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The AEdge Distribution Bands (ADB) indicator can help you predict where an asset’s price may be headed in the near future. It does this by estimating the asset’s future distribution of returns and displaying the results as bands on the chart. The bands show the estimated range of where the price is likely to be in the future.
The ADB also includes a mean reversion hypothesis test, which checks whether or not price has a natural tendency to revert back to the estimate. If the bands are yellow, it means the assumption of reversion does not hold.
The indicator uses a skewed, fat-tailed distribution to predict future price movements. It also takes into account the fact that volatility tends to cluster, meaning low volatility tends to follow low volatility and high volatility tends to follow high volatility. The ADB adjusts the bands and its predictions based on this information to provide the most accurate prediction possible.
AEdge Local Asymmetry Cumulative Probability (LACP) helps you understand what can be thought of as the directional force acting on price changes. It does this by measuring the natural pull up or down of the most recent data and assigning a probability to the current pull. This probability represents the likelihood that the observed pull will stay where it is now, or go lower. For example, a probability of 0.25 means there is a 25% chance that we will see this specific pull or lower, assuming the estimate is correct.
The LACP indicator plots one moving line and three static lines. The moving line represents the cumulative probability. The upper and lower static lines represent the upper and lower percentile thresholds. (A percentile is simply a value on a scale from 0 to 100 that represents cumulative probability). The middle line represents the median, or the 50% probability line.
If the current pull is outside the upper percentile threshold, it means the current pull is more positive (upward) than expected and is more likely to go back down to the median. The opposite is true for the lower percentile threshold. When this happens, it usually means the pull lessens, which can lead to sideways movement or a decrease in what many traders perceive as momentum.
The AEdge Markovian Pivots indicator is based on the idea that most of what happens during a certain time period (like a day if on a 1-minute chart) is influenced by what happened during the previous time period (for example the previous day). The indicator measures how price moved during the previous time period and uses this information to create pivot lines that forecast areas of what can be thought of as support and resistance for the current time period. It can plot up to 10 lines up and 10 lines down.
The Markovian Pivots indicator views asset prices as a Markovian process, which is a sequence of events where the probability of each event depends only on the state we arrive at in the previous event. While asset prices do have some memory and are influenced by cyclical and fractal patterns, these effects are small compared to the influence of the previous time period in the short-term. Therefore, it is reasonable to assume that the distribution of price changes within the current time period will be highly influenced by the distribution of price changes in the previous time period.
No matter if you subscribe to the Markovian assumption or not, we are very confident that you’ll be surprised at how accurate this indicator can be!
The AEdge Bar Volatility Estimator indicator helps you predict the volatility of the current bar based on the estimated volatility of the previous bar. It separates the upward volatility and downward volatility so you can see which direction is more volatile. This indicator can be useful for assessing potential risk and creating ranges for how far the current bar is likely to move depending on its direction.
There are three main settings for the AEdge Bar Volatility Estimator:
1. Data Source: This sets the input for the indicator. The default is the close price of the main chart, but you can choose any other data source on your chart.
2. Number of Bars: This setting determines how many time periods are used to estimate the parameters for the distribution that the volatility is based on. You can use the Autocorrelogram indicator from the Researcher Pack to find the optimal sample size by looking at the autocorrelation of absolute log %-change and seeing when it is no longer significantly different from 0.
3. Target: This specifies what the model should predict. If you select Average, the model will predict the average bar volatility. Select Tail-End to predict wilder moves, or select Extreme Tail-End to predict extreme moves.
The AEdge Directional Change Probability indicator helps you predict the likelihood that an asset’s price will soon change direction. It does this by considering both price and time and measuring cyclical events. This indicator can help you identify when an asset’s price is likely to change from going up to going down or sideways, or from going down to going up or sideways.
This indicator is based on the assumption that these events are a type of Poisson process, which means that the average number of events per time unit is almost constant and the events are mutually exclusive and independently distributed. This assumption is reasonable because there is no sign of significant autocorrelation in the observed events.
There are several potential uses for this indicator in your analysis. For example, you can use it to see when a price move is likely to reach its limit and change direction, or to identify unusual price moves.
