An event-based framework of directional changes (DC) and overshoots maps financial market (FM) price time series into the so-called intrinsic time where events are the time scale of the price time series. This allows for multi-scale analysis of financial data. In the light of this, this paper formulates DC event approach into three automated trading strategies for investments in the FMs: ZI-Directional Change Trading (DCT0), DCT1, and DCT2. The main idea is to use intrinsic time scale based on DC events to learn the size and the direction of periodic patterns from the asset price historical dataset. Using simulation models of Saudi Stock Market, we evaluate the returns of the automated DC trading strategies. The analysis revealed interesting results and evidence that the proposed strategies can indeed generate effective trading for investors with a high rate of returns. The results of this study can be used further to develop decision support systems and autonomous trading agent strategies for the FM.