UP DOWN Indicator 1Title: UP DOWN Indicator based on ADX Strategy - Accurate Signal Provider with Enhanced Success Potential
Description:
The Martingale ADX Indicator is a groundbreaking tool meticulously crafted to offer traders unparalleled precision in signal generation and risk management. Leveraging the power of the Average Directional Index (ADX), this indicator provides 100% non-repaint signals on the current candle, guiding traders to opportune and prepare for trade entry with remarkable accuracy.
With a focus on empowering traders across various financial markets, including Forex and Binary Options, this ADX Strategy-1 Indicator introduces a unique approach to trading dynamics. By seamlessly integrating the renowned Martingale Step-1 risk management strategy, this indicator not only minimizes losses but also enhances the potential for success, even in volatile market conditions.
Key Features:
Non-Repaint Signals: The Martingale ADX Indicator stands as a testament to reliability, offering 100% non-repaint signals. Traders can trust in the consistency and not removing losing Signals which is very important to trust the previous generated signals also, eliminating uncertainties and facilitating confident decision-making.
ADX-Based Precision: Built upon the robust framework of the Average Directional Index (ADX), this indicator delivers precise signals tailored to prevailing market trends and volatility levels. Whether trading in longer timeframes or engaging in Binary Options, traders can rely on the Martingale Step-1 ADX Indicator for superior insights.
Next Candle Trading: Seamlessly integrated into trading strategies, signals from the Martingale ADX Indicator prompt action on the subsequent candle. This real-time approach ensures traders stay ahead of market movements, seizing opportunities as they emerge. Giving Signals Once Candle ahead makes traders to prepare early and decide whether they want to enter the trade on presented Signal or not as per their own experience too. If the trading candle is loss then the very next candle shall be used for taking Martingale Sep-1 to enhance the Accuracy.
Enhanced Success Potential: With Martingale Step-1 risk management, this ADX Indicator offers more than just signal accuracy – it presents the potential for heightened success rates. Through strategic position sizing and leveraging experience and Price Action insights, traders can elevate overall accuracy to levels ranging from 80% to 90%.
Conclusion:
The UP DOWN Strategy-1 Indicator represents a paradigm shift in trading technology, combining precision signal generation with advanced risk management strategies. Whether you're a seasoned trader or just starting your journey, this indicator empowers you to navigate financial markets with confidence and achieve consistent results.
Experience the difference with the Martingale ADX Indicator – where reliability meets profitability, and success becomes attainable with every trade.
Trade wisely, and may your ventures be marked by prosperity and fulfillment.
Pardon for any descriptive language grammatical error and comment about this indicator and to get my other strategy as well. Happy trading !!
Risk Disclaimer:
Trading in financial markets carries inherent risks and should be approached with caution. It is imperative to exercise sound judgment and trade only with funds that you can afford to lose. We strongly advise against using borrowed funds for trading purposes. First practice on demo for own learning then make decision wisely.
Recherche dans les scripts pour "accuracy"
Monte Carlo Future Moves [ChartPrime]ORIGINS AND HISTORICAL BACKGROUND:
Prior to the the advent of the Monte Carlo method, examining well-understood deterministic problems via simulation generally utilized statistical sampling to gauge uncertainty estimations. The Monte Carlo (MC) approach inverts this paradigm by modeling with probabilistic metaheuristics to address deterministic problems. Addressing Buffon's needle problem, an early form of the Monte Carlo method estimated π (3.14159) by dropping needles on a floor. Later, the modern MC inception primarily began when Stanislaw Ulam was playing solitaire games while experiencing illness and recovery.
Ulam further developed, applied, and ascribed "Monte Carlo" as a classified code name to maintain a level of secrecy for the modern method applications during collaborative investigations on neutron diffusion and collision intricacies with John von Neumann. Despite having relevant data, physicist's conventional deterministic mathematical methods were unable to solve mysterious "neutronion problems". Monte Carlo filled in the gaps necessary to resolve this perplexing neutron problem with innovative statistics, and the resilient MC continues onward to have diverse application in many fields of science. MC also extends into the realm of relevance within finance.
APPLICATION IN FINANCE:
Building on its historical roots, the Monte Carlo method's transition into finance opened new avenues for risk assessment and predictive analysis. In financial markets, characterized by uncertainty and complex variables, this method offers a powerful tool for simulating a wide range of scenarios and assessing probabilities of different outcomes. By employing probabilistic models to predict price movements, the Monte Carlo method helps in creating more resilient and informed trading strategies. This approach is particularly valuable in options pricing, portfolio management, and risk assessment, where understanding the range of potential outcomes is crucial for making sound investment decisions. Our indicator utilizes this methodology, blending traditional financial analysis with advanced statistical techniques.
THE INDICATOR:
The Monte Carlo Future Moves (ChartPrime) indicator is designed to predict future price movements. It simulates various possible price paths, showing the likelihood of different outcomes. We have designed it to be simple to use and understand by displaying lines indicating the most likely bullish and bearish outcomes. The arrows point to these areas making it intuitive to understand. Also included is extreme price levels shown in blue and yellow. This is the most likely extreme range that the price will move to. The outcome distribution is there to show you the range of outcomes along with a visual representation of the possible future outcomes. To make things more user friendly we have also included a representation of this distribution as a background heatmap. The brighter the price level, the more likely the price will end at that level. Finally, we have also included a market bias indication on the side that shows you the general bullish/bearish probabilities.
HOW TO USE:
To use this indicator you want to first assess the market bias. From there you want to target the most likely polar outcome. You can use the range of outcomes to assess your risk and set a stop within a reasonable range of the desired target. By default the indicator projects 10 steps into the future, however this can be easily adjusted in the settings. Generally this indicator excels at mid-term estimations and may yield inconclusive results if the prediction period is too short or too long. You can change the granularity of the outcomes to give you a more or less detailed view of the future. That being said, a lower resolution can make the predictions less useful while a higher resolution can give you a less useful picture. If you decide to use a higher resolution we have included an option to smooth the final result. This is intended to reduce the uncertainty and noise in the predicted outcomes. It is advised to use the minimum level of smoothing possible as a high level of smoothing will greatly reduce the accuracy.
INPUT SECTION:
Derivative Source changes how the indicator sees the price movements. When you set this to Candle it will use the difference between the open and close of each candle. If set to Move, it will use the difference between closing prices. If you are in a market with gaps, you might want to use Candle as this will prevent the indicator from seeing gaps.
Number of Simulations is a crucial setting as it is the core of this indicator. This determines the number of simulations the indicator will use to get its final result. By default it is set to 1000 as we feel like that is around the minimum number of simulations required to get a reasonable output while maintaining stability. In tests the maximum number of simulations we have been able to consistently achieve is 2000.
Lookback is the number of historical candles to account for. A lookback that is too short will not have enough data to accurately assess the likelihood of a price movement, while a period that is too large can make the data less relevant. By default this is set to 1000 as we feel like this is a reasonable tradeoff between volume of data and relevance.
Steps Into Future is the prediction period. By default we have picked a period of 10 steps as this has a good balance between accuracy and usability. The more steps into the future you go, the more uncertain the future outcome will be.
Outcome Granularity controls the precision of the simulated outcomes. By default this is set to 40 as its a good balance between resolution and accuracy.
Outcome Smoothing allows you to smooth the outcome distribution. By default this is set to 0 as it is generally not needed for lower resolutions. Smoothing levels beyond 2 are not recommended as it will negatively impact the output.
Returns Granularity controls the level of definition in the collected price movements. This directly impacts indicator performance and is set to 50 by default because its a good balance between fidelity and usability. When this number is too small, the simulations will be less accurate while numbers too large will negatively impact the probabilities of the movements.
Drift is the trend component in the simulation. This adds the directionality of the simulations by biasing the movements in the current direction of the market. We have included both the standard formula for drift and linear regression. Both methods are well suited for simulating future price movements and have their own advantages. The drift period is set to 100 by default as its a good balance between current and historical directionality. You may want to increase or decrease this number depending on the current market conditions but it is advised to use a period that isn't too small. If your period is too small it can skew the outcomes too much resulting in poor performance. When this is set to 0 it will use the same period as your lookback.
Volatility Adjust , adjusts the simulation to include current volatility. This makes sure that the price movements in the simulation reflects the current market conditions better by making sure that each price move is at least a minimum size.
Returns Style allows you to pick between using percent moves and log returns. We have opted to make percent move the default as it is more intuitive for beginners however both settings yield similar results. Log returns can be less cpu intensive so it might be desirable for longer term predictions.
Precision adjusts the rounding of used when collecting the frequency of price movement sizes. By default this is set to 4 as its is fairly accurate without impacting performance too much. A larger number will make the indicator more precise but at the cost of cpu time. Precision levels that are too small can greatly reduce the accuracy of the simulation and even break the indicator all together.
Update Every Bar allows you to recalculate the prediction every bar and is there for you if you want to strictly use the market bias. It is not recommended to enable this feature but it is there for flexibility.
Side of Chart allows you to pick what side of the price action you want the visuals to be on. When its set to the right everything will be to the right of the starting point and when its set to Left it will position everything to the left of the starting point.
Move Visualization is there to give you an arrow to the most likely bullish and bearish moves. It is meant as a visual aid and visualization tool. The color of these arrows use the same colors as the distribution.
Most Likely Move is a horizontal line that indicates the most likely move. It is positioned in the same location as the Move Visualization.
Standard Deviation is horizontal lines at the extremities of the simulated price action. These represent the most likely range of the future outcomes. You can adjust the multiplier of the standard deviation but by default it is set to 2.
Most Likely Direction is a vertical bar that shows you the sum of the up and down probabilities. It is there to show you the bias of the outcomes and guide you in decision making.
Max Probability Zone is a horizontal line that highlights the location of the highest probability move. You can think of it almost like the POC in a volume distribution but in this case it is the "most likely" single outcome.
Outcome Distribution allows you to toggle the distribution on or off. This is the distribution of all of the simulated outcomes. You can toggle the scale width of the distribution to fit your visual style.
Distribution Text toggles the probability text inside of the distribution bars. When you have a large number for the outcome granularity this text may not be visible and you may want to disable this feature.
Background is a heatmap of the outcome distribution. This allows you to visualize the underlying distribution without the need for the distribution histogram. The brighter the color, the more likely the outcome is for that level. It can be useful for visualizing the range of possible outcomes.
Starting Line is simply a horizontal line indicating the starting point of the simulation. It just the opening price for the starting position.
Extend Lines allows you to extend the lines and background past the prediction period.
CONCLUSION:
With its intuitive visuals and flexible settings, the Monte Carlo Future Moves (ChartPrime) indicator is practice and easy to use. It brings clarity to price movement predictions, helping you to build confidence in your strategies. This indicator not only reflects the evolution of technical analysis but also touches on data-driven insights.
Enjoy
Advanced Technical Range and Expectancy Estimator [SS]Hello everyone,
This indicator is a from of momentum based probability modelling. It is derived from my own approaches to probability modelling but just simplified a bit.
How it works:
The indicator looks at various technical, including stochastics, RSI, MFI and Z-Score, to determine the likely sentiment. All of these, with the exception of Z-Score, are momentum based indicators and can alert us to likely sentiment. However, instead of us making the subjective determination ourselves as to whether the RSI or MFI or Stochastics are bullish, the indicator will look at previous instances of these occurrences, and tally the bullish and bearish follow throughs that happened. It will also calculate the average target price that was hit, under similar conditions, on the same timeframe.
The Z-Score is your "tie breaker". It is not a momentum based indicator and measures something a little different (the standard deviation and over-extension of the stock). For this reason, it provides an alternative assessment and tends to be a bit more reliable in times of low momentum.
Back-test Results:
The indicator back-tests itself over the previous 100 candles. I have limited it to 100 candles for pragmatic considerations (it has to back-test each technical individually and increasing the BT length will slow and potentially error out the indicator) as well as accuracy considerations.
One thing I have noticed in my years of trying to crack the code and develop probability models for tickers, is historical accuracy doesn't always matter because sentiment is always changing. You need to see what it has done over the most recent 100 to 200 candles.
There are two back-test windows, one for the price targets and the other for the sentiment accuracy. The most effective/most accurate will highlight green, the least effective/least accurate will highlight red:
In the image above, you can see that the most accurate predictor of sentiment is Z-Score, with a 90.32% accuracy rate over the past 100 candles.
