Tri-State SupertrendTri-State Supertrend: Buy, Sell, Range
( Credits: Based on "Pivot Point Supertrend" by LonesomeTheBlue.)
Tri-State Supertrend incorporates a range filter into a supertrend algorithm.
So in addition to the Buy and Sell states, we now also have a Range state.
This avoids the typical "whipsaw" problem: During a range, a standard supertrend algorithm will fire Buy and Sell signals in rapid succession. These signals are all false signals as they lead to losing positions when acted on.
In this case, a tri-state supertrend will go into Range mode and stay in this mode until price exits the range and a new trend begins.
I used Pivot Point Supertrend by LonesomeTheBlue as a starting point for this script because I believe LonesomeTheBlue's version is superior to the classic Supertrend algorithm.
This indicator has two additional parameters over Pivot Point Supertrend:
A flag to turn the range filter on or off
A range size threshold in percent
With that last parameter, you can define what a range is. The best value will depend on the asset you are trading.
Also, there are two new display options.
"Show (non-) trendline for ranges" - determines whether to draw the "trendline" inside of a range. Seeing as there is no trend in a range, this is usually just visual noise.
"Show suppressed signals" - allows you to see the Buy/Sell signals that were skipped by the range filter.
How to use Tri-State Supertrend in a strategy
You can use the Buy and Sell signals to enter positions as you would with a normal supertrend. Adding stop loss, trailing stop etc. is of course encouraged and very helpful. But what to do when the Range signal appears?
I currently run a strategy on LDO based on Tri-State Supertrend which appears to be profitable. (It will quite likely be open sourced at some point, but it is not released yet.)
In that strategy, I experimented with different actions being taken when the Range state is entered:
Continue: Just keep last position open during the range
Close: Close the last position when entering range
Reversal: During the range, execute the OPPOSITE of each signal (sell on "buy", buy on "sell")
In the backtest, it transpired that "Continue" was the most profitable option for this strategy.
How ranges are detected
The mechanism is pretty simple: During each Buy or Sell trend, we record price movement, specifically, the furthest move in the trend direction that was encountered (expressed as a percentage).
When a new signal is issued, the algorithm checks whether this value (for the last trend) is below the range size set by the user. If yes, we enter Range mode.
The same logic is used to exit Range mode. This check is performed on every bar in a range, so we can enter a buy or sell as early as possible.
I found that this simple logic works astonishingly well in practice.
Pros/cons of the range filter
A range filter is an incredibly useful addition to a supertrend and will most likely boost your profits.
You will see at most one false signal at the beginning of each range (because it takes a bit of time to detect the range); after that, no more false signals will appear over the range's entire duration. So this is a huge advantage.
There is essentially only one small price you have to pay:
When a range ends, the first Buy/Sell signal you get will be delayed over the regular supertrend's signal. This is, again, because the algorithm needs some time to detect that the range has ended. If you select a range size of, say, 1%, you will essentially lose 1% of profit in each range because of this delay.
In practice, it is very likely that the benefits of a range filter outweigh its cost. Ranges can last quite some time, equating to many false signals that the range filter will completely eliminate (all except for the first one, as explained above).
You have to do your own tests though :)
Komut dosyalarını "algo" için ara
Cryptosmart Trading Tool (by heswaikcrypt)Introducing the Cryptosmart Trading Tool (CSTP) - An optimized into Market Sentiment and direction tool
The Cryptosmart Trading Tool (CSTP) is an advanced indicator developed to provide valuable insights into market sentiment and direction. This tool combines existing TA tools and intelligently develops smart algorithms to empower traders with a deeper understanding of market dynamics. Some classic elements are included in the scripting, such as the exponential moving average (EMA), volume, and Relative Strength Index (RSI), to provide a comprehensive analysis of market conditions. By combining these indicators, the script aims to capture different aspects of market sentiment and enhance the accuracy of the analysis.
The Cryptosmart Trading Tool (CSTP) incorporates a unique algorithm that combines trend following analysis, momentum analysis, and volume analysis to provide insights into market sentiment and price action.
Trend Following Analysis:
The algorithm utilizes two exponential moving averages (EMAs): EMA1 and EMA2.
When EMA1 crosses above EMA2, it indicates an uptrend (isUptrend).
When EMA1 crosses below EMA2, it indicates a downtrend.
You adjust the input value to suit your trading strategy, however, 7, 8, 21, 34, and 200 have been tested to produce a fine tuned output.
The bar color indicates blue for bullish sentiment (is uptrend) and white for bearish sentiment (is downtrend).
Momentum Analysis:
The relative strength index (RSI) is calculated based on the closing prices and the specified RSI length.
RSI values above 70 indicate overbought conditions (isOverbought).
RSI values below 30 indicate oversold conditions (isOversold).
Using the isOversoldExtreme and isOverboughtExtreme, the CSTP algorithm detect extreme over bought and oversold conditions and alert with label color green and red.
Volume Analysis:
The algorithm calculates the average volume over a specified length (averageVolume).
The volume ratio is obtained by dividing the current volume by the average volume.
High volume activity is identified when the volume ratio is greater than 1 (isHighVolume).
Major Flip and Arrow Plots:
Major bullish or bearish flips are identified when EMA1 crosses above EMA2 with RSI values above 50 and high volume activity (isBullishFlip) or when EMA1 crosses below EMA2 with RSI values below 50 and high volume activity (isBearishFlip).
Arrow plots are used to display trend direction, upward arrows for major bullish flips and downward arrows for major bearish flips.
The algorithm calculates the bullBearRatio and RSIValueAtFlip to capture the volume ratio and RSI values at major flips.
The bullishRatio and bearishRatio variables store the volume ratio values for the corresponding trend conditions.
Labels are also displayed on the chart to provide information about EMA values and RSI values. This can be independently disabled by the user
The uniqueness of the CSTP algorithm lies in its combination of trend following analysis, momentum analysis, and volume analysis. By considering these factors, the algorithm provides insights into market sentiment and price action. The use of EMAs, RSIs, and volume ratios allows traders to identify potential trends, overbought/oversold conditions, and high volume activity. The visual representation of bar colors and arrows enhances the ease of understanding the sentiment and major flips. CSTP is uniquely presented by using dots, arrows, candlestick colors, and shape labels to indicate the market scenario. This is explained below.
By leveraging multiple indicators and analysis techniques, CSTP aims to provide traders with a holistic understanding of market dynamics and enhance their decision-making process.
It's important to note that while the individual components used in CSTP are not new or unique on their own, the specific algorithm, parameters, and calculations used within the script are what make it distinctive and valuable. By carefully integrating these components, CSTP generates results that are greater than the sum of its parts, providing traders with a comprehensive analysis of market conditions.
Through extensive research, analysis, and testing, we have created a useful tool, fine-tuned to optimize the accuracy and reliability of the script's output, which can assist traders in making more informed trading decisions.
How to Use:
1. Apply the CSTP Script:
- Apply the CSTP script to your TradingView chart to start analyzing market conditions. (Access instructions can be found in the author's details section.)
- Ensure you have the latest version of TradingView to access all the features and functionalities.
2. Customize Parameters:
- Customize the input variables to match your trading preferences and adapt the tool to different markets.
- Experiment with different settings, such as RSI Length and EMA Lengths, to find the optimal configuration for your trading strategy.
3. Interpret the Color-Coded Bars and Wave Labels:
- Green bars indicate bullish sentiment, suggesting potential buying opportunities.
- Red bars indicate bearish sentiment, indicating potential selling opportunities.
- Blue and white bars represent sentiment backed by smart money liquidity, adding an extra layer of analysis.
- The wave labels provide insights into market structure and potential wave patterns.
4. Combine with Candlestick philosophy strategy and parameters used:
- Wait for candlestick closure before making trading decisions based on CSTP's analysis.
- Consider the EMA (yellow) line as an additional tool to confirm entry or exit points.
- Combining CSTP's analysis with candlestick patterns can enhance your decision-making process and improve trade timing.
- Volume Analysis: Compares the current volume to the Simple Moving Average (SMA) of volume using the RSI Length parameter to determine high-volume periods.
- Color-Coded Bars: The color of the bars represents different market sentiments based on all the parameters used including Relative strength index, bullish and bearish
divergence and volume conditions.
- Open Close Cross (OCC) Alerts: Generates dot alert with color code (red=Bearish, green=Bullish) when there is a crossover or crossunder between the close and open
prices
Important Notes:
- Candlestick color matter a lot as then show the sentiment of the market at a given time. and it is an added advantage for a trader to understand candlestick Psychology.
Candlestick conditions
I will use this BINANCE:MTLUSDT chart to explain how it works
Long green Arrow: Bullish call, with green isBullish arrow
Long red Arrow: Bearish call, with isBearish arrow
Blue with red wick and tape: this indicate a bearish sentiment but with some bullish volume, this position is dice which requires a proper understanding of entry and exit. when if this said candle stick closes below the EMA line, wait for the the next candle after it t determining your move. If the next one closes above it, then the direction is still bullish, else the direction has flipped bearish. (special scenario: in the range or consolidative market phase, you may need to wait 3-7 day candle close before you decide. use the coloration as guide to help with your decision making).
Blue with green wick and tape: this indicated strong bullish sentiment backed by liquidity to push. it is important to not the candle close, if the candle closes above the EMA (7 and/or 21) that validates the move, else, you may need to wait for the next candle close to determine the move and momentum of the market. Example is the $COOMPUST chart
White with green wick and tape: this works just like the "Blue candlestick with red wick and tape". follow same procedure
White with red wick and tape: White candle with red wick, indicates bearish sentiment backed by available market liquidity at the time.
If you see the market moving upward and the candlestick keep closing with white color, it is an indication of inorganic move (Check BITFINEX:SUIUST ) the best thing to do is to wait at resistance. a similar scenario can be seen here
Market test:
below are picture of the indicator tested on different assets
CRYPTOCAP:BNB
AUD
Tesla
it is best to book an entry after an arrow indicate (especially for a bullish market) and the candle closes above the EMA (Yellow line).
Risk management.
- ALWAYS PROTECT YOUR PROFIT WHEN YOU SEE ON. THE MARKET IS DYNAMIC
- Trading involves risks, and no tool can guarantee absolute accuracy in predicting market direction. Conduct thorough research and exercise caution when making trading decisions.
- Apply proper risk management strategies and adjust position sizes according to your risk tolerance.
- Stay updated with market news and events that may impact your trading decisions.
Conclusion:
The Cryptosmart Trading Tool (CSTP) provides traders with a powerful advantage by offering valuable insights into market sentiment and direction. To gain access or trial, refer to the author's details section. This indicator combines various analysis techniques to provide a comprehensive view of the market. Remember to apply your own analysis and expertise in conjunction with CSTP for optimal results.
This indicator combines my 8years of trading experience. Enjoy
Disclaimer:
Trading involves risks, and the CSTP script is designed to assist traders by providing valuable insights. It should be used as a supplement to your own analysis and expertise. Exercise caution and make informed trading decisions based on your own research.
Trend & Pullback Toolkit (Expo)█ Overview
The Trend & Pullback Trading Toolkit is an all-encompassing suite of tools designed for serious traders who want a comprehensive trend approach. It empowers traders to align their strategies with prevailing market trends, thereby mitigating risk while maximizing profit potential.
The Toolkit helps traders spot, analyze, and react to market trends, pullbacks, and significant trends. It combines multiple trading methodologies, such as the Elliott Wave theory, cyclical analysis, retracement analysis, strength analysis, volatility analysis, and pivot analysis, to provide a thorough understanding of the market. All these tools can help traders detect trends, pullbacks, and major shifts in the overall trend. By integrating different methodologies, this toolkit offers a multifaceted approach to analyzing market trends.
In essence, the Trend & Pullback Toolkit is the complete package for traders seeking to detect, evaluate, and act upon market trends and pullbacks while being prepared for major trend shifts.
The Trend & Pullback Toolkit works in any market and timeframe for discretionary analysis and includes many oscillators and features, but first, let us define what a cycle is:
█ What is a cycle
This involves the analysis of recurring patterns or events in the market that repeat over a specific period. Cycles can exist in various time frames and can be identified and analyzed with various tools, including some types of oscillators or time-based analysis methods.
Traders must also be aware that cycles do not always repeat perfectly and can often shift, evolve, or disappear entirely.
█ Features & How They Work
Elliott Wave Cycles: This is a method of technical analysis that traders use to analyze financial market cycles and forecast market trends. Elliott Wave theory asserts that markets move in repetitive cycles, which traders can analyze to predict future price movement. The core principle behind the theory is that market prices alternate between an impulsive, or driving phase, and a corrective phase on all time scales of trend. This pattern forms a fractal, meaning it's a self-similar pattern that repeats regardless of the degree or size of the waves.
The Elliott Wave Cycle Feature uses the principle of the Elliott Wave to identify trends and pullbacks in real-time.
Ratio Wave Cycle: This method elaborates on the concept of how negative volatility, or the degree of variation in the negative returns of a financial instrument, influences the effectiveness of a relative price move. Essentially, it delves into the relationship between the negative fluctuations in the market and the resulting relative price change, exploring how the two aspects interact with each other.
The central concept is that trends are generally more stable and predictable than rapid retracements. Therefore, the indicator calculates the relationship between these two market movements. By doing so, it establishes a trend-based identification system. This system aids in forecasting future market movements, allowing traders to make informed decisions based on these predictions. Essentially, it uses the calculated relationship to discern the overall direction (trend) of the market despite temporary counter-movements (retracements), thereby providing a more robust trading signal.
Periodic Wave Cycle: Thi refers to patterns or events in price action that recur over a specific time period. Periodic cycles can range from short-term intraday cycles (like the tendency for stock market volatility to be high at the opening and close of trading) to long-term cycles trend cycles. Traders use this to predict future price movements and trends.
By identifying the phases of a cycle, traders can predict key turning points in the market.
Retracement Cycles: Retracements are temporary price reversals that occur within a larger trend. These retracements are a common occurrence in all markets and timeframes, representing a pause or counter-move within a larger prevailing trend. Retracements can be driven by a variety of factors, including profit-taking, market uncertainty, or a change in market fundamentals. Despite these periodic reversals, the overall trend (upwards or downwards) often continues after the retracement is complete.
Fibonacci retracement functions are primarily used to identify potential retracement levels.
Volatility Cycle: A volatility cycle refers to the periodic changes in the degree of dispersion or variability of a security's returns, expressed as a standard deviation or variance. This feature uses both measures.
