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!
Educational
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!
Grid + Trade Annotations & Liquidations## Introducing the “Grid + Trade Annotations & Liquidations” Pine Script Strategy
Imagine you could overlay a perfectly-spaced price grid on your favorite chart, backtest a simple moving-average crossover, see exactly where trades would have fired off in the past—and even know at what price you’d have been liquidated if you were running at 10× leverage. That’s exactly what this all-in-one TradingView **Pine Script® v6** strategy delivers.
### Why you’ll love it
* **Visual clarity:** A fixed-interval horizontal grid, centered on each bar’s close, helps you instantly spot round-number levels (e.g. every \$0.50).
* **Trade annotations:** Every historical entry/exit is automatically marked with arrows and labels—no more scrolling through Trade History.
* **Liquidation lines:** For each entry, the script computes your theoretical liquidation price, based on your chosen leverage, and draws it as a dashed line.
* **Performance metrics:** Total return, maximum drawdown, Sharpe ratio, and win rate are calculated and displayed on-chart, so you don’t have to wrestle with spreadsheets.
---
## How it’s structured
The code lives in a single **strategy**—add it via **Pine Editor → New Strategy** and click **Add to Chart**. Internally, it’s broken into four main sections:
1. **Grid setup:**
* **Inputs:** `gridStep`, `aboveLines`, `belowLines`, `gridColor`, `gridStyle`, `gridWidth`.
* **Persistent array:** stores `line` objects so they survive bar updates.
* **Draw/update logic:** on each confirmed historical bar, the script either recreates all lines (when you change the count) or simply repositions them around the new close.
2. **Entry/exit logic & annotations:**
* **Example system:** 20-period vs. 50-period simple moving-average crossover.
* **Labels & shapes:**
* Green triangles for long entries/exits, red for short.
* A “Long Liq:” or “Short Liq:” label at the point of entry.
3. **Liquidation calculations:**
* **Formula:**
* Long: `P_liq = P_entry × (1 − 1⁄L)`
* Short: `P_liq = P_entry × (1 + 1⁄L)`
* Let the script draw a dashed red (for longs) or dashed green (for shorts) line at each `P_liq`.
4. **Performance metrics:**
* **Built-ins:**
* `strategy.netprofit_percent` → total return %
* `strategy.max_drawdown_percent` → max drawdown %
* `strategy.wintrades` / `strategy.closedtrades` → win rate %
* **Sharpe ratio:** manually computed from per-bar returns, assuming a user-defined risk-free rate and bars-per-year count.
---
## Using & customizing the strategy
1. **Add to your chart.**
* Copy the full script into Pine Editor, select **Strategy**, and hit **Add to Chart**.
2. **Tune your grid.**
* **`Grid Interval ($)`**: e.g. `0.50` for \$0.50 steps.
* **`Lines Above`/`Below`**: how many lines to show on each side of the current price.
* **`Grid Style`**: choose Solid, Dashed, or Dotted; set line width and opacity via the color picker.
3. **Adjust your trading logic.**
* Out of the box, the script uses SMA(20) vs. SMA(50). Swap in any `ta.*` indicator calls you like.
4. **Set leverage & capital.**
* **`Leverage`**: affects the liquidation price.
* **`Initial Capital`** and **`Order Size`**: the strategy uses 100% of equity per trade by default—you can change that in the `strategy()` call.
5. **Review performance.**
* Metrics show up in the Strategy Tester and on-chart label.
* If you want data in the Data Window, expand the script’s name to see the hidden plots for return, drawdown, Sharpe, and win rate.
---
## Behind the code
Below is a high-level walkthrough of the key snippets:
```pinescript
//@version=6
strategy("Grid + Annotations & Liquidations", overlay=true,
initial_capital=100000, default_qty_type=strategy.percent_of_equity,
default_qty_value=100)
// ─ Grid inputs & style mapping ────────────────────────────────────────
gridStep = input.float(0.50, "Grid Interval ($)", minval=0)
aboveLines = input.int(5, "Lines Above", minval=0)
belowLines = input.int(5, "Lines Below", minval=0)
gridColor = input.color(color.new(color.gray, 80), "Grid Color")
gridStyle = input.string("Dashed", "Grid Style", options= )
gridWidth = input.int(1, "Grid Line Width", minval=1, maxval=5)
gridStyleConst = gridStyle == "Solid" ? line.style_solid :
gridStyle == "Dotted"? line.style_dotted :
line.style_dashed
```
* We map a simple string choice into Pine’s `line.style_*` constants.