The most accurate predictor of price is MFI, with a 60% (for bull targets) and 42% (for bear targets)accuracy rate.
Anchoring Points:
The indicator permits you to anchor by two points. The default setting is anchoring by previous candle. If you plan to use this as an oscillator, to see the current prediction for the current candle you are viewing, then you will need to leave this default setting. It will pull the data from the previous candle and give you the data for the current candle you are on.
If you are assess the likely sentiment for the next day after the day has closed off, you will want to anchor by current candle. This will take the current technicals that the day has closed off with and run the assessment for you.
Customizability
You can customize the technicals by source and length of assessment.
They are all defaulted to the traditional settings of these indicators, but if you want to customize your model to try and improve or enhance accuracy in one way or another, you are free and able to do so!
I do suggest leaving the defaults as they seem to work particular well :-).
Thresholds
Thresholds are the tolerance levels that we permit for our technical search range. If you want them to be exactly identical, then you can set it to 0. If you want it to be extremely similar, you can set it to 0.01. This will hone in on the ranges you are interest in and you can see how it affects your accuracy by reviewing the results in the back-test tables.
Keep Static Colour Option
I want to make a quick note on the "Keep Static Colour" option that is in your settings menu.
The primary table that shows you the probability and price targets change colours based on the accuracy of the assessment. This is so, if you are using a mobile device or smaller screen and can't have the back-test results open at the same time, you can see still which are the most reliable results. However, if you have the back-test tables open and you find these colour changes too distracted, you can toggle on the "Keep Static Colour" and it will resort the colour of the table to a solid white:
Show Technicals
The indicator can show you the current technical values if you are using it in place of an oscillator. Its less pivotal as its making the assessment for you, but just for your reference if you want to see what the current MFI, Z-Score or Stochastics etc. are, you have that option as well.
All Timeframes Permitted
You can view Weekly, Monthly, Hourly, 5 minute, 1 minute, its all supported!
That's the indicator in a nutshell.
Hope you enjoy and leave your questions below.
Safe trades everyone!
Ahsan Tufail Precise MA Crossover Filter for Reliable SignalsIntroduction:
In the ever-evolving world of Forex trading, strategies that provide a competitive edge are highly sought after. The Moving Average (MA) crossover technique is a popular long-term approach, but its vulnerability to false signals can lead to potential losses. To overcome this challenge, we introduce a game-changing MA crossover filter designed to weed out false signals and unlock the full potential of this strategy. In this article, we delve into the mechanics of this filter, providing a comprehensive analysis of its components and how it enhances the accuracy of buy and sell signals.
The Power of the MA Crossover Filter:
The essence of our MA crossover filter lies in the integration of a specialized indicator that operates on a scale of 0 to 100. This ingenious indicator dynamically measures the distance between the middle Bollinger band and either the upper or lower Bollinger band. By analyzing the values of the last 504 candlesticks, it maps the range from 50 to 100 for the largest and smallest distances between the middle and upper Bollinger bands. Similarly, for values ranging from 0 to 50, it measures the distance between the middle and lower Bollinger bands.
Unveiling the Signal Execution Process:
The brilliance of this filter is revealed in its meticulous execution of buy and sell signals, which significantly reduces false crossovers. Let's explore the process step-by-step:
Buy Signal Precision:
To initiate a buy signal, the price must be positioned above the 200-period Simple Moving Average (SMA).
The filter validates the crossover by checking the indicator's value, ensuring it falls below the threshold of 25.
Sell Signal Accuracy:
For a sell signal, the price must be below the 200-period Simple Moving Average (SMA).
The filter confirms the crossover by verifying the indicator's value, which should exceed the threshold of 75.
This selective approach ensures that only high-confidence crossovers are considered, maximizing the potential for profitable trades.
Fine-Tuning the Filter for Optimal Performance:
While the MA crossover filter exhibits its prowess in GBPUSD and EURUSD currency pairs, it may require adjustments for other pairs. Currency pairs possess unique characteristics, and adapting the filter to specific behavior is crucial for its success.
To fine-tune the filter for alternative currency pairs, traders should conduct rigorous backtesting and analyze historical price data. By experimenting with indicator threshold values, traders can calibrate the filter to accurately match the dynamics of the target currency pair. This iterative process allows for customization, ultimately resulting in a finely-tuned filter that aligns with the unique behavior of the selected market.
Conclusion:
The MA crossover filter represents a paradigm shift in long-term Forex trading strategies. By intelligently filtering false signals, this precision tool unleashes the true potential of the MA crossover technique, elevating its profitability and enhancing overall trading performance. While no strategy guarantees absolute success, incorporating this filter empowers traders with a heightened level of confidence in their buy and sell signals. Embracing the power of this innovative filter can be a transformative step towards mastering Forex profits and staying ahead in the dynamic world of currency trading.
Ultimate Balance OscillatorIntroducing the Ultimate Balance Oscillator: A Powerful Trading Indicator
Built upon the renowned Rate of Change (ROC), Relative Strength Index (RSI), Commodity Channel Index (CCI), Williams Percent Range, and Average Directional Index (ADX) from TradingView, this indicator equips traders with an unparalleled understanding of market dynamics.
What sets the Ultimate Balance Oscillator apart is its meticulous approach to weighting. Each component is assigned a weight that reflects its individual significance, while carefully mitigating the influence of highly correlated signals. This strategic weighting methodology ensures an unbiased and comprehensive representation of market sentiment, eliminating dominance by any single indicator.
Key Features and Benefits:
1. Comprehensive Market Analysis: The Ultimate Balance Oscillator provides a comprehensive view of market conditions, enabling traders to discern price trends, evaluate momentum shifts, identify overbought or oversold levels, and gauge the strength of prevailing trends. This holistic perspective empowers traders to make well-informed decisions based on a thorough understanding of the market.
2. Enhanced Signal Accuracy: With its refined weighting approach, the Ultimate Balance Oscillator filters out noise and emphasizes the most relevant information. This results in heightened signal accuracy, providing traders with a distinct advantage in identifying optimal entry and exit points. Say goodbye to unreliable signals and welcome a more precise and dependable trading experience.
3. Adaptability to Various Trading Scenarios: The Ultimate Balance Oscillator transcends the constraints of specific markets or timeframes. It seamlessly adapts to diverse trading scenarios, accommodating both short-term trades and long-term investments. Traders can customize this indicator to suit their preferred trading style and effortlessly navigate ever-changing market conditions.
4. Simplicity and Ease of Use: The Ultimate Balance Oscillator simplifies trading analysis by providing a single line on the chart. Its straightforward interpretation and seamless integration into trading strategies make decision-making effortless. By observing bullish or bearish crossovers with the moving average, recognizing overbought or oversold levels, and tracking the overall trend of the oscillator, traders can make well-informed decisions with confidence.
5. Real-time Alerts: Stay ahead of the game with the Ultimate Balance Oscillator's customizable alert system. Traders can set up personalized alerts for bullish or bearish crossovers, breaches of overbought or oversold thresholds, or any specific events that align with their trading strategy. Real-time notifications enable timely action, ensuring traders never miss lucrative trading opportunities.
The Ultimate Balance Oscillator is a robust trading companion, empowering traders to make shrewd and calculated decisions. Embrace its power and elevate your trading endeavors to new heights of precision and success. Discover the potential of the Ultimate Balance Oscillator and unlock a world of trading possibilities.
Machine Learning : Cosine Similarity & Euclidean DistanceIntroduction:
This script implements a comprehensive trading strategy that adheres to the established rules and guidelines of housing trading. It leverages advanced machine learning techniques and incorporates customised moving averages, including the Conceptive Price Moving Average (CPMA), to provide accurate signals for informed trading decisions in the housing market. Additionally, signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation are utilised to enhance the signal quality and improve trading accuracy.
Features:
Market Analysis: The script utilizes advanced machine learning methods such as Lorentzian, Euclidean distance, and Cosine similarity to analyse market conditions. These techniques measure the similarity and distance between data points, enabling more precise signal identification and enhancing trading decisions.
Cosine similarity:
Cosine similarity is a measure used to determine the similarity between two vectors, typically in a high-dimensional space. It calculates the cosine of the angle between the vectors, indicating the degree of similarity or dissimilarity.
In the context of trading or signal processing, cosine similarity can be employed to compare the similarity between different data points or signals. The vectors in this case represent the numerical representations of the data points or signals.
Cosine similarity ranges from -1 to 1, with 1 indicating perfect similarity, 0 indicating no similarity, and -1 indicating perfect dissimilarity. A higher cosine similarity value suggests a closer match between the vectors, implying that the signals or data points share similar characteristics.
Lorentzian Classification:
Lorentzian classification is a machine learning algorithm used for classification tasks. It is based on the Lorentzian distance metric, which measures the similarity or dissimilarity between two data points. The Lorentzian distance takes into account the shape of the data distribution and can handle outliers better than other distance metrics.
Euclidean Distance:
Euclidean distance is a distance metric widely used in mathematics and machine learning. It calculates the straight-line distance between two points in Euclidean space. In two-dimensional space, the Euclidean distance between two points (x1, y1) and (x2, y2) is calculated using the formula sqrt((x2 - x1)^2 + (y2 - y1)^2).
Dynamic Time Windows: The script incorporates a dynamic time window function that allows users to define specific time ranges for trading. It checks if the current time falls within the specified window to execute the relevant trading signals.
Custom Moving Averages: The script includes the CPMA, a powerful moving average calculation. Unlike traditional moving averages, the CPMA provides improved support and resistance levels by considering multiple price types and employing a combination of Exponential Moving Averages (EMAs) and Simple Moving Averages (SMAs). Its adaptive nature ensures responsiveness to changes in price trends.
Signal Processing Techniques: The script applies signal processing techniques such as Know sure thing, Rational Quadratic, and sigmoid transformation to enhance the quality of the generated signals. These techniques improve the accuracy and reliability of the trading signals, aiding in making well-informed trading decisions.
Trade Statistics and Metrics: The script provides comprehensive trade statistics and metrics, including total wins, losses, win rate, win-loss ratio, and early signal flips. These metrics offer valuable insights into the performance and effectiveness of the trading strategy.
Usage:
Configuring Time Windows: Users can customize the time windows by specifying the start and finish time ranges according to their trading preferences and local market conditions.
Signal Interpretation: The script generates long and short signals based on the analysis, custom moving averages, and signal processing techniques. Users should pay attention to these signals and take appropriate action, such as entering or exiting trades, depending on their trading strategies.
Trade Statistics: The script continuously tracks and updates trade statistics, providing users with a clear overview of their trading performance. These statistics help users assess the effectiveness of the strategy and make informed decisions.
Conclusion:
With its adherence to housing trading rules, advanced machine learning methods, customized moving averages like the CPMA, and signal processing techniques such as Lorentzian, Euclidean distance, Cosine similarity, Know sure thing, Rational Quadratic, and sigmoid transformation, this script offers users a powerful tool for housing market analysis and trading. By leveraging the provided signals, time windows, and trade statistics, users can enhance their trading strategies and improve their overall trading performance.
Disclaimer:
Please note that while this script incorporates established tradingview housing rules, advanced machine learning techniques, customized moving averages, and signal processing techniques, it should be used for informational purposes only. Users are advised to conduct their own analysis and exercise caution when making trading decisions. The script's performance may vary based on market conditions, user settings, and the accuracy of the machine learning methods and signal processing techniques. The trading platform and developers are not responsible for any financial losses incurred while using this script.
By publishing this script on the platform, traders can benefit from its professional presentation, clear instructions, and the utilisation of advanced machine learning techniques, customised moving averages, and signal processing techniques for enhanced trading signals and accuracy.
I extend my gratitude to TradingView, LUX ALGO, and JDEHORTY for their invaluable contributions to the trading community. Their innovative scripts, meticulous coding patterns, and insightful ideas have profoundly enriched traders' strategies, including my own.
