Name: Algorithmic Sports Modeling in Python: Quantitative Strategies, Portfolio Optimization, and Execution Architecture
SKU: 68912894
Price: 1414.00 TWD
Availability: InStock
Author: Bérenger, Luca
ISBN: 9798174118751

portada Algorithmic Sports Modeling in Python: Quantitative Strategies, Portfolio Optimization, and Execution Architecture

Algorithmic Sports Modeling in Python: Quantitative Strategies, Portfolio Optimization, and Execution Architecture

Type

Physical Book

Publisher

Independently published

Author

Bérenger, Luca

Format

Paperback

ISBN13

9798174118751

Algorithmic Sports Modeling in Python: Quantitative Strategies, Portfolio Optimization, and Execution Architecture

Bérenger, Luca (Author) · Independently published · Paperback

New Book Imported to Taiwan

Delivery: 23 Oct - 02 Nov

NT$ 1,414

NT$ 1,414

Add to Wishlist

Synopsis "Algorithmic Sports Modeling in Python: Quantitative Strategies, Portfolio Optimization, and Execution Architecture"

Reactive Publishing Master the Quantitative Engineering of Modern Sports Analytics and Automated Market Execution The sports analytics landscape has shifted from basic statistics to complex quantitative modeling and automated execution. Modern markets require the same mathematical rigor, risk discipline, and low-latency architecture as high-frequency financial trading. Algorithmic Sports Modeling in Python provides a complete, developer-focused blueprint for designing, testing, and deploying robust quantitative sports models using modern Python tools. This hands-on guide bridges the gap between theoretical data science and production-ready system architecture. You will learn to construct scalable data pipelines, implement predictive statistical models, optimize dynamic bet sizing, and build automated execution frameworks designed to handle real-world latency, slippage, and market friction. Inside, you will explore how to: Build High-Throughput Data Pipelines: Ingest, clean, and structure high-frequency sports data using Polars and DuckDB for ultra-fast processing. Develop Advanced Predictive Models: Implement Poisson distribution, Monte Carlo simulations, state-space models, and machine learning algorithms tailored to dynamic sports event outcomes. Perform Rigorous Backtesting: Construct robust backtesting engines that account for exchange fees, line movements, market impact, and out-of-sample validity. Apply Quantitative Portfolio Management: Implement Kelly Criterion variants, fractional allocation strategies, and variance-minimization techniques to manage bankroll drawdown and maximize risk-adjusted growth. Deploy Live Execution Pipelines: Design asynchronous execution routines using asyncio to connect directly to live exchange APIs, automate order routing, and monitor real-time system state. Whether you are a quantitative analyst, software engineer, or experienced trader seeking to translate financial engineering principles into sports analytics, this book provides the production-grade code, architectural patterns, and mathematical foundations necessary to build institutional-grade execution systems.