True Range Breakout [racer8]TRB (True Range Breakout) plots the current TR (true range) as well as the previous TR high over n periods. If the current TR is greater than the previous TR high, then the TR histogram will become red. Red signals high volatility. Enter trades only when the histogram is above the TR high line. Happy trading! 🥳
在腳本中搜尋"Volatility"
AVWTR (Average Volume Weighted True Range)A better tool to measure market volatility. The true ranges are weighted by the volume and averaged through the specified period.
AlignedMA and Cumulative HighLow Strategy V2Based on earlier strategy published - AlignedMA and Cumulative HighLow Strategy. Adjustments are done in entry and exit criteria to make it work for shares.
Modified to preserve existing entry criteria + additional MA shift condition. Exit criteria is set based on supertrend and trailing stops.
Most of the parameters are already optimized. You only need to alter SupertrendMult for individual shares based on individual share volatility. Usually works within 2-4.
There might be bit of repainting. I am unable to understand if there is any. Any suggestions on further improvements welcome :)
Note to moderators : I have used 1000 as initial capital with 100% on each trade. As strategy does not compound - I believe this is reasonable. I have kept this setting as this makes it easier to compare with buy and hold return.
Separated ATR - evoThis script plots two ATR (Average True Range) values, one based on only bullish and the other based on only bearish bars. If the current bar is positive, the negative ATR will use its last known negative bar for the calculation. You can smooth bar directions by using the Heikin Ashi setting.
Use this the same way how you would use the regular ATR indicator, but with the added value of knowing which side of the market has more volatility.
Strategy - Bobo PAPATRHi I've revamped this bot mentioned in the linked idea to make it work with v4 of pine. In doing so there are some very significant changes to how it works. The main one is that it no longer uses traditional daily pivot calculations to calculate the bands. It creates a more dynamic intraday set of pivot points based on recent price action rather than yesterday's ohlc. As published, the bot is tuned for a 15 min time frame. But it actually works well on lower time frames you just need to adjust the lookback periods in settings a bit to re tune it. It's also tuned to ES really but will need tweaking for a different instrument at the very least.
The basic concept is recent price action is used to calculate a 'middle' around which red and green bands are located. Their position or width is largely determined by recent volatility. The middle line is again calculated from recent price action. The three lines from that form a tradeable range with green at the top and red at the bottom. The strategy is simple enough, it shorts as it sinks from outside red, and longs when rising above green. The basic principle being that once you enter that range you have a high probability of hitting the middle before you hit your stop loss. So the basic principle is you are trying to capture the inherent ranginess of liquid indices like S&P 500. That back and forth movement that happens. The bot is capturing this by fading extremes of a recent range but the problem with that is you'dd get murdered in a strong trend. To mitigate that there is a trend calculation running in the background the will prevent trading against firm trends mostly. So the bot should trade mostly in rangy conditions because that is what it is trying to do.
Bot will close issue close signals automatically upon crossing the middle, it also will close automatically at predefined stops or limits. These values are denominated in market mintick values. For example the CFD SPX500 has a mintick of 0.1. Therefore a stop value of 100 will equate to 10 points on the index. If trading the same market via ES1! the mintick value is different - 0.25. So in this case a value of 40 is required to set the stop at 10 points.
Anyway shout if you have questions. Hope it's useful.
TVC:SPX OANDA:SPX500USD
SuperTrendSuperTrend is one of the most common ATR based trailing stop indicators.
In this version you can change the ATR calculation method from the settings. Default method is RMA, when the alternative method is SMA.
The indicator is easy to use and gives an accurate reading about an ongoing trend. It is constructed with two parameters, namely period and multiplier. The default values used while constructing a superindicator are 10 for average true range or trading period and three for its multiplier.
The average true range (ATR) plays an important role in 'Supertrend' as the indicator uses ATR to calculate its value. The ATR indicator signals the degree of price volatility.
The buy and sell signals are generated when the indicator starts plotting either on top of the closing price or below the closing price. A buy signal is generated when the ‘Supertrend’ closes above the price and a sell signal is generated when it closes below the closing price.
It also suggests that the trend is shifting from descending mode to ascending mode. Contrary to this, when a ‘Supertrend’ closes above the price, it generates a sell signal as the colour of the indicator changes into red.
A ‘Supertrend’ indicator can be used on equities, futures or forex, or even crypto markets and also on daily, weekly and hourly charts as well, but generally, it fails in a sideways-moving market.
I had converted Supertrend indicator code for various platforms like Metastock in 2017, but in this TradingView version special credit goes to everget - Alex Orekhov which gave a great inspiration to look my indicators better with highlights, signals and alarms. Thank you Alex.
(Poshtrader) Bollinger Band SqueezeThe Bollinger Band Squeeze is a trading strategy designed to find consolidations with decreasing volatility. In its simplest form, this strategy is neutral and the ensuing break can be up or down. Traders, therefore, must employ other aspects of technical analysis to formulate a trading bias to act before the break or confirm the break. Acting before the break will improve the risk-reward ratio.
Adaptive Pivot (HLC3)SUMMARY:
Standard Pivot (HLC3) with ATR leeway added to make it adaptive to market volatility.
DESCRIPTION:
Adaptive Pivot is an indicator utilizing the simplicity of HLC3 Pivots as a turning point (and sometimes a trend indicator) while addressing it's fixed and inflexible nature.
