SMA Crossover Candle Body SizeThis indicator allows you to filter the candle body size of a SMA crossover. This helps to eliminate times when price is consolidating and constantly crossing above or below. By adjusting the candle body size to say something like 15, you'll only receive alerts when significant size candles cross and hold above or below your desired SMA.
Ketidakstabilan
VWAP Deviation Channels with Probability (Lite)VWAP Deviation Channels with Probability (Lite)
Version 1.2
Overview
This indicator is a powerful tool for intraday traders, designed to identify high-probability areas of support and resistance. It plots the Volume-Weighted Average Price (VWAP) as a central "value" line and then draws statistically-based deviation channels around it.
Its unique feature is a dynamic probability engine that analyzes thousands of historical price bars to calculate and display the real-time likelihood of the price touching each of these deviation levels. This provides a quantifiable edge for making trading decisions.
Core Concepts Explained
This indicator is built on three key concepts:
The VWAP (Volume-Weighted Average Price): The dotted midline of the channels is the session VWAP. Unlike a Simple Moving Average (SMA) which only considers price, the VWAP incorporates volume into its calculation. This makes it a much more significant benchmark, as it represents the true average price where the most business has been transacted during the day. It's heavily used by institutional traders, which is why price often reacts strongly to it.
Standard Deviation Channels: The channels above and below the VWAP are based on standard deviations. Standard deviation is a statistical measure of volatility.
- Wide Bands: When the channels are wide, it signifies high volatility.
- Narrow Bands: When the channels are tight and narrow, it signifies low volatility and
consolidation (a "squeeze").
The Conditional Probability Engine: This is the heart of the indicator. For every deviation level, the script displays a percentage. This percentage answers a very specific question:
"Based on thousands of previous bars, when the last candle had a certain momentum (bullish or bearish), what was the historical probability that the price would touch this specific level?"
The probabilities are calculated separately depending on whether the previous candle was green (bullish) or red (bearish). This provides a nuanced, momentum-based edge. The level with the highest probability is highlighted, acting as a "price magnet."
How to Use This Indicator
Recommended Timeframes:
This indicator is designed specifically for intraday trading. It works best on timeframes like the 1-minute, 5-minute, and 15-minute charts. It will not display correctly on daily or higher timeframes.
Recommended Trading Strategy: Mean Reversion
The primary strategy for this indicator is "Mean Reversion." The core idea is that as the price stretches to extreme levels far away from the VWAP (the "mean"), it is statistically more likely to "snap back" toward it.
Here is a step-by-step guide to trading this setup:
1. Identify the Extreme: Wait for the price to push into one of the outer deviation bands (e.g., the -2, -3, or -4 bands for a buy setup, or the +2, +3, or +4 bands for a sell setup).
2. Look for the High-Probability Zone: Pay close attention to the highlighted probability label. This is the level that has historically acted as the strongest magnet for price. A touch of this level represents a high-probability area for a potential reversal.
3. Wait for Confirmation: Do not enter a trade just because the price has touched a band. Wait for a confirmation candle that shows momentum is shifting.
- For a Buy: Look for a strong bullish candle (e.g., a green engulfing candle or a hammer/pin
bar) to form at the lower bands.
- For a Sell: Look for a strong bearish candle (e.g., a red engulfing candle or a shooting star)
to form at the upper bands.
Define Your Exit:
- Take Profit: A logical primary target for a mean reversion trade is the VWAP (midLine).
- Stop Loss: A logical place for a stop-loss is just outside the next deviation band. For
example, if you enter a long trade at the -3 band, your stop loss could be placed just
below the -4 band.
Disclaimer: This indicator is a tool for analysis and should not be considered a standalone trading system. Trading involves significant risk, and past performance is not indicative of future results. Always use this indicator in conjunction with other forms of analysis and sound risk management practices.
Iceberg DetectorThis Pine-script indicator helps you spot potential “iceberg” order activity by highlighting bars where volume spikes well above its average while price movement remains unusually muted. It’s purely a heuristic—no true bid/ask or futures order‐flow data is used—so treat every signal as an invitation to investigate, not as a standalone buy/sell trigger.
How It Works • Volume vs. Volume-SMA: The script compares each bar’s total volume to an N-bar simple moving average. • Price Movement vs. Movement-SMA: It measures the bar’s percent change (|close–open|/open×100) against its own N-bar SMA. • Sensitivity Slider: From 1 (loose filter) to 10 (strict filter), you control how extreme the volume spike (and muted move) must be to fire a signal. • Pivot-Style Extremes Filter: Short signals only appear when price is at or very near a recent local high, and long signals only when price is at or very near a recent local low. This dramatically cuts down “noise” on lower timeframes—script execution halts on intraday charts below 1 H.
How to Use
Apply to an hourly (or higher) chart.
Tweak “Length” parameters for your preferred look-back on volume and movement SMAs.
Adjust “Sensitivity” from 1 (more signals, weaker divergences) up to 10 (very rare, extreme divergences).
