DJT Strategy - The Art of the DipWHAT THIS IS
A satirical — but mechanically honest — volatility-event mean-reversion strategy for index charts (ES, SPX, SPY, NQ...). It trades one hypothesis, known to Wall Street as the TACO trade ("Trump Always Chickens Out"): when a policy Announcement detonates the VIX, the sell-off is usually walked back within days — a "90-day pause," a "very productive call," a clarification that the tariffs apply primarily to penguins. The dip, having been artisanally manufactured, is bought.
The jokes are in the labels. The engine underneath is a real VIX-spike fade with staged exits, and every decision is lookahead-clean.
HOW IT WORKS — ENTRY (Chaos Detection)
• Covfefe Threshold — VIX trades ≥ 12% (default) above YESTERDAY'S CONFIRMED daily close. Not today's repainting value — yesterday's close is final data the moment today begins.
• Flash Tantrum — fast intraday VIX rate-of-change (default 8% over 6 bars) to catch the 2:37 PM post that ends four decades of trade policy in under 280 characters.
• Minimum VIX floor (default 18) — below this the market is not scared, it is merely golfing.
• Vol-curve confirmation (default ON) — requires VIX9D > VIX. Genuine event panic inverts the front of the volatility curve: 9-day vol pricing above 30-day is the fingerprint of a real scare. If the curve isn't inverted, even the market doesn't believe the post, and the signal is skipped. This is the filter that separates an actual tantrum from a slow-drift vol day.
When everything aligns, the strategy goes long the chart symbol at the next bar open.
HOW IT WORKS — EXITS (the TACO Protocol, staged like the walk-back itself)
• GREAT CALL — the first reassuring headline: VIX Δ falls back under 8% (default) → take half the position off.
• CONCEPTS OF A PLAN — VIX reverts to within 4% of yesterday's close: a Framework of a Concept of a Deal has been reached → close the rest.
• YOU'RE FIRED — fixed stop loss (default 1.5%). Sometimes he does not, in fact, chicken out. This is the apology budget.
• DECLARE VICTORY — fixed profit target (default 2.5%). Exit into strength and take credit for the bounce you predicted after it happened.
• NEWS CYCLE EXPIRY — time stop (default 78 bars ≈ one full RTH session on 5m). After one news cycle, a newer, more beautiful crisis replaces this one and the edge is gone.
EXTRAS
• Escalation sizing — at FULL COVFEFE (2× the spike threshold) the position gets the BIGLY multiplier. Peak fear is peak walk-back probability. This is either alpha or a margin call; many people are saying both.
• Post-trade cooldown so a single escalating tweetstorm can't chain entries.
• RTH-only entries (default ON), plus an optional "Prime Posting Hours" filter (cable-news breakfast block + post-lunch Executive Time).
• "Believe Me" mode (default OFF) — experimental fade of VIX-crush euphoria, for days when everyone believes The Deal is real this time.
• ♟️ 4D CHESS MODE — reverses every signal, for users who believe there is, in fact, a plan. Exits are direction-aware, so the joke is mechanically sound. If this mode outperforms, please tell no one.
• CHAOS-O-METER™ dashboard — live chaos grade from 🏌️ GOLFING to 🚨 FULL COVFEFE, vol-curve status, a factory-floor "DAYS SINCE LAST TANTRUM" safety sign (resets constantly, as is tradition), deal accounting (Deals made / Fake news / Deals honored: TBD), net P&L denominated in $TRUMP at a peg of your choosing, and a Sharpe ratio readout that is simply THE BEST RATIO.
• 🗽 Liberation Day (April 2) is marked annually — heightened tantrum risk, observed like a holiday, because it is one now.
NO REPAINTING / NO LOOKAHEAD
The reference VIX level is yesterday's confirmed daily close, pulled with the standard non-repainting pattern (close of the daily feed with lookahead on — final the moment today starts). Signals are evaluated on confirmed chart-timeframe bars and orders fill at the next bar open. Everything the strategy decides is knowable at decision time — which is more than can be said for the policy it trades.
