Advertisement
algorithmic trading with python chris conlan: Algorithmic Trading with Python Chris Conlan, 2020-04-09 Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn. After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline. This book maintains a high standard of reprocibility. All code and data is self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities. Algorithmic Trading with Python (2020) is the spiritual successor to Automated Trading with R (2016). This book covers more content in less time than its predecessor due to advances in open-source technologies for quantitative analysis. |
algorithmic trading with python chris conlan: Automated Trading with R Chris Conlan, 2016-09-28 Learn to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage’s API, and the source code is plug-and-play. Automated Trading with R explains automated trading, starting with its mathematics and moving to its computation and execution. You will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform. The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage. This book will: Provide a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders Offer an understanding of the internal mechanisms of an automated trading system Standardize discussion and notation of real-world strategy optimization problems What You Will Learn Understand machine-learning criteria for statistical validity in the context of time-series Optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library Best simulate strategy performance in its specific use case to derive accurate performance estimates Understand critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital Who This Book Is For Traders/practitioners at the retail or small fund level with at least an undergraduate background in finance or computer science; graduate level finance or data science students |
algorithmic trading with python chris conlan: An Introduction to Algorithmic Trading Edward Leshik, Jane Cralle, 2011-04-04 Interest in algorithmic trading is growing massively – it’s cheaper, faster and better to control than standard trading, it enables you to ‘pre-think’ the market, executing complex math in real time and take the required decisions based on the strategy defined. We are no longer limited by human ‘bandwidth’. The cost alone (estimated at 6 cents per share manual, 1 cent per share algorithmic) is a sufficient driver to power the growth of the industry. According to consultant firm, Aite Group LLC, high frequency trading firms alone account for 73% of all US equity trading volume, despite only representing approximately 2% of the total firms operating in the US markets. Algorithmic trading is becoming the industry lifeblood. But it is a secretive industry with few willing to share the secrets of their success. The book begins with a step-by-step guide to algorithmic trading, demystifying this complex subject and providing readers with a specific and usable algorithmic trading knowledge. It provides background information leading to more advanced work by outlining the current trading algorithms, the basics of their design, what they are, how they work, how they are used, their strengths, their weaknesses, where we are now and where we are going. The book then goes on to demonstrate a selection of detailed algorithms including their implementation in the markets. Using actual algorithms that have been used in live trading readers have access to real time trading functionality and can use the never before seen algorithms to trade their own accounts. The markets are complex adaptive systems exhibiting unpredictable behaviour. As the markets evolve algorithmic designers need to be constantly aware of any changes that may impact their work, so for the more adventurous reader there is also a section on how to design trading algorithms. All examples and algorithms are demonstrated in Excel on the accompanying CD ROM, including actual algorithmic examples which have been used in live trading. |
algorithmic trading with python chris conlan: An Introduction to Algorithmic Finance, Algorithmic Trading and Blockchain Satya Chakravarty, Palash Sarkar, 2020-08-20 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. |
algorithmic trading with python chris conlan: Fast Python Chris Conlan, 2020-05-31 Fast Python aggressively rehashes the basics of Python programming in order to dispel myths and misconceptions about how to write fast code. Readers equipped with the lessons from this book will be able to test, diagnose, and optimize out performance bottlenecks in their own work. For each algorithm discussed, readers will walk through numerous progressively faster methods of programming it, all while picking up bits of fundamental knowledge about time complexity, memory efficiency, data structures, multi-threading, and vectorization. As such, this book is relevant to veterans looking refresh their methods and to computer science students navigating Algorithms 101. This book maintains a high standard of reproducibility. All of the graphics, tables, and code profiles contained in this book are fully reproducible and available to anyone in a public GitHub repository. |
