MTF Pivots with lines (Nearest to the Price)In this indicator, I demonstrate the MTF pivots with lines connecting each pivot. These pivots are represented by lines, and there is a filter to display the nearest line to the current price.
Educational
Ema King for intraday 3-5mThe EMA Crossover Buy/Sell Indicator is a TradingView script designed to help traders identify potential entry and exit points using two Exponential Moving Averages (EMAs). The indicator works as follows:
Buy Signal:
When the EMA11 crosses above the EMA 55, then genrate buy signal for up trend
Sell Signal:
When the EMA11 crosses below the EMA 55, then genrate sell signal for down trend
Summary
This indicator uses the EMA crossover strategy to generate buy and sell signals. It provides a clear, visual representation of market trends, helping traders identify potential trade opportunities with ease.
To remove false positives, combine this with other indicators.
Disclaimer: This analysis is for educational purposes only. NSE:NIFTY
Please assess your own risk tolerance and conslut with a financial advisor before trading.
[Mad] Triple Bollinger Bands MTF_3xThis For My Friend Amit G it have 3 boilinder band and moving average
Averaging Stratagy on TW ( CryptoBus ) 🛠 Averaging Strategy — умное усреднение для трейдинга на TradingView!
📊 Стратегия анализирует тренд и корректирует входы, снижая влияние волатильности.
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Bitcoin Log Growth Regression Curve (Tommy)1. Introduction
Bitcoin’s price had historically exhibited exponential growth, meaning its growth rate increased over time rather than remaining constant. To analyze this behavior, I employed a logarithmic scale. The logarithmic transformation converted the exponential growth pattern into a linear relationship, which was easier to analyze using linear regression techniques.
2. What Is a Logarithmic Function?
A logarithmic function (in this case, using base 10) transforms a number x into log10(x). For example, log10(100) = 2 because 10^2 = 100. This transformation compresses data that spans several orders of magnitude into a smaller, more manageable scale. In my analysis, the logarithmic transformation was applied to both the time variable (expressed as “weekly bar numbers”) and the price. This approach converted an exponential relationship into a linear one.
3. Selection of Key Coordinates
Key turning points in Bitcoin’s historical price were selected, representing the cycle peaks and bottoms. Since TradingView charts begin on 2009 10 05, I converted these dates into “weekly bar numbers. The chosen coordinates were as follows:
- Cycle Peaks (Highs):
(2011-06 06-$32) -> (80, $32)
(2013-11-25, $1240) -> (208, $1240)
(2017-12-11, $19804) -> (451, $19804)
(2021-11-08, $68997) -> (623, $68997)
- Cycle Bottoms (Lows):
(2011-11-21, $2) -> (102, $2)
(2015-08-17, $162) -> (298, $162)
(2018-12-10, $3124) -> (471, $3124)
(2022-11-21, $15473) -> (677, $15473)
These points were chosen because they marked significant turning points in Bitcoin’s price cycles.
4. Conversion to Linear Form
The values then were converted to their linear form by applying the base 10 logarithm. This transformation allowed the function to be expressed in a linear state as:
y = a * x + b
where:
- x is the logarithm of the weekly bar number, and
- y is the logarithm of the price.
This step was essential for enabling linear regression on these values. After applying the base 10 logarithm, the coordinates became:
- Cycle Peaks (Highs):
(1.903, 1.505)
(2.318, 3.093)
(2.654, 4.296)
(2.795, 4.839)
- Cycle Bottoms (Lows):
(2.009, 0.301)
(2.474, 2.210)
(2.673, 3.494)
(2.830, 4.190)
5. Linear Regression
A linear regression was then performed on these log transformed points separately for the highs and lows. The best fit lines obtained were:
- Cycle Peaks (Highs):
y = 3.711 * x – 5.541
In exponential form, this translates to:
Price = 10^(3.711 * log(x)-5.541)
- Cycle Bottoms (Lows):
y = 4.782 * x – 9.385
In exponential form, this translates to:
Price = 10^(4.782 * log(x)-9.385)
6. Visualization through Log Channel with Offsets
To provide additional context and to visually assess potential overextensions or undervaluations relative to the long term trend, channels were drawn around the regression lines by applying a percentage offset. For example, with a 20% offset:
- Upper Channel:
Upper=Regression Value * (1 + 0.2)
- Lower Channel:
Lower=Regression Value * (1 - 0.2)
These channels help illustrate how far the current price deviates from the trend line and can signal potential reversal points if the price moves significantly outside these boundaries.
