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Discover the inner workings of today's forex market, the essential risks in forex algo trading, and how to mitigate them Key FeaturesBuild trading applications with research and without advanced Python programming skillsDive into professional fx trading while enhancing your trading apps to be more accurateDevelop simple yet efficient backtesting applications to help keep your expectations realisticBook Description Algorithm-based trading is a popular choice for Python programmers due to its apparent simplicity. However, very few traders get the results they want, partly because they aren't able to capture the complexity of the factors that influence the market. Getting Started with Forex Trading Using Python helps you understand the market and build an application that reaps desirable results. The book is a comprehensive guide to everything that is market-related: data, orders, trading venues, and risk. From the programming side, you'll learn the general architecture of trading applications, systemic risk management, de-facto industry standards such as FIX protocol, and practical examples of using simple Python codes. You'll gain an understanding of how to connect to data sources and brokers, implement trading logic, and perform realistic tests. Throughout the book, you'll be encouraged to further study the intricacies of algo trading with the help of code snippets. By the end of this book, you'll have a deep understanding of the fx market from the perspective of a professional trader. You'll learn to retrieve market data, clean it, filter it, compress it into various formats, apply trading logic, emulate the execution of orders, and test the trading app before trading live. What you will learnExplore the forex market organization and operationsUnderstand the sources of alpha and the concept of algo tradingGet a grasp on typical risks and ways to mitigate themUnderstand fundamental and technical analysisConnect to data sources and check the integrity of market dataUse API and FIX protocol to send ordersTranslate trading ideas into codeRun reliable backtesting emulating real-world market conditionsWho this book is for This book is for financial traders and python developers who are interested in forex trading. Academic researchers looking to focus on practical applications will find this book useful. This book can also help established fx market professionals who want to take the first steps in algo trading. Familiarity with Python and object-oriented programming within the scope of an online course or self-study is a must. Knowledge of network protocols and interfaces is a plus but not a prerequisite, as is specific knowledge about markets and trading.
Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading. You'll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for algorithmic trading Learn how to retrieve financial data from public and proprietary data sources Explore vectorization for financial analytics with NumPy and pandas Master vectorized backtesting of different algorithmic trading strategies Generate market predictions by using machine learning and deep learning Tackle real-time processing of streaming data with socket programming tools Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms
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.
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
Take your blockchain and Web3 development skills to the next level by building real-world full-stack DeFi applications with Solidity and JavaScript Key Features Gain the knowledge you need to start implementing DeFi principles in practice Learn how to build full-stack real-world DeFi products from scratch with step-by-step instructions Leverage tools like Hardhat, Ethers.js, Node.js, React.js, Solidity, and Web3 for effective DeFi application development Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionEnter the world of Decentralized Finance (DeFi) with Building Full Stack DeFi Applications. Understand how this blockchain-based financial technology, designed to manage crypto assets, runs independently without centralized financial institutions like banks and brokerages, eliminating the fees that banks and other financial companies charge for using their services. This book will show you how DeFi solutions are built with smart contracts running on blockchains and how they allow users to gain and earn crypto assets based on the trust of the smart contracts. This book uncovers the inner workings of DeFi by guiding you through the mathematical foundations and teaching you how to build real-world DeFi products with Solidity and JavaScript. As you progress through the chapters, you’ll learn how to implement smart contracts of liquidity pools to trade cryptocurrencies and implement staking, including farming features that allow users to earn. You’ll also find out how to create asset pools that allow users to lend and borrow cryptocurrencies and generate interest. Additionally, you’ll discover how to use Web3 libraries to build the frontend of DeFi products. By the end of this book, you’ll will be well acquainted with popular tools, libraries, and design patterns for implementing a full-stack DeFi application with Web3 and Solidity.What you will learn Understand the key concepts and principles of DeFi and how it works Get to grips with smart contract development to solve complex problems Build your experience in designing, building, and deploying Web3 applications Implement liquidity pools and swapping features for seamless crypto exchanges Develop staking and farming features for DeFi applications Create smart contracts for crypto loans integrated with Web3 libraries Who this book is for If you are a blockchain developer experienced in Web3 and Solidity development, or anyone interested in learning about blockchain and DeFi technologies, this book is for you. Product managers, executives, and other management professionals looking to start or delve into a DeFi project will also benefit from this book, as will developers and architects with basic blockchain knowledge who want to advance their skills in building full-stack DeFi products. Experience with Solidity, JavaScript, and Web3 will help you get the most out of this book.
