[Autoview][Alerts]Blank R0.13BThis is a fork of JustUncleL's
Dual MA Ribbons R0.13
It is now a blank template for making new strategies / alerts for autoview
The changes are as follows:
Removed actual algo
Establish functions for long Signal, long Close Signal and short Signal, short Close Signal to minimize the places code must be edited to update / replace algos
Make allow Long and allow short and invert trade directions independent options
Added support for alternate candle types
Added autoset backtest period feature, and optional coloring
Moved strategy calls in to functions so they can all be commented out or activated / disabled in a single block at the top of the script
Cerca negli script per "algo"
Top Bottom Finder Public version- Jayy This script plots a 6 algos from the Coles/Hawkins "Midas Technical Analysis" book:
Top finder / Bottom Finder (Levine Algo by Bob English)* - onlinelibrary.wiley.com
MIDAS VWAP Gen-1) -
MIDAS VWAP average and deltas
VWAP (Gen-1) using a date or a bar n number can be initiated at bar 0 - useful for a new IPO
Standard Deviation of MIDAS VWAP
MIDAS Displacement Channels (Coles) - edmond.mires.co
An%20Anchored%20VWAP%20Channel%20For%20Congested%20Markets.pdf
* for better results with topfinder and bottomfinder use the companion TB-F Matcher script.
See wiki for a synopsis: en.wikipedia.org
Relevant info can be found in: Midas Technical Analysis: A VWAP Approach to Trading and Investing in Today’s Markets by
Andrew Coles, David G. Hawkins Copyright © 2011 by Andrew Coles and David G. Hawkins.
Appendix C: TradeStation Code for the MIDAS Topfinder/Bottomfinder Curves ported to Tradingview
This script requires a working understanding of "Midas Technical Analysis" Google "Midas Technical Analysis" and a variety of information will appear.
To find fit the curve as described in the Midas book a companion script is required that will after a few manual iterative inputs guide you to the appropriate D value for the for input into this program ( see the TB-F Matcher script). You might also try the Midas average and Deltas as described in the book. I have added the 2nd, 3rd and 4th multiples of Delta.
The advantage is that there is no curve fitting. You still need to select a starting point for Midas or the topfinder bottomfinder (TB_F)
or the VWAP.
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
See the notes in the script below
Cheers Jayy
Volume Range EventsChanges in the feelings (positive, negative, neutral) in the market concerning the valuation of an instrument are often preceded with sudden outbursts of buying and selling frenzies. The aim of this indicator is to report such outbursts. We can see them as expansions of volume, sometimes 10 times more than usual. and as extensions of the trading range, also sometimes 10 times more than usual (e.g. usual range is 10 cent suddenly a whole dollar.) The changes are calculated in such a way that these fit between plus and minus 100 percent, the bars are scaled in some sort of logarithmic way. The Emoline is the same as the one in the True Balance of Power indicator, which I already published
ONLY RISES ARE EVENTS
Sometimes analysts are tempted to give meaning to low volume or small ranges. These simply mean that the market has little interest in trading this instrument. I believe that in such cases the trader needs to wait for expansion and extension events to happen, then he can make a better guess of where the market is heading. As events often mark the beginning or ending of a trend, this indicator provides an early and clear signal, because it doesn’t bother us about non-events.
WHAT IS USUAL?
If the algorithm would use an average as a normal to scale volume or range events, then previous peaks will act as spoilers by making the average so high that a following peak is scaled too small. I developed a function, usual() , that kicks out all extremes of a ‘population of values’ and which returns the average of the non-extreme values. It can be called with any serial. This function is called by both algorithms that report volume and range peaks, which guarantees that the results are really comparable. As this function has a fixed look back of 8 periods, we might state that ‘usual’ is a short lived relative value. I think this doesn’t matter for the practical use of the indicator.
COLORING AND INTERPRETATION
I follow the categories in the ‘Better Volume Indicator’, published by LeazyBear, these are:
1. Climactic Volumes, event >40 % (this means peak is 1.5 X usual)
LIME: Climax Buying Volume, direction up, range event also > 30 %
RED: Climax Selling Volume, direction down, range event also > 30 %
AQUA: Climax Churning Volume, both directions, range event < 30%
2. Smaller Volumes, event <40 %
GREEN: Supportive Volume, both directions, if combined with range event
BLUE: Churning Volume, both directions, if not combined with range event (Professional Trading)
3. Just Range Events
BLACK histogram bars (Amateurish Trading)
BUY & SELL VOLUME TO PRICE PRESSURE by @XeL_ArjonaBUY & SELL PRICE TO VOLUME PRESSURE
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by: Stocks & Commodities V. 21:10 (68-72): "Bull And Bear Balance Indicator by Vadim Gimelfarb"
Normalisation (Filter) from Karthik Marar's VSA work: karthikmarar.blogspot.mx
Buy to Sell Convergence / Divergence and Volume Pressure Counterforce Histogram Ideas by: @XeL_Arjona
WHAT IS THIS?
The following indicators try to acknowledge in a K-I-S-S approach to the eye (Keep-It-Simple-Stupid), the two most important aspects of nearly every trading vehicle: -- PRICE ACTION IN RELATION BY IT'S VOLUME --
Volume Pressure Histogram: Columns plotted in positive are considered the dominant Volume Force for the given period. All "negative" columns represents the counterforce Vol.Press against the dominant.
Buy to Sell Convergence / Divergence: It's a simple adaptation of the popular "Price Percentage Oscillator" or MACD but taking Buying Pressure against Selling Pressure Averages, so given a Positive oscillator reading (>0) represents Bullish dominant Trend and a Negative reading (<0) a Bearish dominant Trend. Histogram is the diff between RAW Volume Pressures Convergence/Divergence minus Normalised ones (Signal) which helps as a confirmation.
Volume bars are by default plotted from RAW Volume Pressure algorithms, but they can be as well filtered with Karthik Marar's approach against a "Total Volume Average" in favor to clean day to day noise like HFT.
ALL NEW IDEAS OR MODIFICATIONS to these indicators are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. -- 2015
BUY & SELL VOLUME TO PRICE PRESSURE by @XeL_ArjonaBUY & SELL PRICE TO VOLUME PRESSURE
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by: Stocks & Commodities V. 21:10 (68-72): "Bull And Bear Balance Indicator by Vadim Gimelfarb"
Normalisation (Filter) from Karthik Marar's VSA work: karthikmarar.blogspot.mx
Buy to Sell Convergence / Divergence and Volume Pressure Counterforce Histogram Ideas by: @XeL_Arjona
WHAT IS THIS?
The following indicators try to acknowledge in a K-I-S-S approach to the eye (Keep-It-Simple-Stupid), the two most important aspects of nearly every trading vehicle: -- PRICE ACTION IN RELATION BY IT'S VOLUME --
Volume Pressure Histogram: Columns plotted in positive are considered the dominant Volume Force for the given period. All "negative" columns represents the counterforce Vol.Press against the dominant.
Buy to Sell Convergence / Divergence: It's a simple adaptation of the popular "Price Percentage Oscillator" or MACD but taking Buying Pressure against Selling Pressure Averages, so given a Positive oscillator reading (>0) represents Bullish dominant Trend and a Negative reading (<0) a Bearish dominant Trend. Histogram is the diff between RAW Volume Pressures Convergence/Divergence minus Normalised ones (Signal) which helps as a confirmation.
Volume bars are by default plotted from RAW Volume Pressure algorithms, but they can be as well filtered with Karthik Marar's approach against a "Total Volume Average" in favor to clean day to day noise like HFT.
ALL NEW IDEAS OR MODIFICATIONS to these indicators are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. -- 2015
BUY & SELL VOLUME PRESSURE by @XeL_ArjonaBUY & SELL PRICE TO VOLUME PRESSURE
By Ricardo M Arjona @XeL_Arjona
DISCLAIMER:
The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.
The embedded code and ideas within this work are FREELY AND PUBLICLY available on the Web for NON LUCRATIVE ACTIVITIES and must remain as is.
Pine Script code MOD's and adaptations by @XeL_Arjona with special mention in regard of:
Buy (Bull) and Sell (Bear) "Power Balance Algorithm" by: Stocks & Commodities V. 21:10 (68-72): "Bull And Bear Balance Indicator by Vadim Gimelfarb"
Normalisation (Filter) from Karthik Marar's VSA work: karthikmarar.blogspot.mx
Buy to Sell Convergence / Divergence and Volume Pressure Counterforce Histogram Ideas by: @XeL_Arjona
WHAT IS THIS?
The following indicators try to acknowledge in a K-I-S-S approach to the eye (Keep-It-Simple-Stupid), the two most important aspects of nearly every trading vehicle: -- PRICE ACTION IN RELATION BY IT'S VOLUME --
Volume Pressure Histogram: Columns plotted in positive are considered the dominant Volume Force for the given period. All "negative" columns represents the counterforce Vol.Press against the dominant.