HOW TO USE
• Chart: an index or index future (ES1!, SPX, SPY, NQ1!...). Intraday timeframes; defaults tuned around 5m.
• Both vol symbols are inputs — swap in VXN for NQ, or your regional vol index pair for non-US indexes.
• Strategy properties: $1,000,000 initial capital (sized for index futures notional), 2 contracts per trade by default (so the half-off scale-out has a half; at the default 1.5% stop on ES this risks roughly 1% of equity per trade), $4.50/contract commission, 1 tick slippage. margin_long/margin_short are explicitly 0 — Pine v6's default of 100 silently rejects futures entries whose notional exceeds capital; if you fork this for leveraged instruments, keep that line.
DISCLAIMER
This is satire with a working strategy attached, published for education and entertainment. The VIX spikes are, regrettably, real; the edge may not be. Backtest results on manufactured dips do not guarantee future walk-backs. Not financial advice — frankly, it barely qualifies as advice.
Strategy
Indicator
Parkinson Range Oscillator [BackQuant]Parkinson Range Oscillator
Overview
Parkinson Range Oscillator is a volatility regime indicator built around the Parkinson volatility estimator , a high-low based variance model originally proposed as a more statistically efficient alternative to close-to-close volatility. Instead of measuring volatility from closing returns, this script measures volatility from the intrabar price range using ln(H/L), then converts it into a normalized oscillator (z-score) so you can identify volatility expansion vs compression relative to the asset’s own history.
The indicator is designed to answer questions like:
Is volatility currently elevated or suppressed relative to its baseline?
Is volatility expanding (risk rising) or compressing (coiling)?
How extreme is the current vol state in percentile terms?
How does range-based vol compare to a more common ATR-based vol read?
It plots:
A Parkinson-based volatility z-score oscillator with gradient fills.
A signal line (EMA) for expansion/compression transitions.
An ATR-based z-score for context comparison.
A dashboard with current vol %, z-score, percentile rank, regime label, and ATR z-score.
Where Parkinson volatility comes from (origin and intuition)
The Parkinson estimator comes from academic finance and the study of volatility estimation. The key insight is simple:
The daily high and low contain more information about variability than the close alone.
Close-to-close volatility only uses one price per bar (the close), throwing away intrabar information. The high-low range captures the realized dispersion inside the bar, so under ideal assumptions it can estimate variance more efficiently.
The Parkinson model is derived assuming:
Price follows a continuous-time diffusion process (often framed like geometric Brownian motion).
No drift matters for the variance estimate over the interval.
No jumps and no microstructure distortions (idealized).
Even though real markets violate these assumptions (gaps, jumps, wicks from order flow), the estimator remains useful because:
Range is still a strong proxy for realized volatility.
It reacts to intrabar expansion earlier than close-based methods.
It is less dependent on where the bar closes.
Core Parkinson formula (what the script implements)
Parkinson variance for a window of n bars is:
Var = (1 / (4 * n * ln(2))) * Σ
This script computes it in the common rolling form:
logHL2 = (ln(high/low))²
parkVar = SMA(logHL2, n) / (4 * ln(2))
parkVol = sqrt(parkVar) * 100
Key details:
ln(H/L) makes the range scale-invariant (percent-like), so it behaves more consistently across price levels.
Squaring gives variance contribution.
The 1/(4 ln 2) constant comes from the expected distribution of high-low range under a Brownian diffusion.
sqrt converts variance to standard deviation (volatility).
*100 expresses it as a percentage for readability.
So parkVol is a “range-based realized volatility proxy” in percent terms.
Why range-based volatility behaves differently than ATR
ATR measures average true range, which is a linear range magnitude measure (high-low plus gaps). Parkinson uses ln(H/L) which is:
Log-scaled (closer to a return-based measure).
More directly tied to variance estimation theory.
In practice:
ATR can be driven by gaps and absolute range.
Parkinson is driven by proportional range and tends to emphasize how wide the bar is relative to its price level.
