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MA Sabres [LuxAlgo]The "MA Sabres" indicator highlights potential trend reversals based on a moving average direction. Detected reversals are accompanied by an extrapolated "Sabre" looking shape that can be used as support/resistance and as a source of breakouts. 🔶 USAGE If a selected moving average (MA) continues in the same direction for a certain time, a change in that direction could signify a potential reversal. In this publication, when a trend change occurs, a sabre-shaped figure is drawn which can be used as support/resistance: A sabre can be indicative of a direction, however, it can also act as a stop-loss when the price should go in the opposite direction: Or show potential areas of interest: 🔶 DETAILS This publication will look for a change in direction after the MA went in the same direction during x consecutive bars (settings: " Reversal after x bars in the same direction "). Then a circle-shaped drawing will be drawn 1 bar back, at the previous high/low, dependable of the previous direction. From there originates a sabre-shaped figure where the tip lies as far as the user-set MA length. The angle of the "sabre" relies on the ATR of the previous 14 bars. Less volatility will create a flatter sabre while the opposite is true when there is more volatility in the previous 14 bars. The sabre is created by the latest feature, polylines , which enables us to connect several 'points', resulting in a polyline.new() object. Do note that sabres are offset by one bar to the past to align their locations. 🔶 SETTINGS MA Type: SMA, EMA, SMMA (RMA), HullMA, WMA, VWMA, DEMA, TEMA, NONE (off) Length: this sets the length of MA, and the length of the sabre shape Previous Trend Duration: After the MA direction is the same for x consecutive bars, the first time the direction changes, a sabre is drawn
مؤشرات Pine Script™
من ‎LuxAlgo‎
69
Machine Learning: Optimal RSI [YinYangAlgorithms]This Indicator, will rate multiple different lengths of RSIs to determine which RSI to RSI MA cross produced the highest profit within the lookback span. This ‘Optimal RSI’ is then passed back, and if toggled will then be thrown into a Machine Learning calculation. You have the option to Filter RSI and RSI MA’s within the Machine Learning calculation. What this does is, only other Optimal RSI’s which are in the same bullish or bearish direction (is the RSI above or below the RSI MA) will be added to the calculation. You can either (by default) use a Simple Average; which is essentially just a Mean of all the Optimal RSI’s with a length of Machine Learning. Or, you can opt to use a k-Nearest Neighbour (KNN) calculation which takes a Fast and Slow Speed. We essentially turn the Optimal RSI into a MA with different lengths and then compare the distance between the two within our KNN Function. RSI may very well be one of the most used Indicators for identifying crucial Overbought and Oversold locations. Not only that but when it crosses its Moving Average (MA) line it may also indicate good locations to Buy and Sell. Many traders simply use the RSI with the standard length (14), however, does that mean this is the best length? By using the length of the top performing RSI and then applying some Machine Learning logic to it, we hope to create what may be a more accurate, smooth, optimal, RSI. Tutorial: This is a pretty zoomed out Perspective of what the Indicator looks like with its default settings (except with Bollinger Bands and Signals disabled). If you look at the Tables above, you’ll notice, currently the Top Performing RSI Length is 13 with an Optimal Profit % of: 1.00054973. On its default settings, what it does is Scan X amount of RSI Lengths and checks for when the RSI and RSI MA cross each other. It then records the profitability of each cross to identify which length produced the overall highest crossing profitability. Whichever length produces the highest profit is then the RSI length that is used in the plots, until another length takes its place. This may result in what we deem to be the ‘Optimal RSI’ as it is an adaptive RSI which changes based on performance. In our next example, we changed the ‘Optimal RSI Type’ from ‘All Crossings’ to ‘Extremity Crossings’. If you compare the last two examples to each other, you’ll notice some similarities, but overall they’re quite different. The reason why is, the Optimal RSI is calculated differently. When using ‘All Crossings’ everytime the RSI and RSI MA cross, we evaluate it for profit (short and long). However, with ‘Extremity Crossings’, we only evaluate it when the RSI crosses over the RSI MA and RSI <= 40 or RSI crosses under the RSI MA and RSI >= 60. We conclude the crossing when it crosses back on its opposite of the extremity, and that is how it finds its Optimal RSI. The way we determine the Optimal RSI is crucial to calculating which length is currently optimal. In this next example we have zoomed in a bit, and have the full default settings on. Now we have signals (which you can set alerts for), for when the RSI and RSI MA cross (green is bullish and red is bearish). We also have our Optimal RSI Bollinger Bands enabled here too. These bands allow you to see where there may be Support and Resistance within the RSI at levels that aren’t static; such as 30 and 70. The length the RSI Bollinger Bands use is the Optimal RSI Length, allowing it to likewise change in correlation to the Optimal RSI. In the example above, we’ve zoomed out as far as the Optimal RSI Bollinger Bands go. You’ll notice, the Bollinger Bands may act as Support and Resistance locations within and outside of the RSI Mid zone (30-70). In the next example we will highlight these areas so they may be easier to see. Circled above, you may see how many times the Optimal RSI faced Support and Resistance locations on the Bollinger Bands. These Bollinger Bands may give a second location for Support and Resistance. The key Support and Resistance may still be the 30/50/70, however the Bollinger Bands allows us to have a more adaptive, moving form of Support and Resistance. This helps to show where it may ‘bounce’ if it surpasses any of the static levels (30/50/70). Due to the fact that this Indicator may take a long time to execute and it can throw errors for such, we have added a Setting called: Adjust Optimal RSI Lookback and RSI Count. This settings will automatically modify the Optimal RSI Lookback Length and the RSI Count based on the Time Frame you are on and the Bar Indexes that are within. For instance, if we switch to the 1 Hour Time Frame, it will adjust the length from 200->90 and RSI Count from 30->20. If this wasn’t adjusted, the Indicator would Timeout. You may however, change the Setting ‘Adjust Optimal RSI Lookback and RSI Count’ to ‘Manual’ from ‘Auto’. This will give you control over the ‘Optimal RSI Lookback Length’ and ‘RSI Count’ within the Settings. Please note, it will likely take some “fine tuning” to find working settings without the Indicator timing out, but there are definitely times you can find better settings than our ‘Auto’ will create; especially on higher Time Frames. The Minimum our ‘Auto’ will create is: Optimal RSI Lookback Length: 90 RSI Count: 20 The Maximum it will create is: Optimal RSI Lookback Length: 200 RSI Count: 30 If there isn’t much bar index history, for instance, if you’re on the 1 Day and the pair is BTC/USDT you’ll get < 4000 Bar Indexes worth of data. For this reason it is possible to manually increase the settings to say: Optimal RSI Lookback Length: 500 RSI Count: 50 But, please note, if you make it too high, it may also lead to inaccuracies. We will conclude our Tutorial here, hopefully this has given you some insight as to how calculating our Optimal RSI and then using it within Machine Learning may create a more adaptive RSI. Settings: Optimal RSI: Show Crossing Signals: Display signals where the RSI and RSI Cross. Show Tables: Display Information Tables to show information like, Optimal RSI Length, Best Profit, New Optimal RSI Lookback Length and New RSI Count. Show Bollinger Bands: Show RSI Bollinger Bands. These bands work like the TDI Indicator, except its length changes as it uses the current RSI Optimal Length. Optimal RSI Type: This is how we calculate our Optimal RSI. Do we use all RSI and RSI MA Crossings or just when it crosses within the Extremities. Adjust Optimal RSI Lookback and RSI Count: Auto means the script will automatically adjust the Optimal RSI Lookback Length and RSI Count based on the current Time Frame and Bar Index's on chart. This will attempt to stop the script from 'Taking too long to Execute'. Manual means you have full control of the Optimal RSI Lookback Length and RSI Count. Optimal RSI Lookback Length: How far back are we looking to see which RSI length is optimal? Please note the more bars the lower this needs to be. For instance with BTC/USDT you can use 500 here on 1D but only 200 for 15 Minutes; otherwise it will timeout. RSI Count: How many lengths are we checking? For instance, if our 'RSI Minimum Length' is 4 and this is 30, the valid RSI lengths we check is 4-34. RSI Minimum Length: What is the RSI length we start our scans at? We are capped with RSI Count otherwise it will cause the Indicator to timeout, so we don't want to waste any processing power on irrelevant lengths. RSI MA Length: What length are we using to calculate the optimal RSI cross' and likewise plot our RSI MA with? Extremity Crossings RSI Backup Length: When there is no Optimal RSI (if using Extremity Crossings), which RSI should we use instead? Machine Learning: Use Rational Quadratics: Rationalizing our Close may be beneficial for usage within ML calculations. Filter RSI and RSI MA: Should we filter the RSI's before usage in ML calculations? Essentially should we only use RSI data that are of the same type as our Optimal RSI? For instance if our Optimal RSI is Bullish (RSI > RSI MA), should we only use ML RSI's that are likewise bullish? Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None? KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using. Machine Learning Length: How far back is our Machine Learning going to keep data for. k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for? Fast ML Data Length: What is our Fast ML Length? This is used with our Slow Length to create our KNN Distance. Slow ML Data Length: What is our Slow ML Length? This is used with our Fast Length to create our KNN Distance. If you have any questions, comments, ideas or concerns please don't hesitate to contact us. HAPPY TRADING!
