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Ultimate Screener TemplateHello Traders With the Ultimate Strategy Template , I shared a template to connect any indicator to this template and get backtesting results in less than a few minutes Now sharing another template ... many traders ask me to develop for them a screener/scanner based on a custom indicator on TradingView. The current TradingView screeners are great but don't allow for much customization; as we can only select pre-defined filters I don't know when we'll be able to natively screen among hundreds of assets with a custom indicator... then.... I created this template for the time being.... which should A whole new world The Ultimate Screener Template screens over 38 securities. What's very cool is that you'll only need to create 1 alert to scan over 38 securities: Explanation with this image here I totally understand that not all TradingView accounts have a lot of alarms; with this template, you can multiply the number of custom alerts you're initially allowed to. What if I need to set alerts on more than 38 securities? Pretty straightforward... you can create alerts only if the indicator is added to your chart first. So if you need let's say 100 securities, add this indicator 3 times on your chart (38*3 > 100) and then... edit the securities for your 3 indicators How to update your Screener settings Alert mode As per Pinescript reference , this template allows setting the alert frequency Possible values are: - alert.freq_all (all function calls trigger the alert) - alert.freq_once_per_bar (the first function call during the bar triggers the alert) - alert.freq_once_per_bar_close (the function call triggers the alert only when it occurs during the last script iteration of the real-time bar when it closes). All-time-high and All-time-low This template shows how to capture ATH and ATL alerts across many securities I used the functions from Quantnomad: Whenever a new ATH or ATL is made, the screener will send a personalized alarm with a personalized text based on the security triggering this alarm The code is pretty straightforward and shows you the part you'll need to update to transform your favorite custom indicator into a powerful screener. If anything is unclear in the code, please leave a comment and I'll respond as soon as possible All the best Dave
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Daveatt tarafından
45
Flawless Victory Strategy - 15min BTC Machine Learning StrategyHello everyone, I am a heavy Python programmer bringing machine learning to TradingView. This 15 minute Bitcoin Long strategy was created using a machine learning library and 1 year of historical data in Python. Every parameter is hyper optimized to bring you the most profitable buy and sell signals for Bitcoin on the 15min chart. The historical Bitcoin data was gathered from Binance API, in case you want to know the best exchange to use this long strategy. It is a simple Bollinger Band and RSI strategy with two versions included in the tradingview settings. The first version has a Sharpe Ratio of 7.5 which is amazing, and the second version includes the best stop loss and take profit positions with a Sharpe Ratio of 2.5 . Let me talk a little bit more about how the strategy works. The buy signal is triggered when close price is less than lower Bollinger Band at Std Dev 1, and the RSI is greater than a certain value. The sell signal is triggered when close price is greater than upper Bollinger Band at Std Dev 1, and the RSI is greater than a certain value. What makes this strategy interesting is the parameters the Machine Learning library found when backtesting for the best Sharpe Ratio. I left my computer on for about 28 hours to fully backtest 5000 EPOCHS and get the results. I was able to create a great strategy that might be one of TradingView's best strategies out on the website today. I will continue to apply machine learning to all my strategies from here on forward. Please Let me know if you have any questions or certain strategies you would like me to hyper optimize for you. I'm always willing to create profitable strategies! P.S. You can always pyramid this strategy for more gains! I just don't add pyramiding when creating my strategies because I want to show you the true win/loss ratio based buying one time and one selling one time. I feel like when creating a strategy that includes pyramiding right off the bat falsifies the win rate. This is my way of being transparent with you all. Have fun trading!
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Trebor_Namor tarafından
199
Windowed Volume Weighted Moving AverageIntroduction The concept of windowing was briefly introduced in the Blackman filter post, however windowing is more than just some window functions, and isn't exclusively used in filter design. Today we will use windowing with the volume weighted moving average, a moving average that weight the price with volume in order to be more reactive when volume is high, that is the moving average is more reactive when the market is more active. The use of windowing in the vwma allow to enhance its performance in the frequency domain which result in a smoother output. Note that i made a similar indicator long ago, but at that time I was not great at all with math and pinescript in general and the indicator was therefore wrong, i want to remind to the community that i'am not a professional, only an enthusiast, I never claimed to be a master coder and i'am totally open to receive criticism, if I sounded like bragging in the past I apologize, at 20 years old it is still easy to act like a kid, the information contained in my posts is only shared in order to help others but also myself, since sharing is also a way to learn more effectively. That said lets go with the indicator. Windowing Windowing consist on applying a window function to a signal, by applying i mostly talk about multiplying, this process is mostly used with windowed sinc filters in order to reduce ripples in the pass/stop band, but can be used with any kind of filters in order to have better frequency domain performance, the only thing we need to do is to multiply the filter weights by a window function. In order to understand windowing it is useful to visualize this process and understand spectral leakage. Remember that we can describe