Strength Cycle: Gauges the power of a market trend and its inherent impulses. This feature offers a broad perspective on the cyclical nature of markets, which alternate between periods of strength, often referred to as bull markets, and periods of weakness, known as bear markets. It effectively tracks the direction, intensity, and cyclic patterns of market behavior.
Let us define the difference between strength and impulse:
Strength: This refers to the power or force behind a price move. In trading, this refers to the momentum or volume supporting a price move.
Impulse: In the context of trading, an impulse usually refers to a strong move in price. Impulse moves are typically followed by corrective moves against the trend.
Pivot Cycles: Pivot cycles refer to the observation of recurring price patterns or turning points in the market. Pivots can be defined as significant highs or lows that act as potential reversal or support/resistance points. Pivot point analysis helps traders understand the prevailing market sentiment. Overall, pivot cycles provide traders with a framework to identify potential market turning points and price levels of interest.
█ How to use the Trend & Pullback Toolkit
Elliott Wave Cycles
Ratio Wave Cycle
Periodic Wave Cycle
Retracement Cycles
Volatility Cycle:
Strength Cycle
Pivot Cycles
█ Why is this Trend & Pullback Toolkit Needed?
The core philosophy of this toolkit revolves around the popular adage in trading circles: "The trend is your friend." This toolkit ensures that you are always in sync with the trend, thereby increasing the chances of successful trades.
Here's an overview of the key benefits:
Trend Identification: The toolkit includes sophisticated algorithms and indicators that help identify the prevailing trend in the market. These algorithms analyze price patterns, momentum, volume, and other factors to determine the direction and strength of the trend.
Risk Reduction: By enabling traders to trade with the trend, this toolkit reduces the risk of betting against market momentum.
Profit Maximization: Trading with the trend increases the likelihood of successful trades.
Advanced Analysis Tools: The toolkit includes tools that provide a deeper insight into market dynamics. These tools enable a multi-dimensional analysis of market trends, from Elliott Wave cycles and period cycles to retracement cycles, ratio wave cycles, pivot cycles, and strength cycles.
User-friendly Interface: Despite its sophistication, the toolkit is designed with user-friendliness in mind. It allows for customization and presents data in easy-to-understand formats.
Versatility: The toolkit is versatile and can be used across different markets - stocks, forex, commodities, and cryptocurrencies. This makes it a valuable resource for all types of traders.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ In conclusion, The Trading Toolkit is a powerful ally for any trader, offering the capabilities to navigate the complexities of the market with ease. Whether you're a novice or an experienced trader, this toolkit provides a structured and systematic approach to trading.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
MCumulativeDelta* MCumulativeDelta Indicator *
The MCumulativeDelta Indicator shows the Buying / Selling pressure that is happening in the market. The Delta is powered by the *MBox Precision Delta* Algorithm. This indicator serves to show overall Accumulation and Distribution of the BUYERS and the SELLERS. It becomes possible to gauge if the market is overall Bullish or Bearish. This helps determine trade direction and keeping out of other trades that are counter to what the overall Buying / Selling is showing.
* WHAT THE SCRIPT DOES *
The script draws a histogram that can either be positive or negative. When the histogram is positive it means there are more Buyers in the Market. When the histogram is negative it means there are more sellers in the market. The more positive the histogram gets, the more BUYERS are flooding the market. The more negative the histogram gets, the more SELLERS are flooding the market. When the histogram switches over from negative to positive it is a Bullish sign of Buying. When the histogram switches over from positive to negative, it is a Bearish sign of Selling.
* HOW TO USE IT *
As the histogram becomes more negative, this shows that the SELLERS have taken control of the markets. Conversely, as the histogram becomes more positive, this shows that the Buyers have taken control of the markets. The side that is in control is the direction to generally place trades in, and at the same time filter out trades of the opposite direction.
* HOW IT WORKS *
The MCumulativeDelta histogram on the chart represents overall Buying / Selling. This is the DELTA (difference) between the BUYING and the SELLING. Taking the total BUYING and subtracting the total of SELLING, we produce the DELTA (difference) between the Buying / Selling and this is what is drawn by the histogram.
Unlike other Cumulative Delta indicators which determine delta from the Up / Down wick and just multiply by volume (not a true delta), the MCumulativeDelta indicator uses a sophisticated algorithm that analyzes price movement corresponding to volume movement.
The way the DELTA, BUYING, and SELLING is calculated is computed by the *MBox Precision Delta* Algorithm. The algorithm considers the following data points when making it's computation
1. Price moving up on increasing volume
2. Price moving up on decreasing volume
3. Price moving horizontally on increasing volume
4. Price moving horizontally on decreasing volume
5. Price moving down on increasing volume
6. Price moving down on decreasing volume
Using these data points allows MCumulativeDelta to effectively compute and define the following scenarios
1. Accumulation / Distribution
2. Buying / Selling Exhaustion
3. Buying / Selling EFFORT / NO RESULT
Once the scenario is determined, it will greatly aid in trade decision making. These scenarios are explained in the examples below
* EXAMPLE AND USE CASES *
- Accumulation Example -
When you see a large amount of BUYING (large positive histogram) and price entering an up trend, this is indicative of Accumulation and you would be looking for PULLBACKS to get into the up trend move.
- Distribution Example -
When you see a large amount of SELLING (large negative histogram) and price entering a down trend, this is indicative of Distribution and you would be looking for pullbacks to get into the down trend move.
- Buying EXHAUSTION Divergence -
As price makes higher highs, but the MCumulativeDelta histogram drops (becomes less positive) on the higher highs, it means BUYERS are exhausted. Potentially a reversal or change in behavior in the markets.
- Selling EXHAUSTION Divergence -
As price makes lower lows, but the MCumulativeDelta histogram contracts (becomes less negative) on the lower lows, it means SELLERS are exhausted. Potentially a reversal or change in behavior in the markets.
- BUYING EFFORT / NO RESULT -
As the MCumulativeDelta histogram increases positively, but price fails to make higher highs, it is a sign of EFFORT / NO RESULT on behalf of the Buyers. In this case Buyers are pushing hard to move price up, but are unable to, due to being OVERBOUGHT. If this is accompanied by visible SELLING, it would be a good short entry.
- SELLING EFFORT / NO RESULT -
As the MCumulativeDelta histogram increases negatively, but price fails to make lower lows, it is a sign of EFFORT / NO RESULT on behalf of the Sellers. In this case Sellers are pushing hard to move price down, but are unable to, due to being OVERSOLD. If this is accompanied by visible BUYING, it would be a good long entry.
* SETTING ALERTS *
- FOR CROSSING FROM BUYING TO SELLING OR SELLING TO BUYING -
To be alerted when the histogram crosses over from Buying to Selling (Positive to Negative) or Selling to Buying (Negative to Positive)
1. Right Click Chart -> Add Alert...
2. Select Condition to be "MCumulativeDelta"
3. Select "Crossing" with Value = 0
4. Options set "Once Per Bar Close"
5. Customize Any other Alert Options you want
* AUTHOR *
This script is published by MBoxWave LLC
Smart Money Concepts Probability (Expo)█ Overview
The Smart Money Concept Probability (Expo) is an indicator developed to track the actions of institutional investors, commonly known as "smart money." This tool calculates the likelihood of smart money being actively engaged in buying or selling within the market, referred to as the "smart money order flow."
The indicator measures the probability of three key events: Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS ). These probabilities are displayed as percentages alongside their respective levels, providing a straightforward and immediate understanding of the likelihood of smart money order flow.
Finally, the backtested results are shown in a table, which gives traders an understanding of the historical performance of the current order flow direction.
█ Calculations
The algorithm individually computes the likelihood of the events ( CHoCH , SMS , and BMS ). A positive score is assigned for events where the price successfully breaks through the level with the highest probability, and a negative score when the price fails to do so. By doing so, the algorithm determines the probability of each event occurring and calculates the total profitability derived from all the events.
█ Example
In this case, we have an 85% probability that the price will break above the upper range and make a new Break Of Structure and only a 16.36% probability that the price will break below the lower range and make a Change Of Character.
█ Settings
The Structure Period sets the pivot period to use when calculating the market structure.
The Structure Response sets how responsive the market structure should be. A low value returns a more responsive structure. A high value returns a less responsive structure.
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS )
The insights provided by this tool help traders gain an understanding of the smart money order flow direction, which can be used to determine the market trend.
█ Any Alert function call
An alert is sent when the price breaks the upper or lower range, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Timeframe
Probability percentage
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Breakout Probability (Expo)█ Overview
Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels.
The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias.
█ Calculations
The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line.
█ Example
In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made.
█ Settings
Percentage Step
The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one.
Disable 0.00% values
If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values.
Number of Lines
Set the number of levels you want to display.
Show Statistic Panel
Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not)
█ Any Alert function call
An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Bias
Probability percentage
The first level high and low price
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit.
The indicator can be used for setting a stop loss based on where the price is most likely not to reach.
The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
MoonFlag Converging BandsThis script form a cloud that is made from multiple lines that are each similar to a moving average.
However, each line is different to moving averages as it uses an algorithm that is nonlinear, 'overshoot moving averages' better explains how they work.
A cloud (visible on the indicator plot) is formed from multiple 'overshoot moving average' lines, each with a different lookback length.
A single variable is provided in the settings which extends all lines which form the cloud.
So the cloud is formed from the max and min from multiple 'nonlinear' moving averages.
What is interesting here is that, ....when the cloud lines narrow or converge..... ,this signifies that all moving averages are narrowing.
However, as the algo does not use standard moving averages - it is a bit more spicy and has some merit with predicting a big or biggish move in advance, before it happens.
So, the overshoot moving averages have a predictive quality.
Whereas, standard moving averages always lag the present time price action.
Indeed, most indicators are based on moving averages and lag the price action.
I'll try and explain how the overshoot moving average works...
Each line which forms the cloud gives an indication of the price trend momentum.
So if the price action rises above a line. the line will follow and move up, however, when the price action reduces momentum or starts to move downwards, the underlying momentum will push the line to overshoot the price action. Hence the price action crossing lines (or extending beyond the cloud) can indicate a change in momentum of a price trend.
There is also a median line shown which can be quite useful. If the price action stays about the median, this would suggest increasing bullish momentum. Then if the price action crosses the median - this is reasonable grounds to think about getting out of a trade as a change in momentum, on multiple timeframes has occured.
So, ... why is this wavecloud important or how is it useful.
When the wavecloud gets narrow - this generally means that all moving averages are converging. However, moving averages lag real-time price action and therefore lack a predictive speculation. With the waveclound presented in this indicator, when the wavecloud narrows this can suggest/predict a sizeable move is about to happen. In the settings, there is a narrowing % variable which can be adjusted depending on which coin or timeframe someone is working with. If there is a lot of background shading (faster timeframes)- decrease the % narrowing. Conversely, if there is insufficient background lines (with longer timeframes), increase the narrowing %.
There are a few trends which are exceptions to predicting a big move. One is that the price trend continues at a steady pace and hence the wavecloud narrows on a steadily increasing or decreasing price.
Another is that the price is choppy and just goes up and down throwing all moving averages or most indicators into a non useful state. However, adjust the narrowing % for whatever price action is in play at the time and you might find you can neatly pick out a big price change.
So, which way does a big price action move go, up or down, I'll leave this one to you. If one is trying to find the end point of a massive bull run - there might be a wavecloud narrowing at the top, just before the price suddenly drops. If its sometime after a big crash and the price action has already been through a choppy phase, its possibly time for a big rise after one last sharp drop. There are all sorts of price action wavecloud formations however, nothing very predictive in terms of suggesting when a big move might be soon to happen is otherwise available. (Although I did find my other script 'Volume Effectiveness' has some merits.)
Timeframe is an important factor with this algorithm. I think the 4hour timeframe with bitcoin is reasonable. I've not extensively tested with other coins however, faster timeframes always render unpredictable results. Also if the timeframe is too long - its difficult to suggest what is going to happen in the near future.
FATL, SATL, RFTL, & RSTL Digital Signal Filter Smoother [Loxx]FATL, SATL, RFTL, & RSTL Digital Signal Filter (DSP) Smoother is is a baseline indicator with DSP processed source inputs
What are digital indicators: distinctions from standard tools, types of filters.
To date, dozens of technical analysis indicators have been developed: trend instruments, oscillators, etc. Most of them use the method of averaging historical data, which is considered crude. But there is another group of tools - digital indicators developed on the basis of mathematical methods of spectral analysis. Their formula allows the trader to filter price noise accurately and exclude occasional surges, making the forecast more effective in comparison with conventional indicators. In this review, you will learn about their distinctions, advantages, types of digital indicators and examples of strategies based on them.
Two non-standard strategies based on digital indicators
Basic technical analysis indicators built into most platforms are based on mathematical formulas. These formulas are a reflection of market behavior in past periods. In other words, these indicators are built based on patterns that were discovered as a result of statistical analysis, which allows one to predict further trend movement to some extent. But there is also a group of indicators called digital indicators. They are developed using mathematical analysis and are an algorithmic spectral system called ATCF (Adaptive Trend & Cycles Following). In this article, I will tell you more about the components of this system, describe the differences between digital and regular indicators, and give examples of 2 strategies with indicator templates.
ATCF - Market Spectrum Analysis Method
There is a theory according to which the market is chaotic and unpredictable, i.e. it cannot be accurately analyzed. After all, no one can tell how traders will react to certain news, or whether some large investor will want to play against the market like George Soros did with the Bank of England. But there is another theory: many general market trends are logical, and have a rationale, causes and effects. The economy is undulating, which means it can be described by mathematical methods.
Digital indicators are defined as a group of algorithms for assessing the market situation, which are based exclusively on mathematical methods. They differ from standard indicators by the form of analysis display. They display certain values: price, smoothed price, volumes. Many standard indicators are built on the basis of filtering the minute significant price fluctuations with the help of moving averages and their variations. But we can hardly call the MA a good filter, because digital indicators that use spectral filters make it possible to do a more accurate calculation.
Simply put, digital indicators are technical analysis tools in which spectral filters are used to filter out price noise instead of moving averages.
The display of traditional indicators is lines, areas, and channels. Digital indicators can be displayed both in the form of lines and in digital form (a set of numbers in columns, any data in a text field, etc.). The digital display of the data is more like an additional source of statistics; for trading, a standard visual linear chart view is used.
All digital models belong to the category of spectral analysis of the market situation. In conventional technical indicators, price indications are averaged over a fixed period of time, which gives a rather rough result. The use of spectral analysis allows us to increase trading efficiency due to the fact that digital indicators use a statistical data set of past periods, which is converted into a “frequency” of the market (period of fluctuations).
