McRib Bull Market Indicator# McRib Bull Market Indicator
## Overview
The McRib Bull Market Indicator is a unique technical analysis tool that marks McDonald's McRib sandwich release dates on your trading charts. While seemingly unconventional, this indicator serves as a fascinating historical reference point for market analysis, particularly for studying periods of market expansion.
## Key Features
- Visual yellow labels marking verified McRib release dates from 2012 to 2024
- Clean, unobtrusive design that overlays on any chart timeframe
- Covers both U.S. and international releases (including UK and Australia)
## Historical Reference Points
The indicator includes release dates from:
- December 2012
- October-December 2014
- January 2015
- October 2016
- November 2017
- October 2018
- October 2019
- December 2020
- October 2022
- November 2023
- December 2024
## Usage Guide
1. Add the indicator to any chart by searching for "McRib Bull Market Indicator"
2. The indicator will automatically display yellow labels above price candles on McRib release dates
3. Use these reference points to:
- Analyze market conditions during McRib releases
- Study potential correlations between releases and market movements
- Compare market behavior across different McRib release periods
- Identify any patterns in market expansion phases coinciding with releases
## Trading Application
While initially created as a novelty indicator, it can be used to:
- Mark specific historical points of reference for broader market analysis
- Study potential market psychology around major promotional events
- Compare seasonal market patterns with recurring release dates
- Analyze market expansion phases that coincide with releases
Remember: While this indicator provides interesting historical reference points, it should be used as part of a comprehensive trading strategy rather than as a standalone trading signal.
Komut dosyalarını "2024年5月2日+深交所+股票成交额+新浪财经" için ara
XAUUSD 10-Minute StrategyThis XAUUSD 10-Minute Strategy is designed for trading Gold vs. USD on a 10-minute timeframe. By combining multiple technical indicators (MACD, RSI, Bollinger Bands, and ATR), the strategy effectively captures both trend-following and reversal opportunities, with adaptive risk management for varying market volatility. This approach balances high-probability entries with robust volatility management, making it suitable for traders seeking to optimise entries during significant price movements and reversals.
Key Components and Logic:
MACD (12, 26, 9):
Generates buy signals on MACD Line crossovers above the Signal Line and sell signals on crossovers below the Signal Line, helping to capture momentum shifts.
RSI (14):
Utilizes oversold (below 35) and overbought (above 65) levels as a secondary filter to validate entries and avoid overextended price zones.
Bollinger Bands (20, 2):
Uses upper and lower Bollinger Bands to identify potential overbought and oversold conditions, aiming to enter long trades near the lower band and short trades near the upper band.
ATR-Based Stop Loss and Take Profit:
Stop Loss and Take Profit levels are dynamically set as multiples of ATR (3x for stop loss, 5x for take profit), ensuring flexibility with market volatility to optimise exit points.
Entry & Exit Conditions:
Buy Entry: T riggered when any of the following conditions are met:
MACD Line crosses above the Signal Line
RSI is oversold
Price drops below the lower Bollinger Band
Sell Entry: Triggered when any of the following conditions are met:
MACD Line crosses below the Signal Line
RSI is overbought
Price moves above the upper Bollinger Band
Exit Strategy: Trades are closed based on opposing entry signals, with adaptive spread adjustments for realistic exit points.
Backtesting Configuration & Results:
Backtesting Period: July 21, 2024, to October 30, 2024
Symbol Info: XAUUSD, 10-minute timeframe, OANDA data source
Backtesting Capital: Initial capital of $700, with each trade set to 10 contracts (equivalent to approximately 0.1 lots based on the broker’s contract size for gold).
Users should confirm their broker's contract size for gold, as this may differ. This script uses 10 contracts for backtesting purposes, aligned with 0.1 lots on brokers offering a 100-contract specification.
Key Backtesting Performance Metrics:
Net Profit: $4,733.90 USD (676.27% increase)
Total Closed Trades: 526
Win Rate: 53.99%
Profit Factor: 1.44 (1.96 for Long trades, 1.14 for Short trades)
Max Drawdown: $819.75 USD (56.33% of equity)
Sharpe Ratio: 1.726
Average Trade: $9.00 USD (0.04% of equity per trade)
This backtest reflects realistic conditions, with a spread adjustment of 38 points and no slippage or commission applied. The settings aim to simulate typical retail trading conditions. However, please adjust the initial capital, contract size, and other settings based on your account specifics for best results.
Usage:
This strategy is tuned specifically for XAUUSD on a 10-minute timeframe, ideal for both trend-following and reversal trades. The ATR-based stop loss and take profit levels adapt dynamically to market volatility, optimising entries and exits in varied conditions. To backtest this script accurately, ensure your broker’s contract specifications for gold align with the parameters used in this strategy.
TASC 2024.11 Ultimate Strength Index█ OVERVIEW
This script implements the Ultimate Strength Index (USI) indicator, introduced by John Ehlers in his article titled "Ultimate Strength Index (USI)" from the November 2024 edition of TASC's Traders' Tips . The USI is a modified version of Wilder's original Relative Strength Index (RSI) that incorporates Ehlers' UltimateSmoother lowpass filter to produce an output with significantly reduced lag.
█ CONCEPTS
Many technical indicators, including the RSI, lag due to their heavy reliance on historical data. John Ehlers reformulated the RSI to substantially reduce lag by applying his UltimateSmoother filter to upward movements ( strength up - SU ) and downward movements ( strength down - SD ) in the time series, replacing the standard process of smoothing changes with rolling moving averages (RMAs). Ehlers' recent works, covered in our recent script publications, have shown that the UltimateSmoother is an effective alternative to other classic averages, offering notably less lag in its response.
Ehlers also modified the RSI formula to produce an index that ranges from -1 to +1 instead of 0 to 100. As a result, the USI indicates bullish conditions when its value moves above 0 and bearish conditions when it falls below 0.
The USI retains many of the strengths of the traditional RSI while offering the advantage of reduced lag. It generally uses a larger lookback window than the conventional RSI to achieve similar behavior, making it suitable for trend trading with longer data lengths. When applied with shorter lengths, the USI's peaks and valleys tend to align closely with significant turning points in the time series, making it a potentially helpful tool for timing swing trades.
█ CALCULATIONS
The first step in the USI's calculation is determining each bar's strength up (SU) and strength down (SD) values. If the current bar's close exceeds the previous bar's, the calculation assigns the difference to SU. Otherwise, SU is zero. Likewise, if the current bar's close is below the previous bar's, it assigns the difference to SD. Otherwise, SD is zero.
Next, instead of the RSI's typical smoothing process, the USI's calculation applies the UltimateSmoother to the short-term average SU and SD values, reducing high-frequency chop in the series with low lag.
Finally, this formula determines the USI value:
USI = ( Ult (SU) − Ult (SD)) / ( Ult (SU) + Ult (SD)),
where Ult (SU) and Ult (SD) are the smoothed average strength up and strength down values.
TASC 2024.10 Adaptive Oscillator Threshold█ OVERVIEW
This script introduces a more dynamic approach to generating trading signals using the RSI indicator and a threshold that adapts to price trends and dispersion. This methodology comes from Francesco Bufi's article "Overbought/Oversold Oscillators: Useless Or Just Misused" from the October 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
According to Francesco Bufi's observations, an oscillator-based buy signal should have a threshold that varies with the trend direction: higher during uptrends and lower during downtrends. Additionally, the level should decrease as the distance from the price to its mean increases to reduce signals in volatile conditions. Accordingly, Bufi proposes a formula for an adaptive buy level whose value is proportional to the trend (linear regression slope) and inversely proportional to the typical distance between price and its mean (standard deviation). Traders can apply this method to any oscillator to add adaptivity without modifying the oscillator's calculations, as it's simply an adaptive technique for interpreting the calculated values.
This script demonstrates the application of Bufi's Adaptive Threshold (BAT) in a simple RSI-based strategy and allows users to compare its performance to the traditional fixed-threshold approach. Bufi's observations suggest that using the BAT instead of a static threshold can help improve the backtest performance of oscillator-based systems.
