Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc.
The HADES method is:
fit & forecast = a + b * (1 / alpha + T - 1)
T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints.
y = input time series
a = y, if no previous data exists
b = 0, if no previous data exists
otherwise:
a = alpha * y + (1 - alpha) * a
b = alpha * (a - a ) + (1 - alpha) * b
alpha = 1 / sqrt(len * 4)
len = min(ceil(exp(1 / sig)), available data)
sig = sqrt(Absolute net change in y / Sum of absolute changes in y)
For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1
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The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval:
prediction interval = data fit +- resoduals fit * k
forecasting interval = data opf +- errors fit * k
where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t
...
How-to:
0) Apply to your data where it makes sense, eg. tick data;
1) Use power transform to compensate for multiplicative behavior in case it's there;
2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point;
3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy;
4) Use one-point forecast for nowcasting;
5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks.
I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one.
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Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method?
1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods;
2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it;
3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value;
4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻
R&D; log & explanations
If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe
Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity.
In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process.
To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws.
Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset.
So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median.
Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are:
alpha = 1 / length
alpha = 2 / (length + 1)
The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on:
alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and
alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on...
It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing.
From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise.
What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast).
Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements:
alpha = sqrt(1 / length * 2) / 2
which simplifies to:
alpha = 1 / sqrt(len * 8)
^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements.
Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median.
Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn:
paste.pics
^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression.
I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula:
alpha = sqrt(1 / length) / 2
which simplifies to:
alpha = 1 / sqrt(len * 4)
Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough.
Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part.
Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology.
There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added.
As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution.
Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect.
Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths.
And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals.
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I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default.
Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place.
The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data.
That exp() in the len formula was the last piece that made it all work both on synthetic and on real data.
^^ a standalone script calculating optimal dynamic window size
Omg, THAT took time to write. Comment and/or text me if you need
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"Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻
∞
Cerca negli script per "如何用wind搜索股票的发行价和份数"
Dynamic Score SMA [QuantAlgo]Dynamic Score SMA 📈🌊
The Dynamic Score SMA by QuantAlgo offers a powerful trend-following approach that combines the simplicity of the Simple Moving Average (SMA) with an innovative dynamic trend scoring technique . By continuously evaluating price movement relative to the SMA over a customizable window, this indicator adapts to varying market conditions, providing traders and investors with clearer, more adaptable trend signals. With this dynamic scoring approach, the Dynamic Score SMA helps identify trend shifts, allowing for more strategic decision-making.
🌟 Conceptual Foundation and Innovation
At the core of the Dynamic Score SMA is its dynamic trend score system , which assesses price movements by comparing them to the SMA over a series of historical data points. This technique goes beyond traditional SMA indicators by offering a dynamic, probabilistic evaluation of trend strength, delivering a more responsive and nuanced view of market direction. The integration of this scoring system enables traders and investors to navigate both trending and sideway markets with greater confidence and precision.
⚙️ Technical Composition and Calculation
The Dynamic Score SMA leverages the Simple Moving Average to establish a baseline trend, with customizable SMA length to control the indicator’s sensitivity. The dynamic trend scoring technique then evaluates price behavior relative to the SMA over a specified window, generating a trend score that reflects the current market bias.
When the score crosses the designated uptrend or downtrend thresholds, the indicator signals a potential trend shift. By adjusting the SMA length, window duration, and thresholds, users can refine the indicator’s responsiveness to match their preferred trading or investing strategy, making it suitable for both volatile and steady markets.
📈 Features and Practical Applications
Customizable SMA Length: Set the length of the SMA to control how sensitive the trend is to price changes. Longer lengths produce smoother trends, while shorter lengths increase responsiveness.
Window Length for Dynamic Scoring: Adjust the window length to determine how many data points are considered in the dynamic trend score calculation, allowing for more tailored analysis of recent versus long-term trends.
Uptrend/Downtrend Thresholds: Define thresholds for triggering trend signals. Higher thresholds reduce sensitivity, providing clearer signals in volatile markets, while lower thresholds capture shorter-term movements.
Bar and Background Coloring: Visual cues, including bar coloring and background fills, provide a quick reference for current trend direction, making it easier to monitor market conditions.
Trend Confirmation: The dynamic trend scoring system verifies trend strength, offering more reliable entry and exit points by filtering out potential false signals.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score SMA to your favourites, then apply it to your chart. Customize the SMA length, window size, and thresholds to match your trading or investing preferences.
👀 Monitor Trend Shifts: Observe the trend in relation to the SMA and watch for signals when the score crosses key thresholds. Bar and/or background coloring will help identify the current trend direction and any shifts in momentum.
🔔 Set Alerts: Configure alerts for significant trend crossovers and reversals, enabling you to act on market changes in real-time without needing constant chart observation.
💫 Summary and Usage Tips
The Dynamic Score SMA by QuantAlgo is a sophisticated trend-following indicator that combines the familiarity of the SMA with a dynamic trend scoring system, providing a more adaptable and probabilistic approach to trend analysis. By tailoring the SMA length, scoring window, and thresholds, traders and investors can fine-tune the indicator for both short-term adjustments and long-term trend following. For optimal use, adjust sensitivity based on market volatility, and rely on the visual cues for clear trend confirmation. Whether you’re navigating choppy markets or stable trends, the Dynamic Score SMA offers a refined approach to capturing market direction with enhanced precision.
Mag7 IndexThis is an indicator index based on cumulative market value of the Magnificent 7 (AAPL, MSFT, NVDA, TSLA, META, AMZN, GOOG). Such an indicator for the famous Mag 7, against which your main security can be benchmarked, was missing from the TradingView user library.
The index bar values are calculated by taking the weighted average of the 7 stocks, relative to their market cap. Explicitly, we are multiplying each bar period's total outstanding stock amount by the OHLC of that period for each stock and dividing that value by the combined sum of outstanding stock for the 7 corporations. OHLC is taken for the extended trading session.
The index dynamically adjusts with respect to the chosen main security and the bars/line visible in the chart window; that is, the first close value is normalized to the main security's first close value. It provides recalculation of the performance in that chart window as you scroll (this isn't apparent in the demo chart above this description).
It can be useful for checking market breadth, or benchmarking price performance of the individual stock components that comprise the Magnificent 7. I prefer comparing the indicator to the Nasdaq Composite Index (IXIC) or S&P500 (SPX), but of course you can make comparisons to any security or commodity.
Settings Input Options:
1) Bar vs. Line - view as OHLC colored bars or line chart. Line chart color based on close above or below the previous period close as green or red line respectively.
2) % vs Regular - the final value for the window period as % return for that window or index value
3) Turn on/off - bottom right tile displaying window-period performance
Inspired by the simpler NQ 7 Index script by @RaenonX but with normalization to main security at start of window and additional settings input options.
Please provide feedback for additional features, e.g., if a regular/extended session option is useful.
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
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Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
Educational Inidicators - Ichimoku CloudThis indicator is part of the Indicator Educational Series, intended to help newer traders understand and interact with various indicators. The goal is to allow users to gain a stronger understanding of an indicator's underlying philosophy, and visually see how changes to an indicator's parameters affects the trades suggested by that indicator.
The scripts in this series are all open source, with the code broken up into logical section and notated so beginner users can also understand some PineScript fundamentals.
Please understand that no indicator presented in and of itself constitutes a complete trading strategy. Rather, this series is to help users determine which indicators make sense to them, and which ones to combine to create their own trading strategy. All material presented is purely for educational purposes.
Presented here is the Ichimoku Cloud.
The Ichimoku Cloud was developed by Goichi Hosada, and first published in the late 1960s. It is used by traders to understand price momentum, and help forecast future price movements.
The indicator at its core can be understood from four component parts:
The Conversion Line - An average of the highest and lowest price in a given window. Typically, this is a "fast" average, and as such, this line has the lowest period
The Base Line - An average of the highest and lowest price in a given window. This is a "slower" average than the Conversion Line, and as such should have a larger period than the Conversion Line
Leading Span A - The average of the Conversion Line and the Base Line
[*}Leading Span B - An average of the highest and lowest price in a given window. This is the "slowest" average of all three, and as such should have the largest period
When plotted, the Conversion Line (orange by default), Base Line (purple by default), Leading Span A (blue by default), and Leading Span B (red by defaults) are all drawn on the chart along with the price candles. The area between the Leading Span A and Leading Span B lines are also shaded depending on which of the two lines is greater: whenever Leading Span A is greater the area is shaded positively (blue by default), whenever Leading Span B is greater the area is shaded negatively (red by defaults).
One interesting feature of the Ichimoku Cloud is that it drawn a certain number of candles forward. What this means is that where the cloud is drawn on the chart is reflective of prices that have occurred a number of candles in the past. This is done intentionally to help traders see how the current price is moving in relation to historical price movements on the asset.
See below for how the indicators look in their default colors on the chart
These indicators can then be used to start analyzing the price movement, and making trade decisions.
The first inference we can make is the momentum of the price. Since the lines are drawn from averages of varying speeds, the shaded area between the Leading Span lines can tell us whether the momentum is bullish (up) or bearish (down).