Because the indicator is just a single line in the chart, the price may go near it but never touch it. Or it can go pass through it and never retest it again. In an attempt to lessen these from occurring, we can combine pivots with average true range (ATR). This is the specific formula I applied in this indicator:
>Upper Pivot = HLC3 + ATR
>Lower Pivot = HLC3 - ATR
This creates a kind of a range or cloud around the Pivot, making it possibly a more accurate indicator for market turning points.
ADJUSTABLE PARAMETERS:
The usual ATR parameters are included in this indicator:
>ATR_Length = input(14, title="ATR Length", minval=1)
>ATR_Smoothing = input(title="ATR Smoothing", defval="RMA", options="RMA", "SMA", "EMA", "WMA")
Added to the usual ones is this:
>ATR_Multiplier = input(1, title="ATR Multiplier", minval=0.1)
which modifies the extent of the ATR (similar to Chandelier Exit) as it is added/subtracted from the pivot values.
Pivot’s timeframe is also adjustable:
>Pivot_Timeframe = input("3M", title='Pivot Resolution')
Note: I did not lock the type to input.resolution to allow for more possible timeframes.
OTHER PARAMETERS
Indicator color will change to green when the open is above the HLC3 Pivot and change to red when the reverse is true.
Dynamically Adjustable Moving AverageIntroduction
The Dynamically Adjustable Moving Average (AMA) is an adaptive moving average proposed by Jacinta Chan Phooi M’ng (1) originally provided to forecast Asian Tiger's futures markets. AMA adjust to market condition in order to avoid whipsaw trades as well as entering the trending market earlier. This moving average showed better results than classical methods (SMA20, EMA20, MAC, MACD, KAMA, OptSMA) using a classical crossover/under strategy in Asian Tiger's futures from 2014 to 2015.
Dynamically Adjustable Moving Average
AMA adjust to market condition using a non-exponential method, which in itself is not common, AMA is described as follow :
1/v * sum(close,v)
where v = σ/√σ
σ is the price standard deviation.
v is defined as the Efficacy Ratio (not be confounded with the Efficiency Ratio) . As you can see v determine the moving average period, you could resume the formula in pine with sma(close,v) but in pine its not possible to use the function sma with variables for length, however you can derive sma using cumulation.
sma ≈ d/length where d = c - c_length and c = cum(close)
So a moving average can be expressed as the difference of the cumulated price by the cumulated price length period back, this difference is then divided by length. The length period of the indicator should be short since rounded version of v tend to become less variables thus providing less adaptive results.
AMA in Forex Market
In 2014/2015 Major Forex currencies where more persistent than Asian Tiger's Futures (2) , also most traded currency pairs tend to have a strong long-term positive autocorrelation so AMA could have in theory provided good results if we only focus on the long term dependency. AMA has been tested with ASEAN-5 Currencies (3) and still showed good results, however forex is still a tricky market, also there is zero proof that switching to a long term moving average during ranging market avoid whipsaw trades (if you have a paper who prove it please pm me) .
Conclusion
An interesting indicator, however the idea behind it is far from being optimal, so far most adaptive methods tend to focus more in adapting themselves to market complexity than volatility. An interesting approach would have been to determine the validity of a signal by checking the efficacy ratio at time t . Backtesting could be a good way to see if the indicator is still performing well.
References
(1) J.C.P. M’ng, Dynamically adjustable moving average (AMA’) technical
analysis indicator to forecast Asian Tigers’ futures markets, Physica A (2018),
doi.org
(2) www.researchgate.net
(3) www.ncbi.nlm.nih.gov
DD ATR bandsThe top band is ATR added to candle high (with given length and multiplication). The bottom one is analogic.
Created for finding initial stop loss for entry on low timeframes. Use band value at last major high/low to place the stop loss at.
It shows prices with acceptable risk and a reasonable margin for market volatility.
Brooks Type Signal BarIndicates "strong bars" similar to how Al Brooks defines them in his book-- these don't necessarily trigger entries but can be points of interest.
2-3 points as a signal bar size seems to work well, depending upon volatility.
btcATR Bitfinex [csg]A simple script that uses the average true range indicator and compares it against bitcoin's market cap to obtain a visual representation of bitcoin's historical volatility.
A version of this indicator will be made for other coins once tradingview gives us access to the market cap data
Bollinger Band Percent Width Crossing RSIBollinger Band width is hard to follow on log charts, but it's an excellent indicator for volatility. When BBand % width crosses down through periods of low crosses down through periods where RSI is low, you may be able to count on a substantial reversal over the intermediate or long-term.
Directional Movement Bands [DW]This is a simple experimental study designed to outline trend activity and volatility.
In this study, the amount of change between current source and source of a specified lookback is calculated, then added to and subtracted from current source.
Next an exponential moving average is taken of the values for smoothing over the specified period.
Lastly, a midline is generated by taking the median of both bands.
Volatility Finite Volume Elements Strategy The FVE is a pure volume indicator. Unlike most of the other indicators
(except OBV), price change doesn?t come into the equation for the FVE
(price is not multiplied by volume), but is only used to determine whether
money is flowing in or out of the stock. This is contrary to the current trend
in the design of modern money flow indicators. The author decided against a
price-volume indicator for the following reasons:
- A pure volume indicator has more power to contradict.
