Bottom Up - Slope Trend DetectorSlope Trend Detector by Bottom Up
This indicator is a simple slope trend detector which highlights clearly current market bias.
It uses an EWMA to get a smoother moving average on which to identify the trend by monitoring the slope. EWMA reduces noise and gives a more reliable trend reading.
It isn't subject to repaint and sends an alert whenever the trend changes.
It shows two moving averages simultaneously, a faster one and a slower one, whose periods can be customized by the user, to have a clear reading of the current market condition, allowing to distinguish retracements from long-term structural changes.
Add to chart. Turn on alerts. Happy trading!
Bottom Up - The Ecosystem Designed for Traders
bottomup.finance
Trend Analysis
The ApexThis is a proprietary technical indicator developed by The Apex Trading Firm. It utilizes a custom trend following logic based on trends and momentum filtering. This script is strictly for internal use by authorized firm personnel only. Unauthorized distribution is prohibited.
Ichimoku Horizon MTFIchimoku Horizon MTF — Multi-TimeFrame Ichimoku with auto-map
Overview
Ichimoku Horizon MTF plots classic Ichimoku on your current chart timeframe and projects up to three higher timeframes (TF1 / TF2 / TF3) onto the same chart. The goal is to keep one clean chart while still seeing higher-timeframe Ichimoku context (for example Daily / Weekly / Monthly).
On the screen, you typically see:
– the full Ichimoku set for your chart timeframe (Tenkan, Kijun, Kumo, Chikou),
– the same structure projected from TF1–TF3, each with its own colour family,
– an optional Kumo Midline (extra line, not part of original Ichimoku, used as a visual helper),
– and an optional TF banner showing which higher timeframes are currently mapped.
And optionally: A Kumo Midline (midpoint between SSA and SSB)
Kumo Midline (non-original Ichimoku addition)
The Kumo Midline is a personal addition.
It is not part of the original Ichimoku.
It is provided for informational and visual purposes only, as a helper to read the centre of the cloud.
It is calculated as the simple average of the two cloud boundaries:
Midline= SSA + SSB / 2
You can turn it ON/OFF globally, and also separately for each timeframe (Chart / TF1 / TF2 / TF3). The same logic (including the optional Midline) is applied to TF1 / TF2 / TF3 and projected onto your main chart.
Auto-map & timeframe presets
You control how TF1 / TF2 / TF3 are selected using two mechanisms:
Auto-map TF from chart (ON/OFF)
When ON, the script automatically chooses a profile based on your chart timeframe
(Scalp / Intraday / Swing / Long Term / Investment / Macro).
TF1 / TF2 / TF3 are always higher than the chart timeframe, with a consistent progression.
Preset Time Frame (when Auto-map = OFF)
When OFF, you choose a fixed preset, for example:
Scalp S — 1m / 5m / 15m
Intraday L — 1H / 4H / 1D
Swing — 1D / 1W / 1M
Investment — 1M / 3M / 6M
Macro — 3M / 6M / 12M
If you choose Custom, you manually set TF1 / TF2 / TF3 using the three “Timeframe selection” inputs in the TF1 / TF2 / TF3 sections.
This allows you to switch quickly between scalp / intraday / swing / macro profiles without editing any code.
TF banner (legend) & display options
The indicator includes an optional TF banner (a small panel) that acts as a timeframe legend:
Shows the active profile name (Scalp, Intraday M, Intraday XL, Swing, Long Term, Macro, Custom). Displays the three mapped timeframes (TF1 / TF2 / TF3) in short form (5m, 1H, 4H, 1D, 1W, 1M, etc.). Shows “TK / KJ” with small coloured squares for Tenkan and Kijun for each TF, matching the line colours on the chart.
You can control:
Show / hide the banner.
Position: Top / Bottom, Left / Center / Right.
Text colour, background, text size.
Each block (Chart, TF1, TF2, TF3) also has its own toggles for:
Tenkan
Kijun
Chikō
SSA
SSB
Kumo fill
Kumo Midline
This lets you keep only what you really need (for example: just HTF Kijun + HTF Kumo).
Colour design
Special care has been taken with the colour design:
Each timeframe uses its own colour family
(for example: warm colours for the chart timeframe, green for TF1, blue for TF2, neutral/grey for TF3), so the chart stays readable even when all TFs are displayed at once.Kumo fills are semi-transparent to provide context without hiding price action. Defaults are tuned for light charts, and every colour can be customised if you prefer another palette.
Built-in alerts
The script includes a small set of ready-to-use alerts, controlled by:
A global “Enable alerts” switch
A built-in cooldown to avoid alert spam
Available conditions (on the chart timeframe):
TK > KJ (UP) – Tenkan crosses above Kijun (filtered by a bullish HTF bias).
TK < KJ (DOWN) – Tenkan crosses below Kijun (filtered by a bearish HTF bias).
Kumo Breakout (UP) – Close breaks above the cloud (with bullish HTF filter).
Kumo Breakout (DOWN) – Close breaks below the cloud (with bearish HTF filter).
All TF Bullish – Chart + TF1 + TF2 + TF3 all have Tenkan > Kijun (full bullish alignment).
All TF Bearish – Chart + TF1 + TF2 + TF3 all have Tenkan < Kijun (full bearish alignment).
HTF Confirms (BULL) – Bullish TK cross on the chart timeframe, with HTF support and price above the cloud.
HTF Confirms (BEAR) – Bearish TK cross on the chart timeframe, with HTF confirmation and price below the cloud.
To use them:
Add an alert on the indicator,
Choose one of these conditions,
Use “Once per bar close” for cleaner signals.
No-repaint logic
Higher-timeframe data is fetched using request.security() with:
barmerge.gaps_off
barmerge.lookahead_off
This means:
No artificial lookahead,
No repainting,
Apart from the normal forward shift of the Ichimoku cloud, which is how standard Ichimoku works by design.
If the chart get “stuck on the left”
stuck on the left side, or misaligned. If you see the TF banner or right-side labels not updating correctly: Clear the TradingView app cache (or restart the app / browser)
Reload the chart. This usually forces TradingView to redraw all tables and labels correctly and fixes the display issue.
Disclaimer
This script is provided for educational and informational purposes only.
