Libsan | لایب سان

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

Learn Algorithmic Trading: Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis - Original PDF

نویسندگان: Sebastien Donadio, Sourav Ghosh خلاصه: 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.

Fractal Energy Trading: Four Simple Rules to Profit In Any Market & Any Timeframe [Print Replica] Kindle Edition - Original PDF

نویسندگان: Doc Severson خلاصه: his book is dedicated to my father Allan for taking the ultimate risk as a young man so that the rest of us had the freedom to pursue opportunity. Lib- erty is a very powerful concept and one that he fought for with his life. This book is also dedicated to my mother Charmaine who understood at an early age that opportunity cost was defined by “staying put,” that not having the freedom to express one’s talents was akin to imprisonment, and that tea must only be brewed whilst boiling.

Algorithmic Trading Methods: Applications Using Advanced Statistics, Optimization, and Machine Learning Techniques - Original PDF

نویسندگان: Robert Kissell خلاصه: 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.

A Machine Learning based Pairs Trading Investment Strategy - Original PDF

نویسندگان: Simão Moraes Sarmento, Nuno Horta 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: Euan Sinclair 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: A. J. Veal 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: Jiri Pik, Sourav Ghosh 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: Laurent Bernut 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: Chen, Jun; Tsang, Edward 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: Shmuel Oluwa 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.

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