7. Meaning and Application of the Model
This model was designed to analyze Bitcoin’s long term trend by:
• Assessing Market Conditions:By comparing the current Bitcoin price to the regression channels, one can gauge whether Bitcoin is trading above (potentially overextended) or below (potentially undervalued) its long term trend.
• Forecasting Reversal Points: Significant deviations from the channel boundaries may indicate that a price correction or reversal is imminent.
• Supporting Investment Decisions: The regression function, together with its channels, provides a quantitative framework for evaluating the current market state and estimating future price targets, helping to guide strategic entry or exit decisions.
Based on the derived model and current market conditions, today's analysis indicates that Bitcoin's support levels lie approximately between $24,000 and $36,000, while its resistance levels are in the range of about $134,000 to $202,000.
8. Summary
This model came together by pinpointing key turning points in Bitcoin’s price history and converting the dates into weekly bar numbers. I then applied a base 10 logarithm to both the time and price data, which transformed Bitcoin’s exponential growth into a straight-line relationship. Using linear regression on these log transformed values, I derived trend equations for the peaks and bottoms, and added channels with a set offset to highlight areas of potential support and resistance. Overall, this approach offers a clear, data-driven way to gauge Bitcoin’s long-term trend and anticipate potential market turning points.
1. 서론
비트코인은 창시 이후 상상할 수 없을 정도의 상승세를 보여주었으며, 그 폭발적인 성장률은 시간이 흐를수록 걷잡을 수 없이 가속화되었습니다. 이러한 현상은 단순한 우연이 아니라, 전 세계 투자자들과 주요 기관들의 열렬한 관심을 이끌어내며 비트코인이 주요 자산으로 자리잡게 된 배경이 되었습니다. 저는 최근에 이 비범한 상승세의 이면을 보다 깊이 이해하고자 로그 기반으로 간단한 비트코인 추세 채널링 모델을 만들어봤습니다.
2. 로그함수란?
로그함수는 어떤 숫자를, 특정 밑을 기준으로 그 크기를 지수 형태로 표현해 주는 함수입니다. 예를 들어, 100이라는 숫자는 10의 몇 제곱인지 나타내면 10^2 = 100이므로, log(100)는 2가 됩니다. 즉, 로그함수는 아주 크거나 작은 값을 다루기 쉽게 숫자 범위를 압축해 주는 역할을 합니다. 제 분석에서는 ‘주간 바 번호’라는 시간 변수와 가격 데이터에 모두 로그 변환을 적용하여, 본래 기하급수적으로 증가하는 데이터를 선형 관계로 바꿔서 보다 쉽게 이해하고 분석할 수 있도록 하였습니다. 참고로 본 분석에서는 로그 10진법을 사용하였습니다.
3. 주요 고점/저점 선택
역대 비트코인의 가격 사이클에서 중요한 변곡점들을 고점 저점 각각 4개씩 도출했습니다. 트레이딩뷰의 BTCUSD:INDEX 차트는 2009년 10월 05일부터 시작되므로, 각 날짜가 차트 상에서 몇 번째 봉에 해당하는지를 계산하였습니다. 좌표는 다음과 같습니다:
사이클 상단 (고점):
(2011-06 06-$32) -> (80, $32)
(2013-11-25, $1240) -> (208, $1240)
(2017-12-11, $19804) -> (451, $19804)
(2021-11-08, $68997) -> (623, $68997)
사이클 하단 (저점):
(2011-11-21, $2) -> (102, $2)
(2015-08-17, $162) -> (298, $162)
(2018-12-10, $3124) -> (471, $3124)
(2022-11-21, $15473) -> (677, $15473)
해당 변곡점들은 차트 분석 좀 하시는 분들은 아시겟지만, 역대 비트코인 가격 사이클에서 가장 중요한 고점과 저점들입니다.
4. 데이터 변환
이후 선택된 값들에 10진 로그를 적용하여 기하급수적으로 증가하는 데이터를 선형 관계로 전환하였습니다. 자, 우리 어렸을 때 수학시간에 다 배운 1차 방정식인데, 기억하시죠? 다음과 같이 선형적 함수로 표현될 수 있습니다:
y = a * x + b
- x: 날짜(몇 번째 주봉인지)
- y: 비트코인 로그 값
로그를 적용한 결과, 좌표는 다음과 같이 바뀌었습니다. 이제 선형 회귀 분석이 가능한 데이터 포맷이 되었네요.
