Biquad High Pass FilterThis indicator utilizes a biquad high pass filter to filter out low-frequency components from price data, helping traders focus on high-frequency movements and detect rapid changes in trends.
The Length parameter determines the cutoff frequency of the filter, affecting how quickly the filter responds to changes in price. A shorter length allows the filter to react more quickly to high-frequency movements.
The Q Factor controls the sharpness of the filter. A higher Q value results in a more precise filtering effect by narrowing the frequency band. However, be cautious when setting the Q factor too high, as it can induce resonance, amplifying certain frequencies and potentially making the filter less effective by introducing unwanted noise.
Key Features of Biquad Filters
Biquad filters are a type of digital filter that provides a combination of low-pass, high-pass, band-pass, and notch filtering capabilities. In this implementation, the biquad filter is configured as a high pass filter, which allows high-frequency signals to pass while attenuating lower-frequency components. This is particularly useful in trading to highlight rapid price movements, making it easier to spot short-term trends and patterns.
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. The customizable length and Q factor allow for flexible adaptation to different trading strategies and market conditions. Designed for real-time charting, the biquad filter operates efficiently without significant lag, ensuring timely analysis.
By incorporating this biquad high pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into rapid price movements, leading to more informed trading decisions.
M-oscillator
Internal Bar Strength IBS [Anan]This indicator calculates and displays the Internal Bar Strength (IBS) along with its moving average. The IBS is a measure that represents where the closing price is relative to the high-low range of a given period.
█ Main Formula
The core of this indicator is the Internal Bar Strength (IBS) calculation. The basic IBS formula is:
ibs = (close - low) / (high - low)
I enhanced the original formula by incorporating a user-defined length parameter. This modification allows for greater flexibility in analysis and interpretation. The extended version enables users to adjust the indicator's length according to their specific needs or market conditions. Notably, setting the length parameter to 1 reproduces the behavior of the original formula, maintaining backward compatibility while offering expanded functionality:
ibs = (close - ta.lowest(low, ibs_length)) / (ta.highest(high, ibs_length) - ta.lowest(low, ibs_length))
Where:
- `close` is the closing price of the current bar
- `lowest low` is the lowest low price over the specified IBS length
- `highest high` is the highest high price over the specified IBS length
█ Key Features
- Calculates IBS using a user-defined length
- Applies a moving average to the IBS values
- Offers multiple moving average types
- Includes optional Bollinger Bands or Donchian Channel overlays
- Visualizes bull and bear areas
█ Inputs
- IBS Length: The period used for IBS calculation
- MA Type: The type of moving average applied to IBS (options: SMA, EMA, SMMA, WMA, VWMA, Bollinger Bands, Donchian)
- MA Length: The period used for the moving average calculation
- BB StdDev: Standard deviation multiplier for Bollinger Bands
█ How to Use and Interpret
1. IBS Line Interpretation:
- IBS values range from 0 to 1
- Values close to 1 indicate the close was near the high, suggesting a bullish sentiment
- Values close to 0 indicate the close was near the low, suggesting a bearish sentiment
- Values around 0.5 suggest the close was near the middle of the range
2. Overbought/Oversold Conditions:
- IBS values above 0.8 (teal zone) may indicate overbought conditions
- IBS values below 0.2 (red zone) may indicate oversold conditions
- These zones can be used to identify potential reversal points
3. Trend Identification:
- Consistent IBS values above 0.5 may indicate an uptrend
- Consistent IBS values below 0.5 may indicate a downtrend
4. Using Moving Averages:
- The yellow MA line can help smooth out IBS fluctuations
- Crossovers between the IBS and its MA can signal potential trend changes
5. Bollinger Bands/Donchian Channel:
- When enabled, these can provide additional context for overbought/oversold conditions
- IBS touching or exceeding the upper band may indicate overbought conditions
- IBS touching or falling below the lower band may indicate oversold conditions
Remember that no single indicator should be used in isolation. Always combine IBS analysis with other technical indicators, price action analysis, and broader market context for more reliable trading decisions.
MTF-Colored EMA Difference and Stochastic indicatorThis indicator combines two popular technical analysis tools: the Exponential Moving Average (EMA) and the Stochastic Oscillator, with the added flexibility of analyzing them across multiple time frames. It visually represents the difference between two EMAs and the crossover signals from the Stochastic Oscillator, providing a comprehensive view of the market conditions.
Components:
EMA Difference Histogram :
EMA Calculation : The indicator calculates two EMAs (EMA1 and EMA2) for the selected time frame.
EMA Difference : The difference between EMA1 and EMA2 is plotted as a 4 coloured histogram.
Stochastic Oscillato r:
Calculation : The %K and %D lines of the Stochastic Oscillator are calculated for the selected time frame.
Additional Confirmation via Colors :
Green: %K is above %D, indicating a bullish signal.
Red: %K is below %D, indicating a bearish signal.
Entry and Exit Strategies
Entry Strategy :
Bullish Entry :
Condition 1: The histogram is Dark green (indicating a strong upward trend).
Condition 2: The Stochastic colour is green (%K is above %D).
Bearish Entry :
Condition 1: The histogram is Dark Red (indicating a strong downward trend).
Condition 2: The Stochastic colour is red (%K is below %D).
Exit Strategy:
Bullish Exit:
Condition: The Stochastic colour turns red (%K crosses below %D).
Bearish Exit:
Condition: The Stochastic colour turns green (%K crosses above %D).
Additional Considerations:
Time Frame Selection : The chosen time frame for both the EMA and Stochastic calculations should align with the trader’s strategy (e.g., daily for swing trading, hourly for intraday trading).
Risk Management : Implement stop-loss orders to manage risk effectively. The stop-loss can be placed below the recent swing low for long positions and above the recent swing high for short positions.
Confirmation : Consider using this indicator in conjunction with other technical analysis tools to confirm signals and reduce the likelihood of false entries and exits.
Nebula SAR Echo📈 Overview:
The "Nebula SAR Echo" is a sophisticated technical analysis tool designed for traders seeking enhanced trend detection. This indicator combines the robust Parabolic SAR mechanism with gradient color coding to provide clear visual insights into market trends.
🎯 Key Features:
Advanced Parabolic SAR Calculation:
Utilizes dynamic coefficients for more responsive and accurate trend detection.
Highlights trend reversals with visual markers for immediate identification.
Gradient Color Coding:
Gradient colors dynamically reflect the strength and direction of the trend.
Bullish trends are represented in shades of green, while bearish trends are shown in shades of red.
User-Friendly Customization:
Easily adjustable parameters for acceleration factors and gradient color use.
💡 Benefits:
Enhanced Decision Making:
Combines real-time trend analysis to assist traders in making more informed decisions.
Visual Clarity:
Clear visual markers and gradient color coding simplify the interpretation of market trends.
Helps traders quickly identify key turning points and potential future price paths.
🔍 Use Cases:
Trend Identification:
Ideal for identifying ongoing trends and potential reversals in various market conditions.
Useful for both short-term trading strategies and long-term investment planning.
Risk Management:
Gradient color coding aids in assessing trend strength and potential volatility.
Traders can set more precise stop-loss and take-profit levels based on the trend strength.
⚙️ How to Use:
1. Parameter Setup:
Set the desired acceleration factors (start, increment, and max) for the Parabolic SAR.
Enable or disable gradient colors based on personal preference.
2. Interpretation:
Use the SAR values and gradient colors to gauge current market trends.
3. Alerts:
Set up alert conditions for bullish and bearish reversals to stay notified of significant market changes.
🔹 Conclusion:
The "Nebula SAR Echo" is a versatile and powerful tool for traders who require an in-depth analysis of market trends. By leveraging the advanced Parabolic SAR calculation and gradient color coding, this indicator provides a comprehensive view of market conditions, making it an indispensable addition to any trader's toolkit.
Bitcoin Puell Multiple (BPM)The Bitcoin Puell Multiple is a key indicator for evaluating buying and selling opportunities based on the profitability of Bitcoin miners.
The Idea
The Bitcoin Puell Multiple is a ratio that measures the daily profitability of Bitcoin miners in relation to the historical annual average of this profitability. It is calculated by dividing the amount of newly issued Bitcoins (in USD) each day by the 365-day moving average of that same amount. This indicator provides valuable information on Bitcoin's market cycles, helping investors to identify periods when Bitcoin is potentially undervalued or overvalued.
How to Use
To use the Bitcoin Puell Multiple, investors watch for extreme levels of the indicator. A high Puell Multiple suggests that miners are making exceptionally high profits compared to the previous year, which could indicate an overvaluation of Bitcoin and a selling opportunity (red zones). Conversely, a low Puell Multiple indicates that miners' earnings are low relative to history, suggesting an undervaluation of Bitcoin and a potential buying opportunity (green zones). The trigger thresholds for these zones can be configured in the tool's parameters.
What makes this tool different from the other "Puell Multiple" scripts available is that it is up to date in terms of its data sources, with a more precise calculation, and allows you to view the entire history.
Zone trigger limits and their visualization, as well as colors, are all configurable via the tool parameters.
Here, for example, is a configuration with more sensitive trigger levels and a different color:
Consecutive Closes Above/Below 3 SMA with Z-Score BandsA simple indicator that measures consecutive closes above & below the 3-period simple moving average. An upper and lower Z-score has been calculated to indicate where the 4 standard deviations of the last 60 bars sits.
Useful for identifying directional runs in price.
D2MAThe script is called "D2MA" (Distance to Moving Average). It calculates the distance between the closing price and a user-selected type of moving average (MA). It also plots this distance on a chart, allowing users to see how far the price is from the chosen moving average. Users can choose to display this distance as either an absolute value or as a percentage.
Input Parameters
Length (len): The number of bars (or periods) used to calculate the moving average.
Source (src): The price data used for calculations, typically the closing price.
Percentage Distance (pc): A boolean option to display the distance as a percentage instead of an absolute value.
MA Type (maType): The type of moving average to use.
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Hull Moving Average (HMA)
Arnaud Legoux Moving Average (ALMA)
Triple Exponential Moving Average (T3)
Power Weighted Moving Average (PWMA)
The script includes functions to calculate different types of moving averages:
The difference between the source price (e.g., closing price) and the calculated moving average. if Distance as Percentage , the distance expressed as a percentage of the moving average value.
Plotting the Data
Signal Line: The signal line changes colour (green or red) based on whether the distance is increasing or decreasing.
Visual Representation
How to Use
Identify Trends: By seeing how far the price is from a selected moving average, traders can gauge the strength of a trend.
Spot Reversals: Significant deviations from the moving average can signal potential reversals.
Empirical Kaspa Power Law Full Model v3.1🔶 First we need to understand what Power Laws are.
Power laws are mathematical relationships where one quantity varies as a power of another. They are prevalent in both natural and social systems, describing phenomena such as earthquake magnitudes, word frequencies, and wealth distributions. In a power-law relationship, a change in one quantity results in a proportional change in another, typically following a consistent and predictable mathematical pattern.
🔶 Why Do Power Laws work for Bitcoin and Kaspa?
Power laws work for Bitcoin and Kaspa due to the underlying principles of network dynamics and growth patterns that these cryptocurrencies exhibit. Here's how:
1. Network Growth and User Adoption:
Both Bitcoin and Kaspa grow as more users join their networks. The value of these networks often increases in a manner consistent with Metcalfe’s Law, which states that the value of a network is proportional to the square of its number of users. This relationship is a form of a power law, where network effects lead to exponential growth as more users participate.
2. Mining and Hash Rate:
The mining difficulty and hash rate in cryptocurrencies like Bitcoin and Kaspa adjust based on network activity. As more miners join, the difficulty increases to maintain a stable rate of block production. This self-adjusting mechanism creates feedback loops that can be described by power laws, ensuring the stability and security of the network over time.