Buy to Sell Convergence / Divergence: It's a simple adaptation of the popular "Price Percentage Oscillator" or MACD but taking Buying Pressure against Selling Pressure Averages, so given a Positive oscillator reading (>0) represents Bullish dominant Trend and a Negative reading (<0) a Bearish dominant Trend. Histogram is the diff between RAW Volume Pressures Convergence/Divergence minus Normalised ones (Signal) which helps as a confirmation.
Volume bars are by default plotted from RAW Volume Pressure algorithms, but they can be as well filtered with Karthik Marar's approach against a "Total Volume Average" in favor to clean day to day noise like HFT.
ALL NEW IDEAS OR MODIFICATIONS to these indicators are Welcome in favor to deploy a better and more accurate readings. I will be very glad to be notified at Twitter: @XeL_Arjona
Any important addition to this work MUST REMAIN PUBLIC by means of CreativeCommons CC & TradingView. -- 2015
Machine Learning | Adaptive Trend Signals [Bitwardex]⚙️🧠Machine Learning | Adaptive Trend Signals
🔷Overview
Machine Learning | Adaptive Trend Signals is a Pine Script™ v6 indicator designed to visualize market trends and generate signals through a combination of volatility clustering, Gaussian smoothing, and adaptive trend calculations. Built as an overlay indicator, it integrates advanced techniques inspired by machine learning concepts, such as K-Means clustering, to adapt to changing market conditions. The script is highly customizable, includes a backtesting module, and supports alert conditions, making it suitable for traders exploring trend-based strategies and developers studying volatility-driven indicator design.
🔷Functionality
The indicator performs the following core functions:
• Volatility Clustering: Uses K-Means clustering to categorize market volatility into high, medium, and low states, adjusting trend sensitivity accordingly.
• Trend Calculation: Computes adaptive trend lines (SmartTrend) based on volatility-adjusted standard deviation, smoothed RSI, and ADX filters.
• Signal Generation: Identifies potential buy and sell points through trend line crossovers and directional confirmation.
• Backtesting Module: Tracks trade outcomes based on the SmartTrend3 value, displaying win rate and total trades.
• Visualization: Plots trend lines with gradient colors and optional signal markers (bullish 🐮 and bearish 🐻).
• Alerts: Provides configurable alerts for trend shifts and volatility state changes.
🔷Technical Methodology
Volatility Clustering with K-Means
The indicator employs a K-Means clustering algorithm to classify market volatility, measured via the Average True Range (ATR), into three distinct clusters:
• Data Collection: Gathers ATR values over a user-defined training period (default: 100 bars).
• Centroid Initialization: Sets initial centroids at the highest, lowest, and midpoint ATR values within the training period.
• Iterative Clustering: Assigns ATR data points to the nearest centroid, recalculates centroid means, and repeats until convergence.
• Dynamic Adjustment: Assigns a volatility state (high, medium, or low) based on the closest centroid, adjusting the trend factor (e.g., tighter for high volatility, wider for low volatility).
This approach allows the indicator to adapt its sensitivity to varying market conditions, providing a data-driven foundation for trend calculations.
🔷Gaussian Smoothing
To enhance signal clarity and reduce noise, the indicator applies Gaussian kernel smoothing to:
• RSI: Smooths the Relative Strength Index (calculated from OHLC4) to filter short-term fluctuations.
• SmartTrend: Smooths the primary trend line for a more stable output.
The Gaussian kernel uses a sigma value derived from the user-defined smoothing length, ensuring mathematically consistent noise reduction.
🔷SmartTrend Calculation
The pineSmartTrend function is the core of the indicator, producing three trend lines:
• SmartTrend: The primary trend line, calculated using a volatility-adjusted standard deviation, smoothed RSI, and ADX conditions.
• SmartTrend2: A secondary trend line with a wider factor (base factor * 1.382) for signal confirmation.
SmartTrend3: The average of SmartTrend and SmartTrend2, used for plotting and backtesting.
Key components of the calculation include:
• Dynamic Standard Deviation: Scales based on ATR relative to its 50-period smoothed average, with multipliers (1.0 to 1.4) applied according to volatility thresholds.
• RSI and ADX Filters: Requires RSI > 50 for bullish trends or < 50 for bearish trends, alongside ADX > 15 and rising to confirm trend strength.
Volatility-Adjusted Bands: Constructs upper and lower bands around price action, adjusted by the volatility cluster’s dynamic factor.
🔷Signal Generation
The generate_signals function generates signals as follows:
• Buy Signal: Triggered when SmartTrend crosses above SmartTrend2 and the price is above SmartTrend, with directional confirmation.
• Sell Signal: Triggered when SmartTrend crosses below SmartTrend2 and the price is below SmartTrend, with directional confirmation.
Directional Logic: Tracks trend direction to filter out conflicting signals, ensuring alignment with the broader market context.
Signals are visualized as small circles with bullish (🐮) or bearish (🐻) emojis, with an option to toggle visibility.
🔷Backtesting
The get_backtest function evaluates signal outcomes using the SmartTrend3 value (rather than closing prices) to align with the trend-based methodology.
It tracks:
• Total Trades: Counts completed long and short trades.
• Win Rate: Calculates the percentage of trades where SmartTrend3 moves favorably (higher for longs, lower for shorts).
Position Management: Closes opposite positions before opening new ones, simulating a single-position trading system.
Results are displayed in a table at the top-right of the chart, showing win rate and total trades. Note that backtest results reflect the indicator’s internal logic and should not be interpreted as predictive of real-world performance.
🔷Visualization and Alerts
• Trend Lines: SmartTrend3 is plotted with gradient colors reflecting trend direction and volatility cluster, accompanied by a secondary line for visual clarity.
• Signal Markers: Optional buy/sell signals are plotted as small circles with customizable colors.
• Alerts: Supports alerts for:
• Bullish and bearish trend shifts (confirmed on bar close).
Transitions to high, medium, or low volatility states.
🔷Input Parameters
• ATR Length (default: 14): Period for ATR calculation, used in volatility clustering.
• Period (default: 21): Common period for RSI, ADX, and standard deviation calculations.
• Base SmartTrend Factor (default: 2.0): Base multiplier for volatility-adjusted bands.
• SmartTrend Smoothing Length (default: 10): Length for Gaussian smoothing of the trend line.
• Show Buy/Sell Signals? (default: true): Enables/disables signal markers.
• Bullish/Bearish Color: Customizable colors for trend lines and signals.
🔷Usage Instructions
• Apply to Chart: Add the indicator to any TradingView chart.
• Configure Inputs: Adjust parameters to align with your trading style or market conditions (e.g., shorter ATR length for faster markets).
• Interpret Output:
• Trend Lines: Use SmartTrend3’s direction and color to gauge market bias.
• Signals: Monitor bullish (🐮) and bearish (🐻) markers for potential entry/exit points.
• Backtest Table: Review win rate and total trades to understand the indicator’s behavior in historical data.
• Set Alerts: Configure alerts for trend shifts or volatility changes to support manual or automated trading workflows.
• Combine with Analysis: Use the indicator alongside other tools or market context, as it is designed to complement, not replace, comprehensive analysis.
🔷Technical Notes
• Data Requirements: Requires at least 100 bars for accurate volatility clustering. Ensure sufficient historical data is loaded.
• Market Suitability: The indicator is designed for trend detection and may perform differently in ranging or volatile markets due to its reliance on RSI and ADX filters.
• Backtesting Scope: The backtest module uses SmartTrend3 values, which may differ from price-based outcomes. Results are for informational purposes only.
• Computational Intensity: The K-Means clustering and Gaussian smoothing may increase processing time on lower timeframes or with large datasets.
🔷For Developers
The script is modular, well-commented, encouraging reuse and modification with proper attribution.
Key functions include:
• gaussianSmooth: Applies Gaussian kernel smoothing to any data series.
• pineSmartTrend: Computes adaptive trend lines with volatility and momentum filters.
• getDynamicFactor: Adjusts trend sensitivity based on volatility clusters.
• get_backtest: Evaluates signal performance using SmartTrend3.
Developers can extend these functions for custom indicators or strategies, leveraging the volatility clustering and smoothing methodologies. The K-Means implementation is particularly useful for adaptive volatility analysis.
🔷Limitations
• The indicator is not predictive and should be used as part of a broader trading strategy.
• Performance varies by market, timeframe, and parameter settings, requiring user experimentation.
• Backtest results are based on historical data and internal logic, not real-world trading conditions.
• Volatility clustering assumes sufficient historical data; incomplete data may affect accuracy.
🔷Acknowledgments
Developed by Bitwardex, inspired by machine learning concepts and adaptive trading methodologies. Community feedback is welcome via TradingView’s platform.
🔷 Risk Disclaimer
Trading involves significant risks, and most traders may incur losses. Bitwardex AI Algo is provided for informational and educational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument . The signals, metrics, and features are tools for analysis and do not guarantee profits or specific outcomes. Past performance is not indicative of future results. Always conduct your own due diligence and consult a financial advisor before making trading decisions.