Watch for red triangles above bars (Iceberg-Short) and green triangles below (Iceberg-Long).
Important Disclaimers • This is NOT a genuine order-flow or footprint tool—it only approximates delta by bar direction. • Always contextualize Short signals near the lower end of a range or support zone, and Long signals near the upper end of a range or resistance zone. • Use additional confirmation (price patterns, larger-timeframe pivots, traditional volume/price analysis) before risking real capital.
By combining volume spikes with muted price action at range extremes, you gain a fresh lens on where hidden large orders might be lurking—without needing a dedicated order-flow feed. Use it as an idea‐generator, not as gospel
Market Structure + VIX long & shortThis indicator is an indicator for the dominance of Bigs long and short trading. I added all the indicators of CNN's put call ratio, cpc, and pcce. Bigs long is dangerous, so take a conservative approach with LL or HL, and use it for alert purposes. If possible, try to check CNN's put call ratio directly. The Bigs Short indicator is quite useful. In particular, strong short signals will be useful.
Volatility Zones (STDEV %)This indicator displays the relative volatility of an asset as a percentage, based on the standard deviation of price over a custom length.
🔍 Key features:
• Uses standard deviation (%) to reflect recent price volatility
• Classifies volatility into three zones:
Low volatility (≤2%) — highlighted in blue
Medium volatility (2–4%) — highlighted in orange
High volatility (>4%) — highlighted in red
• Supports visual background shading and colored line output
• Works on any timeframe and asset
📊 This tool is useful for identifying low-risk entry zones, periods of expansion or contraction in price behavior, and dynamic market regime changes.
You can adjust the STDEV length to suit your strategy or timeframe. Best used in combination with your entry logic or trend filters.
Bollinger Bands Levels | VTS Pro📊 Bollinger Bands Levels | VTS Pro
by Alireza Mossaheb
This advanced Bollinger Bands indicator takes your technical analysis to the next level by providing dynamic price bands along with customizable horizontal levels and labels. Whether you're a trend trader or a mean reversion strategist, this tool adapts to your workflow.
🔧 Key Features:
Three Modes: Choose between Strong (20, 2), Weak (10, 1.5), or Custom settings for full control.
Multi-Timeframe Support: Plot Bollinger Bands from any higher or lower timeframe.
Multiple MA Types: Select from SMA, EMA, RMA (SMMA), WMA, and VWMA for the basis line.
Visual Enhancements:
Optional background fill between bands
Stylized horizontal lines with labels (Top/Mid/Low)
Customizable line style, width, and color
Smart Labeling: Automatically names levels based on timeframe and mode.
Improved Plot Logic: Line width bug fixed for smoother rendering across presets.
🧠 Ideal For:
Spotting volatility squeezes or expansions
Confirming support/resistance with upper/lower bands
Creating confluence zones using higher timeframe Bollinger levels
Momentum Trajectory Suite📈 Momentum Trajectory Suite
🟢 Overview
Momentum Trajectory Suite is a multi-faceted indicator designed to help traders evaluate trend direction, volatility conditions, and behavioral sentiment in a single consolidated view.
By combining a customizable Trajectory EMA, adaptive Bollinger Bands, and a Greed vs. Fear heatmap, this tool empowers traders to identify directional bias, measure momentum strength, and spot potential reversals or continuation setups.
🧠 Concept
This indicator merges three classic techniques:
Trend Analysis: Trajectory EMA highlights the prevailing directional momentum by smoothing price action over a customizable period.
Volatility Envelopes: Bollinger Bands adapt to dynamic price swings, showing overbought/oversold extremes and periods of contraction or expansion.
Behavioral Sentiment: A Greed vs. Fear heatmap combines RSI and MACD Histogram readings to visualize when markets are dominated by buying enthusiasm or selling pressure.
The combination is designed to help traders interpret market context more effectively than using any single component alone.
🛠️ How to Use the Indicator
Trajectory EMA:
Use the blue EMA line to assess overall trend direction.
Price closing above the EMA may indicate bullish momentum; closing below may indicate bearish bias.
Buy/Sell Signals:
Green circles appear when price crosses above the EMA (potential long entry).
Red circles appear when price crosses below the EMA (potential exit or short entry).
Bollinger Bands:
Monitor upper/lower bands for overbought and oversold price extremes.
Narrowing bands may signal upcoming volatility expansion.
Greed vs. Fear Heatmap:
Green histogram bars indicate bullish sentiment when RSI exceeds 60 and MACD Histogram is positive.
Red histogram bars indicate bearish sentiment when RSI is below 40 and MACD Histogram is negative.
Gray bars indicate neutral or mixed conditions.
Background Color Zones:
The chart background shifts to green when EMA slope is positive and red when negative, providing quick directional cues.
All inputs are adjustable in settings, including EMA length, Bollinger Band parameters, and oscillator configurations.
📊 Interpretation
Bullish Conditions:
Price above the Trajectory EMA, background green, and Greed heatmap active.
