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Indigo Python for Asset Management | Erin Mills Town Centre
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The asset management industry is undergoing a paradigm shift toward automation, transparency, and data-driven decision-making. Traditional tools (Excel, Bloomberg) are being replaced by programmable, scalable solutions. Yet most finance professionals lack accessible, practical training in applying Python to real portfolio problems. Python for Asset Management fills that gap. The book empowers non-programmers - portfolio managers, risk analysts, and students - to implement advanced models themselves. It responds to the growing demand for quantitative literacy in finance, especially in sustainable investing and smart beta strategies, areas of active research for both of the authors. Features 31 hands-on Python exercises with real data and executable code. Complete GitHub repository (MIT License) with all scripts, data pipelines, and results. Step-by-step implementation of VaR (historical, parametric, Monte Carlo), bond immunization, and factor models. Real-world decision tools - e.g., build a bullet/barbell/ladder bond portfolio, run Brinson-Fachler attribution, or backtest smart beta vs. index. Immediate applicability - every exercise produces a deliverable (e.g., optimal weights, risk report, attribution table) ready for client meetings. Focus on practical asset management workflows, not just theory.
The asset management industry is undergoing a paradigm shift toward automation, transparency, and data-driven decision-making. Traditional tools (Excel, Bloomberg) are being replaced by programmable, scalable solutions. Yet most finance professionals lack accessible, practical training in applying Python to real portfolio problems. Python for Asset Management fills that gap. The book empowers non-programmers - portfolio managers, risk analysts, and students - to implement advanced models themselves. It responds to the growing demand for quantitative literacy in finance, especially in sustainable investing and smart beta strategies, areas of active research for both of the authors. Features 31 hands-on Python exercises with real data and executable code. Complete GitHub repository (MIT License) with all scripts, data pipelines, and results. Step-by-step implementation of VaR (historical, parametric, Monte Carlo), bond immunization, and factor models. Real-world decision tools - e.g., build a bullet/barbell/ladder bond portfolio, run Brinson-Fachler attribution, or backtest smart beta vs. index. Immediate applicability - every exercise produces a deliverable (e.g., optimal weights, risk report, attribution table) ready for client meetings. Focus on practical asset management workflows, not just theory.
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