Bollinger bands and Keltner bands
█ OVERVIEW
This is a Bollinger band Script, with Keltner Channel Bands and for scalping.
█ CONCEPTS
1 — Bollinger Bands, The Bollinger band component is make out from two part, the standard component represented by a white cloud.
This is the 2nd deviation and 3rd deviation filled.
The second part linear weight
This is the 2nd deviation, and is filled from the 3rd deviation from the standard bands. This give the effect of cutting in and out to proved a support and resistance cloud.
2 — Keltner bands, this is the normal calculation, nothing special, by default this is off and can be turned back on in styles.
3 Deviations with ALMA as the Basis
3 — Non-traditional basis
Like with you can change the base moving average
but to a lesser extent.
option are:
sma = Simple Moving Average
ema = Exponential Moving Average
wma = Weighted Moving Average
vwma = Volume Weighted Moving Average
rma = Running Moving Average
alma = Arnaud Legoux Moving Average
hma = Hull Moving Average
█ OTHER SECTIONS
• LIMITATIONS: again this is base on moving average so there is a lag factor, the cloud do not work without volume so if the indicator can not pull volume data you are left with the standard bands.
• NOTE this is a trimmed version of still functions the same.
• THANKS: rumpypumpydumpy for the inspiration
Cari dalam skrip untuk "KELTNER"
BB and KeltnerStrategy using the Bollinger band with the Keltner Channels. Watch for the Bollinger bands (blue/red lines) squeezing inside the Keltner Channels (pink dots) for a breakout - this could be up or down so use in conjunction with other indicators.
Original Keltner with Support And ResistanceThis indicator is based on the original Keltner Channels using typical price and calculating the 10 period average of high - low
Typical price = (high + low + close)/3
In this case, I've taken Typical price as (open + high + low + close)/4 on the advice of John Bollinger from his book Bollinger on Bollinger Bands.
Buy Line = 10 Period Typical Price Average + 10 Period Average of (High - Low)
Sell Line = 10 Period Typical Price Average - 10 Period Average of (High - Low)
This is the basis for the indicator. I've added the highest of the Buy Line and lowest of the Sell Line for the same period which acts as Support and Resistance.
If price is trending below the Lowest of Sell Line, take only sell trades and the Lowest Line acts as resistance.
If price is trending above the Highest of Buy Line, take only buy trades and the Highest Line acts as support.
Stochastic + Keltner Channels for ScalpingSimple arrow indicator, indicating the direction go the next slight movement. This indicator will work on any time frame or market.
How does this indicator work?
It will use Stochastic and Keltner Channels to detect potential reversals depending on the frequency you choose in the indicator's settings. The higher the frequency, the fewer candles will be used in the calculation.
When to use this indicator?
It will work better in higher time frames for low volatility indicators. You can mix with other indicators like RSI or ADX. This way, you will be able to check if the time selected frame has enough volatility to move the price enough to cover the spreads and fees of your broker.
When to exit the trade after the signal from this indicator?
A good target would be for 1x ATR value and stop-loss 2x the ATR value. Doing trailing stop will reduce your risk and secure some profits, but make sure to use values for possible fakeouts
Can this indicator be used alone as the main source of entry signal for the trades?
You can use it alone, but I recommend mixing with other trend-based indicators, like Moving Averages, so you get the best results. Since it's for scalping purposes, small moments, and reversals, it doesn't have the trend filter, but it can work trading in favor of a significant trend as well
this is a better version of my other script Scalping Arrows
EMA Keltner Channel 1D100/200 EMAs, along with Keltner Bands based off them. Colors correspond to actions you should be ready to take in the area. Use to set macro mindset.
Uses the security function to display only the 1D values.
Red= Bad
Orange = Not as Bad, but still Bad.
Yellow = Warning, might also be Bad.
Purple = Dip a toe in.
Blue = Give it a shot but have a little caution.
Green = It's second mortgage time.
Zero Lag Keltner ChannelsThis is Keltner Channelz (KC) with Zero Lag Moving Average (ZLMA as base). It is smoother and has less lag than the original (EMA/SMA) variant.
It also can be used as a trend indicator and trend confirmation indicator. The upper and lower bands are green if it is an up trend, and red if a down trend. If both have the same color it is a stronger trend.
