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Statistical Package for the Trading Sciences [SS] This is SPTS. It stands for Statistical Package for the Trading Sciences. Its a play on SPSS (Statistical Package for the Social Sciences) by IBM (software that, prior to Pinescript, I would use on a daily basis for trading). Let's preface this indicator first: This isn't so much an indicator as it is a project. A passion project really. This has been in the works for months and I still feel like its incomplete. But the plan here is to continue to add functionality to it and actually have the Pinecoding and Tradingview community contribute to it. As a math based trader, I relied on Excel, SPSS and R constantly to plan my trades. Since learning a functional amount of Pinescript and coding a lot of what I do and what I relied on SPSS, Excel and R for, I use it perhaps maybe a few times a week. This indicator, or package, has some of the key things I used Excel and SPSS for on a daily and weekly basis. This also adds a lot of, I would say, fairly complex math functionality to Pinescript. Because this is adding functionality not necessarily native to Pinescript, I have placed most, if not all, of the functionality into actual exportable functions. I have also set it up as a kind of library, with explanations and tips on how other coders can take these functions and implement them into other scripts. The hope here is that other coders will take it, build upon it, improve it and hopefully share additional functionality that can be added into this package. Hence why I call it a project. Okay, let's get into an overview: Current Functions of SPTS: SPTS currently has the following functionality (further explanations will be offered below): Ability to Perform a One-Tailed, Two-Tailed and Paired Sample T-Test, with corresponding P value. Standard Pearson Correlation (with functionality to be able to calculate the Pearson Correlation between 2 arrays). Quadratic (or Curvlinear) correlation assessments. R squared Assessments. Standard Linear Regression. Multiple Regression of 2 independent variables. Tests of Normality (with Kurtosis and Skewness) and recognition of up to 7 Different Distributions. ARIMA Modeller (Sort of, more details below) Okay, so let's go over each of them! T-Tests So traditionally, most correlation assessments on Pinescript are done with a generic Pearson Correlation using the "ta.correlation" argument. However, this is not always the best test to be used for correlations and determine effects. One approach to correlation assessments used frequently in economics is the T-Test assessment. The t-test is a statistical hypothesis test used to determine if there is a significant difference between the means of two groups. It assesses whether the sample means are likely to have come from populations with the same mean. The test produces a t-statistic, which is then compared to a critical value from the t-distribution to determine statistical significance. Lower p-values indicate stronger evidence against the null hypothesis of equal means. A significant t-test result, indicating the rejection of the null hypothesis, suggests that there is statistical evidence to support that there is a significant difference between the means of the two groups being compared. In practical terms, it means that the observed difference in sample means is unlikely to have occurred by random chance alone. Researchers typically interpret this as evidence that there is a real, meaningful difference between the groups being studied. Some uses of the T-Test in finance include: Risk Assessment: The t-test can be used to compare the risk profiles of different financial assets or portfolios. It helps investors assess whether the differences in returns or volatility are statistically significant. Pairs Trading: Traders often apply the t-test when engaging in pairs trading, a strategy that involves trading two correlated securities. It helps determine when the price spread between the two assets is statistically significant and may revert to the mean. Volatility Analysis: Traders and risk managers use t-tests to compare the volatility of different assets or portfolios, assessing whether one is significantly more or less volatile than another. Market Efficiency Tests: Financial researchers use t-tests to test the Efficient Market Hypothesis by assessing whether stock price movements follow a random walk or if there are statistically significant deviations from it. Value at Risk (VaR) Calculation: Risk managers use t-tests to calculate VaR, a measure of potential losses in a portfolio. It helps assess whether a portfolio's value is likely to fall below a certain threshold. There are many other applications, but these are a few of the highlights. SPTS permits 3 different types of T-Test analyses, these being the One Tailed T-Test (if you want to test a single direction), two tailed T-Test (if you are unsure of which direction is significant) and a paired sample t-test. Which T is the Right T? Generally, a one-tailed t-test is used to determine if a sample mean is significantly greater than or less than a specified population mean, whereas a two-tailed t-test assesses if the sample mean is significantly different (either greater or less) from the population mean. In contrast, a paired sample t-test compares two sets of paired observations (e.g., before and after treatment) to assess if there's a significant difference in their means, typically used when the data points in each pair are related or dependent. So which do you use? Well, it depends on what you want to know. As a general rule a one tailed t-test is sufficient and will help you pinpoint directionality of the relationship (that one ticker or economic indicator has a significant affect on another in a linear way). A two tailed is more broad and looks for significance in either direction. A paired sample t-test usually looks at identical groups to see if one group has a statistically different outcome. This is usually used in clinical trials to compare treatment interventions in identical groups. It's use in finance is somewhat limited, but it is invaluable when you want to compare equities that track the same thing (for example SPX vs SPY vs ES1!) or you want to test a hypothesis about an index and a leveraged share (for example, the relationship between FNGU and, say, MSFT or NVDA). Statistical Significance In general, with a t-test you would need to reference a T-Table to determine the statistical significance of the degree of Freedom and the T-Statistic. However, because I wanted Pinescript to full fledge replace SPSS and Excel, I went ahead and threw the T-Table into an array, so that Pinescript can make the determination itself of the actual P value for a t-test, no cross referencing required :-). Left tail (Significant): Both tails (Significant): Distributed throughout (insignificant): As you can see in the images above, the t-test will also display a bell-curve analysis of where the significance falls (left tail, both tails or insignificant, distributed throughout). That said, I have not included this function for the paired sample t-test because that is a bit more nuanced. But for the one and two tailed assessments, the indicator will provide you the P value. Pearson Correlation Assessment I don't think I need to go into too much detail on this one. I have put in functionality to quickly calculate the Pearson Correlation of two array's, which is not currently possible with the "ta.correlation" function. Quadratic (Curvlinear) Correlation Not everything in life is linear, sometimes things are curved! The Pearson Correlation is great for linear assessments, but tends to under-estimate the degree of the relationship in curved relationships. There currently is no native function to t-test for quadratic/curvlinear relationships, so I went ahead and created one. You can see an example of how Quadratic and Pearson Correlations vary when you look at CME_MINI:ES1! against AMEX:DIA for the past 10 ish months: Pearson Correlation: Quadratic Correlation: One or the other is not always the best, so it is important to check both! R-Squared Assessments: The R-squared value, or the square of the Pearson correlation coefficient (r), is used to measure the proportion of variance in one variable that can be explained by the linear relationship with another variable. It represents the goodness-of-fit of a linear regression model with a single predictor variable. R-Squared is offered in 3 separate forms within this indicator. First, there is the generic R squared which is taking the square root of a Pearson Correlation assessment to assess the variance. The next is the R-Squared which is calculated from an actual linear regression model done within the indicator. The first is the R-Squared which is calculated from a multiple regression model done within the indicator. Regardless of which R-Squared value you are using, the meaning is the same. R-Square assesses the variance between the variables under assessment and can offer an insight into the goodness of fit and the ability of the model to account for the degree of variance. Here is the R Squared assessment of the SPX against the US Money Supply: Standard Linear Regression The indicator contains the ability to do a standard linear regression model. You can convert one ticker or economic indicator into a stock, ticker or other economic indicator. The indicator will provide you with all of the expected information from a linear regression model, including the coefficients, intercept, error assessments, correlation and R2 value. Here is AAPL and MSFT as an example: Multiple Regression Oh man, this was something I really wanted in Pinescript, and now we have it! I have created a function for multiple regression, which, if you export the function, will permit you to perform multiple regression on any variables available in Pinescript! Using this functionality in the indicator, you will need to select 2, dependent variables and a single independent variable. Here is an example of multiple regression for NASDAQ:AAPL using NASDAQ:MSFT and NASDAQ:NVDA : And an example of SPX using the US Money Supply (M2) and AMEX:GLD : Tests of Normality: Many indicators perform a lot of functions on the assumption of normality, yet there are no indicators that actually test that assumption! So, I have inputted a function to assess for normality. It uses the Kurtosis and Skewness to determine up to 7 different distribution types and it will explain the implication of the distribution. Here is an example of SP:SPX on the Monthly Perspective since 2010: And NYSE:BA since the 60s: And NVDA since 2015: ARIMA Modeller Okay, so let me disclose, this isn't a full fledge ARIMA modeller. I took some shortcuts. True ARIMA modelling would involve decomposing the seasonality from the trend. I omitted this step for simplicity sake. Instead, you can select between using an EMA or SMA based approach, and it will perform an autogressive type analysis on the EMA or SMA. I have tested it on lookback with results provided by SPSS and this actually works better than SPSS' ARIMA function. So I am actually kind of impressed. You will need to input your parameters for the ARIMA model, I usually would do a 14, 21 and 50 day EMA of the close price, and it will forecast out that range over the length of the EMA. So for example, if you select the EMA 50 on the daily, it will plot out the forecast for the next 50 days based on an autoregressive model created on the EMA 50. Here is how it looks on AMEX:SPY : You can also elect to plot the upper and lower confidence bands: Closing Remarks So that is the indicator/package. I do hope to continue expanding its functionality, but as of now, it does already have quite a lot of functionality. I really hope you enjoy it and find it helpful. This. Has. Taken. AGES! No joke. Between referencing my old statistics textbooks, trying to remember how to calculate some of these things, and wanting to throw my computer against the wall because of errors in the code, this was a task, that's for sure. So I really hope you find some usefulness in it all and enjoy the ability to be able to do functions that previously could really only be done in external software. As always, leave your comments, suggestions and feedback below! Take care!