3. Price Behavior:
Astrophysicist Giovanni Santostasi discovered that Bitcoin’s price follows a power-law distribution over time. This means that despite short-term volatility, Bitcoin’s long-term price behavior is predictable and adheres to specific mathematical patterns. Santostasi's model provides a framework for understanding Bitcoin’s price movements and forecasting future trends. He also discovered that Kaspa might be following a power-law aswell but it might be to early to tell because Kaspa hasn't been around for too long(2years).
4. Resource Allocation and System Stability:
As the price of Bitcoin or Kaspa increases, more resources are allocated to mining, leading to more sophisticated mining operations. This iterative process of investment and technological advancement follows a power-law pattern, driving the growth and stability of the network.
In summary, the application of power laws to Bitcoin and Kaspa offers a structured framework for understanding their price movements, network growth, and overall stability. These principles provide valuable predictive tools for long-term forecasting, helping to explain the dynamic behavior of these cryptocurrencies.
🔶 What does it look like on a chart?
Here is the Kaspa power law plotted on the KaspaUSD chart. Notice that the y-axis is in logarithmic scale. Unfortunately, TradingView does not allow the x-axis to be in logarithmic scale, which would otherwise make the power law appear as a straight line.
🔶 All the features of the Empirical Kaspa Power Law Full Model
This indicator includes a variety of scripts and tools, meticulously designed and developed to navigate the Kaspa market effectively.
🔹 Power Law & Deviation bands
The decision to use the lower two bands, marking an area between -40% to -50% below the power law, is based on historical analysis. Historically, this range has proven to be a great buying opportunity. In the case of Bitcoin, the bottom typically lies around -60% from the power law. However, for Kaspa, the bottom appears to be less distant from the power law. This discrepancy can be attributed to the differing supply dynamics of the two. Bitcoin undergoes a halving event approximately every four years, significantly reducing the rate at which new coins are introduced into circulation. This cyclical halving can lead to larger price fluctuations and a greater deviation from the power law. In contrast, Kaspa employs a more gradual reduction in its emission rate, with a 5% decrease each month. This consistent and incremental reduction helps Kaspa's price follow the power law more closely, resulting in less pronounced deviations. Consequently, the bottom for Kaspa tends to be closer to the power law, typically around -40% to -50%, rather than the -60% observed with Bitcoin.
The top two deviation bands are fitted to a few bubble data points, which are honestly not very reliable compared to the bottom bands that are based on a larger number of data points. When examining Bitcoin, we see that the bottoms are quite predictable due to the availability of thousands of data points, making it easier to identify patterns and trends.
However, predicting the tops is significantly more challenging because we lack a substantial amount of data for the peaks. This limited data makes it difficult to draw reliable conclusions about the upper deviation bands. As a result, while the bottom bands offer a robust framework for analysis, the top bands should be approached with caution due to their lesser reliability.
🔹 Alternating Sine wave
In observing the price behavior of Kaspa, an intriguing pattern emerges: it tends to follow a roughly four-month cycle. This cycle appears to alternate between smaller and larger waves. To capture this pattern, the sine wave in our indicator is designed to follow the power law, with both the top and bottom of the wave adjusting according to it.
Here's a simple explanation of how this works:
1. Four-Month Cycle: Empirically, Kaspa’s price seems to oscillate over approximately 120 days. This cycle includes periods of growth and decline, repeating every four months. Within these cycles, we observe alternating phases one smaller and one larger in amplitude.
2. Power Law Influence: The sine wave component of our indicator is not arbitrary; it follows a power law that predicts the general price trend of Kaspa. The power law essentially provides a baseline that reflects the longer-term price trajectory.
3. Diminishing Returns and Smoothing: To model diminishing returns, we adjust the amplitude of the sine wave over time, making it smaller as the cycle progresses. This helps to capture the natural tendency for price movements to become less volatile over longer periods. Additionally, the bottom of the sine wave adheres to the power law, ensuring it remains consistent with the overall trend.
🔹 Sine wave Cycle Start & End
Color transitions play a crucial role in visualizing different phases of the four-month cycle.
Based on empirical data, Kaspa experiences approximately 60 days of downward price action following each cycle peak, a period we refer to as the bear phase. This phase is followed by the bull phase, which also lasts around 60 days. To indicate the cycle peak, we have added a colored warning on the sine wave.
Cycle Start (Purple): The sine wave starts with a purple color, marking the beginning of a new cycle. This bull phase often represents a potential bottom or accumulation zone where prices are lower and stable, offering a strategic point for entering the market.
Cycle Top (Red): As the cycle progresses, the sine wave transitions through colors until it reaches red. This red phase indicates the top of the cycle, where the price is likely peaking. It's a critical area for investors to consider dollar-cost averaging (DCA) out of Kaspa, as it signifies a period of potential overvaluation and heightened risk.
These color transitions provide a visual guide to the market's cyclical nature, helping investors identify optimal entry and exit points. By following the sine wave's color changes, you can better time your investments, entering at the start of the cycle and considering exits as the cycle tops out.
🔹 Colored Deviation from the Power Law Bubbles
In trading, having a clear visual signal can significantly enhance decision-making, especially when dealing with complex models like power laws. This inspired the creation of the "deviation bubbles" in my indicator, which provides an intuitive, color-coded visual queue to help me, and other traders, better grasp market deviations and make timely trading decisions.
Here's a breakdown of how the deviation bubbles work:
1. Power Law Reference: The core of the indicator calculates a theoretical price level (the power law price) for Kaspa.
2. Deviation Calculation: For each day, the indicator computes the percentage deviation of the actual closing price from this power law price. This tells how much the market price diverges from the theoretically expected level.
3. Color-Coding Based on Deviation:
The deviation is categorized into various ranges (e.g., ≥ 100%, 90-100%, 80-90%, etc.).
Each range is assigned a distinct color, from red for extreme positive deviations to blue for extreme negative deviations.
This gradient helps in quickly identifying significant market deviations.
By integrating these bubbles into the chart, the indicator offers a simple yet powerful visual tool, aiding in recognizing critical market conditions without the need to delve into complex calculations manually. This approach not only enhances the ease of trading but also helps in overcoming the hesitation often faced when pulling the trigger on trades.
🔹 Projected Power Law Bands
Extends the current power law bands into the future using the same formula that defines the current power law.
Visual Representation: Dotted lines on the chart indicate the projected power law price and deviation bands.
Limitations: TradingView restricts how far these projections can extend, typically up to a reasonable future period.
These projected bands help anticipate future price movements, aiding in more informed trading decisions.
🔹 Projected Sine Wave
This projection continues to calculate the phase and amplitude, adjusting for diminishing returns and cycle transitions. It also estimates the future power law price, ensuring the projection reflects potential market dynamics.
Visual Representation: The projected sine wave is shown with dotted blue lines, providing a clear visual of the expected trend, aiding traders in their decision-making process.
Limitations: Again, TradingView restricts how far these projections can extend, typically up to a reasonable future period.
🔶 Why are all these different scripts made into one indicator?
As a trader and crypto analyst, I needed specific tools and customizations that no other indicator offered. Being a visual person, I rely heavily on visual triggers such as colors and patterns to make trading decisions. Initially, I developed this indicator for my personal use to enhance my market analysis with these visual cues. However, after sharing my insights, other traders expressed interest in using it. In response, I expanded the functionality and added various options to cater to a broader range of users.
This comprehensive indicator integrates multiple features into one tool, providing a powerful and flexible solution for analyzing market trends and making informed trading decisions. The use of colors and visual elements helps in quickly identifying key signals and market phases. The customizable options allow you to fine-tune the indicator to suit your specific needs, making it a versatile tool for both novice and experienced traders.
🔶 Usage & Settings:
This indicator is best used on the Daily chart for KASUSD - crypto because it uses a power law formula based on days.
🔹 Using the Indicator for 4-Month Cycles:
For traders interested in playing the 4-month cycles, this indicator provides a straightforward strategy. When the bubbles turn purple or the sine wave shows the purple start color, it signals a good time to dollar-cost average (DCA) into the market. Conversely, when the bubbles turn red or the cycle top is near, indicated by a red color, it’s time to DCA out of the Kaspa market. This visual approach helps traders make timely decisions based on color-coded signals, simplifying the trading process.
Historically, it was nearly impossible to accurately time all the 4-month cycle tops because they alternate each time. Without the combination of multiple scripts in this indicator, identifying these cyclical patterns and their respective peaks was extremely challenging. This integrated tool now provides a clear and reliable method for detecting these critical points, enhancing trading effectiveness.
🔹 Combining the visual queues for market extremes
The chart above illustrates the alignment of visual cues indicating market extremes. Notably, these visual cues—marked by red and purple boxes—historically pinpoint areas of extreme value or opportunities. When red aligns with red and purple aligns with purple, these zones have consistently indicated significant market extremes.
Understanding and recognizing these patterns provides a strategic advantage. By identifying these visual triggers, traders can plan and execute informed trades with greater confidence whenever similar scenarios unfold in the future.
Kaspa is perhaps one of the most cyclical and predictable cryptocurrencies in the market. Given its consistent behavior, traders might wonder why they would trade anything else. As long as there are no signs indicating a change in Kaspa's cyclical nature, there is no reason to make significant alterations to our predictions. This makes Kaspa an attractive option for traders seeking reliable and repeatable trading opportunities.
🔹 Settings & customization:
As a visually-oriented trader, it is essential to customize the appearance of indicators to effectively navigate the Kaspa market. The Indicator offers extensive customization options, allowing users to modify the colors of various elements to suit their preferences. For example, users can adjust the colors of the deviation bubbles, deviation bands, sine wave, and power law to enhance visual clarity and focus on specific data points. This level of personalization not only enhances the overall user experience but also ensures that the visual representation aligns with unique trading strategies, making it easier to interpret complex market data.
Additionally, users can change the power law inputs and other parameters as shown in the image. For instance, the Power Law Intercept and Power Law Slope can be manually adjusted, allowing traders to update these values. This flexibility is crucial as the future power law for Kaspa may evolve/change.
🔶 Limitations
Like any technical analysis tool, the Empirical Kaspa Power Law Full Model indicator has limitations. It's based on historical data, which may not always accurately predict future market movements.
🔶 Credits
I want to thank Dr. Giovanni Santostasi · Professor of physics and Mathematics.
He was one of the first who applied the concept of the power law to Bitcoin's price movements, which has been instrumental in providing insights into the long-term growth and potential future value of Bitcoin. Giovanni also offers coding classes on his Discord, which I attended. He personally taught me how to code specific things in Pine Editor and Python, sparking my interest in developing my own indicator.
Additionally, I would like to extend my gratitude to the following individuals for their invaluable contributions in terms of ideas, theories, formulas, testing, and guidance:
Forgowork, PlanC, Miko Genno, Chancellor, SavingFace, Kaspapero, JJ Venema.
HRC - Hash Rate Capitulation [Da_Prof]The HRC (Hash Rate Capitulation) indicator is a measure of hash rate trend strength. It is the fractional difference between a long and a short simple moving average (SMA) of the bitcoin hash rate. Historically, the 21-day and 105-day SMA work well for this indicator. The hash rate generally increases over time, but when the short SMA crosses below the longer-term SMA, it shows that miners are removing significant hash from the system. This state can be considered a miner "capitulation". Historically, this has marked depressed BTC prices and has led to higher prices within some months. Shout out to foosmoo, the hash rate oscillator indicator prompted this presentation.
Flush Percent RangeFans of Woodies CCI may recognize the approach to this one. This is my attempt at using the same methods but for taking the highs and lows into account without the standard deviation of the CCI. The smoothness of other oscillators may not be ideal however the Williams Percent Range is a fast stochastic that also operates within a channel. This provides an alternative yet still complex view for the virtuoso. A unique feature is total utilization of the weighted moving average, from the standard to the more complex. A fun fact is the Hull Moving Average is actually calculated using weighted moving averages.
How to use:
The base length is for accuracy, the fast length is for catching all the moves(even the wrong ones sometimes.)
The bars back option will not flip the histogram/base trend to its bullish/bearish alternative until the base plot remains on the latter half of the oscillator for a certain number of bars. This can be set to zero if desired.