It does not constitute financial or investment advice and should not be used as a standalone signal provider.
Always do your own analysis and use proper risk management before taking any trade.
Thanks for using Ichimoku Horizon MTF.
Clean Industry DataClean Industry Data – Overview
Clean Industry Data is a utility tool designed to give traders an instant, structured view of key fundamental and volatility metrics directly on the chart. The script displays a compact, customizable information panel containing:
Industry & Sector
Market Cap and Free-Float Market Cap
Free-Float Percentage
Average Daily Rupee Volume
Relative Volume (R.Vol) based on daily volume
% from 10 / 21 / 50 EMAs (calculated on daily closes)
ADR (14-day) with threshold-based indicators
ATR (current timeframe) with colour-coded risk cues
All volume-based statistics are anchored to daily data, ensuring the values remain consistent across all timeframes. The display table supports flexible positioning, custom background/text colours, and adjustable text size.
This script is ideal for traders who want a quick, accurate snapshot of a stock’s liquidity, volatility, and broader classification — without digging through multiple menus or external sources.
Trade-Pilot v3.0Trade Pilot — Smart Trade Mapping for Real Decisions ✨
Trade Pilot is designed to give traders a clear, self-explanatory view of each opportunity on the chart.
It interprets market-maker behavior and translates it into fully mapped trade setups that anyone can read instantly.
Instead of raw alerts, Trade-Pilot presents complete trade frameworks:
entry ➝ stop zone ➝ logical targets — all aligned visually in a clean, structured flow.
Key Advantages
🎯 Fully Framed Trades
Each signal comes with a defined entry area, balanced stop zone, and actionable target path.
📊 Multiple Trade Styles in One Tool
Provides short-term, medium-term, long-range, and investment-grade setups — each rendered with its own visual clarity.
🧭 Market-Maker Logic, Simplified
Instead of revealing mechanics, the indicator expresses complex behavior through intuitive chart structures.
📐 Clear Risk-to-Reward Mapping
The layout makes position sizing and expectation management straightforward and visual.
🧹 Auto-Managed Visuals
Labels, zones, and structures update and clean themselves as trades progress or complete — no clutter, no noise.
🔍 High-Quality Signal Presentation
Every setup is shown consistently, helping traders stay disciplined and focused.
Why Traders Use It
Trade-Pilot is built for traders who value clarity, consistency, and decision-ready insights.
It shows you the trade — not the calculations.
It gives structure — not confusion.
It enhances your chart — instead of overwhelming it.
If your trading style depends on understanding the full picture at a glance, Trade-Pilot will feel like a natural extension of your workflow.
⚠️ Disclaimer:
This indicator is for educational and informational purposes only. It is not investment advice. Past performance is not indicative of future results. Always conduct your own analysis and trade responsibly.
Free trial : t.me ( just send your TV username )
Sector Rotation - Risk Preference Indicator# Sector Rotation - Risk Preference Indicator
## Overview
This indicator measures market risk appetite by comparing the relative strength between **Aggressive** and **Defensive** sectors. It provides a clean, single-line visualization to help traders identify market sentiment shifts and potential trend reversals.
## How It Works
The indicator calculates a **Bullish/Bearish Ratio** by dividing the average price of aggressive sector ETFs by defensive sector ETFs, then normalizing to a baseline of 100.
**Formula:**
- Ratio = (Aggressive Sectors Average / Defensive Sectors Average) × 100
**Interpretation:**
- **Ratio > 100**: Risk-on sentiment (Aggressive sectors outperforming Defensive)
- **Ratio < 100**: Risk-off sentiment (Defensive sectors outperforming Aggressive)
- **Ratio ≈ 100**: Neutral (Both sector groups performing equally)
## Default Sectors
**Defensive Sectors** (Safe havens during uncertainty):
- XLP - Consumer Staples Select Sector SPDR Fund
- XLU - Utilities Select Sector SPDR Fund
- XLV - Health Care Select Sector SPDR Fund
**Aggressive Sectors** (Growth-oriented, higher risk):
- XLK - Technology Select Sector SPDR Fund
- XBI - SPDR S&P Biotech ETF
- XRT - SPDR S&P Retail ETF
## Features
✅ **Fully Customizable Sectors** - Choose any ETFs/tickers for each sector group
✅ **Smoothing Control** - Adjustable SMA period to reduce noise (default: 2)
✅ **Clean Visualization** - Single blue line for easy interpretation
✅ **Multi-timeframe Support** - Works on any timeframe
✅ **Lightweight** - Minimal calculations for fast performance
## Settings
### Defensive Sectors Group
- **Defensive Sector 1**: First defensive ETF ticker (default: XLP)
- **Defensive Sector 2**: Second defensive ETF ticker (default: XLU)
- **Defensive Sector 3**: Third defensive ETF ticker (default: XLV)
### Aggressive Sectors Group
- **Aggressive Sector 1**: First aggressive ETF ticker (default: XLK)
- **Aggressive Sector 2**: Second aggressive ETF ticker (default: XBI)
- **Aggressive Sector 3**: Third aggressive ETF ticker (default: XRT)
### Display Settings
- **Smoothing Length**: SMA period for ratio smoothing (default: 2, range: 1-50)
- Lower values = More responsive but noisier
- Higher values = Smoother but more lagging
## Use Cases
### 1. Market Regime Identification
- **Rising Ratio (trending up)** → Bull market / Risk-on environment
- Aggressive sectors leading, investors chasing growth
- Favorable for long positions in tech, growth stocks
- **Falling Ratio (trending down)** → Bear market / Risk-off environment
- Defensive sectors leading, investors seeking safety
- Consider defensive positioning or short opportunities
### 2. Divergence Analysis
- **Bullish Divergence**: Price makes new lows but ratio rises
- Suggests underlying strength returning
- Potential market bottom forming
- **Bearish Divergence**: Price makes new highs but ratio falls
- Suggests weakening momentum
- Potential market top forming
### 3. Trend Confirmation
- **Strong uptrend + Rising ratio** → Confirmed bullish trend
- **Strong downtrend + Falling ratio** → Confirmed bearish trend
- **Uptrend + Falling ratio** → Weakening trend, watch for reversal
- **Downtrend + Rising ratio** → Potential trend exhaustion
## Best Practices
⚠️ **Timeframe Selection**
- Recommended: Daily, 4H, 1H for cleaner signals
- Lower timeframes (15m, 5m) may produce noisy signals
⚠️ **Complementary Analysis**
- Use alongside price action and volume analysis
- Combine with support/resistance levels
- Not designed as a standalone trading system
⚠️ **Market Conditions**
- Most effective in trending markets
- Less reliable during ranging/consolidation periods
- Works best in liquid, well-traded sectors
⚠️ **Customization Tips**
- Can substitute with international sectors (EWU, EWZ, etc.)