Go from the bare basics to implementing your own automatic trading algorithm and become a cryptocurrency trading pro Key Features Excel at crypto trading with structured methodologies, practical examples, and real-time trading scenarios Go from the theoretical know-how to developing and testing your own strategy Transform manual trades into an automated algorithm for nonstop trades Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn today's fast-paced digital age, cryptocurrencies have emerged as a revolutionary financial asset class, capturing the attention of investors and traders worldwide. However, navigating the world of cryptocurrency trading can be overwhelming for beginners. Zero to Hero in Cryptocurrency Trading acts as a guiding light to navigate this complex realm. This comprehensive guide to cryptocurrency trading empowers you to go from a novice trader to a proficient investor by helping you implement your own trading strategy. As you progress, you’ll gain structured trading knowledge through hands-on examples and real-time scenarios, bolstered by trading psychology and money management techniques. You’ll be able to automate your manual trades with an algorithm that works even while you sleep. You’ll also benefit from interactive teaching methods, including screenshots, charts, and drawings to help decode market operations and craft your unique edge in the dynamic crypto world. As an added bonus, you’ll receive ready-to-use templates to identify useful indicators, test your strategy, and even maintain a trading journal. By the end of this book, you’ll be well-equipped to trade cryptocurrencies and automate manual trading to give you an edge in the markets.What you will learn Master trading psychology and prevent emotions from sabotaging trades Manage risks by identifying and tailoring specific risk profiles Interpret, assess, and integrate technical indicators in your trading Get to grips with trading on a centralized exchange Get a deeper understanding of risk and money management Gain an edge by identifying trading patterns Automate the patterns into a strategy for a bot that operates 24/7 Who this book is forThis book is for finance and investment professionals, crypto market enthusiasts, and anyone new to trading who wants to kickstart their cryptocurrency trading journey. A basic understanding of cryptocurrencies is a must, but prior trading experience is not necessary.
The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover statistical inefficiencies in financial markets and exploit them through algorithmic trading. Author Yves Hilpisch shows practitioners, students, and academics in both finance and data science practical ways to apply machine learning and deep learning algorithms to finance. Thanks to lots of self-contained Python examples, you'll be able to replicate all results and figures presented in the book. In five parts, this guide helps you: Learn central notions and algorithms from AI, including recent breakthroughs on the way to artificial general intelligence (AGI) and superintelligence (SI) Understand why data-driven finance, AI, and machine learning will have a lasting impact on financial theory and practice Apply neural networks and reinforcement learning to discover statistical inefficiencies in financial markets Identify and exploit economic inefficiencies through backtesting and algorithmic trading--the automated execution of trading strategies Understand how AI will influence the competitive dynamics in the financial industry and what the potential emergence of a financial singularity might bring about
This is not just another book with yet another trading system. This is a complete guide to developing your own systems to help you make and execute trading and investing decisions. It is intended for everyone who wishes to systematise their financial decision making, either completely or to some degree. Author Robert Carver draws on financial theory, his experience managing systematic hedge fund strategies and his own in-depth research to explain why systematic trading makes sense and demonstrates how it can be done safely and profitably. Every aspect, from creating trading rules to position sizing, is thoroughly explained. The framework described here can be used with all assets, including equities, bonds, forex and commodities. There is no magic formula that will guarantee success, but cutting out simple mistakes will improve your performance. You'll learn how to avoid common pitfalls such as over-complicating your strategy, being too optimistic about likely returns, taking excessive risks and trading too frequently. Important features include: - The theory behind systematic trading: why and when it works, and when it doesn't. - Simple and effective ways to design effective strategies. - A complete position management framework which can be adapted for your needs. - How fully systematic traders can create or adapt trading rules to forecast prices. - Making discretionary trading decisions within a systematic framework for position management. - Why traditional long only investors should use systems to ensure proper diversification, and avoid costly and unnecessary portfolio churn. - Adapting strategies depending on the cost of trading and how much capital is being used. - Practical examples from UK, US and international markets showing how the framework can be used. Systematic Trading is detailed, comprehensive and full of practical advice. It provides a unique new approach to system development and a must for anyone considering using systems to make some, or all, of their investment decisions.
Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, you’ll quickly grasp Python’s fundamentals, working with the built-in data structures and functions. Then you’ll move on to building your very own webapp, exploring database management, exception handling, and data wrangling. If you’re intrigued by what you can do with context managers, decorators, comprehensions, and generators, it’s all here. This second edition is a complete learning experience that will help you become a bonafide Python programmer in no time. Why does this book look so different? Based on the latest research in cognitive science and learning theory, Head First Pythonuses a visually rich format to engage your mind, rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multi-sensory learning experience is designed for the way your brain really works.
The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.