Path of the Planets🪐 Path of the Planets
Path of the Planets is an open-source Pine Script™ v6 indicator. It is inspired by W.D. Gann’s Path of Planets chart, specifically the Chart 5-9 artistic replica by Patrick Mikula "shown below". The script visualizes planetary positions so you can explore possible correlations with price. It overlays geocentric and heliocentric longitudes and declinations using the AstroLib library and includes an optional positions table that shows, at a glance, each body’s geocentric longitude, heliocentric longitude, and declination. This is an educational tool only and not trading advice.
Key Features
Start point: Choose a date and time to begin plotting so studies can align with market events.
Adjustments: Mirror longitudes and shift by 360° multiples to re-frame cycles.
Planets: Toggle geocentric and heliocentric longitudes and declinations for Sun, Mercury, Venus, Earth, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto. Moon declination is available.
Positions table: Optional color-coded table (bottom-right) with three columns labeled Geo, Helio, and Dec. Values show degrees with the zodiac sign for the longitudes and degrees for declinations.
Visualization: Solid lines for geocentric longitudes, circles for heliocentric longitudes, and columns for declinations. Includes a zero-declination reference line.
How It Works
Converts bar timestamps to Julian days via AstroLib.
Fetches positions with AstroLib types: geocentric (0), heliocentric (1), and declination (3).
Normalizes longitudes to the −180° to +180° range, applies optional mirroring and 360° shifts, and converts longitudes to zodiac sign labels for the table.
Plots and the table update only on and after the selected start time.
Usage Tips
Apply on daily or higher timeframes when studying broader cycles. For degrees, use the left scale.
Limitations at the moment: default latitude, longitude, and timezone are set to 0; aspects and retrogrades are not included; the focus is on raw paths.
License and Credits
Dependency: @BarefootJoey Astrolib
Contributions and observations are welcome.
Dönemler
GOLDEN SCALP DEXDescription:
This tool is designed for traders who want to capture momentum shifts that occur within larger timeframe structures. The strategy identifies unique breakout opportunities inside the HTF cycle and provides precise entry and exit signals without cluttering the chart.
The system focuses on:
Pinpointing immediate momentum breakouts
Clean chart visualization with intuitive buy/sell markers
Full backtesting capability through TradingView’s Strategy Tester
Customizable position sizing for futures, forex, and crypto traders
It is especially useful for traders who prefer fast, rule-based signals during high-impact intraday moves.
⚠️ Disclaimer: This is a trading tool, not financial advice. Results may vary depending on market conditions, and proper risk management is essential.
Daily Open/Close + Weekday ADR (price & pips) Market Maker TTMarket development for trading weekly highs and lows during consolidation markets
Weekly High/Low ZonesMarket Makers Use weekly highs and lows before reversing price and that should help during london and new york session
Psych Zones – Continuous 250-pip Bands (clamped boxes)Market Makers liquidation and reversal zones for swing trading
Psych Zones – Single 750-pip Range (000 to 750)Market structure on each range of the market, use this if you are counter-trend trading or looking to exit out of a trade.
Psych Levels – 250 pip gridMarket Test: Each 250 Pip, Institutional market behavior works in market rotations
ETH/SOL 1D Dynamic Trend Core - Indicator v46🚀 Dynamic Trend Core
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. It offers both a quantitative backtesting engine and a rich, intuitive visual interface.
Its core philosophy is simple: confirmation. The system seeks to filter out market noise by requiring a confluence of conditions—trend, momentum, price action, and volume—to be in alignment before a signal is considered valid.
⚙️ Core Logic Components
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that determines the underlying market trend (Bullish, Bearish, or Consolidation).
Confirmation & Momentum: Signals are confirmed with a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum aligns with the primary trend.
Advanced Filtering Layers: A suite of optional filters allows for robust customization:
Volume & ADX: Ensure sufficient market participation and trend strength.
Market Regime: Uses the total crypto market cap to gauge broad market health.
Multi-Timeframe (MTF): Aligns signals with the dominant weekly trend.
