Book of Fish: Universal Deep DiveAhoy, Captain. 🏴☠️
Here is your official Angler’s Manual for the Book of Fish: Universal Deep Dive. This guide translates every pixel on your TradingView chart into nautical instruction so you can navigate the currents and land the big catch.
Print this out, tape it to your monitor, and respect the code of the sea.
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📖 The Angler’s Manual: How to Fish
A Guide to the "Universal Deep Dive" Indicator
🌊 1. Check the Current (Background Color)
Before you cast a line, you must know which way the river is flowing.
• Green Water (Background): The tide is coming in. The broad market (Advancers) is beating the losers.
o The Rule: We prefer Longs (Calls). Swimming upstream against the green current is dangerous.
• Red Water (Background): The tide is going out. The market is heavy.
o The Rule: We prefer Shorts (Puts). Don't fight the gravity.
Captain’s Note: If your specific fish (stock) is Green while the water is Red, that’s a Monster Fish (Relative Strength). It’s strong, but keep a tight drag—if it gets tired, the current will drag it down fast.
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🐟 2. Identify the Species (Candle Colors)
The color of your bars tells you exactly what strategy to deploy.
🟢 The Marlin (Ultra Bull)
• Visual: Green Candles. Price is riding above the Yellow Wave (20 EMA), and the Yellow Wave is above the White Whale (200 EMA).
• Strategy: Trend Following.
• How to Fish:
o Wait for the fish to swim down and touch the Yellow Wave.
o If it bounces? CAST! (Enter Long).
o Target: Let it run until the trend bends.
🔴 The Barracuda (Ultra Bear)
• Visual: Red Candles. Price is diving below the Yellow Wave, and the Yellow Wave is below the White Whale.
• Strategy: Trend Following (Short).
• How to Fish:
o Wait for the fish to jump up and hit the Yellow Wave.
o If it rejects? CAST! (Enter Short).
🟠 The Bottom Feeder (No Man’s Zone)
• Visual: Orange or Lime Candles. The price is fighting the trend (e.g., Price is below Yellow, but Yellow is still above White).
• Strategy: Reversion to Mean (Scalping).
• How to Fish:
o You are catching small fry here.
o Target: The Purple Anchor (VWAP) or the White Whale (200 EMA).
o Rule: As soon as it hits the Anchor or the Whale, cut the line and take your profit. Do not hold for a home run.
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🎣 3. The Tackle Box (Signals & Icons)
These shapes are your triggers. They tell you when to strike.
Icon Name Meaning Action
▲ (Green Triangle) 3-Bar Play THE STRIKE. Momentum is breaking out after a rest. ENTER NOW. This is the sharpest hook in the box. Trend is resuming.
🔷 (Blue Diamond) Inside Bar The Nibble. Price is coiling/resting. Set a trap. Place a stop-entry slightly above the diamond (for longs).
⚫ (Black Dots) The Squeeze Calm Waters. Volatility has died. DO NOT CAST. Wait. When the dots disappear, the storm (and the move) begins.
9️⃣ (Red/Green Number) Exhaustion Full Net. The school has swum too far in one direction. Take Profits. A Red 9 at the top means the bull run is tired. A Green 9 at the bottom means the bear dive is ending.
✖️ (Purple Cross) RSI Snag Hazard. The engine is overheated (Overbought/Oversold). Don't add weight. The line might snap if you buy here.
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🗺️ 4. The Map (The Lines)
• The Yellow Wave (20 EMA): Your surfboard. In a strong trend, price should surf this line. If it closes below it, the surf is over.
• The White Whale (200 EMA): The deep ocean trend. This is massive support/resistance. We generally do not short above the Whale or long below it.
• The Purple Anchor (VWAP): The average price. Prices love to return here when they get lost in the No Man's Zone.
• The Dotted Lines (PDH/PDL): The Horizon. Previous Day High (Green) and Low (Red). Crossing these means you are entering open ocean (Discovery Mode).
⚓ The Captain's Code
1.Don't force the fish. If the chart is chopping (Gray candles), stay on the dock.
2.Respect the '9'. When you see that number, lock in some gains.
3.The Trend is your Friend. Green Candles + Green Background = Smooth Sailing.
Fair winds and following seas.
ความผันผวน
India VIX Tray - DynamicIndia VIX Table
Shows INDIAVIX value as a tray in Chart with Dynamic colour change according to Low Volatility, Moderate Volatility, High Volatility.
XAUUSD Macro Anomaly Pulses (Chart XAU) - sudoXAUUSD Macro Anomaly Pulses
A simple pulse indicator that highlights when XAUUSD moves in a way that macro conditions cannot fully explain
Overview
This indicator marks candles on XAUUSD that behave differently than what the broader market suggests should happen.
Instead of looking at XAUUSD alone, this tool compares gold’s actual movement to an expected movement based on:
Other gold cross pairs (XAUJPY, XAUAUD, XAUCHF)
The U.S. Dollar Index (DXY), inverted
The US30 index (Dow Jones)
When XAUUSD moves much stronger or weaker than this macro-based expectation, the indicator plots a small pulse (a circle) directly on the candle.
Purpose
This indicator helps you quickly see when a candle on XAUUSD is acting “out of character” compared to normal macro flow. In other words:
“Did XAUUSD move in a way that makes sense with the rest of the market, or did something weird happen?”
These unusual moves often signal:
Liquidity grabs
Stop hunts
News-driven spikes
False breakouts
Front-running of macro shifts
How It Works
It reads the XAUUSD candles directly from the chart.
This ensures pulses stick to your candles correctly.
It pulls data from basket legs (XAUJPY, XAUAUD, XAUCHF) and macro symbols (DXY, US30) using security calls.
It converts each symbol into a simple % return per candle.
It builds an “expected” gold move using weighted inputs:
Average return of gold crosses
Inverse return of DXY
Return of US30
It calculates the “residual,” which means:
actual XAU return - expected macro return
It turns that into a Z-score to measure how extreme the deviation is.
If the Z-score is too high or too low, the script marks the candle:
Aqua pulse below bar = unusually strong move
Fuchsia pulse above bar = unusually weak move
How to Interpret the Pulses
Aqua Pulse (below candle) – Bullish anomaly
XAUUSD moved stronger than the macro environment suggests.
Meaning:
-Possible liquidity grab upward
-Possible early trend move
-Possible false breakout
-Price may be overreacting
Fuchsia Pulse (above candle) – Bearish anomaly
XAUUSD moved weaker than expected.
Meaning:
-Possible liquidity sweep downward
-Possible aggressive sell-side event
-Possible exhaustion
-Price may be taking liquidity before reversing
Typical Use Cases
-Spot moments when gold acts independently of macro
-Identify candles that might signal a reversal or a trap
-Confirm whether a breakout is real or suspicious
-Filter trades by macro alignment
-Help understand when XAUUSD is reacting to news or liquidity instead of fundamentals
Inputs Explained
- Z-score Lookback – How many candles are considered normal behavior
- Z-threshold – How extreme a move must be before it is marked
- Basket / DXY / US30 weights – How much influence each macro component has
ORB Algo - BitcoinGENERAL SUMMARY
We present our new ORB Algo indicator! ORB stands for "Opening Range Breakout," a common trading strategy. The indicator can analyze the market trend in the current session and generate Buy/Sell, Take Profit, and Stop Loss signals. For more information about the indicator's analysis process, you can read the “How Does It Work?” section of the description.