مؤشرات Pine Script™
من ‎YinYangAlgorithms‎
21
Moving Average TransformThe MAT is essentially a different kind of smoothed moving average. It is made to filter out data sets that deviate from the specified absolute threshold and the result becomes a smoothing function. The goal here, inspired by time series analysis within mathematical study, is to eliminate data anomalies and generate a more accurate trendline. Functionality: This script calculates a filtered average by: Determining the mean of the entire data series. Initializing sum and count variables. Iterating through the data to filter values that deviate from the mean beyond the threshold. Calculating a filtered mean based on the filtered data. The filtered mean is then passed through a moving average function, where various types of moving averages like SMA, EMA, DEMA, TEMA, and ALMA can be applied. Some popular averages such as the HMA were omitted due to their heavy dependency on weighing specific data points. Some information from "Time Series Analysis" regarding deviations Definition of Anomaly: An anomaly or outlier is a data point that differs significantly from other observations in the dataset. It can be caused by various reasons such as measurement errors, data entry errors, or genuine extreme observations. Impact on Mean: The mean (or average) of a dataset is calculated by summing all the values and dividing by the number of values. Since the mean is sensitive to extreme values, even a single outlier can significantly skew the mean. Example: Consider a simple time series dataset: . The value "150" is an anomaly in this context. If we calculate the mean with this outlier, it is (10 + 12 + 11 + 9 + 150) / 5 = 38.4. However, if we exclude the outlier, the mean becomes (10 + 12 + 11 + 9) / 4 = 10.5. The presence of the outlier has substantially increased the mean. Accuracy and Representativeness: While the mean calculated without outliers might be more "accurate" in the sense of being more representative of the central tendency of the bulk of the data, it's essential to note that anomalies might convey important information about the system being studied. Blindly removing or ignoring them might lead to overlooking significant events or phenomena. Approaches to Handle Anomalies? Detection and Removal Robust Statistics Transformation
مؤشرات Pine Script™
من ‎NantzOS‎
2
Volume and Price Z-Score [Multi-Asset] - By LeviathanThis script offers in-depth Z-Score analytics on price and volume for 200 symbols. Utilizing visualizations such as scatter plots, histograms, and heatmaps, it enables traders to uncover potential trade opportunities, discern market dynamics, pinpoint outliers, delve into the relationship between price and volume, and much more. A Z-Score is a statistical measurement indicating the number of standard deviations a data point deviates from the dataset's mean. Essentially, it provides insight into a value's relative position within a group of values (mean). - A Z-Score of zero means the data point is exactly at the mean. - A positive Z-Score indicates the data point is above the mean. - A negative Z-Score indicates the data point is below the mean. For instance, a Z-Score of 1 indicates that the data point is 1 standard deviation above the mean, while a Z-Score of -1 indicates that the data point is 1 standard deviation below the mean. In simple terms, the more extreme the Z-Score of a data point, the more “unusual” it is within a larger context. If data is normally distributed, the following properties can be observed: - About 68% of the data will lie within ±1 standard deviation (z-score between -1 and 1). - About 95% will lie within ±2 standard deviations (z-score between -2 and 2). - About 99.7% will lie within ±3 standard deviations (z-score between -3 and 3). Datasets like price and volume (in this context) are most often not normally distributed. While the interpretation in terms of percentage of data lying within certain ranges of z-scores (like the ones mentioned above) won't hold, the z-score can still be a useful measure of how "unusual" a data point is relative to the mean. The aim of this indicator is to offer a unique way of screening the market for trading opportunities by conveniently visualizing where current volume and price activity stands in relation to the average. It also offers features to observe the convergent/divergent relationships between asset’s price movement and volume, observe a single symbol’s activity compared to the wider market activity and much more. Here is an overview of a few important settings. Z-SCORE TYPE ◽️ Z-Score Type: Current Z-Score Calculates the z-score by comparing current bar’s price and volume data to the mean (moving average with any custom length, default is 20 bars). This indicates how much the current bar’s price and volume data deviates from the average over the specified period. A positive z-score suggests that the current bar's price or volume is above the mean of the last 20 bars (or the custom length set by the user), while a negative z-score means it's below that mean. Example: Consider an asset whose current price and volume both show deviations from their 20-bar averages. If the price's Z-Score is +1.5 and the volume's Z-Score is +2.0, it means the asset's price is 1.5 standard deviations above its average, and its trading volume is 2 standard deviations above its average. This might suggest a significant upward move with strong trading activity. ◽️ Z-Score Type: Average Z-Score Calculates the custom-length average of symbol's z-score. Think of it as a smoothed version of the Current Z-Score. Instead of just looking at the z-score calculated on the latest bar, it considers the average behavior over the last few bars. By doing this, it helps reduce sudden jumps and gives a clearer, steadier view of the market. Example: Instead of a single bar, imagine the average price and volume of an asset over the last 5 bars. If the price's 5-bar average Z-Score is +1.0 and the volume's is +1.5, it tells us that, over these recent bars, both the price and volume have been consistently above their longer-term averages, indicating sustained increase. ◽️ Z-Score Type: Relative Z-Score Calculates a relative z-score by comparing symbol’s current bar z-score to the mean (average z-score of all symbols in the group). This is essentially a z-score of a z-score, and it helps in understanding how a particular symbol's activity stands out not just in its own historical context, but also in relation to the broader set of symbols being analyzed. In other words, while the primary z-score tells you how unusual a bar's activity is for that specific symbol, the relative z-score informs you how that "unusualness" ranks when compared to the entire group's deviations. This can be particularly useful in identifying symbols that are outliers even among outliers, indicating exceptionally unique behaviors or opportunities. Example: If one asset's price Z-Score is +2.5 and volume Z-Score is +3.0, but the group's average Z-Scores are +0.5 for price and +1.0 for volume, this asset’s Relative Z-Score would be high and therefore stand out. This means that asset's price and volume activities are notably high, not just by its own standards, but also when compared to other symbols in the group. DISPLAY TYPE ◽️ Display Type: Scatter Plot The Scatter Plot is a visual tool designed to represent values for two variables, in this case the Z-Scores of price and volume for multiple