Bollinger Bands and Keltner Channel (BB and KC)Script that combines 2 Bollinger Bands (Std Dev = 1 and Std Dev = 2) with Keltner Channel (Std Dev = 1.5)
Keltner Channel Strategy by Kevin DaveyKeltner Channel Strategy Description
The Keltner Channel Strategy is a volatility-based trading approach that uses the Keltner Channel, a technical indicator derived from the Exponential Moving Average (EMA) and Average True Range (ATR). The strategy helps identify potential breakout or mean-reversion opportunities in the market by plotting upper and lower bands around a central EMA, with the channel width determined by a multiplier of the ATR.
Components:
1. Exponential Moving Average (EMA):
The EMA smooths price data by placing greater weight on recent prices, allowing traders to track the market’s underlying trend more effectively than a simple moving average (SMA). In this strategy, a 20-period EMA is used as the midline of the Keltner Channel.
2. Average True Range (ATR):
The ATR measures market volatility over a 14-period lookback. By calculating the average of the true ranges (the greatest of the current high minus the current low, the absolute value of the current high minus the previous close, or the absolute value of the current low minus the previous close), the ATR captures how much an asset typically moves over a given period.
3. Keltner Channel:
The upper and lower boundaries are set by adding or subtracting 1.5 times the ATR from the EMA. These boundaries create a dynamic range that adjusts with market volatility.
Trading Logic:
• Long Entry Condition: The strategy enters a long position when the closing price falls below the lower Keltner Channel, indicating a potential buying opportunity at a support level.
• Short Entry Condition: The strategy enters a short position when the closing price exceeds the upper Keltner Channel, signaling a potential selling opportunity at a resistance level.
The strategy plots the upper and lower Keltner Channels and the EMA on the chart, providing a visual representation of support and resistance levels based on market volatility.
Scientific Support for Volatility-Based Strategies:
The use of volatility-based indicators like the Keltner Channel is supported by numerous studies on price momentum and volatility trading. Research has shown that breakout strategies, particularly those leveraging volatility bands such as the Keltner Channel or Bollinger Bands, can be effective in capturing trends and reversals in both trending and mean-reverting markets  .
Who is Kevin Davey?
Kevin Davey is a highly respected algorithmic trader, author, and educator, known for his systematic approach to building and optimizing trading strategies. With over 25 years of experience in the markets, Davey has earned a reputation as an expert in quantitative and rule-based trading. He is particularly well-known for winning several World Cup Trading Championships, where he consistently demonstrated high returns with low risk.
Keltner Channel - APEXKeltner Channels are volatility-based bands built above and below an exponential moving average. This indicator is similar to Bollinger Bands but instead of Standart deviation of using the standard deviation uses the Average True Range (ATR) to set channel distance. The Keltner Channels it present for only allow EMA addition and EMA smoothing. Therefore I have taken the liberty to add those as default.
You can use the channels in its form to help you get into more volatile moves and thus getting out faster. I wanted to show an approach of screening area below a moving average by adding min max ATR. In Apex you will have to use 2 Keltner Channels with 2 Signals to build this up.
This feature will be available first in v1 APEX.
Keltner Channels MTFKeltner Channels MTF | Adapted 🌌
Navigate the market’s wild waves with these Keltner Channels, a sleek spin on AlchimistOfCrypto’s Bollinger Bands! This Pine Script v6 indicator tracks price action like a radar, highlighting trends with scientific precision. 🧪
Key Features:
Customizable Channels: Adjust period and multiplier to map market volatility, signaling potential reversals when prices hit the upper or lower bands. 📈
MA Options: Switch between Exponential or Simple Moving Average for trend clarity. ⚙️
Band Styles: Select Average True Range, True Range, or Range to define volatility edges. 📏
Glow Effect: Illuminate bands with 8 vibrant themes (Neon, Grayscale, etc.) for visual pop. ✨
Trend Signals: Spot bullish/bearish shifts with glowing circles, flagging momentum changes. 💡
Alerts: Catch price breakouts or trend reversals at band edges, warning of potential market U-turns. 🚨
Perfect for traders decoding market trends with a touch of cosmic style! 🌠
Keltner Channel + RSIKeltner Channel and RSI use together. They tell us when the trend will turn.
Fave fun!!
Simple Keltner ChannelsThis script is just a fun little project I decided to do. It serves as a way for me to practice my coding and was not made with the intent of making money.
Scalping Trading System bot Crypto and StocksThis is a trend trading strategy scalping bot that can work with any type of market. However I concluded my tests so far with Crypto, Stocks and Forex, and with optimizations always could be found some profitable settings.
