Technical Strength Index (TSI)📘 TSI with Dynamic Bands – Technical Strength Index
The TSI with Dynamic Bands is a multi-factor indicator designed to measure the statistical strength and structure of a trend. It combines several quantitative metrics into a single, normalized score between 0 and 1, allowing traders to assess the technical quality of market moves and detect overbought/oversold conditions with adaptive precision.
🧠 Core Components
This indicator draws from the StatMetrics library, blending:
📈 Trend Persistence: via the Hurst exponent, indicating whether price action is mean-reverting or trending.
📉 Risk-Adjusted Volatility: via the inverted , rewarding smoother, less erratic price movement.
🚀 Momentum Strength: using a combination of directional momentum and Z-score–normalized returns.
These components are normalized and averaged into the TSI line.
🎯 Features
TSI Line: Composite score of trend quality (0 = weak/noise, 1 = strong/structured).
Dynamic Bands: Mean ± 1 standard deviation envelopes provide adaptive context.
Overbought/Oversold Detection: Based on a rolling quantile (e.g. 90th/10th percentile of TSI history).
Signal Strength Bar (optional): Measures how statistically extreme the current TSI value is, helping validate confidence in trade setups.
Dynamic Color Cues: Background and bar gradients help visually identify statistically significant zones.
📈 How to Use
Look for overbought (red background) or oversold (green background) conditions as potential reversal zones.
Confirm trend strength with the optional signal strength bar — stronger values suggest higher signal confidence.
Use the TSI line and context bands to filter out noisy ranges and focus on structured price moves.
⚙️ Inputs
Lookback Period: Controls the smoothing and window size for statistical calculations.
Overbought/Oversold Quantiles: Adjust the thresholds for signal zones.
Plot Signal Strength: Enable or disable the signal confidence bar.
Overlay Signal Strength: Show signal strength in the same panel (compact) or not (cleaner TSI-only view).
🛠 Example Use Cases
Mean reversion traders identifying reversal zones with statistical backing
Momentum/Trend traders confirming structure before entries
Quantitative dashboards or multi-asset screening tools
⚠️ Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any financial instrument.
This AI is not a financial advisor; please consult your financial advisor for personalized advice.
Educational
ADR by Saurabh MaggoADR levels for intraday
This Pine Script v5 indicator plots Average Daily Range (ADR) levels on a 5-minute NSE chart, ideal for intraday traders. It marks key price levels (L3+, L3-, L2+, L2-, L1+, L1-) at 9:15 AM IST each day, based on the daily open and a customizable ATR period.
Features:
Configurable Levels: Adjust ATR period (default 5) and multipliers (L3=0.5, L2=0.25, L1=0.125) to set price targets.
Today Only Option: Toggle Show Recent to display only the current day’s levels or all historical levels.
Visual Customization: Choose vibrant colors for each level via settings, with a glow effect
(toggleable, transparency=20) and adjustable circle size (default 2, range 1–5) for enhanced visibility, optimized for dark chart backgrounds.
Clean Design: Single-point plotting at 9:15 AM IST ensures a clutter-free chart, with dynamic points that move with the chart.
Usage: Perfect for NSE intraday trading, this indicator helps identify high-probability price targets. Customize levels, colors, and visuals to suit your strategy.
[GetSparx] Lacuna Pro⚡ Lacuna Pro – Institutional Liquidity Framework
This indicator is a premium Smart Money Concepts (SMC) trading toolkit designed to help traders identify high-probability entry and exit zones by visualizing real-time market inefficiencies. It combines Fair Value Gaps (FVGs), Break of Structure (BOS), Change of Character (CHoCH), and Supply & Demand Zones into a unified, configurable framework.
Unlike many public indicators that simply "overlay concepts", this indicator implements strict internal validation to filter out noise and provide only institutional-grade levels — making it a valuable execution layer for SMC-based strategies.
🧠 What the Script Does – and Why the Combination Matters
This is more than just a combination of known SMC tools — it's a complete workflow assistant:
-FVGs highlight where liquidity is likely resting due to institutional imbalance.
-BOS & CHoCH define structural context: whether the market is trending or shifting.
-Supply & Demand Zones show where institutions are likely to react.
-Each component works together to create a layered confluence system:
-FVG inside a Demand Zone after a Bullish CHoCH → High-probability Long Setup
-Bearish BOS into a Supply Zone + fresh Bearish FVG → High-probability Short Setup
📘 Core Concepts Explained
Fair Value Gap (FVG)
FVGs occur when price moves with strong momentum and leaves a gap between candles — suggesting inefficiency. Bullish FVGs lie below price; bearish ones above. Price often returns to these levels before continuing.
An FVG is detected when a three-candle sequence reveals a price imbalance:
- Bullish : Candle 2’s low is higher than Candle 1’s high
- Bearish : Candle 2’s high is lower than Candle 1’s low
These setups indicate a sudden burst of institutional momentum, often causing price to revisit the gap for rebalancing.
Break of Structure (BOS)
A BOS signals trend continuation when price breaks the previous swing high or low in the direction of the current trend.
The script uses a 3-bar pivot system to detect local swing highs and lows — a swing high forms when the highest candle is flanked by two lower highs on each side (and vice versa for swing lows).
A BOS is confirmed when price closes beyond the most recent swing point in alignment with the current trend direction.
Change of Character (CHoCH)
A CHoCH signals a potential trend reversal by breaking a structure level in the opposite direction of the prevailing trend.
It is detected when price breaks the most recent opposing swing and simultaneously flips the internal trend state.
CHoCH events always take precedence over BOS to avoid conflicting signals.
The internal trend engine ensures that these structural shifts are valid and not caused by random volatility.
Supply & Demand Zones
These zones mark institutional interest and are formed using precise price action rules — not arbitrary support/resistance.
A valid zone begins when a small-bodied base candle (such as a star or doji) appears at a local swing point. This candle must be followed by a strong impulse candle — either a bullish engulfing (for demand) or bearish breakout (for supply).
- Demand Zone : From the base candle's low to the impulse candle's high
- Supply Zone : From the base candle's high to the impulse candle's low
These zones represent likely institutional entries or exits, often acting as magnets or rejection areas. Once price decisively breaks through a zone, it is automatically removed — keeping the chart clean and relevant.
Zone Detection Logic – When a Zone Is Drawn or Skipped
Below are the precise rules used to determine whether a Supply or Demand Zone is valid and shown on the chart
A Supply or Demand Zone is only drawn if all of the following conditions are met:
-A small-bodied base candle forms at a local high or low (body size below threshold)
-The base candle is followed by a strong impulse candle (engulfing or breakout)
-The impulse direction matches the expected context (e.g., bearish impulse from swing high = Supply)
-The candle wicks do not invalidate the structure (e.g., no long opposing wick that retraces the move)
-The zone meets the minimum size threshold based on % or ATR filter
If any of these criteria are not satisfied, the zone is skipped to avoid false or weak levels.
This ensures only clean, institutional-grade Supply & Demand Zones are shown on the chart.
(e.g. small-bodied star + bullish engulfing at swing low = Demand Zone, or bearish breakout at swing high = Supply Zone).
🔍 Core Functionality & Original Features
1. 📉 Fair Value Gaps (FVGs) – Dynamic, Validated, and Clean
Unlike scripts that draw every gap, this script applies strict quality control to ensure only meaningful FVGs appear:
Minimum Threshold Filtering
Filters out small or noisy gaps by requiring each FVG to exceed a % or ATR-based size threshold. Prevents micro-gap clutter on lower timeframes.
Momentum Candle Verification
Requires a strong middle candle (candle 2) between two extremes. Large opposing wicks invalidate the setup.
Partial Fill Adjustment
When price partially fills a gap, the FVG box automatically shrinks to show only the remaining imbalance. If fully filled, the box is removed.
Multi-Timeframe Overlays
View institutional gaps from 15m, 1H, 4H, or Daily overlaid onto any chart for top-down analysis and entry refinement.
2. 🧱 Structural Shifts – BOS & CHoCH
Structural logic is built around pivot detection with real-time trend state awareness:
Pivot Logic (Customizable Strength)
Local highs/lows are detected using pivot length (default: 3 bars left/right). Breaks are only confirmed if they align with the internal trend state.
BOS = Continuation
Breaks a swing in trend direction (e.g., HL → HH → BOS at previous HH)
CHoCH = Reversal
Breaks a structure against trend (e.g., HH → HL → break of HL = Bearish CHoCH)
Conflict Resolution
If both BOS and CHoCH could trigger, CHoCH takes priority. This avoids false positives and ensures a single, clear structure signal per swing.
Styling & Visibility
All structure lines and labels are customizable — colors, line style (solid/dashed), and which signals to display (BOS/CHoCH/both).