BTC Cycle Analysis: Uses Halving or Mayer Multiple models to position trades within historical macro cycles.
Delta Zones: An additional filter to confirm signals with recent buy or sell pressure detected in candle wicks.
📊 The On-Chart Command Center
The strategy's real power comes from its on-chart visual feedback system, which provides full transparency into the engine's decision-making process.
Note: For the dashboard to update in real-time, you must enable "Recalculate on every tick" in the script's settings.
Power Core Gauge: Located at the bottom-center, this gauge is the heart of the system. It displays the number of active filter conditions met (e.g., 6/7) and "powers up" by glowing brightly as a signal becomes fully confirmed.
Live Conditions Panel: In the bottom-right corner, this panel acts as a detailed pre-flight checklist. It shows the real-time status of every single filter, helping you understand exactly why a trade is (or is not) being triggered.
Energized Trendline: The main SAMA trendline changes color and brightness based on the strength and direction of the trend, providing immediate visual context.
Halving Cycle Visualization: An optional visual guide to the phases of the Bitcoin halving cycle.
Delta Zone Pressure Boxes: A visual guide that draws boxes around candles exhibiting significant buying or selling pressure.
🛠️ How to Use
Operation Mode: "Alerts-Only Mode" for generating live signals.
Configure Strategy: Start with the default filters. If a potential trade setup is missed, check the Live Conditions Panel to see exactly which filter blocked the signal. Adjust the filters to suit your specific asset and timeframe.
Manage Risk: Adjust the Risk & Exit settings to match your personal risk tolerance.
BTC Dynamic Trend Core - Indicator v46🚀 Dynamic Trend Core
The Dynamic Trend Core is a sophisticated, multi-layer trading engine designed to identify high-probability, trend-following opportunities. It offers both a quantitative backtesting engine and a rich, intuitive visual interface.
Its core philosophy is simple: confirmation. The system seeks to filter out market noise by requiring a confluence of conditions—trend, momentum, price action, and volume—to be in alignment before a signal is considered valid.
⚙️ Core Logic Components
Primary Trend Analysis (SAMA): The foundation is a Self-Adjusting Moving Average (SAMA) that determines the underlying market trend (Bullish, Bearish, or Consolidation).
Confirmation & Momentum: Signals are confirmed with a blend of the Natural Market Slope and a Cyclic RSI to ensure momentum aligns with the primary trend.
Advanced Filtering Layers: A suite of optional filters allows for robust customization:
Volume & ADX: Ensure sufficient market participation and trend strength.
Market Regime: Uses the total crypto market cap to gauge broad market health.
Multi-Timeframe (MTF): Aligns signals with the dominant weekly trend.
BTC Cycle Analysis: Uses Halving or Mayer Multiple models to position trades within historical macro cycles.
Delta Zones: An additional filter to confirm signals with recent buy or sell pressure detected in candle wicks.
📊 The On-Chart Command Center
The strategy's real power comes from its on-chart visual feedback system, which provides full transparency into the engine's decision-making process.
Note: For the dashboard to update in real-time, you must enable "Recalculate on every tick" in the script's settings.
Power Core Gauge: Located at the bottom-center, this gauge is the heart of the system. It displays the number of active filter conditions met (e.g., 6/7) and "powers up" by glowing brightly as a signal becomes fully confirmed.
Live Conditions Panel: In the bottom-right corner, this panel acts as a detailed pre-flight checklist. It shows the real-time status of every single filter, helping you understand exactly why a trade is (or is not) being triggered.
Energized Trendline: The main SAMA trendline changes color and brightness based on the strength and direction of the trend, providing immediate visual context.
Halving Cycle Visualization: An optional visual guide to the phases of the Bitcoin halving cycle.
Delta Zone Pressure Boxes: A visual guide that draws boxes around candles exhibiting significant buying or selling pressure.
🛠️ How to Use
Indicator version of BTC DTC Strategy: "Alerts-Only Mode" for generating live signals.