Indicators
SImple Moving Average
Exponential Moving Average
Keltner Channels
MACD Histogram
Stochastics
Rules for entry
long= Close of the candle bigger than both moving averages and close of the candle is between the top and bot levels from Keltner. At the same time the macd histogram is negative and stochastic is below 50.
short= Close of the candle smaller than both moving averages and close of the candle is between the top and bot levels from Keltner. At the same time the macd histogram is positive and stochastic is above 50.
Rules for exit
We exit when we meet an opposite reverse order.
This strategy has no risk management inside, so use it with caution !
STD-Adaptive T3 Channel w/ Ehlers Swiss Army Knife Mod. [Loxx]STD-Adaptive T3 Channel w/ Ehlers Swiss Army Knife Mod. is an adaptive T3 indicator using standard deviation adaptivity and Ehlers Swiss Army Knife indicator to adjust the alpha value of the T3 calculation. This helps identify trends and reduce noise. In addition. I've included a Keltner Channel to show reversal/exhaustion zones.
What is the Swiss Army Knife Indicator?
John Ehlers explains the calculation here: www.mesasoftware.com
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Keltner Channel [LINKUSDT] 1HThis is a long-only strategy tested on LINK/USDT, 1 hour bar, from Feb 2019. The entry is determined by the breakout of upper Keltnel Channel and when the +DI is higher than 32. Instead of a fixed stop-loss from the original script , I change the exit to the middle band of the Keltnel Channel. 1st profit target will close 20% of the position. 2nd profit target will close 30% of the position. While the remaining 50% position will be closed when the price closes below the middle band of the Keltnel Channel, to take advantage of big trend. All parameters are adjustable. I added another option to enable or disable the ribbon trend filter.
My thoughts: For the same period, LINK appreciated 3000%. So I guess most in and out strategies couldn’t beat a buy and hold strategy during this period. But this doesn’t mean that this strategy is not feasible as each strategy is designed to only take advantage of a certain pattern or behavior of the market. Also, short term strategies allow you to use leverage and hence enable you to use you capital efficiently. Commission is set to 0.1%, taking account of the slippage.
Suggestion: Please perform walk forward analysis before you use real money for trading. Parameters need to be adjusted from time to time depends on your analysis. Can try using ATR for profit targets as over a longer term, the volatility might drop hence a high fixed % profit targets might not be realistic.
Any suggestions are welcome!
Mean Reversion Trading V1Overview
This is a simple mean reversion strategy that combines RSI, Keltner Channels, and MACD Histograms to predict reversals. Current parameters were optimized for NASDAQ 15M and performance varies depending on asset. The strategy can be optimized for specific asset and timeframe.
How it works
Long Entry (All must be true):
1. RSI < Lower Threshold
2. Close < Lower KC Band
3. MACD Histogram > 0 and rising
4. No open trades
Short Entry (All must be true):
1. RSI > Upper Threshold
2. Close > Upper KC Band
3. MACD Histogram < 0 and falling
4. No open trades
Long Exit:
1. Stop Loss: Average position size x ( 1 - SL percent)
2. Take Profit: Average position size x ( 1 + TP percent)
3. MACD Histogram crosses below zero
Short Exit:
1. Stop Loss: Average position size x ( 1 + SL percent)
2. Take Profit: Average position size x ( 1 - TP percent)
3. MACD Histogram crosses above zero
Settings and parameters are explained in the tooltips.
Important
Initial capital is set as 100,000 by default and 100 percent equity is used for trades
Keltner Hull Suite [QuantAlgo]🟢 Overview
The Keltner Hull Suite combines Hull Moving Average positioning with double-smoothed True Range banding to identify trend regimes and filter market noise. The indicator establishes upper and lower volatility bounds around the Hull MA, with the trend line conditionally updating only when price violates these boundaries. This mechanism distinguishes between genuine directional shifts and temporary price fluctuations, providing traders and investors with a systematic framework for trend identification that adapts to changing volatility conditions across multiple timeframes and asset classes.