symbols. Each symbol has it's own dot with x and y coordinates: X-Axis: Represents the Z-Score of price. A symbol further to the right indicates a higher positive deviation in its price from its average, while a symbol to the left indicates a negative deviation. Y-Axis: Represents the Z-Score of volume. A symbol positioned higher up on the plot suggests a higher positive deviation in its trading volume from its average, while one lower down indicates a negative deviation. Here are some guideline insights of plot positioning: - Top-Right Quadrant (High Volume-High Price): Symbols in this quadrant indicate a scenario where both the trading volume and price are higher than their respective mean. - Top-Left Quadrant (High Volume-Low Price): Symbols here reflect high trading volumes but prices lower than the mean. - Bottom-Left Quadrant (Low Volume-Low Price): Assets in this quadrant have both low trading volume and price compared to their mean. - Bottom-Right Quadrant (Low Volume-High Price): Symbols positioned here have prices that are higher than their mean, but the trading volume is low compared to the mean. The plot also integrates a set of concentric squares which serve as visual guides: - 1st Square (1SD): Encapsulates symbols that have Z-Scores within ±1 standard deviation for both price and volume. Symbols within this square are typically considered to be displaying normal behavior or within expected range. - 2nd Square (2SD): Encapsulates those with Z-Scores within ±2 standard deviations. Symbols within this boundary, but outside the 1 SD square, indicate a moderate deviation from the norm. - 3rd Square (3SD): Represents symbols with Z-Scores within ±3 standard deviations. Any symbol outside this square is deemed to be a significant outlier, exhibiting extreme behavior in terms of either its price, its volume, or both. By assessing the position of symbols relative to these squares, traders can swiftly identify which assets are behaving typically and which are showing unusual activity. This visualization simplifies the process of spotting potential outliers or unique trading opportunities within the market. The farther a symbol is from the center, the more it deviates from its typical behavior. ◽️ Display Type: Columns In this visualization, z-scores are represented using columns, where each symbol is presented horizontally. Each symbol has two distinct nodes: - Left Node: Represents the z-score of volume. - Right Node: Represents the z-score of price. The height of these nodes can vary along the y-axis between -4 and 4, based on the z-score value: - Large Positive Columns: Signify a high or positive z-score, indicating that the price or volume is significantly above its average. - Large Negative Columns: Represent a low or negative z-score, suggesting that the price or volume is considerably below its average. - Short Columns Near 0: Indicate that the price or volume is close to its mean, showcasing minimal deviation. This columnar representation provides a clear, intuitive view of how each symbol's price and volume deviate from their respective averages. ◽️ Display Type: Circles In this visualization style, z-scores are depicted using circles. Each symbol is horizontally aligned and represented by: - Solid Circle: Represents the z-score of price. - Transparent Circle: Represents the z-score of volume. The vertical position of these circles on the y-axis ranges between -4 and 4, reflecting the z-score value: - Circles Near the Top: Indicate a high or positive z-score, suggesting the price or volume is well above its average. - Circles Near the Bottom: Represent a low or negative z-score, pointing to the price or volume being notably below its average. - Circles Around the Midline (0): Highlight that the price or volume is close to its mean, with minimal deviation. ◽️ Display Type: Delta Columns There's also an option to utilize Z-Score Delta Columns. For each symbol, a single column is presented, depicting the difference between the z-score of price and the z-score of volume. The z-score delta essentially captures the disparity between how much the price and volume deviate from their respective mean: - Positive Delta: Indicates that the z-score of price is greater than the z-score of volume. This suggests that the price has deviated more from its average than the volume has from its own average. Such a scenario could point to price movements being more significant or pronounced compared to the changes in volume. - Negative Delta: Represents that the z-score of volume is higher than the z-score of price. This might mean that there are substantial volume changes, yet the price hasn't moved as dramatically. This can be indicative of potential build-up in trading interest without an equivalent impact on price. - Delta Close to 0: Means that the z-scores for price and volume are almost equal, indicating their deviations from the average are in sync. ◽️ Display Type: Z-Volume/Z-Price Heatmap This visualization offers a heatmap either for volume z-scores or price z-scores across all symbols. Here's how it's presented: Each symbol is allocated its own horizontal row. Within this row, bar-by-bar data is displayed using a color gradient to represent the z-score values. The heatmap employs a user-defined gradient scale, where a chosen "cold" color represents low z-scores and a chosen "hot" color signifies high z-scores. As the z-score increases or decreases, the colors transition smoothly along this gradient, providing an intuitive visual indication of the z-score's magnitude. - Cold Colors: Indicate values significantly below the mean (negative z-score) - Mild Colors: Represent values close to the mean, suggesting minimal deviation. - Hot Colors: Indicate values significantly above the mean (positive z-score) This heatmap format provides a rapid, visually impactful means to discern how each symbol's price or volume is behaving relative to its average. The color-coded rows allow you to quickly spot outliers. VOLUME TYPE The "Volume Type" input allows you to choose the nature of volume data that will be factored into the volume z-score calculation. The interpretation of indicator’s data changes based on this input. You can opt between: - Volume (Regular Volume): This is the classic measure of trading volume, which represents the volume traded in a given time period - bar. - OBV (On-Balance Volume): OBV is a momentum indicator that accumulates volume on up bars and subtracts it on down bars, making it a cumulative indicator that sort of measures buying and selling pressure. Interpretation Implications: - For Volume Type: Regular Volume: Positive Z-Score: Indicates that the trading volume is above its average, meaning there's unusually high trading activity . Negative Z-Score: Suggests that the trading volume is below its average, signifying unusually low trading activity. - For Volume Type: OBV: Positive Z-Score: Signifies that “buying pressure” is above its average. Negative Z-Score: Signifies that “selling pressure” is above its average. When comparing Z-Score of OBV to Z-Score of price, we can observe several scenarios. If Z-Price and Z-Volume are convergent (have similar z-scores), we can say that the directional price movement is supported by volume. If Z-Price and Z-Volume are divergent (have very different z-scores or one of them being zero), it suggests a potential misalignment between price movement and volume support, which might hint at possible reversals or weakness.
