ICT Structure Levels (ST/IT/LT) - v7 (by Jonas E)ICT Structure Levels (ST/IT/LT) – Neighbor-Wick Pivots
This indicator is designed for traders following ICT-style market structure analysis. It identifies Short-Term (ST), Intermediary (IT), and Long-Term (LT) swing highs and lows, but with a stricter filter that reduces false signals.
Unlike standard pivot indicators, this script requires not only that a bar makes a structural high/low, but also that the neighboring bars’ extremes are formed by wicks rather than flat-bodied candles. This wick condition helps confirm that the level is a true liquidity sweep and not just random price action.
How it works (conceptual):
Detects pivots based on user-defined left/right bars.
Validates that extremes on both sides of the pivot are wick-driven (high > body for highs, low < body for lows).
Marks valid STH/STL, ITH/ITL, and LTH/LTL directly on the chart with optional price labels.
Uses ATR offset for better label readability.
Alerts can be enabled to notify when a new structural level is confirmed.
How to use it:
Map market structure across multiple layers (ST/IT/LT).
Identify true liquidity grabs and avoid false highs/lows.
Integrate with Break of Structure (BOS) and Change of Character (CHoCH) strategies.
Combine with other ICT concepts (Order Blocks, Fair Value Gaps, Liquidity Pools).
What makes it unique:
Most pivot indicators mark every high/low indiscriminately. This script filters pivots using wick validation, which significantly reduces noise and focuses only on the levels most relevant to liquidity-based trading strategies.
Циклический анализ
Auto-Fit Growth Trendline# **Theoretical Algorithmic Principles of the Auto-Fit Growth Trendline (AFGT)**
## **🎯 What Does This Algorithm Do?**
The Auto-Fit Growth Trendline is an advanced technical analysis system that **automates the identification of long-term growth trends** and **projects future price levels** based on historical cyclical patterns.
### **Primary Functionality:**
- **Automatically detects** the most significant lows in regular periods (monthly, quarterly, semi-annually, annually)
- **Constructs a dynamic trendline** that connects these historical lows
- **Projects the trend into the future** with high mathematical precision
- **Generates Fibonacci bands** that act as dynamic support and resistance levels
- **Automatically adapts** to different timeframes and market conditions
### **Strategic Purpose:**
The algorithm is designed to identify **fundamental value zones** where price has historically found support, enabling traders to:
- Identify optimal entry points for long positions
- Establish realistic price targets based on mathematical projections
- Recognize dynamic support and resistance levels
- Anticipate long-term price movements
---
## **🧮 Core Mathematical Foundations**
### **Adaptive Temporal Segmentation Theory**
The algorithm is based on **dynamic temporal partition theory**, where time is divided into mathematically coherent uniform intervals. It uses modular transformations to create bijective mappings between continuous timestamps and discrete periods, ensuring each temporal point belongs uniquely to a specific period.
**What does this achieve?** It allows the algorithm to automatically identify natural market cycles (annual, quarterly, etc.) without manual intervention, adapting to the inherent periodicity of each asset.
The temporal mapping function implements a **discrete affine transformation** that normalizes different frequencies (monthly, quarterly, semi-annual, annual) to a space of unique identifiers, enabling consistent cross-temporal comparative analysis.
---
## **📊 Local Extrema Detection Theory**
### **Multi-Point Retrospective Validation Principle**
Local minima detection is founded on **relative extrema theory with sliding window**. Instead of using a simple minimum finder, it implements a cross-validation system that examines the persistence of the extremum across multiple historical periods.
**What problem does this solve?** It eliminates false minima caused by temporal volatility, identifying only those points that represent true historical support levels with statistical significance.
This approach is based on the **statistical confirmation principle**, where a minimum is only considered valid if it maintains its extremum condition during a defined observation period, significantly reducing false positives caused by transitory volatility.
---
## **🔬 Robust Interpolation Theory with Outlier Control**
### **Contextual Adaptive Interpolation Model**
The mathematical core uses **piecewise linear interpolation with adaptive outlier correction**. The key innovation lies in implementing a **contextual anomaly detector** that identifies not only absolute extreme values, but relative deviations to the local context.