Configure Strategy: Start with the default filters. If a potential trade setup is missed, check the Live Conditions Panel to see exactly which filter blocked the signal. Adjust the filters to suit your specific asset and timeframe.
Manage Risk: Adjust the Risk & Exit settings to match your personal risk tolerance.
Ichimoku Trading Signals 2Swing Trading (Strategy 1, H4+ timeframes)
Use the Kumo Cloud to identify the trend: price above a green cloud = uptrend; price below a red cloud = downtrend.
Entry signals occur when price or the Tenkan-sen line crosses the Kijun-sen line, confirmed by Chikou Span momentum.
Exit triggers when price crosses back through the Kijun-sen or when Tenkan-sen crosses back below (for long positions) or above (for short positions).
Place stop-loss orders just beyond the nearest swing low/high candle cluster to manage risk tightly.
Smart Money Time (SMT)SMT Divergence – 90m / 30m / 10m (Prev-Cycle, Real-Time, Trailing)
Purpose:
This indicator finds SMT (Smart Money Technique) divergences between two related markets (e.g., CME_MINI:NQ1! vs CME_MINI:ES1! ). It does this per 90m/30m/10m cycles and only compares the current cycle to the immediately previous cycle —never older. It supports three cycle granularities:
90-minute cycles (9 blocks from 02:30–16:00 NY time)
30-minute sub-cycles (27 blocks)
10-minute sub-cycles (81 blocks; exactly 3 per 30-minute cycle)
For each cycle, the script tracks each symbol’s extreme (highest high for potential bearish SMT, lowest low for potential bullish SMT). When the leader sets a new extreme vs its own previous cycle while the lagger fails to do so vs its previous cycle, an SMT divergence is formed and plotted on the chart in real time. Lines trail as price makes new extremes within the same cycle.
What you’ll see on the chart:
A line from the previous cycle’s extreme to the current cycle’s extreme on the symbol pane where the indicator is applied (Primary A).
An optional text label at the current extreme (e.g., “90m SMT”, “30m SMT”, “10m SMT”).
Lines update (“trail”) as the current cycle goes on. When a new cycle begins, tracking resets for that cycle.
Default styling (editable):
90m SMT: solid, width 1, black
30m SMT: solid, width 1, black
10m SMT: dotted, width 1, black
You can toggle the text on/off and change width, style, and colors separately for 90m, 30m, and 10m.
Signals (definitions)
Bearish SMT: One market makes a Higher High vs its own previous cycle, while the other fails to make a Higher High vs its previous cycle.
Bullish SMT: One market makes a Lower Low vs its own previous cycle, while the other fails to make a Lower Low vs its previous cycle.
The line is drawn on Primary A by default.
Settings (explained)
Symbols
Primary Symbol A – the chart’s “leader/lagger” pane the script draws on.
Comparison Symbol B – the second market used for SMT checks.
Detection toggles
Detect SMT: 90m / 30m / 10m – turn on/off detection for each timeframe.
Note: The script always compares current cycle ↔ previous cycle only.
Validate candle direction
When enabled, the bar that makes the new extreme must also close in the confirming direction on that same market:
Bearish SMT: the bar that made the new Higher High must be a down close (close < open).
Bullish SMT: the bar that made the new Lower Low must be an up close (close > open).
This filter removes many “wick-only” probes and reduces false positives.
Turn off if you prefer to register SMTs on any intrabar extreme, regardless of bar close.
Delete SMT when invalidated
After an SMT forms, if the lagger later breaks the previous extreme it initially failed to break, the divergence is considered invalid and the script deletes the line and its label. (An “SMT invalidated” alert can fire if alerts are enabled.)
Enable alerts
Fires on SMT formed (separate messages for 90m/30m/10m and bullish/bearish) and on SMT invalidated.
To use, click Create Alert on the indicator and choose “Any alert() function call”. Use “Once per bar” (or per bar close) to taste.
Appearance – per timeframe (90m / 30m / 10m)
Bullish/Bearish colors, Line width, Line style, Show text (toggle subtitle label).