مؤشرات Pine Script™
من ‎LeviathanCapital‎
22
MTF Moving AveragesThe MTF Moving Averages indicator allows users to plot multiple moving averages on different timeframes within the same chart on TradingView. This indicator supports four different timeframes: daily, weekly, monthly, and intraday. For each timeframe, users can choose up to four moving averages to plot. They can also select the type of moving average (SMA, EMA, or WMA), the source (e.g., close price), and the length of each moving average. Additionally, users have the option to enable a "Trend Suite" for the second moving average on the daily timeframe. The Trend Suite adds 2 moving averages with source low and high. In the intraday timeframe, the second moving average is calculated and plotted based on the daily timeframe. The indicator provides customization options for colors, allowing users to define the colors for each moving average line. The settings in the indicator are designed in a clear and organized manner. Have fun
مؤشرات Pine Script™
من ‎fyntrade‎
Trend Gaussian Channels [DeltaAlgo]This Script Introduces The Use Of The Gaussian Channel Concepts This indicator consists of three lines: a central line that represents the moving average, and an upper and lower band that represent the volatility of the price movements. The Gaussian channels is a concept consists of an upper & lower bands along with the basis; the mid band. The use of the Gaussian bands are simple, as described below.👇 Use Case: There are many ways the Gaussian channel indicator can be used! 1. Look for the price to touch or cross the upper/lower bands of the Gaussian Channel Indicator. This indicates that the price has reached an high level of volatility. a reversal or correction may be imminent. 2. Wait for confirmation of the potential reversal or correction. This can be in the form of a bearish or bullish candlestick pattern, or a signal from another technical indicator. a. For this reason I have implemented some signals that indicate trend shifts & candle colors to clearly display the switching between a bullish sentiment or bearish. 3. Enter a trade in the direction of the reversal or correction. For example, if the price touches the upper band and a bearish candlestick pattern occurs or if you get a bearish signal, enter a short trade. If the price touches the lower band and indicates bullish candlestick pattern or bullish signal, enter a long trade. Sometimes this band can act as a support & resistance, THIS is not always the case as it is not meant to be used as support & resistance.
مؤشرات Pine Script™
من ‎GeorgeWashington1‎
3
REMA CROSSOVER BY JUGNUThis indicator triggers alerts for long and short positions on DAILY TIME FRAME for SWING trades based on the conditions which described below. This script will generate alerts when the following conditions are met: LONG POSITION: RSI(14) above 50. EMA(5) crosses above EMA(10). Indicator Triangle Green below price bars SHORT POSITION: RSI(14) below 50. EMA(5) crosses down EMA(10). Indicator Triangle RED above price bars This script plots green and red triangles below and above the price bars to indicate long and short alert conditions, respectively. It also triggers alerts when these conditions are met.
مؤشرات Pine Script™
من ‎whoismudasar‎
1
VAcc (Velocity & Acceleration)VAcc (Velocity & Acceleration) is a momentum indicator published by Scott Cong in Stocks & Commodities V. 41:09 (8–15). It applies concepts from physics, namely velocity and acceleration, to financial markets. VAcc functions similarly to the popular MACD (Moving Average Convergence Divergence) indicator when using a longer lookback period, but produces more responsive results. With shorter periods, VAcc exhibits characteristics reminiscent of the stochastic oscillator. 🟠 Algorithm The average velocity over the past n periods is defined as ((C - C_n) / n + (C - C_{n-1}) / (n - 1) + … + (C - C_i) / i + (C - C_1) / 1) / n At its core, the velocity is a weighted average of the rate of change over the past n periods. The calculation of the acceleration follows a similar process, where it’s defined as ((V - V_n) / n + (V - V_{n - 1}) / (n - 1) + … + (V - V_i) / i + (V - V_1) / 1) / n 🟠 Comparison with MACD A comparison of VAcc and MACD on the daily Nasdaq 100 (NDX) chart from August 2022 helps demonstrate VAcc's improved sensitivity. Both indicators utilized a lookback period of 26 days and smoothing of 9 periods. The VAcc histogram clearly shows a divergence forming, with momentum weakening as prices reached new highs. In contrast, the corresponding MACD histogram significantly lagged in confirming the divergence, highlighting VAcc's ability to identify subtle shifts in trend momentum more immediately than the traditional MACD.
مؤشرات Pine Script™
من ‎AlphaViz‎
Hull WavesThe Hull Waves indicator is based on the Hull Moving Averages (HMA), which are special moving averages that stand out for their ability to filter out market noise and offer a clearer view of price trends. Compared to traditional moving averages, HMAs are more responsive yet smoother, allowing traders to capture significant price movements without getting overwhelmed by short-term fluctuations. The HMAs integrated into Hull Waves provide two distinct perspectives on the price trend: 8-period HMA: This short-term HMA is extremely reactive and closely follows price changes. It is ideal for capturing short-term trading signals while the medium-term 21-period HMA offers a more balanced view of price trends and identifies medium-term trends. By crossing HMAs, traders can efficiently identify trend reversal points or strong market continuations. Another feature of the indicator is the “fan” of dynamic lines, which acts as a visual float for price candles, allowing traders to quickly evaluate trading opportunities. The "fan" or float of dynamic lines represents a visual representation of the candle's price movements. These lines extend from the start point to the end point, like an open fan. This visual approach makes the market dynamics immediately evident. Strategy: Long Entry Signal (Buy): When the Hull Waves range shows a series of upward sloping lines and the Hull Moving Averages (e.g. 8-period HMA) crosses the 21-period HMA upwards, it is a long entry signal. Confirmation of the signal can come from an increase in trader volume or other supporting indicators. Place a buy order at the next closing price. Short Entry Signal (Sell): When the Hull Waves range shows a series of downward sloping lines and the Hull Moving Averages (e.g. 8-period HMA) crosses the 21-period HMA downward, it is a short entry signal. Confirm the signal with an increase in trader volume or other relevant indicators. Place a sell order at the next closing price. Exit Signal (Closing a Position): To close a long position, wait for a signal reversal, such as the Hull Moving Averages crossing downwards or a change in the Hull Waves range. To close a short position, wait for a signal reversal, such as the Hull Moving Averages crossing higher or a change in the Hull Waves range.
