Algorithmic Trading

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Algorithmic and High-Frequency Trading

Algorithmic and High-Frequency Trading
  • Author : Álvaro Cartea,Sebastian Jaimungal,José Penalva
  • Publisher :
  • Release Date :2015-08-06
  • Total pages :356
  • ISBN : 1107091144
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Summary : A straightforward guide to the mathematics of algorithmic trading that reflects cutting-edge research.

Algorithmic Trading

Algorithmic Trading
  • Author : Ernie Chan
  • Publisher :
  • Release Date :2013-05-28
  • Total pages :224
  • ISBN : 1118460146
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Summary : 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

Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading
  • Author : Stefan Jansen
  • Publisher :
  • Release Date :2018-12-31
  • Total pages :516
  • ISBN : 1789342716
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Summary : Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental, and alternative data to research alpha factors Design and fine-tune supervised, unsupervised, and reinforcement learning models Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn Integrate machine learning models into a live trading strategy on Quantopian Evaluate strategies using reliable backtesting methodologies for time series Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow Work with reinforcement learning for trading strategies in the OpenAI Gym Who this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

Quantitative Trading

Quantitative Trading
  • Author : Ernie Chan
  • Publisher :
  • Release Date :2009-01-12
  • Total pages :208
  • ISBN : 9780470466261
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Summary : 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.

Algorithmic Trading

Algorithmic Trading
  • Author : Jeffrey Bacidore
  • Publisher :
  • Release Date :2020-06-15
  • Total pages :329
  • ISBN : 9780578715230
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Summary : 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.

A Guide to Creating A Successful Algorithmic Trading Strategy

A Guide to Creating A Successful Algorithmic Trading Strategy
  • Author : Perry J. Kaufman
  • Publisher :
  • Release Date :2016-02-01
  • Total pages :192
  • ISBN : 1119224748
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Summary : Turn insight into profit with guru guidance toward successful algorithmic trading A Guide to Creating a Successful Algorithmic Trading Strategy provides the latest strategies from an industry guru to show you how to build your own system from the ground up. If you're looking to develop a successful career in algorithmic trading, this book has you covered from idea to execution as you learn to develop a trader's insight and turn it into profitable strategy. You'll discover your trading personality and use it as a jumping-off point to create the ideal algo system that works the way you work, so you can achieve your goals faster. Coverage includes learning to recognize opportunities and identify a sound premise, and detailed discussion on seasonal patterns, interest rate-based trends, volatility, weekly and monthly patterns, the 3-day cycle, and much more—with an emphasis on trading as the best teacher. By actually making trades, you concentrate your attention on the market, absorb the effects on your money, and quickly resolve problems that impact profits. Algorithmic trading began as a "ridiculous" concept in the 1970s, then became an "unfair advantage" as it evolved into the lynchpin of a successful trading strategy. This book gives you the background you need to effectively reap the benefits of this important trading method. Navigate confusing markets Find the right trades and make them Build a successful algo trading system Turn insights into profitable strategies Algorithmic trading strategies are everywhere, but they're not all equally valuable. It's far too easy to fall for something that worked brilliantly in the past, but with little hope of working in the future. A Guide to Creating a Successful Algorithmic Trading Strategy shows you how to choose the best, leave the rest, and make more money from your trades.

Algorithmic Trading and Stocks Essential Training

Algorithmic Trading and Stocks Essential Training
  • Author : N.A
  • Publisher :
  • Release Date :2018
  • Total pages :329
  • ISBN :
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Summary : Learn how to develop and test a rules-based trading strategy and program a simple trading algorithm for buying and selling stocks.

Electronic and Algorithmic Trading Technology

Electronic and Algorithmic Trading Technology
  • Author : Kendall Kim
  • Publisher :
  • Release Date :2010-07-27
  • Total pages :224
  • ISBN : 9780080548869
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Summary : 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 Algorithmic Trading Cookbook

Python Algorithmic Trading Cookbook
  • Author : Pushpak Dagade
  • Publisher :
  • Release Date :2020-08-28
  • Total pages :542
  • ISBN : 1838982515
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Summary : Ever wondered what it takes to be an algorithmic trading professional? Look no further, this recipe-based guide will help you uncover various common and not-so-common challenges faced while devising efficient and powerful algo trading strategies. You will implement various Python libraries to conduct key tasks in the algorithmic trading ecosystem.