May signal trend continuation and increased buying pressure.
Bearish Conditions:
Price below the Trajectory EMA, background red, and Fear heatmap active.
May signal momentum breakdown or potential continuation to the downside.
Volatility Clues:
Wide Bollinger Bands = trending, volatile market.
Narrow Bollinger Bands = low volatility and possible breakout setup.
Signal Confirmation:
Consider combining signals (e.g., EMA crossover + Greed/Fear heatmap + Bollinger Band touch) for higher-confidence entries.
📝 Notes
The script does not repaint or use future data.
Suitable for multiple timeframes (intraday to daily).
May be combined with other confirmation tools or price action analysis.
⚠️ Disclaimer
This script is for educational and informational purposes only and does not constitute financial advice. Trading carries risk and past performance is not indicative of future results. Always perform your own due diligence before making trading decisions.
Weekly Range PlotterThe Weekly Range Plotter is a dynamic market structure tool designed to help traders visualize critical high and low levels from specific days of the week and the previous week's range. It provides key visual anchors to support analysis of market behavior, including range compression/expansion and directional bias.
Williams VIX For Bottoms [DCD]Williams VIX Original - Authentic Volatility Fear Gauge
What This Indicator Does
The Williams VIX Fix measures market fear by calculating how far current lows deviate from recent highs, identifying potential market bottoms during high volatility periods. This implementation provides Larry Williams' original formula in its purest form.
How It Works
Core Formula:
VIX Fix = ((Highest High over 22 periods - Current Low) / Highest High over 22 periods) × 100
The calculation process:
Measures Relative Distance: Compares current low to highest high over lookback period
Converts to Percentage: Normalizes values for cross-market comparison
Applies Statistical Analysis: Uses Bollinger Bands (2 std dev) around VIX Fix values
Filters with Percentiles: 85th percentile threshold removes noise
Signal Generation
Green Flash Signals trigger when either condition is met:
VIX Fix exceeds upper Bollinger Band (2 standard deviations above 20-period MA)
VIX Fix exceeds Range High (85th percentile of recent values)
This dual-condition approach reduces false signals while capturing genuine volatility spikes.
What Makes This Original
Pure Formula Implementation: Uses Williams' exact original calculation without modifications
Dual Confirmation System: Combines Bollinger Bands with percentile analysis
Professional Visualization: Histogram display, background highlighting, and live value table
Comprehensive Alerts: Signal start/end notifications plus Green Flash alerts
How to Use
Primary Purpose: Spot high-probability reversal zones during market fear climaxes
Signal Interpretation:
Green triangle + background highlight = High volatility reversal zone
Higher VIX Fix values = Stronger fear/better reversal potential
Use with price action confirmation for best results
Optimal Settings:
Timeframes: 4H, Daily, Weekly
Markets: All (stocks, crypto, forex, commodities)
Combine with support levels and candlestick patterns
Key Parameters:
VIX Fix Length (22): Lookback period for highest high
Std Dev Multiplier (2.0): Bollinger Band sensitivity
Percentile High (0.85): Only top 15% of readings trigger signals
The VIX Fix excels at identifying market fear climaxes that coincide with significant price bottoms, making it valuable for swing traders seeking high-probability entries during market stress.
Historical Volatility with HV Average & High/Low Trendlines
### 📊 **Indicator Title**: Historical Volatility with HV Average & High/Low Trendlines
**Version**: Pine Script v5
**Purpose**:
This script visualizes market volatility using **Historical Volatility (HV)** and enhances analysis by:
* Showing a **moving average** of HV to identify volatility trends.
* Marking **high and low trendlines** to highlight extremes in volatility over a selected period.
---
### 🔧 **Inputs**:
1. **HV Length (`length`)**:
Controls how many bars are used to calculate Historical Volatility.
*(Default: 10)*
2. **Average Length (`avgLength`)**:
Number of bars used for calculating the moving average of HV.
*(Default: 20)*
3. **Trendline Lookback Period (`trendLookback`)**:
Number of bars to look back for calculating the highest and lowest values of HV.
*(Default: 100)*
---
### 📈 **Core Calculations**:
1. **Historical Volatility (`hv`)**:
$$
HV = 100 \times \text{stdev}\left(\ln\left(\frac{\text{close}}{\text{close} }\right), \text{length}\right) \times \sqrt{\frac{365}{\text{period}}}
$$
* Measures how much the stock price fluctuates.
* Adjusts annualization factor depending on whether it's intraday or daily.
2. **HV Moving Average (`hvAvg`)**:
A simple moving average (SMA) of HV over the selected `avgLength`.
3. **HV High & Low Trendlines**:
* `hvHigh`: Highest HV value over the last `trendLookback` bars.
* `hvLow`: Lowest HV value over the last `trendLookback` bars.
---
### 🖍️ **Visual Plots**:
* 🔵 **HV**: Blue line showing raw Historical Volatility.