🟢 How It Works
The calculation foundation begins with the Hull Moving Average, a weighted moving average designed to minimize lag while maintaining smoothness:
hullMA = ta.hma(priceSource, hullPeriod)
The indicator then calculates true range and applies dual exponential smoothing to create a volatility measure that responds more quickly to volatility changes than traditional ATR implementations while maintaining stability through the double-smoothing process:
tr = ta.tr(true)
smoothTR = ta.ema(tr, keltnerPeriod)
doubleSmooth = ta.ema(smoothTR, keltnerPeriod)
deviation = doubleSmooth * keltnerMultiplier
Dynamic support and resistance boundaries are constructed by applying the multiplier-scaled volatility deviation to the Hull MA, creating upper and lower bounds that expand during volatile periods and contract during consolidation:
upperBound = hullMA + deviation
lowerBound = hullMA - deviation
The trend line employs a conditional update mechanism that prevents premature trend reversals. The system maintains the current trend line until price action violates the respective boundary, at which point the trend line snaps to the violated bound:
if upperBound < trendLine
trendLine := upperBound
if lowerBound > trendLine
trendLine := lowerBound
Directional bias determination compares the current trend line value against its previous value, establishing bullish conditions when rising and bearish conditions when falling. Signal generation occurs on state transitions, triggering alerts when the trend state shifts from neutral or opposite direction:
trendUp = trendLine > trendLine
trendDown = trendLine < trendLine
longSignal = trendState == 1 and trendState != 1
shortSignal = trendState == -1 and trendState != -1
The visualization layer creates a trend band by plotting both the current trend line and a two-bar shifted version, with the area between them filled to create a visual channel that reinforces directional conviction.
🟢 How to Use This Indicator
▶ Long and Short Signals: The indicator generates long/buy signals when the trend state transitions to bullish (trend line begins rising) and short/sell signals when transitioning to bearish (trend line begins falling). These state changes represent structural shifts in momentum where price has broken through the adaptive volatility bands, confirming directional commitment.
▶ Trend Band Dynamics: The spacing between the main trend line and its shifted counterpart creates a visual band whose width reflects trend strength and momentum consistency. Expanding bands indicate accelerating directional movement and strong trend persistence, while contracting or flattening bands suggest decelerating momentum, potential trend exhaustion, or impending consolidation. Monitoring band width provides early warning of regime transitions from trending to range-bound conditions.
▶ Preconfigured Presets: Three optimized parameter sets accommodate different trading styles and timeframes. Default (14, 20, 2.0) provides balanced trend identification suitable for daily charts and swing trading, Fast Response (10, 14, 1.5) delivers aggressive signal generation optimized for intraday scalping and momentum trading on 1-15 minute timeframes, while Smooth Trend (18, 30, 2.5) offers conservative trend confirmation ideal for position trading on 4-hour to daily charts with enhanced noise filtration.
▶ Built-in Alerts: Three alert conditions enable automated monitoring - Bullish Trend Signal triggers on long setup confirmation, Bearish Trend Signal activates on short setup confirmation, and Trend Change alerts on any directional transition. These notifications allow you to respond to regime shifts without continuous chart monitoring.
▶ Color Customization: Five visual themes (Classic, Aqua, Cosmic, Ember, Neon, plus Custom) accommodate different chart backgrounds and display preferences, ensuring optimal contrast and visual clarity across trading environments.
Keltner Channels Linda RaschkeThis channel is a second version designed by the famous american analyst Linda Raschke; it allows you to find the main trend and to implement a trend-following strategy. You can open a new long/short position when the price cross up the basis and the upper/lower band (jointly with other indicators).
Keltner Channels Oscillator v3V2 with Bollinger Bands
Planning to make an STO/KCO hybrid and a model strategy for an all-in-one indicator.
Keltner Channel StrategyOverview
The Keltner Channel Strategy is a powerful trend-following and mean-reversion system that leverages the Keltner Channels, EMA crossovers, and ATR-based stop-losses to optimize trade entries and exits. This strategy has proven to be highly effective, particularly when applied to Gold (XAUUSD) and other commodities with strong trend characteristics.
📈 How It Works
This strategy incorporates two trading approaches: 1️⃣ Keltner Channel Reversal Trades – Identifies overbought and oversold conditions when price touches the outer bands.
2️⃣ Trend Following Trades – Uses the 9 EMA & 21 EMA crossover, with confirmation from the 50 EMA, to enter trades in the direction of the trend.
🔍 Entry & Exit Criteria
📊 Keltner Channel Entries (Reversal Strategy)
✅ Long Entry: When the price crosses below the lower Keltner Band (potential reversal).
✅ Short Entry: When the price crosses above the upper Keltner Band (potential reversal).
⏳ Exit Conditions:
Long positions close when price crosses back above the mid-band (EMA-based).