Features of the new ORB Algo indicator:
Buy/Sell Signals
Up to 3 Take Profit Signals
Stop Loss Signals
Buy/Sell, Take Profit, and Stop Loss Alerts
Fully Customizable Algorithm
Session Control Panel
Backtesting Control Panel
HOW DOES IT WORK?
This indicator works best on the 1-minute timeframe. The idea is that the trend of the current session can be predicted by analyzing the market for a period of time after the session begins. However, each market has its own dynamics, and the algorithm will require fine-tuning to achieve the best possible performance. For this reason, we implemented a Backtesting Panel that shows the past performance of the algorithm on the current ticker with your current settings. Always remember that past performance does not guarantee future results.
Here are the steps of the algorithm explained briefly:
The algorithm follows and analyzes the first 30 minutes (adjustable) of the session.
Then, it checks for breakouts above or below the opening range high or low.
If a breakout occurs in either direction, the algorithm will look for retests of the breakout. Depending on the sensitivity setting, there must be 0 / 1 / 2 / 3 failed retests for the breakout to be considered reliable.
If the breakout is reliable, the algorithm will issue an entry signal.
After entering the position, the algorithm will wait for the Take-Profit or Stop-Loss zones to be reached and send a signal if any of them occur.
If you wonder how the indicator determines the Take-Profit and Stop-Loss zones, you can check the Settings section of the description.
UNIQUENESS
Although some indicators display the opening range of the session, they often fall short in features such as indicating breakouts, entries, and Take-Profit & Stop-Loss zones. We are also aware that different markets have different dynamics, and tuning the algorithm for each market is crucial for better results. That is why we decided to make the algorithm fully customizable.
In addition to this, our indicator includes a detailed backtesting panel so you can see the past performance of the algorithm on the current ticker. While past performance does not guarantee future results, we believe that a backtesting panel is necessary to fine-tune the algorithm. Another strength of the indicator is that it offers multiple options for detecting Take-Profit and Stop-Loss zones, allowing traders to choose the one that fits their style best.
⚙️ SETTINGS
Keep in mind that the best timeframe for this indicator is the 1-minute timeframe.
TP = Take-Profit
SL = Stop-Loss
EMA = Exponential Moving Average
OR = Opening Range
ATR = Average True Range
1. Algorithm
ORB Timeframe → This setting determines how long the algorithm will analyze the market after a new session begins before issuing signals. It is important to experiment with this option and find the optimal setting for the current ticker. More volatile stocks will require a higher value, while more stable stocks can use a shorter one.
Sensitivity → Determines how many failed retests are required before taking an entry. Higher sensitivity means fewer retests are needed to consider the breakout reliable.
If you believe the ticker makes strong moves after breaking out, use high sensitivity.
If the ticker doesn’t define the trend immediately after a breakout, use low sensitivity.
(High = 0 Retests, Medium = 1 Retest, Low = 2 Retests, Lowest = 3 Retests)
Breakout Condition → Determines how the algorithm detects breakouts.
Close = The bar must close above OR High for bullish breakouts or below OR Low for bearish breakouts.
EMA = The bar’s EMA must be above/below the OR Lines instead of relying on the closing price.
TP Method → Method used to determine TP zones.
Dynamic = Searches for the bar where price stops following the current trend and reverses. It uses an EMA, and when the bar’s close crosses the EMA, a TP is placed.
ATR = Determines TP zones before the trade happens, using the ATR of the entry bar. This option also displays the TP zones on the ORB panel.
→ The Dynamic method generally performs better, while the ATR method is safer and more conservative.
EMA Length → Sets the length of the EMA used in both the Dynamic TP method and the “EMA Breakout Condition.” The default value usually performs well, but you can experiment to find the optimal length for the current ticker.
Stop-Loss → Defines where the SL zone will be placed.
Safer = SL is placed closer to OR High in bullish entries and closer to OR Low in bearish entries.
Balanced = SL is placed in the middle of OR High & OR Low.
Risky = SL is placed farther away, giving more room for movement.
Adaptive SL → Activates only if the first TP zone is reached.
Enabled = After the 1st TP hits, SL moves to the entry price, making the position risk-free.
Disabled = SL never changes.
PDH/PDL Sweep & Rejection - sudoPDH/PDL Sweep + Rejection
This indicator identifies classic liquidity sweeps of the previous day's high or low, then confirms whether price rejected that level with force. It is built to highlight moments when the market takes liquidity and immediately snaps back in the opposite direction, a behavior often linked to failed breakouts, engineered stops, or clean reversals. The tool marks these events directly on the chart so you can see them without manually watching the daily levels.
What it detects
The indicator focuses on two events:
PDH sweep and rejection
Price breaks above the previous day's high, overshoots the level by a meaningful amount, and then closes back below the high.
PDL sweep and rejection
Price breaks below the previous day's low, overshoots, and then closes back above the low.
These are structural liquidity events, not random wicks. The script checks for enough overshoot and strong bar range to confirm it was a genuine stop grab rather than noise.
How it works
The indicator evaluates each bar using the following logic:
1. Previous day levels
It pulls yesterday's high and low directly from the daily timeframe. These act as the PDH and PDL reference points for intraday trading.
2. Overshoot measurement
After breaking the level, price must push far enough beyond it to qualify as a sweep. Instead of using arbitrary pips, the required overshoot is scaled relative to ATR. This keeps the logic stable across different assets and volatility conditions.
3. Range confirmation
The bar must be larger than normal compared to ATR. This ensures the sweep happened with momentum and not because of small, choppy price movement.
4. Rejection close
A valid signal only prints if price closes back inside the previous day's range.
For a PDH sweep, the bar must close below PDH.
For a PDL sweep, the bar must close above PDL.
This confirms a failed breakout and a rejection.
What gets placed on the chart
Red downward triangle above the bar: Previous Day High sweep and rejection
Lime upward triangle below the bar: Previous Day Low sweep and rejection
The markers appear exactly on the bar where the sweep and rejection occurred.
How traders can use this
Identify potential reversals
Sweeps often occur when algorithms target liquidity pools. When followed by a strong rejection, the market may be preparing for a reversal or rotation.
Avoid chasing breakouts
A clear sweep warns that a breakout attempt failed. This can prevent traders from entering at the worst possible location.
Time entries at extremes
The markers help you see where the market grabbed stops and immediately turned. These areas can become high quality entry zones in both trend continuation and countertrend setups.
Support liquidity based models
The indicator aligns naturally with trading frameworks that consider liquidity, displacement, failed breaks, and microstructure shifts.
Add confidence to confluence-based setups
Combine sweeps with displacement, FVGs, or higher timeframe levels to refine entry timing.
Why this indicator is helpful
It automates a pattern that traders often identify manually. Sweeps are easy to miss in fast markets, and this tool eliminates the need to constantly monitor daily levels. By marking only the events that show overshoot plus rejection plus significant range, it filters out the weak or false signals and leaves only meaningful liquidity events.
Displacement Pulse Markers - sudoThis indicator is designed to highlight sudden and meaningful bursts of price movement. These bursts are called displacement pulses. A pulse appears when price expands with force, closes near the extreme of its own bar, and breaks through a recent structural level. The indicator places small circles above or below the candle to signal these moments so that traders can quickly spot abnormal movement and potential shifts in market intent.
