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[bjb] tEMAs - Triple Exponential Moving Averages, by BlueJayBirdIs it not a pain in the butt not to find a simple script like this one in TV? I didn't find one, so I made it. Features: - Simple script with 3 simple Exponential Moving Averages. Special features: - Settable time resolution. - Colored fill between EMA20/50 pair, and between EMA50/200 pair. More useful than you think: - I've found it specially useful for detecting Elliot Wave patterns. The fill colors make them stand out. Use EMA20/50 pair for this. - The resolution change makes EMAs studies from higher timeframes, at lower time frames, more accurate. Try from 1m timeframe, setting the tEMAs resolution to 1h. Like, follow and comment. 💯
Penunjuk Pine Script®
oleh BlueJayBird
Fibonacci Moving Averages [UkutaLabs]█ OVERVIEW The Fibonacci Moving Averages are a toolkit which allows the user to configure different types of Moving Averages based on key Fibonacci numbers. Moving Averages are used to visualise short-term and long-term support and resistance which can be used as a signal where price might continue or retrace. Moving Averages serve as a simple yet powerful tool that can help traders in their decision-making and help foster a sense of where the price might be moving next. The aim of this script is to simplify the trading experience of users by automatically displaying a series of useful Moving Averages, allowing the user to easily configure multiple at once depending on their trading style. █ USAGE This script will automatically plot 5 Moving Averages, each with a period of a key Fibonacci Level (5, 8, 13, 21 and 34). Both the Source and Type of the Moving Averages can be configured by the user (see all options below under SETTINGS), making this a versatile trading tool that can provide value in a wide variety of trading styles. █ SETTINGS Configuration • MA Source: Determines the source of the Moving Averages (open, high, low, close, hl2, hlc3, ohlc4, hlcc4) • MA Source: Determines the type of the Moving Averages (SMA, EMA, VWMA, WMA, HMA, RMA) Colors • 5: Determines the color of the 5 period Moving Average • 8: Determines the color of the 8 period Moving Average • 13: Determines the color of the 13 period Moving Average • 21: Determines the color of the 21 period Moving Average • 34: Determines the color of the 34 period Moving Average
Penunjuk Pine Script®
oleh UkutaLabs
Parametric Corrective Linear Moving AveragesImpulse responses can fully describe their associated systems, for example a linearly weighted moving average (WMA) has a linearly decaying impulse response, therefore we can deduce that lag is reduced since recent values are the ones with the most weights, the Blackman moving average (or Blackman filter) has a bell shaped impulse response, that is mid term values are the ones with the most weights, we can deduce that such moving average is pretty smooth, the least squares moving average has negative weights, we can therefore deduce that it aim to heavily reduce lag, and so on. We could even estimate the lag of a moving average by looking at its impulse response (calculating the lag of a moving average is the aim of my next article with Pinescripters) . Today a new moving average is presented, such moving average use a parametric rectified linear unit function as weighting function, we will see that such moving average can be used as a low lag moving average as well as a signal moving average, thus creating a moving average crossover system. Finally we will estimate the LSMA using the proposed moving average. Correctivity And The Parametric Rectified Linear Unit Function Lot of terms are used, each representing one thing, lets start with the easiest one,"corrective". In some of my posts i may have used the term "underweighting", which refer to the process of attributing negative weights to the input of a moving average, a corrective moving average is simply a moving average underweighting oldest values of the input, simply put most of the low lag moving averages you'll find are corrective. This term was used by Aistis Raudys in its paper "Optimal Negative Weight Moving Average for Stock Price Series Smoothing" and i felt like it was a more elegant term to use instead of "low-lag". Now we will describe the parametric rectified linear unit function (PReLU), this function is the one used as weighting function and is not that complex. This function has two inputs, alpha , and x , in short if x is greater than 0, x remain unchanged, however if x is lower than 0, then the function output is alpha × x , if alpha is equal to 1 then the function is equivalent to an identity function, if alpha is equal to 0 then the function is equivalent to a rectified unit function. PReLU is mostly used in neural network design as an activation function, i wont explain to you how neural networks works but remember that neural networks aim to mimic the neural networks in the brain, and the activation function mimic the process of neuron firing. Its a super interesting topic because activation functions