AVR Trading Checklist PanelChecklist indicator,
✅ 4 checklist-punten
✅ Automatische score (0–100%)
✅ Visueel paneel met donkere stijl
✅ "AVR TRADING" als header
✅ Discount/Premium kleurvlak
✅ ✅ en ❌ symbolen
✅ Gele accenten
Educational
KingJakesFx CRTThis TradingView indicator is a comprehensive tool that identifies and marks significant high and low points of Candle Range Type (CRT) candles. Its standout feature is the ability to visualize these key levels across multiple timeframes, allowing traders to maintain awareness of important price zones even when analyzing shorter timeframes.
The indicator extends high and low lines into the future, creating dynamic support and resistance levels that help anticipate potential price reactions. With extensive customization options, users can tailor the visual appearance of lines, labels, and alerts to match their trading setup and preferences.
Perfect for traders who analyze multiple timeframes and want to maintain awareness of significant price levels, this indicator combines powerful technical analysis with flexible visual customization to enhance any trading strategy.
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action."
The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators."
Implementation Methodology
Component Selection
Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components:
Credit Spreads (High Yield Credit Spread)
As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite.
Volatility Measures (VIX)
Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite.
Equity/Bond Performance Ratio (SPY/IEF)
According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior."
Commodity Ratio (Oil/Gold)
Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator."
Standardization Process
Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ
This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics.
Signal Integration
The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process."
The final index is scaled to a 0-100 range, with:
Values above 70 indicating "Risk-On" market conditions
Values below 30 indicating "Risk-Off" market conditions
Values between 30-70 representing neutral risk sentiment
Limitations and Differences from Original Implementation
Proprietary Components
The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability."
Technical Limitations
Pine Script v6 imposes certain constraints that prevent full replication:
Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization.
Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints.
Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds.
Empirical Performance
While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points."
References
Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187.
Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337.
Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680.
Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898.
Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541.
Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194.
Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150.
Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566.
Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide."
Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287.
Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425.
Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper.
Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
Super Strategy Indicator Zeenu This indicator predicts the continuous 20 % price moves and this is created for educational purpose only.
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Intraday Fibs RetracementFibonacci (Fibs) levels are often used by traders as a way to find support and resistance, based on the Fibonacci sequence. These levels are widely used in technical analysis to identify potential reversal points in the price of an asset.
Fibs retracement draws lines at these Fibs level between a significant high and low point on a price chart.
What it shows:
This indicator will automatically draw Fibs Retracement Levels on your chart without any manual work.
It is designed to be used for day trading, especially in scenarios where a ticker gaps up/down large compared to the prior day close. (i.e. scenario where the difference of day's open and prior day close is large)
The drawing will happen on each trading day the moment trading hours open, and will NOT draw during pre-market and post-market.
User can see the line of each Fibs level, labelled with the Fib percentage and price value for the corresponding levels.
User will specify a start and end point of Fibs and based on the choice the indicator will automatically compute the other user defined Fibs levels and display on the chart.
How to use it:
The Fib levels drawn can be a potential support and resistance zone. Therefore in scenario where you already have a position and are approaching one of these levels it could be a point to close out some or all the position as you are approaching a resistance. On the other hand when price do approach these levels you could enter a position for a reversal trade. These are few ways to use the indicator but there are other ways that can be used, which can be found out by researching "Fibonacci (Fibs) Retracement".
In the example on the chart you can see a price bounce from the 0.7886 Fibs level on this particular day, where the price gapped up and was coming down after market hours opened.
Key settings:
1. Fibs Retracement Start and end Point: User selects where the Fibs levels should be drawn.
Available Options are:
Start Points:
Market Open
Market Open High (Dependent on the time frame you are on)
Pre-market High
Day's High
End Points:
Previous Day Close
Previous Day Low
Previous Day High
Pre-market Low (Current Day)
Day's Low
2. Custom Fib Levels: User can manually enter the Fib levels they want to see. (Max 9)
Default values are: 0,0.236,0.382,0.5,0.618,0.786,1,1.618,2.618.
3. Display settings: User can specify the line colour, thickness and style.
4. Label Setting: User can choose to turn on/off the labels for the each Fibs Level. Label will show the fib percentage and the corresponding price. User can also choose the location of the labels, defined by an offset from the current candle.
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If anything is not clear please let me know!
Statistical Reliability Index (SRI)Statistical Reliability Index (SRI)
The Statistical Reliability Index (SRI) is a professional financial analysis tool designed to assess the statistical stability and reliability of market conditions. It combines advanced statistical methods to gauge whether current market trends are statistically consistent or prone to erratic behavior. This allows traders to make more informed decisions when navigating trending and choppy markets.
Key Concepts:
1. Extrapolation of Cumulative Distribution Functions (CDF)
What is CDF?
A Cumulative Distribution Function (CDF) is a statistical tool that models the probability of a random variable falling below a certain value.
How it’s used in SRI:
The SRI utilizes the 95th percentile CDF of recent returns to estimate the likelihood of extreme price movements. This helps identify when a market is experiencing statistically significant changes, crucial for forecasting potential breakouts or breakdowns.
Weight in SRI:
The weight of the CDF extrapolation can be adjusted to emphasize its impact on the overall reliability index, allowing customization based on the trader's preference for tail risk analysis.
2. Bias Factor (BF)
What is the Bias Factor?
The Bias Factor measures the ratio of the current market price to the expected mean price calculated over a defined period. It represents the deviation from the typical price level.
How it’s used in SRI:
A higher bias factor indicates that the current price significantly deviates from the historical average, suggesting a potential mean reversion or trend exhaustion.
Weight in SRI:
Adjusting the Bias Factor weight lets users control how much this deviation influences the SRI, balancing between momentum trading and mean reversion strategies.
3. Coefficient of Variation (CV)
What is CV?
The Coefficient of Variation (CV) is a statistical measure that expresses the ratio of the standard deviation to the mean. It indicates the relative variability of asset returns, helping gauge the risk-to-return consistency.
How it’s used in SRI:
A lower CV indicates more stable and predictable price behavior, while a higher CV signals increased volatility. The SRI incorporates the inverse of the normalized CV to reflect price stability positively.
