bought higher than it sold, and to make matters worse it paid.1 fee. We do it for you. The input is never the same, and we cannot simply rely on a bunch of if this do that code. This is risky business, since there are no guarantees,.e.: a system cannot accurately tell (given historical events) whether that market price is at a valley or not it can only make a calculated guess. When we write code we usually have a clear goal in mind, thus we know what the output should be given some clearly defined input. The threshold in the graph above is drawn 2 std. Artificial Intelligence, one may develop as many models as he/she wishes to, or at least, as many he/she has the guts. The whole point of the process is trying to find some patterns that are pretty obvious to the human eye, but also that these patterns are reoccurring throughout history and hopefully will continue to do so in the future. Pay close attention to where it generates the Buy/Sell signals they appear to be way more optimal than in the previous three examples, but far from perfect. Lets quickly see what it means. Box plot representation of prices.
Hopefully within a few weeks Ill plug it in and write a new post showing my results. Actually, all of the above can generate ROI of over 100 if there were no trading fees, since that is how exchanges prevent us from becoming millionaires over night. For example, a crypto-asset that doesnt have a real product in market will fundamentally rank as a poor investment choice, even if it manages to get listed in top 10 crypto-currencies by trading volume. 96 of the data points lie within 2-std.
These were achieved by utilizing some default indicators which I had to adjust in several ways prior to applying. I only had to tweak adjust it according to my needs.
If the parameters are sub-optimal, so is the outcome. To make our job easier we have to introduce math and statistics to aid. The reward/punishment is expressed as a number, and so we train the system to optimize itself for obtaining the highest possible score. Most Algos have a correlation with the market,.e. Its pretty hard to test and verify new hypotheses while simultaneously tweaking its many parameters and trying different values.
And thats the whole point, we try to make a calculated guess, that is the plausibility of being at a valley/peak and triggering a trading signal (either buy or sell). Deviation above and below the average value. Will make better trading decisions (both long- short-term) than humans.