The factor controls the chop on the various levels. A higher number will increase it.
The oscillator levels are measuring slope, price relative to the average, and a summation of percent changes between the two. Both the baseline/histogram and the levels have color coding for bullishness, bearishness, and indecision(depending on the factor.) The fast line matches the indecision color by default. This is all customizable.
There are many potential ways to trade with this indicator. From hooks back toward the trend and range line crossovers to divergence and reversals. It's important to note the current performance of the oscillator levels. Time cycles may come in handy along with other forecasting tools.
Lastly, there are optional linear regression lines plotted on the chart. They're synchronized to the lengths in the oscillator. This is an additional visual aid to provide context to the direction of the channel.
Overall the Flush Percent Range is for analyzing multiple regression models within a single price channel. No smoothing, fast averages, and specified timeframes of highs/lows. Credit to Larry Williams for the original calculation and Ken Woods for design/methodology inspiration.
Deviation in Euclidean Distance from the Kaspa Power Law v3.0🔶 First we need to understand what Power Laws are.
Power laws are mathematical relationships where one quantity varies as a power of another. They are prevalent in both natural and social systems, describing phenomena such as earthquake magnitudes, word frequencies, and wealth distributions. In a power-law relationship, a change in one quantity results in a proportional change in another, typically following a consistent and predictable mathematical pattern.
🔶 Why Do Power Laws work for Bitcoin and Kaspa?
Power laws work for Bitcoin and Kaspa due to the underlying principles of network dynamics and growth patterns that these cryptocurrencies exhibit. Here's how:
1. Network Growth and User Adoption:
Both Bitcoin and Kaspa grow as more users join their networks. The value of these networks often increases in a manner consistent with Metcalfe’s Law, which states that the value of a network is proportional to the square of its number of users. This relationship is a form of a power law, where network effects lead to exponential growth as more users participate.
2. Mining and Hash Rate:
The mining difficulty and hash rate in cryptocurrencies like Bitcoin and Kaspa adjust based on network activity. As more miners join, the difficulty increases to maintain a stable rate of block production. This self-adjusting mechanism creates feedback loops that can be described by power laws, ensuring the stability and security of the network over time.
3. Price Behavior:
Astrophysicist Giovanni Santostasi discovered that Bitcoin’s price follows a power-law distribution over time. This means that despite short-term volatility, Bitcoin’s long-term price behavior is predictable and adheres to specific mathematical patterns. Santostasi's model provides a framework for understanding Bitcoin’s price movements and forecasting future trends. He also discovered that Kaspa might be following a power-law aswell but it might be to early to tell because Kaspa hasn't been around for too long(2years).
4. Resource Allocation and System Stability:
As the price of Bitcoin or Kaspa increases, more resources are allocated to mining, leading to more sophisticated mining operations. This iterative process of investment and technological advancement follows a power-law pattern, driving the growth and stability of the network.
In summary, the application of power laws to Bitcoin and Kaspa offers a structured framework for understanding their price movements, network growth, and overall stability. These principles provide valuable predictive tools for long-term forecasting, helping to explain the dynamic behavior of these cryptocurrencies.
🔶 What does it look like on a chart?
Here is the Kaspa power law plotted on the KaspaUSD chart. Notice that the y-axis is in logarithmic scale. Unfortunately, TradingView does not allow the x-axis to be in logarithmic scale, which would otherwise make the power law appear as a straight line.
🔶 What is the deviation in Euclidean Distance from the Power Law?
Euclidean distance is a way to measure the straight-line distance between two points in a multi-dimensional space. When applied to a power law, it measures how far a data point is from the value predicted by the power-law formula.
🔶 Why are we measuring the Euclidean Distance from the Power Law & Discovery
On June 2, 2024, Plan C on Twitter announced a significant discovery: he and Dr. Giovanni Santostasi found that by examining the Euclidean distance from the Bitcoin power law, normalizing the data, and plotting it on an oscillator, it is possible to predict or time the market. In his post, Plan C hinted at the concept of "two-dimensional deviation," describing the result as the ultimate tool for navigating Bitcoin cycles. So, applying this technique to Kaspa, the only other cryptocurrency that might follow a power law might be a great idea!
This discovery leverages the power-law principles to create a sophisticated market timing tool, potentially offering insights into both Bitcoin and Kaspa's price movements.
🔶 Visual Representation of the Normalized Deviation in Euclidean Distance from the Kaspa Power Law
Steps Involved to visualize the indicator/oscillator:
1. Power Law Calculation:
The theoretical price is computed using the Power Law formula. This formula is based on the number of days since Kaspa's genesis block, simulating an ideal price growth trajectory.
2. Deviation Calculation:
For each day, the actual price is compared against the power law price for a range of days around the current date. The Euclidean Distance in days is the smallest number of days (either past or future) where this deviation is minimized.
3. Normalization:
The raw deviations over a fixed window are scaled to fit within a range of -100, 100. This normalized value is then smoothed using a simple moving average to produce a more readable oscillator.
4. Dynamic Coloring:
The oscillator's line color changes dynamically based on its value, providing an intuitive visual cue for traders.
🔶 Using the Oscillator
This indicator is best used on the Daily chart for KASUSD - crypto because it uses a power law formula based on days.
Identify Extremes:
When the oscillator shows high positive or negative values, it signals potential market extremes. This can help traders decide when to buy (when the market is oversold) or sell (when the market is overbought).
Values near -100 or 100 indicate significant deviation from the power law, highlighting potential market extremes.
🔶 Indicator Option's & Settings
Smooth Trends:
The smoothed line of the oscillator helps filter out market noise, allowing traders to focus on broader trends rather than short-term fluctuations.
Customize Your Analysis with Adjustable Price Sources:
One of the standout features of the Oscillator is its flexibility in using different price sources. You can customize the price source to better suit your trading style and analysis needs.
Price Source Selection:
The indicator allows you to choose the price source for its calculations. By default, it uses the average price of the daily candle (OHLC4), but you can adjust this to other price metrics such as the closing price, opening price, or any custom input.
Using Different Price Sources:
Using the daily candle average provides a balanced view of the day's trading activity, smoothing out intraday volatility.
Custom daily price sources:
Daily Highs:
Setting the price source to the daily high can help identify the maximum deviation when the market reaches its highest point during the day. This can be useful for spotting overbought conditions and potential resistance levels.
Daily Lows:
Conversely, using the daily low as the price source can highlight when the market hits its lowest point, indicating potential oversold conditions and support levels.
This flexibility ensures that the oscillator can be tailored to different trading strategies, allowing you to refine your analysis and make more informed decisions based on the price metrics most relevant to you.
By leveraging the Kaspa Power Law Deviation Oscillator, traders can gain a clearer perspective on market movements, making more informed decisions based on the deviation from a theoretically ideal price path. This tool adds another layer of insight to your trading strategy, helping you navigate the market with greater confidence.
🔶 LIMITATIONS
Like any technical analysis tool, the Deviation in Euclidean distance from the Kaspa Power law indicator has limitations. It's based on historical data, which may not always accurately predict future market movements.
ADX with Donchian Channels
The "ADX with Donchian Channels" indicator combines the Average Directional Index (ADX) with Donchian Channels to provide traders with a powerful tool for identifying trends and potential breakouts.
Features:
Average Directional Index (ADX):
The ADX is used to quantify the strength of a trend. It helps traders determine whether a market is trending or ranging.
Adjustable parameters for ADX smoothing and DI length allow traders to fine-tune the sensitivity of the trend strength measurement.
Donchian Channels on ADX:
Donchian Channels are applied directly to the ADX values to highlight the highest high and lowest low of the ADX over a specified period.
The upper and lower Donchian Channels can signal potential trend breakouts when the ADX value moves outside these bounds.
The middle Donchian Channel provides a reference for the average trend strength.
Visualization:
The indicator plots the ADX line in red to clearly display the trend strength.
The upper and lower Donchian Channels are plotted in blue, with a green middle line to represent the average.
The area between the upper and lower Donchian Channels is filled with a blue shade to visually emphasize the range of ADX values.
Default Settings for Scalping:
Donchian Channel Length: 10
Standard Deviation Multiplier: 1.58
ADX Length: 2
ADX Smoothing Length: 2
These default settings are optimized for scalping, offering a quick response to changes in trend strength and potential breakout signals. However, traders can adjust these settings to suit different trading styles and market conditions.
How to Use:
Trend Strength Identification: Use the ADX line to identify the strength of the current trend. Higher ADX values indicate stronger trends.
Breakout Signals: Monitor the ADX value in relation to the Donchian Channels. A breakout above the upper channel or below the lower channel can signal a potential trend continuation or reversal.
Range Identification: The filled area between the Donchian Channels provides a visual representation of the ADX range, helping traders identify when the market is ranging or trending.
This indicator is designed to enhance your trading strategy by combining trend strength measurement with breakout signals, making it a versatile tool for various market conditions.
Glitch IndexGlitch Index is an oscillator from an unknown origin that is discovered in 2013 as a lua indicator taken from MetaStock days and we are not really sure how far back the original idea goes.
How it Works?
As I found this indicator and looking at it's code in different platform I can see it comes back from a basic idea of getting a price value, calculating it's smoothed average with a set multiplier and getting the difference then presenting it on a simplified scale. It appears to be another interpretation of figuring out price acceleration and velocity. The main logic is calculated as below:
price = priceSet(priceType)
_ma = getAverageName(price, MaMethod, MaPeriod)
rocma = ((_ma - _ma ) * 0.1) + 1
maMul = _ma * rocma
diff = price - maMul
gli_ind = (diff / price) * -10
How to Use?
Glitch Index can be used based on different implementations and along with your already existing trading system as a confirmation. Yoıu can use it as a Long signal when the histogram crosses inner levels or you can use it as an overbough and oversold signals when the histogram crosses above outter levels and gets back in the range between outter and inner levels.
You can customise the settings and set your prefered inner and outter levels in indicator settings along with gradient or static based coloring and modify the code as you see fit. The coloring code is set below:
gli_col = gli_ind > outterLevel ? color.green : gli_ind < -outterLevel ? color.red : gli_ind > innerLevel ? color.rgb(106, 185, 109, 57) : gli_ind < -innerLevel ? color.rgb(233, 111, 111, 40) : color.new(color.yellow, 60)
gradcol = color.from_gradient(gli_ind, -outterLevel, outterLevel, color.red, color.green)
colorSelect = colorType == "Gradient" ? gradcol : gli_col
Cosine smoothed stochasticDescription
The "Cosine Smoothed Stochastic" indicator leverages advanced Fourier Transform techniques to smooth the traditional Stochastic Oscillator. This approach enhances the signal's reliability and reduces noise, providing traders with a more refined and actionable indicator.
The Stochastic Oscillator is a popular momentum indicator that measures the current price relative to the high-low range over a specified period. It helps identify overbought and oversold conditions, signaling potential trend reversals. By smoothing this indicator with Fourier Transform techniques, we aim to reduce false signals and improve its effectiveness.
The indicator comprises three main components:
Cosine Function: A custom function to compute the cosine of an input scaled by a frequency tuner.
Kernel Function: Utilizes the cosine function to create a smooth kernel, constrained to positive values within a specific range.
Kernel Regression and Multi Cosine: Perform kernel regression over a lookback period, with the multi cosine function summing these regressions at varying frequencies for a composite smooth signal.
Additionally, the indicator includes a volume oscillator to complement the smoothed stochastic signals, providing insights into market volume trends.
Features
Fourier Transform Smoothing: Advanced smoothing technique to reduce noise.
Volume Oscillator: Dynamic volume-based oscillator for additional market insights.
Customizable Inputs: Users can configure key parameters like regression lookback period, tuning coefficient, and smoothing length.
Visual Alerts: Buy and sell signals based on smoothed stochastic crossovers.
Usage
The indicator is designed for trend-following and momentum-based trading strategies . It helps identify overbought and oversold conditions, trend reversals, and potential entry and exit points based on smoothed stochastic values and volume trends.