Whenever Leading Span A, the faster of the two lines, is above Leading Span B, that means that price is moving upward faster than it typically has, ergo we are in Bullish Momentum. On the chart, this is indicated in two ways:
The area is shaded positively (blue by default)
A green upward triangle is added to the chart to indicate where the momentum first turned Bullish
Whenever Leading Span A is below Leading Span B, that means that price is moving downward faster than it typically has, ergo we are in Bearish Momentum. On the chart, this is indicated in two ways:
The area is shaded negatively (red by default)
A red downward triangle is added to the chart to indicate where the momentum first turned Bearish
The next inference we can make is possible trading points. When we're in a period of momentum, as determined above, we know that price is going up or down, depending on the momentum we're in. We can then use the Conversion Line, Base Line, and the Price itself to confirm a good trade price.
When the asset is in Bullish Momentum, and the Conversion Line, our fastest average, is above the Base Line, our mid speed average, we know that the price is coming up quickly in the short term. When the Base Line and current Price are also above the cloud, then we have triple confirmation that price is going up, and we should enter a Long position. On the chart, this point is indicated with a green flag.
When the asset is in Bearish Momentum, and the Conversion Line is below the Base Line, we know that the price is going down quickly in the short term. When the Base Line and current Price are also below the cloud, then we have triple confirmation that price is going down, and we should enter a Short position. On the chart, this point is indicated with a red flag.
The script presented here also allows users to customize the various parameters of the Ichimoku Cloud, and visually see how analysis is affected by these changes. This is designed to allow users to modify parameters as they see fit, within certain constraints, to find the best set for them. The lines, cloud, and chart indicators will all update automatically with the users' inputs.
Paytience DistributionPaytience Distribution Indicator User Guide
Overview:
The Paytience Distribution indicator is designed to visualize the distribution of any chosen data source. By default, it visualizes the distribution of a built-in Relative Strength Index (RSI). This guide provides details on its functionality and settings.
Distribution Explanation:
A distribution in statistics and data analysis represents the way values or a set of data are spread out or distributed over a range. The distribution can show where values are concentrated, values are absent or infrequent, or any other patterns. Visualizing distributions helps users understand underlying patterns and tendencies in the data.
Settings and Parameters:
Main Settings:
Window Size
- Description: This dictates the amount of data used to calculate the distribution.
- Options: A whole number (integer).
- Tooltip: A window size of 0 means it uses all the available data.
Scale
- Description: Adjusts the height of the distribution visualization.
- Options: Any integer between 20 and 499.
Round Source
- Description: Rounds the chosen data source to a specified number of decimal places.
- Options: Any whole number (integer).
Minimum Value
- Description: Specifies the minimum value you wish to account for in the distribution.
- Options: Any integer from 0 to 100.
- Tooltip: 0 being the lowest and 100 being the highest.
Smoothing
- Description: Applies a smoothing function to the distribution visualization to simplify its appearance.
- Options: Any integer between 1 and 20.
Include 0
- Description: Dictates whether zero should be included in the distribution visualization.
- Options: True (include) or False (exclude).
Standard Deviation
- Description: Enables the visualization of standard deviation, which measures the amount of variation or dispersion in the chosen data set.
- Tooltip: This is best suited for a source that has a vaguely Gaussian (bell-curved) distribution.
- Options: True (enable) or False (disable).
Color Options
- High Color and Low Color: Specifies colors for high and low data points.
- Standard Deviation Color: Designates a color for the standard deviation lines.
Example Settings:
Example Usage RSI
- Description: Enables the use of RSI as the data source.
- Options: True (enable) or False (disable).
RSI Length
- Description: Determines the period over which the RSI is calculated.
- Options: Any integer greater than 1.
Using an External Source:
To visualize the distribution of an external source:
Select the "Move to" option in the dropdown menu for the Paytience Distribution indicator on your chart.
Set it to the existing panel where your external data source is placed.
Navigate to "Pin to Scale" and pin the indicator to the same scale as your external source.
Indicator Logic and Functions:
Sinc Function: Used in signal processing, the sinc function ensures the elimination of aliasing effects.
Sinc Filter: A filtering mechanism which uses sinc function to provide estimates on the data.
Weighted Mean & Standard Deviation: These are statistical measures used to capture the central tendency and variability in the data, respectively.
Output and Visualization:
The indicator visualizes the distribution as a series of colored boxes, with the intensity of the color indicating the frequency of the data points in that range. Additionally, lines representing the standard deviation from the mean can be displayed if the "Standard Deviation" setting is enabled.
The example RSI, if enabled, is plotted along with its common threshold lines at 70 (upper) and 30 (lower).
Understanding the Paytience Distribution Indicator
1. What is a Distribution?
A distribution represents the spread of data points across different values, showing how frequently each value occurs. For instance, if you're looking at a stock's closing prices over a month, you may find that the stock closed most frequently around $100, occasionally around $105, and rarely around $110. Graphically visualizing this distribution can help you see the central tendencies, variability, and shape of your data distribution. This visualization can be essential in determining key trading points, understanding volatility, and getting an overview of the market sentiment.
2. The Rounding Mechanism
Every asset and dataset is unique. Some assets, especially cryptocurrencies or forex pairs, might have values that go up to many decimal places. Rounding these values is essential to generate a more readable and manageable distribution.
Why is Rounding Needed? If every unique value from a high-precision dataset was treated distinctly, the resulting distribution would be sparse and less informative. By rounding off, the values are grouped, making the distribution more consolidated and understandable.
Adjusting Rounding: The `Round Source` input allows users to determine the number of decimal places they'd like to consider. If you're working with an asset with many decimal places, adjust this setting to get a meaningful distribution. If the rounding is set too low for high precision assets, the distribution could lose its utility.
3. Standard Deviation and Oscillators
Standard deviation is a measure of the amount of variation or dispersion of a set of values. In the context of this indicator:
Use with Oscillators: When using oscillators like RSI, the standard deviation can provide insights into the oscillator's range. This means you can determine how much the oscillator typically deviates from its average value.
Setting Bounds: By understanding this deviation, traders can better set reasonable upper and lower bounds, identifying overbought or oversold conditions in relation to the oscillator's historical behavior.
4. Resampling
Resampling is the process of adjusting the time frame or value buckets of your data. In the context of this indicator, resampling ensures that the distribution is manageable and visually informative.
Resample Size vs. Window Size: The `Resample Resolution` dictates the number of bins or buckets the distribution will be divided into. On the other hand, the `Window Size` determines how much of the recent data will be considered. It's crucial to ensure that the resample size is smaller than the window size, or else the distribution will not accurately reflect the data's behavior.
Why Use Resampling? Especially for price-based sources, setting the window size around 500 (instead of 0) ensures that the distribution doesn't become too overloaded with data. When set to 0, the window size uses all available data, which may not always provide an actionable insight.
5. Uneven Sample Bins and Gaps
You might notice that the width of sample bins in the distribution is not uniform, and there can be gaps.
Reason for Uneven Widths: This happens because the indicator uses a 'resampled' distribution. The width represents the range of values in each bin, which might not be constant across bins. Some value ranges might have more data points, while others might have fewer.
Gaps in Distribution: Sometimes, there might be no data points in certain value ranges, leading to gaps in the distribution. These gaps are not flaws but indicate ranges where no values were observed.
In conclusion, the Paytience Distribution indicator offers a robust mechanism to visualize the distribution of data from various sources. By understanding its intricacies, users can make better-informed trading decisions based on the distribution and behavior of their chosen data source.
Rolling MACDThis indicator displays a Rolling Moving Average Convergence Divergence . Contrary to MACD indicators which use a fix time segment, RMACD calculates using a moving window defined by a time period (not a simple number of bars), so it shows better results.
This indicator is inspired by and use the Close & Inventory Bar Retracement Price Line to create an MACD in different timeframes.
█ CONCEPTS
If you are not already familiar with MACD, so look at Help Center will get you started www.tradingview.com
The typical MACD, short for moving average convergence/divergence, is a trading indicator used in technical analysis of stock prices, created by Gerald Appel in the late 1970s. It is designed to reveal changes in the strength, direction, momentum, and duration of a trend in a stock's price.
The MACD indicator(or "oscillator") is a collection of three time series calculated from historical price data, most often the closing price. These three series are: the MACD series proper, the "signal" or "average" series, and the "divergence" series which is the difference between the two. The MACD series is the difference between a "fast" (short period) exponential moving average (EMA), and a "slow" (longer period) EMA of the price series. The average series is an EMA of the MACD series itself.
Because RMACD uses a moving window, it does not exhibit the jumpiness of MACD plots. You can see the more jagged MACD on the chart above. I think both can be useful to traders; up to you to decide which flavor works for you.
█ HOW TO USE IT
Load the indicator on an active chart (see the Help Center if you don't know how).
Time period
By default, the script uses an auto-stepping mechanism to adjust the time period of its moving window to the chart's timeframe. The following table shows chart timeframes and the corresponding time period used by the script. When the chart's timeframe is less than or equal to the timeframe in the first column, the second column's time period is used to calculate RMACD:
Chart Time
timeframe period
1min 🠆 1H
5min 🠆 4H
1H 🠆 1D
4H 🠆 3D
12H 🠆 1W
1D 🠆 1M
1W 🠆 3M
You can use the script's inputs to specify a fixed time period, which you can express in any combination of days, hours and minutes.