- Can use crypto sectors (DeFi vs Layer1, etc.)
- Adjust smoothing based on trading style (day trading = 2-5, swing = 10-20)
## Display Options
### Default View (overlay=false)
- Shows in separate pane below chart
- Dedicated scale for ratio values
### Alternative View
- Can be moved to main chart pane (drag indicator)
I typically overlay this indicator on the SPY daily chart to observe divergences. I don’t focus on specific values but rather on the direction of the trend.
The author is not responsible for any trading losses incurred using this indicator.
## Support & Feedback
For questions, feature requests, or bug reports:
- Comment below
- Send a private message
- Check for updates regularly
If you find this indicator useful, please:
- ⭐ Leave a like/favorite
- 💬 Share your experience in comments
- 📊 Share charts showing interesting patterns
Sequential Exhaustion 9/13 [Crypto Filter] - PyraTimeConcept: The Exhaustion Meter
This indicator is a customized version of the Sequential count, a powerful tool used by institutional traders to measure buyer and seller exhaustion. It looks for a sequence of 9 (Setup) or 13 (Countdown) consecutive candles that satisfy specific price criteria.
The purpose is simple: To tell you when a trend has run out of fuel.
Key Differentiators (The Value)
Due to the high volatility of the crypto market, standard Sequential indicators print too many false signals ("13s") during a strong trend. This custom version solves that problem with two core filters:
1. Trend Filter (EMA 200): If enabled, the indicator will automatically hide all Sell signals when the price is above the 200 EMA, protecting the user from shorting an uptrend (and vice-versa).
2. Color Confirmation: It will not print a signal unless the closing candle color matches the direction (e.g., no Red 13 sell signals on Green Candles). This drastically cleans up the chart.
Understanding the Numbers
The numbers appearing above and below the candles are your exhaustion meter.
* The "9" (Setup): Indicates a short-term trend is nearing exhaustion.
* The "13" (Countdown): Indicates the trend is statistically complete and a reversal is highly probable.
The Actionable Strategy (The PyraTime Rule)
This indicator is designed to be your Exit Tool. Use it to determine when to take profit from an existing trade.
* Example: You enter Long at the GPM Time Line. When the PyraTD prints a Red 9 or Red 13, you take profit immediately.
Final Note
Use the integrated visibility settings to turn off signals (e.g., hide 9s or Sells) to customize the view to your preferred trading style.
Disclaimer: This tool measures mathematical exhaustion and is part of the PyraTime system. It is not financial advice.
MTF Trading Helper & Multi AlertsHi dear fellows, I´m using this indicator for my trading, so every then and when I will publish updates on this one.
This indicator should help to identify the right trading setup. I´m using it to trade index futures and stocks.
MTF Trading Helper & Multi Alerts
Overview
This indicator provides a clear visual representation of trend direction across three timeframes. It helps traders identify trend alignment, potential reversals, and optimal entry/exit points by analyzing the relationship between different smoothed timeframes.
You can set up multiple alerts (as one alert in Tradingview)
How It Works
The indicator displays three colored circles representing the smoothed candle direction on three different timeframes:
Bottom plot represents the overall trend direction, the plot in the middle shows intermediate momentum, and the one on top captures short-term price action.
When a color change occurs, the circle appears in a darker shade to highlight the transition.
🟢 Green = Bullish - 🔴 Red = Bearish
This change can also trigger multiple alerts.
Timeframe Settings - important
Choose between two trading setups, either for:
Intraday 1-minute candles or 1h for swing trading. Set up your chart accordingly to that timeframe.
Intraday | 1Min chart candles
Swing | 1 hour chart candles
Plots
TF3 represents the overall trend direction (bottom), TF2 shows intermediate momentum (middle), and TF1 captures short-term price action (top).
Interpretation & Strategy Alerts
1. Trend Bullish (TF3 turns Green)
The higher timeframe has shifted bullish - a potential new uptrend is forming.
Example: You're watching ES-mini on the Intraday setting. TF3 turns green after being red for several days. This signals the broader trend may be shifting bullish - consider looking for long opportunities.
2. Trend Bearish (TF3 turns Red)
The higher timeframe has shifted bearish - consider protecting profits or exiting long positions.
Example: You hold a long position in Es-mini. TF3 turns red, indicating the macro trend is weakening. This is your signal to take profits or tighten stop-losses.
3. Possible Accumulation (TF3 Red + TF2 turns Green)
While the overall trend is still bearish, the medium timeframe shows buying pressure. Smart money may be accumulating - watch closely for a potential trend reversal.
Example: Es-mini has been in a downtrend (TF3 red). Suddenly TF2 turns green while TF3 remains red. This could indicate institutional buying before a reversal. Don't buy yet, but add it to your watchlist and wait for confirmation.
4. Trend Continuation (TF3 Green + TF2 turns Green)
The medium timeframe realigns with the bullish macro trend - a potential buying opportunity as momentum returns to the uptrend.
Example: Es-mini is in an uptrend (TF3 green). After a pullback, TF2 was red but now turns green again. The pullback appears to be over - this is a trend continuation signal and a potential entry point.
5. Buy the Dip (TF3 + TF2 Green + TF1 turns Green)
All timeframes are now aligned bullish. The short-term pullback is complete and price is resuming the uptrend - optimal entry for short-term trades.
Example: Es-mini is trending up (TF3 + TF2 green). A small dip caused TF1 to turn red briefly. When TF1 turns green again, all three timeframes are aligned - this is your "Buy the Dip" signal with strong confirmation.