Inputs
Cosine Kernel Setup:
varient: Choose between "Tuneable" and "Stepped" regression types.
lookbackR: Lookback period for regression.
tuning: Tuning coefficient for frequency adjustment.
Stochastic Calculation:
volshow: Toggle to show the volume oscillator.
emalength: Smoothing period for the Stochastic Oscillator.
lookback_period, m1, m2: Parameters for the Stochastic Oscillator lookback and moving averages.
How It Works
Stochastic Oscillator:
Computes the stochastic %K and smoothes it with an EMA.
Further smoothes %K using the multi cosine function.
Volume Oscillator:
Calculates short and long EMAs of volume and derives the oscillator as the percentage difference.
Plots volume oscillator columns with dynamic coloring based on the oscillator's value and change.
Visual Representation:
Plots smoothed stochastic lines with colors indicating bullish, bearish, overbought, and oversold conditions.
Uses plotchar to mark crossovers between current and previous values of d.
Displays overbought and oversold levels with filled regions between them.
Chart Example
To understand the indicator better, refer to the clean and annotated chart provided. The script is used without additional scripts to maintain clarity. The chart includes:
Smoothed Stochastic Lines: Colored according to trend conditions.
Volume Oscillator: Plotted as columns for visual volume trend analysis.
Overbought/Oversold Levels: Clearly marked levels with filled regions between them.
Alert Conditions
The indicator sets up alerts for buy and sell signals when the smoothed stochastic crosses over or under its previous value. These alerts can be used for automated trading systems or manual trading signals.
breakthrough of the indicators method :
Initialization and Inputs:
The indicator starts by defining necessary inputs, such as the lookback period for regression, tuning coefficient, and smoothing parameters for the Stochastic Oscillator and volume oscillator.
Cosine Function and Kernel Creation:
The cosine function is defined to compute the cosine of an input scaled by a frequency tuner.
The kernel function utilizes this cosine function to create a smoothing kernel, which is constrained to positive values within a specific range.
Kernel Regression:
The kernel regression function iterates over the lookback period, calculating weighted sums of the source values using the kernel function. This produces a smoothed value by dividing the accumulated weighted values by the total weights.
Multi Cosine Smoothing:
The multi cosine function combines multiple kernel regressions at different frequencies, summing these results and averaging them to achieve a composite smoothed value.
Stochastic Calculation and Smoothing:
The traditional Stochastic Oscillator is calculated, and its %K value is smoothed using an EMA.
The smoothed %K is further refined using the multi cosine function, resulting in a more reliable and less noisy signal.
Volume Oscillator Calculation:
The volume oscillator calculates short and long EMAs of the volume and derives the oscillator as the percentage difference between these EMAs. The result is plotted with dynamic coloring to indicate volume trends.
Plotting and Alerts:
The indicator plots the smoothed stochastic lines , overbought/oversold levels, and volume oscillator on the chart.
Buy and sell alerts are set up based on crossovers of the smoothed stochastic values, providing traders with actionable signals.
MTF WaveTrend [CryptoSea]The MTF WaveTrend Indicator is a sophisticated tool designed to enhance market analysis through multi-timeframe WaveTrend calculations. This tool is built for traders who seek to identify market momentum and potential reversals with higher accuracy.
In the example below, we can see all the choosen timeframes agree on bearish momentum.
Key Features
Multi-Timeframe WaveTrend Analysis: Tracks WaveTrend values across multiple timeframes to provide a comprehensive view of market momentum.
Customizable Colour Rules: Offers three different colour rules (Traditional, WT1 0 Rule, WT1 & WT2 0 Rule) to suit various trading strategies.
Timeframe Visibility Control: Allows users to enable or disable specific timeframes, providing flexibility in analysis.
Clear Visual Indicators: Uses color-coded squares and labels to clearly display WaveTrend status across different timeframes.
Candle Colouring Option: Includes a setting for neutral candle coloring to enhance chart readability.
This example shows what can happen when all timeframes start alligning with eachother.
How it Works
WaveTrend Calculation: Computes the WaveTrend oscillator by applying a series of exponential moving averages and scaling calculations.
Multi-Timeframe Data Aggregation: Utilizes the `request.security` function to gather and display WaveTrend values from various timeframes without repainting issues.
Conditional Plotting: Displays visual cues only when higher timeframes align with the selected timeframe, ensuring relevant and reliable signals.
Dynamic Colour Rules: Adjusts the indicator colors based on the chosen rule, whether it's a traditional crossover, WT1 crossing zero, or both WT1 & WT2 crossing zero.
Traditional: Colors are determined by the relationship between WT1 and WT2. If WT1 is greater than WT2, it is bullish (bullColour), otherwise bearish (bearColour).
WT1 0 Rule: Colors are based on whether WT1 is above or below zero. WT1 above zero is bullish (bullColour), below zero is bearish (bearColour).
WT1 & WT2 0 Rule: A more complex rule where both WT1 and WT2 need to be above zero for a bullish signal (bullColour) or both below zero for a bearish signal (bearColour). If WT1 and WT2 are not in agreement, a neutral color (neutralColour) is displayed.
This indicator will make sure that the lowest timeframe you can see data from will be the timeframe you are on. This is to avoid false signals as you cannot display 3 x 5 minute candles whilst looking at the 15 minute candle.
Application
Strategic Decision-Making: Assists traders in making informed decisions by providing detailed analysis of WaveTrend movements across different timeframes.
Trend Confirmation: Reinforces trading strategies by confirming potential reversals with multi-timeframe WaveTrend analysis.
Customized Analysis: Adapts to various trading styles with extensive input settings that control the display and sensitivity of WaveTrend data.
The MTF WaveTrend Indicator by is an invaluable addition to a trader's toolkit, offering depth and precision in market trend analysis to navigate complex market conditions effectively.
KNN OscillatorOverview
The KNN Oscillator is an advanced technical analysis tool designed to help traders identify potential trend reversals and market momentum. Using the K-Nearest Neighbors (KNN) algorithm, this oscillator normalizes KNN values to create a dynamic and responsive indicator. The oscillator line changes color to reflect the market sentiment, providing clear visual cues for trading decisions.
Key Features
Dynamic Color Oscillator: The line changes color based on the oscillator value – green for positive, red for negative, and grey for neutral.
Advanced KNN Algorithm: Utilizes the K-Nearest Neighbors algorithm for precise trend detection.
Normalized Values: Ensures the oscillator values are normalized to align with the stock price range, making it applicable to various assets.
Easy Integration: Can be easily added to any TradingView chart for enhanced analysis.
How It Works
The KNN Oscillator leverages the K-Nearest Neighbors algorithm to calculate the average distance of the nearest neighbors over a specified period. These values are then normalized to match the stock price range, ensuring they are comparable across different assets. The oscillator value is derived by taking the difference between the normalized KNN values and the source price. The line's color changes dynamically to provide an immediate visual indication of the market's state:
Green: Positive values indicate upward momentum.
Red: Negative values indicate downward momentum.
Grey: Neutral values indicate a stable or consolidating market.
Usage Instructions
Trend Reversal Detection: Use the color changes to identify potential trend reversals. A shift from red to green suggests a bullish reversal, while a shift from green to red indicates a bearish reversal.
Momentum Analysis: The oscillator's value and color help gauge market momentum. Strong positive values (green) indicate strong upward momentum, while strong negative values (red) indicate strong downward momentum.
Market Sentiment: The dynamic color changes provide an easy-to-understand visual representation of market sentiment, helping traders make informed decisions quickly.
Confirmation Tool: Use the KNN Oscillator in conjunction with other technical indicators to confirm signals and improve the accuracy of your trades.
Scalability: Applicable to various timeframes and asset classes, making it a versatile tool for all types of traders.
DeQuex Algo BISTIntroduction:
The DeQuex Algo is an advanced technical analysis tool designed to help traders identify high-probability entry and exit points in the Borsa Istanbul (BIST) market. This updated version incorporates an adaptive MACD to reduce false signals and improve the overall reliability of the indicator.
Key Features:
1. Adaptive MACD: The script utilizes an adaptive MACD that dynamically adjusts to market volatility, reducing the occurrence of false signals often associated with traditional MACD implementations.
2. RSI Confirmation: In addition to the adaptive MACD, the DeQuex Algo also considers RSI readings to provide stronger confirmation for buy and sell signals.
3. Signal Types:
- Buy Signal: Triggered when the adaptive MACD crosses above its signal line.
- Sell Signal: Triggered when the adaptive MACD crosses below its signal line.
- Strong Buy Signal: Triggered when both the adaptive MACD and RSI cross above their respective thresholds, indicating a high-probability bullish setup.
- Strong Sell Signal: Triggered when both the adaptive MACD and RSI cross below their respective thresholds, indicating a high-probability bearish setup.
4. Price Bar Highlighting: The script color-codes price bars to provide a visual representation of the current trend. Green bars indicate an uptrend, red bars indicate a downtrend, and purple bars signify a period of consolidation or uncertainty. This feature allows traders to quickly assess the market context at a glance.
5. Customizable Alerts: Users can enable alerts for each signal type, ensuring they never miss a potential trading opportunity.
6. Dynamic Support and Resistance: The DeQuex Algo incorporates dynamic support and resistance levels based on market volatility. These levels are plotted using an innovative approach that combines Donchian channels with a Kalman filter for smoother, more reliable zones.
7. User-Friendly Inputs: The script provides a range of input parameters, allowing traders to fine-tune the indicator's sensitivity and adapt it to their preferred trading style and timeframe.
How to Use:
1. Add the DeQuex Algo indicator to your TradingView chart.
2. Customize the input parameters as desired, or use the default settings.
3. Enable alerts for your preferred signal types.
4. Look for buy and sell signals based on the adaptive MACD and RSI readings, paying attention to the color-coded price bars for additional context.
5. Consider the dynamic support and resistance levels when planning your entries, exits, and stop-loss placements.
Please note that while the DeQuex Algo is designed to identify high-probability setups, no indicator is perfect, and false signals may still occur. Always use proper risk management and consider other factors, such as market sentiment and fundamental analysis, when making trading decisions.
We hope that the DeQuex Algo will be a valuable addition to your trading toolbox, and we welcome any feedback or suggestions for further improvement.
Best regards,
BrandonJames1337
TR:
İşte güncellenmiş DeQuex Algo göstergeniz için önerilen bir açıklama:
Giriş:
DeQuex Algo, yatırımcıların Borsa İstanbul (BIST) piyasasında yüksek olasılıklı giriş ve çıkış noktalarını belirlemelerine yardımcı olmak için tasarlanmış gelişmiş bir teknik analiz aracıdır. Bu güncellenmiş sürüm, yanlış sinyalleri azaltmak ve göstergenin genel güvenilirliğini artırmak için uyarlanabilir bir MACD içerir.
Temel Özellikler:
1. Uyarlanabilir MACD: Komut dosyası, piyasa oynaklığına dinamik olarak ayarlanan ve genellikle geleneksel MACD uygulamalarıyla ilişkili yanlış sinyallerin oluşumunu azaltan uyarlanabilir bir MACD kullanır.
2. RSI Onayı: Uyarlanabilir MACD'ye ek olarak DeQuex Algo, alım ve satım sinyalleri için daha güçlü onay sağlamak üzere RSI okumalarını da dikkate alır.
3. Sinyal Türleri:
- Alış Sinyali: Uyarlanabilir MACD sinyal çizgisinin üzerine çıktığında tetiklenir.
- Satış Sinyali: Uyarlanabilir MACD sinyal çizgisinin altından geçtiğinde tetiklenir.
- Güçlü Alış Sinyali: Hem uyarlanabilir MACD hem de RSI kendi eşiklerinin üzerine çıktığında tetiklenir ve yüksek olasılıklı bir yükseliş düzenine işaret eder.
- Güçlü Satış Sinyali: Hem uyarlanabilir MACD hem de RSI kendi eşiklerinin altına düştüğünde tetiklenir ve yüksek olasılıklı bir düşüş düzenine işaret eder.