Altcoin Reversal or Correction DetectionINDICATOR OVERVIEW: Altcoin Reversal or Correction Detection
Altcoin Reversal or Correction Detection is a powerful crypto-specific indicator designed exclusively for altcoins by analyzing their RSI values across multiple timeframes alongside Bitcoin’s RSI. Since BTC's price movements have a strong influence on altcoins, this tool helps traders better understand whether a reversal or correction signal is truly reliable or just noise. Even if an altcoin appears oversold or overbought, it may continue trending with BTC—so this indicator gives you the full picture.
The indicator is optimized for CRYPTO MARKETS only. Not suitable for BTC itself—this is a precision tool built only for ALTCOINS only.
This indicator is not only for signals but also serves as a tool for observing all the information from different timeframes of BTC and altcoins collectively.
How the Calculation Works: Algorithm Overview
The Altcoin Reversal or Correction Detection indicator relies on an algorithm that compares the RSI values of the altcoin across multiple timeframes with Bitcoin's RSI values. This allows the indicator to identify key market moments where a reversal or correction might occur.
BTC-Altcoin RSI Correlation: The algorithm looks for the correlation between Bitcoin's price movements and the altcoin's price actions, as BTC often influences the direction of altcoins. When both Bitcoin and the altcoin show either overbought or oversold conditions in a significant number of timeframes, the indicator signals the potential for a reversal or correction.
Multi-Timeframe Confirmation: Unlike traditional indicators that may focus on a single timeframe, this tool checks multiple timeframes for both BTC and the altcoin. When the same overbought/oversold conditions are met across multiple timeframes, it confirms the likelihood of a trend reversal or correction, providing a more reliable signal. The more timeframes that align with this pattern, the stronger the signal becomes.
Overbought/Oversold Conditions & Extreme RSI Values: The algorithm also takes into account the size of the RSI values, especially focusing on extreme overbought and oversold levels. The greater the RSI values are in these extreme regions, the stronger the potential reversal or correction signal. This means that not only do multiple timeframes need to confirm the condition, but the magnitude of the overbought or oversold RSI level plays a crucial role in determining the strength of the signal.
Signal Strength Levels: The signals are classified into three levels:
Early Signal
Strong Signal
Very Strong Signal
By taking into account the multi-timeframe analysis of both BTC and the altcoin RSI values, along with the magnitude of these RSI values, the indicator offers a highly reliable method for detecting potential reversals and corrections.
Who Is This Indicator Suitable For?
This indicator can also be used to detect reversal points, but it is especially effective for scalping. It highlights potential correction points, making it perfect for quick entries during smaller market pullbacks or short-term trend shifts, which is more suitable for scalpers looking to capitalize on short-term movements
Integration with other tools
Use this tool alongside key Support and Resistance zones to further enhance your trade by filtering for even better quality entries and focusing only on high-quality reversal or correction setups. It can be also used with other indicators and suitable with other personalised strategies.
ThinkTech AI SignalsThink Tech AI Strategy
The Think Tech AI Strategy provides a structured approach to trading by integrating liquidity-based entries, ATR volatility thresholds, and dynamic risk management. This strategy generates buy and sell signals while automatically calculating take profit and stop loss levels, boasting a 64% win rate based on historical data.
Usage
The strategy can be used to identify key breakout and retest opportunities. Liquidity-based zones act as potential accumulation and distribution areas and may serve as future support or resistance levels. Buy and sell zones are identified using liquidity zones and ATR-based filters. Risk management is built-in, automatically calculating take profit and stop loss levels using ATR multipliers. Volume and trend filtering options help confirm directional bias using a 50 EMA and RSI filter. The strategy also allows for session-based trading, limiting trades to key market hours for higher probability setups.
Settings
The risk/reward ratio can be adjusted to define the desired stop loss and take profit calculations. The ATR length and threshold determine ATR-based breakout conditions for dynamic entries. Liquidity period settings allow for customized analysis of price structure for support and resistance zones. Additional trend and RSI filters can be enabled to refine trade signals based on moving averages and momentum conditions. A session filter is included to restrict trade signals to specific market hours.
Style
The strategy includes options to display liquidity lines, showing key support and resistance areas. The first 15-minute candle breakout zones can also be visualized to highlight critical market structure points. A win/loss statistics table is included to track trade performance directly on the chart.
This strategy is intended for descriptive analysis and should be used alongside other confluence factors. Optimize your trading process with Think Tech AI today!
Mark Hours/Minutes (Formula + Minutes)This Pine Script code is a TradingView indicator that analyzes the hour and minutes of each candle in a 1-minute timeframe and plots a red triangle above the candle if one of the following conditions is met:
Sum/Difference Condition: The sum or the absolute difference of the hours and minutes is equal to 29, 35, or 71, with a tolerance of +/- 1.
Minutes Condition: The minutes are equal to 00, 29, or 35.
This indicator is based on the Goldbach theory and the "algo path" concept popularized by Hopiplaka, which posits that algorithmic trading paths often initiate from minute values of 00, 29, and 35. Use this indicator according to your trading strategy.
Stop/Take BoundsThe Stop/Take Bounds indicator is tool for setting dynamic stop-loss and take-profit levels based on percentage distance from the price. Unlike traditional ATR-based methods, this indicator allows traders to set stop levels as a fixed percentage of the price and define the take-profit multiple.
- Stop-loss distanceis determined as a percentage of the current price (e.g., 1% means the stop-loss is always 1% away from the price).
- Take-profit distance is calculated by multiplying the stop-loss distance by a user-defined multiplier (e.g., a multiplier of 2 places the take-profit level twice as far as the stop-loss).
- The indicator plots red lines for stop-loss levels and green lines for take-profit levels, making it easy to visualize risk-to-reward scenarios.
How to Use
1. Set Stop-Loss Distance (%) – Define how far the stop-loss should be from the price.
2. Set Take-Profit Multiplier – Choose how many times larger the take-profit should be compared to the stop-loss.
3. Apply to Long and Short Trades – The indicator automatically plots levels for both long and short positions.
4. Use in Manual or Algorithmic Trading – Ideal for discretionary traders as well as for integration into algorithmic strategies.
Use Cases
- Risk Management – Helps maintain disciplined risk-to-reward ratios.
- Strategy Development – Can be used in the creation of algorithmic trading systems.
- Trailing Stop Simulation – Can act as a trailing stop mechanism when used dynamically.
This indicator is a great addition to any trading strategy!
Quarterly Theory ICT 01 [TradingFinder] XAMD + Q1-Q4 Sessions🔵 Introduction
The Quarterly Theory ICT indicator is an advanced analytical system based on the concepts of ICT (Inner Circle Trader) and fractal time. It divides time into quarterly periods and accurately determines entry and exit points for trades by using the True Open as the starting point of each cycle. This system is applicable across various time frames including annual, monthly, weekly, daily, and even 90-minute sessions.
Time is divided into four quarters: in the first quarter (Q1), which is dedicated to the Accumulation phase, the market is in a consolidation state, laying the groundwork for a new trend; in the second quarter (Q2), allocated to the Manipulation phase (also known as Judas Swing), sudden price changes and false moves occur, marking the true starting point of a trend change; the third quarter (Q3) is dedicated to the Distribution phase, during which prices are broadly distributed and price volatility peaks; and the fourth quarter (Q4), corresponding to the Continuation/Reversal phase, either continues or reverses the previous trend.
By leveraging smart algorithms and technical analysis, this system identifies optimal price patterns and trading positions through the precise detection of stop-run and liquidity zones.
With the division of time into Q1 through Q4 and by incorporating key terms such as Quarterly Theory ICT, True Open, Accumulation, Manipulation (Judas Swing), Distribution, Continuation/Reversal, ICT, fractal time, smart algorithms, technical analysis, price patterns, trading positions, stop-run, and liquidity, this system enables traders to identify market trends and make informed trading decisions using real data and precise analysis.
♦ Important Note :
This indicator and the "Quarterly Theory ICT" concept have been developed based on material published in primary sources, notably the articles on Daye( traderdaye ) and Joshuuu . All copyright rights are reserved.
🔵 How to Use
The Quarterly Theory ICT strategy is built on dividing time into four distinct periods across various time frames such as annual, monthly, weekly, daily, and even 90-minute sessions. In this approach, time is segmented into four quarters, during which the phases of Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal appear in a systematic and recurring manner.
The first segment (Q1) functions as the Accumulation phase, where the market consolidates and lays the foundation for future movement; the second segment (Q2) represents the Manipulation phase, during which prices experience sudden initial changes, and with the aid of the True Open concept, the real starting point of the market’s movement is determined; in the third segment (Q3), the Distribution phase takes place, where prices are widely dispersed and price volatility reaches its peak; and finally, the fourth segment (Q4) is recognized as the Continuation/Reversal phase, in which the previous trend either continues or reverses.
