RATIOPLOTTER.EU

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Enter two financial asset tickers (e.g., T-MSFT/T-GOOGL) to plot their ratio, relative strength. You can search tickers and copy them into a basket using the ticker selector.

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Want to create plots with your own data series or price to sales plots?

Try our price to x plot which supports your own data vectors.

Suggested Ratio Plot Ticker Pairs (Free Tier)

Plot Icon Legend: (No medal icon) Free Plot: Free and unlimited. Does not require login. Premium Plot: Allowed for non-subscribers up to 8/month. Requires Google Login. Charts not listed below (custom charts) will be in this category. Premium & Subscriber Plot: Requires Paid Subscription. This category is not applicable to ratio plots. They are all allowed for non-subscribers.

Trading Notes & Guide

General Notes

Currency Consistency: Ensure tickers are in the same currency to avoid exchange rate noise affecting the ratio.
See the currency on the Ticker Selector Page.

ETF Consistency: When plotting the ratio between two ETF's, always check that both are either distributing or accumulating. If this is not the case dividends can effect the results significantly!

Beta Correlation: Ensure the beta (calculated from montly periodic returns) is significant. Otherwise, you may be trading noise, and there will be no reliable reversal to the mean.

Recommendation: Always check the seasonality plot of the ticker you intend to swing trade; ensure the historical seasonality isn't moving against your position.

R2 and Beta Analysis Summary for Ratio Trading

To ensure a Ratio trade is based on structural correlation rather than random price movement, the R2 and Beta must BOTH be significant.

A low R2 indicates that Asset A and Asset B are "decoupled," meaning any apparent pattern in the Ratio is likely statistical noise with no mathematical pressure to return to the mean. Conversely, a high R2 confirms the assets are "tethered," making mean-reversion strategies reliable.

Besides the R2 we also need to make sure the Beta is healthy. The Beta between two assets indicates how much the numerator moves relative to the denominator. To use the Beta you can use this simple formula: Expected Numerator performance ≈ Denominator performance × Beta value.

A static Beta may mask recent shifts in relative volatility. To ensure your position sizing remains accurate, compare the overall Beta with the rolling Beta windows in the bottom dashboard pane. Monitoring these short-term fluctuations helps distinguish between normal market noise and a fundamental shift in how aggressively one asset moves compared to the other.

Statistical Warning: The R2 Validity Test
The following table defines the boundaries between random noise and actionable correlation based R2 values:

0.00 to 0.30 Pure Noise

EXTREME CAUTION.
The assets are practically decoupled. No statistical reason for the Ratio to return to the mean.
Assets with a R2 value in this range can be used for diversifying portfolios.

0.30 to 0.70 Weak Link

CAUTION.
Significant risk of "drift" where the Ratio diverges permanently from historical averages.
Assets with an R2 value in this range provide little value for both diversification and mean reversion trading.

0.70 to 1.00 Reliable Link

OPTIMAL.
Assets are tethered. High probability of mean reversion when the Ratio stretches.
Assets with an R2 value in this range are ideal for mean reversion trading.

Statistical Warning: The Beta Validity Test
The following table defines the boundaries between Inverse Ratio, Low volatility and high volatility asset pairs.

-1.50 to -0.50 Inverse Relationship

ATTENTION.
The assets move in opposite directions. A Beta of -1.0 indicates equal volatility but inverse movement. Assets in this range are primarily used for hedging strategies rather than standard Ratio trading.

0.00 to 0.50 Low Volatility

EXTREME CAUTION.
The numerator asset is significantly less volatile than the denominator. The ratio will move slowly unless position sizing is adjusted.
Assets with Betas in this range can be used for diversifying portfolios.

0.50 to 1.50 The sweet spot

OPTIMAL.
Both assets exhibit similar volatility profiles, meaning neither will easily overpower the other during price swings. This balance makes the pair optimal for standard 1:1 ratio trading, allowing for predictable mean reversion without the need for complex position sizing adjustments.

1.50 to 2.0+ Very high Volatility

CAUTION.
The numerator is highly volatile relative to the denominator. While the assets may be correlated, this severe imbalance requires careful position sizing to prevent the numerator from overpowering the trade.

*Note: RatioPlotter.eu uses periodic returns (relative changes) to ensure the Beta and R2 reflect the structural relationship regardless of the absolute currency price of the assets.