Text Options
Text color, Text size, Text offset (vertical spacing from the extreme).
How to use
Add to chart and select two related markets, e.g., NQ (A) vs ES (B).
Choose which cycles to monitor (90m / 30m / 10m).
Optionally enable Validate candle direction to demand a confirming close on the bar that made the extreme.
Watch for plotted SMTs:
Bearish SMT (HH vs no HH) often signals potential weakness
Bullish SMT (LL vs no LL) often signals potential strength
Use SMTs as context/confluence—e.g., near session highs/lows, liquidity pools, PD arrays, or your own levels. Combine with structure, order flow, and risk rules.
Turn on alerts to be notified when SMTs form or are invalidated in real time.
Notes & behavior
New-York session timing: Cycles are fixed windows in America/New_York and automatically handle DST.
Real-time & trailing: Lines trail to the most recent extreme within the same cycle but there’s no look-ahead across cycles.
No repaint across cycles: Each signal is strictly current cycle vs previous cycle.
Cleanup: On invalidation the script deletes the label first, then the line, preventing orphan labels.
Tips
10m SMTs are more frequent/noisier; 90m are fewer but more meaningful. Many traders look for multi-frame agreement (e.g., a 30m SMT aligning with a fresh 10m SMT).
If you want fewer signals, keep Validate candle direction on; if you want maximum sensitivity, turn it off.
Disclaimer: Educational use only. Markets are risky; do your own research and manage risk responsibly.
Market Sessions [odnac]
This indicator highlights the three main global market sessions (USA, Europe, Asia) and their overlaps directly on the chart.
It helps traders quickly identify active trading periods and potential high-liquidity overlaps.
Features:
Customizable start and end times for each session
Optional daily dividers with weekday labels
Session markers displayed as circles above the candles
Overlap sessions displayed in distinct colors
Adjustable opacity for better chart visibility
Option to hide weekends
Sessions included:
USA Market Session (default 13:30–20:00 UTC)
Europe Market Session (default 07:00–16:00 UTC)
Asia Market Session (default 00:00–09:00 UTC)
Overlaps: USA + Europe, USA + Asia, Europe + Asia
This tool is designed for intraday timeframes (1m–60m) and can be useful for scalping, day trading, or session-based strategies.
DMI MTF Color Table v5DMI Multi-Timeframe Color Table v5
A comprehensive DMI (Directional Movement Index) table that displays trend direction and strength across multiple timeframes simultaneously. This indicator helps traders quickly assess market conditions and identify confluence across different time horizons.
Features:
Multi-timeframe analysis (7 configurable timeframes)
Color-coded cells based on trend strength and direction
Real-time current market condition display
Customizable strength thresholds and color schemes
Multiple display modes (All, DI+ Only, DI- Only, ADX Only)
Text-based strength classifications (STRONG/MEDIUM/WEAK)
Directional bias indicators (BULL/BEAR)
How It Works:
The table shows DI+, DI-, and ADX values across your chosen timeframes with intelligent color coding:
Green shades indicate bullish momentum (DI+ > DI-)
Red shades indicate bearish momentum (DI- > DI+)
Color intensity reflects trend strength based on ADX values
Current market condition appears in top-right corner
Display Options:
Toggle numerical values, strength text, and timeframe labels
Adjustable table size and transparency
Customizable color schemes for all conditions
Optional current timeframe DMI plot overlay
Educational Use:
This tool is designed for educational purposes to help understand multi-timeframe analysis and DMI interpretation. All trading decisions should be based on your own analysis and risk management.
Credits:
Original concept and development by Profitgang. If you use or modify this script, please provide appropriate credit to the original author.
Note: This indicator is for analysis purposes only. Past performance does not guarantee future results. Always conduct your own research and consider your risk tolerance before making trading decisions.
Daily Distribution Range - Amplitude Probability DashboardSummary
This indicator provides a powerful statistical deep-dive into an asset's daily distribution range, amplitude and volatility. It moves beyond simple range indicators by calculating the historical probability of a trading day reaching certain amplitude levels.