사이클 상단 (고점):
(1.903, 1.505)
(2.318, 3.093)
(2.654, 4.296)
(2.795, 4.839)
사이클 하단 (저점):
(2.009, 0.301)
(2.474, 2.210)
(2.673, 3.494)
(2.830, 4.190)
5. 선형 회귀 분석
고점과 저점 데이터를 로그 스케일로 변환한 후 각각 선형 회귀 분석을 수행하여 최적화된 추세선을 도출해봤습니다. 그 결과, 아래와 같은 방정식이 나왔네요:
사이클 상단 (고점):
y = 3.711 * x – 5.541
지수 함수로 변환하면:
가격 = 10^(3.711 * log(x)-5.541)
사이클 하단 (저점):
y = 4.782 * x – 9.385
지수 함수로 변환하면:
가격 = 10^(4.782 * log(x)-9.385)
6. 로그 채널에 편차 적용
하나의 회귀선으로 모든 변곡점을 정확히 포착하는 것은 어렵기 때문에, 육안으로 확인했을 때 주요 고점과 저점들이 어느 정도 포함될 수 있도록 적절한 범위를 설정하였습니다. 이를 위해 회귀선 위아래로 일정 비율의 편차(오프셋)을 적용하여 채널을 형성하였습니다. 예를 들어, 20%의 오프셋을 적용하면 다음과 같이 계산됩니다:
사이클 상단 = 회귀 값 * (1 + 0.2)
사이클 하단 = 회귀 값 * (1 - 0.2)
이 채널을 활용하면 비트코인의 가격이 장기 추세 대비 어느 정도 위치해 있는지 한눈에 파악할 수 있습니다. 만약 가격이 채널을 크게 벗어난다면, 시장이 과열되었거나 반대로 저평가되었을 가능성이 높다고 해석할 수 있습니다.
7. 해당 모델의 시사점
- 시장 상황 평가: 현재 비트코인 가격이 본 채널과 비교했을 때 과열(고평가) 상태인지, 혹은 침체(저평가) 상태인지 어느정도 큰 그림에서 가늠해볼 수 있습니다.
- 변곡점 예측: 가격이 채널의 상단이나 하단에 도달할 경우, 일정 수준의 조정이나 방향 전환이 나타날 가능성이 높습니다.
현재 시장 상황과 모델이 도출한 결과를 바탕으로 보면, 장기적으로 비트코인의 주요 지지 구간은 약 $24,000 ~ $36,000, 저항 구간은 $134,000 ~ $202,000 수준으로 나타납니다.
8. 결론
이번 회귀 모델을 구축하면서, 비트코인의 가격 움직임이 단순한 우연이 아니라 일정한 패턴을 따르고 있음을 다시 한번 확인할 수 있었습니다. 단순히 과거 데이터를 대입해 본 것이지만, 로그 스케일과 선형 회귀를 활용하니 꽤 직관적인 추세선이 도출되었고, 이를 통해 장기적인 흐름에서 가격이 어디쯤 위치해 있는지를 객관적으로 볼 수 있었습니다.
물론, 이 채널이 절대적인 예측 도구는 아닙니다. 시장은 항상 변수가 많고, 예상치 못한 이벤트가 터질 수도 있습니다. 하지만 적어도 큰 그림에서 시장이 과열되었는지, 혹은 저평가된 구간에 들어섰는지를 판단하는 데는 유용한 프레임워크가 될 수 있다고 생각합니다.
선형 차트로 보면, 시간이 갈수록 채널이 시사하는 지지/저항 구간의 범위가 넓어집니다. 따라서 미래로 갈수록 예측이 더욱 어려워지는 것이 이 시장의 본질일지도 모릅니다. 결국, 시장이 어느 방향으로든 극단적인 움직임을 보일 가능성은 점점 커지며, 이런 불확실성을 감안한 대응 전략이 더욱 중요하고 생각합니다.
CHANDAN DARBAR Support & ResistanceAuto Hourly Pivot & Price-Cross Signals
by shrinathdarbar250
This indicator automatically calculates hourly pivot points using the previous hour’s high, low, and close values. It displays the key pivot levels (Pivot, R1, S1, R2, S2, R3, S3) on your chart with dynamic coloring—red if the level is above the current price (acting as potential resistance) and green if below (acting as potential support).
