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Trixter Quantum

49 USD

Demo downloaded: 21

Published: 16 September 2026

Current version: 1.0

Trixter Quantum

Experts
Andrei Samokhin
Andrei Samokhin
Russia

913
Welcome to my seller page. I develop applications in the fields of AI and machine learning, and I am also passionate about trading systems. My products are based on various machine learning techniques. I research and apply state-of-the-art algorithms for time series analysis and am constantly
Version: 1.0
Activations: 15

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 stop-loss is 10 times smaller than the take-profit). At the same time, based on test results, this scalping bot remains profitable and does not ride out losses. It is an excellent system for those who are not willing to wait long for a position to close while risking their deposit. Short stop-losses serve as a rigorous test of the system, proving that Trixter Quantum's signals are accurate rather than random, and protecting against deep drawdowns.

Key Principles

• Analysis of local entanglement: mathematical mapping and evaluation of the correlation between adjacent states (patterns) to pinpoint entry points.

• Gold volatility analysis across multiple timeframes and indicator periods.

• Volatility grouping to identify effective patterns (ranging from 100 to 3,000).

• Statistical verification: high mathematical expectation of profit and statistical significance.

• Patterns that fail verification are flagged as noise—no trades are opened in these scenarios.

• Mandatory 2-year forward test required before approval for live accounts.

Trading Logic Features

• Trades are opened via stop orders placed at a predefined distance from the price (breakout trading).

• If a stop order is not triggered (the price does not hit the level) and the signal disappears, the order is canceled.

• Open positions are managed with a stop-loss and take-profit, and are closed at market price upon receiving a counter-signal.

• The robot does not use martingale, grid strategies, arbitrage, or other high-risk methods.

• Recommended timeframes: from M5 to H1. The optimal setting is H1 XAUUSD.

• Flexible adjustments for trading activity are available.

• An ECN or STP account with a minimum deposit starting at $200 is recommended.

Main Settings

• Allow BUY signals, Allow SELL signals: Enable or disable buy/sell trades based on market direction confidence.

• Signals sensitivity, Filter sensitivity: Threshold for signal sensitivity and noise filtering. Default value is 0.5; can be increased to 0.7–0.9 for cleaner signals.

• Filter by trading hours: Restricts trading times. Default setting is 24/7.

Capital Management Settings

• Distance in points for stop order: Distance from the current price to the stop order. Default: 250. Lower values yield more trades; higher values result in a more conservative approach.

• Max positions + orders number: Maximum number of simultaneously open positions (no averaging). Default: 3.

• Additional configurable parameters: delay between order placements, maximum spread control, and stop-loss/take-profit levels.

Important: Default settings are already optimized. Do not modify them unless necessary. Always save the standard set-file to allow rolling back to factory parameters.

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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 cons

49 USD

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 s

49 USD

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

49 USD

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 algorithm finds k

49 USD

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 more h

49 USD

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 u

49 USD

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 Ma

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The robust covariance (mincovdet) method, or Minimum Covariance Determinant (MCD) , is a robust method for estimating the covariance matrix and mean vector that is resistant to outliers in the data. The idea of the method Classical sample covariance is very sensitive to "contamination." MCD solves this problem by finding a "clean" subset of observations that does not contain outliers and computing standard estimates (mean and covariance) only on it. What MCD (mincovdet) is used for 1. Detection

49 USD

SVM solves a binary classification problem: it separates two classes. But One-Class SVM is trained only on data from one class ("normal" examples) and builds a boundary around them (a hyperplane in the transformed space) that separates this region from everything else. Everything that falls inside the region is considered normal, everything outside is an anomaly. How it works Data is mapped into a feature space (via a kernel, e.g., RBF). The algorithm looks for a hyperplane maximally distant fro

49 USD

Artificial intelligence-based scalping trading system - the latest development in the field of AI and trading. Since I am immersed in the development process and write many trading systems for my clients on order, I do not have time to test them myself. I invite respected buyers to share their trading reports with me; I am always open to fruitful cooperation. If any of the trading systems sparks your interest - be sure to write to me so that there is feedback. There is a special promo price of $

49 USD

AI-based scalping trading system - the latest development in the field of AI and trading. This system differs from the previous one by increased historical profitability . It uses special learning algorithms for trading with a high profit factor. Since I am immersed in the development process and write many trading systems for my clients on order, I do not have time to test them myself. I invite respected buyers to share their trading reports with me; I am always open to fruitful cooperation. If

49 USD

Trixter Night Scalper is ready to install and awaits your instructions — to trade at night or around the clock. My method allows combining several unique strategies into one trading system that applies the most successful trading strategy depending on market conditions. You can be sure that the bot will open a trade only according to the most favorable scenario and will ignore situations in which trading is undesirable. I recommend choosing night trading because the risk per trade will be minima

100 USD

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Trixter Quantum

Andrei Samokhin