The results are presented in a clean, interactive dashboard that highlights the current day's performance in real-time, allowing traders to instantly gauge if the current volatility is normal, unusually high, or unusually low compared to history.
This tool is designed to help traders answer a critical question: "Based on past behavior, what is the likelihood that today's range will be at least X%?"
Key Concepts Explained
1. Daily Amplitude (%)
The indicator first calculates the amplitude (or range) of every historical daily candle and expresses it as a percentage of that day's opening price.
Formula: (Daily High - Daily Low) / Daily Open * 100
This normalization allows for a consistent volatility comparison across different price levels and time periods.
2. Cumulative Probability Distribution
Instead of showing the probability of a day's final range falling into a small, exclusive bin (e.g., "exactly between 1.0% and 1.5%"), this indicator uses a cumulative model. It answers the question, "What is the probability that the daily range will be at least a certain value?"
For example, if the row for "≥ 2%" shows a probability of 12.22%, it means that historically, 12.22% of all trading days have had a total range of 2% or more. This is incredibly useful for risk management and setting realistic expectations.
Core Features
Statistical Dashboard: Presents all data in a clear, easy-to-read table on your chart.
Cumulative Probability Model: Instantly see the historical probability of the daily range reaching or exceeding key percentage levels.
Real-Time Highlight & Arrow (→): The dashboard isn't just historical. It actively tracks the current, unfinished day's amplitude and highlights the corresponding row with a color and an arrow (→). This provides immediate context for the current session's price action.
Timeframe Independent: You can use this indicator on any chart timeframe (e.g., 5-minute, 1-hour, 4-hour), and it will always fetch and calculate using the correct daily data.
Clean & Professional UI: Features a monospace font for perfect alignment and a simple, readable design.
Fully Customizable: Easily adjust the dashboard's position, text size, and the amount of historical data used for the analysis.
How to Use & Interpret the Data
This indicator is not a trading signal but a powerful tool for statistical context and decision-making.
Risk Management: If you see that an asset has only a 5% historical probability of moving more than 3% in a day, you can set stop-losses more intelligently and avoid being overly aggressive with your targets on a typical day.
Setting Profit Targets: Gauge realistic intra-day profit targets. If a stock is already up 2.5% and has historically only moved more than 3% on rare occasions, you might consider taking profits.
Options Trading: Volatility is paramount for options. This tool helps you visualize the expected range of movement, which can inform decisions on strike selection for strategies like iron condors or straddles.
Identifying Volatility Regimes: Quickly see if the current day is a "normal" low-volatility day or an "abnormal" high-volatility day that could signal a major market event or trend initiation.
Dashboard Breakdown
→ (Arrow): Points to the bin corresponding to the current, live day's amplitude.
Amplitude Level: The minimum amplitude threshold. The format "≥ 1.5%" means "greater than or equal to 1.5%".
Days Reaching Level: The raw number of historical days that had an amplitude equal to or greater than the level in the first column.
Prob. of Reaching Level (%): The percentage of total days that reached that amplitude level (Days Reaching Level / Total Days Analyzed).
Settings
Position: Choose where the dashboard appears on your chart.
Text Size: Adjust the font size for better readability on your screen resolution.
Max Historical Days to Analyze: Set the lookback period for the statistical analysis. A larger number provides a more robust statistical sample but may take slightly longer to load initially.
Enjoy this tool and use it to add a new layer of statistical depth to your trading analysis.
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.
SigmoidCycle Oscillator [LuminoAlgo]Purpose:
The SineCycle Oscillator measures price momentum using sigmoid function mathematics (S-curve transformation) borrowed from neural network theory. It generates an oscillator that fluctuates around 1.0, identifying momentum shifts and potential reversal points.
