitative financial analysis. Focusing on advanced methods for modelling financial markets in the context of practical financial applications, it will cover data, software and techniques that will enable the reader to implement and interpret quantitative methodologies, specifically for trading and investment. Includes contributions from an international team of academics and quantitative asset managers from Morgan Stanley, Barclays Global Investors, ABN AMRO and Credit Suisse First Boston. Fills the gap for a book on applied quantitative investment & trading models Provides details of how to combine various models to manage and trade a portfolio

Advances in Financial Machine Learning

Advances in Financial Machine Learning Book
Author : Marcos Lopez de Prado
Publisher : John Wiley & Sons
Release : 2018-01-23
ISBN : 1119482119
Language : En, Es, Fr & De

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Book Description :

Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.

An Introduction to Algorithmic Finance Algorithmic Trading and Blockchain

An Introduction to Algorithmic Finance  Algorithmic Trading and Blockchain Book
Author : Satya Chakravarty,Palash Sarkar
Publisher : Emerald Group Publishing
Release : 2020-08-20
ISBN : 1789738938
Language : En, Es, Fr & De

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Book Description :

The purpose of the book is to provide a broad-based accessible introduction to three of the presently most important areas of computational finance, namely, option pricing, algorithmic trading and blockchain. This will provide a basic understanding required for a career in the finance industry and for doing more specialised courses in finance.

Big Data and Machine Learning in Quantitative Investment

Big Data and Machine Learning in Quantitative Investment Book
Author : Tony Guida
Publisher : John Wiley & Sons
Release : 2019-03-25
ISBN : 1119522196
Language : En, Es, Fr & De

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Book Description :

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.

What Hedge Funds Really Do

What Hedge Funds Really Do Book
Author : Philip J. Romero,Tucker Balch
Publisher : Business Expert Press
Release : 2014-08-22
ISBN : 1631570900
Language : En, Es, Fr & De

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Book Description :

What Hedge Funds Do provides a needed complement to journalistic accounts of the hedge fund industry, to deepen the understanding of non-specialist readers such as policymakers, journalists, and individual investors. What do hedge funds really do? These lightly-regulated funds continually innovate new investing and trading strategies to take advantage of temporary mispricing of assets (when their market price deviates from their intrinsic value). These techniques are shrouded in mystery, which permits hedge fund managers to charge exceptionally high fees. While the details of each funds' approach are carefully guarded trade secrets, this book draws the curtain back on the core building blocks of many hedge fund strategies Beyond the book's instructional goals, What Hedge Funds Do provides a needed complement to journalistic accounts of the hedge fund industry, to deepen the understanding of non-specialist readers such as policymakers, journalists, and individual investors. It is written by a fund practitioner and computer scientist (Balch), in collaboration with a public policy economist and finance academic (Romero).

Algorithmic Short Selling with Python

Algorithmic Short Selling with Python Book
Author : Laurent Bernut,Michael Covel
Publisher : Packt Publishing Ltd
Release : 2021-09-30
ISBN : 1801810397
Language : En, Es, Fr & De

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Book Description :

Leverage Python source code to revolutionize your short selling strategy and to consistently make profits in bull, bear, and sideways markets Key FeaturesUnderstand techniques such as trend following, mean reversion, position sizing, and risk management in a short-selling contextImplement Python source code to explore and develop your own investment strategyTest your trading strategies to limit risk and increase profitsBook Description If you are in the long/short business, learning how to sell short is not a choice. Short selling is the key to raising assets under management. This book will help you demystify and hone the short selling craft, providing Python source code to construct a robust long/short portfolio. It discusses fundamental and advanced trading concepts from the perspective of a veteran short seller. This book will take you on a journey from an idea (“buy bullish stocks, sell bearish ones”) to becoming part of the elite club of long/short hedge fund algorithmic traders. You'll explore key concepts such as trading psychology, trading edge, regime definition, signal processing, position sizing, risk management, and asset allocation, one obstacle at a time. Along the way, you'll will discover simple methods to consistently generate investment ideas, and consider variables that impact returns, volatility, and overall attractiveness of returns. By the end of this book, you'll not only become familiar with some of the most sophisticated concepts in capital markets, but also have Python source code to construct a long/short product that investors are bound to find attractive. What you will learnDevelop the mindset required to win the infinite, complex, random game called the stock marketDemystify short selling in order to generate alpa in bull, bear, and sideways marketsGenerate ideas consistently on both sides of the portfolioImplement Python source code to engineer a statistically robust trading edgeDevelop superior risk management habitsBuild a long/short product that investors will find appealingWho this book is for This is a book by a practitioner for practitioners. It is designed to benefit a wide range of people, including long/short market participants, quantitative participants, proprietary traders, commodity trading advisors, retail investors (pro retailers, students, and retail quants), and long-only investors. At least 2 years of active trading experience, intermediate-level experience of the Python programming language, and basic mathematical literacy (basic statistics and algebra) are expected.