This strategy, by harnessing the concepts of fractal time and smart algorithms, enables precise analysis of price patterns across multiple time frames and, through the identification of key points such as stop-run and liquidity zones, assists traders in optimizing their trading positions. Utilizing real market data and dividing time into Q1 through Q4 allows for a comprehensive and multi-level technical analysis in which optimal entry and exit points are identified by comparing prices to the True Open.
Thus, by focusing on keywords like Quarterly Theory ICT, True Open, Accumulation, Manipulation, Distribution, Continuation/Reversal, ICT, fractal time, smart algorithms, technical analysis, price patterns, trading positions, stop-run, and liquidity, the Quarterly Theory ICT strategy acts as a coherent framework for predicting market trends and developing trading strategies.
🔵b]Settings
Cycle Display Mode: Determines whether the cycle is displayed on the chart or on the indicator panel.
Show Cycle: Enables or disables the display of the ranges corresponding to each quarter within the micro cycles (e.g., Q1/1, Q1/2, Q1/3, Q1/4, etc.).
Show Cycle Label: Toggles the display of textual labels for identifying the micro cycle phases (for example, Q1/1 or Q2/2).
Table Display Mode: Enables or disables the ability to display cycle information in a tabular format.
Show Table: Determines whether the table—which summarizes the phases (Q1 to Q4)—is displayed.
Show More Info: Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
🔵 Conclusion
Quarterly Theory ICT provides a fractal and recurring approach to analyzing price behavior by dividing time into four quarters (Q1, Q2, Q3, and Q4) and defining the True Open at the beginning of the second phase.
The Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal phases repeat in each cycle, allowing traders to identify price patterns with greater precision across annual, monthly, weekly, daily, and even micro-level time frames.
Focusing on the True Open as the primary reference point enables faster recognition of potential trend changes and facilitates optimal management of trading positions. In summary, this strategy, based on ICT principles and fractal time concepts, offers a powerful framework for predicting future market movements, identifying optimal entry and exit points, and managing risk in various trading conditions.
AO/AC Trading Zones Strategy [Skyrexio] Overview
AO/AC Trading Zones Strategy leverages the combination of Awesome Oscillator (AO), Acceleration/Deceleration Indicator (AC), Williams Fractals, Williams Alligator and Exponential Moving Average (EMA) to obtain the high probability long setups. Moreover, strategy uses multi trades system, adding funds to long position if it considered that current trend has likely became stronger. Combination of AO and AC is used for creating so-called trading zones to create the signals, while Alligator and Fractal are used in conjunction as an approximation of short-term trend to filter them. At the same time EMA (default EMA's period = 100) is used as high probability long-term trend filter to open long trades only if it considers current price action as an uptrend. More information in "Methodology" and "Justification of Methodology" paragraphs. The strategy opens only long trades.
Unique Features
No fixed stop-loss and take profit: Instead of fixed stop-loss level strategy utilizes technical condition obtained by Fractals and Alligator to identify when current uptrend is likely to be over. In some special cases strategy uses AO and AC combination to trail profit (more information in "Methodology" and "Justification of Methodology" paragraphs)
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Multilayer trades opening system: strategy uses only 10% of capital in every trade and open up to 5 trades at the same time if script consider current trend as strong one.
Short and long term trend trade filters: strategy uses EMA as high probability long-term trend filter and Alligator and Fractal combination as a short-term one.
Methodology
The strategy opens long trade when the following price met the conditions:
1. Price closed above EMA (by default, period = 100). Crossover is not obligatory.
2. Combination of Alligator and Williams Fractals shall consider current trend as an upward (all details in "Justification of Methodology" paragraph)
3. Both AC and AO shall print two consecutive increasing values. At the price candle close which corresponds to this condition algorithm opens the first long trade with 10% of capital.
4. If combination of Alligator and Williams Fractals shall consider current trend has been changed from up to downtrend, all long trades will be closed, no matter how many trades has been opened.
5. If AO and AC both continue printing the rising values strategy opens the long trade on each candle close with 10% of capital while number of opened trades reaches 5.
6. If AO and AC both has printed 5 rising values in a row algorithm close all trades if candle's low below the low of the 5-th candle with rising AO and AC values in a row.
Script also has additional visuals. If second long trade has been opened simultaneously the Alligator's teeth line is plotted with the green color. Also for every trade in a row from 2 to 5 the label "Buy More" is also plotted just below the teeth line. With every next simultaneously opened trade the green color of the space between teeth and price became less transparent.
Strategy settings
In the inputs window user can setup strategy setting:
EMA Length (by default = 100, period of EMA, used for long-term trend filtering EMA calculation).
User can choose the optimal parameters during backtesting on certain price chart.
Justification of Methodology
Let's explore the key concepts of this strategy and understand how they work together. We'll begin with the simplest: the EMA.
The Exponential Moving Average (EMA) is a type of moving average that assigns greater weight to recent price data, making it more responsive to current market changes compared to the Simple Moving Average (SMA). This tool is widely used in technical analysis to identify trends and generate buy or sell signals. The EMA is calculated as follows:
1.Calculate the Smoothing Multiplier:
Multiplier = 2 / (n + 1), Where n is the number of periods.
2. EMA Calculation
EMA = (Current Price) × Multiplier + (Previous EMA) × (1 − Multiplier)
In this strategy, the EMA acts as a long-term trend filter. For instance, long trades are considered only when the price closes above the EMA (default: 100-period). This increases the likelihood of entering trades aligned with the prevailing trend.
Next, let’s discuss the short-term trend filter, which combines the Williams Alligator and Williams Fractals. Williams Alligator
Developed by Bill Williams, the Alligator is a technical indicator that identifies trends and potential market reversals. It consists of three smoothed moving averages:
Jaw (Blue Line): The slowest of the three, based on a 13-period smoothed moving average shifted 8 bars ahead.
Teeth (Red Line): The medium-speed line, derived from an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, calculated using a 5-period smoothed moving average shifted 3 bars forward.
When the lines diverge and align in order, the "Alligator" is "awake," signaling a strong trend. When the lines overlap or intertwine, the "Alligator" is "asleep," indicating a range-bound or sideways market. This indicator helps traders determine when to enter or avoid trades.
Fractals, another tool by Bill Williams, help identify potential reversal points on a price chart. A fractal forms over at least five consecutive bars, with the middle bar showing either:
Up Fractal: Occurs when the middle bar has a higher high than the two preceding and two following bars, suggesting a potential downward reversal.
Down Fractal: Happens when the middle bar shows a lower low than the surrounding two bars, hinting at a possible upward reversal.
Traders often use fractals alongside other indicators to confirm trends or reversals, enhancing decision-making accuracy.
How do these tools work together in this strategy? Let’s consider an example of an uptrend.
When the price breaks above an up fractal, it signals a potential bullish trend. This occurs because the up fractal represents a shift in market behavior, where a temporary high was formed due to selling pressure. If the price revisits this level and breaks through, it suggests the market sentiment has turned bullish.
The breakout must occur above the Alligator’s teeth line to confirm the trend. A breakout below the teeth is considered invalid, and the downtrend might still persist. Conversely, in a downtrend, the same logic applies with down fractals.
In this strategy if the most recent up fractal breakout occurs above the Alligator's teeth and follows the last down fractal breakout below the teeth, the algorithm identifies an uptrend. Long trades can be opened during this phase if a signal aligns. If the price breaks a down fractal below the teeth line during an uptrend, the strategy assumes the uptrend has ended and closes all open long trades.
By combining the EMA as a long-term trend filter with the Alligator and fractals as short-term filters, this approach increases the likelihood of opening profitable trades while staying aligned with market dynamics.
Now let's talk about the trading zones concept and its signals. To understand this we need to briefly introduce what is AO and AC. The Awesome Oscillator (AO), developed by Bill Williams, is a momentum indicator designed to measure market momentum by contrasting recent price movements with a longer-term historical perspective. It helps traders detect potential trend reversals and assess the strength of ongoing trends.
The formula for AO is as follows:
AO = SMA5(Median Price) − SMA34(Median Price)
where:
Median Price = (High + Low) / 2
SMA5 = 5-period Simple Moving Average of the Median Price
SMA 34 = 34-period Simple Moving Average of the Median Price
The Acceleration/Deceleration (AC) Indicator, introduced by Bill Williams, measures the rate of change in market momentum. It highlights shifts in the driving force of price movements and helps traders spot early signs of trend changes. The AC Indicator is particularly useful for identifying whether the current momentum is accelerating or decelerating, which can indicate potential reversals or continuations. For AC calculation we shall use the AO calculated above is the following formula:
AC = AO − SMA5(AO) , where SMA5(AO)is the 5-period Simple Moving Average of the Awesome Oscillator
When the AC is above the zero line and rising, it suggests accelerating upward momentum.
When the AC is below the zero line and falling, it indicates accelerating downward momentum.