How it works
The indicator evaluates each bar for three conditions:
Range expansion relative to volatility
The bar must be larger than normal. It compares the bar range to ATR and requires that range to exceed a multiple of ATR. When this condition is met, the bar is considered a large or forceful bar.
Close location within the bar
The bar has to close near its own high or low. A close near the top suggests strong buying force. A close near the bottom suggests strong selling force. The user can adjust what percentage qualifies as near the top or bottom.
Break of recent structure
The bar must break a recent pivot level. For bullish pulses, the high of the bar must exceed the highest high of the past N bars. For bearish pulses, the low must break the lowest low of the past N bars. This confirms that the move did not merely expand but actually displaced prior structure.
When all conditions align
A bullish displacement pulse is marked with a small aqua circle below the bar.
A bearish displacement pulse is marked with a fuchsia circle above the bar.
The result is a clean on chart visualization of where price produced meaningful displacement.
How traders can use this
Spot abnormal momentum
Pulses can highlight areas where price behaves with more force than usual. These events often appear around news, liquidity sweeps, or algorithmic shifts.
Identify possible regime changes
A pulse that breaks structure while closing near the extreme may signal a transition from a ranging environment to a trending one. It does not predict direction but flags where displacement actually occurred.
Support narrative building
When combined with levels, zones, or other frameworks, pulses can confirm whether the market had enough strength to break through an area with conviction.
Filter trades or refine entries
Some traders may choose to trade in the direction of recent pulses during trending conditions. Others may only enter a trade after a pulse confirms that the market has shifted away from compression.
Track where the market is imbalanced
A pulse visually marks whether buyers or sellers were able to generate strong initiative movement. These points often become useful reference zones for continuation or rejection analysis.
Why this indicator is useful
It reduces complex logic into simple visual markers. Instead of scanning bar by bar for structural breaks, volatility expansions, and close strength, the indicator does this automatically and highlights only the bars that meet all criteria. This keeps the chart clean while still providing precision about where displacement actually occurred.
Linear Trajectory & Volume StructureThe Linear Trajectory & Volume Structure indicator is a comprehensive trend-following system designed to identify market direction, volatility-adjusted channels, and high-probability entry points. Unlike standard Moving Averages, this tool utilizes Linear Regression logic to calculate the "best fit" trajectory of price, encased within volatility bands (ATR) to filter out market noise.
It integrates three core analytical components into a single interface:
Trend Engine: A Linear Regression Curve to determine the mean trajectory.
Volume Verification: Filters signals to ensure price movement is backed by market participation.
Market Structure: Identifies previous high-volume supply and demand zones for support and resistance analysis.
2. Core Components and Logic
The Trajectory Engine
The backbone of the system is a Linear Regression calculation. This statistical method fits a straight line through recent price data points to determine the current slope and direction.
The Baseline: Represents the "fair value" or mean trajectory of the asset.
The Cloud: Calculated using Average True Range (ATR). It expands during high volatility and contracts during consolidation.
Trend Definition:
Bullish: Price breaks above the Upper Deviation Band.
Bearish: Price breaks below the Lower Deviation Band.
Neutral/Chop: Price remains inside the cloud.
Smart Volume Filter
The indicator includes a toggleable volume filter. When enabled, the script calculates a Simple Moving Average (SMA) of the volume.
High Volume: Current volume is greater than the Volume SMA.
Signal Validation: Reversal signals and structure zones are only generated if High Volume is present, reducing the likelihood of trading false breakouts on low liquidity.
Volume Structure (Smart Liquidity)
The script automatically plots Support (Demand) and Resistance (Supply) boxes based on pivot points.
Creation: A box is drawn only if a pivot high or low is formed with High Volume (if the volume filter is active).
Mitigation: The boxes extend to the right. If price breaks through a zone, the box turns gray to indicate the level has been breached.
3. Signal Guide
Trend Reversals (Buy/Sell Labels)
These are the primary signals indicating a potential change in the macro trend.
BUY Signal: Appears when price closes above the upper volatility band after previously being in a downtrend.
SELL Signal: Appears when price closes below the lower volatility band after previously being in an uptrend.
Pullbacks (Small Circles)
These are continuation signals, useful for adding to positions or entering an existing trend.
Long Pullback: The trend is Bullish, but price dips momentarily below the baseline (into the "discount" area) and closes back above it.
Short Pullback: The trend is Bearish, but price rallies momentarily above the baseline (into the "premium" area) and closes back below it.
4. Configuration and Settings
Trend Engine Settings
Trajectory Length: The lookback period for the Linear Regression. This is the most critical setting for tuning sensitivity.
Channel Multiplier: Controls the width of the cloud.
1.0: Aggressive. Results in narrower bands and earlier signals, but more false positives.
1.5: Balanced (Default).
2.0+: Conservative. Creates a wide channel, filtering out significant noise but delaying entry signals.
Signal Logic
Show Trend Reversals: Toggles the main Buy/Sell labels.
Show Pullbacks: Toggles the re-entry circle signals.
Smart Volume Filter: If checked, signals require above-average volume. Unchecking this yields more signals but removes the volume confirmation requirement.
Volume Structure
Show Smart Liquidity: Toggles the Support/Resistance boxes.
Structure Lookback: Defines how many bars constitute a pivot. Higher numbers identify only major market structures.
Max Active Zones: Limits the number of boxes on the chart to prevent clutter.
5. Timeframe Optimization Guide
To maximize the effectiveness of the Linear Trajectory, you must adjust the Trajectory Length input based on your trading style and timeframe.
Scalping (1-Minute to 5-Minute Charts)
Recommended Length: 20 to 30
Multiplier: 1.2 to 1.5
Logic: Fast-moving markets require a shorter lookback to react quickly to micro-trend changes.
Day Trading (15-Minute to 1-Hour Charts)
Recommended Length: 55 (Default)
Multiplier: 1.5
Logic: A balance between responsiveness and noise filtering. The default setting of 55 is standard for identifying intraday sessions.
Swing Trading (4-Hour to Daily Charts)
Recommended Length: 89 to 100
Multiplier: 1.8 to 2.0
Logic: Swing trading requires filtering out intraday noise. A longer length ensures you stay in the trade during minor retracements.
6. Dashboard (HUD) Interpretation
The Head-Up Display (HUD) provides a summary of the current market state without needing to analyze the chart visually.
Bias: Displays the current trend direction (BULLISH or BEARISH).
Momentum:
ACCELERATING: Price is moving away from the baseline (strong trend).
WEAKENING: Price is compressing toward the baseline (potential consolidation or reversal).
Volume: Indicates if the current candle's volume is HIGH or LOW relative to the average.
Disclaimer
*Trading cryptocurrencies, stocks, forex, and other financial instruments involves a high level of risk and may not be suitable for all investors. This indicator is a technical analysis tool provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of profit. Past performance of any trading system or methodology is not necessarily indicative of future results.
GCM MACD based Range OscillatorGCM MACD based Range Oscillator (MRO)
Introduction
The GCM MACD based Range Oscillator (MRO) is a hybrid technical indicator that combines the momentum-tracking capabilities of the classic MACD (Moving Average Convergence Divergence) with a custom Range Oscillator.
The core problem this script solves is normalization. Usually, Range Oscillators and MACD Histograms operate on vastly different scales, making it impossible to overlay them accurately. This script dynamically scales the Range Oscillator to fit within the recent amplitude of the MACD Histogram, allowing traders to visualize volatility and momentum on a single, unified interface.