Key Features:
Hourly Pivot Calculation: Automatically computes pivot levels using standard formulas based on the previous hour’s data.
Dynamic Coloring: Pivot lines change color in real time to indicate whether they are above (red) or below (green) the current price.
Price-Cross Signals: Generates a BUY signal when the price crosses above any resistance level and a SELL signal when the price crosses below any support level.
Automatic Updates: Pivot lines are refreshed every hour—old lines are deleted when a new hour begins, keeping your chart clean and up-to-date.
This indicator is ideal for traders looking to quickly identify dynamic support and resistance levels and capture potential breakout or reversal opportunities based on hourly market structure.
Rick Auto FiboIndicador de Fibo Automatica ajustavel ao periodo, com niveis de entradas e alvos baseados em fibonacci.
Automatic Fibo Indicator adjustable to the period, with entry levels and targets based on Fibonacci
EMA & SMMA Pro Trend Indicator EMA & SMMA Combo is a powerful TradingView indicator that combines the Exponential Moving Average (EMA) and Smoothed Moving Average (SMMA) for enhanced trend analysis.
Features:
✅ EMA Calculation – Tracks price trends with a customizable length and offset.
✅ Smoothing Options – Choose between different moving average types, including SMA, EMA, SMMA (RMA), WMA, and VWMA.
✅ Bollinger Bands Integration – Add upper and lower bands for volatility analysis.
✅ SMMA Calculation – Provides a smooth trend-following average for better signal clarity.
This indicator helps traders identify trends, spot reversals, and optimize entry/exit points efficiently. 🚀
Soso Supply & Demand A simple indicator that points out demand and supply, combined with confirmations like choch on lower time frames works well. I will try to improve it and test it more.
Fresh Imbalance by VAGAFX🔥 Fresh Imbalances Indicator – Spot High-Probability Trade Setups Instantly! 🔥
Unlock the power of institutional order flow with the Fresh Imbalances Indicator, inspired by VAGAFX’s cutting-edge Smart Money Concepts. This tool is designed for traders who seek precision in identifying fresh imbalances—key areas where price is likely to react due to unfilled institutional orders.
Reversal Candle patternThis is a great scalping indicator alert, it can show you where the liquidity of the previous high/low is taken and to follow the direction of the bullish or bearish pattern. Remember to look for this pattern either on a lower prices for bullish and higher for bearish gives the best results.
High-Low Breakout strategyThis is the candle pattern where the high is taken and the low liquidity is also taken of the previous candle. This then sets the market for either a bullish run or bearish, usually you see this kind of candle in a new breakout. Set previous candle lookback to 1. Best on 3 minutes time frame but will give very good signal on 1hr, even 4hr candles. Is designed for the nasdaq but can also be used in Gold. Do your backtest to verify alerts before you use it.
Forward Curve Visualization ToolProvide the spot symbol and the futures product root, and the script automatically scans all relevant contracts for you—no more tedious manual searches. The result is a clean, intuitive chart showing the live forward curve in real time.
It also detects contango or backwardation conditions (based on spot < F1 < F2 < F3).
Future Features:
Plot historical snapshots of the curve (1 day, 1 week, or 1 month ago) to understand market trends over time.
Display additional metrics such as annualized basis, cost of carry (CoC), and even volume or open interest for deeper insights.
If you trade futures and watch the forward curve, this script will give you the actionable data you need and get more ideas or features you’d like to see. Let’s build them together!
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
TOTAL ZONESIT indicates the actual zones in the live market used for pro traders to identify traps in the live market
Adidas SeasonaxTesting a Seasonal Trading Strategy by Giles Coghlan
According to the analyst's observations, Adidas stock experiences a seasonal decline every year between February 19 and March 7. Following this, a rebound occurs with a 90% probability between March 8 and May 15, Coghlan claims. To test this idea, a strategy was developed that opens a long position on March 8 and closes it on May 15. Read more here .