When the AC is below zero line and rising it suggests the decelerating the downtrend momentum. When AC is above the zero line and falling, it suggests the decelerating the uptrend momentum.
Now let's discuss the trading zones concept and how it can create the signal. Zones are created by the combination of AO and AC. We can divide three zone types:
Greed zone: when the AO and AC both are rising
Red zone: when the AO and AC both are decreasing
Gray zone: when one of AO or AC is rising, the other is falling
Gray zone is considered as uncertainty. AC and AO are moving in the opposite direction. Strategy skip such price action to decrease the chance to stuck in the losing trade during potential sideways. Red zone is also not interesting for the algorithm because both indicators consider the trend as bearish, but strategy opens only long trades. It is waiting for the green zone to increase the chance to open trade in the direction of the potential uptrend. When we have 2 candles in a row in the green zone script executes a long trade with 10% of capital.
Two green zone candles in a row is considered by algorithm as a bullish trend, but now so strong, that's the reason why trade is going to be closed when the combination of Alligator and Fractals will consider the the trend change from bullish to bearish. If id did not happens, algorithm starts to count the green zone candles in a row. When we have 5 in a row script change the trade closing condition. Such situation is considered is a high probability strong bull market and all trades will be closed if candle's low will be lower than fifth green zone candle's low. This is used to increase probability to secure the profit. If long trades are initiated, the strategy continues utilizing subsequent signals until the total number of trades reaches a maximum of 5. Each trade uses 10% of capital.
Why we use trading zones signals? If currently strategy algorithm considers the high probability of the short-term uptrend with the Alligator and Fractals combination pointed out above and the long-term trend is also suggested by the EMA filter as bullish. Rising AC and AO values in the direction of the most likely main trend signaling that we have the high probability of the fastest bullish phase on the market. The main idea is to take part in such rapid moves and add trades if this move continues its acceleration according to indicators.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.12.31. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 10%
Maximum Single Position Loss: -9.49%
Maximum Single Profit: +24.33%
Net Profit: +4374.70 USDT (+43.75%)
Total Trades: 278 (39.57% win rate)
Profit Factor: 2.203
Maximum Accumulated Loss: 668.16 USDT (-5.43%)
Average Profit per Trade: 15.74 USDT (+1.37%)
Average Trade Duration: 60 hours
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 4h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
Arpeet MACDOverview
This strategy is based on the zero-lag version of the MACD (Moving Average Convergence Divergence) indicator, which captures short-term trends by quickly responding to price changes, enabling high-frequency trading. The strategy uses two moving averages with different periods (fast and slow lines) to construct the MACD indicator and introduces a zero-lag algorithm to eliminate the delay between the indicator and the price, improving the timeliness of signals. Additionally, the crossover of the signal line and the MACD line is used as buy and sell signals, and alerts are set up to help traders seize trading opportunities in a timely manner.
Strategy Principle
Calculate the EMA (Exponential Moving Average) or SMA (Simple Moving Average) of the fast line (default 12 periods) and slow line (default 26 periods).
Use the zero-lag algorithm to double-smooth the fast and slow lines, eliminating the delay between the indicator and the price.
The MACD line is formed by the difference between the zero-lag fast line and the zero-lag slow line.
The signal line is formed by the EMA (default 9 periods) or SMA of the MACD line.
The MACD histogram is formed by the difference between the MACD line and the signal line, with blue representing positive values and red representing negative values.
When the MACD line crosses the signal line from below and the crossover point is below the zero axis, a buy signal (blue dot) is generated.
When the MACD line crosses the signal line from above and the crossover point is above the zero axis, a sell signal (red dot) is generated.
The strategy automatically places orders based on the buy and sell signals and triggers corresponding alerts.
Advantage Analysis
The zero-lag algorithm effectively eliminates the delay between the indicator and the price, improving the timeliness and accuracy of signals.
The design of dual moving averages can better capture market trends and adapt to different market environments.
The MACD histogram intuitively reflects the comparison of bullish and bearish forces, assisting in trading decisions.
The automatic order placement and alert functions make it convenient for traders to seize trading opportunities in a timely manner, improving trading efficiency.
Risk Analysis
In volatile markets, frequent crossover signals may lead to overtrading and losses.
Improper parameter settings may cause signal distortion and affect strategy performance.
The strategy relies on historical data for calculations and has poor adaptability to sudden events and black swan events.
Optimization Direction
Introduce trend confirmation indicators, such as ADX, to filter out false signals in volatile markets.
Optimize parameters to find the best combination of fast and slow line periods and signal line periods, improving strategy stability.
Combine other technical indicators or fundamental factors to construct a multi-factor model, improving risk-adjusted returns of the strategy.
Introduce stop-loss and take-profit mechanisms to control single-trade risk.
Summary
The MACD Dual Crossover Zero Lag Trading Strategy achieves high-frequency trading by quickly responding to price changes and capturing short-term trends. The zero-lag algorithm and dual moving average design improve the timeliness and accuracy of signals. The strategy has certain advantages, such as intuitive signals and convenient operation, but also faces risks such as overtrading and parameter sensitivity. In the future, the strategy can be optimized by introducing trend confirmation indicators, parameter optimization, multi-factor models, etc., to improve the robustness and profitability of the strategy.
Classic Nacked Z-Score ArbitrageThe “Classic Naked Z-Score Arbitrage” strategy employs a statistical arbitrage model based on the Z-score of the price spread between two assets. This strategy follows the premise of pair trading, where two correlated assets, typically from the same market sector, are traded against each other to profit from relative price movements (Gatev, Goetzmann, & Rouwenhorst, 2006). The approach involves calculating the Z-score of the price spread between two assets to determine market inefficiencies and capitalize on short-term mispricing.
Methodology
Price Spread Calculation:
The strategy calculates the spread between the two selected assets (Asset A and Asset B), typically from different sectors or asset classes, on a daily timeframe.
Statistical Basis – Z-Score:
The Z-score is used as a measure of how far the current price spread deviates from its historical mean, using the standard deviation for normalization.
Trading Logic:
• Long Position:
A long position is initiated when the Z-score exceeds the predefined threshold (e.g., 2.0), indicating that Asset A is undervalued relative to Asset B. This signals an arbitrage opportunity where the trader buys Asset B and sells Asset A.
• Short Position:
A short position is entered when the Z-score falls below the negative threshold, indicating that Asset A is overvalued relative to Asset B. The strategy involves selling Asset B and buying Asset A.
Theoretical Foundation
This strategy is rooted in mean reversion theory, which posits that asset prices tend to return to their long-term average after temporary deviations. This form of arbitrage is widely used in statistical arbitrage and pair trading techniques, where investors seek to exploit short-term price inefficiencies between two assets that historically maintain a stable price relationship (Avery & Sibley, 2020).
Further, the Z-score is an effective tool for identifying significant deviations from the mean, which can be seen as a signal for the potential reversion of the price spread (Braucher, 2015). By capturing these inefficiencies, traders aim to profit from convergence or divergence between correlated assets.
Practical Application
The strategy aligns with the Financial Algorithmic Trading and Market Liquidity analysis, emphasizing the importance of statistical models and efficient execution (Harris, 2024). By utilizing a simple yet effective risk-reward mechanism based on the Z-score, the strategy contributes to the growing body of research on market liquidity, asset correlation, and algorithmic trading.
The integration of transaction costs and slippage ensures that the strategy accounts for practical trading limitations, helping to refine execution in real market conditions. These factors are vital in modern quantitative finance, where liquidity and execution risk can erode profits (Harris, 2024).
References
• Gatev, E., Goetzmann, W. N., & Rouwenhorst, K. G. (2006). Pairs Trading: Performance of a Relative-Value Arbitrage Rule. The Review of Financial Studies, 19(3), 1317-1343.
• Avery, C., & Sibley, D. (2020). Statistical Arbitrage: The Evolution and Practices of Quantitative Trading. Journal of Quantitative Finance, 18(5), 501-523.
• Braucher, J. (2015). Understanding the Z-Score in Trading. Journal of Financial Markets, 12(4), 225-239.
• Harris, L. (2024). Financial Algorithmic Trading and Market Liquidity: A Comprehensive Analysis. Journal of Financial Engineering, 7(1), 18-34.
Auto-Adjusting Kalman Filter by TenozenNew year, new indicator! Auto-Adjusting Kalman Filter is an indicator designed to provide an adaptive approach to trend analysis. Using the Kalman Filter (a recursive algorithm used in signal processing), this algo dynamically adjusts to market conditions, offering traders a reliable way to identify trends and manage risk! In other words, it's a remaster of my previous indicator, Kalman Filter by Tenozen.
What's the difference with the previous indicator (Kalman Filter by Tenozen)?
The indicator adjusts its parameters (Q and R) in real-time using the Average True Range (ATR) as a measure of market volatility. This ensures the filter remains responsive during high-volatility periods and smooth during low-volatility conditions, optimizing its performance across different market environments.