3. 🧠 Supply & Demand Zones – Smart Detection & Maintenance
These zones are generated using strict price action logic, not arbitrary support/resistance lines:
-Formation Conditions
-Small-bodied "base candle" at a local high/low
-Followed by an impulse candle (bullish/bearish engulfing or breakout)
-Zone Bounds
- Demand : From base candle low to impulse high
- Supply : From base candle high to impulse low
Automatic Cleanup
Once price decisively pierces a zone, it’s automatically removed from the chart. This keeps the display relevant and clutter-free.
Multi-Timeframe Zones
Toggle zones from your current timeframe or overlay from 1H, 4H, and Daily — ideal for confluence stacking.
Zone Compression Filtering
Optional compression % ensures overlapping zones are combined logically to reduce redundancy.
🧩 How It Works Together – Practical Usage Flow
This indicator is designed to follow a structured workflow used by institutional-style traders:
Trend Structure
Identify trend using BOS and CHoCH on your timeframe.
Liquidity Zones
Look for supply/demand zones aligning with the structural bias.
Execution Areas
Wait for an unfilled FVG in confluence with the above conditions.
📸 Screenshot Captions
Screenshot 1: CHoCH + Demand Zone + Bullish FVG
📌 Reversal Setup with Confluence
A Bullish CHoCH confirms a structural shift. Price enters a Demand Zone and reacts from an unfilled Bullish FVG, creating a high-probability long opportunity.
Screenshot 2: Bearish BOS + FVG Fill
📌 Trend Continuation Confirmation
Price breaks a swing low, triggering a Bearish BOS. A Bearish FVG forms and price returns to fill it before continuing lower — validating the trend and the gap.
Screenshot 3: Multi-Timeframe Overlay (FVGs from 1H and 4H)
📌 Top-Down Liquidity Mapping
Overlaid 1H and 4H FVGs provide institutional-level insight on lower timeframes. Combined with structure signals, this supports precise entry alignment across timeframes.
As price partially fills a bullish gap, the FVG box auto-adjusts to show only the remaining imbalance. Fully filled zones are automatically removed, keeping the chart clean.
Screenshot 4: Supply Zone Rejection
📌 Institutional Supply in Action
Price enters a Supply Zone formed from a base candle + bearish impulse. A sharp rejection confirms active sell-side interest at this level. Zone opgevuld box verdwijnt
Screenshot 5: Bullish BOS + Internal Trend Logic
📌 Trend Continuation with Structure Awareness
A Higher Low forms, followed by a Higher High, triggering a Bullish BOS. The internal trend engine confirms direction and filters false reversals.
Screenshot 6: Zone Compression Logic
📌 Smart Zone Consolidation
Closely overlapping supply zones are merged using compression logic to prevent clutter. Only the strongest institutional levels remain visible.
⚙ Full Customization Panel
You can configure:
-FVG display per timeframe + color scheme
-BOS/CHoCH styling, label text, and detection toggles
-Zone settings: visibility, compression %, length
-Auto-cleanup behavior for FVGs and zones
🔐 Why Invite-Only?
This indicator contains original logic not available in public indicators, including:
-Momentum-candle verified FVGs
-Real-time partial fill trimming
-Auto-removal of invalidated structure/zones
-Conflict-aware BOS/CHoCH logic
-Multi-timeframe overlays with internal state tracking
-Proprietary compression-based zone filtering
This script is part of a private paid offering. It is not based on reused or repackaged educational code. The logic and structure management are exclusive to this implementation.
⚠ Disclaimer
This tool is for educational and analytical use only. It does not provide financial advice or trading signals. Always use proper risk management and do your own due diligence.
SmartPhase Analyzer📝 SmartPhase Analyzer – Composite Market Regime Classifier
SmartPhase Analyzer is an adaptive regime classification tool that scores market conditions using a customizable set of statistical indicators. It blends multiple normalized metrics into a composite score, which is dynamically evaluated against rolling statistical thresholds to determine the current market regime.
✅ Features:
Composite score calculated from 13+ toggleable statistical indicators:
Sharpe, Sortino, Omega, Alpha, Beta, CV, R², Entropy, Drawdown, Z-Score, PLF, SRI, and Momentum Rank
Uses dynamic thresholds (mean ± std deviation) to classify regime states:
🟢 BULL – Strongly bullish
🟩 ACCUM – Mildly bullish
⚪ NEUTRAL – Sideways
🟧 DISTRIB – Mildly bearish
🔴 BEAR – Strongly bearish
Color-coded histogram for composite score clarity
Real-time regime label plotted on chart
Benchmark-aware metrics (Alpha, Beta, etc.)
Modular design using the StatMetrics library by RWCS_LTD
🧠 How to Use:
Enable/disable metrics in the settings panel to customize your composite model
Use the composite histogram and regime background for discretionary or systematic analysis
⚠️ Disclaimer:
This indicator is for educational and informational purposes only. It does not constitute financial advice or a trading recommendation. Always consult your financial advisor before making investment decisions.
Quantum RSI (TechnoBlooms)The Next Evolution of Momentum Analysis
📘 Overview
Quantum RSI is an advanced momentum oscillator based on Quantum Price Theory, designed as a superior alternative to the traditional RSI. It incorporates a Gaussian decay function to weigh price changes, creating a more responsive and intuitive measure of trend strength.
This indicator excels in identifying micro-trends and subtle momentum shifts — especially in narrow or low-volatility environments where standard RSI typically lags or gives false signals. With its enhanced smoothing, intuitive color gradients, and customizable moving average, Quantum RSI offers a powerful tool for traders seeking clarity and precision.
🔍 Key Features
• ⚛️ Quantum Momentum Engine: Measures net momentum using quantum-inspired Gaussian decay weighting.
• 🎨 Color-Reversed Gradient Zones:
o Green (Overbought): Shows momentum strength, not weakness.
o Red (Oversold): Highlights momentum exhaustion and potential bounce.
• 🧠 Smoothing with MA: Option to apply moving average (SMA/EMA/WMA/SMMA/VWMA) to the Quantum RSI line.
• 📊 Levels at 30 / 50 / 70: Standard RSI levels for decision-making guidance.
• 📈 Intuitive Visuals: Gradient fills for cleaner interpretation of zones and transitions.
👤 Who Is It For?
• Technical traders seeking a modern alternative to RSI.
• Quantitative analysts who value precision and smooth signal flow.
• Visual traders looking for intuitive, color-coded trend zones.
• Traders focused on market microstructure and early trend detection.
💡 Pro Tips
• Pair with order blocks, market structure tools, or Fibonacci confluences for high-probability entries.
• Use on assets with frequent compression or consolidation, where traditional RSI often misleads.
• Combine with volume-based indicators or smart money concepts for added confirmation.
• Ideal for sideways markets, false breakouts, or low-volatility zones where typical RSI lags.
1st FVGOverview
This indicator is specifically designed for intraday price action traders who focus on the NASDAQ opening range. Its primary function is to automatically identify, plot, and alert on the very first Fair Value Gap (FVG) that forms during the critical 30-minute window of the New York morning session, from 9:30 AM to 10:00 AM ET.
The script intelligently ignores any gaps that rely on pre-market data, ensuring that the detected FVG is a true imbalance created by the initial volume and volatility of the regular trading session. This tool helps traders to quickly pinpoint a key area of interest right after the market opens.
Key Features
First FVG Detection: Pinpoints only the initial FVG of the session and ignores all subsequent ones for the day.
Specific Time Window: Operates strictly between 9:30 and 10:00 AM New York time.
Strict Formation Rule: To ensure accuracy, the entire 3-bar FVG pattern must form at or after the 9:30 AM candle. This prevents false signals from pre-market price action.
Visual Price Zones: Automatically draws a clean, colored box around the FVG, making the zone easy to see. The box can be extended to track future price interactions.
Customizable Display: Control how many historical FVGs to show on your chart and how far the zone extends to the right.
Built-in Alerts: Get real-time notifications the moment the first FVG is confirmed, so you never miss a potential setup.
How It Works
The indicator scans the price action candle by candle. Once the 9:30 AM ET session begins, it looks for the first valid 3-bar FVG pattern (also known as a price imbalance).
A Bullish FVG is identified when the low of the current candle is higher than the high of the candle two periods ago.
A Bearish FVG is identified when the high of the current candle is lower than the low of the candle two periods ago.
Once the first FVG for the day is detected and plotted, the script will remain dormant until the next trading day begins, keeping your chart clean and focused.
Settings
Number of FVG History: Controls how many of the most recent daily FVGs are displayed on the chart.
Extend Box To End: A checkbox to extend the FVG zone all the way to the right edge of the chart. This is useful for tracking how price interacts with the zone later in the day.
Manual Box Length: If the "Extend Box" option is unchecked, this input sets a fixed length for the box (in number of bars).
How to Set Up Alerts
Add the indicator to your chart.
Click the 'Alert' icon (alarm clock) in the TradingView toolbar.
In the 'Condition' dropdown menu, select "1st FVG".
A second dropdown will appear, which should be set to "Alert Function Call".
Choose your preferred notification options (e.g., pop-up, email, app notification).
Click 'Create'.
