Automatizing Price Negotiation in Commodities Markets

This is an introductory work to trade automatization of the futures market, so far operated by human traders. We are not focusing on maximizing individual profits of any trader as done in many studies, but rather we try to build a stable electronic trading system allowing to obtain a fair price, based on supply and demand dynamics, in order to avoid speculative bubbles and crashes. In our setup, producers and consumers release regularly their forecasts of output and consumption respectively. Automated traders will use this information to negotiate price of the underlying commodity. We suggested a set of analytical criteria allowing to measure the efficiency of the automatic trading strategy in respect to market stability.Automated Traders, Optimal Strategies, Futures Market, Commodities Trading

The effects of periodic and continuous market environments on the performance of trading agents

Simulation experiments are conducted on simple continuous double auction (CDA) markets based on the experimental economics work of Vernon Smith. CDA models within experimental economics usually consist of a sequence of discrete trading periods or “days”, with allocations of stock and currency replenished at the start of each day, a situation we call “periodic” replenishment. In our experiments we look at both periodic and continuous-replenishment versions of the CDA. In this we build on the work of Cliff and Preist (2001) with human subjects, but we replace human traders with Zero Intelligence Plus (ZIP) trading agents, a minimal algorithm that can produce equilibrating market behaviour in CDA models. Our results indicate that continuous-replenishment (CR) CDA markets are similar to conventional periodic CDA markets in their ability to show equilibration dynamics. Secondly we show that although both models produce the same behaviour of price formation, they are different playing fields, as periodic markets are more efficient over time than their continuous counterparts. We also find, however, that the volume of trade in periodic CDA markets is concentrated in the early period of each trading day, and the market is in this sense inefficient. We look at whether ZIP agents require different parameters for optimal behaviour in each market type, and find that this is indeed the case. Overall, our conclusions mirror earlier findings on the robustness of the CDA, but we stress that a CR-CDA marketplace equilibrates in a different way to a periodic one

Characterization and Modeling of Spectrum Trading Markets

Telecommunication regulators are facing increasing pressure to make spectrum resources more widely available to new wireless services and providers. In spectrum trading markets, buyers and sellers determine the assignments of spectrum and, possibly, its uses. These markets are being considered or implemented by the regulatory bodies of many countries as a way to provide increasing efficiency in the use of spectrum and attend the demand for this resource. This work describes a classification for the implementation of spectrum trading markets and a way to model them and identify the conditions for their viability. Specifically, we make use of Agent-Based Computational Economics (ACE) to model the participants in these markets, analyze the behaviors that emerge from the interactions of its participants and determine the conditions for viable markets. Our results, provide guidelines that can be used by regulators and wireless service providers for the design and implementation of these markets

Optimal Strategies for Automated Traders in a Producer-Consumer Futures Market

The aim of this work is to show how automated traders can operate a futures market. First, we established some hypothesises on the properties of the ’correct’ price pattern which translates accurately the underlying moves in the supply/demand balance and the nominal price, then mathematical measures were derived allowing to estimate the efficiency of a given trading strategy. As a starting step, we applied our approach to a simplified market setup where only two automated traders, a producer and a consumer, can trade. They receive a stream of forecasts on supply and demand levels and they should react instantaneously by adjusting these forecasts, then issuing sale and buy orders. Later, we suggested a parameterized trading strategy for the two automatons. Finally, we obtained by simulation the optimal parameters of this strategy in some particular cases.Automated traders; optimal strategies; agent based

Incorporating Price Information in Blockchain-based Energy Trading

Blockchain-based peer-to-peer (P2P) ecosystem is well suited for distributed energy trading as it is inherently decentralised. In a distributed energy trading, an auctioneer passes unspent reservations to the next auctioneer, as dictated by the passing mechanism. However, traditional P2P energy trading systems used passing mechanisms that only partially consider the auction capability of the next auctioneer. We propose iPass, which incorporates price information when passing unspent auction reservations in P2P energy trading environment. The three performance metrics applied to measure the trading efficiency are (a) auction convergence time, (b) the number of auction settlements, and (c) the economic surplus of buyers and sellers. We simulated the proposed mechanism in Hyperledger Fabric, a permissioned blockchain framework. Hyperledger Fabric manages the data storage and smart contracts. Experiments show iPass is more efficient compared to existing passing mechanisms

Una breve revisión del Modelado y Simulación basados en agentes

Agent Based Modeling is a relatively new approach to simulating socio economic systems.Its main advantage is the use of entities called agents to model a system in order to study the behaviour and, most important, the emergent properties of it. Agents often represent people, animals, companies, natural entities, etc., and could be in a wide range from totally autonomous to basically reactive. The programming of agents using decision rules o life cycles makes the approach more versatile and flexible.This article shows the basic concepts underlying the agent based modeling, its advantages and disadvantages, as well as, the wide range of application of it.El modelado y simulación basados en agentes, es un enfoque relativamente nuevo para simular sistemas socio-económicos. Su principal ventaja es el uso de entidades autónomas, denomina- das agentes, para modelar un sistema, con el que es posible estudiar el comportamiento del mismo y, lo que es más importante, sus propiedades emergentes. Los agentes a menudo repre- sentan gente, animales, compañías, entidades naturales, etc., y pueden situarse en un amplio rango de comportamiento que va desde los totalmente autónomos a los básicamente reactivos. La programación de agentes usando reglas de decisión o ciclo de vida, confiere a este enfoque mayor versatilidad y flexibilidad.Este artículo además de explicar someramente sus principios, muestra el campo de aplicación del mismo, señalando sus ventajas y desventajas

Designing the Market Game for a Commodity Trading Simulation

In this paper, we propose to design a market game that (a) can be used in modeling and studying commodity trad-ing scenarios, and (b) can be used in capturing human traders ’ behaviors. Specifically, we demonstrate the useful-ness of this commodity trading game in a single-commodity futures trading scenario. A pilot experiment was run with a mixture of human traders and an autonomous agent that emulates the aggregated market condition, with the assump-tion that this autonomous agent would hint each of its action through a public announcement. We show that the informa-tion collected from this simulation can be used to extract the pattern of successful human traders. Finally, we elaborate on the potential of this market game in studying autonomous commodity trading. 1

Automatizing Price Negotiation in Commodities Markets

This is an introductory work to trade automatization of the futures market, so far operated by human traders. We are not focusing on maximizing individual profits of any trader as done in many studies, but rather we try to build a stable electronic trading system allowing to obtain a fair price, based on supply and demand dynamics, in order to avoid speculative bubbles and crashes. In our setup, producers and consumers release regularly their forecasts of output and consumption respectively. Automated traders will use this information to negotiate price of the underlying commodity. We suggested a set of analytical criteria allowing to measure the efficiency of the automatic trading strategy in respect to market stability