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

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

Preliminary aerodynamic design methodology for aero engine lean direct injection combustors

The Lean Direct Injection (LDI) combustor is one of the low-emissions combustors with great potential in aero-engine applications, especially those with high overall pressure ratio. A preliminary design tool providing basic combustor sizing information and qualitative assessment of performance and emission characteristics of the LDI combustor within a short period of time will be of great value to designers. In this research, the methodology of preliminary aerodynamic design for a second-generation LDI (LDI-2) combustor was explored. A computer code was developed based on this method covering the design of air distribution, combustor sizing, diffuser, dilution holes and swirlers. The NASA correlations for NOx emissions are also embedded in the program in order to estimate the NOx production of the designed LDI combustor. A case study was carried out through the design of an LDI-2 combustor named as CULDI2015 and the comparison with an existing rich-burn, quick-quench, lean-burn combustor operating at identical conditions. It is discovered that the LDI combustor could potentially achieve a reduction in liner length and NOx emissions by 18% and 67%, respectively. A sensitivity study on parameters such as equivalence ratio, dome and passage velocity and fuel staging is performed to investigate the effect of design uncertainties on both preliminary design results and NOx production. A summary on the variation of design parameters and their impact is presented. The developed tool is proved to be valuable to preliminarily evaluate the LDI combustor performance and NOx emission at the early design stage