algorithmic trading with python chris conlan: Genetic Algorithms and Genetic Programming in Computational Finance Shu-Heng Chen, 2012-12-06 After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including financial forecasting, trading strategies development, cash flow management, option pricing, portfolio management, volatility modeling, arbitraging, and agent-based simulations of artificial stock markets. Two tutorial chapters are also included to help readers quickly grasp the essence of these tools. Finally, a menu-driven software program, Simple GP, accompanies the volume, which will enable readers without a strong programming background to gain hands-on experience in dealing with much of the technical material introduced in this work. |
algorithmic trading with python chris conlan: Combinatorial Optimization in Communication Networks Maggie Xiaoyan Cheng, Yingshu Li, Ding-Zhu Du, 2006-07-02 Combinatorial optimization algorithms are used in many applications including the design, management, and operations of communication networks. The objective of this book is to advance and promote the theory and applications of combinatorial optimization in communication networks. Each chapter of the book is written by an expert dealing with theoretical, computational, or applied aspects of combinatorial optimization. Topics covered in the book include the combinatorial optimization problems arising in optical networks, wireless ad hoc networks, sensor networks, mobile communication systems, and satellite networks. A variety of problems are addressed using combinatorial optimization techniques, ranging from routing and resource allocation to QoS provisioning. |
algorithmic trading with python chris conlan: A Framework for Visualizing Information E.H. Chi, 2002-04-30 Fundamental solutions in understanding information have been elusive for a long time. The field of Artificial Intelligence has proposed the Turing Test as a way to test for the smart behaviors of computer programs that exhibit human-like qualities. Equivalent to the Turing Test for the field of Human Information Interaction (HII), getting information to the people that need them and helping them to understand the information is the new challenge of the Web era. In a short amount of time, the infrastructure of the Web became ubiquitious not just in terms of protocols and transcontinental cables but also in terms of everyday devices capable of recalling network-stored data, sometimes wire lessly. Therefore, as these infrastructures become reality, our attention on HII issues needs to shift from information access to information sensemaking, a relatively new term coined to describe the process of digesting information and understanding its structure and intricacies so as to make decisions and take action. |
algorithmic trading with python chris conlan: Computational Modeling and Visualization of Physical Systems with Python Jay Wang, 2015-12-21 Computational Modeling, by Jay Wang introduces computational modeling and visualization of physical systems that are commonly found in physics and related areas. The authors begin with a framework that integrates model building, algorithm development, and data visualization for problem solving via scientific computing. Through carefully selected problems, methods, and projects, the reader is guided to learning and discovery by actively doing rather than just knowing physics. |
algorithmic trading with python chris conlan: Algorithmic Trading with Interactive Brokers Matthew Scarpino, 2019-09-03 Through Interactive Brokers, software developers can write applications that read financial data, scan for contracts, and submit orders automatically. Individuals can now take advantage of the same high-speed decision making and order placement that professional trading firms use.This book walks through the process of developing applications based on IB's Trader Workstation (TWS) programming interface. Beginning chapters introduce the fundamental classes and functions, while later chapters show how they can be used to implement full-scale trading systems. With an algorithmic system in place, traders don't have to stare at charts for hours on end. Just launch the trading application and let the TWS API do its work.The material in this book focuses on Python and C++ coding, so readers are presumed to have a basic familiarity with one of these languages. However, no experience in financial trading is assumed. If you're new to the world of stocks, bonds, options, and futures, this book explains what these financial instruments are and how to write applications capable of trading them. |
algorithmic trading with python chris conlan: Multi-objective Group Decision Making Jie Lu, Da Ruan, 2007 This book proposes a set of models to describe fuzzy multi-objective decision making (MODM), fuzzy multi-criteria decision making (MCDM), fuzzy group decision making (GDM) and fuzzy multi-objective group decision-making problems, respectively. It also gives a set of related methods (including algorithms) to solve these problems. One distinguishing feature of this book is that it provides two decision support systems software for readers to apply these proposed methods. A set of real-world applications and some new directions in this area are then described to further instruct readers how to use these methods and software in their practice. |
algorithmic trading with python chris conlan: Data Analytics in Bioinformatics Rabinarayan Satpathy, Tanupriya Choudhury, Suneeta Satpathy, Sachi Nandan Mohanty, Xiaobo Zhang, 2021-01-20 Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more. |
