Discussing the article: "Feature Engineering With Python And MQL5 (Part II): Angle Of Price"
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Check out the new article: Feature Engineering With Python And MQL5 (Part II): Angle Of Price.
There are many posts in the MQL5 Forum asking for help calculating the slope of price changes. This article will demonstrate one possible way of calculating the angle formed by the changes in price in any market you wish to trade. Additionally, we will answer if engineering this new feature is worth the extra effort and time invested. We will explore if the slope of the price can improve any of our AI model's accuracy when forecasting the USDZAR pair on the M1.
One key limitation of trend lines is that they are defined subjectively. Therefore, a trader can arbitrarily adjust his trend lines to create an analysis that supports his perspective, even if his perspective is wrong. Therefore, it is only natural to try to define trend lines in a more robust approach. Most traders hope to do this by calculating the slope created by changes in price levels. The key assumption is that, knowing the slope is equivalent to knowing the direction of the trend line formed by price action.
We have now arrived at the first obstacle to be overcome, defining the slope. Most traders attempt to define the slope created by price as the difference in price divided by the difference in time. There are several limitations to this approach. Firstly, equity markets are closed over the weekend. In our MetaTrader 5 terminals, the time that elapsed whilst the markets were closed in not recorded, it must be inferred from the data at hand. Therefore, when using such a simple model, we must keep in mind that the model does not account for the time that elapsed over the weekend. This means that, if price levels gaped over the weekend, then our estimation of the slope will be overinflated.
It should be immediately obvious that the slope calculated by our current approach will be very sensitive to our representation of time. If we chose to ignore the time that elapsed over the weekend, as we stated earlier, we will obtain overinflated coefficients. And if we account for the time over the weekend, we will obtain relatively smaller coefficients. Therefore, under our current model, it is possible to obtain 2 different slope calculations when analyzing the same data. This is undesirable. We would prefer our calculation to be deterministic. Meaning that, our calculation of the slope will always be the same, if we are analyzing the same data.
To overcome these limitations, I'd like to propose an alternative calculation. We could instead calculate the slope formed by price by using the difference in opening price divided by the difference in close price. We have substituted time from the x-axis. This new quantity informs us how sensitive the close price is to changes in the open price. If the absolute value of this quantity is > 1, then that tells us that large changes in the open price, have little effect on the close price. Likewise, if the absolute value of the quantity is < 1, then that informs us that small changes in the open price, could have large effects on the close price. Additionally, if the coefficient of the slope is negative, than that informs us that the open price and the close price tend to change in opposite directions.
Author: Gamuchirai Zororo Ndawana