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 Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book 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 learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data Who 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. Table of Contents Machine Learning for Trading - From Idea to Execution Market and Fundamental Data - Sources and Techniques Alternative Data for Finance - Categories and Use Cases Financial Feature Engineering - How to Research Alpha Factors Portfolio Optimization and Performance Evaluation The Machine Learning Process Linear Models - From Risk Factors to Return Forecasts The ML4T Workflow - From Model to Strategy Backtesting (N.B. Please use the Look Inside option to see further chapters)

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 Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a research and strategy development process to apply predictive modeling to trading decisions Leverage NLP and deep learning to extract tradeable signals from market and alternative data Book DescriptionThe 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 learn Leverage market, fundamental, and alternative text and image data Research and evaluate alpha factors using statistics, Alphalens, and SHAP values Implement machine learning techniques to solve investment and trading problems Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio Create a pairs trading strategy based on cointegration for US equities and ETFs Train a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data Who 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.

Die Anfängerhausarbeit im Staatsrecht ist ein Baustein für die Leistungsnachweise der Zwischenprüfung an nahezu allen juristischen Fakultäten. Häufig kommt diese in Gestalt einer Ferienhausarbeit am Ende des 2./3. Semesters auf die Anfänger im Staatsrecht zu. Die wissenschaftliche Arbeitsweise, ein stringenter Lösungsweg mit Stellungnahme zu allen im Sachverhalt aufgeworfenen Problemen und schließlich die formale Abfassung der Arbeit bedeutet für Studienanfängerinnen und -anfänger absolutes Neuland. Dieses Anleitungsbuch liefert einen auf die Probleme zu Studienbeginn zugeschnittenen Leitfaden zur Herangehensweise und formalen Bearbeitung einer Hausarbeit und zehn nachvollziehbar und mustergültig gelöste Fälle. Das Werk bietet also kompaktes Prüfungstraining aus einem Guss. Es ist inhaltlich eng verzahnt mit den Schwerpunkte-Lehrbüchern von Kingreen/Poscher, Grundrechte. Staatsrecht II, und Degenhart, Staatsrecht I. Staatsorganisationsrecht. Für die Neuauflage wurden sämtliche Kapitel und Hausarbeiten überarbeitet und aktualisiert. Hinzugekommen ist eine Fallbearbeitung zu grundrechtlichen Fragen einer Impfpflicht. Das Kapitel "Gestaltungsrichtlinien" behandelt vertiefter als zuvor die an Bedeutung gewinnenden Online-Quellen (einschließlich Blogs). Den Leserinnen und Lesern ist ein gewinnbringender Fundus an Prüfungs- und Lehrerfahrung weiterhin sicher!

Mein Gott, war das früher alles aufregend! Damals, mit dreizehn, vierzehn, tobten die Schmetterlinge nur so durch die Bäuche, Eltern und Geschwister nervten unsäglich und dann gab es auch noch Ärger in der Schule. Wie gut, dass man sich der besten Freundin anvertrauen konnte - und dem Tagebuch, in dem man sich hemmungslos über Liebe, Leid und Lappalien ausließ. Nun haben einige - inzwischen erwachsen gewordene - Teenager ihre Tagebücher wieder hervorgekramt und präsentieren große Dramen, schräge Geschichten und aufrichtiges Pathos aus dem Wechselbad der Gefühle, das wir Pubertät nennen.

"This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code. This course will allow you to utilize Principal Component Analysis, and to visualize and interpret the results of your datasets such as the ones in the above description. You will also be able to apply hard and soft clustering methods (k-Means and Gaussian Mixture Models) to assign segment labels to customers categorized in your sample data sets."--Resource description page.

"In this video course you will understand the assumptions, advantages, and disadvantages of various popular clustering algorithms, and then learn how to apply them to different data sets for analysis. You will apply the Latent Dirichlet Allocation algorithm to model topics, which you can use as an input for a recommendation engine just like the New York Times did. You will be using cutting-edge, nonlinear dimensionality techniques (also called manifold learning)--such as T-SNE and UMAP--and autoencoders (unsupervised deep learning) to assess and visualize the information content in a higher dimension. You will be looking at K-Means, density-based clustering, and Gaussian mixture models. You will see hierarchical clustering through bottom-up and top-down strategies. You will go from preprocessing text to recommending interesting articles. Through this course, you will learn and apply concepts needed to ensure your mastery of unsupervised algorithms in Python."--Resource description page.