* `gridStep` drives the spacing in dollars.
```pinescript
// Persist & update lines only when needed
var line gridLines = array.new_line()
if barstate.islastconfirmedhistory
total = aboveLines + belowLines + 1
if array.size(gridLines) != total
// delete & recreate
…
else
// only reposition
…
```
* Wrapping all drawing in `barstate.islastconfirmedhistory` avoids repaint issues.
* The script deletes and rebuilds lines only when you change `aboveLines`/`belowLines`, otherwise it simply moves them.
```pinescript
// MA crossover logic & liquidation labels
fast = ta.sma(close, 20)
slow = ta.sma(close, 50)
if ta.crossover(fast, slow)
strategy.entry("Long", strategy.long)
liq = close * (1 - 1.0 / leverage)
label.new(bar_index, low, text="Long Liq: " + str.tostring(liq))
line.new(…, y1=liq, y2=liq, color=color.red, style=line.style_dashed)
```
* Entries trigger both the `strategy.entry` call and a pair of visual cues: a label and a dashed line at the computed liquidation price.
```pinescript
// Performance metrics: draw from built-ins + manual Sharpe
totalRet = strategy.netprofit_percent
maxDD = strategy.max_drawdown_percent
winRate = strategy.closedtrades > 0 ?
(strategy.wintrades / strategy.closedtrades)*100 : 0
// Manual Sharpe calculation
… accumulate per-bar % returns … compute mean, stddev … apply formula …
```
* TradingView gives us return, drawdown, and trade counts out of the box.
* We calculate Sharpe ourselves so you can adjust the risk-free rate and periods per year.
---
## Wrapping up
This one-file strategy is designed to be both **educational** and **practical**:
* **Learn by reading:** every section is commented so you can see how Pine v6 handles arrays, loops, strategy functions, and labels.
* **Customize for your edge:** swap in your own indicators, change leverage, or hook up alerts.
* **Publish & share:** drop it into your public repo with this story as your README, and fellow traders will know exactly how to use it.
Feel free to fork, file issues, or submit pull requests. Happy charting—and may your grid lines always align!
Moving Averages with ADR%/ATR/52W TableOption to select Moving Averages as per different time frames.
ADR%: It should be above 5% as it is a sign of strength and stop loss should be lower than the ADR%. It should be calculated for last 20 Days.
ATR%: It is calculated as per the previous 14 Candles.
Minervini’s 52-Week High and Low Principles:
52-Week High:
Key Principle: Minervini prefers stocks trading near or at their 52-week highs, as this indicates strong bullish momentum and institutional buying interest. A stock at or close to its 52-week high is often breaking out of consolidation or resistance, signaling potential for further upside.
Criteria:
The stock should ideally be within 25% of its 52-week high (i.e., no more than 25% below the high). This is considered the “pivot point” or “buy zone” where the stock is still in a strong uptrend.
A breakout above the 52-week high, especially on high volume, is a bullish signal, often marking the start of a new uptrend.
Rationale: Stocks near their 52-week highs are less likely to face overhead resistance (supply from previous buyers at higher prices) and are attractive to momentum traders and institutions.
52-Week Low:
Key Principle: Minervini advises avoiding stocks trading close to their 52-week lows, as they often indicate weakness, lack of demand, or bearish sentiment. Instead, he looks for stocks that are significantly above their 52-week lows, demonstrating strength and recovery.
Criteria:
The stock should be at least 30% above its 52-week low to confirm it has moved away from a downtrend and is showing relative strength.
Stocks too close to their 52-week lows are considered risky, as they may be in a prolonged downtrend or lack institutional support.
Rationale: A stock well above its 52-week low has likely absorbed selling pressure and is attracting buyers, indicating a healthier trend and potential for further gains.
Application in Trading:
Stock Selection: Minervini uses these criteria as part of his SEPA (Specific Entry Point Analysis) methodology to filter stocks. Stocks meeting the 52-week high/low criteria are more likely to be in a “Stage 2” uptrend (per his adaptation of Stan Weinstein’s stage analysis).
Breakout Strategy: He focuses on buying stocks breaking out from consolidation patterns (e.g., volatility contractions, cup-and-handle) near their 52-week highs, ideally with strong volume and tight price action.