T3 JMA KAMA VWMAEnhancing Trading Performance with T3 JMA KAMA VWMA Indicator
Introduction
In the dynamic world of trading, staying ahead of market trends and capitalizing on volume-driven opportunities can greatly influence trading performance. To address this, we have developed the T3 JMA KAMA VWMA Indicator, an innovative tool that modifies the traditional Volume Weighted Moving Average (VWMA) formula to increase responsiveness and exploit high-volume market conditions for optimal position entry. This article delves into the idea behind this modification and how it can benefit traders seeking to gain an edge in the market.
The Idea Behind the Modification
The core concept behind modifying the VWMA formula is to leverage more responsive moving averages (MAs) that align with high-volume market activity. Traditional VWMA utilizes the Simple Moving Average (SMA) as the basis for calculating the weighted average. While the SMA is effective in providing a smoothed perspective of price movements, it may lack the desired responsiveness to capitalize on short-term volume-driven opportunities.
To address this limitation, our T3 JMA KAMA VWMA Indicator incorporates three advanced moving averages: T3, JMA, and KAMA. These MAs offer enhanced responsiveness, allowing traders to react swiftly to changing market conditions influenced by volume.
T3 (T3 New and T3 Normal):
The T3 moving average, one of the components of our indicator, applies a proprietary algorithm that provides smoother and more responsive trend signals. By utilizing T3, we ensure that the VWMA calculation aligns with the dynamic nature of high-volume markets, enabling traders to capture price movements accurately.
JMA (Jurik Moving Average):
The JMA component further enhances the indicator's responsiveness by incorporating phase shifting and power adjustment. This adaptive approach ensures that the moving average remains sensitive to changes in volume and price dynamics. As a result, traders can identify turning points and anticipate potential trend reversals, precisely timing their position entries.
KAMA (Kaufman's Adaptive Moving Average):
KAMA is an adaptive moving average designed to dynamically adjust its sensitivity based on market conditions. By incorporating KAMA into our VWMA modification, we ensure that the moving average adapts to varying volume levels and captures the essence of volume-driven price movements. Traders can confidently enter positions during periods of high trading volume, aligning their strategies with market activity.
Benefits and Usage
The modified T3 JMA KAMA VWMA Indicator offers several advantages to traders looking to exploit high-volume market conditions for position entry:
Increased Responsiveness: By incorporating more responsive moving averages, the indicator enables traders to react quickly to changes in volume and capture short-term opportunities more effectively.
Enhanced Entry Timing: The modified VWMA aligns with high-volume periods, allowing traders to enter positions precisely during price movements influenced by significant trading activity.
Improved Accuracy: The combination of T3, JMA, and KAMA within the VWMA formula enhances the accuracy of trend identification, reversals, and overall market analysis.
Comprehensive Market Insights: The T3 JMA KAMA VWMA Indicator provides a holistic view of market conditions by considering both price and volume dynamics. This comprehensive perspective helps traders make informed decisions.
Analysis and Interpretation
The modified VWMA formula with T3, JMA, and KAMA offers traders a valuable tool for analyzing volume-driven market conditions. By incorporating these advanced moving averages into the VWMA calculation, the indicator becomes more responsive to changes in volume, potentially providing deeper insights into price movements.
When analyzing the modified VWMA, it is essential to consider the following points:
Identifying High-Volume Periods:
The modified VWMA is designed to capture price movements during high-volume periods. Traders can use this indicator to identify potential market trends and determine whether significant trading activity is driving price action. By focusing on these periods, traders may gain a better understanding of the market sentiment and adjust their strategies accordingly.
Confirmation of Trend Strength:
The modified VWMA can serve as a confirmation tool for assessing the strength of a trend. When the VWMA line aligns with the overall trend direction, it suggests that the current price movement is supported by volume. This confirmation can provide traders with additional confidence in their analysis and help them make more informed trading decisions.
Potential Entry and Exit Points:
One of the primary purposes of the modified VWMA is to assist traders in identifying potential entry and exit points. By capturing volume-driven price movements, the indicator can highlight areas where market participants are actively participating, indicating potential opportunities for opening or closing positions. Traders can use this information in conjunction with other technical analysis tools to develop comprehensive trading strategies.
Interpretation of Angle and Gradient:
The modified VWMA incorporates an angle calculation and color gradient to further enhance interpretation. The angle of the VWMA line represents the slope of the indicator, providing insights into the momentum of price movements. A steep angle indicates strong momentum, while a shallow angle suggests a slowdown. The color gradient helps visualize this angle, with green indicating bullish momentum and purple indicating bearish momentum.
Conclusion
By modifying the VWMA formula to incorporate the T3, JMA, and KAMA moving averages, the T3 JMA KAMA VWMA Indicator offers traders an innovative tool to exploit high-volume market conditions for optimal position entry. This modification enhances responsiveness, improves timing, and provides comprehensive market insights.
Enjoy checking it out!
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Credits to:
◾ @cheatcountry – Hann Window Smoothing
◾ @loxx – T3
◾ @everget – JMA
S2BU2 True Range BandsWhat it is:
A twist on the DMI and ATR indicators.
How it is calculated:
What you see is a basic 14-period EMA calculated based on the opening price of the candle.
Extending from this base is the average true range, showing the expected realistic movement in that direction.
How you may interpret what you see:
Extended movement outside of those bands indicates a strong trend and also indicates a bad time to enter. In a sideways market the best time to get in and out is whenever the chart crosses the upper/lower bands. As usual, do not use this indicator alone to determine your entries/exits. If you find a regular behaviour related to this indicator that is not mentioned here feel free to reach out to me. I will consider adding it the list
How you may improve accuracy:
Most obvious - combine with different indicators measuring different things. This indicator measures potential direction. You may combine it with a simple trendindicator, momentum-indicator, etc. There is little use in combining it with another indicator measuring the same thing. When using 2 indicators for the same aspect, pick the one that is more reliable or reacts faster - depending on your preference and strategy.
Another Option to improve accuracy is to overlap the indicator on several timeframes. Dissonance is weakness. Ressonance is strength. if you see many tfs showing the same movement it may be a good time to get in - you will have to judge it yourself though ;)
Internal Pivot Pattern [LuxAlgo]The Internal Pivot Pattern indicator is a novel method allowing traders to detect pivots without excessive delay on the chart timeframe, by using the lower timeframe data from a candle.
It features custom colors for candles and zigzag lines to help identify trends. A dashboard showing the accuracy of the pattern is also included.
🔶 USAGE
We define a pivot as the occurrence where the middle candle over a specific interval (for example, the most recent 21 bars) is the highest (pivot high) or the lowest (pivot low). This method commonly allows for identifying swing highs/lows on a trader's chart; however, this pattern can only be identified after a specific number of bars has been formed, rendering this pattern useless for real-time detection of swing highs/lows.
This indicator uses a different approach, removing the need to wait for candles to form on the user chart; instead, we check the lower timeframe data of the current candle and evaluate for the presence of a pivot given the internal data, effectively providing pivot confirmation at the candle close.
An internal pivot low pattern is indicative of a potential uptrend, while an internal pivot high is indicative of a potential downtrend.
Candles are colored based on the last internal pivot detected, with blue candle colors indicating that the most recent internal pivot is a pivot low, indicating an uptrend, while an orange candle color indicates that the most recent internal pivot is a pivot high, indicating a downtrend.
🔹 Timeframes
The timeframe setting allows controlling the amount of lower timeframe data to consider for the internal pivot detection. This setting must be lower than the user's chart timeframe.
Using a timeframe significantly lower than the user chart timeframe will evaluate a larger amount of data for the pivot detection, making it less frequent, while using a timeframe closer to the chart timeframe can make the internal pivot detection more frequent, and more prone to false positives.
🔹 Accuracy Dashboard
The Accuracy Dashboard allows evaluating how accurate the detected patterns are as a percentage, with a pattern being judged accurate if subsequent patterns are detected higher or lower than a previous one.
For example, an internal pivot low is judged accurate if the following internal pivot is higher than it, indicating that higher highs have been made.
This dashboard can be useful to determine the timeframe setting to maximize the respective internal pivot accuracy.
🔶 SETTINGS
Timeframe: Timeframe for detecting internal swings
Accuracy Dashboard: Enable or disable the Accuracy Dashboard.
🔹 Style
Internal Pivot High: Color of the dot displayed upon the detection of an internal pivot high
Internal Pivot Low: Color of the dot displayed upon the detection of an internal pivot low
Zig-Zag: Color of the zig-zag segments connecting each internal pivot
Candles: Enable candle coloring, with control over the color of the candles highlighting the detected trend
Simple Decesion Matrix Classification Algorithm [SS]Hello everyone,
It has been a while since I posted an indicator, so thought I would share this project I did for fun.
This indicator is an attempt to develop a pseudo Random Forest classification decision matrix model for Pinescript.
This is not a full, robust Random Forest model by any stretch of the imagination, but it is a good way to showcase how decision matrices can be applied to trading and within Pinescript.
As to not market this as something it is not, I am simply calling it the "Simple Decision Matrix Classification Algorithm". However, I have stolen most of the aspects of this machine learning algo from concepts of Random Forest modelling.
How it works:
With models like Support Vector Machines (SVM), Random Forest (RF) and Gradient Boosted Machine Learning (GBM), which are commonly used in Machine Learning Classification Tasks (MLCTs), this model operates similarity to the basic concepts shared amongst those modelling types. While it is not very similar to SVM, it is very similar to RF and GBM, in that it uses a "voting" system.
What do I mean by voting system?
How most classification MLAs work is by feeding an input dataset to an algorithm. The algorithm sorts this data, categorizes it, then introduces something called a confusion matrix (essentially sorting the data in no apparently order as to prevent over-fitting and introduce "confusion" to the algorithm to ensure that it is not just following a trend).
From there, the data is called upon based on current data inputs (so say we are using RSI and Z-Score, the current RSI and Z-Score is compared against other RSI's and Z-Scores that the model has saved). The model will process this information and each "tree" or "node" will vote. Then a cumulative overall vote is casted.
How does this MLA work?
This model accepts 2 independent variables. In order to keep things simple, this model was kept as a three node model. This means that there are 3 separate votes that go in to get the result. A vote is casted for each of the two independent variables and then a cumulative vote is casted for the overall verdict (the result of the model's prediction).
The model actually displays this system diagrammatically and it will likely be easier to understand if we look at the diagram to ground the example:
In the diagram, at the very top we have the classification variable that we are trying to predict. In this case, we are trying to predict whether there will be a breakout/breakdown outside of the normal ATR range (this is either yes or no question, hence a classification task).
So the question forms the basis of the input. The model will track at which points the ATR range is exceeded to the upside or downside, as well as the other variables that we wish to use to predict these exceedences. The ATR range forms the basis of all the data flowing into the model.
Then, at the second level, you will see we are using Z-Score and RSI to predict these breaks. The circle will change colour according to "feature importance". Feature importance basically just means that the indicator has a strong impact on the outcome. The stronger the importance, the more green it will be, the weaker, the more red it will be.
We can see both RSI and Z-Score are green and thus we can say they are strong options for predicting a breakout/breakdown.
So then we move down to the actual voting mechanisms. You will see the 2 pink boxes. These are the first lines of voting. What is happening here is the model is identifying the instances that are most similar and whether the classification task we have assigned (remember out ATR exceedance classifier) was either true or false based on RSI and Z-Score.
These are our 2 nodes. They both cast an individual vote. You will see in this case, both cast a vote of 1. The options are either 1 or 0. A vote of 1 means "Yes" or "Breakout likely".
However, this is not the only voting the model does. The model does one final vote based on the 2 votes. This is shown in the purple box. We can see the final vote and result at the end with the orange circle. It is 1 which means a range exceedance is anticipated and the most likely outcome.
The Data Table Component
The model has many moving parts. I have tried to represent the pivotal functions diagrammatically, but some other important aspects and background information must be obtained from the companion data table.
If we bring back our diagram from above:
We can see the data table to the left.
The data table contains 2 sections, one for each independent variable. In this case, our independent variables are RSI and Z-Score.
The data table will provide you with specifics about the independent variables, as well as about the model accuracy and outcome.
If we take a look at the first row, it simply indicates which independent variable it is looking at. If we go down to the next row where it reads "Weighted Impact", we can see a corresponding percent. The "weighted impact" is the amount of representation each independent variable has within the voting scheme. So in this case, we can see its pretty equal, 45% and 55%, This tells us that there is a slight higher representation of z-score than RSI but nothing to worry about.
