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Automated trading is a novel field of study in which computer programs are put in charge of deciding when and how to trade financial instruments. Intelligent agents, with their ability to act autonomously and to adapt and interact with the environment, seem like an obvious choice for the development of automated trading systems. The aim of this article is to analyze how well intelligent agents suit this task. We implemented a set of autonomous currency trading agents, using an architecture that consists of an ensemble of classification and regression models, a case-based reasoning system and an expert system. A total of six trading agents were implemented, each being responsible for trading one of the following currency pair in the Forex market: EUR/USD, EUR/JPY, EUR/CHF, USD/JPY, USD/CHF and CHF/JPY. The agents simulated trades over a period of 23 months, having all achieved a reasonable profit trading independently. However, their strategies resulted in relatively high drawdows. In order to decrease the risk inherent to these high drawdowns, the same simulation was performed while making the agents share the monetary resources. As expected, this strategy of investment diversification originated better results. Still, when the trading costs were taken into consideration, the overall trading performance was less than impressive. That was due to the fact that each agent performed too many trades, and the cost associated with the trading commissions became prohibitively high. We were able to lessen the impact of the trading costs in the total profit by integrating the agents in a multi-agent system, in which the agents communicated with each other before opening new trades. This allowed them to calculate the intended exposure to the market, which in turn enabled them to avoid redundant trades. Under simulation and using low leverage, this multi-agent system obtained a 55.7% profit in 23 months of trading, with a 9.0% maximum drawdown.
Terms Version: Weak breakout, then TR 31:39 Kwon, Y., Moon, B.: Daily Stock Prediction Using Neuro-genetic Hybrids. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 2203–2214. Springer, Heidelberg (2003)CrossRefGoogle Scholar
Nigeria Non-bank foreign exchange companies offer currency exchange and international payments to private individuals and companies. These are also known as "foreign exchange brokers" but are distinct in that they do not offer speculative trading but rather currency exchange with payments (i.e., there is usually a physical delivery of currency to a bank account).
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Foreign exchange, more commonly known as Forex or FX, relates to buying and selling currencies with the purpose of making profit off the changes in their value. As the biggest market in the world by far, larger than the stock market or any other, there is high liquidity in the forex market. Therefore, the forex market attracts many traders, beginners and experienced alike.
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For example USD/INR, here we are stating state per USD is 67.3420 rupees. The first currency of a currency pair is called the base currency, and the second currency is called the quote currency. The currency pair shows how much of the quote currency is needed to purchase one unit of the base currency. This is just a basic concept of currency trading.
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