The Science of Algorithmic Trading and Portfolio Management, with its emphasis on algorithmic trading processes and current trading models, sits apart from others of its kind. Robert Kissell, the first author to discuss algorithmic trading across the various asset classes, provides key insights into ways to develop, test, and build trading algorithms. Readers learn how to evaluate market impact models and assess performance across algorithms, traders, and brokers, and acquire the knowledge to implement electronic trading systems. This valuable book summarizes market structure, the formation of prices, and how different participants interact with one another, including bluffing, speculating, and gambling. Readers learn the underlying details and mathematics of customized trading algorithms, as well as advanced modeling techniques to improve profitability through algorithmic trading and appropriate risk management techniques. Portfolio management topics, including quant factors and black box models, are discussed, and an accompanying website includes examples, data sets supplementing exercises in the book, and large projects. - Prepares readers to evaluate market impact models and assess performance across algorithms, traders, and brokers. - Helps readers design systems to manage algorithmic risk and dark pool uncertainty. - Summarizes an algorithmic decision making framework to ensure consistency between investment objectives and trading objectives.

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.

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key FeaturesImplement machine learning algorithms to build, train, and validate algorithmic modelsCreate your own algorithmic design process to apply probabilistic machine learning approaches to trading decisionsDevelop neural networks for algorithmic trading to perform time series forecasting and smart analyticsBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learnImplement machine learning techniques to solve investment and trading problemsLeverage market, fundamental, and alternative data to research alpha factorsDesign and fine-tune supervised, unsupervised, and reinforcement learning modelsOptimize portfolio risk and performance using pandas, NumPy, and scikit-learnIntegrate machine learning models into a live trading strategy on QuantopianEvaluate strategies using reliable backtesting methodologies for time seriesDesign and evaluate deep neural networks using Keras, PyTorch, and TensorFlowWork with reinforcement learning for trading strategies in the OpenAI GymWho this book is for Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

While statistical arbitrage has faced some tough times?as markets experienced dramatic changes in dynamics beginning in 2000?new developments in algorithmic trading have allowed it to rise from the ashes of that fire. Based on the results of author Andrew Pole?s own research and experience running a statistical arbitrage hedge fund for eight years?in partnership with a group whose own history stretches back to the dawn of what was first called pairs trading?this unique guide provides detailed insights into the nuances of a proven investment strategy. Filled with in-depth insights and expert advice, Statistical Arbitrage contains comprehensive analysis that will appeal to both investors looking for an overview of this discipline, as well as quants looking for critical insights into modeling, risk management, and implementation of the strategy.

Discover foundational and advanced techniques in quantitative equity trading from a veteran insider In Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage, distinguished physicist-turned-quant Dr. Michael Isichenko delivers a systematic review of the quantitative trading of equities, or statistical arbitrage. The book teaches you how to source financial data, learn patterns of asset returns from historical data, generate and combine multiple forecasts, manage risk, build a stock portfolio optimized for risk and trading costs, and execute trades. In this important book, you’ll discover: Machine learning methods of forecasting stock returns in efficient financial markets How to combine multiple forecasts into a single model by using secondary machine learning, dimensionality reduction, and other methods Ways of avoiding the pitfalls of overfitting and the curse of dimensionality, including topics of active research such as “benign overfitting” in machine learning The theoretical and practical aspects of portfolio construction, including multi-factor risk models, multi-period trading costs, and optimal leverage Perfect for investment professionals, like quantitative traders and portfolio managers, Quantitative Portfolio Management will also earn a place in the libraries of data scientists and students in a variety of statistical and quantitative disciplines. It is an indispensable guide for anyone who hopes to improve their understanding of how to apply data science, machine learning, and optimization to the stock market.

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

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