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Fractal Adaptive Moving Average (FRAMA)

Machine Learning: VWAP [YinYangAlgorithms]Machine Learning: VWAP aims to use Machine Learning to Identify the best location to Anchor the VWAP at. Rather than using a traditional fixed length or simply adjusting based on a Date / Time; by applying Machine Learning we may hope to identify crucial areas which make sense to reset the VWAP and start anew. VWAP’s may act similar to a Bollinger Band in the sense that they help to identify both Overbought and Oversold Price locations based on previous movements and help to identify how far the price may move within the current Trend. However, unlike Bollinger Bands, VWAPs have the ability to parabolically get quite spaced out and also reset. For this reason, the price may never actually go from the Lower to the Upper and vice versa (when very spaced out; when the Upper and Lower zones are narrow, it may bounce between the two). The reason for this is due to how the anchor location is calculated and in this specific Indicator, how it changes anchors based on price movement calculated within Machine Learning. This Indicator changes the anchor if the Low < Lowest Low of a length of X and likewise if the High > Highest High of a length of X. This logic is applied within a Machine Learning standpoint that likewise amplifies this Lookback Length by adding a Machine Learning Length to it and increasing the lookback length even further. Due to how the anchor for this VWAP changes, you may notice that the Basis Line (Orange) may act as a Trend Identifier. When the Price is above the basis line, it may represent a bullish trend; and likewise it may represent a bearish trend when below it. You may also notice what may happen is when the trend occurs, it may push all the way to the Upper or Lower levels of this VWAP. It may then proceed to move horizontally until the VWAP expands more and it may gain more movement; or it may correct back to the Basis Line. If it corrects back to the basis line, what may happen is it either uses the Basis Line as a Support and continues in its current direction, or it will change the VWAP anchor and start anew. Tutorial: If we zoom in on the most recent VWAP we can see how it expands. Expansion may be caused by time but generally it may be caused by price movement and volume. Exponential Price movement causes the VWAP to expand, even if there are corrections to it. However, please note Volume adds a large weighted factor to the calculation; hence Volume Weighted Average Price (VWAP). If you refer to the white circle in the example above; you’ll be able to see that the VWAP expanded even while the price was correcting to the Basis line. This happens due to exponential movement which holds high volume. If you look at the volume below the white circle, you’ll notice it was very large; however even though there was exponential price movement after the white circle, since the volume was low, the VWAP didn’t expand much more than it already had. There may be times where both Volume and Price movement isn’t significant enough to cause much of an expansion. During this time it may be considered to be in a state of consolidation. While looking at this example, you may also notice the color switch from red to green to red. The color of the VWAP is related to the movement of the Basis line (Orange middle line). When the current basis is > the basis of the previous bar the color of the VWAP is green, and when the current basis is < the basis of the previous bar, the color of the VWAP is red. The color may help you gauge the current directional movement the price is facing within the VWAP. You may have noticed there are signals within this Indicator. These signals are composed of Green and Red Triangles which represent potential Bullish and Bearish momentum changes. The Momentum changes happen when the Signal Type: The High/Low or Close (You pick in settings) Crosses one of the locations within the VWAP. Bullish Momentum change signals occur when : Signal Type crosses OVER the Basis Signal Type crosses OVER the lower level Bearish Momentum change signals occur when: Signal Type crosses UNDER the Basis Signal Type Crosses UNDER the upper level These signals may represent locations where momentum may occur in the direction of these signals. For these reasons there are also alerts available to be set up for them. If you refer to the two circles within the example above, you may see that when the close goes above the basis line, how it mat represents bullish momentum. Likewise if it corrects back to the basis and the basis acts as a support, it may continue its bullish momentum back to the upper levels again. However, if you refer to the red circle, you’ll see if the basis fails to act as a support, it may then start to correct all the way to the lower levels, or depending on how expanded the VWAP is, it may just reset its anchor due to such drastic movement. You also have the ability to disable Machine Learning by setting ‘Machine Learning Type’ to ‘None’. If this is done, it will go off whether you have it set to: Bullish Bearish Neutral For the type of VWAP you want to see. In this example above we have it set to ‘Bullish’. Non Machine Learning VWAP are still calculated using the same logic of if low < lowest low over length of X and if high > highest high over length of X. Non Machine Learning VWAP’s change much quicker but may also allow the price to correct from one side to the other without changing VWAP Anchor. They may be useful for breaking up a trend into smaller pieces after momentum may have changed. Above is an example of how the Non Machine Learning VWAP looks like when in Bearish. As you can see based on if it is Bullish or Bearish is how it favors the trend to be and may likewise dictate when it changes the Anchor. When set to neutral however, the Anchor may change quite quickly. This results in a still useful VWAP to help dictate possible zones that the price may move within, but they’re also much tighter zones that may not expand the same way. We will conclude this Tutorial here, hopefully this gives you some insight as to why and how Machine Learning VWAPs may be useful; as well as how to use them. Settings: VWAP: VWAP Type: Type of VWAP. You can favor specific direction changes or let it be Neutral where there is even weight to both. Please note, these do not apply to the Machine Learning VWAP. Source: VWAP Source. By default VWAP usually uses HLC3; however OHLC4 may help by providing more data. Lookback Length: The Length of this VWAP when it comes to seeing if the current High > Highest of this length; or if the current Low is < Lowest of this length. Standard VWAP Multiplier: This multiplier is applied only to the Standard VWMA. This is when 'Machine Learning Type' is set to 'None'. Machine Learning: Use Rational Quadratics: Rationalizing our source may be beneficial for usage within ML calculations. Signal Type: Bullish and Bearish Signals are when the price crosses over/under the basis, as well as the Upper and Lower levels. These may act as indicators to where price movement may occur. 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!
