On-Line Portfolio Selection Strategy Based on Weighted Moving Average Asymmetric Mean Reversion

Mean reversion is an important property for constructing efficient on-line portfolio selection strategy. The existing strategies mostly suppose that the mean reversion is multi-period symmetric or single-period asymmetric. However, the mean reversion is multi-period and asymmetric in the real market. Taking this into account, on-line strategies based on multi-period asymmetric mean reversion is proposed. With designing multi-piecewise loss function and imitating passive aggressive algorithm, we propose a new on-line strategy WMAAMR. This strategy runs in linear time, and thus is suitable for large-scale trading applications. Empirical results on four real markets show that WMAAMR can achieve better results and bear higher transaction cost rate

Online portfolio selection: A survey

Ministry of Education, Singapore under its Academic Research Funding Tier

Optimal algorithmic trading and market microstructure

The efficient frontier is a core concept in Modern Portfolio Theory. Based on this idea, we will construct optimal trading curves for different types of portfolios. These curves correspond to the algorithmic trading strategies that minimize the expected transaction costs, i.e. the joint effect of market impact and market risk. We will study five portfolio trading strategies. For the first three (single-asset, general multi-asseet and balanced portfolios) we will assume that the underlyings follow a Gaussian diffusion, whereas for the last two portfolios we will suppose that there exists a combination of assets such that the corresponding portfolio follows a mean-reverting dynamics. The optimal trading curves can be computed by solving an N-dimensional optimization problem, where N is the (pre-determined) number of trading times. We will solve the recursive algorithm using the "shooting method", a numerical technique for differential equations. This method has the advantage that its corresponding equation is always one-dimensional regardless of the number of trading times N. This novel approach could be appealing for high-frequency traders and electronic brokers.quantitative finance; optimal trading; algorithmic trading; systematic trading; market microstructure

Supplier asset allocation in a pool-based electricity market

A power supplier in a pool-based market needs to allocate his generation capacities to participate in contract and spot markets. In this paper, the optimal portfolio selection theory is introduced for this purpose. A model applying this theory is proposed to solve the supplier asset allocation problem. Real market data are used in a numerical study to test the proposed model. The results show that different asset allocation solutions can yield very different risk-return tradeoffs for a supplier, and the proposed method can be potentially useful in suppliers' decision making. © 2007 IEEE.published_or_final_versio

Automatic Game Parameter Tuning using General Video Game Agents

Automatic Game Design is a subfield of Game Artificial Intelligence that aims to study the usage of AI algorithms for assisting in game design tasks. This dissertation presents a research work in this field, focusing on applying an evolutionary algorithm to video game parameterization. The task we are interested in is player experience. N-Tuple Bandit Evolutionary Algorithm (NTBEA) is an evolutionary algorithm that was recently proposed and successfully applied in game parameterization in a simple domain, which is the first experiment included in this project. To further investigating its ability in evolving game parameters, We applied NTBEA to evolve parameter sets for three General Video Game AI (GVGAI) games, because GVGAI has variety supplies of video games in different types and the framework has already been prepared for parameterization. 9 positive increasing functions were picked as target functions as representations of the player expected score trends. Our initial assumption was that the evolved games should provide the game environments that allow players to obtain score in the same trend as one of these functions. The experiment results confirm this for some functions, and prove that the NTBEA is very much capable of evolving GVGAI games to satisfy this task