a signal as the sum of sine/cosine waves of different frequencies, amplitude and phase, leakage is an effect that appear with signals having discontinuities, that is when a signal non periodic. This figure show a non periodic sine wave of frequency 0.1, a non periodic signal will have is last sample value different from its first sample value, if we where to do its fourier transform we wouldn't end up with a single bin at 0.1 but with more bins, this is spectral leakage, the discontinuities in the signal create additional frequency components. In order to reduce leakage we must make the signal approximately periodic, this is done by making use of window functions. A window function is symmetric and relatively smooth, all we have to do is to multiply our first non periodic signal with the window function. We end up with the following windowed signal : The signal is approximately periodic and leakage has been reduced. Now that we have seen that, it might be useful to see why it is useful in filters. Remember that the Fourier transform of the filter weights gives us its frequency response, if our weights introduce leakage we end up with ripples, so windowing the filter weights might help reduce the ripples in the frequency response, which result in a smoother filter output. Volume Weighted Moving Average A volume weighted moving average is a FIR filter who use volume as filter kernel, therefore the frequency response of this filter always change, it is therefore not wrong to qualify the vwma as an adaptive moving average. Higher volume mean higher weighting of the current closing price value, which therefore produce a more reactive output. However the smoothness of the moving average is relatively poor. Windowed Volume Weighted Moving Average The proposed moving average has a length setting who control the moving average period, and various options that we will describe below. The first option is the type of window, there are many windows, certains more complex than others, here 3 windows are proposed, the famous Blackman window, the Bartlett, and finally the Hanning window, they provide each different level of smoothness. lets compare our moving average with period 100 with a vwma of the same period. Our moving average in red, and the vwma in blue. As you can see the results are smoother. The power parameter is used in order to give an even higher weighting to closing prices with high volume, this create a more boxy output. Below is a comparison with a vwma in blue and a powered vwma in red with power = 2 without windowing : We can then apply a window, here i will choose the Blackman window : Conclusion A new moving average based on windowed volume weighting has been proposed. The result are smoother which might therefore reduce whipsaw trades. I wish i could have explained things better, unfortunately windowing isn't something i use much, i wanted to post this moving average earlier this year. I will be off in France for 1 week, my flight is tomorrow in the morning, therefore i don't think i'll have the possibility to make other posts this year. I want to profit from this occasion to review my year in tradingview. Many indicators have been posted, some being extremely bad and others really interesting, this year introduced my attempts on estimating the lsma efficiently, the linear channels, an attempt on making lines and remain the first indicator from the v4 i posted if i'am right. Then came the efficient auto-line, who gained some popularity quite fast. Then finally the %G oscillator and the recursive bands where posted, and remain some of the favorites indicators i made. I also wanted to leave this year due to studies, that i totally abandoned, i'am thankful that i chosen to stay. I also want to express my apologies to any member that i could have offended, i think that i'am not a mean person but i certainly not contest the fact that i'am clumsy, even in my work, however my clumsiness is far greater when it comes to interact with other peoples or a group of peoples, i don't want to hurt anyone, if i made anything that made you feel bad then i'am sincerely sorry, and hope we can start this new year from 0. Finally i thank the tradingview community for their interest and curiosity, i thank all the great coders who work on making pinescript a better scripting language, i also thank the tradingview staff for their work this year. I wish you all a merry christmas, and an happy new year. Thanks for reading.
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alexgrover tarafından
7
Peak Valley Estimation StrategyIntroduction Its the first strategy that i post here, so don't expect ground breaking stuff, when testing my indicators i always used prorealtime and not tradingview. This strategy use signals generated by the peak/valley estimator indicator i posted long ago, i think the signals generated where sometimes quite accurate in some markets thus providing potential material for a profitable strategy. The indicator use 3 parameters, therefore the optimisation process is not easy, but i selected what i judged good parameters values at first glance. The strategy is in its more simple form without stop or anything, the detection of peaks and valley can allow for tighter stops since we expect the price to reverse, but take into account that sops and take profits are parameters subject to optimization process except if selected with strict money management rules and not profit optimization. Of course trading the strategy in this form is far from being great, if we take into account the market non stationarity then we might expect loss during trending markets. Trend strength indicators could help switch from a reversal to breakout strategy thus maybe providing more control. I really hope you find an use for the strategy. Notes Its been three long years since i started tradingview, and i put more efforts in my indicators than in my studies and life overall, this have created complicated situations and i can't afford to follow up with this, therefore i announce that in the end of june i will leave tradingview for quite a long time, at least until i have my degree. I announce it in advance in case some of you want helps of any kind. I will post all the indicators, both in progress and finished i have made during those three years. I hope you can all understand. Thanks for reading !