The filter resets on a user-defined timeframe, aligning its calculations with dominant trends and reducing sensitivity to short-term noise. This helps maintain consistency with the broader market structure.
A confidence metric, derived from the deviation of price from the Kalman filter line (measured in ATR multiples), is visualized as a heatmap:
Green : Bullish confidence (higher values indicate stronger trends).
Red : Bearish confidence (higher values indicate stronger trends).
Gray : Neutral zone (low confidence, suggesting caution).
This provides a clear, objective measure of trend strength.
How it works?
The Kalman Filter estimates the "true" price by filtering out market noise. It operates in two steps, that is, prediction and update. Prediction is about projection the current state (price) forward. Update is about adjusting the prediction based on the latest price data. The filter's parameters (Q and R) are scaled using normalized ATR, ensuring adaptibility to changing market conditions. So it means that, Q (Process Noise) increases during high volatility, making the filter more responsive to price changes and R (Measurement Noise) increases during low volatility, smoothing out the filter to avoid overreacting to minor fluctuations. Also, the trend confidence is calculated based on the deviation of price from the Kalman filter line, measured in ATR multiples, this provides a quantifiable measure of trend strength, helping traders assess market conditions objectively.
How to use?
Use the Kalman Filter line to identify the prevailing trend direction. Trade in alignment with the filter's slope for higher-probability setups.
Look for pullbacks toward the Kalman Filter line during strong trends (high confidence zones)
Utilize the dynamic stop-loss and take-profit levels to manage risk and lock in profits
Confidence Heatmap provides an objective measure of market sentiment, helping traders avoid low-confidence (neutral) zones and focus on high-probability opportunities
Guess that's it! I hope this indicator helps! Let me know if you guys got some feedback! Ciao!
VWAP Fibonacci Bands (Zeiierman)█ Overview
The VWAP Fibonacci Bands is a sophisticated yet user-friendly indicator designed to assist traders in visualizing market trends, volatility, and potential support/resistance levels. Developed by Zeiierman, this tool integrates the MIDAS (Market Interpretation Data Analysis System) methodology with Standard Deviation Bands and user-defined Fibonacci levels to provide a comprehensive market analysis framework.
This indicator is built for traders who want a dynamic and customizable approach to understanding market movements, offering features that adapt to varying market conditions. Whether you're a scalper, swing trader, or long-term investor.
█ How It Works
⚪ Anchor Point System
The indicator begins its calculations based on an anchor point, which can be set to:
A specific date for historical analysis or alignment with significant market events.
A timeframe-based reset, dynamically restarting calculations at the beginning of each selected period (e.g., daily, weekly, or monthly).
This dual-anchor method ensures flexibility, allowing the indicator to align with various trading strategies.
⚪ MIDAS Calculation
The MIDAS calculation is central to this indicator. It uses cumulative price and volume data to compute a volume-weighted average price (VWAP), offering a trendline that reflects the true value weighted by trading activity.
⚪ Standard Deviation Bands
The upper and lower bands are calculated using the standard deviation of price movements around the MIDAS line.
⚪ Fibonacci Levels
User-defined Fibonacci ratios are used to plot additional support and resistance levels between the bands. These levels provide visual cues for potential price reversals or trend continuations.
█ How to Use
⚪ Trend Identification
Uptrend: The price remains above the MIDAS line.
Downtrend: The price stays below the MIDAS line and aligns with the lower bands.
⚪ Support and Resistance
The upper and lower bands act as support and resistance levels.
Fibonacci levels provide intermediate zones for potential price reversals.
⚪ Volatility Analysis
Wider bands indicate periods of high volatility.
Narrower bands suggest low-volatility conditions, often preceding breakouts.
⚪ Overbought/Oversold Conditions
Look for the price beyond the upper or lower bands to identify extreme conditions.
█ Settings
Set Anchor Method
Anchor Method: Choose between Timeframe or Date to define the starting point of calculations.
Anchor Timeframe: For Timeframe mode, specify the interval (e.g., Daily, Weekly).
Anchor Date: For Date mode, set the exact starting date for historical alignment.
Set Std Dev Multiplier
Controls the width of the bands:
Higher values widen the bands, filtering out minor fluctuations.
Lower values tighten the bands for more responsive analysis.
Set Fibonacci Levels
Define custom Fibonacci ratios (e.g., 0.236, 0.382) to plot intermediate levels between the bands.
█ Tips for Fine-Tuning
⚪ For Trend Trading:
Use higher Std Dev Multipliers to focus on long-term trends and avoid noise. Adjust Anchor Timeframe to Weekly or Monthly for broader trend analysis.
⚪ For Reversal Trading:
Tighten the bands with a lower Std Dev Multiplier.
Use shorter anchor timeframes for intraday reversals (e.g., Hourly).
⚪ For Volatile Markets:
Increase the Std Dev Multiplier to accommodate wider price swings.
⚪ For Quiet Markets:
Decrease the Std Dev Multiplier to highlight smaller fluctuations.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Leverage Aware Trade OptimizerWelcome to the Leverage-Aware Trade Optimizer (LATO)! I’m thrilled to have you exploring this dynamic algorithm! LATO combines advanced market oscillation tracking, leverage-aware trade optimization, and real-time market analysis to help you make smarter, more informed trading decisions. Whether you're just starting or you’re an experienced trader, LATO provides powerful tools and insights to enhance your strategies. LATO is here to support you in optimizing your trades with precision, so feel free to dive in and explore all the features. Let’s make your trading experience as effective and rewarding as possible. Safe trading!
Leverage-Aware Trade Optimizer (LATO)
Short Title: LATO
Category: Trading Tools / Technical Analysis
Overview
The Leverage-Aware Trade Optimizer (LATO) is a powerful algorithm designed to track and analyze market oscillations, identify reversal zones, and provide dynamic trading levels for optimal decision-making. With built-in risk management features, LATO enhances traders’ ability to make well-informed decisions based on a comprehensive range of market indicators, including price oscillations, probabilities, and leverage-related risks.
Key Features
Comprehensive Market Oscillation Tracking: LATO utilizes advanced indicators such as the Indexed Position Oscillator (IPO), Candle Relative Percentage (CRP), and Oscillating Range Indicator (ORI) to track price fluctuations and detect key market oscillations, providing a detailed view of price movements.
Dynamic Price Levels for Trading Decisions: The script calculates critical price levels such as WAP, WBP, XAP, and XBP. These weighted and expanded prices help identify potential support and resistance zones for accurate trade entries and exits.
Reversal Detection and Trend Identification: LATO is designed to recognize top and bottom reversal zones using user-defined thresholds (e.g., upper_reversal, lower_reversal). The algorithm signals potential trend changes with event markers such as UP, DOWN, UIP, and DIP, enabling traders to anticipate market reversals.
Risk and Leverage Mapping: By estimating liquidation levels for various leverage values (5x, 10x, 20x, etc.), LATO assists in risk management, helping traders visualize leverage exposure and optimize their trades according to risk tolerance.
Integrated Visualization and Event Labels: LATO enhances visual analysis by plotting key levels, trend lines, and event markers on the chart. Custom labels summarize critical values, including SOD (Sell Odds), BOD (Buy Odds), ORI (Oscillating Range Indicator), and PVI (Price Volatility Index), offering a quick, actionable summary for traders.
User Inputs
Orders Deviation (order_deviation): Controls the deviation for calculating trade levels.
Top Reversal (upper_reversal): Sets the threshold for the upper reversal zone.
Bottom Reversal (lower_reversal): Sets the threshold for the lower reversal zone.
How It Works
LATO tracks market oscillations through the Indexed Position Oscillator (IPO) and Candle Relative Percentage (CRP), dynamically adjusting as the market fluctuates. The algorithm then identifies key levels using weighted prices (e.g., WAP, WBP) and generates reversal signals based on defined thresholds.
Once the Leverage-Aware Trade Optimizer (LATO) is applied to a chart, it automatically calculates dynamic support and resistance levels and identifies potential buying or selling opportunities. The script also plots liquidation zones based on different leverage levels and visualizes these areas through color-coded lines.
Use Case Scenarios
Trend Reversal Detection: Identify when the market is likely to reverse based on the ORI and price action.
Dynamic Price Levels: Use the weighted price levels and trend lines to pinpoint entry/exit points.
Leverage Risk Management: Monitor liquidation levels and use them for managing risk while trading with leverage.
Oscillation Tracking: Track key oscillations for detecting overbought or oversold conditions.
Alert Setup for LATO
You can set up alerts based on the key conditions like UP, DOWN, UIP, and DIP, as well as specific market movements.
Down Trend Alert (DOWN): Alerts when there’s a downtrend, triggered by a crossover of WBP and BL5, with specific conditions for ORI and SOD.
Up Trend Alert (UP): Alerts when there’s an uptrend, triggered by a crossunder of WAP and SL5, with ORI below -0.5.