Pine Script™ indicator
by YinYangAlgorithms
12
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!
Pine Script™ indicator
by YinYangAlgorithms
19
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!
Pine Script™ indicator
by YinYangAlgorithms
1
Machine Learning : Cosine Similarity & Euclidean DistanceIntroduction: This script implements a comprehensive trading strategy that adheres to the established rules and guidelines of housing trading. It leverages advanced machine learning techniques and incorporates customised moving averages, including the Conceptive Price Moving Average (CPMA), to provide accurate signals for informed trading decisions in the housing market. Additionally, signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation are utilised to enhance the signal quality and improve trading accuracy. Features: Market Analysis: The script utilizes advanced machine learning methods such as Lorentzian, Euclidean distance, and Cosine similarity to analyse market conditions. These techniques measure the similarity and distance between data points, enabling more precise signal identification and enhancing trading decisions. Cosine similarity: Cosine similarity is a measure used to determine the similarity between two vectors, typically in a high-dimensional space. It calculates the cosine of the angle between the vectors, indicating the degree of similarity or dissimilarity. In the context of trading or signal processing, cosine similarity can be employed to compare the similarity between different data points or signals. The vectors in this case represent the numerical representations of the data points or signals. Cosine similarity ranges from -1 to 1, with 1 indicating perfect similarity, 0 indicating no similarity, and -1 indicating perfect dissimilarity. A higher cosine similarity value suggests a closer match between the vectors, implying that the signals or data points share similar characteristics. Lorentzian Classification: Lorentzian classification is a machine learning algorithm used for classification tasks. It is based on the Lorentzian distance metric, which measures the similarity or dissimilarity between two data points. The Lorentzian distance takes into account the shape of the data distribution and can handle outliers better than other distance metrics. Euclidean Distance: Euclidean distance is a distance metric widely used in mathematics and machine learning. It calculates the straight-line distance between two points in Euclidean space. In two-dimensional space, the Euclidean distance between two points (x1, y1) and (x2, y2) is calculated using the formula sqrt((x2 - x1)^2 + (y2 - y1)^2). Dynamic Time Windows: The script incorporates a dynamic time window function that allows users to define specific time ranges for trading. It checks if the current time falls within the specified window to execute the relevant trading signals. Custom Moving Averages: The script includes the CPMA, a powerful moving average calculation. Unlike traditional moving averages, the CPMA provides improved support and resistance levels by considering multiple price types and employing a combination of Exponential Moving Averages (EMAs) and Simple Moving Averages (SMAs). Its adaptive nature ensures responsiveness to changes in price trends. Signal Processing Techniques: The script applies signal processing techniques such as Know sure thing, Rational Quadratic, and sigmoid transformation to enhance the quality of the generated signals. These techniques improve the accuracy and reliability of the trading signals, aiding in making well-informed trading decisions. Trade Statistics and Metrics: The script provides comprehensive trade statistics and metrics, including total wins, losses, win rate, win-loss ratio, and early signal flips. These metrics offer valuable insights into the performance and effectiveness of the trading strategy. Usage: Configuring Time Windows: Users can customize the time windows by specifying the start and finish time ranges according to their trading preferences and local market conditions. Signal Interpretation: The script generates long and short signals based on the analysis, custom moving averages, and signal processing techniques. Users should pay attention to these signals and take appropriate action, such as entering or exiting trades, depending on their trading strategies. Trade Statistics: The script continuously tracks and updates trade statistics, providing users with a clear overview of their trading performance. These statistics help users assess the effectiveness of the strategy and make informed decisions. Conclusion: With its adherence to housing trading rules, advanced machine learning methods, customized moving averages like the CPMA, and signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation, this script offers users a powerful tool for housing market analysis and trading. By leveraging the provided signals, time windows, and trade statistics, users can enhance their trading strategies and improve their overall trading performance. Disclaimer: Please note that while this script incorporates established tradingview housing rules, advanced machine learning techniques, customized moving averages, and signal processing techniques, it should be used for informational purposes only. Users are advised to conduct their own analysis and exercise caution when making