Parkinson often reacts sharply when wicks expand even if closes are stable.
Normalization into an oscillator (making it comparable through time)
Raw volatility values are hard to interpret across regimes because every market has different “normal.” This script normalizes Parkinson volatility against its own rolling baseline using a z-score:
parkMA = SMA(parkVol, baselineLen)
parkSD = stdev(parkVol, baselineLen)
osc = (parkVol - parkMA) / parkSD
Interpretation:
osc = 0 means current vol is at its baseline average.
osc = +1 means 1 standard deviation above normal (high vol).
osc = -1 means 1 standard deviation below normal (compressed).
osc > +2 flags extreme expansion states.
This is the core output. It turns “volatility” into “volatility regime” in standardized units.
Signal line and expansion/compression transitions
The oscillator is smoothed with an EMA to create a signal line:
signal = EMA(osc, signalLen)
Then transitions are defined as:
Expansion cross: crossover(osc, signal) and osc > 0
Compression cross: crossunder(osc, signal) and osc < 0
Why the extra osc > 0 and osc < 0 conditions:
It prevents treating small oscillations around zero as meaningful.
It forces expansion signals to occur in above-average volatility territory.
It forces compression signals to occur in below-average volatility territory.
So signals are regime-confirming, not constant cross spam.
Percentile rank (how extreme is vol relative to the past)
In addition to the z-score, the script computes the percentile rank of the raw Parkinson volatility:
pctRank = percentrank(parkVol, pctRankLookback)
Interpretation:
pctRank near 90–100 means current vol is among the highest levels seen in that lookback.
pctRank near 0–10 means it is among the lowest (compression).
Z-score tells you “how many SDs from mean.” Percentile tells you “how rare is this state historically.” Those are different but complementary.
ATR comparison line (context, not the main engine)
The indicator also computes an ATR-based volatility proxy and normalizes it in the same way:
atrVol = ATR(n) / close * 100
atrOsc = zscore(atrVol, baselineLen)
This gives you a direct visual comparison:
If Parkinson oscillator is high but ATR oscillator isn’t, range expansion may be happening in a way ATR is not emphasizing (or vice versa).
If both agree, you have stronger confirmation of a true volatility regime shift.
ATR is included as a “common benchmark,” not as the primary signal.
Regime classification (human-readable state mapping)
The script labels regimes from osc:
osc > 2.0 → EXTREME
osc > 1.0 → HIGH
osc > 0.0 → ABOVE AVG
osc > -1.0 → BELOW AVG
else → COMPRESSED
This is a practical mapping for dashboards and quick reads. It is not pretending that 2.0 is a universal constant, it is just a standardized “rare expansion” threshold.
Coloring follows the same logic:
More positive = more “expansion” coloring (bearCol).
More negative = more “compression” coloring (bullCol).
Note: the color naming is semantic here:
“Low Vol / Compression” is bullCol because compression often precedes trend expansion opportunities.
“High Vol / Expansion” is bearCol because high vol often implies risk, disorder, liquidation, or unstable conditions.
You can interpret those however you prefer, the tool is measuring volatility regime, not directional bias.
Plot design (why the oscillator is split into positive/negative)
The oscillator is split into two series:
oscPos = osc if osc > 0 else na
oscNeg = osc if osc < 0 else na
This is purely for visuals:
Positive region is drawn with expansion color and expansion gradient fill to zero.
Negative region is drawn with compression color and compression gradient fill to zero.
This makes it obvious at a glance which side of “normal volatility” you’re on.
How to interpret the indicator correctly
1) The oscillator is volatility regime, not price direction
High osc does not mean price will go down. It means the market is moving violently relative to its baseline. That can occur in:
Selloffs, liquidations, panic.
Breakouts and momentum expansions.
News-driven repricing.
Low osc does not mean price will go up. It means the market is quiet relative to baseline:
Ranges, coils, low realized movement.
Slow grind trends with suppressed pullbacks.
Pre-breakout compressions.
