CorrelationMulti-Timeframe Correlation Indicator
This Pine Script indicator measures the correlation between the current symbol and a reference symbol (default: GLD) across three different timeframes. It provides traders with valuable insights into how assets move in relation to each other over short, medium, and long-term periods.
Key Features
Multiple Timeframe Analysis: Calculates correlation coefficients over three customizable periods (default: 20, 50, and 200 bars)
Visual Reference Lines: Displays horizontal lines at +1, 0, and -1 to indicate perfect positive correlation, no correlation, and perfect negative correlation
Color-Coded Outputs: Shows short-term correlation in green, medium-term in yellow, and long-term in red for easy visual interpretation
Understanding Correlation
The correlation coefficient measures the statistical relationship between two data series, ranging from -1 to +1:
+1: Perfect positive correlation (both assets move together in the same direction)
0: No correlation (movements are random and independent)
-1: Perfect negative correlation (assets move in opposite directions)
How To Use This Indicator
Market Relationships: Identify how strongly your current asset correlates with the reference symbol
Diversification Analysis: Find assets with negative correlations to build a diversified portfolio
Divergence Opportunities: Watch for changes in correlation patterns that might signal trading opportunities
Trend Confirmation: Use correlation with benchmark assets to confirm broader market trends
Customization Options
Reference Symbol: Change the default GLD to any other symbol you want to compare against
Period Lengths: Adjust the short, medium, and long timeframes to match your trading strategy and timeframe
This indicator helps traders make more informed decisions by understanding the interrelationships between different assets across various timeframes, potentially improving portfolio construction and risk management strategies. Indicator
Correlation TableThis indicator displays a vertical table that shows the correlation between the asset currently loaded on the chart and up to 32 selected trading pairs. It offers the following features:
Chart-Based Correlation: Correlations are calculated based on the asset you have loaded in your chart, providing relevant insights for your current market focus.
Configurable Pairs: Choose from a list of 32 symbols (e.g., AUDUSD, EURUSD, GBPUSD, etc.) with individual checkboxes to include or exclude each pair in the correlation analysis.
Custom Correlation Length: Adjust the lookback period for the correlation calculation to suit your analysis needs.
Optional EMA Smoothing: Enable an Exponential Moving Average (EMA) on the price data, with a configurable EMA length, to smooth the series before calculating correlations.
Color-Coded Output: The table cells change color based on the correlation strength and direction—neutral, bullish (green), or bearish (red)—making it easy to interpret at a glance.
Clear Table Layout: The indicator outputs a neatly organized vertical table with headers for "Pair" and "Correlation," ensuring the information is displayed cleanly and is easy to understand.
Ideal for traders who want a quick visual overview of how different instruments correlate with their current asset, this tool supports informed multi-asset analysis
ITALIANO:
Questo indicatore visualizza una tabella verticale che mostra la correlazione tra l'asset attualmente caricato sul grafico e fino a 32 coppie di trading selezionate. Offre le seguenti funzionalità:
Correlazione basata sul grafico: le correlazioni vengono calcolate in base all'asset caricato nel grafico, fornendo informazioni pertinenti per il tuo attuale focus di mercato.
Coppie configurabili: scegli da un elenco di 32 simboli (ad esempio, AUDUSD, EURUSD, GBPUSD, ecc.) con caselle di controllo individuali per includere o escludere ciascuna coppia nell'analisi della correlazione.
Lunghezza di correlazione personalizzata: regola il periodo di lookback per il calcolo della correlazione in base alle tue esigenze di analisi.
Smoothing EMA opzionale: abilita una media mobile esponenziale (EMA) sui dati dei prezzi, con una lunghezza EMA configurabile, per smussare la serie prima di calcolare le correlazioni.
Output codificato a colori: le celle della tabella cambiano colore in base alla forza e alla direzione della correlazione, neutra, rialzista (verde) o ribassista (rosso), rendendola facile da interpretare a colpo d'occhio.
Clear Table Layout: l'indicatore genera una tabella verticale ordinatamente organizzata con intestazioni per "Coppia" e "Correlazione", assicurando che le informazioni siano visualizzate in modo chiaro e siano facili da comprendere.
