NDLI: Máquina de vector soporte multidimensional para la negociación de divisas en el mercado Forex

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Máquina de vector soporte multidimensional para la negociación de divisas en el mercado Forex

Content Provider	Semantic Scholar
Author	Calderón, Daniel Castaño Tobón, Juan Diego Marín
Copyright Year	2018
Abstract	Nowadays, Forex market is one of the most negotiated markets thanks to its given characteristics. The characteristics of this market such as the speed on the price changes, the high volatility, the liquidity and the multiple leverage options, new trading platforms have arised in order to allow people to use robots in order to negotiate automatically, these robots can be designed with artificial intelligence techniques, with these techniques, the robot is able to detect patterns that no human can see, besides, it can learn as it trades in the market. In this thesis a model is designed from an artificial intelligence technique called support vector machine, which can be used in the Forex market for automated trading using technical indicators as the model inputs, these indicators were chosen based on bibliographic review about different negotiation strategies, finally the model is validated using Backtesting and the model efficiency is determined by the profit generated for itself in the market. Last, according to the results thown by the model, three types of efficiencies were identified: Efficiency in regard to the profits, in which the model is efficient thanks to the yields generated through the backtest period; the operative efficiency, in which the model shows a high error percentage, this indicates that the trading robot is not trustworthy; last, the efficiency based on the risk/profit factor, shows that the model is inefficient because of the profitability obtained within the backtest period versus the risk in the Forex market.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://repository.eia.edu.co/bitstream/11190/2217/1/CastanoDaniel_2018_MaquinaVectorSoporte.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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