Risk Management: Stocks too far from their 52-week highs or too close to their 52-week lows may have higher risk, either due to overextension or lack of momentum.
Dynamic Color Logic in Your Script:
Based on our previous discussions, your Pine Script incorporates Minervini’s criteria for dynamic coloring in the ADR%/ATR/52W Table:
Below 52-Week High: Text turns green if the stock is within -25% to 0% of the 52-week high (i.e., high_52w_dist >= -25 and high_52w_dist <= 0), highlighting stocks in the bullish “buy zone.”
Above 52-Week Low: Text turns green if the stock is ≥30% above the 52-week low (i.e., low_52w_dist >= 30), indicating strength and distance from weakness.
These thresholds align with Minervini’s principles to visually flag stocks meeting his momentum criteria.
Integration with Your Pine Script:
Your script already implements Minervini’s 52-week high/low principles in the table’s dynamic color logic. Here’s how it reflects his strategy:
Below 52-Week High (high_52w_dist): The condition high_52w_dist >= -25 and high_52w_dist <= 0 ensures the stock is within 25% of its 52-week high, marking it as a potential candidate for a breakout or continuation trade.
Above 52-Week Low (low_52w_dist): The condition low_52w_dist >= 30 confirms the stock is at least 30% above its 52-week low, filtering out weak stocks and highlighting those with bullish strength.
The table displays these metrics on intraday and daily charts, using daily data via request.security for accurate calculations, which supports Minervini’s focus on daily price action for entry points.
ADR% / ATR / 52W Range TableIndicator to know the -
Average Dynamic Range: Usual parameter is of 20 Days and ideally it should be above 5%. Stop Loss should not be more than the ADR.
ATR: It is measured for 14 days.
Below 52-Week High: As per Mark Minervini, stock should be in uptrend and less than 25% below its 52-Weeks High.
Above 52-Low: Stock should be minimum 30% above its 52-Week Low. The Higher the better.
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.
Daily EMA Crossover with Previous High/Low Breakgive buy and sell signal arrow sign when below condition met on candlestick chart of trading view. show buy arrow signal when 9 EMA cross above 20EMA upside and same time broken 1 previous day ago high price. show see arrow signal when 9 EMA cross below 20EMA upside and same time broken 1 previous day ago low price. Please create a pine code for trading view , Time frame should be 1 day char
Balance-Tilt Indicator – Micro Range Equilibrium Tool (V2)The Extreme Conditions Background Gradient Tint indicator is designed to visually highlight extreme conditions in the market by applying dynamic background color changes based on a specific score—referred to as the tilt_score. This indicator helps traders quickly identify potential critical points in price movement, which could indicate a turning point, overbought, or oversold conditions.
Key Features:
Dynamic Background Tint:
- The indicator changes the background color to provide a visual cue based on the value of the tilt_score:
Green Tint: When the tilt_score exceeds 0.85, a green background is applied, signaling a highly bullish condition. The opacity of the tint decreases as the score rises above 0.85, providing a gradient effect.
Red Tint: When the tilt_score falls below -0.85, a red background is applied, signaling a highly bearish condition. The opacity of the red tint decreases as the score becomes more negative, again creating a gradient effect.
Neutral Zone (No Tint): For values of the tilt_score between -0.85 and 0.85, no background tint is applied, indicating neutral or moderate market conditions.
- Opacity Adjustment Based on Score:
The opacity of the background color is dynamically adjusted depending on how extreme the tilt_score is. The further the score deviates from the neutral range (i.e., above 0.85 or below -0.85), the more intense the color becomes, making it easier for traders to spot extreme market conditions at a glance.
- Visual Alerts for Extreme Conditions:
The green tint alerts traders to a strong uptrend or bullish momentum, while the red tint alerts to a strong downtrend or bearish momentum. These visual cues help traders identify potential reversal points, exhaustion, or other significant market shifts.
How to Use the Indicator:
- Spotting Market Extremes:
Bullish Signal: When the background turns green, with a tilt_score higher than 0.85, it suggests that the market may be entering an extreme bullish phase. Traders can look for continuation signals, confirm with other indicators, or prepare for a potential pullback if the score continues to rise sharply.
Bearish Signal: When the background turns red, with a tilt_score lower than -0.85, it indicates an extreme bearish market condition. Traders may look for potential reversal patterns or prepare for a continuation of the downtrend if the score continues to decrease.