2) Compression regimes are often “setup states”
When osc is deeply negative (compressed), it often indicates that realized movement has collapsed. In many markets this precedes:
Breakouts (vol expansion from compression).
Trend acceleration.
Mean reversion bursts.
But compression can also persist. This is why the script includes signal crosses and percentile rank to judge when compression is shifting.
3) Expansion regimes are often “risk states”
When osc is positive and rising, the environment is more chaotic:
Stops are more likely to be hit.
Mean reversion can get violent.
Trend continuation can be strong but timing becomes harder.
In those regimes, the tool can be used to:
Reduce leverage.
Widen stops (if your system supports it).
Switch to volatility-aware sizing.
Wait for stabilization if you trade mean reversion.
4) Use percentile rank to identify “rare” volatility
Two markets can both show osc = +1, but one might be at the 95th percentile and the other at the 70th depending on distribution shape. Percentile tells you whether the current vol is truly rare in that lookback.
Cross dots (how to treat them)
ExpansionCross and CompressionCross are not buy/sell signals. They are “volatility phase change” markers:
ExpansionCross: vol regime moving up, above baseline, acceleration risk increases.
CompressionCross: vol regime moving down, below baseline, quieting environment.
These are useful for:
Strategy toggles (trend mode vs chop mode).
Sizing changes.
Timing filters (avoid entries during extreme expansion if your edge hates noise).
Dashboard (what it gives you at a glance)
The table summarizes everything that matters without you needing to interpret plots manually:
Parkinson Vol %: current raw range-based volatility level.
Z-Score: current standardized regime reading.
Percentile: rarity of current vol in the lookback.
Regime: discrete label based on z-score thresholds.
ATR Z-Score: comparison metric in standardized units.
The dashboard is positioned and sized via inputs so it can fit different chart layouts.
Parameter tuning guidance
Parkinson Length
Controls how quickly the raw Parkinson vol responds:
Shorter = more reactive to immediate range changes.
Longer = smoother volatility estimate, less noisy.
Baseline Length
Controls what “normal” means:
Long baseline (like 100) creates stable regime definitions.
Short baseline makes z-scores jump around and can overreact.
Signal Length
Controls how quickly you detect regime turning points:
Short signal = more crosses, earlier detection, more noise.
Long signal = fewer crosses, later detection, cleaner regime shifts.
Percentile Lookback
Controls rarity context:
252 approximates one trading year on daily charts.
On intraday, it becomes “252 bars,” so adjust to match your horizon.
Limitations and what to watch for
Parkinson assumes continuous diffusion. Jumps and gaps can distort it.
Wicks caused by illiquidity can inflate ln(H/L) and produce false “expansion.”
Z-score assumes the baseline distribution is reasonably stable. If volatility distribution shifts structurally, your z-scores can be biased until baseline catches up.
Percentile rank is lookback-dependent. Different lookbacks can change “rarity” classification materially.
Summary
Parkinson Range Oscillator converts a statistically grounded high-low volatility estimator into a regime oscillator by z-scoring Parkinson volatility against its own rolling baseline. It highlights expansion vs compression states with clear gradients, flags volatility phase changes via oscillator-signal crosses, ranks current volatility by percentile for rarity context, and overlays an ATR-based z-score for comparison. This makes it a practical tool for volatility-aware trading, regime filtering, sizing adjustments, and identifying compression-to-expansion transitions. Indicator
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. Indicator
Indicator
Volatility patterns / Flowly Indicators- 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.
Indicator
Volatility Risk Premium (VRP) 1.0ENGLISH
This indicator (V-R-P) calculates the (one month) Volatility Risk Premium for S&P500 and Nasdaq-100.
V-R-P is the premium hedgers pay for over Realized Volatility for S&P500 and Nasdaq-100 index options.
The premium stems from hedgers paying to insure their portfolios, and manifests itself in the differential between the price at which options are sold (Implied Volatility) and the volatility the S&P500 and Nasdaq-100 ultimately realize (Realized Volatility).