6. Sell the Dip (TF3 + TF2 Green + TF1 turns Red)
Short-term weakness within an uptrend. This can be used to take partial profits, wait for a better entry, or trail stops tighter.
Example: You're long on ES-mini with TF3 and TF2 green. TF1 turns red, indicating short-term selling pressure. Consider taking partial profits here and wait for TF1 to turn green again (Buy the Dip) to add back to your position.
How to Use
Choose your scenario: Select "Intraday" 1min-chart for day trading or "Swing" 1h-chart for swingtrading
Enable alerts: Turn on the strategy alerts you want to receive in the settings
Wait for signals: Let the indicator notify you when conditions align
Confirm with price action: Always use additional confirmation before entering trades
Best Practices
✅ Use TF3 as your trend filter - only take longs when TF3 turns green and hold them :)
✅ Use TF2 for timing - wait for TF2 to align with TF3 for swings.
✅ Use TF2 for early entries (accumulation phase) when TF3 is still red. Watch out!
✅ Use TF1 for entries when TF3 and TF2 are green. Only buy if TF1 is red. Keep it short and sweet.
✅ Combine with support/resistance levels for better entries
✅ Use proper risk management - no indicator is 100% accurate
Disclaimer
This indicator is for educational purposes only. Past performance does not guarantee future results. Always do your own research and use proper risk management. Never risk more than you can afford to lose.
Viprasol Elite Advanced Pattern Scanner# 🚀 Viprasol Elite Advanced Pattern Scanner
## Overview
The **Viprasol Elite Advanced Pattern Scanner** is a sophisticated technical analysis tool designed to identify high-probability double bottom (DISCOUNT) and double top (PREMIUM) patterns with unprecedented accuracy. Unlike basic pattern detectors, this elite scanner employs an AI-powered quality scoring system to filter out false signals and highlight only the most reliable trading opportunities.
## 🎯 Key Features
### Advanced Pattern Detection
- **DISCOUNT Patterns** (Double Bottoms): Identifies bullish reversal zones where price may bounce
- **PREMIUM Patterns** (Double Tops): Detects bearish reversal zones where price may decline
- Multi-point validation system (5-point structure)
- Symmetry analysis with customizable tolerance
### 🤖 AI Quality Scoring System
Each pattern receives a quality score (0-100) based on:
- **Symmetry Analysis** (32% weight): How closely the two bottoms/tops match
- **Trend Context** (22% weight): Strength of the preceding trend using ADX
- **Volume Profile** (22% weight): Volume confirmation at key points
- **Pattern Depth** (16% weight): Significance of the pattern's price range
- **Structure Quality** (16% weight): Overall pattern formation quality
Quality Grades:
- ⭐ **ELITE** (88-100): Highest probability setups
- ✨ **VERY STRONG** (77-87): Strong trade opportunities
- ✓ **STRONG** (67-76): Valid patterns with good potential
- ○ **VALID** (65-66): Acceptable patterns meeting minimum criteria
### 🎯 Intelligent Target System
Three target modes per pattern direction:
- **Conservative**: 0.618 Fibonacci extension (safer, closer targets)
- **Balanced**: 1.0 extension (moderate risk/reward)
- **Aggressive**: 1.618 extension (higher risk/reward)
Targets automatically adjust based on pattern quality score.
### 🔧 Advanced Filtering Options
- **Volatility Filter (ATR)**: Excludes patterns during extreme volatility
- **Momentum Filter (ADX)**: Ensures sufficient trend strength
- **Liquidity Filter (Volume)**: Confirms adequate trading volume
### 📊 Pattern Lifecycle Management
- Real-time neckline tracking with extension multiplier
- Pattern invalidation after extended wait period
- Breakout/breakdown confirmation
- Reversal detection (pattern failure scenarios)
- Target achievement tracking
### 🌈 Premium Visual System
- Color-coded quality levels
- Cyber-themed color scheme (Neon Green/Hot Pink/Purple/Cyan)
- Transparent fills for pattern zones
- Dynamic labels with pattern information
- Elite dashboard showing live pattern stats
## 📈 How To Use
### Basic Setup
1. Add indicator to your chart
2. Enable desired patterns (DISCOUNT and/or PREMIUM)
3. Adjust quality threshold (default: 65) - higher = fewer but better signals
4. Set your preferred target mode
### Trading DISCOUNT Patterns (Bullish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakout above neckline (point 5)
4. Stop loss below the lowest bottom
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break below bottoms = SHORT signal)
### Trading PREMIUM Patterns (Bearish)
1. Wait for pattern detection (labeled points 1-4)
2. Check quality score on dashboard
3. Entry on breakdown below neckline (point 5)
4. Stop loss above the highest top
5. Target shown automatically based on your mode
6. ⚠️ Watch for pattern failure (break above tops = LONG signal)
## ⚙️ Input Settings Guide
### 🔍 Detection Engine
- **Left/Right Pivots**: Higher = fewer but cleaner patterns (default: 6/4)
- **Min Pattern Width**: Minimum bars between bottoms/tops (default: 12)
- **Symmetry Tolerance**: Max % difference allowed between levels (default: 1.8%)
- **Extension Multiplier**: How long to wait for breakout (default: 2.2x pattern width)
### ⭐ Quality AI
- **Min Quality Score**: Only show patterns above this score (default: 65)
- **Weight Distribution**: Customize what matters most (symmetry/trend/volume/depth/structure)
### 🔧 Filters
- **Volatility Filter**: Avoid choppy markets (recommended: ON)
- **Momentum Filter**: Ensure trend strength (recommended: ON)
- **Liquidity Filter**: Volume confirmation (recommended: ON)
### 💎 Target System
- Choose target aggression for each pattern type and direction
- Higher quality patterns get adjusted targets automatically
## 🎨 Visual Customization
- Adjust colors for DISCOUNT/PREMIUM patterns
- Set quality-based color coding
- Customize label sizes
- Toggle dashboard visibility and position
- Show/hide historical patterns
## 🚨 Alert System
Set up TradingView alerts for:
- 🚀 **LONG Signals**: DISCOUNT breakout, PREMIUM failure
- 📉 **SHORT Signals**: PREMIUM breakdown, DISCOUNT failure
- ✅ **Target Achievement**: When price hits your target
## 💡 Pro Tips
1. **Higher Timeframes = Better Signals**: Patterns on 4H, Daily, Weekly are more reliable
2. **Quality Over Quantity**: Focus on ELITE and VERY STRONG grades
3. **Combine with Trend**: DISCOUNT in uptrend, PREMIUM in downtrend = best results
4. **Watch Pattern Failures**: Failed patterns often provide strong counter-trend signals
5. **Adjust for Your Style**: Intraday traders use Conservative, swing traders use Aggressive
## 🔒 Pattern Invalidation
Patterns become invalid if:
- No breakout/breakdown within extension period
- Support/resistance levels are broken prematurely
- Pattern shown in faded colors = no longer active
## ⚠️ Risk Disclaimer
This indicator is a tool for technical analysis and does not guarantee profitable trades. Always:
- Use proper risk management
- Combine with other analysis methods
- Never risk more than you can afford to lose
- Past performance does not indicate future results
Harmonic Sniper Trigger [Fisher] - PyraTime**Concept: Precision Momentum**
The Harmonic Sniper Trigger is a custom-tuned implementation of the Fisher Transform, designed specifically to identify sharp market reversals with zero lag. Unlike standard moving averages that react slowly to price changes, the Fisher Transform uses Gaussian probability to convert price into a normal distribution, creating clear, sharp turning points.