algorithmic trading with python chris conlan: Algorithms for Visual Design Using the Processing Language Kostas Terzidis, 2009-04-08 As the first book to share the necessary algorithms for creating code to experiment with design problems in the processing language, this book offers a series of generic procedures that can function as building blocks and encourages you to then use those building blocks to experiment, explore, and channel your thoughts, ideas, and principles into potential solutions. The book covers such topics as structured shapes, solid geometry, networking and databases, physical computing, image processing, graphic user interfaces, and more. |
algorithmic trading with python chris conlan: Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics Yi Pan, Min Li, Jianxin Wang, 2013-11-12 Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics An in-depth look at the latest research, methods, and applications in the field of protein bioinformatics This book presents the latest developments in protein bioinformatics, introducing for the first time cutting-edge research results alongside novel algorithmic and AI methods for the analysis of protein data. In one complete, self-contained volume, Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics addresses key challenges facing both computer scientists and biologists, arming readers with tools and techniques for analyzing and interpreting protein data and solving a variety of biological problems. Featuring a collection of authoritative articles by leaders in the field, this work focuses on the analysis of protein sequences, structures, and interaction networks using both traditional algorithms and AI methods. It also examines, in great detail, data preparation, simulation, experiments, evaluation methods, and applications. Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics: Highlights protein analysis applications such as protein-related drug activity comparison Incorporates salient case studies illustrating how to apply the methods outlined in the book Tackles the complex relationship between proteins from a systems biology point of view Relates the topic to other emerging technologies such as data mining and visualization Includes many tables and illustrations demonstrating concepts and performance figures Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics is an essential reference for bioinformatics specialists in research and industry, and for anyone wishing to better understand the rich field of protein bioinformatics. |
algorithmic trading with python chris conlan: A Tutorial on Elliptic PDE Solvers and Their Parallelization Craig C. Douglas, Gundolf Haase, Ulrich Langer, 2003-01-01 This compact yet thorough tutorial is the perfect introduction to the basic concepts of solving partial differential equations (PDEs) using parallel numerical methods. In just eight short chapters, the authors provide readers with enough basic knowledge of PDEs, discretization methods, solution techniques, parallel computers, parallel programming, and the run-time behavior of parallel algorithms to allow them to understand, develop, and implement parallel PDE solvers. Examples throughout the book are intentionally kept simple so that the parallelization strategies are not dominated by technical details. |
algorithmic trading with python chris conlan: Computational Information Geometry Frank Nielsen, Frank Critchley, Christopher T. J. Dodson, 2016-11-24 This book focuses on the application and development of information geometric methods in the analysis, classification and retrieval of images and signals. It provides introductory chapters to help those new to information geometry and applies the theory to several applications. This area has developed rapidly over recent years, propelled by the major theoretical developments in information geometry, efficient data and image acquisition and the desire to process and interpret large databases of digital information. The book addresses both the transfer of methodology to practitioners involved in database analysis and in its efficient computational implementation. |
algorithmic trading with python chris conlan: Blockchain Explained Srihari Kapu, 2020-12-08 This book offers the most anticipated solution to the blockchain and digital financial questions that are present in the minds of many. It points us to where it all started, where we are at, and a careful and well-informed analysis of what the future holds regarding financial transactions and the growth of cryptocurrency and blockchain technology. The world is consciously taking giant strides into the digital aspect of accounting. With the advent of blockchain and various forms of digital money, it is pertinent for every enthusiastic young mind to understand the basics of the market. The book takes a sneak peek into the future of blockchain and financial technology tech with real-life examples, illustrations, and analysis to tailor the mind of the public to the right path. The industry’s most important terminologies and concepts are broken down into bits for everyone. Every page of the book keeps you more informed about a particular subject matter. |
