OPEN-SOURCE SCRIPT

Multi Time Frame Exponential Moving Average and dasboard

This Pine script, titled "Multi Time Frame Exponential Moving Average (MTF EMA)," provides an innovative approach for traders who wish to track trends across multiple timeframes without having to switch between different charts. It combines two main features: an indicator displaying exponential moving averages (EMA) on five different time periods, as well as a compact dashboard that synthesizes this information on a single chart window.

The originality of this script lies in its ability to provide a comprehensive analysis of EMA trends across different time intervals, allowing traders to quickly and clearly understand the market dynamics without having to navigate between multiple charts. Rather than switching from one chart to another to observe trends on different time scales, traders can now consult a single dashboard to obtain all the necessary information.

The script uses exponential moving averages (EMA) to identify trends over five time periods: 5 minutes, 15 minutes, 1 hour, 4 hours, and 1 day. The values of the EMAs are calculated based on the closing prices of candles. Bullish or bearish trends are indicated by upward or downward arrows respectively, making it easy to interpret the information on the dashboard.

To use this script, traders can simply add it to their chart on the TradingView platform. They can customize the parameters of the exponential moving averages according to their preferences and choose between a dark or light theme for the dashboard. Then, they can observe trends on different time scales directly on the dashboard, enabling them to make informed trading decisions.

In summary, this script offers a practical and innovative solution for tracking trends across multiple timeframes, combining the efficiency of exponential moving averages with the convenience of a dashboard centralized on a single chart. This allows traders to save time and stay informed about market movements effectively and efficiently.
Moving Averagesmultitimeframe

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