**Why is this important?** Financial markets contain extreme events (crashes, bubbles) that can distort projections. This system identifies and appropriately weights them without completely eliminating them, preserving directional information while attenuating distortions.
### **Implicit Bayesian Smoothing Algorithm**
When an outlier is detected (deviation >300% of local average), the system applies a **simplified Kalman filter** that combines the current observation with a local trend estimation, using a weight factor that preserves directional information while attenuating extreme fluctuations.
---
## **📈 Stabilized Extrapolation Theory**
### **Exponential Growth Model with Dampening**
Extrapolation is based on a **modified exponential growth model with progressive dampening**. It uses multiple historical points to calculate local growth ratios, implements statistical filtering to eliminate outliers, and applies a dampening factor that increases with extrapolation distance.
**What advantage does this offer?** Long-term projections in finance tend to be exponentially unrealistic. This system maintains short-to-medium term accuracy while converging toward realistic long-term projections, avoiding the typical "exponential explosions" of other methods.
### **Asymptotic Convergence Principle**
For long-term projections, the algorithm implements **controlled asymptotic convergence**, where growth ratios gradually converge toward pre-established limits, avoiding unrealistic exponential projections while preserving short-to-medium term accuracy.
---
## **🌟 Dynamic Fibonacci Projection Theory**
### **Continuous Proportional Scaling Model**
Fibonacci bands are constructed through **uniform proportional scaling** of the base curve, where each level represents a linear transformation of the main curve by a constant factor derived from the Fibonacci sequence.
**What is its practical utility?** It provides dynamic resistance and support levels that move with the trend, offering price targets and profit-taking points that automatically adapt to market evolution.
### **Topological Preservation Principle**
The system maintains the **topological properties** of the base curve in all Fibonacci projections, ensuring that spatial and temporal relationships are consistently preserved across all resistance/support levels.
---
## **⚡ Adaptive Computational Optimization**
### **Multi-Scale Resolution Theory**
It implements **automatic multi-resolution analysis** where data granularity is dynamically adjusted according to the analysis timeframe. It uses the **adaptive Nyquist principle** to optimize the signal-to-noise ratio according to the temporal observation scale.
**Why is this necessary?** Different timeframes require different levels of detail. A 1-minute chart needs more granularity than a monthly one. This system automatically optimizes resolution for each case.
### **Adaptive Density Algorithm**
Calculation point density is optimized through **adaptive sampling theory**, where calculation frequency is adjusted according to local trend curvature and analysis timeframe, balancing visual precision with computational efficiency.
---
## **🛡️ Robustness and Fault Tolerance**
### **Graceful Degradation Theory**
The system implements **multi-level graceful degradation**, where under error conditions or insufficient data, the algorithm progressively falls back to simpler but reliable methods, maintaining basic functionality under any condition.
**What does this guarantee?** That the indicator functions consistently even with incomplete data, new symbols with limited history, or extreme market conditions.
### **State Consistency Principle**
It uses **mathematical invariants** to guarantee that the algorithm's internal state remains consistent between executions, implementing consistency checks that validate data structure integrity in each iteration.
---
## **🔍 Key Theoretical Innovations**
### **A. Contextual vs. Absolute Outlier Detection**
It revolutionizes traditional outlier detection by considering not only the absolute magnitude of deviations, but their relative significance within the local context of the time series.
**Practical impact:** It distinguishes between legitimate market movements and technical anomalies, preserving important events like breakouts while filtering noise.
### **B. Extrapolation with Weighted Historical Memory**
It implements a memory system that weights different historical periods according to their relevance for current prediction, creating projections more adaptable to market regime changes.
**Competitive advantage:** It automatically adapts to fundamental changes in asset dynamics without requiring manual recalibration.
### **C. Automatic Multi-Timeframe Adaptation**
It develops an automatic temporal resolution selection system that optimizes signal extraction according to the intrinsic characteristics of the analysis timeframe.