- The number of buyers or sellers (which is assessed by volume) will be the same,
regardless of the price fluctuation.
- Price-volume indicators tend to spike excessively at breakouts or breakdowns.
This study is an addition to FVE indicator. Indicator plots different-coloured volume
bars depending on volatility.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not
Volatility Finite Volume Elements Strategy The FVE is a pure volume indicator. Unlike most of the other indicators
(except OBV), price change doesn?t come into the equation for the FVE
(price is not multiplied by volume), but is only used to determine whether
money is flowing in or out of the stock. This is contrary to the current trend
in the design of modern money flow indicators. The author decided against a
price-volume indicator for the following reasons:
- A pure volume indicator has more power to contradict.
- The number of buyers or sellers (which is assessed by volume) will be the same,
regardless of the price fluctuation.
- Price-volume indicators tend to spike excessively at breakouts or breakdowns.
This study is an addition to FVE indicator. Indicator plots different-coloured volume
bars depending on volatility.
Moving CO-covariance (covariance on covariance)This is Covariance on Covariance. It shows you how much a given covariance period has deviated from it mean over another defined period. Because it is a time series, It can allow you to spot changes in how covariance changes. You can apply trend lines, Fibonacci retracements, etc. This is also volume weighting covariance.
This is not a directional indicator nor is moving covariance. This is used for forecasting volatility. This must be used in conjunction with moving covariance.
[Volatility] [Gain & Loss] - OverviewFX:EURUSD
Indicator Overview: Volatility & Gain/Loss - Forex Pair Analysis
This indicator, " —Overview" , is designed for users interested in analyzing the volatility and gain/loss metrics of multiple forex pairs. The tool is especially useful for traders aiming to assess currency pair volatility alongside gain and loss percentages over selected periods. It enables a clearer understanding of pair behavior and aids in decision-making.
Key Features
Customizable Volatility and Gain/Loss Periods : Define your preferred calculation periods and timeframes for both volatility and gain/loss to tailor the indicator to specific trading strategies. Multi-Pair Analysis : This indicator supports up to six forex pairs (default pairs include EURUSD, GBPUSD, USDJPY, USDCHF, AUDUSD, and USDCAD) and allows you to adjust these pairs as needed. Visual Ranking : Forex pairs are sorted by volatility, displaying the highest pairs at the top for quick reference. Top Gain/Loss Highlighting : The pair with the maximum gain and the pair with the maximum loss are highlighted in the table, making it easy to identify the best and worst performers at a glance.
Indicator Settings
Volatility Settings : Period : Adjust the number of periods used in the ATR (Average True Range) calculation. A default period of 14 is set. Timeframe : Select a timeframe (e.g., Daily, Weekly) for volatility calculation to match your analysis preference.
Gain/Loss Settings : Period : Choose the number of periods for gain/loss calculation. The default is set to 1. Timeframe : Select the timeframe for gain/loss calculation, independent of the volatility timeframe.
Symbol Selection : Configure up to six forex pairs. By default, popular forex pairs are pre-loaded but can be customized to include other currency pairs.
Output and Visualization
Table Display : This indicator displays data in a neatly structured table positioned in the top-right corner of your chart. Columns : Includes columns for the Forex Pair, Volatility Percentage, Gain Percentage, and Loss Percentage. Color Coding : Volatility is displayed in a standard color for clear readability. Gain values are highlighted in green, and Loss values are highlighted in red, allowing for quick visual differentiation. Highlighting : Rows representing the pair with the highest gain and the pair with the most significant loss are especially highlighted for emphasis.
How to Use
Volatility Analysis : This metric gives insight into the average price range movements for each pair over the specified period and timeframe, helping you evaluate the potential for rapid price changes. Gain/Loss Tracking : Gain or loss percentages show the pair's recent performance, allowing you to observe whether a currency pair is trending positively or negatively over the chosen period. Comparative Pair Ranking : Use the table to identify pairs with the highest volatility and extremes in gain or loss to guide trading decisions based on market conditions.
Ideal For
Swing Traders and Day Traders looking to understand short-term market fluctuations in currency pairs. Risk Management : Helps traders gauge pairs with higher risk (volatility) and recent performance (gain/loss) for informed position sizing and risk control.
This indicator is a comprehensive tool for visualizing and analyzing key forex pairs, making it an essential addition for traders looking to stay updated on volatility trends and recent price changes.
Bar metrics / quantifytools— Overview
Rather than eyeball evaluating bullishness/bearishness in any given bar, bar metrics allow a quantified approach using three basic fundamental data points: relative close, relative volatility and relative volume. These data points are visualized in a discreet data dashboard form, next to all real-time bars. Each value also has a dot in front, representing color coded extremes in the values.
Relative close represents position of bar's close relative to high and low, high of bar being 100% and low of bar being 0%. Relative close indicates strength of bulls/bears in a given bar, the higher the better for bulls, the lower the better for bears. Relative volatility (bar range, high - low) and relative volume are presented in a form of a multiplier, relative to their respective moving averages (SMA 20). A value of 1x indicates volume/volatility being on par with moving average, 2x indicates volume/volatility being twice as much as moving average and so on. Relative volume and volatility can be used for measuring general market participant interest, the "weight of the bar" as it were.
