موضوعات
آموزش و پرورش
ادبیات و زبان
پزشکی، دندانپزشکی و داروسازی
تاریخ و جغرافیا
داستان و رمان
دیگر
دین و فلسفه
روانشناسی
ریاضیات و آمار
سلامتی، تناسب اندام و رژیم غذایی
شیمی و پلیمر
علوم اجتماعی و حقوق
علوم زیستی و بیوتکنولوژی
فیزیک و نجوم
کامپیوتر و اینترنت
کتابهای کودکان و داستان
کسب و کار و اقتصاد
کشاورزی و دامپزشکی و غذا
معماری
مهندسی و فناوری
هنر و تئاتر
محصولات
A Machine Learning based Pairs Trading Investment Strategy - Original PDF
نویسندگان: خلاصه: This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.
Positional Option Trading: An Advanced Guide - Original PDF
نویسندگان: خلاصه: You know nothing, Jon Snow. —Ygritte in A Storm of Swords by George R. R. Martin. He is not the only one. We are not in a time where reason is valued. In economics, the idea that marginal tax cuts pay for themselves is still advanced, even though all evidence says they don't. Forty percent of Americans do not believe in evolution. Forty-five percent believe in ghosts. These beliefs are not based on any evidence. They are manifestations of another philosophy, whether it is economic, religious, or sociological. Usually these opinions reveal more about what people want to be true rather than any facts that they know. And many people know few facts anyway. Evidence is seen as irrelevant and arguments are won by those who shout loudest and have the best media skills
Research Methods for Leisure & Tourism: A Practical Guide - Original PDF
نویسندگان: خلاصه: This best selling text offers a practical guide to the methodology and techniques of conducting research in Leisure and Tourism. Covering both qualitative and quantitative methods, this completely revised and updated third edition is essential reading for all students and leisure managers evaluating and planning the services they offer
Hands-On Financial Trading with Python: A practical guide to using Zipline and other Python libraries for backtesting trading strategies - Original PDF
نویسندگان: خلاصه: 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. Table of Contents Introduction to algorithmic trading Exploratory Data Analysis in Python High-speed Scientific Computing using NumPy Data Manipulation and Analysis with Pandas Data Visualization using Matplotlib Statistical Estimation, Inference, and Prediction Financial Market Data Access in Python Introduction to Zipline and PyFolio Fundamental algorithmic trading strategies
Algorithmic Short-Selling with Python: Refine your algorithmic trading edge, consistently generate investment ideas, and build a robust long/short product - Original PDF
نویسندگان: خلاصه: Leverage Python source code to revolutionize your short selling strategy and to consistently make profits in bull, bear, and sideways markets Key Features • Understand techniques such as trend following, mean reversion, position sizing, and risk management in a short-selling context • Implement Python source code to explore and develop your own investment strategy • Test your trading strategies to limit risk and increase profits Book 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 when the markets are down. This book will help you demystify and rehabilitate the short-selling craft, providing Python source code to construct a robust long/short portfolio. It explains everything you have ever read about short selling from a long-only perspective. 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 learn • Develop the mindset required to win the infinite, complex, random game called the stock market • Demystify short selling in order to make consistent profits from bull, bear, and sideways markets • Generate ideas consistently on both sides of the portfolio • Implement Python source code to engineer a statistically robust trading edge • Perform superior risk management for high returns • Build a long/short product that investors will find appealing Who 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
Detecting regime change in computational finance: data science, machine learning and algorithmic trading - Original PDF
نویسندگان: خلاصه: "Based on interdisciplinary research into "Directional Change", a new data-driven approach to financial data analysis, Detecting Regime Change in Computational Finance: Data Science, Machine Learning and, Algorithmic Trading applies machine learning to financial market monitoring and algorithmic trading. Directional Change is a new way of summarizing price changes in the market. Instead of sampling prices at fixed intervals (such as daily closing in time series), it samples prices when the market changes direction ("zigzag"). By sampling data in a different way, the book lays out concepts which enable the extraction of information that other market participants may not be able to see. The book includes a Foreword by Richard Olsen and explores the following topics: Data science: as an alternative to time series, price movements in a market can be summarised as directional changes Machine learning for regime change detection: historical regime changes in a market can be discovered by a Hidden Markov Model Regime characterisation: normal and abnormal regimes in historical data can be characterised using indicators defined under Directional Change Market Monitoring: by using historical characteristics of normal and abnormal regimes, one can monitor the market to detect whether the market regime has changed Algorithmic trading: regime tracking information can help us to design trading algorithms It will be of great interest to researchers in computational finance, machine learning, and data science"--;Background and literature survey -- Regime change detection using directional change indicators -- Classification of normal and abnormal regimes in financial markets -- Tracking regime changes using directional change indicators -- Algorithmic trading based on regime change tracking.