Summary for the period from 01.01.2006 to 13.02.2025:
Expected return: 9.5% (analyst's forecast), 8.8% (average profit per trade based on backtest results)
Expected win rate: 90% (analyst's forecast), 84.2% (win rate based on backtest results)
-----------------------------------------------------------------------------------------------------------------
Проверка сезонной торговой стратегии от Giles Coghlan
По наблюдению аналитика, ежегодно в акциях Adidas в период с 19 февраля по 7 марта наблюдается сезонное падение. После него с вероятность 90% происходит отскок в период с 8 марта по 15 мая, утверждает Coghlan. Для проверки идеи была создана стратегия, которая открывает лонг-позицию 8 марта и закрывает ее 15 мая. Подробнее читайте тут .
Резюме за период с 01.01.2006 по 13.02.2025:
Ожидаемая доходность: 9,5% (прогноз аналитика), 8,8% (средний профит на сделку по результатам бэктеста)
Ожидаемый винрейт: 90% (прогноз аналитика), 84,2% (винрейт по результатам бэктеста)
AUrum Electron LevelsBreakout - Pullback - Retrace, strategy and method
Visual on GOLD ( XAUUSD ) - Look and observe - then decide
The basis of analysis is to determine the Break and Closed prices.
If it is above the +0.2% level, follow the direction of the momentum to take a BUY position with a target objective at the +0.8% level and with a risk target at the -0.2% level
If it is below the -0.2% level, follow the direction of the momentum to take a SELL position with a target objective at the -0.8% level and with a risk target at the +0.2% level
Disclaimer:
Do you own Back/Foward test
Do your own research
Four LevelsManually input 4 levels on chart
Daily High: Thin Red Line
Daily Low: Thin Green Line
Weekly High: Thick Red Line
Weekly Low: Thick Green Line
Son Model ICT [TradingFinder] HTF DOL H1 + Sweep M15 + FVG M1🔵 Introduction
The ICT Son Model setup is a precise trading strategy based on market structure and liquidity, implemented across multiple timeframes. This setup first identifies a liquidity level in the 1-hour (1H) timeframe and then confirms a Market Structure Shift (MSS) in the 5-minute (5M) timeframe to validate the trend. After confirmation, the price forms a new swing in the 5-minute timeframe, absorbing liquidity.
Once this level is broken, traders typically drop to the 30-second (30s) timeframe and enter trades based on a Fair Value Gap (FVG). However, since access to the 30-second timeframe is not available to most traders, we take the entry signal directly from the 5-minute timeframe, using the same liquidity zones and confirmed breakouts to execute trades. This approach simplifies execution and makes the strategy accessible to all traders.
This model operates in two setups :
Bullish ICT Son Model and Bearish ICT Son Model. In the bullish setup, liquidity is first accumulated at the lows of the 1-hour timeframe, and after confirming a market structure shift, a long position is initiated. Conversely, in the bearish setup, liquidity is first drawn from higher levels, and upon confirmation of a bearish trend, a short position is executed.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The ICT Son Model setup is designed around liquidity analysis and market structure shifts and can be applied in both bullish and bearish market conditions. The strategy first identifies a liquidity level in the 1-hour (1H) timeframe and then confirms a Market Structure Shift (MSS) in the 5-minute (5M) timeframe.
After this shift, the price forms a new swing, absorbing liquidity. When this level is broken in the 5-minute timeframe, the trader enters based on a Fair Value Gap (FVG). While the ideal entry is in the 30-second (30s) timeframe, due to accessibility constraints, we take entry signals directly from the 5-minute timeframe.
🟣 Bullish Setup
In the Bullish ICT Son Model, the 1-hour timeframe first identifies liquidity at the market lows, where price sweeps this level to absorb liquidity. Then, in the 5-minute timeframe, an MSS confirms the bullish shift.
After confirmation, the price forms a new swing, absorbing liquidity at a higher level. The price then retraces into a Fair Value Gap (FVG) created in the 5-minute timeframe, where the trader enters a long position, placing the stop-loss below the FVG.
🟣 Bearish Setup
In the Bearish ICT Son Model, liquidity at higher market levels is identified in the 1-hour timeframe, where price sweeps these levels to absorb liquidity. Then, in the 5-minute timeframe, an MSS confirms the bearish trend.
After confirmation, the price forms a new swing, absorbing liquidity at a lower level. The price then retraces into a Fair Value Gap (FVG) created in the 5-minute timeframe, where the trader enters a short position, placing the stop-loss above the FVG.
🔵 Settings
Swing period : You can set the swing detection period.
