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QuantDirection Analytics

Updated: May 30, 2023

What are they and What do they do?

In general, the Analytics capabilities of the platform attempt to utilize the rich data that exists around price movements relative to their expected moves and deliver greater insights than just the actual frequency of those moves occurring in the past. The platform’s general search capabilities allow you to see actual results of price moves across hundreds of conditions. The Analytics operate as a layer above this data and bring out probabilities that have occurred when these conditions interact with each other.


For example, if we are considering a trade and want to know what the probability has been that if price crosses the midline that it won't cross back on Friday’s market close, we could then use the analytics to find that relationship. In this example, Analytics utilizes a combination of two price conditions; both the likelihood that price will cross the midline, and the likelihood that it doesn’t cross back by Friday’s market close.


The rationale behind the analytics extends from the basis for Quantitative Analysis of the price data itself. We are anchoring our directional price movements and measuring them in relation to what is expected by option holders. Since we are observing the raw probabilities of these movements occurring over time it follows then that we can also observe the interactions of these movements as they occur together.


While these price moves do occur independently and we can observe and quantify the correlation of those movements, it is the behavior following those movements that is studied by the analytics. For example, we see that when price moves from the upper midline to the midpoint it moves up to the upper expected move 80% of the time over the last four years (93 occurrences). The other percentage of the time it does not. But where does it go after that? And when it touches the midline after that, how likely is it to go back up to the midpoint?


As another example, let’s look at the condition where price has breached the upper expected move. We observe that 83% of the time it will go back and touch the upper expected move line. When that occurs, what is the probability it will continue to move down below the upper EM and stay there? What is the probability that it will bounce off that EM line and continue higher?

With QuantDirection Analytics we can also compare price moves in the aggregate. For example, which category has the highest probability correlation? Which in which price zone do price conditions tend to move most predictably? Which Volatility levels? Are there certain Volatility levels where certain zone price levels become the most predicable? Which time intervals are the most predictable?


They Analytics utilize probabilities and frequencies to derive their calculations. The reliability of the findings can be limited by the frequencies of the occurrences since standard deviation is inversely correlated with the data sample size. As such, this information should not be considered definitive but should be viewed as another instructional data point that can be a part of the investor’s overall analysis and diligence.






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