**Result:** A single indicator that functions optimally from 1-minute to monthly charts without manual adjustments.
### **D. Intelligent Asymptotic Convergence**
It introduces the concept of controlled asymptotic convergence in financial extrapolations, where long-term projections converge toward realistic limits based on historical fundamentals.
**Added value:** Mathematically sound long-term projections that avoid the unrealistic extremes typical of other extrapolation methods.
---
## **📊 Complexity and Scalability Theory**
### **Optimized Linear Complexity Model**
The algorithm maintains **linear computational complexity** O(n) in the number of historical data points, guaranteeing scalability for extensive time series analysis without performance degradation.
### **Temporal Locality Principle**
It implements **temporal locality**, where the most expensive operations are concentrated in the most relevant temporal regions (recent periods and near projections), optimizing computational resource usage.
---
## **🎯 Convergence and Stability**
### **Probabilistic Convergence Theory**
The system guarantees **probabilistic convergence** toward the real underlying trend, where projection accuracy increases with the amount of available historical data, following **law of large numbers** principles.
**Practical implication:** The more history an asset has, the more accurate the algorithm's projections will be.
### **Guaranteed Numerical Stability**
It implements **intrinsic numerical stability** through the use of robust floating-point arithmetic and validations that prevent overflow, underflow, and numerical error propagation.
**Result:** Reliable operation even with extreme-priced assets (from satoshis to thousand-dollar stocks).
---
## **💼 Comprehensive Practical Application**
**The algorithm functions as a "financial GPS"** that:
1. **Identifies where we've been** (significant historical lows)
2. **Determines where we are** (current position relative to the trend)
3. **Projects where we're going** (future trend with specific price levels)
4. **Provides alternative routes** (Fibonacci bands as alternative targets)
This theoretical framework represents an innovative synthesis of time series analysis, approximation theory, and computational optimization, specifically designed for long-term financial trend analysis with robust and mathematically grounded projections.
MMA, Mid-Price Moving Averages (Open + Close Based MAs)📝 Script Description
This script introduces a custom set of moving averages based on the mid-price, calculated as the average of the open and close prices:
Mid Price = (Open + Close) / 2
Instead of traditional close-based MAs, this approach reflects the average sentiment throughout the trading session, offering a smoother and more realistic view of price action.
🔍 Key Features:
✅ Gap-aware smoothing
Captures opening gaps, offering a better representation of intraday shifts.
✅ Reduced noise
Less vulnerable to sharp closing moves or one-off spikes, making it easier to identify true trend breaks or supports.
✅ Closer to actual flow
Reflects a more natural midline of price movement, ideal for traders who prioritize clean, sustained trends.
✅ Better support/resistance alignment
Especially useful for identifying stable uptrends and minimizing false breakout signals.
📐 Included Moving Averages:
MA 5
MA 10
MA 20
MA 60
MA 120
MA 200
(All based on mid-price, not close)
🎯 Recommended For:
Traders seeking smoother and more reliable trendlines
Those who want a more realistic depiction of support and resistance
Ideal for filtering out noisy movements while focusing on clean, straight-moving charts
Daily Seasonality Strength + Prediction TableDaily Seasonality Strength + Prediction Table
Return Estimates:
This indicator uses historical price data to calculate average returns for each day (of the week or month) and uses these to predict the next day’s return.
Seasonality Strength:
It measures seasonality strength by comparing predicted returns with actual returns, using the inverse of MSE (higher values mean stronger seasonality).
supports up to 10 assets
This script is for informational and educational purposes only. It does not constitute financial, investment, or trading advice. I am not a financial advisor. Any decisions you make based on this indicator are your own responsibility. Always do your own research and consult with a qualified financial professional before making any investment decisions.
Past performance is no guarantee of future results. The value of the instruments may fluctuate and is not guaranteed
Transfer Function Filter [theUltimator5]The Transfer Function Filter is an engineering style approach to transform the price action on a chart into a frequency, then filter out unwanted signals using Butterworth-style filter approach.