Hands-On Financial Modeling with Excel for Microsoft 365: Build your own practical financial models for effective forecasting, valuation, trading, and growth analysis, 2nd Edition - Original PDF
نویسندگان: خلاصه: Explore a variety of Excel features, functions, and productivity tips for various aspects of financial modeling Key Features Explore Excel's financial functions and pivot tables with this updated second edition Build an integrated financial model with Excel for Microsoft 365 from scratch Perform financial analysis with the help of real-world use cases Book Description Financial modeling is a core skill required by anyone who wants to build a career in finance. Hands-On Financial Modeling with Excel for Microsoft 365 explores financial modeling terminologies with the help of Excel. Starting with the key concepts of Excel, such as formulas and functions, this updated second edition will help you to learn all about referencing frameworks and other advanced components for building financial models. As you proceed, you'll explore the advantages of Power Query, learn how to prepare a 3-statement model, inspect your financial projects, build assumptions, and analyze historical data to develop data-driven models and functional growth drivers. Next, you'll learn how to deal with iterations and provide graphical representations of ratios, before covering best practices for effective model testing. Later, you'll discover how to build a model to extract a statement of comprehensive income and financial position, and understand capital budgeting with the help of end-to-end case studies. By the end of this financial modeling Excel book, you'll have examined data from various use cases and have developed the skills you need to build financial models to extract the information required to make informed business decisions. What you will learn Identify the growth drivers derived from processing historical data in Excel Use discounted cash flow (DCF) for efficient investment analysis Prepare detailed asset and debt schedule models in Excel Calculate profitability ratios using various profit parameters Obtain and transform data using Power Query Dive into capital budgeting techniques Apply a Monte Carlo simulation to derive key assumptions for your financial model Build a financial model by projecting balance sheets and profit and loss Who this book is for This book is for data professionals, analysts, traders, business owners, and students who want to develop and implement in-demand financial modeling skills in their finance, analysis, trading, and valuation work. Even if you don't have any experience in data and statistics, this book will help you get started with building financial models. Working knowledge of Excel is a prerequisite.
Developing High Frequency Trading Systems: Learn how to implement high-frequency trading from scratch with C++ or Java basics - Original PDF
نویسندگان: خلاصه: Use your programming skills to create and optimize high-frequency trading systems in no time with Java, C++, and Python Key Features • Learn how to build high-frequency trading systems with ultra-low latency • Understand the critical components of a trading system • Optimize your systems with high-level programming techniques Book Description The world of trading markets is complex, but it can be made easier with technology. Sure, you know how to code, but where do you start? What programming language do you use? How do you solve the problem of latency? The Developing High-Frequency Trading Systems book answers all these questions. This practical guide will help you navigate the fast-paced world of algorithmic trading and show you how to build a high-frequency trading system from complex technological components, supported by accurate data. Starting off with an introduction to high-frequency trading, exchanges, and the critical components of a trading system, the book quickly moves on to the nitty-gritty of optimizing hardware and your operating system for low-latency trading, such as bypassing the kernel, memory allocation, and the danger of context switching. Monitoring your system’s performance is vital, so you’ll also get up to speed with logging and statistics. As you move beyond the traditional high-frequency trading programming languages, such as C++ and Java, you’ll learn how to use Python to achieve high levels of performance. And what book on trading would be complete without diving into cryptocurrency? By the end of this book, you’ll be ready to take on the markets with high-frequency trading systems. What you will learn • Understand the architecture of high-frequency trading systems • Boost system performance to achieve the lowest possible latency • Leverage the power of Python, C++, and Java to build your trading systems • Bypass your kernel and optimize your operating system • Use static analysis to improve code development • Use C++ templates and Java multithreading for super-low latency • Apply your knowledge to cryptocurrency trading Who This Book Is For This book is for software engineers, quantitative developers or researchers, and DevOps engineers who want to understand the technical side of high-frequency trading systems and the optimizations that are needed to get to ultra-low latency systems. Prior experience working with C++ and Java will help you grasp the topics covered in this book.