By default, the time period currently used is displayed in the lower-right corner of the chart. The script's inputs allow you to hide the display or change its size and location.
Minimum Window Size
This input field determines the minimum number of values to keep in the moving window, even if these values are outside the prescribed time period. This mitigates situations where a large time gap between two bars would cause the time window to be empty, which can occur in non-24x7 markets where large time gaps may separate contiguous chart bars, namely across holidays or trading sessions. For example, if you were using a 1D time period and there is a two-day gap between two bars, then no chart bars would fit in the moving window after the gap. The default value is 10 bars.
//
This indicator should make trading easier and improve analysis. Nothing is worse than indicators that give confusingly different signals.
I hope you enjoy my new ideas
best regards
Chervolino
Adaptive Average Vortex Index [lastguru]As a longtime fan of ADX, looking at Vortex Indicator I often wondered, where is the third line. I have rarely seen that anybody is calculating it. So, here it is: Average Vortex Index - an ADX calculated from Vortex Indicator. I interpret it similarly to the ADX indicator: higher values show stronger trend. If you discover other interpretation or have suggestions, comments are welcome.
Both VI+ and VI- lines are also drawn. As I use adaptive length calculation in my other scripts (based on the libraries I've developed and published), I have also included the possibility to have an adaptive length here, so if you hate the idea of calculating ADX from VI, you can disable that line and just look at the adaptive Vortex Indicator.
Note that as with all my oscillators, all the lines here are renormalized to -1..1 range unlike the original Vortex Indicator computation. To do that for VI+ and VI- lines, I subtract 1 from their values. It does not change the shape or the amplitude of the lines.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers . I do not know, which combination works best, so you can experiment.
If no Adaptation is selected ( None option), you can set Length directly. If an Adaptation is selected, then Cycle multiplier can be set.
The oscillator also has the option to configure the internal smoothing function with Window setting. By default, RMA is used (like in ADX calculation). Fast Default option is using half the length for smoothing. Triangle , Hamming and Hann Window algorithms are some better smoothers suggested by John F. Ehlers.
After the oscillator a Moving Average can be applied. The following Moving Averages are included: SMA , RMA, EMA , HMA , VWMA , 2-pole Super Smoother, 3-pole Super Smoother, Filt11, Triangle Window, Hamming Window, Hann Window, Lowpass, DSSS.
Postfilter options are applied last:
Stochastic - Stochastic
Super Smooth Stochastic - Super Smooth Stochastic (part of MESA Stochastic ) by John F. Ehlers
Inverse Fisher Transform - Inverse Fisher Transform
Noise Elimination Technology - a simplified Kendall correlation algorithm "Noise Elimination Technology" by John F. Ehlers
Momentum - momentum (derivative)
Except for Inverse Fisher Transform , all Postfilter algorithms can have Length parameter. If it is not specified (set to 0), then the calculated Slow MA Length is used. If Filter/MA Length is less than 2 or Postfilter Length is less than 1, they are calculated as a multiplier of the calculated oscillator length.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Rolling VWAP█ OVERVIEW
This indicator displays a Rolling Volume-Weighted Average Price. Contrary to VWAP indicators which reset at the beginning of a new time segment, RVWAP calculates using a moving window defined by a time period (not a simple number of bars), so it never resets.
█ CONCEPTS
If you are not already familiar with VWAP, our Help Center will get you started.
The typical VWAP is designed to be used on intraday charts, as it resets at the beginning of the day. Such VWAPs cannot be used on daily, weekly or monthly charts. Instead, this rolling VWAP uses a time period that automatically adjusts to the chart's timeframe. You can thus use RVWAP on any chart that includes volume information in its data feed.
Because RVWAP uses a moving window, it does not exhibit the jumpiness of VWAP plots that reset. You can see the more jagged VWAP on the chart above. We think both can be useful to traders; up to you to decide which flavor works for you.
█ HOW TO USE IT
Load the indicator on an active chart (see the Help Center if you don't know how).
Time period
By default, the script uses an auto-stepping mechanism to adjust the time period of its moving window to the chart's timeframe. The following table shows chart timeframes and the corresponding time period used by the script. When the chart's timeframe is less than or equal to the timeframe in the first column, the second column's time period is used to calculate RVWAP:
Chart Time
timeframe period
1min 🠆 1H
5min 🠆 4H
1H 🠆 1D
4H 🠆 3D
12H 🠆 1W
1D 🠆 1M
1W 🠆 3M
You can use the script's inputs to specify a fixed time period, which you can express in any combination of days, hours and minutes.
By default, the time period currently used is displayed in the lower-right corner of the chart. The script's inputs allow you to hide the display or change its size and location.
Minimum Window Size
This input field determines the minimum number of values to keep in the moving window, even if these values are outside the prescribed time period. This mitigates situations where a large time gap between two bars would cause the time window to be empty, which can occur in non-24x7 markets where large time gaps may separate contiguous chart bars, namely across holidays or trading sessions. For example, if you were using a 1D time period and there is a two-day gap between two bars, then no chart bars would fit in the moving window after the gap. The default value is 10 bars.
█ NOTES
If you are interested in VWAP indicators, you may find the VWAP Auto Anchored built-in indicator worth a try.
For Pine Script™ coders
The heart of this script's calculations uses the `totalForTimeWhen()` function from the ConditionalAverages library published by PineCoders . It works by maintaining an array of values included in a time period, but without a for loop requiring a lookback from the current bar, so it is much more efficient.
We write our Pine Script™ code using the recommendations in the User Manual's Style Guide .
Look first. Then leap.
Adaptive MA constructor [lastguru]Adaptive Moving Averages are nothing new, however most of them use EMA as their MA of choice once the preferred smoothing length is determined. I have decided to make an experiment and separate length generation from smoothing, offering multiple alternatives to be combined. Some of the combinations are widely known, some are not. This indicator is based on my previously published public libraries and also serve as a usage demonstration for them. I will try to expand the collection (suggestions are welcome), however it is not meant as an encyclopaedic resource, so you are encouraged to experiment yourself: by looking on the source code of this indicator, I am sure you will see how trivial it is to use the provided libraries and expand them with your own ideas and combinations. I give no recommendation on what settings to use, but if you find some useful setting, combination or application ideas (or bugs in my code), I would be happy to read about them in the comments section.
The indicator works in three stages: Prefiltering, Length Adaptation and Moving Averages.
Prefiltering is a fast smoothing to get rid of high-frequency (2, 3 or 4 bar) noise.
Adaptation algorithms are roughly subdivided in two categories: classic Length Adaptations and Cycle Estimators (they are also implemented in separate libraries), all are selected in Adaptation dropdown. Length Adaptation used in the Adaptive Moving Averages and the Adaptive Oscillators try to follow price movements and accelerate/decelerate accordingly (usually quite rapidly with a huge range). Cycle Estimators, on the other hand, try to measure the cycle period of the current market, which does not reflect price movement or the rate of change (the rate of change may also differ depending on the cycle phase, but the cycle period itself usually changes slowly).
Chande (Price) - based on Chande's Dynamic Momentum Index (CDMI or DYMOI), which is dynamic RSI with this length
Chande (Volume) - a variant of Chande's algorithm, where volume is used instead of price
VIDYA - based on VIDYA algorithm. The period oscillates from the Lower Bound up (slow)
VIDYA-RS - based on Vitali Apirine's modification of VIDYA algorithm (he calls it Relative Strength Moving Average). The period oscillates from the Upper Bound down (fast)
Kaufman Efficiency Scaling - based on Efficiency Ratio calculation originally used in KAMA
Deviation Scaling - based on DSSS by John F. Ehlers
Median Average - based on Median Average Adaptive Filter by John F. Ehlers
Fractal Adaptation - based on FRAMA by John F. Ehlers
MESA MAMA Alpha - based on MESA Adaptive Moving Average by John F. Ehlers
MESA MAMA Cycle - based on MESA Adaptive Moving Average by John F. Ehlers, but unlike Alpha calculation, this adaptation estimates cycle period
Pearson Autocorrelation* - based on Pearson Autocorrelation Periodogram by John F. Ehlers
DFT Cycle* - based on Discrete Fourier Transform Spectrum estimator by John F. Ehlers
Phase Accumulation* - based on Dominant Cycle from Phase Accumulation by John F. Ehlers
Length Adaptation usually take two parameters: Bound From (lower bound) and To (upper bound). These are the limits for Adaptation values. Note that the Cycle Estimators marked with asterisks(*) are very computationally intensive, so the bounds should not be set much higher than 50, otherwise you may receive a timeout error (also, it does not seem to be a useful thing to do, but you may correct me if I'm wrong).
The Cycle Estimators marked with asterisks(*) also have 3 checkboxes: HP (Highpass Filter), SS (Super Smoother) and HW (Hann Window). These enable or disable their internal prefilters, which are recommended by their author - John F. Ehlers. I do not know, which combination works best, so you can experiment.
Chande's Adaptations also have 3 additional parameters: SD Length (lookback length of Standard deviation), Smooth (smoothing length of Standard deviation) and Power (exponent of the length adaptation - lower is smaller variation). These are internal tweaks for the calculation.
Length Adaptaton section offer you a choice of Moving Average algorithms. Most of the Adaptations are originally used with EMA, so this is a good starting point for exploration.
