Robust optimization of algorithmic trading systems

GAs (Genetic Algorithms) and GP (Genetic Programming) are investigated for finding robust Technical Trading Strategies (TTSs). TTSs evolved with standard GA/GP techniques tend to suffer from over-fitting as the solutions evolved are very fragile to small disturbances in the data. The main objective of this thesis is to explore optimization techniques for GA/GP which produce robust TTSs that have a similar performance during both optimization and evaluation, and are also able to operate in all market conditions and withstand severe market shocks. In this thesis, two novel techniques that increase the robustness of TTSs and reduce over-fitting are described and compared to standard GA/GP optimization techniques and the traditional investment strategy Buy & Hold. The first technique employed is a robust multi-market optimization methodology using a GA. Robustness is incorporated via the environmental variables of the problem, i.e. variablity in the dataset is introduced by conducting the search for the optimum parameters over several market indices, in the hope of exposing the GA to differing market conditions. This technique shows an increase in the robustness of the solutions produced, with results also showing an improvement in terms of performance when compared to those offered by conducting the optimization over a single market. The second technique is a random sampling method we use to discover robust TTSs using GP. Variability is introduced in the dataset by randomly sampling segments and evaluating each individual on different random samples. This technique has shown promising results, substantially beating Buy & Hold. Overall, this thesis concludes that Evolutionary Computation techniques such as GA and GP combined with robust optimization methods are very suitable for developing trading systems, and that the systems developed using these techniques can be used to provide significant economic profits in all market conditions

A Review of Energy Management of Renewable Multisources in Industrial Microgrids

This review aims to consolidate recent advancements in power control within microgrids and multi-microgrids. It specifically focuses on analyzing the comparative benefits of various architectures concerning energy sharing and demand cost management. The paper provides a comprehensive technical analysis of different architectures found in existing literature, which are designed for energy management and demand cost optimization. In summary, this review paper provides a thorough examination of power control in microgrids and multi-microgrids and compares different architectural approaches for energy management and demand cost optimization

Portfolio implementation risk management using evolutionary multiobjective optimization

Portfoliomanagementbasedonmean-varianceportfoliooptimizationissubjecttodifferent sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancybetweentargetandpresentportfolios,causedbytradingstrategies,mayexposeinvestors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix.Sandra Garcia-Rodriguez and David Quintana acknowledge financial support granted by the Spanish Ministry of Economy and Competitivity under grant ENE2014-56126-C2-2-R. Roman Denysiuk and Antonio Gaspar-Cunha were supported by the Portuguese Foundation for Science and Technology under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico-LA 25-2013-2014-Strategic Project-LA 25-2013-2014).info:eu-repo/semantics/publishedVersio

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

Interconnecting industrial multi-microgrids using bidirectional hybrid energy links

Sharing and exchange energy among nearby industrial microgrids are crucial, especially with high energy requirements for their production targets and costly energy storage systems that may be oversized for their operations. Facilitating energy exchange can provide an economic advantage for industrial production by utilizing cheaper energy sources and reducing production costs. This manuscript presents an efficient approach for transferring large energy packets with minimal energy losses using high-voltage direct current (HVDC) energy transmission. The manuscript methodology focuses on implementing an industrial multi-microgrid using a modular multilevel converter. This converter utilizes two power link channels: a three-phase AC and an HVDC link, creating a hybrid energy transmission between microgrids. When a substantial amount of energy to transfer, the HVDC method enhances overall efficiency by reducing copper losses and mitigating issues associated with the AC link, such as harmonics and skin effects. The modular multilevel converter topology offers high flexibility and the use of fewer converters. Additionally, the HVDC link eliminates distance restrictions for energy transfer between industrial microgrids. A case study illustrates the functionality of this topology, demonstrating optimized power transfer and decreased energy losses. This methodology allows industrial microgrids to enhance energy efficiency and productivity while minimizing operational costs

A survey on portfolio optimisation with metaheuristics.

A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize return and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its return but also interactively define its risk as usually measured by a portfolio variance. This presents a combinatorial optimisation problem that involves selection of both a number of assets as well as its quantity (weight or proportion or units). The problem is extremely complex due to a large number of selectable assets. Furthermore, the problem is dynamic and stochastic in nature with a number of constraints presenting a complex model which is difficult to solve for exact solution. In the last decade research publications have reported the applications of metaheuristic-based optimisation methods with some success., This paper presents a review of these reported models, optimisation problem formulations and metaheuristic approaches for portfolio optimisation

What is the growth potential of green innovation? An assessment of EU climate policy options

This paper provides a model-based analysis of the cost-efficiency of different EU climate policy options that could direct innovation in the private sector towards an environmentally sustainable growth path. Our objective is to assess different policy options in order to identify an appropriate policy-mix of environmental and innovation market instruments in terms of their cost-effectiveness. For this purpose, we develop a fully-dynamic, multisectoral DSGE model with endogenous technological change where we specifically identify its environmental content and we calibrate the model for the EU and the rest of the world. Our results suggest that an appropriate policy mix should intensively stimulate R&D in the green sectors in the short-run and phase-it out by spreading the R&D support to all sectors of the economy in the medium-term. Although intuitive, the orders of magnitude presented in this paper should be interpreted with caution by taking into account the underlying assumptions of the model and identification of green innovation data.Carbon revenue recycling, climate change, directed technical change, double dividend, dynamic general equilibrium model, endogenous growth, R&D

A robust vehicle to grid aggregation framework for electric vehicles charging cost minimization and for smart grid regulation

In this paper, we propose an optimal hierarchical bi-directional aggregation algorithm for the electric vehicles (EVs) integration in the smart grid (SG) using Vehicle to Grid (V2G) technology through a network of Charging Stations (CSs). The proposed model forecasts the power demand and performs Day-ahead (DA) load scheduling in the SG by optimizing EVs charging/discharging tasks. This method uses EVs and CSs as the voltage and frequency stabilizing tools in the SG. Before penetrating EVs in the V2G mode, this algorithm determines the on arrival EVs State of Charge (SOC) at CS, obtains projected park/departure time information from EV owners, evaluates their battery degradation cost prior to charging. After obtaining all necessary data, it either uses EV in the V2G mode to regulates the SG or charge it according to the owner request but, it ensure desired SOC on departure. The robustness of the proposed algorithm has been tested by using IEEE-32 Bus-Bars based power distribution in which EVs are integrated through five CSs. Two intense case studies have been carried out for the appropriate performance validation of the proposed algorithm. Simulations are performed using electricity pricing data from PJM and to test the EVs behaviour 3 types of EVs having different specifications are penetrated. Simulation results have proved that the proposed model is capable of integrating EVs in the voltage and frequency stabilization and it also simultaneously minimizes approximately $1500 in term of charging cost for EVs contributing in the V2G mode each day. Particularly, during peak hours this algorithm provides effective grid stabilization services.info:eu-repo/semantics/publishedVersio