How It Works (The Math)
1. MACD Calculation: The script calculates a standard MACD (Fast MA - Slow MA) and its Signal line to derive the MACD Histogram.
2. Weighted Range Oscillator: Instead of a simple RSI or Stochastic, this script uses a volatility-based calculation. It compares the current Close to a Weighted Moving Average (derived from price deltas).
3. Dynamic Fitting: The script looks back 100 bars to find the maximum amplitude of the MACD Histogram. It then normalizes the Range Oscillator values to match this amplitude.
4. Bands & Coloring:
o Slope Coloring: Both the MACD and the Oscillator change color based on their slope. Green indicates rising values (bullish pressure), and Red indicates falling values (bearish pressure).
o Fixed Bands: Horizontal bands are placed at +0.75 and -0.75 relative to the scaled data to act as Overbought and Oversold zones, with a yellow-tinted background for visibility.
How to Use This Indicator
• Trend Confirmation: When both the MACD line and the Range Oscillator are green, the trend is strongly bullish. When both are red, the trend is bearish.
• Contraction & Expansion: The yellow zone (between -0.75 and +0.75) represents the "equilibrium" or ranging area. Breakouts above the Upper Band (+0.75) usually signal strong expansion or overbought conditions, while drops below the Lower Band (-0.75) signal oversold conditions.
• The "Fill" Gap: The space between the Range Oscillator line and the MACD line is filled. A widening gap between these two metrics can indicate a divergence between pure price action (Range) and momentum (MACD).
• High/Low Marks: Small markers are plotted on the most recent 3 candles to show the exact High and Low oscillation points for short-term entries.
Settings Included
• Range Length & Multiplier: Adjust the sensitivity of the Range Oscillator.
• MACD Inputs: Customizable Fast, Slow, and Signal lengths, with options for SMA or EMA types.
• Visuals: Fully customizable colors for Rising/Falling trends, band opacity, and line thickness.
How this follows House Rules
1. Originality:
o Rule: You cannot simply upload a generic MACD.
o Compliance: This is not a standard MACD. It is a complex script that performs mathematical normalization to fit two different indicator types onto one scale. The "Dynamic Fitting" logic makes it unique.
2. Description Quality:
o Rule: You must explain the math and how to read the signals.
o Compliance: The description above details the "Weighted MA logic" and the "Dynamic Fitting" process. It avoids saying "Buy when Green" (which is low effort) and instead explains why it turns green (slope analysis).
3. Visuals:
o Rule: Plots must be clear and not cluttered.
o Compliance: The script uses overlay=false (separate pane). The specific colors you requested (#37ff0c, #ff0014, and the Yellow tint) are high-contrast and distinct, making the chart easy to read.
4. No "Holy Grail" Claims:
o Rule: Do not promise guaranteed profits.
o Compliance: The description uses terms like "Trend Confirmation" and "Signal," avoiding words like "Guaranteed," "Win-rate," or "No Repaint."
OTA ATR Stop BufferOTA ATR indicator calculates and displays the Daily Average True Range (ATR), and two customizable ATR percentage values in a clean table format. It provides values in ticks and points, helping traders set stop-loss buffers based on market volatility.
Fair Value Gaps (Custom)Fair Value Gaps (FVG) - Custom
A comprehensive Fair Value Gap indicator designed for futures traders, offering multi-timeframe analysis with full customization of colors, opacity, and visual elements per timeframe.
What are Fair Value Gaps?
Fair Value Gaps (FVGs) are three-candle patterns where a gap exists between the high of the first candle and the low of the third candle (bullish) or between the low of the first candle and the high of the third candle (bearish). These imbalances often act as support/resistance zones where price tends to return.
Key Features
Multi-Timeframe Support
5 independent timeframe slots
View higher timeframe FVGs on lower timeframe charts
Each timeframe has its own color, opacity, label, and midline settings
Flexible Fill Methods
Any Touch — FVG filled when price touches the zone
Midpoint Reached — FVG filled when price reaches 50% of the zone
Wick Sweep — FVG filled when wick passes through entire zone
Body Beyond — FVG filled when candle body closes beyond the zone
Visual Customization
Per-timeframe color AND opacity control via color picker
Optional midline display per timeframe
Customizable labels with fill percentage display
Optional borders with style/width settings
Boxes can extend to chart edge or fixed bar length
Dashboard & Alerts
Real-time FVG count dashboard (Bull/Bear above/below price)
Alert conditions: Price enters FVG, Midline cross, New FVG formed, FVG filled
Recommended Settings for ES/NQ Futures
Min Gap Size: 8 ticks (2 points)
Fill Method: Body Beyond (most conservative)
Default Opacity: 10% (adjust per timeframe as needed)
Usage Tips
Use higher timeframe FVGs as key support/resistance zones
Watch for confluence when multiple timeframe FVGs overlap
Midline often acts as the first target/reaction point
Combine with other confluence factors (order blocks, volume, etc.)
NC-ALPHA INDEX [Pro Pane] - Smart Money Flow01. THE PROBLEM: MARKET CAP IS A LAGGING INDICATOR
Standard crypto indices (like Coin50 or Total Market Cap) are weighted by capitalization. This is a flawed model for active traders because it prioritizes "Dino Coins"—older assets with massive supplies but very little active volume or price discovery. They are heavy, slow, and hide the real story.
02. THE SOLUTION: VOLUME-VELOCITY WEIGHTING
The NC-ALPHA INDEX is designed for SMC (Smart Money Concepts) traders who need to see where the real liquidity is flowing right now.
Instead of static weighting, this script dynamically adjusts the influence of each asset based on its Real-Time Dollar Volume.
High Volume = High Impact: If a specific asset (e.g., SOL, HYPE, or PEPE) is attracting massive liquidity inflow, its weight in the index increases instantly.
Low Volume = Low Impact: Assets with no volume ("Zombie coins") have minimal impact on the index line, preventing false signals.
03. THE "MARKET DRIVERS" BASKET
The index tracks a curated basket of 10 high-velocity assets representing the current market meta:
1 - Kings: BTC, ETH
2 - Market Leaders: SOL, BNB
3 - High Beta / L1s: SUI
Sector Proxies: DOGE (Memes), HYPE (DEX/Perps), AAVE (DeFi), LINK (Infra), XRP.
04. HOW TO TRADE WITH IT
A. The Divergence (Trap Detector) If Bitcoin is making a Higher High (HH) at a Key Resistance, but the NC-ALPHA Index is making a Lower High (LH) or stagnating:
Signal: The pump is unsupported by broad liquidity. It is likely a "Fake Pump" driven by wash trading or isolated manipulation. High probability of an SFP (Swing Failure Pattern).
B. The HUD (Heads-Up Display) The dashboard on the chart shows you exactly what is moving the market.
Look at the "W%" (Weight) column.
Signal: If an Altcoin (like SUI or HYPE) suddenly exceeds 15-20% weight, a Sector Rotation is occurring. Stop watching BTC and focus on that asset.
05. TECHNICAL NOTES
Crash Proof: Built with advanced nz() data handling to prevent the "disappearing line" bug common in composite indices.
Usage Rule: For accurate calculation, use this indicator on 24/7 Crypto Charts (BTC, ETH, SOL) rather than Traditional Finance charts (VIX, SPX) to avoid weekend data gaps.
