Signal Settings

Overview#

The Signal Settings for Lune Elara let you control the generation and behavior of trade entry signals. These settings give you a high degree of customization. You can 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. You can understand the conceptual approach as follows:

Lune Elara uses an 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 order flow, based on your selections.

The model continuously learns and updates its understanding of how these features influence future price movements, so it can adapt to changing market conditions. This adaptive approach keeps the signal logic relevant as market behavior changes. 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	Enables or disables the generation of long (buy) signals.
Short Trades	Enables or disables the generation of short (sell) signals.

Prediction & Sensitivity#

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

Setting	Description	Options / Recommended
Prediction Target Mode	Configures what the AI model predicts. "Standard Return" predicts the next bar's return, while "Maximum Favorable Excursion" predicts directional profit potential.	Standard ReturnMaximum Favorable Excursion
Prediction Horizon	Sets the number of bars ahead for the AI to predict (only used in MFE mode).	Range: 1 - 50Recommended: 3 - 10
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: 0.8 - 2.0
Signal Sensitivity	Controls the responsiveness and frequency of signals. Lower values are more sensitive and produce more signals.	Range: 0.1 - 10.0Recommended: 1.0 - 3.0
Learning Rate	Controls how quickly the AI model adapts to new market data. Lower values result in slower, more stable adaptation.	Range: 0.01 - 0.50Recommended: 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 regime changes.	Range: 50 - 2000Recommended: 300 - 800

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.	Disabled, Return Smoothing, Volatility Analysis, Distribution Skewness, Distribution Kurtosis, Trend Memory, Price Efficiency, Volume Analysis, Flow Analysis, Volatility Regimes, Market Efficiency, Regime Detection, Pattern Detection, Change Detection, Price Filtering, Velocity Tracking, Trend Filtering, State Tracking, Momentum Analysis, Sharpe Ratio, Win Rate, Profit Factor
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 enable advanced, experimental systems for signal processing. Use at your own risk.

Setting	Description	Range / Recommended
Target Sharpe	Sets the performance threshold when using the Sharpe Ratio feature.	Range: 0.01 - 5.0Recommended: 1.0 - 2.5
Target Win Rate	Sets the performance threshold when using the Win Rate feature.	Range: 0.01 - 0.99Recommended: 0.40 - 0.75
Target Profit Factor	Sets the performance threshold when using the Profit Factor feature.	Range: 1.0 - 5.0Recommended: 1.2 - 2.5
Price Filtering Process Noise	Controls the noise parameter for the Kalman Filter calculations used in Price Filtering.	Range: 0.001 - 0.1Recommended: 0.005 - 0.02
Pattern Detection Sensitivity	Adjusts the sensitivity for Pattern Detection and Regime Detection features.	Range: 0.01 - 1.0Recommended: 0.05 - 0.2

Best Practices & Usage#

• Select diverse features: choose a diverse set that captures different aspects of the market (e.g., one for trend, one for volatility, one for volume). This lets the model build a more robust understanding.
• Balance sensitivity and confirmation: lowering Signal Sensitivity generates more signals but may increase false positives. Higher values give stronger confirmation but may result in fewer opportunities. Find a balance that suits your risk tolerance.
• Adjust lookbacks for your timeframe: on a lower timeframe, consider shorter Lookback periods to make the strategy more responsive. For higher timeframes, longer Lookback periods can give more stable signals.
• Use MFE for clearer directional bias: the "Maximum Favorable Excursion" Prediction Target Mode can suit strategies that aim to capture larger, more directional moves, since it focuses on profit potential rather than the next bar's return.
• Start with recommended values: the recommended values give a solid starting point for most markets. Use them as a baseline, then carefully adjust based on the asset and timeframe you are trading.
• Tune one thing at a time: when you optimize the settings, adjust only one parameter at a time. This helps you clearly understand the effect of each change during backtesting.