SMA - Simple Moving Average
RMA - Running Moving Average
EMA - Exponential Moving Average
HMA - Hull Moving Average
VWMA - Volume Weighted Moving Average
2-pole Super Smoother - 2-pole Super Smoother by John F. Ehlers
3-pole Super Smoother - 3-pole Super Smoother by John F. Ehlers
Filt11 -a variant of 2-pole Super Smoother with error averaging for zero-lag response by John F. Ehlers
Triangle Window - Triangle Window Filter by John F. Ehlers
Hamming Window - Hamming Window Filter by John F. Ehlers
Hann Window - Hann Window Filter by John F. Ehlers
Lowpass - removes cyclic components shorter than length (Price - Highpass)
DSSS - Derivation Scaled Super Smoother by John F. Ehlers
There are two Moving Averages that are drown on the chart, so length for both needs to be selected. If no Adaptation is selected ( None option), you can set Fast Length and Slow Length directly. If an Adaptation is selected, then Cycle multiplier can be selected for Fast and Slow MA.
More information on the algorithms is given in the code for the libraries used. I am also very grateful to other TradingView community members (they are also mentioned in the library code) without whom this script would not have been possible.
Indicators & Conditions Test Framework [DTU]Hello All,
This script is a framework to build strategies by combining indicators and conditions (long, short, exits). You are able to analyze your strategies in realtime by changing the input parameters related to indicators, conditions and their combinations.
OVERVIEW
With this Study/Strategy framework, you will be able to create strategy conditions, display them on the chart, and test them using existing indicators as well as external and custom indicators that you can add.
The main purpose of the Framework is to choose your indicators to be used in the conditions and test your strategy by producing your "Long, short, Exit long, Exit short" combinations.
Although may be, it can be a bit difficult and complicated at first start, but you can understand the logic on its use in a very short time.
Notes:
I removed external links off descriptive images and video to be comply with Trading view violation House Rules
Since I am new in the community and still trying to understand the pine script language I can make errors and violations on my script. Please Inform me on any issue that I made..
HOW TO
STEP 1: SETTINGS ______________________________________________________________________________________________________
SOURCE, TIMEFRAME, SECURITY
Select the Source, timeframe and Secure type that your indicators will use.
Here, the Secure entry consists of 3 parts and the f_security function is used to determine it.
a)Secure
This option is defined as reducing repaint in tradingview calculations as much as possible. The following function is used.
request.security(_symbol, _res, _src , lookahead=barmerge.lookahead_on)
b)Semi Secure
While this option can reduce repaint in tradingview calculations as much as possible, it is less secure. The following function is used.
request.security(_symbol, _res, _src )
c)Repaint
This option turns on the repaint feature. The following function is used.
request.security(_symbol, _res, _src ) : na
Ind Source:
You can the source that indicators will use their own calculations
Ext Source:
You can import external Indicator sources from here . It appears on condition/combination area as "EXT".
To export the External indicator plot it with a title. It will be visible in source dropdown input
PERIOD , ALERTS...
Period:
Determine your strategy testing period by selecting start and end date/time
(!!! According to your tradingview subscription, it takes the last 5000, 10000.. bars.
The extra bar option may cause problems such as not appearing in the calculations or errors).
Plot Alerts:
Plot condition result as alerts arrows on the chart's bottom for "LONG" and the top for "SHORT" entries, exits
Close on opposite:
When selected, a long entry gets closed when a short entry opens and vice versa
Show Profit:
It appears if script is in strategy mode (not in study) this can display current or open profit for better reanalyzing your strategy entry exit points. (Currently under development)
PLOT TYPE OPERATIONS
This option has 4 entries
a) Mult
Sets the multiplier for the selected Plot Type (stochastic, Percentrank, Org Range (-1,1) ) except for "Original" in the range (-1,1).
EXAMPLE: When 1000 is selected, the indicator in the range of (-1,1) will appear in the range of (-1000, 1000) on the screen.
b) Shift
It determines the shift that will appear on the screen for the selected Plot Type (stochastic, Percentrank,Org Range (-1,1) ) in the range (-1,1) other than "Original".
EXAMPLE: When Shift:35000 and mult:1000 are selected, the indicator will appear in the range (34000, 36000) on the screen.
c) Smooth
This option (only for Stochastic & PercentRank) allows to smooth the indicator to be displayed.
Here, tradinview ta.swma function is used.
b) hline
Adjusts the horizontal lines to appear on the screen according to the mult factor for the range (-1,1)
The lines represent the values (-1, -05, 0, 05, 1)
STEP 2: INDICATORS ______________________________________________________________________________________________________
You need to choose indicators that you can use in strategy conditions.
Here, the indicators come from the dturkuler/lib_Indicators_DT open script library defined in the code
In addition, you can add the indicators that you will create in the area defined in the code to this list..
You can also import external indicators and test them with other variables on the system..
You can choose a maximum of 5 indicators that you can use in total. (can be increased in new versions)
Indicators are categorized in 3 main sections
Indicator Selection:
You can select your indicators from this area
a)Moving Averages
These are indicators such as EMA, SMA that you can show on the stock. They come from the library.
These indicators are fed from Settings/source. Only the length value can be used as a parameter.
In addition, line colors can be changed..
As of now, there are 28 indicators in the library in total and 5 indicators are left as future use for this field for now.
b)Other Indicators
These are different indicators from the stock value such as RSI, COG. They come from the library. These indicators are fed from Settings/source.
Only the length value can be used as a parameter. In addition, line colors can be changed.
As of now, there are 24 indicators in the library in total and 5 indicators are left as a future use for this field for now.
c)Custom Indicators
These indicators are the ones you can create by programming yourself in the source code..
The area at the bottom of the settings screen is reserved for the parameters of this type of indicators.
Indicator Length:
You can update your selected indicator length value from here. (Not: it doesn't work for custom indicators since they have their parameter on cust. Ind. input screen )
Indicator Plot Type:
Next to the indicators, there is an input selection field about how they will be displayed on the screen.
a)Original
The indicator is displayed on the screen with its current values. It is an ideal solution for displaying moving average indicators such as (EMA, SMA) over current stock.
Since the values of indicators such as (RSI, COB) are low (-100,100 : -1.1), they appear at the bottom of the screen and make analysis difficult.
For this reason, other options may be more suitable for these.
b)Stochastic
The indicator is displayed on the screen with stochastic calculation in the range of -1.1.
It uses the stochastic(50) calculation method to spread indicators such as (RSI, COB) over the range (-1,1).
Indicators in this selection can be fixed and monitored under stock on the screen with the parameters under the Plot Type section.
You can see the original values of the relevant indicator on the Data Window screen.
(!!! Do not use the values on the chart in your condition calculations. Instead, get the values from Data Window)
c)PercentRank
The indicator is displayed on the screen with stochastic calculation in the range of -1.1. .
Since the values of indicators such as (RSI, COB) are low (-100,100 : -1.1), they appear at the bottom of the screen and make analysis difficult.
Indicators in this selection can be fixed and monitored under stock on the screen with the parameters under the Plot Type section.
You can see the original values of the relevant indicator on the Data Window screen
((!!! Do not use the values on the chart in your condition calculations. Instead, get the values from Data Window)
d)Org Range (-1,1)
If your indicator is in the range of -1.1, your indicator will be displayed on the screen with its original calculation in the range of -1.1.
Indicators in this selection can be fixed and monitored under stock on the screen with the parameters under the Plot Type section.
You can see the original values of the relevant indicator on the Data Window screen.
(!!! Do not use the values on the chart in your fitness calculations. Instead, get the values from Data Window)
STEP 2 NOTES:
STEP 3: CONDITIONS ______________________________________________________________________________________________________
After choosing the indicators you will use in the conditions, you move on to the "CONDITIONS" section.
There are 4 conditions type here.
• LONG ENTRY CONDITION
• SHORT ENTRY CONDITION
• LONG CLOSE CONDITION
• SHORT CLOSE CONDITION
The use of each condition is the same.
There are 3 combinations you can use in each condition. (can be increased in new versions)
a)COMBINATIONS
There are 3 combinations you can use in each condition. (can be increased in new versions)
Each combination are build from 4 parts
1)1st Indicator
If set to "NONE" this combination will not be used on calculations. You can select
IND1-5: from indicators (See above),
EXT: value from externally imported indicator
Stock built-in values: close, open...
2)Operator
Selected Operator compares 1st Indicator with the 2nd one. You can select different operators such as
crossover, crossunder, cross,>,<,=....
3)2nd Indicator
This indicator will be compared with the 1st one via selected Operator. You can select
IND1-5: from indicators (See above),
VALUE: a float value defined in the combinations value parameter
EXT: value from externally imported indicator
Stock builtin values: close,open...
