doc: add description about indicators generated by chatgpt

pkg/indicator/alma.go

@@ -10,6 +10,14 @@ import (
 // Refer: Arnaud Legoux Moving Average
 // Refer: https://capital.com/arnaud-legoux-moving-average
 // Also check https://github.com/DaveSkender/Stock.Indicators/blob/main/src/a-d/Alma/Alma.cs
+//
+// The Arnaud Legoux Moving Average (ALMA) is a technical analysis indicator that is used to smooth price data and reduce the lag associated
+// with traditional moving averages. It was developed by Arnaud Legoux and is based on the weighted moving average, with the weighting factors
+// determined using a Gaussian function. The ALMA is calculated by taking the weighted moving average of the input data using weighting factors
+// that are based on the standard deviation of the data and the specified length of the moving average. This resulting average is then plotted
+// on the price chart as a line, which can be used to make predictions about future price movements. The ALMA is typically more responsive to
+// changes in the underlying data than a simple moving average, but may be less reliable in trending markets.
+//
 // @param offset: Gaussian applied to the combo line. 1->ema, 0->sma
 // @param sigma: the standard deviation applied to the combo line. This makes the combo line sharper
 //go:generate callbackgen -type ALMA

pkg/indicator/atrp.go

@@ -11,6 +11,14 @@ import (
 // ATRP is the average true range percentage
 // See also https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atrp
 //
+// The Average True Range Percentage (ATRP) is a technical analysis indicator that measures the volatility of a security's price. It is
+// calculated by dividing the Average True Range (ATR) of the security by its closing price, and then multiplying the result by 100 to convert
+// it to a percentage. The ATR is a measure of the range of a security's price, taking into account gaps between trading periods and any limit
+// moves (sharp price movements that are allowed under certain exchange rules). The ATR is typically smoothed using a moving average to make it
+// more responsive to changes in the underlying price data. The ATRP is a useful indicator for traders because it provides a way to compare the
+// volatility of different securities, regardless of their individual prices. It can also be used to identify potential entry and exit points
+// for trades based on changes in the security's volatility.
+//
 // Calculation:
 //
 //     ATRP = (Average True Range / Close) * 100

pkg/indicator/cci.go

@@ -10,6 +10,15 @@ import (
 // Refer: Commodity Channel Index
 // Refer URL: http://www.andrewshamlet.net/2017/07/08/python-tutorial-cci
 // with modification of ddof=0 to let standard deviation to be divided by N instead of N-1
+//
+// The Commodity Channel Index (CCI) is a technical analysis indicator that is used to identify potential overbought or oversold conditions
+// in a security's price. It was originally developed for use in commodity markets, but can be applied to any security that has a sufficient
+// amount of price data. The CCI is calculated by taking the difference between the security's typical price (the average of its high, low, and
+// closing prices) and its moving average, and then dividing the result by the mean absolute deviation of the typical price. This resulting value
+// is then plotted as a line on the price chart, with values above +100 indicating overbought conditions and values below -100 indicating
+// oversold conditions. The CCI can be used by traders to identify potential entry and exit points for trades, or to confirm other technical
+// analysis signals.
+
 //go:generate callbackgen -type CCI
 type CCI struct {
 	types.SeriesBase

pkg/indicator/dema.go

@@ -7,6 +7,13 @@ import (
 
 // Refer: Double Exponential Moving Average
 // Refer URL: https://investopedia.com/terms/d/double-exponential-moving-average.asp
+//
+// The Double Exponential Moving Average (DEMA) is a technical analysis indicator that is used to smooth price data and reduce the lag
+// associated with traditional moving averages. It is calculated by taking the exponentially weighted moving average of the input data,
+// and then taking the exponentially weighted moving average of that result. This double-smoothing process helps to eliminate much of the noise
+// in the original data and provides a more accurate representation of the underlying trend. The DEMA line is then plotted on the price chart,
+// which can be used to make predictions about future price movements. The DEMA is typically more responsive to changes in the underlying data
+// than a simple moving average, but may be less reliable in trending markets.
 