Built by KheopsCrypto for the SMC Community.
Volatility Regime NavigatorA guide to understanding VIX, VVIX, VIX9D, VVIX/VIX, and the Composite Risk Score
1. Purpose of the Indicator
This dashboard summarizes short-term market volatility conditions using four core volatility metrics.
It produces:
• Individual readings
• A combined Regime classification
• A Composite Risk Score (0–100)
• A simplified Risk Bucket (Bullish → Stress)
Use this to evaluate market fragility, drift potential, tail-risk, and overall risk-on/off conditions.
This is especially useful for intraday ES/NQ trading, expected-move context, and understanding when breakouts or fades have edge.
2. The Four Core Volatility Inputs
(1) VIX — Baseline Equity Volatility
• < 16: Complacent (easy drift-up, but watch for fragility)
• 16–22: Healthy, normal volatility → ideal trading conditions
• > 22: Stress rising
• > 26: Tail-risk / risk-off environment
(2) VIX9D — Short-Term Event Vol
Measures 9-day implied volatility. Reacts to immediate news/events.
• < 14: Strongly bullish (drift regime)
• 14–17: Bullish to neutral
• 17–20: Event risk building
• > 20: Short-term stress / caution
(3) VVIX — Volatility of VIX (fragility index)
Tracks volatility of volatility.
• < 100: “Bullish, Bullish” — very low fragility
• 100–120: Normal
• 120–140: Fragile
• > 140: Stress, hedging pressure
(4) VVIX/VIX Ratio — Microstructure Risk-On/Risk-Off
One of the most sensitive indicators of market confidence.
• 5.0–6.5: Strongest “normal/bullish” zone
• < 5.0: Bottom-stalking / fear regime
• > 6.5: Complacency → vulnerable to reversals
• > 7.5: Fragile / top-risk
3. Composite Risk Score (0–100)
The dashboard converts all four inputs into a single score.
Score Interpretation
• 80–100 → Bullish - Drift regime. Shallow pullbacks. Upside favored.
• 60–79 → Normal - Healthy tape. Balanced two-way trading.
• 40–59 → Fragile - Choppy, failed breakouts, thinner liquidity.
• 20–39 → Risk-Off - Downside tails active. Favor fades and defensive behavior.
• < 20 → Stress - Crisis or event-driven tape. Avoid longs.
Score updates every bar.
4. Regime Label
Independent of the composite score, the script provides a Regime classification based on combinations of VIX + VVIX/VIX:
• Bullish+ → Buying is easy, tape lifts passively
• Normal → Cleanest and most tradable conditions
• Complacent → Top-risk; be careful chasing upside
• Mixed → Signals conflict; chop potential
• Bottom Stalk → High VIX, low VVIX/VIX (capitulation signatures)
A trailing “+” or “*” indicates additional bullish or caution overlays from VIX9D/VVIX.
5. How to Use the Dashboard in Trading
When Bullish (Score ≥ 80):
• Expect drift-up behavior
• Downside limited unless catalyst hits
• Structure favors breakouts and trend continuation
• Mean reversion trades have lower expectancy
When Normal (Score 60–79):
• The “playbook regime”
• Breakouts and mean reversion both valid
• Best overall trading environment
When Fragile (Score 40–59):
• Expect chop
• Breakouts fail
• Take quicker profits
• Avoid overleveraged directional bets
When Risk-Off (20–39):
• Favor fades of strength
• Downside tails activate
• Trend-following short setups gain edge
• Respect volatility bands
When Stress (<20):
• Avoid long exposure
• Do not chase dips
• Expect violent, news-sensitive behavior
• Position sizing becomes critical
6. Quick Summary
• VIX = weather
• VIX9D = short-term storm radar
• VVIX = foundation stability
• VVIX/VIX = confidence vs fragility
• Composite Score = overall regime health
• Risk Bucket = simple “what do I do?” label
This dashboard gives traders a high-confidence, low-noise view of equity volatility conditions in real time.
Price Velocity TachometerA visual gauge that breaks price action into a tachometer-style display, showing how fast price is moving up or down in real time. It measures price velocity in ticks per second and converts that momentum into an easy-to-read, center-zero meter—green when price accelerates upward, red when it accelerates downward. Ideal for spotting microbursts of momentum, shifts in pressure, and real-time strength behind each move.
Disclaimer:
This indicator is provided for informational and educational purposes only. Trading involves risk, and the user assumes all responsibility for any decisions or outcomes resulting from its use. Use at your own risk.
PVV StochRSI TrendAnother Price, Volume, Volatility Trend indicator. This one has an RSI factor to it.
Have fun and change what you want.
Adjusting the inputs to the timeframe traded on is encouraged.
Hash SupertrendHash Supertrend is a visually enhanced Supertrend-based indicator designed by Hash Capital Research, tuned specifically for crypto trend trading on Solana (SOL) and Bitcoin (BTC). It combines institutional-style color coding, an optional session time filter, and production-ready alerts for systematic and discretionary traders alike.
What This Indicator Is
Hash Supertrend is a trend-following volatility band indicator built on TradingView’s native ta.supertrend() function.
It’s optimized and visually styled for:
High-volatility crypto pairs (especially SOL/USDT, SOL/USD, BTC/USDT, BTC/USD)
Timeframes typically used by crypto traders (from 5m scalping to 4H swing and 1D trend following)
The script is an indicator, not a strategy:
It does not place trades or show backtest results.
It provides clear trend states, flips, and alerts that you can plug into your own execution stack or manual trading.
Key Features
✅ Tuned for Crypto (Solana & Bitcoin)
Parameters are chosen to respond well to the volatility profile of SOL and BTC, reducing noise while still catching strong moves.
✅ Non-repainting Supertrend Core
Uses TradingView’s built-in ta.supertrend — values may move intrabar as the bar forms, but once a bar closes, the historical line and signals do not repaint.
✅ Fluorescent Trend Visualization
Bright green for bullish phases
Bright red for bearish phases
Adaptive color intensity based on user setting
✅ Glow Layer & Trend Zones
Glow effect around the Supertrend line for instant visual recognition
Optional filled zones between price and line for “trend cloud” style visualization
✅ Time Filter (Session Control)
Option to only mark signals during specific hours for those wanting to integrate with webhooks
Designed for traders who avoid certain sessions (e.g., low-liquidity hours)
✅ Signal Dots & Alerts
Tiny green dots for bullish flips
Tiny red dots for bearish flips
Professional, preconfigured alerts for:
Long Entry
Short Entry
Any Trend Change
Filtered signals outside trading hours (for monitoring only)
The core logic is built on:
ATR Length (ATR Length) Default: 16
Lower values (7–10): more sensitive, more signals, more noise
Higher values (12–20): smoother, fewer but stronger trend signals
Factor (Factor) Default: 3.11
Lower values (1.5–2.5): tighter bands, earlier entries, higher whipsaws
Higher values (3.0–4.0+): wider bands, later entries, stronger trend confirmation
The indicator reads direction from ta.supertrend and classifies:
Bullish Trend: direction < 0
Bearish Trend: direction > 0
A trend flip happens when direction changes sign:
longSignal: Supertrend flips from above price to below price (bearish → bullish)
shortSignal: Supertrend flips from below price to above price (bullish → bearish)
PVV Trend Line (Lower Study)Doing my best to create something is uses rate of change on the Price, volume, and volatility. I know it's not perfect, but it does it's job for me.
