Building an effective trading strategy on capital markets should start with finding a market inefficiency which is reflected in some price pattern or anomaly. There are two main approaches to identify and eventually exploit such inefficiencies. One of them is to apply some sophisticated data-mining tools and the other is based on creating a mathematical model of the phenomenon. This research is focused on the second approach which means that a model is created and used to develop a trading strategy.

One of such inefficiencies which can be observed in many markets is price oscillations caused by feedback from the price curve which occurs when it reaches some particular level or after a big change in quotations when it takes some time to stabilize the price curve. Such levels are called resistance or support levels in technical analysis. In control engineering such phenomenon when signal change exceeds its target level is called overshoot and may cause decreasing signal oscillations before it stabilizes on the target level.

Identifying and modeling market inefficiency does not lead directly to an effective trading strategy. In order to be useful it must be predictable and significant enough to provide profits which overcome costs. Validating trading strategy requires considering conditions close to real trading. In order to achieve that it is needed to take into account not only loses but also other costs like slippage or transaction costs. In this research I study the described phenomenon and the possibility of building an effective trading strategy based on its model and make the attempt to determine conditions which impacts the strategy performance.

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