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The traditional stock market trading methods have been developing during centures. Manual trading on the stock exchange is gradually disappearing. To replace it there is a progress of information technology coming in play today. The trading decisions are influenced by many psychological factors they can not discard. An alternative to human emotions on the stock exchange can only be trading machines or trading robots. The trading robot is a software package, which has an algorithm on how the transactions with stocks should be made and under which conditions they should be triggered. Trading robots can be so sophisticated that they can almost exclude any kind of risks to a certain degree. There was analysed and classified a varitey of existing strategies. This work proposes an implementation of a trading robot based on Breakout Volatility Filter trading algorithm. The algorithm developed in work is Adaptive Reversal Parametric Breakout (ARPB) Strategy. The most important basis principle of the proposed ARPB strategy is that the strategy adoptes itself to a daily volatility rate and basicly "learns" a new market conjuncture to minimize the number of poor quality trades.

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MSc, MBA Alexander Muzychenko is the reseacher in stock markted trading and information systems. Focuses on new ideas of algorythms and automation in the market. Within this research presents Adaptive Reversal Parametric Breakout Strategy and implements it in real Automated trading System written with C. The testing is done in real markets.

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