This indicator allows you to analyze market structure by isolating or removing different frequency components of price movement—similar to how engineers filter signals in control systems and electrical circuits.
🔎 Features
Four Filter Types
1) Low Pass Filter – Smooths price data, highlighting long-term trends while filtering out short-term noise. This filter acts similar to an EMA, removing noisy signals, resulting in a smooth curve that follows the price of the stock relative to the filter cutoff settings.
Real world application for low pass filter - Used in power supplies to provide a clean, stable power level.
2) High Pass Filter – Removes slow-moving trends to emphasize short-term volatility and rapid fluctuations. The high pass filter removes the "DC" level of the chart, removing the average price moves and only outputting volatility.
Real world application for high pass filter - Used in audio equalizers to remove low-frequency noise (like rumble) while allowing higher frequencies to pass through, improving sound clarity.
3) Band Pass Filter – Allows signals to plot only within a band of bar ranges. This filter removes the low pass "DC" level and the high pass "high frequency noise spikes" and shows a signal that is effectively a smoothed volatility curve. This acts like a moving average for volatility.
Real world application for band pass filter - Radio stations only allow certain frequency bands so you can change your radio channel by switching which frequency band your filter is set to.
4) Band Stop Filter – Suppresses specific frequency bands (cycles between two cutoffs). This filter allows through the base price moving average, but keeps the high frequency volatility spikes. It allows you to filter out specific time interval price action.
Real world application for band stop filter - If there is prominent frequency signal in the area which can cause unnecessary noise in your system, a band stop filter can cancel out just that frequency so you get everything else
Configurable Parameters
• Cutoff Periods – Define the cycle lengths (in bars) to filter. This is a bit counter-intuitive with the numbering since the higher the bar count on the low-pass filter, the lower the frequency cutoff is. The opposite holds true for the high pass filter.
• Filter Order – Adjust steepness and responsiveness (higher order = sharper filtering, but with more delay).
• Overlay Option – Display Low Pass & Band Stop outputs directly on the price chart, or in a separate pane. This is enabled by default, plotting the filters that mimic moving averages directly onto the chart.
• Source Selection – Apply filters to close, open, high, low, or custom sources.
Histograms for Comparison
• BS–LP Histogram – Shows distance between Band Stop and Low Pass filters.
• BP–HP Histogram – Highlights differences between Band Pass and High Pass filters.
Histograms give the visualization of a pseudo-MACD style indicator
Visual & Informational Aids
• Customizable colors for each filter line.
• Optional zero-line for histogram reference.
• On-chart info table summarizing active filters, cutoff settings, histograms, and filter order.
📊 Use Cases
Trend Detection – Use the Low Pass filter to smooth noise and follow underlying market direction.
Volatility & Cycle Analysis – Apply High Pass or Band Pass to capture shorter-term patterns.
Noise Suppression – Deploy Band Stop to remove specific choppy frequencies.
Momentum Insight – Watch the histograms to spot divergences and relative filter strength.
BTC Power Law Valuation BandsBTC Power Law Rainbow
A long-term valuation framework for Bitcoin based on Power Law growth — designed to help identify macro accumulation and distribution zones, aligned with long-term investor behavior.
🔍 What Is a Power Law?
A Power Law is a mathematical relationship where one quantity varies as a power of another. In this model:
Price ≈ a × (Time)^b
It captures the non-linear, exponentially slowing growth of Bitcoin over time. Rather than using linear or cyclical models, this approach aligns with how complex systems, such as networks or monetary adoption curves, often grow — rapidly at first, and then more slowly, but persistently.
🧠 Why Power Law for BTC?
Bitcoin:
Has finite supply and increasing adoption.
Operates as a monetary network , where Metcalfe’s Law and power laws naturally emerge.
Exhibits exponential growth over logarithmic time when viewed on a log-log chart .
This makes it uniquely well-suited for power law modeling.
🌈 How to Use the Valuation Bands
The central white line represents the modeled fair value according to the power law.