I am using 30-day Implied Volatility (IV) and 21-day Realized Volatility (HV) as the basis for my calculation, as one month of IV is based on 30 calendaristic days and one month of HV is based on 21 trading days.
At first, the indicator appears blank and a label instructs you to choose which index you want the V-R-P to plot on the chart. Use the indicator settings (the sprocket) to choose one of the indices (or both).
Together with the V-R-P line, the indicator will show its one year moving average within a range of +/- 15% (which you can change) for benchmarking purposes. We should consider this range the “normalized” V-R-P for the actual period.
The Zero Line is also marked on the indicator.
Interpretation
When V-R-P is within the “normalized” range, … well... volatility and uncertainty, as it’s seen by the option market, is “normal”. We have a “premium” of volatility which should be considered normal.
When V-R-P is above the “normalized” range, the volatility premium is high. This means that investors are willing to pay more for options because they see an increasing uncertainty in markets.
When V-R-P is below the “normalized” range but positive (above the Zero line), the premium investors are willing to pay for risk is low, meaning they see decreasing uncertainty and risks in the market, but not by much.
When V-R-P is negative (below the Zero line), we have COMPLACENCY. This means investors see upcoming risk as being lower than what happened in the market in the recent past (within the last 30 days).
CONCEPTS:
Volatility Risk Premium
The volatility risk premium (V-R-P) is the notion that implied volatility (IV) tends to be higher than realized volatility (HV) as market participants tend to overestimate the likelihood of a significant market crash.
This overestimation may account for an increase in demand for options as protection against an equity portfolio. Basically, this heightened perception of risk may lead to a higher willingness to pay for these options to hedge a portfolio.
In other words, investors are willing to pay a premium for options to have protection against significant market crashes even if statistically the probability of these crashes is lesser or even negligible.
Therefore, the tendency of implied volatility is to be higher than realized volatility, thus V-R-P being positive.
Realized/Historical Volatility
Historical Volatility (HV) is the statistical measure of the dispersion of returns for an index over a given period of time.
Historical volatility is a well-known concept in finance, but there is confusion in how exactly it is calculated. Different sources may use slightly different historical volatility formulas.
For calculating Historical Volatility I am using the most common approach: annualized standard deviation of logarithmic returns, based on daily closing prices.
Implied Volatility
Implied Volatility (IV) is the market's forecast of a likely movement in the price of the index and it is expressed annualized, using percentages and standard deviations over a specified time horizon (usually 30 days).
IV is used to price options contracts where high implied volatility results in options with higher premiums and vice versa. Also, options supply and demand and time value are major determining factors for calculating Implied Volatility.
Implied Volatility usually increases in bearish markets and decreases when the market is bullish.
For determining S&P500 and Nasdaq-100 implied volatility I used their volatility indices: VIX and VXN (30-day IV) provided by CBOE.
Warning
Please be aware that because CBOE doesn’t provide real-time data in Tradingview, my V-R-P calculation is also delayed, so you shouldn’t use it in the first 15 minutes after the opening.
This indicator is calibrated for a daily time frame.
ESPAŇOL
Este indicador (V-R-P) calcula la Prima de Riesgo de Volatilidad (de un mes) para S&P500 y Nasdaq-100.
V-R-P es la prima que pagan los hedgers sobre la Volatilidad Realizada para las opciones de los índices S&P500 y Nasdaq-100.
La prima proviene de los hedgers que pagan para asegurar sus carteras y se manifiesta en el diferencial entre el precio al que se venden las opciones (Volatilidad Implícita) y la volatilidad que finalmente se realiza en el S&P500 y el Nasdaq-100 (Volatilidad Realizada).
Estoy utilizando la Volatilidad Implícita (IV) de 30 días y la Volatilidad Realizada (HV) de 21 días como base para mi cálculo, ya que un mes de IV se basa en 30 días calendario y un mes de HV se basa en 21 días de negociación.
Al principio, el indicador aparece en blanco y una etiqueta le indica que elija qué índice desea que el V-R-P represente en el gráfico. Use la configuración del indicador (la rueda dentada) para elegir uno de los índices (o ambos).