4)Value
When the 2nd indicator field is "VALUE", value area compares the entered value.
ex: 1st indicator="open", op=">", 2nd indicator="VALUE", value=3000.12 means is(close>3000.12)
In other conditions, it compares the previous values of the indicator.
ex: 1st indicator="open", op=">" 2nd indicator is "close" and value is 2 means is(open>close )
EXAMPLES:
indicator 1= "IND1", Operator=">", indicator 2= "IND2" => is(IND1>IND2)
indicator 1= "IND1", Operator=">", indicator 2= "VALUE", "0.1" => is(IND1>0.9)
indicator 1= "IND2", Operator="crossover", indicator 2= "IND1" => is(IND2 crossover IND1) : like a=ta.crossover(IND2, IND1)
indicator 1= "IND1", Operator="<", indicator 2= "close" => is(IND1>close)
indicator 1= "IND1", Operator="<", indicator 2= "EXT" => is(IND1>EXT) , EXT mean external imported indicator that define on settings section
indicator 1= "IND1", Operator="<", indicator 2= "IND1", Value="1" => is (IND1>IND1 )
b)JOIN COMBINATIONS
Each combination in Condition is compared with the next one via JOIN operator
The join operator can be selected as AND or OR.
Examples:
1st combination= is(IND1>0.9) true
2nd combination= is(IND2 crossover IND1) false
1st combination "AND" 2ndcombination" => false (is(IND1>0.9) AND is(IND2 crossover IND1))
1st combination "OR" 2nd combination" => true (is(IND1>0.9) OR is(IND2 crossover IND1))
STEP 3 NOTES:
When the 2nd indicator field is "VALUE", value area compares the entered value. In other conditions, it compares the previous values of the indicator.
In cases where "VALUE" is not selected, integer values must be entered in this field. (float should not be entered. ie 1, 2 should be entered)
!!!If the 1st indicator is "NONE" in the combination, that combination is cancelled.
Each combination returns true/false, allowing the selected value to be compared with another value
Example: EMA(21)>EMA(50) returns true under all conditions or (EMA(21) crossover EMA(50)) returns true when passed.
You can use , Value of 5 indicators (IND1-IND5) or (VALUE) that you have defined in combinations or import indicator (EXT) or stock values (close, open, high...) in your calculations.
combination Compares the 1st indicator with 2nd indicator via the operator.
STEP 4: CUSTOM INDICATORS ______________________________________________________________________________________________________
There is an area in the code for designing Custom Indicators.
Here you can design your own indicators and use them in the framework.
You can also create unlimited parameters for your indicators in the SETTINGS custom indicator field.
For now, only 3 Custom indicators have been defined.
Examples are entered in the code for custom indicators.
STEP 4 NOTES:
Including / updating custom to the code is explained in the source code
• LIMITATIONS:
!!! According to your tradingview subscription, it takes the last 5000, 10000.. bars. More bar options may cause problems such as not appearing in the calculations or errors.
• RAMBLINGS:
• NOTES [ /i]
This Script can be used as an indicator if the last strategy parts in the code are commented out and converted to the initial strategy study.
It was originally prepared for my use with my own strategy framework and has export functions accordingly.
When integrated to my own strategy framework it brings many more features over strategy definition of trades.
• TODO [ /i]
TODO: Add tooltips to the settings screen
TODO: Add double triple, Quatr factor for all indicators (convert any indicator to factor2-4 facotr. ex: EMA to DEMA, TEMA, QEMA...)
TODO: Add factorized Fibo avg range indicator (good for trend definition and entry exit points)
TODO: Add bands to the indicator and conditions
TODO: Add debug window for exporting indicator's parameters
TODO: Add isRising(value) isFalling(value), is...(value) .... to combinations (they can be used as custom indicator also
TODO: Reassess condition entry screen for user friendly GUI
TODO: Increase # conditions from 3 to 4
TODO: Reassess strategy entries, exit and close (should be improved)
TODO: Add Alerts, Condiional alerts for indicator (study) part
TODO: Create export function v3 for Pinecoders Indicator framework
• THANKS:
For Pine script format docs RicardoSantos .
For Pine script coding standards Pinecoders .
For moving average script used on library s RodrigoKazuma .
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.
Signal-to-Noise Ratio█ SNR Trend/Noise + Statistical Significance (Single-TF)
A momentum-quality and divergence suite that blends directional Signal-to-Noise, Sharpe/T-tests, and clean divergence lines—on one panel.
█ Overview
This tool quantifies how directional price action is ( SNR ) and how statistically credible its payoff is ( Sharpe & t-stats )—then draws regular/hidden divergences directly on the oscillator. It’s built to separate “looks like a trend” from “is a robust, risk-adjusted edge,” so you can fade or follow with intent.
Single timeframe (uses chart TF), single pane, no price overlay.
Two SNR modes : regression slope / residual stdev (trend vs path noise) and slope / ATR (trend vs range volatility).
Sharpe & t-Statistics : classic t-mean and t-eff (autocorrelation-aware via n_eff).
t-Slope : significance of the price trend itself (corr(price, time)).
Divergence engine : crisp lines on the indicator (scale-safe), regular & hidden, with alerts.
█ Concepts
Directional SNR (trend/noise) — Regression slope normalized by residual stdev (or ATR). Sign: > 0 up, < 0 down. Magnitude ≈ trend cleanliness.
Raw Sharpe (μ/σ) & t-Statistic — Payoff quality.
t = Sharpe × √n
t-eff scales confidence by an effective sample size n_eff from lag-1 autocorr.
t-Slope — t-stat of the regression slope via correlation of price with time.
Δ (SNR − Sharpe) — “Structure premium”: how much trendiness isn’t showing up in Sharpe (useful for fade vs follow).
█ Key Features
Two SNR engines: Slope/Residuals (path-noise aware) and Slope/ATR (range-vol aware).
Sharpe, t-mean , and t-eff (autocorr-aware confidence).
t-Slope for trend significance.
Divergence Suite : regular & hidden divergences, indicator-to-indicator lines, compact shapes, const-title alerts.
Clean UX: zero line, optional Δ plot, scale-safe (no price axis contamination).
█ How It Works
SNR : computes regression slope over Window (bars) and normalizes by residual stdev (or ATR ) → directional signal vs path noise.
Sharpe & t-stats : arithmetic returns over the same window; t-eff replaces n with n_eff from lag-1 autocorr (set by Correlation Window (bars) ).
Divergences : pivot-based (user L/R). On confirmation, draws a line between the last two indicator pivots (HH vs LH, LL vs HL). Separate colors for regular/hidden and optional alerts.
█ Usage & Playbooks
Trend-Following (quality trend)
Trade with SNR sign when SNR > threshold and t-stat > threshold (e.g., t_eff > 1.65 ). Trail by ATR or residual noise.
Mean-Reversion / Carry (drift)
Prefer when Sharpe/t are strong but SNR is modest (good payoff without a steep path). Use smaller targets and faster exits.
Contrarian on Exhaustion
Watch Δ = SNR − Sharpe spikes and regular divergences (price HH/LL, indicator LH/HL). Fade tactically into the mean/regression; stops beyond the pivot.
Hidden Divergences
Use with a simple trend filter (SNR sign or EMA slope) to time continuations after pullbacks.
█ Inputs (most relevant)
Window (bars) — length for SNR/Sharpe/t and t-Slope.
Correlation Window (bars) — length for ρ₁ when computing n_eff .
SNR Mode — Slope/Residuals or Slope/ATR.
Thresholds — Min SNR, Min Raw Sharpe, Min t-Statistic.
Divergences — enable/disable, source (SNR / t-eff / t-mean / t-slope), pivot L/R, hidden on/off, trend filter, colors, line style/width.
Extras — show Δ(SNR−Sharpe), show t-Slope.
█ Quick Summary (EN)
SNR (trend/noise)
= directional price slope normalized by “noise” (regression residual stdev or ATR).
Measures how directional the move is per bar.
Sensitive to the structure of the path (candles aligning along a line).
Can remain high even if single bars are choppy, as long as the overall trajectory is tilted.
Raw Sharpe (μ/σ of returns)
= mean of arithmetic returns / standard deviation.
Measures how well risk is “paid” within the lookback (risk-adjusted return).
Sensitive to the distribution of returns (volatility, outliers).
Penalizes turbulent regimes even if directionality exists.
What changes in trading (operationally)
SNR↑ & Sharpe↑ : directional trend with well-compensated risk → “quality trend”.
Playbook : classic trend-following (pullback/breakout), tight stops (ATR/residuals), longer holds, scaling allowed.
SNR↑ & Sharpe↓ : trend exists but is noisy/unstable (high vol, spikes).
Playbook : tighter confirmations (micro-pullbacks), reduced size, wider stops, faster/partial TP. For contrarians, good spot to seek exhaustion.
SNR≈0 & Sharpe≈0 : range / no edge.
Playbook : stay flat or very tactical mean-reversion with small targets.
SNR↓ & Sharpe↓ : noisy downtrend (mirror of SNR↑ & Sharpe↓, but short side).
Playbook : selective shorts; same risk rules as above.
Sharpe↑ but low SNR : slow drift with compressed volatility (good μ/σ but little slope).
Playbook : intraday carry/scalps with small targets; not ideal for pure trend-following.
Practical rules
Use SNR for directional bias and trend quality; use Sharpe to gauge conviction (position sizing & management).
Entries : require SNR consistent with direction; filter with Sharpe > threshold to avoid poorly paid trends.
Stops/TP : with high SNR → trail on ATR/residuals; with low Sharpe → closer TPs and wider stops (or smaller size).
