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Author: Sofien Kaabar
Publisher:
ISBN:
Category :
Languages : en
Pages : 282
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Book Description
What is this book all about?This book is a modest attempt at presenting a more modern version of Technical Analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around technical indicators which is exactly the purpose of this book. I believe it is time to be creative and invent our own indicators that fit our profiles. Having had more success with custom indicators than conventional ones, I have decided to share my findings. The following chapters present new indicators that are the fruit of my research as well as indicators created by brilliant people. I also include the functions to create the indicators in Python and provide how to best use them as well as back-testing results. What am I going to gain?You will gain exposure to many new indicators and concepts that will change the way you think about trading and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized?The order of chapters is not important, although reading the introductory technical chapter is helpful. The book is divided into three parts: part 1 deals with trend-following indicators, part 2 deals with contrarian indicators, part 3 deals with market timing indicators, and finally, part 4 deals with risk and performance indicators.What do you mean when you say this book is dynamic and not static?This means that everything inside gets updated regularly with new material on my Medium profile. I always publish new findings and strategies. Make sure to follow me.What level of knowledge do I need to follow this book?Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python.
Author: Sofien Kaabar
Publisher:
ISBN:
Category :
Languages : en
Pages : 282
Get Book Here
Book Description
What is this book all about?This book is a modest attempt at presenting a more modern version of Technical Analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around technical indicators which is exactly the purpose of this book. I believe it is time to be creative and invent our own indicators that fit our profiles. Having had more success with custom indicators than conventional ones, I have decided to share my findings. The following chapters present new indicators that are the fruit of my research as well as indicators created by brilliant people. I also include the functions to create the indicators in Python and provide how to best use them as well as back-testing results. What am I going to gain?You will gain exposure to many new indicators and concepts that will change the way you think about trading and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized?The order of chapters is not important, although reading the introductory technical chapter is helpful. The book is divided into three parts: part 1 deals with trend-following indicators, part 2 deals with contrarian indicators, part 3 deals with market timing indicators, and finally, part 4 deals with risk and performance indicators.What do you mean when you say this book is dynamic and not static?This means that everything inside gets updated regularly with new material on my Medium profile. I always publish new findings and strategies. Make sure to follow me.What level of knowledge do I need to follow this book?Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python.
Author: Eryk Lewinson
Publisher: Packt Publishing Ltd
ISBN: 1789617324
Category : Computers
Languages : en
Pages : 426
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Book Description
Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
Author: Sofien Kaabar
Publisher:
ISBN:
Category :
Languages : en
Pages : 330
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Book Description
Trading strategies come in different shapes and colors, and having a detailed view on their structure and functioning is very useful towards the path of creating a robust and profitable trading system. The book presents various technical strategies and the way to back-test them in Python. You can think of the book as a mix between introductory Python and an Encyclopedia of trading strategies with a touch of reality.
Author: Domenico Starnini
Publisher: Domenico Starnini
ISBN:
Category : Education
Languages : it
Pages : 92
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Book Description
Transform your trading game with 'Build Technical Indicators in Python.' This comprehensive guide offers practical, hands-on guidance for traders and analysts of all levels. With a focus on implementing technical analysis with Python libraries such as TA-Lib and Pandas TA-Lib, you'll gain the skills and knowledge to optimize your trading strategies and improve your success rate. With easy-to-follow instructions and real-world examples, this book is a must-have resource for anyone looking to succeed in today's fast-paced financial markets. Don't miss out on this opportunity to take your trading to the next level. Purchase 'Build Technical Indicators in Python' today and start your journey towards trading success!