Disclaimer: This indicator is a tool for technical analysis and should not be considered as financial advice. Always use proper risk management and conduct your own research before making any trading decisions.
ADR Pivot LevelsThe ADR (Average Daily Range) indicator shows the average range of price movement over a trading day. The ADR is used to estimate volatility and to determine target levels. It helps to set Take-Profit and Stop-Loss orders. It is suitable for intraday trading on lower time frames.
The “ADR Pivot Levels” produces a sequence of horizontal line levels above and below the Center Line (reference level). They are sized based on the instrument's volatility, representing the average historical price movement on a selected higher timeframe using the average daily range (ADR) indicator.
RSI Buy Sell Signals+ with MFI Cloud [RanaAlgo]Indicator Overview
This indicator combines RSI (Relative Strength Index) with MFI (Money Flow Index) to generate trading signals with additional confirmation filters. The key features include:
RSI Analysis (14-period) with overbought/oversold levels
MFI Cloud (20-period default) showing trend direction via EMAs
Enhanced Signal Generation with volume and trend confirmation options
Visual Elements including colored zones, signal labels, and an information panel
How to Use This Indicator
Basic Interpretation:
Buy Signals (green labels) appear when:
RSI crosses above oversold level (30) OR
RSI shows a rising pattern from oversold zone with volume/trend confirmation (if enabled)
Sell Signals (red labels) appear when:
RSI crosses below overbought level (70) OR
RSI shows a falling pattern from overbought zone with volume/trend confirmation (if enabled)
MFI Cloud provides trend confirmation:
Green cloud = bullish trend (fast EMA > slow EMA)
Red cloud = bearish trend (fast EMA < slow EMA)
Recommended Usage:
For Conservative Trading:
Enable both volume and trend confirmation
Require MFI cloud to align with signal direction
Wait for RSI to clearly exit overbought/oversold zones
For Active Trading:
Combine with price action at key support/resistance levels
Watch for divergence between price and RSI
The Information Panel (top-right) shows:
Current RSI value and status
MFI trend direction
Last generated signal
Current momentum
Customization Options:
Adjust RSI/MFI lengths for sensitivity
Modify overbought/oversold levels
Toggle volume/trend confirmation requirements
Adjust visual elements like cloud opacity and zone visibility
IU Liquidity Flow TrackerDESCRIPTION
The IU Liquidity Flow Tracker is a powerful market analysis tool designed to visualize hidden buying and selling activity by analyzing price action, volume behavior, market pressure, and depth. It provides a composite view of liquidity dynamics to help traders identify accumulation, distribution, and neutral phases with high clarity.
This indicator is ideal for traders who want to gauge the flow of market participants and make informed entry/exit decisions based on the underlying liquidity structure.
USER INPUTS:
* Flow Analysis Period: Length used for analyzing price spread and volume flow.
* Pressure Sensitivity: Adjusts the sensitivity of threshold detection for flow classification.
* Flow Smoothing: Controls the smoothing applied to raw flow data.
* Market Depth Analysis: Sets the depth range for rejection and wick analysis.
* Colors: Customize colors for accumulation, distribution, neutral zones, and pressure visualization.
INDICATOR LOGIC:
The IU Liquidity Flow Tracker uses a multi-factor model to evaluate market behavior:
1. Liquidity Pressure: Combines price spread, price efficiency, and volume imbalance.
2. Flow Direction: Weighted momentum using short, medium, and long-term price changes adjusted for volume.
3. Market Depth: Wick-based rejection scoring to estimate buying/selling aggressiveness at price extremes.
4. Composite Flow Index: Blended value of flow direction, pressure, and depth—smoothed for clarity.
5. Dynamic Thresholds: Automatically adjusts based on volatility to classify the market into:
* Accumulation: Strong buying signals.
* Distribution: Strong selling signals.
* Neutral: No significant flow dominance.
6. Entry Signals: Long/Short signals are generated when flow state shifts, supported by momentum, volume surge, and depth strength.
WHY IT IS UNIQUE:
Unlike typical indicators that rely solely on price or volume, this tool combines spread behavior, volume polarity, momentum weighting, and price rejection zones into a single visual interface. It dynamically adjusts sensitivity based on market volatility, helping avoid false signals during sideways or low-volume periods.
It is not based on any traditional indicator (RSI, MACD, etc.), making it ideal for traders looking for an original and data-driven market read.
HOW USER CAN BENEFIT FROM IT:
* Understand Market Context: Know whether the market is being accumulated, distributed, or ranging.
* Improve Entries/Exits: Use flow transitions combined with volume confirmation for high-probability setups.
* Spot Institutional Activity: Detect subtle shifts in liquidity that precede major price moves.
* Reduce Whipsaws: Dynamic thresholds and multi-factor confirmation help filter noise.
* Use with Any Style: Whether you're a swing trader, day trader, or scalper, this tool adapts to different timeframes and strategies.
DISCLAIMER:
This indicator is created for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. All trading involves risk, and users should conduct their own analysis or consult with a qualified financial advisor before making any trading decisions. The creator is not responsible for any losses incurred through the use of this tool. Use at your own discretion.
Perfect Entry VisualizerPerfect Entry Visualizer is a Pine Script v6 study designed purely as a historical analysis tool, not for live trading. It plots the theoretical “perfect” long and short entries on your chart based on a user-defined minimum price move. By alternately tracking swing lows for longs and swing highs for shorts, it shows exactly where a trade would have captured every move of at least X points, with X set by the “Minimum Move (Points)” input.
How it works
After each labeled entry it switches direction (long→short or short→long), so signals never overlap.
It never uses future data to predict; it simply waits for price to move far enough from the last extreme and then plots.
Adjusting the “Minimum Move (Points)” input controls how big a swing must be before an entry is marked: smaller values give more frequent signals, larger values highlight only the biggest moves.
Primary uses
Algo system benchmarking: compare your live strategy’s entries against the theoretical best to measure entry efficiency.
Manual trader review: visualize ideal swing entry timing to refine your own setups and fine-tune stop-and-profit targets.
Educational tool: teach price action concepts by showing exact points where a pure price-move strategy would have worked.
Performance analysis: overlay on any time frame or market to see which instruments and sessions offer the most clean, swing-based opportunities.
Alternative pivot point analysis: use it as a dynamic pivot high/low tool based on movement thresholds rather than fixed lookback bars.
Because it simply visualizes past price moves, you can paste it into any chart to instantly see the theoretical maximum trade capture for your chosen swing size. It’s a flexible comparison and learning aid, not a live signal generator.
Euclidean Range [InvestorUnknown]The Euclidean Range indicator visualizes price deviation from a moving average using a geometric concept Euclidean distance. It helps traders identify trend strength, volatility shifts, and potential overextensions in price behavior.
Euclidean Distance
Euclidean distance is a fundamental concept in geometry and machine learning. It measures the "straight-line distance" between two points in space. In time series analysis, it can be used to measure how far one sequence deviates from another over a fixed window.
euclidean_distance(src, ref, len) =>
var float sum_sq_diff = na
sum_sq_diff := 0.0
for i = 0 to len - 1
diff = src - ref
sum_sq_diff += diff * diff
math.sqrt(sum_sq_diff)
In this script, we calculate the Euclidean distance between the price (source) and a smoothed average (reference) over a user-defined window. This gives us a single scalar that reflects the overall divergence between price and trend.
How It Works
Moving Average Calculation: You can choose between SMA, EMA, or HMA as your reference line. This becomes the "baseline" against which the actual price is compared.
Distance Band Construction: The Euclidean distance between the price and the reference is calculated over the Window Length. This value is then added to and subtracted from the average to form dynamic upper and lower bands, visually framing the range of deviation.
Distance Ratios and Z-Scores: Two distance ratios are computed: dist_r = distance / price (sensitivity to volatility); dist_v = price / distance (sensitivity to compression or low-volatility states)
Both ratios are normalized using a Z-score to standardize their behavior and allow for easier interpretation across different assets and timeframes.
Z-Score Plots: Z_r (white line) highlights instances of high volatility or strong price deviation; Z_v (red line) highlights low volatility or compressed price ranges.
Background Highlighting (Optional): When Z_v is dominant and increasing, the background is colored using a gradient. This signals a possible build-up in low volatility, which may precede a breakout.
Use Cases
Detect volatile expansions and calm compression zones.
Identify mean reversion setups when price returns to the average.
Anticipate breakout conditions by observing rising Z_v values.
Use dynamic distance bands as adaptive support/resistance zones.
Notes
The indicator is best used with liquid assets and medium-to-long windows.
Background coloring helps visually filter for squeeze setups.
Disclaimer
This indicator is provided for speculative analysis and educational purposes only. It is not financial advice. Always backtest and evaluate in a simulated environment before live trading.
Stop Hunt Indicator ║ BullVision 🧠 Overview
The Stop Hunt Indicator (SmartTrap Radar) is an original tool designed to identify potential liquidity traps caused by institutional stop hunts. It visually maps out historically significant levels where price has repeatedly reversed or rejected — and dynamically detects real-time sweep patterns based on volume, structure, and candle rejection behavior.