Contrarian : look for divergences like very high SNR but weak Sharpe (extended trend, poorly paid) or Sharpe spikes without SNR rise (episodic/non-structural move).
t-Statistic (of the mean)
t = Sharpe × √n
where μ = mean return, σ = stdev of returns, n = bars in lookback.
Hypothesis test : how significantly different from zero the mean return is.
H0: μ = 0. Typical thresholds: ~1.65 (one-tailed 5%), 1.96 (two-tailed 5%), 2.58 (1%). If |t| exceeds the threshold with the correct sign, the edge is statistically meaningful.
Relation to Sharpe : t is Sharpe scaled by √n; for the same Sharpe, larger n ⇒ larger t (more confidence).
Use : quality filter. Example (intraday): go long only if t > 1.65 and SNR > threshold . Be cautious when SNR is high but t is weak → geometric trend but poor payoff.
Time-series caveats
Returns are not i.i.d.: autocorrelation/heteroskedasticity can inflate t. Quick fix: use an effective sample size
n_eff ≈ n × (1 − ρ1) / (1 + ρ1)
then t_eff = Sharpe × √(n_eff)
Beware overfitting: calibrate thresholds out-of-sample.
Bonus: t-stat of the slope (true trend)
Test the statistical significance of the price trend itself:
t_slope = r × √((n − 2) / (1 − r²)) where r = corr(price, time) .
Pairs naturally with directional SNR (slope/noise): SNR says how clean the trend is; t_slope says whether the slope is statistically different from zero.
█ Alerts
Bearish Divergence , Bullish Divergence
Hidden Bearish , Hidden Bullish
Dynamic Score Supertrend [QuantAlgo]Dynamic Score Supertrend 📈🚀
The Dynamic Score Supertrend by QuantAlgo introduces a sophisticated trend-following tool that combines the well-known Supertrend indicator with an innovative dynamic trend scoring technique . By tracking market momentum through a scoring system that evaluates price behavior over a customizable window, this indicator adapts to changing market conditions. The result is a clearer, more adaptive tool that helps traders and investors detect and capitalize on trend shifts with greater precision.
💫 Conceptual Foundation and Innovation
At the core of the Dynamic Score Supertrend is the dynamic trend score system , which measures price movements relative to the Supertrend’s upper and lower bands. This scoring technique adds a layer of trend validation, assessing the strength of price trends over time. Unlike traditional Supertrend indicators that rely solely on ATR calculations, this system incorporates a scoring mechanism that provides more insight into trend direction, allowing traders and investors to navigate both trending and choppy markets with greater confidence.
✨ Technical Composition and Calculation
The Dynamic Score Supertrend utilizes the Average True Range (ATR) to calculate the upper and lower Supertrend bands. The dynamic trend scoring technique then compares the price to these bands over a customizable window, generating a trend score that reflects the current market direction.
When the score exceeds the uptrend or downtrend thresholds, it signals a possible shift in market direction. By adjusting the ATR settings and window length, the indicator becomes more adaptable to different market conditions, from steady trends to periods of higher volatility. This customization allows users to refine the Supertrend’s sensitivity and responsiveness based on their trading or investing style.
📈 Features and Practical Applications
Customizable ATR Settings: Adjust the ATR length and multiplier to control the sensitivity of the Supertrend bands. This allows the indicator to smooth out noise or react more quickly to price shifts, depending on market conditions.
Window Length for Dynamic Scoring: Modify the window length to adjust how many data points the scoring system considers, allowing you to tailor the indicator’s responsiveness to short-term or long-term trends.
Uptrend/Downtrend Thresholds: Set thresholds for identifying trend signals. Increase these thresholds for more reliable signals in choppy markets, or lower them for more aggressive entry points in trending markets.
Bar and Background Coloring: Visual cues such as bar coloring and background fills highlight the direction of the current trend, making it easier to spot potential reversals and trend shifts.
Trend Confirmation: The dynamic trend score system provides a clearer confirmation of trend strength, helping you identify strong, sustained movements while filtering out false signals.
⚡️ How to Use
✅ Add the Indicator: Add the Dynamic Score Supertrend to your favourites, then apply it to your chart. Adjust the ATR length, multiplier, and dynamic score settings to suit your trading or investing strategy.
👀 Monitor Trend Shifts: Track price movements relative to the Supertrend bands and use the dynamic trend score to confirm the strength of a trend. Bar and background colors make it easy to visualize key trend shifts.
🔔 Set Alerts: Configure alerts when the dynamic trend score crosses key thresholds, so you can act on significant trend changes without constantly monitoring the charts.
🌟 Summary and Usage Tips
The Dynamic Score Supertrend by QuantAlgo is a robust trend-following tool that combines the power of the Supertrend with an advanced dynamic scoring system. This approach provides more adaptable and reliable trend signals, helping traders and investors make informed decisions in trending markets. The customizable ATR settings and scoring thresholds make it versatile across various market conditions, allowing you to fine-tune the indicator for both short-term momentum and long-term trend following. To maximize its effectiveness, adjust the settings based on current market volatility and use the visual cues to confirm trend shifts. The Dynamic Score Supertrend offers a refined, probabilistic approach to trading and investing, making it a valuable addition to your toolkit.
Rolling Correlation with Bitcoin V1.1 [ADRIDEM]Overview
The Rolling Correlation with Bitcoin script is designed to offer a comprehensive view of the correlation between the selected ticker and Bitcoin. This script helps investors understand the relationship between the performance of the current ticker and Bitcoin over a rolling period, providing insights into their interconnected behavior. Below is a detailed presentation of the script and its unique features.
Unique Features of the New Script
Bitcoin Comparison : Allows users to compare the correlation of the current ticker with Bitcoin, providing an analysis of their relationship.
Customizable Rolling Window : Enables users to set the length for the rolling window, adapting to different market conditions and timeframes. The default value is 252 bars, which approximates one year of trading days, but it can be adjusted as needed.
Smoothing Option : Includes an option to apply a smoothing simple moving average (SMA) to the correlation coefficient, helping to reduce noise and highlight trends. The smoothing length is customizable, with a default value of 4 bars.
Visual Indicators : Plots the smoothed correlation coefficient between the current ticker and Bitcoin, with distinct colors for easy interpretation. Additionally, horizontal lines help identify key levels of correlation.
Dynamic Background Color : Adds dynamic background colors to highlight areas of strong positive and negative correlations, enhancing visual clarity.
Originality and Usefulness
This script uniquely combines the analysis of rolling correlation for a current ticker with Bitcoin, providing a comparative view of their relationship. The inclusion of a customizable rolling window and smoothing option enhances its adaptability and usefulness in various market conditions.
Signal Description
The script includes several features that highlight potential insights into the correlation between the assets:
Rolling Correlation with Bitcoin : Plotted as a red line, this represents the smoothed rolling correlation coefficient between the current ticker and Bitcoin.
Horizontal Lines and Background Color : Lines at -0.5, 0, and 0.5 help to quickly identify regions of strong negative, weak, and strong positive correlations.
These features assist in identifying the strength and direction of the relationship between the current ticker and Bitcoin.
Detailed Description
Input Variables
Length for Rolling Window (`length`) : Defines the range for calculating the rolling correlation coefficient. Default is 252.
Smoothing Length (`smoothing_length`) : The number of periods for the smoothing SMA. Default is 4.
Bitcoin Ticker (`bitcoin_ticker`) : The ticker symbol for Bitcoin. Default is "BINANCE:BTCUSDT".
Functionality
Correlation Calculation : The script calculates the daily returns for both Bitcoin and the current ticker and computes their rolling correlation coefficient.
```pine
bitcoin_close = request.security(bitcoin_ticker, timeframe.period, close)
bitcoin_dailyReturn = ta.change(bitcoin_close) / bitcoin_close
current_dailyReturn = ta.change(close) / close
rolling_correlation = ta.correlation(current_dailyReturn, bitcoin_dailyReturn, length)
```
Smoothing : A simple moving average is applied to the rolling correlation coefficient to smooth the data.
```pine
smoothed_correlation = ta.sma(rolling_correlation, smoothing_length)
```
Plotting : The script plots the smoothed rolling correlation coefficient and includes horizontal lines for key levels.
```pine
plot(smoothed_correlation, title="Rolling Correlation with Bitcoin", color=color.rgb(255, 82, 82, 50), linewidth=2)
h_neg1 = hline(-1, "-1 Line", color=color.gray)
h_neg05 = hline(-0.5, "-0.5 Line", color=color.red)
h0 = hline(0, "Zero Line", color=color.gray)
h_pos05 = hline(0.5, "0.5 Line", color=color.green)
h1 = hline(1, "1 Line", color=color.gray)
fill(h_neg1, h_neg05, color=color.rgb(255, 0, 0, 90), title="Strong Negative Correlation Background")
fill(h_neg05, h0, color=color.rgb(255, 165, 0, 90), title="Weak Negative Correlation Background")
fill(h0, h_pos05, color=color.rgb(255, 255, 0, 90), title="Weak Positive Correlation Background")
fill(h_pos05, h1, color=color.rgb(0, 255, 0, 90), title="Strong Positive Correlation Background")
```
How to Use
Configuring Inputs : Adjust the rolling window length and smoothing length as needed. Ensure the Bitcoin ticker is set to the desired asset for comparison.
