Signal Settings

Overview

The Signal Settings for Lune Stratos provide detailed control over its unique signal generation engine. This strategy is designed to identify complex market patterns by combining longer-term trend analysis with shorter-term price movements. These settings allow you to adjust how the strategy weighs different types of analysis, fine-tune its sensitivity, and configure its adaptive capabilities to match your trading preferences.

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 Stratos uses a multi-phase process to generate trading signals. It begins by identifying the current market "regime"—such as a bull trend, bear trend, or a ranging environment. The strategy then calculates a series of advanced statistical features from the price data.

These features are used to power specialized sub-models, including a momentum model and a mean-reversion model. A key part of the strategy is its ability to dynamically adjust the importance (weight) of each model based on the detected market regime. By combining the outputs of these models, the strategy generates a final, high-confidence trading signal that is always adapted to the current market's character. All signals are confirmed on the close of a price bar to ensure they do not repaint.

Settings

These settings control the core logic that Lune Stratos uses to generate trade signals.

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.

Core Lookback Periods

These settings define the main timeframes used for market analysis.

Setting	Description	Range / Recommended
Short-Term Lookback	Sets the period for short-term analysis and volatility detection.	Range: 1 - 2000 Recommended: 10 - 30
Medium-Term Lookback	Sets the period for medium-term trend analysis and regime detection.	Range: 1 - 2000 Recommended: 30 - 80
Long-Term Lookback	Sets the period for long-term statistical analysis and establishing a baseline for market behavior.	Range: 1 - 2000 Recommended: 50 - 200

Signal Sensitivity & Thresholds

These settings adjust the strategy's overall responsiveness and its reaction to market conditions.

Setting	Description	Range / Recommended
Signal Sensitivity	Controls the overall frequency of signals. Lower values result in more signals.	Range: 0.1 - 5.0 Recommended: 1.0 - 3.0
Regime Sensitivity	Controls how quickly the system adapts to changes in the market regime.	Range: 0.1 - 5.0 Recommended: 1.0 - 3.0
Volatility Weight	Controls the importance of volatility in classifying the market regime and generating signals.	Range: 0.1 - 3.0 Recommended: 1.0 - 2.5

Model Feature Weights

These settings control the importance of different analytical models in the final signal calculation.

Setting	Description	Range / Recommended
Momentum Weight	Controls the influence of momentum-based features.	Range: 0.0 - 1.0 Recommended: 0.2 - 0.5
Mean Reversion Weight	Controls the influence of mean-reversion-based features.	Range: 0.0 - 1.0 Recommended: 0.2 - 0.5
Information Weight	Controls the influence of information-theory-based features.	Range: 0.0 - 1.0 Recommended: 0.1 - 0.4

Advanced Signal Configuration

These settings provide additional layers of filtering and adaptability.

Setting	Description	Options / Recommended
Feature Weight	Controls the sensitivity of the internal feature calculations.	Range: 1 - 10 Recommended: 2 - 5
Noise Filter Strength	Controls the strength of the adaptive noise filter. Higher values create smoother signals.	Range: 0.01 - 0.99 Recommended: 0.5 - 0.8
Adaptive Mode	Controls how the strategy's parameters adapt to market conditions.	• Static: Parameters are fixed. • Dynamic: Parameters adjust based on recent market conditions. • Regime-Aware: Parameters adjust based on the detected market regime. Recommended: Dynamic or Regime-Aware

Best Practices & Usage

• Adjust Weights to Your View: Use the Model Feature Weights to align the strategy with your market outlook. If you believe the market is trending, you could increase the Momentum Weight. If you expect the market to be range-bound, you could increase the Mean Reversion Weight.

• Use Adaptive Modes: For most situations, setting the Adaptive Mode to Dynamic or Regime-Aware is recommended. This allows the strategy to automatically adjust its internal parameters to the current market environment.

• Control Signal Frequency: The primary way to manage how many signals you get is by adjusting the Signal Sensitivity. A lower value will increase the number of signals, while a higher value will make the filter more selective, resulting in fewer trades.

• Filter Out Market Noise: In choppy or unpredictable markets, increasing the Noise Filter Strength can help to smooth the signal output and reduce the number of false signals.