Ideale per i trader che desiderano una rapida panoramica visiva di come diversi strumenti siano correlati con il loro asset corrente, questo strumento supporta un'analisi multi-asset informata Indicator
Cross-Asset Correlation Trend IndicatorCross-Asset Correlation Trend Indicator
This indicator uses correlations between the charted asset and ten others to calculate an overall trend prediction. Each ticker is configurable, and by analyzing the trend of each asset, the indicator predicts an average trend for the main asset on the chart. The strength of each asset's trend is weighted by its correlation to the charted asset, resulting in a single average trend signal. This can be a rather robust and effective signal, though it is often slow.
Functionality Overview :
The Cross-Asset Correlation Trend Indicator calculates the average trend of a charted asset based on the correlation and trend of up to ten other assets. Each asset is assigned a trend signal using a simple EMA crossover method (two customizable EMAs). If the shorter EMA crosses above the longer one, the asset trend is marked as positive; if it crosses below, the trend is negative. Each trend is then weighted by the correlation coefficient between that asset’s closing price and the charted asset’s closing price. The final output is an average weighted trend signal, which combines each trend with its respective correlation weight.
Input Parameters :
EMA 1 Length : Sets the period of the shorter EMA used to determine trends.
EMA 2 Length : Sets the period of the longer EMA used to determine trends.
Correlation Length : Defines the lookback period used for calculating the correlation between the charted asset and each of the other selected assets.
Asset Tickers : Each of the ten tickers is configurable, allowing you to set specific assets to analyze correlations with the charted asset.
Show Trend Table : Toggle to show or hide a table with each asset’s weighted trend. The table displays green, red, or white text for each weighted trend, indicating positive, negative, or neutral trends, respectively.
Table Position : Choose the position of the trend table on the chart.
Recommended Use :
As always, it’s essential to backtest the indicator thoroughly on your chosen asset and timeframe to ensure it aligns with your strategy. Feel free to modify the input parameters as needed—while the defaults work well for me, they may need adjustment to better suit your assets, timeframes, and trading style.
As always, I wish you the best of luck and immense fortune as you develop your systems. May this indicator help you make well-informed, profitable decisions! Indicator
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Historical Correlation [LuxAlgo]The Historical Correlation tool aims to provide the historical correlation coefficients of up to 10 pairs of user-defined tickers starting from a user-defined point in time.
Users can choose to display the historical values as lines or the most recent correlation values as a heat map.
🔶 USAGE
This tool provides historical correlation coefficients, the correlation coefficient between two assets highlight their linear relationship and is always within the range (-1, 1).
It is a simple and easy to use statistical tool, with the following interpretation:
Positive correlation (values close to +1.0): the two assets move in sync, they rise and fall at the same time.
Negative correlation (values close to -1.0): the two assets move in opposite directions: when one goes up, the other goes down and vice versa.
No correlation (values close to 0): the two assets move independently.
The user must confirm the selection of the anchor point in order for the tool to be executed; this can be done directly on the chart by clicking on any bar, or via the date field in the settings panel.
For the parameter Anchor period , the user can choose between the following values NONE, HOURLY, DAILY, WEEKLY, MONTHLY, QUARTERLY and YEARLY. If NONE is selected, there will be no resetting of the calculations, otherwise the calculations will start from the first bar of the new period.
There is a wide range of trading strategies that make use of correlation coefficients between assets, some examples are:
Pair Trading: Traders may wish to take advantage of divergences in the price movements of highly positively correlated assets; even highly positively correlated assets do not always move in the same direction; when assets with a correlation close to +1.0 diverge in their behavior, traders may see this as an opportunity to buy one and sell the other in the expectation that the assets will return to the likely same price behavior.
Sector rotation: Traders may want to favor some sectors that are expected to perform in the next cycle, tracking the correlation between different sectors and between the sector and the overall market.
Diversification: Traders can aim to have a diversified portfolio of uncorrelated assets. From a risk management perspective, it is useful to know the correlation between the assets in your portfolio, if you hold equal positions in positively correlated assets, your risk is tilted in the same direction, so if the assets move against you, your risk is doubled. You can avoid this increased risk by choosing uncorrelated assets so that they move independently.
Hedging: Traders may want to hedge positions with correlated assets, from a hedging perspective, if you are long an asset, you can hedge going long a negative correlated asset or going short a positive correlated asset.
Traders generally need to develop awareness, a key point is to be aware of the relationships between the assets we hold or trade, the historical correlation is an invaluable tool in our arsenal which allows us to make better informed decisions.
On this chart we have an example of historical correlations for several futures markets.
