Advanced Portfolio Optimization With Excel Python
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Advanced Portfolio Optimization With Excel Python
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Author : Hayden Van Der Post
language : en
Publisher: Independently Published
Release Date : 2025-04-03
Advanced Portfolio Optimization With Excel Python written by Hayden Van Der Post and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-03 with Business & Economics categories.
Reactive Publishing Advanced Portfolio Optimization with Excel & Python Master Quantitative Investing with Real-World Applications Unlock the full power of modern portfolio theory, machine learning, and quantitative finance using two of the most accessible tools in your arsenal: Excel and Python. This advanced guide is designed for serious investors, analysts, and finance professionals who want to go beyond basic models and learn how to engineer high-performance portfolios. Inside, you'll find a deep dive into risk-adjusted strategies, multi-factor models, regime switching, Monte Carlo simulations, Black-Litterman adjustments, and more-anchored by code and practical Excel frameworks you can apply immediately. Whether you're managing capital or building algorithms, this book offers you the tools to: Construct robust portfolios with modern optimization techniques Combine fundamental and technical factors in allocation decisions Apply risk-parity, volatility targeting, and regime-based tilts Leverage Python for backtesting and Excel for scenario analysis Bridge academic theory with real-world portfolio management With a dual emphasis on financial insight and hands-on execution, this book is ideal for those who want more than just theory-it's for builders, quants, and future fund managers.
Advanced Portfolio Optimization
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Author : Dany Cajas
language : en
Publisher:
Release Date : 2025-04-10
Advanced Portfolio Optimization written by Dany Cajas and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-10 with Business & Economics categories.
Python In Excel 2026 Edition
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Author : Hayden Van Der Post
language : en
Publisher: Independently Published
Release Date : 2025-12
Python In Excel 2026 Edition written by Hayden Van Der Post and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12 with Business & Economics categories.
Reactive Publishing Python-in-Excel is no longer an experiment. By 2026, it has become the new default for advanced financial modeling, automation, analytics, and strategic decision-making. This book shows you exactly how to harness it. Python-in-Excel 2026 Edition is the definitive guide for modern finance teams, FP&A professionals, data analysts, and anyone who needs to build intelligent models without leaving Excel. Blending the comfort of spreadsheets with the power of Python, this handbook gives you a complete operating system for next-generation financial workflows. Inside, you'll learn how to design scalable forecasting models, automate repetitive tasks, integrate machine learning and statistical tools, build real-time dashboards, and connect Python directly into Excel's formulas, tables, and PowerBI reporting layers. Every chapter is engineered to help you move faster, think more strategically, and deliver insights at a level that was impossible before. Whether you're building a dynamic FP&A forecast model, designing a Monte Carlo simulation, cleaning messy financial data, creating an automation pipeline, or using Python to replace dozens of complex Excel formulas, this book gives you a complete blueprint for the future of spreadsheet-native analytics. What You'll Learn - The 2026 Python-in-Excel architecture How Microsoft integrates Python, Excel, PowerBI, and automation into a single analytics environment. - Next-generation forecasting & FP&A workflows Scenario modeling, rolling forecasts, driver-based planning, and automated sensitivity analysis. - Python-based financial modeling inside Excel Portfolio analytics, optimization, capital allocation, options pricing, volatility models, and time-series forecasting. - Data transformation & automation pipelines Clean, shape, merge, and automate large datasets directly in Excel using Python. - Machine learning in Excel for finance Regression, classification, clustering, anomaly detection, demand forecasting, and predictive analytics. - Advanced Excel formulas powered by Python Replace long formulas with clean Python blocks. Build hybrid models combining Excel + Pandas/Numpy. - Executive-grade reporting Link Excel, Python, and PowerBI into an end-to-end reporting system that updates automatically. - Workflow engineering for modern teams Versioning, quality checks, automation triggers, model governance, and collaborative development. Who This Book Is For This book is built for: - Financial analysts - FP&A professionals and CFO teams - Data analysts and business intelligence specialists - Quantitative finance practitioners - Excel power users ready to level up - Anyone who wants the speed of Excel and the power of Python in one unified system By the end of this book, you'll have a fully modernized analytics stack, all inside Excel. Python-in-Excel is the future of spreadsheet modeling. This is the handbook that shows you how to master it.