Ticker Data Usage, Data Sourcing and Ticker Functions

Ticker Data Sources
Our data is provided from a variety of sources: The Federal Reserve (FED-* tickers), The World Bank (WB-* tickers), The International Monetary Fund (IMF-* tickers), Tiingo (T-* tickers covering: stocks, ETFs, etc), Eurostat (ES-* tickers), Ourselves (MATH-* tickers), Yourself (USR-* tickers for uploaded). The ticker data sources vary according to ticker prefix as you can see. Our ticker selector allows browsing each ticker and their description and placing them on a basket for use. The menu has links allwoing the upload of your own data which goes into the USR-* ticker space.
Default Ticker Column Projections & Adjustments
Let's take T-GOOGL as an example. When the projection variable is left blank, the ratio plotter will use the default projection variable which is -AdjClose (close price adjusted for both dividends and splits). This means that when you input T-GOOGL into the ratio plotter it will actually plot the "Tiingo split and dividend adjusted close price" of GOOGL: T-GOOGL-AdjClose (T-GOOGL-AdjClose by T-MSFT-AdjClose). If you want to plot the split (but not dividend) adjusted close price of GOOGL you can input T-GOOGL-SplitAdjClose. All projection columns are provided by Tiingo, with exception of the -SplitAdjClose column which is calculated by our system. For a complete explanation for beginners see our article: Understanding column projections.
ADD() Element-wise adding
The ADD() function performs element-wise addition between two vectors, or between a vector and a literal scalar value. Example: ADD(T-AAPL, T-MSFT) or ADD(T-GLD, 10.5).
CLOSE_GAPS() Interpolating and extrapolating sparse data
The CLOSE_GAPS() function resolves interior and exterior `NaN` gaps in sparse datasets (like European Union Eurostat macroeconomic vectors). It requires 4 arguments: the ticker, interpolation method ('LINEAR'), extrapolation method ('FORWARD_FILL', 'NONE', 'LINEAR'), and extrapolation days. Example: CLOSE_GAPS(ES-teicp000-coicop18, 'LINEAR', 'FORWARD_FILL', 60).
CURVE_FROM_VECTOR() Custom data vectors
The CURVE_FROM_VECTOR() function allows transforming your own data vectors into plottable data. It creates a vector from user-entered data points, useful for proprietary data. Example usage: CURVE_FROM_VECTOR([198.27, 211.92, 245.12, 281.72, 318.27], '2026-06-30', 'last', '1y', 'linear', 'forward_fill'). Arguments represent the array of values, anchor date, anchor position ('first' or 'last'), frequency ('1y', '1q', '1m'), interpolation ('linear', 'forward_fill'), and extrapolation ('none', 'forward_fill', 'linear').
LAG() Time-shifted underlying vector
The LAG() ticker function is used to create a lagged version of an asset. This shifts a dataset to the right by an amount of days equal to the lag value. To write custom LAG() tickers, simply input your ticker of choice and lag value between the parenthesis, separated by a comma. Example: LAG(T-AAPL, 10). It is highly useful for autocorrelation studies.
LINEAR_COMBINATION() Weighted sum of multiple vectors
The LINEAR_COMBINATION() function calculates a weighted mathematical sum across multiple vectors. It takes two arrays as arguments: an array of coefficients and an array of tickers or expressions. Example: LINEAR_COMBINATION([1.5, -0.5], [T-AAPL, T-MSFT]).
LOG() Logarithmic transformation
The LOG() function applies a logarithmic transformation to a vector given a specific mathematical base. It accepts the ticker and the mathematical base as arguments. Example: LOG(T-GOOGL, 2) or LOG(T-MSFT, 10). If you want you can use EPSILON constant which is not a natural logarithm constant, but just a logarithm on a tiny base: LOG(T-SPY, EPSILON). We encourage using LOG on the base two as it is the fastest to compute for you and will allow caching of the computation for others to benefit. The LOG on base 2 will also mean you are measuring how long it takes for the time series to double, a difference in 1 point is a doubling.
LOOK_FORWARD() Shifting data forward in time
The LOOK_FORWARD() function is the mathematical inverse of LAG(). It brings future values forward in time to the current date coordinate, shifting the vector left toward the epoch. Example: LOOK_FORWARD(T-AAPL, 30). The second argument 30 is the number of days to look forward. All our data for time series is measured in days.
MATH- Custom Mathematical Tickers
The MATH- tickers are custom, idealized mathematical baseline tickers generated by our system (e.g. MATH-CAGR_PCT-2 for 2% exponential growth, MATH-CONST_VEC-1 for a constant flatline value of 1). To find the list of valid MATH tickers go to the Ticker Selector Page. Note that MATH tickers have a truth boundary projected far into the future (9999-12-31).
MINUS() Element-wise subtraction
The MINUS() function calculates the element-wise subtraction of the second argument from the first. It supports vector-vector, vector-scalar, and scalar-vector subtraction. Example: MINUS(T-AAPL, T-MSFT).
MOVING_AVERAGE() Trailing Simple Moving Average
The MOVING_AVERAGE() function computes a simple rolling mean over a specified lookback window of days. Example: MOVING_AVERAGE(T-SPY, 200) calculates the 200-day trailing moving average.
MULT() Element-wise multiplication
The MULT() function computes the element-wise multiplication of two synchronized vectors. Example: MULT(T-COM, T-USDCHF) performs a currency conversion of the T-COM broad commodity investment basket to Swiss Francs (most of our tickers are in USD and that ticker price currency can be seen on the ticker selector page). T-COM is the: DIREXION AUSPICE BROAD COMMODITY STRATEGY ETF.
RAT() Calculating Ratio Between Two Tickers
The RAT() ticker function works by calculating the relative outperformance (division) between two tickers as a new dataset. To write custom RAT() tickers, simply input your two tickers of choice separated by a comma. Example: RAT(T-AAPL, T-MSFT). Example of comparing Google's outperformance to an idealized CAGR curve: RAT(T-GOOGL,T-MSFT)/MATH-CAGR_PCT-2 (requires login with Google).
REGRESSION() and RESIDUALS() Functions