Weight in SRI:
By adjusting the CV weight, users can prioritize consistent price movements over erratic volatility, aligning the indicator with risk tolerance and strategy preferences.
Interpreting the SRI:
1. SRI Plot:
The SRI plot dynamically changes color to reflect market conditions:
Aqua Line: Indicates uptrend stability, signaling statistically consistent upward movements.
Fuchsia Line: Indicates downtrend stability, where statistically reliable downward movements are present.
The overlay background shifts between colors:
Aqua Background: Signifies statistical stability, where trends are historically consistent.
Fuchsia Background: Indicates statistical instability, often associated with trend uncertainty.
Yellow Background: Marks choppy periods, where statistical data suggests that market conditions are not conducive to reliable trading.
2. SRI Volatility Plot:
Displays the volatility of the SRI itself to detect when the indicator is stable or unstable:
Blue Area Fill: Signifies that the SRI is stable, indicating trending conditions.
Yellow Area Fill: Represents choppy or unstable SRI movements, suggesting sideways or unreliable market conditions.
A Chop Threshold Line (dotted yellow) highlights the maximum acceptable SRI volatility before the market is considered too unpredictable.
3. Stability Assessment:
Stable Trend (No Chop):
The SRI is smooth and consistent, often accompanied by aqua or fuchsia lines.
Volatility remains below the chop threshold, indicating a low-risk, trend-following environment.
Chop Mode:
The SRI becomes erratic, and the volatility plot spikes above the threshold.
Marked by a yellow shaded background, indicating uncertain and non-trending conditions.
[Trend Identification:
Use the color-coded SRI line and background to determine uptrend or downtrend reliability.
Be cautious when the SRI volatility plot shows yellow, as this signals trading conditions may not be reliable.
Practical Use Cases:
Trend Confirmation:
Utilize the SRI plot color and background to confirm whether a detected trend is statistically reliable.
Chop Mode Filtering:
During yellow chop periods, it is advisable to reduce trading activity or adopt range-bound strategies.
Strategy Filter:
Combine the SRI with trend-following indicators (like moving averages) to enhance entry and exit accuracy.
Volatility Monitoring:
Pay attention to the SRI volatility plot, as spikes often precede erratic price movements or trend reversals.
Disclaimer:
The Statistical Reliability Index (SRI) is a technical analysis tool designed to aid in market stability assessment and trend validation. It is not intended as a standalone trading signal generator. While the SRI can help identify statistically reliable trends, it is essential to incorporate additional technical and fundamental analysis to make well-informed trading decisions.
Trading and investing involve substantial risk, and past performance does not guarantee future results. Always use risk management practices and consult with a financial advisor to tailor strategies to your individual risk profile and objectives.
Position Size Calculatorusing the settings you can edit your portfolio balance and desired risk, helps you calculate everything required about position sizing and helps you NOT lose more than intended + 10% deviation on top of that.
TCP | Money Management indicator | Crypto Version📌 TCP | Money Management Indicator | Crypto Version
A robust, multi-target risk and capital management indicator tailored for crypto traders. Whether you're trading spot, perpetual futures, or leverage tokens, this tool empowers you with precise control over risk, reward, and position sizing—directly on your chart. Eliminate guesswork and trade with confidence.
🔰 Introduction: Master Your Capital, Master Your Trades
Poor money management is the number one reason traders lose their accounts, even with solid strategies. The TCP Money Management Indicator, built by Trade City Pro (TCP), solves this problem by providing a structured, rule-based approach to capital allocation.
Want to dive deeper into the concept of money management? Check out our comprehensive tutorial on TradingView, " TradeCityPro Academy: Money Management ", to understand the principles that power this indicator and transform your trading mindset.
This indicator equips you to:
• Calculate optimal position sizes based on your capital, risk percentage, and leverage
• Set up to 5 customizable take-profit targets with partial close percentages
• Access real-time metrics like Risk-to-Reward (R/R), USD profit, and margin usage
• Trade with discipline, avoiding emotional or inconsistent decisions
💸 Money Management Formula
The indicator uses a professional capital allocation model:
Position Size = (Capital × Risk %) ÷ (Stop Loss % × Leverage)
From this, it calculates:
• Total risk amount in USD
• Optimal position size for your trade
• Margin required for each take-profit target
• Adjusted R/R for each target, accounting for partial position closures
🛠 How to Use
Enter Trade Parameters: Input your capital, risk %, leverage, entry price, and stop-loss price.
Set Take-Profit Targets: Enable 1 to 5 take-profit levels and specify the percentage of the position to close at each.
Real-Time Calculations: The indicator automatically computes:
• R/R ratio for each target
• Profit in USD for each partial close
• Margin used per target (in % and USD)
Visualize Your Trade:
• Price levels for entry, stop-loss, and take-profits are plotted on the chart.
• A dynamic info panel on the left side displays all key metrics.
🔄 Dynamic Adjustments: As each take-profit target is hit and a portion of the position is closed, the indicator recalculates the remaining position size, expected profit, R/R, and margin for subsequent targets. This ensures accuracy and reflects real-world trade behavior.
📊 Table Overview
The left-side panel provides a clear snapshot:
• Trade Setup: Capital, entry price, stop-loss, risk amount, and position size
• Per Target: Percentage closed, R/R, profit in USD, and margin used
• Summary: Total expected profit across all targets
⚙️ Settings Panel
• Total Capital ($): Your account size for the trade
• Risk per Trade (%): The percentage of capital you’re willing to risk
• Leverage: The leverage applied to the trade
• Entry/Stop-Loss Prices: Define your trade’s risk zone
• Take-Profit Targets (1–5): Set price levels and percentage to close at each
🔍 Use Case Example
Imagine you have $1,000 capital, risking 1%, using 10x leverage:
• Entry: $100 | Stop-Loss: $95
• TP1: $110 (close 50%) | TP2: $115 (close 50%)
The indicator calculates the exact position size, profit at each target, and margin allocation in real time, with all metrics displayed on the chart.
✅ Why Traders Love It
• Precision: No more manual calculations or guesswork
• Versatility: Works on all crypto pairs (BTC, ETH, altcoins, etc.)