It's useful use it, if it's not then don't.
You will need to change settings for the time frame you want to trade on.
EMA Smoothed Standard Error Bands-zrbb-EMA Smoothed Standard Error Bands-zrbb-
The Standard Error Bands (SEM) indicator is primarily used in market analysis to measure price volatility, assess trend strength, and identify potential market reversals or consolidation zones. Similar to Bollinger Bands, it is typically based on linear regression lines rather than simple moving averages, providing traders with a visual range of price fluctuations around its average trend.
Specific functions include:
* Measuring Volatility: The width of the SEM directly reflects market volatility. When price trends are stable, the bandwidth typically contracts, indicating that data points are clustered around the mean; conversely, when market volatility increases, the bandwidth expands, indicating greater price dispersion.
* Assessing Trend Strength and Direction: This indicator can show the direction of the current trend and assess its strength by observing the price's position within the bands. If the price consistently touches or trades near the boundary on one side of the band, it usually indicates a strong trend in that direction.
* Identifying Overbought/Oversold Signals: While not a strictly overbought/oversold indicator, when the price touches or breaks through the upper or lower band, it may indicate that the market is in a state of extreme volatility in the short term, potentially leading to a price pullback or reversal.
Predicting Potential Trend Ends or Consolidation: When the standard error band begins to expand significantly, it can be a signal that the momentum of the current trend is weakening, and the market may be about to enter a consolidation phase or the trend may be about to reverse.
Assisting Decision Making and Risk Management: Traders use the boundary lines as potential support and resistance levels to help determine entry and exit points or set stop-loss levels, thereby managing trading risk.
In summary, the standard error band is a dynamic volatility tool that helps traders better understand market behavior by quantifying the degree to which prices deviate from their predicted trend, providing an important reference, especially in judging the continuation of trends and potential turning points.
标准误差带（Standard Error Bands）指标在市场分析中主要用于衡量价格波动性、判断趋势强度以及识别潜在的市场反转或盘整区域。它类似于布林带（Bollinger Bands），但通常基于线性回归线而不是简单的移动平均线，为交易者提供了价格围绕其平均趋势波动的视觉范围。
具体作用包括：
衡量波动性：标准误差带的宽度直接反映了市场的波动性。当价格趋势稳定时，带宽通常会收缩，表明数据点聚集在均值附近；相反，当市场波动加剧时，带宽会扩张，表明价格离散程度增大。
判断趋势强度和方向：该指标可以显示当前趋势的方向，并通过观察价格在带内的位置来评估趋势的强度。如果价格持续触及或运行在某一侧的边界附近，通常意味着该方向的趋势强劲。
识别超买/超卖信号：虽然不是严格意义上的超买/超卖指标，但当价格触及或突破上轨或下轨时，可能预示着市场短期内处于极端的波动状态，可能会出现价格回调或反转。
预测潜在的趋势结束或盘整：当标准误差带开始显著扩张时，这可能是一个信号，表明当前趋势的动能正在减弱，市场可能即将进入盘整期或趋势即将反转。
辅助决策和风险管理：交易者利用边界线作为潜在的支撑位和阻力位，帮助确定进场、出场点位或设置止损水平，从而管理交易风险。
总之，标准误差带是一个动态的波动率工具，它通过量化价格偏离其预测趋势的程度，帮助交易者更清晰地理解市场行为，尤其是在判断趋势的持续性和潜在转折点方面提供了重要参考。
ATR Levels Trade PlanOverview
This indicator is a trade management tool designed to help traders visualize volatility-based targets and stop-losses instantly. By anchoring calculations to the Daily Opening Price and the Average True Range (ATR), it projects objective, mathematical support and resistance levels for the current session.
How It Works
The script detects the start of the trading day (or a manually defined period) and draws a vertical marker. From there, it projects horizontal lines representing key multiples of the ATR:
Green Line: Opening Price (The baseline).
Blue Lines (Targets): +0.5 ATR, +1.0 ATR, and +2.0 ATR. These serve as dynamic profit-taking zones based on current market volatility.
Orange Line (Stop Loss): -2.0 ATR. A standard volatility-based stop level.
Red Line (Emergency Exit): -3.0 ATR. A level indicating extreme adverse moves.
Multi-Ticker Database & Date Verification This version includes a built-in configuration menu capable of storing unique trade plans for up to 20 different stocks.
20-Slot Memory: You can pre-load the Ticker Symbol, Planned Open, and ATR for up to 20 individual assets in the settings.
Date/Period of Trade: Each slot includes a "Date" field (YYYYMMDD). This assigns the manual values to a specific trading session.
Default Behavior (Auto-Fallback): The indicator intelligently scans the database when you switch charts.
If the Ticker matches a slot AND the Date matches the current session, it loads your manual values.
If the Ticker is not in the database, or if the Date is expired (from a previous day), the script automatically defaults to the live Daily Open and standard ATR-14.
Key Features
Clean Visuals: Uses the Drawing API to plot lines only on the current/last bar, keeping historical price action clean and uncluttered.
Text Customization: Users can align text to the Right, Left, or Center, adjust the offset distance, and change text size to fit their chart layout.
Flexible Alerts: Includes a dedicated "Alert Configuration" menu. Users can toggle alerts on/off for individual lines (e.g., enable the Stop Loss alert but disable the +0.5 ATR alert). All enabled settings work via a single "Any alert() function call."
Settings
Stock Database: 20 configuration groups to input Ticker, Date, Open, and ATR.
Global/Fallback Values: Input custom Open/ATR prices (leave at 0 for automatic) to be used if the specific stock is not in the database.
Text & Alignment: Adjust label position, offset, and size.
Alert Configuration: Checkboxes to enable/disable alerts for specific price levels.
Methodology The levels are calculated using the standard formula: Level = Opening Price + (Multiplier * ATR)
[longshorti] Auto Fibonacci Grid (Long/Short) 🌟 Auto Fibonacci Grid (Long/Short) — Smart Retracement Tool
The Auto Fibonacci Grid (Long/Short) is an advanced trading utility designed to automate the process of identifying key Fibonacci retracement levels for both bullish and bearish swings. This indicator provides traders with precise zones for potential entries during market corrections.
✨ Key Features and Originality:
True Auto-Detection: The script automatically analyzes the market impulse within the lookback window to determine if the current grid should be calculated for a Bullish (Long) or Bearish (Short) scenario.
Impulse Filtered Alerts: A custom alert system triggers only when the price enters your designated key zone and when the underlying market impulse exceeds a user-defined Minimum Impulse Percentage. This is crucial for filtering out false signals generated by weak, consolidating movements.
Dynamic Correction Zones: Define any range of Fibonacci levels (e.g., 0.5 to 0.618) to be highlighted as your Key Zone (Buy or Sell Zone), with dedicated color schemes for Long and Short setups.
Visual Tracking: Fills between levels dynamically change color to indicate the impulse direction and track which zones have already been successfully tested by the price action.
🧠 How It Works:
The indicator scans the last N bars (Fixed Window Lookback) to identify the Low and High of the swing. It then compares the bar indices to determine the final direction. The calculateFibPrice function internally adapts to project correction levels from the High down (for Long) or from the Low up (for Short), ensuring the grid is always applied correctly to the impulse.
