Signal Settings

Overview

The Signal Settings for Lune Momento allow you to control how the strategy generates trade signals. These settings are designed to be highly customizable, enabling you to adjust the strategy's sensitivity to different market behaviors. By balancing the influence of trends, mean reversion, and volatility, you can fine-tune the algorithm to align with your trading style and the specific 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 Momento uses a unique approach that combines long-term trend analysis with short-term momentum to capture complex, non-linear market patterns. The strategy's core engine first identifies the dominant market character—whether it is trending, mean-reverting, or moving unpredictably. Based on this real-time analysis, it then weighs different factors like trend strength and volatility to generate a signal. This adaptive process allows the strategy to focus on momentum in trending markets and identify potential turning points in range-bound markets. 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 Momento uses to generate trade 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 market analysis and volatility detection.

Range: 1 - 2000 Recommended: 10 - 30

Medium-Term Lookback

Sets the period for medium-term trend analysis and pattern detection.

Range: 1 - 2000 Recommended: 30 - 80

Long-Term Lookback

Sets the period for long-term statistical analysis and for identifying the overall market regime.

Range: 1 - 2000 Recommended: 50 - 200

Signal Sensitivity & Weighting

These settings adjust the strategy's overall responsiveness and the importance of different analytical models.

Setting
Description
Range / Recommended

Signal Sensitivity

Controls the overall frequency of signals. Lower values result in more signals, while higher values apply more filtering for higher-quality signals.

Range: 0.1 - 5.0 Recommended: 1.0 - 3.0

Trend Weight

Controls how much importance is given to trend-following analysis when generating signals.

Range: 0.0 - 5.0 Recommended: 1.0 - 2.5

Mean Reversion Weight

Controls how much importance is given to mean reversion analysis when generating signals.

Range: 0.0 - 5.0 Recommended: 0.5 - 2.0

Advanced Signal Configuration

These settings provide additional layers of filtering and analysis.

Setting
Description
Range / Recommended

Volatility Weight

Controls how much importance is given to volatility analysis when generating signals.

Range: 0.0 - 5.0 Recommended: 0.4 - 2.0

Noise Filter Strength

Controls the strength of the adaptive noise filter. Higher values create smoother signal generation but may introduce more lag.

Range: 0.0 - 2.0 Recommended: 0.4 - 1.5

Best Practices & Usage

  • Tailor the Strategy to the Market: If you are trading a market that is strongly trending, consider increasing the Trend Weight and decreasing the Mean Reversion Weight. For a market that is moving sideways or is range-bound, do the opposite.

  • Adjust Sensitivity for Signal Frequency: Use the Signal Sensitivity setting as your primary tool for controlling the number of trades. If you want more trading opportunities, lower the value. If you prefer fewer, higher-confirmation trades, increase the value.

  • Use the Noise Filter in Choppy Markets: If you notice the strategy is generating too many signals due to market "noise" or chop, slightly increasing the Noise Filter Strength can help smooth out the signals and reduce false entries.

  • Start with Default Settings: The recommended values provide a balanced starting point. It is best to begin with these settings and then make small adjustments to one parameter at a time to see how it affects performance in backtesting.

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