— Features
Users can gauge past bar metrics using lookback via input menu. Past bars, especially recent ones, are helpful for giving context for current bar metrics. Lookback bars are highlighted on the chart using a yellow box and metrics presented on the data dashboard with lookback symbols:
To inspect bar metric data and its implications, users can highlight bars with specified bracket values for each metric:
When bar highlighter is toggled on and desired bar metric values set, alert for the specified combination can be toggled on via alert menu. Note that bar highlighter must be enabled in order for alerts to function.
— Visuals
Bar metric dots are gradient colored the following way:
Relative volatility & volume
0x -> 1x / Neutral (white) -> Light (yellow)
1x -> 1.7x / Light (yellow) -> Medium (orange)
1.7x -> 2.4x / Medium (orange) -> Heavy (red)
Relative close
0% -> 25% / Heavy bearish (red) -> Light bearish (dark red)
25% -> 45% / Light bearish (dark red) -> Neutral (white)
45% - 55% / Neutral (white)
55% -> 75% / Neutral (white) -> Light bullish (dark green)
75% -> 100% / Light bullish (dark green) -> Heavy bullish (green)
All colors can be adjusted via input menu. Label size, label distance from bar (offset) and text format (regular/stealth) can be adjusted via input menu as well:
— Practical guide
As interpretation of bar metrics is highly contextual, it is especially important to use other means in conjunction with the metrics. Levels, oscillators, moving averages, whatever you have found useful for your process. In short, relative close indicates directional bias and relative volume/volatility indicates "weight" of directional bias.
General interpretation
High relative close, low relative volume/volatility = mildly bullish, bias up/consolidation
High relative close, medium relative volume/volatility = bullish, bias up
High relative close, high relative volume/volatility = exuberantly bullish, bias up/down depending on context
Medium relative close, low relative volume/volatility = noise, no bias
Medium relative close, medium to high relative volume/volatility = indecision, further evidence needed to evaluate bias
Low relative close, low relative volume/volatility = mildly bearish, bias down/consolidation
Low relative close, medium relative volume/volatility = bearish, bias down
Low relative close, high relative volume/volatility = exuberantly bearish, bias down/up depending on context
Nuances & considerations
As to relative close, it's important to note that each bar is a trading range when viewed on a lower timeframe, ES 1W vs. ES 4H:
When relative close is high, bulls were able to push price to range high by the time of close. When relative close is low, bears were able to push price to range low by the time of close. In other words, bulls/bears were able to gain the upper hand over a given trading range, hinting strength for the side that made the final push. When relative close is around middle range (40-60%), it can be said neither side is clearly dominating the range, hinting neutral/indecision bias from a relative close perspective.
As to relative volume/volatility, low values (less than ~0.7x) imply bar has low market participant interest and therefore is likely insignificant, as it is "lacking weight". Values close to or above 1x imply meaningful market participant interest, whereas values well above 1x (greater than ~1.3x) imply exuberance. This exuberance can manifest as initiation (beginning of a trend) or as exhaustion (end of a trend):
Topological Market Stress (TMS) - Quantum FabricTopological Market Stress (TMS) - Quantum Fabric
What Stresses The Market?
Topological Market Stress (TMS) represents a revolutionary fusion of algebraic topology and quantum field theory applied to financial markets. Unlike traditional indicators that analyze price movements linearly, TMS examines the underlying topological structure of market data—detecting when the very fabric of market relationships begins to tear, warp, or collapse.
Drawing inspiration from the ethereal beauty of quantum field visualizations and the mathematical elegance of topological spaces, this indicator transforms complex mathematical concepts into an intuitive, visually stunning interface that reveals hidden market dynamics invisible to conventional analysis.
Theoretical Foundation: Topology Meets Markets
Topological Holes in Market Structure
In algebraic topology, a "hole" represents a fundamental structural break—a place where the normal connectivity of space fails. In markets, these topological holes manifest as:
Correlation Breakdown: When traditional price-volume relationships collapse
Volatility Clustering Failure: When volatility patterns lose their predictive power
Microstructure Stress: When market efficiency mechanisms begin to fail
The Mathematics of Market Topology
TMS constructs a topological space from market data using three key components:
1. Correlation Topology
ρ(P,V) = correlation(price, volume, period)
Hole Formation = 1 - |ρ(P,V)|
When price and volume decorrelate, topological holes begin forming.
2. Volatility Clustering Topology
σ(t) = volatility at time t
Clustering = correlation(σ(t), σ(t-1), period)
Breakdown = 1 - |Clustering|
Volatility clustering breakdown indicates structural instability.
3. Market Efficiency Topology
Efficiency = |price - EMA(price)| / ATR
Measures how far price deviates from its efficient trajectory.