█ DISCLAIMER
This strategy script educates users on the trading systems outlined by the TASC article. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script.
TASC 2024.09 Precision Trend Analysis█ OVERVIEW
This script introduces an approach for detecting and confirming trends in price series based on digital signal processing principles, as presented by John Ehlers in the "Precision Trend Analysis" article from the September 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
Traditional trend-following indicators, such as moving averages , are lowpass filters that pass low-frequency components in a series and remove high-frequency components. Because lowpass filters preserve lengthy cycles in the data while attenuating shorter cycles, such filters have unavoidable lag that impacts the timeliness of trading signals.
In his article, John Ehlers presents an alternative approach that combines two highpass filters with different lengths to remove undesired high-frequency content via cancellation . Highpass filters have nearly zero lag. As such, the resulting trend indicator from this approach is very responsive to changes in the price series, with peaks and valleys that closely align with those of the price data. The indicator signifies an uptrend when its value is positive (i.e., above the balance point) and a downtrend when it is negative.
Subsequently, John Ehlers demonstrates that one can use the trend indicator's rate of change (ROC) to determine the onset of new trend movements. The ROC is zero at peaks and valleys in the trend indicator. Therefore, when the ROC crosses above zero, it signifies the onset or continuation of an uptrend. Likewise, the ROC crossing below zero indicates the onset or continuation of a downtrend. Note, however, that because the ROC does not preserve lower-frequency information, it can produce whipsaw trading signals in sideways or continuously trending price series.
This script implements both the trend indicator and its ROC along with the following on-chart signals:
• Green and red arrows that indicate the possible onset or continuation of an uptrend and downtrend, respectively
• Bar and plot colors that signify the sign (direction) of the trend indicator
█ CALCULATIONS
The math behind the trend indicator comes from digital filter design principles. The first step applies a digital highpass filter that attenuates long cycles with periods above the user-specified critical period. The default value is 250 bars, representing roughly one year for instruments such as stocks on the daily timeframe. The next step applies a highpass filter with a shorter period (40 bars by default). The difference between these filters determines the trend indicator, which preserves cyclic components between 40 and 250 bars by default while attenuating and eliminating others. The ROC represents the scaled one-bar difference in the trend indicator.
Grayscale GSOL Solana Financials [NeoButane]This script shows Grayscale's GSOL financials based on the information from their website. Investors and traders like to use financials when making the decision to buy, sell, or hold.
►Usage
This script is specific to GSOL. Investors and traders use financials when making the decision to buy, sell, or hold. How one interprets financials is up to the individual. For example, investors who believe a Solana ETF is coming soon can view the "% Discount / Premium to NAV", which is currently over 600%, and decide not to buy because the premium would collapse if an ETF began trading.
►Configuration
Data select the data you'd like to display.
Show Highest label show the highest value of the entire data set.
Line Color an expression of self.
Extrapolate Data Using Average or Last Known Value Shows a line beyond the dataset, using the average of all past data or the last data point to predict newer data. % Discount / Premium to NAV, Share Premium, and SOL Per Share are supported.
→Data retrieved from Grayscale
AUM assets under management.
NAV net asset value.
Market Price market price of GSOL.
Shares Outstanding number of shares held in the open market.
→Data retrieved from Grayscale, modified by me
% Discount / Premium to NAV the % away NAV is from the market price of GSOL.
Formula: (GSOL - NAV) / NAV
Share Premium the actual $ premium of GSOL to its NAV.
Formula: GSOL - NAV
SOL Per Share the amount of SOL 1 share of GSOL can redeem. This is derived using Kraken's SOLUSD daily close prices.
Formula: Kraken's SOLUSD / NAV
SOL Price Using Market Price Premium the price of SOL if GSOL's market price was "correct" and the SOL Per Share ratio remained the same.
Formula: GSOL / SOL Per Share
►How this works
Grayscale has a spreadsheet of historical data available on their GSOL page. Since financials are not available for OTC:GSOL, I placed all the data into arrays to emulate a symbol's price (y) coordinates. UNIX time for each day, also in an array, is used as the time (x) coordinates. The UNIX arrays and data arrays are then looped to plot as lines, with data y2 being the next data point, making it appear as a continuous line.
Grayscale's GSOL was downloaded spreadsheet and opened in Excel. SOLUSD prices were exported using TradingView export function. The output of information was pasted into Pine Script. For matching up Kraken's SOLUSD prices to each Grayscale's data since GSOL does not trade daily, dates were converted to UNIX and matched with xlookup(). A library or seed will be used in the future for updating.
References
Data retrieved from Grayscale's website 2024/08/04.
www.grayscale.com
Quantity of Solana held by the trust can be seen in their filings. Ctrl + F "Quantity of
SOL "
www.grayscale.com
Q1 2024: www.grayscale.com
The high premium can partly be explained by private placement currently being closed. This means private sales can't dilute share value.
www.etf.com
TASC 2024.08 Volume Confirmation For A Trend System█ OVERVIEW
This script demonstrates the use of volume data to validate price movements based on the techniques Buff Pelz Dormeier discusses in his "Volume Confirmation For A Trend System" article from the August 2024 edition of TASC's Traders' Tips . It presents a trend-following system implementation that utilizes a combination of three indicators: the Average Directional Index (ADX), the Trend Thrust Indicator (TTI), and the Volume Price Confirmation Indicator (VPCI).
█ CONCEPTS
In his article, Buff Pelz Dormeier recounts his search for an optimal trend-following strategy enhanced with volume data, starting with a simple system combining the ADX , MACD , and OBV indicators. Even in these early tests, the author observed that the volume confirmation from OBV notably improved trading performance. Subsequently, the author replaced OBV with his VPCI, which considers the proportional weights of volume and price, to enhance the validation of trend momentum. Lastly, the author explored the inclusion of his TTI, a modified MACD that features volume-based enhancements, as a strategy component for improved trend-following performance.
According to the author's research, the ADX+TTI+VPCI system outperformed similar strategies he tested in the article, yielding significantly higher returns and enhanced perceived reliability. Because the system's design revolves around catching pronounced trends, it performs best with a portfolio of individual stocks. The author applies the system in the article by allocating 5% of the equity to long positions in S&P 500 components that meet the ADX+TTI+VPCI entry criteria (see the Calculations section below for details). He uses the proceeds from closing positions to enter new positions in other stocks meeting the screening criteria, holding any excess proceeds in cash.
█ CALCULATIONS
The TTI is similar to the MACD. Its calculation entails the following steps:
Calculate fast (short-term) and slow (long-term) volume-weighted moving averages (VWMAs).
Compute the volume multiple (VM) as the square of the ratio of the fast VWMA to the slow VWMA.
Adjust these averages by multiplying the fast VWMA by the VM and dividing the slow VWMA by the VM.
Calculate the difference between the adjusted VWMAs to determine the TTI value, and take the average of that series to determine the signal line value.
The VPCI utilizes differences and ratios between VWMAs and corresponding simple moving averages (SMAs) to provide an alternative volume-price confirmation tool. Its calculation is as follows:
Subtract the slow SMA from the VWMA of the same length to calculate the volume-price confirmation/contradiction (VPC) value.
Divide the fast VWMA by the corresponding fast SMA to determine the volume-price ratio (VPR).
Divide the short-term VWMA by the long-term VWMA to calculate the VM.
Compute the VPCI as the product of the VPC, VPR, and VM values.
The long entry criteria of the ADX+TTI+VPCI system are as follows:
The ADX is above 30.
The TTI crosses above its signal line.
The VPCI is above 0, confirming the trend.
Signals to close positions occur when the VPCI is below 0, indicating a contradiction .
NOTE: Unlike in the article, this script applies the ADX+TTI+VPCI system to one stock at a time , not a portfolio of S&P 500 constituents.
█ DISCLAIMER
This strategy script educates users on the trading system outlined by the TASC article. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script.