Mathematical Foundation:
This indicator applies the sigmoid logistic function concept: y = 1/(1+e^-x) , which creates an S-shaped curve. In financial markets context, this transformation:
- Maps price changes to a bounded range (-1 to +1)
- Provides non-linear sensitivity (high near zero, low at extremes)
- Naturally filters outliers without lag penalty
Calculation Process:
1. Statistical Normalization: Price deviations are measured from a moving average baseline and scaled by recent volatility (standard deviation over N periods)
2. Sigmoid Transformation: Normalized values undergo S-curve transformation, which weights small movements linearly but compresses large movements logarithmically
3. Dual Timeframe Analysis:
• Short window: User-defined period (N)
• Long window: Double period (2N)
• Ratio calculation: Short sigmoid average ÷ Long sigmoid average
4. Volatility-Weighted Smoothing: Final values use exponential smoothing where the smoothing factor adjusts based on the coefficient of variation (volatility/mean ratio)
What Makes This Different:
Unlike linear momentum oscillators (RSI, Stochastic) that use fixed mathematical relationships, the sigmoid transformation creates variable sensitivity zones. This mimics how professional traders mentally weight price movements.
Trading Application:
Signal Types:
- Momentum: Green (>1.0) = bullish, Red (<1.0) = bearish
- Reversals: 1.0 line crosses with volume confirmation
- Divergence: Price makes new high/low, oscillator doesn't
- Exhaustion: Extended readings (>1.2 or <0.8) suggest overextension
Optimal Conditions:
- Works best: Trending markets with clear swings
- Avoid: Low volume, ranging markets under 1% daily movement
- Timeframes: 4H and above for reliability
Parameter Guidelines:
- Length 8-10: Day trading (expect more whipsaws)
- Length 14-20: Swing trading (balanced signals)
- Length 25-30: Position trading (fewer, stronger signals)
Limitations:
- Lag increases with higher length settings
- Can give false signals during news-driven spikes
- Requires additional confirmation in choppy markets
Trading Framework:
Based on momentum persistence theory - assumes trends continue until sigmoid curve flattens (indicating momentum exhaustion). The mathematical model captures both mean reversion (extreme readings) and trend following (mid-range readings) characteristics.
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.
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## **🎯 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).
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## **💼 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
Possible Deviations | Session Fibs📌 Session Fibs with Confluence Detection
This script automatically plots custom Fibonacci extensions from key market sessions and candles, giving you a structured view of intraday levels that matter:
Asia Session (20:00–00:00 NY time)
→ Marks the session high/low, draws fib projections, and shades the range.
London 4:00 AM (1H) Candle
→ Captures the 1-hour “last leg” move into London and projects fib levels.
New York Open Key Candles
→ 8:30 AM and 9:30 AM (5-minute) candles with fib projections.
⚡ Features
Custom fib set (0, 0.5, ±0.618, ±2.0, ±2.25, ±2.5, ±3.0, ±3.25, ±3.5, ±4.0, ±4.25, ±4.5, 4.618).
Adjustable line extension mode (none, right N bars, infinite right).
Toggleable labels & text color, with placement options (on line / left of line).
Asia session box highlight for visual clarity.
Confluence detection: automatically checks for overlapping fib levels between
Asia ↔ London (same day)
Today ↔ Previous day (optional)
→ Highlights overlaps with dashed lines + labels (e.g., LON -3.25 ≈ ASIA -2.25).
🎯 Use Case
Designed for traders who track session ranges and liquidity sweeps, this tool makes it easy to spot:
Intraday fib alignment between Asia and London.
Key NYO candles in relation to overnight ranges.
High-probability confluence zones for entries/exits.
SMC - OB/Breaker Block/Bos/ChoCh (DeadCat) Based on analyzing your Pine Script code, here are comprehensive descriptions that should comply with TradingView's house rules:
Script 1: "PO3 Liquidity w/ CISD (DeadCat)"
Description:
This indicator implements the Power of Three (PO3) liquidity concept combined with Change in State of Delivery (CISD) pattern recognition for Smart Money Concepts (SMC) trading. The script operates on multi-timeframe analysis using automated timeframe selection.