مؤشرات Pine Script™
من ‎AleSaira‎
3
[AIO] Multi Collection Moving Averages 140 MA TypesAll In One Multi Collection Moving Averages. Since signing up 2 years ago, I have been collecting various Сollections. I decided to get it into a decent shape and make it one of the biggest collections on TV, and maybe the entire internet. And now I'm sharing my collection with you. 140 Different Types of Moving Averages are waiting for you. Specifically : " AARMA | Adaptive Autonomous Recursive Moving Average ADMA | Adjusted Moving Average ADXMA | Average Directional Moving Average ADXVMA | Average Directional Volatility Moving Average AHMA | Ahrens Moving Average ALF | Ehler Adaptive Laguerre Filter ALMA | Arnaud Legoux Moving Average ALSMA | Adaptive Least Squares ALXMA | Alexander Moving Average AMA | Adaptive Moving Average ARI | Unknown ARSI | Adaptive RSI Moving Average AUF | Auto Filter AUTL | Auto-Line BAMA | Bryant Adaptive Moving Average BFMA | Blackman Filter Moving Average CMA | Corrected Moving Average CORMA | Correlation Moving Average COVEMA | Coefficient of Variation Weighted Exponential Moving Average COVNA | Coefficient of Variation Weighted Moving Average CTI | Coral Trend Indicator DEC | Ehlers Simple Decycler DEMA | Double EMA Moving Average DEVS | Ehlers - Deviation Scaled Moving Average DONEMA | Donchian Extremum Moving Average DONMA | Donchian Moving Average DSEMA | Double Smoothed Exponential Moving Average DSWF | Damped Sine Wave Weighted Filter DWMA | Double Weighted Moving Average E2PBF | Ehlers 2-Pole Butterworth Filter E2SSF | Ehlers 2-Pole Super Smoother Filter E3PBF | Ehlers 3-Pole Butterworth Filter E3SSF | Ehlers 3-Pole Super Smoother Filter EDMA | Exponentially Deviating Moving Average (MZ EDMA) EDSMA | Ehlers Dynamic Smoothed Moving Average EEO | Ehlers Modified Elliptic Filter Optimum EFRAMA | Ehlers Modified Fractal Adaptive Moving Average EHMA | Exponential Hull Moving Average EIT | Ehlers Instantaneous Trendline ELF | Ehler Laguerre filter EMA | Exponential Moving Average EMARSI | EMARSI EPF | Edge Preserving Filter EPMA | End Point Moving Average EREA | Ehlers Reverse Exponential Moving Average ESSF | Ehlers Super Smoother Filter 2-pole ETMA | Exponential Triangular Moving Average EVMA | Elastic Volume Weighted Moving Average FAMA | Following Adaptive Moving Average FEMA | Fast Exponential Moving Average FIBWMA | Fibonacci Weighted Moving Average FLSMA | Fisher Least Squares Moving Average FRAMA | Ehlers - Fractal Adaptive Moving Average FX | Fibonacci X Level GAUS | Ehlers - Gaussian Filter GHL | Gann High Low GMA | Gaussian Moving Average GMMA | Geometric Mean Moving Average HCF | Hybrid Convolution Filter HEMA | Holt Exponential Moving Average HKAMA | Hilbert based Kaufman Adaptive Moving Average HMA | Harmonic Moving Average HSMA | Hirashima Sugita Moving Average HULL | Hull Moving Average HULLT | Hull Triple Moving Average HWMA | Henderson Weighted Moving Average IE2 | Early T3 by Tim Tilson IIRF | Infinite Impulse Response Filter ILRS | Integral of Linear Regression Slope JMA | Jurik Moving Average KA | Unknown KAMA | Kaufman Adaptive Moving Average & Apirine Adaptive MA KIJUN | KIJUN KIJUN2 | Kijun v2 LAG | Ehlers - Laguerre Filter LCLSMA | 1LC-LSMA (1 line code lsma with 3 functions) LEMA | Leader Exponential Moving Average LLMA | Low-Lag Moving Average LMA | Leo Moving Average LP | Unknown LRL | Linear Regression Line LSMA | Least Squares Moving Average / Linear Regression Curve LTB | Unknown LWMA | Linear Weighted Moving Average MAMA | MAMA - MESA Adaptive Moving Average MAVW | Mavilim Weighted Moving Average MCGD | McGinley Dynamic Moving Average MF | Modular Filter MID | Median Moving Average / Percentile Nearest Rank MNMA | McNicholl Moving Average MTMA | Unknown MVSMA | Minimum Variance SMA NLMA | Non-lag Moving Average NWMA | Dürschner 3rd Generation Moving Average (New WMA) PKF | Parametric Kalman Filter PWMA | Parabolic Weighted Moving Average QEMA | Quadruple Exponential Moving Average QMA | Quick Moving Average REMA | Regularized Exponential Moving Average REPMA | Repulsion Moving Average RGEMA | Range Exponential Moving Average RMA | Welles Wilders Smoothing Moving Average RMF | Recursive Median Filter RMTA | Recursive Moving Trend Average RSMA | Relative Strength Moving Average - based on RSI RSRMA | Right Sided Ricker MA RWMA | Regressively Weighted Moving Average SAMA | Slope Adaptive Moving Average SFMA | Smoother Filter Moving Average SMA | Simple Moving Average SSB | Senkou Span B SSF | Ehlers - Super Smoother Filter P2 SSMA | Super Smooth Moving Average STMA | Unknown SWMA | Self-Weighted Moving Average SW_MA | Sine-Weighted Moving Average TEMA | Triple Exponential Moving Average THMA | Triple Exponential Hull Moving Average TL | Unknown TMA | Triangular Moving Average TPBF | Three-pole Ehlers Butterworth TRAMA | Trend Regularity Adaptive Moving Average TSF | True Strength Force TT3 | Tilson (3rd Degree) Moving Average VAMA | Volatility Adjusted Moving Average VAMAF | Volume Adjusted Moving Average Function VAR | Vector Autoregression Moving Average VBMA | Variable Moving Average VHMA | Vertical Horizontal Moving Average VIDYA | Variable Index Dynamic Average VMA | Volume Moving Average VSO | Unknown VWMA | Volume Weighted Moving Average WCD | Unknown WMA | Weighted Moving Average XEMA | Optimized Exponential Moving Average ZEMA | Zero Lag Moving Average ZLDEMA | Zero-Lag Double Exponential Moving Average ZLEMA | Ehlers - Zero Lag Exponential Moving Average ZLTEMA | Zero-Lag Triple Exponential Moving Average ZSMA | Zero-Lag Simple Moving Average " Don't forget that you can use any Moving Average not only for the chart but also for any of your indicators without affecting the code as in my example. But remember that some MAs are not designed to work with anything other than a chart. All MA and Code lists are sorted strictly alphabetically by short name (A-Z). Each MA has its own number (ID) by which you can display the Moving Average you need. Next to the ID selection there are tooltips with short names and their numbers. Use them. The panel below will help you to read the Name of the selected MA. Because of the size of the collection I think this is the optimal and most convenient use. Correct me if this is not the case. Unknown - Some MAs I collected so long ago that I lost the full real name and couldn't find the authors. If you recognize them, please let me know. I have deliberately simplified all MAs to input just Source and Length. Because the collection is so large, it would be quite inconvenient and difficult to customize all MA functions (multipliers, offset, etc.). If you need or like any MA you will still have to take it from my collection for your code. I tried to leave the basic MA settings inside function in first strings. I have tried to list most of the authors, but since the bulk of the collection was created a long time ago and was not intended for public publication I could not find all of them. Some of the features were created from scratch or may have been slightly modified, so please be careful. If you would like to improve this collection, please write to me in PM. Also Credits, Likes, Awards, Loves and Thanks to :     @alexgrover     @allanster     @andre_007     @auroagwei     @blackcat1402     @bsharpe     @cheatcountry     @CrackingCryptocurrency     @Duyck     @ErwinBeckers     @everget     @glaz     @gotbeatz26107     @HPotter     @io72signals     @JacobAmos     @JoshuaMcGowan     @KivancOzbilgic     @LazyBear     @loxx     @LuxAlgo     @MightyZinger     @nemozny     @NGBaltic     @peacefulLizard50262     @RicardoSantos     @StalexBot     @ThiagoSchmitz     @TradingView     — 𝐀𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 ! So just a Big Thank You to everyone who has ever and anywhere shared their codes.