TASC 2024.07 Gaps and Extreme Closes█ OVERVIEW
This script, inspired by Perry Kaufman's article "Trading Opening Gaps and Extreme Closes in Stocks" from the TASC's July 2024 edition of Traders' Tips , provides analytical insights into stock price behaviors following significant price moves. The information about the frequency, pullbacks, and closing patterns of these extreme price movements can aid in developing more effective trading strategies by understanding what to expect during volatile market conditions.
█ CONCEPTS
Perry Kaufman's article investigates the behavior of stock prices following substantial opening gaps and extreme closing moves to identify patterns and expectations that traders can utilize to make informed decisions. The motivation behind the article is to offer traders a more scientific approach to understanding price movements during volatile market conditions, particularly during earnings season or significant economic events. Kaufman's analysis reveals that stock prices have a history of exhibiting certain behaviors after substantial price gaps and extreme closes. This script follows Perry Kaufman's study and helps provide insight into how prices often behave after significant price changes. This analysis can help traders establish price movement expectations and potential strategies for trading such occurrences.
█ CALCULATIONS
Input Parameters:
This script offers users the choice to analyze "Opening Gaps" or "Extreme Closes" for price movements of different predefined magnitudes in a specified direction ("Upward" or "Downward").
Outputs:
Based on the specified inputs, the script performs the following calculations for the active ticker displayed on the chart:
Frequency of Extreme Price Movements : Quantifies the occurrences of directional price movements within predefined percentage ranges.
Average Pullbacks : Computes the average retracement (pullback) from analyzed price movements within each percentage range.
Average Closes : Analyzes the typical closing behavior relative to the directional price movements within each range.
The script organizes the results from these calculations within the table on a separate chart pane, providing users with helpful insights into how a stock historically behaved following significant price movements.
TASC 2024.06 REIT ETF Trading System█ OVERVIEW
This strategy script demonstrates the application of the Real Estate Investment Trust (REIT) ETF trading system presented in the article by Markos Katsanos titled "Is The Price REIT?" from TASC's June 2024 edition of Traders' Tips .
█ CONCEPTS
REIT stocks and ETFs offer a simplified, diversified approach to real estate investment. They exhibit sensitivity to interest rates, often moving inversely to interest rate and treasury yield changes. Markos Katsanos explores this relationship and the correlation of prices with the broader market to develop a trading strategy for REIT ETFs.
The script employs Bollinger Bands and Donchian channel indicators to identify oversold conditions and trends in REIT ETFs. It incorporates the 10-year treasury yield index (TNX) as a proxy for interest rates and the S&P 500 ETF (SPY) as a benchmark for the overall market. The system filters trade entries based on their behavior and correlation with the REIT ETF price.
█ CALCULATIONS
The strategy initiates long entries (buy signals) under two conditions:
1. Oversold condition
The weekly ETF low price dips below the 15-week Bollinger Band bottom, the closing price is above the value by at least 0.2 * ATR ( Average True Range ), and the price exceeds the week's median.
Either of the following:
– The TNX index is down over 15% from its 25-week high, and its correlation with the ETF price is less than 0.3.
– The yield is below 2%.
2. Uptrend
The weekly ETF price crosses above the previous week's 30-week Donchian channel high.
The SPY ETF is above its 20-week moving average.
Either of the following:
– Over ten weeks have passed since the TNX index was at its 30-week high.
– The correlation between the TNX value and the ETF price exceeds 0.3.
– The yield is below 2%.
The strategy also includes three exit (sell) rules:
1. Trailing (Chandelier) stop
The weekly close drops below the highest close over the last five weeks by over 1.5 * ATR.
The TNX value rises over the latest 25 weeks, with a yield exceeding 4%, or its value surges over 15% above the 25-week low.
2. Stop-loss
The ETF's price declines by at least 8% of the previous week's close and falls below the 30-week moving average.
The SPY price is down by at least 8%, or its correlation with the ETF's price is negative.
3. Overbought condition
The ETF's value rises above the 100-week low by over 50%.
The ETF's price falls over 1.5 * ATR below the 3-week high.
The ETF's 10-week Stochastic indicator exceeds 90 within the last three weeks.
█ DISCLAIMER
This strategy script educates users on the system outlined by the TASC article. However, note that its default properties might not fully represent real-world trading conditions for an individual. By default, it uses 10% of equity as the order size and a slippage amount of 5 ticks. Traders should adjust these settings and the commission amount when using this script. Additionally, since this strategy utilizes compound conditions on weekly data to trigger orders, it will generate significantly fewer trades than other, higher-frequency strategies.
NZTInstitutionalLevelDESCRIPTION IN ENGLISH
🔶 INTRODUCTION
NZTInstitutionalLevel is an indicator for the TradingView platform designed to display institutional levels on a price chart. This script is based on the concept of calculating significant price levels that can be used for both long-term trading and intraday operations. The indicator calculates and visualizes the levels at which large market participants , such as institutional investors and large funds , can actively participate. The displayed levels are very important , as psychologically people tend to buy or sell at these levels, which makes them a reliable support in the analysis
🔶 CONTENT
The indicator uses the analysis of support and resistance levels , which are often tested by major market players . These levels represent prices that have historically experienced significant price movements due to large trading volumes, making them relevant for future trading decisions. You may notice that price often reverses or tests these round levels. These levels are a powerful pillar of price action analysis.
🔶 KEY FEATURES
The indicator displays institutional (bank) levels . Thanks to which you can easily determine the position of major players and the direction of their capital.
Visualization customization:
Users can customize the display of levels by selecting color, thickness and line style (solid, dotted, dashed).
Adaptability:
The script adapts the level step size depending on the current price of the asset and the selected time interval, which allows it to be used in various trading conditions and for assets with different volatility and price range.
Automatic scaling:
The number of displayed levels changes depending on the selected time interval, allowing traders to focus only on significant levels without overloading the chart with unnecessary information.
🔶 SETTINGS
🔹 Show Institutional Levels (Показывать институциональные уровни)
Allows you to disable or enable the display of institutional levels.
🔹 Level color (Цвет уровней)
Allows you to customize the color of the levels.
🔹 Level thickness (Толщина уровней)
Allows you to adjust the thickness of the levels.
🔹 Level style (Стиль уровней)
Allows you to customize the levels' style.
🔶 RECOMMENDATIONS FOR USE
To use the indicator, activate it on the desired price chart through the TradingView indicator menu. Once activated, adjust the visibility, color, style and thickness of the levels according to your preferences. The indicator will automatically calculate and display institutional levels based on the current asset price and configured parameters . These levels can serve as potential points for placing buy or sell orders, setting stop losses, or taking profits.
The indicator was developed by Temirlan Tolegenov for NZT Trader Community , May 2024, Prague, Czech Republic.
ОПИСАНИЕ НА РУССКОМ ЯЗЫКЕ
🔶 ВСТУПЛЕНИЕ
NZTInstitutionalLevel – это индикатор для платформы TradingView, предназначенный для отображения институциональных уровней на ценовом графике . Этот скрипт основан на концепции вычисления значимых ценовых уровней , которые могут быть использованы как для долгосрочной торговли, так и для интрадей-операций . Индикатор рассчитывает и визуализирует уровни , на которых могут активно участвовать крупные участники рынка , такие как институциональные инвесторы и большие фонды . Отображаемые уровни очень важны , так как психологически люди склонны покупать или продавать на этих уровнях , что делает их надежной опорой при анализе.
🔶 СОДЕРЖАНИЕ
Индикатор использует анализ уровней поддержки и сопротивления , которые часто тестируются крупными игроками рынка . Эти уровни представляют собой цены, на которых исторически происходили значительные движения цен за счет больших объемов торгов, что делает их релевантными для будущих торговых решений. Вы можете заметить, что цена часто разворачивается или тестирует эти круглые уровни. Эти уровни являются мощной основой анализа price action.
