Signal Settings

Overview

The Signal Settings for Lune Stratos 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 an 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 Stratos uses a sophisticated, adaptive AI model powered by a machine learning engine to identify trading opportunities. The strategy dynamically 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 features an online learning system with an advanced optimizer, allowing it to continuously learn from new market data and update its internal parameters without repainting. Its unique forward-looking design is trained to predict the probability of a significant price move occurring several bars into the future. This adaptive approach ensures the signal logic remains effective and relevant as market behavior changes. All signals are confirmed on the close of a price bar.

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, while higher values provide stronger filtering.	Range: 0.01 - 5.0 Recommended: 1.0 - 3.0
Signal Confidence Threshold	The minimum required signal confidence for a trade entry. A higher value requires stronger confirmation from the AI before a signal is generated.	Range: 0.01 - 0.99 Recommended: 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 - 50 Recommended: 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 - 10.0 Recommended: 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 - 0.20 Recommended: 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 - 2000 Recommended: 300 - 800

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

Advanced Signal Settings

These settings provide additional control over the AI model's learning process to improve stability and prevent overfitting.

Setting	Description	Range / Recommended
Overfitting Protection	Strength of the regularization mechanism to prevent the AI from overfitting to market noise. Higher values create simpler, more generalized models.	Range: 0.0 - 0.1 Recommended: 0.005 - 0.02
Optimizer Momentum	Controls the momentum factor for the adaptive learning algorithm. Higher values result in smoother parameter updates.	Range: 0.0 - 0.99 Recommended: 0.85 - 0.95
Optimizer Decay	The decay factor for the adaptive learning rate optimizer. Higher values lead to more stable learning rates.	Range: 0.9 - 0.999 Recommended: 0.995 - 0.999

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 Confidence: Lowering Signal Sensitivity and Signal Confidence 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.