Multi-Scale Topological Analysis
Markets exist across multiple temporal scales simultaneously. TMS analyzes topology at three distinct scales:
Micro Scale (3-15 periods): Immediate structural changes, market microstructure stress
Meso Scale (10-50 periods): Trend-level topology, medium-term structural shifts
Macro Scale (50-200 periods): Long-term structural topology, regime-level changes
The final stress metric combines all scales:
Combined Stress = 0.3×Micro + 0.4×Meso + 0.3×Macro
How TMS Works
1. Topological Space Construction
Each market moment is embedded in a multi-dimensional topological space where:
- Price efficiency forms one dimension
- Correlation breakdown forms another
- Volatility clustering breakdown forms the third
2. Hole Detection Algorithm
The indicator continuously scans this topological space for:
Hole Formation: When stress exceeds the formation threshold
Hole Persistence: How long structural breaks maintain
Hole Collapse: Sudden topology restoration (regime shifts)
3. Quantum Visualization Engine
The visualization system translates topological mathematics into intuitive quantum field representations:
Stress Waves: Main line showing topological stress intensity
Quantum Glow: Surrounding field indicating stress energy
Fabric Integrity: Background showing structural health
Multi-Scale Rings: Orbital representations of different timeframes
4. Signal Generation
Stable Topology (✨): Normal market structure, standard trading conditions
Stressed Topology (⚡): Increased structural tension, heightened volatility expected
Topological Collapse (🕳️): Major structural break, regime shift in progress
Critical Stress (🌋): Extreme conditions, maximum caution required
Inputs & Parameters
🕳️ Topological Parameters
Analysis Window (20-200, default: 50)
Primary period for topological analysis
20-30: High-frequency scalping, rapid structure detection
50: Balanced approach, recommended for most markets
100-200: Long-term position trading, major structural shifts only
Hole Formation Threshold (0.1-0.9, default: 0.3)
Sensitivity for detecting topological holes
0.1-0.2: Very sensitive, detects minor structural stress
0.3: Balanced, optimal for most market conditions
0.5-0.9: Conservative, only major structural breaks
Density Calculation Radius (0.1-2.0, default: 0.5)
Radius for local density estimation in topological space
0.1-0.3: Fine-grained analysis, sensitive to local changes
0.5: Standard approach, balanced sensitivity
1.0-2.0: Broad analysis, focuses on major structural features
Collapse Detection (0.5-0.95, default: 0.7)
Threshold for detecting sudden topology restoration
0.5-0.6: Very sensitive to regime changes
0.7: Balanced, reliable collapse detection
0.8-0.95: Conservative, only major regime shifts
📊 Multi-Scale Analysis
Enable Multi-Scale (default: true)
- Analyzes topology across multiple timeframes simultaneously
- Provides deeper insight into market structure at different scales
- Essential for understanding cross-timeframe topology interactions
Micro Scale Period (3-15, default: 5)
Fast scale for immediate topology changes
3-5: Ultra-fast, tick/minute data analysis
5-8: Fast, 5m-15m chart optimization
10-15: Medium-fast, 30m-1H chart focus
Meso Scale Period (10-50, default: 20)
Medium scale for trend topology analysis
10-15: Short trend structures
20-25: Medium trend structures (recommended)
30-50: Long trend structures
Macro Scale Period (50-200, default: 100)
Slow scale for structural topology
50-75: Medium-term structural analysis
100: Long-term structure (recommended)
150-200: Very long-term structural patterns
⚙️ Signal Processing
Smoothing Method (SMA/EMA/RMA/WMA, default: EMA) Method for smoothing stress signals
SMA: Simple average, stable but slower
EMA: Exponential, responsive and recommended
RMA: Running average, very smooth
WMA: Weighted average, balanced approach
Smoothing Period (1-10, default: 3)
Period for signal smoothing
1-2: Minimal smoothing, noisy but fast
3-5: Balanced, recommended for most applications
6-10: Heavy smoothing, slow but very stable
Normalization (Fixed/Adaptive/Rolling, default: Adaptive)
Method for normalizing stress values
Fixed: Static 0-1 range normalization
Adaptive: Dynamic range adjustment (recommended)
Rolling: Rolling window normalization
🎨 Quantum Visualization
Fabric Style Options:
Quantum Field: Flowing energy visualization with smooth gradients
Topological Mesh: Mathematical topology with stepped lines
Phase Space: Dynamical systems view with circular markers
Minimal: Clean, simple display with reduced visual elements
Color Scheme Options:
Quantum Gradient: Deep space blue → Quantum red progression
Thermal: Black → Hot orange thermal imaging style
Spectral: Purple → Gold full spectrum colors
Monochrome: Dark gray → Light gray elegant simplicity
Multi-Scale Rings (default: true)
- Display orbital rings for different time scales
- Visualizes how topology changes across timeframes
- Provides immediate visual feedback on cross-scale dynamics
Glow Intensity (0.0-1.0, default: 0.6)
Controls the quantum glow effect intensity
0.0: No glow, pure line display
0.6: Balanced, recommended setting
1.0: Maximum glow, full quantum field effect
📋 Dashboard & Alerts
Show Dashboard (default: true)
Real-time topology status display
Current market state and trading recommendations
Stress level visualization and fabric integrity status
Show Theory Guide (default: true)
Educational panel explaining topological concepts
Dashboard interpretation guide
Trading strategy recommendations
Enable Alerts (default: true)
Extreme stress detection alerts
Topological collapse notifications
Hole formation and recovery signals
Visual Logic & Interpretation
Main Visualization Elements
Quantum Stress Line
Primary indicator showing topological stress intensity
Color intensity reflects current market state
Line style varies based on selected fabric style
Glow effect indicates stress energy field