Core Methodology: The indicator identifies C2 liquidity sweeps by detecting when price breaks previous period highs/lows and then reverses back above/below those levels. It specifically looks for:
C2 Buy Setup: When current low breaks previous period low but closes back above it
C2 Sell Setup: When current high breaks previous period high but closes back below it
CISD Pattern Detection: The script implements sophisticated CISD (Change in State of Delivery) pattern recognition by:
Tracking the first break of previous HTF high/low levels
Identifying imbalance candles (gaps between consecutive candles)
Confirming CISD when price reclaims the imbalance level within 2 HTF periods
Validating setups only when both liquidity sweep AND CISD confirmation occur
Visual Components:
HTF Candles: Displays higher timeframe candle structure on current chart
Trading Zones: Shows zones between HTF open and equilibrium levels
CISD Lines: Marks confirmed change in state of delivery levels
C2/C4 Labels: Identifies liquidity sweep entry points and potential continuation setups
Market Structure: Optional HH/HL/LH/LL pivot markers
Unique Features:
Automatic timeframe calculation (15m→4H, 1H→1D, etc.)
Real-time HTF period countdown
Setup invalidation tracking when stops are hit
Progressive setup confirmation (C2→C4 evolution)
Bias filter for directional trading preferences
Usage: C2 setups provide initial entry opportunities after confirmed liquidity sweeps with CISD confirmation. C4 setups offer additional entries when HTF equilibrium conditions align favorably. The indicator helps traders identify institutional liquidity grabs followed by genuine directional moves.
Script 2: "SMC Toolkit (DeadCat)"
Description:
This comprehensive Smart Money Concepts toolkit provides institutional-level market structure analysis with automated Order Block (OB) and Breaker Block (BB) zone identification, plus Break of Structure (BOS) and Change of Character (ChoCh) detection.
Market Structure Algorithm: The indicator uses a sophisticated pivot-based algorithm to identify and track market structure progression:
Uptrend: HH→HL→HH sequence tracking
Downtrend: LL→LH→LL sequence tracking
Trend Changes: Automatic ChoCh detection when structure breaks occur
Order Block Logic:
Bullish OB Zones: Created at Higher Lows (HL) and Lower Lows (LL) during uptrends
Bearish OB Zones: Created at Lower Highs (LH) and Higher Highs (HH) during downtrends
Uses last bearish candle before bullish moves (and vice versa) to define precise zone boundaries
Breaker Block Logic:
Bullish BB Zones: Former resistance that becomes support after HH/LH breaks
Bearish BB Zones: Former support that becomes resistance after LL/HL breaks
Automatically transitions when structure points are breached
Zone Management: The script employs intelligent zone lifecycle management:
Creates new zones only at confirmed structure points
Makes previous zones transparent when new structure is confirmed
Maintains zone relevance through dynamic extension
Limits total zones to prevent chart clutter
BOS vs ChoCh Detection:
BOS (Break of Structure): Continuation patterns when trend highs/lows are exceeded
ChoCh (Change of Character): Reversal patterns when pullback levels are broken against trend
Requires 2-candle confirmation before finalizing structure changes
Visual Enhancements:
Color-coded zones with transparency controls
Directional arrows (▲/▼) in zone labels
Customizable line styles and text sizing
Clean market structure progression tracking
Originality: This toolkit combines traditional SMC concepts with enhanced zone boundary calculation using multi-candle analysis and intelligent zone lifecycle management, providing more precise entry/exit levels than standard implementations.
AI Gold Liquidity Breakout CatcherTitle: Gold AI Liquidity Breakout Catcher
Description:
Indicator Philosophy and Originality:
This indicator is not merely a collection of separate tools, but an integrated trading framework designed to improve decision-making by ensuring signal confluence. The core philosophy is that high-probability trading signals occur when multiple, distinct analysis methodologies align.
The originality of this script lies in how it systematically combines a leading signal (the Liquidity Breakout) with multiple, independent lagging confirmation tools (the Classic Filters, the Hull MA, and the Range Filter). A user can see a primary breakout signal and immediately validate its strength against the broader trend defined by the Hull MA, the intermediate trend from the Range Filter, and the specific conditions of the classic filters.