Colored bands represent deviations from the model in logarithmic space, acting as macro zones:
🔵 Lower Bands: Deep value / Accumulation zones.
🟡 Mid Bands: Fair value.
🔴 Upper Bands: Euphoria / Risk of macro tops.
📐 Smart Money Concepts (SMC) Alignment
Accumulation: Occurs when price consolidates near lower bands — often aligning with institutional positioning.
Markup: As price re-enters or ascends the bands, we often see breakout behavior and trend expansion.
Distribution: When price extends above upper bands, potential for exit liquidity creation and distribution events.
Reversion: Historically, price mean-reverts toward the model — rarely staying outside the bands for long.
This makes the model useful for:
Cycle timing
Long-term DCA strategy zones
Identifying value dislocations
Filtering short-term noise
⚠️ Disclaimer
This tool is for educational and informational purposes only . It is not financial advice. The power law model is a non-predictive, mathematical framework and does not guarantee future price movements .
Always use additional tools, risk management, and your own judgment before making trading or investment decisions.
Lanzadera)The Lanzadera Indicator is designed to identify market momentum and potential breakout opportunities. It works as a dynamic tool that helps traders detect price acceleration zones, providing clear signals for possible entries and exits. With customizable settings and a user-friendly design, this indicator is suitable for both beginners and experienced traders.
Use it to:
Spot momentum shifts before major price movements.
Enhance your breakout trading strategies.
Gain a clearer view of market dynamics with visual alerts.
This indicator is built for traders who want precision, discipline, and a professional edge in their decision-making process.
GEXStrik BarS EUR USDGEX strike bars on the left side measure the prevailing amount of positive or negative gamma. On the right side, the volume of calls and puts traded in the region.
STOCK EXCHANGE + SILVER BULLET FRAMESThis script is an updated version of the " NY/LDN/TOK Stock Exchange Opening Hours " script.
Objective
Displays global stock exchange sessions (New York, London, Tokyo) with session frames, highs/lows, and opening lines. Includes ICT Silver Bullet windows (NY, London, Tokyo) with configurable shading. Past sessions are frozen at close, ongoing sessions update dynamically until closure, and upcoming sessions are pre-drawn. Fully customizable with options for weekends, labels, padding, opacity, and individual session toggles.
It is designed to help traders quickly interpret market context, liquidity zones, and session-based price behavior.
Main Features
Past sessions (historical data)
• Session Frames:
• Each box is frozen at the session’s close.
• The left edge aligns with the opening time, while the right edge is fixed at the closing time.
• The top and bottom reflect the highest and lowest prices during the session.
• Session Labels:
• Names (NY, LDN, TOK) displayed above the frame, aligned left, in the same color as the frame.
• Opening Lines:
• Vertical dotted lines mark the start of each session.
Ongoing and upcoming sessions (live market)
• Dynamic Session Frames:
• The right edge is locked at the future close time.
• The top and bottom update in real time as new highs and lows form.
• Labels and Lines:
• The session label is visible above the active frame.
• Opening lines are drawn as soon as the session begins.
Silver Bullet Time Windows (ICT concept)
• Highlights key liquidity windows within sessions:
• New York: 10:00–11:00 and 14:00–15:00
• London: 08:00–09:00
• Tokyo: 09:00–10:00
• Silver Bullet zones are shaded with configurable opacity (default 5%).
Customization and Options
• Enable or disable individual sessions (NY, London, Tokyo).
• Toggle weekend display (frames and Silver Bullets).
• Adjust label size, padding, and text visibility.
• Control frame opacity (default 0%).
• Optimized memory management with automatic pruning of old graphical objects.
TrendBreaks & MA Divergence v1.3 — couleurs perso (panel)clean and easy predictive mouvements and swing stratagy
Multi SMA by GreenDecodeThis indicator, created by GreenDecode, plots eight Simple Moving Averages (SMAs) with customizable lengths and resolutions. Each SMA can be toggled on or off, and the colors are distinctly set for easy identification: SMA1 (yellow), SMA2 (cyan), SMA3 (green), SMA4 (red), SMA5 (blue), SMA6 (lightblue), SMA7 (teal), and SMA8 (gold). The SMAs are calculated to avoid repainting by using the 'lookahead=barmerge.lookahead_off' parameter, ensuring historical accuracy. Ideal for technical analysis to identify trends and potential reversal points on various timeframes.
