|
no
experience
|
8
products
|
38
demo versions
|
|
0
jobs
|
0
signals
|
0
subscribers
|
Elliptic Envelope is a parametric machine learning algorithm designed to detect outliers and anomalies in multidimensional data. The method assumes that "normal" data (the main sample) follows a multidimensional normal (Gaussian) distribution. Operating Principle Ellipsoid Formation: In the feature space, the algorithm builds a hyperellipsoid around the central group of points. The boundary of this ellipsoid is determined by the covariance matrix and the mean values of the features. Use of
Do you love high-frequency, high-speed trading with low risks? Then Trixter Quantum was created specifically for you. Thanks to precise entries and high mathematical expectation, the bot trades with short stop-losses while capturing large movements in points. Its core algorithm is based on the analysis of local entanglement of adjacent states (patterns), allowing it to instantly detect hidden microstructural shifts in the market. The stop-loss to take-profit ratio can reach up to 1:10 (the
Love high-frequency, high-speed trading with low risk? Then this bot is created specifically for you. Thanks to precise entries and high expected payoff, the bot trades with tight stops while capturing large point movements. The stop-loss to take-profit ratio can reach up to 1:10—meaning the stop-loss is 10 times smaller than the take-profit—while this scalping bot remains profitable and does not hold onto losing trades, according to test results. This is an excellent system for those who are
This bot trades gold level breakouts! Key Principles Gold volatility analysis across multiple timeframes and indicator periods. Volatility grouping to find effective patterns (from 100 to 3000). Statistical verification: high expected mathematical payoff and statistical significance. Patterns that fail verification are marked as noise — no trades are opened in such situations. Mandatory 2-year forward test before being allowed on a real account. Trading Logic Features Opening trades via stop
Local Outlier Factor (LOF) Algorithm is a machine learning method for finding anomalies in data that cannot be detected using global thresholds. Unlike simple methods, LOF looks for local outliers by comparing the density of points in their immediate vicinity. How it works The main idea of LOF is simple: an anomaly is a point whose surrounding object density is significantly lower than the density around its neighbors . Selecting the neighborhood. For each point, the
Decision tree is a supervised machine learning algorithm that operates on the principle of sequential "Yes/No" questions. It is a tree-like structure where: Internal nodes — are checks of some feature (e.g., "Age > 18?"). Branches — are the results of the check (Yes/No). Leaves — are the final decision (answer). The construction process is that the algorithm automatically finds the most important feature at each step (using criteria such as Gini Index or Entropy ) and splits the data into
Isolation Forest is a machine learning algorithm for anomaly (outlier) detection . How the algorithm works: Random partitioning: The algorithm repeatedly selects a random feature and a random threshold value to split the data, until each point ends up in its own separate "cell" (or until the maximum depth is reached). Isolation depth: Anomalies are "isolated" faster because they require fewer splits to be separated from the rest of the data (they are located far from dense clusters). Forest
Trixter KNN Gold is an intelligent trading system based on gold volatility analysis. The bot uses smart pattern search based on the nearest neighbors method (KNN). The found patterns are processed by two classifiers, after which the model is exported to the ONNX format. Key principles Analysis of gold volatility across multiple timeframes and indicator periods. Volatility grouping to find effective patterns (from 100 to 3000). Statistical validation: high mathematical expectation of profit and