🔶 КЛЮЧЕВЫЕ ОСОБЕННОСТИ
Индикатор отображает институциональные (банковские/круглые) уровни. Благодаря чему вы легко сможете определить позиции крупных игроков и направление их капиталов.
Настройка визуализации:
Пользователи могут настроить отображение уровней, выбрав цвет, толщину и стиль линий (сплошные, пунктирные, точками).
Адаптивность:
Скрипт адаптирует размер шага уровня в зависимости от текущей цены актива и выбранного временного интервала, что позволяет использовать его в различных торговых условиях и для активов с разной волатильностью и ценовым диапазоном.
Автоматическое масштабирование:
Количество отображаемых уровней меняется в зависимости от выбранного временного интервала, позволяя трейдерам сосредоточиться только на значимых уровнях, не перегружая график лишней информацией.
🔶 НАСТРОЙКИ
🔹 Показывать институциональные уровни
Позволяет отключить или включить отображение институциональных уровней.
🔹 Цвет уровней
Позволяет настроить цвет уровней.
🔹 Толщина уровней
Позволяет регулировать толщину уровней.
🔹 Стиль уровней
Позволяет настроить стиль уровней.
🔶 РЕКОМЕНДАЦИИ К ИСПОЛЬЗОВАНИЮ
Для использования индикатора, активируйте его на желаемом ценовом графике через меню индикаторов TradingView. После активации, н астройте видимость, цвет, стиль и толщину уровней в соответствии с вашими предпочтениями. Индикатор автоматически рассчитает и отобразит институциональные уровни , основываясь на текущей цене актива и настроенных параметрах . Эти уровни могут служить потенциальными точками для размещения ордеров на покупку или продажу, установления стоп-лоссов или взятия прибыли.
Индикатор разработан Темирланом Толегеновым для международного сообщества NZT Trader , Май 2024, Прага, Чешская Республика.
The indicator is published in accordance and respect to all House Rules of the TradingView platform.
Индикатор опубликован в соответствии и уважением ко всем внутренним правилами платформы TradingView.
TASC 2024.04 The Ultimate Smoother█ OVERVIEW
This script presents an implementation of the digital smoothing filter introduced by John Ehlers in his article "The Ultimate Smoother" from the April 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
The UltimateSmoother preserves low-frequency swings in the input time series while attenuating high-frequency variations and noise. The defining input parameter of the UltimateSmoother is the critical period , which represents the minimum wavelength (highest frequency) in the filter's pass band. In other words, the filter attenuates or removes the amplitudes of oscillations at shorter periods than the critical period.
According to Ehlers, one primary advantage of the UltimateSmoother is that it maintains zero lag in its pass band and minimal lag in its transition band, distinguishing it from other conventional digital filters (e.g., moving averages ). One can apply this smoother to various input data series, including other indicators.
█ CALCULATIONS
Ehlers derived the UltimateSmoother using inspiration from the design principles he learned from his experience with analog filters , as described in the original publication. On a technical level, the UltimateSmoother's unique response involves subtracting a high-pass response from an all-pass response . At very low frequencies (lengthy periods), where the high-pass filter response has virtually no amplitude, the subtraction yields a frequency and phase response practically equivalent to the input data. At other frequencies, the subtraction achieves filtration through cancellation due to the close similarities in response between the high-pass filter and the input data.
TASC 2024.03 Rate of Directional Change█ OVERVIEW
This script implements the Rate of Directional Change (RODC) indicator introduced by Richard Poster in the "Taming The Effects Of Whipsaw" article featured in the March 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
In his article, Richard Poster discusses an approach to potentially reduce false trend-following strategy entry signals due to whipsaws in forex data. The RODC indicator is central to this approach. The idea behind RODC is that one can characterize market whipsaw as alternating up and down ZigZag segments. By counting the number of up and down segments within a lookback window, the RODC indicator aims to identify if the window contains a significant whipsaw pattern:
RODC = 100 * Segments / Window Size (bars)
Larger RODC values suggest elevated whipsaw in the calculation window, while smaller values signify trending price activity.
█ CALCULATIONS
• For each price bar, the script iterates through the lookback window to identify up and down segments.
• If the price change between subsequent bars within the window is in the direction opposite to the current segment and exceeds the specified threshold , the calculation interprets the condition as a reversal point and the start of a new segment.
• The script uses the number of segments within the window to calculate RODC according to the above formula.
• Finally, the script applies a simple moving average to smoothen the RODC data.
Users can change the length of the lookback window , the threshold value, and the smoothing length in the "Inputs" tab of the script's settings.
TASC 2024.02 Price-Time Filtering█ OVERVIEW
This script implements a price-time trend filter proposed by Alfred François Tagher in the “Trend Identification By Price And Time Filtering” article from the February 2024 edition of TASC's Traders' Tips .
█ CONCEPTS
In his article, Alfred François Tagher proposed a rule set designed to minimize the impact of stochastic price movements, facilitating the identification of larger-scale trends. The rules are:
• If the most recent week's close exceeds the previous week's high, the trend is up.
• If the most recent week's close is below the previous week's low, the trend is down.
• The trend remains unchanged until one of the above conditions occurs.
Similar rules can also apply to monthly bars.
The author argues that this approach integrates characteristics of conventional price action and time dynamics filters, so he refers to it as price-time filtering .
█ CALCULATIONS
This script applies the above price-time filtering rules and offers multiple ways to view the results on a chart:
• In the "Oscillator" view mode, the script counts and displays the number of bars in the uptrend and downtrend.
• In the "Linebreak" view mode, the trend filter is presented in a format resembling a point-and-figure (P&F) chart , with the length of each bar corresponding to the high-low range of the respective trend.
• In both view modes, the script offers bar coloring of the main chart based on the identified trend.
TASC 2024.01 Gap Momentum System█ OVERVIEW
TASC's January 2024 edition of Traders' Tips features an article titled “Gap Momentum” by Perry J. Kaufman. The article discusses how a trader might create a momentum strategy based on opening gap data. This script implements the Gap Momentum system presented therein.
█ CONCEPTS
In the article, Perry J. Kaufman introduces Gap Momentum as a cumulative series constructed in the same way as On-Balance Volume (OBV) , but using gap openings (today’s open minus yesterday’s close).
To smoothen the resulting time series (i.e., obtain the " signal line "), the author applies a simple moving average . Subsequently, he proposes the following two trading rules for a long-only trading system:
• Enter a long position when the signal line is moving higher.
• Exit when the signal line is moving lower.
█ CALCULATIONS
The calculation of Gap Momentum involves the following steps:
1. Calculate the ratio of the sum of positive gaps over the past N days to the sum of negative gaps (absolute values) over the same time period.
2. Add the resulting gap ratio to the cumulative time series. This time series is the Gap Momentum.
3. Keep moving forward, as in an N-day moving average.
CM_VixFix_RSI_HMA200_TrailStop_vFinal📌 CM_VixFix_RSI_HMA200_TrailStop – vFinal | BTC 30-Minute Strategy (Long & Short)
This strategy combines volatility-based market shock detection (Williams Vix Fix), trend confirmation (HMA200), and momentum filtering (RSI) to generate high-probability trade entries. It is engineered for BTC/USDT on the 30-minute timeframe, with carefully tuned parameters through extensive backtesting.
🎯 Core Components:
WVF (Williams Vix Fix): Identifies volatility spikes, acting as a proxy for oversold conditions or panic drops.
RSI (Relative Strength Index): Generates directional momentum signals, with separate thresholds for long and short entries.
HMA200: Filters trades based on the prevailing market trend. Trades are only allowed in the direction of HMA slope and position.
ATR-Based Trailing Stop Engine: Activates after a minimum profit threshold is hit. Combines dynamic trailing logic with a Hard Stop (maximum loss limit) to mitigate risk.
⚠️ Short-Side Challenges & Solutions
During development, the short-side trades exhibited a lower win rate, a common behavior in crypto bull-biased markets. To address this, we implemented:
RSI trigger reduced to 20 to capture only high-momentum sell-offs.