regroup many functions that can be used for technical indicators, one example being the inverse fisher RSI who make use of the hyperbolic tangent function. Finally the term parametric used here refer to the ability of the user to change the aspect of the weighting function thanks to certain settings, thinking about it, it isn't a common things for moving averages indicators to let the user modify the characteristics of the weighting function, an exception being the Arnaud Legoux moving average (ALMA) which weighting function is a gaussian function, the user can control the peak and width of the function. The Indicator The indicator has two moving averages displayed on the chart, a trigger moving average (in blue) and a signal moving average (in red), their crosses can generate signals. The length parameter control the filter length, with higher values of length filtering longer term price fluctuations. The percentage of negative weights parameter aim to determine the percentage of negative weights in the weighting function, note that the signal moving average won't use the same amount and will use instead : 100 - Percentage , this allow to reverse the weighting function thus creating a more lagging output for signal. Note that this parameter is caped at 50, this is because values higher than 50 would make the trigger moving average become the signal moving average, in short it inverse the role of the moving averages, that is a percentage of 25 would be the same than 75. In red the moving average using 25% of negative weights, in blue the same moving average using 14% percent of negative weights. In theory, more negative weights = less lag = more overshoots. Here the trigger MA in blue has 0% of negative weights, the trigger MA in green has however 35% of negative weights, the difference in lag can be clearly seen. In the case where there is 0% of negative weights the trigger become a simple WMA while the signal one become a moving average with linearly increasing weights. The corrective factor is the same as alpha in PReLU, and determine the steepness of the negative weights line, this parameter is constrained in a range of (0,1), lower values will create a less steep negative weights line, this parameter is extremely useful when we want to reduce overshoots, an example : here the corrective factor is equal to 1 (so the weighting function is an identity function) and we use 45% of negative weights, this create lot of overshoots, however a corrective factor of 0.5 reduce them drastically : Center Of Linearity The impulse response of the signal moving average is inverse to the impulse response of the trigger moving average, if we where to show them together we would see that they would crosses at a point, denoted center of linearity, therefore the crosses of each moving averages correspond to the cross of the center of linearity oscillator and 0 of same period. This is also true with the center of gravity oscillator, linear covariance oscillator and linear correlation oscillator. Of course the center of linearity oscillator is way more efficient than the proposed indicator, and if a moving average crossover system is required, then the wma/sma pair is equivalent and way more efficient, who would know that i would propose something with more efficient alternatives ? xD Estimating A Least Squares Moving Average I guess...yeah...but its not my fault you know !!! Its a linear weighting function ! What can i do about it ? The least squares moving average is corrective, its weighting function is linearly decreasing and posses negative weights with an amount of negative weights inferior to 50%, now we only need to find the exact percentage amount of negative weights. How to do it ? Well its not complicated if we recall the estimation with the WMA/SMA combination. So, an LSMA of period p is equal to : 3WMA(p) - 2SMA(p) , each coefficient of the combination can give us this percentage, that is 2/3*100 = 33.333 , so there are 33.33% percent of negative weights in the weighting function of the least squares moving average. In blue the trigger moving average with percentage of negative values et to 33.33, and in green the lsma of both period 50. Conclusion Altho inefficient, the proposed moving averages remain extremely interesting. They make use of the PReLU function as weighting function and allow the user to have a more accurate control over the characteristics of the moving averages output such as lag and overshoot amount, such parameters could even be made adaptive. We have also seen how to estimate the least squares moving average, we have seen that the lsma posses 33.333...% of negative weights in its weighting function, another useful information. The lsma is always behind me, not letting me focus on cryptobot super profit indicators using massive amount of labels, its like each time i make an indicator, the lsma come back, like a jealous creature, she want the center of attention, but you know well that the proposed indicator is inefficient ! Inefficient elegance (effect of the meds) . Thanks for reading !