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alexgrover tarafından
15
CM Stochastic POP Method 1 - Jake Bernstein_V1A good friend ucsgears recently published a Stochastic Pop Indicator designed by Jake Bernstein with a modified version he found. I spoke to Jake this morning and asked if he had any updates to his Stochastic POP Trading Method. Attached is a PDF Jake published a while back (Please read for basic rules, which also Includes a New Method). I will release the Additional Method Tomorrow. Jake asked me to share that he has Updated this Method Recently. Now across all symbols he has found the Stochastic Values of 60 and 30 to be the most profitable. NOTE - This can be Significantly Optimized for certain Symbols/Markets. Jake Bernstein will be a contributor on TradingView when Backtesting/Strategies are released. Jake is one of the Top Trading System Developers in the world with 45+ years experience and he is going to teach how to create Trading Systems and how to Optimize the correct way. Below are a few Strategy Results....Soon You Will Be Able To Find Results Like This Yourself on TradingView.com BackTesting Results Example: EUR-USD Daily Chart Since 01/01/2005 Strategy 1: Go Long When Stochastic Crosses Above 60. Go Short When Stochastic Crosses Below 30. Exit Long/Short When Stochastic has a Reverse Cross of Entry Value. Results: Total Trades = 164 Profit = 50, 126 Pips Win% = 38.4% Profit Factor = 1.35 Avg Trade = 306 Pips Profit ***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6 Strategy 2: Rules - Proprietary Optimization Jake Will Teach. Only Added 1 Additional Exit Rule. Results: Total Trades = 164 Profit = 62, 876 Pips!!! Win% = 38.4% Profit Factor = 1.44 Avg Trade = 383 Pips Profit ***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6 Strategy 3: Rules - Proprietary Optimization Jake Will Teach. Only added 1 Additional Exit Rule. Results: Winning Percent Increases to 72.6%!!! , Same Amount of Trades. ***Most Consecutive Wins = 21 ...Most Consecutive Losses = 4 Indicator Includes: -Ability to Color Candles (CheckBox In Inputs Tab) Green = Long Trade Blue = No Trade Red = Short Trade -Color Coded Stochastic Line based on being Above/Below or In Between Entry Lines. Link To Jakes PDF with Rules dl.dropboxusercontent.com
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ChrisMoody tarafından
44
Volume BombI am republishing to use a clean chart. Previous one had too much of a mess. Idea for TradingView: Please allow us to change out the charts after publishing. I like to know when volume spikes (only when it spikes). I am not interested in seeing the rest of the volume bars. I created this indicator to show me when it explodes (i.e. the name "Volume Bomb" , plus it sounds cool). This indicator only shows you when volume exceeds the EMA of volume by whatever multiplier you set. Default settings are the current volume with 10 EMA. Yellow arrowup will appear when volume is at 1.5x the 10 EMA. Adjust it to your liking and particular stock.
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repo32 tarafından
10
2 MA + Strat Candle ColorsThe "2 MA + Strat Candle Colors" indicator combines two customizable moving averages (MAs) with a strategic candle-coloring system to help traders analyze trends and price action. Here’s a breakdown of its features: 1. Two Moving Averages (MAs): MA 1 & MA 2 Settings: Users can select between 7 MA types for each line: SMA, EMA, WMA, HMA, VWMA, LSMA, SMMA. Adjustable periods and price sources (e.g., close, open) for both MAs. Default settings: MA 1 = 9-period EMA, MA 2 = 20-period EMA. Plotting: MA 1 is blue, MA 2 is red (colors customizable via inputs). Crossovers between the MAs can signal trend changes. 2. Strategic Candle Coloring: Candles are colored based on their relationship to the previous candle: Green (Bullish): "Two-Up Bar" – current high > prior high, and low does not break prior low. Red (Bearish): "Two-Down Bar" – current low < prior low, and high does not break prior high. Purple (Outside Bar): "Three Bar" – current candle engulfs the prior candle (higher high and lower low). Yellow (Inside Bar): "One Bar" – current candle is contained within the prior candle’s range. Candle coloring is based on:
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CSP_22 tarafından
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel. █ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values. █ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical  Volatility  Rank, and Historical  Volatility  Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models. Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias. Pros:   • Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator. Cons:   • The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.   • Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.   • Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute   deviation makes the historical volatility more stable with extreme values. Parkinson Hv Estimator:   • Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.) Garman-Klass Estimator:   • Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.   • The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility. Rogers-Satchell Estimator:   • Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.   • The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.   • Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility. Garman-Klass Yang-Zhang extension:   • Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.   • The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies. Yang-Zhang Estimator:   • The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.   • This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.   • 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV. EWMA Estimator: • EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.   • EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.   • RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.   • However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH. Adjusted Mean Absolute Deviation Estimator:   • This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.   • It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.   • The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.   • In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility. Adjusted Median Absolute Deviation Estimator:   • This is another estimator that does not use standard deviation to measure volatility.   • Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.   • The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators. █ FEATURES   • You can select the volatility estimator models in the Volatility Model input   • Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.   • Alpha is used to adjust the Yang Zhang volatility estimator value.   • Percentile Length is used to Adjust Percentile coloring lookbacks.   • The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.   • When percentile coloring is off, it won’t show the gradient color.   • You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.   • When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon. • You can choose the plot style, such as lines, columns, areas in the plotstyle input.   • When the show information panel is on, a small panel will display on the right.   • The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value. █ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move). █ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
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Motgench tarafından
ADX Forecast Colorful [DiFlip]ADX Forecast Colorful Introducing one of the most advanced ADX indicators available — a fully customizable analytical tool that integrates forward-looking forecasting capabilities. ADX Forecast Colorful is a scientific evolution of the classic ADX, designed to anticipate future trend strength using linear regression. Instead of merely reacting to historical data, this indicator projects the future behavior of the ADX, giving traders a strategic edge in trend analysis. ⯁ Real-Time ADX Forecasting For the first time, a public ADX indicator incorporates linear regression (least squares method) to forecast the future behavior of ADX. This breakthrough approach enables traders to anticipate trend strength changes based on historical momentum. By applying linear regression to the ADX, the indicator plots a projected trendline n periods ahead — helping users make more accurate and timely trading decisions. ⯁ Highly Customizable The indicator adapts seamlessly to any trading style. It offers a total of 26 long entry conditions and 26 short entry conditions, making it one of the most configurable ADX tools on TradingView. Each condition is fully adjustable, enabling the creation of statistical, quantitative, and automated strategies. You maintain full control over the signals to align perfectly with your system. ⯁ Innovative and Science-Based This is the first public ADX indicator to apply least-squares predictive modeling to ADX dynamics. Technically, it embeds machine learning logic into a traditional trend-strength indicator. Using linear regression as a predictive engine adds powerful statistical rigor to the ADX, turning it into an intelligent, forward-looking signal generator. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental method in statistics and machine learning used to model the relationship between a dependent variable y and one or more independent variables x. The basic formula for simple linear regression is: y = β₀ + β₁x + ε Where: y  = predicted value (e.g., future ADX) x  = explanatory variable (e.g., bar index or time) β₀ = intercept β₁ = slope (rate of change) ε   = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the ADX projection based on recent trends. ⯁ Least Squares Estimation To minimize the error, the regression coefficients are calculated as: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Where: Σ = summation x̄ and ȳ = means of x and y i ranges from 1 to n (number of data points) These formulas provide the best linear unbiased estimator under Gauss-Markov conditions — assuming constant variance and linearity. ⯁ Linear Regression in Machine Learning Linear regression is a foundational algorithm in supervised learning. Its power in producing quantitative predictions makes it essential in AI systems, predictive analytics, time-series forecasting, and automated trading. Applying it to the ADX essentially places an intelligent forecasting engine inside a classic trend tool. ⯁ Visual Interpretation Imagine an ADX time series like this: Time → ADX  → The regression line smooths these values and projects them n periods forward, creating a predictive trajectory. This forecasted ADX line can intersect with the actual ADX, offering smarter buy and sell signals. ⯁ Summary of Scientific Concepts Linear Regression: Models variable relationships with a straight line. Least Squares: Minimizes prediction errors for best fit. Time-Series Forecasting: Predicts future values using historical data. Supervised Learning: Trains models to predict outcomes from inputs. Statistical Smoothing: Reduces noise and highlights underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Based on rigorous statistical theory. Unprecedented: First public ADX using least-squares forecast modeling. Smart: Uses machine learning logic. Forward-Looking: Generates predictive, not just reactive, signals. Customizable: Flexible for any strategy or timeframe. ⯁ Conclusion By merging ADX and linear regression, this indicator enables traders to predict market momentum rather than merely follow it. ADX Forecast Colorful is not just another indicator — it’s a scientific leap forward in technical analysis. With 26 fully configurable entry conditions and smart forecasting, this open-source tool is built for creating cutting-edge quantitative strategies. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What is the ADX? The Average Directional Index (ADX) is a technical analysis indicator developed by J. Welles Wilder. It measures the strength of a trend in a market, regardless of whether the trend is up or down. The ADX is an integral part of the Directional Movement System, which also includes the Plus Directional Indicator (+DI) and the Minus Directional Indicator (-DI). By combining these components, the ADX provides a comprehensive view of market trend strength. ⯁ How to use the ADX? The ADX is calculated based on the moving average of the price range expansion over a specified period (usually 14 periods). It is plotted on a scale from 0 to 100 and has three main zones: Strong Trend: When the ADX is above 25, indicating a strong trend. Weak Trend: When the ADX is below 20, indicating a weak or non-existent trend. Neutral Zone: Between 20 and 25, where the trend strength is unclear. ⯁ Entry Conditions Each condition below is fully configurable and can be combined to build precise trading logic. 