Consolidation Range Detector [Pt]█ Author's Note:
After extensively reviewing the existing consolidation detection tools in the TradingView library, I found that none fully met my expectations. Some tools were overly sensitive, producing too many invalid ranges, while others lacked the necessary sensitivity. Consequently, I decided to develop my own tool. I hope that you, fellow traders, find it valuable and enjoy using it.
█ Description:
The Consolidation Range Detector is a sophisticated TradingView tool designed to identify and visualize periods of price consolidation on any financial chart. This indicator employs advanced algorithms to detect ranges where price movements are confined, helping traders spot potential breakout zones and make informed trading decisions.
█ Key Features:
► Customizable Detection Sensitivity: Adjust the sensitivity of the detection algorithm to suit your trading strategy, ensuring a precise fit within the consolidation range.
► Dynamic Coloring: Choose between random or fixed colors for the consolidation ranges, with options to match different background color schemes (Dark, Light, Neutral).
► Visual Clarity: Highlight detected consolidation ranges directly on the chart with customizable color schemes to enhance visibility and provide clear visual cues.
► ATR-Based Validation: Ensures detected consolidation ranges are significant and reliable by using the Average True Range (ATR) for validation.
█ User-Defined Inputs:
► Minimum Detection Bars: Set the minimum number of bars required to detect a consolidation range.
► Max Range Multiplier: Define the maximum range for detection as a multiple of the ATR.
► Detection Sensitivity: Adjust the sensitivity of the detection algorithm. Higher values mean a tighter fit within the consolidation range.
► Color Options: Choose the color for the consolidation range boxes and decide whether to use random colors.
► Color Scheme (Background): Select a color scheme for the chart background (Dark, Light, Neutral).
█ How It Works:
► Range Detection: The indicator scans the chart for potential consolidation ranges based on user-defined parameters. It calculates the average price and ATR to determine the significance of the range.
► Validation: Each detected range is validated based on criteria such as ATR threshold, range validity, average price comparison, and the number of touches at the range boundaries.
► Visualization: Validated ranges are highlighted on the chart with colored boxes, providing a clear visual cue of potential consolidation zones.
█ Usage Examples:
► Example 1:
The image below showcases the Consolidation Range Detector in action on a chart of S&P 500 E-mini Futures. The indicator highlights several consolidation ranges with different colors, demonstrating its ability to adapt to varying market conditions and visually emphasize key areas of price consolidation. The annotations for breakouts and price reactions are manually marked to illustrate the practical application of the tool in identifying potential trading opportunities based on these key areas.
█ Practical Applications:
► Identify Breakout Zones: Use the detected consolidation ranges to identify potential breakout zones, helping to anticipate significant price movements.
► Identify Key Price Levels: The tool helps in pinpointing key price levels where there is a high probability of significant price reactions, providing crucial insights for trading strategies.
► Enhance Technical Analysis: Integrate the Consolidation Range Detector into your existing technical analysis toolkit to improve the accuracy of your trading decisions.
█ Conclusion:
The Consolidation Range Detector is a powerful tool for traders looking to identify periods of price consolidation and potential breakout zones. With its customizable settings and advanced detection algorithms, it provides a reliable and visual method to enhance your trading strategy. Whether you're a beginner or an experienced trader, this indicator can add significant value to your technical analysis.
█ Cautionary Note:
While the Consolidation Range Detector is a powerful tool, it's important to combine it with other indicators and analysis methods for comprehensive trading decisions. Always consider market context and external factors when interpreting detected consolidation ranges.
Search in scripts for "algo"
MTF Workbench [WinWorld]WHAT IS THIS?
This is MTF Workbench — an indicator, which is based on World Class SMC, but has one main feature — multi-timeframe analysis.
WHY MAKING MTF FEATURE AS A SEPARATE INDICATOR?
We weren't able to implement this feature in the World Class SMC itself due to huge size and complexity of the script, so we have re-written the entire script and optimized it to implement MTF and decided to make a separate script for MTF features in order to not make World Class SMC any heavier, because otherwise the script would probably not even load up on the chart.
WHAT ARE THE FEATURES?
MTF Workbench has two features for now: dashboard and structure mapping. But there will be more soon!
DASHBOARD
Dashboard gathers data from 4 different timeframes and visualize the results in the nice little table on the chart. It is useful to have a dashboard because it visualizes important data in a simple way.
The settings of the dashboard are:
- Position. this settings has 2 subsettings: vertical position (bottom, middle, top) and horizontal position (left, center, right). These subsettings allow you to place dashboard on any side of the chart;
- Text size. This settings defines size of the text in the dashboard, simple as that;
- Timeframe #1, #2, ..., #4. These four settings allow you to choose 4 different timeframes for the table to gather data from.
How to read the dashboard:
- The colour of the specific data cell is the current trend of selected timeframe;
- IDM ⧖ — price has not reached IDM yet;
- IDM ✓ — price grabbed IDM.
This is it for dashboard, now for structure mapping.
STRUCTURE MAPPING
By structure we mean IDM, BoS and ChoCh (if you don't what this means, refer to World Class SMC description to learn the terms, we won't explain it here). In our main indicator structure was only drawn for the timeframe you were currently using, but now you can choose whatever timeframe you want to get structure from!
Why do this matter? Well, this feature alone allows to perform so called intern-structure analysis, because now you will able to compare current timeframe's structure to a higher timeframe's structure and get an a sufficient amount of edge about what Smart Money are doing.