This indicator serves as the *Trigger* component of the PyraTime system. While Time Cycles tell you *when* to look, this indicator tells you *what* to do.
Key Features
Visual Signal Markers : Prints clear "B" (Buy) and "S" (Sell) labels on the oscillator pane for instant recognition.
Trend Fills : Dynamic Green/Red shading between the signal lines makes it easy to identify trend direction at a glance.
Integrated Alerts: Fully compatible with TradingView alerts, allowing you to be notified the second momentum flips.
How to Use This Indicator
This tool is designed to filter out noise and identify the exact moment a trend reverses.
1. Wait for the Setup: Do not trade every signal. This indicator is most powerful when price is approaching a key support/resistance level or a specific Time Pivot.
2. The Trigger: When the Fisher line crosses the Signal line (changing from Red to Green or vice versa), it confirms that momentum has mathematically shifted.
3. The Execution: Use this crossover as your entry signal *only* if it aligns with your broader market thesis.
Best Practice:
Use this in conjunction with a Time-Cycle indicator (such as the GPM Architecture).
Scenario: Price hits a Vertical Time Line.
Action: Wait for this Fisher indicator to print a "B" or "S".
Result: You enter exactly at the pivot, minimizing drawdown.
Disclaimer: This tool is for technical analysis purposes only. Past performance does not guarantee future results.
Zig Zag & Trendlines with Dynamic Threshold ATRPercentage Zig Zag with Dynamic Threshold
This Pine Script indicator is an advanced Zig Zag tool that identifies and tracks price pivots based on a percentage move required for reversal, offering a clear visual representation of volatility-adjusted trends.
Core Functionality (The Reversal Threshold):
Unlike standard Zig Zag indicators that use a fixed price difference, this indicator calculates the required reversal size (%X) dynamically using the Average True Range (ATR).
It calculates the ATR as a percentage of the current price (ATR%).
The final threshold is this ATR% multiplied by a user-defined factor (default 3x).
This means the reversal threshold is wider during volatile periods and narrower during quiet periods, adapting automatically to market conditions. Users can optionally revert to a fixed percentage if desired.
Trend Extension Lines:
The indicator draws two unique, dynamic trend lines connecting the last two significant Highs and the last two significant Lows. Crucially, these lines do not wait for the entire Zig Zag leg to confirm:
If the price is actively forming a new up-leg, the High Extension Line connects the last confirmed High to the current extreme high of the active move.
The Low Extension Line functions similarly for the downtrend.
This feature allows the user to visualize dynamic support and resistance levels based on the current, active trend structure defined by the percentage threshold.
Order Blocks V5 by GaryIn financial markets, Order Blocks are a powerful Price Action concept representing large-scale buying/selling by institutional investors or major capital at specific price ranges. They often signal potential reversals or trend continuations. However, standard Order Block definitions are often overly broad, generating excessive noise in real-market conditions and leading to misjudgments.
Order Blocks V5 was developed to address this pain point. It integrates complementary technical tools and flexible analysis logic to help you screen high-quality, reliable trading opportunities.
Core Features & Functions:
1. Dual Structure Detection
Swing Order Blocks: Identifies large-scale Order Blocks formed on the primary trend structure based on your custom Swing Length (e.g., 50 bars). These blocks typically indicate significant market turning points.
Internal Order Blocks: Detects smaller-scale Order Blocks within trends using a shorter Internal Structure Length (e.g., 5 bars). This helps capture short-term pullback and reversal opportunities.
2. Complementary Technical Tools for Filtering
Built-in Bollinger Bands (For Reference): The indicator displays Bollinger Bands (customizable length and standard deviation) directly on the chart. While it doesn’t include automatic Bollinger Bands filtering logic, you can use this tool to assess market volatility and overbought/oversold conditions manually. For example, prioritize Internal Order Block signals when price touches or nears the upper/lower bands—adding a discretionary filter to reduce false signals.
Moving Averages for Trend Context: Integrated with 5-period, 10-period, 20-period, 40-period, and 60-period EMAs. Use these to judge the current trend direction:
Focus on bullish Order Block signals when EMAs are in a bullish alignment (uptrend).
Focus on bearish Order Block signals when EMAs are in a bearish alignment (downtrend).
This helps you trade with the trend and filter low-quality counter-trend signals.
3. "Touch & Reversal" Signals
The indicator not only marks Order Blocks but also intelligently monitors price interactions with them. A price retest of an Order Block is inherently noteworthy.
More importantly, when price touches an Order Block and then:
Breaks above the high of the touch bar (for bullish Order Blocks), or
Breaks below the low of the touch bar (for bearish Order Blocks),
The indicator instantly generates prominent labels like B-SOB (Buy - Swing Order Block) or S-IOB (Sell - Internal Order Block) on the chart—signaling a potential reversal confirmed by market action.