📈 BUY 🅰️  Signal Validity: The signal will remain valid for X bars . 🅰️  Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 📉 SELL 🅰️  Signal Validity: The signal will remain valid for X bars . 🅰️  Signal Sequence: Configurable as AND or OR . 🅰️ +DI > -DI 🅰️ +DI < -DI 🅰️ +DI > ADX 🅰️ +DI < ADX 🅰️ -DI > ADX 🅰️ -DI < ADX 🅰️ ADX > Threshold 🅰️ ADX < Threshold 🅰️ +DI > Threshold 🅰️ +DI < Threshold 🅰️ -DI > Threshold 🅰️ -DI < Threshold 🅰️ +DI (Crossover) -DI 🅰️ +DI (Crossunder) -DI 🅰️ +DI (Crossover) ADX 🅰️ +DI (Crossunder) ADX 🅰️ +DI (Crossover) Threshold 🅰️ +DI (Crossunder) Threshold 🅰️ -DI (Crossover) ADX 🅰️ -DI (Crossunder) ADX 🅰️ -DI (Crossover) Threshold 🅰️ -DI (Crossunder) Threshold 🔮 +DI (Crossover) -DI Forecast 🔮 +DI (Crossunder) -DI Forecast 🔮 ADX (Crossover) +DI Forecast 🔮 ADX (Crossunder) +DI Forecast 🤖 Automation All BUY and SELL conditions are compatible with TradingView alerts, making them ideal for fully or semi-automated systems. ⯁ Unique Features Linear Regression: (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Condition Table: BUY/SELL Condition Labels: BUY/SELL Plot Labels in the Graph Above: BUY/SELL Automate and Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill" Linear Regression (Forecast) Signal Validity: The signal will remain valid for X bars Signal Sequence: Configurable as AND/OR Table of Conditions: BUY/SELL Conditions Label: BUY/SELL Plot Labels in the graph above: BUY/SELL Automate & Monitor Signals/Alerts: BUY/SELL Background Colors: "bgcolor" Background Colors: "fill"
Pine Script® göstergesi
DiFlip tarafından
Thirdeyechart Index Weekly DoomsdayIndex Weekly – Version 3 (Dynamic Strength Ranking) The Index Weekly Dynamic Ranking Version is a professional TradingView indicator designed to give traders a real-time, high-level view of global index momentum. Unlike static tables, this version dynamically ranks indices by weekly strength, placing the strongest index at the top and the weakest at the bottom. Each symbol is displayed with color-coded values—blue for positive weekly momentum, red for negative—making it immediately clear which markets are performing strongly and which are under pressure. This indicator calculates weekly percentage changes for all selected indices using: pct_week = ((close_week – open_week) / open_week) * 100 The results are compiled into a ranked table, so symbols automatically reorder themselves based on current strength. This dynamic ranking allows traders to quickly spot the most dominant indices and adjust their strategy accordingly. The table is fully visual and easy to read, with distinct coloring for up and down momentum, providing both clarity and speed for decision-making. The version is ideal for traders who want to combine global macro perspective with technical setups, as it shows not only the direction of individual indices but also which markets are leading or lagging. By following the strongest index first, traders can align their positions with global momentum rather than relying on a single static chart. This approach makes weekly index tracking more technical, more advanced, and closer to an institutional-style dashboard, similar to what professional terminals like Bloomberg offer, while remaining lightweight and easy to use on TradingView. Disclaimer This tool is for educational and analytical purposes only. It does not provide buy/sell signals or financial advice. Trading involves risk, and all decisions remain the responsibility of the user. © 2025 Ajik Boy. All rights reserved. Redistribution or commercial use without permission is prohibited.
Pine Script® göstergesi
Spiderweb11 tarafından
Hybrid Flow Master📊 Hybrid Flow Master - Professional Trading Indicator Overview Hybrid Flow Master is an advanced all-in-one trading indicator that combines Smart Money Concepts, institutional order flow analysis, and multi-timeframe confluence scoring to identify high-probability trade setups. Designed for both scalpers and swing traders across all markets (Forex, Crypto, Stocks, Indices). 🎯 Key Features 1. Intelligent Confluence System (0-100% Scoring) Proprietary scoring algorithm that weighs multiple factors Only signals when minimum confidence threshold is met Real-time probability calculations for each setup Signal quality grading: A+, A, B, C ratings 2. Smart Money Concepts (SMC) Automatic Order Block detection (bullish/bearish) Fair Value Gap (FVG) identification Market structure analysis (Higher Highs, Lower Lows) Swing high/low tracking with visual markers 3. Multi-Timeframe Analysis Higher timeframe trend filter for confluence Customizable HTF periods (1H, 4H, Daily, etc.) Prevents counter-trend trades Aligns entries with major trends 4. Volume Flow Analysis Volume spike detection with customizable thresholds Volume delta calculations (buying vs selling pressure) Institutional footprint identification Background highlighting for high-volume bars 5. Advanced Risk Management ATR-based stop loss calculation Automatic take profit levels Customizable risk/reward ratios (1:1, 1:2, 1:3+) Visual SL/TP lines on chart Position sizing guidance 6. Professional Dashboard Real-time HUD displaying: Market bias (Bullish/Bearish/Neutral) Higher timeframe trend status Current confluence percentage Volume status (Normal/High) RSI reading with color coding ATR volatility measure Signal quality grade 7. Smart Alert System Bullish confluence signals Bearish confluence signals Volume spike notifications Customizable alert messages Works with mobile app notifications 📈 What Makes It Unique? ✅ No Repainting - All signals are confirmed and final ✅ Probability-Based - Shows confidence level, not just binary signals ✅ Multi-Factor Confluence - Combines structure, volume, momentum, and HTF analysis ✅ Clean Interface - Toggle individual components on/off ✅ Works on All Timeframes - From 1-minute scalping to daily swing trading ✅ Universal Markets - Forex, Crypto, Stocks, Indices, Commodities 🎨 Customization Options Adjustable swing detection length Volume threshold settings Minimum confluence score filter Custom color schemes Dashboard position (4 corners) Show/hide individual components Risk/reward ratio adjustment ATR multiplier for stops 📊 Best Used For: ✔️ Scalping (1m - 15m charts) ✔️ Day Trading (15m - 1H charts) ✔️ Swing Trading (4H - Daily charts) ✔️ Trend Following ✔️ Reversal Trading ✔️ Breakout Trading 💡 How to Use: Add indicator to chart - Works immediately with default settings Set your timeframe - Choose your trading style Wait for signals - Green BUY or Red SELL labels with confidence % Check confluence score - Higher % = better quality setup Review dashboard - Confirm market bias and HTF trend Manage risk - Use provided SL/TP levels or adjust to your preference Set alerts - Get notified of high-probability setups ⚙️ Recommended Settings: For Scalping (1m-5m): Swing Length: 5-7 Min Confluence: 70% HTF: 15m or 1H For Day Trading (15m-1H): Swing Length: 10-15 Min Confluence: 60% HTF: 4H or Daily For Swing Trading (4H-Daily): Swing Length: 15-20 Min Confluence: 50-60% HTF: Weekly 📚 Indicator Components: ✦ Market Structure Detection ✦ Order Block Identification ✦ Fair Value Gaps (FVG) ✦ Volume Analysis ✦ RSI (14) ✦ MACD (12, 26, 9) ✦ ATR (14) ✦ Multi-Timeframe Trend ✦ Confluence Scoring Algorithm 🚀 Performance Notes: Optimized for speed and efficiency Minimal CPU usage Clean chart presentation Limited drawing objects (no chart clutter) Works on all TradingView plans ⚠️ Important Notes: This indicator is a tool to assist trading decisions, not financial advice Always use proper risk management (1-2% per trade recommended) Backtest on your preferred market and timeframe Combine with your own analysis and strategy Past performance does not guarantee future results 🔔 Alert Setup: Right-click indicator name → "Add Alert" → Choose: "Bullish Confluence Signal" for buy setups "Bearish Confluence Signal" for sell setups "Volume Spike Alert" for unusual activity 💬 Support: For questions, suggestions, or custom modifications, feel free to message me directly through TradingView.
Pine Script® göstergesi
kariyappavinay tarafından
CVD – Visible Range Candles & Line (Cumulative Delta Volume)Disclaimer: This indicator is provided for informational and educational purposes only. It does not constitute investment advice, trading advice, or a recommendation to buy or sell any financial instrument. The author assumes no liability for any losses, damages, or errors arising from use or misuse of this script. Please test thoroughly and use at your own risk. ________________________________________________________________________________ Purpose This indicator provides a fast and clear visualization of Cumulative Delta Volume (CVD) for the currently visible chart range in TradingView. It helps traders identify buy/sell volume pressure and market sentiment over any custom timeframe, with full control over reset intervals and chart style. Key Features CVD by Visible Bars: Dynamically calculates CVD only for bars currently visible on the chart, so scrolling and zooming always rescale the line and candles to your view. Style Selection: Choose line or candlestick display. Candles include both standard OHLC and optional Heikin Ashi smoothing. Automatic Resets: Restart CVD accumulation at the beginning of every day, week, month, or quarter. Choose ‘None’ for ongoing accumulation. Fully Custom Colors: Line color, candle body, wick, border – all optimized for clarity and customizable via the indicator’s Style tab. Autoscale Support: Always fits your timeframe. No need to adjust scale manually. Zero-Level Reference: Includes a horizontal zero line for quick reversal detection. Input Parameters Style: "Line" or "Candles" – controls visual type Heikin Ashi candles: Enable smoothing for candle view Show Line: Toggle CVD line visibility Reset CVD: Options: None, Daily, Weekly, Monthly, Quarterly How To Use Add the indicator to your TradingView chart. Select your preferred visual style (Line or Candles). Choose reset frequency based on your trading timeframe. Customize colors in the Style tab (line, candle up/down, wick, border). Scroll or zoom on the chart – the indicator’s range always fits the currently visible bars. Typical Use Cases Intraday traders tracking open/close session volume delta Swing traders identifying quarterly or monthly market accumulations Visualizing buy/sell pressure divergence at reversal points Comparing volume sentiment across flexible chart intervals Formula Delta calculation: Delta=volume×(sign(close−open)) Accumulation: Resets at user-chosen intervals, CVD plotted for only visible bars Author Created by Ronen Cohen
Pine Script® göstergesi
ronenc775 tarafından
ATR Volatility AlertsOverview: This is a dynamic alert tool based on the Average True Range (ATR), designed to help traders detect sudden price movements that exceed normal volatility levels. Whether you are trading breakouts or monitoring for abnormal spikes, this indicator visualizes these events on the chart and triggers system alerts when the price move exceeds your specified ATR multiplier. Key Features: Fully Customizable ATR Range: You can adjust the ATR Length (Default: 14) and the Multiplier (Default: 1.5x). Tip: Increase the multiplier (e.g., to 2.0 or 3.0) to catch only extreme volatility, or lower it for scalping smaller moves. Visual Chart Signals: Visual markers appear instantly when a bar's movement exceeds the ATR threshold. Green Triangle: Indicates an Upward Spike. Red Triangle: Indicates a Downward Spike. Flexible System Alerts: Designed to integrate seamlessly with TradingView's alert system. You can choose from three specific alert directions based on your strategy: 1.Price Spike Up: Triggers only on sharp upward moves. 2.Price Spike Down: Triggers only on sharp downward moves. 3.Bidirectional Volatility Alert: Triggers on BOTH huge pumps and dumps. How to Set Alerts: Click the "Create Alert" button in TradingView. Select ATR Volatility Alerts in the "Condition" dropdown. Choose the specific logic you need: · Select Price Spike Up for bullish monitoring. · Select Price Spike Down for bearish monitoring. · Select Bidirectional Volatility Alert to watch for any volatility expansion.
