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Optimization

*Auto Backtest & Optimize EngineFull-featured Engine for Automatic Backtesting and parameter optimization. Allows you to test millions of different combinations of stop-loss and take profit parameters, including on any connected indicators. ⭕️ Key Futures Quickly identify the optimal parameters for your strategy. Automatically generate and test thousands of parameter combinations. A simple Genetic Algorithm for result selection. Saves time on manual testing of multiple parameters. Detailed analysis, sorting, filtering and statistics of results. Detailed control panel with many tooltips. Display of key metrics: Profit, Win Rate, etc.. Comprehensive Strategy Score calculation. In-depth analysis of the performance of different types of stop-losses. Possibility to use to calculate the best Stop-Take parameters for your position. Ability to test your own functions and signals. Customizable visualization of results. Flexible Stop-Loss Settings: • Auto ━ Allows you to test all types of Stop Losses at once(listed below). • S.VOLATY ━ Static stop based on volatility (Fixed, ATR, STDEV). • Trailing ━ Classic trailing stop following the price. • Fast Trail ━ Accelerated trailing stop that reacts faster to price movements. • Volatility ━ Dynamic stop based on volatility indicators. • Chandelier ━ Stop based on price extremes. • Activator ━ Dynamic stop based on SAR. • MA ━ Stop based on moving averages (9 different types). • SAR ━ Parabolic SAR (Stop and Reverse). Advanced Take-Profit Options: • R:R: Risk/Reward ━ sets TP based on SL size. • T.VOLATY ━ Calculation based on volatility indicators (Fixed, ATR, STDEV). Testing Modes: • Stops ━ Cyclical stop-loss testing • Pivot Point Example ━ Example of using pivot points • External Example ━ Built-in example how test functions with different parameters • External Signal ━ Using external signals ⭕️ Usage ━ First Steps: When opening, select any point on the chart. It will not affect anything until you turn on Manual Start mode (more on this below). The chart will immediately show the best results of the default Auto mode. You can switch Part's to try to find even better results in the table. Now you can display any result from the table on the chart by entering its ID in the settings. Repeat steps 3-4 until you determine which type of Stop Loss you like best. Then set it in the settings instead of Auto mode. * Example: I flipped through 14 parts before I liked the first result and entered its ID so I could visually evaluate it on the chart. Then select the stop loss type, choose it in place of Auto mode and repeat steps 3-4 or immediately follow the recommendations of the algorithm. Now the Genetic Algorithm at the bottom right will prompt you to enter the Parameters you need to search for and select even better results. Parameters must be entered All at once before they are updated. Enter recommendations strictly in fields with the same names. Repeat steps 5-6 until there are approximately 10 Part's left or as you like. And after that, easily pour through the remaining Parts and select the best parameters. ━ Example of the finished result. ━ Example of use with Takes You can also test at the same time along with Take Profit. In this example, I simply enabled Risk/Reward mode and immediately specified in the TP field Maximum RR, Minimum RR and Step. So in this example I can test (3-1) / 0.1 = 20 Takes of different sizes. There are additional tips in the settings. ━ * Soon you will start to understand how the system works and things will become much easier. * If something doesn't work, just reset the engine settings and start over again. * Use the tips I have left in the settings and on the Panel. ━ Details: Sort ━ Sorting results by Score, Profit, Trades, etc.. Filter ━ Filtring results by Score, Profit, Trades, etc.. Trade Type ━ Ability to disable Long\Short but only from statistics. BackWin ━ Backtest Window Number of Candle the script can test. Manual Start ━ Enabling it will allow you to call a Stop from a selected point. which you selected when you started the engine. * If you have a real open position then this mode can help to save good Stop\Take for it. 1 - 9 Сheckboxs ━ Allow you to disable any stop from Auto mode. Ex Source - Allow you to test Stops/Takes from connected indicators. Connection guide: //@version=6 indicator("My script") rsi = ta.rsi(close, 14) buy = not na(rsi) and ta.crossover (rsi, 40) // OS = 40 sell = not na(rsi) and ta.crossunder(rsi, 60) // OB = 60 Signal = buy ? +1 : sell ? -1 : 0 plot(Signal, "🔌Connector🔌", display = display.none) * Format the signal for your indicator in a similar style and then select it in Ex Source. ⭕️ How it Works Hypothesis of Uniform Distribution of Rare Elements After Mixing. 