- Use in Conjunction with Other Indicators:
While this background tint provides a useful visual cue, it is recommended to use it alongside other technical indicators (such as RSI, MACD, or moving averages) to confirm trends or identify overbought/oversold conditions. The tilt_score focuses on extreme price movement and should be seen as a supplementary tool to help validate trade setups.
- Avoiding Overreaction:
A background shift to extreme colors (green or red) does not automatically indicate a reversal but rather a strong trend condition. Ensure confirmation with other technical tools before making trade decisions based purely on these background signals.
Conclusion:
The Extreme Conditions Background Gradient Tint is a straightforward, effective tool for identifying extreme market conditions. Its dynamic color-changing feature helps you quickly detect potential trend reversals or confirmation of strong trends. By monitoring the tint and combining it with other analysis tools, you can enhance your market timing and improve trading decisions based on visual cues from price action.
Futures Globex Session (Auto Session Times)This indicator will automatically change the globex session start and stop times relative to the product you are trading. For it to work correctly you have to be on the continuous unadjusted chart (MES1!, MCL1!, etc..)
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
stockan – Oscillator Matrixstockan – Oscillator Matrix
A multi-layer RSI-based momentum & trend tool with signal smoothing, short-segment regression lines, fade-style histogram, reversal markers, and a blocky heat-bar.
stockan is designed to give you a crystal-clear view of short-term momentum shifts and trend bias all in one pane. It builds upon the classic 0–100 RSI by:
Smoothing it with a quick SMA to generate a clean crossover signal.
Drawing tiny linear-regression segments on both RSI and its signal line to highlight the slope (i.e. local trend direction) over a handful of bars.
Filling between RSI and signal in green/red so you can instantly see when momentum flips.
Plotting a soft-fade histogram of (RSI – signal), where stronger moves produce more opaque bars, while smaller divergences fade into the background.
Marking local peaks and troughs on the RSI curve with dots—perfect for fine-tuning entries or exits.
Rendering a bottom “heat” strip as blocky columns that switch from red to green once RSI crosses your chosen threshold, giving you a persistent bias indicator.
🔧Inputs & Settings
You can customize every aspect of stockan in the Indicator Settings:
Price Source (default = Close)
Choose which price series (Open, High, Low, Close, or a custom series) you want the RSI to use.
Oscillator Length (RSI) (default = 14)
The look-back period for the RSI calculation. Shorter values make the oscillator more sensitive.
Signal Smoothing (default = 3)
The length of the simple moving average applied to the RSI. Higher values produce slower, cleaner signals.
Trend-LR Length (default = 20)
Number of bars used in each linear-regression segment. Longer lengths smooth trends but react more slowly.
Heat Threshold (default = 50)
The cutoff level (on the 0–100 RSI scale) above which the bottom heat-blocks turn green.
Histogram Max for Fade (default = 20)
The absolute difference (RSI – signal) that maps to 100% opacity in the histogram. Smaller differences fade out; larger ones stand out.
🚀 How to Use stockan
Identify Momentum Shifts
Watch for the green/red fill to flip—when the RSI line crosses above its signal, green fill indicates building bullish momentum; red indicates bearish pressure.
Sense Short-Term Trend with Mini-Regression Lines
The tiny sloping segments on both RSI and signal lines give an immediate visual cue: upward-tilted segments = short-term uptrend, downward = downtrend.
Gauge Strength with the Fade-Style Histogram
Opaque bars mean strong momentum divergence; faint bars mean weak or consolidating moves. Use these to avoid low-conviction signals.
Fine-Tune Entries & Exits Using Reversal Dots
Gray dots mark local RSI highs (possible short setups), green dots mark local lows (possible long entries).
Confirm Bias with the Heat-Bar
A steady green row at the bottom tells you RSI has been above your threshold consistently—ideal for trend-following. A red row suggests caution or counter-trend trades.
🎯 Benefits
All-in-One Pane: No need to juggle RSI, MA, histogram and custom script separately.
Clean Visuals: Soft fades and blocky heat bars reduce clutter and highlight what matters.
Non-Repainting: Uses only closed-bar data; once a bar is closed, nothing moves or disappears.
Highly Customizable: Every length, threshold, color and transparency can be adjusted in Settings.
Lightweight & Self-Contained: Pure Pine v5—no external libraries, no proprietary code—fully compliant with TradingView’s policies.
FeraTrading Sessions High/LowThe FeraTradiang Sessions High/Low Indicator plots precise high and low levels for the New York, London, and Asian trading sessions — without any clutter.