This synergy, where different components work together to validate a single event, is the primary value and reason for this mashup. It provides a structured, multi-layered confirmation process within a single tool, which is not achievable by adding these indicators separately to the chart.
This indicator is a comprehensive technical analysis tool designed to identify potential trading opportunities and provide supplemental trend analysis. It features a primary signal engine based on pivot trendline breakouts, a sophisticated confirmation layer using classic technical indicators, and three separate modules for discretionary analysis: an ICT-based structure plotter, a highly customizable Hull Moving Average (HMA), and a volatility-adaptive Range Filter. This document provides a detailed, transparent explanation of all underlying logic.
1. Core Engine: Pivot-Based Liquidity Trendline Signals
The indicator's foundational signal is generated from a custom method we call "Liquidity Trendlines," which aims to identify potential shifts in momentum.
How It Works:
The script first identifies significant swing points in the price using ta.pivothigh() and ta.pivotlow().
It then draws a trendline connecting consecutive pivot points.
A "Liquidity Breakout" signal (liquidity_plup for buy, liquidity_pldn for sell) is generated when the price closes decisively across this trendline, forming the basis for a potential trade.
2. The Signal Confirmation Process: Multi-Layered Filtering System
A raw Liquidity Breakout signal is only a starting point. To enhance reliability, the signal must pass through a series of user-enabled filters. A final Buy or Sell signal is only plotted if all active filter conditions are met simultaneously.
General & Smart Trend Filters: Use a combination of EMAs, DMI (ADX), and market structure to define the trend.
RSI & MACD Filters: Used for momentum confirmation.
Directional Body Strength Filter: A custom filter that validates the signal based on the strength and direction of the signal candle's body (bodyUpOK / bodyDownOK).
Support & Resistance (S&R) Filter: Blocks signals forming too close to key S&R zones.
Higher Timeframe (HTF) Filter: Provides confluence by checking the trend on higher timeframes.
3. Visual Aid 1: ICT-Based Structure & Premium/Discount Zones
This module is for visual and discretionary analysis only and does not directly influence the automated Buy/Sell signals.
ICT Market Structure: Plots labels for CHoCH, SMS, and BMS based on a Donchian-channel-like logic.
ICT Premium & Discount Zones: When enabled, it draws colored zones corresponding to Premium, Discount, and Equilibrium levels.
4. Visual Aid 2: Hull Moving Average (HMA) Integration
This is another independent tool for trend analysis. It does not affect the primary signals but has its own alerts and serves as a powerful visual confirmation layer.
Functionality: Includes multiple Hull variations (HMA, THMA, EHMA), customizable colors based on trend, and the ability to pull data from a higher timeframe.
5. Visual Aid 3: Range Filter Integration
This module is a volatility-adaptive trend filter that provides its own set of signals and visuals. It is designed to be a standalone trend analysis tool integrated within the indicator for additional confluence.
How It Works: The Range Filter calculates a dynamic volatility threshold based on the average range of the price. A central filter line moves up or down only when the price exceeds this threshold, effectively filtering out market noise.
Visuals: It plots the central filter line and upper/lower bands that create a volatility channel. It can also color the price bars based on the trend.
Signals & Alerts: The Range Filter generates its own "Manual Buy" and "Manual Sell" signals when the price crosses the filter line after a change in trend direction. These signals have their own dedicated alerts.
6. Risk Management & Additional Features
TP/SL Calculations: Automatically calculates Take Profit and Stop Loss levels for the primary signals based on the ATR.
Multi-Timeframe (MTF) Scanner: A dashboard that monitors the final Buy/Sell signal status across multiple timeframes.
Session Filter & Alerts: Allows for restricting trades to specific market sessions and configuring alerts for any valid signal.
By combining breakout detection with a rigorous confirmation process and multiple supplemental analysis tools, this indicator provides a structured and transparent approach to trading.