Fourier theory provides the following spectral ranging of the trend duration:
low frequency range (0-4) - a reflection of a long trend of 2 months or more
medium frequency range (5-40) - the trend lasts 10-60 days, thus it is referred to as a correction
high frequency range (41-130) - price noise that lasts for several days
The ATCF algorithm is built on the basis of spectral analysis and includes a set of indicators created using digital filters. Its consists of indicators and filters:
FATL: Built on the basis of a low-frequency digital trend filter
SATL: Built on the basis of a low-frequency digital trend filter of a different order
RFTL: High frequency trend line
RSTL: Low frequency trend line
Inclucded:
4 DSP filters
Bar coloring
Keltner channels with variety ranges and smoothing functions
Bollinger bands
40 Smoothing filters
33 souce types
Variable channels
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs.
Versions:
- 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers
- 2016, TASC September, "Measuring Market Cycles"
Description
The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
How to use this indicator
The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators.
Usage Example 1
Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period.
Usage Example 2
Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Mikolaj Zakrzowski - Adjusted Mayer MultipleAuthor - Publication: Mikołaj Zakrzowski, Marek Zatwarnicki
Author - Algorithm: Mikołaj Zakrzowski
Author - Code: Marek Zatwarnicki, Derek Gruening
Inspired by: Mayer Multiple by Trace Mayer
Category: Technical Analysis
Type: Indicator
Timeframe: 1D Only
Index: INDEX:BTCUSD Only
About:
According to Willy Woo Mayer Multiple is "A way to gauge the current price of Bitcoin against its long range historical price movements (200 day moving average), the Mayer Multiple highlights when Bitcoin is overbought or oversold in the context of longer time frames".
My friend, Mikolaj Zakrzowski, decided to modify and adjust this indicator so that it could be normalized. This procedure allows for easier interpretation, and clear signals of the end of the ups and downs of a given Bitcoin cycle.
How to use:
BUY - Buy some Bitcoin , when label on last candle shows "Buy".
SELL- Sell some Bitcoin , when label on last candle shows "Sell".
Formula:
- Mayer Multiple - Close / ta. sma (close, 200)
- Formula for normalization is an intellectual property of Mikolaj Zakrzowski.
Overfitting: The presented algorithm is characterized by log regresion determined as of 01/01/2022. Tests with historical data show that the algorithm is very likely to work equally well the following years.
Disclaimer: Past good results do not guarantee future trading success. Please use the algorithm with caution and support it with your knowledge. Published algorithm decisions are not financial advice.
Market Maker Volatility Diameter V2 by Hawkeye Charting***German Description below***
Hey guys,
we are proud to publish the Market Maker Volatility Diameter V2!
Our goal with this indicator is to provide an All-in-one indicator, combining some special tools of open source scripts as well as some of our own developments and the algorithm of our MMVD V1.
We will create a video series very soon, where we will explain each aspect of the tool, your options and of course our trading strategies with this indicator.
You have the following technical tools and information combined in this indicator, which can each be shown and hidden:
- Psychological Ranges (Weekly Opening High/ Low for Crypto and Forex)
- Market Maker Sessions (Sydney, Asia, London, NY)
- Trade Cloud (algorithm developed by Hawkeye Charting)
- Fibonacci Cloud (inspired by watching paid offerings, coded by Hawkeye Charting)
- Display Moving Averages (select the visualization of up to 6 moving averages. You can change for each of these 6 MA's the type and the length.)
- Display Major Trend Cloud (developed by Hawkeye Charting)
- PVSRA Candle Colors
- Vector Candle Zones
- Pivots
- Pivot Fibonacci Levels (developed by Hawkeye Charting)
- OHLC-Levels
- Average Daily, Weekly, Monthly Ranges
- Volume Profile for Intraday Trading for up to 8 days.
We hope especially for people, who can not afford the Pro offering from TradingView, to give access to a good indicator, which includes many tools and alerts.
Our goal is to lower the barriers for new entrants and of course to protect people, to pay for indicators, which are completely insane priced.
Only, that you get an idea: the whole indicator has only cost me about 100 h of work (for a single person!), and I'm no Pine script expert, so don't get fooled when someone offers you insane amounts for an indicator...
There is no holy grail. Each indicator works only with calculations on previous data.
We appreciate seeing that you guys like this work, so please leave a like and a follow and share this indicator.
*****German Description*****
Hey Leute,
wir sind stolz, unsere 2. Version des Market Maker Volatility Diameter zu veröffentlichen!
Unser Ziel ist es, mit diesem Indikator eine All-In-One Lösung anzubieten, welche einige nicht ganz geläufige Tools sowie unsere eigenen Entwicklungen und natürlich den Algorithmus des MMVD V1 vereinen.
Wir werden in naher Zukunft eine Video Serie veröffentlichen, in welcher wir Stück für Stück jeden Aspekt des Werkzeugs, die Einstellungsmöglichkeiten sowie unsere Trading Strategien mit diesem Indikator erklären werden.
Ihr habt die folgenden technischen Werkzeuge und Informationen in diesem Indikator vereint, welche jede einzeln an- oder abgewählt und eingestellt werden können:
- Psychological Ranges (Weekly Opening High/ Low für Krypto and Forex)
- Market Maker Sessions (Sydney, Asia, London, NY)
- Trade Cloud (Algorithmus von Hawkeye Charting entwickelt)
- Fibonacci Cloud (inspiriert von der Beobachtung eines Paid-Indikators, Code geschrieben von Hawkeye Charting)
- Moving Averages (Ihr könnt die Darstellung von bis zu 6 Gleitenden Durchschnitten auswählen und für jeden dieser Durchschnitte den Typ und die Länge ändern.)
- Display Major Trend Cloud (entwickelt von Hawkeye Charting)
- PVSRA Candle Colors
- Vector Candle Zones
- Pivots
- Pivot Fibonacci Levels (entwickelt von Hawkeye Charting)
- OHLC-Levels
- Average Daily, Weekly, Monthly Ranges
- Volume Profile für Intraday Trading, Darstellungsmöglichkeit für 3-8 Tage
Wir hoffen, dass wir speziell für Leute, die sich nicht das PRO-Abo aufwärts von TradingView leisten können, Zugang zu einem guten Indikator, welche viele Werkzeuge und Alarme vereint gewährleisten zu können.
Unser Ziel ist es, die Eintrittsbarrieren für neue Marktteilnehmer senken und natürlich Leute vor wahnsinnigen Paid-Angeboten beschützen zu können.
Nur, damit ihr eine Vorstellung bekommt: den gesamten Indikator hat mich lediglich 100h Arbeit gekostet (für eine einzelne Person!), und ich bin kein Pine Script Experte. Also lasst euch bitte nicht verar******, wenn euch Paid-Angebote erreichen, mit dem Versprechen, den "zu 95% erfolgreich" Indikator erwerben zu können.
Es gibt keinen heiligen Gral, jeder Indikator arbeitet nur mit Berechnung von Vergangenheitswerten.
Wir würden uns riesig freuen, wenn euch diese Arbeit gefällt und ihr uns Likes und Follows hinterlasst und ihr diesen Indikator teilt.
Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto)
Description:
This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms.
To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest
neighbours of the current indicator(s) values.
The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours,
sees their state and thus classifies the current point.
The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours+++/Days
Relative Strength(RSMK) + Perks - Markos KatsanosIf you are desperately looking for a novel RSI, this isn't that. This is another lesser known novel species of indicator. Hot off the press, in multiple stunning color schemes, I present my version of "Relative Strength (RSMK)" employing PSv4.0, originally formulated by Markos Katsanos for TASC - March 2020 Traders Tips. This indicator is used to compare performance of an asset to a market index of your choosing. I included the S&P 500 index along side the Dow Jones and the NASDAQ indices selectively by an input() in "Settings". You may comparatively analyze other global market indices by adapting the code, if you are skilled enough in Pine to do so.
With this contribution to the Tradingview community, also included is MY twin algorithmic formulation of "Comparative Relative Strength" as a supplementary companion indicator. They are eerily similar, so I decided to include it. You may easily disable my algorithm within the indicator "Settings". I do hope you may find both of them useful. Configurations are displayed above in multiple scenarios that should be suitable for most traders.
As always, I have included advanced Pine programming techniques that conform to proper "Pine Etiquette". For those of you who are newcomers to Pine Script, this script may also help you understand advanced programming techniques in Pine and how they may be utilized in a most effective manner. Utilizing the "Power of Pine", I included the maximum amount of features I could surmise in an ultra small yet powerful package, being less than a 60 line implementation at initial release.
Unfortunately, there are so many Pine mastery techniques included, I don't have time to write about all of them. I will have to let you discover them for yourself, excluding the following Pine "Tricks and Tips" described next. Of notable mention with this release, I have "overwritten" the Pine built-in function ema(). You may overwrite other built-in functions too. If you weren't aware of this Pine capability, you now know! Just heed caution when doing so to ensure your replacement algorithms are 100% sound. My ema() will also accept a floating point number for the period having ultimate adjustability. Yep, you heard all of that properly. Pine is becoming more impressive than `impressive` was originally thought of...
Features List Includes:
Dark Background - Easily disabled in indicator Settings->Style for "Light" charts or with Pine commenting
AND much, much more... You have the source!
The comments section below is solely just for commenting and other remarks, ideas, compliments, etc... regarding only this indicator, not others. When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
Trade Manager/Pnl and Risk-Reward Panel (Plug&Play)Hello traders
The Trade Manager Standalone is finally back and with many more built-in features.
I. 💎 SCRIPTS ACCESS AND TRIALS 💎
1. No TRIAL is available for that script. Available only with one-time payment on my website .
2. My website URL is in this script signature at the very bottom (you'll have to scroll down a bit and going past the long description) and in my profile status available here : Daveatt
Due to the new scripts publishing house rules, I won't mention the URL here directly. As I value my partnership with TradingView very much, I prefer showing you the way for finding them :)
3. Many video tutorials explaining clearly how all our indicators work are available on your website > guides section.
4. You may also contact me directly for more information
II. 🔎 What is a Trade Manager?🔎
2.1 Concept
Standalone Trade Manager compatible with any indicator.
Once connected, whenever you'll update your Algorithm Builders or your indicator, the Trade Manager Stop-loss, take-profit levels, and analytics get updated automatically. #bold #statement #but #actually #true
2.2 How hard is it to update your indicator?
We'll send to our customers, a comprehensive and easy tutorial, to make any indicator compatible.
I guarantee you, it should take no more than 2 minutes per indicator. We made it easy, fun, and awesome. #bolder #statement
III. The amazing benefits of our🔌&🕹️ (Plug&Play) system
Hope you're ready to be impressed. Because, what I'm about to introduce, is my best-seller feature - and available across many of my indicators.
In TradingView, there is a feature called "Indicator on Indicator" meaning you can use an external indicator as a data source for another indicator.
I'm using that feature to connect any external indicator to our Trade Manager (Plug & Play) - hence the plug and play name. Please don't make it a plug and pray :) it's supposed to help you out, not to stress you even more
Let's assume you want to connect your RSI divergence to your Trade Manager.
I mentioned an RSI divergence but you may connect any oscillator (MACD, On balance volume, stochastic RSI, True Strenght index, and many more..) or non-oscillator (divergence, trendline break, higher highs/lower lows, candlesticks pattern, price action, harmonic patterns, ...) indicators.
THE SKY IS (or more likely your imagination) is the limit :)
Fear no more. The Plug&Play technology allows you to connect it and use it the backtest calculations.
This is not magic ✨, neither is sorcery, but certainly is way beyond the most awesome thing I've ever developed on TradingView (even across all brokers I know). #bold #statement #level #9000
TradingView is the best trading platform by far and I'm very grateful to offer my indicators on their website.
To connect your external indicator to ours, we're using a native TradingView feature, which is not available for all users.
It depends on your TradingView subscription plan ( More info here )
If you intend to use our Algorithm Plug&Play indicator, and/or our Backtest Plug&Play suites, then you must upgrade your TradingView account to enjoy those features.
We value our relationship with our customers seriously, and that's why we're warning you that a compatible TradingView account type is required - at least PRO+ or PREMIUM to add more than 1 Plug&Play indicator per account.
We go in-depth on our website why the Plug&Play is an untapped opportunity for many traders out there - URL available on my profile status and signature
IV. 🧰 Features 🧰
4.1 Stop-Loss Management
For what's following, let's assume that 2 is the stop-loss value you inserted in the indicator, and the Algorithm Builder gives a BUY signal.
This is NOT a recommendation at all, only an example to explain how this feature works.
- %Trailing: The Stop-Loss starts 2% away from the entry price - and will move up (because we're on a BUY trade as per our example) every time your trade will gain 2% profit
- Percentage: The Stop-Loss stays static 2% away from the entry price. There is no trailing here
- TP Trailing: This is a very awesome feature. The stop-loss is set 2% away when the trades start.
When the TP1 is hit, the stop-loss will be moved to the Entry price (also called breakeven).
When the TP2 is hit, the SL is moved to the previous TP1 position
- Fixed: Set the Stop-Loss at a fixed position (value should be in currency/units)
4.2 Take Profits Management
You can manage up to 2 take profit levels defined as a percentage or price value.
The expected input is in percentage value (for instance, setting the % target of TP1 to 2% will set the TP1 level 2% away from the entry price
4.3 Built-in Trade Manager
This is very likely the most loved utility script that we shared on TradingView.
It's included in your Algorithm Builder - Single Trend+, and will certainly help you immensely to analyze your charts and your trades.
We made sure that all the graphical elements on the chart will be updated in real-time whenever our user change anything on the indicator configuration.
You'll also be able to change the Trade Manager labels positions as you wish :)
4.4 Built-in Risk-to-Reward Panel
The good stuff doesn't stop here.
You'll notice that this sometimes green (when in a LONG), sometimes red (when in a SHORT) panel at the right of your chart.
It displays for the selected trading algorithmic (see 2.3.2 above), a ton of useful real-time analytics.
- Entry Price: the price when the Algorithm Builder will give a signal.
- The Trade PnL in percentage.
- Entry Stop Loss: Distance (in currency/units) between the selected stop-loss algorithm (percent, trailing, TP trailing, etc.) and the entry price.
- Entry TP1: Distance (in currency/units) between the entry price and the first take profit
- Entry TP2: Distance (in currency/units) between the entry price and the second take profit
- Risk/Reward TP1: Using the Stop-loss distance at entry, and Take Profit 1 at entry to compute the risk-to-reward ratio.
- Risk/Reward TP2: Using the Stop-loss distance at entry, and Take Profit 2 at entry to compute the risk-to-reward ratio.