Upper Reversal Alert (UIP): Alerts when ORI crosses below the lower_reversal threshold.
Downward Reversal Alert (DIP): Alerts when ORI crosses above the upper_reversal threshold.
Conclusion
The Leverage-Aware Trade Optimizer (LATO) is a comprehensive trading tool designed for traders seeking to optimize their trade entries and exits. By combining multiple indicators, dynamic price levels, and reversal zone detection, LATO offers an advanced approach to market analysis and decision-making. Whether you’re trading with leverage or simply looking for trend confirmation, LATO provides the insights you need to maximize your trading potential.
Notes
This script is designed to be used on any time frame.
Adjust the order_deviation parameter based on the asset volatility you are trading.
The reversal thresholds (upper and lower) should be fine-tuned depending on market conditions.
Absolute Strength Index [ASI] (Zeiierman)█ Overview
The Absolute Strength Index (ASI) is a next-generation oscillator designed to measure the strength and direction of price movements by leveraging percentile-based normalization of historical returns. Developed by Zeiierman, this indicator offers a highly visual and intuitive approach to identifying market conditions, trend strength, and divergence opportunities.
By dynamically scaling price returns into a bounded oscillator (-10 to +10), the ASI helps traders spot overbought/oversold conditions, trend reversals, and momentum changes with enhanced precision. It also incorporates advanced features like divergence detection and adaptive signal smoothing for versatile trading applications.
█ How It Works
The ASI's core calculation methodology revolves around analyzing historical price returns, classifying them into top and bottom percentiles, and normalizing the current price movement within this framework. Here's a breakdown of its key components:
⚪ Returns Lookback
The ASI evaluates historical price returns over a user-defined period (Returns Lookback) to measure recent price behavior. This lookback window determines the sensitivity of the oscillator:
Shorter Lookback: Higher responsiveness to recent price movements, suitable for scalping or high-volatility assets.
Longer Lookback: Smoother oscillator behavior is ideal for identifying larger trends and avoiding false signals.
⚪ Percentile-Based Thresholds
The ASI categorizes returns into two groups:
Top Percentile (Winners): The upper X% of returns, representing the strongest upward price moves.
Bottom Percentile (Losers): The lower X% of returns, capturing the sharpest downward movements.
This percentile-based normalization ensures the ASI adapts to market conditions, filtering noise and emphasizing significant price changes.
⚪ Oscillator Normalization
The ASI normalizes current returns relative to the top and bottom thresholds:
Values range from -10 to +10, where:
+10 represents extreme bullish strength (above the top percentile threshold).
-10 indicates extreme bearish weakness (below the bottom percentile threshold).
⚪ Signal Line Smoothing
A signal line is optionally applied to the ASI using a variety of moving averages:
Options: SMA, EMA, WMA, RMA, or HMA.
Effect: Smooths the ASI to filter out noise, with shorter lengths offering higher responsiveness and longer lengths providing stability.
⚪ Divergence Detection
One of ASI's standout features is its ability to detect and highlight bullish and bearish divergences:
Bullish Divergence: The ASI forms higher lows while the price forms lower lows, signaling potential upward reversals.
Bearish Divergence: The ASI forms lower highs while the price forms higher highs, indicating potential downward reversals.
█ Key Differences from RSI
Dynamic Adaptability: ASI adjusts to market conditions through percentile-based scaling, while RSI uses static thresholds.
█ How to Use ASI
⚪ Trend Identification
Bullish Strength: ASI above zero suggests upward momentum, suitable for trend-following trades.
Bearish Weakness: ASI below zero signals downward momentum, ideal for short trades or exits from long positions.
⚪ Overbought/Oversold Levels
Overbought Zone: ASI in the +8 to +10 range indicates potential exhaustion of bullish momentum.
Oversold Zone: ASI in the -8 to -10 range points to potential reversal opportunities.
⚪ Divergence Signals
Look for bullish or bearish divergence labels to anticipate trend reversals before they occur.
⚪ Signal Line Crossovers
A crossover between the ASI and its signal line (e.g., EMA or SMA) can indicate a shift in momentum:
Bullish Crossover: ASI crosses above the signal line, signaling potential upside.
Bearish Crossover: ASI crosses below the signal line, suggesting downside momentum.
█ Settings Explained
⚪ Absolute Strength Index
Returns Lookback: Sets the sensitivity of the oscillator. Shorter periods detect short-term changes, while longer periods focus on broader trends.
Top/Bottom Percentiles: Adjust thresholds for defining winners and losers. Narrower percentiles increase sensitivity to outliers.
Signal Line Type: Choose from SMA, EMA, WMA, RMA, or HMA for smoothing.
Signal Line Length: Fine-tune the responsiveness of the signal line.
⚪ Divergence
Divergence Lookback: Adjusts the period for detecting divergence. Use longer lookbacks to reduce noise.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Adaptive Volatility-Scaled Oscillator [AVSO] (Zeiierman)█ Overview
The Adaptive Volatility-Scaled Oscillator (AVSO) is a dynamic trading indicator that measures and visualizes volatility-adjusted market behavior. By scaling various metrics (such as volume, price changes, standard deviation, ATR, and Yang-Zhang volatility) and applying adaptive smoothing, AVSO helps traders identify market conditions where volatility deviates significantly from the norm.
This indicator uses standardized scaling (Z-Score logic) to highlight periods of abnormally high or low volatility relative to recent history. With gradient coloring and clear volatility zones, AVSO provides a visually intuitive way to analyze market volatility and adapt trading strategies accordingly.
█ How It Works
⚪ Scaling Metrics: The indicator scales user-selected metrics (e.g., volume, ATR, standard deviation) relative to the market and price, providing a standardized volatility measure.
⚪ Z-Score Standardization: The scaled metric is normalized using a Z-Score to measure how far current volatility deviates from its recent mean.
Positive Z-Score: Above-average volatility.
Negative Z-Score: Below-average volatility.
⚪ Adaptive Smoothing: An Adaptive EMA smooths the Z-Score, dynamically adjusting its length based on the strength of the volatility. Stronger deviations result in shorter smoothing, increasing responsiveness.
█ Unique Feature: Yang-Zhang Volatility
The Yang-Zhang volatility estimator sets this indicator apart by providing a more robust and accurate measure of volatility compared to traditional methods like ATR or standard deviation.
⚪ What Makes Yang-Zhang Volatility Unique?
Comprehensive Calculation: It combines overnight price gaps (log returns from the previous close to the current open) and intraday price movements (high, low, and close).
Accurate for Gapped Markets: Traditional volatility measures can misrepresent price movement when significant gaps occur between sessions. Yang-Zhang accounts for these gaps, making it highly reliable for assets prone to overnight price jumps, such as stocks, cryptocurrencies, and futures.
Adaptable to Real Market Conditions : By including both close-to-open returns and intraday volatility, it provides a balanced and adaptive measure that captures the full volatility picture.
⚪ Why This Matters to Traders
Better Volatility Insights: Yang-Zhang offers a clearer view of true market volatility, especially in markets with price gaps or uneven trading sessions.
Improved Trade Timing: By identifying volatility spikes and calm periods more effectively, traders can time their entries and exits with greater confidence.
█ How to Use
Identify High and Low Volatility
A high Z-Score (>2) indicates significant market volatility. This can signal momentum-driven moves, breakouts, or areas of increased risk.
A low Z-Score (<-2) suggests low volatility or a calm market environment. This often occurs before a potential breakout or reversal.
Trade Signals
High Volatility Zones (background highlight): Monitor for potential breakouts, trend continuations, or reversals.
Low Volatility Zones: Anticipate range-bound conditions or upcoming volatility spikes.
█ Settings
Source: Select the price source for scaling calculations (close, high, low, open).
Metric Measure: Choose the volatility measure:
Volume: Scales raw volume.
Close: Uses closing price changes.
Standard Deviation: Price dispersion.
ATR: Average True Range.
Yang: Yang-Zhang volatility estimate.
Bars to Analyze: Number of historical bars used to calculate the mean and standard deviation of the scaled metric.
ATR / Standard Deviation Period: Lookback period for ATR or Standard Deviation calculation.
Yang Volatility Period: Period for the Yang-Zhang volatility estimator.
Smoothing Period: Base smoothing length for the adaptive smoothing line.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Newday_smaThis algorithm is based on SMA (Simple Moving Average) to identify price trends, detecting "positive price zones" (where the price is above the SMA) and "negative price zones" (where the price is below the SMA), and then connecting turning points within those zones with lines.
Key Steps:
SMA Period Selection: The user can select the SMA period to be 5, 10, or 20.
SMA Calculation: The SMA of the current price is calculated based on the selected period.
Identify Positive and Negative Price Zones:
Positive Price Zone: When the closing price is higher than the SMA, it’s considered a positive price zone.
Negative Price Zone: When the closing price is lower than the SMA, it’s considered a negative price zone.