4. Highly Customizable
Tailor the indicator to your trading style and instruments with adjustable parameters:
Display colors and transparency for both Order Block types.
Detection lengths for swing and internal structures.
Option to remove Order Block boxes after price breaks.
Sensitivity of Touch & Reversal signals (e.g., max signals per block, minimum bars between touches).
Toggle visibility of Bollinger Bands and individual EMAs.
How to Use Order Blocks V5 to Enhance Your Trading?
1. Identify Trend Direction: Use the built-in EMA system (e.g., 20/40/60 EMA alignment) to determine if the market is in an uptrend, downtrend, or consolidation.
2. Locate Key Zones: Focus on green (bullish) and red (bearish) Order Block boxes automatically drawn on the chart—these are potential support/resistance areas.
3. Apply Discretionary Filtering:
Use Bollinger Bands to gauge volatility: Avoid signals in narrow-range (low-volatility) markets; prioritize signals when price approaches extreme bands.
Combine with trend direction from EMAs to filter 逆势 (counter-trend) signals.
4. Wait for Confirmation:
Conservative Strategy: Enter trades only after price retests an Order Block and triggers a Touch & Reversal signal (e.g., B-... or S-... labels).
Aggressive Strategy: Monitor price when it first touches an Order Block, combining with indicators like RSI or MACD to identify potential entry points.
5. Risk Management: Place stop-loss orders outside the Order Block box to filter false breaks.
Risk Disclaimer: No indicator guarantees 100% win rate. Use this tool as part of your trading system, combine it with other analysis methods, and strictly follow risk management rules.
Download, test it out, and share your feedback in the comments!
EMA Scalp PRO ema cros+heikin-ashi-vol-atr EMA Scalp PRO – indicator is a visual scalping helper designed mainly for crypto pairs on lower timeframes (10–30m). It is NOT an automated trading strategy but a trend and momentum signal tool that helps the user take more disciplined entries.
Core logic:
• Core signals when EMA 9 crosses EMA 21 (bullish or bearish crossover)
• Higher–timeframe trend filter with EMA 144 and optional EMA 200
• Momentum filter with RSI
• Liquidity/volume filter using Volume SMA with a dynamic multiplier
• Directional filter using Heikin Ashi trend (bull / bear)
• Consolidation detection with ATR, EMA distance and ADX, plus a separate breakout condition
• Cooldown bars after each signal to reduce overtrading and noise
The script plots:
• Long / Short signals with labels directly on the chart
• Exit signals when EMA 9 makes a reverse crossover against EMA 21
• An information table (mode, trend, market state, ATR ratio, RSI, volume, etc.) to quickly assess current market conditions
Important:
• This indicator is strictly for educational and informational purposes.
• It does NOT provide financial or investment advice.
• The user must apply their own risk management (position sizing, SL/TP) and always test the tool on historical data or in paper trading before using it in live markets.
Tomie Tèo EMA 9 / 21EMA 9 / 21 Crossover momentum Signal. If retest happens after Crossover show obvious correlation with crossover => Enter
價漲量增 + 力度 + 艾爾德 精簡版這是一套結合三大核心邏輯的多維強勢趨勢偵測系統：
PUVU 價漲量增：確認價格突破是否具備真實量能。
Strength 力度指標：整合 ROC、RSI 斜率、MACD 動能三項數據，轉換為 0–100 的標準化強度分數。
Elder Impulse System：以視覺化 K 棒顏色呈現趨勢動能變化。
此外，本工具加入 Trend Bias 趨勢偏向濾網、極端反手模式、精準信號三角形與可視化面板，
可用於判斷市場是否具備持續性動能、突破是否可信、反轉是否具備條件。
本指標適用於：
趨勢交易
波段突破
盤整突破偵測
高勝率強勢區辨識
多品種分析（加密貨幣、外匯、指數、股票）
此版本可用於觀察趨勢方向、尋找可能的交易機會與賣出時機。
For English users:
This script provides trend analysis, volume confirmation, strength scoring, and impulse-based visualization to assist traders in identifying potential breakouts and market conditions.
Bookmap Style Aggressor Bubbles
This indicator is designed to emulate the visual aesthetic of professional Order Flow software (such as Bookmap) directly within TradingView. It replaces the traditional candlestick view with a clean "Microstructure" Step Line and highlights significant volume events using dynamic "Aggressor Bubbles."
This tool is perfect for traders who practice Order Flow analysis, Scalping, or VSA (Volume Spread Analysis) and want to visualize the relative intensity of buyers and sellers without the noise of traditional wicks and bodies.
1. How it Works
Since TradingView Pine Script operates on OHLCV (Level 1) data, this indicator uses a heuristic model to approximate Order Flow dynamics:
Aggressor Bubbles (Volume Spikes):
The script calculates a Relative Volume (RVOL) metric by comparing the current bar's volume against a 50-period Simple Moving Average (SMA).
If the current volume exceeds a user-defined threshold (e.g., 2.0x the average), a bubble is plotted.
Size: The bubble size scales dynamically based on how massive the volume spike is (Small, Normal, Large, Huge).
Direction (Color): The aggressor side is approximated using the price action of the bar. If Close >= Open, it is treated as Buy Aggression (Green). If Close < Open, it is treated as Sell Aggression (Red).
Microstructure Price Line:
Standard candles can obscure the immediate path of price. This indicator includes a Step Line option that plots the closing price. This mimics the "Last Price" feed seen in DOM-based software, allowing you to see exactly where price held or broke.
2. Features
Smart Filtering: Filters out low-volume noise. You only see bubbles when "Whales" or significant liquidity changes occur.
Visual Customization: Fully adjustable colors for Buy/Sell bubbles and the price line.
Alert System: Includes a built-in alert that triggers whenever a significant Aggressor Bubble appears, allowing you to be notified of high-activity moments instantly.
Clean Aesthetic: Optimized for Dark Mode/Black backgrounds.
3. How to Use
Chart Setup (Important): For the best experience, hide your standard candles. Go to Chart Settings > Symbol and uncheck Body, Borders, and Wick.
Settings: Set your background to Black.
Interpretation:
Breakouts: Look for large bubbles pushing price through a key level. This indicates strong momentum.
