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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
This book provides a comprehensive look at the challenges of keeping up with liquidity needs and technology advancements. It is also a sourcebook for understandable, practical solutions on trading and technology.
The true meaning of investment discipline is to trade only when you rationally expect that you will achieve your desired objective. Accordingly, managers must thoroughly understand why they trade. Because trading is a zero-sum game, good investment discipline also requires that managers understand why their counterparties trade. This book surveys the many reasons why people trade and identifies the implications of the zero-sum game for investment discipline. It also identifies the origins of liquidity and thus of transaction costs, as well as when active investment strategies are profitable. The book then explains how managers must measure and control transaction costs to perform well. Electronic trading systems and electronic trading strategies now dominate trading in exchange markets throughout the world. The book identifies why speed is of such great importance to electronic traders, how they obtain it, and the trading strategies they use to exploit it. Finally, the book analyzes many issues associated with electronic trading that currently concern practitioners and regulators.
The design of trading algorithms requires sophisticated mathematical models backed up by reliable data. In this textbook, the authors develop models for algorithmic trading in contexts such as executing large orders, market making, targeting VWAP and other schedules, trading pairs or collection of assets, and executing in dark pools. These models are grounded on how the exchanges work, whether the algorithm is trading with better informed traders (adverse selection), and the type of information available to market participants at both ultra-high and low frequency. Algorithmic and High-Frequency Trading is the first book that combines sophisticated mathematical modelling, empirical facts and financial economics, taking the reader from basic ideas to cutting-edge research and practice. If you need to understand how modern electronic markets operate, what information provides a trading edge, and how other market participants may affect the profitability of the algorithms, then this is the book for you.
The use of ICT applications has dipped into almost every aspect of the business sector, including trade. With the volume of e-commerce increasing, international traders must switch their rules and practices to e-trade to survive in such a competitive market. However, the complexity of international trade, which covers customs processes, different legislation, specific documentation requirements, different languages, different currencies, and different payment systems and risk, presents its own challenges in this transition. Tools and Techniques for Implementing International E-Trading Tactics for Competitive Advantage examines the multidisciplinary approach of international e-trade as it applies to information technology, digital marketing, digital communication, online reputation management, and different legislation and risks. The content within this publication examines digital advertising, consumer behavior, and e-commerce and is designed for international traders, entrepreneurs, business professionals, researchers, academicians, and students.
Argues that post-crisis Wall Street continues to be controlled by large banks and explains how a small, diverse group of Wall Street men have banded together to reform the financial markets.
Handbook of Multi-Commodity Markets and ProductsOver recent decades, the marketplace has seen an increasing integration, not only among different types of commodity markets such as energy, agricultural, and metals, but also with financial markets. This trend raises important questions about how to identify and analyse opportunities in and manage risks of commodity products. The Handbook of Multi-Commodity Markets and Products offers traders, commodity brokers, and other professionals a practical and comprehensive manual that covers market structure and functioning, as well as the practice of trading across a wide range of commodity markets and products. Written in non-technical language, this important resource includes the information needed to begin to master the complexities of and to operate successfully in today’s challenging and fluctuating commodity marketplace. Designed as a practical practitioner-orientated resource, the book includes a detailed overview of key markets – oil, coal, electricity, emissions, weather, industrial metals, freight, agricultural and foreign exchange – and contains a set of tools for analysing, pricing and managing risk for the individual markets. Market features and the main functioning rules of the markets in question are presented, along with the structure of basic financial products and standardised deals. A range of vital topics such as stochastic and econometric modelling, market structure analysis, contract engineering, as well as risk assessment and management are presented and discussed in detail with illustrative examples to commodity markets. The authors showcase how to structure and manage both simple and more complex multi-commodity deals. Addressing the issues of profit-making and risk management, the book reveals how to exploit pay-off profiles and trading strategies on a diversified set of commodity prices. In addition, the book explores how to price energy products and other commodities belonging to markets segmented across specific structural features. The Handbook of Multi-Commodity Markets and Products includes a wealth of proven methods and useful models that can be selected and developed in order to make appropriate estimations of the future evolution of prices and appropriate valuations of products. The authors additionally explore market risk issues and what measures of risk should be adopted for the purpose of accurately assessing exposure from multi-commodity portfolios. This vital resource offers the models, tools, strategies and general information commodity brokers and other professionals need to succeed in today’s highly competitive marketplace.
Algorithmic Trading and Quantitative Strategies provides an in-depth overview of this growing field with a unique mix of quantitative rigor and practitioner’s hands-on experience. The focus on empirical modeling and practical know-how makes this book a valuable resource for students and professionals. The book starts with the often overlooked context of why and how we trade via a detailed introduction to market structure and quantitative microstructure models. The authors then present the necessary quantitative toolbox including more advanced machine learning models needed to successfully operate in the field. They next discuss the subject of quantitative trading, alpha generation, active portfolio management and more recent topics like news and sentiment analytics. The last main topic of execution algorithms is covered in detail with emphasis on the state of the field and critical topics including the elusive concept of market impact. The book concludes with a discussion on the technology infrastructure necessary to implement algorithmic strategies in large-scale production settings. A git-hub repository includes data-sets and explanatory/exercise Jupyter notebooks. The exercises involve adding the correct code to solve the particular analysis/problem.
The book provides detailed coverage of?Single order algorithms, such as Volume-Weighted Average Price (VWAP), Time-Weighted-Average Price (TWAP), Percent of Volume (POV), and variants of the Implementation Shortfall algorithm. ?Multi-order algorithms, such as Pairs Trading and Portfolio Trading algorithms.?Smart routers, including "smart market", "smart limit", and dark aggregators.?Trading performance measurement, including trading benchmarks, "algo wheels", trading cost models, and other measurement issues.
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.