Signal Settings

Overview#

The Signal Settings for Lune Stratos let you control how trade entry signals are generated and how they behave. These settings give you a high degree of customization, letting you fine-tune the strategy's responsiveness with an 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 works as follows.

Lune Stratos uses an adaptive AI model powered by a machine learning engine to identify trading opportunities. The strategy builds a feature vector from a range of market characteristics, such as volatility, trend memory, and price efficiency, based on your selections.

The model uses an online learning system with an adaptive optimizer, so it can continuously learn from new market data and update its internal parameters without repainting. Its forward-looking design is trained to predict the probability of a significant price move several bars into the future. This keeps the signal logic 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 - 5.0Recommended: 1.0 - 3.0
Signal Confidence Threshold	The minimum signal confidence 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
Prediction Horizon	Sets the number of bars ahead for the AI to predict. A shorter horizon is more reactive, while a longer horizon captures 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 give 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

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.	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 give you more control over the AI model's learning process to improve stability and prevent overfitting.

Setting	Description	Range / Recommended
Overfitting Protection	Strength of the regularization that prevents the AI from overfitting to market noise. Higher values create simpler, more generalized models.	Range: 0.0 - 0.1Recommended: 0.005 - 0.02
Optimizer Momentum	Controls the momentum factor for the adaptive learning algorithm. Higher values give smoother parameter updates.	Range: 0.0 - 0.99Recommended: 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 - 0.999Recommended: 0.995 - 0.999

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 sensitivity and confidence: Lowering Signal Sensitivity and Signal Confidence generates more signals but may increase the number of false positives. Higher values give stronger confirmation but may give 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 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 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 during backtesting.