 //go:generate callbackgen -type DEMA
 type DEMA struct {

pkg/indicator/dmi.go

@@ -10,8 +10,15 @@ import (
 // Refer: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/trend/adx.py
 //
 // Directional Movement Index
-// an indicator developed by J. Welles Wilder in 1978 that identifies in which
-// direction the price of an asset is moving.
+//
+// The Directional Movement Index (DMI) is a technical analysis indicator that is used to identify the direction and strength of a trend
+// in a security's price. It was developed by J. Welles Wilder and is based on the concept of the +DI and -DI lines, which measure the strength
+// of upward and downward price movements, respectively. The DMI is calculated by taking the difference between the +DI and -DI lines, and then
+// smoothing the result using a moving average. This resulting line is called the Average Directional Index (ADX), and is used to identify whether
+// a security is trending or not. If the ADX is above a certain threshold, typically 20, it indicates that the security is in a strong trend,
+// and if it is below that threshold it indicates that the security is in a sideways or choppy market. The DMI can be used by traders to confirm
+// the direction and strength of a trend, or to identify potential entry and exit points for trades.
+
 //go:generate callbackgen -type DMI
 type DMI struct {
 	types.IntervalWindow

pkg/indicator/drift.go

@@ -10,6 +10,14 @@ import (
 // Refer: https://tradingview.com/script/aDymGrFx-Drift-Study-Inspired-by-Monte-Carlo-Simulations-with-BM-KL/
 // Brownian Motion's drift factor
 // could be used in Monte Carlo Simulations
+//
+// In the context of Brownian motion, drift can be measured by calculating the simple moving average (SMA) of the logarithm
+// of the price changes of a security over a specified period of time. This SMA can be used to identify the long-term trend
+// or bias in the random movement of the security's price. A security with a positive drift is said to be trending upwards,
+// while a security with a negative drift is said to be trending downwards. Drift can be used by traders to identify potential
+// entry and exit points for trades, or to confirm other technical analysis signals.
+// It is typically used in conjunction with other indicators to provide a more comprehensive view of the security's price.
+
 //go:generate callbackgen -type Drift
 type Drift struct {
 	types.SeriesBase

pkg/indicator/emv.go

@@ -6,6 +6,11 @@ import (
 
 // Refer: Ease of Movement
 // Refer URL: https://www.investopedia.com/terms/e/easeofmovement.asp
+// The Ease of Movement (EOM) is a technical analysis indicator that is used to measure the relationship between the volume of a security
+// and its price movement. It is calculated by dividing the difference between the high and low prices of the security by the total
+// volume of the security over a specified period of time, and then multiplying the result by a constant factor. This resulting value is
+// then plotted on the price chart as a line, which can be used to make predictions about future price movements. The EOM is typically
+// used to identify periods of high or low trading activity, and can be used to confirm other technical analysis signals.
 
 //go:generate callbackgen -type EMV
 type EMV struct {

pkg/indicator/fisher.go

@@ -7,6 +7,15 @@ import (
 	"github.com/c9s/bbgo/pkg/types"
 )
 
+// Fisher Transform
+//
+// The Fisher Transform is a technical analysis indicator that is used to identify potential turning points in the price of a security.
+// It is based on the idea that prices tend to be normally distributed, with most price movements being small and relatively insignificant.
+// The Fisher Transform converts this normal distribution into a symmetrical, Gaussian distribution, with a peak at zero and a range of -1 to +1.
+// This transformation allows for more accurate identification of price extremes, which can be used to make predictions about potential trend reversals.
+// The Fisher Transform is calculated by taking the natural logarithm of the ratio of the security's current price to its moving average,
+// and then double-smoothing the result. This resulting line is called the Fisher Transform line, and can be plotted on the price chart
+// along with the security's price.
 //go:generate callbackgen -type FisherTransform
 type FisherTransform struct {
 	types.SeriesBase

pkg/indicator/gma.go

@@ -7,6 +7,13 @@ import (
 )
 
 // Geometric Moving Average
+//
+// The Geometric Moving Average (GMA) is a technical analysis indicator that uses the geometric mean of a set of data points instead of
+// the simple arithmetic mean used by traditional moving averages. It is calculated by taking the nth root of the product of the last n
+// data points, where n is the length of the moving average. The resulting average is then plotted on the price chart as a line, which can
+// be used to make predictions about future price movements. Because the GMA gives more weight to recent data points, it is typically more
+// responsive to changes in the underlying data than a simple moving average.
+
 //go:generate callbackgen -type GMA
 type GMA struct {
 	types.SeriesBase