• Flexibility: Perfect for scalping, swing trading, or futures strategies
• Universal: Compatible with all timeframes
• Transparency: Fully manual, with clear and reliable outputs
🧩 Built by Trade City Pro (TCP)
Developed by TCP, a trusted name in trading tools, used by over 150,000 traders worldwide. This indicator is coded in Pine Script v5, ensuring compatibility with TradingView’s platform.
🧾 Final Notes
• No Auto-Trading: This is a manual tool for disciplined traders
• No Repainting: All calculations are accurate and non-repainting
• Tested: Rigorously validated across major crypto pairs
• Publish-Ready: Built for seamless use on TradingView
🔗 Resources
• Money Management Tutorial: Learn the fundamentals of capital management with our detailed guide: TradeCityPro Academy: Money Management
• TradingView Profile: Explore more tools by TCP on TradingView
Order Block with BoSHere’s a professional and concise description you can use for publishing your **TradingView script** titled **"Order Block with BoS"**:
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### 📌 **Description for TradingView Publication:**
**"Order Block with Break of Structure (BoS)"** is a powerful price action-based indicator designed to identify potential reversal zones and momentum shifts using **Order Block** detection combined with **Break of Structure (BoS)** confirmation.
### 🔍 **Key Features:**
* **Order Block Detection**: Highlights bullish and bearish order blocks using precise candle structure logic.
* **Break of Structure (BoS)**: Confirms structural breaks above swing highs or below swing lows to validate potential trend continuation or reversal.
* **Dynamic ATR Filter**: Uses a 14-period ATR with dynamic thresholds to confirm significant moves, filtering out weak breakouts.
* **Visual Aids**:
* Color-coded **boxes** to mark detected Order Blocks.
* **Arrows** at BoS confirmation points when ATR confirms strong momentum.
* Optional **dashed BoS lines** to show where price broke structure.
### ⚙️ **Customizable Inputs**:
* `Swing Length`: Defines the sensitivity of swing high/low detection.
* `Show Break of Structure`: Toggle on/off BoS confirmation lines.
* `Candle Lookback`: Number of historical candles to consider.
This indicator is ideal for traders who incorporate **smart money concepts**, **market structure analysis**, or **institutional order flow** strategies.
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Would you like me to help write the **strategy** version of this or translate the description into another language for international audiences?
Hybrid: RSI + Breakout + DashboardHybrid RSI + Breakout Strategy
Adaptive trading system that switches modes based on market regime:
Ranging: Buys when RSI < 30 and sells when RSI > 70.
Trending: Enters momentum breakouts only in the direction of the 200-EMA bias, with ADX confirming trend strength.
Risk Management: Trailing stop locks profits and caps drawdown.
Optimized for BTC, ETH, and SOL on 1 h–1 D charts; back-tested from 2017 onward. Educational use only—run your own tests before deploying live funds.
cc AJGB Candle Range Finder with TableOverview:
The "cc AJGB Candle Range Finder with Table" is a versatile Pine Script indicator designed to identify and visualize price ranges within the 1 minute charts based on UTC+2 Time Zone. Unlike traditional range indicators, it offers three unique calculation methods to define ranges based on minute and hour interactions, displays ranges as boxes with labeled point values, and summarizes average range sizes in a customizable table. This tool is ideal for analyzing price ranges of specific time based ranges.
Features:
Customizable Time Range: Users specify a start and end minute (0-59) to define the range period (e.g., 29th to 35th minute).
Three Calculation Methods:
Minute Only: Uses the minute of each bar to identify ranges (e.g., matches user-specified minutes).
Minute - Hour: Adjusts the minute by subtracting the hour, allowing for dynamic range detection across hourly cycles.
Minute + Hour: Combines minute and hour values for a unique range calculation, useful for specific intraday patterns.
Visual Output: Draws boxes around detected ranges, with labels showing the start/end minutes and range size in points.
Summary Table: Displays the average range size (in points) for each method, with customizable position, colors, and text size.
How It Works:
The indicator evaluates each bar’s timestamp in (UTC+2 ONLY) to match user-specified minutes using one or more selected methods. When a start minute is detected, it tracks the high and low prices until the end minute, drawing a box to highlight the range and labeling it with the range size in points. A table summarizes the average range size for each method, helping traders assess typical price movements during the specified period.
Market Analysis: Compare range sizes across different methods to understand intraday volatility patterns.
Settings Customization: Adjust colors, table position, and label sizes to suit your chart preferences.
Settings:
Range to Find: Set start and end minutes.
Range Selection: Enable/disable each method and customize colors.
Range Label Size: Choose label size (Tiny to Huge).
Table Settings: Configure table position (Top, Bottom, Left, Right), sub-position, text size, and colors.
Notes:
Only works on 1 minute charts
The indicator works best using Start Times that are lower than the End Times.
Ensure the chart is set to UTC+2 Time Zone for accurate range detection.
Why It’s Unique:
Unlike standard range indicators that focus on sessions or fixed periods, this tool allows precise minute-based range detection with three distinct calculation methods, offering flexibility for data gathering. The interactive table provides quick insights into average range sizes.
ADX and DI - Trader FelipeADX and DI - Trader Felipe
This indicator combines the Average Directional Index (ADX) and the Directional Indicators (DI+ and DI-) to help traders assess market trends and their strength. It is designed to provide a clear view of whether the market is in a trending phase (either bullish or bearish) and helps identify potential entry and exit points.
What is ADX and DI?
DI+ (Green Line):
DI+ measures the strength of upward (bullish) price movements. When DI+ is above DI-, it signals that the market is experiencing upward momentum.
DI- (Red Line):
DI- measures the strength of downward (bearish) price movements. When DI- is above DI+, it suggests that the market is in a bearish phase, with downward momentum.
ADX (Blue Line):
ADX quantifies the strength of the trend, irrespective of whether it is bullish or bearish. The higher the ADX, the stronger the trend:
ADX > 20: Indicates a trending market (either up or down).
ADX < 20: Indicates a weak or sideways market with no clear trend.
Threshold Line (Gray Line):
This horizontal line, typically set at 20, represents the threshold for identifying whether the market is trending or not. If ADX is above 20, the market is considered to be in a trend. If ADX is below 20, it suggests that the market is not trending and is likely in a consolidation phase.