We can clearly see how positively correlated the Nasdaq100 and Dow30 are with the SP500 over the whole period, or how the correlation between the Euro and the SP500 falls from almost +85% to almost -4% since 2021.
As we can see, correlations, like everything else in the market, are not static and vary over time depending on many factors, from macro to technical and everything in between.
🔹 Heatmap
The chart above shows the tool with the default settings and the Drawing Mode set to 'HEATMAP'.
We can see the current correlation between the assets, in this case the FX pairs.
The highest positive correlation is +90% (+0.90) between EURUSD and GBPUSD.
The highest negative correlation is -78% (-0.78) between EURUSD and USDJPY.
The pair with no correlation is AUDUSD and EURCAD with 1% (0.01)
On the above chart we can see the current correlations for the futures markets.
Currently, the assets that are less correlated to the SP500 are NaturalGas and the Euro, the more positive correlations are Nasdaq100 and Dow20, and the more negative correlations are the Yen, Treasury Bonds and 10-Year Notes.
🔶 DETAILS
🔹 Anchor Period
This chart shows the standard FX correlations with the Anchor Period set to `MONTHLY`.
We can clearly see how the calculations restart with the new month, in this case we can clearly see the differences between the correlations from month to month.
Let us look at the correlation coefficient between GBPUSD and USDJPY
In January, their correlation started at close to -100%, rose to close to +50%, only to fall to close to 0% and remain there for the second half of the month.
In February it was -90% in the first few days of the month and is now around -57%.
And between AUDUSD and EURCAD
Last month their correlation was negative for most of the month, reaching -70% and ending around -14%.
This month their correlation has never gone below +21% and at the time of writing is close to +53%.
🔶 SETTINGS
Anchor point: Starting point from which the tool is executed
Anchor period: At the beginning of each new period, the tool will reset the calculations
Pairs from 1 to 10: For each pair of tickers, you can: enable/disable the pair, select the color and specify the two tickers from which you wish to obtain the correlation
🔹 Style
Drawing Mode: Output style, `LINES` will show the historical correlations as lines, `HEATMAP` will show the current correlations with a color gradient from green for correlations near 1 to red for correlations near -1.
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Correlation Coefficient - DXY & XAUPublishing my first indicator on PulseWire. Essentially a modification of the Correlation Coefficient indicator, that displays a 2 ticker symbols' correlation coefficient vs, the chart presently loaded.. You can modify the symbols, but the default uses DXY and XAU, which have been displaying strong negative correlation.
As with the built-in CC (Correlation Coefficient) indicator, readings are taken the same way:
Positive Correlation = anything above 0 | stronger as it moves up towards 1 | weaker as it moves back down towards 0
Negative Correlation = anything below 0 | stronger moving down towards -1 | weaker moving back up towards 0
This is primarily created to work with the Bitcoin weekly chart, for comparing DXY and Gold (XAU) price correlations (in advance, when possible). If you change the chart timeframe to something other than weekly, consider playing with the Length input, which is set to 35 by default where I think it best represents correlations with Bitcoin's weekly timeframe for DXY and Gold.
The intention is that you might be able to determine future direction of Bitcoin based on positive or negative correlations of Gold and/or the US Dollar Index. DXY has been making peaks and valleys prior to Bitcoin since after March 2020 black swan event, where it peaked just after instead. In the future, it may flip over again and Bitcoin may hit major highs or lows prior to DXY, again. So, keep an eye on the charts for all 3, as well as the indicator correlations.
Currently, we've moved back into negative correlation between Bitcoin and DXY, and positive correlation with Bitcoin and Gold:
Negative Correlation b/w Bitcoin and DXY - if DXY moves up, Bitcoin likely moves down, or if DXY moves down, Bitcoin likely moves up (or if Bitcoin were to move first before DXY, as it did on March 2020, instead)
Positive Correlation b/w Bitcoin and Gold - Bitcoin and Gold will likely move up or down with each other.
DXY is represented by the green histogram and label, Gold is represented by the yellow histogram and label. Again, you can modify the tickers you want to check against, and you can modify the colors for their histograms / labels.
The inspiration from came from noticing areas of same date or delayed negative correlation between Bitcoin and DXY, here is one of my most recent posts about that:
Please let me know if you have any questions, or would like to see updates to the indicator to make it easier to use or add more useful features to it.
I hope this becomes useful to you in some way. Thank you for your support!