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Steversteves tarafından
27
Nadaraya-Watson non repainting [LPWN]// ENGLISH The problem of the wonderfuls Nadaraya-Watson indicators is that they repainting, @jdehorty made an aproximation of the Nadaraya-Watson Estimator using raational Quadratic Kernel so i used this indicator as inspiration i just added the Upper and lower band using ATR with this we get an aproximation of Nadaraya-Watson Envelope without repainting Settings: Bandwidth. This is the number of bars that the indicator will use as a lookback window. Relative Weighting Parameter. The alpha parameter for the Rational Quadratic Kernel function. This is a hyperparameter that controls the smoothness of the curve. A lower value of alpha will result in a smoother, more stretched-out curve, while a lower value will result in a more wiggly curve with a tighter fit to the data. As this parameter approaches 0, the longer time frames will exert more influence on the estimation, and as it approaches infinity, the curve will become identical to the one produced by the Gaussian Kernel. Color Smoothing. Toggles the mechanism for coloring the estimation plot between rate of change and cross over modes. ATR Period. Period to calculate the ATR (upper and lower bands) Multiplier. Separation of the bands // SPANISH El problema de los maravillosos indicadores de Nadaraya-Watson es que repintan, @jdehorty hizo una aproximación delNadaraya-Watson Estimator usando un Kernel cuadrático racional, así que usé este indicador como inspiración y solo agregamos la banda superior e inferior usando ATR con esto obtenemos una aproximación de Nadaraya-Watson Envelope sin volver a pintar Configuración: Banda ancha. Este es el número de barras que el indicador utilizará como ventana retrospectiva. Parámetro de ponderación relativa. El parámetro alfa para la función Rational Quadratic Kernel. Este es un hiperparámetro que controla la suavidad de la curva. Un valor más bajo de alfa dará como resultado una curva más suave y estirada, mientras que un valor más bajo dará como resultado una curva más ondulada con un ajuste más ajustado a los datos. A medida que este parámetro se acerque a 0, los marcos de tiempo más largos ejercerán más influencia en la estimación y, a medida que se acerque al infinito, la curva será idéntica a la que produce el Gaussian Kernel. Suavizado de color. Alterna el mecanismo para colorear el gráfico de estimación entre la tasa de cambio y los modos cruzados. Período ATR. Periodo para calcular el ATR (bandas superior e inferior) Multiplicador. Separación de las bandas
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Lupown tarafından
15
MACD histogram relative open/closePrelude This script makes it easy to capture MACD Histogram open/close for automated trading. There seems to be no "magic" value for MACD Histogram that always works as a cut-off for trade entry/exit, because of the variation in market price over time. The idea behind this script is to replicate the view of the MACD graph we (humans) see on the screen, in mathematics, so the computer can approximately detect when the curve is opening/closing. Math The maths for this is composed of 2 sections - 1. Entry - i. To trigger entry, we normalize the Histogram value by first determining the lowest and highest values on the MACD curves (MACD, Signal & Hist). ii. The lowest and highest values are taken over the "Frame of reference" which is a hyperparameter. iii. Once the frame of reference is determined, the entry cutoff param can be defined with respect to the values from (i) (10% by default) 2. Exit To trigger an exit, a trader searches for the point where the Histogram starts to drop "steeply". To convert the notion of "steep" into mathematics - i. Take the max histogram value reached since last MACD curve flip ii. Define the cutoff with reference to the value from (i) (30% by default) Plots Gray - Dead region Blue - Histogram opening Red - Histogram is closing Notes A good value for the frame of reference can be estimated by looking at the timescale of the graph you generally work with during manual trading. For me, that turned out to be ~2.5 hours. (as shown in the above graph) For a 3-minute ticker, frame of reference = 2.5 * 60 / 3 = 50 Which is the default given in this script. Ultimately, it is up to you to do grid search and find these hyperparams for the stock and ticker size you're working with. Also, this script only serves the purpose of detecting the Histogram curve opening/closing. You may want to add further checks to perform proper trading using MACD.