⚙️ Settings Overview:
The script includes comprehensive settings for:
Grid Mode: Auto Detect, Force Bullish, or Force Bearish.
Impulse Filter: Set the minimum percentage (0% = Off) required for alerts to trigger.
MFI/RSI Settings: Used for additional signal confirmation (internal logic).
Display & Style: Full control over line colors, fill colors, and text sizes.
SMC Statistical Liquidity Walls [PhenLabs]📊 SMC Statistical Liquidity Walls
Version: PineScript™ v6
📌 Description
The SMC Statistical Liquidity Walls indicator is designed to visualize market volatility and potential reversal zones using advanced statistical modeling. Unlike traditional Bollinger Bands that use simple lines, this script utilizes an “Inverted Sigmoid” opacity function to create a “fog of war” effect. This visualizes the density of liquidity: the further price moves from the equilibrium (mean), the “harder” the liquidity wall becomes.
This tool solves the problem of over-trading in low-probability areas. By automatically mapping “Premium” (Resistance) and “Discount” (Support) zones based on Standard Deviation (SD), traders can instantly see when price is overextended. The result is a clean, intuitive overlay that helps you identify high-probability mean reversion setups without cluttering your chart with manual drawings.
🚀 Points of Innovation
Inverted Sigmoid Logic: A custom mathematical function maps Standard Deviation to opacity, creating a realistic “wall” density effect rather than linear gradients.
Dynamic “Solidity”: The indicator is transparent at the center (Equilibrium) and becomes visually solid at the edges, mimicking physical resistance.
Separated Directional Bias: distinct Red (Premium) and Green (Discount) coding helps SMC traders instantly recognize expensive vs. cheap pricing.
Smart “Safe” Deviation: Includes fallback logic to handle calculation errors if deviation hits zero, ensuring the indicator never crashes during data gaps.
🔧 Core Components
Basis Calculation: Uses a Simple Moving Average (SMA) to determine the market’s equilibrium point.
Standard Deviation Zones: Calculates 1SD, 2SD, and 3SD levels to define the statistical extremes of price action.
Sigmoid Alpha Calculation: Converts the SD distance into a transparency value (0-100) to drive the visual gradient.
🔥 Key Features
Automated Premium/Discount Zones: Red zones indicate overbought (Premium) areas; Green zones indicate oversold (Discount) areas.
Customizable Density: Users can adjust the “Steepness” and “Midpoint” of the sigmoid curve to control how fast the walls become solid.
Integrated Alerts: Built-in alert conditions trigger when price hits the “Solid” wall (2SD or higher), perfect for automated trading or notifications.
Visual Clarity: The center of the chart remains clear (high transparency) to keep focus on price action where it matters most.
🎨 Visualization
Equilibrium Line: A gray line representing the mean price.
Gradient Fills: The space between bands fills with color that increases in opacity as it moves outward.
Premium Wall: Upper zones fade from transparent red to solid red.
Discount Wall: Lower zones fade from transparent green to solid green.
📖 Usage Guidelines
Range Period: Default 20. Controls the lookback period for the SMA and Standard Deviation calculation.
Source: Default Close. The price data used for calculations.
Center Transparency: Default 100 (Clear). Controls how transparent the middle of the chart is.
Edge Transparency: Default 45 (Solid). Controls the opacity of the outermost liquidity wall.
Wall Steepness: Default 2.5. Adjusts how aggressively the gradient transitions from clear to solid.
Wall Start Point: Default 1.5 SD. The deviation level where the gradient shift begins to accelerate.
✅ Best Use Cases
Mean Reversion Trading: Enter trades when price hits the solid 2SD or 3SD wall and shows rejection wicks.
Take Profit Targets: Use the Equilibrium (Gray Line) as a logical first target for reversal trades.
Trend Filtering: Do not initiate new long positions when price is deep inside the Red (Premium) wall.
⚠️ Limitations
Lagging Nature: As a statistical tool based on Moving Averages, the walls react to past price data and may lag during sudden volatility spikes.
Trending Markets: In strong parabolic trends, price can “ride” the bands for extended periods; mean reversion should be used with caution in these conditions.
💡 What Makes This Unique
Physics-Based Visualization: We treat liquidity as a physical barrier that gets denser the deeper you push, rather than just a static line on a chart.
🔬 How It Works
Step 1: The script calculates the mean (SMA) and the Standard Deviation (SD) of the source price.
Step 2: It defines three zones above and below the mean (1SD, 2SD, 3SD).
Step 3: The custom `get_inverted_sigmoid` function calculates an Alpha (transparency) value based on the SD distance.
Step 4: Plot fills are colored dynamically, creating a seamless gradient that hardens at the extremes to visualize the “Liquidity Wall.”
💡 Note
For best results, combine this indicator with Price Action confirmation (such as pin bars or engulfing candles) when price touches the solid walls.
ATR Based Stoploss LineThis indicator dynamically plots a horizontal stop-loss level using an RMA-based Average True Range (ATR). The stop value is calculated from the current closing price minus ATR (with optional multiplier) to provide a systematic risk reference during active price movement. A fixed line extends across recent bars for clear visualization, with the stop-loss price displayed at the midpoint of that line for intuitive charting. This tool should be strictly used for breakout environments, aligned with your risk management protocol, and always confirmed with volume analysis before execution. The intent is to drive disciplined entries, strengthen downside protection, and support robust trade management in volatile market conditions.
Hierarchical Hidden Markov ModelHierarchical Hidden Markov Models (HHMMs) are an advanced version of standard Hidden Markov Models (HMMs). While HMMs model systems with a single layer of hidden states, each transitioning to other states based on fixed probabilities, HHMMs introduce multiple layers of hidden states. This hierarchical structure allows for more complex and nuanced modeling of systems, making HHMMs particularly useful in representing systems with nested states or regimes. In HHMMs, the hidden states are organized into levels, where each state at a higher level is defined by a set of states at a lower level. This nesting of states enables the model to capture longer-term dependencies in the time series, as each state at a higher level can represent a broader regime, and the states within it can represent finer sub-regimes. For example, in financial markets, a high-level state might represent a general market condition like high volatility, while the nested lower-level states could represent more specific conditions such as trending or oscillating within the high volatility regime.
The hierarchical nature of HHMMs is facilitated through the concept of termination probabilities. A termination probability is the probability that a given state will stop emitting observations and transition control back to its parent state. This mechanism allows the model to dynamically switch between different levels of the hierarchy, thereby modeling the nested structure effectively. Beside the transition, emission and initial probabilities that generally define a HMM, termination probabilities distinguish HHMMs from HMMs because they define when the process in a sub-state concludes, allowing the model to transition back to the higher-level state and potentially move to a different branch of the hierarchy.
In financial markets, HHMMs can be applied similiarly to HMMs to model latent market regimes such as high volatility, low volatility, or neutral, along with their respective sub-regimes. By identifying the most likely market regime and sub-regime, traders and analysts can make informed decisions based on a more granular probabilistic assessment of market conditions. For instance, during a high volatility regime, the model might detect sub-regimes that indicate different types of price movements, helping traders to adapt their strategies accordingly.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. These posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequence. Out-of-sample predictions, on the other hand, offer a forward-looking evaluation to test the model's predictive capability.