algorithmic trading with python chris conlan: Computational Linguistics and Talking Robots Roland Hausser, 2011-08-19 The practical task of building a talking robot requires a theory of how natural language communication works. Conversely, the best way to computationally verify a theory of natural language communication is to demonstrate its functioning concretely in the form of a talking robot, the epitome of human–machine communication. To build an actual robot requires hardware that provides appropriate recognition and action interfaces, and because such hardware is hard to develop the approach in this book is theoretical: the author presents an artificial cognitive agent with language as a software system called database semantics (DBS). Because a theoretical approach does not have to deal with the technical difficulties of hardware engineering there is no reason to simplify the system – instead the software components of DBS aim at completeness of function and of data coverage in word form recognition, syntactic–semantic interpretation and inferencing, leaving the procedural implementation of elementary concepts for later. In this book the author first examines the universals of natural language and explains the Database Semantics approach. Then in Part I he examines the following natural language communication issues: using external surfaces; the cycle of natural language communication; memory structure; autonomous control; and learning. In Part II he analyzes the coding of content according to the aspects: semantic relations of structure; simultaneous amalgamation of content; graph-theoretical considerations; computing perspective in dialogue; and computing perspective in text. The book ends with a concluding chapter, a bibliography and an index. The book will be of value to researchers, graduate students and engineers in the areas of artificial intelligence and robotics, in particular those who deal with natural language processing. |
algorithmic trading with python chris conlan: A Framework of Software Measurement Horst Zuse, 2013-02-06 No detailed description available for A Framework of Software Measurement. |
algorithmic trading with python chris conlan: Memory Architecture Exploration for Programmable Embedded Systems Peter Grun, Nikil D. Dutt, Alexandru Nicolau, 2003 This book presents a compiler-in-the-loop exploration strategy for alternative memory architectures, allowing for effective matching of the target application to the processor-memory architecture. This new approach for memory architecture exploration replaces the traditional black-box view of the memory system. The utility of the approach is illustrated for a set of large, real-life benchmarks. Material is of interest to different groups in the embedded systems-on-chip field, including researchers and students in memory architecture, CAD developers, and system designers. Grun is affiliated with the Center for Embedded Computer Systems, University of California-Irvine. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com). |
algorithmic trading with python chris conlan: Scientific Data Analysis using Jython Scripting and Java Sergei V. Chekanov, 2010-08-05 Scientific Data Analysis using Jython Scripting and Java presents practical approaches for data analysis using Java scripting based on Jython, a Java implementation of the Python language. The chapters essentially cover all aspects of data analysis, from arrays and histograms to clustering analysis, curve fitting, metadata and neural networks. A comprehensive coverage of data visualisation tools implemented in Java is also included. Written by the primary developer of the jHepWork data-analysis framework, the book provides a reliable and complete reference source laying the foundation for data-analysis applications using Java scripting. More than 250 code snippets (of around 10-20 lines each) written in Jython and Java, plus several real-life examples help the reader develop a genuine feeling for data analysis techniques and their programming implementation. This is the first data-analysis and data-mining book which is completely based on the Jython language, and opens doors to scripting using a fully multi-platform and multi-threaded approach. Graduate students and researchers will benefit from the information presented in this book. |
algorithmic trading with python chris conlan: Metadata for Digital Resources Muriel Foulonneau, Jenn Riley, 2014-01-23 This book assists information professionals in improving the usability of digital objects by adequately documenting them and using tools for metadata management. It provides practical advice for libraries, archives, and museums dealing with digital collections in a wide variety of formats and from a wider variety of sources. This book is forward-thinking in its approach to using metadata to drive digital library systems, and will be a valuable resource for those creating and managing digital resources as technologies for using those resources grow and change. - Provides practical guidance on the key choices that information professionals in libraries, archives, and museums must make when defining and implementing a metadata strategy - Provides insight on the new area of metadata librarianship while positions are opening in many organizations and many professionals worldwide are charged with managing and sharing metadata - Focuses on metadata usability and the careful definition of what a digital library system must do in order to define a metadata strategy |