* And yes, this feature only works for analyzing higher timeframes!
The structure itself is plotted the same way as it is in our main indicator, but we also add timeframe to the specific structure event (event is when price reaches IDM, BoS or ChoCh lines) so you could differentiate internal-structure events from any other events.
Live structure is also available in this indicator.
WHY USE THIS INDICATOR?
Even though there a lot of structure mapping indicators with MTF features, they don't have what MTF Workbench has — the correct core structure-mapping algorithm. We took our core structure-mapping algorithm and put it into MTF Workbench to finally bring MTF analysis to life to work state-of-the-art structure-mapping algorithm, which gives any user a huge edge in the market by a very simple reason — this algorithm actually works. Our algorithm proved itself to be efficient and it helps map structure without human intervention, which is a huge leap in smart money trading. To this day we were not able to find an algorithm which would match the quality of our algo! Which why we think making an MTF version of our algorithm is a good thing to do, because now users can finally work with current timeframe and see information about structure from other timeframes using only ONE chart. If you are smart-money trader, you understand that this is a HUGE thing.
For PineScript moderators
We know the rule not publish slightly modifie version of some indicator as another indicator, but this is not a slightly different version. MTF Workbench was completely re-writtten from scratch and optimized so it could fint PineSript's code restrictions such as 500 max local scopes, which World Class SMC with MTF Workbench's features exceeded way too far.
Also, by referencing our World Class SMC indicator we don't promote it in any way. The reference is only made with purposes of
1) Informational reference to help users learn specific terms.
2) Informational reference to some of the World Class SMC features to give users a clue about what exactly MTF Workbench does.
We hope that you will find a great use from MTF Workbench as we did and it will help your level up your edge!
Sincerely, WinWorld Team.
Automating wealth creation since 2022.
Grid by Volatility (Expo)█ Overview
The Grid by Volatility is designed to provide a dynamic grid overlay on your price chart. This grid is calculated based on the volatility and adjusts in real-time as market conditions change. The indicator uses Standard Deviation to determine volatility and is useful for traders looking to understand price volatility patterns, determine potential support and resistance levels, or validate other trading signals.
█ How It Works
The indicator initiates its computations by assessing the market volatility through an established statistical model: the Standard Deviation. Following the volatility determination, the algorithm calculates a central equilibrium line—commonly referred to as the "mid-line"—on the chart to serve as a baseline for additional computations. Subsequently, upper and lower grid lines are algorithmically generated and plotted equidistantly from the central mid-line, with the distance being dictated by the previously calculated volatility metrics.
█ How to Use
Trend Analysis: The grid can be used to analyze the underlying trend of the asset. For example, if the price is above the Average Line and moves toward the Upper Range, it indicates a strong bullish trend.
Support and Resistance: The grid lines can act as dynamic support and resistance levels. Price tends to bounce off these levels or breakthrough, providing potential trade opportunities.
Volatility Gauge: The distance between the grid lines serves as a measure of market volatility. Wider lines indicate higher volatility, while narrower lines suggest low volatility.
█ Settings
Volatility Length: Number of bars to calculate the Standard Deviation (Default: 200)
Squeeze Adjustment: Multiplier for the Standard Deviation (Default: 6)
Grid Confirmation Length: Number of bars to calculate the weighted moving average for smoothing the grid lines (Default: 2)
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Buy/Sell Toolkit (Expo)█ Overview
The Buy/Sell Toolkit is a comprehensive trading tool designed to provide a holistic approach to trading. It brings together essential trading indicators and features in one place, simplifying the trading process and offering valuable insights into the market.
The indicator serves as an all-inclusive solution for traders seeking in-depth technical insights. While the Buy/Sell Toolkit can be utilized alongside other technical analysis methods, it can also be used as a standalone toolkit, adaptable to any trading style. In addition, each feature is thoughtfully integrated because not all technical indicators are suitable for every market condition or trading style.
The Buy/Sell toolkit works in any market and timeframe for discretionary analysis and includes many features:
█ Features
Buy/Sell signals: This feature provides real-time Buy/Sell trading signals for any market and timeframe. These signals are based on the trend.
Contrarian Signals: This feature provides real-time contrarian signals to take a position against the prevailing market trend.
Ultimate Trend: This feature assists in identifying the overall trend of the market, recognizing whether the market is in an uptrend, downtrend, or sideways.
Trend Advisor: The Trend Advisor helps traders understand the trend's strength, duration, and direction.
Trend Reversal: This feature identifies potential points where the current market may reverse within a trend. It's basically a trend-following line based on reversal calculation; it helps traders catch trend continuation setups.
Momentum Average: This indicator measures the rate of change in prices to identify the strength of the current trend. It can be beneficial for spotting potential price breakouts or warning of a market slowdown and pullbacks.
Take Profit Points: This feature suggests optimal points to exit a trade and lock in profits. It determines these points by using various factors such as volatility, support and resistance levels, and historical price movements.
Candle Coloring, Arithmetic Candlesticks, including Arithmetic Heikin Ashi: This feature provides an excellent visual aid to assist traders in recognizing patterns, identifying trends, and optimizing their trading strategies. The Arithmetic Candlesticks help smooth out price volatility and identify market trends more clearly.
Reversal Cloud: This innovative feature provides a graphical representation of potential price reversal zones. The cloud helps traders visualize where the price might reverse its trend.