4. Fiyat Çubuğu Vurgulama: Komut dosyası, mevcut eğilimin görsel bir temsilini sağlamak için fiyat çubuklarını renk kodlarıyla kodlar. Yeşil çubuklar yükseliş trendini, kırmızı çubuklar düşüş trendini ve mor çubuklar ise konsolidasyon veya belirsizlik dönemini gösterir. Bu özellik, yatırımcıların piyasa bağlamını bir bakışta hızlı bir şekilde değerlendirmelerine olanak tanır.
5. Özelleştirilebilir Uyarılar: Kullanıcılar her sinyal türü için uyarıları etkinleştirerek potansiyel bir alım satım fırsatını asla kaçırmamalarını sağlayabilir.
6. Dinamik Destek ve Direnç: DeQuex Algo, piyasa oynaklığına dayalı dinamik destek ve direnç seviyeleri içerir. Bu seviyeler, daha yumuşak ve daha güvenilir bölgeler için Donchian kanallarını Kalman filtresiyle birleştiren yenilikçi bir yaklaşım kullanılarak çizilir.
7. Kullanıcı Dostu Girişler: Komut dosyası, yatırımcıların göstergenin hassasiyetini ince ayarlamalarına ve tercih ettikleri ticaret tarzına ve zaman dilimine uyarlamalarına olanak tanıyan bir dizi giriş parametresi sağlar.
Nasıl Kullanılır:
1. DeQuex Algo göstergesini TradingView grafiğinize ekleyin.
2. Giriş parametrelerini istediğiniz gibi özelleştirin veya varsayılan ayarları kullanın.
3. Tercih ettiğiniz sinyal türleri için uyarıları etkinleştirin.
4. Ek bağlam için renk kodlu fiyat çubuklarına dikkat ederek uyarlanabilir MACD ve RSI okumalarına dayalı alım ve satım sinyallerini arayın.
5. Girişlerinizi, çıkışlarınızı ve stop-loss yerleşimlerinizi planlarken dinamik destek ve direnç seviyelerini göz önünde bulundurun.
DeQuex Algo yüksek olasılıklı kurulumları belirlemek için tasarlanmış olsa da, hiçbir göstergenin mükemmel olmadığını ve yine de yanlış sinyallerin oluşabileceğini lütfen unutmayın. Alım satım kararları verirken her zaman uygun risk yönetimini kullanın ve piyasa duyarlılığı ve temel analiz gibi diğer faktörleri göz önünde bulundurun.
DeQuex Algo'nun ticaret araç kutunuza değerli bir katkı sağlayacağını umuyor ve daha fazla iyileştirme için her türlü geri bildirim veya öneriyi memnuniyetle karşılıyoruz.
Saygılarımla,
BrandonJames1337
Modern Trend IdentifierThis is an update by Lightangel112 to Trendilo (Open-Source).
Thanks @ Lightangel112
The Modern Trend Identifier (MTI) is a sophisticated technical analysis tool designed for traders and analysts seeking to accurately determine market trends. This indicator leverages the Arnaud Legoux Moving Average (ALMA) to smooth price data and calculate percentage changes, providing a clearer and more responsive trend analysis. MTI is engineered to highlight trend direction with visual cues, fill areas between the indicator and its bands, and color bars based on trend direction, making it a powerful tool for identifying market momentum and potential reversals.
Capabilities
Smoothing and Trend Calculation:
Utilizes ALMA to smooth price data, reducing noise and providing a clearer view of the trend.
Calculates percentage changes in price over a user-defined lookback period.
Dynamic Range Adjustment:
Normalizes the ALMA percentage change values to ensure they stay within a -100 to 100 range.
Uses a combination of linear and smoothstep compression to handle extreme values without losing sensitivity.
Trend Direction and Highlighting:
Determines the trend direction based on the relationship between the smoothed ALMA percentage change and dynamically adjusted RMS (Root Mean Square) bands.
Colors the trend line to visually indicate whether the market is in an uptrend, downtrend, or neutral state.
Dynamic Threshold Calculation:
Calculates dynamic thresholds using percentile ranks to adapt to changing market conditions.
Visualization Enhancements:
Fills areas between the ALMA percentage change line and its RMS bands to provide a clear visual indication of the trend strength.
Offers the option to color price bars based on the identified trend direction.
Customizable Settings:
Provides extensive customization options for lookback periods, smoothing parameters, ALMA settings, band multipliers, and more.
Allows users to enable or disable various visual enhancements and customize their appearance.
Use Cases
Trend Identification:
MTI helps traders identify the current market trend, whether it's bullish, bearish, or neutral. This can be particularly useful for trend-following strategies.
Momentum Analysis:
By highlighting areas of strong momentum, MTI enables traders to spot potential breakouts or breakdowns. This can be useful for both entry and exit decisions.
Support and Resistance Levels:
The dynamic threshold bands can act as support and resistance levels. Traders can use these levels to set stop-loss and take-profit orders.
Divergence Detection:
MTI can help in identifying divergences between price and the indicator, which can signal potential trend reversals. This is useful for traders looking to capitalize on trend changes.
Risk Management:
The fill areas and colored bars provide clear visual cues about trend strength and direction, aiding in better risk management. Traders can adjust their positions based on the strength of the trend.
Backtesting:
The extensive customization options allow traders to backtest different settings and parameters to optimize their trading strategies for various market conditions.
Multiple Timeframes:
MTI can be applied to multiple timeframes, from intraday charts to daily, weekly, or monthly charts, making it a versatile tool for traders with different trading styles.
Example Scenarios
Day Trading:
A day trader can use MTI on a 5-minute chart to identify intraday trends. By adjusting the lookback period and smoothing parameters, the trader can quickly spot potential entry and exit points based on short-term momentum changes.
Swing Trading:
A swing trader might apply MTI to a 4-hour chart to identify medium-term trends. The dynamic thresholds can help in setting appropriate stop-loss levels, while the trend direction highlighting aids in making informed decisions about holding or exiting positions.
Position Trading:
For a position trader using a daily chart, MTI can help identify the overarching trend. The trader can use the fill areas and bar coloring to assess the strength of the trend and make decisions about entering or exiting long-term positions.
Market Analysis:
An analyst could use MTI to study historical price movements and identify patterns. By examining how the indicator reacted to past market conditions, the analyst can gain insights into potential future price movements.
In summary, the Modern Trend Identifier (MTI) is a versatile and powerful tool that enhances trend analysis with advanced smoothing techniques, dynamic adjustments, and comprehensive visual cues. It is designed to meet the needs of traders and analysts across various trading styles and timeframes, providing clear and actionable insights into market trends and momentum.
Updated with the following:
Additions and Enhancements in MTI
Grouped Inputs with Descriptive Tooltips:
Inputs are organized into groups for better clarity.
Each input parameter includes a descriptive tooltip.
Dynamic Threshold Calculation:
Added dynamic threshold calculation using percentile ranks to adapt to changing market conditions.
Normalization and Compression:
Added normalization factor to ensure plots are within -100 to 100 range.
Introduced smoothstep function for smooth transition and selectively applied linear and smoothstep compression to values outside -80 to 100 range.
Enhanced Visualization:
Highlighted trend direction with RGB colors.
Enhanced fill areas between the ALMA percentage change line and its RMS bands.
Colored price bars based on the identified trend direction.
RMS Lines Adjustment:
Dynamically adjusted RMS calculation without strict capping.
Ensured RMS lines stay below fill areas to maintain clarity.
Descriptive and Organized Code:
Enhanced code clarity with detailed comments.
Organized code into logical sections for better readability and maintenance.
Key Differences and Improvements.
Input Customization:
Trendilo: Inputs are simple and ungrouped.
MTI: Inputs are grouped and include tooltips for better user guidance.
Trend Calculation:
Trendilo: Uses ALMA and calculates percentage change.
MTI: Enhanced with normalization, compression, and dynamic threshold calculation.
Normalization and Compression:
Trendilo: No normalization or compression applied.
MTI: Normalizes values to -100 to 100 range and applies smoothstep compression to handle extreme values.
Dynamic RMS Adjustment:
Trendilo: Simple RMS calculation.
MTI: Dynamically adjusted RMS calculation to ensure clarity in visualization.
Visual Enhancements:
Trendilo: Basic trend highlighting and filling.
MTI: Enhanced visual cues with RGB colors, dynamic threshold bands, and improved fill areas.
Code Clarity:
Trendilo: Functional but lacks detailed comments and organization.
MTI: Well-organized, extensively commented code for better readability and maintainability.
[GYTS-Pro] Flux Composer🧬 Flux Composer (Professional Edition)
🌸 Confluence indicator in GoemonYae Trading System (GYTS) 🌸
The Flux Composer is a powerful tool in the GYTS suite that is designed to aggregate signals from multiple Signal Providers, apply advanced decaying functions, and offer customisable and advanced confluence mechanisms. This allows making informed decisions by considering the strength and agreement ("when all stars align") of various input signals.
🌸 --------- TABLE OF CONTENTS --------- 🌸
1️⃣ Main Highlights
2️⃣ Flux Composer’s Features
Multi Signal Provider support
Advanced decaying functions
Customisable Flux confluence mechanisms
Actionable trading experience
Filtering options
User-friendly experience
Upgrades compared to Community Edition
3️⃣ User Guide
Selecting Signal Providers
Connecting Signal Providers to the Flux Composer
Understanding the Flux
Tuning the decaying functions
Choosing Flux confluence mechanism
Choosing sensitivity
Utilising the filtering options
Interpreting the Flux for trading signals
4️⃣ Limitations
🌸 ------ 1️⃣ --- MAIN HIGHLIGHTS --- 1️⃣ ------ 🌸
- Signal aggregation : Combines signals from multiple different 📡 Signal Providers, each of which can be tuned and adjusted independently.
- Decaying function : Utilises advanced decaying functions to model the diminishing effect of signals over time, ensuring that recent signals have more weight. In addition to the decaying effect, the "quality" of the original signals (e.g. a "strong" GDM from WaveTrend 4D ) are accounted for as well.
- Flux confluence mechanism : The aggregation of all decaying functions form the "Flux", which is the core signal measurement of the Flux Composer. Multiple mechanisms are available for creating the Flux and effectively using it for actionable trading signals.
- Visualisation : Provides detailed visualisation options to help users understand and tune the contributions of individual Signal Providers and their decaying functions.
- Backtesting : The 🧬 Flux Composer is a core component of the TradingView suite of the 🌸 GoemonYae Trading System (GYTS) 🌸. It connects multiple 📡 Signal Providers, such as the WaveTrend 4D, and processes their signals to produce a unified "Flux". This Flux can then be used by the GYTS "🎼 Order Orchestrator" for backtesting and trade automation.
🌸 ------ 2️⃣ --- FLUX COMPOSER'S FEATURES --- 2️⃣ ------ 🌸
Let's delve into more details...
💮 1. Multi Signal Provider support
Using the name of the GYTS "🎼 Order Orchestrator" as an analogy: Imagine a symphony where each instrument plays its own unique part, contributing to the overall harmony. The Flux Composer operates similarly, integrating multiple Signal Providers to create a comprehensive and robust trading signal -- the "Flux". Currently, it supports up to four streams from the WaveTrend 4D's ’s Gradient Divergence Measure (GDM) and another four streams from the Quantile Median Cross (QMC). These can be either four "Professional Edition" Signal Providers or eight "Community Editions".
Note that the GDM includes 2 different continuous signals and the QMC 3 different continuous signals (from different frequencies). This means that the Community Edition can handle 2*2 + 2*3 = 10 different continuous signals and the Professional Edition as much as 20.
As GYTS evolves, more Signal Providers will be added; at the moment of releasing the Flux Composer, only WaveTrend 4D is publicly available.