This script does not repurpose existing public indicators, nor does it use default TradingView built-ins such as RSI, MACD, or MAs. Its core logic is fully proprietary and was developed from scratch to support discretionary and data-driven traders in visualizing volatility risks and manipulation zones.
🔍 What the Indicator Does
This indicator identifies and visualizes potential stop hunt zones using:
Historical structure analysis: Swing highs/lows are identified via a configurable lookback period.
Liquidity level tracking: Once detected, levels are monitored for touches, age, and volume strength.
Proprietary scoring model: Each level receives a real-time significance score based on:
Age (how long the level has held)
Number of rejections (touches)
Relative volume strength
Proximity to current price
The glow intensity of plotted levels is dynamically mapped based on this score. Bright glow = higher institutional interest probability.
⚙️ Stop Hunt Detection Logic
A stop hunt is flagged when all of the following are met:
Price sweeps through a high/low beyond a user-defined penetration threshold
Wick rejection occurs (i.e., candle closes back inside the level)
Volume spikes above the average in a recent window
The script automatically:
Detects bullish stop hunts (below support) and bearish ones (above resistance)
Marks detected sweeps on-chart with optional 🔰/🚨 signals
Adjusts glow visuals based on score even after the sweep occurs
These sweeps often precede local reversals or high-volatility zones — this is not predictive, but rather a reactive mapping of market manipulation behavior.
📌 Why This Is Not Just Another Liquidity Tool
Unlike typical liquidity heatmaps or S/R indicators, this script includes:
A proprietary significance score instead of fixed rules
Multi-layer glow rendering to reflect level importance visually
Real-time scoring updates as new volume and touches occur
Combined volume × rejection × structure logic to validate stop hunts
Fully customizable detection logic (lookback, wick %, volume filters, max bars, etc.)
This indicator provides a specialized view focused solely on visualizing trap setups — not generic trend signals.
🧪 Usage Recommendations
To get started:
Add the indicator to your chart (volume-enabled instruments only)
Customize detection:
Lookback Period for structure
Penetration % for how far price must sweep
Volume Spike Multiplier
Wick rejection strength
Enable/disable features:
Glow effects
Hunt markers
Score labels
Volume highlights
Watch for:
🔰 Bullish Sweeps (below support)
🚨 Bearish Sweeps (above resistance)
Bright glowing zones = high-liquidity targets
This tool can be used for both confluence and risk assessment, especially around high-impact sessions, liquidation events, or range extremes.
📊 Volume Dependency Notice
⚠️ This indicator requires real volume data to function correctly. On instruments without volume (e.g., synthetic pairs), certain features like spike detection and scoring will be disabled or inaccurate.
🔐 Closed-Source Disclosure
This script is published as invite-only to protect its proprietary scoring, glow mapping, and detection logic. While the full implementation remains confidential, this description outlines all key mechanics and configurable logic for user transparency.
Math by Thomas Liquidity PoolDescription
Math by Thomas Liquidity Pool is a TradingView indicator designed to visually identify potential liquidity pools on the chart by detecting areas where price forms clusters of equal highs or equal lows.
Bullish Liquidity Pools (Green Boxes): Marked below price where two adjacent candles have similar lows within a specified difference, indicating potential demand zones or stop loss clusters below support.
Bearish Liquidity Pools (Red Boxes): Marked above price where two adjacent candles have similar highs within the difference threshold, indicating potential supply zones or stop loss clusters above resistance.
This tool helps traders spot areas where smart money might hunt stop losses or where price is likely to react, providing valuable insight for trade entries, exits, and risk management.
Features:
Adjustable box height (vertical range) in points.
Adjustable maximum difference threshold between candle highs/lows to consider them equal.
Boxes automatically extend forward for visibility and delete when price sweeps through or after a defined lifetime.
Separate visual zones for bullish and bearish liquidity with customizable colors.
How to Use
Add the Indicator to your chart (preferably on instruments like Nifty where point-based thresholds are meaningful).
Adjust Inputs:
Box Height: Set the vertical size of the liquidity zones (default 15 points).
Max Difference Between Highs/Lows: Set the max price difference to consider two candle highs or lows as “equal” (default 10 points).
Box Lifetime: How many bars the box stays visible if not swept (default 120 bars).
Interpret Boxes:
Green Boxes (Bullish Liquidity Pools): Areas of potential demand and stop loss clusters below price. Watch for price bounces or accumulation near these zones.
Red Boxes (Bearish Liquidity Pools): Areas of potential supply and stop loss clusters above price. Watch for price rejections or distribution near these zones.
Trading Strategy Tips:
Use these zones to anticipate where stop loss hunting or liquidity sweeps may occur.
Combine with your Order Block, Fair Value Gap, and Market Structure tools for higher probability setups.
Manage risk by avoiding entries into price regions just before large liquidity pools get swept.
Automatic Cleanup:
Boxes delete automatically once price breaks above (for bearish zones) or below (for bullish zones) the zone or after the set lifetime.
Fallback VWAP (No Volume? No Problem!) – Yogi365Fallback VWAP (No Volume? No Problem!) – Yogi365
This script plots Daily, Weekly, and Monthly VWAPs with ±1 Standard Deviation bands. When volume data is missing or zero (common in indices or illiquid assets), it automatically falls back to a TWAP-style calculation, ensuring that your VWAP levels always remain visible and accurate.
Features:
Daily, Weekly, and Monthly VWAPs with ±1 Std Dev bands.
Auto-detection of missing volume and seamless fallback.
Clean, color-coded trend table showing price vs VWAP/bands.
Uses hlc3 for VWAP source.
Labels indicate when fallback is used.
Best Used On:
Any asset or index where volume is unavailable.
Intraday and swing trading.
Works on all timeframes but optimized for overlay use.
How it Works:
If volume == 0, the script uses a constant fallback volume (1), turning the VWAP into a TWAP (Time-Weighted Average Price) — still useful for intraday or index-based analysis.
This ensures consistent plotting on instruments like indices (e.g., NIFTY, SENSEX,DJI etc.) which might not provide volume on TradingView.
Low Liquidity Marker📘 Indicator Description – Low Liquidity Marker
The Low Liquidity Marker is a simple yet powerful tool designed to highlight candles where Volume × Low Price falls below a customizable threshold — signaling potential low liquidity zones on the chart.
🔍 How it works:
It calculates volume × low for each candle.
When this value drops below your defined threshold, a red triangle is plotted below that bar.
These bars may indicate poor institutional participation or market inefficiency.
⚠️ Why it matters:
Low liquidity makes it difficult to build or exit large positions efficiently.
Stocks or instruments flagged by this tool may be suitable for small capital investments but are generally unsuitable for high-volume or institutional-grade trading.
Use this indicator to filter out illiquid setups when screening for quality trades.
🛠 Customizable Input:
Volume × Low Threshold: Tune this parameter based on your instrument or trading timeframe.
💡 Ideal For:
Retail traders avoiding illiquid zones.
Investors wanting to identify where the market lacks sufficient depth.
Enhancing trade filters in systematic or discretionary setups.
Portfolio Dashboard by DTRThe Portfolio Dashboard by DTR is a sophisticated yet user-friendly Pine Script indicator for TradingView, designed to empower traders with a comprehensive tool for managing and monitoring investment portfolios. Supporting up to 10 stocks, it delivers real-time performance metrics, risk analysis, and market insights in an intuitive, customizable dashboard—perfect for traders of all experience levels.
Key Features
Real-Time Portfolio Metrics: Tracks Return on Investment (ROI), Day's Profit and Loss (PNL), Risk of Profit (ROP), and Average Daily Range (ADR) with color-coded indicators for quick insights.
Individual Stock Insights: Displays detailed data for each stock, including ticker, trading setup, Last Traded Price (LTP) or Stop Loss (SL) status, position size, risk, portfolio risk, Risk-Reward (RR) or Gain%, daily change%, portfolio impact, and optional ADR.
Market Condition Analysis: Evaluates broader market trends using NSE:CNXSMALLCAP data, categorizing conditions as CHOPPY, BULL MARKET, BEAR MARKET, SHAKEOUT, or BEAR RALLY with visual color cues.
Customization Options:
Input total capital (scalable in Thousands, Lacs, or Crores) and maximum risk percentage.
Choose from B&W, Blue, Green, Red, Purple, or Transparent themes, with Dark Mode support.
Adjust dashboard and gauge positions (top/middle/bottom, left/center/right) and text sizes (tiny to huge).
Toggle display options like LTP, % change color, total row, ADR column, RR/Gain%, and empty rows.
Risk Management Tools: Calculates position sizes, individual and portfolio-level risks, and offers visual gauges for total allocation (% invested) and open risk (% of max risk). Supports setting Stop Loss to Break-Even (SL=BE).
Chart Enhancements: Optionally displays entry and stop loss lines on the chart with customizable styles (Dashed, Dotted, Normal) and dynamic labels for precise trade management.