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devxpy tarafından
1
Other altcoins BTC capitalization histogram [peregringlk]Introduction ========== This study is intented to be used in combination with my other study "Other alts compensated cap". Read its description, in particular, it's rationale, to understand why I have removed the big capitalized altcoins from these studies. The middle indicator in the image is that other study, while the indicator in the buttom of the image is that one. It shows, in form of histogram, the BTC capitalization change rate (per candle, using closes) of the "OTHERS" altcoins together with the inverse of the BTCUSD price change rate per candle. NOTE: I call the change rate to the multiplier factor of price from bar to bar. For example, a change rate of 1.20 means +20% respect to "yesterday", and a change rate of 0.80 means -20%. The idea is to know what are altcoin markets (against BTC) doing after each BTC price change. Definitions ========= I will use ALT from now one as the name of an index or fictional coin that represents the average price of all other altcoins combined. I'll use then ALTUSD to represent the price against USD of such fictional coin (= the OTHERS capitalization, as if the USD capitalization of altcoins were the USD price of ALT), and ALTBTC to represent the same price but against BTC (calculated by taking ALTUSD/BITSTAMP:BTCUSD; the choosing of BITSTAMP is because it's the market with a longer history in tradingview). Since I use the "OTHERS" security, I cannot know the real altcoin index so I can only estimate by using the capitalization. CIX100 could be a solution, but it is too recent in time as to inspect past price actions. Description ========= For example, let's assume BTCUSD decreases by 20% today. It would cause a fall in ALTUSD of 20% (just maths). So, what should it happen in ALTBTC to preserve the original ALTUSD price? People should buy alts in BTC markets by a factor of 1/0.8 = 1.25. Or in other words, unless there are a +25% grow in ALTBTC, ALTUSD would see a decrease in value. This is what the histogram shows. The red columns shows the ALTBTC change rate per candle, while each green column shows what is the required change rate in ALTBTC required to preserve its ALTUSD value (capitalization). In other words, the green columns are the "targets" to preserve USD capitalization in ALTBTC, while the red histogram shows the actual changes. Also, it shows two curves. There are just the change rate accumulation during some customizable interval (the same for both lines, and 7 by default; or the "week" for daily candles). The green line is the accumulated "target" change rate within that period of time (the accumulated product of the last `interval` change rates), and the red line is the actual change rate for the same `interval` candles. Interpretation ============ If red column values are bigger than the green ones (green column is negative, and red column is positive; or both are positives but the red one "put outs", or both are negative but the red column doesn't "put out"), OTHERS USD capitalization has increased. If red column values are lower than the green ones (green column is positive and red column is negative; or both are positives but the red one doesn't "put out"; or both are negative but the red column "put outs"), OTHERS USD capitalization has decreased. The same for the continuous lines: if the red line is above the green one, OTHERS USD capitalization has increased during "the past week". Otherwise, it has decreased. The added value of this indicator is that it allows you to know "why". For example, if a green column is positive, and its corresponding red column is positive as well, but below the green one, the capitalization has decreased but BECAUSE the btc price has fallen, not because there was a sellof in alts. Actually, there was some buys (the ALTBTC price increased); it just it was not enough to counteract the btc fall. That can be clearly seen in the remarked candle in the plot, the "coronavirus" sellof. The BTCUSD fall was huge (the hugest in BTC history), and the green column is telling you that to preserve the capitalization a lot of buys were required. However, that didn't happen. Actually, the OTHER alts were pretty quiet (the red column is tiny), causing a massive indirect loss of capitalization. Also, with the curves, you can know if there was a total gainning or loss of capitalization during the past few days or candles. Also you can try to spot the beginning of alts seasons by crosses between red and green lines: if the red lines crosses above the green one (because there was a continuous sequence of red columns above green ones), it means that, potentially, were are at the beginning of an alt season because people are accumulating. Table of cases =========== - if the green column is positive (BTCUSD is down) - if the red column is positive (ALTBTC is up) - bigger than the green column: ALTBTC buys are stronger than required by arbitrage and have counteracted and overcome the BTC fall. - shorter than the green column: there have been some buys but not enough, so the BTCUSD fall has not been fully counteracted. - if the red column is negative (ALTBTC is down): the loss is double: BTCUSD have lost value + ALTBTC is bleeding. - If the green column is negative (BTCUSD is up) - if the red column is negative (ALTBTC is down) - bigger than the green column: ALTBTC sells are so strong that have counteracted the BTC increase in value, causing a loss of USD value. - shorter than the green column: there have been sells but overall the ALTUSD price has increased. - if the red column is positive (ALTBTC is up): the gain is double: BTCUSD has gain value + ALTBTC is also growing.
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peregringlk tarafından
MTF Damiani Volatmeter v3.2Damiani_volatmeter.mq4 v3.2 | Copyright © 2006,2007 Luis Guilherme Damiani | It is a transplant of an indicator to judge the range market price. The original is judged by the two curves, but this indicator shows the difference between the two curves. If it is 0 or less, it can be judged as a range. The red and green lines show the strength of this hourly trend, and if the range is below zero, the background is painted red. The blue and orange lines indicate the strength of the trend of the upper leg, and if the market price is below zero, the background is painted blue. I think that the background color will be purple if the market price is both strong and below zero. レンジ相場を判定するインジケーターを移植したものです。 本来のものは２本の曲線で判断するのですが、このインジケーターでは２本の曲線の差を表示しています。 0以下ならレンジと判定できます。 赤と緑の線はこの時間足のトレンドの強さを示し、ゼロ以下のレンジ相場なら、背景を赤く塗っています。 青とオレンジ色の線は上位足のトレンドの強さを示し、ゼロ以下のレンジ相場なら、背景を青く塗っています。 両方ゼロ以下の強いレンジ相場なら背景色が紫色のなると思います。
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xinolia tarafından
4
Mean Reversion IndicatorThis is a mean reversion indicator that anticipates a local trend reversion. Basically, it is a channel with the mid-line serving as a moving mean baseline. Each of the two curves run up and down within this channel bouncing off from the top and bottom bounds. Touching the bounds serves as an indication of a local trend reversal. The reversal signal is stronger when there exists a resonance (symmetry) in the two curves. The background histogram shows a Karobein oscillator that contributes support or resistance for the signal.
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capissimo tarafından
6
SMIIOLThis indicator generates long signals. The operation of the indicator is as follows; First, true strength index is calculated with closing prices. We call this the "ergodic" curve. Then the average of the ergodic (ema) is calculated to obtain the "signal" curve. To calculate the "oscillator", the signal is subtracted from ergodic (oscillator = ergodic - signal). The last variable to be used in the calculation is the average volume, calculated with sma. Calculation for long signal; - If the ergodic curve cross up the lower band and, - If the hma slope is positive, If all the above conditions are fullfilled, the long input signal is issued with "Buy" label.
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iilter tarafından
Adaptive Quadratic Kernel EnvelopeThis study draws a fair-value curve from a quadratic-weighted (Nadaraya-Watson) regression. Alpha sets how sharply weights decay inside the look-back window, so you trade lag against smoothness with one slider. Band half-width is ATRslow times a bounded fast/slow ATR ratio, giving an instant response to regime shifts without overshooting on spikes. Work in log space when an instrument grows exponentially, equal percentage moves then map to equal vertical steps. NearBase and FarBase define a progression of adaptive thresholds, useful for sizing exits or calibrating mean-reversion logic. Non-repaint mode keeps one-bar delay for clean back-tests, predictive mode shows the zero-lag curve for live decisions. Key points - Quadratic weights cut phase error versus Gaussian or SMA-based envelopes. - Dual-ATR scaling updates width on the next bar, no residual lag. - Log option preserves envelope symmetry across multi-decade data. - Alpha provides direct control of curvature versus noise. - Built-in alerts trigger on the first adaptive threshold, ready for automation. Typical uses Trend bias from the slope of the curve. Entry timing when price pierces an inner threshold and momentum stalls. Breakout confirmation when closes hold beyond outer thresholds while volatility expands. Stops and targets anchored to chosen thresholds, automatically matching current noise.