Maximum Trading Gains with Anchored VWAP: The Perfect Combination of Price, Time, and Volume - Original PDF
نویسندگان: خلاصه: My goal for this book is to give you a thorough understanding of the VWAP and the AVWAP so you can learn how to interpret market action more accurately. This knowledge will allow you to make better trades.
From Data to Trade: A Machine Learning Approach to Quantitative Trading - Original PDF
نویسندگان: خلاصه: Machine Learning has revolutionized the field of quantitative trading, enabling traders to develop and implement sophisticated trading strategies that leverage large amounts of data and advanced modeling techniques. In this book, we provide a comprehensive overview of Machine Learning for quantitative trading, covering the fundamental concepts, techniques, and applications of Machine Learning in the financial industry. We start by introducing the key concepts and challenges of Machine Learning for quantitative trading, including feature engineering, model selection, and backtesting. We then delve into the various Machine Learning approaches that are commonly used in quantitative trading, including supervised learning, unsupervised learning, and reinforcement learning. We also discuss the challenges and best practices of implementing Machine Learning models in the live market, including the role of data quality, the importance of risk management, and the need for ongoing model monitoring and validation. Throughout the book, we provide numerous examples and case studies to illustrate the concepts and techniques discussed, and we also include practical tips and resources to help traders and practitioners get started with Machine Learning for quantitative trading. This book is an essential resource for anyone looking to gain a deeper understanding of how Machine Learning is transforming the world of finance. This groundbreaking work offers a unique perspective on the use of Machine Learning in the financial markets, as it was created by an advanced Artificial Intelligence (AI) using its own Machine Learning algorithms to analyze vast amounts of data and construct a comprehensive guide on the subject. Machine Learning is a type of artificial intelligence that enables computers to learn and adapt without being explicitly programmed. It involves the use of algorithms and statistical models to analyze data and make predictions or decisions based on the patterns and trends that it identifies. In Machine Learning, a computer is trained to recognize patterns in data by being presented with a large number of examples of the patterns that it should recognize. As the computer processes these examples, it "learns" the characteristics of the patterns and becomes better at recognizing them. Once the computer has learned to recognize the patterns, it can then be used to make predictions or decisions based on new data that it has not seen before. There are many different types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. Each type of Machine Learning involves a different approach to training the computer and making predictions or decisions based on the data. Machine Learning is used in a wide range of applications, including image and speech recognition, natural language processing (NLP), recommendation systems, and fraud detection. It has the potential to transform many different industries by automating tasks that would be difficult or impossible for humans to perform, and by enabling computers to make decisions and predictions based on data in a way that is more accurate and efficient than human judgment. In “From Data to Trade: A Quantitative Approach to Machine Learning,” readers will learn about the latest techniques and approaches for using Machine Learning in quantitative trading, as well as practical advice for implementing these methods in their own trading strategies. From basic concepts to advanced techniques, this book covers it all and is an invaluable resource for traders at any level of experience.آیا کتاب مورد نظر هنوز بر روی سایت قرار نگرفته است؟ جای نگرانی نیست! کافی است بر روی گزینه سفارش کتاب کلیک کرده و درخواست خود را ثبت کنید. در کمتر از چند ساعت کتاب شما را آماده خواهیم کرد.