Summary of How to Use the Indicator:
Trend Confirmation: Use ADX > 20 to confirm a trending market. If ADX is below 20, avoid trading.
Long Entry: Enter a long position when DI+ > DI- and ADX > 20.
Short Entry: Enter a short position when DI- > DI+ and ADX > 20.
Avoid Sideways Markets: Do not trade when ADX is below 20. Look for other strategies for consolidation phases.
Exit Strategy: Exit the trade if ADX starts to decline or if the DI lines cross in the opposite direction.
Combine with Other Indicators: Use additional indicators like RSI, moving averages, or support/resistance to filter and confirm signals.
Yearly History Calendar-Aligned Price up to 10 Years)Overview
This indicator helps traders compare historical price patterns from the past 10 calendar years with the current price action. It overlays translucent lines (polylines) for each year’s price data on the same calendar dates, providing a visual reference for recurring trends. A dynamic table at the top of the chart summarizes the active years, their price sources, and history retention settings.
Key Features
Historical Projections
Displays price data from the last 10 years (e.g., January 5, 2023 vs. January 5, 2024).
Price Source Selection
Choose from Open, Low, High, Close, or HL2 ((High + Low)/2) for historical alignment.
The selected source is shown in the legend table.
Bulk Control Toggles
Show All Years : Display all 10 years simultaneously.
Keep History for All : Preserve historical lines on year transitions.
Hide History for All : Automatically delete old lines to update with current data.
Individual Year Settings
Toggle visibility for each year (-1 to -10) independently.
Customize color and line width for each year.
Control whether to keep or delete historical lines for specific years.
Visual Alignment Aids
Vertical lines mark yearly transitions for reference.
Polylines are semi-transparent for clarity.
Dynamic Legend Table
Shows active years, their price sources, and history status (On/Off).
Updates automatically when settings change.
How to Use
Configure Settings
Projection Years : Select how many years to display (1–10).
Price Source : Choose Open, Low, High, Close, or HL2 for historical alignment.
History Precision : Set granularity (Daily, 60m, or 15m).
Daily (D) is recommended for long-term analysis (covers 10 years).
60m/15m provides finer precision but may only cover 1–3 years due to data limits.
Adjust Visibility & History
Show Year -X : Enable/disable specific years for comparison.
Keep History for Year -X : Choose whether to retain historical lines or delete them on new year transitions.
Bulk Controls
Show All Years : Display all 10 years at once (overrides individual toggles).
Keep History for All / Hide History for All : Globally enable/disable history retention for all years.
Customize Appearance
Line Width : Adjust polyline thickness for better visibility.
Colors : Assign unique colors to each year for easy identification.
Interpret the Legend Table
The table shows:
Year : Label (e.g., "Year -1").
Source : The selected price type (e.g., "Close", "HL2").
Keep History : Indicates whether lines are preserved (On) or deleted (Off).
Tips for Optimal Use
Use Daily Timeframes for Long-Term Analysis :
Daily (1D) allows 10+ years of data. Smaller timeframes (60m/15m) may have limited historical coverage.
Compare Recurring Patterns :
Look for overlaps between historical polylines and current price to identify potential support/resistance levels.
Customize Colors & Widths :
Use contrasting colors for years you want to highlight. Adjust line widths to avoid clutter.
Leverage Global Toggles :
Enable Show All Years for a quick overview. Use Keep History for All to maintain continuity across transitions.
Example Workflow
Set Up :
Select Projection Years = 5.
Choose Price Source = Close.
Set History Precision = 1D for long-term data.
Customize :
Enable Show Year -1 to Show Year -5.
Assign distinct colors to each year.
Disable Keep History for All to ensure lines update on year transitions.
Analyze :
Observe how the 2023 close prices align with 2024’s price action.
Use vertical lines to identify yearly boundaries.
Common Questions
Why are some years missing?
Ensure the chart has sufficient historical data (e.g., daily charts cover 10 years, 60m/15m may only cover 1–3 years).
How do I update the data?
Adjust the Price Source or toggle years/history settings. The legend table updates automatically.
Rocky's Dynamic DikFat Supply & Demand ZonesDynamic Supply & Demand Zones
Overview
The Dynamic Supply & Demand Zones indicator identifies key supply and demand levels on your chart by detecting pivot highs and lows. It draws customizable boxes around these zones, helping traders visualize areas where price may react. With flexible display options and dynamic box behavior, this tool is designed to assist in identifying potential support and resistance levels for various trading strategies.
Key Features
Pivot-Based Zones: Automatically detects supply (resistance) and demand (support) zones using pivot highs and lows on the chart’s timeframe.
Dynamic Box Sizing: Boxes shrink when price enters them, reflecting reduced zone strength, and stop adjusting once price fully crosses through.
Customizable Display: Choose to show current-day boxes, historical boxes, or all boxes, with an option to update past box colors dynamically.
Session-Based Extension: Boxes can extend to the current bar or stop at 4:00 PM of the creation day’s 9:30 AM–4:00 PM trading session (ideal for stock markets).
Color Coding: Borders change color based on price position:
Green for demand zones (price above the box).
Red for supply zones (price below the box).
White for neutral zones (price inside the box).
User-Friendly Inputs: Adjust pivot lookback periods, box visibility, extension behavior, and colors via intuitive input settings.
How It Works
Zone Detection: The indicator uses pivot highs and lows to define supply and demand zones, plotting boxes between these levels.
Box Behavior:
Boxes are created when pivot highs and lows are confirmed, with no overlap with the previous box.
When price enters a box, it shrinks to reflect interaction, stopping once price exits completely.
Boxes can extend to the current bar or end at 4:00 PM of the creation day (or next trading day if created after 4:00 PM or on weekends).
Display Options:
Current Only: Shows boxes created on the current day.
Historical Only: Shows boxes from previous days, with optional color updates.
All Boxes: Shows all boxes, with an option to hide historical box color updates.
Performance: Limits the number of boxes to 200 to ensure smooth performance, removing older boxes as needed.
Inputs
Pivot Look Right/Left: Set the number of bars (default: 2) to confirm pivot highs and lows.
