This repository is the companion code for the Packt book 'Learn Algorithmic Trading'. It contains a collection of Python scripts, data files (pickle, CSV, HDF5), and a FIX protocol simulator organized by book chapters. The code demonstrates algorithmic trading strategies (mean reversion, trend following, pairs trading, turtle trading), technical indicators (EMA, MACD, RSI, Bollinger Bands), machine learning models (KNN, SVM, logistic regression, linear/ridge/lasso), backtesting frameworks (for-loop and event-based), risk management metrics, and a FIX 4.4 simulator with custom FX fields. The repository is primarily educational and not intended for production use. It exhibits significant technical drift: dependencies are not pinned, data sources (Yahoo Finance via pandas_datareader) are deprecated, Python 2.7 compatibility is assumed, and the code relies on very old library versions (pandas 0.14.1/0.15.2). Reproducibility is low due to missing environment definitions and hardcoded paths. However, some components (OrderManager, FIX simulator, risk measures) are well-structured and could be extracted for reuse with minimal adaptation.

This repository is a companion to the Packt book 'Python Feature Engineering Cookbook' by Soledad Galli. It contains over 70 Jupyter notebooks organized by chapter, each demonstrating a specific feature engineering technique for machine learning, including missing data imputation, categorical encoding, variable transformation, discretization, outlier handling, date/time features, scaling, feature creation, and text feature extraction. The notebooks use public datasets (Titanic, Boston Housing, Credit Approval UCI, 20 Newsgroups) and implement techniques both manually with pandas and using specialized libraries like feature_engine, category_encoders, and featuretools. While educational and self-contained, the code is not production-grade and relies on outdated library versions and deprecated datasets (e.g., load_boston), making it a tutorial resource rather than a reusable software project. The repository is stale—last updated around 2020—with no ongoing maintenance, and missing dependencies (e.g., category_encoders not listed in requirements.txt) further reduce reproducibility.

The repository is the official code bundle for the book 'Pandas Cookbook - Second Edition' by Packt Publishing. It contains Jupyter notebooks for each chapter, demonstrating pandas functionality for data manipulation, analysis, and visualization. The notebooks use a variety of real-world datasets to illustrate concepts such as data cleaning, filtering, grouping, reshaping, and time series analysis. Additionally, there are two demo directories that provide a reusable module for loading and cleaning Kaggle survey data, along with tests. The repository serves as an educational resource for learning pandas through practical examples.