'This hypothesis states that if an array of N elements contains K valid elements, then after mixing, these valid elements will be approximately uniformly distributed.' 'This means that in a random sample of k elements, the proportion of valid elements should closely match their proportion in the original array, with some random variation.' 'According to the central limit theorem, repeated sampling will result in an average count of valid elements following a normal distribution.' 'This supports the assumption that the valid elements are evenly spread across the array.' 'To test this hypothesis, we can conduct an experiment:' 'Create an array of 1,000,000 elements.' 'Select 1,000 random elements (1%) for validation.' 'Shuffle the array and divide it into groups of 1,000 elements.' 'If the hypothesis holds, each group should contain, on average, 1~ valid element, with minor variations.' * I'd like to attach more details to My hypothesis but it won't be very relevant here. Since this is a whole separate topic, I will leave the minimum part for understanding the engine. Practical Application To apply this hypothesis, I needed a way to generate and thoroughly mix numerous possible combinations. Within Pine, generating over 100,000 combinations presents significant challenges, and storing millions of combinations requires excessive resources. I developed an efficient mechanism that generates combinations in random order to address these limitations. While conventional methods often produce duplicates or require generating a complete list first, my approach guarantees that the first 10% of possible combinations are both unique and well-distributed. Based on my hypothesis, this sampling is sufficient to determine optimal testing parameters. Most generators and randomizers fail to accommodate both my hypothesis and Pine's constraints. My solution utilizes a simple Linear Congruential Generator (LCG) for pseudo-randomization, enhanced with prime numbers to increase entropy during generation. I pre-generate the entire parameter range and then apply systematic mixing. This approach, combined with a hybrid combinatorial array-filling technique with linear distribution, delivers excellent generation quality. My engine can efficiently generate and verify 300 unique combinations per batch. Based on the above, to determine optimal values, only 10-20 Parts need to be manually scrolled through to find the appropriate value or range, eliminating the need for exhaustive testing of millions of parameter combinations. For the Score statistic I applied all the same, generated a range of Weights, distributed them randomly for each type of statistic to avoid manual distribution. Score ━ based on Trade, Profit, WinRate, Profit Factor, Drawdown, Sharpe & Sortino & Omega & Calmar Ratio. ⭕️ Notes For attentive users, a little tricks :) To save time, switch parts every 3 seconds without waiting for it to load. After 10-20 parts, stop and wait for loading. If the pause is correct, you can switch between the rest of the parts without loading, as they will be cached. This used to work without having to wait for a pause, but now it does slower. This will save a lot of time if you are going to do a deeper backtest. Sometimes you'll get the error “The scripts take too long to execute.” For a quick fix you just need to switch the TF or Ticker back and forth and most likely everything will load. The error appears because of problems on the side of the site because the engine is very heavy. It can also appear if you set too long a period for testing in BackWin or use a heavy indicator for testing. Manual Start - Allow you to Start you Result from any point. Which in turn can help you choose a good stop-stick for your real position. * It took me half a year from idea to current realization. This seems to be one of the few ways to build something automatic in backtest format and in this particular Pine environment. There are already better projects in other languages, and they are created much easier and faster because there are no limitations except for personal PC. If you see solutions to improve this system I would be glad if you share the code. At the moment I am tired and will continue him not soon. Also You can use my previosly big Backtest project with more manual settings(updated soon)