For more details, please check the guides section of my website. Links are in my signature and profile status.
4.5 Built-in PnL real-time calculations
YES!!!! you read it correctly
The panel displays the risk-to-reward ratios but also the PnL (Profit and Loss in percentage value) of the current and last trade
V. 🔔 Alerts 🔔
We enabled the alerts on the:
1. Stop-Loss
2. Take Profit 1
3. Take Profit 2
VI. 🤖 Compatible with trading bots? 🤖
I'm very aware of all existing solutions out there allowing us to capture the TradingView alerts (Instabot, ProfitView, ...) and forwarding them to the brokers to automatize your trading.
You'll find a more detailed answer on our website.
If you have any doubt or question, please hit me up directly or ask in the comments section of this script.
I'll never claim I have the best trading methodology or the best indicators.
You only will judge and I'll appreciate all the questions and feedback you're sending my way.
They help me a ton to develop indicators based on all the requests I received.
Kind regards,
Dave
SMC - OB/Breaker Block/Bos/ChoCh (DeadCat) Based on analyzing your Pine Script code, here are comprehensive descriptions that should comply with TradingView's house rules:
Script 1: "PO3 Liquidity w/ CISD (DeadCat)"
Description:
This indicator implements the Power of Three (PO3) liquidity concept combined with Change in State of Delivery (CISD) pattern recognition for Smart Money Concepts (SMC) trading. The script operates on multi-timeframe analysis using automated timeframe selection.
Core Methodology: The indicator identifies C2 liquidity sweeps by detecting when price breaks previous period highs/lows and then reverses back above/below those levels. It specifically looks for:
C2 Buy Setup: When current low breaks previous period low but closes back above it
C2 Sell Setup: When current high breaks previous period high but closes back below it
CISD Pattern Detection: The script implements sophisticated CISD (Change in State of Delivery) pattern recognition by:
Tracking the first break of previous HTF high/low levels
Identifying imbalance candles (gaps between consecutive candles)
Confirming CISD when price reclaims the imbalance level within 2 HTF periods
Validating setups only when both liquidity sweep AND CISD confirmation occur
Visual Components:
HTF Candles: Displays higher timeframe candle structure on current chart
Trading Zones: Shows zones between HTF open and equilibrium levels
CISD Lines: Marks confirmed change in state of delivery levels
C2/C4 Labels: Identifies liquidity sweep entry points and potential continuation setups
Market Structure: Optional HH/HL/LH/LL pivot markers
Unique Features:
Automatic timeframe calculation (15m→4H, 1H→1D, etc.)
Real-time HTF period countdown
Setup invalidation tracking when stops are hit
Progressive setup confirmation (C2→C4 evolution)
Bias filter for directional trading preferences
Usage: C2 setups provide initial entry opportunities after confirmed liquidity sweeps with CISD confirmation. C4 setups offer additional entries when HTF equilibrium conditions align favorably. The indicator helps traders identify institutional liquidity grabs followed by genuine directional moves.
Script 2: "SMC Toolkit (DeadCat)"
Description:
This comprehensive Smart Money Concepts toolkit provides institutional-level market structure analysis with automated Order Block (OB) and Breaker Block (BB) zone identification, plus Break of Structure (BOS) and Change of Character (ChoCh) detection.
Market Structure Algorithm: The indicator uses a sophisticated pivot-based algorithm to identify and track market structure progression:
Uptrend: HH→HL→HH sequence tracking
Downtrend: LL→LH→LL sequence tracking
Trend Changes: Automatic ChoCh detection when structure breaks occur
Order Block Logic:
Bullish OB Zones: Created at Higher Lows (HL) and Lower Lows (LL) during uptrends
Bearish OB Zones: Created at Lower Highs (LH) and Higher Highs (HH) during downtrends
Uses last bearish candle before bullish moves (and vice versa) to define precise zone boundaries
Breaker Block Logic:
Bullish BB Zones: Former resistance that becomes support after HH/LH breaks
Bearish BB Zones: Former support that becomes resistance after LL/HL breaks
Automatically transitions when structure points are breached
Zone Management: The script employs intelligent zone lifecycle management:
Creates new zones only at confirmed structure points
Makes previous zones transparent when new structure is confirmed
Maintains zone relevance through dynamic extension
Limits total zones to prevent chart clutter
BOS vs ChoCh Detection:
BOS (Break of Structure): Continuation patterns when trend highs/lows are exceeded
ChoCh (Change of Character): Reversal patterns when pullback levels are broken against trend
Requires 2-candle confirmation before finalizing structure changes
Visual Enhancements:
Color-coded zones with transparency controls
Directional arrows (▲/▼) in zone labels
Customizable line styles and text sizing
Clean market structure progression tracking
Originality: This toolkit combines traditional SMC concepts with enhanced zone boundary calculation using multi-candle analysis and intelligent zone lifecycle management, providing more precise entry/exit levels than standard implementations.
Volume Profile Grid [Alpha Extract]A sophisticated volume distribution analysis system that transforms market activity into institutional-grade visual profiles, revealing hidden support/resistance zones and market participant behavior. Utilizing advanced price level segmentation, bullish/bearish volume separation, and dynamic range analysis, the Volume Profile Grid delivers comprehensive market structure insights with Point of Control (POC) identification, Value Area boundaries, and volume delta analysis. The system features intelligent visualization modes, real-time sentiment analysis, and flexible range selection to provide traders with clear, actionable volume-based market context.
🔶 Dynamic Range Analysis Engine
Implements dual-mode range selection with visible chart analysis and fixed period lookback, automatically adjusting to current market view or analyzing specified historical periods. The system intelligently calculates optimal bar counts while maintaining performance through configurable maximum limits, ensuring responsive profile generation across all timeframes with institutional-grade precision.
// Dynamic period calculation with intelligent caching
get_analysis_period() =>
if i_use_visible_range
chart_start_time = chart.left_visible_bar_time
current_time = last_bar_time
time_span = current_time - chart_start_time
tf_seconds = timeframe.in_seconds()
estimated_bars = time_span / (tf_seconds * 1000)
range_bars = math.floor(estimated_bars)
final_bars = math.min(range_bars, i_max_visible_bars)
math.max(final_bars, 50) // Minimum threshold
else
math.max(i_periods, 50)
🔶 Advanced Bull/Bear Volume Separation
Employs sophisticated candle classification algorithms to separate bullish and bearish volume at each price level, with weighted distribution based on bar intersection ratios. The system analyzes open/close relationships to determine volume direction, applying proportional allocation for doji patterns and ensuring accurate representation of buying versus selling pressure across the entire price spectrum.
🔶 Multi-Mode Volume Visualization
Features three distinct display modes for bull/bear volume representation: Split mode creates mirrored profiles from a central axis, Side by Side mode displays sequential bull/bear segments, and Stacked mode separates volumes vertically. Each mode offers unique insights into market participant behavior with customizable width, thickness, and color parameters for optimal visual clarity.
// Bull/Bear volume calculation with weighted distribution
for bar_offset = 0 to actual_periods - 1
bar_high = high
bar_low = low
bar_volume = volume
// Calculate intersection weight
weight = math.min(bar_high, next_level) - math.max(bar_low, current_level)
weight := weight / (bar_high - bar_low)
weighted_volume = bar_volume * weight
// Classify volume direction
if bar_close > bar_open
level_bull_volume += weighted_volume
else if bar_close < bar_open
level_bear_volume += weighted_volume
else // Doji handling
level_bull_volume += weighted_volume * 0.5
level_bear_volume += weighted_volume * 0.5
🔶 Point of Control & Value Area Detection
Implements institutional-standard POC identification by locating the price level with maximum volume accumulation, providing critical support/resistance zones. The Value Area calculation uses sophisticated sorting algorithms to identify the price range containing 70% of trading volume, revealing the market's accepted value zone where institutional participants concentrate their activity.
🔶 Volume Delta Analysis System
Incorporates real-time volume delta calculation with configurable dominance thresholds to identify significant bull/bear imbalances. The system visually highlights price levels where buying or selling pressure exceeds threshold percentages, providing immediate insight into directional volume flow and potential reversal zones through color-coded delta indicators.
// Value Area calculation using 70% volume accumulation
total_volume_sum = array.sum(total_volumes)
target_volume = total_volume_sum * 0.70
// Sort volumes to find highest activity zones
for i = 0 to array.size(sorted_volumes) - 2
for j = i + 1 to array.size(sorted_volumes) - 1
if array.get(sorted_volumes, j) > array.get(sorted_volumes, i)
// Swap and track indices for value area boundaries
// Accumulate until 70% threshold reached
for i = 0 to array.size(sorted_indices) - 1
accumulated_volume += vol
array.push(va_levels, array.get(volume_levels, idx))
if accumulated_volume >= target_volume
break
❓How It Works
🔶 Weighted Volume Distribution
Implements proportional volume allocation based on the percentage of each bar that intersects with price levels. When a bar spans multiple levels, volume is distributed proportionally based on the intersection ratio, ensuring precise representation of trading activity across the entire price spectrum without double-counting or volume loss.
🔶 Real-Time Profile Generation
Profiles regenerate on each bar close when in visible range mode, automatically adapting to chart zoom and scroll actions. The system maintains optimal performance through intelligent caching mechanisms and selective line updates, ensuring smooth operation even with maximum resolution settings and extended analysis periods.
🔶 Market Sentiment Analysis
Features comprehensive volume analysis table displaying total volume metrics, bullish/bearish percentages, and overall market sentiment classification. The system calculates volume dominance ratios in real-time, providing immediate insight into whether buyers or sellers control the current price structure with percentage-based sentiment thresholds.
🔶 Visual Profile Mapping
Provides multi-layered visual feedback through colored volume bars, POC line highlighting, Value Area boundaries, and optional delta indicators. The system supports profile mirroring for alternative perspectives, line extension for future reference, and customizable label positioning with detailed price information at critical levels.
Why Choose Volume Profile Grid
The Volume Profile Grid represents the evolution of volume analysis tools, combining traditional volume profile concepts with modern visualization techniques and intelligent analysis algorithms. By integrating dynamic range selection, sophisticated bull/bear separation, and multi-mode visualization with POC/Value Area detection, it provides traders with institutional-quality market structure analysis that adapts to any trading style. The comprehensive delta analysis and sentiment monitoring system eliminates guesswork while the flexible visualization options ensure optimal clarity across all market conditions, making it an essential tool for traders seeking to understand true market dynamics through volume-based price discovery.
52SIGNAL RECIPE AMA Momentum Vector═══52SIGNAL RECIPE AMA Momentum Vector═══
◆ Overview
52SIGNAL RECIPE AMA Momentum Vector is an advanced technical indicator based on Adaptive Moving Average (AMA), integrating volume filtering and gradient zone visualization to provide comprehensive analysis of price trends and momentum.
It automatically adjusts to market conditions by calculating efficiency ratios, reducing noise while clearly capturing significant trends. The volume confirmation system helps traders identify high-probability entry and exit points with precision.
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◆ Key Features
• Adaptive Moving Average: Smart moving average that automatically adjusts based on market conditions
• Volume Filter Integration: Double-confirmation of important price movements through volume analysis
• Momentum Gradient Zones: Intuitive visualization of trend strength through color gradation
• Signal Confirmation System: Generation of high-reliability buy/sell signals by combining multiple factors
• Trend Direction Identification: Clear color distinction between bullish and bearish market conditions
• Automatic Adaptation: Intelligent design that self-adjusts to various market situations
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◆ Technical Foundation
■ AMA Calculation Principles
• Efficiency Ratio (ER): Measures how efficiently price moves in one direction
• Dynamic Smoothing Coefficient: Automatically adjusts faster or slower based on market conditions
• Adaptive Algorithm: Less sensitive during sideways markets, more responsive during trending markets
• Noise Reduction Function: Filters out meaningless price movements while capturing important signals
■ Momentum Vector Implementation
• Trend-Price Distance Calculation: Measures trend strength by the distance between AMA and current price
• Color Gradation: Visual system where color intensity changes proportionally to trend strength
• ATR-Based Adjustment: Automatically adjusts gradient zone width according to market volatility
• Directional Color Distinction: Intuitive display with blue/cyan for uptrends and red for downtrends
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◆ Practical Applications
■ Price Trend Interpretation
• Trend Direction Assessment:
▶ Price above AMA with blue gradation indicates ongoing bullish momentum
▶ Price below AMA with red gradation indicates ongoing bearish momentum
• Momentum Strength Verification:
▶ Deeper gradient colors mean stronger momentum and healthier trends
▶ Lighter gradient colors suggest weakening momentum and potential reversal
■ Trading Strategy Utilization
• Trend Following Strategy:
▶ Buy signal when price crosses above AMA with increased volume
▶ Sell signal when price crosses below AMA with increased volume
• Momentum Confirmation Trading:
▶ Deep gradation increases confidence in trend continuation for entry decisions
▶ Multiple consecutive candles staying on one side of AMA increases trend reliability
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◆ Advanced Configuration Options
■ Input Parameter Guide
• Fast Period (Default: 2)
▶ 1-2: Responds very quickly to price changes. Suitable for short-term trading.
▶ 3-5: Moderate response that reduces frequent signals.
▶ 6-10: Slower response but captures only more definitive trends.
• Slow Period (Default: 30)
▶ 20-25: AMA moves faster. Good for shorter timeframe trading.
▶ 26-35: Balanced speed suitable for most market conditions.
▶ 36-50: AMA moves slowly, smoothly following long-term trends.
• Efficiency Ratio Period (Default: 10)
▶ 5-8: Focuses more on recent price movements. Responds quickly to changes.
▶ 9-12: Balanced period suitable for most situations.
▶ 13-20: Considers longer-term price movements, ignoring temporary fluctuations.
• Volume Average Period (Default: 20)
▶ 10-15: Compares with the average volume of the last 10-15 days. More sensitive to changes.
▶ 16-25: Compares with the average volume of approximately the last month. Balanced setting.
▶ 26-50: Compares with long-term average volume, capturing only truly significant volume changes.
• Volume Threshold Multiplier (Default: 1.2)
▶ 1.0-1.1: Recognizes volume just 10% above average as valid.
▶ 1.2-1.5: Requires volume 20-50% higher than average (e.g., 1.2 means 120% of average).
▶ 1.6-2.0: Recognizes only very high volume at least 1.6 times (160%) above average.
■ Timeframe-Specific Recommended Settings
• Short Timeframes (5min-1hr):
Fast Period 2, Slow Period 20, Efficiency Ratio Period 8
→ Responds quickly to price changes, suitable for day trading.