Short positions close when price crosses back below the mid-band (EMA-based).
📈 Trend Following Entries (Momentum Strategy)
✅ Long Entry: When the 9 EMA crosses above the 21 EMA, and price is above the 50 EMA (bullish momentum).
✅ Short Entry: When the 9 EMA crosses below the 21 EMA, and price is below the 50 EMA (bearish momentum).
⏳ Exit Conditions:
Long positions close when the 9 EMA crosses back below the 21 EMA.
Short positions close when the 9 EMA crosses back above the 21 EMA.
📌 Risk Management & Profit Targeting
ATR-based Stop-Losses:
Long trades: Stop set at 1.5x ATR below entry price.
Short trades: Stop set at 1.5x ATR above entry price.
Take-Profit Levels:
Long trades: Profit target 2x ATR above entry price.
Short trades: Profit target 2x ATR below entry price.
🚀 Why Use This Strategy?
✅ Works exceptionally well on Gold (XAUUSD) due to high volatility.
✅ Combines reversal & trend strategies for improved adaptability.
✅ Uses ATR-based risk management for dynamic position sizing.
✅ Fully automated alerts for trade entries and exits.
🔔 Alerts
This script includes automated TradingView alerts for:
🔹 Keltner Band touches (Reversal signals).
🔹 EMA crossovers (Momentum trades).
🔹 Stop-loss & Take-profit activations.
📊 Ideal Markets & Timeframes
Best for: Gold (XAUUSD), NASDAQ (NQ), Crude Oil (CL), and trending assets.
Recommended Timeframes: 15m, 1H, 4H, Daily.
⚡️ How to Use
1️⃣ Add this script to your TradingView chart.
2️⃣ Select a 15m, 1H, or 4H timeframe for optimal results.
3️⃣ Enable alerts to receive trade notifications in real time.
4️⃣ Backtest and tweak ATR settings to fit your trading style.
🚀 Optimize your Gold trading with this Keltner Channel Strategy! Let me know how it performs for you. 💰📊
Keltner Channel+EMA with Buy/Sell SignalsIndicator Name: Double Keltner Channel with EMA (Buy/Sell Signals)
Description:
This indicator is designed to help traders identify potential trend reversals and generate buy/sell signals in volatile markets. It combines two Keltner Channels with different sensitivities (multipliers of 2.6 and 3.8) to visualize dynamic support and resistance levels. The addition of a 20-period EMA helps confirm trend direction and filter out potential false signals.
How the Indicator Works:
• Keltner Channels: These bands dynamically adjust to changing market volatility, offering a visual representation of potential price ranges. The 2.6 multiplier Keltner Channel (KC) is more sensitive to price changes, potentially highlighting short-term reversals, while the 3.8 multiplier KC focuses on broader trend shifts.
• 20-period EMA: This widely used trend indicator helps smooth out price fluctuations and identify the underlying direction of the market.
• Buy Signals: Generated when a candle's low touches or crosses below either Keltner Channel's lower band, and within the next 6 candles, that same candle closes above the 20 EMA. This combination suggests a potential rejection of lower prices (support) and a possible resumption of the uptrend.
• Sell Signals: Mirror the buy signal logic but are triggered when the candle's high touches or crosses above either Keltner Channel's upper band and then closes below the 20 EMA within the next 6 candles. This indicates a potential rejection of higher prices (resistance) and a possible shift to a downtrend.
How to Use the Indicator:
1. Identify the Trend: Use the 20 EMA to determine the overall trend direction. Look for buy signals primarily in uptrends and sell signals in downtrends.
2. Confirm with RSI : While not included in this indicator, consider using a separate Relative Strength Index (RSI) with a length of 10, SMA type, MA length of 14, and standard deviation of 2. Look for oversold conditions (RSI below 20) to confirm buy signals and overbought conditions (RSI above 80) to confirm sell signals.
3.Apply Risk Management: Always use appropriate risk management techniques, such as stop-loss orders, to protect your capital.
Key Points:
• This indicator is most effective in trending markets.
• It is not a standalone trading system and should be used in conjunction with other analysis tools and confirmation.
• The Keltner Channel multiplier values can be adjusted to suit your trading style and risk tolerance.
Important Disclaimer:
This indicator is a modification of the original Keltner Channel code and is intended for educational and informational purposes only.
It does not constitute financial advice. Always conduct your own research and consult with a qualified financial advisor before making any investment decisions.
