OPEN-SOURCE SCRIPT
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.
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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