• Medium Timeframes (4hr-daily):
Fast Period 2, Slow Period 30, Efficiency Ratio Period 10
→ Most balanced setting for general swing trading.
• Long Timeframes (daily-weekly):
Fast Period 2, Slow Period 40, Efficiency Ratio Period 14
→ Optimized for smoothly tracking longer trends.
■ Market-Specific Recommended Settings
• Stock Market:
Volume Threshold 1.2, Volume Average Period 20
→ Signal is valid when volume is 20% above average.
• Forex Market:
Volume Threshold 1.5, Efficiency Ratio Period 12
→ Forex requires higher volume to be meaningful and slightly longer efficiency measurement.
• Cryptocurrency Market:
Volume Threshold 1.3, Fast Period 2, Slow Period 25
→ Settings optimized for highly volatile cryptocurrencies.
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◆ Synergy with Other Indicators
• Moving Averages: Trend reliability increases when AMA and key moving averages point in the same direction
• RSI/Stochastic: Powerful reversal signals when AMA crossovers occur in overbought/oversold zones
• MACD: Signal probability greatly increases when MACD histogram direction changes coincide with AMA crossovers
• Bollinger Bands: Trend strength can be determined by AMA's position within Bollinger Bands
• Support/Resistance Levels: Success probability dramatically increases when AMA breakouts occur at key price levels
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◆ Conclusion
AMA Momentum Vector provides accurate price trend analysis by combining the advanced features of adaptive moving averages with momentum visualization technology.
It perfectly adapts to constantly changing market environments through its self-adjusting algorithm and generates highly reliable trading signals through its volume confirmation system.
Users can optimize the indicator for their trading style and market conditions with simple parameter adjustments, enabling effective trading decisions that comprehensively consider price direction, momentum strength, and volume confirmation.
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※ Disclaimer: Past performance does not guarantee future results. Always use appropriate risk management strategies.
═══52SIGNAL RECIPE AMA Momentum Vector═══
◆ 개요
52SIGNAL RECIPE AMA Momentum Vector는 적응형 이동평균(AMA)을 기반으로 한 고급 기술적 지표로, 볼륨 필터링과 그라데이션 존 시각화를 통합하여 가격 추세와 모멘텀을 종합적으로 분석합니다.
시장 효율성 비율을 자동으로 계산하여 시장 상황에 맞게 스스로 조정되며, 노이즈는 줄이고 중요한 추세는 선명하게 포착합니다. 또한 볼륨 확인 시스템을 통해 높은 확률의 매매 시점을 정확하게 식별할 수 있도록 도와줍니다.
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◆ 주요 특징
• 적응형 이동평균: 시장 상황에 따라 자동으로 조정되는 스마트한 이동평균선
• 볼륨 필터 통합: 중요한 가격 움직임을 볼륨으로 한번 더 확인
• 모멘텀 그라데이션 존: 색상 그라데이션으로 추세의 강도를 직관적으로 시각화
• 신호 확인 시스템: 여러 요소를 종합하여 신뢰도 높은 매수/매도 신호 생성
• 추세 방향 식별: 상승세와 하락세를 색상으로 명확하게 구분
• 자동 적응 기능: 다양한 시장 상황에 알아서 맞춰지는 지능형 설계
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◆ 기술적 기반
■ AMA 계산 원리
• 효율성 비율 (ER): 가격이 얼마나 효율적으로 한 방향으로 움직이는지 측정
• 동적 평활화 계수: 시장 상황에 따라 빠르거나 느리게 자동 조절되는 계수
• 적응형 알고리즘: 횡보장에서는 둔감하게, 추세장에서는 민감하게 반응
• 노이즈 감소 기능: 무의미한 가격 움직임은 걸러내고 중요한 신호만 포착
■ 모멘텀 벡터 구현
• 추세-가격 거리 계산: AMA와 현재 가격 사이의 거리로 추세 강도 측정
• 색상 그라데이션: 추세 강도에 비례하여 색상 농도가 변하는 시각화 시스템
• ATR 기반 조정: 시장 변동성에 맞춰 그라데이션 영역 너비 자동 조절
• 방향성 색상 구분: 상승세는 파란색/청록색, 하락세는 빨간색으로 직관적 표시
─────────────────────────────────────
◆ 실용적 응용
■ 가격 추세 해석
• 추세 방향 판단:
▶ 가격이 AMA 위에 있고 파란색 그라데이션이 보이면 상승 모멘텀 진행 중
▶ 가격이 AMA 아래에 있고 빨간색 그라데이션이 보이면 하락 모멘텀 진행 중
• 모멘텀 강도 확인:
▶ 그라데이션 색상이 진할수록 모멘텀이 강하고 추세가 건강함을 의미
▶ 그라데이션 색상이 옅을수록 모멘텀이 약해지고 있으며 반전 가능성 시사
■ 트레이딩 전략 활용
• 추세 추종 전략:
▶ 가격이 AMA를 상향 돌파하고 볼륨이 증가하면 매수 신호
▶ 가격이 AMA를 하향 돌파하고 볼륨이 증가하면 매도 신호
• 모멘텀 확인 트레이딩:
▶ 진한 그라데이션은 추세 지속 가능성이 높음을 의미하므로 진입 확신 강화
▶ 여러 캔들이 연속해서 AMA 한쪽에 머물면 추세의 신뢰도가 높아짐
─────────────────────────────────────
◆ 고급 설정 옵션
■ 인풋 파라미터 가이드
• 빠른 기간 (Fast Period) (기본값: 2)
▶ 1-2: 가격 변화에 매우 빠르게 반응합니다. 단기 거래에 적합합니다.
▶ 3-5: 적당히 반응하여 잦은 신호를 줄여줍니다.
▶ 6-10: 반응이 느리지만 더 확실한 추세만 포착합니다.
• 느린 기간 (Slow Period) (기본값: 30)
▶ 20-25: AMA가 더 빠르게 움직입니다. 짧은 시간 거래에 좋습니다.
▶ 26-35: 균형 잡힌 속도로 대부분의 시장 상황에 적합합니다.
▶ 36-50: AMA가 천천히 움직여 장기 추세를 부드럽게 따라갑니다.
• 효율성 비율 기간 (Efficiency Ratio Period) (기본값: 10)
▶ 5-8: 최근 가격 움직임에 더 집중합니다. 변화에 빠르게 반응합니다.
▶ 9-12: 균형 잡힌 기간으로 대부분의 상황에 적합합니다.
▶ 13-20: 더 긴 기간의 가격 움직임을 고려하여 일시적인 변동을 무시합니다.
• 볼륨 평균 기간 (Volume Average Period) (기본값: 20)
▶ 10-15: 최근 10-15일의 평균 볼륨과 비교합니다. 변화에 민감합니다.
▶ 16-25: 지난 약 한 달간의 평균 볼륨과 비교합니다. 균형 잡힌 설정입니다.
▶ 26-50: 장기 평균 볼륨과 비교하여 정말 큰 볼륨 변화만 포착합니다.
• 볼륨 임계값 승수 (Volume Threshold Multiplier) (기본값: 1.2)
▶ 1.0-1.1: 평균보다 약 10% 정도만 높아도 유효한 볼륨으로 인정합니다.
▶ 1.2-1.5: 평균보다 20~50% 높은 볼륨을 요구합니다(예: 1.2는 평균의 120%).
▶ 1.6-2.0: 평균의 최소 1.6배(160%) 이상 되는 매우 높은 볼륨만 인정합니다.
■ 타임프레임별 추천 설정
• 짧은 시간 차트 (5분-1시간):
빠른 기간 2, 느린 기간 20, 효율성 비율 기간 8
→ 가격 변화에 빠르게 반응하며 단타에 적합합니다.
• 중기 차트 (4시간-일봉):
빠른 기간 2, 느린 기간 30, 효율성 비율 기간 10
→ 일반적인 스윙 트레이딩에 가장 균형 잡힌 설정입니다.
• 장기 차트 (일봉-주봉):
빠른 기간 2, 느린 기간 40, 효율성 비율 기간 14
→ 더 긴 추세를 매끄럽게 추적하는 데 최적화되었습니다.
■ 시장별 추천 설정
• 주식 시장:
볼륨 임계값 1.2, 볼륨 평균 기간 20
→ 평균보다 20% 많은 볼륨이 있을 때 신호가 유효합니다.
• 외환 시장:
볼륨 임계값 1.5, 효율성 비율 기간 12
→ 외환은 볼륨이 더 높아야 의미가 있으며, 약간 더 긴 효율성 측정이 필요합니다.
• 암호화폐 시장:
볼륨 임계값 1.3, 빠른 기간 2, 느린 기간 25
→ 변동성이 큰 암호화폐에 최적화된 설정입니다.
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◆ 다른 지표와의 시너지
• 이동평균선: AMA와 주요 이동평균선이 같은 방향을 가리킬 때 추세 신뢰도 상승
• RSI/스토캐스틱: 과매수/과매도 구간에서 AMA 교차 발생 시 강력한 반전 신호
• MACD: MACD 히스토그램 방향 변화와 AMA 교차가 일치하면 신호 확률 대폭 증가
• 볼린저 밴드: AMA가 볼린저 밴드 내에서 어떤 위치에 있는지로 추세 강도 판단
• 지지/저항 레벨: 중요 가격대에서 AMA 돌파 시 성공 확률이 크게 증가
─────────────────────────────────────
◆ 결론
AMA Momentum Vector는 적응형 이동평균의 고급 기능과 모멘텀 시각화 기술을 결합하여 정확한 가격 추세 분석을 제공합니다.
자체 조정 알고리즘으로 시시각각 변하는 시장 환경에 완벽하게 적응하며, 볼륨 확인 시스템을 통해 신뢰도 높은 매매 신호를 생성합니다.
사용자는 간단한 파라미터 조정으로 자신의 거래 스타일과 시장 상황에 맞게 지표를 최적화할 수 있어, 가격 방향, 모멘텀 강도, 볼륨 확인을 종합적으로 고려한 효과적인 거래 결정을 내릴 수 있습니다.
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※ 면책 조항: 과거 성과가 미래 결과를 보장하지 않습니다. 항상 적절한 리스크 관리 전략을 사용하세요.
ATRWhat the Indicator Shows:
A compact table with four cells is displayed in the bottom-left corner of the chart:
| ATR | % | Level | Lvl+ATR |
Explanation of the Columns:
ATR — The averaged daily range (volatility) calculated with filtering of abnormal bars (extremely large or small daily candles are ignored).
% — The percentage of the daily ATR that the price has already covered today (the difference between the daily Open and Close relative to ATR).
Level — A custom user-defined level set through the indicator settings.
Lvl+ATR — The sum of the daily ATR and the user-defined level. This can be used, for example, as a target or stop-loss reference.
Color Highlighting of the "%" Cell:
The background color of the "%" ATR cell changes depending on the value:
✅ If the value is less than 10% — the cell is green (market is calm, small movement).
➖ If the value is between 10% and 50% — no highlighting (average movement, no signal).
🟡 If the value is between 50% and 70% — the cell is yellow (movement is increasing, be alert).
🔴 If the value is above 70% — the cell is red (the market is actively moving, high volatility).
Key Features:
✔ All ATR calculations and percentage progress are performed strictly based on daily data, regardless of the chart's current timeframe.
✔ The indicator is ideal for intraday traders who want to monitor daily volatility levels.
✔ The table always displays up-to-date information for quick decision-making.
✔ Filtering of abnormal bars makes ATR more stable and objective.
What is Adaptive ATR in this Indicator:
Instead of the classic ATR, which simply averages the true range, this indicator uses a custom algorithm:
✅ It analyzes daily bars over the past 100 days.
✅ Calculates the range High - Low for each bar.
✅ If the bar's range deviates too much from the average (more than 1.8 times higher or lower), the bar is considered abnormal and ignored.
✅ Only "normal" bars are included in the calculation.
✅ The average range of these normal bars is the adaptive ATR.
Detailed Algorithm of the getAdaptiveATR() Function:
The function takes the number of bars to include in the calculation (for example, 5):
The average of the last 5 normal bars is calculated.
pinescript
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adaptiveATR = getAdaptiveATR(5)
Step-by-Step Process:
An empty array ranges is created to store the ranges.
Daily bars with indices from 1 to 100 are iterated over.
For each bar:
🔹 The daily High and Low with the required offset are loaded via request.security().
🔹 The range High - Low is calculated.
🔹 The temporary average range of the current array is calculated.
🔹 The bar is checked for abnormality (too large or too small).
🔹 If the bar is normal or it's the first bar — its range is added to the array.
Once the array accumulates the required number of bars (count), their average is calculated — this is the adaptive ATR.
If it's not possible to accumulate the required number of bars — na is returned.
Что показывает индикатор:
На графике внизу слева отображается компактная таблица из четырех ячеек:
ATR % Уровень Ур+ATR
Пояснения к столбцам:
ATR — усреднённый дневной диапазон (волатильность), рассчитанный с фильтрацией аномальных баров (слишком большие или маленькие дневные свечи игнорируются).
% — процент дневного ATR, который уже "прошла" цена на текущий день (разница между открытием и закрытием относительно ATR).
Уровень — пользовательский уровень, который задаётся вручную через настройки индикатора.
Ур+ATR — сумма уровня и дневного ATR. Может использоваться, например, как ориентир для целей или стопов.
Цветовая подсветка ячейки "%":
Цвет фона ячейки с процентом ATR меняется в зависимости от значения:
✅ Если значение меньше 10% — ячейка зелёная (рынок пока спокоен, маленькое движение).
➖ Если значение от 10% до 50% — фон не подсвечивается (среднее движение, нет сигнала).
🟡 Если значение от 50% до 70% — ячейка жёлтая (движение усиливается, повышенное внимание).
🔴 Если значение выше 70% — ячейка красная (рынок активно движется, высокая волатильность).
Особенности работы:
✔ Все расчёты ATR и процентного прохождения производятся исключительно по дневным данным, независимо от текущего таймфрейма графика.
✔ Индикатор подходит для трейдеров, которые торгуют внутри дня, но хотят ориентироваться на дневные уровни волатильности.
✔ В таблице всегда отображается актуальная информация для принятия быстрых торговых решений.
✔ Фильтрация аномальных баров делает ATR более устойчивым и объективным.
Что такое адаптивный ATR в этом индикаторе
Вместо классического ATR, который просто усредняет истинный диапазон, здесь используется собственный алгоритм:
✅ Он берет дневные бары за последние 100 дней.
✅ Для каждого из них рассчитывает диапазон High - Low.
✅ Если диапазон бара слишком сильно отличается от среднего (более чем в 1.8 раза больше или меньше), бар считается аномальным и игнорируется.