trading decisions. The script's performance may vary based on market conditions, user settings, and the accuracy of the machine learning methods and signal processing techniques. The trading platform and developers are not responsible for any financial losses incurred while using this script. By publishing this script on the platform, traders can benefit from its professional presentation, clear instructions, and the utilisation of advanced machine learning techniques, customised moving averages, and signal processing techniques for enhanced trading signals and accuracy. I extend my gratitude to TradingView, LUX ALGO, and JDEHORTY for their invaluable contributions to the trading community. Their innovative scripts, meticulous coding patterns, and insightful ideas have profoundly enriched traders' strategies, including my own.
Pine Script™ indicator
by chhagansinghmeena
74
FRAMA and Candlestick Patterns [CSM]FRAMA (Fractal Adaptive Moving Average) is a technical analysis indicator that adapts its smoothing period according to the market's volatility, allowing it to provide accurate signals in all market conditions. This indicator script plots the FRAMA on a chart and generates buy and sell signals based on the FRAMA and candlestick patterns. It also includes an option to filter signals based on bullish and bearish engulfing patterns. To detect candlestick patterns, the script imports the "BankNifty_CSM" library from the creator's public library on TradingView. The FRAMA calculation is done using a loop that iterates over the last "length" number of bars, with the smoothing factor adjusted based on the "fracDim" parameter. The buy and sell signals are generated based on the position of the current price relative to the FRAMA line. If the "engulfing" parameter is set to true, the signals are further filtered based on bullish and bearish engulfing patterns. Overall, this script combines various technical indicators and candlestick pattern recognition to provide a complete trading strategy. However, as with any trading strategy, it should be thoroughly backtested and evaluated before using it in a live trading environment.
Pine Script™ indicator
by chhagansinghmeena
5
AI-Bank-Nifty Tech AnalysisThis code is a TradingView indicator that analyzes the Bank Nifty index of the Indian stock market. It uses various inputs to customize the indicator's appearance and analysis, such as enabling analysis based on the chart's timeframe, detecting bullish and bearish engulfing candles, and setting the table position and style. The code imports an external script called BankNifty_CSM, which likely contains functions that calculate technical indicators such as the RSI, MACD, VWAP, and more. The code then defines several table cell colors and other styling parameters. Next, the code defines a table to display the technical analysis of eight bank stocks in the Bank Nifty index. It then defines a function called get_BankComponent_Details that takes a stock symbol as input, requests the stock's OHLCV data, and calculates several technical indicators using the imported CSM_BankNifty functions. The code also defines two functions called get_EngulfingBullish_Detection and get_EngulfingBearish_Detection to detect bullish and bearish engulfing candles. Finally, the code calculates the technical analysis for each bank stock using the get_BankComponent_Details function and displays the results in the table. If the engulfing input is enabled, the code also checks for bullish and bearish engulfing candles and displays buy/sell signals accordingly. The FRAMA stands for "Fractal Adaptive Moving Average," which is a type of moving average that adjusts its smoothing factor based on the fractal dimension of the price data. The fractal dimension reflects self-similarity at different scales. The FRAMA uses this property to adapt to the scale of price movements, capturing short-term and long-term trends while minimizing lag. The FRAMA was developed by John F. Ehlers and is commonly used by traders and analysts in technical analysis to identify trends and generate buy and sell signals. I tried to create this indicator in Pine. In this context, "RS" stands for "Relative Strength," which is a technical indicator that compares the performance of a particular stock or market sector against a benchmark index. The "Alligator" is a technical analysis tool that consists of three smoothed moving averages. Introduced by Bill Williams in his book "Trading Chaos," the three lines are called the Jaw, Teeth, and Lips of the Alligator. The Alligator indicator helps traders identify the trend direction and its strength, as well as potential entry and exit points. When the three lines are intertwined or close to each other, it indicates a range-bound market, while a divergence between them indicates a trending market. The position of the price in relation to the Alligator lines can also provide signals, such as a buy signal when the price crosses above the Alligator lines and a sell signal when the price crosses below them. In addition to these, we have several other commonly used technical indicators, such as MACD, RSI, MFI (Money Flow Index), VWAP, EMA, and Supertrend. I used all the built-in functions for these indicators from TradingView. Thanks to the developer of this TradingView Indicator. I also created a BankNifty Components Table and checked it on the dashboard.
