Signal Settings

Overview#

Signal Settings for Lune Vertex let you control how trade entry signals are generated and behave. These settings give you a high degree of customization, letting you tune the strategy's responsiveness through its AI-driven engine. By setting up the prediction model, selecting market features, and adjusting sensitivity, you can tailor the signal logic to your trading style and the market you analyze.

Signal Generation Logic#

To protect our proprietary algorithms, the exact mechanics of the signal logic are not disclosed. You can understand the conceptual approach as follows:

Lune Vertex uses a classification AI model that sorts the market into three states: bullish, bearish, or neutral. The strategy builds a feature vector from a wide range of market characteristics, such as volatility, trend memory, and price efficiency, based on your selections.

The model learns online, so it continuously updates its understanding of how these features influence future price movements. This lets it adapt to changing market conditions. Signals are generated only when the model has high conviction that the market is entering a strong bullish or bearish phase, and it ignores periods of uncertainty. 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	Turns the generation of long (buy) signals on or off.
Short Trades	Turns the generation of short (sell) signals on or off.

AI & Sensitivity#

These settings define the AI's prediction target and its overall responsiveness.

Setting	Description	Options / Recommended
Signal Confidence Threshold	The minimum signal confidence needed for a trade entry. A higher value needs stronger confirmation from the AI before a signal is generated.	Range: 0.01 - 0.99Recommended: 0.6 - 0.8
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
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	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: 1.0 - 3.0
Learning Rate	Sets how quickly the AI model adapts to new market data. Lower values give slower, more stable adaptation, while higher values give faster but potentially less stable learning.	Range: 0.01 - 0.20Recommended: 0.03 - 0.10

Dynamic Feature Selection#

Select up to eight market features for the AI model to analyze. The model dynamically weighs the importance of each selected feature.

Setting	Description	Options
Feature 1-8 Type	Selects the type of market feature to analyze. Each option captures a different market characteristic.	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

Advanced Signal Settings#

These settings turn on advanced, experimental systems for signal processing. Use at your own risk.

Setting	Description	Range / Recommended
Overfitting Protection	Strength of the regularization mechanism that prevents the AI from overfitting to market noise. Higher values create simpler, more generalized models.	Range: 0.0 - 0.1Recommended: 0.0001 - 0.01
Gradient Clipping	Clips the internal learning gradients to prevent extreme updates and improve the model's stability.	Range: 0.0 - 10.0Recommended: 0.5 - 2.0

Best Practices & Usage#

• Select diverse features: When choosing features, select a varied set that captures different aspects of the market (for example, one for trend, one for volatility, one for volume). This lets the model build a more robust understanding.
• Balance confidence and signal frequency: A lower Signal Confidence Threshold generates more signals but may increase false positives. Higher values give 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, use 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 give a solid starting point for most markets. Use them as a baseline, then adjust based on the asset and timeframe you trade.
• Tune one thing at a time: When you optimize the settings, adjust only one parameter at a time. This helps you see the effect of each change during backtesting.