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Julien_Eche tarafından
2
Linear Regression Forecast (ADX Adaptive)Linear Regression Forecast (ADX Adaptive) This indicator is a dynamic price projection tool that combines multiple linear regression forecasts into a single, adaptive forecast curve. By integrating trend strength via the ADX and directional bias, it aims to visualize how price might evolve in different market environments—from strong trends to mean-reverting conditions. Core Concept: This tool builds forward price projections based on a blend of linear regression models with varying lookback lengths (from 2 up to a user-defined max). It then adjusts those projections using two key mechanisms: ADX-Weighted Forecast Blending In trending conditions (high ADX), the model follows the raw forecast direction. In ranging markets (low ADX), the forecast flips or reverts, biasing toward mean-reversion. A logistic transformation of directional bias, controlled by a steepness parameter, determines how aggressively this blending reacts to price behavior. Volatility Scaling The forecast’s magnitude is scaled based on ADX and directional conviction. When trends are unclear (low ADX or neutral bias), the projection range expands to reflect greater uncertainty and volatility. How It Works: Regression Curve Generation For each regression length from 2 to maxLength, a forward projection is calculated using least-squares linear regression on the selected price source. These forecasts are extrapolated into the future. Directional Bias Calculation The forecasted points are analyzed to determine a normalized bias value in the range -1 to +1, where +1 means strongly bullish, -1 means strongly bearish, and 0 means neutral. Logistic Bias Transformation The raw bias is passed through a logistic sigmoid function, with a user-defined steepness. This creates a probability-like weight that favors either following or reversing the forecast depending on market context. ADX-Based Weighting ADX determines the weighting between trend-following and mean-reversion modes. Below ADX 20, the model favors mean-reversion. Above 25, it favors trend-following. Between 20 and 25, it linearly blends the two. Blended Forecast Curve Each forecast point is blended between trend-following and mean-reverting values, scaled for volatility. What You See: Forecast Lines: Projected future price paths drawn in green or red depending on direction. Bias Plot: A separate plot showing post-blend directional bias as a percentage, where +100 is strongly bullish and -100 is strongly bearish. Neutral Line: A dashed horizontal line at 0 percent bias to indicate neutrality. User Inputs: -Max Regression Length -Price Source -Line Width -Bias Steepness -ADX Length and Smoothing Use Cases: Visualize expected price direction under different trend conditions Adjust trading behavior depending on trending vs ranging markets Combine with other tools for deeper analysis Important Notes: This indicator is for visualization and analysis only. It does not provide buy or sell signals and should not be used in isolation. It makes assumptions based on historical price action and should be interpreted with market context.
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Tradenometry tarafından
2
Exponential growthPurpose The indicator plots an exponential curve based on historical price data and supports toggling between exponential regression and linear logarithmic regression. It also provides offset bands around the curve for additional insights. Key Inputs 1. yxlogreg and dlogreg: These are the "Endwert" (end value) and "Startwert" (start value) for calculating the slope of the logarithmic regression. 2. bars: Specifies how many historical bars are considered in the calculation. 3.offsetchannel: Adds an adjustable percentage-based offset to create upper and lower bands around the main exponential curve. Default: 1 (interpreted as 10% bands). 4.lineareregression log.: A toggle to switch between exponential function and linear logarithmic regression. Default: false (exponential is used by default). 5.Dynamic Labels: Creates a label showing the calculated regression values and historical bars count at the latest bar. The label is updated dynamically. Use Cases Exponential Growth Tracking: Useful for assets or instruments exhibiting exponential growth trends. Identifying Channels: Helps identify support and resistance levels using the offset bands. Switching Analysis Modes: Flexibility to toggle between exponential and linear logarithmic analysis.
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Embit0one tarafından
1
2-Year - Fed Rate SpreadThe “2-Year - Fed Rate Spread” is a financial indicator that measures the difference between the 2-Year Treasury Yield and the Federal Funds Rate (Fed Funds Rate). This spread is often used as a gauge of market sentiment regarding the future direction of interest rates and economic conditions. Calculation • 2-Year Treasury Yield: This is the return on investment, expressed as a percentage, on the U.S. government’s debt obligations that mature in two years. • Federal Funds Rate: The interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The indicator calculates the spread by subtracting the Fed Funds Rate from the 2-Year Treasury Yield: {2-Year - Fed Rate Spread} = {2-Year Treasury Yield} - {Fed Funds Rate} Interpretation: • Positive Spread: A positive spread (2-Year Treasury Yield > Fed Funds Rate) typically suggests that the market expects the Fed to raise rates in the future, indicating confidence in economic growth. • Negative Spread: A negative spread (2-Year Treasury Yield < Fed Funds Rate) can indicate market expectations of a rate cut, often signaling concerns about an economic slowdown or recession. When the spread turns negative, the indicator’s background turns red, making it visually easy to identify these periods. How to Use: • Trend Analysis: Investors and analysts can use this spread to assess the market’s expectations for future monetary policy. A persistent negative spread may suggest a cautious approach to equity investments, as it often precedes economic downturns. • Confirmation Tool: The spread can be used alongside other economic indicators, such as the yield curve, to confirm signals about the direction of interest rates and economic activity. Research and Academic References: The 2-Year - Fed Rate Spread is part of a broader analysis of yield spreads and their implications for economic forecasting. Several academic studies have examined the predictive power of yield spreads, including those that involve the 2-Year Treasury Yield and Fed Funds Rate: 1. Estrella, Arturo, and Frederic S. Mishkin (1998). “Predicting U.S. Recessions: Financial Variables as Leading Indicators.” The Review of Economics and Statistics, 80(1): 45-61. • This study explores the predictive power of various financial variables, including yield spreads, in forecasting U.S. recessions. The authors find that the yield spread is a robust leading indicator of economic downturns. 2. Estrella, Arturo, and Gikas A. Hardouvelis (1991). “The Term Structure as a Predictor of Real Economic Activity.” The Journal of Finance, 46(2): 555-576. • The paper examines the relationship between the term structure of interest rates (including short-term spreads like the 2-Year - Fed Rate) and future economic activity. The study finds that yield spreads are significant predictors of future economic performance. 3. Rudebusch, Glenn D., and John C. Williams (2009). “Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve.” Journal of Business & Economic Statistics, 27(4): 492-503. • This research investigates why the yield curve, particularly spreads involving short-term rates like the 2-Year Treasury Yield, remains a powerful tool for forecasting recessions despite changes in monetary policy. Conclusion: The 2-Year - Fed Rate Spread is a valuable tool for market participants seeking to understand future interest rate movements and potential economic conditions. By monitoring the spread, especially when it turns negative, investors can gain insights into market sentiment and adjust their strategies accordingly. The academic research supports the use of such yield spreads as reliable indicators of future economic activity.