If there was a major over-respresentation of greater than 30 or 40%, then the model would risk being skewed and voting too heavily in favour of 1 variable over the other.
If we move down from there we will see the next row reads "independent accuracy". The voting of each independent variable's accuracy is considered separately. This is one way we can determine feature importance, by seeing how well one feature augments the accuracy. In this case, we can see that RSI has the greatest importance, with an accuracy of around 87% at predicting breakouts. That makes sense as RSI is a momentum based oscillator.
Then if we move down one more, we will see what each independent feature (node) has voted for. In this case, both RSI and Z-Score voted for 1 (Breakout in our case).
You can weigh these in collaboration, but its always important to look at the final verdict of the model, which if we move down, we can see the "Model prediction" which is "Bullish".
If you are using the ATR breakout, the model cannot distinguish between "Bullish" or "Bearish", must that a "Breakout" is likely, either bearish or bullish. However, for the other classification tasks this model can do, the results are either Bullish or Bearish.
Using the Function:
Okay so now that all that technical stuff is out of the way, let's get into using the function. First of all this function innately provides you with 3 possible classification tasks. These include:
1. Predicting Red or Green Candle
2. Predicting Bullish / Bearish ATR
3. Predicting a Breakout from the ATR range
The possible independent variables include:
1. Stochastics,
2. MFI,
3. RSI,
4. Z-Score,
5. EMAs,
6. SMAs,
7. Volume
The model can only accept 2 independent variables, to operate within the computation time limits for pine execution.
Let's quickly go over what the numbers in the diagram mean:
The numbers being pointed at with the yellow arrows represent the cases the model is sorting and voting on. These are the most identical cases and are serving as the voting foundation for the model.
The numbers being pointed at with the pink candle is the voting results.
Extrapolating the functions (For Pine Developers:
So this is more of a feature application, so feel free to customize it to your liking and add additional inputs. But here are some key important considerations if you wish to apply this within your own code:
1. This is a BINARY classification task. The prediction must either be 0 or 1.
2. The function consists of 3 separate functions, the 2 first functions serve to build the confusion matrix and then the final "random_forest" function serves to perform the computations. You will need all 3 functions for implementation.
3. The model can only accept 2 independent variables.
I believe that is the function. Hopefully this wasn't too confusing, it is very statsy, but its a fun function for me! I use Random Forest excessively in R and always like to try to convert R things to Pinescript.
Hope you enjoy!
Safe trades everyone!
Cypher Harmonic Pattern [TradingFinder] Cypher Pattern Detector🔵 Introduction
The Cypher Pattern is one of the most accurate and advanced harmonic patterns, introduced by Darren Oglesbee. The Cypher pattern, utilizing Fibonacci ratios and geometric price analysis, helps traders identify price reversal points with high precision. This pattern consists of five key points (X, A, B, C, and D), each playing an important role in determining entry and exit points in the financial markets.
The reversal point typically occurs in the XD region, with the Fibonacci ratio ranging between 0.768 and 0.886. This zone is referred to as the Potential Reversal Zone (PRZ), where traders anticipate price changes to occur.
The Cypher harmonic pattern is popular among professional traders due to its high accuracy in identifying market trends and reversal points. The pattern appears in two forms: bullish Cypher pattern and bearish Cypher pattern.
In the bullish Cypher pattern, after a price correction, the price moves upward, while in the bearish Cypher pattern, the price moves downward after a temporary increase. These patterns help traders use technical analysis to identify strong reversal points in the PRZ and execute more optimal trades.
Bullish Cypher Pattern :
Bearish Cypher Pattern :
🔵 How to Use
The Cypher pattern is one of the most complex and precise harmonic patterns, leveraging Fibonacci ratios to help traders identify price reversals. This pattern is comprised of five key points, each playing a critical role in determining entry and exit points.
The Cypher pattern appears in two main types :
Bullish Cypher pattern : This pattern appears as an M shape on the chart and indicates a trend reversal to the upside after a price correction. Traders can prepare for buying after identifying this pattern in technical analysis.
Bearish Cypher pattern : This pattern appears as a W shape and signals the start of a downtrend after a temporary price increase. Traders can use this pattern to enter short positions.
🟣 How to Identify the Cypher Pattern on a Chart
Identifying the Cypher pattern requires precision and the use of advanced technical analysis tools. The pattern consists of four main legs, each identified using Fibonacci ratios and geometric analysis.
To spot the Cypher pattern on a chart, first, identify the five key points : X, A, B, C, and D.
XA leg : The initial move from point X to A.
AB leg : The first correction after the XA move, where the price moves to point B.
BC leg : After the correction, the price moves upwards to point C.
CD leg : The final price move that reaches point D, where a price reversal is expected.
In a bullish Cypher pattern, point D indicates the start of a new uptrend, while in a bearish Cypher pattern, point D signals the beginning of a downtrend. Correctly identifying these points helps traders determine the best time to enter a trade.
🟣 How to Trade Using the Cypher Pattern
Once the Cypher pattern is identified on the chart, traders can use it to set entry and exit points. Point D is the key point for trade entry. In the bullish Cypher pattern, the trader can enter a long position after point D forms, while in the bearish Cypher pattern, point D serves as the ideal point for entering a short position.
🟣 Entering a Buy Trade with the Bullish Cypher Pattern
In a bullish Cypher pattern, traders wait for the price to reach point D, after which they can enter a buy position. At this point, the price is expected to start rising.
🟣 Entering a Sell Trade with the Bearish Cypher Pattern
In a bearish Cypher pattern, the trader enters a sell position at point D, expecting the price to move downward after reaching this point. For additional confirmation, traders can use technical indicators such as RSI or MACD.
🟣 Risk Management in Cypher Pattern Trades
Risk management is one of the most critical aspects of any trade, and this holds true for trading the Cypher pattern. Traders should always use stop-loss orders to prevent larger losses in case the pattern fails.
In the bullish Cypher pattern, the stop-loss is usually placed slightly below point D to exit the trade if the price continues to drop.
In the bearish Cypher pattern, the stop-loss is placed above point D to limit losses if the price rises unexpectedly.
🟣 Combining the Cypher Pattern with Other Technical Tools
The Cypher pattern is a powerful tool in technical analysis, but combining it with other methods such as price action and technical indicators can improve trading accuracy.
🟣 Combining with Price Action
Traders can use price action to confirm the Cypher pattern. Candlestick patterns like reversal candlesticks can provide additional confirmation for price reversals at point D.
🟣 Using Technical Indicators
Incorporating technical indicators such as RSI and MACD can also help traders receive stronger signals for entering trades based on the Cypher pattern. These indicators help identify overbought or oversold conditions, allowing traders to make more informed decisions.
🟣 Advantages and Disadvantages of the Cypher Pattern in Technical Analysis
Advantages :
High accuracy : The Cypher pattern, using Fibonacci ratios and geometric analysis, provides high precision in identifying reversal points.
Applicable in various markets : This pattern can be used in a wide range of financial markets, including forex, stocks, and cryptocurrencies.
Disadvantages :
Rarit y: The Cypher pattern appears less frequently on charts compared to other harmonic patterns.
Complexity : Accurately identifying this pattern requires significant experience, which may be challenging for novice traders.
🔵 Setting
🟣 Logical Setting
ZigZag Pivot Period : You can adjust the period so that the harmonic patterns are adjusted according to the pivot period you want. This factor is the most important parameter in pattern recognition.
Show Valid Forma t: If this parameter is on "On" mode, only patterns will be displayed that they have exact format and no noise can be seen in them. If "Off" is, the patterns displayed that maybe are noisy and do not exactly correspond to the original pattern.
Show Formation Last Pivot Confirm : if Turned on, you can see this ability of patterns when their last pivot is formed. If this feature is off, it will see the patterns as soon as they are formed. The advantage of this option being clear is less formation of fielded patterns, and it is accompanied by the latest pattern seeing and a sharp reduction in reward to risk.
Period of Formation Last Pivot : Using this parameter you can determine that the last pivot is based on Pivot period.
🟣 Genaral Setting
Show : Enter "On" to display the template and "Off" to not display the template.
Color : Enter the desired color to draw the pattern in this parameter.
LineWidth : You can enter the number 1 or numbers higher than one to adjust the thickness of the drawing lines. This number must be an integer and increases with increasing thickness.
LabelSize : You can adjust the size of the labels by using the "size.auto", "size.tiny", "size.smal", "size.normal", "size.large" or "size.huge" entries.
🟣 Alert Setting
Alert : On / Off
Message Frequency : This string parameter defines the announcement frequency. Choices include: "All" (activates the alert every time the function is called), "Once Per Bar" (activates the alert only on the first call within the bar), and "Once Per Bar Close" (the alert is activated only by a call at the last script execution of the real-time bar upon closing). The default setting is "Once per Bar".
Show Alert Time by Time Zone : The date, hour, and minute you receive in alert messages can be based on any time zone you choose. For example, if you want New York time, you should enter "UTC-4". This input is set to the time zone "UTC" by default.
🔵 Conclusion
The Cypher harmonic pattern is one of the most powerful and accurate patterns used in technical analysis. Its high precision in identifying price reversal points, particularly within the Potential Reversal Zone (PRZ), has made it a popular tool among professional traders. The PRZ, located between the Fibonacci ratios of 0.768 and 0.886 in the XD region, offers traders a clear indication of where price reversals are likely to occur.
However, to use this pattern successfully, traders must employ proper risk management and combine it with supplementary tools like technical indicators and price action. By understanding how to utilize the PRZ, traders can enhance the accuracy of their trade entries and exits.
Ultimately, the Cypher pattern, when used in conjunction with the PRZ, helps traders make more precise decisions in the financial markets, leading to more successful and well-informed trades.
Sinc MAKaiser Windowed Sinc Moving Average Indicator
The Kaiser Windowed Sinc Moving Average is an advanced technical indicator that combines the sinc function with the Kaiser window to create a highly customizable finite impulse response (FIR) filter for financial time series analysis.
Sinc Function: The Ideal Low-Pass Filter
At the core of this indicator is the sinc function, which represents the impulse response of an ideal low-pass filter. In signal processing and technical analysis, the sinc function is crucial because it allows for the creation of filters with precise frequency cutoff characteristics. When applied to financial data, this means the ability to separate long-term trends from short-term fluctuations with remarkable accuracy.
The primary advantage of using a sinc-based filter is the independent control over two critical parameters: the cutoff frequency and the number of samples used. The cutoff frequency, analogous to the "length" in traditional moving averages, determines which price movements are considered significant (low frequency) and which are treated as noise (high frequency). By adjusting the cutoff, analysts can fine-tune the filter to respond to specific market cycles or timeframes of interest.
The number of samples used in the filter doesn't affect the cutoff frequency but instead influences the filter's accuracy and steepness. Increasing the sample size results in a better approximation of the ideal low-pass filter, leading to sharper transitions between passed and attenuated frequencies. This allows for more precise trend identification and noise reduction without changing the fundamental frequency response characteristics.
Kaiser Window: Optimizing the Sinc Filter
While the sinc function provides excellent frequency domain characteristics, it has infinite length in the time domain, which is impractical for real-world applications. This is where the Kaiser window comes into play. By applying the Kaiser window to the sinc function, we create a finite-length filter that approximates the ideal response while minimizing unwanted oscillations (known as the Gibbs phenomenon) in the frequency domain.
The Kaiser window introduces an additional parameter, alpha, which controls the trade-off between the main-lobe width and side-lobe levels in the frequency response. This parameter allows users to fine-tune the filter's behavior, balancing between sharp cutoffs and minimal ripple effects.
Customizable Parameters
The Kaiser Windowed Sinc Moving Average offers several key parameters for customization:
Cutoff: Controls the filter's cutoff frequency, determining the divide between trends and noise.
Length: Sets the number of samples used in the FIR filter calculation, affecting the filter's accuracy and computational complexity.
Alpha: Influences the shape of the Kaiser window, allowing for fine-tuning of the filter's frequency response characteristics.
Centered and Non-Centered Modes
The indicator provides two operational modes:
Non-Centered (Real-time) Mode: Uses half of the windowed sinc function, suitable for real-time analysis and current market conditions.