Interpreting the Indicator : Use the plotted correlation coefficient and horizontal lines to assess the strength and direction of the relationship between the current ticker and Bitcoin.
Signal Confirmation : Look for periods of strong positive or negative correlation to identify potential co-movements or divergences. The background colors help to highlight these key levels.
This script provides a detailed comparative view of the correlation between the current ticker and Bitcoin, aiding in more informed decision-making by highlighting the strength and direction of their relationship.
AI Channels (Clustering) [LuxAlgo]The AI Channels indicator is constructed based on rolling K-means clustering, a common machine learning method used for clustering analysis. These channels allow users to determine the direction of the underlying trends in the price.
We also included an option to display the indicator as a trailing stop from within the settings.
🔶 USAGE
Each channel extremity allows users to determine the current trend direction. Price breaking over the upper extremity suggesting an uptrend, and price breaking below the lower extremity suggesting a downtrend. Using a higher Window Size value will return longer-term indications.
The "Clusters" setting allows users to control how easy it is for the price to break an extremity, with higher values returning extremities further away from the price.
The "Denoise Channels" is enabled by default and allows to see less noisy extremities that are more coherent with the detected trend.
Users who wish to have more focus on a detected trend can display the indicator as a trailing stop.
🔹 Centroid Dispersion Areas
Each extremity is made of one area. The width of each area indicates how spread values within a cluster are around their centroids. A wider area would suggest that prices within a cluster are more spread out around their centroid, as such one could say that it is indicative of the volatility of a cluster.
Wider areas around a specific extremity can indicate a larger and more spread-out amount of prices within the associated cluster. In practice price entering an area has a higher chance to break an associated extremity.
🔶 DETAILS
The indicator performs K-means clustering over the most recent Window Size prices, finding a number of user-specified clusters. See here to find more information on cluster detection.
The channel extremities are returned as the centroid of the lowest, average, and highest price clusters.
K-means clustering can be computationally expensive and as such we allow users to determine the maximum number of iterations used to find the centroids as well as the number of most historical bars to perform the indicator calculation. Do note that increasing the calculation window of the indicator as well as the number of clusters will return slower results.
🔶 SETTINGS
Window Size: Amount of most recent prices to use for the calculation of the indicator.
Clusters": Amount of clusters detected for the calculation of the indicator.
Denoise Channels: When enabled, return less noisy channels extremities, disabling this setting will return the exact centroids at each time but will produce less regular extremities.
As Trailing Stop: Display the indicator as a trailing stop.
🔹 Optimization
This group of settings affects the runtime performance of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
Z-HistogramIt is possible to approximate the underlying distribution of a random variable by using what is called an "Histogram". In order to construct an histogram one must first split the data into several intervals (also called bins) often of the same size and count the number of values falling within each intervals, the histogram plot is then constructed with the X axis representing the measured variable and the Y axis representing the frequency.
The proposed script aim to estimate the underlying distribution of a rolling z-score by constructing its histogram, here the histogram consist of 13 bins of width 0.5 rolling standard deviations. The length setting define the rolling z-score period, the window setting define the number of past data to be counted, finally using the "Total" option (true by default) will count all the rolling z-scores values since the first bar, in order to use the window setting make sure to uncheck the "Total" option.
DISPLAY
In order to see the entirety of the histogram make sure to double click on the indicator window and to have all the lower panels (text notes, pine editor...etc) hidden, finally make sure to zoom-in in order to see the frequency numbers displayed.
Z-Histogram on BTCUSD 15 min TF, the blue bins represent intervals situated over 0 while red bins represent intervals situated under 0. Here σ represent the X-axis in standard deviations, the histogram start with a bin situated at σ = -3 which count the number of times the rolling z-score was within -3 and -2.5, the histogram end with the bin situated at σ = 3 which count the number of time the rolling z-score was within 3 and 3.5.
It is also possible to look at the shape of the histogram without having the indicator window at full size.
INTERPREATION
An histogram can give really interesting information such as overall trend direction and strength. The direction can be measured by looking at the skewness of the histogram, with a negative skewness (the peak of the histogram situated at the right from the center) representing down-trending variations and positive skewness (the peak of the histogram situated at the left from the center) representing up-trending variations, while a symmetrical histogram could represent a ranging market. The farther away the peak of the histogram is situated from the center, the stronger the trend.
Another interesting characteristic is the tailedness of the histogram, which can give information about the cleanliness of the trend, for example a positive skew and high tailedness would represent a clean up-trend, as it could suggest less variations contrary to the main trend.
An histogram applied to the rolling z-score can give various useful information. As a recall the rolling z-score of the price measure the distance between the closing price and its moving average in term of rolling standard deviations, for example if the rolling z-score is equal to 2 it means that the closing price is currently 2 rolling standard deviations over its moving average.
Lets for example analyze the histogram using INTC 15 min tf with a window of 456 bars and rolling z-score of length = 100 in order to review longer term variations.
We can see from the histogram that the uptrend visible on the chart is represented by the bins situated over 0 having an overall higher frequency than the bins under 0, we can see that the closing price tended to stay between 1 and 1.5 rolling standard deviations over its period 100 moving average. Here bins under 0 accounts for retracements in the trend.
IN SUMMARY
An histogram can give various information regarding the price evolution of a security, the proposed script aim to plot the histogram of a rolling z-score. Now this script might not be too useful but it was fun to make, also it does not mean that an histogram is not an useful tool in the context of trading, the only thing required is a god implementation of it (like volume profiles for example)
In this post we have also reviewed some important statistical concepts such as distributions, z-score, skewness and tailedness, each being extremely important in the quantitative trading field.
Thx for reading !
BEST Dollar Cost AverageHello traders
This is an upgraded version of my Dollar Cost Average (Data Window) script
1 - What is Dollar-Cost Averaging ( DCA )?
Dollar-Cost Averaging is a strategy that allows an investor to buy the same dollar amount of investment at regular intervals. The purchases occur regardless of the asset's price.
I hope you're hungry because that one is a biggie and gave me a few headaches. Happy that it's getting out of my way finally and I can offer it
🔸 This indicator will analyze for the defined date range, how a dollar-cost average ( DCA ) method would have performed (green panel) versus investing all the hard earnt money at the beginning (orange panel)
=> green versus orange
2- What's on the menu today?
My indicator works with all asset classes and with the daily/weekly/monthly inputs.
⚠️⚠️⚠️ However, results are only visible on the DAILY timeframe chart
As always, let's review quickly the different fields so that you'll understand how to use it (and I won't get spammed with questions in DM ^^)
🔸 Use current resolution: if checked will use the resolution of the chart
🔸 The timeframe used for DCA: different timeframe to be used if Use current resolution is unchecked
🔸 Amount invested in your local currency: The amount in Fiat money that will be invested at each period selected above
🔸 Starting Date
🔸 Ending Date
🔹 The script screenshot shows a DCA with 100 USD invested daily from 01.01.2017 to 01.28.2020
3- Bonus (DATA WINDOW)
🔸 Please check this screenshot to understand what you're supposed to see: Data window
And a quick video that I did months ago explaining how we can use this data window effectively
4 - Specifications used
I got the idea from this website dcabtc.com and the result shown by this website and my indicator are very interesting in general and for your own trading
The formula used for the DCA calculation is the one from the Investopedia website.
Best regards and best of luck
Dave
Currency Correlation indicator for the major currenciesThis Pine script creates a currency correlation graph with 6 correlations in a separate window below the main chart.
The indicator supports the following 8 currencies: AUD, CAD, CHF, EUR, GBP, JPY, NZD, USD
Correlations can be selected to be either related to the base or the counter currency (default is base). The length of the correlation can be chosen (default is 10).
Correlations are given for related currencies e.g. for all AUD pairs.
If Base currency is selected and e.g. AUDCAD is displayed in the main window, then the correlation window will compare AUDCAD to: AUDCHF, AUDEUR, AUDGBP, AUDJPY, AUDNZD, AUDUSD
If Counter currency is seleted and e.g. AUDCAD is displayed in the main window, then the correlation window will compare AUDCAD to: CHFCAD, EURCAD, GBPCAD, JPYCAD, NZDCAD, USDCAD
Many of the above currency pairs are not real pairs. But they are availabe in Pine script to enable e.g. correlation calculations.
The advantage is that e.g. the comparison between AUDCAD and EURAUD will give a positive correlation, if AUD is gaining in strenght and EUR and CAD are not changing in strenght.
Eventhough price is moving in the opposite direction the correlation is positive in the AUDCAD vs EURAUD example.
Post 9/21 EMA Cross — Paint X Bars* Watches for **9 EMA crossing the 21 EMA** (a classic momentum/trend trigger).
* When a cross happens, it **paints exactly X bars** after the cross in a color you choose:
* **Bullish cross (9 > 21):** paints your bullish color for X bars.
* **Bearish cross (9 < 21):** paints your bearish color for X bars.
* You decide whether the **cross bar itself counts** as the first painted bar.
* Optionally plots the 9 & 21 EMAs so you can see the cross visually.
# Why that’s useful
* **Focus:** It reduces noise by spotlighting the **immediate post‑cross window** when momentum often continues.
* **Discipline:** “Exactly X bars” forces consistency, avoiding “just one more bar” bias.
