Financial Software Engineering with Python and C++: Advanced Techniques for Algorithmic Trading, Risk Management, and Financial Modeling

Name: Financial Software Engineering with Python and C++: Advanced Techniques for Algorithmic Trading, Risk Management, and Financial Modeling
Brand: Independently Published
SKU: 9798185206034
Price: 4527.0 INR
Availability: OutOfStock

by Reactive Publishing , James Preston , Alice Schwartz

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ISBN13: 9798185206034
Binding: Paperback
Subject: N/A
Publisher: Independently Published
Publisher Imprint: Independently Published
Publication Date: July 1, 2026
Pages: 454
Original Price: USD 41.99
Language: English
Edition: N/A
Item Weight: 545 grams
BISAC Subject(s): Finance / Financial Engineering

About The Book

Reactive Publishing

In today's fast-paced financial markets, building robust, scalable, and high-performance systems is essential for success. This book equips developers, quantitative analysts, and financial engineers with the advanced skills needed to design, implement, and optimize sophisticated financial applications using the complementary strengths of Python and C++.

What You'll Learn

Algorithmic Trading Systems: Develop end-to-end trading strategies, from signal generation and backtesting to low-latency execution engines. Master Python libraries like pandas, NumPy, and scikit-learn alongside C++ for performance-critical components such as market data feeds and order matching.
Risk Management Frameworks: Implement advanced techniques including Value-at-Risk (VaR), stress testing, Monte Carlo simulations, and real-time portfolio risk analytics. Learn how to balance Python's rapid prototyping capabilities with C++'s speed for production-grade risk systems.
Financial Modeling: Build complex models for derivatives pricing, fixed income, portfolio optimization, and stochastic processes. Explore hybrid architectures that leverage Python for high-level modeling and data science while using C++ (with libraries like QuantLib) for computationally intensive calculations.
Software Engineering Best Practices in Finance: Cover topics such as concurrency and multithreading in C++, memory management, interfacing Python with C++ (via pybind11, SWIG, or Cython), microservices for trading platforms, testing, deployment, and regulatory compliance (e.g., audit trails and data integrity).
Performance Optimization and Scalability: Techniques for handling massive market data volumes, high-frequency trading (HFT) considerations, GPU acceleration, and cloud-native financial applications.

Who This Book Is For

Quantitative developers and software engineers transitioning into or deepening their expertise in fintech.
Traders and analysts seeking to automate and optimize their strategies with production-quality code.
Students and professionals in financial engineering, computational finance, or computer science who want practical, hands-on guidance combining two of the most powerful languages in the industry.

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