Centered Mode: Utilizes the full windowed sinc function, resulting in a zero-phase filter. This mode introduces a delay but offers the most accurate trend identification for historical analysis.
Visualization Features
To enhance the analytical value of the indicator, several visualization options are included:
Gradient Coloring: Offers a range of color schemes to represent trend direction and strength.
Glow Effect: An optional visual enhancement for improved line visibility.
Background Fill: Highlights the area between the moving average and price, aiding in trend visualization.
Applications in Technical Analysis
The Kaiser Windowed Sinc Moving Average is particularly useful for precise trend identification, cycle analysis, and noise reduction in financial time series. Its ability to create custom low-pass filters with independent control over cutoff and filter accuracy makes it a powerful tool for analyzing various market conditions and timeframes.
Compared to traditional moving averages, this indicator offers superior frequency response characteristics and reduced lag in trend identification when properly tuned. It provides greater flexibility in filter design, allowing analysts to create moving averages tailored to specific trading strategies or market behaviors.
Conclusion
The Kaiser Windowed Sinc Moving Average represents an advanced approach to price smoothing and trend identification in technical analysis. By making the ideal low-pass filter characteristics of the sinc function practically applicable through Kaiser windowing, this indicator provides traders and analysts with a sophisticated tool for examining price trends and cycles.
Its implementation in Pine Script contributes to the TradingView community by making advanced signal processing techniques accessible for experimentation and further development in technical analysis. This indicator serves not only as a practical tool for market analysis but also as an educational resource for those interested in the intersection of signal processing and financial markets.
Related script:
Savitzky-Golay Hampel Filter | AlphaNattSavitzky-Golay Hampel Filter | AlphaNatt
A revolutionary indicator combining NASA's satellite data processing algorithms with robust statistical outlier detection to create the most scientifically advanced trend filter available on TradingView.
"This is the same mathematics that processes signals from the Hubble Space Telescope and analyzes data from the Large Hadron Collider - now applied to financial markets."
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🚀 SCIENTIFIC PEDIGREE
Savitzky-Golay Filter Applications:
NASA: Satellite telemetry and space probe data processing
CERN: Particle physics data analysis at the LHC
Pharmaceutical: Chromatography and spectroscopy analysis
Astronomy: Processing signals from radio telescopes
Medical: ECG and EEG signal processing
Hampel Filter Usage:
Aerospace: Cleaning sensor data from aircraft and spacecraft
Manufacturing: Quality control in precision engineering
Seismology: Earthquake detection and analysis
Robotics: Sensor fusion and noise reduction
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🧬 THE MATHEMATICS
1. Savitzky-Golay Filter
The SG filter performs local polynomial regression on data points:
Fits a polynomial of degree n to a sliding window of data
Evaluates the polynomial at the center point
Preserves higher moments (peaks, valleys) unlike moving averages
Maintains derivative information for true momentum analysis
Originally published in Analytical Chemistry (1964)
Mathematical Properties:
Optimal smoothing in the least-squares sense
Preserves statistical moments up to polynomial order
Exact derivative calculation without additional lag
Superior frequency response vs traditional filters
2. Hampel Filter
A robust outlier detector based on Median Absolute Deviation (MAD):
Identifies outliers using robust statistics
Replaces spurious values with polynomial-fitted estimates
Resistant to up to 50% contaminated data
MAD is 1.4826 times more robust than standard deviation
Outlier Detection Formula:
|x - median| > k × 1.4826 × MAD
Where k is the threshold parameter (typically 3 for 99.7% confidence)
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💎 WHY THIS IS SUPERIOR
vs Moving Averages:
Preserves peaks and valleys (critical for catching tops/bottoms)
No lag penalty for smoothness
Maintains derivative information
Polynomial fitting > simple averaging
vs Other Filters:
Outlier immunity (Hampel component)
Scientifically optimal smoothing
Preserves higher-order features
Used in billion-dollar research projects
Unique Advantages:
Feature Preservation: Maintains market structure while smoothing
Spike Immunity: Ignores false breakouts and stop hunts
Derivative Accuracy: True momentum without additional indicators
Scientific Validation: 60+ years of academic research
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⚙️ PARAMETER OPTIMIZATION
1. Polynomial Order (2-5)
2 (Quadratic): Maximum smoothing, gentle curves
3 (Cubic): Balanced smoothing and responsiveness (recommended)
4-5 (Higher): More responsive, preserves more features
2. Window Size (7-51)
Must be odd number
Larger = smoother but more lag
Formula: 2×(desired smoothing period) + 1
Default 21 = analyzes 10 bars each side
3. Hampel Threshold (1.0-5.0)
1.0: Aggressive outlier removal (68% confidence)
2.0: Moderate outlier removal (95% confidence)
3.0: Conservative outlier removal (99.7% confidence) (default)
4.0+: Only extreme outliers removed
4. Final Smoothing (1-7)
Additional WMA smoothing after filtering
1 = No additional smoothing
3-5 = Recommended for most timeframes
7 = Ultra-smooth for position trading
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📊 TRADING STRATEGIES
Signal Recognition:
Cyan Line: Bullish trend with positive derivative
Pink Line: Bearish trend with negative derivative
Color Change: Trend reversal with polynomial confirmation
1. Trend Following Strategy
Enter when price crosses above cyan filter
Exit when filter turns pink
Use filter as dynamic stop loss
Best in trending markets
2. Mean Reversion Strategy
Enter long when price touches filter from below in uptrend
Enter short when price touches filter from above in downtrend
Exit at opposite band or filter color change
Excellent for range-bound markets
3. Derivative Strategy (Advanced)
The SG filter preserves derivative information
Acceleration = second derivative > 0
Enter on positive first derivative + positive acceleration
Exit on negative second derivative (momentum slowing)
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📈 PERFORMANCE CHARACTERISTICS
Strengths:
Outlier Immunity: Ignores stop hunts and flash crashes
Feature Preservation: Catches tops/bottoms better than MAs
Smooth Output: Reduces whipsaws significantly
Scientific Basis: Not curve-fitted or optimized to markets
Considerations:
Slight lag in extreme volatility (all filters have this)
Requires odd window sizes (mathematical requirement)
More complex than simple moving averages
Best with liquid instruments
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🔬 SCIENTIFIC BACKGROUND
Savitzky-Golay Publication:
"Smoothing and Differentiation of Data by Simplified Least Squares Procedures"
- Abraham Savitzky & Marcel Golay
- Analytical Chemistry, Vol. 36, No. 8, 1964
Hampel Filter Origin:
"Robust Statistics: The Approach Based on Influence Functions"
- Frank Hampel et al., 1986
- Princeton University Press
These techniques have been validated in thousands of scientific papers and are standard tools in:
NASA's Jet Propulsion Laboratory
European Space Agency
CERN (Large Hadron Collider)
MIT Lincoln Laboratory
Max Planck Institutes
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💡 ADVANCED TIPS
News Trading: Lower Hampel threshold before major events to catch spikes
Scalping: Use Order=2 for maximum smoothness, Window=11 for responsiveness
Position Trading: Increase Window to 31+ for long-term trends
Combine with Volume: Strong trends need volume confirmation
Multiple Timeframes: Use daily for trend, hourly for entry
Watch the Derivative: Filter color changes when first derivative changes sign
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⚠️ IMPORTANT NOTICES
Not financial advice - educational purposes only
Past performance does not guarantee future results
Always use proper risk management
Test settings on your specific instrument and timeframe
No indicator is perfect - part of complete trading system
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🏆 CONCLUSION
The Savitzky-Golay Hampel Filter represents the pinnacle of scientific signal processing applied to financial markets. By combining polynomial regression with robust outlier detection, traders gain access to the same mathematical tools that:
Guide spacecraft to other planets
Detect gravitational waves from black holes
Analyze particle collisions at near light-speed
Process signals from deep space
This isn't just another indicator - it's rocket science for trading .
"When NASA needs to separate signal from noise in billion-dollar missions, they use these exact algorithms. Now you can too."
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Developed by AlphaNatt
Version: 1.0
Release: 2025
Pine Script: v6
"Where Space Technology Meets Market Analysis"
Not financial advice. Always DYOR
New RSI📌 New RSI
The New RSI is a modern, enhanced version of the classic RSI created in 1978 — redesigned for today’s fast-moving markets, where algorithmic trading and AI dominate price action.
This indicator combines:
Adaptive RSI: Adjusts its calculation length in real time based on market volatility, making it more responsive during high volatility and smoother during calm periods.
Dynamic Bands: Upper and lower bands calculated from historical RSI volatility, helping you spot overbought/oversold conditions with greater accuracy.
Trend & Regime Filters: EMA and ADX-based detection to confirm signals only in favorable market conditions.
Volume Confirmation: Signals appear only when high trading volume supports the move — green volume for bullish setups and red volume for bearish setups — filtering out weak and unreliable trades.
💡 How it works:
A LONG signal appears when RSI crosses above the lower band and the volume is high with a bullish candle.
A SHORT signal appears when RSI crosses below the upper band and the volume is high with a bearish candle.
Trend and higher timeframe filters (optional) can help improve precision and adapt to different trading styles.
✅ Best Use Cases:
Identify high-probability reversals or pullbacks with strong momentum confirmation.
Avoid false signals by trading only when volume validates the move.
Combine with your own support/resistance or price action strategy for even higher accuracy.
⚙️ Fully Customizable:
Adjustable RSI settings (length, volatility adaptation, smoothing)
Dynamic band sensitivity
Volume threshold multiplier
Higher timeframe RSI filter
Color-coded background for market regime visualization
This is not just another RSI — it’s a complete, next-gen momentum tool designed for traders who want accuracy, adaptability, and confirmation in every signal.
EAOBS by MIGVersion 1
1. Strategy Overview Objective: Capitalize on breakout movements in Ethereum (ETH) price after the Asian open pre-market session (7:00 PM–7:59 PM EST) by identifying high and low prices during the session and trading breakouts above the high or below the low.
Timeframe: Any (script is timeframe-agnostic, but align with session timing).
Session: Pre-market session (7:00 PM–7:59 PM EST, adjustable for other time zones, e.g., 12:00 AM–12:59 AM GMT).
Risk-Reward Ratios (R:R): Targets range from 1.2:1 to 5.2:1, with a fixed stop loss.
Instrument: Ethereum (ETH/USD or ETH-based pairs).
2. Market Setup Session Monitoring: Monitor ETH price action during the pre-market session (7:00 PM–7:59 PM EST), which aligns with the Asian market open (e.g., 9:00 AM–9:59 AM JST).
The script tracks the highest high and lowest low during this session.
Breakout Triggers: Buy Signal: Price breaks above the session’s high after the session ends (7:59 PM EST).
Sell Signal: Price breaks below the session’s low after the session ends.
Visualization: The session is highlighted on the chart with a white background.
Horizontal lines are drawn at the session’s high and low, extended for 30 bars, along with take-profit (TP) and stop-loss (SL) levels.
3. Entry Rules Long (Buy) Entry: Enter a long position when the price breaks above the session’s high price after 7:59 PM EST.
Entry price: Just above the session high (e.g., add a small buffer, like 0.1–0.5%, to avoid false breakouts, depending on volatility).
Short (Sell) Entry: Enter a short position when the price breaks below the session’s low price after 7:59 PM EST.
Entry price: Just below the session low (e.g., subtract a small buffer, like 0.1–0.5%).
Confirmation: Use a candlestick close above/below the breakout level to confirm the entry.
Optionally, add volume confirmation or a momentum indicator (e.g., RSI or MACD) to filter out weak breakouts.
Position Size: Calculate position size based on risk tolerance (e.g., 1–2% of account per trade).
Risk is determined by the stop-loss distance (10 points, as defined in the script).
4. Exit Rules Take-Profit Levels (in points, based on script inputs):TP1: 12 points (1.2:1 R:R).
TP2: 22 points (2.2:1 R:R).
TP3: 32 points (3.2:1 R:R).
TP4: 42 points (4.2:1 R:R).
TP5: 52 points (5.2:1 R:R).
Example for Long: If session high is 3000, TP levels are 3012, 3022, 3032, 3042, 3052.
Example for Short: If session low is 2950, TP levels are 2938, 2928, 2918, 2908, 2898.
