Fast Python aggressively rehashes the basics of Python programming in order to dispel myths and misconceptions about how to write fast code. Readers equipped with the lessons from this book will be able to test, diagnose, and optimize out performance bottlenecks in their own work.
For each algorithm discussed, readers will walk through numerous progressively faster methods of programming it, all while picking up bits of fundamental knowledge about time complexity, memory efficiency, data structures, multi-threading, and vectorization. As such, this book is relevant to veterans looking refresh their methods and to computer science students navigating Algorithms 101.
This book maintains a high standard of reproducibility. All of the graphics, tables, and code profiles contained in this book are fully reproducible and available to anyone in a public GitHub repository.
Where to Buy
Available at Amazon.
Algorithmic Trading with Python
Algorithmic Trading with Python discusses modern quant trading methods in Python with a heavy focus on pandas, numpy, and scikit-learn.
After establishing an understanding of technical indicators and performance metrics, readers will walk through the process of developing a trading simulator, strategy optimizer, and financial machine learning pipeline.
This book maintains a high standard of reprocibility. All code and data is self-contained in a GitHub repo. The data includes hyper-realistic simulated price data and alternative data based on real securities.
Algorithmic Trading with Python (2020) is the spiritual successor to Automated Trading with R (2016). This book covers more content in less time than its predecessor due to advances in open-source technologies for quantitative analysis.
Where to Buy
Available at Amazon.
The Blender Python API
Understand the Blender Python API to allow for precision 3D modeling and add-on development. Follow detailed guidance on how to create precise geometries, complex texture mappings, optimized renderings, and much more.
This book is a detailed, user-friendly guide to understanding and using Blender’s Python API for programmers and 3D artists. Blender is a popular open source 3D modeling software used in advertising, animation, data visualization, physics simulation, photorealistic rendering, and more. Programmers can produce extremely complex and precise models that would be impossible to replicate by hand, while artists enjoy numerous new community-built add-ons.
The Blender Python API is an unparalleled programmable visualization environment. Using the API is made difficult due to its complex object hierarchy and vast documentation. You will become familiar with data structures and low-level concepts in both modeling and rendering with special attention given to optimizing procedurally generated models.
- Discusses modules of the API as analogs to human input modes in Blender
- Reviews low-level and data-level manipulation of 3D objects in Blender Python
- Details how to deploy and extend projects with external libraries
- Provides organized utilities of novel and mature API abstractions for general use in add-on development
Where to Buy
Automated Trading with R
Automated Trading with R provides all the tools you need to trade algorithmically with your existing brokerage, from data management, to strategy optimization, to order execution, using free and publicly available data. Connect to your brokerage’s API, and the source code is plug-and-play.
Automated Trading with R explains the broad topic of automated trading, starting with its mathematics and moving to its computation and execution. Readers will gain a unique insight into the mechanics and computational considerations taken in building a back-tester, strategy optimizer, and fully functional trading platform.
The platform built in this book can serve as a complete replacement for commercially available platforms used by retail traders and small funds. Software components are strictly decoupled and easily scalable, providing opportunity to substitute any data source, trading algorithm, or brokerage.
- Provides a flexible alternative to common strategy automation frameworks, like Tradestation, Metatrader, and CQG, to small funds and retail traders.
- Offers an understanding of the internal mechanisms of an automated trading system.
- Standardizes discussion and notation of real-world strategy optimization problems.
What You’ll Learn
- To optimize strategies, generate real-time trading decisions, and minimize computation time while programming an automated strategy in R and using its package library.
- How to best simulate strategy performance to derive accurate performance estimates.
- Important optimization criteria for statistical validity in the context of time series.
- An understanding of critical real-world variables pertaining to portfolio management and performance assessment, including latency, drawdowns, varying trade size, portfolio growth, and penalization of unused capital.
Visit our forum site, r.chrisconlan.com, to discuss and build on finance and programming topics detailed in the text. The site contains information on how to obtain the source code, instructions and reference materials for new R users, and hosts the text’s developer community. The community is encouraged to modify and contribute improvements to the Community Platform hosted at Github.