* 🔴 **HV Average**: Red line (thicker) indicating smoothed HV trend.
* 🟢 **HV High**: Green horizontal line marking volatility peaks.
* 🟠 **HV Low**: Orange horizontal line marking volatility lows.
---
### ✅ **Usage**:
* **High HV**: Indicates increased risk or potential breakout conditions.
* **Low HV**: Suggests consolidation or calm markets.
* **Cross of HV above Average**: May signal rising volatility (e.g., before breakout).
* **Touching High/Low Levels**: Helps identify volatility extremes and possible reversal zones.
Velocity + Momentum (SMA-Based)Velocity + Momentum (SMA-Based) is a clean, powerful oscillator that measures price acceleration using SMA-derived velocity and dual momentum signals. This tool is ideal for identifying directional shifts, exhaustion points, and early entries across any market or timeframe.
How It Works:
This indicator calculates velocity as the distance between the current close and a simple moving average of the open price. Then, it applies two smoothed moving averages to this velocity line:
• Internal Momentum (shorter-term smoothing)
• External Momentum (longer-term context, hidden by default)
The result is a layered view of how fast price is moving and whether that move is gaining or losing strength.
How to Use:
• The green/red histogram shows current velocity (positive = bullish, negative = bearish)
• The teal/maroon line tracks internal momentum and provides short-term signal turns
• The black/gray (hidden) line reflects external momentum and supports broader trend alignment
• Watch for crosses above/below the zero line for confirmation of directional strength
• Use the built-in alerts to catch real-time shifts in all three layers of movement: velocity, internal, and external
Why It's Useful:
• Detects subtle transitions before price structure changes
• Helps filter out noise by comparing short-term vs long-term motion
• Ideal for scalpers, swing traders, and trend-followers alike
• Pairs well with structure-based tools or price action zones
• Works on any asset and timeframe
This indicator simplifies momentum analysis by giving you actionable, multi-layered feedback on how price is accelerating — and when that’s likely to reverse.
Bid/Ask Volume Tension with Rolling Avg📊 Bid/Ask Volume Tension with Rolling Average
This indicator is designed to help traders identify pivotal moments of buildup, exhaustion, or imbalance in the market by calculating the tension between buy and sell volume.
🔍 How It Works:
Buy volume is approximated when the candle closes higher than or equal to its open.
Sell volume is approximated when the candle closes below its open.
Both are smoothed using an EMA (Exponential Moving Average) for noise reduction.
Tension is calculated as the absolute difference between smoothed buy and sell volume.
A rolling average of tension shows the baseline for normal behavior.
When instant tension rises significantly above the rolling average, it often signals:
A build-up before a large move
Aggressive order flow imbalances
Potential reversals or breakouts
🧠 How to Use:
Watch the orange line (instant tension) for spikes above the aqua line (rolling average).
Purple background highlights show when tension exceeds a customizable multiple of the average — a potential setup zone.
Use this indicator alongside:
Price action (candlestick structure)
Support/resistance
Liquidity zones or order blocks
⚙️ Settings:
Smoothing Length: Controls the responsiveness of buy/sell volume smoothing.
Rolling Avg Window: Defines the lookback period for the baseline tension.
Buildup Threshold: Triggers highlight zones when tension exceeds this multiple of the average.
🧪 Best For:
Spotting pre-breakout tension
Detecting volume-based divergences
Confirming order flow imbalances
Currency Volatility Index (CVI)This Currency Volatility Index (CVI) indicator aggregates the realized volatility of the eight “major” FX pairs into a single, tradable series—much like an FX-version of the VIX. Here’s what it does step by step:
Inputs & Settings
• Volatility Length (default 20 days): the lookback over which daily log-returns’ standard deviation is computed.
• Data Timeframe (default Daily): the resolution at which price data is fetched for each pair.
• Smoothing Length (default 5): the period of a simple moving average applied to the raw, averaged volatility (in %).
Pair-by-Pair Volatility Calculation
For each hard-coded symbol (EURUSD, GBPUSD, USDJPY, USDCHF, AUDUSD, USDCAD, NZDUSD, EURGBP):
Pull the series of daily closes.
Compute the series of log-returns: ln(today’s close / yesterday’s close).
Calculate the standard deviation of those log-returns over your lookback.
Annualize it (×√252) to convert daily volatility into an annualized figure.
Aggregation
The eight annualized volatilities are averaged (equal weights).
The resulting number is then multiplied by 100 to express it as a percentage.
Smoothing & Plotting
A simple moving average over the aggregated volatility smooths out spikes.
The smoothed CVI (%) is plotted as a standalone line below price charts.
Visualization Aids
A small table in the top-right corner shows each pair’s current volatility in percent.
A dynamic label on the final bar prints the latest CVI value directly on the chart.
Why use it?
Gives a one-stop measure of overall FX market turbulence.
Helps you compare “quiet” vs. “volatile” regimes across currencies.