Pine Script™ indicator
by chhagansinghmeena
38
Cong Adaptive Moving AverageDr. Scott Cong's new adaptation of an adaptive moving average (AMA), featured in TASC March 2023. It adjusts its parameters automatically according to the volatility of market, tracking price closely in trending movement, staying flat in congestion areas. Perry Kaufman’s adaptive moving average, first described in his 1995 book Smarter Trading, is a great example of how an AMA can self-adjust to adapt to changing environments. This indicator presents a new scheme for an adaptive moving average that is responsive, smooth, and robust.
Pine Script™ indicator
by Forex_Station_Centaur
1
Ostinated Adjustable Fractal with AlertThis indicator (Ostinated Adjustable Fractal with Alert) is like the traditional William Fractals. However, it created to overcome two disadvantages of the William Fractals: 1. Set minimum value to 1. This a big advantage over traditional William Fractal that has a minimum value of 2. 2. Alerts: setting alert with the traditional William Fractal is very difficult. We have created this indicator to simplify setting fractal alerts. Like and comment. Enjoy!
Pine Script™ indicator
by luxurylifestylemanifestation
2
JC MAs: SMA, WMA, EMA, DEMA, TEMA, ALMA, Hull, Kaufman, FractalThe best collection of moving averages anywhere. I know, because I searched, couldn't find the right collection, and so wrote it myself! ------------------------------------------------------------------------------- Notable features that either aren't found anywhere else...or at least in one place: ------------------------------------------------------------------------------- • The "Triple Exponential Moving Average", is actually that mathematically - rather than "three seperate EMA graphs", as is commonly found on Trading View. • Includes exotic moving averages: Hull Moving Average (HMA), Kaufman's Adaptive Moving Average (KAMA), and Fractal Apaptive Moving Average (FrAMA). • Each moving average has its own user-definable averaging length in DAYS, rather than an abstract "length". This is respected even for different graphing resolutions, and different chart views - even for the more exotic MAs. • Days can be fractional. • A master time resolution ("Timeframe") is also user-definable. And unlike most other moving average charts, this won't affect the internal "length" variable (specified days are still respected), it only changes the graphing resolution. You can also specify to use chart's resolution - which, as you know, is not very useful for moving averages - yet so many moving average scripts on Trading View don't let you specify otherwise. • If every CPU cycle counts, you can set "days" to 0 to prevent a particular unneeded moving average from being calculated at all. • Includes a custom moving average that is unique, if you're looking for a tiny edge in TA to beat everyone else looking at the same stuff: a customizable weighted blend of SMA, TEMA, HMA, KAMA, and FrMA. (Note: The weights for these blends don't have to add up to 100, they will self-level no matter what they add up to.) • By default, the averages are color-coded according to rainbow order of light spectrum frequency, relative to approximate responsiveness to current price: Red (SMA) is the laziest, violet (FrAMA) is the most hyper, and green is in the middle. ------------------------------------------------------------------------------- Contains the following moving averages, in order of responsiveness: ------------------------------------------------------------------------------- • Simple Moving Average (SMA) • Arnaud Legoux Moving Average (ALMA) • Exponential Moving Average (EMA) • Weighted Moving Average (WMA) • Blend average of SMA and TEMA (JCBMA) • Double Exponential Moving Average (DEMA) • Triple Exponential Moving Average (TEMA) • Hull Moving Average (HMA) • Kaufman's Adaptive Moving Average (KAMA) • Fractal Apaptive Moving Average (FrAMA) Note: There are a few extreme edge cases where the graphs won't render, which are obvious. (Because they won't render.) In which case, all you need to do is choose a more sane master resolution ("Timeframe") relative to the timeframe of the chart. This is more about the limits of Trading View, than specific script bugs. ------------------------------------------------------------------------------- Includes reworked code snippets ------------------------------------------------------------------------------- • "Kaufman Moving Average Adaptive (KAMA)" by HPotter • "FRAMA (Ehlers true modified calculation)" by nemozny • Which in turn was based on "Fractal Adaptive Moving Average (real one)" by Shizaru
Pine Script™ indicator
by rareHeron59465
6
(FRAMA) Fractal Adaptive Moving AverageThis script plots UP TO 4 FRAMAs (Fractal Adaptive Moving Average), a particularly useful adaptive MA which uses fractal price theory to "adapt" itself, or better, to auto-adjust its parameters. It's quite reactive, but its major strength resides in its ability to filter the noise during lateral price movements, avoiding some false signal provided by more common moving averages. By default, the script plots the basic FRAMA version, the one firstly developed by John Ehlers, but also provides the possibility to play with the parameters, to obtain a modified FRAMA. Unfortunately I'm not able to provide web sources about FRAMA here, due to Tradingview policies. So search online if you need more details about FRAMAs. Please show me your support if you like my scripts. Hope you enjoy them.