Trend Cloud: Similar to the Reversal Cloud, this feature visualizes the prevailing market trend, making it easy for traders to understand the direction of the market at a glance.
Signal Optimizer: The Signal Optimizer is a powerful tool that optimizes the Buy/Sell and contrarian signals based on win-rate or performance. It automatically applies the best settings to the signals, freeing traders from the task of constantly adjusting them. This helps traders to get the most reliable signals automatically, enhancing their trading efficiency.
█ How to use the Buy/Sell Toolkit?
Here are a few illustrative examples to provide traders with a better understanding of the Toolkit's practical usage. These examples showcase the combination of features, but it's important to note that they serve as demonstrations, and we encourage traders to explore and adapt the features to align with their unique trading styles.
Buy/Sell Signals & Take Profit
Optimized Buy/Sell signals & Candle Color + Trend Advisor + Reversal Cloud
Contrarian Signals & Take Profit
,with Reversal Cloud
Optimized Contrarian Signals & Ultimate Trend & Reversal Cloud
Trend Cloud
Filter signals with Trend Cloud
█ Why is this Buy/Sell Toolkit Needed?
The Buy/Sell Toolkit is an exceptional tool for traders because it consolidates several critical trading indicators into a single, user-friendly platform. The Toolkit's holistic approach to market analysis can enhance decision-making, reduce guesswork, and improve overall trading performance. Additionally, it allows traders to customize their approach according to the market conditions and their trading style.
The Toolkit's automated features, such as the Signal Optimizer, save time and effort, making it easier for both new and experienced traders. In addition, its comprehensive suite of features ensures traders have all the information they need to make informed trading decisions. All these features make the Buy/Sell Toolkit a powerful ally in any trader's arsenal.
Here's why this Toolkit is essential:
Comprehensive Market Analysis: The Toolkit offers a wide range of indicators and tools for comprehensive market analysis, from trend detection to momentum analysis. This reduces the need for multiple tools and allows for a more efficient trading process. By providing a host of indicators like Buy/Sell signals, Contrarian Signals, Trend Analysis, and Momentum Average, the Toolkit helps traders make well-informed decisions based on comprehensive data and trend analysis.
Automation and Time-Saving: The Signal Optimizer automatically applies the best settings to the signals based on win rate or performance. This saves time and ensures the signals' reliability, reducing, it makes the trading process efficient and hassle-free.
Versatility: The Toolkit is versatile and can be used for various financial markets, including stocks, forex, commodities, or cryptocurrencies. Regardless of the market you trade in, the Buy/Sell Toolkit has something to offer.
Visual Tools: The Toolkit provides visual tools like Reversal Cloud, Trend Cloud, Trend lines, Candle coloring, and much more, which are excellent for visualizing market trends and potential reversal zones. This can make the process of understanding market movements more intuitive and less intimidating, especially for novice traders.
Confirmation: By using multiple indicators in conjunction with each other, traders can confirm signals and improve the accuracy of their trades.
Learning and Development: The Toolkit serves as an excellent resource for both novice and experienced traders to learn about different trading indicators, how they interact, and how to use them effectively.
█ Any Alert Function Call
This function allows traders to combine any feature and create customized alerts. These alerts can be set for various conditions and customized according to the trader's strategy or preferences.
█ How are the features calculated? - Overview
The Toolkit combines many of our existing premium indicators and new technical analysis algorithms to analyze the market. This overview covers how the main features are calculated.
Buy/Sell
The core function calculates the Exponential Weighting for a given time series X over a period T. The time series is based on absolute price changes. It focuses on the magnitude of price changes from one period to the next, irrespective of the direction (up or down). This type of time series can be used to measure the volatility of a price series, as it quantifies the size of price movements. It's useful in scenarios where the direction of the change is not as important as the magnitude of the change.
Contrarian Signals
Our contrarian signals are based on deviation from the expected range value. The algorithm quantifies the amount of variation or dispersion in a set of trading ranges. Non-expected values are the fundamental core of the signal generation process.
Ultimate Trend
The Ultimate trend calculates an adaptive smoothing momentum function by first determining the directional price movement and then applying smoothing to the positive and negative price changes. It then uses these values to calculate a form of Variable Moving Average (VMA), where the smoothing factor is adjusted based on a normalized measure of the relative difference between the Positive and Negative Directional values.
Trend Advisor
It's a form of Moving Averages that are applied to the price chart using three different weighting functions, simple weighting, price volatility smoothing constant weighting, and the traditional EMA weighting function.
Trend Reversal and Cloud
The function uses the information on how much the current price compared to the relative historical price fluctuates over a specific period and automatically updates its equilibrium value at new price changes.
Momentum Average
Essentially, it uses a modified version of the relative rate of change over a certain period.
Take Profit
The take profit uses similar range price functions as the contrarian signals, where a take profit signal is triggered at extremely abnormal values.
Candles
Note, Using and Backtesting on non-standard charts produces unrealistic results since it does not represent the closing price. The candles are based on a smoothing process that finds the best smoothing coefficient for the current data, using close as time series.