algorithmic trading with python chris conlan: Algorithms and Data Structures Helmut Knebl, 2020-10-31 This is a central topic in any computer science curriculum. To distinguish this textbook from others, the author considers probabilistic methods as being fundamental for the construction of simple and efficient algorithms, and in each chapter at least one problem is solved using a randomized algorithm. Data structures are discussed to the extent needed for the implementation of the algorithms. The specific algorithms examined were chosen because of their wide field of application. This book originates from lectures for undergraduate and graduate students. The text assumes experience in programming algorithms, especially with elementary data structures such as chained lists, queues, and stacks. It also assumes familiarity with mathematical methods, although the author summarizes some basic notations and results from probability theory and related mathematical terminology in the appendices. He includes many examples to explain the individual steps of the algorithms, and he concludes each chapter with numerous exercises. |
algorithmic trading with python chris conlan: Algorithmic Equity Osonde A. Osoba, Benjamin Boudreaux, Jessica M. Saunders, J. Luke Irwin, Pam A. Mueller, Samantha Cherney, 2019 This report is an examination of pathologies in social institutions' use of algorithmic decisionmaking processes. The primary focus is understanding how to evaluate the equitable use of algorithms across a range of specific applications. |
algorithmic trading with python chris conlan: Algorithmic Trading Ernie Chan, 2013-05-28 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, Managing Director, Manager Selection & Portfolio Construction, 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 |
algorithmic trading with python chris conlan: Software Process Modeling Silvia T. Acuna, Natalia Juristo, 2005-03-10 This book brings together experts to discuss relevant results in software process modeling, and expresses their personal view of this field. It is designed for a professional audience of researchers and practitioners in industry, and graduate-level students. |
algorithmic trading with python chris conlan: Process Analytics Seyed-Mehdi-Reza Beheshti, Boualem Benatallah, Sherif Sakr, Daniela Grigori, Hamid Reza Motahari-Nezhad, Moshe Chai Barukh, Ahmed Gater, Seung Hwan Ryu, 2016-03-28 This book starts with an introduction to process modeling and process paradigms, then explains how to query and analyze process models, and how to analyze the process execution data. In this way, readers receive a comprehensive overview of what is needed to identify, understand and improve business processes. The book chiefly focuses on concepts, techniques and methods. It covers a large body of knowledge on process analytics – including process data querying, analysis, matching and correlating process data and models – to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern process analytics. Following an introduction to basic business process and process analytics concepts, it describes the state of the art in this area before examining different analytics techniques in detail. In this regard, the book covers analytics over different levels of process abstractions, from process execution data and methods for linking and correlating process execution data, to inferring process models, querying process execution data and process models, and scalable process data analytics methods. In addition, it provides a review of commercial process analytics tools and their practical applications. The book is intended for a broad readership interested in business process management and process analytics. It provides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in business process management to graduate courses in business process analytics. Lastly, it offers professionals a reference guide to the state of the art in commercial tools and techniques, complemented by many real-world use case scenarios. |
algorithmic trading with python chris conlan: Algorithmic Trading Jeffrey Bacidore, 2021-02-16 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. |
algorithmic trading with python chris conlan: Animated Algorithms Peter Gloor, Scott Dynes, Irene Lee, 1993 |
algorithmic trading with python chris conlan: Data Structures and Advanced Algorithms Rachel Xin, Tony Lee, Elisabeth Feng, 2020-08-07 The purpose of this book is to teach you, a budding programmer, basics of Object-Oriented Programming, data structures, and advanced algorithms using Python version 3.8. Unlike many books currently on the market, a background in math is not required to read and understand this book as the data structures and concepts will be explained in simple terms. |
algorithmic trading with python chris conlan: Algorithmics David Harel, 1987 Software -- Programming Techniques. |
algorithmic trading with python chris conlan: Numerical Methods, Software, and Analysis John Rischard Rice, 1983 Mathematics and computer science background. Numerical software. Errors, roud-off, and stabilitly. Models and formulas for numerical computations. Interpolation. Matrices and linear equations. Differentiation and integration. Nonlinear equations. Ordinary differential equations. Partial differential equations. Approximation of functions and data. Software practice, costs, and engineering. Software performance evaluation. The validation of numerical computations. Protran. |