Multicriteria Portfolio Construction With Python
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Author : Elissaios Sarmas
language : en
Publisher: Springer Nature
Release Date : 2020-10-17
Multicriteria Portfolio Construction With Python written by Elissaios Sarmas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-17 with Business & Economics categories.
This book covers topics in portfolio management and multicriteria decision analysis (MCDA), presenting a transparent and unified methodology for the portfolio construction process. The most important feature of the book includes the proposed methodological framework that integrates two individual subsystems, the portfolio selection subsystem and the portfolio optimization subsystem. An additional highlight of the book includes the detailed, step-by-step implementation of the proposed multicriteria algorithms in Python. The implementation is presented in detail; each step is elaborately described, from the input of the data to the extraction of the results. Algorithms are organized into small cells of code, accompanied by targeted remarks and comments, in order to help the reader to fully understand their mechanics. Readers are provided with a link to access the source code through GitHub. This Work may also be considered as a reference which presents the state-of-art research on portfolio construction with multiple and complex investment objectives and constraints. The book consists of eight chapters. A brief introduction is provided in Chapter 1. The fundamental issues of modern portfolio theory are discussed in Chapter 2. In Chapter 3, the various multicriteria decision aid methods, either discrete or continuous, are concisely described. In Chapter 4, a comprehensive review of the published literature in the field of multicriteria portfolio management is considered. In Chapter 5, an integrated and original multicriteria portfolio construction methodology is developed. Chapter 6 presents the web-based information system, in which the suggested methodological framework has been implemented. In Chapter 7, the experimental application of the proposed methodology is discussed and in Chapter 8, the authors provide overall conclusions. The readership of the book aims to be a diverse group, including fund managers, risk managers, investment advisors, bankers, private investors, analytics scientists, operations researchers scientists, and computer engineers, to name just several. Portions of the book may be used as instructional for either advanced undergraduate or post-graduate courses in investment analysis, portfolio engineering, decision science, computer science, or financial engineering.
Portfolio Optimization With R Rmetrics
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Author :
language : en
Publisher: Rmetrics
Release Date :
Portfolio Optimization With R Rmetrics written by and has been published by Rmetrics this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Quantitative Portfolio Optimization
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Author : Miquel Noguer Alonso
language : en
Publisher: John Wiley & Sons
Release Date : 2025-01-29
Quantitative Portfolio Optimization written by Miquel Noguer Alonso and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-29 with Business & Economics categories.
Expert guidance on implementing quantitative portfolio optimization techniques In Quantitative Portfolio Optimization: Theory and Practice, renowned financial practitioner Miquel Noguer, alongside physicists Alberto Bueno Guerrero and Julian Antolin Camarena, who possess excellent knowledge in finance, delve into advanced mathematical techniques for portfolio optimization. The book covers a range of topics including mean-variance optimization, the Black-Litterman Model, risk parity and hierarchical risk parity, factor investing, methods based on moments, and robust optimization as well as machine learning and reinforcement technique. These techniques enable readers to develop a systematic, objective, and repeatable approach to investment decision-making, particularly in complex financial markets. Readers will gain insights into the associated mathematical models, statistical analyses, and computational algorithms for each method, allowing them to put these techniques into practice and identify the best possible mix of assets to maximize returns while minimizing risk. Topics explored in this book include: Specific drivers of return across asset classes Personal risk tolerance and it#s impact on ideal asses allocation The importance of weekly and monthly variance in the returns of specific securities Serving as a blueprint for solving portfolio optimization problems, Quantitative Portfolio Optimization: Theory and Practice is an essential resource for finance practitioners and individual investors It helps them stay on the cutting edge of modern portfolio theory and achieve the best returns on investments for themselves, their clients, and their organizations.
Excel Meets Python
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Author : Reactive Publishing
language : en
Publisher: Independently Published
Release Date : 2025-01-03
Excel Meets Python written by Reactive Publishing and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Business & Economics categories.