Dual confirmation: RSI must also be below its EMA(21) and price below EMA(100).
MaxBars filter (10 candles): Prevents multiple short entries in tight ranges or low-volatility zones.
As a result:
Short trades now yield a Risk/Reward ratio above 3.4
Average short trade profit is ~3x the average loss, making short entries valuable despite lower hit rate.
📊 Performance Summary (Backtest: BTCUSDT, 30-Min, July 2024–July 2025)
Metric Long Short Overall
Total Trades 401 50 451
Win Rate 49.6% 30.0% 47.4%
Avg PnL 311 USDT 1,229 USDT 413 USDT
Risk/Reward (Avg K/L) 1.05 3.48 1.16
🚨 Disclaimer
This strategy is not a plug-and-play black box. While signals are statistically validated, we strongly recommend using this tool in conjunction with:
Volume and time-of-day filters
Fundamental/macro overlays (especially around Fed announcements or CPI data)
A broader risk management framework
Note: This strategy has been optimized exclusively for BTC/USDT on the 30-minute timeframe. If applied to other assets or timeframes, recalibration is necessary.
RSI-Adaptive T3 + Squeeze Momentum Strategy✅ Strategy Guide: RSI-Adaptive T3 + Squeeze Momentum Strategy
📌 Overview
The RSI-Adaptive T3 + Squeeze Momentum Strategy is a dynamic trend-following strategy based on an RSI-responsive T3 moving average and Squeeze Momentum detection .
It adapts in real-time to market volatility to enhance entry precision and optimize risk.
⚠️ This strategy is provided for educational and research purposes only.
Past performance does not guarantee future results.
🎯 Strategy Objectives
The main objective of this strategy is to catch the early phase of a trend and generate consistent entry signals.
Designed to be intuitive and accessible for traders from beginner to advanced levels.
✨ Key Features
RSI-Responsive T3: T3 length dynamically adjusts according to RSI values for adaptive trend detection
Squeeze Momentum: Combines Bollinger Bands and Keltner Channels to identify trend buildup phases
Visual Triggers: Entry signals are generated from T3 crossovers and momentum strength after squeeze release
📊 Trading Rules
Long Entry:
When T3 crosses upward, momentum is positive, and the squeeze has just been released.
Short Entry:
When T3 crosses downward, momentum is negative, and the squeeze has just been released.
Exit (Reversal):
When the opposite condition to the entry is triggered, the position is reversed.
💰 Risk Management Parameters
Pair & Timeframe: BTC/USD (30-minute chart)
Capital (simulated): $30,00
Order size: `$100` per trade (realistic, low-risk sizing)
Commission: 0.02%
Slippage: 2 pips
Risk per Trade: 5%
Number of Trades (backtest period): 181
📊 Performance Overview
Symbol: BTC/USD
Timeframe: 30-minute chart
Date Range: January 1, 2024 – July 3, 2025
Win Rate: 47.8%
Profit Factor: 2.01
Net Profit: 173.16 (units not specified)
Max Drawdown: 5.77% or 24.91 (0.79%)
⚙️ Indicator Parameters
Indicator Name: RSI-Adaptive T3 + Squeeze Momentum
RSI Length: 14
T3 Min Length: 5
T3 Max Length: 50
T3 Volume Factor: 0.7
BB Length: 27 (Multiplier: 2.0)
KC Length: 20 (Multiplier: 1.5, TrueRange enabled)
🖼 Visual Support
T3 slope direction, squeeze status, and momentum bars are visually plotted on the chart,
providing high clarity for quick trend analysis and execution.
🔧 Strategy Improvements & Uniqueness
Inspired by the RSI Adaptive T3 by ChartPrime and Squeeze Momentum Indicator by LazyBear ,
this strategy fuses both into a hybrid trend-reversal and momentum breakout detection system .
Compared to traditional trend-following methods, it excels at capturing early trend signals with greater sensitivity .
✅ Summary
The RSI-Adaptive T3 + Squeeze Momentum Strategy combines momentum detection with volatility-responsive risk management.
With a strong balance between visual clarity and practicality, it serves as a powerful tool for traders seeking high repeatability.
⚠️ This strategy is based on historical data and does not guarantee future profits.
Always use appropriate risk management when applying it.
VARNI-LINE-CHART//@version=5
indicator("VARNI-LINE-CHART",shorttitle = "VARNI-LINE-CHART", overlay=false)
// Input for Index and Expiry Date
spot_ = input.string("BANKNIFTY", title = "Spot Symbol", options = , group = "Index")
tooltip_day = "Enter the day of the expiry. Add 0 in front if the day is a single digit. For example: 05 instead of 5"
tooltip_month = "Enter the month of the expiry. Add 0 in front if the month is a single digit. For example: 06 instead of 6"
tooltip_year = "Enter the year of the expiry. Use the last two digits of the year. For example: 24 instead of 2024"
_day = input.string("13", title = "Expiry Day", tooltip = tooltip_day, group="Expiry Date")
_month = input.string("02", title = "Expiry Month", tooltip = tooltip_month, group="Expiry Date")
_year = input.string("25", title = "Expiry Year", tooltip = tooltip_year, group="Expiry Date")
// Input for Strikes
tooltip_ = "You can select any Strike, and choose to include both strikes or just one"
strike_ce = input.int(23500, "Call Strike", tooltip = tooltip_,step = 50, group = "Select Strike")
strike_pe = input.int(23500, "Put Strike", tooltip = tooltip_,step = 50, group = "Select Strike")
var string spot = na
if spot_ == "SENSEX"
spot := "BSX"
else if spot_ == "BANKEX"
spot := "BKX"
else
spot := spot_
// Option to include both strikes
strike_choice = input.string("Combined", title = "Select Strike", options = , group = "Select Strike")
// Generate symbols for Call and Put options
var string symbol_CE = spot + _year + _month + _day + "C" + str.tostring(strike_ce)
var string symbol_PE = spot + _year + _month + _day + "P" + str.tostring(strike_pe)
// Request security data for both Call and Put options
= request.security(symbol_CE, timeframe.period, )
= request.security(symbol_PE, timeframe.period, )
call_volume = request.security( symbol_CE, timeframe.period , volume )
put_volume = request.security( symbol_PE, timeframe.period , volume )
var float combined_open = 0
var float combined_high = 0
var float combined_low = 0
var float combined_close = 0
var float combined_vol = 0
// Calculate combined premium based on strike choice
if strike_choice == "Combined"
combined_open := call_open + put_open
combined_close := call_close + put_close
combined_high := math.max(combined_open, combined_close)
combined_low := math.min(combined_open, combined_close)
combined_vol := call_volume + put_volume
else if strike_choice == "Only Call"
combined_open := call_open
combined_close := call_close
combined_high := call_high
combined_low := call_low
combined_vol := call_volume
else
combined_open := put_open
combined_close := put_close
combined_high := put_high
combined_low := put_low
combined_vol := put_volume
// Plot combined premium as a line chart
plot(combined_close, title = "Combined Premium", color = combined_close > combined_open ? color.green : color.red, linewidth = 2)
// Indicator selection
use_ema_crossover = input.bool(false, title = "Use EMA Crossover", group = "Indicators")
use_supertrend = input.bool(false, title = "Use Supertrend", group = "Indicators")