✅ Только нормальные бары попадают в расчёт.
✅ В итоге считается среднее из диапазонов этих нормальных баров — это и есть адаптивный ATR.
Подробный алгоритм функции getAdaptiveATR()
Функция принимает количество баров для расчёта (например, 5):
Считается 5 последних нормальных баров
pinescript
Копировать
Редактировать
adaptiveATR = getAdaptiveATR(5)
Пошагово:
Создаётся пустой массив ranges для хранения диапазонов.
Перебираются дневные бары с индексами от 1 до 100.
Для каждого бара:
🔹 Через request.security() подгружаются дневные High и Low с нужным смещением.
🔹 Считается диапазон High - Low.
🔹 Считается временное среднее диапазона по текущему массиву.
🔹 Проверяется, не является ли бар аномальным (слишком большой или маленький).
🔹 Если бар нормальный или это самый первый бар — его диапазон добавляется в массив.
Как только массив набирает заданное количество баров (count), берётся их среднее значение — это и есть адаптивный ATR.
Если не удалось набрать нужное количество баров — возвращается na.
Aetherium Institutional Market Resonance EngineAetherium Institutional Market Resonance Engine (AIMRE)
A Three-Pillar Framework for Decoding Institutional Activity
🎓 THEORETICAL FOUNDATION
The Aetherium Institutional Market Resonance Engine (AIMRE) is a multi-faceted analysis system designed to move beyond conventional indicators and decode the market's underlying structure as dictated by institutional capital flow. Its philosophy is built on a singular premise: significant market moves are preceded by a convergence of context , location , and timing . Aetherium quantifies these three dimensions through a revolutionary three-pillar architecture.
This system is not a simple combination of indicators; it is an integrated engine where each pillar's analysis feeds into a central logic core. A signal is only generated when all three pillars achieve a state of resonance, indicating a high-probability alignment between market organization, key liquidity levels, and cyclical momentum.
⚡ THE THREE-PILLAR ARCHITECTURE
1. 🌌 PILLAR I: THE COHERENCE ENGINE (THE 'CONTEXT')
Purpose: To measure the degree of organization within the market. This pillar answers the question: " Is the market acting with a unified purpose, or is it chaotic and random? "
Conceptual Framework: Institutional campaigns (accumulation or distribution) create a non-random, organized market environment. Retail-driven or directionless markets are characterized by "noise" and chaos. The Coherence Engine acts as a filter to ensure we only engage when institutional players are actively steering the market.
Formulaic Concept:
Coherence = f(Dominance, Synchronization)
Dominance Factor: Calculates the absolute difference between smoothed buying pressure (volume-weighted bullish candles) and smoothed selling pressure (volume-weighted bearish candles), normalized by total pressure. A high value signifies a clear winner between buyers and sellers.
Synchronization Factor: Measures the correlation between the streams of buying and selling pressure over the analysis window. A high positive correlation indicates synchronized, directional activity, while a negative correlation suggests choppy, conflicting action.
The final Coherence score (0-100) represents the percentage of market organization. A high score is a prerequisite for any signal, filtering out unpredictable market conditions.
2. 💎 PILLAR II: HARMONIC LIQUIDITY MATRIX (THE 'LOCATION')
Purpose: To identify and map high-impact institutional footprints. This pillar answers the question: " Where have institutions previously committed significant capital? "
Conceptual Framework: Large institutional orders leave indelible marks on the market in the form of anomalous volume spikes at specific price levels. These are not random occurrences but are areas of intense historical interest. The Harmonic Liquidity Matrix finds these footprints and consolidates them into actionable support and resistance zones called "Harmonic Nodes."
Algorithmic Process:
Footprint Identification: The engine scans the historical lookback period for candles where volume > average_volume * Institutional_Volume_Filter. This identifies statistically significant volume events.
Node Creation: A raw node is created at the mean price of the identified candle.
Dynamic Clustering: The engine uses an ATR-based proximity algorithm. If a new footprint is identified within Node_Clustering_Distance (ATR) of an existing Harmonic Node, it is merged. The node's price is volume-weighted, and its magnitude is increased. This prevents chart clutter and consolidates nearby institutional orders into a single, more significant level.
Node Decay: Nodes that are older than the Institutional_Liquidity_Scanback period are automatically removed from the chart, ensuring the analysis remains relevant to recent market dynamics.
3. 🌊 PILLAR III: CYCLICAL RESONANCE MATRIX (THE 'TIMING')
Purpose: To identify the market's dominant rhythm and its current phase. This pillar answers the question: " Is the market's immediate energy flowing up or down? "
Conceptual Framework: Markets move in waves and cycles of varying lengths. Trading in harmony with the current cyclical phase dramatically increases the probability of success. Aetherium employs a simplified wavelet analysis concept to decompose price action into short, medium, and long-term cycles.
Algorithmic Process:
Cycle Decomposition: The engine calculates three oscillators based on the difference between pairs of Exponential Moving Averages (e.g., EMA8-EMA13 for short cycle, EMA21-EMA34 for medium cycle).
Energy Measurement: The 'energy' of each cycle is determined by its recent volatility (standard deviation). The cycle with the highest energy is designated as the "Dominant Cycle."
Phase Analysis: The engine determines if the dominant cycles are in a bullish phase (rising from a trough) or a bearish phase (falling from a peak).
Cycle Sync: The highest conviction timing signals occur when multiple cycles (e.g., short and medium) are synchronized in the same direction, indicating broad-based momentum.
🔧 COMPREHENSIVE INPUT SYSTEM
Pillar I: Market Coherence Engine
Coherence Analysis Window (10-50, Default: 21): The lookback period for the Coherence Engine.
Lower Values (10-15): Highly responsive to rapid shifts in market control. Ideal for scalping but can be sensitive to noise.
Balanced (20-30): Excellent for day trading, capturing the ebb and flow of institutional sessions.
Higher Values (35-50): Smoother, more stable reading. Best for swing trading and identifying long-term institutional campaigns.
Coherence Activation Level (50-90%, Default: 70%): The minimum market organization required to enable signal generation.
Strict (80-90%): Only allows signals in extremely clear, powerful trends. Fewer, but potentially higher quality signals.
Standard (65-75%): A robust filter that effectively removes choppy conditions while capturing most valid institutional moves.
Lenient (50-60%): Allows signals in less-organized markets. Can be useful in ranging markets but may increase false signals.
Pillar II: Harmonic Liquidity Matrix
Institutional Liquidity Scanback (100-400, Default: 200): How far back the engine looks for institutional footprints.
Short (100-150): Focuses on recent institutional activity, providing highly relevant, immediate levels.
Long (300-400): Identifies major, long-term structural levels. These nodes are often extremely powerful but may be less frequent.
Institutional Volume Filter (1.3-3.0, Default: 1.8): The multiplier for detecting a volume spike.
High (2.5-3.0): Only registers climactic, undeniable institutional volume. Fewer, but more significant nodes.
Low (1.3-1.7): More sensitive, identifying smaller but still relevant institutional interest.
Node Clustering Distance (0.2-0.8 ATR, Default: 0.4): The ATR-based distance for merging nearby nodes.
High (0.6-0.8): Creates wider, more consolidated zones of liquidity.
Low (0.2-0.3): Creates more numerous, precise, and distinct levels.
Pillar III: Cyclical Resonance Matrix
Cycle Resonance Analysis (30-100, Default: 50): The lookback for determining cycle energy and dominance.
Short (30-40): Tunes the engine to faster, shorter-term market rhythms. Best for scalping.
Long (70-100): Aligns the timing component with the larger primary trend. Best for swing trading.
Institutional Signal Architecture
Signal Quality Mode (Professional, Elite, Supreme): Controls the strictness of the three-pillar confluence.
Professional: Loosest setting. May generate signals if two of the three pillars are in strong alignment. Increases signal frequency.
Elite: Balanced setting. Requires a clear, unambiguous resonance of all three pillars. The recommended default.
Supreme: Most stringent. Requires perfect alignment of all three pillars, with each pillar exhibiting exceptionally strong readings (e.g., coherence > 85%). The highest conviction signals.
Signal Spacing Control (5-25, Default: 10): The minimum bars between signals to prevent clutter and redundant alerts.
🎨 ADVANCED VISUAL SYSTEM
The visual architecture of Aetherium is designed not merely for aesthetics, but to provide an intuitive, at-a-glance understanding of the complex data being processed.
Harmonic Liquidity Nodes: The core visual element. Displayed as multi-layered, semi-transparent horizontal boxes.
Magnitude Visualization: The height and opacity of a node's "glow" are proportional to its volume magnitude. More significant nodes appear brighter and larger, instantly drawing the eye to key levels.
Color Coding: Standard nodes are blue/purple, while exceptionally high-magnitude nodes are highlighted in an accent color to denote critical importance.
🌌 Quantum Resonance Field: A dynamic background gradient that visualizes the overall market environment.
Color: Shifts from cool blues/purples (low coherence) to energetic greens/cyans (high coherence and organization), providing instant context.
Intensity: The brightness and opacity of the field are influenced by total market energy (a composite of coherence, momentum, and volume), making powerful market states visually apparent.
💎 Crystalline Lattice Matrix: A geometric web of lines projected from a central moving average.
Mathematical Basis: Levels are projected using multiples of the Golden Ratio (Phi ≈ 1.618) and the ATR. This visualizes the natural harmonic and fractal structure of the market. It is not arbitrary but is based on mathematical principles of market geometry.
🧠 Synaptic Flow Network: A dynamic particle system visualizing the engine's "thought process."
Node Density & Activation: The number of particles and their brightness/color are tied directly to the Market Coherence score. In high-coherence states, the network becomes a dense, bright, and organized web. In chaotic states, it becomes sparse and dim.
⚡ Institutional Energy Waves: Flowing sine waves that visualize market volatility and rhythm.
Amplitude & Speed: The height and speed of the waves are directly influenced by the ATR and volume, providing a feel for market energy.
📊 INSTITUTIONAL CONTROL MATRIX (DASHBOARD)
The dashboard is the central command console, providing a real-time, quantitative summary of each pillar's status.
Header: Displays the script title and version.
Coherence Engine Section:
State: Displays a qualitative assessment of market organization: ◉ PHASE LOCK (High Coherence), ◎ ORGANIZING (Moderate Coherence), or ○ CHAOTIC (Low Coherence). Color-coded for immediate recognition.
Power: Shows the precise Coherence percentage and a directional arrow (↗ or ↘) indicating if organization is increasing or decreasing.
Liquidity Matrix Section:
Nodes: Displays the total number of active Harmonic Liquidity Nodes currently being tracked.
Target: Shows the price level of the nearest significant Harmonic Node to the current price, representing the most immediate institutional level of interest.
Cycle Matrix Section:
Cycle: Identifies the currently dominant market cycle (e.g., "MID ") based on cycle energy.
Sync: Indicates the alignment of the cyclical forces: ▲ BULLISH , ▼ BEARISH , or ◆ DIVERGENT . This is the core timing confirmation.
Signal Status Section:
A unified status bar that provides the final verdict of the engine. It will display "QUANTUM SCAN" during neutral periods, or announce the tier and direction of an active signal (e.g., "◉ TIER 1 BUY ◉" ), highlighted with the appropriate color.
🎯 SIGNAL GENERATION LOGIC
Aetherium's signal logic is built on the principle of strict, non-negotiable confluence.
Condition 1: Context (Coherence Filter): The Market Coherence must be above the Coherence Activation Level. No signals can be generated in a chaotic market.
Condition 2: Location (Liquidity Node Interaction): Price must be actively interacting with a significant Harmonic Liquidity Node.
For a Buy Signal: Price must be rejecting the Node from below (testing it as support).
For a Sell Signal: Price must be rejecting the Node from above (testing it as resistance).
Condition 3: Timing (Cycle Alignment): The Cyclical Resonance Matrix must confirm that the dominant cycles are synchronized with the intended trade direction.
Signal Tiering: The Signal Quality Mode input determines how strictly these three conditions must be met. 'Supreme' mode, for example, might require not only that the conditions are met, but that the Market Coherence is exceptionally high and the interaction with the Node is accompanied by a significant volume spike.
Signal Spacing: A final filter ensures that signals are spaced by a minimum number of bars, preventing over-alerting in a single move.
🚀 ADVANCED TRADING STRATEGIES
The Primary Confluence Strategy: The intended use of the system. Wait for a Tier 1 (Elite/Supreme) or Tier 2 (Professional/Elite) signal to appear on the chart. This represents the alignment of all three pillars. Enter after the signal bar closes, with a stop-loss placed logically on the other side of the Harmonic Node that triggered the signal.
The Coherence Context Strategy: Use the Coherence Engine as a standalone market filter. When Coherence is high (>70%), favor trend-following strategies. When Coherence is low (<50%), avoid new directional trades or favor range-bound strategies. A sharp drop in Coherence during a trend can be an early warning of a trend's exhaustion.
Node-to-Node Trading: In a high-coherence environment, use the Harmonic Liquidity Nodes as both entry points and profit targets. For example, after a BUY signal is generated at one Node, the next Node above it becomes a logical first profit target.
⚖️ RESPONSIBLE USAGE AND LIMITATIONS
Decision Support, Not a Crystal Ball: Aetherium is an advanced decision-support tool. It is designed to identify high-probability conditions based on a model of institutional behavior. It does not predict the future.
Risk Management is Paramount: No indicator can replace a sound risk management plan. Always use appropriate position sizing and stop-losses. The signals provided are probabilistic, not certainties.
Past Performance Disclaimer: The market models used in this script are based on historical data. While robust, there is no guarantee that these patterns will persist in the future. Market conditions can and do change.
Not a "Set and Forget" System: The indicator performs best when its user understands the concepts behind the three pillars. Use the dashboard and visual cues to build a comprehensive view of the market before acting on a signal.
Backtesting is Essential: Before applying this tool to live trading, it is crucial to backtest and forward-test it on your preferred instruments and timeframes to understand its unique behavior and characteristics.
🔮 CONCLUSION
The Aetherium Institutional Market Resonance Engine represents a paradigm shift from single-variable analysis to a holistic, multi-pillar framework. By quantifying the abstract concepts of market context, location, and timing into a unified, logical system, it provides traders with an unprecedented lens into the mechanics of institutional market operations.
It is not merely an indicator, but a complete analytical engine designed to foster a deeper understanding of market dynamics. By focusing on the core principles of institutional order flow, Aetherium empowers traders to filter out market noise, identify key structural levels, and time their entries in harmony with the market's underlying rhythm.