Negativna selekcija - Apsolutna mera algoritamskog izvršenja proizvoljnog naloga

Algorithmic trading is an automated process of order execution on electronic stock markets. It can be applied to a broad range of financial instruments, and it is  characterized by a signicant investors' control over the execution of his/her orders, with the principal goal of finding the right balance between costs and risk of not (fully) executing an order. As the measurement of execution performance gives information whether best execution is achieved, a signicant number of diffeerent benchmarks is  used in practice. The most frequently used are price benchmarks, where some of them are determined before trading (Pre-trade benchmarks), some during the trading  day (In-traday benchmarks), and some are determined after the trade (Post-trade benchmarks). The two most dominant are VWAP and Arrival Price, which is along with other pre-trade price benchmarks known as the Implementation Shortfall (IS). We introduce Negative Selection as a posteriori measure of the execution algorithm performance. It is based on the concept of Optimal Placement, which represents the ideal order that could be executed in a given time win-dow, where the notion of ideal means that it is an order with the best execution price considering  market  conditions  during the time window. Negative Selection is dened as a difference between vectors of optimal and executed orders, with vectors dened as a quantity of shares at specied price positionsin the order book. It is equal to zero when the order is optimally executed; negative if the order is not (completely) filled, and positive if the order is executed but at an unfavorable price. Negative Selection is based on the idea to offer a new, alternative performance measure, which will enable us to find the  optimal trajectories and construct optimal execution of an order. The first chapter of the thesis includes a list of notation and an overview of denitions and theorems that will be used further in the thesis. Chapters 2 and 3 follow with a  theoretical overview of concepts related to market microstructure, basic information regarding benchmarks, and theoretical background of algorithmic trading. Original results are presented in chapters 4 and 5. Chapter 4 includes a construction of optimal placement, definition and properties of Negative Selection. The results regarding the properties of a Negative Selection are given in [35]. Chapter 5 contains the theoretical background for stochastic optimization, a model of the optimal execution formulated as a stochastic optimization problem with regard to Negative Selection, as well as original work on nonmonotone line search method [31], while numerical results are in the last, 6th chapter.Algoritamsko trgovanje je automatizovani proces izvršavanja naloga na elektronskim berzama. Može se primeniti na širok spektar nansijskih instrumenata kojima se trguje na berzi i karakteriše ga značajna kontrola investitora nad izvršavanjem njegovih naloga, pri čemu se teži nalaženju pravog balansa izmedu troška i rizika u vezi sa izvršenjem naloga. S ozirom da se merenjem performasi izvršenja naloga određuje da li je postignuto najbolje izvršenje, u praksi postoji značajan broj različitih pokazatelja. Najčešće su to pokazatelji cena, neki od njih se određuju pre trgovanja (eng. Pre-trade), neki u toku trgovanja (eng. Intraday), a neki nakon trgovanja (eng. Post-trade). Dva najdominantnija pokazatelja cena su VWAP i Arrival Price koji je zajedno sa ostalim "pre-trade" pokazateljima cena poznat kao Implementation shortfall (IS). Pojam negative selekcije se uvodi kao "post-trade" mera performansi algoritama izvršenja, polazeći od pojma optimalnog naloga, koji predstavlja idealni nalog koji se  mogao izvrsiti u datom vremenskom intervalu, pri ćemu se pod pojmom "idealni" podrazumeva nalog kojim se postiže najbolja cena u tržišnim uslovima koji su vladali  u toku tog vremenskog intervala. Negativna selekcija se definiše kao razlika vektora optimalnog i izvršenog naloga, pri čemu su vektori naloga defisani kao količine akcija na odgovarajućim pozicijama cena knjige naloga. Ona je jednaka nuli kada je nalog optimalno izvršen; negativna, ako nalog nije (u potpunosti) izvršen, a pozitivna ako je nalog izvršen, ali po nepovoljnoj ceni. Uvođenje mere negativne selekcije zasnovano je na ideji da se ponudi nova, alternativna, mera performansi i da se u odnosu na nju nađe optimalna trajektorija i konstruiše optimalno izvršenje naloga. U prvom poglavlju teze dati su lista notacija kao i pregled definicija i teorema  neophodnih za izlaganje materije. Poglavlja 2 i 3 bave se teorijskim pregledom pojmova i literature u vezi sa mikrostrukturom tržišta, pokazateljima trgovanja i algoritamskim trgovanjem. Originalni rezultati su predstavljeni u 4. i 5. poglavlju. Poglavlje 4 sadrži konstrukciju optimalnog naloga, definiciju i osobine negativne selekcije. Teorijski i praktični rezultati u vezi sa osobinama negativna selekcije dati su u [35]. Poglavlje 5 sadrži teorijske osnove stohastičke optimizacije, definiciju modela za optimalno izvršenje, kao i originalni rad u vezi sa metodom nemonotonog linijskog pretraživanja [31], dok 6. poglavlje sadrži empirijske rezultate

Prospect theory-based portfolio optimisation: An empirical study and analysis using intelligent algorithms

The first author was part funded by RFBR (grant 14-01-00140)

MATLAB

This excellent book represents the final part of three-volumes regarding MATLAB-based applications in almost every branch of science. The book consists of 19 excellent, insightful articles and the readers will find the results very useful to their work. In particular, the book consists of three parts, the first one is devoted to mathematical methods in the applied sciences by using MATLAB, the second is devoted to MATLAB applications of general interest and the third one discusses MATLAB for educational purposes. This collection of high quality articles, refers to a large range of professional fields and can be used for science as well as for various educational purposes