Penunjuk Pine Script®
oleh alexgrover
10
AllMA Trend Radar [trade_lexx]📈 AllMA Trend Radar is your universal trend analysis tool! 📊 What is AllMA Trend Radar? AllMA Trend Radar is a powerful indicator that uses various types of Moving Averages (MA) to analyze trends and generate trading signals. The indicator allows you to choose from more than 30 different types of moving averages and adjust their parameters to suit your trading style. 💡 The main components of the indicator 📈 Fast and slow moving averages The indicator uses two main lines: - Fast MA (blue line): reacts faster to price changes - Slow MA (red line): smoother, reflects a long-term trend The combined use of fast and slow MA allows you to get trend confirmation and entry/exit points from the market. 🔄 Wide range of moving averages There are more than 30 types of moving averages at your disposal: - SMA: Simple moving average - EMA: Exponential moving average - WMA: Weighted moving average - DEMA: double exponential MA - TEMA: triple exponential MA - HMA: Hull Moving Average - LSMA: Moving average of least squares - JMA: Eureka Moving Average - ALMA: Arnaud Legoux Moving Average - ZLEMA: moving average with zero delay - And many others! 🔍 Indicator signals 1️⃣ Fast 🆚 Slow MA signals (intersection and ratio of fast and slow MA) Up/Down signals (intersection) - Buy (Up) signal: - What happens: the fast MA crosses the slow MA from bottom to top - What does the green triangle with the "Buy" label under the candle look like - What does it mean: a likely upward trend reversal or an uptrend strengthening - Sell signal (Down): - What happens: the fast MA crosses the slow MA from top to bottom - What does it look like: a red triangle with a "Sell" mark above the candle - What does it mean: a likely downtrend reversal or an increase in the downtrend Greater/Less signals (ratio) - Buy signal (Greater): - What happens: the fast MA becomes higher than the slow MA - What does it look like: a green triangle with a "Buy" label under the candle - What does it mean: the formation or confirmation of an uptrend - Sell signal (Less): - What happens: the fast MA becomes lower than the slow MA - What does it look like: a red triangle with a "Sell" mark above the candle - What does it mean: the formation or confirmation of a downtrend 2️⃣ Signals ⚡️ Fast MA (fast MA and price) Up/Down signals (intersection) - Buy signal (Up Fast): - What happens: the price crosses the fast MA from bottom to top - What does it look like: a green triangle with a "Buy" label under the candle - What does it mean: a short-term price growth signal - Sell signal (Down Fast): - What happens: the price crosses the fast MA from top to bottom - What does it look like: a red triangle with a "Sell" label above the candle - What does it mean: a short-term price drop signal Greater/Less signals (ratio) - Buy signal (Greater Fast): - What happens: the price is getting higher than the fast MA - What does it look like: a green triangle with a "Buy" label under the candle - What does it mean: the price is above the fast MA, which indicates an upward movement - Sell signal (Less Fast): - What happens: the price is getting lower than the fast MA - What does it look like: a red triangle with a "Sell" mark above the candle - What does it mean: the price is under the fast MA, which indicates a downward movement 3️⃣ Signals 🐢 Slow MA (slow MA and price) Up/Down signals (intersection) - Buy signal (Up Slow): - What happens: the price crosses the slow MA from bottom to top - What does it look like: a green triangle with a "Buy" label under the candle - What does it mean: a potential medium-term upward trend reversal - Sell signal (Down Slow): - What happens: the price crosses the slow MA from top to bottom - What does it look like: a red triangle with a "Sell" label above the candle - What does it mean: a potential medium-term downward trend reversal Greater/Less signals (ratio) - Buy signal (Greater Slow): - What happens: the price is getting above the slow MA - What does it look like: a green triangle with a "Buy" label under the candle - What does it mean: the price is above the slow MA, which indicates a strong upward movement - Sell signal (Less Slow): - What is happening: the price is getting below the slow MA - What does it look like: a red triangle with a "Sell" mark above the candle - What does it mean: the price is under the slow MA, which indicates a strong downward movement 🛠 Filters to filter out false signals 1️⃣ Minimum distance between the signals - What it does: sets the minimum number of candles between signals of the same type - Why it is needed: it prevents the appearance of too frequent signals, especially during periods of high volatility - How to set it up: Set a different value for each signal type (default: 3-5 bars) - Example: if the value is 3 for Up/Down signals, after the buy signal appears, the next buy signal may appear no earlier than 3 bars later 2️⃣ Advanced indicator filters 🔍 RSI Filter - What it does: Checks the Relative Strength Index (RSI) value before generating a signal - Why it is needed: it helps to avoid countertrend entries and catch reversal points - How to set up: - For buy signals (🔋 Buy): set the RSI range, usually in the oversold zone (for example, 1-30) - For sell signals (🪫 Sell): set the RSI range, usually in