We designed this tool for simplicity, clarity and accuracy, automatically adjusting to any timeframe and time zone — no manual setup required.
🔍 Key Features:
Clean horizontal lines marking session highs and lows
Lines start at the actual high/low
Session times:
New York: 09:30 – 17:00
London: 03:00 – 08:00
Asian: 18:00 – 03:00
Real-time updates that trail live candles
Only shows the most relevant sessions:
Yesterday’s NY
Last night’s Asia + morning continuation
Today’s London
Fully customizable:
Session colors
Label toggle
Line extension settings
Enable extended trading hours on your chart for best results.
Whether you're trading futures, forex, or crypto, this indicator provides clean session context without the mess. Open-source for extra customization and designed for real-time usability.
Stockan Momentum MeterStockan Momentum Meter (SMM)
Advanced Momentum Acceleration Oscillator
Version: 1.0 | Category: Momentum Oscillator | Type: Open Source
Detailed Technical Specification
Key Features
Dual-Layer Momentum Calculation
Calculates momentum using double derivative of price (ROC of ROC)
First Layer: Standard Rate of Change (ROC)
Second Layer: Momentum of Momentum (ROC applied to first ROC)
Signal Smoothing System
EMA filtering of raw momentum values
Adaptive smoothing based on user-defined length
4-State Color Coding
Quadrant-based visualization system:
Strong Bullish (Green): Histogram > Threshold
Moderate Bullish (Blue): 0 < Histogram ≤ Threshold
Moderate Bearish (Orange): -Threshold ≤ Histogram < 0
Strong Bearish (Red): Histogram < -Threshold
Dynamic Threshold System
Adjustable baseline levels for sensitivity control
Symmetrical upper/lower boundaries
Detailed Working Mechanism
Calculation Pipeline
Raw Momentum (momo):
momo = ROC(ROC(close, length), length)
Measures acceleration/deceleration in price movements
Double derivation filters out noise while capturing momentum shifts
Smoothed Signal (ema_momo):
ema_momo = EMA(momo, length)
Creates reference line for momentum comparison
Reduces whipsaws in volatile markets
Histogram Value:
histogram = momo - ema_momo
Visualizes difference between raw and smoothed momentum
Positive values = accelerating momentum
Negative values = decelerating momentum
PARAMETER CONFIGURATION
Momentum Length (Default: 14)
Range: 1-100 | Controls historical window for momentum calculations
Base Line Threshold (Default: 0.0)
Range: 0-100 | Determines sensitivity for color changes
COLOR CODING SYSTEM
GREEN Signals:
Histogram value ABOVE threshold level = Strong bullish momentum
BLUE Signals:
Positive values BETWEEN 0 and threshold = Moderate bullish pressure
ORANGE Signals:
Negative values BETWEEN 0 and -threshold = Moderate bearish pressure
RED Signals:
Histogram value BELOW -threshold = Strong bearish momentum
Key Benefits for Traders
Early Reversal Detection
Identifies momentum exhaustion before price reversal occurs
Divergence Spotting
Clear visualization of:
Bullish divergence (Price ↓ + Histogram ↑)
Bearish divergence (Price ↑ + Histogram ↓)
Trend Strength Measurement
Histogram height indicates momentum intensity
Multi-Timeframe Compatibility
Works effectively on:
Scalping (1-15min)
Swing Trading (1H-4H)
Position Trading (Daily-Weekly)
Customizable Sensitivity
Adjust threshold levels for:
Day traders (higher threshold = fewer signals)
Long-term investors (lower threshold = more sensitivity)
Usage Scenarios
Bullish Signal
Green histogram crossing above threshold
Blue → Green color transition
Bearish Signal
Red histogram crossing below negative threshold
Orange → Red color transition
Confirmation Tool
Use with trend indicators (EMA, MACD):
Green histogram + Price above 200 EMA = Strong uptrend
Red histogram + Price below 200 EMA = Strong downtrend
KEY ADVANTAGES OVER POPULAR INDICATORS
Faster Signals vs RSI
Detects momentum shifts earlier through double ROC calculation
Clearer Visuals vs MACD
Four-color system replaces confusing line crossovers with instant visual cues
Better Filtering vs Stochastic
Dual-layer calculation reduces market noise more effectively
Custom Sensitivity
Adjustable threshold outperforms fixed settings in traditional oscillators
How to Use
Add to chart from TradingView Public Library
Default settings work for most timeframes
Adjust parameters based on:
Aggressive trading: Reduce length (10-12)
Conservative trading: Increase length (20-25)
Combine with:
Trendlines for breakout confirmation
Volume indicators for signal validation
Notes
Best Performance: Ranging markets with clear support/resistance
Risk Management: Use with stop-loss (2x ATR recommended)
Limitations: May give false signals during low-volume periods
New York Open Markerheyo,
surprisingly there aren't many indicators available which does the simple thing of just marking the 9:30 EST open or 10:00 am. also just having them present while backtesting makes it so much more easier to understand the pa.