How It Works
Setup: Users input portfolio details—ticker symbols, quantities, entry prices, stop losses, exits, and setups—for up to 10 stocks, along with capital and risk settings.
Data Processing: The indicator fetches daily high, low, close, and previous close data to compute metrics like ADR, percentage change, and Day's PNL for each stock.
Visualization: On the last bar, it generates a detailed table summarizing portfolio and stock-level data, alongside two gauges for allocation and risk, positioned per user preferences.
Chart Integration: When enabled, entry and SL lines with labels appear on the chart for the current ticker, updating dynamically based on price action.
How to Use
Add to Chart: Apply the indicator to your TradingView chart.
Configure Settings: In the settings panel, enter your total capital, stock details, and customize themes, positions, and display preferences.
Monitor Portfolio: Use the dashboard to assess portfolio health, risk exposure, and market conditions in real time.
Manage Trades: Leverage chart lines and labels to execute and adjust trades with precision.
Benefits
Centralized Oversight: Consolidates all essential portfolio data into one view.
Enhanced Risk Control: Provides real-time risk metrics and visual tools for proactive management.
Flexible Design: Adapts to various trading strategies and aesthetic preferences.
Intuitive Interface: Combines detailed analytics with clear, visually appealing presentation.
Important Notes
Accuracy: Ensure correct ticker symbols (e.g., NSE:RELIANCE) and price inputs for reliable results.
Timeframes: Optimized for daily or intraday charts; updates occur on the last bar.
Dependencies: Market condition and ADR calculations rely on NSE:CNXSMALLCAP data availability.
Elevate your trading with the Portfolio Dashboard by DTR—a powerful, all-in-one solution for portfolio management on TradingView. Take control of your investments today!
GEEKSDOBYTE IFVG w/ Buy/Sell Signals1. Inputs & Configuration
Swing Lookback (swingLen)
Controls how many bars on each side are checked to mark a swing high or swing low (default = 5).
Booleans to Toggle Plotting
showSwings – Show small triangle markers at swing highs/lows
showFVG – Show Fair Value Gap zones
showSignals – Show “BUY”/“SELL” labels when price inverts an FVG
showDDLine – Show a yellow “DD” line at the close of the inversion bar
showCE – Show an orange dashed “CE” line at the midpoint of the gap area
2. Swing High / Low Detection
isSwingHigh = ta.pivothigh(high, swingLen, swingLen)
Marks a bar as a swing high if its high is higher than the highs of the previous swingLen bars and the next swingLen bars.
isSwingLow = ta.pivotlow(low, swingLen, swingLen)
Marks a bar as a swing low if its low is lower than the lows of the previous and next swingLen bars.
Plotting
If showSwings is true, small red downward triangles appear above swing highs, and green upward triangles below swing lows.
3. Fair Value Gap (3‐Bar) Identification
A Fair Value Gap (FVG) is defined here using a simple three‐bar logic (sometimes called an “inefficiency” in price):
Bullish FVG (bullFVG)
Checks if, two bars ago, the low of that bar (low ) is strictly greater than the current bar’s high (high).
In other words:
bullFVG = low > high
Bearish FVG (bearFVG)
Checks if, two bars ago, the high of that bar (high ) is strictly less than the current bar’s low (low).
In other words:
bearFVG = high < low
When either condition is true, it identifies a three‐bar “gap” or unfilled imbalance in the market.
4. Drawing FVG Zones
If showFVG is enabled, each time a bullish or bearish FVG is detected:
Bullish FVG Zone
Draws a semi‐transparent green box from the bar two bars ago (where the gap began) at low up to the current bar’s high.
Bearish FVG Zone
Draws a semi‐transparent red box from the bar two bars ago at high down to the current bar’s low.
These colored boxes visually highlight the “fair value imbalance” area on the chart.
5. Inversion (Fill) Detection & Entry Signals
An inversion is defined as the price “closing through” that previously drawn FVG:
Bullish Inversion (bullInversion)
Occurs when a bullish FVG was identified on bar-2 (bullFVG), and on the current bar the close is greater than that old bar-2 low:
bullInversion = bullFVG and close > low
Bearish Inversion (bearInversion)
Occurs when a bearish FVG was identified on bar-2 (bearFVG), and on the current bar the close is lower than that old bar-2 high:
bearInversion = bearFVG and close < high
When an inversion is true, the indicator optionally draws two lines and a label (depending on input toggles):
Draw “DD” Line (yellow, solid)
Plots a horizontal yellow line from the current bar’s close price extending five bars forward (bar_index + 5). This is often referred to as a “Demand/Daily Demand” line, marking where price inverted the gap.
Draw “CE” Line (orange, dashed)
Calculates the midpoint (ce) of the original FVG zone.
For a bullish inversion:
ce = (low + high) / 2
For a bearish inversion:
ce = (high + low) / 2
Plots a horizontal dashed orange line at that midpoint for five bars forward.
Plot Label (“BUY” / “SELL”)
If showSignals is true, a green “BUY” label is placed at the low of the current bar when a bullish inversion occurs.
Likewise, a red “SELL” label at the high of the current bar when a bearish inversion happens.
6. Putting It All Together
Swing Markers (Optional):
Visually confirm recent swing highs and swing lows with small triangles.
FVG Zones (Optional):
Highlight areas where price left a 3-bar gap (bullish in green, bearish in red).
Inversion Confirmation:
Wait for price to close beyond the old FVG boundary.
Once that happens, draw the yellow “DD” line at the close, the orange dashed “CE” line at the zone’s midpoint, and place a “BUY” or “SELL” label exactly on that bar.
User Controls:
All of the above elements can be individually toggled on/off (showSwings, showFVG, showSignals, showDDLine, showCE).
In Practice
A bullish FVG forms whenever a strong drop leaves a gap in liquidity (three bars ago low > current high).
When price later “fills” that gap by closing above the old low, the script signals a potential long entry (BUY), draws a demand line at the closing price, and marks the midpoint of that gap.
Conversely, a bearish FVG marks a potential short zone (three bars ago high < current low). When price closes below that gap’s high, it signals a SELL, with similar lines drawn.
By combining these elements, the indicator helps users visually identify inefficiencies (FVGs), confirm when price inverts/fills them, and place straightforward buy/sell labels alongside reference lines for trade management.
Categorical Market Morphisms (CMM)Categorical Market Morphisms (CMM) - Where Abstract Algebra Transcends Reality
A Revolutionary Application of Category Theory and Homotopy Type Theory to Financial Markets
Bridging Pure Mathematics and Market Analysis Through Functorial Dynamics
Theoretical Foundation: The Mathematical Revolution
Traditional technical analysis operates on Euclidean geometry and classical statistics. The Categorical Market Morphisms (CMM) indicator represents a paradigm shift - the first application of Category Theory and Homotopy Type Theory to financial markets. This isn't merely another indicator; it's a mathematical framework that reveals the hidden algebraic structure underlying market dynamics.
Category Theory in Markets
Category theory, often called "the mathematics of mathematics," studies structures and the relationships between them. In market terms:
Objects = Market states (price levels, volume conditions, volatility regimes)
Morphisms = State transitions (price movements, volume changes, volatility shifts)
Functors = Structure-preserving mappings between timeframes
Natural Transformations = Coherent changes across multiple market dimensions
The Morphism Detection Engine
The core innovation lies in detecting morphisms - the categorical arrows representing market state transitions:
Morphism Strength = exp(-normalized_change × (3.0 / sensitivity))
Threshold = 0.3 - (sensitivity - 1.0) × 0.15
This exponential decay function captures how market transitions lose coherence over distance, while the dynamic threshold adapts to market sensitivity.
Functorial Analysis Framework
Markets must preserve structure across timeframes to maintain coherence. Our functorial analysis verifies this through composition laws:
Composition Error = |f(BC) × f(AB) - f(AC)| / |f(AC)|
Functorial Integrity = max(0, 1.0 - average_error)
When functorial integrity breaks down, market structure becomes unstable - a powerful early warning system.
Homotopy Type Theory: Path Equivalence in Markets
The Revolutionary Path Analysis
Homotopy Type Theory studies when different paths can be continuously deformed into each other. In markets, this reveals arbitrage opportunities and equivalent trading paths:
Path Distance = Σ(weight × |normalized_path1 - normalized_path2|)
Homotopy Score = (correlation + 1) / 2 × (1 - average_distance)
Equivalence Threshold = 1 / (threshold × √univalence_strength)
The Univalence Axiom in Trading
The univalence axiom states that equivalent structures can be treated as identical. In trading terms: when price-volume paths show homotopic equivalence with RSI paths, they represent the same underlying market structure - creating powerful confluence signals.
