Markovian Pivots

AEdge Markovian Pivots is based on the idea that most (if not all) of what happens between the open and the close of a time-based bar (like your typical candlestick bars) depends mainly on what happened in the previous bar of the same time frame. As an example of this idea, take a 1-minute chart. Most of what happens during the current day will depend on what happened the previous day.

Markovian Pivots explores this idea by measuring how price moved during the previous higher time frame bar and translate that into pivot lines (up to 10 pivots up and 10 down) that forecast areas of support and resistance for the duration of the current higher time frame bar.

More formally, Markovian Pivots see asset prices as a Markovian process (also known as a Markov chain, which is a sequence of events where the probability of each event depends only on the state we arrive at in the previous event). We know this is not entirely true for asset prices since memory for asset prices has been shown to be cyclical and fractal. However, in the short term, the effect attained from the cyclical and fractal nature of asset prices is quite small compared to the effect attained from the state of the previous bar, and so, assuming that the distribution of price changes within the current bar will be highly influence by the distribution of price changes of the previous bar is a reasonable assumption to make.

There are multiple possible uses for this indicator in your analysis. Here are a few examples:

  • Finding areas of where price may stop (because for it to be able to continue further it would have to be an outlier). Remember that this does not mean price will reverse, just that it is more likely for it to stop than it is for it to shoot right through.
  • Find different areas of point of control that price may hover around.
  • Find the predicted range of price for the day in order to manage risk.

The indicator settings have three main parameters:

  1. The first parameter, Data Source, sets the input to the indicator. This defaults to the close price of the main chart. You can change this to any other data source found on your chart.
  2. The second parameter, Allow Negative Pivot Levels, toggles whether or not the indicator can predict negative levels.
  3. The third parameter, Time Window for Fitting, determines what time window should be predicted. By default, the indicator automatically selects the appropriate time window by looking at the chart time frame. However, this can be overridden by changing this setting to one of the supported time windows. Remember though, that the lower the time frame, the lower this setting has to be, since it does not pull any higher time frame data. It is just a time window on the current chart and so the data used to calculate the prediction is the data available on the current chart time frame, not data from a higher time frame. For example, if we are on a 1-minute chart and select 3 Days, it will use 3 days’ worth of 1-minute data to calculate the prediction for the next 3 days.