Algorithmic Trading and Finance Models with Python, R, and Stata Essential Training

Algorithmic Trading and Finance Models with Python, R, and Stata Essential Training
  • Author : Michael McDonald
  • Publisher :
  • Release Date :2019
  • Total pages :329
  • ISBN :
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Summary :

Algorithmic Trading

Algorithmic Trading
  • Author : IntroBooks Team
  • Publisher :
  • Release Date :
  • Total pages :329
  • ISBN :
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Summary : Algorithmic trading is an exchange mechanism where computers make choices about what to buy and sell in the money markets. The purpose of algorithmic trading would be to either make money by buying lower and selling higher or to minimize transaction costs by effectively buying or selling large volumes of financial commodities. Depending on those guidelines, the computer determines when and how much to buy and sell. And these norms are designed by manual efforts. Algorithmic Trading typically involves understanding of the financial marketing domain, programming, and knowledge related to data sciences. Algorithmic trading can be broken down into two segments: *The revelation of market inefficiencies: People are looking in the markets for something unfair that they can leverage. To illustrate, if two exchanges value a similar financial product differently, there may be a variance. *People devise a plan to exploit the business incompetence they have detected. It entails determining the ideal moment to buy and sell, the exact quantity to buy and sell, and how to end the trading operations.

An Introduction to Algorithmic Trading

An Introduction to Algorithmic Trading
  • Author : Edward Leshik,Jane Cralle
  • Publisher :
  • Release Date :2011-04-04
  • Total pages :272
  • ISBN : 0470689544
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Summary : CD-ROM includes examples and algorithms in Microsoft Excel spreadsheets.

Algorithmic Trading Methods

Algorithmic Trading Methods
  • Author : Robert Kissell
  • Publisher :
  • Release Date :2020-09-08
  • Total pages :612
  • ISBN : 0128156317
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Summary : 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.

Essays on Algorithmic Trading

Essays on Algorithmic Trading
  • Author : Markus Gsell
  • Publisher :
  • Release Date :2010-07-09
  • Total pages :226
  • ISBN : 3838261143
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Summary : Technological innovations are altering the traditional value chain in securities trading. Hitherto the order handling, i.e. the appropriate implementation of a general trading decision into particular orders, has been a core competence of brokers. Labeled as Algorithmic Trading, the automation of this task recently found its way both into the brokers' portfolio of service offerings as well as to their customers' trading desks. The software performing the order handling thereby constantly monitors the market(s) in real-time and further evaluates historical data to dynamically determine appropriate points in time for trading. Within only a few years, this technology propagated itself among market participants along the entire value chain and has nowadays gained a significant market share on securities markets worldwide. Surprisingly, there has been only little research analyzing the impact of this special type of trading on markets. Markus Gsell's book aims at closing this gap by analyzing the drivers for adoption of this technology, the impact the application of this technology has on markets on a macro level, i.e. how the market outcome is affected, as well as on a micro level, i.e. how the exhibited trading behavior of these automated traders differs from normal traders' behavior.

Learn Algorithmic Trading

Learn Algorithmic Trading
  • Author : Sebastien Donadio,Sourav Ghosh
  • Publisher :
  • Release Date :2019-11-07
  • Total pages :394
  • ISBN : 1789342147
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Summary : Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading strategies Key Features Understand the power of algorithmic trading in financial markets with real-world examples Get up and running with the algorithms used to carry out algorithmic trading Learn to build your own algorithmic trading robots which require no human intervention Book Description It’s now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate. You’ll start with an introduction to algorithmic trading, along with setting up the environment required to perform the tasks in the book. You’ll explore the key components of an algorithmic trading business and aspects you’ll need to take into account before starting an automated trading project. Next, you’ll focus on designing, building and operating the components required for developing a practical and profitable algorithmic trading business. Later, you’ll learn how quantitative trading signals and strategies are developed, and also implement and analyze sophisticated trading strategies such as volatility strategies, economic release strategies, and statistical arbitrage. Finally, you’ll create a trading bot from scratch using the algorithms built in the previous sections. By the end of this book, you’ll be well-versed with electronic trading markets and have learned to implement, evaluate and safely operate algorithmic trading strategies in live markets. What you will learn Understand the components of modern algorithmic trading systems and strategies Apply machine learning in algorithmic trading signals and strategies using Python Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more Quantify and build a risk management system for Python trading strategies Build a backtester to run simulated trading strategies for improving the performance of your trading bot Deploy and incorporate trading strategies in the live market to maintain and improve profitability Who this book is for This book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.