"In all chaos there is a cosmos, in all disorder a secret order." - Carl Jung
— Dskyz, Trade with insight. Trade with confluence. Trade with Aetherium.
PRICE MOVEMENT STATISTICS# Price Movement Statistics - Advanced Pattern Recognition System
## Foundation
Price Movement Statistics (PMS) represents a fundamentally different approach to market analysis compared to traditional indicators like RSI, Moving Averages, or Bollinger Bands. While most indicators rely on mathematical transformations of price data, PMS implements a **machine learning-inspired nearest-neighbor algorithm** that compares current market conditions against thousands of historical patterns across multiple correlated instruments.
### What Makes This Original
Unlike standard indicators that follow predetermined formulas, PMS:
1. **Multi-Symbol Pattern Database**: Analyzes up to 4 different but correlated symbols simultaneously, creating a massive historical pattern database that single-symbol indicators cannot access
2. **8-Feature Normalized Vector Comparison**: Converts each candlestick into 8 numerical features (body-to-range ratios, wick proportions, relative positioning, momentum characteristics) and uses Manhattan distance calculations to find statistically similar historical situations
3. **Forward-Looking Statistical Validation**: Instead of just identifying patterns, PMS tracks what actually happened 1-5 bars after similar patterns occurred historically, providing probabilistic forecasts with sample sizes and confidence levels
4. **Adaptive Similarity Scoring**: Uses real-time distance calculations between current conditions and historical patterns, allowing traders to see exactly how many similar cases existed and their outcomes
## Technical Methodology Explained
### Pattern Recognition Engine
The core algorithm transforms each market condition into a normalized 8-dimensional vector containing:
- Short vs. long-term range ratios computed using proprietary envelope calculations
- Price position relative to recent ranges using adaptive scaling methods
- Volatility comparisons across multiple timeframes with logarithmic return analysis
- Momentum divergences between short and long-term linear regression slopes
- Volume behavior patterns using statistical deviation scoring
- Candlestick structure metrics including ATR ratios and boundary touch frequencies
### Advanced Code Architecture
**Multi-Symbol Data Pipeline**: The system employs Pine Script's `request.security()` function in a sophisticated loop structure that simultaneously processes up to 4 different instruments. Each symbol contributes its own 8-feature vector, creating a 32-dimensional search space that dramatically expands pattern recognition capabilities beyond single-symbol analysis.
**Adaptive Normalization Engine**: Rather than using simple percentage changes, the code implements a custom `scale_adaptive()` function that ranks current values against rolling historical distributions. This percentile-based approach ensures pattern recognition remains consistent across different market volatility regimes and price levels.
**Distance Matrix Calculations**: The matching algorithm runs nested loops through thousands of historical bars, computing Manhattan distances for each potential match. The code optimizes performance by using vectorized operations and early termination conditions when similarity thresholds aren't met.
**Forward-Looking Analysis Pipeline**: Once matches are identified, the system implements a sophisticated outcome tracking mechanism that categorizes future price movements, volume behaviors, and candle characteristics. This requires careful index management to avoid look-ahead bias while maintaining real-time calculation efficiency.
### Similarity Matching Process
1. **Data Normalization**: Features are processed through custom percentile ranking against 500-bar rolling windows
2. **Distance Calculation**: Optimized Manhattan distance computation across 8-dimensional vectors with early exit conditions
3. **Multi-Symbol Aggregation**: Matches from different symbols are weighted and combined using statistical averaging techniques
4. **Threshold Filtering**: Dynamic similarity boundaries that adapt to market volatility conditions
5. **Outcome Analysis**: Forward-looking statistical compilation with bias tracking and magnitude calculations
### Statistical Output Generation
The system's proprietary aggregation engine provides:
- **Win/Loss Ratios**: Calculated from actual forward-price movements with statistical weighting
- **Sample Sizes**: Match counts across all symbols with confidence scoring algorithms
- **Average Magnitude**: Expected move calculations using historical outcome distributions
- **Volume Context**: Pattern-specific volume analysis using normalized scoring methods
- **Directional Bias**: Multi-timeframe probability calculations with cross-symbol validation
## Why This Approach is Worth the Investment
### Beyond Traditional Indicators
Standard indicators like RSI or MACD give you oversold/overbought signals or momentum divergences, but they don't answer the crucial question: "What happened historically when similar conditions occurred?" PMS bridges this gap by providing:
1. **Quantified Probabilities**: Instead of subjective pattern recognition, you get actual win rates and sample sizes
2. **Cross-Market Validation**: Patterns confirmed across multiple correlated instruments carry more statistical weight
3. **Sample Size Transparency**: You can see whether a signal is based on 5 occurrences or 500, adjusting confidence accordingly
4. **Magnitude Expectations**: Historical data shows not just direction, but expected move sizes
### Practical Trading Applications
**Entry Timing**: When PMS shows >70% historical win rate with 100+ matches, you have statistical evidence supporting your entry rather than relying on visual pattern interpretation.
**Risk Management**: Historical magnitude data helps size positions appropriately based on expected adverse moves in similar past situations.
**Confirmation**: Multi-symbol analysis provides cross-market confirmation that single-symbol indicators cannot offer.
## How to Use the System
### Signal Interpretation
- **Bias Ratio >1.5**: Historically bullish (more winning long trades than losing ones)
- **Bias Ratio <0.67**: Historically bearish (more winning short trades than losing ones)
- **Sample Size >50**: High confidence (sufficient historical data)
- **Sample Size <20**: Low confidence (limited historical precedent)
### Setup Optimization
- **Symbol Selection**: Choose 3-4 correlated instruments (e.g., stock + sector ETF + index, or currency pairs with base currency relationships)
- **Timeframe Coordination**: Use higher timeframes for broader context, lower timeframes for precise entry timing
- **Threshold Adjustment**: Lower similarity thresholds find more specific matches; higher thresholds increase sample sizes
## Technical Requirements and Limitations
**Data Depth**: Requires minimum 1000 bars per symbol for meaningful analysis; 3000+ bars recommended for optimal performance.
**Computational Load**: Real-time pattern matching across multiple symbols and thousands of historical bars requires TradingView's advanced Pine Script capabilities.
**Market Applicability**: Most effective in liquid markets with sufficient historical data; less reliable in newly listed instruments or during unprecedented market conditions.
## Important Disclaimers
This system identifies historical statistical patterns under similar conditions—it does not predict future movements with certainty. Effectiveness depends on intelligent symbol selection, appropriate timeframe usage, and integration with proper risk management. Past performance patterns do not guarantee future results, and all trading involves substantial risk of loss.
The algorithm's sophistication lies not in complex mathematical formulas, but in its ability to efficiently search through massive historical datasets and quantify pattern outcomes—something impossible to do manually and unavailable in standard technical indicators.
Institutional Volume Profile# Institutional Volume Profile (IVP) - Advanced Volume Analysis Indicator
## Overview
The Institutional Volume Profile (IVP) is a sophisticated technical analysis tool that combines traditional volume profile analysis with institutional volume detection algorithms. This indicator helps traders identify key price levels where significant institutional activity has occurred, providing insights into market structure and potential support/resistance zones.
## Key Features
### 🎯 Volume Profile Analysis
- **Point of Control (POC)**: Identifies the price level with the highest volume activity
- **Value Area**: Highlights the price range containing a specified percentage (default 70%) of total volume
- **Multi-Row Distribution**: Displays volume distribution across 10-50 price levels for detailed analysis
- **Customizable Period**: Analyze volume profiles over 10-500 bars
### 🏛️ Institutional Volume Detection
- **Pocket Pivot Volume (PPV)**: Detects bullish institutional buying when up-volume exceeds recent down-volume peaks
- **Pivot Negative Volume (PNV)**: Identifies bearish institutional selling when down-volume exceeds recent up-volume peaks
- **Accumulation Detection**: Spots potential accumulation phases with high volume and narrow price ranges
- **Distribution Analysis**: Identifies distribution patterns with high volume but minimal price movement
### 🎨 Visual Customization Options
- **Multiple Color Schemes**: Heat Map, Institutional, Monochrome, and Rainbow themes
- **Bar Styles**: Solid, Gradient, Outlined, and 3D Effect rendering
- **Volume Intensity Display**: Visual intensity based on volume magnitude
- **Flexible Positioning**: Left or right side profile placement
- **Current Price Highlighting**: Real-time price level indication
### 📊 Advanced Visual Features
- **Volume Labels**: Display volume amounts at key price levels
- **Gradient Effects**: Multi-step gradient rendering for enhanced visibility
- **3D Styling**: Shadow effects for professional appearance
- **Opacity Control**: Adjustable transparency (10-100%)
- **Border Customization**: Configurable border width and styling
## How It Works
### Volume Distribution Algorithm
The indicator analyzes each bar within the specified period and distributes its volume proportionally across the price levels it touches. This creates an accurate representation of where trading activity has been concentrated.
### Institutional Detection Logic
- **PPV Trigger**: Current up-bar volume > highest down-volume in lookback period + above volume MA
- **PNV Trigger**: Current down-bar volume > highest up-volume in lookback period + above volume MA
- **Accumulation**: High volume + narrow range + bullish close
- **Distribution**: Very high volume + minimal price movement
### Value Area Calculation
Starting from the POC, the algorithm expands both upward and downward, adding volume until reaching the specified percentage of total volume (default 70%).
## Configuration Parameters
### Profile Settings
- **Profile Period**: 10-500 bars (default: 50)
- **Number of Rows**: 10-50 levels (default: 24)
- **Profile Width**: 10-100% of screen (default: 30%)
- **Value Area %**: 50-90% (default: 70%)
### Institutional Analysis
- **PPV Lookback Days**: 5-20 periods (default: 10)
- **Volume MA Length**: 10-200 periods (default: 50)
- **Institutional Threshold**: 1.0-2.0x multiplier (default: 1.2)
### Visual Controls
- **Bar Style**: Solid, Gradient, Outlined, 3D Effect
- **Color Scheme**: Heat Map, Institutional, Monochrome, Rainbow
- **Profile Position**: Left or Right side
- **Opacity**: 10-100%
- **Show Labels**: Volume amount display toggle
## Interpretation Guide
### Volume Profile Elements
- **Thick Horizontal Bars**: High volume nodes (strong support/resistance)
- **Thin Horizontal Bars**: Low volume nodes (weak levels)
- **White Line (POC)**: Strongest support/resistance level
- **Blue Highlighted Area**: Value Area (fair value zone)
### Institutional Signals
- **Blue Triangles (PPV)**: Bullish institutional buying detected
- **Orange Triangles (PNV)**: Bearish institutional selling detected
- **Color-Coded Bars**: Different colors indicate institutional activity types
### Color Scheme Meanings
- **Heat Map**: Red (high volume) → Orange → Yellow → Gray (low volume)
- **Institutional**: Blue (PPV), Orange (PNV), Aqua (Accumulation), Yellow (Distribution)
- **Monochrome**: Grayscale intensity based on volume
- **Rainbow**: Color-coded by price level position
## Trading Applications
### Support and Resistance
- POC acts as dynamic support/resistance
- High volume nodes indicate strong price levels
- Low volume areas suggest potential breakout zones
### Institutional Activity
- PPV above Value Area: Strong bullish signal
- PNV below Value Area: Strong bearish signal
- Accumulation patterns: Potential upward breakouts
- Distribution patterns: Potential downward pressure
### Market Structure Analysis
- Value Area defines fair value range
- Profile shape indicates market sentiment
- Volume gaps suggest potential price targets
## Alert Conditions
- PPV Detection at current price level
- PNV Detection at current price level
- PPV above Value Area (strong bullish)
- PNV below Value Area (strong bearish)
## Best Practices
1. Use multiple timeframes for confirmation
2. Combine with price action analysis
3. Pay attention to volume context (above/below average)
4. Monitor institutional signals near key levels
5. Consider overall market conditions
## Technical Notes
- Maximum 500 boxes and 100 labels for optimal performance
- Real-time calculations update on each bar close
- Historical analysis uses complete bar data
- Compatible with all TradingView chart types and timeframes
---
*This indicator is designed for educational and informational purposes. Always combine with other analysis methods and risk management strategies.*
Why EMA Isn't What You Think It IsMany new traders adopt the Exponential Moving Average (EMA) believing it's simply a "better Simple Moving Average (SMA)". This common misconception leads to fundamental misunderstandings about how EMA works and when to use it.
EMA and SMA differ at their core. SMA use a window of finite number of data points, giving equal weight to each data point in the calculation period. This makes SMA a Finite Impulse Response (FIR) filter in signal processing terms. Remember that FIR means that "all that we need is the 'period' number of data points" to calculate the filter value. Anything beyond the given period is not relevant to FIR filters – much like how a security camera with 14-day storage automatically overwrites older footage, making last month's activity completely invisible regardless of how important it might have been.
EMA, however, is an Infinite Impulse Response (IIR) filter. It uses ALL historical data, with each past price having a diminishing - but never zero - influence on the calculated value. This creates an EMA response that extends infinitely into the past—not just for the last N periods. IIR filters cannot be precise if we give them only a 'period' number of data to work on - they will be off-target significantly due to lack of context, like trying to understand Game of Thrones by watching only the final season and wondering why everyone's so upset about that dragon lady going full pyromaniac.
If we only consider a number of data points equal to the EMA's period, we are capturing no more than 86.5% of the total weight of the EMA calculation. Relying on he period window alone (the warm-up period) will provide only 1 - (1 / e^2) weights, which is approximately 1−0.1353 = 0.8647 = 86.5%. That's like claiming you've read a book when you've skipped the first few chapters – technically, you got most of it, but you probably miss some crucial early context.
▶️ What is period in EMA used for?
What does a period parameter really mean for EMA? When we select a 15-period EMA, we're not selecting a window of 15 data points as with an SMA. Instead, we are using that number to calculate a decay factor (α) that determines how quickly older data loses influence in EMA result. Every trader knows EMA calculation: α = 1 / (1+period) – or at least every trader claims to know this while secretly checking the formula when they need it.
Thinking in terms of "period" seriously restricts EMA. The α parameter can be - should be! - any value between 0.0 and 1.0, offering infinite tuning possibilities of the indicator. When we limit ourselves to whole-number periods that we use in FIR indicators, we can only access a small subset of possible IIR calculations – it's like having access to the entire RGB color spectrum with 16.7 million possible colors but stubbornly sticking to the 8 basic crayons in a child's first art set because the coloring book only mentioned those by name.