you can turn off 10:00am marker if you don't use it.
good luck.
SS Galaxy TRI-Force Setup EMASuper trend Indicator (to identify trend direction and reversals)
10 EMA and 21 EMA Crossover (to time entry and exit)
ATR in Percentagethe script calculates ATR in percentage instead of money units, useful for comparison
Created by me using ChatGPT orders.
hope it is useful you can put alerts too
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
EMA Strategy with DOUBLE EMAStrategy Concept
This strategy uses two exponential moving averages (EMAs) to confirm the market trend and generate non-repeating buy/sell signals based on trend alignment:
Fast EMA (e.g., 50-period) detects short-term trend.
Slow EMA (e.g., 200-period) confirms the broader trend.
Slope of each EMA is used to determine whether the trend is up, down, or flat.
The strategy avoids "flat" areas unless allowed through settings.
⚙️ Signal Generation Logic
Buy Signal (Long Entry):
First, the fast EMA must turn bullish.
Then, the slow EMA must also confirm the bullish trend.
Only if both are aligned, a buy signal is triggered.
After a buy signal, no new buy will occur until a sell happens.
Sell Signal (Short Entry):
Same logic in reverse:
Fast EMA turns bearish → wait for slow EMA to confirm.
Only then, a sell signal is triggered.
After a sell, no new sell can happen until a buy is triggered.
Real-Time Price Line by Candle ColorThis indicator draws a horizontal line at the current price that updates in real time on each candle. The line:
Extends infinitely left and right
Changes color based on the current candle:
🟢 Green if the candle is bullish (close ≥ open)
🔴 Red if the candle is bearish (close < open)
Automatically clears and redraws each bar to reflect the latest price and direction
Use this as a simple but effective visual aid to track the live price and its directional bias.
ADX Supertrend | [DeV]The "ADX Supertrend" indicator is a user-friendly tool that blends two popular trading indicators—the Supertrend and the Average Directional Index (ADX)—to help traders spot trends and make smarter trading decisions. By combining these two, it offers a clearer picture of when a market is trending strongly and in which direction, while cutting down on misleading signals. Here’s a straightforward explanation of how each part works, how they team up, the benefits of using them together, and why the ADX makes the Supertrend even better.
Supertrend:
It's like a guide that follows the market’s price movements to tell you whether prices are trending up or down. It creates two lines, one above and one below the price, based on how much the market is bouncing around (its volatility). When the price moves above the upper line, it signals an uptrend (a good time to buy), and the indicator draws a line below the price to show support. When the price drops below the lower line, it signals a downtrend (a potential time to sell), and the line appears above the price as resistance. The Supertrend is great because it adjusts to market conditions, widening the gap between lines in wild markets and tightening it in calm ones.
Average Directional Index:
The ADX is all about measuring how strong a trend is, without caring whether it’s going up or down. Think of it as a meter that tells you if the market is charging forward with purpose or just drifting aimlessly. It uses a scale from 0 to 100, where higher numbers mean a stronger trend. For example, an ADX above 25 often suggests a solid trend worth paying attention to, while a low ADX signals a sleepy, sideways market. The ADX also looks at whether buyers or sellers are in control to confirm the trend’s direction.
Confluence:
The Supertrend is great at spotting trends, but it can be a bit trigger-happy, giving signals in markets that aren’t really trending. That’s where the ADX shines. It acts like a quality control check, making sure the Supertrend’s signals only count when the market is moving with conviction. By filtering out weak or messy trends, the ADX helps you avoid wasting time on trades that fizzle out. It also double-checks the trend’s direction, so you’re not just guessing whether buyers or sellers are in charge. This teamwork means you get signals that are more reliable and less likely to lead you astray, especially in tricky markets where prices bounce around without a clear path.