مؤشرات Pine Script™
من ‎HALDRO‎
5
G Channel with Arrows 1. Channel Calculation: - The indicator calculates an upper channel ( `UpperBuffer` ) and a lower channel ( `LowerBuffer `) based on the input parameters `ChannelPeriod` . - The channels are determined by a dynamic calculation that considers the current price ( `src` ) and the previous values of the upper and lower channels (` aBuffer` and `bBuffer` ). 2. Middle Channel: - The middle channel ( `MiddleBuffer` ) is the average of the upper and lower channels, providing a central reference line. 3. Exponential Moving Average (EMA): - The script calculates an Exponential Moving Average (`EMAValue`) based on the closing prices with a specified period (`EMAPeriod`). 4. Channel Plots: - Plots for the upper, lower, and middle channels are displayed on the chart, each with a distinctive color and style. 5. Fill Between Channels: - The space between the upper and middle channels is filled with a blue color (`#1900ff`), and the space between the lower and middle channels is filled with a red color (`#f70a0a`). 6. EMA Line: - The EMA line is plotted on the chart in green. 7. Buy and Sell Signals: - Buy signals ( `buySignal` ) are generated when the EMA crosses above the middle channel. - Sell signals ( `sellSignal` ) are generated when the EMA crosses below the middle channel. - Arrows are plotted at the respective locations of buy and sell signals. 8. Breakout Arrows: - Additional arrows are plotted when the closing price breaks out above the upper channel (green arrow) or below the lower channel (red arrow). 9. User Input Parameters: - Traders can customize the input parameters such as `ChannelPeriod` and `EMAPeriod` to adjust the sensitivity of the channels and the EMA. Overall, the indicator provides traders with a visual representation of price channels, an EMA trend reference, and signals for potential buy/sell opportunities and breakout points. It can be used as part of a trading strategy to identify trends, reversals, and potential entry/exit points in the market.
مؤشرات Pine Script™
من ‎ClearTradingMind‎
1
Moving averages & clouds Hi all! This is a script that lets you have 3 moving averages (of a user defined type) and maybe have an alternative cloud (fill) between them. The cloud can be customized and turned on/off in the "style" tab for the indicator. Alerts can be configured to fire on up/down/all crosses and are activated when the whole candle has crossed the morning average. A higher time frame can be configured for the moving averages. You can hide the moving average, but show the cloud: You can have multiple clouds: You can have moving averages from a higher time frame (here from weekly time frame on a daily chart): Best of trading luck!
مؤشرات Pine Script™
من ‎mickes‎
Fiboborsa+BistTitle: "Fiboborsa+Bist Indicator for TradingView" Description: The "Fiboborsa+Bist" indicator is a powerful tool designed for TradingView users. This indicator offers a comprehensive set of technical indicators to assist you in your technical analysis and trading decisions. Features: Simple Moving Averages (SMA): You can enable or disable SMA with different periods (20, 50, 100, 200) to observe different timeframes and trends. SMA Strategy: Use SMA crossovers to determine trends. Watch for the 20-period SMA crossing above the 50-period SMA for a bullish signal. For a bearish signal, observe the 50-period SMA crossing below the 100-period SMA. Exponential Moving Averages (EMA): Similar to SMA, you can enable or disable EMA with different periods (5, 8, 14, 21, 34, 55, 89, 144, 233) for more precise trend analysis. EMA Strategy: Use EMA crossovers and crossunders for short-term trend changes. A buy signal may occur when the 5-period EMA crosses above the 14-period EMA, while a crossunder suggests a selling opportunity. Weighted Moving Averages (WMA): Customize WMA settings with various periods (5, 13, 21, 34, 89, 144, 233, 377, 610, 987) to suit your trading style. WMA Strategy: Use WMA crossovers to verify trends. When the 13-period WMA crosses above the 34-period WMA, it may indicate an uptrend. Buy and Sell Signals: The indicator provides buy and sell signals based on EMA crossovers and crossunders. Strong signals are also highlighted. EMA Buy and Sell Strategy: Make informed trading decisions using buy and sell signals generated by EMA crossovers and crossunders. Ichimoku Cloud: You can enable the Ichimoku Cloud for a clear visual representation of support and resistance levels. Ichimoku Strategy: Use the Ichimoku Cloud to determine trend direction. Entering long positions is common when the price is above the cloud and considering short positions when it's below the cloud. Verify the trend with the Chikou Span. Bollinger Bands: Easily visualize price volatility by enabling the Bollinger Bands feature. Bollinger Bands Strategy: Bollinger Bands help you visualize price volatility. Look for potential reversal points when the price touches or crosses the upper or lower bands. Use the "Fiboborsa+Bist" indicator to enhance your trading strategies and make informed decisions in the dynamic world of financial markets. Additional Information: Bollinger Bands: Bollinger Bands are a technical analysis tool used to monitor price volatility and determine overbought or oversold conditions. This indicator consists of three components: Middle Moving Average (SMA): Typically, a 20-day SMA is used. Upper Band: Calculated by adding two times the standard deviation to the SMA. Lower Band: Calculated by subtracting two times the standard deviation from the SMA. As the price moves between these two bands, it becomes possible to identify potential buying or selling points by comparing its height or low with these bands. Ichimoku Cloud: The Ichimoku Cloud is a comprehensive indicator used for trend identification, defining support and resistance levels, and measuring trend strength. The Ichimoku Cloud comprises five key components: Tenkan Sen (Conversion Line): Used to identify short-term trends. Kijun Sen (Base Line): Used to identify medium-term trends. Senkou Span A (Leading Span A): Calculated as (Tenkan Sen + Kijun Sen) / 2 and shows future support and resistance levels. Senkou Span B (Leading Span B): Calculated as (highest high + lowest low) / 2 and indicates future support and resistance levels. Chikou Span (Lagging Line): Enables tracking the price backward. The Ichimoku Cloud interprets a price above the cloud as an uptrend and below the cloud as a downtrend. The Chikou Span assists in verifying the current trend. ADDITIONAL STRATEGY WITH RSI AND MACD INDICATORS **Strategy: Two-Stage Trading Strategy Using RSI, MACD, and Fiboborsa+Bist Indicators** **Stage 1: Determining the Trend and Selecting the Trading Direction** 1. **Trend Identification with Fiboborsa+Bist Indicator:** - Analyze the simple moving averages (SMA), exponential moving averages (EMA), and weighted moving averages (WMA) used with the Fiboborsa+Bist indicator. These indicators will provide information about the direction of the market trend. 2. **Identifying Overbought and Oversold Conditions with RSI:** - Use the RSI indicator to identify overbought (70 and above) and oversold (30 and below) conditions. This helps in measuring the strength of the trend. If RSI enters the overbought zone, a downward correction is likely. If RSI enters the oversold zone, an upward correction is probable. 3. **Evaluating Momentum with MACD:** - Examine price momentum using the MACD indicator. When the MACD line crosses above the signal line, it may indicate an increasing upward momentum. Conversely, a downward cross can suggest an increasing downward momentum. **Stage 2: Generating Buy and Sell Signals** 4. **Combining RSI, MACD, and Fiboborsa+Bist Indicators:** - To generate a buy signal, wait for RSI to move out of the oversold region into an uptrend and for the MACD line to cross above the signal line. - To generate a sell signal, wait for RSI to move out of the overbought region into a downtrend and for the MACD line to cross below the signal line. 5. **Confirmation with Fiboborsa+Bist Indicator:** - When you receive a buy or sell signal, use the Fiboborsa+Bist indicator to confirm the market trend. Confirming the trend can strengthen your trade signals. 6. **Setting Stop-Loss and Take-Profit Levels:** - Remember to manage risk when opening buy or sell positions. Set stop-loss and take-profit levels to limit your risk. 7. **Monitor and Adjust Your Trades:** - Continuously monitor your trade positions and adjust your strategy as per market conditions. This two-stage trading strategy offers the ability to determine trends and generate trade signals using different indicators. However, every trading strategy involves risks, so risk management and practical application are essential. Also, it's recommended to test this strategy in a demo account before using it in a real trading account.
