Volatilityspike — Indicatori e strategie

GARCH Volume Volatility [MarkitTick]Title: GARCH Volume Volatility Description Overview The GARCH Volume Volatility (GV) indicator is a sophisticated quantitative tool designed to analyze the rate of change in market participation. While the vast majority of technical indicators focus on Price Volatility (how much price moves), this script focuses on Volume Volatility (how unstable the participation is). Market volume is rarely distributed evenly; it tends to cluster. Periods of high activity are often followed by more high activity, and periods of calm tend to persist. This behavior is known as "heteroskedasticity." This script utilizes an Exponentially Weighted Moving Average (EWMA) model—a core component of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) frameworks—to model these changing variance regimes. By isolating volume volatility from raw volume data, this tool helps traders distinguish between sustainable liquidity flows and erratic, unsustainable volume shocks that often precede market reversals or breakouts. Methodology and Calculations 1. Logarithmic vs. Percentage Returns The foundation of this indicator is the calculation of "Volume Returns"—the period-over-period change in volume. - The script defaults to Logarithmic Returns. In financial statistics, log returns are preferred because they normalize data that can vary wildly in magnitude (such as cryptocurrency volume spikes), providing a more symmetric view of changes. - Users can opt for standard percentage changes if they prefer a linear approach. 2. Variance Proxy (Squared Returns) To measure volatility, the direction of the volume change (up or down) matters less than the magnitude. The script squares the returns to create a "Variance Proxy." This ensures that a massive drop in volume is treated with the same statistical weight as a massive spike in volume—both represent a significant change in the volatility of participation. 3. GARCH-Style Smoothing (EWMA) Standard Moving Averages (SMA) treat all data points in the lookback period equally. However, volatility is dynamic. This script uses an EWMA model with a tunable "Lambda" (Decay Factor). - The Recursive Formula: The current calculation relies on a weighted average of the current variance and the previous period's smoothed variance. - Memory Effect: This allows the indicator to "remember" recent volatility shocks while gradually letting their influence fade. This mimics the GARCH process of conditional variance. 4. Dynamic Statistical Thresholds The final output is the Volatility (square root of variance). To make this data actionable, the script calculates a dynamic upper and lower limit based on the standard deviation (Z-Score) of the volatility itself over a user-defined lookback period. How to Use The indicator plots a histogram that categorizes the market into four distinct volatility regimes: 1. High Volatility (Red Histogram) Trigger: Volatility > High Band (Upper Standard Deviation). Interpretation: This signals an extreme anomaly in volume stability. This is not just "high volume," but "erratic volume behavior." This often occurs at: - Capitulation bottoms (panic selling). - Euphoric tops (blow-off tops). - Major news events or earnings releases. 2. Elevated Volatility (Maroon Histogram) Trigger: Volatility > Mean Average. Interpretation: The market is in an active state. Participation is changing rapidly, but within statistically normal bounds. This is common during healthy, trending moves where new participants are entering the market steadily. 3. Normal/Low Volatility (Green Histogram) Trigger: Volatility is within the lower bands. Interpretation: The market volume is stable. There are no sudden shocks in participation. This is typical of consolidation phases or "creeping" trends where the price drifts without significant volume conviction. 4. Extremely Low Volatility (Bright Green/Transparent) Trigger: Volatility < Low Band. Interpretation: The "calm before the storm." When volume volatility collapses to near-zero, it implies that the market has reached a state of equilibrium or disinterest. Historically, volatility is cyclical; periods of extreme compression often lead to violent expansion. Settings and Configuration Core Settings - Use EWMA: When checked (Default), uses the recursive GARCH-style calculation. If unchecked, it reverts to a simple SMA of variance, which is less sensitive to recent shocks but more stable. - Log Returns: Uses natural log for calculations. Highly recommended for assets with exponential growth or large volume ranges. - Length: The baseline period for the calculation. - Threshold Lookback: The number of bars used to calculate the Mean and Standard Deviation bands. - EWMA Lambda: The decay factor (0.0 to 1.0). A value of 0.94 is standard for risk metrics. -- Higher Lambda (e.g., 0.98): The indicator reacts slower and is smoother (long memory). -- Lower Lambda (e.g., 0.80): The indicator reacts very fast to new data (short memory). Visuals - Show Thresholds: Toggles the visibility of the statistical bands on the chart. - High Band (StdDev): The multiplier for the upper warning zone. Default is 1.5 deviations. Increasing this to 2.0 or 3.0 will filter for only the most extreme events. Disclaimer This tool is for educational and technical analysis purposes only. Breakouts can fail (fake-outs), and past geometric patterns do not guarantee future price action. Always manage risk and use this tool in conjunction with other forms of analysis.

Volatility % (Standard Deviation of Returns)This script takes closing prices of candles to measure the Standard Deviation (σ) which is then used to calculate the volatility by taking the stdev of the last 30 candles and multiplying it by the root of the trading days in a year, month and week. It then multiplies that number by 100 to show a percentage. Default settings are annual volatility (252 candles, red), monthly volatility (30 candles, blue) and weekly volatility (5 candles, green) if you use daily candles. It is open source so you can increase the number of candles with which the stdev is calculated, and change the number of the root that multiplies the stdev.