Pine Script® göstergesi
Sewell_ tarafından
FVG ATRFVG ATR — Fair Value Gap Size Measured in ATR Units This Pine Script v6 indicator detects Fair Value Gaps and displays their size as a ratio of the Average True Range, providing traders with a normalized measurement of gap significance across different market conditions and timeframes.​ Key Features Automatic FVG Detection The indicator identifies bullish and bearish Fair Value Gaps using the standard three-candle pattern. Bullish FVGs occur when the current low exceeds the high from two bars ago, while bearish FVGs occur when the current high falls below the low from two bars ago.​ ATR Ratio Calculation Each detected FVG is measured against the current Average True Range at the moment of detection. The ratio is displayed as a compact label next to the gap, showing values like "ATR: 0.75" or "ATR: 1.41". This normalization allows comparison of gap significance across volatile and calm market periods.​ Minimal Visual Footprint Labels are displayed directly on the chart without boxes or lines, using customizable text sizes from tiny to large. The default tiny size ensures the chart remains uncluttered while providing essential information at a glance.​ Highly Customizable Display All visual aspects are configurable through input parameters, including label position (top, middle, or bottom of gap), text size, text color, optional background, and horizontal offset from the detection candle.​ Customizable Parameters Detection Settings Detect Bullish FVG: Enable or disable detection of bullish gaps. Default is enabled.​ Detect Bearish FVG: Enable or disable detection of bearish gaps. Default is enabled.​ Min Size (pips): Filter out small gaps below the specified threshold. One pip equals 10 ticks for most Forex pairs. Default is 10 pips.​ ATR Calculation ATR Period: Period length for Average True Range calculation. Default is 14, adjustable to match your trading strategy.​ Label Settings Label Position: Vertical placement of the text label relative to the FVG zone. Options are Top, Middle, or Bottom. Default is Middle.​ Label Size: Text size from Tiny (smallest), Small, Normal, to Large. Default is Tiny for minimal chart clutter.​ Text Color: Custom color for label text. Default is white for visibility on dark themes.​ Show Background: Toggle to display labels with a colored background box or as transparent text only. Default is disabled for cleaner appearance.​ Background Color: Custom color for label background when enabled. Default is semi-transparent gray.​ Label Offset (bars): Horizontal distance in bars between the detection candle and the label. Set to 0 for labels directly on the candle, or increase for separation. Default is 0.​ Recommended Use Cases Multi-Timeframe Analysis Compare FVG significance across different timeframes by observing ATR ratios. A 1.5 ATR gap on the 1-hour chart may indicate different significance than the same ratio on the daily chart.​ Volatility-Adjusted Trading Use ATR ratios to filter for only the most significant gaps. For example, only trade FVGs with ratios above 1.0 to focus on gaps larger than typical price movement.​ Risk Management Size positions based on gap magnitude relative to current volatility. Larger ATR ratios may warrant tighter stops or smaller position sizes.​ Market Efficiency Analysis Track how quickly and completely different-sized gaps get filled. Gaps with higher ATR ratios may take longer to fill or act as stronger support and resistance zones.​ Technical Details This indicator is written in Pine Script v6 and follows all recommended coding standards including strict 4-space indentation, lazy boolean evaluation, and proper type declarations. The script uses array-based storage to maintain up to 500 labels simultaneously.​ The ATR ratio is calculated at the moment of FVG detection and remains fixed, never repainting. The calculation divides the FVG height (distance between gap boundaries) by the current ATR value using the specified period. Division by zero is protected with conditional logic.​ Label positioning uses the xloc.bar_index and yloc.price system for precise placement. The horizontal offset parameter allows traders to adjust label spacing based on chart zoom level and personal preference. Text formatting uses str.tostring with two decimal places for clear ratio display.​ Important Notes The indicator never repaints as all FVG detections and ATR calculations are fixed upon bar confirmation. Labels persist on the chart until the maximum label count is reached, at which point the oldest labels are automatically removed by TradingView.​ For optimal performance on charts with many FVGs, consider increasing the minimum pip size filter or using smaller label sizes. The tiny size option provides the smallest possible text for maximum chart clarity.​ Installation and Usage Copy the source code into the TradingView Pine Editor and add the indicator to your chart. The overlay parameter is set to true, allowing labels to display directly on price candles. Configure all parameters through the indicator settings panel to match your trading style and visual preferences.​ 100% Pine Script v6 indicator — No repaint — Open source
Pine Script® göstergesi
Andre_lpz tarafından