Pine Script® indicator
by HALDRO
16
Walk Forward PatternsINTRO In Euclidean geometry, every mathematical output has a planar projection. 'Walk Forward Patterns' can be considered a practical example of this concept. On the other hand, this indicator might also be viewed as an experiment in 'how playing with Lego as a child contributes to time series analysis' :) OVERVIEW This script dynamically generates the necessary optimization and testing ranges for Walk Forward Analysis based on user-defined bar count and length inputs. It performs automatic calculations for each step, offers 8 different window options depending on the inputs, and visualizes the results dynamically. I should also note that most of the window models consist of original patterns I have created. ADDITIONAL INFO : WHAT IS WALK FORWARD ANALYSIS? Although it is not the main focus of this indicator, providing a brief definition of Walk Forward Analysis can be helpful in correctly interpreting the results it generates. Walk Forward Analysis (WFA) is a systematic method for optimizing parameters and validating trading strategies. It involves dividing historical data into variable segments, where a strategy is first optimized on an in-sample period and then tested on an out-of-sample period. This process repeats by shifting the windows forward, ensuring that each test evaluates the strategy on unseen data, helping to assess its robustness and adaptability in real market conditions. ORIGINALITY There are very few studies on Walk Forward Analysis in TradingView. Even worse, there are no any open-source studies available. Someone has to start somewhere, I suppose. And in my personal opinion, determining the optimization and backtest intervals is the most challenging part of WFA. These intervals serve as a prerequisite for automated parameter optimization. I felt the need to publish this pattern module, which I use in my own WFA models, partly due to this gap on community scripts. INDICATOR MECHANICS To use the indicator effectively, you only need to perform four simple tasks: Specify the total number of bars in your chart in the 'Bar Index' parameter. Define the optimization (In-Sample Test) length. Define the testing (Out-Of-Sample Test) length. Finally, select the window type. The indicator automatically models everything else (including the number of steps) based on your inputs. And the result; you now have a clear idea of which bars to use for your Walk Forward tests! A COMMONLY USED WINDOW SELECTION METHOD: ROLLING A more concrete definition of Walk Forward Analysis, specifically for the widely used Rolling method, can be described as follows: Parameters that have performed well over a certain period are identified (Optimization: In-Sample). These parameters are then tested on a shorter, subsequent period (Backtest: Out-of-Sample). The process is repeated forward in time (At each step, the optimization and backtest periods are shifted by the backtest length). If the cumulative percentage profit obtained from the backtest results is greater than half of the historical optimization profit, the strategy is considered "successful." If the strategy is successful, the most recent (untested) optimization values are used for live trading. OTHER WINDOW OPTIONS ANCHORED: That's a pattern based on progressively expanding optimization ranges at each step. Backtest ranges move forward in a staircase-like manner. STATIC: Optimization ranges remain fixed, while backtest ranges are shifted forward. BLOCKED: Optimization ranges are shifted forward in groups of three blocks. Backtest ranges are also shifted in a staircase manner, even at the cost of creating gaps from the optimization end bars. TRIANGULAR: Optimization ranges are shifted forward in triangular regions, while backtest ranges move in a staircase pattern. RATIO: The optimization length increases by 25% of the initial step’s fixed length at each step. In other words, the length grows by 25% of the first step's length incrementally. Backtest ranges always start from the bar where the optimization ends. FIBONACCI: A variation of the Ratio method, where the optimization shift factor is set to 0.618 RANDOM WALK Unlike the window models explained above, we can also generate optimization and backtest ranges completely randomly—offering almost unlimited variations! When you select the "Random" option in the "Window" parameter on the indicator interface, random intervals are generated based on various trigonometric calculations. By changing the numerical value in the '🐒' parameter, you can create entirely unique patterns. WHY THE 🐒 EMOJI? Two reasons. First, I think that as humanity, we are a species of tailless primates who become happy when we understand things :). At least evolutionarily. The entire history of civilization is built on the effort to express the universe in a scale we can comprehend. 