Pine Script® göstergesi
EdgeTools tarafından
Dynamic Gradient Filter Sigmoid Functions: History and Mathematical Basis: Sigmoid functions have a rich history in mathematics and are widely used in various fields, including statistics, machine learning, and signal processing. The term "sigmoid" originates from the Greek words "sigma" (meaning "S-shaped") and "eidos" (meaning "form" or "type"). The sigmoid curve is characterized by its smooth S-shaped appearance, which allows it to map any real-valued input to a bounded output range, typically between 0 and 1. The most common form of the sigmoid function is the logistic function: Logistic Function (σ): Defined as σ(x) = 1 / (1 + e^(-x)), where: 'x' is the input value, 'e' is Euler's number (approximately 2.71828). This function was first introduced by Belgian mathematician Pierre François Verhulst in the 1830s to model population growth with limiting factors. It gained popularity in the early 20th century when statisticians like Ronald Fisher began using it in regression analysis. Specific Sigmoid Functions Used in the Indicator: sig(val): The 'sig' function in this indicator is a modified version of the logistic function, clamping a value between 0 and 1 on the sigmoid curve. siga(val): The 'siga' function adjusts values between -1 and 1 on the sigmoid curve, offering a centered variation of the sigmoid effect. sigmoid(val): The 'sigmoid' function provides a standard implementation of the logistic function, calculating the sigmoid value of the input data. Adaptive Smoothing Factor: The ' adaptiveSmoothingFactor(gradient, k)' function computes a dynamic smoothing factor for the filter based on the gradient of the price data and the user-defined sensitivity parameter 'k' . Gradient: The gradient represents the rate of change in price, calculated as the absolute difference between the current and previous close prices. Sensitivity (k): The 'k' parameter adjusts how quickly the filter reacts to changes in the gradient. Higher values of 'k' lead to a more responsive filter, while lower values result in smoother outputs. Usage in the Indicator: The "close" value refers to the closing price of each period in the chart's time frame The indicator calculates the gradient by measuring the absolute difference between the current "close" price and the previous "close" price. This gradient represents the strength or magnitude of the price movement within the chosen time frame. The "close" value plays a pivotal role in determining the dynamic behavior of the "Dynamic Gradient Filter," as it directly influences the smoothing factor. What Makes This Special: The "Dynamic Gradient Filter" indicator stands out due to its adaptive nature and responsiveness to changing market conditions. Dynamic Smoothing Factor: The indicator's dynamic smoothing factor adjusts in real-time based on the rate of change in price (gradient) and the user-defined sensitivity '(k)' parameter. This adaptability allows the filter to respond promptly to both minor fluctuations and significant price movements. Smoothed Price Action: The final output of the filter is a smoothed representation of the price action, aiding traders in identifying trends and potential reversals. Customizable Sensitivity: Traders can adjust the 'Sensitivity' parameter '(k)' to suit their preferred trading style, making the indicator versatile for various strategies. Visual Clarity: The plotted "Dynamic Gradient Filter" line on the chart provides a clear visual guide, enhancing the understanding of market dynamics. Usage: Traders and analysts can utilize the "Dynamic Gradient Filter" to: Identify trends and reversals in price movements. Filter out noise and highlight significant price changes. Fine-tune trading strategies by adjusting the sensitivity parameter. Enhance visual analysis with a dynamically adjusting filter line on the chart. Literature: en.wikipedia.org medium.com en.wikipedia.org
Pine Script® göstergesi
Mupsje tarafından
4
Machine Learning using Neural Networks | EducationalThe script provided is a comprehensive illustration of how to implement and execute a simplistic Neural Network (NN) on TradingView using PineScript. It encompasses the entire workflow from data input, weight initialization, implicit neuron calculation, feedforward computation, backpropagation for weight adjustments, generating predictions, to visualizing the Mean Squared Error (MSE) Loss Curve for monitoring the training phase. In the visual example above, you can see that the prediction is not aligned with the actual value. This is intentional for demonstrative purposes, and by incrementing the Epochs or Learning Rate, you will see these two values converge as the accuracy increases. Hyperparameters: Learning Rate, Epochs, and the choice between Simple Backpropagation and a verbose version are declared as script inputs, allowing users to tailor the training process. Initialization: Random initialization of weight matrices (w1, w2) is performed to ensure asymmetry, promoting effective gradient updates. A seed is added for reproducibility. Utility Functions: Functions for matrix randomization, sigmoid activation, MSE loss calculation, data normalization, and standardization are defined to streamline the computation process. Neural Network Computation: The feedforward function computes the hidden and output layer values given the input. Two variants of the backpropagation function are provided for weight adjustment, with one offering a more verbose step-by-step computation of gradients. A wrapper train_nn function iterates through epochs, performing feedforward, loss computation, and backpropagation in each epoch while logging and collecting loss values. Training Invocation: The input data is prepared by normalizing it to a value between 0 and 1 using the maximum standardized value, and the training process is invoked only on the last confirmed bar to preserve computational resources. Output Forecasting and Visualization: Post training, the NN's output (predicted price) is computed, standardized and visualized alongside the actual price on the chart. The MSE loss between the predicted and actual prices is visualized, providing insight into the prediction accuracy. Optionally, the MSE Loss Curve is plotted on the chart, illustrating the loss trajectory through epochs, assisting in understanding the training performance. Customizable Visualization: Various inputs control visualization aspects like Chart Scaling, Chart Horizontal Offset, and Chart Vertical Offset, allowing users to adapt the visualization to their preference. ------------------------------------------------------- The following is this Neural Network structure, consisting of one hidden layer, with two hidden neurons. Through understanding the steps outlined in my code, one should be able to scale the NN in any way they like, such as changing the input / output data and layers to fit their strategy ideas. Additionally, one could forgo the backpropagation function, and load their own trained weights into the w1 and w2 matrices, to have this code run purely for inference. ------------------------------------------------------- While this demonstration does create a “prediction”, it is on historical data. The purpose here is educational, rather than providing a ready tool for non-programmer consumers. Normally in Machine Learning projects, the training process would be split into two segments, the Training and the Validation parts. For the purpose of conveying the core concept in a concise and non-repetitive way, I have foregone the Validation part. However, it is merely the application of your trained network on new data (feedforward), and monitoring the loss curve. Essentially, checking the accuracy on “unseen” data, while training it on “seen” data. ------------------------------------------------------- I hope that this code will help developers create interesting machine learning applications within the Tradingview ecosystem.
Pine Script® göstergesi
Alien_Algorithms tarafından
21
RAS.V2 Strength Index OscillatorHeavily modified version of my previous "Relative Aggregate Strength Oscillator" -Added high/low lines, alma curves,, lrc bands, changed candle calculations + other small things. Replaces the standard RSI indicator with something a bit more insightful. Credits to @wolneyyy - 'Mean Deviation Detector - Throw Out All Other Indicators ' And @algomojo - 'Responsive Coppock Curve' And the default Relative Strength Index The candles are the average of the MFI ,CCI ,MOM and RSI candles, they seemed similar enough in style to me so I created candles out of each and the took the sum of all the candle's OHLC values and divided by 4 to get an average, same as v1 but with some tweaks. Previous Peaks and Potholes visible with the blue horizontal lines which adjust when a new boundary is established. Toggle alma waves or smalrc curves or both to your liking. This indicator is great for calling out peaks and troughs in realtime, although is best when combined with other trusted indicators to get a consensus.
