Signal Settings

Overview#

The Signal Settings for Lune Zentro let you control the generation and behavior of trade entry signals. These settings give you a high degree of customization, letting you fine-tune the strategy's responsiveness through its AI-driven engine. By configuring the prediction model, selecting market features, and adjusting sensitivity, you can tailor the signal logic to fit your 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. The conceptual approach can be understood as follows:

Lune Zentro uses a 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 helps the signal logic stay effective as market behavior changes. All signals are confirmed on the close of a price bar so 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.	Range: 0.1 - 5.0Recommended: 1.0 - 3.0
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.	Range: 0.01 - 1.0Recommended: 0.6 - 0.9
Signal Confidence Threshold	The minimum required probability of a target move being met. A higher value leads to fewer, higher-conviction signals.	Range: 0.01 - 0.99Recommended: 0.6 - 0.8
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.	Range: 1 - 50Recommended: 3 - 15
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.	Range: 0.1 - 10.0Recommended: 0.8 - 2.5
Learning Rate	Controls how quickly the AI model adapts to new market data. Lower values result in slower, more stable adaptation.	Range: 0.01 - 0.20Recommended: 0.03 - 0.10
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.	Range: 5 - 2000Recommended: 300 - 800
Normalization Lookback	Sets the lookback window for feature standardization. A lower value leads to more reactive normalization.	Range: 5 - 2000Recommended: 50 - 200
Enable Adaptive Learning	Allows the model to dynamically learn from changing market conditions. When disabled, the model uses static parameters.	On/Off Toggle

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 help 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.	Range: 1 - 2000Recommended: 50 - 250**

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 lets the model build a fuller understanding.
• Balance sensitivity and confirmation: Lowering the Signal Sensitivity, Strength, and Confidence thresholds generates 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 trade on a lower timeframe, consider shorter Lookback periods to make the strategy more responsive. For higher timeframes, longer Lookback periods can give more stable signals.
• Start with recommended values: The recommended values provide a starting point for most markets. Use them as a baseline, then adjust them based on the specific asset and timeframe you trade.
• Tune one thing at a time: When optimizing the settings, adjust only one parameter at a time. This helps you see the effect of each change on the strategy's performance during backtesting.