مؤشرات Pine Script™
من ‎bistatistics‎
2
TMA Bands with Break Arrow @ClearTradingMind The "TMA Bands with Break Arrow" indicator, developed by ClearTradingMind, is designed to provide traders with insights into potential trend reversals based on the movement of price within a channel defined by the Triangular Moving Average (TMA) and its bands. The TMA is a smoothed moving average, and this indicator adds upper and lower bands to visualize potential breakouts. Key Components: 1. TMA Bands: The indicator plots the upper and lower bands of the TMA channel. These bands represent potential overbought (upper band) and oversold (lower band) conditions. 2. Break Arrows: The indicator generates buy (green triangle up) and sell (red triangle down) arrows when the closing price breaks above the upper band or below the lower band, indicating a potential trend reversal. 3. Background Color: The background color dynamically changes based on the last generated signal. A blue background suggests a recent buy signal, while a red background indicates a recent sell signal. This provides a quick visual reference for the prevailing market sentiment. Usage: 1. Trend Reversals: Traders can use the buy and sell arrows as signals for potential trend reversals. A buy signal suggests a possible upward trend, while a sell signal suggests a potential downward trend. 2. Channel Breakouts: Watch for price breaking above the upper band (buy signal) or below the lower band (sell signal). These breakouts may indicate the start of a new trend. 3. Volatility Analysis: The width of the TMA channel represents volatility. A widening channel suggests increased volatility, while a narrowing channel suggests decreasing volatility. 4. Background Color: The background color provides additional context. A blue background indicates recent bullish sentiment, while a red background suggests recent bearish sentiment. Parameters: - TMA Period: The number of bars used to calculate the Triangular Moving Average. - ATR Period: The number of bars used to calculate the Average True Range (ATR) for determining the width of the TMA channel. - ATR Multiplier: A multiplier applied to the ATR to determine the width of the TMA channel. Note: This indicator is a tool to assist traders in their analysis, and it is recommended to use it in conjunction with other technical and fundamental analysis methods for more comprehensive decision-making. Disclaimer: Trading involves risk, and this indicator does not guarantee profit. Users should conduct thorough analysis and risk management before making trading decisions.
مؤشرات Pine Script™
من ‎ClearTradingMind‎
2
Crossover EMMMCrossover EMMM is an indicator that displays the Madrid Moving Averages (EMMM) and detects crossovers (upward crossings) and crossunders (downward crossings) between two moving averages. It uses two input parameters to define the fast and slow EMMM lengths. The script calculates the EMMM values, their changes, and assigns colors based on the change direction. The fast EMMM is plotted in green or red, and the slow EMMM is plotted in blue or red, depending on the change direction. The script also displays triangle shapes below or above the bars to indicate crossovers and crossunders. The "Madrid Moving Average" (EMMMM) is a type of moving average used in technical analysis to smooth price fluctuations of financial assets, such as stocks or currency pairs. Unlike the Simple Moving Average (SMA), which treats all data equally, the EMMM gives more weight to recent data. This results in the EMMM responding more swiftly to price changes, making it well-suited for identifying short-term trends.
مؤشرات Pine Script™
من ‎buzza‎
1
TTP Pair Slope/HedgePair slope/hedge uses linear regression to calculate the hedge ratio (slope) between the two assets within a period. It allows you to specify a "from" and a "to" candle. Example: "A regression from 1000 candles back in time and ignore the last 100 candles. This would result in making a regression of 900 candles in total." The formula used to perform the regression with the assts X and Y is: Hedge = mean( (X-mean(X))^2 ) —————————————————— mean( (X-mean(X)) * (Y-mean(Y)) ) You can later use the hedge in a chart of X - Hedge * Y (Confirm with 1 / hedge ) If the plot is stationary the period tested should look like stationary. If you cross an imaginary horizontal line across all the values in the period used it should look like a flat channel with values crossing above and below the line. The purpose of this indicator is to help finding the linear regression test used for conintegration analysis. Conintegration assets is one of the requirements to consider assets for pair and hedge trading.
مؤشرات Pine Script™
من ‎TheTradingParrot‎
6
9-20 sma multi timeframe indicatorThis is an indicator to help visualizing the 9 and the 20 sma on 3 different timeframes. When they cross, you will see a cross on the band representing the timeframe. When a trade is favorable the band will color in green for up trend and in red for downtrend: - Conditions in uptrend: Start after the first green candle closed above the 9 sma, Stop after the first red candle closed under the 9 sma - Conditions in downtrend: Start after the first red candle closed below the 9 sma, Stop after the first green candle closed above the 9 sma
مؤشرات Pine Script™
من ‎Elimak07‎
2
Machine Learning: Trend Lines [YinYangAlgorithms]Trend lines have always been a key indicator that may help predict many different types of price movements. They have been well known to create different types of formations such as: Pennants, Channels, Flags and Wedges. The type of formation they create is based on how the formation was created and the angle it was created. For instance, if there was a strong price increase and then there is a Wedge where both end points meet, this is considered a Bull Pennant. The formations Trend Lines create may be powerful tools that can help predict current Support and Resistance and also Future Momentum changes. However, not all Trend Lines will create formations, and alone they may stand as strong Support and Resistance locations on the Vertical. The purpose of this Indicator is to apply Machine Learning logic to a Traditional Trend Line Calculation, and therefore allowing a new approach to a modern indicator of high usage. The results of such are quite interesting and goes to show the impacts a simple KNN Machine Learning model can have on Traditional Indicators. Tutorial: There are a few different settings within this Indicator. Many will greatly impact the results and if any are changed, lots will need ‘Fine Tuning’. So let's discuss the main toggles that have great effects and what they do before discussing the lengths. Currently in this example above we have the Indicator at its Default Settings. In this example, you can see how the Trend Lines act as key Support and Resistance locations. Due note, Support and Resistance are a relative term, as is their color. What starts off as Support or Resistance may change when the price crosses over / under them. In the example above we have zoomed in and circled locations that exhibited markers of Support and Resistance along the Trend Lines. These Trend Lines are all created using the Default Settings. As you can see from the example above; just because it is a Green Upwards Trend Line, doesn’t mean it’s a Support Line. Support and Resistance is always shifting on Trend Lines based on the prices location relative to them. We won’t go through all the Formations Trend Lines make, but the example above, we can see the Trend Lines formed a Downward Channel. Channels are when there are two parallel downwards Trend Lines that are at a relatively similar angle. This means that they won’t ever meet. What may happen when the price is within these channels, is it may bounce between the upper and lower bounds. These Channels may drive the price upwards or downwards, depending on if it is in an Upwards or Downwards Channel. If you refer to the example above, you’ll notice that the Trend Lines are formed like traditional Trend Lines. They don’t stem from current Highs and Lows but rather Machine Learning Highs and Lows. More often than not, the Machine Learning approach to Trend Lines cause their start point and angle to be quite different than a Traditional Trend Line. Due to this, it may help predict Support and Resistance locations at are more uncommon and therefore can be quite useful. In the example above we have turned off the toggle in Settings ‘Use Exponential Data Average’. This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. By Default it is enabled, but as you can see when it is disabled it may create some pretty strong lasting Trend Lines. This is why we advise you ZOOM OUT AS FAR AS YOU CAN. Trend Lines are only displayed when you’ve zoomed out far enough that their Start Point is visible. As you can see in this example above, there were 3 major Upward Trend Lines created in 2020 that have had a major impact on Support and Resistance Locations within the last year. Lets zoom in and get a closer look. We have zoomed in for this example above, and circled some of the major Support and Resistance locations that these Upward Trend Lines may have had a major impact on. Please note, these Machine Learning Trend Lines aren’t a ‘One Size Fits All’ kind of thing. They are completely customizable within the Settings, so that you can get a tailored experience based on what Pair and Time Frame you are trading on. When any values are changed within the Settings, you’ll likely need to ‘Fine Tune’ the rest of the settings until your desired result is met. By default the modifiable lengths within the Settings are: Machine Learning Length: 50 KNN Length:5 Fast ML Data Length: 5 Slow ML Data Length: 30 For example, let's toggle ‘Use Exponential Data Averages’ back on and change ‘Fast ML Data Length’ from 5 to 20 and ‘Slow ML Data Length’ from 30 to 50. As you can in the example above, all of the lines have changed. Although there are still some strong Support Locations created by the Upwards Trend Lines. We will conclude our Tutorial here. Hopefully you’ve learned how to use Machine Learning Trend Lines and will be able to now see some more unorthodox Support and Resistance locations on the Vertical. Settings: Use Machine Learning Sources: If disabled Traditional Trend line sources (High and Low) will be used rather than Rational Quadratics. Use KNN Distance Sorting: You can disable this if you wish to not have the Machine Learning Data sorted using KNN. If disabled trend line logic will be Traditional. Use Exponential Data Average: This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. Machine Learning Length: How strong is our Machine Learning Memory? Please note, when this value is too high the data is almost 'too' much and can lead to poor results. K-Nearest Neighbour (KNN) Length: How many K-Nearest Neighbours are allowed with our Distance Clustering? Please note, too high or too low may lead to poor results. Fast ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 3/5/7 all seem to work well for Fast. Slow ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 20 - 50 all seem to work well for Slow. If you have any questions, comments, ideas or concerns please don't hesitate to contact us. HAPPY TRADING!