💮 2. Advanced decaying functions
A trading signal can be relevant today, less relevant tomorrow, and irrelevant in a week's time. In other words, its relevance diminishes, or decays , over time. The Flux Composer utilises decaying functions that ensure that recent signals carry more weight, while older signals fade away. This is crucial for accurate signal processing. The intensity and decay settings allow for precise control, allowing emphasising certain signals based on their strength and relevance over time. On top of that, unlike binary signals ("buy now"), the Flux Composer utilises the actual values from the Signal Providers, differentiating between the exact quality of signals, and thus offering a detailed representation of the trading landscape. We will illustrate this in a further section.
💮 3. Customisable Flux confluence mechanisms
Another core component of the Flux Composer is the ability of intelligently combining the decaying functions. It offers four sophisticated confluence mechanisms: Amplitude Compression, Accentuated Amplitude Compression, Trigonometric, and GYTSynthesis. Each mechanism has its unique way of processing the Flux, tailored to different trading needs. For instance, the Amplitude Compression method scales the Flux based on recent values, much like the Stochastic Oscillator, while the Trigonometric method uses smooth functions to reduce outliers’ impact. The GYTSynthesis is a proprietary method, striking a balance between signal strength and discriminative power.
We'll discuss this in more detail in the User Guide section.
💮 4. Actionable trading experience
While the mathematical abilities might seem overwhelming, the goal of the Flux Composer is to transform complex signal data into actionable trading signals. When the Flux reaches certain thresholds, it generates clear bullish or bearish signals, making it easy for traders to interpret. The inclusion of upper and lower thresholds (UT and LT) helps in identifying strong signals visually and should be a familiar behaviour similar to how many other indicators operate. Furthermore, the Flux Composer can plot trading signals directly on the oscillator, showing triangle shapes for buy or sell signals. This visual aid is complemented by the possibility to setup TradingView alerts.
💮 5. Filtering options
The Professional Edition also offers filtering options to possibly further improve the quality of Flux signals. Signal streams can be divided into “Signal Flux” and “Filter Flux.” The Filter Flux acts as a gatekeeper, ensuring that only signals meeting the Filter's criteria (which consist of similar UT/LT thresholds) are considered for trading. This dual-layer approach enhances the reliability of trading signals, reducing the chances of false positives.
💮 6. User-friendly experience
GYTS is all about sophisticated, robust methods but also "elegance". One of the interpretations of the latter, is that the users' experience is very important. Despite the Flux Composer's mathematical underpinnings, it offers intuitive settings that with omprehensive tooltips to help with a smooth setup process. For those looking to fine-tune their signals, the Flux Composer allows the visualisation of individual decaying functions. This feature helps users understand the impact of each setting and make informed adjustments. Additionally, the background of the chart can be coloured to indicate the trading direction suggested by the Filter Flux, providing an at-a-glance overview of market conditions.
💮 7. Upgrades compared to Community Edition
Number of signal streams -- At the moment of writing, the Professional Edition works with 4x GDM and 4x QMC signal streams from WaveTrend 4D Signal Provider , while Community Edition (CE) Flux Composer (FC) only works with 2x GDM and 2x QMC signal streams.
Flux confluence mechanism -- CE includes the Amplitude Compression and Trigonometric confluence mechanisms, while the Pro Edition also includes the Accentuated Amplitude Compression and the GYTSynthesis mechanisms.
Signal streams as filters -- The Pro Edition can use Signal Providers as filters.
🌸 ------ 3️⃣ --- USER GUIDE --- 3️⃣ ------ 🌸
💮 1. Selecting Signal Providers
The Flux Composer’s foundation lies in its Signal Providers. When starting with the Flux Composer, using a single Signal Provider can already provide significant value due to the nature of decaying functions. For instance, the WaveTrend 4D signal provider includes up to 5 signal types (GDM and QMC in different frequencies) in a single direction (long/short). Moreover, the various confluence mechanisms that enhance the resulting Flux result in improved discrimination between weak and strong signals. This approach is akin to ensemble learning in machine learning, where multiple models are combined to improve predictive performance.
While using a single Signal Provider is beneficial, the true power of the Flux Composer is realised with multiple Signal Providers. Here are two general approaches to selecting Signal Providers:
Diverse Behaviours
Use Signal Providers with different behaviours, such as WaveTrend 4D on various assets/timeframes or entirely different Signal Providers. This approach leverages diversification to achieve robustness, rooted in the principle that varied sources enhance the overall signal quality. To explain this with an analogy, this strategy aligns with the theory of diversification in portfolio management, where combining uncorrelated assets reduces overall risk. Similarly, combining uncorrelated signals can mitigate the risk of signal failure. A practical example can be integrating a mean-reversion signal with a trend-following signal -- these can balance each other out, providing more stable outputs over different market conditions.
Enhancing a Single Provider
If you consider a particular Signal Provider highly effective, you could improve its robustness by using multiple instances with slight variations. These variations could include different sources (e.g., close, HL2, HLC3), data providers (same asset across different brokers/exchanges), or parameter adjustments. This method mirrors Monte Carlo simulations, often used in risk management and derivative pricing, which involve running many simulations with varied inputs to estimate the probability of different outcomes. By applying similar principles, the strategy becomes less susceptible to overfitting, ensuring the signals are not overly dependent on specific data conditions.
💮 2. Connecting Signal Providers to the Flux Composer
Moving on to practicalities: how do you connect Signal Providers with the Flux Composer? You may have noticed that when you open the drawdown of a data source in a TradingView indicator (with "open", "high", "low", etc.), you also see names from other indicators on your chart. We call these "streams", and the Signal Providers are designed such that they output this stream in a way that the Flux Composer can interpret it. Thus, to connect a Signal Provider with the Flux Composer, you should first have that Signal Provider on your chart. Obviously you should set it up an a way that it seems to provide good signals. After that, in the Data Stream dropdown in the Flux Composer, you can select the stream that is outputted by your Signal Provider. This will always be with a prefix of "🔗 STREAM" (after the Signal Provider's indicator name). See the chart below.
There is one important nuance: when you have multiple (similar) Signal Providers on your chart, it may be hard to select the correct data stream in the Flux Composer as the names of the streams keep repeating when you use identical indicators. So be sure to be attentive as you might end up using the same signals multiple times.
Also, the Signal Providers have an "Indicator name" parameter (and another parameter to repeat this name) that is handy to use when you have multiple Signal Providers on your screen. It is handy to give names that describe the unique settings of that Signal Provider so you can better differentiate what you are looking at on your screen.
💮 3. Understanding the Flux
Let's understand how the Signal Provider's signals are processed. In the chart below, you see we have one Signal Provider (WaveTrend 4D) connected to the Flux Composer and that it gives a bearish QMC signal. The Flux Composer converts this into a decaying function. You can show these functions per Signal Provider when the option "Show decaying function of Signal Provider" is enabled (as it is in the chart).
In our opinion, of crucial importance is the ability to process the quality of signals, rather than just any signal. In mathematical terms, we are interested in continuous signals as these provide a spectrum of values. These signals can reflect varying degrees of market sentiment or trend strength, offering richer information than binary signals, which offer only two states (e.g., buy/sell). Especially in the context of the Flux Composer, where you aggregate multiple signals, it makes a big difference whether you combine 10 weak signals or 10 strong signals. To illustrate this principle, look at the chart below where there are 4 signals of different strengths. As you can see, each of the signals affects the Flux with different intensities.
💮 4. Tuning the decaying functions
As previously mentioned, the decaying functions are a way to give more importance to recent signals while allowing older ones to fade away gradually. This mimics the natural way we assess information, giving more weight to recent events. The decaying functions in the Flux Composer are highly customisable while remaining easy to use. You can adjust the initial intensity , which sets the starting strength of a signal, and the decay rate, which determines how quickly this signal diminishes over time. Let's look at specific examples.
If we add 3 Flux Composers on the chart, connect the same Signal Provider, keep all settings the same with one exception, we get the chart below. Here we have changed the "intensity" parameter of the specific signal. As you can see, the decaying functions are different. The intensity determines the initial strength of the decayed function. Adjusting the intensity allows you to emphasise certain signal types based on their perceived reliability or importance.
Let's now keep the intensity the same ("normal"), but change the "decay" parameter. As you can see in the image below, the decay controls how quickly the signal’s strength diminishes over time. By adjusting the decay, you can model the longevity of the signal’s impact. A faster decay means the signal loses its influence quickly, while a slower decay means it remains relevant for a longer period.
So how do multiple signals interact? You can see this as a simple "stacking of decaying functions" (although there is more to it, see next section). In the chart below we different strenghts of signals and different decay rates to illustrate how the Flux is constructed.
Hopefully this helps with developing some intuition how signals are converted to decaying functions, how you can control them, and how the Flux is constructed. When tuning these parameters, use the visualisation options to see how individual decaying functions contribute to the overall Flux. This helps in understanding and refining the parameters to achieve the desired trading signal behaviour.
💮 5. Choosing Flux confluence mechanism
While we mentioned that the Flux is a "stacking of individual decaying functions", in the back-end, that is not exactly that simple. Like previously mentioned, for GYTS, "elegance" is very important. One of the interpretations is "user friendliness" and the Flux confluence mechanism is one of the essential developments for this characteristic. The Flux confluence mechanism is critical in synthesising the aggregated signals into the Flux. The choice of mechanism affects how the signals are combined and the resulting trading signals. The Professional Edition offers four distinct mechanisms, each with its strengths.
The Amplitude Compression mechanism is intuitive, scaling the Flux based on recent values, intuitively not unlike the method of the well-known Stochastic Oscillator. The Accentuated Amplitude Compression method takes this a step further, giving more weight to strong Flux values. The Trigonometric mechanism smooths the Flux and reduces the impact of outliers, providing a balanced approach. Finally, the GYTSynthesis mechanism, a proprietary approach, balances signal strength and discriminative power, making it easier to tune and generalise.
It's difficult to convey the workings of the Flux confluence mechanism in a chart, but let's take the opportunity to show how the Flux would look like when connecting both one WaveTrend 4D Signal Provider signals to four Flux Composers with default settings, except the Flux confluence mechanism:
You may notice subtle differences between the four methods. They react differently to different values and their overall shape is slightly be different. The Amplitude Compression is more "pointy" and GYTSynthesis doesn't react to low values. There are many nuances, especially in combination with tuning the sensitivity and upper/lower threshold (UT/LT) parameters.
💮 6. Choosing sensitivity
Speaking of the sensitivity , this parameters fine-tunes how responsive the Flux is to the input signals. Higher sensitivity results in more pronounced responses, leading to more frequent trading signals. Lower sensitivity makes the Flux less responsive, resulting in fewer but potentially more reliable signals.
You might think that changing the upper/lower threshold (UT/LT) parameters would be equivalent, but that's not the case. The sensitivity In case of the Amplitude Compression mechanisms, changing the sensitivity would change the relative Flux shape over time, and with the Trigonometric and GYTSynthesis mechanisms, the Flux shape itself (independent of time) would change. In other words, these are all good parameters for tuning.
💮 7. Utilising the filtering options
When choosing the signal stream of a Signal Provider, you can also change the default "Signal" category of that Signal Provider to a "Filter". In the example below, two Signal Providers are connected; the second is set as a filter. You can see that a second row of a Flux is shown in the Flux Composer (this visualisation can be disabled), corresponding with the signals of the second Signal Provider.
Logically, only when the Filter Flux gives a signal in a certain direction, signals from the regular Signal Flux are registered. Generally speaking, for this use case it is handy to set the thresholds for the Filter Flux low and possibly to decrease the decay rate so that the filtering is active for a long enough time.
💮 8. Interpreting the Flux for trading signals
Lastly, the Signal Flux gives buy and sell signals when it crosses the upper/lower thresholds (UT/LT), when the filter allows it (if enabled). This can be visualised with the triangles as you may have seen in the charts in the previous sections. For people using TradingView's alerts -- these would work too out of the box. And finally, for backtesting and possibly trade automation, we will have the GYTS "🎼 Order Orchestrator" that connects with the Flux Composer.
