SUPERALGOS GOVERNANCE
Data Mining Team Proof of Value, January 2023
Digging for insights, finding valuable treasures
January has been a productive month for the Data Mining team as they worked diligently to provide traders with valuable indicators. The team’s efforts were led by three members — Carl, Quantum, and Blaa Johan.
Carl, has done an excellent job with three indicators:
Range Divergence, measuring the current trend strength by calculating the average volume needed to move price and applies that to the current candles volume in order to determine if the candle move is greater than expected, as expected or if it has met some sort of rejection.
OS Volume, this indicator analyzes the volume of trades made in the market and identifies potential changes in market momentum.
and OS Fib Channel, this indicator plots Fibonacci retracements and extensions on a chart based on volume analysis. It helps traders identify potential levels of support and resistance.
Meanwhile, Quantum has made a rich contribution by developing
One-Sided Gauss Channels, this indicator plots a channel on a chart, with the channel lines determined by a one-sided Gaussian distribution. It helps traders identify potential trends and price movements.
T3 Moving Average, this indicator calculates the average price over a set number of periods, but with a triple exponential smoothing factor added to it. This results in a more responsive moving average compared to other traditional moving averages.
MavilimW, this indicator is a modified version of the Weighted Moving Average, with the weighting factor determined by the rate of price change. It helps traders identify potential trends in the market.
and Gann Swing, this indicator uses Gann analysis to identify potential swing points in the market, helping traders determine potential changes in trend direction.
Quantum has also fixed the Fisher Ribbons plotter issue, which was not showing previously.
Lastly, Blaa Johan has done an impressive job by developing:
Mann-Kendall Test: This is a statistical test used to determine whether there is a monotonic trend in a data set, without assuming a particular distribution. It can be used to identify trends in financial market data.
Hurst Exponent: Which measures the persistence of a time series, providing information on the long-term memory of a market. It can be used to identify market trends and predict future price movements. Here it’s max-min normalized.
Hurst Exponent using Yeo-Johnsson transformation and Detrended Fluctuation Analysis, similar to above but it transforms the data series into normal distribution and then use DFA to find the exponent. Z-score normalized. Winner of the longest named indicator?
Ornstein-Uhlenbeck Process: This is a mathematical model used to describe the behavior of a time series. It can be used to analyze market trends and identify potential price movements.
Lyapunov Exponent: This indicator measures the rate of divergence or convergence of nearby points in a time series. It can be used to identify market trends and predict future price movements.
Lyapunov Exponent Levels: A variant from the previous indicator that adds dynamic upper and lower levels based on market volatility.
Daubechies Wavelet Oscillator (BWO): This indicator uses wavelet analysis to identify trends and cycles in market data. It helps traders identify potential price movements and market trends. It also incorporates the RSD t-test that Thomas previous month contributed.
The team continues to work towards delivering innovative and useful indicators for traders. We hope that you will support their efforts in making the world of trading easier and hopefully more profitable.
