Trixter DTR
- Experts
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Andrei Samokhin
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
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:
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Internal nodes — are checks of some feature (e.g., "Age > 18?").
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Branches — are the results of the check (Yes/No).
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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 homogeneous groups until it obtains a pure leaf or reaches constraints.
What is it used for?
Decision trees are versatile and are used in two main types of tasks:
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Classification (categorical answer):
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Assigning a client to the "approved" or "rejected" category in a bank.
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Identifying the species of an animal based on features.
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Determining the probability of buying or selling a financial asset.
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Regression (numeric answer):
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Predicting the price of an apartment based on area and location.
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Predicting the direction of gold movement.
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Main advantages:
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Transparency and interpretability: Unlike neural networks, you can easily explain why the model made a particular decision (it can be drawn and the path from the root to the leaf can be traced).
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Does not require data preparation: Works well with different types of features (numeric and categorical) and is insensitive to outliers.
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Feature selection: Automatically shows which factors are most important.
Bot Trixter DTR is built using this approach.
The Trixter DTR algorithm implements pattern detection using a decision tree (abbreviated as DTR). We start from the assumption that the gold market is sufficiently efficient and most price fluctuations are market noise that cannot be predicted. However, we can isolate fragments of the gold chart (so-called anomalies) in which it is maximally predictable. Thus, we have identified 15% of chart sections that are well predictable.
Key principles
- Analysis of gold volatility across multiple timeframes and indicator periods.
- Grouping volatility to find effective patterns (from 100 to 3000).
- Statistical validation: high mathematical expectation of profit and statistical significance.
- Patterns that do not pass validation are marked as noise — in such situations, trades are not opened.
- Mandatory 2-year forward test before being allowed on a live account.
Trading logic features
- Opening trades with limit orders at a selected distance from the price — minimal slippage.
- If a limit order does not trigger (price does not touch the level) and the signal disappears — the order is deleted.
- Open positions are protected by stop-loss and take-profit, and are closed at market price upon an opposite signal.
- The robot does not use martingale, arbitrage, or other high-risk strategies.
- Recommended timeframes: from M5 to H1. Optimal — H1 XAUUSD.
- Flexible adjustment of trading activity is provided (parameters below).
Main settings
- Allow BUY signals, Allow SELL signals — allow or prohibit buy/sell when confident in the market direction.
- Signals sensitivity, Filter sensitivity — threshold for signal sensitivity and noise filtering. Default value — 0.5. Can be increased to 0.7–0.9 for cleaner signals.
- Filter by trading hours — trading time restrictions. By default — 24/7.
Capital management settings
- Distance in points for limit order — distance from price to limit order. Default: 250. Lower → more trades, higher → more conservative.
- Max positions + orders number — maximum number of simultaneously open positions (no averaging). Default: 3.
- Additionally configurable: delay between openings, maximum spread control, stop-loss and take-profit levels.