the overbought zone (for example, 70-100) - Example: if the RSI = 25 (in the range 1-30), the buy signal will be confirmed 📊 MFI Filter (Cash Flow Index) - What it does: analyzes volumes and the direction of price movement - Why it is needed: confirms signals with data on the activity of cash flows - How to set up: - For buy signals (🔋 Buy): set the MFI range in the oversold zone (for example, 1-25) - For sell signals (🪫 Sell): set the MFI range in the overbought zone (for example, 75-100) - Example: if MFI = 80 (in the range of 75-100), the sell signal will be confirmed 📈 Stochastic Filter - What it does: analyzes the position of the current price relative to the price range - Why it is needed: confirms signals based on overbought/oversold conditions - How to configure: - You can configure the K Length, D Length and Smoothing parameters - For buy signals (🔋 Buy): set the stochastic range in the oversold zone (for example, 1-20) - For sell signals (🪫 Sell): set the stochastic range in the overbought zone (for example, 80-100) - Example: if stochastic = 15 (is in the range of 1-20), the buy signal will be confirmed 🔌 Connecting to trading strategies The indicator provides various connectors to connect to your trading strategies.: 1️⃣ Individual connectors for each type of signal - 🔌Fast vs Slow Up/Down MA Signal🔌: signals for the intersection of fast and slow MA - 🔌Fast vs Slow Greater/Less MA Signal🔌: signals of the ratio of fast and slow MA - 🔌Fast Up/Down MA Signal🔌: signals of the intersection of price and fast MA - 🔌Fast Greater/Less MA Signal🔌: signals of the ratio of price and fast MA - 🔌Slow Up/Down MA Signal🔌: signals of the intersection of price and slow MA - 🔌Slow Greater/Less MA Signal🔌: Price versus slow MA signals 2️⃣ Combined connectors - 🔌Combined Up/Down MA Signal🔌: combines all the crossing signals (Up/Down) - 🔌Combined Greater/Less MA Signal🔌: combines all the signals of the ratio (Greater/Less) - 🔌Combined All MA Signals🔌: combines all signals (Up/Down and Greater/Less) ❗️ All connectors return values: - 1: buy signal - -1: sell signal - 0: no signal 📚 How to start using AllMA Trend Radar 1️⃣ Selection of types of moving averages - Add an indicator to the chart - Select the type and period for the fast MA (default: DEMA with a period of 14) - Select the type and period for the slow MA (default: SMA with a period of 14) - Experiment with different types of MA to find the best combination for your trading style 2️⃣ Signal settings - Turn on the desired signal types (Up/Down, Greater/Less) - Set the minimum distance between the signals - Activate and configure the necessary filters (RSI, MFI, Stochastic) 3️⃣ Checking on historical data - Analyze how the indicator works based on historical data - Pay attention to the accuracy of the signals and the presence of false alarms - Adjust the settings if necessary 4️⃣ Introduction to the trading strategy - Decide which signals will be used to enter the position. - Determine which signals will be used to exit the position. - Connect the indicator to your trading strategy through the appropriate connectors 🌟 Practical application examples Scalping strategy - Fast MA: TEMA with a period of 8 - Slow MA: EMA with a period of 21 - Active signals: Fast MA Up/Down - Filters: RSI (range 1-40 for purchases, 60-100 for sales) - Signal spacing: 3 bars Strategy for day trading - Fast MA: TEMA with a period of 10 - Slow MA: SMA with a period of 20 - Active signals: Fast MA Up/Down and Fast vs Slow Greater/Less - Filters: MFI (range 1-25 for purchases, 75-100 for sales) - Signal spacing: 5 bars Swing Trading Strategy - Fast MA: DEMA with a period of 14 - Slow MA: VWMA with a period of 30 - Active signals: Fast vs Slow Up/Down and Slow MA Greater/Less - Filters: Stochastic (range 1-20 for purchases, 80-100 for sales) - Signal spacing: 8 bars A strategy for positional trading - Fast MA: HMA with a period of 21 - Slow MA: SMA with a period of 50 - Active signals: Slow MA Up/Down and Fast vs Slow Greater/Less - Filters: RSI and MFI at the same time - The distance between the signals: 10 bars 💡 Tips for using AllMA Trend Radar 1. Select the types of MA for market conditions: - For trending markets: DEMA, TEMA, HMA (fast MA) - For sideways markets: SMA, WMA, VWMA (smoothed MA) - For volatile markets: KAMA, AMA, VAMA (adaptive MA) 2. Combine different types of signals: - Up/Down signals work better when moving from a sideways trend to a directional one - Greater/Less signals are optimal for fixing a stable trend 3. Use filters effectively: - The RSI filter works great in trending markets - MFI filter helps to confirm the strength of volume movement - Stochastic filter works well in lateral ranges 4. Adjust the minimum distance between the signals: - Small values (2-3 bars) for short-term trading - Average values (5-8 bars) for medium-term trading - Large values (10+ bars) for long-term trading 5. Use combination connectors: - For more reliable signals, connect the indicator through the combined connectors 💰 With the AllMA Trend Radar indicator, you get a universal trend analysis tool that can be customized for any trading style and timeframe. The combination of different types of moving averages and advanced filters allows you to significantly improve the accuracy of signals and the effectiveness of your trading strategy!