Adaptive MVRV & RSI Strategy V6 (Dynamic Thresholds)Strategy Explanation
This is an advanced Dollar-Cost Averaging (DCA) strategy for Bitcoin that aims to adapt to long-term market cycles and changing volatility. Instead of relying on fixed buy/sell signals, it uses a dynamic, weighted approach based on a combination of on-chain data and classic momentum.
Core Components:
Dual-Indicator Signal: The strategy combines two powerful indicators for a more robust signal:
MVRV Ratio: An on-chain metric to identify when Bitcoin is fundamentally over or undervalued relative to its historical cost basis.
Weekly RSI: A classic momentum indicator to gauge long-term market strength and identify overbought/oversold conditions.
Dynamic, Self-Adjusting Thresholds: The core innovation of this strategy is that it avoids fixed thresholds (e.g., "sell when RSI is 70"). Instead, the buy and sell zones are dynamically calculated based on a long-term (2-year) moving average and standard deviation of each indicator. This allows the strategy to automatically adapt to Bitcoin's decreasing volatility and changing market structure over time.
Weighted DCA (Scaling In & Out): The strategy doesn't just buy or sell a fixed amount. The size of its trades is scaled based on conviction:
Buying: As the MVRV and RSI fall deeper into their "undervalued" zones, the percentage of available cash used for each purchase increases.
Selling: As the indicators rise further into "overvalued" territory, the percentage of the current position sold also increases.
This creates an adaptive system that systematically accumulates during periods of fear and distributes during periods of euphoria, with the intensity of its actions directly tied to the extremity of market conditions.
MACD BILE
📊 How to Interpret
Green histogram → strong bullish momentum, favoring buy/long setups.
Red histogram → strong bearish momentum, favoring sell/short setups.
MACD crossing above Signal → buy signal.
MACD crossing below Signal → sell signal.
Because the cycle is adaptive, the indicator becomes more responsive in volatile markets and more stable during sideways conditions, reducing noise compared to the standard fixed-period MACD.
🔑 Key Advantages over Standard MACD
Adaptive to market conditions → no need to manually choose fixed periods.
Reduces false signals during sideways or ranging markets.
Provides clearer trend detection, especially in highly volatile assets such as crypto, forex, and stocks.
SMC - Complete AnalysisMC COMPLETE TRADING SYSTEM
📊 OVERVIEW
Professional Smart Money Concepts indicator with automated BUY/SELL signals, Entry/SL/TP prices, and 4-level market analysis for disciplined trading.
🎯 MAIN FEATURES
🟢 BUY/🔴 SELL Signals - Clear entry signals with exact prices
📍 ENTRY/SL/TP - Automated price calculations
🎪 Discipline Mode - High-probability setups only
⚡ Confluence Scoring - 6-factor signal validation
🏗️ 4 ANALYSIS LEVELS
Level 1: Market Structure
BOS/CHoCH/MSS detection
Displacement & Range analysis
Internal structure mapping
Level 2: Time-Based
Kill Zones (Asian/London/NY)
Session tracking
Daily/Weekly levels
Level 3: Entry & Risk
Smart entry triggers
Auto risk calculator
Target projections
Level 4: Advanced Analytics
Auto Fibonacci levels
Trend line detection
Smart money flow analysis
Strength meter
⚙️ SETTINGS
Default (Relaxed for more signals):
Minimum Confluence: 3/6
Kill Zone Required: OFF
Strength Bias Required: OFF
Risk per Trade: 2%
Risk:Reward: 3:1
📈 RECOMMENDED PAIRS
EURUSD (Beginners)
GBPUSD (Experienced)
XAUUSD (Best SMC signals)
EURJPY (Good structure)
⏰ BEST TIMEFRAMES
H1 - Recommended balance
H4 - High quality signals
M30 - More frequent signals
🎯 TRADING RULES
Trade ONLY on BUY/SELL signals
Use exact ENTRY/SL/TP prices
Set orders immediately
Wait for SL HIT or TP HIT
No modifications allowed
🔒 DISCIPLINE MODE
Shows signals only when confluence ≥3/6
All other features hidden by default
Simple status table
Forces disciplined trading
💡 USAGE
Wait for BUY or SELL signal
Note ENTRY/SL/TP prices
Execute trade exactly as shown
Hold until exit signal
Repeat
⚠️ IMPORTANT
No signal = No trading
2% risk maximum per trade
London/NY sessions preferred
Patience is key to success
🚀 Professional SMC system for consistent profitability through disciplined trading!
