Hands On Algorithmic Trading With Python
Download Hands On Algorithmic Trading With Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Hands On Algorithmic Trading With Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Hands-On Algorithmic Trading with Python
Artificial intelligence in general and specifically machine learning are becoming increasingly important tools for many industries and enterprises. But one business sector in particular has long since adopted and benefitted from these powerful computing paradigms: investment services. In fact, over the past decade, few other industries and sectors have experienced the frenetic pace of automation as that of the investment management industry, the direct result of algorithmic trading and machine learning technologies. Industry experts estimate that today as much as 70% of the daily trading volume in the United States equity markets is executed algorithmically-by computer programs following a set of predefined rules that span the entire trading process, from idea generation to execution and portfolio management. But although all algorithmic trading is executed by computers, the rules for generating trades are either designed by humans or discovered by machine learning algorithms from training data. Not surprisingly, the ability to create these algorithms, particularly using Python, is in high demand. In this video course, designed for those with a basic level of experience and expertise in trading, investing, and writing code in Python, you learn about the process and technological tools for developing algorithmic trading strategies. You'll examine the pros and cons of algorithmic trading as well as the first steps you'll need to take to "level the playing field" for retail equity investors. You'll explore some of the models that you can apply to formulate trading and investment strategies. You'll also learn about the Pandas library to import, analyze, and visualize data from market, fundamental, and alternative, no-cost sources that are available online. You'll even see how to prepare for competitions that can fund your algorithmic trading strategies. (Note that live trading is beyond the scope of the course.) What you'll learn-and how you can apply it By the end of this video course you'll understand: The advantages and disadvantages of algorithmic trading The different types of models used to generate trading and investment strategies The process and tools used for researching, designing, and developing them Pitfalls of backtesting algorithmic strategies Risk-adjusted metrics for evaluating their performance The paramount importance of risk management and position sizing And you'll be able to: Use the Pandas library to import, analyze, and vis...
Hands-On AI Trading with Python, QuantConnect and AWS
Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab. Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM. Predict market volatility regimes and allocate funds accordingly. Predict daily returns of tech stocks using classifiers. Forecast Forex pairs' future prices using Support Vector Machines and wavelets. Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs. Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications. Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization. Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch. AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.
Hands-On Financial Trading with Python
Build and backtest your algorithmic trading strategies to gain a true advantage in the market Key FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python's data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook Description Creating an effective system to automate your trading can help you achieve two of every trader's key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it's 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. As you progress, you'll pick up lots of skills like 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 to help you get —and stay—ahead of the markets. What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is for If you're a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You don't have to be a fully-fledged programmer to dive into this book, but knowing how to use Python's core libraries and a solid grasp on statistics will help you get the most out of this book.