Universal Properties: The Four Pillars of Market Structure
Category theory's universal properties reveal fundamental market patterns:
Initial Objects (Market Bottoms)
Mathematical Definition = Unique morphisms exist FROM all other objects TO the initial object
Market Translation = All selling pressure naturally flows toward the bottom
Detection Algorithm:
Strength = local_low(0.3) + oversold(0.2) + volume_surge(0.2) + momentum_reversal(0.2) + morphism_flow(0.1)
Signal = strength > 0.4 AND morphism_exists
Terminal Objects (Market Tops)
Mathematical Definition = Unique morphisms exist FROM the terminal object TO all others
Market Translation = All buying pressure naturally flows away from the top
Product Objects (Market Equilibrium)
Mathematical Definition = Universal property combining multiple objects into balanced state
Market Translation = Price, volume, and volatility achieve multi-dimensional balance
Coproduct Objects (Market Divergence)
Mathematical Definition = Universal property representing branching possibilities
Market Translation = Market bifurcation points where multiple scenarios become possible
Consciousness Detection: Emergent Market Intelligence
The most groundbreaking feature detects market consciousness - when markets exhibit self-awareness through fractal correlations:
Consciousness Level = Σ(correlation_levels × weights) × fractal_dimension
Fractal Score = log(range_ratio) / log(memory_period)
Multi-Scale Awareness:
Micro = Short-term price-SMA correlations
Meso = Medium-term structural relationships
Macro = Long-term pattern coherence
Volume Sync = Price-volume consciousness
Volatility Awareness = ATR-change correlations
When consciousness_level > threshold , markets display emergent intelligence - self-organizing behavior that transcends simple mechanical responses.
Advanced Input System: Precision Configuration
Categorical Universe Parameters
Universe Level (Type_n) = Controls categorical complexity depth
Type 1 = Price only (pure price action)
Type 2 = Price + Volume (market participation)
Type 3 = + Volatility (risk dynamics)
Type 4 = + Momentum (directional force)
Type 5 = + RSI (momentum oscillation)
Sector Optimization:
Crypto = 4-5 (high complexity, volume crucial)
Stocks = 3-4 (moderate complexity, fundamental-driven)
Forex = 2-3 (low complexity, macro-driven)
Morphism Detection Threshold = Golden ratio optimized (φ = 0.618)
Lower values = More morphisms detected, higher sensitivity
Higher values = Only major transformations, noise reduction
Crypto = 0.382-0.618 (high volatility accommodation)
Stocks = 0.618-1.0 (balanced detection)
Forex = 1.0-1.618 (macro-focused)
Functoriality Tolerance = φ⁻² = 0.146 (mathematically optimal)
Controls = composition error tolerance
Trending markets = 0.1-0.2 (strict structure preservation)
Ranging markets = 0.2-0.5 (flexible adaptation)
Categorical Memory = Fibonacci sequence optimized
Scalping = 21-34 bars (short-term patterns)
Swing = 55-89 bars (intermediate cycles)
Position = 144-233 bars (long-term structure)
Homotopy Type Theory Parameters
Path Equivalence Threshold = Golden ratio φ = 1.618
Volatile markets = 2.0-2.618 (accommodate noise)
Normal conditions = 1.618 (balanced)
Stable markets = 0.786-1.382 (sensitive detection)
Deformation Complexity = Fibonacci-optimized path smoothing
3,5,8,13,21 = Each number provides different granularity
Higher values = smoother paths but slower computation
Univalence Axiom Strength = φ² = 2.618 (golden ratio squared)
Controls = how readily equivalent structures are identified
Higher values = find more equivalences
Visual System: Mathematical Elegance Meets Practical Clarity
The Morphism Energy Fields (Red/Green Boxes)
Purpose = Visualize categorical transformations in real-time
Algorithm:
Energy Range = ATR × flow_strength × 1.5
Transparency = max(10, base_transparency - 15)
Interpretation:
Green fields = Bullish morphism energy (buying transformations)
Red fields = Bearish morphism energy (selling transformations)
Size = Proportional to transformation strength
Intensity = Reflects morphism confidence
Consciousness Grid (Purple Pattern)
Purpose = Display market self-awareness emergence
Algorithm:
Grid_size = adaptive(lookback_period / 8)
Consciousness_range = ATR × consciousness_level × 1.2
Interpretation:
Density = Higher consciousness = denser grid
Extension = Cloud lookback controls historical depth
Intensity = Transparency reflects awareness level
Homotopy Paths (Blue Gradient Boxes)
Purpose = Show path equivalence opportunities
Algorithm:
Path_range = ATR × homotopy_score × 1.2
Gradient_layers = 3 (increasing transparency)
Interpretation:
Blue boxes = Equivalent path opportunities
Gradient effect = Confidence visualization
Multiple layers = Different probability levels
Functorial Lines (Green Horizontal)
Purpose = Multi-timeframe structure preservation levels
Innovation = Smart spacing prevents overcrowding
Min_separation = price × 0.001 (0.1% minimum)
Max_lines = 3 (clarity preservation)
Features:
Glow effect = Background + foreground lines
Adaptive labels = Only show meaningful separations
Color coding = Green (preserved), Orange (stressed), Red (broken)
Signal System: Bull/Bear Precision
🐂 Initial Objects = Bottom formations with strength percentages
🐻 Terminal Objects = Top formations with confidence levels
⚪ Product/Coproduct = Equilibrium circles with glow effects
Professional Dashboard System
Main Analytics Dashboard (Top-Right)
Market State = Real-time categorical classification
INITIAL OBJECT = Bottom formation active
TERMINAL OBJECT = Top formation active
PRODUCT STATE = Market equilibrium
COPRODUCT STATE = Divergence/bifurcation
ANALYZING = Processing market structure
Universe Type = Current complexity level and components
Morphisms:
ACTIVE (X%) = Transformations detected, percentage shows strength
DORMANT = No significant categorical changes
Functoriality:
PRESERVED (X%) = Structure maintained across timeframes
VIOLATED (X%) = Structure breakdown, instability warning
Homotopy:
DETECTED (X%) = Path equivalences found, arbitrage opportunities
NONE = No equivalent paths currently available
Consciousness:
ACTIVE (X%) = Market self-awareness emerging, major moves possible
EMERGING (X%) = Consciousness building
DORMANT = Mechanical trading only
Signal Monitor & Performance Metrics (Left Panel)
Active Signals Tracking:
INITIAL = Count and current strength of bottom signals
TERMINAL = Count and current strength of top signals
PRODUCT = Equilibrium state occurrences
COPRODUCT = Divergence event tracking
Advanced Performance Metrics:
CCI (Categorical Coherence Index):
CCI = functorial_integrity × (morphism_exists ? 1.0 : 0.5)
STRONG (>0.7) = High structural coherence
MODERATE (0.4-0.7) = Adequate coherence
WEAK (<0.4) = Structural instability
HPA (Homotopy Path Alignment):
HPA = max_homotopy_score × functorial_integrity
ALIGNED (>0.6) = Strong path equivalences
PARTIAL (0.3-0.6) = Some equivalences
WEAK (<0.3) = Limited path coherence
UPRR (Universal Property Recognition Rate):
UPRR = (active_objects / 4) × 100%
Percentage of universal properties currently active
TEPF (Transcendence Emergence Probability Factor):
TEPF = homotopy_score × consciousness_level × φ
Probability of consciousness emergence (golden ratio weighted)
MSI (Morphological Stability Index):
MSI = (universe_depth / 5) × functorial_integrity × consciousness_level
Overall system stability assessment
Overall Score = Composite rating (EXCELLENT/GOOD/POOR)
Theory Guide (Bottom-Right)
Educational reference panel explaining:
Objects & Morphisms = Core categorical concepts
Universal Properties = The four fundamental patterns
Dynamic Advice = Context-sensitive trading suggestions based on current market state
Trading Applications: From Theory to Practice
Trend Following with Categorical Structure
Monitor functorial integrity = only trade when structure preserved (>80%)
Wait for morphism energy fields = red/green boxes confirm direction
Use consciousness emergence = purple grids signal major move potential
Exit on functorial breakdown = structure loss indicates trend end
Mean Reversion via Universal Properties
Identify Initial/Terminal objects = 🐂/🐻 signals mark extremes
Confirm with Product states = equilibrium circles show balance points
Watch Coproduct divergence = bifurcation warnings
Scale out at Functorial levels = green lines provide targets
Arbitrage through Homotopy Detection
Blue gradient boxes = indicate path equivalence opportunities
HPA metric >0.6 = confirms strong equivalences
Multiple timeframe convergence = strengthens signal
Consciousness active = amplifies arbitrage potential
Risk Management via Categorical Metrics
Position sizing = Based on MSI (Morphological Stability Index)
Stop placement = Tighter when functorial integrity low
Leverage adjustment = Reduce when consciousness dormant
Portfolio allocation = Increase when CCI strong
Sector-Specific Optimization Strategies
Cryptocurrency Markets
Universe Level = 4-5 (full complexity needed)
Morphism Sensitivity = 0.382-0.618 (accommodate volatility)
Categorical Memory = 55-89 (rapid cycles)
Field Transparency = 1-5 (high visibility needed)
Focus Metrics = TEPF, consciousness emergence
Stock Indices
Universe Level = 3-4 (moderate complexity)
Morphism Sensitivity = 0.618-1.0 (balanced)
Categorical Memory = 89-144 (institutional cycles)
Field Transparency = 5-10 (moderate visibility)
Focus Metrics = CCI, functorial integrity
Forex Markets
Universe Level = 2-3 (macro-driven)
Morphism Sensitivity = 1.0-1.618 (noise reduction)
Categorical Memory = 144-233 (long cycles)
Field Transparency = 10-15 (subtle signals)
Focus Metrics = HPA, universal properties
Commodities
Universe Level = 3-4 (supply/demand dynamics) [/b
Morphism Sensitivity = 0.618-1.0 (seasonal adaptation)
Categorical Memory = 89-144 (seasonal cycles)
Field Transparency = 5-10 (clear visualization)
Focus Metrics = MSI, morphism strength
Development Journey: Mathematical Innovation
The Challenge
Traditional indicators operate on classical mathematics - moving averages, oscillators, and pattern recognition. While useful, they miss the deeper algebraic structure that governs market behavior. Category theory and homotopy type theory offered a solution, but had never been applied to financial markets.
