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AlgoAlpha
18 de Ene. de 2024 7:27

Median Proximity Percentile [AlgoAlpha]Β 

INDEX:BTCUSD480
Bitcoin all time history indexINDEX

DescripciΓ³n

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πŸ“ŠπŸš€ Introducing the "Median Proximity Percentile" by AlgoAlpha, a dynamic and sophisticated trading indicator designed to enhance your market analysis! This tool efficiently tracks median price proximity over a specified lookback period and finds it's percentile between 2 dynamic standard deviation bands, offering valuable insights for traders looking to make informed decisions.

🌟 Key Features:
  • Color-Coded Visuals: Easily interpret market trends with color-coded plots indicating bullish or bearish signals.
  • Flexibility: Customize the indicator with your preferred price source and lookback lengths to suit your trading strategy.
  • Advanced Alert System: Stay ahead with customizable alerts for key trend shifts and market conditions.


πŸ” Deep Dive into the Code:
  1. Choose your preferred price data source and define lookback lengths for median and EMA calculations.
    priceSource = input.source(close, "Source")
    and
    lookbackLength = input.int(21, minval = 1, title = "Lookback Length")
  2. Calculate median value, price deviation, and normalized value to analyze market position relative to the median.
    medianValue = ta.median(priceSource, lookbackLength)
  3. Determine upper and lower boundaries based on standard deviation and EMA.
    upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
  4. Compute the percentile value to track market position within these boundaries.
    percentileValue = 100 * (normalizedValue - lowerBoundary)/(upperBoundary - lowerBoundary) - 50
  5. Enhance your analysis with Hull Moving Average (HMA) for smoother trend identification.
    emaValue = ta.hma(percentileValue, emaLookbackLength)
  6. Visualize trends with color-coded plots and characters for easy interpretation.
    plotColor = percentileValue > 0 ? colorUp : percentileValue < 0 ? colorDown : na
  7. Set up advanced alerts to stay informed about significant market movements.
    // Alerts alertcondition(ta.crossover(emaValue, 0), "Bullish Trend Shift", "Median Proximity Percentile Crossover Zero Line") alertcondition(ta.crossunder(emaValue, 0), "Bearish Trend Shift", "Median Proximity Percentile Crossunder Zero Line") alertcondition(ta.crossunder(emaValue,emaValue[1]) and emaValue[1] > 90, "Bearish Reversal", "Median Proximity Percentile Bearish Reversal") alertcondition(ta.crossunder(emaValue[1],emaValue) and emaValue[1] < -90, "Bullish Reversal", "Median Proximity Percentile Bullish Reversal")


🚨 Remember, the "Median Proximity Percentile [AlgoAlpha]" is a tool to aid your analysis. It’s essential to combine it with other analysis techniques and market understanding for best results. Happy trading! πŸ“ˆπŸ“‰

Notas de prensa

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Replaces "EMA" with "HMA" in the inputs.

Notas de prensa

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Added Alerts for trend swings
Added the option to turn on/off the noise scatterplot
OscillatorsTrend AnalysisWave AnalysismedianalgoalphaMomentum OscillatorstrendfollowingmeanreversionTechnical Analysispercentilealgotradingchart
Comentarios
Salamandras
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Super wskaΕΊnik ;D
AlgoAlpha
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@Salamandras, Thank you 😁
SniperAIAlgoTest
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Even market is sideways it is showing up or down direction. It is better to show gape when market is sideways.
AlgoAlpha
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@SniperAIAlgoTest, Thank you for your feedback! We'll take that into account for future indicators.
Salamandras
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fajny, dziΔ™ki ;)
Kokiamoun213
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is it printing ?
AlgoAlpha
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@Kokiamoun213, Nope, all real time signals only πŸ‘
manna2020
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Thanks for your indicator. But can you explain what the very tiny green and red dots are for and how are they used?
AlgoAlpha
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@manna2020, They represent the unsmoothed data, which in some situations might be useful as the smoothing causes a certain degree of lag. This is an inevitable phenomenon for every form of smoothed data.
manna2020
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@AlgoAlpha, Thanks for quick response. How do we interpret them?
MΓ‘s