Coiled Moving AveragesThis indicator detects when 3 moving averages converge and become coiled. This indicates volatility contraction which often leads to volatility expansion, i.e. large price movements.
Moving averages are considered coiled when the percent difference from each moving average to the others is less than the Coil Tolerance % input value.
This indicator is unique in that it detects when moving averages converge within a specified percent range. This is in contrast to other indicators that only detect moving average crossovers, or the distance between price and a moving average.
This indicator includes options such as:
- % difference between the MAs to be considered coiled
- type and length of MAs
- background color to indicate when the MAs are coiled
- arrows to indicate if price is above or below the MAs when they become coiled
While coiling predicts an increased probability for volatility expansion, it does not necessarily predict the direction of expansion. However, the arrows which indicate whether price is above or below the moving average coil may increase the odds of a move in that direction. Bullish alignment of the moving averages (faster MAs above the slower MAs) may also increase the odds of a bullish break, while bearish alignment may increase the odds of a bearish break.
Note that mean reversion back to the MA coil is common after initial volatility expansion. This can present an entry opportunity for traders, as mean reversion may be followed by continuation in the direction of the initial break.
Experiment with different settings and timeframes to see how coiled MAs can help predict the onset of volatility.

# Volatilityexpansion

GARCH Volatility Estimation - The Quant ScienceThe GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events.
The general formula of the GARCH model is:
σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1)
where:
σ²(t) is the conditional variance at time t (i.e., squared volatility)
ω is the constant term (intercept) representing the baseline level of volatility
α is the coefficient representing the impact of the squared lagged error term on the conditional variance
ε²(t-1) is the squared lagged error term at the previous time period
β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance
In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset.
HOW TO USE
This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red).
USER INTERFACE
Alpha: select the starting value of Alpha (default value is 0.10).
Beta: select the starting value of Beta (default value is 0.80).
Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20).
Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30).
Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.

TheATR™: Volatility Extremes (VolEx)Volatility is a crucial aspect of financial markets that is closely monitored by traders and investors alike. The traditional Average True Range (ATR) oscillator is a widely used technical indicator for measuring volatility in financial markets. However, there are limitations to the ATR oscillator, as it does not account for changing market conditions and may not adequately reflect extreme price movements. To address these limitations, TheATR has developed the VolEx indicator, which aims to identify extremes in the ATR oscillator by building dynamic thresholds using either a 'percentage' or 'standard deviation' based comparison with the value of the ATR.
The VolEx indicator utilizes a dynamic approach to measure volatility by considering the current level of the ATR oscillator relative to the dynamically generated thresholds. The dynamic thresholds are calculated based on the current ATR value and the chosen method of comparison (either 'percentage' or 'standard deviation'). If the ATR value exceeds the upper dynamic threshold, the market is experiencing high volatility, while a value below the lower dynamic threshold indicates low volatility.
The VolEx indicator offers several advantages over traditional volatility indicators, such as the ATR oscillator. First, it takes into account the changing market conditions and adjusts the thresholds accordingly. Second, it offers flexibility in the choice of the comparison method, allowing traders to tailor the indicator to their specific trading strategies. Finally, it provides clear signals for identifying extremes in volatility, which can be used to inform trading decisions.
In summary, the VolEx indicator developed by TheATR is a dynamic and flexible technical indicator that offers a robust approach to measuring volatility in financial markets. By utilizing dynamic thresholds and allowing for different comparison methods, the VolEx indicator provides a valuable tool for traders and investors seeking to identify extremes in market volatility..
NOTE: It is important to note that volatility, as measured by the VolEx indicator, does not provide any directional bias for the market movement. Rather, it simply indicates the degree to which the market is moving, regardless of direction. Traders and investors must use other technical or fundamental analysis tools to determine the direction of the market and make informed trading decisions based on their individual strategies and risk tolerance.

Volatility Compression Ratio by M-CarloHello traders. I created this simple indicator to use as a FILTER.
He does not provide any operational signals but tells us if we are in a period of volatility compression or expansion and it can work on all market.
This filter works great for all strategies that work on breakouts
The concept is this: I will enter at breakout of a price level that I consider important, only if there is a volatility compression and not in the case of expansion of volatility.
Technically the calculation is very simple:
Step 1: I calculate the ATR at "x" periods, I set 7 by default because I get better results but you can change it as you like using the "atr length" field. You can also choose whether to calculate the ATR via RMA, SMA or EMA.
Step 2: I Calculate a simple average of the previous ATR over a longer period, longer period than set with the "length multiplier" parameter, which multiplies the "atr length" value by "x" times. Here I set the default 3 but you can change it as you like.
Step 3: I divide the ATR value calculated in step1 by its long-term average calculated in step2, obtaining a value that will oscillate above and below the value of 1
So:
if the indicator is above the value of 1 it means that volatility is expanding
If the indicator is below the value of 1 it means that we are in a period of volatility compression (and as we know volatility explodes sooner or later)
If you have any questions write to me and I hope this filter helps you! Have good Trading!

Outside Bar FinderOutside bars occur when the range of a candlestick falls entirely outside of the previous candlestick's range. This indicates indecision and volatility expansion which often leads to changes in trend direction.
This indicator includes options such as:
- The number of consecutive outside bars required to trigger the indicator
- An arrow indicating whether the outside bar is bullish or bearish
- Signal lines to indicate the high and low of the outside bar
Try out this indicator with different options on different timeframes to see if outside bars increase the probability of identifying changes in trend. Breaks or closes outside the signal lines can be used to trigger trade signals.