Identify Turning Points:
In the positive price zone, if the current closing price falls below the SMA, a potential turning point is detected, and the algorithm looks for the lowest point (the lowest high in that zone).
In the negative price zone, if the current closing price rises above the SMA, a potential turning point is detected, and the algorithm looks for the highest point (the highest low in that zone).
Connect the Turning Points:
When transitioning from the negative price zone to the positive price zone, a line is drawn from the lowest point of the negative zone to the highest point of the positive zone.
When transitioning from the positive price zone to the negative price zone, a line is drawn from the highest point of the positive zone to the lowest point of the negative zone.
Dynamic Updates: As new candles form, the algorithm continuously updates the turning points and draws the lines accordingly.
Key Features:
Flexible SMA Period Selection: The user can choose from different SMA periods (5, 10, or 20).
Dynamic Turning Point Recognition: The algorithm dynamically identifies turning points based on the relationship between the price and the SMA, marking fluctuations in price.
Connecting Turning Points: The algorithm connects the key points in positive and negative price zones with lines to help identify price trends.
Use Cases:
This algorithm is useful for technical analysis, especially for short-term trading.
It helps identify support and resistance levels, assisting users in making buy and sell decisions.
Trend Speed Analyzer (Zeiierman)█ Overview
The Trend Speed Analyzer by Zeiierman is designed to measure the strength and speed of market trends, providing traders with actionable insights into momentum dynamics. By combining a dynamic moving average with wave and speed analysis, it visually highlights shifts in trend direction, market strength, and potential reversals. This tool is ideal for identifying breakout opportunities, gauging trend consistency, and understanding the dominance of bullish or bearish forces over various timeframes.
█ How It Works
The indicator employs a Dynamic Moving Average (DMA) enhanced with an Accelerator Factor, allowing it to adapt dynamically to market conditions. The DMA is responsive to price changes, making it suitable for both long-term trends and short-term momentum analysis.
Key components include:
Trend Speed Analysis: Measures the speed of market movements, highlighting momentum shifts with visual cues.
Wave Analysis: Tracks bullish and bearish wave sizes to determine market strength and bias.
Normalized Speed Values: Ensures consistency across different market conditions by adjusting for volatility.
⚪ Average Wave and Max Wave
These metrics analyze the size of bullish and bearish waves over a specified Lookback Period:
Average Wave: This represents the mean size of bullish and bearish movements, helping traders gauge overall market strength.
Max Wave: Highlights the largest movements within the period, identifying peak momentum during trend surges.
⚪ Current Wave Ratio
This feature compares the current wave's size against historical data:
Average Wave Ratio: Indicates if the current momentum exceeds historical averages. A value above 1 suggests the trend is gaining strength.
Max Wave Ratio: Shows whether the current wave surpasses previous peak movements, signaling potential breakouts or trend accelerations.
⚪ Dominance
Dominance metrics reveal whether bulls or bears have controlled the market during the Lookback Period:
Average Dominance: Compares the net difference between average bullish and bearish wave sizes.
Max Dominance: Highlights which side had the stronger individual waves, indicating key power shifts in market dynamics.
Positive values suggest bullish dominance, while negative values point to bearish control. This helps traders confirm trend direction or anticipate reversals.
█ How to Use
Identify Trends: Leverage the color-coded candlesticks and dynamic trend line to assess the overall market direction with clarity.
Monitor Momentum: Use the Trend Speed histogram to track changes in momentum, identifying periods of acceleration or deceleration.
Analyze Waves: Compare the sizes of bullish and bearish waves to identify the prevailing market bias and detect potential shifts in sentiment. Additionally, fluctuations in Current Wave ratio values should be monitored as early indicators of possible trend reversals.
Evaluate Dominance: Utilize dominance metrics to confirm the strength and direction of the current trend.
█ Settings
Maximum Length: Sets the smoothing of the trend line.
Accelerator Multiplier: Adjusts sensitivity to price changes.
Lookback Period: Defines the range for wave calculations.
Enable Table: Displays statistical metrics for in-depth analysis.
Enable Candles: Activates color-coded candlesticks.
Collection Period: Normalizes trend speed values for better accuracy.
Start Date: Limits calculations to a specific timeframe.
Timer Option: Choose between using all available data or starting from a custom date.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Fractal Trend Detector [Skyrexio]Introduction
Fractal Trend Detector leverages the combination of Williams fractals and Alligator Indicator to help traders to understand with the high probability what is the current trend: bullish or bearish. It visualizes the potential uptrend with the coloring bars in green, downtrend - in red color. Indicator also contains two additional visualizations, the strong uptrend and downtrend as the green and red zones and the white line - trend invalidation level (more information in "Methodology and it's justification" paragraph)
Features
Optional strong up and downtrends visualization: with the specified parameter in settings user can add/hide the green and red zones of the strong up and downtrends.
Optional trend invalidation level visualization: with the specified parameter in settings user can add/hide the white line which shows the current trend invalidation price.
Alerts: user can set up the alert and have notifications when uptrend/downtrend has been started, strong uptrend/downtrend started.
Methodology and it's justification
In this script we apply the concept of trend given by Bill Williams in his book "Trading Chaos". This approach leverages the Alligator and Fractals in conjunction. Let's briefly explain these two components.
The Williams Alligator, created by Bill Williams, is a technical analysis tool used to identify trends and potential market reversals. It consists of three moving averages, called the jaw, teeth, and lips, which represent different time periods:
Jaw (Blue Line): The slowest line, showing a 13-period smoothed moving average shifted 8 bars forward.
Teeth (Red Line): The medium-speed line, an 8-period smoothed moving average shifted 5 bars forward.
Lips (Green Line): The fastest line, a 5-period smoothed moving average shifted 3 bars forward.
When the lines are spread apart and aligned, the "alligator" is "awake," indicating a strong trend. When the lines intertwine, the "alligator" is "sleeping," signaling a non-trending or range-bound market. This indicator helps traders identify when to enter or avoid trades.
Williams Fractals, introduced by Bill Williams, are a technical analysis tool used to identify potential reversal points on a price chart. A fractal is a series of at least five consecutive bars where the middle bar has the highest high (for a up fractal) or the lowest low (for a down fractal), compared to the two bars on either side.
Key Points:
Up fractal: Formed when the middle bar shows a higher high than the two preceding and two following bars, signaling a potential turning point downward.
Down fractal: Formed when the middle bar has a lower low than the two surrounding bars, indicating a potential upward reversal.
Fractals are often used with other indicators to confirm trend direction or reversal, helping traders make more informed trading decisions.
How we can use its combination? Let's explain the uptrend example. The up fractal breakout to the upside can be interpret as bullish sign, there is a high probability that uptrend has just been started. It can be explained as following: the up fractal created is the potential change in market's behavior. A lot of traders made a decision to sell and it created the pullback with the fractal at the top. But if price is able to reach the fractal's top and break it, this is a high probability sign that market "changed his opinion" and bullish trend has been started. The moment of breaking is the potential changing to the uptrend. Here is another one important point, this breakout shall happen above the Alligator's teeth line. If not, this crossover doesn't count and the downtrend potentially remaining. The inverted logic is true for the down fractals and downtrend.
According to this methodology we received the high probability up and downtrend changes, but we can even add it. If current trend established by the indicator as the uptrend and alligator's lines have the following order: lips is higher than teeth, teeth is higher than jaw, script count it as a strong uptrend and start print the green zone - zone between lips and jaw. It can be used as a high probability support of the current bull market. The inverted logic can be used for bearish trend and red zones: if lips is lower than teeth and teeth is lower than jaw it's interpreted by the indicator as a strong down trend.
Indicator also has the trend invalidation line (white line). If current bar is green and market condition is interpreted by the script as an uptrend you will see the invalidation line below current price. This is the price level which shall be crossed by the price to change up trend to down trend according to algorithm. This level is recalculated on every candle. The inverted logic is valid for downtrend.
How to use indicator
Apply it to desired chart and time frame. It works on every time frame.
Setup the settings with enabling/disabling visualization of strong up/downtrend zones and trend invalidation line. "Show Strong Bullish/Bearish Trends" and "Show Trend Invalidation Price" checkboxes in the settings. By default they are turned on.
Analyze the price action. Indicator colored candle in green if it's more likely that current state is uptrend, in red if downtrend has the high probability to be now. Green zones between two lines showing if current uptrend is likely to be strong. This zone can be used as a high probability support on the uptrend. The red zone show high probability of strong downtrend and can be used as a resistance. White line is showing the level where uptrend or downtrend is going be invalidated according to indicator's algorithm. If current bar is green invalidation line will be below the current price, if red - above the current price.
Set up the alerts if it's needed. Indicator has four custom alerts called "Uptrend has been started" when current bar closed as green and the previous was not green, "Downtrend has been started" when current bar closed red and the previous was not red, "Uptrend became strong" if script started printing the green zone "Downtrend became strong" if script started printing the red zone.
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test indicators before live implementation.
