Discussing the article: "Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia"
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Check out the new article: Markets Positioning Codex in MQL5 (Part 2): Bitwise Learning, with Multi-Patterns for Nvidia.
We continue our new series on Market-Positioning, where we study particular assets, with specific trade directions over manageable test windows. We started this by considering Nvidia Corp stock in the last article, where we covered 5 signal patterns from the complimentary pairing of the RSI and DeMarker oscillators. For this article, we cover the remaining 5 patterns and also delve into multi-pattern options that not only feature untethered combinations of all ten, but also specialized combinations of just a pair.
The sixth signal that we have spots market moves that have a ‘high-conviction’ by merging slope-metrics of the RSI and DeMarker with the high-low price expansion. This usually signals an increase in trader activity as well as volatility. The buy condition is marked when the RSI slope across 3 bars is positive, and the DeMarker also indicates a similar state across its 3 bars. The price range of the current price bar, where current is taken to represent the most recent completed price bar that would be indexed 1; would also be greater than that of a previous bar at an optimized distance in the past. Once the current close price is within the top 25 percent of its range, this would signal buying pressure. The chart representation for this would be as follows:
RSI slope(3) > 0, DeMarker slope(3) > 0, AND current bar range (high-low) > 1.2 * average(range,10) (i.e., range expansion) — price must close in top 25% of bar.
As a side note, given our long only approach, the sell-requirements, are also achieved when the RSI and DeMarker slopes are negative over a three bar period and the current high-low range has increased more than a past bar-range. This past benchmark is tunable/optimizable, and we are taking its period to match that of our used averages in order to reduce the number of optimized parameters. Readers of course can adjust this, but the rationale here is to be wary of curve fitting.
Author: Stephen Njuki