Pine Script® göstergesi
EsIstTurnt tarafından
hayatguzel trendycurveENG If we are wondering how the trendlines drawn on the hayatguzel indicator look like on the graph, we should use this indicator. Trendlines that are linear in Hg (hayatguzel) are actually curved in the graph. "hayatguzel curve" indicator has capable of plotting horizontal levels but not trendlines in hg indicator. But "hayatguzel trendycurve" indicator has capable of plotting (on the chart) trendlines in hg. First of all, we start by determining the coordinates from the trendlines drawn in hg. The coordinate of trendline beginings is x1,y1. In the continuation of the trendline, the coordinate of the second point taken from anywhere on the trendline is defined as x2,y2. In order to find the x1 and x2 values, the gray bar index chart must be open. After reading the values, the bar index chart can be turned off in the settings. The x coordinates of the trendlines will be the values ​​in this gray bar index graph. You can read these coordinates from the gray numbers in the hg-trendycurve setting at the top left of the graph. The y values ​​are the y axis values ​​in the hg indicator. It should be noted that the ema value in the hayatguzel trendycurve indicator must be the same as the ema value in the hg indicator. Hayatguzel trendycurve indicator is not an indicator that can be used on its own, it should be used together with hayatguzel indicator. TR Hayatguzel indikatöründe çizilen trendline'ların grafik üzerine nasıl göründüğünü merak ediyorsak bu indikatörü kullanmalıyız. Hg'de doğrusal olan trendline'lar doğal olarak grafikte eğriseller. Hayatguzel curve indikatöründe hg'deki sadece yatay seviyeler grafiğe dökülürken bu hayatguzel trendycurve indikatörü ile hg'deki trendline'lar da grafiğe dökülebiliyor. Öncelikle hg'de çizilen trendline'lardan koordinatları belirlemek ile işe başlıyoruz. Trendline'ların başladığı yerin koordinatı x1,y1'dir. Trendline'ın devamında trendline üzerinde herhangi bir yerden alınan ikinci noktanın koordinatı da x2,y2 olarak tanımlandı. x1 ve x2 değerlerini bulabilmek için gri bar index grafiğinin açık olması gerekmektedir. Değerleri okuduktan sonra bar index grafiği ayarlardan kapatılabilir. Trendline'ların x koordinatları bu gri renkli bar index grafiğindeki değerler olacaktır. Bu koordinatları grafikte sol üstte bulunan hg-trendycurve ayalarındaki gri sayılardan okuyabilirsiniz. y değerleri ise hg indikatöründeki y ekseni değerleridir. Unutulmamalı ki hayatguzel trendycurve indikatöründeki ema değeri hg indikatöründeki ema değeri ile aynı olmalıdır. Hayatguzel trendycurve indikatörü kendi başına kullanılabilecek bir indikatör olmayıp hayatguzel indikatörü ile beraber kullanılması gerekmektedir.
Pine Script® göstergesi
hayatguzel tarafından
7
Momentum Strategy (BTC/USDT; 1h) - MACD (with source code)Good morning traders. It's been a while from my last publication of a strategy and today I want to share with you this small piece of script that showed quite interesting result across bitcoin and other altcoins. The macd indicator is an indicator built on the difference between a fast moving average and a slow moving average: this difference is generally plottted with a blue line while the orange line is simply a moving average computed on this difference. Usually this indicator is used in technical analysis for getting signals of buy and sell respectively when the macd crosses above or under its moving average: it means that the distance of the fast moving average (the most responsive one) from the slower one is getting lower than what it-used-to-be in the period considered: this could anticipate a cross of the two moving averages and you want to anticipate this potential trend reversal by opening a long position Of course the workflow is specularly the same for opening short positions (or closing long positions) What this strategy does is simply considering the moving average computed on macd and applying a linear regression on it: in this way, even though the signal can be sligthly delayed, you reduce noise plotting a smooth curve. Then, it simply checks the maximums and the minimums of this curve detecting whenever the changes of the values start to be negative or positive, so it opens a short position (closes long) on the maximum on this curve and it opens a long position (closes short) on the minimum. Of course, I set an option for using this strategy in a conventional way working on the crosses between macd and its moving average. Alternatively you can use this workflow if you prefer. In conclusion, you can use a tons of moving averages: I made a function in pine in order to allw you to use any moving average you want for the two moving averages on which the macd is based or for the moving average computed on the macd PLEASE, BE AWARE THAT THIS TRADING STRATEGY DOES NOT GUARANTEE ANY KIND OF SUCCESS IN ADVANCE. YOU ARE THE ONE AND ONLY RESPONSIBLE OF YOUR OWN DECISIONS, I DON'T TAKE ANY RESPONSIBILITY ASSOCIATED WITH THEM. IF YOU RUN THIS STRATEGY YOU ACCEPT THE POSSIBILITY OF LOOSING MONEY, ALL OF MY PUBBLICATIONS ARE SUPPOSED TO BE JUST FOR EDUCATIONAL PURPOSES. IT IS AT YOUR OWN RISK WHETHER TO USE IT OR NOT But if you make money out of this, please consider to buy me a beer 😜 Happy Trading!
Pine Script® stratejisi
Drun30 tarafından
10
Market Meanness Index-Price ChangesThis is the Market Mean index. It is used to identify if the market is really trending or if it is range bound(random). In theory, a random sample will be mean reverting 75% of the time. This indicator checks to see what how much the market is mean reverting and converts it to a percentage. If the index is around 75 or higher than the price curve of the market is range bound and there is no trend from a statistical standpoint. If the index is below 75 this means the price curve of the market is in fact trending in a direction as the market is not reverting as much as it should if it were truly following a random/range bound price curve.
Pine Script® göstergesi
LevyFlight tarafından
2
ZenAlgo - ADXThis open-source indicator builds upon the official Average Directional Index (ADX) implementation by TradingView. It preserves the core logic of the original ADX while introducing additional visualization features, configurability, and analytical overlays to assist with directional strength analysis. Core Calculation The script computes the ADX, +DI, and -DI based on smoothed directional movement and true range over a user-defined length. The smoothing is performed using Wilder’s method, as in the original implementation. True Range is calculated from the current high, low, and previous close. Directional Movement components (+DM, -DM) are derived by comparing the change in highs and lows between consecutive bars. These values are then smoothed, and the +DI and -DI are expressed as percentages of the smoothed True Range. The difference between +DI and -DI is normalized to derive DX, which is further smoothed to yield the ADX value. The indicator includes a selectable signal line (SMA or EMA) applied to the ADX for crossover-based visualization. Visualization Enhancements Several plots and conditions have been added to improve interpretability: Color-coded histograms and lines visualize DI relative to a configurable threshold (default: 25). Colors follow the ZenAlgo color scheme. Dynamic opacity and gradient coloring are used for both ADX and DI components, allowing users to distinguish weak/moderate/strong directional trends visually. Mirrored ADX is internally calculated for certain overlays but not directly plotted. The script also provides small circles and diamonds to highlight: Crossovers between ADX and its signal line. DI crossing above or below the 25 threshold. Rising ADX confirmed by rising DI values, with point size reflecting ADX strength. Divergence Detection The indicator includes optional detection of fractal-based divergences on the DI curve: Regular and hidden bullish and bearish divergences are identified based on relative fractal highs/lows in both price and DI. Detected divergences are optionally labeled with 'R' (Regular) or 'H' (Hidden), and color-coded accordingly. Fractal points are defined using 5-bar patterns to ensure consistency and reduce false positives. ADX/DI Table When enabled, a floating table displays live values and summaries: ADX value , trend direction (rising/falling), and qualitative strength. DI composite , trend direction, and relative strength. Contextual power dynamics , describing whether bulls or bears are gaining or losing strength. The background colors of the table reflect current trend strength and direction. Interpretation Guidelines ADX indicates the strength of a trend, regardless of its direction. Values below 20 are often considered weak, while those above 40 suggest strong trending conditions. +DI and -DI represent bullish and bearish directional movements, respectively. Crossovers between them are used to infer trend direction. When ADX is rising and either +DI or -DI is dominant and increasing, the trend is likely strengthening. Divergences between DI and price may suggest potential reversals but should be interpreted cautiously and not in isolation. The threshold line (default 25) provides a basic filter for ignoring low-strength conditions. This can be adjusted depending on the market or timeframe. Added Value over Existing Indicators Fully color-graded ADX and DI display for better visual clarity. Optional signal MA over ADX with crossover markers. Rich contextual labeling for both divergence and threshold events. Power dynamics commentary and live table help users contextualize current momentum. Customizable options for smoothing type, divergence display, table position, and visual offsets. These additions aim to improve situational awareness without altering the fundamental meaning of ADX/DI values. Limitations and Disclaimers As with any ADX-based tool, this indicator does not indicate market direction alone —it measures strength, not trend bias. Divergence detection relies on fractal patterns and may lag or produce false positives in sideways markets. Signal MA crossovers and DI threshold breaks are not entry signals , but contextual markers that may assist with timing or filtering other systems. The table text and labels are for visual assistance and do not replace proper technical analysis or market context.
