The engine then attaches 51 different sensors to each spectrum, amounting to 612 sensor readings overall. These sensors take readings of time, velocity, phase, acceleration, and other proprietary components.
Each spectrum is observed and codified based on the instructions fed into the engine. In simple terms, the engine has been told to identify trades that would last, on average, a particular length of time: 1 – 2 months, 4 – 6 months, 12 months, and so on. Then, based on the measurements gained from the 51 sensors, and their relationship to the chosen length of time for a trade, each asset is ranked in its order of strength of reading.
We are looking for assets that show the strongest signals for a Momentum Reversal. The reason we look for this reversal is that it is the ideal point of entrance where the largest profit most often exists. If we can accurately enter the market at this reversal point, many other problems are negated. An old axiom that is often used is, “70% of a good trade is a good entrance”. This is what Qarma algorithms allow us to do. The rest of the Qarma platform simply arranges the information into a useable format for the fund manager to make intelligent portfolio decisions.