Expanded Cloud [LuxAlgo]The Expanded Cloud tool allows traders to identify and follow trends accurately. It is based on the well-known Donchian Channels, but with enhanced features.
It features a trailing cloud that expands with the price and a trading stats dashboard.
🔶 USAGE
The tool is super easy to use. Traders can identify bigger or smaller trends just by adjusting the length from the settings panel.
Trend identification is based on Donchian Channels. An uptrend is indicated when the cloud is located below the price, while a downtrend is indicated when the cloud is above it.
Dots signal the start of a new trend, and the width of the clouds identifies the strength of the price expansion. The wider the cloud, the bigger the move.
The expanded cloud, due to its visual, can also act as a trailing stop.
🔹 Trend Identification
As we can see in the chart above, different length values identify different trends on the same BTC daily chart. Larger values identify larger trends.
🔹 Cloud Expansion
From the settings panel, traders can adjust how the clouds expand based on the Expansion % parameter. It accepts values from 0 to 100, which controls how much of the expansion is taken into account. Higher values will make the cloud expand and get closer to the price faster.
When the cloud moves opposite to the direction of the indicated trend (e.g: the cloud decreases while being below the price), it is often indicative of the end of a retracement, and we can expect the price to move with the indicated trend.
The chart above shows the effect of different Expansion % values.
🔹 Dashboard
The trading statistics dashboard informs traders of key metrics derived from the tool. The following are notable:
PNL: Theoretical profit or loss from all trends identified by the tool in the right scale units.
EXPECT.: Expected value of each trade. It is derived from win rate and risk-to-reward metrics.
AVG: 1st TOUCH: The average number of bars from the beginning of a new trend until the price touches the cloud for the first time.
🔶 SETTINGS
Length: Length for trend detection
Expansion %: Percentage of price expansion for cloud formation
Source: Source of the data
🔹 Dashboard
Show Dashboard: Enable/disable the statistics dashboard
Location: Dashboard location
Size: Dashboard size
ATR Buy, Target, Stop + OverlayATR Buy, Target, Stop + Overlay
This tool is to assist traders with precise trade planning using the Average True Range (ATR) as a volatility-based reference.
This script plots buy, target, and stop-loss levels on the chart based on a user-defined buy price and ATR-based multipliers, allowing for objective and adaptive trade management.
*NOTE* In order for the indicator to initiate plotted lines and table values a non-zero number must be entered into the settings.
What It Does:
Buy Price Input: Users enter a manual buy price (e.g., an executed or planned trade entry).
ATR-Based Target and Stop: The script calculates:
Target Price = Buy + (ATR × Target Multiplier)
Stop Price = Buy − (ATR × Stop Multiplier)
Customizable Timeframe: Optionally override the ATR timeframe (e.g., use daily ATR on a 1-hour chart).
Visual Overlay: Lines are drawn directly on the price chart for the Buy, Target, and Stop levels.
Interactive Table: A table is displayed with relevant levels and ATR info.
Customization Options:
Line Settings:
Adjust color, style (solid/dashed/dotted), and width for Buy, Target, and Stop lines.
Choose whether to extend lines rightward only or in both directions.
Table Settings:
Choose position (top/bottom, left/right).
Toggle individual rows for Buy, Target, Stop, ATR Timeframe, and ATR Value.
Customize text color and background transparency.
How to Use It for Trading:
Plan Your Trade: Enter your intended buy price when planning a trade.
Assess Risk/Reward: The script immediately visualizes the potential stop-loss and target level, helping assess R:R ratios.
Adapt to Volatility: Use ATR-based levels to scale stop and target dynamically depending on current market volatility.
Higher Timeframe ATR: Select a different timeframe for the ATR calculation to smooth noise on lower timeframe charts.
On-the-Chart Reference: Visually track trade zones directly on the price chart—ideal for live trading or strategy backtesting.
Ideal For:
Swing traders and intraday traders
Risk management and trade planning
Traders using ATR-based exits or scaling
Visualizing asymmetric risk/reward setups
How I Use This:
After entering a trade, adding an entry price will plot desired ATR target and stop level for visualization.
Adjusting ATR multiplier values assists in evaluating and planning trades.
Visualization assists in comparing ATR multiples to recent support and resistance levels.
Volatility-Adjusted Momentum Score (VAMS) [QuantAlgo]🟢 Overview
The Volatility-Adjusted Momentum Score (VAMS) measures price momentum relative to current volatility conditions, creating a normalized indicator that identifies significant directional moves while filtering out market noise. It divides annualized momentum by annualized volatility to produce scores that remain comparable across different market environments and asset classes.
The indicator displays a smoothed VAMS Z-Score line with adaptive standard deviation bands and an information table showing real-time metrics. This dual-purpose design enables traders and investors to identify strong trend continuation signals when momentum persistently exceeds normal levels, while also spotting potential mean reversion opportunities when readings reach statistical extremes.
