  In this paper, we present an integrated approach to portfolio construction
and optimization, leveraging high-performance computing capabilities. We first
explore diverse pairings of generative model forecasts and objective functions
used for portfolio optimization, which are evaluated using
performance-attribution models based on LASSO. We illustrate our approach using
extensive simulations of crypto-currency portfolios, and we show that the
portfolios constructed using the vine-copula generative model and the
Sharpe-ratio objective function consistently outperform. To accommodate a wide
array of investment strategies, we further investigate portfolio blending and
propose a general framework for evaluating and combining investment strategies.
We employ an extension of the multi-armed bandit framework and use value models
and policy models to construct eclectic blended portfolios based on past
performance. We consider similarity and optimality measures for value models
and employ probability-matching ("blending") and a greedy algorithm
("switching") for policy models. The eclectic portfolios are also evaluated
using LASSO models. We show that the value model utilizing cosine similarity
and logit optimality consistently delivers robust superior performances. The
extent of outperformance by eclectic portfolios over their benchmarks
significantly surpasses that achieved by individual generative model-based
portfolios over their respective benchmarks.