Penunjuk Pine Script®
oleh trade_lexx
Variety N-Tuple Moving Averages w/ Variety Stepping [Loxx]Variety N-Tuple Moving Averages w/ Variety Stepping is a moving average indicator that allows you to create 1- 30 tuple moving average types; i.e., Double-MA, Triple-MA, Quadruple-MA, Quintuple-MA, ... N-tuple-MA. This version contains 2 different moving average types. For example, using "50" as the depth will give you Quinquagintuple Moving Average. If you'd like to find the name of the moving average type you create with the depth input with this indicator, you can find a list of tuples here: Tuples extrapolated Due to the coding required to adapt a moving average to fit into this indicator, additional moving average types will be added as they are created to fit into this unique use case. Since this is a work in process, there will be many future updates of this indicator. For now, you can choose from either EMA or RMA. This indicator is also considered one of the top 10 forex indicators. See details here: forex-station.com Additionally, this indicator is a computationally faster, more streamlined version of the following indicators with the addition of 6 stepping functions and 6 different bands/channels types. STD-Stepped, Variety N-Tuple Moving Averages STD-Stepped, Variety N-Tuple Moving Averages is the standard deviation stepped/filtered indicator of the following indicator Last but not least, a big shoutout to @lejmer for his help in formulating a looping solution for this streamlined version. this indicator is speedy even at 50 orders deep. You can find his scripts here: www.tradingview.com How this works Step 1: Run factorial calculation on the depth value, Step 2: Calculate weights of nested moving averages factorial(depth) / (factorial(depth - k) * factorial(k); where depth is the depth and k is the weight position Examples of coefficient outputs: 6 Depth: 6 15 20 15 6 7 Depth: 7 21 35 35 21 7 8 Depth: 8 28 56 70 56 28 8 9 Depth: 9 36 34 84 126 126 84 36 9 10 Depth: 10 45 120 210 252 210 120 45 10 11 Depth: 11 55 165 330 462 462 330 165 55 11 12 Depth: 12 66 220 495 792 924 792 495 220 66 12 13 Depth: 13 78 286 715 1287 1716 1716 1287 715 286 78 13 Step 3: Apply coefficient to each moving average For QEMA, which is 5 depth EMA , the calculation is as follows ema1 = ta. ema ( src , length) ema2 = ta. ema (ema1, length) ema3 = ta. ema (ema2, length) ema4 = ta. ema (ema3, length) ema5 = ta. ema (ema4, length) In this new streamlined version, these MA calculations are packed into an array inside loop so Pine doesn't have to keep all possible series information in memory. This is handled with the following code: temp = array.get(workarr, k + 1) + alpha * (array.get(workarr, k) - array.get(workarr, k + 1)) array.set(workarr, k + 1, temp) After we pack the array, we apply the coefficients to derive the NTMA: qema = 5 * ema1 - 10 * ema2 + 10 * ema3 - 5 * ema4 + ema5 Stepping calculations First off, you can filter by both price and/or MA output. Both price and MA output can be filtered/stepped in their own way. You'll see two selectors in the input settings. Default is ATR ATR. Here's how stepping works in simple terms: if the price/MA output doesn't move by X deviations, then revert to the price/MA output one bar back. ATR The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period. Standard Deviation Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. Adaptive Deviation By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility . Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting). The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions. See how this compares to Standard Devaition here: Adaptive Deviation Median Absolute Deviation The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant. Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution. For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated. Efficiency-Ratio Adaptive ATR Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range See how this compares to ATR here: ER-Adaptive ATR Mean Absolute Deviation The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation. This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life. For Pine Coders, this is equivalent of using ta.dev() Bands/Channels See the information above for how bands/channels are calculated. After the one of the above deviations is calculated, the channels are calculated as output +/- deviation * multiplier Signals Green is uptrend, red is downtrend, yellow "L" signal is Long, fuchsia "S" signal is short. Included: Alerts Loxx's Expanded Source Types Bar coloring Signals 6 bands/channels types 6 stepping types Related indicators 3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping STD-Stepped Fast Cosine Transform Moving Average ATR-Stepped PDF MA