Equilibrium Line
Silver line showing average stress level
Reference point for normal market conditions
Helps identify when stress is elevated or suppressed
Upper/Lower Bounds
Red upper bound: High stress threshold
Green lower bound: Low stress threshold
Quantum fabric fill between bounds shows stress field
Multi-Scale Rings
Aqua circles : Micro-scale topology (immediate changes)
Orange circles: Meso-scale topology (trend-level changes)
Provides cross-timeframe topology visualization
Dashboard Information
Topology State Icons:
✨ STABLE: Normal market structure, standard trading conditions
⚡ STRESSED: Increased structural tension, monitor closely
🕳️ COLLAPSE: Major structural break, regime shift occurring
🌋 CRITICAL: Extreme conditions, reduce risk exposure
Stress Bar Visualization:
Visual representation of current stress level (0-100%)
Color-coded based on current topology state
Real-time percentage display
Fabric Integrity Dots:
●●●●● Intact: Strong market structure (0-30% stress)
●●●○○ Stressed: Weakening structure (30-70% stress)
●○○○○ Fractured: Breaking down structure (70-100% stress)
Action Recommendations:
✅ TRADE: Normal conditions, standard strategies apply
⚠️ WATCH: Monitor closely, increased vigilance required
🔄 ADAPT: Change strategy, regime shift in progress
🛑 REDUCE: Lower risk exposure, extreme conditions
Trading Strategies
In Stable Topology (✨ STABLE)
- Normal trading conditions apply
- Use standard technical analysis
- Regular position sizing appropriate
- Both trend-following and mean-reversion strategies viable
In Stressed Topology (⚡ STRESSED)
- Increased volatility expected
- Widen stop losses to account for higher volatility
- Reduce position sizes slightly
- Focus on high-probability setups
- Monitor for potential regime change
During Topological Collapse (🕳️ COLLAPSE)
- Major regime shift in progress
- Adapt strategy immediately to new market character
- Consider closing positions that rely on previous regime
- Wait for new topology to stabilize before major trades
- Opportunity for contrarian plays if collapse is extreme
In Critical Stress (🌋 CRITICAL)
- Extreme market conditions
- Significantly reduce risk exposure
- Avoid new positions until stress subsides
- Focus on capital preservation
- Consider hedging existing positions
Advanced Techniques
Multi-Timeframe Topology Analysis
- Use higher timeframe TMS for regime context
- Use lower timeframe TMS for precise entry timing
- Alignment across timeframes = highest probability trades
Topology Divergence Trading
- Most powerful at regime boundaries
- Price makes new high/low but topology stress decreases
- Early warning of potential reversals
- Combine with key support/resistance levels
Stress Persistence Analysis
- Long periods of stable topology often precede major moves
- Extended stress periods often resolve in regime changes
- Use persistence tracking for position sizing decisions
Originality & Innovation
TMS represents a genuine breakthrough in applying advanced mathematics to market analysis:
True Topological Analysis: Not a simplified proxy but actual topological space construction and hole detection using correlation breakdown, volatility clustering analysis, and market efficiency measurement.
Quantum Aesthetic: Transforms complex topology mathematics into an intuitive, visually stunning interface inspired by quantum field theory visualizations.
Multi-Scale Architecture: Simultaneous analysis across micro, meso, and macro timeframes provides unprecedented insight into market structure dynamics.
Regime Detection: Identifies fundamental market character changes before they become obvious in price action, providing early warning of structural shifts.
Practical Application: Clear, actionable signals derived from advanced mathematical concepts, making theoretical topology accessible to practical traders.
This is not a combination of existing indicators or a cosmetic enhancement of standard tools. It represents a fundamental reimagining of how we measure, visualize, and interpret market dynamics through the lens of algebraic topology and quantum field theory.
Best Practices
Start with defaults: Parameters are optimized for broad market applicability
Match timeframe: Adjust scales based on your trading timeframe
Confirm with price action: TMS shows market character, not direction
Respect topology changes: Reduce risk during regime transitions
Use appropriate strategies: Adapt approach based on current topology state
Monitor persistence: Track how long topology states maintain
Cross-timeframe analysis: Align multiple timeframes for highest probability trades
Alerts Available
Extreme Topological Stress: Market fabric under severe deformation
Topological Collapse Detected: Regime shift in progress
Topological Hole Forming: Market structure breakdown detected
Topology Stabilizing: Market structure recovering to normal
Chart Requirements
Recommended Markets: All liquid markets (forex, stocks, crypto, futures)
Optimal Timeframes: 5m to Daily (adaptable to any timeframe)
Minimum History: 200 bars for proper topology construction
Best Performance: Markets with clear regime characteristics
Academic Foundation
This indicator draws from cutting-edge research in:
- Algebraic topology and persistent homology
- Quantum field theory visualization techniques
- Market microstructure analysis
- Multi-scale dynamical systems theory
- Correlation topology and network analysis
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or provide direct buy/sell signals. Topological analysis reveals market structure characteristics, not future price direction. Always use proper risk management and combine with your own analysis. Past performance does not guarantee future results.
See markets through the lens of topology. Trade the structure, not the noise.
Bringing advanced mathematics to practical trading through quantum-inspired visualization.
Trade with insight. Trade with structure.