use_vwap = input.bool(true, title = "Use VWAP", group = "Indicators")
use_rsi = input.bool(false, title = "Use RSI", group = "Indicators")
use_sma = input.bool(false, title = "Use SMA", group = "Indicators")
pine_supertrend_value(factor, atrPeriod) =>
src = combined_close
atr = ta.atr(atrPeriod)
upperBand = src + factor * atr
lowerBand = src - factor * atr
prevLowerBand = nz(lowerBand )
prevUpperBand = nz(upperBand )
lowerBand := lowerBand > prevLowerBand or combined_close < prevLowerBand ? lowerBand : prevLowerBand
upperBand := upperBand < prevUpperBand or combined_close > prevUpperBand ? upperBand : prevUpperBand
int _direction = na
float superTrend = na
prevSuperTrend = superTrend
if na(atr )
_direction := 1
else if prevSuperTrend == prevUpperBand
_direction := combined_close > upperBand ? -1 : 1
else
_direction := combined_close < lowerBand ? 1 : -1
superTrend := _direction == -1 ? lowerBand : upperBand
superTrend
pine_supertrend_dir(factor, atrPeriod) =>
src = combined_close
atr = ta.atr(atrPeriod)
upperBand = src + factor * atr
lowerBand = src - factor * atr
prevLowerBand = nz(lowerBand )
prevUpperBand = nz(upperBand )
lowerBand := lowerBand > prevLowerBand or combined_close < prevLowerBand ? lowerBand : prevLowerBand
upperBand := upperBand < prevUpperBand or combined_close > prevUpperBand ? upperBand : prevUpperBand
int _direction = na
float superTrend = na
prevSuperTrend = superTrend
if na(atr )
_direction := 1
else if prevSuperTrend == prevUpperBand
_direction := combined_close > upperBand ? -1 : 1
else
_direction := combined_close < lowerBand ? 1 : -1
superTrend := _direction == -1 ? lowerBand : upperBand
_direction
// Input for EMA lengths
fastLength = input.int(7, 'Fast EMA Length', group = "EMA")
slowLength = input.int(12, 'Slow EMA Length', group = "EMA")
// Input for SuperTrend
atrLength = input.int(7, 'ATR Length', group = "SuperTrend")
fac = input.float(2, 'Factor', group = "SuperTrend")
// Input for RSI
rsi_length = input.int(7, 'Length', group="RSI")
rsi_ob_level = input.int(80, 'Overbought', group="RSI")
rsi_os_level = input.int(20, 'Oversold', group="RSI")
// Input for SMA
sma_length = input.int(7, 'SMA Length', group = "SMA")
var float fast_ema = na
var float slow_ema = na
var float supertrend = na
var int direction = na
var float rsi_val = na
var float sma_val = na
var float sumPriceVolume = na
var float sumVolume = na
var float vwap = na
// Fast EMA
if use_ema_crossover
fast_ema := ta.ema(combined_close, fastLength)
slow_ema := ta.ema(combined_close, slowLength)
// Supertrend
if use_supertrend
supertrend := pine_supertrend_value( fac, atrLength)
direction := pine_supertrend_dir( fac, atrLength)
// VWAP
if use_vwap
if (dayofweek != dayofweek )
sumPriceVolume := 0.0
sumVolume := 0.0
vwap := 0.0
sumPriceVolume += combined_close * combined_vol
sumVolume += combined_vol
vwap := sumPriceVolume / sumVolume
// RSI
if use_rsi
rsi_val := ta.rsi(combined_close, rsi_length)
// SMA
if use_sma
sma_val := ta.sma(combined_close, sma_length)
plot(fast_ema, title='Fast EMA', color=color.blue, linewidth=2)
plot(slow_ema, title='Slow EMA', color=color.yellow, linewidth=2)
plot(direction < 0 ? supertrend : na, "Up direction", color = color.green, style=plot.style_linebr)
plot(direction > 0 ? supertrend : na, "Down direction", color = color.red, style=plot.style_linebr)
plot(vwap, title='VWAP', color=color.purple, linewidth=2)
plot(sma_val, title='SMA', color=color.maroon, linewidth=2)
// Define buy and sell conditions based on selected indicators
var bool buy = false
var bool sell = false
var int buyC = 0
var int sellC = 0
if dayofweek != dayofweek
buyC := 0
sellC := 0
if use_ema_crossover
buy := ( ta.crossover(fast_ema, slow_ema) ) and buyC == 0
sell := ( ta.crossunder(fast_ema, slow_ema) ) and sellC == 0
if use_vwap
buy := ( buy ? buy and (combined_close > vwap and combined_close <= vwap ) : (combined_close > vwap and combined_close <= vwap )) and buyC == 0
sell := ( sell ? sell and (combined_close < vwap and combined_close >= vwap ) : (combined_close < vwap and combined_close >= vwap )) and sellC == 0
if use_rsi
buy := ( buy ? buy and ta.crossover(rsi_val, rsi_ob_level) : ta.crossover(rsi_val, rsi_ob_level) ) and buyC == 0
sell := ( sell ? sell and ta.crossunder(rsi_val, rsi_os_level) : ta.crossunder(rsi_val, rsi_os_level) ) and sellC == 0
if use_sma
buy := ( buy ? buy and ta.crossover(combined_close, sma_val) : ta.crossover(combined_close, sma_val) ) and buyC == 0
sell := ( sell ? sell and ta.crossunder(combined_close, sma_val) : ta.crossunder(combined_close, sma_val) ) and sellC == 0
if use_supertrend
buy := ( buy ? direction == -1 : direction == -1 and direction == 1 ) and buyC == 0
sell := ( sell ? direction == 1 : direction == 1 and direction == -1 ) and sellC == 0
if buy
buyC := 1
sellC := 0
if sell
sellC := 1
buyC := 0
// Plot buy and sell signals
plotshape(buy, title = "Buy", text = 'Buy', style = shape.labeldown, location = location.top, color= color.green, textcolor = color.white, size = size.small)
plotshape(sell, title = "Sell", text = 'Sell', style = shape.labelup, location = location.bottom, color= color.red, textcolor = color.white, size = size.small)
// Alert conditions
alertcondition(buy, "Buy Alert", "Buy Signal")
alertcondition(sell, "Sell Alert", "Sell Signal")
📈 Smart Alert System — EMA/MA/Volume/SMC AlertsHere's a detailed description of your custom TradingView **Pine Script v6**:
---
### 📌 **Title**: Smart Alert System — Webhook Ready (with EMA, MA, Volume, and SMC)
This script is designed to **monitor price behavior**, detect important **technical analysis events**, and **send real-time alerts via webhook to a Telegram bot**.
---
## 🔧 SETTINGS
| Setting | Description |
| ----------------------- | ------------------------------------------------------------------------------ |
| `volumeSpikeMultiplier` | Multiplier to determine a volume spike compared to the 20-bar average volume |
| `testAlert` | If `true`, sends a test alert when the indicator is first applied to the chart |
---
## 🔍 COMPONENT BREAKDOWN
### 1. **EMA & MA Calculations**
These indicators are calculated and plotted on the chart:
* **EMAs**: 13, 25, 30, 200 — used for trend and touch detection
* **MAs**: 100, 300 — used for break and retest detection
```pinescript
ema_13 = ta.ema(close, 13)
ema_200 = ta.ema(close, 200)
ma_100 = ta.sma(close, 100)
```
---
### 2. **📈 Volume Spike Detection**
* A volume spike is identified when the current bar's volume is **2x (default)** greater than the 20-period average.
* A red triangle is plotted above such candles.
* A **JSON alert** is triggered.
```pinescript
volSpike = volume > avgVol * volumeSpikeMultiplier
```
---
### 3. **📊 EMA Touch Alerts**
* The script checks if the current close is:
* Within 0.1% of an EMA value **OR**
* Has crossed above/below it.
* If so, it sends an alert with the EMA name.
```pinescript
touch(val, crossed) =>
math.abs(close - val) / val < 0.001 or crossed
```
---
### 4. **📉 MA Break and Retest Alerts**
* A **break** is when price falls **below** a moving average.
* A **retest** is when price climbs **above** the same average after breaking below.