█ In conclusion , The Buy/Sell Toolkit serves as a comprehensive, user-friendly, and efficient trading assistant. It brings automation and intelligent data play-by-play to your fingertips, making it an essential tool for anyone serious about trading.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Channel Based Zigzag [HeWhoMustNotBeNamed]🎲 Concept
Zigzag is built based on the price and number of offset bars. But, in this experiment, we build zigzag based on different bands such as Bollinger Band, Keltner Channel and Donchian Channel. The process is simple:
🎯 Derive bands based on input parameters
🎯 High of a bar is considered as pivot high only if the high price is above or equal to upper band.
🎯 Similarly low of a bar is considered as pivot low only if low price is below or equal to lower band.
🎯 Adding the pivot high/low follows same logic as that of regular zigzag where pivot high is always followed by pivot low and vice versa.
🎯 If the new pivot added is of same direction as that of last pivot, then both pivots are compared with each other and only the extreme one is kept. (Highest in case of pivot high and lowest in case of pivot low)
🎯 If a bar has both pivot high and pivot low - pivot with same direction as previous pivot is added to the list first before adding the pivot with opposite direction.
🎲 Use Cases
Can be used for pattern recognition algorithms instead of standard zigzag. This will help derive patterns which are relative to bands and channels.
Example: John Bollinger explains how to manually scan double tap using Bollinger Bands in this video: www.youtube.com This modified zigzag base can be used to achieve the same using algorithmic means.
🎲 Settings
Few simple configurations which will let you select the band properties. Notice that there is no zigzag length here. All the calculations depend on the bands.
With bands display, indicator looks something like this
Note that pivots do not always represent highest/lowest prices. They represent highest/lowest price relative to bands.
As mentioned many times, application of zigzag is not for buying at lower price and selling at higher price. It is mainly used for pattern recognition either manually or via algorithms. Lets build new Harmonic, Chart patterns, Trend Lines using the new zigzag?
Machine Learning: kNN (New Approach)Description:
kNN is a very robust and simple method for data classification and prediction. It is very effective if the training data is large. However, it is distinguished by difficulty at determining its main parameter, K (a number of nearest neighbors), beforehand. The computation cost is also quite high because we need to compute distance of each instance to all training samples. Nevertheless, in algorithmic trading KNN is reported to perform on a par with such techniques as SVM and Random Forest. It is also widely used in the area of data science.
The input data is just a long series of prices over time without any particular features. The value to be predicted is just the next bar's price. The way that this problem is solved for both nearest neighbor techniques and for some other types of prediction algorithms is to create training records by taking, for instance, 10 consecutive prices and using the first 9 as predictor values and the 10th as the prediction value. Doing this way, given 100 data points in your time series you could create 10 different training records. It's possible to create even more training records than 10 by creating a new record starting at every data point. For instance, you could take the first 10 data points and create a record. Then you could take the 10 consecutive data points starting at the second data point, the 10 consecutive data points starting at the third data point, etc.
By default, shown are only 10 initial data points as predictor values and the 6th as the prediction value.
Here is a step-by-step workthrough on how to compute K nearest neighbors (KNN) algorithm for quantitative data:
1. Determine parameter K = number of nearest neighbors.
2. Calculate the distance between the instance and all the training samples. As we are dealing with one-dimensional distance, we simply take absolute value from the instance to value of x (| x – v |).
3. Rank the distance and determine nearest neighbors based on the K'th minimum distance.
4. Gather the values of the nearest neighbors.
5. Use average of nearest neighbors as the prediction value of the instance.
The original logic of the algorithm was slightly modified, and as a result at approx. N=17 the resulting curve nicely approximates that of the sma(20). See the description below. Beside the sma-like MA this algorithm also gives you a hint on the direction of the next bar move.
Pulu's 3 Moving Averages
Pulu's 3 Moving Averages
Release version 1, date 2021-09-28
This script allows you to customize three sets of moving averages, turn on/off, set color and parameters. It also tags the start date of the last set of moving average if there is. This, release version 1, supports eight moving average algorithms:
ALMA, Arnaud Legoux Moving Average
EMA, Exponential Moving Average
RMA, Adjusted exponential moving average (aka Wilder’s EMA)
SMA, Simple Moving Average
SWMA, Symmetrically-Weighted Moving Average
VWAP, Volume-Weighted Average Price
VWMA, Volume-Weighted Moving Average
WMA, Weighted Moving Average
The availability and function parameters
Func. Availability Parameters
ALMA
MA1, MA2, MA3
source
length
offset
sigma
EMA
RMA
SMA
VWMA
WMA
MA1, MA2, MA3
source
length
SWMA
VWAP
MA1
source
Parameters
Parameter Description
source the series of values to process. The default is to use the closing price to calculate the moving average.
length an integer value that defines the number of bars to calculate the moving average on. The SWMA and VWAP do not use this parameter.
ALMA offset a floating-point value that controls the tradeoff between smoothness (with a value closer to 1) and responsiveness (with a value closer to 0). This parameter is only used by ALMA.
ALMA sigma a floating-point value that specifies the ALMA’s smoothness. The larger this value, the smoother the moving average is. This parameter is only used by ALMA.
I'm not sure if it is needed, so I do not let the three Moving Averages of the script to have indivial algorithm setting. Because that will involve much complicated condition testing and use up more TradingView script lines limit. If you need to combine different algorithms in the three sets of moving averages, or have other ideas, leave a message to let me know; maybe I will try it in the next update.
