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

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

A new geometric approach for sensitivity analysis in linear programming

In this paper, we present a new geometric approach for sensitivity analysis in linear programming that is computationally practical for a decision-maker to study the behavior of the optimal solution of the linear programming problem under changes in program data. First, we fix the feasible domain (fix the linear constraints). Then, we geometrically formulate a linear programming problem. Next, we give a new equivalent geometric formulation of the sensitivity analysis problem using notions of affine geometry which consists write the coefficient vector of the objective function in polar coordinate and determining all the angles for which the solution remains unchanged. Finally, the approach is presented in detail and illustrated with a numerical example

A new geometric approach to multiobjective linear programming problems

In this paper, we present a novel method for solving multiobjective linear programming problems (MOLPP) that overcomes the need to calculate the optimal value of each objective function. This method is a follow-up to our previous work on sensitivity analysis, where we developed a new geometric approach. The first step of our approach is to divide the space of linear forms into a finite number of sets based on a fixed convex polygonal subset of $\mathbb{R}^{2}$. This is done using an equivalence relationship, which ensures that all the elements from a given equivalence class have the same optimal solution. We then characterize the equivalence classes of the quotient set using a geometric approach to sensitivity analysis. This step is crucial in identifying the ideal solution to the MOLPP. By using this approach, we can determine whether a given MOLPP has an ideal solution without the need to calculate the optimal value of each objective function. This is a significant improvement over existing methods, as it significantly reduces the computational complexity and time required to solve MOLPP. To illustrate our method, we provide a numerical example that demonstrates its effectiveness. Our method is simple, yet powerful, and can be easily applied to a wide range of MOLPP. This paper contributes to the field of optimization by presenting a new approach to solving MOLPP that is efficient, effective, and easy to implement

An adaptive method to solve multilevel multiobjective linear programming problems

This paper is a follow-up to a previous work where we defined and generated the set of all the possible compromises of multilevel multiobjective linear programming problems (ML-MOLPP). In this paper, we introduce a new algorithm to solve ML-MOLPP in which the adaptive method of linear programming is nested. First, we start by generating the set of all the possible compromises (set of all non-dominated solutions). After that, an algorithm based on the adaptive method of linear programming is developed to select the best compromise among all the possible compromises achieved. Finally, all the construction stages are carefully checked and illustrated with a numerical example

Attempted Mediation of a Local, Historic, Wildland Trail Dispute:Searching for Incentives and Options with Participatory Action Research

This case study describes an attempt to resolve disputed access to a historic, local, wildland trail in the Colorado Front Range. A local, historic, wildland trail is one that often traverses both public and private wildland and is accessed freely by a local community for non-motorized use, for connection to nature and for social visits. “Historic” implies use as found in the historic record and as reflected in the memories of older and previous residents. “Wildland” denotes an abundance of ecological processes significantly surpassing indications of human activity. Environmental dispute resolution processes that engage diverse stakeholders in dialogue over issues in dispute are a critical component of ecosystem preservation. EDR processes administratively and procedurally recognize the essential value of a communication link to local knowledge, expertise, and volunteership. This thesis assesses the contribution of participatory action research (PAR) in enhancing conventional environmental dispute resolution practices in cases of stakeholder disparity. Because PAR offers the opportunity for locals to empower themselves with knowledge and to pursue stable social outcomes, it can help address stakeholder disparity. In this case, PAR fortified the EDR process so that a clear understanding of the substantive issues in dispute could be derived despite the absence of a key stakeholder. The PAR constructively de-escalated the local trail dispute by providing a focus on the substantive issues. PAR made room for patience and perseverance, dissipating the emotions that had escalated over a perceived injustice. At the same time, the EDR/PAR intervention required significant time and resources of its facilitator, offering many insights about management of such an intensive intervention process.Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/11/EPapp5litcite.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/10/EPapp4guideapp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/9/EPapp3guidetwo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/8/EPapp2guideone.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/7/EPapp1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/6/EDRPAR4eval.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/5/EDRPAR3process.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/4/EDRPAR2dispute.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/3/EDRPAR1intro.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/2/EDRPAR0firstpages.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/57283/1/EDRPAR0covertitle.pd

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