TrendMaster V2TrendMaster V2 is a comprehensive Pine Script indicator designed for TradingView. It combines multiple technical indicators and an advanced scoring logic to provide actionable trading signals. The script is highly customizable, allowing users to adjust trading modes, color themes, and signal filters according to their preferences and risk tolerance. Key Features Composite Scoring System: The script calculates a composite score based on trend, momentum, pattern recognition, volume, volatility, divergence, Pearson correlation, and the CCI index. This score helps identify the best buy or sell opportunities. Customizable Parameters: Users can choose between “Aggressive,” “Balanced,” or “Conservative” trading modes, adjust indicator periods, and customize the color scheme of all visual elements. Confluence Analysis: The script evaluates the number of matching bullish or bearish signals, providing a confluence summary for higher-confidence trades. Visual Signals: Clear visual cues (triangles, circles, crosses) are displayed on the chart for strong buy/sell signals, confluences, and divergences. Information Panels: Two panels display real-time data such as score, RSI, volume, volatility, Pearson, CCI, trend, signal, and mode, along with the confluence status for quick reference. Alert Conditions: The script supports alerts for strong buy/sell signals, confluences, and divergences. How It Works Main Configuration: Users select a trading mode (Aggressive, Balanced, or Conservative) and a color theme (Dark or Light). Custom colors can also be set for bullish, bearish, strong, neutral, and signal elements. Technical Indicators Moving Averages (SMA/EMA) for trend analysis. RSI to assess momentum and overbought/oversold conditions. MACD for trend confirmation. Volume and Volatility (ATR) for market activity evaluation. Advanced Indicators Pearson Correlation to measure trend strength. CCI for cyclic momentum analysis. Pattern Recognition The script identifies common bullish and bearish reversal patterns (e.g., engulfing, hammer, morning/evening star) and continuation patterns (e.g., three white soldiers/black crows). Composite Score Each indicator contributes to a composite score, weighted according to the selected trading mode. The score determines the strength of buy/sell signals. Confluence Analysis The script counts the number of matching bullish or bearish signals, providing a confluence summary for higher-confidence trades. Visual Signals and Alerts Strong buy/sell signals: triangles Confluence signals: circles Divergences: crosses Alerts are triggered for strong buy/sell signals, confluences, and divergences. Usage Instructions Add the script to your TradingView chart. Adjust the settings in the configuration panel to match your trading style. Monitor the information panels and visual signals to spot trading opportunities. Set up alerts for your preferred signal types.
Pine Script® göstergesi
s47marsac tarafından
Average Price Calculator / VisualizerDCA Average Price Calculator - Visualize Your Breakeven & TP! Ever wished you could visualize your trades and instantly see your average entry price right here on TradingView? Especially if you're a DCA (Dollar-Cost Averaging) trader like me, tracking multiple entries can be a hassle. You're constantly switching to a spreadsheet or calculator to figure out your breakeven and take-profit levels. Well I've developed this DCA Average Price Calculator to solve exactly that problem, bringing all your position planning directly onto your chart. What It Does This indicator is a interactive tool designed to calculate the weighted average price of up to 10 separate trade entries. It then plots your crucial breakeven (average price) and a customizable take-profit target directly on your chart, giving you a clear visual of your position. Key Features Up to 10 Order Entries: Plan complex DCA strategies with support for up to ten individual buys. Flexible Size Input: Enter your position size in either USD Amount or Number of Shares/Contracts. The script is smart enough to know which one you're using. Instant Average Price Calculation: Your weighted average price (your breakeven point) is calculated and plotted in real-time as a clean yellow line. Customizable Take-Profit Target: Set your desired profit percentage and see your take-profit level instantly plotted as a green line. Detailed On-Chart Labels: Each order you plot is marked with a detailed label showing the entry price, the number of shares purchased, and the total USD value of that entry. Clean & Uncluttered UI: The main Average and TP labels are intelligently shifted to the right, ensuring they don't overlap with your entry markers, keeping your chart readable. How to Use It - Simple Steps Add the indicator to your chart. Open the script's 'Settings' menu. In the 'Take Profit' section, set your desired profit percentage (e.g., 1 for 1%). Under the 'Orders' section, begin filling in your entries. For each 'Order #', enter the Price. Next, enter the size. You can either fill in the 'Size (USD)' box OR the '/ Shares' box. Leave the one you're not using at 0. As you add orders, the 'Avg' (yellow) and 'TP' (green) lines, along with the blue order labels, will automatically appear and adjust on your chart! Who Is This For? DCA Traders: This is the ultimate tool for you! Position Traders: Keep track of scaling into a larger position over time. Manual Backtesters: Quickly simulate and visualize how a series of buys would have played out. Any Trader who wants a quick and easy way to calculate their average entry without leaving TradingView. I built this tool to improve my own trading workflow, and I hope it helps you as much as it has helped me. If you find it useful, please consider giving it a 'Like' and feel free to leave any feedback or suggestions in the comments! Happy trading
Pine Script® göstergesi
KB0006 tarafından