```pinescript
breakBelow(ma) => close > ma and close < ma
```
---
### 5. 🧠 **SMC Alerts (Break of Structure \ & Change of Character \ )**
These follow **Smart Money Concepts (SMC)**. The script identifies:
* **BOS**: New higher high in uptrend or lower low in downtrend
* **CHOCH**: Opposite of trend, e.g. lower low in uptrend or higher high in downtrend
```pinescript
bos = (high > high ) and (low > low ) and (low > low )
choch = (low < low ) and (high < high ) and (high < high )
```
---
### 6. 🧪 Dummy Test Alert (1-time fire)
* Sends a `"✅ Test Alert Fired"` to verify setup
* Executes **once only** after adding the indicator
```pinescript
var bool sentTest = false
if testAlert and not sentTest
```
---
### 7. 🚀 Alert Delivery (Webhook JSON)
All alerts are sent as a JSON payload that looks like this:
```json
{
"pair": "BTCUSD",
"event": "🔺 Volume Spike",
"timeframe": "15",
"timestamp": "2024-06-29T12:00:00Z",
"volume": "654000"
}
```
This format makes it compatible with your **Telegram webhook server**.
---
### 🔔 Alerts You Can Create in TradingView
* Set **Webhook URL** to your `https://xxxx.ngrok-free.app/tradingview-webhook`
* Use alert condition: `"Any alert()`" — because all logic is internal.
* Select **"Webhook URL"** and leave the message body blank.
---
### 🛠️ Use Cases
* Notify yourself on **EMA interaction**
* Detect **trend shifts or retests**
* Spot **volume-based market interest**
* Get real-time **BOS/CHOCH alerts** for Smart Money strategies
* Alert through **Telegram using your Node.js webhook server**
---
Would you like me to break down the full Pine Script block-by-block as well?
S4_IBS_Mean_Rev_3candleExitOverview:
This is a rules-based, mean reversion strategy designed to trade pullbacks using the Internal Bar Strength (IBS) indicator. The system looks for oversold conditions based on IBS, then enters long trades , holding for a maximum of 3 bars or until the trade becomes profitable.
The strategy includes:
✅ Strict entry rules based on IBS
✅ Hardcoded exit conditions for risk management
✅ A clean visual table summarizing key performance metrics
How It Works:
1. Internal Bar Strength (IBS) Setup:
The IBS is calculated using the previous bar’s price range:
IBS = (Previous Close - Previous Low) / (Previous High - Previous Low)
IBS values closer to 0 indicate price is near the bottom of the previous range, suggesting oversold conditions.
2. Entry Conditions:
IBS must be ≤ 0.25, signaling an oversold setup.
Trade entries are only allowed within a user-defined backtest window (default: 2024).
Only one trade at a time is permitted (long-only strategy).
3. Exit Conditions:
If the price closes higher than the entry price, the trade exits with a profit.
If the trade has been open for 3 bars without showing profit, the trade is forcefully exited.
All trades are closed automatically at the end of the backtest window if still open.
Additional Features:
📊 A real-time performance metrics table is displayed on the chart, showing:
- Total trades
- % of profitable trades
- Total P&L
- Profit Factor
- Max Drawdown
- Best/Worst trade performance
📈 Visual markers indicate trade entries (green triangle) and exits (red triangle) for easy chart interpretation.
Who Is This For?
This strategy is designed for:
✅ Traders exploring systematic mean reversion approaches
✅ Those who prefer strict, rules-based setups with no subjective decision-making
✅ Traders who want built-in performance tracking directly on the chart
Note: This strategy is provided for educational and research purposes. It is a backtested model and past performance does not guarantee future results. Users should paper trade and validate performance before considering real capital.
High Low Levels by JZCustom High Low Levels Indicator - features
Clearly plotted high and low levels for specific trading sessions. This indicator provides visual representations of key price levels during various trading periods. Below are the main features and benefits of this indicator:
1. Display high and low levels for each session
- previous day high/low: display the high and low from the previous day, giving you a better understanding of how the price moves compared to the prior day.
- asia, london, and custom sessions: track the high and low levels for the major trading sessions (asian and london) and two custom user-defined sessions.
2. Complete line and label customization
- custom line appearance: choose the color, line style (solid, dashed, dotted), and line thickness for each trading session. you can also decide if the lines should extend beyond the current price action.
- custom labels: define your own label texts for each custom session. this way, you can label the levels precisely and easily track price movements.
3. Define your own trading sessions
- add up to two custom sessions (custom and custom 2), which can be defined using precise start and end times (hour and minute).
- each custom session allows you to specify the label text for the high and low levels, enabling you to easily differentiate different parts of the day on the chart.
4. Clear and intuitive design
- grouped settings: all settings are grouped based on trading sessions, so you can easily customize every aspect of the visual representation.
- simple toggle on/off: you can easily enable or disable each line (previous day, asia, london, custom 1, custom 2). this allows you to keep your chart clean and focus only on the important levels you need at any moment.
5. Flexible time zones
- time zone settings: set the time zone (utc, europe/london, america/new_york, asia/tokyo) to properly align the timeframes for each level depending on the market you're focusing on.
6. Automatic cleanup of old lines and labels
- old levels removal: automatically remove old lines and labels to prevent clutter on your chart. this ensures that only current, relevant levels for each trading day or session are displayed.
7. Precise plotting and line extension
- accurate level markings: the indicator calculates the precise times when the high and low levels were reached and plots lines that visually represent these levels.
- line extension options: you have the option to extend the high/low lines beyond their point of calculation, which helps with identifying price action trends beyond the current period.
Dec 7, 2024
Release Notes
Changes and Improvements for Users:
1. Customizable Offset for Lines and Labels:
- A new input, `Line and Label Offset`, allows users to control how far the lines and their associated text labels extend. This ensures the labels and lines remain aligned and can be adjusted as needed.
2. Unified Offset Control:
- The same offset value is applied to all types of lines and labels (e.g., Previous Day High/Low, Asia High/Low, London High/Low, and custom sessions). Users can change this in one place to affect the entire script consistently.
3. Enhanced Flexibility:
- Users now have more control over the appearance and position of their lines and labels, making the indicator adaptable to different chart setups and personal preferences.
These updates aim to enhance user convenience and customization, ensuring a more tailored charting experience.
Real-Time Open Levels with Labels + Info TableReal-Time Multi-Timeframe Open Levels with Labels & Info Panel
Overview
This indicator displays real-time opening price levels across multiple timeframes (Monthly, Weekly, Daily, 4H) directly on your chart. It features:
• Dynamic horizontal lines extending through each timeframe period
• Customizable labels with text/colors
• Special 4H line treatment for the last hour (5-min charts only)
• Integrated information panel showing symbol, timeframe, and price changes
! (www.tradingview.com)
*Example showing multiple timeframe levels with labels and info panel*
---
Features & Configuration
1. Monthly Settings
! (www.tradingview.com)
Show Monthly: Toggle visibility of monthly opening price
Color: Semi-transparent blue (#2196F3 at 70% opacity)
Width: 2px line thickness
Style: Solid/Dotted/Dashed
Label: Display "M-Open" text with white text on blue background
2. Weekly Settings
! (www.tradingview.com)
Show Weekly: Toggle weekly opening price visibility
Color: Semi-transparent red (#FF5252 at 70% opacity)
Width: 1px thickness
Style: Dotted by default
Label: "W-Open" text in white on red background
3. Daily Settings
! (www.tradingview.com)
Show Daily: Toggle daily opening price
Color: Amber (#FFA000 at 70% opacity)
Width: 2px thickness
Style: Solid
Label: "D-Open" in white on orange background
---
4. 4-Hour Settings (5-Minute Charts Only)
Special Features for 5-Min Timeframe:
1. Standard 4H Line
• First 3 hours: Green (#4CAF50) dashed line
• Last hour: Bright red solid line (configurable)
• Vertical divider between 3rd/4th hours
2. Configuration Options
• Main 4H Line:
◦ Color/Width/Style for initial 3 hours
◦ Toggle label ("H4-Open") visibility and styling
• Final Hour Enhancement:
*Last Hour Line*
◦ Unique red color and line style
◦ Separate width (1px) and style (Solid)
*Divider Line*
◦ Vertical red dotted line marking last hour
◦ Adjustable position/width/transparency
! (www.tradingview.com)
*4H levels showing 3-hour segment and final hour treatment*
---
5. Info Panel Settings
Positioning:
• Anchor to any chart corner (Top/Bottom + Left/Right combinations)
• Three text sizes: Title (Huge), Change % (Large), Signature (Small)
Display Elements:
• Symbol: Show exchange prefix (e.g., "NASDAQ:")
• Timeframe: Current chart period (e.g., "5m")
• Change %: 24-hour price movement ▲/▼ percentage
• Custom Signature: Add text/username in footer
Styling:
• Semi-transparent white text (#ffffff77)
• Currency pair formatting (e.g., BTC/USD vs BTC-USD)
! (www.tradingview.com)
*Sample info panel with all elements enabled*
---
Usage Tips
1. Multi-Timeframe Context: Use levels to identify key daily/weekly support/resistance
2. 4H Trading: On 5-min charts, watch for price reactions near final hour transition
3. Customization:
• Match line colors to your chart theme
• Use different labels for clarity (e.g., "Weekly Open")
• Disable unused elements to reduce clutter
4. Divider Lines: Helps identify institutional trading periods (hour closes)
---
*Created using Pine Script v6. For optimal performance, use on charts <1H timeframe. ()*
Ehlers Ultimate Bands (UBANDS)UBANDS: ULTIMATE BANDS
🔍 OVERVIEW AND PURPOSE
Ultimate Bands, developed by John F. Ehlers, are a volatility-based channel indicator designed to provide a responsive and smooth representation of price boundaries with significantly reduced lag compared to traditional Bollinger Bands. Bollinger Bands typically use a Simple Moving Average for the centerline and standard deviations from it to establish the bands, both of which can increase lag. Ultimate Bands address this by employing Ehlers' Ultrasmooth Filter for the central moving average. The bands are then plotted based on the volatility of price around this ultrasmooth centerline.
