Autonomous Forex Trading Agents

  title={Autonomous Forex Trading Agents},
  author={Rui Pedro Barbosa and Orlando Belo},
In this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade in the Forex Marketwithout requiring human supervision. [] Key Method The "A Posteriori Knowledge Module", implemented using a Case-Based Reasoning System , enables the agents to learn from empirical experience and is responsible for suggesting how much to invest in each trade.

Autonomous FOREX Trading Agents

An infrastructure for implementing autonomous Forex trading agents without human supervision is described, which is capable of implementing traditional trading algorithms, rules of expert systems and empirical experiences from third parties.

Agents in the market place an exploratory study on using intelligent agents to trade financial instruments

This dissertation documents our exploratory research aimed at investigating the utilization of intelligent agents in the development of automated financial trading strategies. In order to demonstrate

A Diversified Investment Strategy Using Autonomous Agents

By simulating trades with 18 months of out-of-sample data, it will be demonstrated that data mining models can produce profitable predictions, and that the trading risk can be diminished through investment diversification.

Cartesian Genetic Programming for Trading: A Preliminary Investigation

It is shown that CGP is capable in many instances of evolving programs that, when used as trading strategies, lead to modest positive returns.

Hybridizing Data Stream Mining and Technical Indicators in Automated Trading Systems

This paper investigates the use of technical indicators as a means of deciding when to trade in the direction of a classifier's prediction, and compares this "hybrid" technical/data stream mining-based system with a naive system that always trades in thedirection of predicted price movement.

A smart agent to trade and predict foreign exchange market

The purpose of this work is to design a smart agent that makes a buy/sell decision to maximize profitability with no human supervision in Foreign Exchange market.

Multi-agent pre-trade analysis acceleration in FPGA

Performance results show that calculation of technical indicators and trading strategy evaluation to generate trading signals with a latency of 550 ns is achievable and the implementation of a trading engine for pre-trade analysis as a validation scenario is presented.

Money Management for a Foreign Exchange Trading Strategy Using a Fuzzy Inference System

This work proposes using a fuzzy inference system to determine the lot size, which uses input variables that any trading strategy should have access to (which means that any existing strategy can implement the proposed method) and shows that the dynamic lot size using the fuzzy inferenceSystem can help a trading strategy perform better.

Analyzing Market Interactions in a Multi-agent Supply Chain Environment

This work analyzes a supply chain scenario from an economic perspective that involves both component procurement and sales uncertainties and uses data from a multi-agent supply chain management simulation environment (TAC SCM) which simulates a one-year product life-cycle.

Algorithmic Trading Systems: A Multifaceted View of Adoption

  • David BellL. Gana
  • Business, Computer Science
    2012 45th Hawaii International Conference on System Sciences
  • 2012
This paper engages with senior practitioners in the industry and uses interviews and grounded theory analysis to uncover their adoption concerns, and generalises these issues within a framework and guidelines aimed at supporting algorithmic trading system adoption, deployment and development.



Designing a hybrid AI system as a forex trading decision support tool

  • Lean YuK. LaiShouyang Wang
  • Computer Science, Business
    17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)
  • 2005
A hybrid artificial intelligent system integrating neural network and expert system is proposed to support foreign exchange (forex) trading decisions and the effectiveness of the proposed hybrid AI system is illustrated by simulation experiments.

Financial prediction: Some pointers, pitfalls and common errors

  • K. Swingler
  • Economics
    Neural Computing & Applications
  • 2005
This paper investigates the common methods for using neural networks to forecast the future changes in prices of stocks, exchange rates, commodities and other financial time series, and outlines the major pitfalls and common errors to avoid.

Australian Forex Market Analysis using Connectionist Models

An attempt to compare the performance of a TakagiSugeno type neuro-fuzzy system and a feed forward neural network trained using the scaled conjugate gradient algorithm to predict the average monthly forex rates.

Modelling and Trading the EUR / USD Exchange Rate : Do Neural Network Models Perform Better ?

It is concluded that NNR models do have the ability to forecast EUR/USD returns for the period investigated, and add value as a forecasting and quantitative trading tool.

Using Recurrent Neural Networks To Forecasting of Forex

Empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates is reported, showing that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65.

Adaptive Smoothing Neural Networks in Foreign Exchange Rate Forecasting

Empirical analyses and experimental results show that the proposed novel forecasting model outperforms the other comparable models and is an effective alternative approach for foreign exchange rate forecasting.

Comparing ANN Based Models with ARIMA for Prediction of Forex Rates

Experimental results demonstrate that ANN based model can closely forecast the forex market and shows competitive results when compared with BPR based model on the third indicator.

Analysis of hybrid soft and hard computing techniques for forex monitoring systems

  • A. Abraham
  • Computer Science
    2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291)
  • 2002
This work attempts to compare the performance of hybrid soft computing and hard computing techniques to predict the average monthly forex rates one month ahead and it is observed that the proposed hybrid models could predict the Forex rates more accurately than all the techniques when applied individually.

ANN-Based Forecasting of Foreign Currency Exchange Rates

Investigation of artificial neural networks based prediction modeling of foreign currency rates using three learning algorithms shows that significantly close prediction can be made using simple technical indicators without extensive knowledge of market data.

Forecasting Exchange Rates Using Neural Networks for Technical Trading Rules

The main conclusion of this attempt is that ANNs perform well, and that they are often better than linear models.