Reactive Publishing Elevate your financial modeling expertise with Excel Meets Python: 2025 Masterclass in Advanced Financial Modelling and Analytical Integration. This groundbreaking guide is tailored for finance professionals, analysts, and data enthusiasts ready to combine the reliability of Excel with the advanced capabilities of Python for next-level analysis. Inside this book, you'll explore: Advanced Excel Mastery: Gain proficiency in complex formulas, automation, and powerful data visualization techniques to optimize your workflows. Python-Powered Analysis: Harness Python's ability to automate tasks, analyze vast datasets, and generate predictive insights that go beyond traditional Excel. Integrated Solutions: Step-by-step guidance to seamlessly combine Excel and Python, creating innovative, scalable financial models. Real-World Scenarios: Dive into practical applications for budgeting, forecasting, portfolio management, and other critical financial challenges. Whether you're looking to streamline your processes, improve decision-making, or modernize your toolkit, Excel Meets Python offers actionable insights and practical techniques to help you thrive in a fast-paced, data-driven industry. This is your comprehensive resource for staying ahead in the evolving world of financial analysis.
Robust Equity Portfolio Management
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Author : Woo Chang Kim
language : en
Publisher: John Wiley & Sons
Release Date : 2015-11-30
Robust Equity Portfolio Management written by Woo Chang Kim and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-30 with Business & Economics categories.
A comprehensive portfolio optimization guide, with provided MATLAB code Robust Equity Portfolio Management + Website offers the most comprehensive coverage available in this burgeoning field. Beginning with the fundamentals before moving into advanced techniques, this book provides useful coverage for both beginners and advanced readers. MATLAB code is provided to allow readers of all levels to begin implementing robust models immediately, with detailed explanations and applications in the equity market included to help you grasp the real-world use of each technique. The discussion includes the most up-to-date thinking and cutting-edge methods, including a much-needed alternative to the traditional Markowitz mean-variance model. Unparalleled in depth and breadth, this book is an invaluable reference for all risk managers, portfolio managers, and analysts. Portfolio construction models originating from the standard Markowitz mean-variance model have a high input sensitivity that threatens optimization, spawning a flurry of research into new analytic techniques. This book covers the latest developments along with the basics, to give you a truly comprehensive understanding backed by a robust, practical skill set. Get up to speed on the latest developments in portfolio optimization Implement robust models using provided MATLAB code Learn advanced optimization methods with equity portfolio applications Understand the formulations, performances, and properties of robust portfolios The Markowitz mean-variance model remains the standard framework for portfolio optimization, but the interest in—and need for—an alternative is rapidly increasing. Resolving the sensitivity issue and dramatically reducing portfolio risk is a major focus of today's portfolio manager. Robust Equity Portfolio Management + Website provides a viable alternative framework, and the hard skills to implement any optimization method.
Optimal Portfolio Modeling
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Author : Philip McDonnell
language : en
Publisher: John Wiley & Sons
Release Date : 2008-02-13
Optimal Portfolio Modeling written by Philip McDonnell and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-13 with Business & Economics categories.
Optimal Portfolio Modeling is an easily accessible introduction to portfolio modeling for those who prefer an intuitive approach to this discipline. While early chapters provide engaging insights on the statistical properties of markets, this book quickly moves on to illustrate invaluable trading and risk control models based on popular programs such as Excel and the statistical modeling language R. This reliable resource presents modeling formulas that will allow you to effectively maximize the performance, minimize the drawdown, and manage the risk of your portfolio.
Linear And Mixed Integer Programming For Portfolio Optimization
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Author : Renata Mansini
language : en
Publisher: Springer
Release Date : 2015-06-10
Linear And Mixed Integer Programming For Portfolio Optimization written by Renata Mansini and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-10 with Business & Economics categories.
This book presents solutions to the general problem of single period portfolio optimization. It introduces different linear models, arising from different performance measures, and the mixed integer linear models resulting from the introduction of real features. Other linear models, such as models for portfolio rebalancing and index tracking, are also covered. The book discusses computational issues and provides a theoretical framework, including the concepts of risk-averse preferences, stochastic dominance and coherent risk measures. The material is presented in a style that requires no background in finance or in portfolio optimization; some experience in linear and mixed integer models, however, is required. The book is thoroughly didactic, supplementing the concepts with comments and illustrative examples.