# Signal Settings ## Overview The **Signal Settings** for Lune Zentro allow you to control the generation and behavior of trade entry signals. These settings provide a high degree of customization, letting you fine-tune the strategy’s responsiveness by leveraging its advanced, AI-driven engine. By configuring the prediction model, selecting market features, and adjusting sensitivity, you can tailor the signal logic to fit your specific trading style and the market you are analyzing. ### Signal Generation Logic To protect our proprietary algorithms, the exact mechanics of the signal logic are not disclosed. However, the conceptual approach can be understood as follows: Lune Zentro uses a sophisticated, probability-based AI model to identify trading opportunities. The strategy continuously analyzes historical data to determine the statistical probability of a significant, volatility-adjusted price move occurring within a set number of future bars. This core probability is then combined with a weighted analysis of up to eight user-selected market features, such as volatility and trend memory. If enabled, the adaptive learning system dynamically adjusts the importance of each feature based on its recent predictive power. This ensures the signal logic remains effective and relevant as market behavior changes. All signals are confirmed on the close of a price bar to ensure they do not repaint. ## Settings The following settings control the core logic of the signal engine. ### **General Settings** | Setting | Description | | ---------------- | ----------------------------------------------------------- | | **Long Trades** | Enables or disables the generation of long (buy) signals. | | **Short Trades** | Enables or disables the generation of short (sell) signals. | ### AI & Sensitivity These settings define the AI's prediction target and its overall responsiveness. | Setting | Description | Range / Recommended | | ------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------- | | **Signal Sensitivity** | Controls the overall responsiveness and frequency of signals. Lower values are more sensitive and produce more signals. | | | **Signal Strength Threshold** | The minimum signal strength required for a trade entry. A higher value requires stronger confirmation from the AI before a signal is generated. | | | **Signal Confidence Threshold** | The minimum required probability of a target move being met. A higher value leads to fewer, higher-conviction signals. | | | **Prediction Horizon** | Sets the number of bars ahead for the AI to predict. A shorter horizon is more reactive, while a longer horizon is better for capturing larger moves. | | | **Minimum Desired Move (ATR)** | Sets a volatility-normalized minimum move required for a signal, using the Average True Range (ATR). A value of 1.5 means the predicted move must be at least 1.5x the current ATR. | | | **Learning Rate** | Controls how quickly the AI model adapts to new market data. Lower values result in slower, more stable adaptation. | | | **Analysis Lookback Window** | Sets the size of the sliding data window the AI model uses for its analysis. A shorter window adapts faster to recent market changes. | | | **Normalization Lookback** | Sets the lookback window for feature standardization. A lower value leads to more reactive normalization. | | | **Enable Adaptive Learning** | Allows the model to dynamically learn from changing market conditions. When disabled, the model uses static parameters. | | ### Dynamic Feature Selection Select up to eight market features for the AI model to analyze. | Setting | Description | Range / Recommended | | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Feature 1-8 Type** | Selects the type of market feature to analyze. Each option captures a different market characteristic to enhance prediction accuracy. | Trend Filtering, Trend Strength, Trend Memory, Volatility Regimes, Volatility Analysis, Price Filtering, Volatility Smoothing, Volatility Dynamics, Pattern Detection, Price Efficiency, Distribution Skewness, Distribution Kurtosis, Distribution Kurtosis Alt, Risk Analysis, Downside Risk Ratio, Sharpe Ratio, Volume Analysis, Flow Analysis, Price Anchoring, Range Analysis, Signal Entropy, Variance Analysis, Jump Detection, Jump Analysis, Jump Filtering, Higher Moments, Higher Moments Alt, Normalized Volatility, Return Correlation, Return Memory, Return Memory Alt, Sign Correlation, Return Smoothing, Momentum Analysis, Volume-Volatility, Volume Dynamics, Time Trend Correlation | | **Feature 1-8 Lookback** | Sets the number of bars used for the feature's calculation. A lower value is more responsive to recent data. |

| ## Best Practices & Usage * **Select Diverse Features:** When choosing features, select a varied set that captures different aspects of the market (e.g., one for trend, one for volatility, one for volume). This allows the model to build a more robust understanding. * **Balance Sensitivity and Confirmation:** Lowering the Signal Sensitivity, Strength, and Confidence thresholds will generate more signals but may also increase the number of false positives. Higher values provide stronger confirmation but may result in fewer trading opportunities. Find a balance that suits your risk tolerance. * **Adjust Lookbacks for Your Timeframe:** If you are trading on a lower timeframe, consider using shorter Lookback periods to make the strategy more responsive. For higher timeframes, longer Lookback periods can provide more stable and reliable signals. * **Start with Recommended Values:** The recommended values provide a solid starting point for most markets. Use them as a baseline and then carefully adjust them based on the specific asset and timeframe you are trading. * **Tune One Thing at a Time:** When optimizing the settings, adjust only one parameter at a time. This will help you clearly understand the effect of each change on the strategy's performance during backtesting. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.lunefi.com/automated-tradingview-trading-strategies/lune-automated-strategies/lune-zentro/signal-settings.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.