MODEL TESTING:
When the "Test Out of Sample" option is enabled, the indicator plots the selected display settings based on models' out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of data points not included in the training process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probabilities for a particular state suggest that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas lower complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is useful to assess the stability of the model complexity as well as understand where changes come from when a shift happens. A model with irregular complexity values can be strong sign of overfitting, as it suggests that the process that the model is capturing changes siginficantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
Hidden Markov ModelHidden Markov Models (HMMs) are a class of statistical models used to represent systems that follow a Markov process with hidden states. In such models, the system being modeled transitions between a finite number of states, with the probability of each transition dependent only on the current state. The hidden states are not directly observable; instead, we observe a sequence of emissions or outputs generated by these states. HMMs are widely used in various fields, including speech recognition, bioinformatics, and financial market analysis. In the context of financial markets, HMMs can be utilized to model the latent market regimes (e.g., bullish, bearish, or neutral) that influence the observed market data such as asset prices or returns. By estimating the posterior probabilities of these hidden states, traders and analysts can identify the most likely market regime and make informed decisions based on the probabilistic assessment of market conditions.
The Hidden Markov Model (HMM) comprises several states that work together to model the hidden market dynamics. The states represent the unobservable market regimes such as bullish, bearish or neutral. The states are 'hidden' in nature because we need to infer them from the data and cannot directly observe them.
Model components:
Initial Probabilities: These denote the likelihood of starting in each hidden state. They can be related to long-run probabilities, reflecting the overall likelihood of each state across extended periods. In equilibrium, these initial probabilities may converge to the stationary distribution of the Markov chain.
Transition Probabilities: These capture the likelihood of moving between states, including the probability of remaining in the current state. They model how market regimes evolve over time, allowing the HMM to adapt to changing conditions.
Emission Probabilities: Also known as observation likelihoods, these represent the probability of observing specific market data (like returns) given each hidden state. Emission probabilities can be often represented by continuous probability distributions. In our case we are using a laplace distribution with its location parameter reflecting the central tendency of returns in each state and the scale reflecting the dispersion or the magnitude of the returns.
The power of HMMs in financial modeling lies in their ability to capture complex market dynamics probabilistically. By analyzing patterns in market, the model can estimate the likelihood of being in each state at any given time. This can reveal insights into market behavior and dynamics that might not be apparent from data alone.
MODEL FIT:
By default, the indicator displays the posterior probabilities, which represent the likelihood that the market is in a specific hidden state at any given time, based on the observed data and the model fit. It is crucial to understand that these posterior probabilities strictly represent the model fit, reflecting how well the model explains the historical data it was trained on. This model fit is inherently different from out-of-sample predictions, which are generated using data that was not included in the training process. The posterior probabilities from the model fit provide a probabilistic assessment of the state the market was in at a particular time based on the data that came before and after it in the training sequeence. Out-of-sample predictions on the other hand offer a forward-looking evaluation to test the model's predictive capability.
MODEL TEST:
When the "Test Out of Sample” option is enabled, the indicator plots the selected display settings based on models out-of-sample predictions. The display settings for out-of-sample testing include several options:
State Probability option displays the probability of each state at a given time for segments of datapoints that were not included in the traning process. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is rigorously tested on unseen data. These probabilities are calculated using the forward algorithm, which efficiently computes the likelihood of the observed sequence given the model parameters. Higher probability for a particular state indicate a higher likelihood that the market is currently in that state. Traders can use this information to adjust their strategies according to the identified market regime and their statistical features.
Confidence Interval Bands option plots the upper, lower, and median confidence interval bands for predicted values. These bands provide a range within which future values are expected to lie with a certain confidence level. The width of the interval is determined by the current probability of different states in the model and the distribution of data within these states. The confidence level can be specified in the Confidence Interval setting.
Omega Ratio option displays a risk-adjusted performance measure that offers a more comprehensive view of potential returns compared to traditional metrics like the Sharpe ratio. It takes into account all moments of the returns distribution, providing a nuanced perspective on the risk-return tradeoff in the context of the HHMM's identified market regimes. The minimum acceptable return (MAR) used for the calculation of the omega can be specified in the settings of the indicator. The plot displays both the current Omega ratio and a forecasted "N day Omega" ratio. A higher Omega ratio suggests better risk-adjusted performance, essentially comparing the probability of gains versus the probability of losses relative to the minimum acceptable return. The Omega ratio plot is color-coded, green indicates that the long-term forecasted Omega is higher than the current Omega (suggesting improving risk-adjusted returns over time), while red indicates the opposite. Traders can use omega ratio to assess the risk-adjusted forecast of the model, under current market conditions with a specific target return requirement (MAR). By leveraging the HHMM's ability to identify different market states, the Omega ratio provides a forward-looking risk assessment tool, helping traders make more informed decisions about position sizing, risk management, and strategy selection.
Model Complexity option shows the complexity of the model, as well as complexity of individual states if the “complexity components” option is enabled. Model complexity is measured in terms of the entropy expressed through transition probabilities. The used complexity metric can be related to the models entropy rate and is calculated as the sum of the p*log(p) for every transition probability of a given state. Complexity in this context informs us on how complex the models transitions are. A model that might transition between states more often would be characterised by higher complexity, while a model that tends to transition less often would have lower complexity. High complexity can also suggest the model captures noise rather than the underlying market structure also known as overfitting, whereas too low complexity might indicate underfitting, where the model is too simplistic to capture important market dynamics. It is also useful to assess the stability of the model complexity. A model with irregular complexity values can be sign of overfitting, as it suggests that the process that the model is capturing changes significantly over time.
Akaike/Bayesian Information Criterion option plots the AIC or BIC values for the model on both the training and out-of-sample data. These criteria are used for model selection, helping to balance model fit and complexity, as they take into account both the goodness of fit (likelihood) and the number of parameters in the model. The metric therefore provides a value we can use to compare different models with different number of parameters. Lower values generally indicate a better model. AIC is considered more liberal while BIC is considered a more conservative criterion which penalizes the likelihood more. Beside comparing different models, we can also assess how much the AIC and BIC differ between the training sets and test sets. A test set metric, which is consistently significantly higher than the training set metric can point to a drift in the models parameters, a strong drift of model parameters might again indicate overfitting or underfitting the sampled data.
Indicator settings:
- Source : Data source which is used to fit the model
- Training Period : Adjust based on the amount of historical data available. Longer periods can capture more trends but might be computationally intensive.
- EM Iterations : Balance between computational efficiency and model fit. More iterations can improve the model but at the cost of speed.
- Test Out of Sample : turn on predict the test data out of sample, based on the model that is retrained every N bars
- Out of Sample Display: A selection of metrics to evaluate out of sample. Pick among State probability, confidence interval, model complexity and AIC/BIC.
- Test Model on N Bars : set the number of bars we perform out of sample testing on.
- Retrain Model on N Bars: Set based on how often you want to retrain the model when testing out of sample segments
- Confidence Interval : When confidence interval is selected in the out of sample display you can adjust the percentage to reflect the desired confidence level for predictions.
- Omega forecast: Specifies the number of days ahead the omega ratio will be forecasted to get a long run measure.
- Minimum Acceptable Return : Specifies the target minimum acceptable return for the omega ratio calculation
- Complexity Components : When model complexity is selected in the out of sample display, this option will display the complexity of each individual state.
-Bayesian Information Criterion : When AIC/BIC is selected, turning this on this will ensure BIC is calculated instead of AIC.
