Candlesticks have become a key component of platforms and charting programs for financial trading. With these charts, traders can learn underlying patterns for interpreting price action history and forecasts. This A-Z guide shows portfolio managers, quants, strategists, and analysts how to use Python to recognize, scan, trade, and back-test the profitability of candlestick patterns. Financial author, trading consultant, and institutional market strategist Sofien Kaabar shows you how to create a candlestick scanner and indicator so you can compare the profitability of these patterns. With this hands-on book, you'll also explore a new type of charting system similar to candlesticks, as well as new patterns that have never been presented before. With this book, you will: Create and understand the conditions required for classic and modern candlestick patterns Learn the market psychology behind them Use a framework to learn how back-testing trading strategies are conducted Explore different charting systems and understand their limitations Import OHLC historical FX data in Python in different time frames Use algorithms to scan for and reproduce patterns Learn a pattern's potential by evaluating its profitability and predictability

Candlesticks have become a key component of trading platforms and charting programs for financial trading. A candlestick is a financial chart that helps reveal underlying patterns for traders when interpreting price action history and forecasts. We are often taught how specific patterns behave in a certain way, but we have never really been taught how to trade or back-test them. This hands-on guide will teach you how to recognize, scan, trade, and back-test the profitability of candlestick patterns with Python. Furthermore, this book will introduce new patterns that have never been presented before, revealing the fruits of my research.Other topics discussed in the book include creating a candlestick scanner and indicator, as well as comparing the profitability of the patterns. We will also see a new type of charting system which is similar to candlesticks. This proposed book aims to be the A-Z guide to all candlestick patterns!

DESCRIPTION Python's intuitive syntax and beginner-friendly nature makes it an ideal programming language for financial professionals. It acts as a bridge between the world of finance and data analysis. This book will introduce essential concepts in financial analysis methods and models, covering time-series analysis, graphical analysis, technical and fundamental analysis, asset pricing and portfolio theory, investment and trade strategies, risk assessment and prediction, and financial ML practices. The Python programming language and its ecosystem libraries, such as Pandas, NumPy, SciPy, Statsmodels, Matplotlib, Seaborn, Scikit-learn, Prophet, and other data science tools will demonstrate these rooted financial concepts in practice examples. This book will help you understand the concepts of financial market dynamics, estimate the metrics of financial asset profitability, predict trends, evaluate strategies, optimize portfolios, and manage financial risks. You will also learn data analysis techniques using Python programming language to understand the basics of data preparation, visualization, and manipulation in the world of financial data. KEY FEATURES ● Comprehensive guide to Python for financial data analysis and modeling. ● Practical examples and real-world applications for immediate implementation. ● Covers advanced topics like regression, Machine Learning and time series forecasting. WHAT YOU WILL LEARN ● Learn financial data analysis using Python data science libraries and techniques. ● Learn Python visualization tools to justify investment and trading strategies. ● Learn asset pricing and portfolio management methods with Python. ● Learn advanced regression and time series models for financial forecasting. ● Learn risk assessment and volatility modeling methods with Python. WHO THIS BOOK IS FOR This book is designed for financial analysts and other professionals interested in the financial industry with a basic understanding of Python programming and statistical analysis. It is also suitable for students in finance and data science who wish to apply Python tools to financial data analysis and decision-making. TABLE OF CONTENTS 1. Getting Started with Python for Finance 2. Python Tools for Data Analysis: Primer to Pandas and NumPy 3. Financial Data Manipulation with Python 4. Exploratory Data Analysis for Finance 5. Investment and Trading Strategies 6. Asset Pricing and Portfolio Management 7. Time Series Analysis and Financial Data Forecasting 8. Risk Assessment and Volatility Modelling 9. Machine Learning and Deep Learning in Finance 10. Time Series Analysis and Forecasting with FB Prophet Library Appendix A: Python Code Examples for Finance Appendix B: Glossary Appendix C: Valuable Resources

Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar—financial author, trading consultant, and institutional market strategist—introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization. Understand and create machine learning and deep learning models Explore the details behind reinforcement learning and see how it's used in time series Understand how to interpret performance evaluation metrics Examine technical analysis and learn how it works in financial markets Create technical indicators in Python and combine them with ML models for optimization Evaluate the models' profitability and predictability to understand their limitations and potential

Uncover Hidden Insights and Patterns in Your Data Are you ready to delve into the fascinating realm of data mining? "Mastering Data Mining" is your ultimate guide to unlocking the potential of extracting hidden insights and patterns from your data. Whether you're a data scientist aiming to uncover valuable information or a business professional seeking to make informed decisions, this book equips you with the knowledge and techniques to master the art of data mining. Key Features: 1. Journey into Data Mining: Immerse yourself in the world of data mining, understanding its significance, methodologies, and applications. Build a solid foundation that empowers you to extract meaningful insights from complex datasets. 2. Data Exploration and Preparation: Master the art of data exploration and preparation. Learn how to clean, transform, and preprocess data for effective mining. 3. Exploratory Data Analysis: Delve into exploratory data analysis techniques. Explore visualization, statistical summaries, and data profiling to gain a deeper understanding of your dataset. 4. Supervised Learning Techniques: Uncover the power of supervised learning techniques. Learn how to build predictive models for classification and regression tasks, enabling you to make accurate predictions. 5. Unsupervised Learning and Clustering: Discover unsupervised learning and clustering methods. Explore techniques for grouping similar data points and identifying hidden patterns without predefined labels. 6. Association Rule Mining: Master association rule mining for uncovering relationships in data. Learn how to identify frequent itemsets and extract valuable associations. 7. Text and Web Mining: Explore text and web mining techniques. Learn how to extract insights from textual data and discover patterns in web-based information. 8. Time Series Mining: Delve into time series mining for analyzing sequential data. Learn how to forecast trends, identify anomalies, and make predictions based on temporal patterns. 9. Data Mining Tools and Algorithms: Uncover a range of data mining tools and algorithms. Explore classic algorithms and modern techniques for various data mining tasks. 10. Real-World Applications: Gain insights into real-world use cases of data mining across industries. From customer segmentation to fraud detection, explore how organizations leverage data mining for strategic advantage. Who This Book Is For: "Mastering Data Mining" is an indispensable resource for data scientists, analysts, and business professionals who want to excel in uncovering insights from data. Whether you're new to data mining or seeking advanced techniques, this book will guide you through the intricacies and empower you to harness the full potential of data mining. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

An indispensable hands-on guide to financial accounting In light of recent accounting scandals, it is critical that all financial practitioners understand and play by the rules of the accounting field. Starting from the assumption that the reader is not familiar with any accounting jargon, Mastering Financial Accounting Essentials presents material in a way that explains the key features of modern accounting step by step and helps you develop an intuitive understanding of accounting. Each chapter presents important accounting concepts, from inventory valuation methods and the timing of erosion of productive assets to how internal managers calculate ratios and trends to evaluate business efficiency. For those who need to understand the language and law of this discipline in order to communicate effectively with accountants and clients, Mastering Financial Accounting Essentials will be an indispensable guide.

Here is a chapter from Mastering Technical Analysis, a practical examination of the key tools of technical analysis--how they work, why they work, and which work best in specific situations. Written by one of the founding members of the Market Technicians Association, it will provide you with the guidance and insights you need to improve your trading performance, by removing the guesswork from every move you make.

