Využití Pythonu při optimalizaci portfolia

Portfolio optimization is the approach to select the optimal portfolio that provides the most profitable rate of return for each unit of risk taken by an investor. An investment portfolio is the distribution of an investor's assets, alternatively, it is the selection pool of an investor's investments. The objective of this thesis is to validate and compare the out-of-sample performance of the following strategies: navie Strategy, minimum variance and maximum Sharpe ratio. Therefore, we chose thirty stocks that listed on the NASDAQ Composite Index during the past ten years. This thesis is divided into five chapters. The first chapter expounds on the content and structure of the thesis. The second chapter introduces Python. In Chapter 3 we describe the methodology for portfolio optimization. In the fourth chapter, we use Python to compute the naive Strategy, minimum variance and maximu Sharpe ratio portfolios. The last chapter is the conclusion.Optimalizace portfolia je přístup k výběru optimálního portfolia, který hledá nejvyšší míru výnosu za každou jednotku rizika, kterou investor podstupuje. Investiční portfolio je rozložení prostředků investora do aktiv, resp. je to soubor investic investora. Cílem této diplomové práce je ověřit a porovnat out-of-sample výkonnost následujících strategií: naivní strategie, strategie minimálního rozptylu a maximálního Sharpeho poměru. V aplikační části diplomové práce bylo vybráno třicet akcií, které byly v posledních deseti letech součástí indexu NASDAQ Composite. Tato diplomová práce je rozdělena do pěti kapitol. První kapitola objasňuje obsah a strukturu práce. Druhá kapitola popisuje jazyk Python. Ve třetí kapitole je popsána metodika optimalizace portfolia. Ve čtvrté kapitole je použit Python k sestavení a ověření výkonnosti strategií, konkrétně naivní strategie, strategie minimálního rozptylu a maximálního Sharpeho poměru. Poslední kapitolou je závěr.154 - Katedra financídobř

On the modelling and design of environmentally friendly biochar production for soil application

Biochar production through pyrolysis of various agricultural wastes has the potential to effectively reduce waste disposal issues and mitigate the potential impact of global warming. This thesis firstly provided a comprehensive review of the state-of-the-art knowledge on the pyrolysis processing of agricultural waste, its influencing factors, and the multifunctional application of biochar. Meanwhile, machine learning modelling, life cycle assessment, multiple-objective optimization are reviewed in the context of advancing biochar production and applications, providing more effective means of optimising processes and assessing environmental impacts. However, existing studies tend to be targeted at individual machine learning models or environmental assessment approaches. From a time- and cost-saving perspective, the process operating parameters and the type of biomass must be appropriately selected to obtain the desired product yield and characteristics. It is necessary to determine the environmental performance of the process before deciding to apply the technology on a large scale. Thus, this thesis has innovatively developed a framework containing life cycle assessment method, machine learning modelling, multi-objective optimisation and multi-criteria decision making. Key aspects of the study included the comparison of machine learning methods for predicting the influences of agricultural waste compositions and process conditions on biochar production. Specifically, Multi-layer Perceptron Neural Network and Gaussian Process Regression models were compared in terms of their accuracy in predicting biochar yields and properties. An environmental impact assessment framework was developed by combining Machine Learning and Life Cycle Assessment to assess the carbon footprint of biochar production and soil application, highlighting the potential of biochar soil application to achieve negative carbon emissions. By combining Multi-Objective Optimization and Multi-Criteria Decision-Making techniques with Life Cycle Assessment, this study also developed a novel framework to optimise the biochar production process and analysis its environmental impact. Together, this research aimed to support the development of application-oriented biochar process pathways for agricultural waste management and low carbon development, promoting sustainable agricultural practices

Deep learning integrates histopathology and proteogenomics at a pan-cancer level

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features associated with critical clinical outcomes in cancer. We utilize 2,755 H&E-stained histopathological slides from 657 patients across 6 cancer types from CPTAC. Our models effectively recapitulate distinctions readily made by human pathologists: tumor vs. normal (AUROC = 0.995) and tissue-of-origin (AUROC = 0.979). We further investigate predictive power on tasks not normally performed from H&E alone, including TP53 prediction and pathologic stage. Importantly, we describe predictive morphologies not previously utilized in a clinical setting. The incorporation of transcriptomics and proteomics identifies pathway-level signatures and cellular processes driving predictive histology features. Model generalizability and interpretability is confirmed using TCGA. We propose a classification system for these tasks, and suggest potential clinical applications for this integrated human and machine learning approach. A publicly available web-based platform implements these models

Bayesian pathway analysis over brain network mediators for survival data

Technological advancements in noninvasive imaging facilitate the construction of whole brain interconnected networks, known as brain connectivity. Existing approaches to analyze brain connectivity frequently disaggregate the entire network into a vector of unique edges or summary measures, leading to a substantial loss of information. Motivated by the need to explore the effect mechanism among genetic exposure, brain connectivity and time to disease onset, we propose an integrative Bayesian framework to model the effect pathway between each of these components while quantifying the mediating role of brain networks. To accommodate the biological architectures of brain connectivity constructed along white matter fiber tracts, we develop a structural modeling framework that includes a symmetric matrix-variate accelerated failure time model and a symmetric matrix response regression to characterize the effect paths. We further impose within-graph sparsity and between-graph shrinkage to identify informative network configurations and eliminate the interference of noisy components. Extensive simulations confirm the superiority of our method compared with existing alternatives. By applying the proposed method to the landmark Alzheimer's Disease Neuroimaging Initiative study, we obtain neurobiologically plausible insights that may inform future intervention strategies