Cheers,
dudebruhwhoa :) Indicator
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Crypto Correlation MatrixA crypto correlation matrix or table is a tool that displays the correlation between different cryptocurrencies and other financial assets. The matrix provides an overview of the degree to which various cryptocurrencies move in tandem or independently of each other. Each cell represents the correlation between the row and column assets respectively.
The correlation matrix can be useful for traders and investors in several ways:
First, it allows them to identify trends and patterns in the behavior of different cryptocurrencies. By looking at the correlations between different assets, traders can gain insight into the intra-relationships of the crypto market and make more informed trading decisions. For example, if two cryptocurrencies have a high positive correlation, meaning that they tend to move in the same direction, a trader may want to diversify their portfolio by choosing to invest in only one of the two assets.
Additionally, the correlation matrix can help traders and investors to manage risk. By analyzing the correlations between different assets, traders can identify opportunities to hedge their positions or limit their exposure to particular risks. For example, if a trader holds a portfolio of cryptocurrencies that are highly correlated with each other, they may be at greater risk of losses if the market moves against them. By diversifying their portfolio with assets that are less correlated with each other, they can reduce their overall risk.
Some of the unique properties for this specific script are the correlation strength levels in conjunction with the color gradient of cells, intended for clearer readability.
Features:
Supports up to 64 different crypto assets.
Dark/Light mode.
Correlation strength levels and cell coloring.
Adjustable positioning on the chart.
Alerts at the close of a bar. (Daily timeframe or higher recommended)
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Correlation Coefficient: Visible Range Dynamic Average R -Correlation Coefficient with Dynamic Average R (shows R average for the visible chart only, changes as you zoom in or out)
-Label: Vis-Avg-R = Visable Average R
-the Correlation Coefficient function for Pearson's R is taken from "BA🐷 CC" indicator by @balipour (highly recommended; more thorough treatment of R and other stats, but without the dynamic average)
-I wrote this primarily to add a dynamic Average R, showing correlation for arbitrary start times/end times; whether it be the last month, last year, of some specific period from the past (backtest mode)
-I have been using this to get an idea of correlation regimes over time between Bonds vs Stocks (ZB1! vs ES1!).
-As you see from the above, most of 2022 has seen an unusually strong positive correlation between Bonds and Stocks
~~inputs:
-lookback length for calculation of R
-Backtest mode (true by default): displays Average R for ONLY the visible range displayed on any part of chart history (LHS to RHS of screen only)
-source for both Ticker and compared Asset (close, open, high, low, ohlc4.. etc)
~~some other assets worth comparing:
Aussie vs Gold; Aussie vs ES; Btc vs ES; Copper vs ES
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Multi Delta-Agnostic Correlation Coefficient (tartigradia)Display three DACC plots simultaneously, to visualize both directional (up on top, down at bottom) and adirectional DACC (in the middle) simultaneously.
Delta Agnostic Correlation calculates a correlation between two symbols based only on the sign of their changes using a Sign Test (en.m.wikipedia.org), regardless of the amplitude of price change. Compared to a standard Pearson correlation (quantitative test), Sign Test correlations (discrete test) are highly sensitive to directional change with 0 lag, at the expense of lacking sensitivity to quantity correlation (ie, it does not matter if changes are big or small).
Hence, this Delta-Agnostic Correlation Coefficient (DCC or DACC) indicator is better used to detect early changes in correlations, and then confirmation with a typical Pearson correlation or a non-parametric Spearman test or Mutual Information (all three are quantitative tests, hence accounting for quantity and not just direction) can allow to be more sensitive to quantities too and hence be a robust combination to demonstrate strong correlations both in direction and amplitude.
Adequate statistical significance testing, using a two-sided binomial statistical test, is also implemented. Note however that one assumption of the sign test may here be violated: independence of observations for each symbol. If you assume the market is not acting on a random walk, then there is a temporal autocorrelation, and this biases the sign test. However, in practice, the test works well enough.
The directional variants of the test allow to test the correlation hypothesis only if the index symbol goes into one direction. For example, if we suspect that the index symbol is correlated with the current symbol but only when the index symbol is bullish, we can select "Up" to test this hypothesis. Note that given the specificities of how directional and adirectional tests differ in how they work, the default fill is different: zero-value fill for adirectional test to simulate how price action tend to lose momentum during market close periods, previous DCC_MA (= no change in DCC value) during both market close periods and when the direction is opposite for the directional variants of the test, so that while the market is moving opposite, we don't lose the statistical significance built up to now, otherwise it would be nonsensical (for the directional tests).