algorithmic trading with python chris conlan: Hands-On Financial Trading with Python Jiri Pik, Sourav Ghosh, 2021-04-29 Discover how to build and backtest algorithmic trading strategies with Zipline Key Features: Get to grips with market data and stock analysis and visualize data to gain quality insights Find out how to systematically approach quantitative research and strategy generation/backtesting in algorithmic trading Learn how to navigate the different features in Python's data analysis libraries Book Description: Algorithmic trading helps you stay ahead of the markets by devising strategies in quantitative analysis to gain profits and cut losses. The book starts by introducing you to algorithmic trading and explaining why Python is the best platform for developing trading strategies. You'll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. You'll also focus on time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization. What You Will Learn: Discover how quantitative analysis works by covering financial statistics and ARIMA Use core Python libraries to perform quantitative research and strategy development using real datasets Understand how to access financial and economic data in Python Implement effective data visualization with Matplotlib Apply scientific computing and data visualization with popular Python libraries Build and deploy backtesting algorithmic trading strategies Who this book is for: This book is for data analysts and financial traders who want to explore how to design algorithmic trading strategies using Python's core libraries. If you are looking for a practical guide to backtesting algorithmic trading strategies and building your own strategies, then this book is for you. Beginner-level working knowledge of Python programming and statistics will be helpful. |
algorithmic trading with python chris conlan: Methods and Tools for Software Configuration Management David Whitgift, 1991-11-27 A comprehensive guide to the principles and practice of configuration management--the management of software system components during updating or replacement of elements. Features of commercially available tools are described enabling critical evaluation of their effectiveness. Designed primarily as a reference for professional system designers and project managers, it will also be useful to software engineering students. Covers the entire project lifecycle and goes on to discuss topics such as version management, configuration identification, change control, the software library, automated system building and more. |
algorithmic trading with python chris conlan: Complexity Issues in VLSI Frank Thomson Leighton, 1983 This book solves several mathematical problems in the areas of Very Large Scale Integration (VLSI) and parallel computation. In particular, it describes optimal layouts for the shuffle-exchange graph, one of the best known networks for parallel computation. Attempts to design a shuffle-exchange computer have been hampered in part by the fact that, until now, no good layouts for the shuffle-exchange graph were known. The mesh of trees network (which may eventually prove as useful as the shuffle-exchange graph) is introduced and the book shows how it can be used to perform a variety of computations, including sorting and matrix multiplication, in a logarithmic number of steps. Next, the book introduces the tree of meshes, the first planar graph that was discovered not to have a linear-area layout. Most recently, the structure of this graph has been used to develop a general framework for solving VLSI graph layout problems. Finally, the book develops techniques for proving lower bounds on the bisection width, crossing number, and layout area of a graph. These techniques significantly extend the power and range of previous methods. Researchers in the fields of VLSI, parallel computation, and graph theory will find this study of particular value; it is also accessible to anyone with an elementary knowledge of mathematics and computer science. The book is self-contained and presents in a unified and original manner many results scattered in the technical literature, while also covering new and fundamental results for the first time. |
algorithmic trading with python chris conlan: Principles of Software Engineering and Design Marvin V. Zelkowitz, Alan C. Shaw, John D. Gannon, 1979 Concentrates on the design aspects of programming for software engineering, while also covers the full range of software development cycles. |