pkg/indicator/hull.go

@@ -8,6 +8,12 @@ import (
 
 // Refer: Hull Moving Average
 // Refer URL: https://fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/hull-moving-average
+//
+// The Hull Moving Average (HMA) is a technical analysis indicator that uses a weighted moving average to reduce the lag in simple moving averages.
+// It was developed by Alan Hull, who sought to create a moving average that was both fast and smooth. The HMA is calculated by first taking
+// the weighted moving average of the input data using a weighting factor of W, where W is the square root of the length of the moving average.
+// The result is then double-smoothed by taking the weighted moving average of this result using a weighting factor of W/2. This final average
+// forms the HMA line, which can be used to make predictions about future price movements.
 //go:generate callbackgen -type HULL
 type HULL struct {
 	types.SeriesBase

pkg/indicator/macd.go

@@ -7,13 +7,17 @@ import (
 	"github.com/c9s/bbgo/pkg/types"
 )
 
-/*
-macd implements moving average convergence divergence indicator
+// macd implements moving average convergence divergence indicator
+//
+// Moving Average Convergence Divergence (MACD)
+// - https://www.investopedia.com/terms/m/macd.asp
+// - https://school.stockcharts.com/doku.php?id=technical_indicators:macd-histogram
+// The Moving Average Convergence Divergence (MACD) is a technical analysis indicator that is used to measure the relationship between
+// two moving averages of a security's price. It is calculated by subtracting the longer-term moving average from the shorter-term moving
+// average, and then plotting the resulting value on the price chart as a line. This line is known as the MACD line, and is typically
+// used to identify potential buy or sell signals. The MACD is typically used in conjunction with a signal line, which is a moving average
+// of the MACD line, to generate more accurate buy and sell signals.
 
-Moving Average Convergence Divergence (MACD)
-- https://www.investopedia.com/terms/m/macd.asp
-- https://school.stockcharts.com/doku.php?id=technical_indicators:macd-histogram
-*/
 type MACDConfig struct {
 	types.IntervalWindow // 9
 

pkg/indicator/obv.go

@@ -12,6 +12,13 @@ obv implements on-balance volume indicator
 
 On-Balance Volume (OBV) Definition
 - https://www.investopedia.com/terms/o/onbalancevolume.asp
+
+On-Balance Volume (OBV) is a technical analysis indicator that uses volume information to predict changes in stock price.
+The idea behind OBV is that volume precedes price: when the OBV is rising, it means that buyers are becoming more aggressive and
+that the stock price is likely to follow suit. When the OBV is falling, it indicates that sellers are becoming more aggressive and
+that the stock price is likely to decrease. OBV is calculated by adding the volume on days when the stock price closes higher and
+subtracting the volume on days when the stock price closes lower. This running total forms the OBV line, which can then be used
+to make predictions about future stock price movements.
 */
 //go:generate callbackgen -type OBV
 type OBV struct {

pkg/indicator/rma.go

@@ -13,6 +13,14 @@ const MaxNumOfRMATruncateSize = 500
 // Running Moving Average
 // Refer: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/overlap/rma.py#L5
 // Refer: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.ewm.html#pandas-dataframe-ewm
+//
+// The Running Moving Average (RMA) is a technical analysis indicator that is used to smooth price data and reduce the lag associated
+// with traditional moving averages. It is calculated by taking the weighted moving average of the input data, with the weighting factors
+// determined by the length of the moving average. The resulting average is then plotted on the price chart as a line, which can be used to
+// make predictions about future price movements. The RMA is typically more responsive to changes in the underlying data than a simple
+// moving average, but may be less reliable in trending markets. It is often used in conjunction with other technical analysis indicators
+// to confirm signals or provide additional information about the security's price.
+
 //go:generate callbackgen -type RMA
 type RMA struct {
 	types.SeriesBase

pkg/indicator/rsi.go

@@ -8,11 +8,16 @@ import (
 	"github.com/c9s/bbgo/pkg/types"
 )
 
-/*
-rsi implements Relative Strength Index (RSI)
+// rsi implements Relative Strength Index (RSI)
+// https://www.investopedia.com/terms/r/rsi.asp
+//
+// The Relative Strength Index (RSI) is a technical analysis indicator that is used to measure the strength of a security's price. It is
+// calculated by taking the average of the gains and losses of the security over a specified period of time, and then dividing the average gain
+// by the average loss. This resulting value is then plotted as a line on the price chart, with values above 70 indicating overbought conditions
+// and values below 30 indicating oversold conditions. The RSI can be used by traders to identify potential entry and exit points for trades,
+// or to confirm other technical analysis signals. It is typically used in conjunction with other indicators to provide a more comprehensive
+// view of the security's price.
 