Pine Script® göstergesi
ZenAlgo_Official tarafından
RMSD Trend [InvestorUnknown]RMSD Trend is a trend-following indicator that utilizes Root Mean Square Deviation (RMSD) to dynamically construct a volatility-weighted trend channel around a selected moving average. This indicator is designed to enhance signal clarity, minimize noise, and offer quantitative insights into market momentum, ideal for both discretionary and systematic traders. How It Works At its core, RMSD Trend calculates a deviation band around a selected moving average using the Root Mean Square Deviation (similar to standard deviation but with squared errors), capturing the magnitude of price dispersion over a user-defined period. The logic is simple: When price crosses above the upper deviation band, the market is considered bullish (Risk-ON Long). When price crosses below the lower deviation band, the market is considered bearish (Risk-ON Short). If price stays within the band, the market is interpreted as neutral or ranging, offering low-risk decision zones. The indicator also generates trend flips (Long/Short) based on crossovers and crossunders of the price and the RMSD bands, and colors candles accordingly for enhanced visual feedback. Features 7 Moving Average Types: Choose between SMA, EMA, HMA, DEMA, TEMA, RMA, and FRAMA for flexibility. Customizable Source Input: Use price types like close, hl2, ohlc4, etc. Volatility-Aware Channel: Adjustable RMSD multiplier determines band width based on volatility. Smart Coloring: Candles and bands adapt their colors to reflect trend direction (green for bullish, red for bearish). Intra-bar Repainting Toggle: Option to allow more responsive but repaintable signals. Speculation Fill Zones: When price exceeds the deviation channel, a semi-transparent fill highlights potential momentum surges. Backtest Mode Switching to Backtest Mode unlocks a robust suite of simulation features: Built-in Equity Curve: Visualizes both strategy equity and Buy & Hold performance. Trade Metrics Table: Displays the number of trades, win rates, gross profits/losses, and long/short breakdowns. Performance Metrics Table: Includes key stats like CAGR, drawdown, Sharpe ratio, and more. Custom Date Range: Set a custom start date for your backtest. Trade Sizing: Simulate results using position sizing and initial capital settings. Signal Filters: Choose between Long & Short, Long Only, or Short Only strategies. Alerts The RMSD Trend includes six built-in alert conditions: LONG (RMSD Trend) - Trend flips from Short to Long SHORT (RMSD Trend) - Trend flips from Long to Short RISK-ON LONG (RMSD Trend) - Price crosses above upper RMSD band RISK-OFF LONG (RMSD Trend) - Price falls back below upper RMSD band RISK-ON SHORT (RMSD Trend) - Price crosses below lower RMSD band RISK-OFF SHORT (RMSD Trend) - Price rises back above lower RMSD band Use Cases Trend Confirmation: Confirms directional bias with RMSD-weighted confidence zones. Breakout Detection: Highlights moments when price breaks free from historical volatility norms. Mean Reversion Filtering: Avoids false signals by incorporating RMSD’s volatility sensitivity. Strategy Development: Backtest your signals or integrate with a broader system for alpha generation. Settings Summary Display Mode: Overlay (default) or Backtest Mode Average Type: Choose from SMA, EMA, HMA, DEMA, etc. Average Length: Lookback window for moving average RMSD Multiplier: Band width control based on RMS deviation Source: Input price source (close, hl2, ohlc4, etc.) Intra-bar Updating: Real-time updates (may repaint) Color Bars: Toggle bar coloring by trend direction Disclaimer This indicator is provided for educational and informational purposes only. It is not financial advice. Past performance, including backtest results, is not indicative of future results. Use with caution and always test thoroughly before live deployment.
Pine Script® göstergesi
InvestorUnknown tarafından
1
Dskyz (DAFE) AI Adaptive Regime - Beginners VersionDskyz (DAFE) AI Adaptive Regime - Pro: Revolutionizing Trading for All Introduction In the fast-paced world of financial markets, traders need tools that can keep up with ever-changing conditions while remaining accessible. The Dskyz (DAFE) AI Adaptive Regime - Pro is a groundbreaking TradingView strategy that delivers advanced, AI-driven trading capabilities to everyday traders. Available on TradingView (TradingView Scripts), this Pine Script strategy combines sophisticated market analysis with user-friendly features, making it a standout choice for both novice and experienced traders. Core Functionality The strategy is built to adapt to different market regimes—trending, ranging, volatile, or quiet—using a robust set of technical indicators, including: Moving Averages (MA): Fast and slow EMAs to detect trend direction. Average True Range (ATR): For dynamic stop-loss and volatility assessment. Relative Strength Index (RSI) and MACD: Multi-timeframe confirmation of momentum and trend. Average Directional Index (ADX): To identify trending markets. Bollinger Bands: For assessing volatility and range conditions. Candlestick Patterns: Recognizes patterns like bullish engulfing, hammer, and double bottoms, confirmed by volume spikes. It generates buy and sell signals based on a scoring system that weighs these indicators, ensuring trades align with the current market environment. The strategy also includes dynamic risk management with ATR-based stops and trailing stops, as well as performance tracking to optimize future trades. What Sets It Apart The Dskyz (DAFE) AI Adaptive Regime - Pro distinguishes itself from other TradingView strategies through several unique features, which we compare to common alternatives below: | Feature | Dskyz (DAFE) | Typical TradingView Strategies| |---------|-------------|------------------------------------------------------------| | Regime Detection | Automatically identifies and adapts to **four** market regimes | Often static or limited to trend/range detection | | Multi‑Timeframe Analysis | Uses higher‑timeframe RSI/MACD for confirmation | Rarely incorporates multi‑timeframe data | | Pattern Recognition | Detects candlestick patterns **with volume confirmation** | Limited or no pattern recognition | | Dynamic Risk Management | ATR‑based stops and trailing stops | Often uses fixed stops or basic risk rules | | Performance Tracking | Adjusts thresholds based on past performance | Typically static parameters | | Beginner‑Friendly Presets | Aggressive, Conservative, Optimized profiles | Requires manual parameter tuning | | Visual Cues | Color‑coded backgrounds for regimes | Basic or no visual aids | The Dskyz strategy’s ability to integrate regime detection, multi-timeframe analysis, and user-friendly presets makes it uniquely versatile and accessible, addressing the needs of everyday traders who want professional-grade tools without the complexity. -Key Features and Benefits [Why It’s Ideal for Everyday Traders ⚡The Dskyz (DAFE) AI Adaptive Regime - Pro democratizes advanced trading by offering professional-grade tools in an accessible package. Unlike many TradingView strategies that require deep technical knowledge or fail in changing market conditions, this strategy simplifies complex analysis while maintaining robustness. Its presets and visual aids make it easy for beginners to start, while its adaptive features and performance tracking appeal to advanced traders seeking an edge. 🔄Limitations and Considerations Market Dependency: Performance varies by market and timeframe. Backtesting is essential to ensure compatibility with your trading style. Learning Curve: While presets simplify use, understanding regimes and indicators enhances effectiveness. No Guaranteed Profits: Like all strategies, success depends on market conditions and proper execution. The Reddit discussion highlights skepticism about TradingView strategies’ universal success (Reddit Discussion). Instrument Specificity: Optimized for futures (e.g., ES, NQ) due to fixed tick values. Test on other instruments like stocks or forex to verify compatibility. 📌Conclusion The Dskyz (DAFE) AI Adaptive Regime - Pro is a revolutionary TradingView strategy that empowers everyday traders with advanced, AI-driven tools. Its ability to adapt to market regimes, confirm signals across timeframes, and manage risk dynamically. sets it apart from typical strategies. By offering beginner-friendly presets and visual cues, it makes sophisticated trading accessible without sacrificing power. Whether you’re a novice looking to trade smarter or a pro seeking a competitive edge, this strategy is your ticket to mastering the markets. Add it to your chart, backtest it, and join the elite traders leveraging AI to dominate. Trade like a boss today! 🚀 Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. -Dskyz
