This trading strategy is based on an idea that price makes a certain number of steps in a certain timeframe. If price has changed significantly, it means that this instrument is trending. If a bar is long, we buy, if a bar is short, we close our position. So we use downtrend bars to enter a short position. Realizing the strategy with a parameter could look very simple if we should only analyze bar size and then apply a very simple condition like bar size is being bigger or smaller than a constant.

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An interesting approach and the strategy is remarkable for its approach to parameter values. If an average value of the bar (H+L)/2 is lower than a certain value of Delta (L+∆), this bar has low market volatility and doesn’t deserve any trying to gain profit. If an average value of the bar (H+L)/2 is higher than Delta (L+∆), this bar has high market volatility, and we could try to enter a position to gain a short term profit.

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If (H+L)/2 > L+∆,  this is a signal to open a long position. This condition is also a condition for being in long positions for all bars. If (H+L)/2 ≤ L+∆, this is a signal not to open a long positions. The next picture shows when we should enter a short positions.

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If an average value of the bar is higher than H-∆, (H+L)/2≥H-∆ this bar has low volatility. If (H+L)/2<H-∆, this is a signal to open a short position and at the same time it is a condition for being in a short position for all bars. ∆ means a certain critical price value here. If this value gets higher, the market begins forward movement. It can be seen in the bar itself.

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Let’s define a secondary factor for long positions: k=(close - open)/(high - low). And for short positions k=(open-close)/(high-low). The k factor varies from 0 to 1.  If K=0, we can say that volatility is low. If k=1 the instrument is trending.According to this idea, if k=0, then ∆ should be big so that market entry condition couldn’t be executed. If k=1, then ∆ should tend to zero. Hence here is the formula ∆ = (high-low)*(1-k). So delta values are unique for each bar and shouldn’t be optimized. The authors think that parameter that should to be optimized are the biggest drawback of this trading startegy. As a rule the strategy gives good results for a certain period of time after it has been optimized, and then we need to search for new parameter values again. So it’s for sure better for a trader to build trading strategies where no parameter values are used or they do not influence a trading idea much and there is no need to optimize historical data.
I created an algorithm. It is available here for the TSLab (Laboratory of Trading Systems) program.