For more information on the theory behind, see the original DACC indicator, which is the same script but with only one plot: Indicator
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Delta-Agnostic Correlation Coefficient (alt)Calculate a sort of correlation between two symbols based only on the sign of their changes, regardless of the amplitude of price change.
When positive, the two symbols tend to move together. When negative, the symbols move in opposite directions.
Since there is no significance calculation, and that the result is binary, keep in mind that correlation will always tend to go towards 1 or -1 even when there is no correlation. To reduce this issue, an EMA or SMA is applied to smooth out transitions: SMA smoothes over the selected length period but adds lag, whereas EMA smoothes amplitude without any additional lag. Hence, to know if the correlation is true or not, try to look at the amplitude and the number of consecutive days the correlation is maintained (both quantities are related), because when the correlation is spurious, it will tend to switch more or less alternatively between 1 and -1 and hence will hover around 0, whereas if the correlation is true, it will get further away from 0 and closer to 1 or -1.
In addition, since there is some time lag for the correlation to switch sign, the area is colored to know the current candle's correlation, regardless of past data's correlation: blue is a positive correlation (1), yellow is negative. The coloring can allow to know a trend reversal early on, but it's noisy.
Finally, symbols with closing days are better accounted for, with the correlation set to 0 on closed days (e.g., on week-ends), and the area is then colored in gray to signal that there is no new correlation data.
This is an improved fork over the original indicator by alexjvale, please show him some love if you like this work:
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Swing ComparatorHere I bring you an array of methods to compare the swings and consistency between assets.
This indicator is excellent for swing traders and scalpers looking to maximize their profits by examining which of two closely related pairs provides greater price fluctuation during given period.
This indicator works against two assets, which are to be configured in settings.
This indicator has 5 particular plots for you to examine, each which can be considered for you to contemplate which pair for you to next perform a trade on.
First off, let's start with the blue.
The blue is simply a pearson correlation coefficient, thankfully now included in pulsewire. This provides a value of 1 as values show to be close correlation, 0 showing no correlation, and -1 showing negative correlation - meaning an increase in one pair correlates to a decrease in another pair. This will turn green when greater than 0.975, showing a very strong relationship between the two pairs, and red when below -0.975. This is the only plot to be interpreted on a scale from -1 to +1.
Next, we have the purple and yellow background plots, followed by the white and green moving averages. Though similar, these are all slightly different.
For each of these 4 plots, a value greater than 0 indicates greater price swings for your Symbol #1, while a value less than 0 indicates greater price swings for Symbol #2.
These calculations are performed on a per bar basis, meaning you're likely going to be examining bars longer than what you'll normally be trading on. Use confluence, as well as your own judgement for this.
For example, if symbol #1 provides a bar with an open value 1% greater or less than close, providing a 1% swing on a given bar, but symbol #2 provides 2%, the indicator will fall down toward the negative, as Symbol #2 had the greater swing.
First, yellow focuses on only open/close bar values, and thus the body of the candlestick.
Purple, on the other hand, focuses on the wicks of the candle - thus, the high/low values. I've opted to make these two different values as a wick focuses on the embodiment within the time period, and body focuses on the open/close instant.
Next, the green is an extended EMA of the purple - High/Low ratio. This is important to examine trend overtime, and reduce unneeded noise.
Lastly, the white is simply difference in the standard deviation of the particular bars, between the two symbols you have selected. The tends to usually tie up with the green pretty well.
Considering this is going to by nature be very noisy datasets, I have included in settings the option to extend an EMA for everything. They have their default settings, but if you'd like to examine the trend without an EMA, feel free to set it to 1 to eliminate its effects.
I have additionally added the ability to introduce clipping, as well as scale the correlation coefficient to remain visible when examining very short term time scales. In the future, I hope to properly normalize all plots to remain within a -1 to +1 basis. Please be patient as I have multiple projects ongoing.
Suggestions and constructive criticism are very well encouraged.
Anyone is welcome to utilize this in their code, as well, i just ask you provide credit.
As you reduce to time frames less than a day, you will likely have to reduce the coefficient min/max closer to 0.025, or just hide it entirely.
TODO:
Make it look better. Sorry, folks.
Introduce latency between pairs.
Examine significance of a coefficient of determination
Remove static weights and introduce z-score and linear normalization.
Consider adding room for a 3rd pair. This could get ugly, however.
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