The revised and updated edition of the book that changed the way you think about trading In the Second Edition of this groundbreaking book by star trader Jeff Greenblatt, he continues to shares his hard-won lessons on what it takes to be a professional trader, while detailing his proven techniques for mastering market timing. With the help of numerous case studies and charts, Greenblatt develops his original high-probability pattern recognition system which, once mastered, endows its user with a deeper understanding of how the markets really work and boosts the efficiency of any trading methodology. Following in the footsteps of the great W.D. Gann, Jeff Greenblatt helps investors gain greater precision with any instrument they trade, during any time frame. Shows how to combine a variety of technical indicators to pinpoint turning points in the financial markets Makes even the most complex subject matter easy to understand with crystal-clear explanations and step-by-step guidance on all concepts, terms, processes, and techniques Reveals how to use Elliott Wave Analysis, Fibonacci, candlesticks, and momentum indicators to interpret market movements Breakthrough Strategies for Predicting Any Market shares fascinating and enlightening personal anecdotes from Jeff Greenblatt's career along with his candid reflection on developing and maintaining the mental discipline of a successful trader.

Efficiently Capture and Prepare Data for Analysis Are you ready to optimize the way your organization captures and prepares data for analysis? "Mastering Data Ingestion" is your definitive guide to mastering the art of efficiently collecting, transforming, and organizing data for insights. Whether you're a data engineer streamlining data pipelines or a business leader aiming to leverage accurate information, this book equips you with the knowledge and strategies to excel in data ingestion. Key Features: 1. Enter the World of Data Ingestion: Immerse yourself in the realm of data ingestion, understanding its significance, challenges, and opportunities. Build a strong foundation that empowers you to design seamless processes for data collection. 2. Data Collection Techniques: Master various data collection techniques. Learn about batch processing, real-time streaming, and event-driven approaches for ingesting data from diverse sources. 3. Data Transformation and Enrichment: Delve into data transformation and enrichment during ingestion. Explore techniques for cleansing, structuring, and augmenting data to ensure its quality and usability. 4. Ingestion Patterns and Architectures: Uncover the power of data ingestion patterns and architectures. Learn how to design scalable and fault-tolerant data pipelines that handle high volumes of information. 5. Data Formats and Serialization: Explore data formats and serialization techniques. Learn how to handle diverse data structures, choose appropriate serialization methods, and ensure interoperability. 6. Ingestion Tools and Platforms: Discover a range of tools and platforms for data ingestion. Explore ETL (Extract, Transform, Load) tools, message brokers, and cloud-based services for efficient data movement. 7. Real-Time Data Ingestion: Master real-time data ingestion techniques. Learn how to capture and process streaming data for instant insights and timely decision-making. 8. Data Ingestion Best Practices: Delve into best practices for successful data ingestion projects. Learn how to handle data schema evolution, ensure data integrity, and optimize performance. 9. Cloud Data Ingestion: Explore cloud-based data ingestion strategies. Learn how to ingest data from cloud services, integrate with cloud databases, and leverage serverless architectures. 10. Real-World Applications: Gain insights into real-world use cases of data ingestion across industries. From IoT data streams to social media feeds, discover how organizations leverage efficient data collection for competitive advantage. Who This Book Is For: "Mastering Data Ingestion" is an essential resource for data engineers, analysts, and business professionals aiming to excel in efficiently collecting and preparing data for analysis. Whether you're enhancing your technical skills or optimizing data workflows, this book will guide you through the intricacies and empower you to harness the full potential of data ingestion. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com

Exploit the power of data in your business by building advanced predictive modeling applications with Python About This Book Master open source Python tools to build sophisticated predictive models Learn to identify the right machine learning algorithm for your problem with this forward-thinking guide Grasp the major methods of predictive modeling and move beyond the basics to a deeper level of understanding Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move from a conceptual understanding of advanced analytics to an expert in designing and building advanced analytics solutions using Python. You're expected to have basic development experience with Python. What You Will Learn Gain an insight into components and design decisions for an analytical application Master the use Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover the advanced methods to analyze structured and unstructured data Find out how to deploy a machine learning model in a production environment Visualize the performance of models and the insights they produce Scale your solutions as your data grows using Python Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations. In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life Style and approach This book emphasizes on explaining methods through example data and code, showing you templates that you can quickly adapt to your own use cases. It focuses on both a practical application of sophisticated algorithms and the intuitive understanding necessary to apply the correct method to the problem at hand. Through visual examples, it also demonstrates how to convey insights through insightful charts and reporting.