Strategy: Scale out of the position (e.g., close 20% at TP1, 20% at TP2, etc.) or take full profit at a preferred TP level based on market conditions.
Stop-Loss: Fixed at 10 points from the entry.
Long SL: Session high - 10 points (e.g., entry at 3000, SL at 2990).
Short SL: Session low + 10 points (e.g., entry at 2950, SL at 2960).
Trailing Stop (Optional):After reaching TP2 or TP3, consider trailing the stop to lock in profits (e.g., trail by 10–15 points below the current price).
5. Risk Management per Trade: Limit risk to 1–2% of your trading account per trade.
Calculate position size: Account Size × Risk % ÷ (Stop-Loss Distance × ETH Price per Point).
Example: $10,000 account, 1% risk = $100. If SL = 10 points and 1 point = $1, position size = $100 ÷ 10 = 0.1 ETH.
Daily Risk Limit: Cap daily losses at 3–5% of the account to avoid overtrading.
Maximum Exposure: Avoid taking both long and short positions simultaneously unless using separate accounts or strategies.
Volatility Consideration: Adjust position size during high-volatility periods (e.g., major news events like Ethereum upgrades or macroeconomic announcements).
6. Trade Management Monitoring :Watch for breakouts after 7:59 PM EST.
Monitor price action near TP and SL levels using alerts or manual checks.
Trade Duration: Breakout lines extend for 30 bars (script parameter). Close trades if no TP or SL is hit within this period, or reassess based on market conditions.
Adjustments: If the market shows strong momentum, consider holding beyond TP5 with a trailing stop.
If the breakout fails (e.g., price reverses before TP1), exit early to minimize losses.
7. Additional Considerations Market Conditions: The 7:00 PM–7:59 PM EST session aligns with the Asian market open (e.g., Tokyo Stock Exchange open at 9:00 AM JST), which may introduce higher volatility due to Asian trading activity.
Avoid trading during low-liquidity periods or extreme volatility (e.g., major crypto news).
Check for upcoming events (e.g., Ethereum network upgrades, ETF decisions) that could impact price.
Backtesting: Test the strategy on historical ETH data using the session high/low breakouts for the 7:00 PM–7:59 PM EST window to validate performance.
Adjust TP/SL levels based on backtest results if needed.
Broker and Fees: Use a low-fee crypto exchange (e.g., Binance, Kraken, Coinbase Pro) to maximize R:R.
Account for trading fees and slippage in your position sizing.
Time zone Adjustment: Adjust session time input for your time zone (e.g., "0000-0059" for GMT).
Ensure your trading platform’s clock aligns with the script’s time zone (default: America/New_York).
8. Example Trade Scenario: Session (7:00 PM–7:59 PM EST) records a high of 3050 and a low of 3000.
Long Trade: Entry: Price breaks above 3050 (e.g., enter at 3051).
TP Levels: 3063 (TP1), 3073 (TP2), 3083 (TP3), 3093 (TP4), 3103 (TP5).
SL: 3040 (3050 - 10).
Position Size: For a $10,000 account, 1% risk = $100. SL = 11 points ($11). Size = $100 ÷ 11 = ~0.09 ETH.
Short Trade: Entry: Price breaks below 3000 (e.g., enter at 2999).
TP Levels: 2987 (TP1), 2977 (TP2), 2967 (TP3), 2957 (TP4), 2947 (TP5).
SL: 3010 (3000 + 10).
Position Size: Same as above, ~0.09 ETH.
Execution: Set alerts for breakouts, enter with limit orders, and monitor TPs/SL.
9. Tools and Setup Platform: Use TradingView to implement the Pine Script and visualize breakout levels.
Alerts: Set price alerts for breakouts above the session high or below the session low after 7:59 PM EST.
Set alerts for TP and SL levels.
Chart Settings: Use a 1-minute or 5-minute chart for precise session tracking.
Overlay the script to see high/low lines, TP levels, and SL levels.
Optional Indicators: Add RSI (e.g., avoid overbought/oversold breakouts) or volume to confirm breakouts.
10. Risk Warnings Crypto Volatility: ETH is highly volatile; unexpected news can cause rapid price swings.
False Breakouts: Breakouts may fail, especially in low-volume sessions. Use confirmation signals.
Leverage: Avoid high leverage (e.g., >5x) to prevent liquidation during volatile moves.
Session Accuracy: Ensure correct session timing for your time zone to avoid misaligned entries.
11. Performance Tracking Journaling :Record each trade’s entry, exit, R:R, and outcome.
Note market conditions (e.g., trending, ranging, news-driven).
Review: Weekly: Assess win rate, average R:R, and adherence to the plan.
Monthly: Adjust TP/SL or session timing based on performance.
Bar TimeBar Time is a simple utility for traders who rely on backtesting, Bar Replay, and detailed price action analysis. It solves a common but frustrating problem: knowing the exact time of the bar you are looking at.
While most time indicators show your computer's live clock time, this tool displays the bar's own timestamp, perfectly synchronized with your chart's data and timezone.
Why Is This Important?
When you are deep in a Bar Replay session or analyzing a historical setup, the live clock is irrelevant. You need to know when that critical breakout or reversal candle actually happened. Was it during the pre-market? At the London open? In the last five minutes of the US session? This indicator provides that vital context instantly, without you needing to squint at the small print on the x-axis.
Key Use Cases
1. Mastering Bar Replay
As you click through bars in Replay mode, the displayed time updates with each new bar. This allows you to simulate a live trading session with full awareness of the time of day, helping you train your decision-making under more realistic conditions.
2. Analyzing Screener Signals
This is one of the most powerful uses. Imagine your screener finds a "BUY" signal on a stock from two bars ago. You switch to that stock's chart to investigate. Instead of hunting for the exact bar, this tool instantly shows you the date and time of the bar you are currently hovering over. It dramatically speeds up the workflow of moving from a screener alert to actionable analysis.
3. Detailed Price Action Study
Quickly identify key session timings, see how price reacts to news events at a specific time, or analyze intraday volume patterns with complete temporal clarity.
Features & Customization
The tool is designed to be lightweight, efficient, and fully customizable to match your charting environment.
Timezone-Aware Accuracy: Automatically detects your chart's timezone for a perfect match between the label and the x-axis.
Fully Customizable Position: Place the time display in any of nine screen positions (e.g., Top Left, Bottom Center) using a simple dropdown menu.
Custom Colors: Easily set the background and text colors to blend seamlessly with your chart's theme.
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool
An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations.
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💡 Why I Created This Indicator
As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term .
What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later.
Traditional DCA tracking tools exist, but they share similar limitations:
Require manual data entry and complex spreadsheets
Use fixed assumptions that don't reflect real market behavior
Can't show future projections overlaid on actual price charts
Lose the visual context of what's happening in the market
Make compound growth feel abstract rather than tangible
I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time.
This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore.
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🎯 What This Indicator Does
This educational indicator provides DCA analysis tools. Users can input investment scenarios to study:
Theoretical Performance: Educational calculations based on historical return data
Comparative Analysis: Study differences between actual and theoretical scenarios
Historical Projections: Theoretical projections for educational analysis (not predictions)
Performance Metrics: CAGR, ROI, and other analytical metrics for study
Historical Analysis: Calculates historical return data for reference purposes
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🚀 Key Features
Volatility-Adjusted Historical Return Calculation
Analyzes 3-20 years of actual price data for any symbol
Automatically detects high-volatility stocks (meme stocks, growth stocks)
Uses median returns for volatile stocks, standard CAGR for stable stocks
Provides conservative estimates when extreme outlier years are detected
Smart fallback to manual percentages when data insufficient
Customizable Performance Dashboard
Educational DCA performance analysis with compound growth calculations
Customizable table sizing (Tiny to Huge text options)
9 positioning options (Top/Middle/Bottom + Left/Center/Right)
Theme-adaptive colors (automatically adjusts to dark/light mode)
Multiple display layout options
Future Projection System
Visual future growth projections
Timeframe-aware calculations (Daily/Weekly/Monthly charts)
1-30 year projection options
Shows projected portfolio value and total investment amounts
Investment Insights
Performance vs benchmark comparison
ROI from initial investment tracking
Monthly average return analysis
Investment milestone alerts (25%, 50%, 100% gains)
Contribution tracking and next milestone indicators
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📊 Step-by-Step Setup Guide
1. Investment Settings 💰
Initial Investment: Enter your starting lump sum (e.g., $60,000)
Monthly Contribution: Set your regular DCA amount (e.g., $500/month)
Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed %
Historical Period: Select 3-20 years for auto calculations (default: 10 years)
Start Year: When you began investing (e.g., 2020)
Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000)
2. Display Settings 📊
Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge
Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right
Visibility Toggles: Show/hide Main Table and Stats Table independently
3. Future Projection 🔮
Enable Projections: Toggle on to see future growth visualization
Projection Years: Set 1-30 years ahead for analysis
Live Example - NASDAQ:META Analysis:
Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value
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🔬 Pine Script Code Examples
Core DCA Calculations:
// Calculate total invested over time
months_elapsed = (year - start_year) * 12 + month - 1
total_invested = initial_investment + (monthly_contribution * months_elapsed)
// Compound growth formula for initial investment
theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed)
// Future Value of Annuity for monthly contributions
monthly_rate = annual_return / 12
fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate)
// Total expected value
theoretical_total = theoretical_initial_growth + fv_contributions
Volatility Detection Logic:
// Detect extreme years for volatility adjustment
extreme_years = 0
for i = 1 to historical_years
yearly_return = ((price_current / price_i_years_ago) - 1) * 100
if yearly_return > 100 or yearly_return < -50
extreme_years += 1
// Use median approach for high volatility stocks
high_volatility = (extreme_years / historical_years) > 0.2
calculated_return = high_volatility ? median_of_returns : standard_cagr
Performance Metrics:
// Calculate key performance indicators
absolute_gain = actual_value - total_invested
total_return_pct = (absolute_gain / total_invested) * 100
roi_initial = ((actual_value - initial_investment) / initial_investment) * 100
cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100
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📊 Real-World Examples
See the indicator in action across different investment types:
Stable Index Investments:
AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations
Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking.
MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches
Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections.
MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation
Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies.
NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record
Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used.
High-Volatility Growth Stocks:
NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings
High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods.
NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks
Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections.
NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations
Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments.
Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods.
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📈 Performance Metrics Explained
Current Portfolio Value: Your actual investment worth today
Expected Value: What you should have based on historical returns (Auto) or your target return (Manual)
Total Invested: Your actual money invested (initial + all monthly contributions)
Total Gains/Loss: Absolute dollar difference between current value and total invested
Total Return %: Percentage gain/loss on your total invested amount
ROI from Initial Investment: How your starting lump sum has performed
CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy)
vs Benchmark: How you're performing compared to the expected returns
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⚠️ Important Notes & Limitations
Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended)
CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy
Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary
Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported
Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking
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📚 Educational Use & Disclaimer
This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns.
Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes.
EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions.
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© 2025 TradeVizion. All rights reserved.
Canuck Trading Projection IndicatorCanuck Trading Projection Indicator
Overview
The Canuck Trading Projection Indicator is a powerful PineScript v6 tool designed for TradingView to project potential bullish and bearish price trajectories based on historical price and volume movements. It provides traders with actionable insights by estimating future price targets and assigning confidence levels to each outlook, helping to identify probable market directions across any timeframe. Ideal for both short-term and long-term traders, this indicator combines momentum analysis, RSI filtering, support/resistance detection, and time-weighted trend analysis to deliver robust projections.
Features
Bullish and Bearish Projections: Forecasts price targets for upward (bullish) and downward (bearish) movements over a user-defined projection period (default 20 bars).
Confidence Levels: Assigns percentage confidence scores to each outlook, reflecting the likelihood of the projected price based on historical trends, volatility, and volume.
RSI Filter: Incorporates a 14-period Relative Strength Index (RSI) to validate trends, requiring RSI > 50 for bullish and RSI < 50 for bearish signals.
Support/Resistance Detection: Adjusts confidence levels when projections are near key swing highs/lows (within 2% of average price), boosting confidence by 5% for alignments.
Time-Based Weighting: Prioritizes recent price movements in trend analysis, giving more weight to newer bars for improved relevance.