* **Speed:** Color‑coded candles make it easy to scan charts fast (great for intraday work).
# How signals are defined
* **Bullish condition:** `ta.crossover(EMA9, EMA21)` — the fast EMA crosses **up** through the slow EMA.
* **Bearish condition:** `ta.crossunder(EMA9, EMA21)` — the fast EMA crosses **down** through the slow EMA.
# Key inputs (and what they control)
* **Fast EMA Length (default 9)** and **Slow EMA Length (default 21)**
Change these if your system uses different lookbacks (e.g., 8/21 or 10/20).
***CURRENTLY THE EMA REMAINS STATIC ON THE CHART. PLOT EMA FROM EXTERNAL INDICATOR FOR NOW
* **Bars to Paint After a Cross (default 5)**
How many bars get highlighted post‑cross.
* **Include the Cross Bar Itself? (default off)**
Turn on if you want painting to start **on** the cross candle; off to start **after** it.
* **Bullish/Bearish Paint Colors**
Set your preferred colors (e.g., green/red).
* **Plot EMAs on Chart?**
If off, the logic still works; it just hides the EMA lines.
# What you’ll see on the chart
* Candles **recolored** for exactly X bars after each cross, matching the direction.
* (Optional) 9 & 21 EMA lines so you can confirm the cross visually.
* When the X‑bar window ends, candles return to normal until the **next** cross.
# Practical trading uses
* **Entry timing:** Consider entries only during the painted window to align with fresh momentum.
* **Scaling logic:** Scale in/out within the painted window; stop adding when painting ends.
* **Context filter:** Use the paint as a **“go / no‑go” overlay** on top of your pattern or level setups (breakouts, pullbacks to EMA, ORB, etc.).
FIBO SWING mfi by julzALGOOVERVIEW
FIBO SWING mfi by julzALGO blends MFI → RSI → Least‑Squares smoothing to flag overbought/oversold swings and continuously plot Fibonacci retracements from the rolling high/low of the last 200 bars. It’s built to spot momentum shifts while giving you a clean, always‑current fib map of the recent market range.
CORE PRINCIPLES
Hybrid Momentum Signal
Uses MFI to integrate price and volume.
Applies RSI to MFI for momentum clarity.
Smooths the result with Least Squares regression to reduce noise.
Swing Identification
Marks potential swing highs when momentum is overbought.
Marks potential swing lows when momentum is oversold.
Fixed-Window Fibonacci Mapping
Always calculates fib levels from the highest high and lowest low of the last 200 bars.
This keeps fib zones consistent, independent of swing point detection.
Visual Clarity & Non-Repainting Logic
Clean labels for OB/OS zones.
Lines and levels update only as new bars confirm changes.
Adaptability
Works on any market and timeframe.
Adjustable momentum length, OB/OS thresholds, and smoothing.
HOW IT WORKS
Computes Money Flow Index (MFI) from price & volume.
Applies RSI to the MFI for clearer OB/OS momentum.
Smooths the hybrid with a Least Squares (linear regression) filter.
Swing labels appear when OB/OS conditions are met (green = swing low, red = swing high).
Fibonacci retracements are always drawn from the highest high and lowest low of the last 200 bars (rolling window), independent of swing labels.
HOW TO USE
Watch for OB/OS flips to mark potential swing highs/lows.
Use the 200‑bar fib grid as your active map of pullback levels and reaction zones.
Combine fib reactions with your price action/volume cues for confirmation.
Works across markets and timeframes.
SETTINGS
Length – Period for both MFI and RSI.
OB/OS Levels – Overbought/oversold thresholds (default 70/30).
Smooth – Least‑Squares smoothing length.
Fibonacci Window – Fixed at 200 bars in this version (changeable in code via fibLen).
NOTES
Logic is non‑repainting aside from standard bar/label confirmation.
Increase Length on very low timeframes to reduce noise.
Swing labels help context; fibs are always based on the most recent 200‑bar high/low range.
SUMMARY
FIBO SWING mfi by julzALGO is a momentum-plus-price action tool that merges MFI → RSI → smoothing to identify overbought/oversold swings and automatically plot Fibonacci retracements based on the rolling high/low of the last 200 bars.
It’s designed to help traders quickly see potential reversal points and pullback zones, offering visual confluence between momentum shifts and fixed-window price structure.
DISCLAIMER
For educational purposes only. Not financial advice. Trade responsibly with proper risk management.
Time & Sales (Tape) - TableWhat it is
A compact Time & Sales (tape) visualizer rendered as a table on your chart. It reconstructs prints from a lower timeframe (e.g., 1-second) or, optionally, streams them in Live Tick mode. Each row shows Date, Time, Side (▲ up / ▼ down), Last, and Volume (or Price Volume), with a dynamic scale bar summarizing buy vs sell activity across the visible rows.
Note for newcomers
This is not a “go-to” oscillator or a signal generator. It’s an old-school tape reader that shows the market’s pulse in real time—prints, pace, and the balance between buying and selling. It’s descriptive, not predictive. Use it to observe flow and context on top of your existing method (price/volume/structure), not as a stand-alone “magic wand.”
What it shows
Date: user-selectable format (YYYY-MM-DD, DD-MM-YYYY, MM-DD, DD-MM).
Time: formatted in the selected timezone (New York - UTC-5/-4 by default).
Side: ▲ when last price increased vs the previous print, ▼ when it decreased.
Last: last traded price (formatted to symbol tick size).
Volume: either raw Volume or Price Volume (= HLC3 × Volume).
Dynamic Scale Bar: stacked “blocks” showing the proportion of buy vs sell volume over the current table window.
How it works
When Implement By Timeframe
Aggregates prints from a user-selected lower timeframe (using request.security_lower_tf). If the chosen timeframe is invalid (i.e., higher than the chart), it automatically falls back to 1S. The table is rebuilt on the last bar update.
When Implement By Live Tick
Streams prints only while the chart is in real time (using barstate.isrealtime). This mode does not backfill historical ticks; it appends as new ticks arrive.
Inputs
Timezone: maps timestamps to your chosen session/region.
Date Format: YYYY-MM-DD, DD-MM-YYYY, MM-DD, or DD-MM.
Table Location / Size: choose where the table sits and its font size.
Show Dynamic Volume Scale Bar: toggle the header scale bar.
Implement By: Timeframe (lower-TF reconstruction) or Live Tick.
Timeframe: lower-TF source (e.g., 1S). Falls back automatically if invalid.
Length (№ of Rows): number of rows displayed.
Size Type
Volume = raw exchange volume.
Price Volume = HLC3 × Volume (emphasizes prints at higher prices).
Repainting
Does not repaint historical rows.
Timeframe mode reconstructs from completed lower-TF bars and draws a fixed history window.
Live Tick mode only adds rows in real time; no historical backfill.
The table itself is redrawn on the last bar update for layout, but previously printed rows aren’t revised with future data.
Notes & limitations
Lower-TF aggregation depends on data availability for your symbol/plan. If you request very granular timeframes far back in history, data may be limited by platform constraints.
For performance on long histories, reduce Length (№ of Rows) or use Timeframe mode with 1S/5S as appropriate.
“Price Volume” is a derived metric, not exchange-reported volume. Use it if you want to weight volume by price (HLC3).
Use case
This module provides a fast, at-a-glance tape read directly on the chart: spot bursts of buys vs sells, see the pace and size of flow, and reference exact date/time and last price without leaving your layout. It’s designed as a supporting tool for discretionary execution and for documenting trade ideas that reference tape behavior.
Credits
Timezone helper: n00btraders/Timezone (imported library).
Built for Pine v6.
Disclosure
This script is an educational visualization. It does not place trades and is not financial or investment advice.
Prev D/W/M + Asia & London Levels [Oeditrades]Prev D/W/M + Asia & London Levels
Author: Oeditrades
Platform: Pine Script® v6
What it does
Plots only the most recent, fully completed:
Previous Day / Week / Month highs & lows
Asia and London session highs & lows
Levels are drawn as true horizontal lines from the period/session start and extended to the right for easy confluence reading. The script is non-repainting.
How it works
Prev Day/Week/Month: Uses completed HTF candles (high / low ) so values are fixed for the entire next period.
Sessions (NY time): Asia (default 20:00–03:00) and London (default 03:00–08:00) are tracked in America/New_York time. High/low are locked when the session ends, and the line is anchored at that session’s start.
Inputs & customization
Visibility: toggle Previous Day/Week/Month, Asia, London, and labels.
Colors: highs default red; lows default green (user-configurable). Session highs default pink, lows aqua (also editable).
Style: line style (solid/dotted/dashed) and width.
Sessions: editable time windows for Asia and London (still interpreted in New York time).
Disclaimer: optional on-chart disclaimer panel with editable text.
Notes
Works on any timeframe. For intraday charts, the HTF values remain constant until the next HTF bar completes.
If your market’s overnight hours differ, simply adjust the session windows in Inputs.
Lines intentionally show only the latest completed period/session to keep charts clean.
Use cases
Quick view of PDH/PDL, PWH/PWL, PMH/PML for bias and liquidity.
Intraday planning around Asia/London range breaks, retests, and overlaps with prior levels.
Disclaimer
This tool is for educational purposes only and is not financial advice. Markets involve risk; past performance does not guarantee future results.