Elliott Wave - Impulse + Corrective Detector (Demo) เทคนิคการใช้
สำหรับมือใหม่
ดูเฉพาะ Impulse Wave ก่อน
เทรดตาม direction ของ impulse
ใช้ Fibonacci เป็น support/resistance
สำหรับ Advanced
ใช้ Corrective Wave หาจุด reversal
รวม Triangle กับ breakout strategy
ใช้ Complex correction วางแผนระยะยาว
⚙️ การปรับแต่ง
ถ้าเจอ Pattern น้อยเกินไป
ลด Swing Length เป็น 3-4
เพิ่ม Max History เป็น 500
ถ้าเจอ Pattern เยอะเกินไป
เพิ่ม Swing Length เป็น 8-12
ปิด patterns ที่ไม่ต้องการ
สำหรับ Timeframe ต่างๆ
H1-H4: Swing Length = 5-8
Daily: Swing Length = 3-5
Weekly: Swing Length = 2-3
⚠️ ข้อควรระวัง
Elliott Wave เป็น subjective analysis
ใช้ร่วมกับ indicators อื่นๆ
Backtest ก่อนใช้เงินจริง
Pattern อาจเปลี่ยนได้ตลอดเวลา
🎓 สรุป
โค้ดนี้เป็นเครื่องมือช่วยวิเคราะห์ Elliott Wave ที่:
✅ ใช้งานง่าย
✅ ตรวจจับอัตโนมัติ
✅ มี confidence scoring
✅ แสดงผล Fibonacci levels
✅ ส่ง alerts เรียลไทม์
เหมาะสำหรับ: Trader ที่ต้องการใช้ Elliott Wave ในการวิเคราะห์เทคนิค แต่ไม่มีเวลานั่งหา pattern เอง
💡 Usage Tips
For Beginners
Focus on Impulse Waves first
Trade in the direction of impulse
Use Fibonacci as support/resistance levels
For Advanced Users
Use Corrective Waves to find reversal points
Combine Triangles with breakout strategies
Use Complex corrections for long-term planning
⚙️ Customization
If You See Too Few Patterns
Decrease Swing Length to 3-4
Increase Max History to 500
If You See Too Many Patterns
Increase Swing Length to 8-12
Turn off unwanted pattern types
For Different Timeframes
H1-H4: Swing Length = 5-8
Daily: Swing Length = 3-5
Weekly: Swing Length = 2-3
⚠️ Important Warnings
Elliott Wave is subjective analysis
Use with other technical indicators
Backtest before using real money
Patterns can change at any time
🔧 Troubleshooting
No Patterns Showing
Check if you have enough price history
Adjust Swing Length settings
Make sure pattern detection is enabled
Too Many False Signals
Increase confidence threshold requirements
Use higher timeframes
Combine with trend analysis
Performance Issues
Reduce Max History setting
Turn off unnecessary visual elements
Use on liquid markets only
📈 Trading Applications
Entry Strategies
Wave 3 Entry: After Wave 2 completion (61.8%-78.6% retracement)
Wave 5 Target: Equal to Wave 1 or Fibonacci extensions
Corrective Bounce: Trade reversals at C wave completion
Risk Management
Stop Loss: Beyond pattern invalidation levels
Take Profit: Fibonacci extension targets
Position Sizing: Based on pattern confidence
🎓 Summary
This code is an Elliott Wave analysis tool that offers:
✅ Easy to use interface
✅ Automatic pattern detection
✅ Confidence scoring system
✅ Fibonacci level display
✅ Real-time alerts
Perfect for: Traders who want to use Elliott Wave analysis but don't have time to manually identify patterns.