我不確定是否需要，所以我沒有讓腳本的三組移動平均線有各別的算法設置。因為這將涉及更多複雜的條件測試，並使用更多 TradingView 腳本列數限制。如果您需要在三組均線中組合不同的算法，或者有其他想法，請留言告訴我；也許我會在下一次更新中嘗試。
GA - Value at RiskGA Value at Risk is a multifunctional tool. Its main purpose is to plot on the chart the Value at Risk . But it shows also integrated features related to the Volatility.
Value at Risk is a measure of the risk of loss for investments, given normal market conditions, in a period.
It measures and quantifies the level of financial risk. In this case, the risk is within position over a specific time frame.
Defining p as VaR, the probability of a loss greater than VaR is p, at most. Instead, the probability of loss that is less than VaR is 1-p, at least.
The VaR Breach occurs when a loss exceeds the VaR threshold .
For this case, VaR calculation uses the volatility estimation in a time interval. It defines the Probability Confidence according to the Normal Distribution. VaR is a percentile of the Normal Distribution. This is a multiplier of the Standard Deviation that define a Volatility Range.
The Normal Distribution Area around +- the Standard Deviation gives 68% of Confidence. 2 times the Standard Deviation returns a 95% of probability area. 3 time the Standard Deviation the Area returns 99.7% of Confidence.
Knowing VaR modeling, it is possible to determine the amount of a potential loss . Then, it is possible to know if there is enough capital to cover losses. In the same way, higher-than-acceptable risks forces reducing exposure in a financial instrument.
One of its practical use is to estimate the risk of an investment that is already at portfolio. Indeed, this is the purpose of the Value at Risk calculated in this script.
At the VaR Breach that investment has reached its worst scenario. Then, it can be the case to manage that investment into the balanced portfolio.
The Value at Risk does not tell when to enter the market.
Moving Averages
GA Value at Risk bases its calculations on a set of Moving Averages. Every feature of the script uses one of these Moving Averages for its algorithm.
Moving Averages from MA0 to MA8, are the core of each feature of the script.
By default, from MA0 to MA8, Moving Averages use the Fibonacci Series to define their lengths. This happens because of the power of the Golden Ratio in the market behavior.
Instead, the first moving average is an extra resource. Its purpose is to plot a Signal Line on the chart.
The script does not consider plotting every Moving Average on the chart. But it lets you enable the plotting of 7 Moving Averages (from MA0 to MA5 + Signal Line).
It is possible to select the Moving Average Formula to use in the script. This is a setting that affects every Moving Average. Then, it changes also the result of every feature of the script.
The selection is between:
Exponential Moving Average.
Simple Moving Average.
Weighted moving Average.
Simple Moving Averages and Pointers - Full Visibility
Moving Averages and Partial Visibility
The plotting of each Moving Average can be total or partial.
By default, the plotting of Moving Averages and Signal Line is partial.
When the price approaches a Moving Average a little part of the curve becomes visible. This highlights supports or resistances.
Besides, this tracking remains on the chart. Then it shows supports and resistances that the price reached during its progression.
The Partial Visibility Algorithm is a great advantage, ruling how to plot curves. It uses a parameter to set how much of the curves is to plot.
Exponential Moving Averages and Pointers - Partial Visibility
Exponential Moving Averages and Pointers - Full Visibility
Moving Averages and Pointers
As it is clear, it is not necessary to plot entire curves of Moving Averages on the chart. But it becomes relevant to plot Pointers to Moving Averages.
Indeed, the script plots horizontal segments that point to the latest Average Prices.
Every segment has a Label that shows Average Price, Length, and its related Moving Average (from MA0 to MA8). Besides, it is possible to extend the segment to right.
These pointers are a very useful automatization. They point to the Moving Averages. In this way, they show Dynamic Supports and Resistances as horizontal segments.
They are adaptive. Used together with the Volume Profile their progression approaches Edges of High Nodes.
This adaptive behavior makes easy to see when the price reaches Volume High Nodes and slows down.
Moving Average Pointers use the Partial Visibility Algorithm. In this case, the algorithm shows pointers with higher frequency than curves.
Moving Averages Pointers have:
Horizontal Segment as a Pointer with Arrow.
Label with details.
Circle to the current Average Price.
Weighted Moving Averages and Pointers - Full Visibility
Volatility Channels
Having Moving Averages, from MA0 to MA8, it is possible to plot 9 Volatility Channels.
Each Volatility Channel uses one of the Moving Averages, from MA0 to MA8.
Indeed, each Volatility Channel has the same designation of the Moving Average used.
The Standard Deviation defines the Volatility Range. It uses the length of the Moving Average related to the Volatility Channel.
The Volatility Range is unique for each Volatility Channel. In the same way, each Volatility Channel is unique because of its relation to only one Moving Average.
By default, each volatility channel has the 2 value as Standard Deviation Multiplier. This gives 95% of Confidence that the price will stay into the Volatility Range.
Using the Simple Moving Average, each Volatility Channel becomes a Bollinger Bands envelop.
Volatility Channels work very well even using Exponential or Weighted Moving Averages.
MA0 - Volatility Channel
Volatility Channels - From MA0 to MA8
Value at Risk (VaR)
GA Value at Risk plots VaR according to the volatility. The VaR plotting follows the Trend Momentum or Buying-Selling Waves.
By default, VaR follows the Trend Momentum by 2 times the Standard Deviation of MA0. Where MA0 is the first Moving Average and Volatility Channel of the set.
Besides, by default, the calculation of the Value at Risk is adaptive. It does not follow the Volatility Channel Bands. But it changes according to the fast reaction of the price into the Volatility Range.