🟢 How It Works
The indicator calculates annualized momentum using a simple moving average of logarithmic returns over a specified period, then measures annualized volatility through the standard deviation of those same returns over a longer timeframe. The raw VAMS score divides momentum by volatility, creating a risk-adjusted measure where high volatility reduces scores and low volatility amplifies them.
This raw VAMS value undergoes Z-Score normalization using rolling statistical parameters, converting absolute readings into standardized deviations that show how current conditions compare to recent history. The normalized Z-Score receives exponential moving average smoothing to create the final VAMS line, reducing false signals while preserving sensitivity to meaningful momentum changes.
The visualization includes dynamically calculated standard deviation bands that adjust to recent VAMS behavior, creating statistical reference zones. The information table provides real-time numerical values for VAMS Z-Score, underlying momentum percentages, and current volatility readings with trend indicators.
🟢 How to Use
1. VAMS Z-Score Bands and Signal Interpretation
Above Mean Line: Momentum exceeds historical averages adjusted for volatility, indicating bullish conditions suitable for trend following
Below Mean Line: Momentum falls below statistical norms, suggesting bearish conditions or downward pressure
Mean Line Crossovers: Primary transition signals between bullish and bearish momentum regimes
1 Standard Deviation Breaks: Strong momentum conditions indicating statistically significant directional moves worth following
2 Standard Deviation Extremes: Rare momentum readings that often signal either powerful breakouts or exhaustion points
2. Information Table and Market Context
Z-Score Values: Current VAMS reading displayed in standard deviations (σ), showing how far momentum deviates from its statistical norm
Momentum Percentage: Underlying annualized momentum displayed as percentage return, quantifying the directional strength
Volatility Context: Current annualized volatility levels help interpret whether VAMS readings occur in high or low volatility environments
Trend Indicators: Directional arrows and change values provide immediate feedback on momentum shifts and market transitions
3. Strategy Applications and Alert System
Trend Following: Use sustained readings beyond the mean line and 1σ band penetrations for directional trades, especially when VAMS maintains position in upper or lower statistical zones
Mean Reversion: Focus on 2σ extreme readings for contrarian opportunities, particularly effective in sideways markets where momentum tends to revert to statistical norms
Alert Notifications: Built-in alerts for mean crossovers (regime changes), 1σ breaks (strong signals), and 2σ touches (extreme conditions) help monitor multiple instruments for both continuation and reversal setups
ATR FX DashboardATR FX Dashboard – Multi-Timeframe Volatility Monitor
Overview:
The ATR FX Dashboard provides a quick, at-a-glance view of market volatility across multiple timeframes for any forex pair. It uses the well-known Average True Range (ATR) indicator to display real-time volatility information in both pips and percentage terms, helping traders assess potential risk, position sizing, and market conditions.
How It Works:
This dashboard displays:
✔ ATR in Pips — The average price movement over a given timeframe, converted to pips for easy interpretation, automatically adjusting for JPY pairs.
✔ ATR as a Percentage of Price — Shows how significant the ATR is relative to the current price. Higher percentages often signal higher volatility or more active markets.
✔ Color-Coded Volatility Highlights — On the daily timeframe, ATR % cells are color-coded:
Green: High volatility
Orange: Moderate volatility
Red: Low volatility
Timeframes Displayed:
15 Minutes
1 Hour
4 Hour
Daily
This gives traders a clear, multi-timeframe view of short-term and broader market volatility conditions, directly on the chart.
Ideal For:
✅ Forex traders seeking quick, reliable volatility reference points
✅ Day traders and swing traders needing help with risk assessment and position sizing
✅ Anyone using ATR-based strategies or simply wanting to stay aware of changing market conditions
Additional Features:
Toggle option to display or hide ATR % relative to price
Automatic pip conversion for JPY pairs
Simple, clean table layout in the bottom-right corner of the chart
Supports all forex symbols
Disclaimer:
This tool is for informational purposes only and is not financial advice. As with all technical indicators, it should be used in conjunction with other tools and proper risk management.
Z Score Overlay [BigBeluga]🔵 OVERVIEW
A clean and effective Z-score overlay that visually tracks how far price deviates from its moving average. By standardizing price movements, this tool helps traders understand when price is statistically extended or compressed—up to ±4 standard deviations. The built-in scale and real-time bin markers offer immediate context on where price stands in relation to its recent mean.
🔵 CONCEPTS
Z Score Calculation:
Z = (Close − SMA) ÷ Standard Deviation
This formula shows how many standard deviations the current price is from its mean.
Statistical Extremes:
• Z > +2 or Z < −2 suggests statistically significant deviation.
• Z near 0 implies price is close to its average.
Standardization of Price Behavior: Makes it easier to compare volatility and overextension across timeframes and assets.
🔵 FEATURES
Colored Z Line: Gradient coloring based on how far price deviates—
• Red = oversold (−4),
• Green = overbought (+4),
• Yellow = neutral (~0).
Deviation Scale Bar: A vertical scale from −4 to +4 standard deviations plotted to the right of price.