(1) The REGRESSION or RESIDUAL functions are reserved for paying users, apart from the free tier REGRESSION examples present on the log plot page.

(2) When the REGRESSION() function is the outermost function of the expression and is used on the log plotter, it will cause a nice regression plot canvas to display the fitting line and useful regression statistics such as ADF probability and R².

If used on the regular ratio plotter page, it will plot the residuals vector. Example:   REGRESSION(LOG(T-URTH,2)) (this example requires subscription as it uses the LOG function).

The REGRESSION function is very good at modelling time series that grow following a linear trend. Most financial assets grow following an exponential trend, which means that if we apply the LOG function to this asset ticker we obtain a linear trend which can be modelled by the REGRESSION function. Try it, try plotting just LOG(T-GOOGL) to see this straight line tendency.

RET() Rolling Percentage Return
The RET() function calculates the trailing rolling percentage return of an asset over a specified day window. Example: RET(T-SPY, 30) measures the rolling 30-day percentage performance. The return for a day is always measured looking back at the last x days (30 in the example), otherwise we would introduce look-ahead bias into our models of returns. To calculate forward returns you can combine the two functions RET(LOOK_FORWARD(T-GOOGL,30),30). Easy right?
ROLLING_SLOPE() Calculating slope, velocity, or derivating
Calculates the rolling derivative/velocity of an underlying vector. Example: ROLLING_SLOPE(IMF-NGDPD-CHN, 400, 1). The second argument (400) is the lookback window in days to calculate the smoothed slope. The third argument (1) determines the scalar output granularity (e.g., 1 for daily velocity, 365 to annualize the velocity). Example: China GDP Velocity.
SIMUL_TICKER_DIV_REINVEST() Plotting Dividends Reinvested at Your Portfolio Return Rate
You can do this with the function: SIMUL_TICKER_DIV_REINVEST(T-MSFT,0.25,1,'2022-08-01'). This is a function available only for users with the advanced subscription. The first argument is the Ticker simulated, the second is your portfolio CAGR rate (in this case 0.25 = 25% yoy, applied dynamically daily). The third argument is the initial investment in the currency of the stock ticker. The last argument is the start date. See our article on dividend reinvestment simulation.
VOLATILITY() Annualized Rolling Volatility
The VOLATILITY() function calculates the annualized rolling standard deviation of 1-day returns over a designated window of time. Example: VOLATILITY(T-SPY, 50) measures the annualized volatility of the S&P 500 utilizing a 50-day rolling lookback window. The Scatter plot tool has some examples of the VOLATILITY function.
Z_SCALE() Z scaling, bringing data to the normal curve Z score scale, normalizing
You can do this with the function: Z_SCALE(). Examples: Z_SCALE(T-MSFT), Z_SCALE(RAT(T-GLD,T-SLV)), Z_SCALE(ROLLING_SLOPE(T-COM,90,1)). This brings absolute price vectors or ratios to a standardized Normal Curve (Mean 0, StdDev 1) for macro-economic cycle alignment. This function is available only for users with the advanced subscription. The Buffett Indicators Tool allows plotting the Z-Scale spread of the country ETF price to its GDP figures.