What Boxes to Show: Select Current Only, Historical Only, or All Boxes (default: Current Only).
Boxes On/Off: Toggle box visibility (default: on).
Extend Boxes to Current Bar: Choose whether boxes extend to the current bar or stop at 4:00 PM (default: off, stops at 4:00 PM).
Update Past Box Colors: Enable/disable color updates for historical boxes (default: on).
Demand/Supply/Neutral Box Color: Customize border colors (default: green, red, white).
How to Use
Add the indicator to your chart.
Adjust inputs to match your trading style (e.g., pivot lookback, box extension, colors).
Use the boxes to identify potential support (demand) and resistance (supply) zones:
Green-bordered boxes (price above) may act as support.
Red-bordered boxes (price below) may act as resistance.
White-bordered boxes (price inside) indicate active price interaction.
Combine with other analysis tools (e.g., trendlines, indicators) to confirm trade setups.
Monitor box shrinking to gauge zone strength and watch for breakouts when price fully crosses a box.
Understanding Supply and Demand in Stock Trading
In stock trading, supply and demand are fundamental forces driving price movements. Demand refers to the willingness of buyers to purchase a stock at a given price, often creating support levels where buying interest prevents further price declines. Supply represents the willingness of sellers to offload a stock, forming resistance levels where selling pressure halts price increases. These zones are critical because they highlight areas where significant buying or selling activity has occurred, influencing future price behavior.
The importance of supply and demand lies in their ability to reveal where institutional traders, with large orders, have entered or exited the market. Demand zones, often seen at pivot lows, indicate strong buying interest and potential areas for price reversals or bounces. Supply zones, typically at pivot highs, signal heavy selling and possible reversal points for downward moves. By identifying these zones, traders can anticipate where price is likely to stall, reverse, or break out, enabling better entry and exit decisions. This indicator visualizes these zones as dynamic boxes, making it easier to spot high-probability trading opportunities while emphasizing the core market dynamics of supply and demand.
Feedback
This indicator is designed to help traders visualize supply and demand zones effectively. If you have suggestions for improvements, please share your feedback in the comments!
Anomaly Counter-Trend StrategyA mean-reversion style strategy that automatically spots unusually large price moves over a configurable lookback period and takes the opposite side, with full risk-management, commission and slippage modeling—built in Pine Script® v6.
🔎 Overview
ACTS monitors the percent-change over the past N minutes and, when that move exceeds your chosen threshold, enters a counter-trend position (short on a strong rise; long on a sharp fall). It’s ideal for markets that often “overshoot” and snap back, and can be applied on any symbol or timeframe.
⚙️ Key Features
Anomaly Detection: Detect abnormal price swings based on a user-defined % change over a lookback period.
Counter-Trend Entries: Auto-enter short on rise anomalies, long on fall anomalies (with seamless flat↔reverse transitions).
Risk Management: Configurable stop-loss and take-profit in ticks per trade.
Realistic Modeling: Simulates commissions (0.05 % default), slippage (2 ticks), and percent-of-equity sizing.
Immediate Bar-Close Execution: Orders processed on bar close for faster fills.
Visual Aids: Optional on-chart BUY/SELL triangles and background highlights during anomaly periods.
⚙️ Inputs
Input Default Description
Percentage Threshold (%) 2.00 Min % move over lookback to trigger an anomaly.
Lookback Period (Minutes) 15 Number of minutes over which to measure change.
Stop Loss (Ticks) 100 Distance from entry for stop-loss exit.
Take Profit (Ticks) 200 Distance from entry for take-profit exit.
Plot Trade Signal Shapes (on/off) true Show BUY/SELL triangles on chart.
Highlight Anomaly Background true Shade background during anomaly bars.
📊 How to Use
Add to Chart: Apply the script to any ticker & timeframe.
Tune: Adjust your percentage threshold and lookback to match each instrument’s volatility.
Review Backtest: Check built-in strategy performance (drawdown, Sharpe, etc.) under the Strategy Tester tab.
Go Live: Once optimized, link to alerts or your trade execution system.
⚠️ Disclaimer
This script is provided “as-is” for educational purposes and backtesting only. Past performance does not guarantee future results. Always backtest thoroughly, manage your own risk, and consider market conditions before live trading.
Enjoy experimenting—and may your counter-trend entries catch the next big snapback!
Liquidity Sweep DetectorThe Liquidity Sweep Detector represents a technical analysis tool specifically designed to identify market microstructure patterns typically associated with institutional trading activity. According to Harris (2003), institutional traders frequently employ tactics where they momentarily break through price levels to trigger stop orders before redirecting the market in the opposite direction. This phenomenon, commonly referred to as "stop hunting" or "liquidity sweeping," constitutes a significant aspect of institutional order flow analysis (Osler, 2003). The current implementation provides retail traders with a means to identify these patterns, potentially aligning their trading decisions with institutional movements rather than becoming victims of such strategies.
Osler's (2003) research documents how stop-loss orders tend to cluster around significant price levels, creating concentrations of liquidity. Taylor (2005) argues that sophisticated institutional participants systematically exploit these liquidity clusters by inducing price movements that trigger these orders, subsequently profiting from the ensuing price reaction. The algorithmic detection of such patterns involves several key processes. First, the indicator identifies swing points—local maxima and minima—through comparison with historical price data within a definable lookback period. These swing points correspond to what Bulkowski (2011) describes as "significant pivot points" that frequently serve as liquidity zones where stop orders accumulate.
The core detection algorithm utilizes a multi-stage process to identify potential sweeps. For high sweeps, it monitors when price exceeds a previous swing high by a specified threshold percentage, followed by a bearish candle that closes below the original swing high level. Conversely, for low sweeps, it detects when price drops below a previous swing low by the threshold percentage, followed by a bullish candle closing above the original swing low. As noted by Lo and MacKinlay (2011), these price patterns often emerge when large institutional players attempt to capture liquidity before initiating significant directional moves.