Active Z Score Bin: Highlights the current Z-score bin with a “◀” arrow
Context Labels: Clear numeric labels for each Z-level from −4 to +4 along the side.
Live Value Display: Shows exact Z-score on the active level.
Non-intrusive Overlay: Can be applied directly to price chart without changing scaling behavior.
🔵 HOW TO USE
Identify overbought/oversold areas based on +2 / −2 thresholds.
Spot potential mean reversion trades when Z returns from extreme levels.
Confirm strong trends when price remains consistently outside ±2.
Use in multi-timeframe setups to compare strength across contexts.
🔵 CONCLUSION
Z Score Overlay transforms raw price action into a normalized statistical view, allowing traders to easily assess deviation strength and mean-reversion potential. The intuitive scale and color-coded display make it ideal for traders seeking objective, volatility-aware entries and exits.
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Movement WatcherMovement Watcher – Intraday Price Change Alert
This indicator tracks the percentage price movement of a selected symbol (e.g., VIX) from a configurable start time. If the intraday movement crosses a defined threshold (up or down), it triggers a one-time alert per day.
Key Features:
Monitors intraday % change from the specified start time.
Triggers one-time alerts for upper or lower threshold crossings.
Optional end time for monitoring period.
Visual plots and alert markers.
Useful for automated trading via webhook integrations.
This script was designed to work with automated trading tools such as the Trading Automation Toolbox. You can use it to generate alerts based on intraday volatility and route them via webhook for automated strategies.
Volume Zones IndicatorVolume Zones Indicator — VWAP with Dynamic Monthly Volume Zones
This indicator is an enhanced version of the classic VWAP (Volume Weighted Average Price), designed to create clear monthly zones around VWAP based on average price range (ATR) and volume activity.
The core idea is to highlight key zones where price is more likely to reverse or consolidate, based on where significant trading volume occurs.
How does it work?
VWAP is calculated over the last N days (set by the lookbackPeriod input).
Four zones are plotted above and below VWAP, spaced using a multiple of ATR.
Each zone has its own color for clarity:
Blue — closest to VWAP
Red — second band
Green — third band
Orange — outer band (potential breakout or exhaustion zone)
If the current volume exceeds the moving average of volume, it is highlighted directly on the chart. This helps detect accumulation or distribution moments more easily.
What does the trader see?
You see horizontal colored bands on the chart that update at the start of each new month. These zones:
Remain fixed throughout the month
Automatically adjust based on recent volume and volatility
Act as dynamic support/resistance levels
Best used for:
Mean reversion strategies — identifying pullbacks toward value areas
Support and resistance mapping — automatic SR zones based on price/volume behavior
Breakout filtering — when price reaches zone 3 or 4, trend continuation or reversal is likely
Adding volume context to price action — works well with candlestick and pattern analysis
Settings
Lookback Period (Days): VWAP and volume smoothing length
Volume Area Threshold %: Reserved for future functionality
Works on any timeframe; best suited for 4H timeframe.
Zones are calculated and fixed monthly for clean visual context
Combines price structure with actual volume flow for more reliable decision-making
Adaptive Cycle Oscillator with EMADescription of the Adaptive Cycle Oscillator with EMA Pine Script
This Pine Script, titled "Adaptive Cycle Oscillator with EMA", is a custom technical indicator designed for TradingView to help traders analyze market cycles and identify potential buy or sell opportunities. It combines an Adaptive Cycle Oscillator (ACO) with multiple Exponential Moving Averages (EMAs), displayed as colorful, wavy lines, and includes features like buy/sell signals and divergence detection. Below is a beginner-friendly explanation of how the script works, adhering to TradingView's Script Publishing Rules.
What This Indicator Does
The Adaptive Cycle Oscillator with EMA helps you:
Visualize market cycles using an oscillator that adapts to price movements.
Track trends with seven EMAs of different lengths, plotted as a rainbow of wavy lines.
Identify potential buy or sell signals when the oscillator crosses predefined thresholds.
Spot divergences between the oscillator and price to anticipate reversals.
Use customizable settings to adjust the indicator to your trading style.
Note: This is a technical analysis tool and does not guarantee profits. Always combine it with other analysis methods and practice risk management.
Step-by-Step Explanation for New Users
1. Understanding the Indicator
Adaptive Cycle Oscillator (ACO): The ACO analyzes price data (based on high, low, and close prices, or HLC3) to detect market cycles. It smooths price movements to create an oscillator that swings between overbought and oversold levels.
EMAs: Seven EMAs of different lengths are applied to the ACO and scaled based on the market's dominant cycle. These EMAs are plotted as colorful, wavy lines to show trend direction.
Buy/Sell Signals: The script generates signals when the ACO crosses above or below user-defined thresholds, indicating potential entry or exit points.
Divergence Detection: The script identifies bullish or bearish divergences between the ACO and the fastest EMA, which may signal potential reversals.
