Zentropy

This book compiles selected publications authored or co-authored by the editor to present a comprehensive understanding of following topics: (1) fundamentals of thermodynamics, Materials Genome®, and zentropy theory; (2) zentropy theory for prediction of positive and negative thermal expansions. It is noted that while entropy at one scale is well represented by standard statistical mechanics in terms of probability of individual configurations at that scale, the theory capable of counting total entropy of a system from different scales is lacking. The zentropy theory provides a nested form for configurational entropy enabling multiscale modeling to account for disorder and fluctuations from the electronic scale based on quantum mechanics to the experimental scale based on statistical mechanics using free energies of individual configurations rather than their total energies in standard statistical mechanics. The predictions from the zentropy theory demonstrate remarkable agreements with experimental observations for magnetic transitions and associated emergent behaviors of strongly correlated metals and oxides, including singularity and instability at critical points and positive and negative thermal expansions, without the need of additional truncated models and fitting model parameters beyond density function theory. This paves the way to provide the predicted phase equilibrium data for high throughput predictive CALPHAD modeling of complex material systems, and those individual configurations may thus be considered as the genomic building blocks of individual phases in the spirit of Materials Genome®.

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.