By default, VaR follows the main momentum even if the price is moving in opposition to it. This occurs as long as the Trend Momentum persists.
In the settings box, It is possible to select the following of the latest Buying Wave or Selling Wave.
In this case, VaR changes according to the change of Buying Wave or Selling Wave. This means that, on these conditions, VaR follows main swings. Then it follows the weakening and the strengthening of the trend momentum as long as it persists.
The plotting of the Value at Risk can show these features:
Red cycle to show the Value at Risk at the current price.
Look Back Red Line that shows the progression of the Value at Risk.
Label with details.
MA0 - Value at Risk - Not Adaptive
MA0 - Value at Risk - Adaptive
It is possible to use a different Moving Average and Volatility Channel from the set. This affects the calculation and the plotting of the Value at Risk. In this way, the algorithm return the Value at Risk for the short, middle, or long-term.
Then, you can get the Value at Risk for that Financial Instrument, calculated for ~1 year or more so as for 1 month.
The Value at Risk does not tell you when to enter the market. Besides, it does not show you that the trend is changing.
MA3 - Value at Risk - Adaptive
Value at Profit (VaP)
The Value at Profit has a descriptive purpose. It points the Volatility Band that is opposite to the Value at Risk.
I chose Value at Profit as a designation for this feature. It does not tell you where to exit the market.
But is shows what the price progression is pointing on. This happens following the switching between Volatility Ranges.
The VaP follows the Volatility Band where the price tends to converge.
An outperforming or underperforming price is running faster than the average trend. Then when the price runs enough to converge to the Volatility Band, it is over extended or under extended.
At these conditions, the increased buying or selling pressure affects the price behavior. This slows down the price progression.
The Algorithm behind the Value at Profit is adaptive. Then the pointer jumps up and down the Volatility Bands of the 9 Volatility Channels. This occurs according to the price progression, following the switching between Volatility Ranges.
So, the VaP points a Volatility Band as long as the price can have chances to converges on it. Instead, when the price has chances to exceed the Volatility Band, the VaP points to the next one.
The plotting of the Value at Profit occurs enabling its Label with details.
Value at Profit - MA0 Volatility Channel Upper Band
Value at Profit - MA6 Volatility Channel Upper Band
Price Extension
When the price runs far away from the average trend price, GA Value at Risk can plot the price extension.
It shows the distance in percentage of the price from a Moving Average of the set. This tends to highlight conditions where the price is over or under extended.
An overbought or oversold condition precedes the shortening of the Trust. It is a cause of the hesitation of the price to continue its progression. This includes also Climactic Points and Signs of Dominance.
The Price Extension plotting uses a variation of the Partial Visibility Algorithm. It plots the Price Extension Arrow only when there are specific volatility conditions.
When the Partial Visibility is set to 0, the Price Extension Arrow is always visible on the chart.
The plotting of the Price Extension includes a Label with details.
Over Extension - The Price is Outperforming MA0
Under Extension - The Price is Underperforming MA0
Price Extension Coloring for Bars and Line Chart
GA Value at Risk lets you enable the coloring of vertical charts. Green and Red colors mark the over and under extended price on bars, candle sticks, and also on the Line Chart.
The Price Extension Algorithm colors Bars and Line Chart by a momentum function.
Indeed, the coloring happens following Relative Strength Index or Bollinger Bands %B.
These 2 Momentum functions are different. Indeed, they color the chart according to the purpose of their curves.
Coloring the Line Chart, it is necessary to put on front the script visibility.
Overbought and Oversold Conditions on Line Chart by Bollinger Bands %B
Overbought and Oversold Conditions on Candlesticks Chart by Relative Strength Index
Note: I restrict access to the tool. Use the links in my signature field to gain access to the script. Feel free to send me a PM for any question.
Thank you
Girolamo Aloe
Founder of Profiting Me Finance Analytics
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Disclaimer
Nobody in Girolamo Aloe websites and trading view profile is a Financial Advisor. Nothing therein is intended to be constructed as Financial Advice. The content on his websites is for information and educational purposes only.
Trading carries high risk. You should not invest money that you cannot afford to lose. Past performance is not an indication of future results.
KarkadannKarkadann is an indicator derived from a Naberius trading algorithm. It represents a medium ground between our two other algorithms Mammon and Malphas.
It detects the current trend ranges in the market and prints a suggested entry accordingly at assumed trend channel tops & bottoms upon encountering stalled out price action usually indicative of a retracement. As such, Karakadann can be traded on nearly any timeframe.
This algorithm was developed to trade primarily leveraged XBT; however, after exploring larger alt coins and the more traditional markets outside of cryptocurrency we found that Karkadann does better than the average trader regardless of the pair or ticker being traded at the time. Any core changes to the live trading algorithm will be added to this indicator as they are deployed.
Suggested Methods of Operation:
1. Buy and Sell signals represent a possible trading opportunity. Based on our testing, manual traders should use the 15m - 60m for scalping and 240m - 1D for larger swings.
2. Upon signal print, place your limit orders spread throughout the current candles total body range. DO NOT MARKET IN. DO NOT CHASE. If the limit orders don't fill within the following candle regardless of timeframe being traded remove them and re-evaluate.