Visual Style: The indicator uses a rainbow of seven colors (red, orange, yellow, green, blue, indigo, violet) for the EMAs, with wavy lines for a unique visual effect. Static levels (zero, overbought, oversold) are also wavy for consistency.
2. How to Add the Indicator to Your Chart
Open TradingView and load the chart of any asset (e.g., stock, forex, crypto).
Click on the Indicators button at the top of the chart.
Search for "Adaptive Cycle Oscillator with EMA" (or paste the script into TradingView’s Pine Editor if you have access to it).
Click to add the indicator to your chart. It will appear in a separate panel below the price chart.
3. Customizing the Indicator
The script offers several input options to tailor it to your needs:
Base Cycle Length (Default: 20): Sets the initial period for calculating the dominant cycle. Higher values make the indicator slower; lower values make it more sensitive.
Alpha Smoothing (Default: 0.07): Controls how much the ACO smooths price data. Smaller values produce smoother results.
Show Buy/Sell Signals (Default: True): Toggle to display green triangles (buy) and red triangles (sell) on the chart.
Threshold (Default: 0.0): Defines overbought (above threshold) and oversold (below threshold) levels. Adjust to widen or narrow signal zones.
EMA Base Length (Default: 10): Sets the starting length for the fastest EMA. Other EMAs are incrementally longer (12, 14, 16, etc.).
Divergence Lookback (Default: 14): Determines how far back the script looks to detect divergences.
To adjust these:
Right-click the indicator on your chart and select Settings.
Modify the inputs in the pop-up window.
Click OK to apply changes.
4. Reading the Indicator
Oscillator and EMAs: The ACO and seven EMAs are plotted in a separate panel. The EMAs (colored lines) move in a wavy pattern:
Red (fastest) to Violet (slowest) represent different response speeds.
When the faster EMAs (e.g., red, orange) are above slower ones (e.g., blue, violet), it suggests bullish momentum, and vice versa.
Zero Line: A gray wavy line at zero acts as a neutral level. The ACO above zero indicates bullish conditions; below zero indicates bearish conditions.
Overbought/Oversold Lines: Red (overbought) and green (oversold) wavy lines mark threshold levels. Extreme ACO values near these lines may suggest reversals.
Buy/Sell Signals:
Green Triangle (Bottom): Appears when the ACO crosses above the oversold threshold, suggesting a potential buy.
Red Triangle (Top): Appears when the ACO crosses below the overbought threshold, suggesting a potential sell.
Divergences:
Green Triangle (Bottom): Indicates a bullish divergence (price makes a lower low, but the EMA makes a higher low), hinting at a potential upward reversal.
Red Triangle (Top): Indicates a bearish divergence (price makes a higher high, but the EMA makes a lower high), hinting at a potential downward reversal.
5. Using Alerts
You can set alerts for key events:
Right-click the indicator and select Add Alert.
Choose a condition (e.g., "ACO Buy Signal", "Bullish Divergence").
Configure the alert settings (e.g., notify via email, app, or pop-up).
Click Create to activate the alert.
Available alert conditions:
ACO Buy Signal: When the ACO crosses above the oversold threshold.
ACO Sell Signal: When the ACO crosses below the overbought threshold.
Bullish Divergence: When a potential upward reversal is detected.
Bearish Divergence: When a potential downward reversal is detected.
6. Tips for Using the Indicator
Combine with Other Tools: Use the indicator alongside support/resistance levels, candlestick patterns, or other indicators (e.g., RSI, MACD) for confirmation.
Test on Different Timeframes: The indicator works on any timeframe (e.g., 1-minute, daily). Shorter timeframes may produce more signals but with more noise.
Practice Risk Management: Never rely solely on this indicator. Set stop-losses and position sizes to manage risk.
Backtest First: Use TradingView’s Strategy Tester (if you convert the script to a strategy) to evaluate performance on historical data.
Compliance with TradingView’s Script Publishing Rules
This description adheres to TradingView’s Script Publishing Rules (as outlined in the provided link):
No Performance Claims: The description avoids promising profits or specific results, emphasizing that the indicator is a tool for analysis.
Clear Instructions: It provides step-by-step guidance for adding, customizing, and using the indicator.
Risk Disclaimer: It notes that trading involves risks and the indicator should be used with other analysis methods.
No Misleading Terms: Terms like “buy” and “sell” are used to describe signals, not guaranteed actions.
Transparency: The description explains the indicator’s components (ACO, EMAs, signals, divergences) without exaggerating its capabilities.
No External Links: The description avoids linking to external resources or soliciting users.
Educational Tone: It focuses on educating users about the indicator’s functionality.
Limitations
Not a Standalone System: The indicator is not a complete trading strategy. It provides insights but requires additional analysis.
Lagging Nature: As with most oscillators and EMAs, signals may lag behind price movements, especially in fast markets.
False Signals: Signals and divergences may not always lead to successful trades, particularly in choppy markets.
Market Dependency: Performance varies across assets and market conditions (e.g., trending vs. ranging markets).
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator
The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