Author: Sofien Kaabar
Publisher: Independently Published
ISBN:
Category :
Languages : en
Pages : 218
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Book Description
What is this book all about? This book is a modest attempt at presenting a more modern version of technical analysis based on objective measures rather than subjective ones. A sizeable chunk of this beautiful type of analysis revolves around trend-following technical indicators which is what this book covers. I believe it is time to be creative with indicators. The following chapters present trend-following indicators and how to code/use them. The code included in the book is available in the GitHub repository. A QR code link will be provided in the book. What am I going to gain? You will gain exposure to many new indicators and strategies that will change the way you think about trading, and you will find yourself busy experimenting and choosing the strategy that suits you the best. How is it organized? The order of the chapter is not very important, although reading the introductory Python chapter is helpful. The book is divided into four parts: Part 1 deals with different types of moving averages, Part 2 deals with trend-following indicators, Part3 deals with market regime detection techniques, and finally, Part 4 will present many different trend-following technical strategies. What level of knowledge do I need to follow this book? Although a basic or a good understanding of trading and coding is considered very helpful, it is not necessary. At the beginning of the book, I have included a chapter that deals with some Python concepts, but this book is not about Python.
Author: David Aronson
Publisher: John Wiley & Sons
ISBN: 1118160584
Category : Business & Economics
Languages : en
Pages : 572
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Book Description
Evidence-Based Technical Analysis examines how you can apply the scientific method, and recently developed statistical tests, to determine the true effectiveness of technical trading signals. Throughout the book, expert David Aronson provides you with comprehensive coverage of this new methodology, which is specifically designed for evaluating the performance of rules/signals that are discovered by data mining.
Author: Marcos Lopez de Prado
Publisher: John Wiley & Sons
ISBN: 1119482119
Category : Business & Economics
Languages : en
Pages : 400
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Book Description
Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
Author: Stefan Jansen
Publisher: Packt Publishing Ltd
ISBN: 1839216786
Category : Business & Economics
Languages : en
Pages : 822
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Book Description
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Author: Pushpak Dagade
Publisher: Packt Publishing Ltd
ISBN: 1838982515
Category : Computers
Languages : en
Pages : 528
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Book Description
Build a solid foundation in algorithmic trading by developing, testing and executing powerful trading strategies with real market data using Python Key FeaturesBuild a strong foundation in algorithmic trading by becoming well-versed with the basics of financial marketsDemystify jargon related to understanding and placing multiple types of trading ordersDevise trading strategies and increase your odds of making a profit without human interventionBook Description If you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help. Starting by setting up the Python environment for trading and connectivity with brokers, you’ll then learn the important aspects of financial markets. As you progress, you’ll learn to fetch financial instruments, query and calculate various types of candles and historical data, and finally, compute and plot technical indicators. Next, you’ll learn how to place various types of orders, such as regular, bracket, and cover orders, and understand their state transitions. Later chapters will cover backtesting, paper trading, and finally real trading for the algorithmic strategies that you've created. You’ll even understand how to automate trading and find the right strategy for making effective decisions that would otherwise be impossible for human traders. By the end of this book, you’ll be able to use Python libraries to conduct key tasks in the algorithmic trading ecosystem. Note: For demonstration, we're using Zerodha, an Indian Stock Market broker. If you're not an Indian resident, you won't be able to use Zerodha and therefore will not be able to test the examples directly. However, you can take inspiration from the book and apply the concepts across your preferred stock market broker of choice. What you will learnUse Python to set up connectivity with brokersHandle and manipulate time series data using PythonFetch a list of exchanges, segments, financial instruments, and historical data to interact with the real marketUnderstand, fetch, and calculate various types of candles and use them to compute and plot diverse types of technical indicatorsDevelop and improve the performance of algorithmic trading strategiesPerform backtesting and paper trading on algorithmic trading strategiesImplement real trading in the live hours of stock marketsWho this book is for If you are a financial analyst, financial trader, data analyst, algorithmic trader, trading enthusiast or anyone who wants to learn algorithmic trading with Python and important techniques to address challenges faced in the finance domain, this book is for you. Basic working knowledge of the Python programming language is expected. Although fundamental knowledge of trade-related terminologies will be helpful, it is not mandatory.
Author: Wes McKinney
Publisher: "O'Reilly Media, Inc."
ISBN: 1491957611
Category : Computers
Languages : en
Pages : 553
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Book Description
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