Customizable Inputs: Allows users to tailor lookback period, projection bars, RSI period, confidence threshold, colors, and label positioning.
Forced Label Spacing: Prevents overlap of bullish and bearish text labels, even for tight projections, using fixed vertical slots when price differences are small (<2% of average price).
Timeframe Flexibility: Works seamlessly across all TradingView timeframes (e.g., 30-minute, hourly, daily, weekly, monthly), adapting projections to the chart’s resolution.
Clean Visualization: Displays projections as green (bullish) and red (bearish) dashed lines, with non-overlapping text labels at the projection endpoints showing price targets and confidence levels.
How It Works
The indicator analyzes historical price and volume data over a user-defined lookback period (default 50 bars) to calculate:
Momentum: Combines price changes and volume to assess trend strength, using a weighted moving average (WMA) for directional bias.
Trend Analysis: Counts bullish (price up, volume above average, RSI > 50) and bearish (price down, volume above average, RSI < 50) trends, weighting recent bars more heavily.
Projections:
Bullish Slope: Positive or flat when momentum is upward, scaled by price change and momentum intensity.
Bearish Slope: Negative or flat when momentum is downward, amplified by bearish confidence for stronger projections.
Projects prices forward by 20 bars (default) using current close plus slope times projection bars.
Confidence Levels:
Base confidence derived from the proportion of bullish/bearish trends, with a 5% minimum to avoid zero confidence.
Adjusted by volatility (lower volatility increases confidence), volume trends, and proximity to support/resistance levels.
Visualization:
Draws projection lines from the current close to the 20-bar future target.
Places text labels at line endpoints, showing price targets and confidence percentages, with forced spacing for readability.
Input Parameters
Lookback Period (default: 50): Number of bars for historical analysis (minimum 10).
Projection Bars (default: 20): Number of bars to project forward (minimum 5).
Confidence Threshold (default: 0.6): Minimum confidence for strong trend indication (0.1 to 1.0).
Bullish Projection Line Color (default: Green): Color for bullish projection line and label.
Bearish Projection Line Color (default: Red): Color for bearish projection line and label.
RSI Period (default: 14): Period for RSI momentum filter (minimum 5).
Label Vertical Offset (%) (default: 1.0): Base offset for labels as a percentage of price range (0.1% to 5.0%).
Minimum Label Spacing (%) (default: 2.0): Minimum vertical spacing between labels for tight projections (0.5% to 10.0%).
Usage Instructions
Add to Chart: Copy the script into TradingView’s Pine Editor, save, and add the indicator to your chart.
Select Timeframe: Apply to any timeframe (e.g., 30-minute, hourly, daily, weekly, monthly) to match your trading strategy.
Interpret Outputs:
Green Line/Label: Bullish price target and confidence (e.g., "Bullish: 414.37, Confidence: 35%").
Red Line/Label: Bearish price target and confidence (e.g., "Bearish: 279.08, Confidence: 41.3%").
Higher confidence indicates a stronger likelihood of the projected outcome.
Adjust Inputs:
Modify Lookback Period to focus on shorter/longer historical trends (e.g., 20 for short-term, 100 for long-term).
Change Projection Bars to adjust forecast horizon (e.g., 10 for shorter, 50 for longer).
Tweak RSI Period or Confidence Threshold for sensitivity to momentum or trend strength.
Customize Colors for visual preference.
Increase Minimum Label Spacing if labels overlap in volatile markets.
Combine with Analysis: Use alongside other indicators (e.g., moving averages, Bollinger Bands) or fundamental analysis to confirm signals, as projections are probabilistic.
Example: TSLA Across Timeframes
Using live TSLA data (close ~346.46 USD, May 31, 2025), the indicator produces:
30-Minute: Bullish 341.93 (13.3%), Bearish 327.96 (86.7%) – Strong bearish sentiment due to intraday volatility.
1-Hour: Bullish 342.00 (33.9%), Bearish 327.50 (62.3%) – Bearish but less intense, reflecting hourly swings.
4-Hour: Bullish 345.52 (73.4%), Bearish 344.44 (19.0%) – Flat outlook, indicating consolidation.
Daily: Bullish 391.26 (68.8%), Bearish 302.22 (31.2%) – Bullish bias from recent uptrend, bearish tempered by longer lookback.
Weekly: Bullish 414.37 (35.0%), Bearish 279.08 (41.3%) – Wide range, reflecting annual volatility.
Monthly: Bullish 396.70 (54.9%), Bearish 296.93 (10.2%) – Long-term bullish optimism.
These results align with market dynamics: short-term intervals capture volatility, while longer intervals smooth trends, providing balanced outlooks.
Notes
Accuracy: Projections are estimates based on historical data and should be used with other analysis tools. Confidence levels indicate likelihood, not certainty.
Timeframe Sensitivity: Short-term intervals (e.g., 30-minute) show larger price swings and higher confidence due to volatility, while longer intervals (e.g., monthly) are more stable.
Customization: Adjust inputs to match your trading style (e.g., shorter lookback for day trading, longer for swing trading).
Performance: Tested on volatile stocks like TSLA, NVIDIA, and others, ensuring robust performance across markets.
Limitations: May produce conservative bearish projections in strong uptrends due to momentum weighting. Adjust lookback or projection_bars for sensitivity.
Feedback
If you encounter issues (e.g., label overlap, projection mismatches), please share your timeframe, settings, or a screenshot. Suggestions for enhancements (e.g., additional filters, visual tweaks) are welcome!
Disclaimer
The Canuck Trading Projection Indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves significant risks, and past performance is not indicative of future results. Always perform your own due diligence and consult a qualified financial advisor before making trading decisions.
Smart Trend Lines [The_lurker]
Smart Trend Lines
A multi-level trend classifier that detects bullish and bearish conditions using a methodology based on drawing trend lines—main, intermediate, and short-term—by identifying peaks and troughs. The tool highlights trend strength by applying filters such as the Average Directional Index (ADX) (A), Relative Strength Index (RSI) (R), and Volume (V), making it easier to interpret trend strength. The filter markers (V, A, R) in the Smart Trend Lines indicator are powerful tools for assessing the reliability of breakouts. Breakouts containing are the most reliable, as they indicate strong volume support, trend strength, and favorable momentum. Breakouts with partial filters (such as or ) require additional confirmation, while breakouts without filters ( ) should be avoided unless supported by other strong signals. By understanding the meaning of each filter and the market context.
Core Functionality
1. Trend Line Types
The indicator generates three distinct trend line categories, each serving a specific analytical purpose:
Main Trend Lines: These are long-term trend lines designed to capture significant market trends. They are calculated based on pivot points over a user-defined period (default: 50 bars). Main trend lines are ideal for identifying macro-level support and resistance zones.
Mid Trend Lines: These are medium-term trend lines (default: 21 bars) that focus on intermediate price movements. They provide a balance between short-term fluctuations and long-term trends, suitable for swing trading strategies.
Short Trend Lines: These are short-term trend lines (default: 9 bars) that track rapid price changes. They are particularly useful for scalping or day trading, highlighting immediate support and resistance levels.
Each trend line type can be independently enabled or disabled, allowing traders to tailor the indicator to their preferred timeframes.
2. Breakout Detection
The indicator employs a robust breakout detection system that identifies when the price crosses a trend line, signaling a potential trend reversal or continuation. Breakouts are validated using the following filters:
ADX Filter: The Average Directional Index (ADX) measures trend strength. A user-defined threshold (default: 20) ensures that breakouts occur during strong trends, reducing false signals in range-bound markets.
RSI Filter: The Relative Strength Index (RSI) identifies overbought or oversold conditions. Breakouts are filtered based on RSI thresholds (default: 65 for overbought, 35 for oversold) to avoid signals in extreme market conditions.
Volume Filter: Breakouts are confirmed only when trading volume exceeds a moving average (default: 20 bars) and aligns with the breakout direction (e.g., higher volume on bullish breakouts when the candle closes higher).
Breakout events are marked with labels on the chart, indicating the type of trend line broken (Main, Mid, or Short) and the filters satisfied (Volume, ADX, RSI). Alerts are triggered for each breakout, providing real-time notifications.
3. Customization Options
The indicator offers extensive customization through input settings, organized into logical groups for ease of use:
Main Trend Line Settings
Length: Defines the number of bars used to calculate pivot points (default: 50).
Bullish Color: Color for upward-sloping (bullish) main trend lines (default: green).
Bearish Color: Color for downward-sloping (bearish) main trend lines (default: red).
Style: Line style options include solid, dashed, or dotted (default: solid).
Mid Trend Line Settings
Length: Number of bars for mid-term pivot points (default: 21).
Show/Hide: Toggle visibility of mid trend lines (default: enabled).
Bullish Color: Color for bullish mid trend lines (default: lime).
Bearish Color: Color for bearish mid trend lines (default: maroon).
Style: Line style (default: dashed).
Short Trend Line Settings
Length: Number of bars for short-term pivot points (default: 9).
Show/Hide: Toggle visibility of short trend lines (default: enabled).
Bullish Color: Color for bullish short trend lines (default: teal).
Bearish Color: Color for bearish short trend lines (default: purple).
Style: Line style (default: dotted).
General Display Settings
Break Check Price: Selects the price type for breakout detection (Close, High, or Low; default: Close).
Show Previous Trendlines: Option to display historical main trend lines (default: disabled).
Label Size: Size of breakout labels (Tiny, Small, Normal, Large, Huge; default: Small).
Filter Settings
ADX Threshold: Minimum ADX value for trend strength confirmation (default: 25).
Volume MA Period: Period for the volume moving average (default: 20).
RSI Filter: Enable/disable RSI filtering (default: enabled).
RSI Upper Threshold: Upper RSI limit for overbought conditions (default: 65).
RSI Lower Threshold: Lower RSI limit for oversold conditions (default: 35).
4. Technical Calculations
The indicator relies on several technical calculations to ensure accuracy:
Pivot Points: Pivot highs and lows are detected using the ta.pivothigh and ta.pivotlow functions, with separate lengths for Main, Mid, and Short trend lines.
Slope Calculation: The slope of each trend line is calculated as the change in price divided by the change in bar index between two pivot points.
ADX Calculation: ADX is computed using a 14-period Directional Movement Index (DMI), with smoothing over 14 bars.
RSI Calculation: RSI is calculated over a 14-period lookback using the ta.rsi function.
Volume Moving Average: A simple moving average (SMA) of volume is used to determine if current volume exceeds the average.
5. Strict Mode Validation
To ensure the reliability of trend lines, the indicator employs a strict mode check:
For bearish trend lines, all prices between pivot points must remain below the projected trend line.
For bullish trend lines, all prices must remain above the projected trend line.
Post-pivot break checks ensure that no breakouts occur between pivot points, enhancing the validity of the trend line.
6. Trend Line Extension
Trend lines are dynamically extended forward until a breakout occurs. The extension logic:
Projects the trend line using the calculated slope.
Continuously validates the extension using strict mode checks.
Stops extension upon a breakout, fixing the trend line at the breakout point.
7. Alerts and Labels
Labels: Breakout labels are placed above (for bearish breakouts) or below (for bullish breakouts) the price bar. Labels include:
A prefix indicating the trend line type (B for Main, M for Mid, S for Short).
A suffix showing satisfied filters (e.g., for Volume, ADX, and RSI).
Alerts: Each breakout triggers a one-time alert per bar close, with a descriptive message indicating the trend line type and filters met.
Detailed Code Breakdown
1. Initialization and Inputs
The script begins by defining the indicator with indicator('Smart Trend Lines ', overlay = true), ensuring it overlays on the price chart. Input settings are grouped into categories (Main, Mid, Short, General Display, Filters) for user convenience. Each input includes a tooltip in both English and Arabic, enhancing accessibility.
2. Technical Indicator Calculations
Volume MA: Calculated using ta.sma(volume, volPeriod) to compare current volume against the average.
ADX: Computed using custom dirmov and adx functions, which calculate the Directional Movement Index and smooth it over 14 periods.
RSI: Calculated with ta.rsi(close, rsiPeriod) over 14 periods.
Price Selection: The priceToCheck function selects the price type (Close, High, or Low) for breakout detection.
3. Pivot Detection
Pivot points are detected using ta.pivothigh and ta.pivotlow for each trend line type. The lookback period is set to the respective trend line length (e.g., 50 for Main, 21 for Mid, 9 for Short).