مؤشرات Pine Script™
من ‎YinYangAlgorithms‎
1
LBR-Volatility Breakout BarsThe originator of this script is Linda Raschke of LBR Group. This Pine Script code is the version 5 of LBR Paintbars for TradingView, called "LBR-Bars." It was originally coded for TradingView in version 3 by LazyBear. It is a complex indicator that combines various features such as coloring bars based on different conditions, displaying Keltner channels, and showing volatility lines. Let me break down the key components and explain how it works: 1. Inputs Section: This section defines various input parameters that users can adjust when adding the indicator to their charts. These parameters allow users to customize the behavior and appearance of the indicator. Here are some of the key input parameters: - Users can control whether to color bars under different conditions. For example, they can choose to color LBR bars, color bars above/below Kelts, or color non-LBR bars. - Users can choose whether to show volatility lines or shade Keltner channels' area with the Mid being the moving average on the chart. - In the calculation of Keltner channels, users can set the length of the moving average that the Keltner channels use as the mid and then set the Keltner multiplier. If users want to use "True Range" to determine calculations, they can turn it on or off; it defaults to off. - Users can change the calculation of volatility lines and set the length for finding the lowest and highest prices. The user sets the ATR length and multiplier for the ATR. 2. Calculation Section: This section defines the calculation of the upper and lower standard deviation bands based on the input parameters. It uses Exponential Moving Averages (EMAs) and optionally True Range to calculate these bands if turned on. These bands are used in the Keltner channel calculation. 3. Keltner Channel Section: This section calculates the upper, middle, and lower lines of the Keltner channels. It also plots these lines on the chart. The colors and visibility of these lines are controlled by user inputs. 4. Volatility Lines Section: This section calculates the upper and lower volatility lines based on the lowest and highest prices over a specified period and the ATR. It also checks whether the current close price is above or below these lines accordingly. The colors and visibility of these lines are controlled by user inputs. 5. Bar Colors Section: This section determines the color of the bars on the chart based on various conditions. It checks whether the current bar meets conditions like being an LBR bar, being above or below volatility lines, or being in "No Man's Land." The color of the bars is set accordingly based on user inputs. This Pine Script creates an indicator that provides visual cues on the chart based on Keltner channels, volatility lines, and other customizable conditions. Users can adjust the input parameters to tailor the indicator's behavior and appearance to their trading preferences.
مؤشرات Pine Script™
من ‎thebearfib‎
2
Interactive MA Stop Loss [TANHEF]This indicator is "Interactive." Once added to the chart, you need to click the start point for the moving average stoploss. Dragging it afterward will modify its position. Why choose this indicator over a traditional Moving Average? To accurately determine that a wick has crossed a moving average, you must examine the moving average's range on that bar (blue area on this indicator) and ensure the wick fully traverses this area. When the price moves away from a moving average, the average also shifts towards the price. This can make it look like the wick crossed the average, even if it didn't. How is the moving average area calculated? For each bar, the moving average calculation is standard, but when the current bar is involved, its high or low is used instead of the close. For precise results, simply setting the source in a typical moving average calculation to 'Low' or 'High' is not sufficient in calculating the moving average area on a current bar. Moving Average Options: Simple Moving Average Exponential Moving Average Relative Moving Average Weighted Moving Average Indicator Explanation After adding indicator to chart, you must click on a location to begin an entry. The moving average type can be set and length modified to adjust the stoploss. An optional profit target may be added. A symbol is display when the stoploss and profit target are hit. If a position is create that is not valid, "Overlapping MA and Bar" is displayed. Alerts 'Check' alerts to use within indicator settings (stop hit and/or profit target hit). Select 'Create Alert' Set the condition to 'Interactive MA'' Select create. Alert messages can have additional details using these words in between two Curly (Brace) Brackets: {{stop}} = MA stop-loss (price) {{upper}} = Upper MA band (price) {{lower}} = Lower MA band (price) {{band}} = Lower or Upper stoploss (word) {{type}} = Long or Short stop-loss (word) {{stopdistance}} = Stoploss Distance (%) {{targetdistance}} = Target Distance (%) {{starttime}} = Start time of stoploss (day:hour:minute) {{maLength}} = MA Length (input) {{maType}} = MA Type (input) {{target}} = Price target (price) {{trigger}} = Wick or Close Trigger input (input) {{ticker}} = Ticker of chart (word) {{exchange}} = Exchange of chart (word) {{description}} = Description of ticker (words) {{close}} = Bar close (price) {{open}} = Bar open (price) {{high}} = Bar high (price) {{low}} = Bar low (price) {{hl2}} = Bar HL2 (price) {{volume}} = Bar volume (value) {{time}} = Current time (day:hour:minute) {{interval}} = Chart timeframe {{newline}} = New line for text I will add further moving averages types in the future. If you suggestions post them below.
مؤشرات Pine Script™
من ‎TanHef‎
2
Market Performance TableThe Market Performance Table displays the performance of multiple tickers (up to 5) in a table format. The tickers can be customized by selecting them through the indicator settings. The indicator calculates various metrics for each ticker, including the 1-day change percentage, whether the price is above the 50, 20, and 10-day simple moving averages (SMA), as well as the relative strength compared to the 10/20 SMA and 20/50 SMA crossovers. It also calculates the price deviation from the 50-day SMA. The table is displayed on the chart and can be positioned in different locations. Credits for the idea to @Alex_PrimeTrading ;)
مؤشرات Pine Script™
من ‎fyntrade‎
3