The primary purpose of Ultimate Bands is to offer traders a clearer view of potential support and resistance levels that react quickly to price changes while filtering out excessive noise, aiming for nearly zero lag in the indicator band.
🧩 CORE CONCEPTS
Ultrasmooth Centerline: Employs the Ehlers Ultrasmooth Filter as the basis (centerline) for the bands, aiming for minimal lag and enhanced smoothing.
Volatility-Adaptive Width: The distance between the upper and lower bands is determined by a measure of price deviation from the ultrasmooth centerline. This causes the bands to widen during volatile periods and contract during calm periods.
Dynamic Support/Resistance: The bands serve as dynamic levels of potential support (lower band) and resistance (upper band).
🧮 CALCULATION AND MATHEMATICAL FOUNDATION
Ehlers' Original Concept for Deviation:
John Ehlers describes the deviation calculation as: "The deviation at each data sample is the difference between Smooth and the Close at that data point. The Standard Deviation (SD) is computed as the square root of the average of the squares of the individual deviations."
This describes calculating the Root Mean Square (RMS) of the residuals:
Smooth = UltrasmoothFilter(Source, Length)
Residuals = Source - Smooth
SumOfSquaredResiduals = Sum(Residuals ^2) for i over Length
MeanOfSquaredResiduals = SumOfSquaredResiduals / Length
SD_Ehlers = SquareRoot(MeanOfSquaredResiduals) (This is the RMS of residuals)
Pine Script Implementation's Deviation:
The provided Pine Script implementation calculates the statistical standard deviation of the residuals:
Smooth = UltrasmoothFilter(Source, Length) (referred to as _ehusf in the script)
Residuals = Source - Smooth
Mean_Residuals = Average(Residuals, Length)
Variance_Residuals = Average((Residuals - Mean_Residuals)^2, Length)
SD_Pine = SquareRoot(Variance_Residuals) (This is the statistical standard deviation of residuals)
Band Calculation (Common to both approaches, using their respective SD):
UpperBand = Smooth + (NumSDs × SD)
LowerBand = Smooth - (NumSDs × SD)
🔍 Technical Note: The Pine Script implementation uses a statistical standard deviation of the residuals (differences between price and the smooth average). Ehlers' original text implies an RMS of these residuals. While both measure dispersion, they will yield slightly different values. The Ultrasmooth Filter itself is a key component, designed for responsiveness.
📈 INTERPRETATION DETAILS
Reduced Lag: The primary advantage is the significant reduction in lag compared to standard Bollinger Bands, allowing for quicker reaction to price changes.
Volatility Indication: Widening bands indicate increasing market volatility, while narrowing bands suggest decreasing volatility.
Overbought/Oversold Conditions (Use with caution):
• Price touching or exceeding the Upper Band may suggest overbought conditions.
• Price touching or falling below the Lower Band may suggest oversold conditions.
Trend Identification:
• Price consistently "walking the band" (moving along the upper or lower band) can indicate a strong trend.
• The Middle Band (Ultrasmooth Filter) acts as a dynamic support/resistance level and indicates the short-term trend direction.
Comparison to Ultimate Channel: Ehlers notes that the Ultimate Band indicator does not differ from the Ultimate Channel indicator in any major fashion.
🛠️ USE AND APPLICATION
Ultimate Bands can be used similarly to how Keltner Channels or Bollinger Bands are used for interpreting price action, with the main difference being the reduced lag.
Example Trading Strategy (from John F. Ehlers):
Hold a position in the direction of the Ultimate Smoother (the centerline).
Exit that position when the price "pops" outside the channel or band in the opposite direction of the trade.
This is described as a trend-following strategy with an automatic following stop.
⚠️ LIMITATIONS AND CONSIDERATIONS
Lag (Minimized but Present): While significantly reduced, some minimal lag inherent to averaging processes will still exist. Increasing the Length parameter for smoother bands will moderately increase this lag.
Parameter Sensitivity: The Length and StdDev Multiplier settings are key to tuning the indicator for different assets and timeframes.
False Signals: As with any band indicator, false signals can occur, particularly in choppy or non-trending markets.
Not a Standalone System: Best used in conjunction with other forms of analysis for confirmation.
Deviation Calculation Nuance: Be aware of the difference in deviation calculation (statistical standard deviation vs. RMS of residuals) if comparing directly to Ehlers' original concept as described.
📚 REFERENCES
Ehlers, J. F. (2024). Article/Publication where "Code Listing 2" for Ultimate Bands is featured. (Specific source to be identified if known, e.g., "Stocks & Commodities Magazine, Vol. XX, No. YY").
Ehlers, J. F. (General). Various publications on advanced filtering and cycle analysis. (e.g., "Rocket Science for Traders", "Cycle Analytics for Traders").
OHLCVDataOHLCV Data Power Library
Multi-Timeframe Market Data with Mathematical Precision
📌 Overview
This Pine Script library provides structured OHLCV (Open, High, Low, Close, Volume) data across multiple timeframes using mathematically significant candle counts (powers of 3). Designed for technical analysts who work with fractal market patterns and need efficient access to higher timeframe data.
✨ Key Features
6 Timeframes: 5min, 1H, 4H, 6H, 1D, and 1W data
Power-of-3 Candle Counts: 3, 9, 27, 81, and 243 bars
Structured Data: Returns clean OHLCV objects with all price/volume components
Pine Script Optimized: Complies with all security() call restrictions
📊 Timeframe Functions
pinescript
f_get5M_3() // 3 candles of 5min data
f_get1H_27() // 27 candles of 1H data
f_get1D_81() // 81 candles of daily data
// ... and 27 other combinations
🚀 Usage Example
pinescript
import YourName/OHLCVData/1 as OHLCV
weeklyData = OHLCV.f_get1W_27() // Get 27 weekly candles
latestHigh = array.get(weeklyData, 0).high
plot(latestHigh, "Weekly High")
💡 Ideal For
Multi-timeframe analysis
Volume-profile studies
Fractal pattern detection
Higher timeframe confirmation
⚠️ Note
Replace "YourName" with your publishing username
All functions return arrays of OHLCV objects
Maximum lookback = 243 candles
📜 Version History
1.0 - Initial release (2024)