Penunjuk Pine Script®
oleh loxx
1
Any 8 Moving Averages - Alerts, Clouds & PercentagesANY 8 MOVING AVERAGES WITH ALERTS, COLOR CHANGING CLOUDS AND PERCENTAGE GAPS This is a fully customizable moving average cloud with alerts. It has 8 moving averages that can be individually set to any type such as: EMA, SMA, HMA, WMA, VWMA & RMA. Each moving average paints green when price is above it and paints red when price is below it. They include colored clouds between the price and each moving average as well. You can individually change the length, colors, type of moving average and turn them off for those of you that only want a few moving averages on your chart at once. There is also a percentage gap table that tells you how far away the price is from each moving average which are labeled accordingly. You can also set alerts for when price crosses each moving average. ***HOW TO USE*** When all the moving averages are green, buy dips down to the next lower moving average. When all the moving averages are red, short the tops up to the next moving average. Trade in the direction of the trend and wait for all lines to turn one color before taking trades in that direction. Make sure there is a big enough percentage gap to the next moving average before taking a trade. ***MARKETS*** This indicator can be used as a signal on all markets, including stocks, crypto, futures and forex. ***TIMEFRAMES*** This moving average can be used on all timeframes. ***TIPS*** Try using numerous indicators of ours on your chart so you can instantly see the bullish or bearish trend of multiple indicators in real time without having to analyze the data. Some of our favorites are our Auto Fibonacci, Directional Movement Index + Fisher Price Action, Volume Profile With Buy & Sell Pressure, Auto Support And Resistance and Money Flow Index in combination with this moving average cloud. They all have unique features to help you make better and faster trading decisions.
Penunjuk Pine Script®
oleh FriendOfTheTrend
13
Trend Zone Moving Averages📈 Trend Zone Moving Averages The Trend Zone Moving Averages indicator helps traders quickly identify market trends using the 50SMA, 100SMA, and 200SMA. With dynamic background colors, customizable settings, and real-time alerts, this tool provides a clear view of bullish, bearish, and extreme trend conditions. 🔹 Features: Trend Zones with Dynamic Background Colors Green → Bullish Trend (50SMA > 100SMA > 200SMA, price above 50SMA) Red → Bearish Trend (50SMA < 100SMA < 200SMA, price below 50SMA) Yellow → Neutral Trend (Mixed signals) Dark Green → Extreme Bullish (Price above all three SMAs) Dark Red → Extreme Bearish (Price below all three SMAs) Customizable Moving Averages Toggle 50SMA, 100SMA, and 200SMA on/off from the settings. Perfect for traders who prefer a cleaner chart. Real-Time Trend Alerts Get instant notifications when the trend changes: 🟢 Bullish Zone Alert – When price enters a bullish trend. 🔴 Bearish Zone Alert – When price enters a bearish trend. 🟡 Neutral Zone Alert – When trend shifts to neutral. 🌟 Extreme Bullish Alert – When price moves above all SMAs. ⚠️ Extreme Bearish Alert – When price drops below all SMAs. ✅ Perfect for Any Market Works on stocks, forex, crypto, and commodities. Adaptable for day traders, swing traders, and investors. ⚙️ How to Use: Trend Zone Moving Averages Strategy This strategy helps traders identify and trade with the trend using the Trend Zone Moving Averages indicator. It works across stocks, forex, crypto, and commodities. 🟢 Bullish Trend Strategy (Green Background) Objective: Look for buying opportunities when the market is in an uptrend. Entry Conditions: ✅ Background is Green (Bullish Zone). ✅ Price is above the 50SMA (confirming strength). ✅ Price pulls back to the 50SMA and bounces OR breaks above a key resistance level. Stop Loss: 🔹 Place below the most recent swing low or just under the 50SMA. Take Profit: 🔹 First target at the next resistance level or recent swing high. 🔹 Second target if price continues higher—trail stops to lock in profits. 