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TS Aggregated Median Absolute Deviation

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TS Aggregated Median Absolute Deviation (MAD) Indicator Explanation


Overview

The TS Aggregated Median Absolute Deviation (MAD) is a powerful indicator designed for traders looking for momentum-based strategies. By aggregating the Median Absolute Deviation (MAD) across multiple timeframes, it provides a comprehensive view of market dynamics. This indicator helps identify potential reversal points, overbought/oversold conditions, and general market trends by leveraging the concept of MAD, which measures price dispersion from the median.


Signal Generation:

Long Signal: Triggered when the price moves above the aggregated upper band

Short Signal: Triggered when the price moves below the aggregated red band


Alerts:

Real-time alerts are integrated to notify the user of long or short signals when confirmed:

Long Signal Alert: "TS MAD Flipped ⬆LONG⬆"

Short Signal Alert: "TS MAD Flipped ⬇Short⬇"

Optimization:

Adjust thresholds, MAD lengths, and multipliers for each timeframe to suit the specific asset and market conditions.
Experiment with enabling/disabling MAD components to focus on particular timeframes.
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Benefits of the New Version:


Enhanced histogram: shows the percentage deviation with gradient colors based on trend strength and direction:

Positive deviations are shown in purple shades.
Negative deviations are shown in orange/red shades.
Smooth transitions between colors reflect momentum changes in the deviation.
This new histogram improves clarity and allows traders to better analyze the strength of price deviations from the median.

Greater Customization: Users can now tailor overbought/oversold levels to their trading style.

Improved Alerts: Comprehensive notifications help users take timely actions.

Enhanced Visualization: Clearer band normalization and background highlights offer better situational awareness.

Adaptability: OB/OS 2 dynamically adjusts to market volatility, making it more effective in varying conditions.
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Repainting issue should be fixed
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Trend AnalysisVolatility

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