Max Swing Back Method : It is in two modes "All" and "Custom". If it is in "All" mode, it will check all swings, and if it is in "Custom" mode, it will check the swings to the extent you determine.
Max Swing Back : You can set the number of swings that will go back for checking.
FVG Length : Default is 120 Bar.
MSS Length : Default is 80 Bar.
FVG Filter : This refines the number of identified FVG areas based on a specified algorithm to focus on higher quality signals and reduce noise.
Types of FVG filters :
Very Aggressive Filter: Adds a condition where, for an upward FVG, the last candle's highest price must exceed the middle candle's highest price, and for a downward FVG, the last candle's lowest price must be lower than the middle candle's lowest price. This minimally filters out FVGs.
Aggressive Filter: Builds on the Very Aggressive mode by ensuring the middle candle is not too small, filtering out more FVGs.
Defensive Filter: Adds criteria regarding the size and structure of the middle candle, requiring it to have a substantial body and specific polarity conditions, filtering out a significant number of FVGs.
Very Defensive Filter: Further refines filtering by ensuring the first and third candles are not small-bodied doji candles, retaining only the highest quality signals.
🔵 Conclusion
The ICT Son Model setup is a structured and precise method for trade execution based on liquidity analysis and market structure shifts. This strategy first identifies a liquidity level in the 1-hour timeframe and then confirms a trend shift using the 5-minute timeframe.
Trade entries are executed based on Fair Value Gaps (FVGs), which highlight optimal entry points. By applying this model, traders can leverage existing market liquidity to enter high-probability trades. The bullish setup activates when liquidity is swept from market lows and a market structure shift confirms an upward trend, whereas the bearish setup is used when liquidity is drawn from market highs, confirming a downtrend.
This approach enables traders to identify high-probability trade setups with greater precision compared to many other strategies. Additionally, since access to the 30-second timeframe is limited, the strategy remains fully functional in the 5-minute timeframe, making it more practical and accessible for a wider range of traders.
Cluster Reversal Zones📌 Cluster Reversal Zones – Smart Market Turning Point Detector
📌 Category : Public (Restricted/Closed-Source) Indicator
📌 Designed for : Traders looking for high-accuracy reversal zones based on price clustering & liquidity shifts.
🔍 Overview
The Cluster Reversal Zones Indicator is an advanced market reversal detection tool that helps traders identify key turning points using a combination of price clustering, order flow analysis, and liquidity tracking. Instead of relying on static support and resistance levels, this tool dynamically adjusts to live market conditions, ensuring traders get the most accurate reversal signals possible.
📊 Core Features:
✅ Real-Time Reversal Zone Mapping – Detects high-probability market turning points using price clustering & order flow imbalance.
✅ Liquidity-Based Support/Resistance Detection – Identifies strong rejection zones based on real-time liquidity shifts.
✅ Order Flow Sensitivity for Smart Filtering – Filters out weak reversals by detecting real market participation behind price movements.
✅ Momentum Divergence for Confirmation – Aligns reversal zones with momentum divergences to increase accuracy.
✅ Adaptive Risk Management System – Adjusts risk parameters dynamically based on volatility and trend state.
🔒 Justification for Mashup
The Cluster Reversal Zones Indicator contains custom-built methodologies that extend beyond traditional support/resistance indicators:
✔ Smart Price Clustering Algorithm: Instead of plotting fixed support/resistance lines, this system analyzes historical price clustering to detect active reversal areas.
✔ Order Flow Delta & Liquidity Shift Sensitivity: The tool tracks real-time order flow data, identifying price zones with the highest accumulation or distribution levels.
✔ Momentum-Based Reversal Validation: Unlike traditional indicators, this tool requires a momentum shift confirmation before validating a potential reversal.
✔ Adaptive Reversal Filtering Mechanism: Uses a combination of historical confluence detection + live market validation to improve accuracy.
🛠️ How to Use:
• Works well for reversal traders, scalpers, and swing traders seeking precise turning points.
• Best combined with VWAP, Market Profile, and Delta Volume indicators for confirmation.
• Suitable for Forex, Indices, Commodities, Crypto, and Stock markets.
🚨 Important Note:
For educational & analytical purposes only.
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DS1it is used to find boring candle where these boring candles act as demand and supply zones in the lve markets
DS1 Demand & Supply Zones (Extended)it is used to identify the boring candles range used for zone trading