'Knowledge' is an invention born from this effort, which is why we feel happiness when we 'understand'. Second, I can't think of a better metaphor for randomness than a monkey sitting at a keyboard. See: Monkey Test. Anyway, I’m rambling :) NOTES The indicator generates results for up to 100 steps. As the number of steps increases, the table may extend beyond the screen—don’t forget to zoom out! FINAL WORDS I haven’t published a Walk Forward script yet . However, there seem to be examples that can perform parameter optimization in the true sense of the word, producing more realistic results without falling into overfitting in my library. Hopefully, I’ll have the chance to publish one in the coming weeks. Sincerely thanks to Kıvanç Özbilgiç, Robert Pardo, Kevin Davey, Ernest P. Chan for their inspiring publishments. DISCLAIMER That's just a script, nothing more. I hope it helps everyone. Do not forget to manage your risk. And trade as safely as possible. Best of luck! © dg_factor
Pine Script® indicator
by dg_factor
1
Moving Average PropertiesThis indicator calculates and visualizes the Relative Smoothness (RS) and Relative Lag (RL) or call it accuracy of a selected moving average (MA) in comparison to the SMA of length 2 (the lowest possible length for a moving average and also the one closest to the price). Median RS (Relative Smoothness): Interpretation: The median RS represents the median value of the Relative Smoothness calculated for the selected moving average across a specified look-back period (max bar lookback is set at 3000). Significance: A more negative (larger) median RS suggests that the chosen moving average has exhibited smoother price behavior compared to a simple moving average over the analyzed period. A less negative value indicates a relatively choppier price movement. Median RL (Relative Lag): Interpretation: The median RL represents the median value of the Relative Lag calculated for the selected moving average compared to a simple moving average of length 2. Significance: A higher median RL indicates that the chosen moving average tends to lag more compared to a simple moving average. Conversely, lower values suggest less lag in the selected moving average. Ratio of Median RS to Median RL: Interpretation: This ratio is calculated by dividing the median RS by the median RL. Significance: Traders might use this ratio to assess the balance between smoothness and lag in the chosen moving average. This a measure of for every % of lag what is the smoothness achieved. This can be used a benchmark to decide what length to choose for a MA to get an equivalent value between two stocks. For example a TESLA stock on a 15 minute time frame with a length of 12 has a value (ratio of RS/RL) of -150 , where as APPLE stock of length 35 on a 15 minute chart also has a value (ratio of RS/RL) of -150. I imply that a MA of length 12 working on TESLA stock is equivalent to MA of length 35 on a APPLE stock. (THIS IS A EXAMPLE). My assumption is that finding the right moving average length for a stock isn't a one-size-fits-all situation. It's not just about using a fixed length; it's about adapting to the unique characteristics of each stock. I believe that what works for one stock might not work for another because they have different levels of smoothness or lag in their price movements. So, instead of applying the same length to all stocks, I suggest adjusting the length of the moving average to match the values that we know work best for achieving the desired smoothness or lag or its ratio (RS/RL). This way, we're customizing the indicator for each stock, tailoring it to their individual behaviors rather than sticking to a one-size-fits-all approach. Users can choose from various types of moving averages (EMA, SMA, WMA, VWMA, HMA) and customize the length of the moving average. RS measures the smoothness of the MA, while RL measures its lag compared to a simple moving average. The script plots the median RS and RL values, the selected MA, and the ratio of median RS to median RL on the price chart. Traders can use this information to assess the performance of different moving averages and potentially inform their trading decisions.