algorithmic trading with python chris conlan: Mastering IDoc Business Scenarios with SAP NetWeaver PI Michal Krawczyk, Michal Kowalczewski, 2009-06 IDoc integration flows are only efficient if they are well designed. But to design them well, you have to know how to address the difficulties that can arise in various scenarios. So, to solve these problems, you have two possibilities: trial and error, or reading this book. The new edition of this best-selling guide has been completely updated and extended. It not only thoroughly explains the concepts behind IDocs, but also teaches you how to process IDocs via SAP NetWeaver Process Integration in different business scenarios. 1 Expert Advice Learn about the usage, configuration, and administration of IDocs, and familiarize yourself quickly with all monitoring and error handling aspects. 2 Easy-to-Follow Examples Discover how to use the best possible techniques through easy-to-follow examples based on MM (Materials Management) and SD (Sales & Distribution). 3 Technical Details and Business Background Find out about the comprehensive technical details of IDocs, as well as the business background of their implementation — step by step and with the numerous code samples provided. 4 Key Integration Processes Get to know central processes like IDoc monitoring within SAP NetWeaver landscapes and all aspects of exchange development (tunneling, packaging, serialization, mapping). 5 All-New Topics in this 2nd Edition Explore the ALE distribution model, and two of the latest functionalities for IDoc monitoring: SAP Solution Manager and IDoc packaging. |
algorithmic trading with python chris conlan: Natural Language Processing in POP-11 Gerald Gazdar, Christopher S. Mellish, 1989 |
algorithmic trading with python chris conlan: Neuro-Fuzzy Techniques for Intelligent Information Systems Nikola K. Kasabov, Robert Kozma, 1999-03-29 This volume comprises selected chapters that cover contemporary issues of the development and the application of neuro-fuzzy techniques. Developing and using neural networks, fuzzy logic systems, genetic algorithms and statistical methods as separate techniques, or in their combination, have been research topics in several areas such as mathematics, engineering, computer science, physics, economics and finance. Here the latest results in the fields are presented from both theoretical and practical point of view. The volume has four main parts. Part one presents generic techniques and theoretical issues while part two, three and four deal with practically oriented models, systems and implementations. |
ALGORITHMIC | English meaning - Cambridge Dictionary
ALGORITHMIC definition: 1. connected with or using algorithms (= mathematical instructions for calculating an answer to a…. Learn more.
ALGORITHM Definition & Meaning - Merriam-Webster
The current term of choice for a problem-solving procedure, algorithm, is commonly used nowadays for the set of rules a machine (and especially a computer) follows to achieve a …
Algorithmic - definition of algorithmic by The Free Dictionary
A finite set of unambiguous instructions that, given some set of initial conditions, can be performed in a prescribed sequence to achieve a certain goal and that has a recognizable set …
ALGORITHMIC definition in American English - Collins Online …
3 senses: relating to or using algorithms 1. a logical arithmetical or computational procedure that if correctly applied.... Click for more definitions.
Algorithm - Wikipedia
In mathematics and computer science, an algorithm (/ ˈælɡərɪðəm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class of specific problems or to …
What Is an Algorithm? | Definition & Examples - Scribbr
Aug 9, 2023 · Algorithms are fundamental tools for problem-solving in both the digital world and many real-life scenarios. Each time we try to solve a problem by breaking it down into smaller, …
Algorithm | Definition, Types, & Facts | Britannica
Apr 22, 2025 · algorithm, systematic procedure that produces—in a finite number of steps—the answer to a question or the solution of a problem. The name derives from the Latin translation, …
What does Algorithmic mean? - Definitions.net
Algorithmic refers to a procedure, method, or set of instructions designed to perform a particular task or solve a specific problem, typically by a computer. The term is closely associated with …
What is an Algorithm? Algorithm Definition for Computer Science …
Dec 13, 2022 · Simply put, an algorithm is a set of instructions that performs a particular action. Contrary to popular belief, an algorithm is not some piece of code that requires extremely …
What Is An Algorithm? Defining And Applying Algorithms - Forbes
Jan 12, 2024 · In its fundamental form, an algorithm is a process designed to solve a specific problem. It’s a set of instructions that end up in a desired conclusion. If that sounds vague, it’s …
ALGORITHMIC | English meaning - Cambridge Dictionary
ALGORITHMIC definition: 1. connected with or using algorithms (= mathematical instructions for calculating an answer to …
ALGORITHM Definition & Meaning - Merriam-Webster
The current term of choice for a problem-solving procedure, algorithm, is commonly used nowadays for the set of rules a machine (and especially a computer) follows to …
Algorithmic - definition of algorithmic by The Free Dictionary
A finite set of unambiguous instructions that, given some set of initial conditions, can be performed in a prescribed sequence to achieve a certain goal and that has a …
ALGORITHMIC definition in American English - Collins Online …
3 senses: relating to or using algorithms 1. a logical arithmetical or computational procedure that if correctly applied.... Click for more definitions.
Algorithm - Wikipedia
In mathematics and computer science, an algorithm (/ ˈælɡərɪðəm / ⓘ) is a finite sequence of mathematically rigorous instructions, typically used to solve a class …