The indicator maintains historical arrays of detected sweep events with their corresponding timestamps, enabling temporal analysis of market behavior following such events. Visual elements include horizontal lines marking sweep levels, background color highlighting for sweep events, and an information table displaying active sweeps with their corresponding price levels and elapsed time since detection. This visualization approach allows traders to quickly identify potential institutional activity without requiring complex interpretation of raw price data.
Parameter customization includes adjustable lookback periods for swing point identification, sweep threshold percentages for signal sensitivity, and display duration settings. These parameters allow traders to adapt the indicator to various market conditions and timeframes, as markets demonstrate different liquidity characteristics across instruments and periods (Madhavan, 2000).
Empirical studies by Easley et al. (2012) suggest that retail traders who successfully identify and act upon institutional liquidity sweeps may achieve superior risk-adjusted returns compared to conventional technical analysis approaches. However, as cautioned by Chordia et al. (2008), such patterns should be considered within broader market context rather than in isolation, as their predictive value varies significantly with overall market volatility and liquidity conditions.
References:
Bulkowski, T. (2011). Encyclopedia of Chart Patterns (2nd ed.). John Wiley & Sons.
Chordia, T., Roll, R., & Subrahmanyam, A. (2008). Liquidity and market efficiency. Journal of Financial Economics, 87(2), 249-268.
Easley, D., López de Prado, M., & O'Hara, M. (2012). Flow Toxicity and Liquidity in a High-frequency World. The Review of Financial Studies, 25(5), 1457-1493.
Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.
Lo, A. W., & MacKinlay, A. C. (2011). A Non-Random Walk Down Wall Street. Princeton University Press.
Madhavan, A. (2000). Market microstructure: A survey. Journal of Financial Markets, 3(3), 205-258.
Osler, C. L. (2003). Currency Orders and Exchange Rate Dynamics: An Explanation for the Predictive Success of Technical Analysis. Journal of Finance, 58(5), 1791-1820.
Taylor, M. P. (2005). Official Foreign Exchange Intervention as a Coordinating Signal in the Dollar-Yen Market. Pacific Economic Review, 10(1), 73-82.
CRT Finder (WanHakimFX)📈 Liquidity Grab Indicator with MTF Confluence & Alerts
🔍 Overview:
The Liquidity Grab Indicator is designed to detect precise moments when price sweeps liquidity — either by wicking below recent lows (bullish LQH) or above recent highs (bearish LQL) — followed by a clear rejection. It combines this logic with multi-timeframe confirmation and trend filters, making it a powerful tool for identifying high-probability reversal setups.
⚙️ How It Works:
✅ Liquidity Sweep Logic (LQH / LQL)
Bullish (LQH):
Current candle wicks below the previous low
Closes above the previous candle body
Confirms potential bullish reversal
Bearish (LQL):
Current candle wicks above the previous high
Closes below the previous candle body
Confirms potential bearish reversal
✅ Additional Conditions:
Must occur during London or New York sessions.
Requires trend confluence:
LQH = Price must be above SMMA 60/100/200
LQL = Price must be below SMMA 60/100/200
🧠 Multi-Timeframe Confluence:
The indicator scans for LQH/LQL sweeps across:
Daily
4H
1H
30M
15M
If a sweep occurs on any of these timeframes, an alert is triggered and a triangle marker appears on the chart for real-time visual confluence.
📊 Visual Features:
Green/Red labels for active timeframe sweeps.
Dotted wick lines to show liquidity zones from the previous candle.
Colored triangle markers for MTF sweep alerts.
🛠 Strategy Usage:
This indicator is best used as a trigger tool in a confluence-based strategy:
Use higher-timeframe MTF LQH/LQL markers for directional bias.
Wait for matching sweep on your entry timeframe (e.g., M1/M5).
Enter on confirmation candle or break of structure.
Target imbalances, FVGs, or previous highs/lows.
Risk-managed entries using sweep candle's high/low as stop.
📢 Alerts:
✅ Bullish Sweep (LQH) on any timeframe
✅ Bearish Sweep (LQL) on any timeframe
Long Short dom📊 Long Short dom (VI+) — Custom Vortex Trend Strength Indicator
This indicator is a refined version of the Vortex Indicator (VI) designed to help traders identify trend direction, momentum dominance, and potential long/short opportunities based on VI+ and VI– dynamics.
🔍 What It Shows:
• VI+ (Green Line): Measures upward trend strength.
• VI– (Red Line): Measures downward trend strength.
• Histogram (optional): Displays the difference between VI+ and VI–, helping visualize which side is dominant.
• Background Coloring: Highlights bullish or bearish dominance zones.
• Zero Line: A visual baseline to enhance clarity.
• Highest/Lowest Active Lines: Real-time markers for the strongest directional signals.
⸻
🛠️ Inputs:
• Length: Vortex calculation period (default 14).
• Show Histogram: Enable/disable VI+–VI– difference bars.
• Show Trend Background: Toggle colored zones showing trend dominance.
• Show Below Zero: Decide whether to display values that fall below 0 (for advanced use).
⸻
📈 Strategy Insights:
• When VI+ crosses above VI–, it indicates potential long momentum.
• When VI+ crosses below VI–, it signals possible short pressure.
• The delta histogram (VI+ – VI–) helps you quickly see shifts in momentum strength.
• The background shading provides an intuitive visual cue to assess trend dominance at a glance.
⸻
🚨 Built-in Alerts:
• Bullish Cross: VI+ crosses above VI– → possible entry long.
• Bearish Cross: VI+ crosses below VI– → possible entry short.
⸻
✅ Ideal For:
• Trend-following strategies
• Identifying long/short bias
• Confirming entries/exits with momentum analysis
⸻
This tool gives you clean, real-time visual insight into trend strength and shift dynamics, empowering smarter trade decisions with clarity and confidence.
Extended Altman Z-Score ModelThe Extended Altman Z-Score Model represents a significant advancement in financial analysis and risk assessment, building upon the foundational work of Altman (1968) while incorporating contemporary data analytics approaches as proposed by Fung (2023). This sophisticated model enhances the traditional bankruptcy prediction framework by integrating additional financial metrics and modern analytical techniques, offering a more comprehensive approach to identifying financially distressed companies.
