Moving Average

Moving Average Convergence Divergence
Definition: A customized momentum indicator that measures the convergence/divergence of a short length trend line and long length trend line.
Syntax: macd(shortLength, longLength)
Arguments
shortLength: The number of previous values in calculating for short trend.
longLength: The number of previous values in calculating the long trend.
Return: Time series measuring convergence/divergence
Description: The Moving Average Convergence Divergence (MACD) is a popular technical indicator that measures the relationship between two moving averages of an asset's price. It is calculated by subtracting the 26-period exponential moving average (EMA) from the 12-period EMA. The MACD line can be used to identify trend changes, momentum, and potential buy and sell signals.
Example
macdValue = BTC.close -> macd(15, 30)
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Adaptive Average
Syntax: adma(windowSize, longLength, shortLength)
Definition: Calculates the adaptive moving average, which is a moving average that adjusts the smoothing factor based on market volatility.
Arguments:
windowSize: The number of previous values to use in the calculation.
longLength: The number of previous values to use for the long trend line calculation.
shortLength: The number of previous values to use for the short trend line calculation.
Return: Time series of adaptive moving average values
Description: The Adaptive Average is a technical indicator that adjusts the smoothing factor of a moving average based on the volatility of the asset's price. It uses the Efficiency Ratio (ER) to calculate the optimal period for the moving average, which can help to reduce lag and improve accuracy in volatile markets. Traders can use the Adaptive Average to identify trend changes and potential buy and sell signals.
Example:
avgAdaptiveValue = BTC.close -> adma(10, 26, 12)
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Simple Moving Average
Syntax: sma(length)
Arguments: length = the number of previous values used in the calculation
Return: A time series of smoothed values
Description: Simple moving average (SMA) is a widely used technical indicator that calculates the average price of an asset over a specific time period. It is a lagging indicator, meaning it responds slowly to changes in price. Traders use SMAs to identify trends and potential buy and sell signals, with longer periods (e.g., 50-day SMA) used for long-term trends and shorter periods (e.g., 10-day SMA) used for short-term trends.
Example:
avgSimpleValue = BTC.close -> sma(30)
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Linearly Weighted Moving Average
Definition: Moving average, where the most recent values are weighted more heavily than earlier values, with weights decreasing linearly.
Syntax: wma(length)
Arguments: length = the number of previous values used in the calculation
Return: A time series of smoothed values
Description: Linearly Weighted Moving Average (LWMA) is a technical indicator that assigns more weight to recent prices than past prices, giving it a greater responsiveness to current market conditions. It is calculated by multiplying each price point by a specific weight, with the weights increasing linearly over time. Traders use the LWMA to identify trends and potential buy and sell signals, with shorter periods (e.g., 5-day LWMA) used for short-term trends and longer periods (e.g., 50-day LWMA) used for long-term trends.
Example:
avgWeightedValue = BTC.close -> wma(20)
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Exponential Moving Average
Definition: Moving average that applies more weight to the most recent values, with weights decreasing exponentially.
Syntax: ema(length)
Arguments: length = weighting value used in the calculation
Return: A time series of smoothed values
Description: The Exponential Moving Average (EMA) is a technical indicator that gives more weight to recent prices than past prices, making it more responsive to current market conditions than the Simple Moving Average (SMA). It is calculated by using a smoothing factor that emphasizes recent data points more heavily.
Example:
avgExponentialValue = BTC.close -> ema(20)
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Triple Exponential Moving Average
Syntax ema3x(fastLength, slowLength, innerLength)
Arguments
fastLength: the length of the exponential moving average with the shorter lookback
slowLength: the length of the exponential moving average with the longer lookback
innerLength: the length of the exponential moving average of the double exponential moving average
Return A time series of smoothed values
Description A technical indicator that uses a triple smoothing technique to reduce lag and improve accuracy. It is calculated by taking the difference between a single exponential moving average and a double exponential moving average, which is then added to the double exponential moving average.
Example
tripleXavgValue =  BTC.close -> ema3x(10, 26, 12)
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Hull Moving Average
Syntax: hma(length)
Arguments: length = the number of previous values used in the calculation
Return: A time series of smoothed values
Description: Calculated as wavg(2*wavg(length/2) − wavg(length)), sqrt(length)).
Example:
hullValue = BTC.close -> hma(40)
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Tilson Moving Average
Definition: Tilson moving average, also known as generalized double exponential moving average.
Syntax: tilson(length, volumeFactor)
Arguments:
length: the number of previous values used in the calculation
volumeFactor: relative weighting of exponential and double exponential components
Return: A time series of smoothed values
Description: Calculated as: ema(length)*(1+volumeFactor) - ema(ema(length),length)*volumeFactor
Example:
tilsonValue = BTC.close -> tilson(40, 0.7)
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T3 Moving Average
Definition: T3 moving average, from three applications of Tilson moving average.
Syntax: t3(length)
Arguments: length = the number of previous values used in the calculation
Return: A time series of smoothed values
Description: Calculated with three applications of Tilson, using volumeFactor of 0.7: tilson(tilson(tilson(length, 0.7), length, 0.7), length, 0.7)
Example:
t3Value = BTC.close -> t3(40)
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Combine List
Definition: Combines values from a list of input time series. The values from all inputs at each point in time are combined using a specified calculation.
Syntax: combineLists(combine)
Arguments: combine - what calculation to use when combining values
Return: Single time series of combined values from each point in time
Description: Supported combination methods are:
avg, mean - value is the average of values from inputs
max - value is the maximum of values from inputs
min - value is the minimum of values from inputs
sum - value is the sum of values from inputs
The default value is combine=”avg”.
Example:
combined_values = [BTC, ETH, SOL].close -> combine_lists(combine=”max”)
Last modified 8d ago