3. Use standard candles. Heikin Ashi candles are ok but can be deceiving in times of localized price volatility
4. Trade the trend or wait for extreme price action, counter to the trend, to take up positions.
MidnightQuant Buy/Exit SignalsThe MidnightQuant Indicator is a sophisticated trend-following tool designed for traders seeking an edge in market analysis through a multi-symbol, multi-timeframe approach. Built on an enhanced Supertrend algorithm, this indicator goes beyond traditional trend-following methods by integrating advanced features that cater to both novice and experienced traders. Its unique design provides comprehensive market insights, empowering traders to make informed decisions with confidence.
Keep in mind that it was tested mainly with higher timeframes, 4H, 1D, 1W.
Overview:
MidnightQuant is specifically engineered to simplify the complexity of market analysis by monitoring and analyzing multiple currency pairs simultaneously. It combines trend detection, reversal signals, and a user-friendly dashboard to present a holistic view of market conditions. Whether you're trading a single asset or managing a portfolio, MidnightQuant delivers actionable insights in real-time.
Key Features:
Multi-Symbol Trend Analysis:
MidnightQuant's most distinguishing feature is its ability to track and analyze up to ten different currency pairs simultaneously. Unlike traditional indicators that focus on a single asset, this multi-symbol capability provides a broader view of market dynamics, allowing traders to identify correlations and divergences across various pairs. This is particularly useful for traders who want to confirm the strength of a trend across different markets before making a trading decision.
Enhanced Supertrend Algorithm:
At the core of MidnightQuant lies an optimized Supertrend algorithm that has been fine-tuned for both accuracy and responsiveness. The algorithm calculates trend directions by factoring in average true range (ATR) data, which helps in identifying significant price movements while filtering out market noise. This results in more reliable trend detection and fewer false signals, making it a powerful tool for trend-following strategies.
Intuitive Dashboard Display:
The MidnightQuant dashboard is designed to centralize critical information, making it accessible at a glance. It displays four key columns: Potential Reversals, Confirmed Reversals, Bullish Trends, and Bearish Trends. Each column provides a quick summary of the current market state for all tracked symbols, allowing traders to see where potential opportunities lie. This streamlined presentation reduces the need for constant chart monitoring and helps traders focus on the most promising setups.
Visual Signals and Candlestick Integration:
MidnightQuant enhances chart readability by incorporating visual signals directly on the price chart. Buy and sell signals are clearly marked at points where trend reversals are detected, providing immediate entry and exit cues. Additionally, the indicator color-codes candlesticks according to the current trend direction—purple for bullish and light lavender for bearish—enabling traders to instantly gauge market sentiment.
Customizable Alerts:
The indicator includes flexible alert conditions that can be customized according to your trading preferences. Alerts are triggered for trend direction changes, providing timely notifications for potential buy or sell opportunities. This feature is invaluable for traders who need to stay informed of market movements even when they are not actively monitoring their charts.
Trend Reversal Detection:
One of MidnightQuant's core functionalities is its ability to detect and signal trend reversals. The indicator monitors changes in the trend direction with precision, helping traders to identify potential turning points in the market. This feature is particularly useful for swing traders and those who aim to capitalize on shifts in market momentum.
Customizable Settings:
The indicator comes with various settings that allow traders to tailor it to their specific needs. From selecting which symbols to track to adjusting the sensitivity of the Supertrend algorithm, users have full control over how the indicator behaves. This customization ensures that MidnightQuant can be adapted to different trading styles and strategies.
How It Works:
MidnightQuant uses a proprietary calculation based on the Supertrend algorithm, which leverages ATR to dynamically adjust to market volatility. The indicator tracks the midpoint of each trading range and applies a factor that defines the threshold for trend changes. When the closing price crosses this threshold, a new trend is identified, and corresponding signals are generated.
The multi-symbol feature is powered by the request.security function, which allows MidnightQuant to pull in data from multiple symbols and timeframes. This data is then processed through the Supertrend algorithm to determine the trend direction for each symbol, which is subsequently displayed on the dashboard.
The indicator also includes a built-in dashboard that provides a summarized view of market conditions, including potential and confirmed reversals, as well as current trend directions. This dashboard updates in real-time, giving traders a continuously updated snapshot of market sentiment across multiple assets.
Use Cases:
Swing Traders: The trend reversal detection and real-time alerts help swing traders identify potential entry and exit points, making it easier to capitalize on market swings.
GG Short & Long IndicatorGG Short & Long Indicator is a powerful signal indicator with AI
How do indicator signals work?
The main purpose of the indicator is to give a signal that is most likely to bring profit based on historical data. This ORIGINAL trend algorithm gives SHORT and LONG signals when several conditions coincide: 1) Breakout of the average value of the modernized VWAP (this VWAP takes data only from certain time periods and trading sessions, as a result, its breakout most often coincides with the beginning of a strong trend); 2) The previous condition must be confirmed by volumes. I noticed that on some crypto exchanges, depending on whether the breakout is false or true, the volumes are different relative to each other. I applied this knowledge for additional filtering of signals (this point works only on crypto assets, on other assets the algorithm works without taking it into account, maybe later I will refine it); 3) When some of my original formulas to determine overbought (similar in principle to RSI, but more designed to work with the trader algorithm), should not show overbought - so that the entry into the transaction was not at too unfavorable values. To summarize, the algorithm tries to find a balance to determine a true breakout, during which the price will not go too far (for an acceptable RR).
