Trading team composition for the intraday multistock market

Alvim, Leandro G.M.; Milidiú, Ruy Luiz

doi:10.1016/j.dss.2012.09.009

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…The design of agent strategies for electronic markets and decision support has been the focus of attention of many works for a variety of market settings (Sanglier et al ; LeBaron ; Kettera et al ; Ren et al ; Alvim and Milidiú ). From a modeling perspective, Sim () described a market‐driven model for designing agents (buyers or sellers) that applies to a wide range of bargaining situations where agents need to reach a consensus.…”

Section: Related Workmentioning

confidence: 99%

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

Aloud

Fasli

2016

Computational Intelligence

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One of the most critical issues that developers face in developing automatic systems or software agents for electronic markers is that of endowing the agents with appropriate trading strategies. In this paper, we examine the problem in the Foreign Exchange (FX) market and we use an agent-based FX market simulation to examine which trading strategies lead to market states in which the stylized facts (statistical properties) of the simulation match the stylised facts of the actual FX market transactions data. In particular, our goal is to explore the emergence of the stylized facts of the transactions data, when the simulated market is populated with agents using three different strategies: a variation of the zero-intelligence with a constraint (ZI-CV) strategy; the zero-intelligence directional-change event (ZI-DCT0) strategy; and a genetic programmingbased (GP) strategy. A series of experiments were conducted in an existing agent-based FX market with these three strategies and the results were compared against those of a high-frequency transactions dataset from the FX market. Our results show that the ZI-DCT0 agents best reproduce and explain the properties and phenomena observed in the FX market real transactions data. Our study suggests that the observed stylized facts of the FX market transactions data could be the result of introducing a threshold which triggers the agents to respond to fixed periodic patterns in the price time series. The results of this study can be used further to develop decision support systems and autonomous trading agent strategies for the FX market.

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Section: Related Workmentioning

confidence: 99%

Exploring Trading Strategies and Their Effects in the Foreign Exchange Market

Aloud

Fasli

2016

Computational Intelligence

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“…Propomos duas novas formulações para o esses dois problemas e ambas equivalem ao problema de Agendamento de Intervalos Ponderados, que pode ser resolvido de forma computacionalmente eficiente. Ressaltamos que a abordagem por Agendamento de Intervalos Ponderados para o problema de maximização do retorno acumulado está publicada (Alvim e Milidiú, 2013)…”

Section: O Sistema De Trading Propostounclassified

“…Istó e feito a partir da resolução do problema de Agendamento de Intervalos Ponderados. Ressaltamos que a abordagem por Agendamento de Intervalos Ponderados para o problema de maximização do retorno acumulado está publicada (Alvim e Milidiú, 2013) na prestigiosa Decision Support Systems 1 .…”

Section: Contribuiçõesunclassified

Estratégias De Negociação De Ativos Financeiros Utilizando Agendamento Por Intervalos Ponderados

ALVIM¹

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Alvim, Leandro Guimarães Marques; Milidiú, Ruy Luiz (Advisor) . Weighted Interval Scheduling Resolution for Building Financial Market Trading Strategies. Rio de Janeiro, 2013. 80p. DSc. Thesis -Departamento de Informática, Pontifícia Universidade Católica do Rio de Janeiro.There are different types of investors who make up the financial market and produce market opportunities at different time scales. This indicates a heterogeneous market structure. In this thesis, we conjecture that may have more predictive opportunities than others, what motivates research and construction of we denominate multirresolution optimal strategies. For multirresolution strategies there are time series decomposition approaches for operating at different resolutions or proposals for dataset construction according to multirresolution trading optimal decisions. The other approaches, are single resolution. Thus, we address two problems, maximizing cumulative returns and maximizing cumulative returns with risk control. Here, we propose solving the Weighted Interval Scheduling problem to build multirresolution strategies. Our methodology consists of dividing the market day into time intervals, specialize traders by interval and associate a prize to each trader. For the cumulative return maximization problem, the prize corresponds to cumulative returns between days for the associated trader operation interval. For the cumulative return maximization problem with risk control each trader prize corresponds to cumulative return divided by risk with associated operation interval. In order to control the risk, we employ a set of traders by interval and apply the Markowitz Mean-Variance method to find optimal weight for set of traders. Here, we conjecture that controlling each interval risk leads to the overall risk control of the day. For signaling buy and sell orders, our traders use opportunity detectors. These detectors correspond to Machine Learning algorithms that process technical analysis indicators, price and volume data. We conducted experiments for ten of the most liquid BMF&Bovespa stocks to a one year span. Our Trading Team Composition strategy results indicates an average of 0.24% daily profit and a 77.24% anual profit, exceeding by 300% and 380%, respectively, a single resolution strategy. Regarding operational costs, CTT strategy is viable from US$ 50,000.For the cumulative return maximization problem under risk control, our Portfolio Composition by Intervals strategy results indicates an average of 0.179% daily profit and a 55.85% anual profit, exceeding a Markowitz Mean-Variance method. PUC-Rio -Certificação Digital Nº 0812629/CARegarding operational costs, CCI strategy is viable from US$ 2,000,000.Our main contributions are: the Weighted Interval Scheduling approach for building multirresolution strategies and a portfolio composition of traders instead of stocks performances.

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