🔴 Bearish Trend Strategy (Red Background) Objective: Look for shorting opportunities when the market is in a downtrend. Entry Conditions: ✅ Background is Red (Bearish Zone). ✅ Price is below the 50SMA (confirming weakness). ✅ Price pulls back to the 50SMA and rejects OR breaks below a key support level. Stop Loss: 🔹 Place above the most recent swing high or just above the 50SMA. Take Profit: 🔹 First target at the next support level or recent swing low. 🔹 Second target if price keeps falling—trail stops to secure profits. 🌟 Extreme Trend Strategy (Dark Green / Dark Red Background) Objective: Trade with momentum when the market is in a strong trend. Entry Conditions: ✅ Dark Green Background → Extreme Bullish: Price is above all three SMAs (strong uptrend). ✅ Dark Red Background → Extreme Bearish: Price is below all three SMAs (strong downtrend). Trade Execution: 🔹 For longs (Dark Green): Look for breakout entries above resistance or pullbacks to the 50SMA. 🔹 For shorts (Dark Red): Look for breakdown entries below support or rejections at the 50SMA. Risk Management: 🔹 Use tighter stop losses and trail profits aggressively to maximize gains. 🟡 Neutral Trend Strategy (Yellow Background) Objective: Avoid trading or wait for a breakout. What to Do: 🔹 Avoid trading in this zone—price is indecisive. 🔹 Wait for confirmation (background turns green/red) before taking a trade. 🔹 Use alerts to notify you when the trend resumes. 📌 Final Tips Use this strategy with price action for extra confirmation. Combine with support/resistance levels to improve accuracy. Set alerts for trend changes so you never miss an opportunity. Enjoy!
Penunjuk Pine Script®
oleh FindBetterTrades
1
Smart Moving AveragesSmart Moving Averages analyzes the dynamic interplay between price action and multiple moving averages to identify high-probability support and resistance zones. The script's distinguishing features include: Bounce detection that filters out noise by requiring specific penetration thresholds (0.1-1.5%), helping traders identify genuine support tests versus false signals Real-time MA clustering analysis that reveals zones where multiple moving averages converge, indicating potentially stronger support/resistance levels Statistical tracking of bounce success rates for each MA, allowing traders to identify which moving averages are most reliable for the current market conditions Power bounce detection that combines EMA spread analysis with trend confirmation, highlighting especially strong bullish setups Visual stack status system that instantly communicates market health through an intuitive color-coded display showing how many MAs are below price The script helps traders make more informed decisions by quantifying the historical reliability of different moving averages while providing real-time analysis of MA interactions with price. This systematic approach moves beyond simple MA crossovers to identify higher probability trading opportunities.
Penunjuk Pine Script®
oleh marsrides
1
Customizable MTF Multiple Moving AveragesTitle: Customizable Multiple Moving Averages with Dynamic Colors Description: This script allows you to calculate up to three customizable moving averages, offering the flexibility to choose from multiple moving average types: SMA (Simple Moving Average) EMA (Exponential Moving Average) WMA (Weighted Moving Average) VWMA (Volume Weighted Moving Average) SMMA (Smoothed Moving Average) Key Features: Separate Timeframe for Each Moving Average: Each moving average can be calculated on a different timeframe. For instance, you can display a 1D moving average while working on a 4H chart. Dynamic Colors: Moving averages dynamically change color based on their trend: Uptrend Color: When the moving average is increasing compared to the previous bar of its timeframe. Downtrend Color: When the moving average is decreasing. Full Customization: Length: Adjust the period for each moving average. Source: Choose any price data source (e.g., close, open, high, low). Colors: Set custom colors for uptrend and downtrend behavior. Perfect For: Multi-Timeframe Trend Analysis: Observe trends from higher timeframes without switching your current chart. Crossover Strategies: Combine multiple moving averages to identify entry and exit signals. How to Use: Load the Script: Apply it to your chart. Configure Inputs: Adjust each moving average's settings from the input panel. Analyze Trends: Visualize dynamic trend movements with easy-to-identify colors. Example Configuration: Set MA1 to a 50-period EMA on a 4H timeframe. Set MA2 to a 100-period SMMA on a 1D timeframe. Set MA3 to a 200-period VWMA on a 1W timeframe.
Penunjuk Pine Script®
oleh tropicale2018
1