Pine Script® indicator
by federalTacos5392b
Backtest any Indicator v5Happy Trade, here you get the opportunity to backtest any of your indicators like a strategy without converting them into a strategy. You can choose to go long or go short and detailed time filters. Further more you can set the take profit and stop loss, initial capital, quantity per trade and set the exchange fees. You get an overall result table and even a detailed, scroll-able table with all trades. In the Image 1 you see the provided info tables about all Trades and the Result Summary. Further more every trade is marked by a background color, Labels and Levels. An opening Label with the trade direction and trade number. A closing Label again with the trade number, the trades profit in % and the total amount of $ after all past trades. A green line for the take profit level and a red line for the stop loss. Image 1 Example For this description we choose the Stochastic RSI indicator from TradingView as it is. In Image 2 is shown the performance of it with decent settings. Timeframe=45, BTCUSD, 2023-08-01 - 2023-10-20 Stoch RSI: k=30, d=40, RSI-length=140, stoch-length=140 Backtest any Indicator: input signal=Stoch RSI, goLong, take profit=9.1%, stop loss=2.5%, start capital=1000$, qty=5%, fee=0.1%, no Session Filter Image 2 Usage 1) You need to know the name of the boolean (or integer) variable of your indicator which hold the buy condition. Lets say that this boolean variable is called BUY. If this BUY variable is not plotted on the chart you simply add the following code line at the end of your pine script. For boolean (true/false) BUY variables use this: plot(BUY ? 1:0,'Your buy condition hold in that variable BUY',display = display.data_window) And in case your script's BUY variable is an integer or float then use instate the following code line: plot(BUY ,'Your buy condition hold in that variable BUY',display = display.data_window) 2) Probably the name of this BUY variable in your indicator is not BUY. Simply replace in the code line above the BUY with the name of your script's trade condition variable. 3) Save your changed Indicator script. 4) Then add this 'Backtest any Indicator' script to the chart ... 5) and go to the settings of it. Choose under "Settings -> Buy Signal" your Indicator. So in the example above choose . The form is usually: ' : BUY'. Then you see something like Image 2 6) Decide which trade direction the BUY signal should trigger. A go Long or a go Short by set the hook or not. Now you have a backtest of your Indicator without converting it into a strategy. You may change the setting of your Indicator to the best results and setup the following strategy settings like Time- and Session Filter, Stop Loss, Take Profit etc. More of it below in the section Settings Menu. Appereance In the Image 2 you see on the right side the List of Trades . To scroll down you go into the settings again and decrease the scroll value. So you can see all trades that have happened before. In case there is an open trade you will find it at the last position of the list. Every Long trade is green back grounded while Short trades are red. Every trade begins with a label that show goLong or goShort and its number. And ends with another label again with its number, Profit in % and the resulting total amount of cash. If activated you further see the Take Profit as a green line and the Stop Loss as a orange line. In the settings you can set their percentage above or below the entry price. You also see the Result Summary below. Here you find the usual stats of a strategy of all closed trades. The profit after total amount of fees , amount of trades, Profit Factor and the total amount of fees . Settings Menu In the settings menu you will find the following high-lighted sections. Most of the settings have a question mark on their right side. Move over it with the cursor to read specific explanation. Input Signal of your Indicator: Under Buy you set the trade signal of your Indicator. And under Target you set the value when a trade should happen. In the Example with the Stochastic RSI above we used 20. Below you can set the trade direction, let it be go short when hooked or go long when unhooked. Trade Settings & List of Trades: Take Profit set the target price of any trade. Stop Loss set the price to step out when a trade goes the wrong direction. Check mark the List of Trades to see any single trade with their stats. In case that there are more trades as fits in the list you can scroll down the list by decrease the value Scroll . Time Filter: You can set a Start Time or deactivate it by leave it unhooked. The same with End Time . Session Filter: here you can choose to activate it on weekly base. Which days of the week should be trading and those without. And also on daily base from which time on and until trade are possible. Outside of all times and sessions there will be no new trades if activated. Invest Settings: here you can choose the amount of cash to start with. The Quantity percentage define for every trade how much of the cash should be invested and the Fee percentage which have to be payed every trade. Open position and closing position. Other Announcements This Backtest script don't use the strategy functions of TradingView. It is programmed as an indicator. All trades get executed at candle closing. This script use the functionality "Indicator-on-Indicator" from TradingView. Conclusion So now it is your turn, take your promising indicators and connect it to that Backtest script. With it you get a fast impression of how successful your indicator will trade. You don't have to relay on coders who maybe add cheating code lines. Further more you can check with the Time Filter under which market condition you indicator perform the best or not so well. Also with the Session Filter you can sort out repeating good market conditions for your indicator. Even you can check with the GoShort XOR GoLong check mark the trade signals of you indicator in opposite trade direction with one click. And compare your indicators under the same conditions and get the results just after 2 clicks. Thanks to the in-build fee setting you get an impression how much a 0.1% fee cost you in total. Cheers
Pine Script® indicator
by BerlinCode42
14
Supertrend - Optimised Exit We created a small script that will allow you to have a quick look into static SL/PT to choose from. This might save you time, replacing the manual search for optimal SL/PT. We're checking signals of the strategy and computing its performance with a grid of SL/PT selected. We used SuperTrend signals in this example, but it will be straightforward to integrate your signals. In addition to total Return, we compute MAX Dd and Profit Factor. Other metrics can be implemented as well. Thanks to @MUQWISHI for helping code it. Disclaimer Please remember that past performance may not indicate future results. Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting. This post and the script don’t provide any financial advice.