Pine Script® stratejisi
DskyzInvestments tarafından
3
Standard Deviation (fadi)The Standard Deviation indicator uses standard deviation to map out price movements. Standard deviation measures how much prices stray from their average—small values mean steady trends, large ones mean wild swings. Drawing from up to 20 years of data, it plots key levels using customizable Fibonacci lines tied to that standard deviation, giving traders a snapshot of typical price behavior. These levels align with a bell curve: about 68% of price moves stay within 1 standard deviation, 95% within roughly 2, and 99.7% within roughly 3. When prices break past the 1 StDev line, they’re outliers—only 32% of moves go that far. Prices often snap back to these lines or the average, though the reversal might not happen the same day. How Traders Use It If prices surge past the 1 StDev line, traders might wait for momentum to fade, then trade the pullback to that line or the average, setting a target and stop. If prices dip below, they might buy, anticipating a bounce—sometimes a day or two later. It’s a tool to spot overstretched prices likely to revert and/or measure the odds of continuation. Settings Higher Timeframe: Sets the Higher Timeframe to calculate the Standard Deviation for Show Levels for the Last X Days: Displays levels for the specified number of days. Based on X Period: Number of days to calculate standard deviation (e.g., 20 years ≈ 5,040 days). Larger periods smooth out daily level changes. Mirror Levels on the Other Side: Plots symmetric positive and negative levels around the average. Fibonacci Levels Settings: Defines which levels and line styles to show. With mirroring, negative values aren’t needed. Background Transparency: Turn on Background color derived from the level colors with the specified transparency Overrides: Lets advanced users input custom standard deviations for specific tickers (e.g., NQ1! at 0.01296).
Pine Script® göstergesi
fadizeidan tarafından
36
Daily Standard Deviation (fadi)The Daily Standard Deviation indicator uses standard deviation to map out daily price movements. Standard deviation measures how much prices stray from their average—small values mean steady trends, large ones mean wild swings. Drawing from up to 20 years of data, it plots key levels using customizable Fibonacci lines tied to that standard deviation, giving traders a snapshot of typical price behavior. These levels align with a bell curve: about 68% of price moves stay within 1 standard deviation, 95% within roughly 2, and 99.7% within roughly 3. When prices break past the 1 StDev line, they’re outliers—only 32% of moves go that far. Prices often snap back to these lines or the average, though the reversal might not happen the same day. How Traders Use It If prices surge past the 1 StDev line, traders might wait for momentum to fade, then trade the pullback to that line or the average, setting a target and stop. If prices dip below, they might buy, anticipating a bounce—sometimes a day or two later. It’s a tool to spot overstretched prices likely to revert and/or measure the odds of continuation. Settings Open Hour: Sets the trading day’s start (default: 18:00 EST). Show Levels for the Last X Days: Displays levels for the specified number of days. Based on X Period: Number of days to calculate standard deviation (e.g., 20 years ≈ 5,040 days). Larger periods smooth out daily level changes. Mirror Levels on the Other Side: Plots symmetric positive and negative levels around the average. Fibonacci Levels Settings: Defines which levels and line styles to show. With mirroring, negative values aren’t needed. Overrides: Lets advanced users input custom standard deviations for specific tickers (e.g., NQ1! at 0.01296).
Pine Script® göstergesi
fadizeidan tarafından
20
MA Multi-Timeframe [ChartPrime]The MA Multi-Timeframe indicator is designed to provide multi-timeframe moving averages (MAs) for better trend analysis across different periods. This tool allows traders to monitor up to four different MAs on a single chart, each coming from a selectable timeframe and type (SMA, EMA, SMMA, WMA, VWMA). The indicator helps traders gauge both short-term and long-term price trends, allowing for a clearer understanding of market dynamics. ⯁ KEY FEATURES AND HOW TO USE ⯌ Multi-Timeframe Moving Averages : The indicator allows traders to select up to four MAs, each from different timeframes. These timeframes can be set in the input settings (e.g., Daily, Weekly, Monthly), and each moving average can be displayed with its corresponding timeframe label directly on the chart. Example of different timeframes for MAs: ⯌ Moving Average Types : Users can choose from several types of moving averages, including SMA, EMA, SMMA, WMA, and VWMA, making the indicator adaptable to different strategies and market conditions. This flexibility allows traders to tailor the MAs to their preference. Example of different types of MAs: ⯌ Dashboard Display : The indicator includes a built-in dashboard that shows each MA, its timeframe, and whether the price is currently above or below that MA. This dashboard provides a quick overview of the trend across different timeframes, allowing traders to determine whether the overall trend is up or down. Example of trend overview via the dashboard: ⯌ Polyline Representation : Each MA is plotted using polylines to avoid plot functions and create a curves across up to 4000 bars back, ensuring that historical data is visualized clearly for a deeper analysis of how the price interacts with these levels over time. if barstate.islast for i = 0 to 4000 cp.push(chart.point.from_index(bar_index , ma )) polyline.delete(polyline.new(cp, curved = false, line_color = color, line_style = style) ) Example of polylines for moving averages: ⯌ Customization Options : Traders can customize the length of the MAs for all timeframes using a single input. The color, style (solid, dashed, dotted) of each moving average are also customizable, giving users full control over the visual appearance of the indicator on their chart. Example of custom MA styles: ⯁ USER INPUTS MA Type : Select the type of moving average for each timeframe (SMA, EMA, SMMA, WMA, VWMA). Timeframe : Choose the timeframe for each moving average (e.g., Daily, Weekly, Monthly). MA Length : Set the length for the moving averages, which will be applied to all four MAs. Line Style : Customize the style of each MA line (solid, dashed, or dotted). Colors : Set the color for each MA for better visual distinction. ⯁ CONCLUSION The MA Multi-Timeframe indicator is a versatile and powerful tool for traders looking to monitor price trends across multiple timeframes with different types of moving averages. The dashboard simplifies trend identification, while the customizable options make it easy to adapt to individual trading strategies. Whether you're analyzing short-term price movements or long-term trends, this indicator offers a comprehensive solution for tracking market direction.
Pine Script® göstergesi
ChartPrime tarafından
9