-https://www.investopedia.com/terms/r/rsi.asp
-*/
 //go:generate callbackgen -type RSI
 type RSI struct {
 	types.SeriesBase

pkg/indicator/stoch.go

@@ -9,12 +9,16 @@ import (
 
 const DPeriod int = 3
 
-/*
-stoch implements stochastic oscillator indicator
+// Stochastic Oscillator
+// - https://www.investopedia.com/terms/s/stochasticoscillator.asp
+//
+// The Stochastic Oscillator is a technical analysis indicator that is used to identify potential overbought or oversold conditions
+// in a security's price. It is calculated by taking the current closing price of the security and comparing it to the high and low prices
+// over a specified period of time. This comparison is then plotted as a line on the price chart, with values above 80 indicating overbought
+// conditions and values below 20 indicating oversold conditions. The Stochastic Oscillator can be used by traders to identify potential
+// entry and exit points for trades, or to confirm other technical analysis signals. It is typically used in conjunction with other indicators
+// to provide a more comprehensive view of the security's price.
 
-Stochastic Oscillator
-- https://www.investopedia.com/terms/s/stochasticoscillator.asp
-*/
 //go:generate callbackgen -type STOCH
 type STOCH struct {
 	types.IntervalWindow

pkg/indicator/supertrend.go

@@ -12,6 +12,17 @@ import (
 
 var logst = logrus.WithField("indicator", "supertrend")
 
+// The Super Trend is a technical analysis indicator that is used to identify potential buy and sell signals in a security's price. It is
+// calculated by combining the exponential moving average (EMA) and the average true range (ATR) of the security's price, and then plotting
+// the resulting value on the price chart as a line. The Super Trend line is typically used to identify potential entry and exit points
+// for trades, and can be used to confirm other technical analysis signals. It is typically more responsive to changes in the underlying
+// data than other trend-following indicators, but may be less reliable in trending markets. It is important to note that the Super Trend is a
+// lagging indicator, which means that it may not always provide accurate or timely signals.
+//
+// To use Super Trend, identify potential entry and exit points for trades by looking for crossovers or divergences between the Super Trend line
+// and the security's price. For example, a buy signal may be generated when the Super Trend line crosses above the security's price, while a sell
+// signal may be generated when the Super Trend line crosses below the security's price.
+
 //go:generate callbackgen -type Supertrend
 type Supertrend struct {
 	types.SeriesBase

pkg/indicator/tema.go

@@ -7,6 +7,14 @@ import (
 
 // Refer: Triple Exponential Moving Average (TEMA)
 // URL: https://investopedia.com/terms/t/triple-exponential-moving-average.asp
+//
+// The Triple Exponential Moving Average (TEMA) is a technical analysis indicator that is used to smooth price data and reduce the lag
+// associated with traditional moving averages. It is calculated by taking the exponentially weighted moving average of the input data,
+// and then taking the exponentially weighted moving average of that result, and then taking the exponentially weighted moving average of
+// that result. This triple-smoothing process helps to eliminate much of the noise in the original data and provides a more accurate
+// representation of the underlying trend. The TEMA line is then plotted on the price chart, which can be used to make predictions about
+// future price movements. The TEMA is typically more responsive to changes in the underlying data than a simple moving average, but may be
+// less reliable in trending markets.
 
 //go:generate callbackgen -type TEMA
 type TEMA struct {

pkg/indicator/till.go

@@ -8,6 +8,15 @@ const defaultVolumeFactor = 0.7
 
 // Refer: Tillson T3 Moving Average
 // Refer URL: https://tradingpedia.com/forex-trading-indicator/t3-moving-average-indicator/
+//
+// The Tillson T3 Moving Average (T3) is a technical analysis indicator that is used to smooth price data and reduce the lag associated
+// with traditional moving averages. It was developed by Tim Tillson and is based on the exponential moving average, with the weighting
+// factors determined using a modified version of the cubic polynomial. The T3 is calculated by taking the weighted moving average of the
+// input data using weighting factors that are based on the standard deviation of the data and the specified length of the moving average.
+// This resulting average is then plotted on the price chart as a line, which can be used to make predictions about future price movements.
+// The T3 is typically more responsive to changes in the underlying data than a simple moving average, but may be less reliable in trending
+// markets.
+
 //go:generate callbackgen -type TILL
 type TILL struct {
 	types.SeriesBase