The model's architecture is built upon two distinct yet complementary scoring systems. The traditional Altman Z-Score components form the foundation, including Working Capital to Total Assets (X1), which measures a company's short-term liquidity and operational efficiency. Retained Earnings to Total Assets (X2) provides insight into the company's historical profitability and reinvestment capacity. EBIT to Total Assets (X3) evaluates operational efficiency and earning power, while Market Value of Equity to Total Liabilities (X4) assesses market perception and leverage. Sales to Total Assets (X5) measures asset utilization efficiency.
These traditional components are enhanced by extended metrics introduced by Fung (2023), which provide additional layers of financial analysis. The Cash Ratio (X6) offers insights into immediate liquidity and financial flexibility. Asset Composition (X7) evaluates the quality and efficiency of asset utilization, particularly in working capital management. The Debt Ratio (X8) provides a comprehensive view of financial leverage and long-term solvency, while the Net Profit Margin (X9) measures overall profitability and operational efficiency.
The scoring system employs a sophisticated formula that combines the traditional Z-Score with weighted additional metrics. The traditional Z-Score is calculated as 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5, while the extended components are weighted as follows: 0.5 * X6 + 0.3 * X7 - 0.4 * X8 + 0.6 * X9. This enhanced scoring mechanism provides a more nuanced assessment of a company's financial health, incorporating both traditional bankruptcy prediction metrics and modern financial analysis approaches.
The model categorizes companies into three distinct risk zones, each with specific implications for financial stability and required actions. The Safe Zone (Score > 3.0) indicates strong financial health, with low probability of financial distress and suitability for conservative investment strategies. The Grey Zone (Score between 1.8 and 3.0) suggests moderate risk, requiring careful monitoring and additional fundamental analysis. The Danger Zone (Score < 1.8) signals high risk of financial distress, necessitating immediate attention and potential risk mitigation strategies.
In practical application, the model requires systematic and regular monitoring. Users should track the Extended Score on a quarterly basis, monitoring changes in individual components and comparing results with industry benchmarks. Component analysis should be conducted separately, identifying specific areas of concern and tracking trends in individual metrics. The model's effectiveness is significantly enhanced when used in conjunction with other financial metrics and when considering industry-specific factors and macroeconomic conditions.
The technical implementation in Pine Script v6 provides real-time calculations of both traditional and extended scores, offering visual representation of risk zones, detailed component breakdowns, and warning signals for critical values. The indicator automatically updates with new financial data and provides clear visual cues for different risk levels, making it accessible to both technical and fundamental analysts.
However, as noted by Fung (2023), the model has certain limitations that users should consider. It may not fully account for industry-specific factors, requires regular updates of financial data, and should be used in conjunction with other analysis tools. The model's effectiveness can be enhanced by incorporating industry-specific benchmarks and considering macroeconomic factors that may affect financial performance.
References:
Altman, E.I. (1968) 'Financial ratios, discriminant analysis and the prediction of corporate bankruptcy', The Journal of Finance, 23(4), pp. 589-609.
Li, L., Wang, B., Wu, Y. and Yang, Q., 2020. Identifying poorly performing listed firms using data analytics. Journal of Business Research, 109, pp.1–12. doi.org
Quarterly Theory ICT 05 [TradingFinder] Doubling Theory Signals🔵 Introduction
Doubling Theory is an advanced approach to price action and market structure analysis that uniquely combines time-based analysis with key Smart Money concepts such as SMT (Smart Money Technique), SSMT (Sequential SMT), Liquidity Sweep, and the Quarterly Theory ICT.
By leveraging fractal time structures and precisely identifying liquidity zones, this method aims to reveal institutional activity specifically smart money entry and exit points hidden within price movements.
At its core, the market is divided into two structural phases: Doubling 1 and Doubling 2. Each phase contains four quarters (Q1 through Q4), which follow the logic of the Quarterly Theory: Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal.
These segments are anchored by the True Open, allowing for precise alignment with cyclical market behavior and providing a deeper structural interpretation of price action.
During Doubling 1, a Sequential SMT (SSMT) Divergence typically forms between two correlated assets. This time-structured divergence occurs between two swing points positioned in separate quarters (e.g., Q1 and Q2), where one asset breaks a significant low or high, while the second asset fails to confirm it. This lack of confirmation—especially when aligned with the Manipulation and Accumulation phases—often signals early smart money involvement.
Following this, the highest and lowest price points from Doubling 1 are designated as liquidity zones. As the market transitions into Doubling 2, it commonly returns to these zones in a calculated move known as a Liquidity Sweep—a sharp, engineered spike intended to trigger stop orders and pending positions. This sweep, often orchestrated by institutional players, facilitates entry into large positions with minimal slippage.
Bullish :
Bearish :
🔵 How to Use
Applying Doubling Theory requires a simultaneous understanding of temporal structure and inter-asset behavioral divergence. The method unfolds over two main phases—Doubling 1 and Doubling 2—each divided into four quarters (Q1 to Q4).
The first phase focuses on identifying a Sequential SMT (SSMT) divergence, which forms when two correlated assets (e.g., EURUSD and GBPUSD, or NQ and ES) react differently to key price levels across distinct quarters. For example, one asset may break a previous low while the other maintains structure. This misalignment—especially in Q2, the Manipulation phase—often indicates early smart money accumulation or distribution.
Once this divergence is observed, the extreme highs and lows of Doubling 1 are marked as liquidity zones. In Doubling 2, the market gravitates back toward these zones, executing a Liquidity Sweep.
This move is deliberate—designed to activate clustered stop-loss and pending orders and to exploit pockets of resting liquidity. These sweeps are typically driven by institutional forces looking to absorb liquidity and position themselves ahead of the next major price move.
The key to execution lies in the fact that, during the sweep in Doubling 2, a classic SMT divergence should also appear between the two assets. This indicates a weakening of the previous trend and adds an extra layer of confirmation.
🟣 Bullish Doubling Theory
In the bullish scenario, Doubling 1 begins with a bullish SSMT divergence, where one asset forms a lower low while the other maintains its structure. This divergence signals weakening bearish momentum and possible smart money accumulation. In Doubling 2, the market returns to the previous low and sweeps the liquidity zone—breaking below it on one asset, while the second fails to confirm, forming a bullish SMT divergence.
f this move is followed by a bullish PSP and a clear market structure break (MSB), a long entry is triggered. The stop-loss is placed just below the swept liquidity zone, while the target is set in the premium zone, anticipating a move driven by institutional buyers.