Pine Script® indicator
by QuantNomad
10
Logistic RSI, STOCH, ROC, AO, ... by DGTExperimental attemt of applying Logistic Map Equation for some of widly used indicators. With this study "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)" and a custom interpretation of Logistic Map Equation is presented Calculations with Logistic Map Equation makes sense when the calculated results are iterated many times within the same equation. Here is the Logistic Map Equation : Xn+1 = r * Xn * (1 - Xn) Where, the value of r is the key for this equation which changes amazingly the behaviour of the Logistic Map. The value we have asigned for r is less then 1 and greater than 0 ( 0 < r < 1) and in this case the iterations performed with the maximum number of output series allowed by Pine is quite enough for our purpose and thanks to arrays we can easiliy store them for further processing What we have as output: Each iteration result is then plotted (excluding plotting the first iteration), as circles or line based on user preference Values above and below zero level (0) are coloured differently to emphasis bull and bear power Finally Standard Deviation of Array's Elements is ploted as line. Users may choose to display this line only So where it comes the indicators "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)". Those are the indicators whose values are assigned to our key varaiable in the Logistic Map equation forulma which is r Further details regarding Logistic Map can found under the description of “Logistic EMA w/ Signals by DGT” study Disclaimer: Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely The script is for informational and educational purposes only. Use of the script does not constitute professional and/or financial advice. You alone have the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Pine Script® indicator
by dgtrd
104
Optimized Linear Regression ChannelReturn a linear regression channel with a window size within the range (min, max) such that the R-squared is maximized, this allows a better estimate of an underlying linear trend, a better detection of significant historical supports and resistance points, and avoid finding a good window size manually. Settings Min : Minimum window size value Max : Maximum window size value Mult : Multiplicative factor for the rmse, control the channel width. Src : Source input of the indicator Details The indicator displays the specific window size that maximizes the R-squared at the bottom of the lower channel. When optimizing we want to find parameters such that they maximize or minimize a certain function, here the r-squared. The R-squared is given by 1 minus the ratio between the sum of squares (SSE) of the linear regression and the sum of squares of the mean. We know that the mean will always produce an SSE greater or equal to the one of the linear regression, so the R-squared will always be in a (0,1) range. In the case our data has a linear trend, the linear regression will have a better fit, thus having a lower SSE than the SSE of the mean, has such the ratio between the linear regression SSE and the mean SSE will be low, 1 minus this ratio will return a greater result. A lower R-squared will tell you that your linear regression produces a fit similar to the one produced by the mean. The R-squared is also given by the square of the correlation coefficient between the dependent and independent variables. In pinescript optimization can be done by running a function inside a loop, we run the function for each setting and keep the one that produces the maximum or minimum result, however, it is not possible to do that with most built-in functions, including the function of interest, correlation , as such we must recreate a rolling correlation function that can be used inside loops, such functions are generally loops-free, this means that they are not computed using a loop in the first place, fortunately, the rolling correlation function is simply based on moving averages and standard deviations, both can be computed without using a loop by using cumulative sums, this is what is done in the code. Note that because the R-squared is based on the SSE of the linear regression, maximizing the R-squared also minimizes the linear regression SSE, another thing that is minimized is the horizontality of the fit. In the example above we have a total window size of 27, the script will try to find the setting that maximizes the R-squared, we must avoid every data points before the volatile bearish candle, using any of these data points will produce a poor fit, we see that the script avoid it, thus running as expected. Another interesting thing is that the best R-squared is not always associated to the lowest window size. Note that optimization does not fix core problems in a model, with the linear regression we assume that our data set posses a linear trend, if it's not the case, then no matter how many settings you use you will still have a model that is not adapted to your data.