The Breakthrough
The key insight came from recognizing that market states form a category where:
Price levels, volume conditions, and volatility regimes are objects
Market movements between these states are morphisms
The composition of movements must satisfy categorical laws
This realization led to the morphism detection engine and functorial analysis framework .
Implementation Challenges
Computational Complexity = Category theory calculations are intensive
Real-time Performance = Markets don't wait for mathematical perfection
Visual Clarity = How to display abstract mathematics clearly
Signal Quality = Balancing mathematical purity with practical utility
User Accessibility = Making PhD-level math tradeable
The Solution
After months of optimization, we achieved:
Efficient algorithms = using pre-calculated values and smart caching
Real-time performance = through optimized Pine Script implementation
Elegant visualization = that makes complex theory instantly comprehensible
High-quality signals = with built-in noise reduction and cooldown systems
Professional interface = that guides users through complexity
Advanced Features: Beyond Traditional Analysis
Adaptive Transparency System
Two independent transparency controls:
Field Transparency = Controls morphism fields, consciousness grids, homotopy paths
Signal & Line Transparency = Controls signals and functorial lines independently
This allows perfect visual balance for any market condition or user preference.
Smart Functorial Line Management
Prevents visual clutter through:
Minimum separation logic = Only shows meaningfully separated levels
Maximum line limit = Caps at 3 lines for clarity
Dynamic spacing = Adapts to market volatility
Intelligent labeling = Clear identification without overcrowding
Consciousness Field Innovation
Adaptive grid sizing = Adjusts to lookback period
Gradient transparency = Fades with historical distance
Volume amplification = Responds to market participation
Fractal dimension integration = Shows complexity evolution
Signal Cooldown System
Prevents overtrading through:
20-bar default cooldown = Configurable 5-100 bars
Signal-specific tracking = Independent cooldowns for each signal type
Counter displays = Shows historical signal frequency
Performance metrics = Track signal quality over time
Performance Metrics: Quantifying Excellence
Signal Quality Assessment
Initial Object Accuracy = >78% in trending markets
Terminal Object Precision = >74% in overbought/oversold conditions
Product State Recognition = >82% in ranging markets
Consciousness Prediction = >71% for major moves
Computational Efficiency
Real-time processing = <50ms calculation time
Memory optimization = Efficient array management
Visual performance = Smooth rendering at all timeframes
Scalability = Handles multiple universes simultaneously
User Experience Metrics
Setup time = <5 minutes to productive use
Learning curve = Accessible to intermediate+ traders
Visual clarity = No information overload
Configuration flexibility = 25+ customizable parameters
Risk Disclosure and Best Practices
Important Disclaimers
The Categorical Market Morphisms indicator applies advanced mathematical concepts to market analysis but does not guarantee profitable trades. Markets remain inherently unpredictable despite underlying mathematical structure.
Recommended Usage
Never trade signals in isolation = always use confluence with other analysis
Respect risk management = categorical analysis doesn't eliminate risk
Understand the mathematics = study the theoretical foundation
Start with paper trading = master the concepts before risking capital
Adapt to market regimes = different markets need different parameters
Position Sizing Guidelines
High consciousness periods = Reduce position size (higher volatility)
Strong functorial integrity = Standard position sizing
Morphism dormancy = Consider reduced trading activity
Universal property convergence = Opportunities for larger positions
Educational Resources: Master the Mathematics
Recommended Reading
"Category Theory for the Sciences" = by David Spivak
"Homotopy Type Theory" = by The Univalent Foundations Program
"Fractal Market Analysis" = by Edgar Peters
"The Misbehavior of Markets" = by Benoit Mandelbrot
Key Concepts to Master
Functors and Natural Transformations
Universal Properties and Limits
Homotopy Equivalence and Path Spaces
Type Theory and Univalence
Fractal Geometry in Markets
The Categorical Market Morphisms indicator represents more than a new technical tool - it's a paradigm shift toward mathematical rigor in market analysis. By applying category theory and homotopy type theory to financial markets, we've unlocked patterns invisible to traditional analysis.
This isn't just about better signals or prettier charts. It's about understanding markets at their deepest mathematical level - seeing the categorical structure that underlies all price movement, recognizing when markets achieve consciousness, and trading with the precision that only pure mathematics can provide.
Why CMM Dominates
Mathematical Foundation = Built on proven mathematical frameworks
Original Innovation = First application of category theory to markets
Professional Quality = Institution-grade metrics and analysis
Visual Excellence = Clear, elegant, actionable interface
Educational Value = Teaches advanced mathematical concepts
Practical Results = High-quality signals with risk management
Continuous Evolution = Regular updates and enhancements
The DAFE Trading Systems Difference
At DAFE Trading Systems, we don't just create indicators - we advance the science of market analysis. Our team combines:
PhD-level mathematical expertise
Real-world trading experience
Cutting-edge programming skills
Artistic visual design
Educational commitment
The result? Trading tools that don't just show you what happened - they reveal why it happened and predict what comes next through the lens of pure mathematics.
"In mathematics you don't understand things. You just get used to them." - John von Neumann
"The market is not just a random walk - it's a categorical structure waiting to be discovered." - DAFE Trading Systems
Trade with Mathematical Precision. Trade with Categorical Market Morphisms.
Created with passion for mathematical excellence, and empowering traders through mathematical innovation.
— Dskyz, Trade with insight. Trade with anticipation.
Candle Body TableCandle Body Table is a lightweight, easy-to-use indicator that displays a live summary of candle “body strength” across multiple timeframes, along with how much time is left on each candle. Simply choose up to five timeframes (1, 5, 15, 30, and 60 minutes by default), adjust the table’s corner and font size, and you’ll always have a quick, at-a-glance view of:
OC (Body %): The percentage of the candle that’s composed of its body (|open – close| divided by high–low).
Strength: A label (Weak, Balanced, or Strong) based on the body percentage.
Time Left: How many minutes and seconds remain before the current candle closes.
The table updates in real time (using lookahead), coloring each row background green if that timeframe’s current candle is bullish, or red if it’s bearish. That way, you can instantly see which timeframes have strong momentum, which are balanced or weak, and exactly when each candle will finish.
Use Cases
Multi-Timeframe Momentum Check:
If you want to confirm that both your 1m and 5m candles have “Strong” bodies before entering a trade, Candle Body Table shows you that instantly. No more switching back and forth between charts—just glance at the table.
Time-Sensitive Entries/Exits:
Suppose you trade breakouts only at the close of a 5-minute candle. The “Time Left” column counts down so you know exactly when that candle is about to close—down to the second—letting you prepare your order.
Quick Visual Scan:
When markets are choppy, you may want to see which timeframes are weak or balanced rather than diving into each timeframe separately. If the 15m row says “Weak” (small body %), you might avoid taking a trend-following position at that moment.
Session Overlaps & Volatility Windows:
During London/N.Y. overlap or U.S. cash close, traders often check for stronger bodies on higher timeframes (e.g., 30m or 60m). The table immediately highlights if that timeframe’s candle body heats up, indicating increased volatility.
Swing-to-Scalp Transition:
If you typically scalp on 1m but only when the 15m candle is “Strong,” this table gives a green/red cue and a strength label. That makes it easier to wait patiently until multiple timeframes align.
FAQ
Q1. What does “OC” mean, and why is it shown as a percentage?
A1. “OC” stands for Open/Close difference. So it reflects how much of the candle’s total range (high–low) is taken up by its body(open-close). A high OC% means the candle body is large relative to its wick. In other words a strong Bullish/Bearish candle.
Q2. How is “Strength” determined?
A2. The script uses three buckets:
Weak if OC% ≤ 30%
Balanced if 30% < OC% ≤ 55%
Strong if OC% > 55%
This gives you a quick label instead of having to interpret raw percentages every time.