Absorptions: Look for large bubbles appearing at the top/bottom of a range without price follow-through. This often suggests a reversal (Passive limit orders absorbing the aggressive market orders).
4. Technical Disclosure & Limitations
Please note that TradingView Pine Script provides access to OHLCV (History) data, not historical Tick-by-Tick or Level 2 (Depth of Market) data. Therefore, this indicator is a simulation. The "Aggressor" side is derived from bar direction, and the bubbles represent executed volume per bar, not individual tick clusters. It is intended for visual analysis and identifying high-volume nodes relative to recent history.
Renko ScalperWhat it is-
A lightweight Renko Scalper that combines Renko brick direction with an internal EMA trend filter and MACD confirmation to signal high-probability short-term entries. EMAs are used internally (hidden from the chart) so the visual remains uncluttered.
Signals-
Buy arrow: Renko direction turns bullish AND EMA trend up AND MACD histogram positive.
Sell arrow: Renko direction turns bearish AND EMA trend down AND MACD histogram negative.
Consecutive same-direction signals are suppressed (only one arrow per direction until opposite signal).
Visuals-
Buy / Sell arrows (large) above/below bars.
Chart background tints green/red after the respective signal for easy glance recognition.
Inputs:-
Renko Box Size (points)
EMA Fast / EMA Slow
MACD fast/slow/signal lengths
How to use-
Add to chart
Use smaller Renko box sizes for scalping, larger for swing-like entries.
Confirm signal with price action and volume—this indicator is a signal generator, not a full automated system.
Use alerts (built in) to receive Buy / Sell arrow notifications.
Alerts-
Buy Arrow — buySignal
Sell Arrow — sellSignal
Buy Background / Sell Background — background-color state alerts
Recommended settings-
Timeframes: 1m–15m for scalping, 5m for balanced intraday.
Symbols: liquid futures/currency pairs/major crypto.
Disclaimer
This script is educational and not financial advice. Backtest and forward test on a demo account before live use. Past performance is not indicative of future results. Use proper risk management.
Traffic Lights - BETA ZONESTraffic Lights - BETA ZONES
Overview
The Traffic Light indicator is a simple, visual tool designed to help traders gauge market bias, trend strength, and momentum at a glance. It displays three rows of colored dots (like a traffic light) in a separate pane below your chart:
• Green: Bullish signal (go/buy bias).
• Red: Bearish signal (stop/sell bias).
• Orange: Neutral or caution (mixed/uncertain conditions).
This indicator combines price action (via EMA positioning), trend direction (via RSI), and momentum expansion (via RSI + MACD histogram) to provide a layered view of the market. When all three rows align as green or red, it generates Buy or Sell labels on the main chart for potential entry signals.
It's non-repainting in its core logic (Row 2 uses delayed RSI comparison to avoid noise), making it reliable for live trading. Best used on trending markets like forex, stocks, or crypto on timeframes from 15M to Daily.
How It Works
The indicator evaluates three independent "rows" of conditions, each represented by a colored dot:
1. Row 1: Price Action Signal (EMA Touch) This row assesses the overall trend bias based on price's position relative to a slow EMA (default: 50-period).
o Green: Price is cleanly above the EMA (bullish bias).
o Red: Price is cleanly below the EMA (bearish bias).
o Orange: Price is "touching" or within a volatility buffer around the EMA (neutral/caution). The "touch zone" is defined by ATR padding, which can be toggled off for a stricter (green/red only) mode.
2. Row 2: Buyers/Sellers Trend (RSI) This row tracks the underlying trend of buyer/seller strength using RSI (default: 14-period on close). To reduce noise and repainting, it uses a delayed comparison (RSI vs. RSI ):
o Green: RSI is rising (buyers gaining strength).
o Red: RSI is falling (sellers gaining strength). No orange here—it's purely directional.
3. Row 3: Buyers/Sellers Signal (RSI + MACD Histogram) This row focuses on momentum expansion, requiring alignment across RSI zones and MACD histogram:
o Green: RSI > 50 (bull zone), MACD hist > 0 (positive), and histogram is expanding upward.
o Red: RSI < 50 (bear zone), MACD hist < 0 (negative), and histogram is expanding downward.
o Orange: Any mismatch (e.g., pullbacks, consolidations, or weak momentum). MACD defaults: Fast=12, Slow=26, Signal=9.
Signals
• Buy Signal: Triggers a "Buy" label below the bar when all three rows turn green for the first time (crossover from non-aligned).
• Sell Signal: Triggers a "Sell" label above the bar when all three rows turn red for the first time. These are conservative signals—use them for trend confirmation or entries in alignment with your strategy. They don't repaint once fired.
Inputs & Customization
All inputs are grouped for easy tweaking:
• Row 1: Price Action Signal
o Slow EMA Length (default: 50): Adjusts the trend baseline.
o EMA Timeframe (default: empty/current): Use a higher timeframe (e.g., "240" for 4H) for multi-timeframe analysis.
o Enable Orange 'Touch' Zone (default: true): Toggle for strict (green/red only) vs. touch mode.
o ATR Length (default: 3): Volatility period for touch padding.
o Touch Padding (ATR mult, default: 0.15): Widens the orange buffer; set to 0 for wick-touch only.
• Row 2: Buyers/Sellers Trend (RSI)
o RSI Length (default: 14): Period for RSI calculation.
o RSI Source (default: close): Change to high/low/open for different sensitivities.
• Row 3: Buyers/Sellers Signal (RSI + MACD hist)
o MACD Fast/Slow/Signal Lengths (defaults: 12/26/9): Standard MACD settings.
Usage Tips
• Trend Trading: Wait for all-green for long entries or all-red for shorts. Use in conjunction with support/resistance.
• Scalping/Intraday: Enable orange touch zone for more nuance in choppy markets; disable for cleaner signals in trends.
• Multi-Timeframe: Set Row 1 EMA to a higher TF for "big picture" bias while keeping others on current.
• Risk Management: Always combine with stop-losses (e.g., below recent lows for buys). Backtest on your asset/timeframe.
• Limitations: In ranging markets, orange dots may dominate—pair with volatility filters like ADX. Not a standalone system; use as a confirmation tool.