Pine Script® indicator
by alexgrover
29
Momentum adjusted Moving Average by DGTA brand new Moving Average , calculated using Momentum, Acceleration and Probability (Psychological Effect). Momentum adjusted Moving Average(MaMA) is an indicator that measures Price Action by taking into consideration not only Price movements but also its Momentum, Acceleration and Probability. MaMA, provides faster responses comparing to the regular Moving Average Here is the math of the MaMA idea Momentum measures change in price over a specified time period momentum = source – source(length) where, source, indicates current bar’s price value source(length), indicates historical price value of length bars earlier Lets play with this formula and rewrite it by moving source(length) to other side of the equation source = source(length) + momentum to avoid confusion let’s call the source that we aim to predict as adjustedSource adjustedSource = source(length) + momentum looks nice the next value of source simply can be calculated by summing of historical value of the source value and value of the momentum. I wish it was so easy, the formula holds true only when the momentum is conserved/constant/steady but momentum move up or down with the price fluctuations (accelerating or decelerating) Let’s add acceleration effects on our formula, where acceleration is change in momentum for a given length. Then the formula will become as (skipped proof part of acceleration effects, you may google for further details) adjustedSource = source(length) + momentum + 1/2 * acceleration here again the formula holds true when the acceleration is constant and once again it is not the case for trading, acceleration also changes with the price fluctuations Then, how we can benefit from all of this, it has value yet requires additional approaches for better outcome Let’s simulate behaviour with some predictive approach such as using probability (also known as psychological effect ), where probability is a measure for calculating the chances or the possibilities of the occurrence of a random event. As stated earlier above momentum and acceleration are changing with the price fluctuations, by using the probability approach we can add a predictive skill to determine the likelihood of momentum and acceleration changes (remember it is a predictive approach). With this approach, our equations can be expresses as follows adjustedSource = source(length) + momentum * probability adjustedSource = source(length) + ( momentum + 1/2 * acceleration ) * probability , with acceleration effect Finally, we plot MaMA with the new predicted source adjustedSource, applying acceleration effect is made settable by the used from the dialog box, default value is true. What to look for: • Trend Identification • Support and Resistance • Price Crossovers Recommended settings are applied as default settings, if you wish to change the length of the MaMA then you should also adjust length of Momentum (and/or Probability). For example for faster moving average such as 21 period it would be suggested to set momentum length to 13 Alternative usage , set moving average length to 1 and keep rest lengths with default values, it will produce a predictive price line based on momentum and probability. Experience acceleration factor by enabling and disabling it Conclusion MaMA provide an added level of confidence to a trading strategy and yet it is important to always be aware that it implements a predictive approach in a chaotic market use with caution just like with any indicator Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Pine Script® indicator
by dgtrd
79
Momentum Acceleration by DGTItalian physicist Galileo Galilei is usually credited with being the first to measure speed by considering the distance covered and the time it takes. Galileo defined speed as the distance covered during a period of time. In equation form, that is v = Δd / Δt where v is speed, Δd is change in distance, and Δt is change in time. The Greek symbol for delta, a triangle (Δ), means change. Is the speed getting faster or slower? Acceleration will be the answer, acceleration is defined as the rate of change of speed over a set period of time, meaning something is getting faster or slower. Mathematically expressed, acceleration denoted as a is a = Δv / Δt , where Δv is the change in speed and Δt is the change in time. How to apply in trading Lets think about Momentum, Rate of Return, Rate of Change all are calculated in almost same approach with Speed Momentum measures change in price over a specified time period, Rate of Change measures percent change in price over a specified time period, Rate of Return measures the net gain or loss over a specified time period, And Speed measures change in distance over a specified time period So we may state that measuring the change in distance is also measuring the change in price over a specified time period which is length, hence speed can be calculated as (source – source )/length and acceleration becomes (speed – speed )/length In this study acceleration is used as signal line and result plotted as arrows demonstrating bull or bear direction where direction changes can be considered as trading setups Just a little fun, since we deal with speed the short name of the study is named after famous cartoon character Speedy Gonzales Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely Disclaimer: The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Pine Script® indicator