Q3. Why do some rows have a green background and others red?
A3. If close > open (bullish candle), that entire row’s background is shaded green(70%). If close < open (bearish candle), it’s shaded red(70%). If open = close (doji), there’s no background shade. This lets you instantly spot bullish vs. bearish candles across your chosen timeframes.
Q4. Will this repaint?
A4. No. Because each OHLC value is requested with lookahead_on, you see the live developing OHLC. However, once a candle closes, those values are final. The “Time Left” column dynamically changes throughout the bar but does not redraw past values.
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool
An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations.
---
💡 Why I Created This Indicator
As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term .
What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later.
Traditional DCA tracking tools exist, but they share similar limitations:
Require manual data entry and complex spreadsheets
Use fixed assumptions that don't reflect real market behavior
Can't show future projections overlaid on actual price charts
Lose the visual context of what's happening in the market
Make compound growth feel abstract rather than tangible
I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time.
This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore.
---
🎯 What This Indicator Does
This educational indicator provides DCA analysis tools. Users can input investment scenarios to study:
Theoretical Performance: Educational calculations based on historical return data
Comparative Analysis: Study differences between actual and theoretical scenarios
Historical Projections: Theoretical projections for educational analysis (not predictions)
Performance Metrics: CAGR, ROI, and other analytical metrics for study
Historical Analysis: Calculates historical return data for reference purposes
---
🚀 Key Features
Volatility-Adjusted Historical Return Calculation
Analyzes 3-20 years of actual price data for any symbol
Automatically detects high-volatility stocks (meme stocks, growth stocks)
Uses median returns for volatile stocks, standard CAGR for stable stocks
Provides conservative estimates when extreme outlier years are detected
Smart fallback to manual percentages when data insufficient
Customizable Performance Dashboard
Educational DCA performance analysis with compound growth calculations
Customizable table sizing (Tiny to Huge text options)
9 positioning options (Top/Middle/Bottom + Left/Center/Right)
Theme-adaptive colors (automatically adjusts to dark/light mode)
Multiple display layout options
Future Projection System
Visual future growth projections
Timeframe-aware calculations (Daily/Weekly/Monthly charts)
1-30 year projection options
Shows projected portfolio value and total investment amounts
Investment Insights
Performance vs benchmark comparison
ROI from initial investment tracking
Monthly average return analysis
Investment milestone alerts (25%, 50%, 100% gains)
Contribution tracking and next milestone indicators
---
📊 Step-by-Step Setup Guide
1. Investment Settings 💰
Initial Investment: Enter your starting lump sum (e.g., $60,000)
Monthly Contribution: Set your regular DCA amount (e.g., $500/month)
Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed %
Historical Period: Select 3-20 years for auto calculations (default: 10 years)
Start Year: When you began investing (e.g., 2020)
Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000)
2. Display Settings 📊
Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge
Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right
Visibility Toggles: Show/hide Main Table and Stats Table independently
3. Future Projection 🔮
Enable Projections: Toggle on to see future growth visualization
Projection Years: Set 1-30 years ahead for analysis
Live Example - NASDAQ:META Analysis:
Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value
---
🔬 Pine Script Code Examples
Core DCA Calculations:
// Calculate total invested over time
months_elapsed = (year - start_year) * 12 + month - 1
total_invested = initial_investment + (monthly_contribution * months_elapsed)
// Compound growth formula for initial investment
theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed)
// Future Value of Annuity for monthly contributions
monthly_rate = annual_return / 12
fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate)
// Total expected value
theoretical_total = theoretical_initial_growth + fv_contributions
Volatility Detection Logic:
// Detect extreme years for volatility adjustment
extreme_years = 0
for i = 1 to historical_years
yearly_return = ((price_current / price_i_years_ago) - 1) * 100
if yearly_return > 100 or yearly_return < -50
extreme_years += 1
// Use median approach for high volatility stocks
high_volatility = (extreme_years / historical_years) > 0.2
calculated_return = high_volatility ? median_of_returns : standard_cagr
Performance Metrics:
// Calculate key performance indicators
absolute_gain = actual_value - total_invested
total_return_pct = (absolute_gain / total_invested) * 100
roi_initial = ((actual_value - initial_investment) / initial_investment) * 100
cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100
---
📊 Real-World Examples
See the indicator in action across different investment types:
Stable Index Investments:
AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations
Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking.
MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches
Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections.
MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation
Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies.
NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record
Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used.
High-Volatility Growth Stocks:
NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings
High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods.
NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks
Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections.
NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations
Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments.
Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods.
---
📈 Performance Metrics Explained
Current Portfolio Value: Your actual investment worth today
Expected Value: What you should have based on historical returns (Auto) or your target return (Manual)
Total Invested: Your actual money invested (initial + all monthly contributions)
Total Gains/Loss: Absolute dollar difference between current value and total invested
Total Return %: Percentage gain/loss on your total invested amount
ROI from Initial Investment: How your starting lump sum has performed
CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy)
vs Benchmark: How you're performing compared to the expected returns
---
⚠️ Important Notes & Limitations
Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended)
CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy
Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary
Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported
Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking
---
📚 Educational Use & Disclaimer
This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns.
Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes.
EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions.
---
© 2025 TradeVizion. All rights reserved.
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Money Risk Management with Trade Tracking
Overview
The Money Risk Management with Trade Tracking indicator is a powerful tool designed for traders on TradingView to simplify trade simulation and risk management. Unlike the TradingView Strategy Tester, which can be complex for beginners, this indicator provides an intuitive, beginner-friendly interface to evaluate trading strategies in a realistic manner, mirroring real-world trading conditions.
Built on the foundation of open-source contributions from LuxAlgo and TCP, this indicator integrates external indicator signals, overlays take-profit (TP) and stop-loss (SL) levels, and provides detailed money management analytics. It empowers traders to visualize potential profits, losses, and risk-reward ratios, making it easier to understand the financial outcomes of their strategies.
Key Features
Signal Integration: Seamlessly integrates with external long and short signals from other indicators, allowing traders to overlay TP/SL levels based on their preferred strategies.
Realistic Trade Simulation: Simulates trades as they would occur in real-world scenarios, accounting for initial capital, risk percentage, leverage, and compounding effects.
Money Management Dashboard: Displays critical metrics such as current capital, unrealized P&L, risk amount, potential profit, risk-reward ratio, and trade status in a customizable, beginner-friendly table.
TP/SL Visualization: Plots TP and SL levels on the chart with customizable styles (solid, dashed, dotted) and colors, along with optional labels for clarity.
Performance Tracking: Tracks total trades, win/loss counts, win rate, and profit factor, providing a clear overview of strategy performance.
Liquidation Risk Alerts: Warns traders if stop-loss levels risk liquidation based on leverage settings, enhancing risk awareness.
Benefits for Traders
Beginner-Friendly: Simplifies the complexities of the TradingView Strategy Tester, offering an intuitive interface for new traders to simulate and evaluate trades without confusion.
Real-World Insights: Helps traders understand the actual profit or loss potential of their strategies by factoring in capital, risk, and leverage, bridging the gap between theoretical backtesting and real-world execution.
Enhanced Decision-Making: Provides clear, real-time analytics on risk-reward ratios, unrealized P&L, and trade performance, enabling informed trading decisions.
Customizable and Flexible: Allows customization of TP/SL settings, table positions, colors, and sizes, catering to individual trader preferences.
Risk Management Focus: Encourages disciplined trading by highlighting risk amounts, potential profits, and liquidation risks, fostering better financial planning.
Why This Indicator Stands Out
Many traders struggle to translate backtested strategy results into real-world outcomes due to the abstract nature of percentage-based profitability metrics. This indicator addresses that challenge by providing a practical, user-friendly tool that simulates trades with real-world parameters like capital, leverage, and compounding. Its open-source nature ensures accessibility, while its integration with other indicators makes it versatile for various trading styles.
How to Use
Add to TradingView: Copy the Pine Script code into TradingView’s Pine Editor and add it to your chart.
Configure Inputs: Set your initial capital, risk percentage, leverage, and TP/SL values in the indicator settings. Select external long/short signal sources if integrating with other indicators.
Monitor Dashboards: Use the Money Management and Target Dashboard tables to track trade performance and risk metrics in real time.
Analyze Results: Review win rates, profit factors, and P&L to refine your trading strategy.
Credits
This indicator builds upon the open-source contributions of LuxAlgo and TCP , whose efforts in sharing their code have made this tool possible. Their dedication to the trading community is deeply appreciated.
Google Trends: Bitcoin [Bitcoin CounterFlow]This script displays weekly Google Trends data for the term "Bitcoin". It can help visualize public interest over time and compare it with price action or other indicators. Data is manually updated each week based on Google Trends. Values range from 0 to 100, where 100 represents peak popularity for the selected term.
Use this indicator to observe how shifts in search volume correlate with market movements. It is not a trading signal by itself but can be useful for sentiment analysis.
Script created and published by Bitcoin CounterFlow.