🟣 Bearish Doubling Theory
The bearish scenario follows the same structure in reverse. In Doubling 1, a bearish SSMT divergence occurs when one asset prints a higher high while the other fails to do so. This suggests distribution and weakening buying pressure. Then, in Doubling 2, the market returns to the previous high and executes a liquidity sweep, targeting trapped buyers.
A bearish SMT divergence appears, confirming the move, followed by a bearish PSP on the lower timeframe. A short position is initiated after a confirmed MSB, with the stop-loss placed
🔵 Settings
⚙️ Logical Settings
Quarterly Cycles Type : Select the time segmentation method for SMT analysis.
Available modes include : Yearly, Monthly, Weekly, Daily, 90 Minute, and Micro.
These define how the indicator divides market time into Q1–Q4 cycles.
Symbol : Choose the secondary asset to compare with the main chart asset (e.g., XAUUSD, US100, GBPUSD).
Pivot Period : Sets the sensitivity of the pivot detection algorithm. A smaller value increases responsiveness to price swings.
Pivot Sync Threshold : The maximum allowed difference (in bars) between pivots of the two assets for them to be compared.
Validity Pivot Length : Defines the time window (in bars) during which a divergence remains valid before it's considered outdated.
🎨 Display Settings
Show Cycle :Toggles the visual display of the current Quarter (Q1 to Q4) based on the selected time segmentation
Show Cycle Label : Shows the name (e.g., "Q2") of each detected Quarter on the chart.
Show Labels : Displays dynamic labels (e.g., “Q2”, “Bullish SMT”, “Sweep”) at relevant points.
Show Lines : Draws connection lines between key pivot or divergence points.
Color Settings : Allows customization of colors for bullish and bearish elements (lines, labels, and shapes)
🔔 Alert Settings
Alert Name : Custom name for the alert messages (used in TradingView’s alert system).
Message Frequenc y:
All : Every signal triggers an alert.
Once Per Bar : Alerts once per bar regardless of how many signals occur.
Per Bar Close : Only triggers when the bar closes and the signal still exists.
Time Zone Display : Choose the time zone in which alert timestamps are displayed (e.g., UTC).
Bullish SMT Divergence Alert : Enable/disable alerts specifically for bullish signals.
Bearish SMT Divergence Alert : Enable/disable alerts specifically for bearish signals
🔵 Conclusion
Doubling Theory is a powerful and structured framework within the realm of Smart Money Concepts and ICT methodology, enabling traders to detect high-probability reversal points with precision. By integrating SSMT, SMT, Liquidity Sweeps, and the Quarterly Theory into a unified system, this approach shifts the focus from reactive trading to anticipatory analysis—anchored in time, structure, and liquidity.
What makes Doubling Theory stand out is its logical synergy of time cycles, behavioral divergence, liquidity targeting, and institutional confirmation. In both bullish and bearish scenarios, it provides clearly defined entry and exit strategies, allowing traders to engage the market with confidence, controlled risk, and deeper insight into the mechanics of price manipulation and smart money footprints.
Thai Gold BahtIndicator Name: Thai Gold Baht
Short Title: Thai Gold Baht
Purpose
This indicator calculates and visualizes the real-time price of 1 Thai Gold Baht (15.244 grams) based on the global gold price ( XAU/USD ) and the USD/THB exchange rate .
Users can customize gold weight and purity to simulate the local Thai gold market price.
What it does
Retrieves live gold price per troy ounce in USD
Retrieves current USD to Thai Baht exchange rate
Converts the value using user-defined weight and purity
Displays result as a real-time chart
Shows calculation details in the Data Window
Ideal for
Traders tracking Thai gold based on international prices
Analysts comparing local and global bullion markets
Anyone needing a configurable, transparent gold price conversion
Pine Script Functionality
// Uses XAU/USD and USD/THB as inputs
// Calculates 1 Baht Gold (96.5% default purity)
// Outputs the value in THB as a chart line
ชื่ออินดิเคเตอร์: Thai Gold Baht
ชื่อย่อ: Thai Gold Baht
วัตถุประสงค์
อินดิเคเตอร์นี้ใช้คำนวณและแสดงราคาทองคำไทย 1 บาท (15.244 กรัม) แบบเรียลไทม์
โดยอ้างอิงจากราคาทองคำในตลาดโลก ( XAU/USD ) และอัตราแลกเปลี่ยน USD/THB
ผู้ใช้สามารถกำหนดน้ำหนักทองและความบริสุทธิ์เองได้ เพื่อจำลองราคาทองคำในประเทศไทยอย่างแม่นยำ
สิ่งที่อินดิเคเตอร์นี้ทำ
ดึงราคาทองคำแบบเรียลไทม์ต่อทรอยออนซ์ในสกุลเงิน USD
ดึงอัตราแลกเปลี่ยน USD → THB แบบเรียลไทม์
คำนวณราคาจากน้ำหนักและเปอร์เซ็นต์ความบริสุทธิ์ที่ผู้ใช้กำหนด
แสดงผลลัพธ์เป็นกราฟแบบเรียลไทม์ในหน่วยบาทไทย
แสดงรายละเอียดการคำนวณในหน้าต่าง Data Window ของ TradingView
เหมาะสำหรับ
นักเทรดที่ต้องการติดตามราคาทองคำไทยจากราคาทองคำตลาดโลก
นักวิเคราะห์ที่เปรียบเทียบราคาทองคำในประเทศและต่างประเทศ
ผู้ใช้งานที่ต้องการการแปลงราคาทองคำระหว่างประเทศให้โปร่งใสและปรับแต่งได้
การทำงานของ Pine Script
// ใช้ข้อมูล XAU/USD และ USD/THB เป็นอินพุต
// คำนวณราคาทองคำไทย 1 บาท (ความบริสุทธิ์เริ่มต้นที่ 96.5%)
// แสดงผลเป็นเส้นกราฟของราคาทองคำในหน่วยบาทไทย