by dgtrd
133
Elliott Wave Oscillator Signals by DGTElliott Wave Principle , developed by Ralph Nelson Elliott, proposes that the seemingly chaotic behaviour of the different financial markets isn’t actually chaotic. In fact the markets moves in predictable, repetitive cycles or waves and can be measured and forecast using Fibonacci numbers. These waves are a result of influence on investors from outside sources primarily the current psychology of the masses at that given time. Elliott wave predicts that the prices of the a traded currency pair will evolve in waves: five impulsive waves and three corrective waves. Impulsive waves give the main direction of the market expansion and the corrective waves are in the opposite direction (corrective wave occurrences and combination corrective wave occurrences are much higher comparing to impulsive waves) The Elliott Wave Oscillator (EWO) helps identifying where you are in the 5-3 Elliott Waves, mainly the highest/lowest values of the oscillator might indicate a potential bullish/bearish Wave 3. Mathematically expressed, EWO is the difference between a 5-period and 35-period moving average based on the close. In this study instead 35-period, Fibonacci number 34 is implemented for the slow moving average and formula becomes ewo = ema(source, 5) - ema(source, 34) The application of the Elliott Wave theory in real time trading gets difficult because the charts look messy. This study (EWO-S) simplifies the visualization of EWO and plots labels on probable reversals/corrections. The good part is that all plotting’s are performed on the top of the price chart including a histogram (optional and supported on higher timeframes). Additionally optional Keltner Channels Cloud added to help confirming the price actions. What to look for: Plotted labels can be used to follow the Elliott Wave occurrences and most importantly they can be considered as signals for possible trade setup opportunities. Elliott Wave Rules and Fibonacci Retracement/Extensions are suggested to confirm the patters provided by the EWO-S Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Pine Script® indicator
by dgtrd
295
Script Stopwatch - PineCoders FAQ█ WARNING The publication of our LibraryStopwatch has deprecated this publication. █ ORIGINAL DESCRIPTION This script calculates the run time of a Pine script. While its numbers are not very precise and it doesn’t work on all scripts, it will help developers calculate run times more precisely than by hand, and so provides Pine coders with an additional profiling tool to help them optimize their code. How to use the code • Place the code included between the up/down arrows after your script’s input() calls. • Comment out the display modes you don’t want to use. • Save your script, wait and look at the results. • Results show in different colors, depending on the average time per bar: - green for < 5 ms - dark red for < 50 ms - bright red for > 50 ms (the maximum allowed is 200). How the code works The code in this script starts by saving the value of the timenow variable on the first bar. While the time variable returns the time at the beginning of the bar, timenow returns the current time. The code then follows the progression of timenow during the script’s execution. The variable only updates every second, so in between updates the script makes an estimate of the total time elapsed by adding the last average time per bar calculated to each bar that passes until timenow increases by another 1000 because one more second has elapsed since its last update. At that point a new, current average time per bar is calculated and the cycle repeats. The code only calculates elapsed time for the initial run. Once the realtime bar is hit, timing stops so that time spent in the realtime bar does not affect the numbers once they have been calculated on the script’s initial pass over the dataset. Notes • If results show zero elapsed time, it’s most probably because your script executes in less than one second, which is very good. In that case timenow hasn’t changed, so no timing can be calculated. • The code is quirky and doesn’t work on all scripts. • It doesn’t properly time execution of security() calls. • The average time per bar will sometimes vary quite a bit with changes in chart resolution. Look first. Then leap.
Pine Script® indicator
by PineCoders
9