Junto con la línea V-R-P, el indicador mostrará su promedio móvil de un año dentro de un rango de +/- 15% (que puede cambiar) con fines de evaluación comparativa. Deberíamos considerar este rango como el V-R-P "normalizado" para el período real.
La línea Cero también está marcada en el indicador.
Interpretación
Cuando el V-R-P está dentro del rango "normalizado",... bueno... la volatilidad y la incertidumbre, como las ve el mercado de opciones, es "normal". Tenemos una “prima” de volatilidad que debería considerarse normal.
Cuando V-R-P está por encima del rango "normalizado", la prima de volatilidad es alta. Esto significa que los inversores están dispuestos a pagar más por las opciones porque ven una creciente incertidumbre en los mercados.
Cuando el V-R-P está por debajo del rango "normalizado" pero es positivo (por encima de la línea Cero), la prima que los inversores están dispuestos a pagar por el riesgo es baja, lo que significa que ven una disminución, pero no pronunciada, de la incertidumbre y los riesgos en el mercado.
Cuando V-R-P es negativo (por debajo de la línea Cero), tenemos COMPLACENCIA. Esto significa que los inversores ven el riesgo próximo como menor que lo que sucedió en el mercado en el pasado reciente (en los últimos 30 días).
CONCEPTOS:
Prima de Riesgo de Volatilidad
La Prima de Riesgo de Volatilidad (V-R-P) es la noción de que la Volatilidad Implícita (IV) tiende a ser más alta que la Volatilidad Realizada (HV) ya que los participantes del mercado tienden a sobrestimar la probabilidad de una caída significativa del mercado.
Esta sobreestimación puede explicar un aumento en la demanda de opciones como protección contra una cartera de acciones. Básicamente, esta mayor percepción de riesgo puede conducir a una mayor disposición a pagar por estas opciones para cubrir una cartera.
En otras palabras, los inversores están dispuestos a pagar una prima por las opciones para tener protección contra caídas significativas del mercado, incluso si estadísticamente la probabilidad de estas caídas es menor o insignificante.
Por lo tanto, la tendencia de la Volatilidad Implícita es de ser mayor que la Volatilidad Realizada, por lo cual el V-R-P es positivo.
Volatilidad Realizada/Histórica
La Volatilidad Histórica (HV) es la medida estadística de la dispersión de los rendimientos de un índice durante un período de tiempo determinado.
La Volatilidad Histórica es un concepto bien conocido en finanzas, pero existe confusión sobre cómo se calcula exactamente. Varias fuentes pueden usar fórmulas de Volatilidad Histórica ligeramente diferentes.
Para calcular la Volatilidad Histórica, utilicé el enfoque más común: desviación estándar anualizada de rendimientos logarítmicos, basada en los precios de cierre diarios.
Volatilidad Implícita
La Volatilidad Implícita (IV) es la previsión del mercado de un posible movimiento en el precio del índice y se expresa anualizada, utilizando porcentajes y desviaciones estándar en un horizonte de tiempo específico (generalmente 30 días).
IV se utiliza para cotizar contratos de opciones donde la alta Volatilidad Implícita da como resultado opciones con primas más altas y viceversa. Además, la oferta y la demanda de opciones y el valor temporal son factores determinantes importantes para calcular la Volatilidad Implícita.
La Volatilidad Implícita generalmente aumenta en los mercados bajistas y disminuye cuando el mercado es alcista.
Para determinar la Volatilidad Implícita de S&P500 y Nasdaq-100 utilicé sus índices de volatilidad: VIX y VXN (30 días IV) proporcionados por CBOE.
Precaución
Tenga en cuenta que debido a que CBOE no proporciona datos en tiempo real en Tradingview, mi cálculo de V-R-P también se retrasa, y por este motivo no se recomienda usar en los primeros 15 minutos desde la apertura.
Este indicador está calibrado para un marco de tiempo diario.
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Average True Range ShiftThis 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. Indicator
