Unicorn FL25

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Indicator «Unicorn FL»

Class: Volatility , Oscillator

Trading type: Scalping

Time frame: 5 min (Best Results)

Purpose: Reversal trading on current or future impulse

Level of aggressiveness: Conservative & Moderate


Indicator «Unicorn FL» is based on the linear regression and normal distribution. Regression is one of the most popular and effective methods of time series analysis and data prediction. Indicator «Unicorn FL» uses regression analysis as an alternative to the analysis of the moving average. The main aim is data series smoothing to reduce the noise in data. It allows seeing the market as it is, without deformation.

Using a unique author algorithm indicator «Unicorn FL» searches for the optimal buy/sell zones. If they are sufficient indicator generates appropriate trading signals.

Parameters of the «Unicorn FL» indicator

1. Period of Unicorn FL (indicator period, default value = 21, but changes automatically depending on the market volatility ) – is used to calculate the fair value of the asset-based on linear regression .
2. Width – defines the width of the stationary channel (indicated by bold lines). Default value = 22 pips.
3. Sigma – defines the width of the dynamic channel (indicated by usual lines). Default value = 2standard deviations (changes automatically, depending on market volatility ). It allows fitting from 90% to 95% of price values into the channel. All the price values out of the channel are treated as abnormal.

Rules of trading

The indicator is designed to work on the 5-minute time frame
The rules of trading are extremely simple: when the red triangle appears on the chart (sell signal) or a green triangle (a buy signal), a corresponding transaction (sell or buy an asset) should be done. Stops can be placed above/below the red/blue zones (according to your money management system).

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Unicorn FL Explained

This indicator combines the Durbin Watson Test Statistic, Bollinger Band and Stochastic RSI to produce signals for possible price reversal. The signals are displayed by default as green arrows for bullish and red arrows for bearish .

Durbin Watson -
In statistics, the Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation at lag 1 (AR(1) process) in the residuals (prediction errors) from a regression analysis. With the new array function TradingView implemented, we are able to do our calculations on the residuals.

The residual is given by subtracting the actual value (in this case it's log returns) from the prediction value (generated by linear regression ). The following chart displays who we get the error term from the regression.

Then we do our calculation based on the residuals. The formula is basically the sum of the square of the difference between error and it's 1 lagged term divided by the sum of the square of all the error terms. It's a very simple test on the residual sum of squares. The value you get is from 0 to 4.
When DW > 2, there's a possible negative autocorrelation.
When DW < 2, there's a possible positive autocorrelation.
Note: above 2 and below 2 is only possible autocorrelation, it might not be statistically significant.

If we want to know if the autocorrelation presence is significant or not. We need a Durbin Watson Stats Table. Based on the lookback period you use, you can find the critical value on the table (usually the first column).

For the default setting 30 lookbacks, the 5% critical level for positive autocorrelation is 1.35. (We round to 1.4 on the default setting for convenience ). The other side for negative autocorrelation is just 4 - 1.35 = 2.65 due to its symmetry. For other lookbacks like 50, the critical value is 1.5 and 2.5, we just need to check the table to find the corresponding value. The user only has to find the lower threshold, the indicator will automatically give you the 4- lower threshold value on the other side.

As we have mentioned in the autocorrelation function and Hurst exponent . Positive autocorrelation means positive returns are more likely to be followed by positive returns and negative returns are more likely to be followed by negative returns. People may call it trending. Negative autocorrelation means positive returns are more likely to be followed by negative returns and negative returns may be followed by positive returns. People may call it mean-reverting.

The Durbin Watson test only tests the first-order autocorrelation. But there might also be significant autocorrelation in other lagged values.

Stochastic RSI -StochRSI
The Stochastic RSI (StochRSI) is an indicator used in technical analysis that ranges between zero and one (or zero and 100 on some charting platforms) and is created by applying the Stochastic oscillator formula to a set of relative strength index ( RSI ) values rather than to standard price data. Using RSI values within the Stochastic formula gives traders an idea of whether the current RSI value is overbought or oversold.

The StochRSI oscillator was developed to take advantage of both momentum indicators in order to create a more sensitive indicator that is attuned to a specific security's historical performance rather than a generalized analysis of price change.

A StochRSI reading above 0.8 is considered overbought, while a reading below 0.2 is considered oversold. On the zero to 100 scale, above 80 is overbought, and below 20 is oversold.
Overbought doesn't necessarily mean the price will reverse lower, just like oversold doesn't mean the price will reverse higher. Rather the overbought and oversold conditions simply alert traders that the RSI is near the extremes of its recent readings.
A reading of zero means the RSI is at its lowest level in 14 periods (or whatever lookback period is chosen). A reading of 1 (or 100) means the RSI is at the highest level in the last 14 periods.
Other StochRSI values show where the RSI is relative to a high or low.

Bollinger Bands
Bollinger Bands are envelopes plotted at a standard deviation level above and below a simple moving average of the price. Because the distance of the bands is based on standard deviation, they adjust to volatility swings in the underlying price.

Bollinger bands help determine whether prices are high or low on a relative basis. They are used in pairs, both upper and lower bands and in conjunction with a moving average. Further, the pair of bands is not intended to be used on its own.

When the bands tighten during a period of low volatility , it raises the likelihood of a sharp price move in either direction. This may begin a trending move. Watch out for a false move in the opposite direction which reverses before the proper trend begins.

When the bands separated by an unusually large amount, volatility increases and any existing trend may be ending.

Prices have a tendency to bounce within the bands' envelope , touching one band then moving to the other band. You can use these swings to help identify potential profit targets. For example, if a price bounces off the lower band and then crosses above the moving average, the upper band then becomes the profit target.

Price can exceed or hug a band envelope for prolonged periods during strong trends. On divergence with a momentum oscillator, you may want to do additional research to determine if taking additional profits is appropriate for you.
A strong trend continuation can be expected when the price moves out of the bands. However, if prices move immediately back inside the band, then the suggested strength is negated.

To trigger a signal the indicator checks for the following:

( Bullish )
A candle closes above the upper Bollinger Band
The following candle closes within the upper Bollinger Band
The RSI Stochastic is below the set threshold (10 by default)

( Bearish )
A candle closes below the lower Bollinger Band
The following candle closes within the lower Bollinger Band
The RSI Stochastic is above the set threshold (90 by default)

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Release Notes: What`s New

- Candle body resistance Channel
- Support and Resistance Levels
- EMAs
- Hull MA
- Alerts on Buy and Sell Signals
Release Notes: Bugs are fixed
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Author's instructions

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Авторский канал Феруза Алиева