Volatility patterns / quantifytools - Overview Volatility patterns detect various forms of indecisive price action, on a larger scale as a compressed range and on a smaller scale as indecision candles. Indecisive and volatility suppressing price action can be thought of as a spring being pressed down. The more suppression, the more tension is built and eventually released as a spike or series of spikes in volatility. Each volatility pattern is assigned an influence period, during which average and peak relative volatility is recorded and stored to volatility metrics. - Patterns The following scenarios are qualified as indecision candles: inside candles, indecision engulfing candles and volatility shifts. By default, each indecision candle is considered a valid pattern only when another indecision candle has taken place within 3 periods, e.g. prior inside candle + indecision engulfing candle = valid volatility pattern. This measurement is taken to filter noise by looking for multiple hints of pending volatility, rather than just one. Level of tolerated noise can be changed via input menu by using sensitivity setting, by default set to 2. Sensitivity at 1: Any single indecision candle is considered a valid pattern Sensitivity at 2: 2 indecision candles within 3 bars is considered a valid pattern Sensitivity at 3: 2 indecision candles within 2 bars (consecutive) is considered a valid pattern The following scenarios are qualified as range patterns: series of lower highs/higher lows and series of low volatility pivots. A pivot is defined by highest/lowest point in price, by default within 2 periods back and 2 periods forward. When 4 pivots with qualities mentioned above are found, a box indicating compressed range will appear. Both required pivots and pivot definition can be adjusted via input menu. - Influence time and metrics By default, influence time for each volatility pattern is set to 6 candles, a period for which spike(s) in volatility is expected. For each influence period, average relative volatility (volatility relative to volatility SMA 20) and peak relative volatility is recorded and stored to volatility metrics. All metrics used in calculations are visible in "Data Window "tab. Average and peak volatility during influence period will vary depending on chart, timeframe and chosen settings. Tweaking the settings might result in an improvement and is worth experimenting with. - Visuals By default, indecision candles are visualized as yellow lines and range patterns as orange boxes. Influence time periods are respectively visualized as colored candle borders, applied as long as influence time period is active. All colors are fully customizable via input menu. - Practical guide Volatility patterns depict moments of equal strength from both bulls and bears. While this equilibrium is in place, price is stagnant and compresses until either side initiates volatility, releasing the built up tension. On top of hedging and playing the volatility using volatility based instruments, some other methods can be applied to take advantage of the somewhat tricky areas of indecision. Example #1: Trading volatility Volatility is not a bad thing from a trading perspective, but can actually be fertile ground for executing trade setups. Trading volatility influence periods from higher timeframes on lower timeframes gives greater resolution to work with and opportunities to take advantage of the wild swings created. Example #2: Finding bias for patterns Points of confluence where it anyway makes sense to favor one side over the other can be used for establishing bias for indecisive price action as well. At face value, it makes sense to expect bearish reactions at range highs and bullish reactions at range low, for which volatility patterns can provide a catalyst. Example #3: Betting on initiation direction Betting on direction of the first volatile move can easily go against you, but if risk/reward is able to compensate for the poor win rate, it's a valid idea to consider and explore.

Volume Breakout (ValueRay)Easy visuals on, if volume is way over average. Good for Mean Reverting. Higher Volume tends to higher breakout chances. Please whisper me for for ideas how to make this better. Its a very simple script, but got some alpha. If you know how to improve, let me know and i will code it into.

Mid to High daily % - MA & Threshold Purpose of this script is to provide a metric for comparing crypto volatility in terms of the % gain that can be garnished if you buy the midpoint price of the day and sell the high***. I'm specifically using bots that buy non-stop. This metric makes it easy to compare crypto coins while also providing insight on what a take profit % should be if I want to be sure it closes often instead of getting stuck in a position. Added a few moving averages of (Mid-range to High Daily %). When these lines starts to trend down, it's time to lower the take profit % or move on to the next coin. Decided to add a threshold so I could easily mark where I think the (Mid-range to High Daily %) is for most days. Ex. I can mark 10% threshold and can eyeball roughly ~75% of the days in the past month or so were at or above that level. Then I know I have plenty volatility for a bot taking 5% profit. Also if you have plenty of periodic poke-through that month (let's say once a week) you might argue that you can set it to 7% if you're willing to wait about that long. Either way this metric is conservative because it is only the middle of the range to the high, a less conservative version might provide the % gain if you bought the day low and sold the day high. ***Since this calculation only takes the middle of the range and the high of the day into account, red days are volatile against a buyer but to your advantage if you are a seller. BUT if you have plenty of safety buy orders this volatility in price only means your total purchase volume increases and when/if you reach a take profit level you sell more there. Would like to upgrade and add a separate MA line for green days and a separate MA line for red days to discern if that particular coin has a bias. Also would like to include some statistics on how many candles are above or below threshold for a certain period instead of eyeballing.

Average True Range Shift This indicator builds on the idea of the Average True Range (ATR) as a way of measuring volatility. It uses two different ATRs to show a shift in market volatility. It is mainly composed of two moving averages of ATR. One fast moving, which looks back at the previous 5 periods. One slow moving, which looks back at the previous 21 periods. Both ATRs have been normalized (show percentage instead of an absolute amount). The third component of this indicator is the histogram that is created by subtracting the slow moving average, from the fast moving average. By having two ATRs of different lengths, traders can see how short term volatility compares to long term volatility, and how it is shifting over time. When the fast-moving crosses above the slow-moving, it will show a positive value on the histogram, meaning that short term volatility is increasing and higher than normal. When it crosses below, it will show a negative value on the histogram, meaning that short term volatility is decreasing, and lower than normal. There are a variety of ways to utilize this indicator, and it will work in most markets. I find it is best to analyze macro market conditions on daily charts and above, rather than micro intraday moves.