📚 Quick Reference
Pattern Hierarchy (Most to Least Reliable)
Impulse Waves (90% confidence)
Expanded Flats (85% confidence)
Zigzags (80% confidence)
Triangles (75% confidence)
Complex Corrections (70% confidence)
Best Practices
Start with higher timeframes for main trend
Use lower timeframes for precise entries
Always confirm with volume and momentum
Don't trade against strong fundamental news
Keep a trading journal to track performance
Remember: Elliott Wave is an art as much as a science. This tool helps identify potential patterns, but always use your judgment and additional analysis before making trading decisions.
Intraday Spark Chart [AstrideUnicorn]The Intraday Spark Chart (ISC) is a minimalist yet powerful tool designed to track an asset’s performance relative to its daily opening price. Inspired by Nasdaq's trading-floor analog dashboards, it visualizes intraday percentage changes as a color-coded sparkline, helping traders quickly gauge momentum and session bias.
Ideal for: Day trading, scalping, and multi-asset monitoring.
Best paired with: 1m to 4H timeframes (auto-warns on higher TFs).
Key metrics:
Real-time % change from daily open.
Final daily % change (updated at session close).
Daily open price labels for orientation.
HOW TO USE
Visual Guide
Sparkline Plot:
A green area/line indicates price is above the daily open (bullish).
A red area/line signals price is below the daily open (bearish).
The baseline (0%) represents the daily open price.
Session Markers:
The dotted vertical lines separate trading days.
Gray labels near the baseline show the exact daily open price at the start of each session.
Dynamic Labels:
The labels in the upper left corner of each session range display the current (or final) daily % change. Color matches the trend (green/red) for instant readability.
Practical Use Cases
Opening Range Breakouts: Spot early momentum by observing how price reacts to the daily open.
Multi-Asset Screening: Compare intraday strength across symbols by choosing an asset in the indicator settings panel.
Session Close Prep: Anticipate daily settlement by tracking the final % change (useful for futures/swing traders).
SETTINGS
Asset (Input Symbol) : Defaults to the current chart symbol. Choose any asset to monitor its price action without switching charts - ideal for intermarket analysis or correlation tracking.
HTF Bias Signals (Daily EMA Bias + LTF EMA Cross)clean, flexible indicator (Pine v5). It defines higher-timeframe (HTF) bias from Daily and/or Weekly EMAs, then only fires entries on your chart’s timeframe when that bias agrees with a simple fast/slow EMA trigger. It also includes alertconditions so you can automate alerts.
DCA Strategy on Steroids for CryptoThis strategy getting only in Long position for Crypto
Using Fast and Slow moving Averages and Stochastic RSI to get in Long position
Fast and Slow moving Averages - cross-under - I Prefer - or opposite for Bull Market
Stochastic RSI cross-over - 5 and Trend Determined by the Fast moving Average
There is no Stop loss is not for one with small tolerance to getting under
Fast and Slow moving Averages and Stochastic RSI Parameters can be adjust
The bot Use Safe Trades and Price Deviation Determined from the User
Max Safe Trades = 10
Take profit Parameters can be adjust in %
Pepe-USDC is just a example What the bot Can do
Supp_Ress_V1This indicator automatically plots support and resistance levels using confirmed pivot highs and lows, then manages them smartly by merging nearby levels, extending them, and removing them once price breaks through.
It also draws trendlines by connecting valid higher-lows (uptrend) or lower-highs (downtrend), ensuring they slope correctly and have enough spacing between pivots.
In short: it gives you a clean, trader-like map of the most relevant S/R zones and trendlines, updating dynamically as price action unfolds.