For example:
Period 10 → alpha = 0.1818
Period 11 → alpha = 0.1667
What about wanting an alpha of 0.17, which might yield superior returns in your strategy that uses EMA? No whole-number period can provide this! Direct α parameterization offers more precision, much like how an analog tuner lets you find the perfect radio frequency while digital presets force you to choose only from predetermined stations, potentially missing the clearest signal sitting right between channels.
Sidenote: the choice of α = 1 / (1+period) is just a convention from 1970s, probably started by J. Welles Wilder, who popularized the use of the 14-day EMA. It was designed to create an approximate equivalence between EMA and SMA over the same number of periods, even thought SMA needs a period window (as it is FIR filter) and EMA doesn't. In reality, the decay factor α in EMA should be allowed any valye between 0.0 and 1.0, not just some discrete values derived from an integer-based period! Algorithmic systems should find the best α decay for EMA directly, allowing the system to fine-tune at will and not through conversion of integer period to float α decay – though this might put a few traditionalist traders into early retirement. Well, to prevent that, most traditionalist implementations of EMA only use period and no alpha at all. Heaven forbid we disturb people who print their charts on paper, draw trendlines with rulers, and insist the market "feels different" since computers do algotrading!
▶️ Calculating EMAs Efficiently
The standard textbook formula for EMA is:
EMA = CurrentPrice × alpha + PreviousEMA × (1 - alpha)
But did you know that a more efficient version exists, once you apply a tiny bit of high school algebra:
EMA = alpha × (CurrentPrice - PreviousEMA) + PreviousEMA
The first one requires three operations: 2 multiplications + 1 addition. The second one also requires three ops: 1 multiplication + 1 addition + 1 subtraction.
That's pathetic, you say? Not worth implementing? In most computational models, multiplications cost much more than additions/subtractions – much like how ordering dessert costs more than asking for a water refill at restaurants.
Relative CPU cost of float operations :
Addition/Subtraction: ~1 cycle
Multiplication: ~5 cycles (depending on precision and architecture)
Now you see the difference? 2 * 5 + 1 = 11 against 5 + 1 + 1 = 7. That is ≈ 36.36% efficiency gain just by swapping formulas around! And making your high school math teacher proud enough to finally put your test on the refrigerator.
▶️ The Warmup Problem: how to start the EMA sequence right
How do we calculate the first EMA value when there's no previous EMA available? Let's see some possible options used throughout the history:
Start with zero : EMA(0) = 0. This creates stupidly large distortion until enough bars pass for the horrible effect to diminish – like starting a trading account with zero balance but backdating a year of missed trades, then watching your balance struggle to climb out of a phantom debt for months.
Start with first price : EMA(0) = first price. This is better than starting with zero, but still causes initial distortion that will be extra-bad if the first price is an outlier – like forming your entire opinion of a stock based solely on its IPO day price, then wondering why your model is tanking for weeks afterward.
Use SMA for warmup : This is the tradition from the pencil-and-paper era of technical analysis – when calculators were luxury items and "algorithmic trading" meant your broker had neat handwriting. We first calculate an SMA over the initial period, then kickstart the EMA with this average value. It's widely used due to tradition, not merit, creating a mathematical Frankenstein that uses an FIR filter (SMA) during the initial period before abruptly switching to an IIR filter (EMA). This methodology is so aesthetically offensive (abrupt kink on the transition from SMA to EMA) that charting platforms hide these early values entirely, pretending EMA simply doesn't exist until the warmup period passes – the technical analysis equivalent of sweeping dust under the rug.
Use WMA for warmup : This one was never popular because it is harder to calculate with a pencil - compared to using simple SMA for warmup. Weighted Moving Average provides a much better approximation of a starting value as its linear descending profile is much closer to the EMA's decay profile.
These methods all share one problem: they produce inaccurate initial values that traders often hide or discard, much like how hedge funds conveniently report awesome performance "since strategy inception" only after their disastrous first quarter has been surgically removed from the track record.
▶️ A Better Way to start EMA: Decaying compensation
Think of it this way: An ideal EMA uses an infinite history of prices, but we only have data starting from a specific point. This creates a problem - our EMA starts with an incorrect assumption that all previous prices were all zero, all close, or all average – like trying to write someone's biography but only having information about their life since last Tuesday.
But there is a better way. It requires more than high school math comprehension and is more computationally intensive, but is mathematically correct and numerically stable. This approach involves compensating calculated EMA values for the "phantom data" that would have existed before our first price point.
Here's how phantom data compensation works:
We start our normal EMA calculation:
EMA_today = EMA_yesterday + α × (Price_today - EMA_yesterday)
But we add a correction factor that adjusts for the missing history:
Correction = 1 at the start
Correction = Correction × (1-α) after each calculation
We then apply this correction:
True_EMA = Raw_EMA / (1-Correction)
This correction factor starts at 1 (full compensation effect) and gets exponentially smaller with each new price bar. After enough data points, the correction becomes so small (i.e., below 0.0000000001) that we can stop applying it as it is no longer relevant.
Let's see how this works in practice:
For the first price bar:
Raw_EMA = 0
Correction = 1
True_EMA = Price (since 0 ÷ (1-1) is undefined, we use the first price)
For the second price bar:
Raw_EMA = α × (Price_2 - 0) + 0 = α × Price_2
Correction = 1 × (1-α) = (1-α)
True_EMA = α × Price_2 ÷ (1-(1-α)) = Price_2
For the third price bar:
Raw_EMA updates using the standard formula
Correction = (1-α) × (1-α) = (1-α)²
True_EMA = Raw_EMA ÷ (1-(1-α)²)
With each new price, the correction factor shrinks exponentially. After about -log₁₀(1e-10)/log₁₀(1-α) bars, the correction becomes negligible, and our EMA calculation matches what we would get if we had infinite historical data.
This approach provides accurate EMA values from the very first calculation. There's no need to use SMA for warmup or discard early values before output converges - EMA is mathematically correct from first value, ready to party without the awkward warmup phase.
Here is Pine Script 6 implementation of EMA that can take alpha parameter directly (or period if desired), returns valid values from the start, is resilient to dirty input values, uses decaying compensator instead of SMA, and uses the least amount of computational cycles possible.
// Enhanced EMA function with proper initialization and efficient calculation
ema(series float source, simple int period=0, simple float alpha=0)=>
// Input validation - one of alpha or period must be provided
if alpha<=0 and period<=0
runtime.error("Alpha or period must be provided")
// Calculate alpha from period if alpha not directly specified
float a = alpha > 0 ? alpha : 2.0 / math.max(period, 1)
// Initialize variables for EMA calculation
var float ema = na // Stores raw EMA value
var float result = na // Stores final corrected EMA
var float e = 1.0 // Decay compensation factor
var bool warmup = true // Flag for warmup phase
if not na(source)
if na(ema)
// First value case - initialize EMA to zero
// (we'll correct this immediately with the compensation)
ema := 0
result := source
else
// Standard EMA calculation (optimized formula)
ema := a * (source - ema) + ema
if warmup
// During warmup phase, apply decay compensation
e *= (1-a) // Update decay factor
float c = 1.0 / (1.0 - e) // Calculate correction multiplier
result := c * ema // Apply correction
// Stop warmup phase when correction becomes negligible
if e <= 1e-10
warmup := false
else
// After warmup, EMA operates without correction
result := ema
result // Return the properly compensated EMA value
▶️ CONCLUSION
EMA isn't just a "better SMA"—it is a fundamentally different tool, like how a submarine differs from a sailboat – both float, but the similarities end there. EMA responds to inputs differently, weighs historical data differently, and requires different initialization techniques.
By understanding these differences, traders can make more informed decisions about when and how to use EMA in trading strategies. And as EMA is embedded in so many other complex and compound indicators and strategies, if system uses tainted and inferior EMA calculatiomn, it is doing a disservice to all derivative indicators too – like building a skyscraper on a foundation of Jell-O.
The next time you add an EMA to your chart, remember: you're not just looking at a "faster moving average." You're using an INFINITE IMPULSE RESPONSE filter that carries the echo of all previous price actions, properly weighted to help make better trading decisions.
EMA done right might significantly improve the quality of all signals, strategies, and trades that rely on EMA somewhere deep in its algorithmic bowels – proving once again that math skills are indeed useful after high school, no matter what your guidance counselor told you.
Smarter Money Concepts - OBs [PhenLabs]📊 Smarter Money Concepts - OBs
Version: PineScript™ v6
📌 Description
Smarter Money Concepts - OBs (Order Blocks) is an advanced technical analysis tool designed to identify and visualize institutional order zones on your charts. Order blocks represent significant areas of liquidity where smart money has entered positions before major moves. By tracking these zones, traders can anticipate potential reversals, continuations, and key reaction points in price action.
This indicator incorporates volume filtering technology to identify only the most significant order blocks, eliminating low-quality signals and focusing on areas where institutional participation is likely present. The combination of price structure analysis and volume confirmation provides traders with high-probability zones that may attract future price action for tests, rejections, or breakouts.
🚀 Points of Innovation
Volume-Filtered Block Detection : Identifies only order blocks formed with significant volume, focusing on areas with institutional participation
Advanced Break of Structure Logic : Uses sophisticated price action analysis to detect legitimate market structure breaks preceding order blocks
Dynamic Block Management : Intelligently tracks, extends, and removes order blocks based on price interaction and time-based expiration
Structure Recognition System : Employs technical analysis algorithms to find significant swing points for accurate order block identification
Dual Directional Tracking : Simultaneously monitors both bullish and bearish order blocks for comprehensive market structure analysis
🔧 Core Components
Order Block Detection : Identifies institutional entry zones by analyzing price action before significant breaks of structure, capturing where smart money has likely positioned before moves.
Volume Filtering Algorithm : Calculates relative volume compared to a moving average to qualify only order blocks formed with significant market participation, eliminating noise.
Structure Break Recognition : Uses price action analysis to detect legitimate breaks of market structure, ensuring order blocks are identified only at significant market turning points.
Dynamic Block Management : Continuously monitors price interaction with existing blocks, extending, maintaining, or removing them based on current market behavior.
🔥 Key Features
Volume-Based Filtering : Filter out insignificant blocks by requiring a minimum volume threshold, focusing only on zones with likely institutional activity
Visual Block Highlighting : Color-coded boxes clearly mark bullish and bearish order blocks with customizable appearance
Flexible Mitigation Options : Choose between “Wick” or “Close” methods for determining when a block has been tested or mitigated
Scan Range Adjustment : Customize how far back the indicator looks for structure points to adapt to different market conditions and timeframes
Break Source Selection : Configure which price component (close, open, high, low) is used to determine structure breaks for precise block identification
🎨 Visualization
Bullish Order Blocks : Blue-colored rectangles highlighting zones where bullish institutional orders were likely placed before upward moves, representing potential support areas.
Bearish Order Blocks : Red-colored rectangles highlighting zones where bearish institutional orders were likely placed before downward moves, representing potential resistance areas.
Block Extension : Order blocks extend to the right of the chart, providing clear visualization of these significant zones as price continues to develop.
📖 Usage Guidelines
Order Block Settings
Scan Range : Default: 25. Defines how many bars the indicator scans to determine significant structure points for order block identification.
Bull Break Price Source : Default: Close. Determines which price component is used to detect bullish breaks of structure.
Bear Break Price Source : Default: Close. Determines which price component is used to detect bearish breaks of structure.
Visual Settings
Bullish Blocks Color : Default: Blue with 85% transparency. Controls the appearance of bullish order blocks.
Bearish Blocks Color : Default: Red with 85% transparency. Controls the appearance of bearish order blocks.
General Options
Block Mitigation Method : Default: Wick, Options: Wick, Close. Determines how block mitigation is calculated - “Wick” uses high/low values while “Close” uses close values for more conservative mitigation criteria.
Remove Filled Blocks : Default: Disabled. When enabled, order blocks are removed once they’ve been mitigated by price action.
Volume Filter
Volume Filter Enabled : Default: Enabled. When activated, only shows order blocks formed with significant volume relative to recent average.
Volume SMA Period : Default: 15, Range: 1-50. Number of periods used to calculate the average volume baseline.
Min. Volume Ratio : Default: 1.5, Range: 0.5-10.0. Minimum volume ratio compared to average required to display an order block; higher values filter out more blocks.
✅ Best Use Cases
Identifying high-probability support and resistance zones for trade entries and exits
Finding optimal stop-loss placement behind significant order blocks
Detecting potential reversal areas where price may react after extended moves
Confirming breakout trades when price clears major order blocks
Building a comprehensive market structure map for medium to long-term trading decisions
Pinpointing areas where smart money may have positioned before major market moves
⚠️ Limitations
Most effective on higher timeframes (1H and above) where institutional activity is more clearly defined
Can generate multiple signals in choppy market conditions, requiring additional filtering
Volume filtering relies on accurate volume data, which may be less reliable for some securities
Recent market structure changes may invalidate older order blocks not yet automatically removed
Block identification is based on historical price action and may not predict future behavior with certainty
💡 What Makes This Unique
Volume Intelligence : Unlike basic order block indicators, this script incorporates volume analysis to identify only the most significant institutional zones, focusing on quality over quantity.
Structural Precision : Uses sophisticated break of structure algorithms to identify true market turning points, going beyond simple price pattern recognition.
Dynamic Block Management : Implements automatic block tracking, extension, and cleanup to maintain a clean and relevant chart display without manual intervention.
Institutional Focus : Designed specifically to highlight areas where smart money has likely positioned, helping retail traders align with institutional perspectives rather than retail noise.
🔬 How It Works
1. Structure Identification Process :
The indicator continuously scans price action to identify significant swing points and structure levels within the specified range, establishing a foundation for order block recognition.
2. Break Detection :
When price breaks an established structure level (crossing below a significant low for bearish breaks or above a significant high for bullish breaks), the indicator marks this as a potential zone for order block formation.
3. Volume Qualification :
For each potential order block, the algorithm calculates the relative volume compared to the configured period average. Only blocks formed with volume exceeding the minimum ratio threshold are displayed.
4. Block Creation and Management :
Valid order blocks are created, tracked, and managed as price continues to develop. Blocks extend to the right of the chart until they are either mitigated by price action or expire after the designated timeframe.
5. Continuous Monitoring :
The indicator constantly evaluates price interaction with existing blocks, determining when blocks have been tested, mitigated, or invalidated, and updates the visual representation accordingly.
💡 Note:
Order Blocks represent areas where institutional traders have likely established positions and may defend these zones during future price visits. For optimal results, use this indicator in conjunction with other confluent factors such as key support/resistance levels, trendlines, or additional confirmation indicators. The most reliable signals typically occur on higher timeframes where institutional activity is most prominent. Start with the default settings and adjust parameters gradually to match your specific trading instrument and style.