Pine Script™ indicator
by Bezzus
12
Uber Moving Averages [UTS]Uber Moving Averages are four highly customisable moving averages to complement your technical analysis. The optional trend direction visualisation makes it a powerful tool for trend weighted analysis. Moving Averages 16 different Moving Averages are at your disposal. Trend Visualisation If the predominant trend direction is DOWN the moving average is painted red. If the trend direction is UP the moving average is painted in green.
Pine Script™ indicator
by UberTradingSystems
Ehlers Fractal Adaptive Moving Average with Bands [Bitcoinduke]FRAMA – What is it? Fractal Adaptive Moving Average Technical Indicator ( FRAMA ) was developed by John Ehlers, code was implemented by TradingView user Shizaru. I've updated it to Pine Script 4 and added Bands Extension. This indicator is constructed based on the algorithm of the Exponential Moving Average , in which the smoothing factor is calculated based on the current fractal dimension of the price series. Advantages: possibility to follow strong trend movements determine moments of price consolidation ( FRAMA sufficiently slow down at these moments) FRAMA + Bands The FRAMA serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price. Typically the Upper and Lower Bands are set to two standard deviations away from the FRAMA ; however it should be adjusted depending on the task.
Pine Script™ indicator
by Bitduke
TB!G-Scalp Strategy [Alert]Scalping Crypto Assets Made Easy TB!G-Scalp is a TradingView based alert and accompanying strategy PineV3 Script. Backtest vs Realtime When designing the algorithms for this strategy our focus was primarily on ease of use.This results in a beautiful yet easy to use scalping strategy. As input it takes a chart period and only one extra parameter for fine tuning. The backtest results are an accurate representation of it’s real-time behaviour. What makes it tick? Over the last 2 years we collected a lot of market data regarding Bull and Bear behaviours. This previous market behaviour echo’s into the current market trend. By recognising these echo’s we are able to anticipate an upcoming micro reversal which eventually end up being a scalping strategy. Interested Access is provided to a limited amount of people and for the duration that is determined by it’s Alpha Decay Rate. This ADR is expected to become problematic after 2.3 year of usage on a lot of 150 users.
Pine Script™ indicator
by KingRekter
9
[Single] Heartbeat_Live-=The Script=- Tradingviews #1 Custom Alert Script -non repaint- The Script has been developed for over a year now, always striving to build the most efficient and highest ROI script on the market. This script has been designed for trading Bitcoin, but has now been expanded to being very efficient on most market types, including forex and altcoins. The ability to have alerts on our script gives you the rare ability to automate this script using autoview or Gunbot tradingview extension. -Alerts -non-repaint -limit edition -Lifetime updates Altcoin Screenshot: Bitcoin Screenshot: Forex Screenshot: This script is a custom built indicator exclusively used on this script. You are able to fine tune the script to all of your needs, allowing you to adjust period settings, sensitivity and more so you can always have the signals you want. Not only do you get access to the heartbeat script, but you also get access to our library of other non repaint alert scripts, including Renko Alert scripts and Scripts designed Specifically for leverage. For more information gunbot.store *disclaimer* With the purchase of The Script, you are not given access to source code and we do not give trials. We do however give a guarantee of good support and updates where needed to comply with the signals shown above. It could start repainting depending on tradingview's engine updates, as soon that will happen we are on top of it to fix it.
Pine Script™ indicator
by KingRekter
14