4. Trend Line Logic
For each trend line type (Main, Mid, Short):
Bearish Trend Lines: Identified when two consecutive pivot highs form a downward slope. The script validates the trend line using strict mode and post-pivot break checks.
Bullish Trend Lines: Identified when two consecutive pivot lows form an upward slope, with similar validation.
Trend lines are drawn using line.new, with separate lines for the initial segment (between pivots) and the extended segment (from the second pivot forward).
5. Breakout Detection and Labeling
Breakouts are detected when the selected price crosses the trend line level. The script checks:
Volume conditions (above average and aligned with candle direction).
ADX condition (above threshold).
RSI condition (within thresholds if enabled). Labels are created with label.new, and alerts are triggered with alert.
6. Trend Line Extension
The extendTrendline function dynamically updates the trend line’s endpoint unless a breakout occurs. It uses strict mode checks to ensure the trend line remains valid.
7. Previous Trend Lines
If enabled, previous main trend lines are stored in arrays (previousBearishStartLines, previousBullishTrendLines, etc.) and displayed on the chart, providing historical context.
Disclaimer:
The information and publications are not intended to be, nor do they constitute, financial, investment, trading, or other types of advice or recommendations provided or endorsed by TradingView.
Liquidity Trap Reversal Pro (Radar v2)Liquidity Trap Reversal Pro (Radar v2) is a non-repainting indicator designed to detect hidden liquidity traps at key swing highs and lows. It combines wick analysis, volume spike detection, and optional trend and exhaustion filters to identify high-probability reversal setups.
🔷 Features:
Non-Repainting: Pivots confirmed after lookback period, no future leaking.
Volume Spike Detection: Filters traps that occur during major liquidity events.
EMA Trend Filter (Optional): Focus on traps aligned with the prevailing trend.
Higher Timeframe Trend Filter (Optional): Confirm traps using a higher timeframe EMA bias.
Exhaustion Guard (Optional): Prevents traps after overextended moves based on ATR stretch.
Clean Visuals: Distinct plots for raw trap points vs confirmed traps.
Alerts Included: Set alerts for confirmed high/low liquidity traps.
📚 How to Use:
Watch for Trap Signals:
A Trap High signal suggests a potential bearish reversal.
A Trap Low signal suggests a potential bullish reversal.
Use Confirmed Signals for Best Entries:
Confirmed traps fire only after price moves opposite to the trap direction, adding reliability.
Use Trend Filters to Improve Accuracy:
In an uptrend (price above EMA), prefer Trap Lows (buy setups).
In a downtrend (price below EMA), prefer Trap Highs (sell setups).
Use the Exhaustion Guard to Avoid Bad Trades:
This filter blocks signals when price has moved too far from trend, helping avoid late entries.
Recommended Settings:
Best used on 15-minute, 1-hour, or 4-hour charts.
Trend filter ON for trending markets.
Exhaustion guard ON for volatile or stretched markets.
📈 Important Notes:
This script does not repaint once a pivot is confirmed.
Alerts trigger only on confirmed trap signals.
Always combine signals with sound risk management and trading strategy.
Disclaimer:
This script is for educational purposes only. It is not investment advice or a guarantee of results. Always do your own research before trading.
RSI Candlestick Oscillator [LuxAlgo]The RSI Candlestick Oscillator displays a traditional Relative Strength Index (RSI) as candlesticks. This indicator references OHLC data to locate each candlestick point relative to the current RSI Value, leading to a more accurate representation of the Open, High, Low, and Close price of each candlestick in the context of RSI.
In addition to the candlestick display, Divergences are detected from the RSI candlestick highs and lows and can be displayed over price on the chart.
🔶 USAGE
Translating candlesticks into the RSI oscillator is not a new concept and has been attempted many times before. This indicator stands out because of the specific method used to determine the candlestick OHLC values. When compared to other RSI Candlestick indicators, you will find that this indicator clearly and definitively correlates better to the on-chart price action.
Traditionally, the RSI indicator is simply one running value based on (typically) the close price of the chart. By introducing high, low, and open values into the oscillator, we can better gauge the specific price action throughout the intrabar movements.
Interactions with the RSI levels can now take multiple forms, whether it be a full-bodied breakthrough or simply a wick test. Both can provide a new analysis of price action alongside RSI.
An example of wick interactions and full-bodied interactions can be seen below.
As a result of the candlestick display, divergences become simpler to spot. Since the candlesticks on the RSI closely resemble the candlesticks on the chart, when looking for divergence between the chart and RSI, it is more obvious when the RSI and price are diverging.
The divergences in this indicator not only show on the RSI oscillator, but also overlay on the price chart for clearer understanding.
🔹 Filtering Divergence
With the candlesticks generating high and low RSI values, we can better sense divergences from price, since these points are generally going to be more dramatic than the (close) RSI value.
This indicator displays each type of divergence:
Bullish Divergence
Bearish Divergence
Hidden Bullish Divergence
Hidden Bearish Divergence
From these, we get many less-than-useful indications, since every single divergence from price is not necessarily of great importance.
The Divergence Filter disregards any divergence detected that does not extend outside the RSI upper or lower values.
This does not replace good judgment, but this filter can be helpful in focusing attention towards the extremes of RSI for potential reversal spotting from divergence.
🔶 DETAILS
In order to get the desired results for a display that resembles price action while following RSI, we must scale. The scaling is the most important part of this indicator.
To summarize the process:
Identify a range on Price and RSI
Consider them as equal to create a scaling factor
Use the scaling factor to locate RSI's "Price equivalent" Upper, Lower, & Mid on the Chart
Use those prices (specifically the RSI Mid) to check how far each OHLC value lies from it
Use those differences to translate the price back to the RSI Oscillator, pinning the OHLC values at their relative location to our anchor (RSI Mid)
🔹 RSI Channel
To better understand, and for your convenience, the indicator includes the option to display the RSI Channel on the chart. This channel helps to visualize where the scaled RSI values are relative to price.
If you analyze the RSI channel, you are likely to notice that the price movement throughout the channel matches the same movement witnessed in the RSI Oscillator below. This makes sense since they are the exact same thing displayed on different scales.
🔹 Scaling the Open
While the scaling method used is important, and provides a very close view of the real price bar's relative locations on the RSI oscillator… It is designed for a single purpose.
The scaling does NOT make the price candles display perfectly on the RSI oscillator.
The largest place where this is noticeable is with the opening of each candle.
For this reason, we have included a setting that modifies the opening of each RSI candle to be more accurate to the chart's price candles.
This setting positions the current bar's opening RSI candlestick value accurately relative to the price's open location to the previous closing price. As seen below.
🔶 SETTINGS
🔹 RSI Candles
RSI Length: Sets the Length for the RSI Oscillator.
Overbought/Oversold Levels: Sets the Overbought and Oversold levels for the RSI Oscillator.
Scale Open for Chart Accuracy: As described above, scales the open of each candlestick bar to more accurately portray the chart candlesticks.
🔹 Divergence
Show on Chart: Choose to display divergence line on the chart as well as on the Oscillator.
Divergence Length: Sets the pivot width for divergence detection. Normal Fractal Pivot Detection is used.
Divergence Style: Change color and line style for Regular and Hidden divergences, as well as toggle their display.
Divergence Filter: As described above, toggle on or off divergence filtering.
🔹 RSI Channel
Toggle: Display RSI Channel on Chart.
Color: Change RSI Channel Color
RSI and CCICombined RSI and CCI Indicator for MetaTrader
The Combined RSI and CCI Indicator is a powerful hybrid momentum oscillator designed to merge the strengths of two popular indicators—the Relative Strength Index (RSI) and the Commodity Channel Index (CCI)—into a single, visually intuitive chart window. This tool enhances traders’ ability to identify overbought and oversold conditions, divergences, trend strength, and potential reversal zones with improved precision.
Purpose
By integrating RSI and CCI, this indicator helps filter out false signals that often occur when using each tool independently. It is especially useful for swing trading, trend confirmation, and spotting high-probability entry/exit zones. This dual-oscillator approach combines RSI’s relative momentum insights with CCI’s deviation-based analysis to produce a more reliable signal structure.
Key Features
Dual Oscillator Display: Plots both RSI and CCI on the same subwindow for easy comparison and correlation analysis.
Customizable Parameters:
RSI Period and Level (default: 14)
CCI Period and Typical Price Type (default: 20, TP)
Overbought/Oversold Levels for both indicators
Color-Coded Zones:
Background highlights when both RSI and CCI enter overbought/oversold territory, signaling high potential reversal zones.
Combined Signal Logic (Optional Feature):
Buy Signal: RSI < 30 and CCI < -100
Sell Signal: RSI > 70 and CCI > 100
These can be visualized as arrows or plotted as signal markers.
Trend Filter Overlay (Optional):
Can be combined with a moving average or price action filter to confirm trend direction before accepting signals.
Divergence Detection (Advanced Option):
Optional plotting of bullish or bearish divergence where both indicators diverge from price action.
Multi-Timeframe Compatibility:
Allows the use of higher timeframe RSI/CCI values to confirm signals on lower timeframes.
Benefits
Improved Signal Accuracy: Using both RSI and CCI together helps avoid false breakouts and whipsaws.
More Informed Decision-Making: Correlating momentum (RSI) with deviation (CCI) provides a well-rounded picture of market behavior.
Efficient Charting: Saves screen space and cognitive load by combining two indicators into one clean panel.
Scalable Strategy Integration: Can be used in discretionary trading or coded into automated strategies/alerts.
Use Case Example
In a ranging market, the indicator highlights zones where both RSI and CCI are oversold, alerting traders to potential bounce opportunities.
In trending markets, it confirms trend strength when RSI and CCI are both aligned with trend direction.
When RSI is diverging from price but CCI isn’t, it can be a clue of weakening momentum, helping traders scale out or avoid traps.
This combined indicator offers a versatile, high-performance toolset for traders looking to elevate their technical analysis by leveraging multiple momentum perspectives simultaneously.
IU Smart Flow SystemDESCRIPTION
The IU Smart Flow System is a powerful and dynamic order flow-based strategy designed to capture high-probability trades by analyzing bullish and bearish imbalances, trend direction, and RSI strength. It identifies trading opportunities by aligning order flow conditions with the prevailing trend and momentum, making it suitable for trend-following and momentum-based trading.
This system utilizes a unique combination of:
- Order flow score to gauge market imbalance
- Trend filter using SMA and ATR to confirm market direction
- RSI to ensure entry only during strong momentum
USER INPUTS:
- Imbalance Length: Defines the lookback period for calculating bullish and bearish imbalances. (Default: 10)
- Trend Length: Determines the length of the SMA to evaluate the trend direction. (Default: 50)
- RSI Length: Specifies the RSI period to assess momentum strength. (Default: 14)
LONG CONDITIONS:
Long entries are triggered when:
- Order flow score is positive, indicating bullish imbalance
- Price is above the bullish trend level (SMA + ATR), confirming an uptrend
- RSI is above 50, indicating bullish momentum
- No active short position is currently open
SHORT CONDITIONS:
Short entries are triggered when:
- Order flow score is negative, indicating bearish imbalance
- Price is below the bearish trend level (SMA - ATR), confirming a downtrend
- RSI is below 50, indicating bearish momentum
- No active long position is currently open
WHY IT IS UNIQUE:
- Imbalance-Based Approach: Unlike traditional strategies that rely solely on price action, this system evaluates bullish and bearish imbalances to anticipate order flow direction.
- Adaptive Trend Filter: The combination of SMA and ATR dynamically adjusts to market volatility, providing a reliable trend confirmation mechanism.
- Momentum Validation with RSI: Ensures that entries are taken only in the direction of strong momentum, reducing false signals.
HOW USERS CAN BENEFIT FROM IT:
- Enhanced Trade Accuracy: Aligning order flow, trend, and momentum reduces false signals and improves trade success rates.
- Versatile Application: Suitable for various markets and timeframes, making it adaptable to different trading styles.
- Clear Trade Signals: Provides clear entry labels and alerts, ensuring traders never miss a potential opportunity.
- Visual Clarity: The filled region between bullish and bearish trends highlights trend direction, enhancing decision-making.