pkg/indicator/vidya.go

@@ -9,6 +9,13 @@ import (
 
 // Refer: Variable Index Dynamic Average
 // Refer URL: https://metatrader5.com/en/terminal/help/indicators/trend_indicators/vida
+// The Variable Index Dynamic Average (VIDYA) is a technical analysis indicator that is used to smooth price data and reduce the lag
+// associated with traditional moving averages. It is calculated by taking the weighted moving average of the input data, with the
+// weighting factors determined using a variable index that is based on the standard deviation of the data and the specified length of
+// the moving average. This resulting average is then plotted on the price chart as a line, which can be used to make predictions about
+// future price movements. The VIDYA is typically more responsive to changes in the underlying data than a simple moving average, but may
+// be less reliable in trending markets.
+
 //go:generate callbackgen -type VIDYA
 type VIDYA struct {
 	types.SeriesBase

pkg/indicator/volumeprofile.go

@@ -0,0 +1,102 @@
+package indicator
+
+import (
+	"math"
+	"time"
+
+	"github.com/c9s/bbgo/pkg/types"
+)
+
+type trade struct {
+	price     float64
+	volume    float64 // +: buy, -: sell
+	timestamp types.Time
+}
+
+// The Volume Profile is a technical analysis tool that is used to visualize the distribution of trading volume at different price levels
+// in a security. It is typically plotted as a histogram or heatmap on the price chart, with the x-axis representing the price levels and
+// the y-axis representing the trading volume. The Volume Profile can be used to identify areas of support and resistance, as well as
+// potential entry and exit points for trades.
+type VolumeProfile struct {
+	types.IntervalWindow
+	Delta    float64
+	profile  map[float64]float64
+	trades   []trade
+	minPrice float64
+	maxPrice float64
+}
+
+func (inc *VolumeProfile) Update(price, volume float64, timestamp types.Time) {
+	if inc.minPrice == 0 {
+		inc.minPrice = math.Inf(1)
+	}
+	if inc.maxPrice == 0 {
+		inc.maxPrice = math.Inf(-1)
+	}
+	inc.profile[math.Round(price/inc.Delta)] += volume
+	filter := timestamp.Time().Add(-time.Duration(inc.Window) * inc.Interval.Duration())
+	var i int
+	for i = 0; i < len(inc.trades); i++ {
+		td := inc.trades[i]
+		if td.timestamp.After(filter) {
+			break
+		}
+		inc.profile[math.Round(td.price/inc.Delta)] -= td.volume
+	}
+	inc.trades = inc.trades[i : len(inc.trades)-1]
+	inc.trades = append(inc.trades, trade{
+		price:     price,
+		volume:    volume,
+		timestamp: timestamp,
+	})
+	for k, _ := range inc.profile {
+		if k < inc.minPrice {
+			inc.minPrice = k
+		}
+		if k > inc.maxPrice {
+			inc.maxPrice = k
+		}
+	}
+}
+
+// The Point of Control (POC) is a term used in the context of Volume Profile analysis. It refers to the price level at which the most
+// volume has been traded in a security over a specified period of time. The POC is typically identified by looking for the highest
+// peak in the Volume Profile, and is considered to be an important level of support or resistance. It can be used by traders to
+// identify potential entry and exit points for trades, or to confirm other technical analysis signals.
+
+// Get Resistence Level by finding PoC
+func (inc *VolumeProfile) PointOfControlAboveEqual(price float64, limit ...float64) (resultPrice float64, vol float64) {
+	filter := inc.maxPrice
+	if len(limit) > 0 {
+		filter = limit[0]
+	}
+	start := math.Round(price / inc.Delta)
+	vol = math.Inf(-1)
+	for ; start <= filter; start += inc.Delta {
+		abs := math.Abs(inc.profile[start])
+		if vol < abs {
+			vol = abs
+			resultPrice = start
+		}
+
+	}
+	return resultPrice, vol
+}
+
+// Get Support Level by finding PoC
+func (inc *VolumeProfile) PointOfControlBelowEqual(price float64, limit ...float64) (resultPrice float64, vol float64) {
+	filter := inc.minPrice
+	if len(limit) > 0 {
+		filter = limit[0]
+	}
+	start := math.Round(price / inc.Delta)
+	vol = math.Inf(-1)
+	for ; start >= filter; start -= inc.Delta {
+		abs := math.Abs(inc.profile[start])
+		if vol < abs {
+			vol = abs
+			resultPrice = start
+		}
+	}
+	return resultPrice, vol
+}

pkg/indicator/vwap.go

@@ -7,15 +7,21 @@ import (
 	"github.com/c9s/bbgo/pkg/types"
 )
 