🌸 ------ 4️⃣ --- LIMITATIONS --- 4️⃣ ------ 🌸
Only 🌸 GYTS 📡 Signal Providers are supported, as there is a specific method to pass continuous (non-binary) data in the data stream
At the moment of release, only the WaveTrend 4D Signal Provider is available. Other Signal Providers will be gradually released.
[GYTS-CE] Flux Composer🧬 Flux Composer (Community Edition)
🌸 Confluence indicator in GoemonYae Trading System (GYTS) 🌸
The Flux Composer is a powerful tool in the GYTS suite that is designed to aggregate signals from multiple Signal Providers, apply customisable decaying functions, and offer customisable and advanced confluence mechanisms. This allows making informed decisions by considering the strength and agreement ("when all stars align") of various input signals.
🌸 --------- TABLE OF CONTENTS --------- 🌸
1️⃣ Main Highlights
2️⃣ Flux Composer’s Features
Multi Signal Provider support
Advanced decaying functions
Customisable Flux confluence mechanisms
Actionable trading experience
User-friendly experience
3️⃣ User Guide
Selecting Signal Providers
Connecting Signal Providers to the Flux Composer
Understanding the Flux
Tuning the decaying functions
Choosing Flux confluence mechanism
Choosing sensitivity
Interpreting the Flux for trading signals
4️⃣ Limitations
🌸 ------ 1️⃣ --- MAIN HIGHLIGHTS --- 1️⃣ ------ 🌸
- Signal aggregation : Combines signals from multiple different 📡 Signal Providers, each of which can be tuned and adjusted independently.
- Decaying function : Utilises advanced decaying functions to model the diminishing effect of signals over time, ensuring that recent signals have more weight. In addition to the decaying effect, the "quality" of the original signals (e.g. a "strong" GDM from WaveTrend 4D with GDM ) are accounted for as well.
- Flux confluence mechanism : The aggregation of all decaying functions form the "Flux", which is the core signal measurement of the Flux Composer. Multiple mechanisms are available for creating the Flux and effectively using it for actionable trading signals.
- Visualisation : Provides detailed visualisation options to help users understand and tune the contributions of individual Signal Providers and their decaying functions.
- Backtesting : The 🧬 Flux Composer is a core component of the TradingView suite of the 🌸 GoemonYae Trading System (GYTS) 🌸. It connects multiple 📡 Signal Providers, such as the WaveTrend 4D, and processes their signals to produce a unified "Flux". This Flux can then be used by the GYTS "🎼 Order Orchestrator" for backtesting and trade automation.
🌸 ------ 2️⃣ --- FLUX COMPOSER'S FEATURES --- 2️⃣ ------ 🌸
Let's delve into more details...
💮 1. Multi Signal Provider support
Using the name of the GYTS "🎼 Order Orchestrator" as an analogy: Imagine a symphony where each instrument plays its own unique part, contributing to the overall harmony. The Flux Composer operates similarly, integrating multiple Signal Providers to create a comprehensive and robust trading signal -- the "Flux". Currently, it supports up to two streams from the WaveTrend 4D’s Gradient Divergence Measure (GDM) and another two streams from the WaveTrend 4D's Quantile Median Cross (QMC) .
Note that the GDM includes 2 different continuous signals and the QMC 3 different continuous signals (from different frequencies). This means that the Community Edition can handle 2*2 + 2*3 = 10 different continuous signals.
As GYTS evolves, more Signal Providers will be added; at the moment of releasing the Flux Composer, only WaveTrend 4D with GDM and with QMC are publicly available.
💮 2. Advanced decaying functions
A trading signal can be relevant today, less relevant tomorrow, and irrelevant in a week's time. In other words, its relevance diminishes, or decays , over time. The Flux Composer utilises decaying functions that ensure that recent signals carry more weight, while older signals fade away. This is crucial for accurate signal processing. The intensity and decay settings allow for precise control, allowing emphasising certain signals based on their strength and relevance over time. On top of that, unlike binary signals ("buy now"), the Flux Composer utilises the actual values from the Signal Providers, differentiating between the exact quality of signals, and thus offering a detailed representation of the trading landscape. We will illustrate this in a further section.
💮 3. Customisable Flux confluence mechanisms
Another core component of the Flux Composer is the ability of intelligently combining the decaying functions. It offers two sophisticated confluence mechanisms: Amplitude Compression and Trigonometric. Each mechanism has its unique way of processing the Flux, tailored to different trading needs. The Amplitude Compression method scales the Flux based on recent values, much like the Stochastic Oscillator, while the Trigonometric method uses smooth functions to reduce outliers’ impact We'll discuss this in more detail in the User Guide section.
💮 4. Actionable trading experience
While the mathematical abilities might seem overwhelming, the goal of the Flux Composer is to transform complex signal data into actionable trading signals. When the Flux reaches certain thresholds, it generates clear bullish or bearish signals, making it easy for traders to interpret. The inclusion of upper and lower thresholds (UT and LT) helps in identifying strong signals visually and should be a familiar behaviour similar to how many other indicators operate. Furthermore, the Flux Composer can plot trading signals directly on the oscillator, showing triangle shapes for buy or sell signals. This visual aid is complemented by the possibility to setup TradingView alerts.
💮 5. User-friendly experience
GYTS is all about sophisticated, robust methods but also "elegance". One of the interpretations of the latter, is that the users' experience is very important. Despite the Flux Composer's mathematical underpinnings, it offers intuitive settings that with omprehensive tooltips to help with a smooth setup process. For those looking to fine-tune their signals, the Flux Composer allows the visualisation of individual decaying functions. This feature helps users understand the impact of each setting and make informed adjustments.
🌸 ------ 3️⃣ --- USER GUIDE --- 3️⃣ ------ 🌸
💮 1. Selecting Signal Providers
The Flux Composer’s foundation lies in its Signal Providers. When starting with the Flux Composer, using a single Signal Provider can already provide significant value due to the nature of decaying functions. For instance, the WaveTrend 4D signal provider includes up to two GDM and three QMC signals in a single direction (long/short). Moreover, the various confluence mechanisms that enhance the resulting Flux result in improved discrimination between weak and strong signals. This approach is akin to ensemble learning in machine learning, where multiple models are combined to improve predictive performance.
While using a single Signal Provider is beneficial, the true power of the Flux Composer is realised with multiple Signal Providers. Here are two general approaches to selecting Signal Providers:
Diverse Behaviours
Use Signal Providers with different behaviours, such as WaveTrend 4D on various assets/timeframes or entirely different Signal Providers. This approach leverages diversification to achieve robustness, rooted in the principle that varied sources enhance the overall signal quality. To explain this with an analogy, this strategy aligns with the theory of diversification in portfolio management, where combining uncorrelated assets reduces overall risk. Similarly, combining uncorrelated signals can mitigate the risk of signal failure. A practical example can be integrating a mean-reversion signal with a trend-following signal -- these can balance each other out, providing more stable outputs over different market conditions.
Enhancing a Single Provider
If you consider a particular Signal Provider highly effective, you could improve its robustness by using multiple instances with slight variations. These variations could include different sources (e.g., close, HL2, HLC3), data providers (same asset across different brokers/exchanges), or parameter adjustments. This method mirrors Monte Carlo simulations, often used in risk management and derivative pricing, which involve running many simulations with varied inputs to estimate the probability of different outcomes. By applying similar principles, the strategy becomes less susceptible to overfitting, ensuring the signals are not overly dependent on specific data conditions.
💮 2. Connecting Signal Providers to the Flux Composer
Moving on to practicalities: how do you connect Signal Providers with the Flux Composer? You may have noticed that when you open the drawdown of a data source in a TradingView indicator (with "open", "high", "low", etc.), you also see names from other indicators on your chart. We call these "streams", and the Signal Providers are designed such that they output this stream in a way that the Flux Composer can interpret it. Thus, to connect a Signal Provider with the Flux Composer, you should first have that Signal Provider on your chart. Obviously you should set it up an a way that it seems to provide good signals. After that, in the Data Stream dropdown in the Flux Composer, you can select the stream that is outputted by your Signal Provider. This will always be with a prefix of "🔗 STREAM" (after the Signal Provider's indicator name). See the chart below.
There is one important nuance: when you have multiple (similar) Signal Providers on your chart, it may be hard to select the correct data stream in the Flux Composer as the names of the streams keep repeating when you use identical indicators. So be sure to be attentive as you might end up using the same signals multiple times.
Also, the Signal Providers have an "Indicator name" parameter (and another parameter to repeat this name) that is handy to use when you have multiple Signal Providers on your screen. It is handy to give names that describe the unique settings of that Signal Provider so you can better differentiate what you are looking at on your screen.
💮 3. Understanding the Flux
Let's understand how the Signal Provider's signals are processed. In the chart below, you see we have one Signal Provider (WaveTrend 4D) connected to the Flux Composer and that it gives a bearish QMC signal. The Flux Composer converts this into a decaying function. You can show these functions per Signal Provider when the option "Show decaying function of Signal Provider" is enabled (as it is in the chart).
In our opinion, of crucial importance is the ability to process the quality of signals, rather than just any signal. In mathematical terms, we are interested in continuous signals as these provide a spectrum of values. These signals can reflect varying degrees of market sentiment or trend strength, offering richer information than binary signals, which offer only two states (e.g., buy/sell). Especially in the context of the Flux Composer, where you aggregate multiple signals, it makes a big difference whether you combine 10 weak signals or 10 strong signals. To illustrate this principle, look at the chart below where there are 4 signals of different strengths. As you can see, each of the signals affects the Flux with different intensities.
💮 4. Tuning the decaying functions
As previously mentioned, the decaying functions are a way to give more importance to recent signals while allowing older ones to fade away gradually. This mimics the natural way we assess information, giving more weight to recent events. The decaying functions in the Flux Composer are highly customisable while remaining easy to use. You can adjust the initial intensity , which sets the starting strength of a signal, and the decay rate, which determines how quickly this signal diminishes over time. Let's look at specific examples.
If we add 3 Flux Composers on the chart, connect the same Signal Provider, keep all settings the same with one exception, we get the chart below. Here we have changed the "intensity" parameter of the specific signal. As you can see, the decaying functions are different. The intensity determines the initial strength of the decayed function. Adjusting the intensity allows you to emphasise certain signal types based on their perceived reliability or importance.
Let's now keep the intensity the same ("normal"), but change the "decay" parameter. As you can see in the image below, the decay controls how quickly the signal’s strength diminishes over time. By adjusting the decay, you can model the longevity of the signal’s impact. A faster decay means the signal loses its influence quickly, while a slower decay means it remains relevant for a longer period.
So how do multiple signals interact? You can see this as a simple "stacking of decaying functions" (although there is more to it, see next section). In the chart below we use different "intensity" and "decay" parameters to discuss how the Flux is created.
Hopefully this helps with developing some intuition how signals are converted to decaying functions, how you can control them, and how the Flux is constructed. When tuning these parameters, use the visualisation options to see how individual decaying functions contribute to the overall Flux. This helps in understanding and refining the parameters to achieve the desired trading signal behaviour.
💮 5. Choosing Flux confluence mechanism
While we mentioned that the Flux is a "stacking of individual decaying functions", in the back-end, that is not exactly that simple. Like previously mentioned, for GYTS, "elegance" is very important. One of the interpretations is "user friendliness" and the Flux confluence mechanism is one of the essential developments for this characteristic. The Flux confluence mechanism is critical in synthesising the aggregated signals into the Flux. The choice of mechanism affects how the signals are combined and the resulting trading signals. The Community Edition offers two distinct mechanisms, each with its strengths.
The Amplitude Compression mechanism is intuitive, scaling the Flux based on recent values, intuitively not unlike the method of the well-known Stochastic Oscillator. On the other hand, the Trigonometric mechanism smooths the Flux and reduces the impact of outliers, providing a balanced approach. It's difficult to convey the workings of the Flux confluence mechanism in a chart, but let's take the opportunity to show how the Flux would look like when connecting both GDM and QMC signals to two Flux Composers with default settings, except the Flux confluence mechanism:
You can notice that the upper Flux Converter (FC) triggered two signals while the other FC triggered only one. There are more nuances, especially in combination with tuning the sensitivity and upper/lower threshold (UT/LT) parameters.