— Dskyz , for DAFE Trading Systems
Standard Deviation OscillatorStandard Deviation Oscillator (STDEV OSC) v1.1
Description
The Standard Deviation Oscillator transforms traditional volatility measurements into a dynamic oscillator that fluctuates between 0 and 100. This advanced technical analysis tool helps traders identify periods of extreme volatility and potential market turning points.
Features
Normalized volatility readings (0-100 scale)
Dynamic color changes based on volatility levels
Customizable overbought/oversold thresholds
Built-in alert conditions
Adaptive calculation using rolling windows
Clean, professional visualization
Indicator Parameters
Length: 20; Calculation period for standard deviation
Source: close; Price source for calculations
Overbought Level: 70; Upper threshold for high volatility
Oversold Level: 30; Lower threshold for low volatility
Visual Components
- Main Oscillator Line: Changes color based on current level
- Red: Above overbought level
- Green: Below oversold level
- Blue: Normal range
- Reference Lines:
- Overbought level (default: 70)
- Oversold level (default: 30)
- Middle line (50)
Alert Conditions
1. Volatility High Alert
- Triggers when oscillator crosses above the overbought level
- Useful for identifying potential market tops or breakout scenarios
2. Volatility Low Alert
- Triggers when oscillator crosses below the oversold level
- Helps identify potential market bottoms or consolidation periods
Risk Adjustment Tool
- Scale position sizes inversely to oscillator readings
- Reduce exposure during extremely high volatility periods
- Increase position sizes during normal volatility conditions
Best Practices
1. Timeframe Selection
- Best suited for 1H, 4H, and Daily charts
- Adjust length parameter based on timeframe
2. Confirmation
- Use in conjunction with trend indicators
- Confirm signals with price action patterns
- Consider overall market context
3. Parameter Optimization
- Backtest different length settings
- Adjust overbought/oversold levels based on asset
- Consider market conditions when setting alerts
Technical Notes
- Built in PineScript v5
- Optimized for TradingView platform
- Uses rolling window calculations for better adaptability
- Compatible with all trading instruments
- Minimal performance impact on charts
Version History
- v1.1: Added dynamic coloring, customizable levels, and alert conditions
- v1.0: Initial release with basic oscillator functionality
Disclaimer
This technical indicator is provided for educational and informational purposes only. Past performance is not indicative of future results. Always conduct thorough testing and use proper risk management techniques.
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Tags: #TechnicalAnalysis #Volatility #Trading #Oscillator #TradingView #PineScript
Larry Conners Vix Reversal II Strategy (approx.)This Pine Script™ strategy is a modified version of the original Larry Connors VIX Reversal II Strategy, designed for short-term trading in market indices like the S&P 500. The strategy utilizes the Relative Strength Index (RSI) of the VIX (Volatility Index) to identify potential overbought or oversold market conditions. The logic is based on the assumption that extreme levels of market volatility often precede reversals in price.
How the Strategy Works
The strategy calculates the RSI of the VIX using a 25-period lookback window. The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is often used to identify overbought and oversold conditions in assets.
Overbought Signal: When the RSI of the VIX rises above 61, it signals a potential overbought condition in the market. The strategy looks for a RSI downtick (i.e., when RSI starts to fall after reaching this level) as a trigger to enter a long position.
Oversold Signal: Conversely, when the RSI of the VIX drops below 42, the market is considered oversold. A RSI uptick (i.e., when RSI starts to rise after hitting this level) serves as a signal to enter a short position.
The strategy holds the position for a minimum of 7 days and a maximum of 12 days, after which it exits automatically.
Larry Connors: Background
Larry Connors is a prominent figure in quantitative trading, specializing in short-term market strategies. He is the co-author of several influential books on trading, such as Street Smarts (1995), co-written with Linda Raschke, and How Markets Really Work. Connors' work focuses on developing rules-based systems using volatility indicators like the VIX and oscillators such as RSI to exploit mean-reversion patterns in financial markets.
Risks of the Strategy
While the Larry Connors VIX Reversal II Strategy can capture reversals in volatile market environments, it also carries significant risks:
Over-Optimization: This modified version adjusts RSI levels and holding periods to fit recent market data. If market conditions change, the strategy might no longer be effective, leading to false signals.
Drawdowns in Trending Markets: This is a mean-reversion strategy, designed to profit when markets return to a previous mean. However, in strongly trending markets, especially during extended bull or bear phases, the strategy might generate losses due to early entries or exits.
Volatility Risk: Since this strategy is linked to the VIX, an instrument that reflects market volatility, large spikes in volatility can lead to unexpected, fast-moving market conditions, potentially leading to larger-than-expected losses.
Scientific Literature and Supporting Research
The use of RSI and VIX in trading strategies has been widely discussed in academic research. RSI is one of the most studied momentum oscillators, and numerous studies show that it can capture mean-reversion effects in various markets, including equities and derivatives.
Wong et al. (2003) investigated the effectiveness of technical trading rules such as RSI, finding that it has predictive power in certain market conditions, particularly in mean-reverting markets .
The VIX, often referred to as the “fear index,” reflects market expectations of volatility and has been a focal point in research exploring volatility-based strategies. Whaley (2000) extensively reviewed the predictive power of VIX, noting that extreme VIX readings often correlate with turning points in the stock market .
Modified Version of Original Strategy
This script is a modified version of Larry Connors' original VIX Reversal II strategy. The key differences include:
Adjusted RSI period to 25 (instead of 2 or 4 commonly used in Connors’ other work).