-/*
-vwap implements the volume weighted average price (VWAP) indicator:
+// vwap implements the volume weighted average price (VWAP) indicator:
+//
+// Volume Weighted Average Price (VWAP) Definition
+// - https://www.investopedia.com/terms/v/vwap.asp
+//
+// Volume-Weighted Average Price (VWAP) Explained
+// - https://academy.binance.com/en/articles/volume-weighted-average-price-vwap-explained
+//
+// The Volume Weighted Average Price (VWAP) is a technical analysis indicator that is used to measure the average price of a security
+// over a specified period of time, with the weighting factors determined by the volume of the security. It is calculated by taking the
+// sum of the product of the price and volume for each trade, and then dividing that sum by the total volume of the security over the
+// specified period of time. This resulting average is then plotted on the price chart as a line, which can be used to make predictions
+// about future price movements. The VWAP is typically more accurate than other simple moving averages, as it takes into account the
+// volume of the security, but may be less reliable in markets with low trading volume.
 
-Volume Weighted Average Price (VWAP) Definition
-- https://www.investopedia.com/terms/v/vwap.asp
-
-Volume-Weighted Average Price (VWAP) Explained
-- https://academy.binance.com/en/articles/volume-weighted-average-price-vwap-explained
-*/
 //go:generate callbackgen -type VWAP
 type VWAP struct {
 	types.SeriesBase

pkg/indicator/vwma.go

@@ -7,16 +7,21 @@ import (
 	"github.com/c9s/bbgo/pkg/types"
 )
 
-/*
-vwma implements the volume weighted moving average (VWMA) indicator:
+// vwma implements the volume weighted moving average (VWMA) indicator:
+//
+// Calculation:
+//	pv = element-wise multiplication of close prices and volumes
+//	VWMA = SMA(pv, window) / SMA(volumes, window)
+//
+// Volume Weighted Moving Average
+//- https://www.motivewave.com/studies/volume_weighted_moving_average.htm
+//
+// The Volume Weighted Moving Average (VWMA) is a technical analysis indicator that is used to smooth price data and reduce the lag
+// associated with traditional moving averages. It is calculated by taking the weighted moving average of the input data, with the
+// weighting factors determined by the volume of the security. This resulting average is then plotted on the price chart as a line,
+// which can be used to make predictions about future price movements. The VWMA is typically more accurate than other simple moving
+// averages, as it takes into account the volume of the security, but may be less reliable in markets with low trading volume.
 
-Calculation:
-	pv = element-wise multiplication of close prices and volumes
-	VWMA = SMA(pv, window) / SMA(volumes, window)
-
-Volume Weighted Moving Average
-- https://www.motivewave.com/studies/volume_weighted_moving_average.htm
-*/
 //go:generate callbackgen -type VWMA
 type VWMA struct {
 	types.SeriesBase
@@ -79,7 +84,6 @@ func (inc *VWMA) PushK(k types.KLine) {
 	inc.Update(k.Close.Float64(), k.Volume.Float64())
 }
 
-
 func (inc *VWMA) CalculateAndUpdate(allKLines []types.KLine) {
 	if len(allKLines) < inc.Window {
 		return

pkg/indicator/zlema.go

@@ -7,6 +7,12 @@ import (
 
 // Refer: Zero Lag Exponential Moving Average
 // Refer URL: https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
+//
+// The Zero Lag Exponential Moving Average (ZLEMA) is a technical analysis indicator that is used to smooth price data and reduce the
+// lag associated with traditional moving averages. It is calculated by taking the exponentially weighted moving average of the input
+// data, and then applying a digital filter to the resulting average to eliminate any remaining lag. This filtered average is then
+// plotted on the price chart as a line, which can be used to make predictions about future price movements. The ZLEMA is typically more
+// responsive to changes in the underlying data than a simple moving average, but may be less reliable in trending markets.
 
 //go:generate callbackgen -type ZLEMA
 type ZLEMA struct {