If you have feedback or suggestions, drop a comment below! Happy trading 🚦
Gaussian Hidden Markov ModelA Hidden Markov Model (HMM) is a statistical model that assumes an underlying process is a Markov process with unobservable (hidden) states. In the context of financial data analysis, a HMM can be particularly useful because it allows for the modeling of time series data where the state of the market at a given time depends on its state in the previous time period, but these states are not directly observable from the market data. When we say that a state is "unobservable" or "hidden," we mean that the true state of the process generating the observations at any time is not directly visible or measurable. Instead, what is observed is a set of data points that are influenced by these hidden states.
The HMM uses a set of observed data to infer the sequence of hidden states of the model (in our case a model with 3 states and Gaussian emissions). It comprises three main components: the initial probabilities, the state transition probabilities, and the emission probabilities. The initial probabilities describe the likelihood of starting in a particular state. The state transition probabilities describe the likelihood of moving from one state to another, while the emission probabilities (in our case emitted from Gaussian probability density functions, in the image red yellow and green Laplace probability densitty functions) describe the likelihood of the observed data given a particular state.
MODEL FIT
Posterior
By default, the indicator displays the posterior distribution as fitted by training a 3-state Gaussian HMM. The posterior refers to the probability distribution of the hidden states given the observed data. In the case of your Gaussian HMM with three states, the posterior represents the probabilities that the model assigns to each of these three states at each time point, after observing the data. The term "posterior" comes from Bayes' theorem, where it represents the updated belief about the model's states after considering the evidence (the observed data).
In the indicator, the posterior is visualized as the probability of the stock market being in a particular volatility state (high vol, medium vol, low vol) at any given time in the time series. Each day, the probabilities of the three states sum to 1, with the plot showing color-coded bands to reflect these state probabilities over time. It is important to note that the posterior distribution of the model fit tells you about the performance of the model on past data. The model calculates the probabilities of observations for all states by taking into account the relationship between observations and their past and future counterparts in the dataset. This is achieved using the forward-backward algorithm, which enables us to train the HMM.
Conditional Mean
The conditional mean is the expected value of the observed data given the current state of the model. For a Gaussian HMM, this would be the mean of the Gaussian distribution associated with the current state. It’s "conditional" because it depends on the probabilities of the different states the model is in at a given time. This connects back to the posterior probability, which assigns a probability to the model being in a particular state at a given time.
Conditional Standard Deviation Bands
The conditional standard deviation is a measure of the variability of the observed data given the current state of the model. In a Gaussian HMM, each state has its own emission probability, defined by a Gaussian distribution with a specific mean and standard deviation. The standard deviation represents how spread out the data is around the mean for each state. These bands directly relate to the emission probabilities of the HMM, as they describe the likelihood of the observed values given the current state. Narrow bands suggest a lower standard deviation, indicating the model is more confident about the data's expected range when in that state, while wider bands indicate higher uncertainty and variability.
Transition Matrix
The transition matrix in a HMM is a key component that characterizes the model. It's a square matrix representing the probabilities of transitioning from one hidden state to another. Each row of the transition matrix must sum up to 1 since the probabilities of moving from a given state to all possible subsequent states (including staying in the same state) must encompass all possible outcomes.
For example, we can see the following transition probabilities in our model:
Going from state X: to X (0.98), to Y (0.02), to Z (0)
Going from state Y: to X (0.03), to Y (0.96), to Z (0.01)
Going from state Z: to X (0), to Y (0.11), to Z (0.89)
MODEL TEST
When the "Test Out of Sample” option is enabled, the indicator plots models out-of-sample predictions. This is particularly useful for real-time identification of market regimes, ensuring that the model's predictive capability is rigorously tested on unseen data. The indicator displays the out of sample posterior probabilities which are calculated using the forward algorithm. Higher probability for a particular state indicate that the model is predicted a higher likelihood that the market is currently in that state. Evaluating the models performance on unseen data is crucial in understanding how well the model explains data that are not included in its training process.
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise.
█ OVERVIEW
We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning).
█ Detrend Fluctuation Analysis
Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent.
The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:
• Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.
• According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).
• Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned.
█ Calculation
DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series.
Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.)
We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression.
We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale.
As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement.
The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window.
Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements.
The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases.
If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation).
If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation.
If the slope = 0.5, It means the series follows a random walk.
█ FEATURES
• Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.
• When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)
• “show filter” option applies a smoothing moving average to smooth the exponent.
• Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2.
• For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues.
The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot.
When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use.
█ How to Use
Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.
• In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.
• When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.
• Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa.
However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant.
So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency.
The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right.
An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future.
However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal.
The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead.
Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4).
Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
Chandelier Exit + Pivots + MA + Swing High/LowIt combines four indicators.
For use in the Hero course.
Support & Resistance + VolumeThis script is an advanced technical analysis tool designed to automatically identify institutional Support and Resistance zones, while analyzing the activity (Volume) within these zones. It automatically cleans up the chart to keep only relevant information.
Key Features:
Automatic Zone Detection:
Supports (Green): Identified based on major swing lows (Pivots).
Resistances (Red): Identified based on major swing highs (Pivots).
The width of the zones automatically adapts to market volatility (based on ATR) to remain relevant regardless of the timeframe.
Smart Merging:
To avoid cluttering the chart with overlapping lines, the script detects if a new support or resistance forms within an existing zone.
If so, it does not create a new box but expands the existing zone. This allows you to visualize consolidated "liquidity zones" rather than scattered lines.
Cumulative Volume Profile:
This is the core strength of this indicator. It calculates the total volume traded inside each zone since its creation.
Every time price revisits a zone, the candle's volume is added to the total.
Display: Volume is shown as whole numbers with a $ symbol (e.g., 300 500$) for precise reading.
Interpretation: A zone with very high volume indicates a strong battle between buyers and sellers, making the zone harder to break.
Historical Management (Broken Zones):
If the price crosses and closes beyond a zone (valid breakout), the zone changes appearance immediately.
It turns Gray, stops extending to the right, and the label displays the text "Cassé" (Broken). This allows you to keep a visual trace of past key levels without disturbing current analysis.