Overbought and oversold levels modified to 61 and 42, respectively.
Specific holding period (7 to 12 days) is predefined to reduce holding risk.
These modifications aim to adapt the strategy to different market environments, potentially enhancing performance under specific volatility conditions. However, as with any system, constant evaluation and testing in live markets are crucial.
References
Wong, W. K., Manzur, M., & Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Applied Financial Economics, 13(7), 543-551.
Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17.
Volatility System by W. WilderVolatility System (Volatility Stops) Similarity
Most traders adjust their stops over time in the direction of the trend in order to lock in profits. Apart from moving averages, one of the most popular techniques is trailing stops using a multiple of Average True Range. There are several variations:
The original Volatility System(Volatility Stops), introduced by Welles Wilder in his 1978 book: New Concepts in Technical Trading Systems
Chandelier exits introduced by Alexander Elder in Come Into My Trading Room (2002) trail the stops from Highs or Lows rather than Closing Price
Average True Range Trailing Stops are similar to the above, but include a ratchet mechanism to prevent stops moving down during an up-trend or rising during a down-trend, as ATR increases
WillTrend intoduced by Larry Williams in 1988
Comparison of systems
All the systems under consideration have one common ingredient - ATR. ATR was developed by Welles Wilder and described in his book in 1978, also in this book the Volatility System was described, which in the future became known as Volatility Stops.
In fact, Wilder is the father of such systems due to the presence of ATR in the calculation of this type of indicator.
The main difference of Volatility System
Followers such as Larry Williams and Alexander Elder made minor changes to the value based on the ATR, mainly focusing on changing the base to which this value is added or subtracted.
Larry Williams uses the square root of 5 as a multiplier and calculates the ATR with a period of 66, and Alexander Elder uses a multiplier of 2.5-3.5 applying it to the ATR with a period of 22. Both authors changed the original value for ATR and multiplier calculations. Alexander Elder is closest to the original Welles Wilder calculation, which used a multiplier of 2.8.-3.1 applying it to an ATR with a period of 7.
As a reference, Elder took the Highest High(22) from which he subtracts ATR*Multiplier in an uptrend or the Lowest Low(22) to which he adds ATR*Multiplier to obtain the turning point (SAR).
Larry Williams uses the average price of extremes (Highest High(10) + Lowest Low(10)) / 2 as a reference base to which he adds or subtracts the ATR*Multilpyer values.
Both systems differ from the original, because Wilder used Significan Close(SIC) in his calculations. SIC is the maximum closing price during an uptrend and the minimum closing price during a downtrend, which
does not go beyond the current trade, as in other systems. To calculate the base when a trend changes, bars that are outside the current trend will be used when calculating WillTrend and Chandelier Exit, in contrast to the Volatility System, which takes SIC values only within the current trade. This is the main difference from subsequent developments of similar systems.
Improvements made
The original Volatility System is present as an indicator on TradingView, but it is an improved version with the addition of a ratchet and works differently from the original Weilder system.
List of improvements:
Added the ability to remove the ratchet. You need to turn off the "Trail one way" checkbox in the setting menu. When this function is turned off, the system will operate in the author-inventor mode. On some instruments, the original system works much better than the improved ratchet system, which cannot be turned off.
Added the ability to use Highest High and Lowest Low as a base instead of the closing price.
Volatility Stops Formula Description
Welles Wilder's system uses Closing Price and incorporates a stop-and-reverse feature (as with his Parabolic SAR).
Determine the initial trend direction
Calculate the Significant Close ("SIC"): the highest close reached in an up-trend or the lowest close in a down-trend
Calculate Average True Range ("ATR") for the selected period (7 days in this example)
Multiply ATR by the Multiple (3.0 in this example, best values author describes as 2.8-3.1)
The first stop is calculated in day 7 and plotted for day 8
If an up-trend, the first stop is SIC - 3 * ATR, otherwise SIC + 3 * ATR for a down-trend
Repeat each day until price closes below the stop (or above in a down-trend)
Set SIC equal to the latest Close, reverse the trend and continue.
Chandelier Exit Description
Chandelier Exits subtract a multiple of Average True Range ("ATR") from the highest high for the selected period. Using the default settings as an example:
Highest High in last 22 days - 3 * ATR for 22 days
In a down-trend the formula is reversed:
Lowest Low in last 22 days + 3 * ATR for 22 days
The time period must be long enough to capture the highest point of the recent up-trend: too short and the stops move downward; too long and the high may be taken from a previous down-trend.
It is not essential to use the same period for up and down trends; down-trends are notoriously faster than up-trends and may benefit from a shorter time period.
The multiple of 3 may be varied, but most traders settle between 2.5 and 3.5.
WillTrend Description
Larry Williams is prefer to used the Square Root from 5 as a multiplayer for ATR. SQRT(5) = 2.236
WillTrend subtract a multiple of Average True Range ("ATR") from the Middle Price (Highest High for the selected period + Lowest Low for the selected period / 2).
(Highest High in last 10 days + Lowest Low in last 10 days) / 2 - 2.236 * ATR for 66 days
In a down-trend the formula is reversed:
(Highest High in last 10 days + Lowest Low in last 10 days) / 2 + 2.236 * ATR for 66 days