💮 6. Choosing sensitivity
Speaking of the sensitivity , this parameters fine-tunes how responsive the Flux is to the input signals. Higher sensitivity results in more pronounced responses, leading to more frequent trading signals. Lower sensitivity makes the Flux less responsive, resulting in fewer but potentially more reliable signals.
You might think that changing the upper/lower threshold (UT/LT) parameters would be equivalent, but that's not the case. The sensitivity In case of the Amplitude Compression mechanism, changing the sensitivity would change the relative Flux shape over time, and with the Trigonometric mechanism, the Flux shape itself (independent of time) would change. In other words, these are all good parameters for tuning.
💮 8. Interpreting the Flux for trading signals
Lastly, the Signal Flux gives buy and sell signals when it crosses the upper/lower thresholds (UT/LT) This can be visualised with the triangles as you may have seen in the charts in the previous sections. For people using TradingView's alerts -- these would work out of the box. And finally, for backtesting and possibly trade automation, we will have the GYTS "🎼 Order Orchestrator" that connects with the Flux Composer.
🌸 ------ 4️⃣ --- LIMITATIONS --- 4️⃣ ------ 🌸
Only 🌸 GYTS 📡 Signal Providers are supported, as there is a specific method to pass continuous (non-binary) data in the data stream
At the moment of release, only WaveTrend 4D with GDM and with QMC are available. Other Signal Providers will be gradually released.
Persistent Homology Based Trend Strength OscillatorPersistent Homology Based Trend Strength Oscillator
The Persistent Homology Based Trend Strength Oscillator is a unique and powerful tool designed to measure the persistence of market trends over a specified rolling window. By applying the principles of persistent homology, this indicator provides traders with valuable insights into the strength and stability of uptrends and downtrends, helping to inform better trading decisions.
What Makes This Indicator Original?
This indicator's originality lies in its application of persistent homology , a method from topological data analysis, to financial markets. Persistent homology examines the shape and features of data across multiple scales, identifying patterns that persist as the scale changes. By adapting this concept, the oscillator tracks the persistence of uptrends and downtrends in price data, offering a novel approach to trend analysis.
Concepts Underlying the Calculations:
Persistent Homology: This method identifies features such as clusters, holes, and voids that persist as the scale changes. In the context of this indicator, it tracks the duration and stability of price trends.
Rolling Window Analysis: The oscillator uses a specified window size to calculate the average length of uptrends and downtrends, providing a dynamic view of trend persistence over time.
Threshold-Based Trend Identification: It differentiates between uptrends and downtrends based on specified thresholds for price changes, ensuring precision in trend detection.
How It Works:
The oscillator monitors consecutive changes in closing prices to identify uptrends and downtrends.
An uptrend is detected when the closing price increase exceeds a specified positive threshold.
A downtrend is detected when the closing price decrease exceeds a specified negative threshold.
The lengths of these trends are recorded and averaged over the chosen window size.
The Trend Persistence Index is calculated as the difference between the average uptrend length and the average downtrend length, providing a measure of trend persistence.
How Traders Can Use It:
Identify Trend Strength: The Trend Persistence Index offers a clear measure of the strength and stability of uptrends and downtrends. A higher value indicates stronger and more persistent uptrends, while a lower value suggests stronger and more persistent downtrends.
Spot Trend Reversals: Significant shifts in the Trend Persistence Index can signal potential trend reversals. For instance, a transition from positive to negative values might indicate a shift from an uptrend to a downtrend.
Confirm Trends: Use the Trend Persistence Index alongside other technical indicators to confirm the strength and duration of trends, enhancing the accuracy of your trading signals.
Manage Risk: Understanding trend persistence can help traders manage risk by identifying periods of high trend stability versus periods of potential volatility. This can be crucial for timing entries and exits.
Example Usage:
Default Settings: Start with the default settings to get a feel for the oscillator’s behavior. Observe how the Trend Persistence Index reacts to different market conditions.
Adjust Thresholds: Fine-tune the positive and negative thresholds based on the asset's volatility to improve trend detection accuracy.
Combine with Other Indicators: Use the Persistent Homology Based Trend Strength Oscillator in conjunction with other technical indicators such as moving averages, RSI, or MACD for a comprehensive analysis.
Backtesting: Conduct backtesting to see how the oscillator would have performed in past market conditions, helping you to refine your trading strategy.
FaikValThe "FaikVal" indicator is a powerful tool designed to help traders analyze relative price movements between a base asset and up to three comparison assets. This indicator uses exponential moving averages (EMA) and normalization techniques to identify potential overbought and oversold situations.
Functions and Applications:
Comparison of Price Ratios: The indicator calculates the ratio of the closing price of the base asset to the closing prices of three user-defined comparison assets. This allows for direct comparative analysis and helps identify relative strengths and weaknesses.
EMA Calculations: Two EMAs are calculated for each price ratio (with configurable periods). The difference between these two EMAs serves as the basis for further calculations.
Normalization: The calculated values are normalized over a defined period, helping to smooth out extreme values and facilitate analysis. This normalization transforms the values onto a scale from -100 to 100.
Optional Smoothing: Optional smoothing of the normalized values can be enabled to further reduce short-term fluctuations and generate clearer signals.
Visual Signals: The indicator plots three lines (one for each comparison ratio), representing the normalized values. Additionally, horizontal lines are displayed at +60, -60, and 0 to mark overbought and oversold zones as well as neutral areas.
Customizability: Users can adjust the periods of the EMAs, the length of the normalization period, and the smoothing period. They can also specify which of the three indicators should be displayed.
Applications:
Relative Strength Analysis: Identify whether the base asset is performing stronger or weaker compared to other markets or instruments.
Trend Confirmation: Confirm existing trends by analyzing the movements of the base asset relative to the comparison assets.
Overbought and Oversold Signals: Use the displayed values and horizontal lines to identify potential market turning points and determine entry or exit points.
!!! It works best on the weekly and daily chart for swing trading. It is a set up tool, to determin weather you should go long or short and not a market timing tool. For timing you could use concepts like trend and supply and demand!!!
The "FaikVal" indicator offers versatile and detailed analysis, making it particularly useful for traders seeking deeper insights into relative price strength and weakness.
intellect_city - World Cycle - Ath & Atl - Logarithmic - Signal.Indicator Overview
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Signal - The Pi Cycle Top and Bottom Oscillator is an adaptation of the original Pi Cycle Top chart. It compares the 111-Day Moving Average circle and the 2 * 350-Day Moving Average circle of Bitcoin’s Price. These two moving averages were selected as 350 / 111 = 3.153; An approximation of the important mathematical number Pi.
When the 111-Day Moving Average circle reaches the 2 * 350-Day Moving Average circle, it indicates that the market is becoming overheated. That is because the mid time frame momentum reference of the 111-Day Moving Average has caught up with the long timeframe momentum reference of the 2 * 350-Day Moving Average.
Historically this has occurred within 3 days of the very top of each market cycle.
When the 111 Day Moving Average circle falls back beneath the 2 * 350 Day Moving Average circle, it indicates that the market momentum of that cycle is significantly cooling down. The oscillator drops down into the lower green band shown where the 111 Day Moving Average is moving at a 75% discount relative to the 2 * 350 Day Moving Average.
Historically, this has highlighted broad areas of bear market lows.
IMPORTANT: You need to set a LOGARITHMIC graph. (The function is located at the bottom right of the screen)
IMPORTANT: The INTELLECT_city indicator is made for signal purchases of sales, there is also a strategic one from INTELLECT_city
IMPORTANT: The Chart shows all cycles, both buying and selling.
IMPORTANT: Suitable timeframes are 1 daily (recommended) and 1 weekly
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Описание на русском:
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Обзор индикатора
INTELLECT_city - World Cycle - ATH & ATL - Timeframe 1D and 1W - Logarithmic - Signal - Логарифмический - Сигнал - Осциллятор вершины и основания цикла Пи представляет собой адаптацию оригинального графика вершины цикла Пи. Он сравнивает круг 111-дневной скользящей средней и круг 2 * 350-дневной скользящей средней цены Биткойна. Эти две скользящие средние были выбраны как 350/111 = 3,153; Приближение важного математического числа Пи.
Когда круг 111-дневной скользящей средней достигает круга 2 * 350-дневной скользящей средней, это указывает на то, что рынок перегревается. Это происходит потому, что опорный моментум среднего временного интервала 111-дневной скользящей средней догнал опорный момент импульса длинного таймфрейма 2 * 350-дневной скользящей средней.
Исторически это происходило в течение трех дней после вершины каждого рыночного цикла.
Когда круг 111-дневной скользящей средней опускается ниже круга 2 * 350-дневной скользящей средней, это указывает на то, что рыночный импульс этого цикла значительно снижается. Осциллятор опускается в нижнюю зеленую полосу, показанную там, где 111-дневная скользящая средняя движется со скидкой 75% относительно 2 * 350-дневной скользящей средней.
Исторически это высветило широкие области минимумов медвежьего рынка.
ВАЖНО: Выставлять нужно ЛОГАРИФМИЧЕСКИЙ график. (Находиться функция с правой нижней части экрана)
ВАЖНО: Индикатор INTELLECT_city сделан для сигнальных покупок продаж, есть также и стратегический от INTELLECT_сity
ВАЖНО: На Графике видны все циклы, как на покупку так и на продажу.
ВАЖНО: Подходящие таймфреймы 1 дневной (рекомендовано) и 1 недельный
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Beschreibung - Deutsch
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Indikatorübersicht
INTELLECT_city – Weltzyklus – ATH & ATL – Zeitrahmen 1T und 1W – Logarithmisch – Signal – Der Pi-Zyklus-Top- und Bottom-Oszillator ist eine Anpassung des ursprünglichen Pi-Zyklus-Top-Diagramms. Er vergleicht den 111-Tage-Gleitenden-Durchschnittskreis und den 2 * 350-Tage-Gleitenden-Durchschnittskreis des Bitcoin-Preises. Diese beiden gleitenden Durchschnitte wurden als 350 / 111 = 3,153 ausgewählt; eine Annäherung an die wichtige mathematische Zahl Pi.
Wenn der 111-Tage-Gleitenden-Durchschnittskreis den 2 * 350-Tage-Gleitenden-Durchschnittskreis erreicht, deutet dies darauf hin, dass der Markt überhitzt. Das liegt daran, dass der Momentum-Referenzwert des 111-Tage-Gleitenden-Durchschnitts im mittleren Zeitrahmen den Momentum-Referenzwert des 2 * 350-Tage-Gleitenden-Durchschnitts im langen Zeitrahmen eingeholt hat.
Historisch gesehen geschah dies innerhalb von 3 Tagen nach dem Höhepunkt jedes Marktzyklus.
Wenn der Kreis des 111-Tage-Durchschnitts wieder unter den Kreis des 2 x 350-Tage-Durchschnitts fällt, deutet dies darauf hin, dass die Marktdynamik dieses Zyklus deutlich nachlässt. Der Oszillator fällt in das untere grüne Band, in dem der 111-Tage-Durchschnitt mit einem Abschlag von 75 % gegenüber dem 2 x 350-Tage-Durchschnitt verläuft.
Historisch hat dies breite Bereiche mit Tiefstständen in der Baisse hervorgehoben.
WICHTIG: Sie müssen ein logarithmisches Diagramm festlegen. (Die Funktion befindet sich unten rechts auf dem Bildschirm)
WICHTIG: Der INTELLECT_city-Indikator dient zur Signalisierung von Käufen oder Verkäufen, es gibt auch einen strategischen Indikator von INTELLECT_city
WICHTIG: Das Diagramm zeigt alle Zyklen, sowohl Kauf- als auch Verkaufszyklen.
WICHTIG: Geeignete Zeitrahmen sind 1 täglich (empfohlen) und 1 wöchentlich