But the most important thing is that the parameters to customize the algorithm are governed by our original AI algorithm. It can adjust the indicator in two modes: 1) Settings are selected based on the most profitable historical settings. 2) The settings are selected based not only on historical profitability, but also on winrate, frequency of trades, and a few other items that we will not disclose (so the code is closed) - we consider this approach as a priority, because according to our observations, it gives the highest performance compared to manual tuning. In addition, AI simply simplifies the work with the indicator - you do not need to adjust the settings manually for different trading pairs or timeframes, AI will do it all by itself and immediately give the ready result (backtest) on the table.
How to trade?
After the signal is issued, the indicator determines the recommended levels to close the trade (green dots). Stop loss should be placed behind the corresponding gray SL mark. Levels for closing a deal (TP) and the level of stop loss setting (SL) are also determined automatically for the selected pair and TF, based on volatility and selected indicator settings
To make a trade, you can also use the built-in “Support and Resistance Zones” tool, which displays ranges on the chart based on the modernized ATR, from which the price is more likely to rebound (here I also used my own approach, where in addition to the classic ATR formula, I also used volumes from certain crypto exchanges to determine more accurate price rebound zones)
These zones are also adjusted by AI - the algorithm compares several dozens of variations of these zones (with different settings) and chooses the one that best fits the current settings of the signal algorithm. For example, if the indicator is set up for frequent trades - the zones will be updated faster and will be less deep than if the indicator is set up for medium-term trading
If desired, you can customize the indicator manually using the corresponding section of the settings. Each paramater has a tooltip describing how and what it affects.
Statistisc panel
The panel can be divided into 2 conditional parts:
1) Statistics for each individual TP for the selected strategy. It shows the winrate and gross profit, if you fix a trade on a single target completely
2) Total trading result, if you trade clearly according to the strategy and fix the position by equal hours on 4 TPs. The total trading result is displayed for the current indicator settings, it also shows the best, worst and optimal of the possible indicator settings and the trading result of these settings on the side.
How do setup the indicator?
The indicator has preset settings for several major pairs and timeframes. These are fixed settings specifically selected for individual pairs and timeframes. You can use these presets, or you can choose one of the adaptive settings, which will AUTOMATICALLY select the best/optimal indicator settings.
I recommend choosing the “Adaptive Optimal” preset, as it uses more data to determine the optimal indicator settings and according to my observations this method works better in comparison to manual indicator settings or the “Adaptive Best” preset
Or you can use the manual settings, as mentioned earlier.
Intellect_city - Halvings Bitcoin CycleWhat is halving?
The halving timer shows when the next Bitcoin halving will occur, as well as the dates of past halvings. This event occurs every 210,000 blocks, which is approximately every 4 years. Halving reduces the emission reward by half. The original Bitcoin reward was 50 BTC per block found.
Why is halving necessary?
Halving allows you to maintain an algorithmically specified emission level. Anyone can verify that no more than 21 million bitcoins can be issued using this algorithm. Moreover, everyone can see how much was issued earlier, at what speed the emission is happening now, and how many bitcoins remain to be mined in the future. Even a sharp increase or decrease in mining capacity will not significantly affect this process. In this case, during the next difficulty recalculation, which occurs every 2014 blocks, the mining difficulty will be recalculated so that blocks are still found approximately once every ten minutes.
How does halving work in Bitcoin blocks?
The miner who collects the block adds a so-called coinbase transaction. This transaction has no entry, only exit with the receipt of emission coins to your address. If the miner's block wins, then the entire network will consider these coins to have been obtained through legitimate means. The maximum reward size is determined by the algorithm; the miner can specify the maximum reward size for the current period or less. If he puts the reward higher than possible, the network will reject such a block and the miner will not receive anything. After each halving, miners have to halve the reward they assign to themselves, otherwise their blocks will be rejected and will not make it to the main branch of the blockchain.
The impact of halving on the price of Bitcoin
It is believed that with constant demand, a halving of supply should double the value of the asset. In practice, the market knows when the halving will occur and prepares for this event in advance. Typically, the Bitcoin rate begins to rise about six months before the halving, and during the halving itself it does not change much. On average for past periods, the upper peak of the rate can be observed more than a year after the halving. It is almost impossible to predict future periods because, in addition to the reduction in emissions, many other factors influence the exchange rate. For example, major hacks or bankruptcies of crypto companies, the situation on the stock market, manipulation of “whales,” or changes in legislative regulation.
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Table - Past and future Bitcoin halvings:
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Date: Number of blocks: Award:
0 - 03-01-2009 - 0 block - 50 BTC
1 - 28-11-2012 - 210000 block - 25 BTC
2 - 09-07-2016 - 420000 block - 12.5 BTC
3 - 11-05-2020 - 630000 block - 6.25 BTC
4 - 20-04-2024 - 840000 block - 3.125 BTC
5 - 24-03-2028 - 1050000 block - 1.5625 BTC
6 - 26-02-2032 - 1260000 block - 0.78125 BTC
7 - 30-01-2036 - 1470000 block - 0.390625 BTC
8 - 03-01-2040 - 1680000 block - 0.1953125 BTC
9 - 07-12-2043 - 1890000 block - 0.09765625 BTC
10 - 10-11-2047 - 2100000 block - 0.04882813 BTC
11 - 14-10-2051 - 2310000 block - 0.02441406 BTC
12 - 17-09-2055 - 2520000 block - 0.01220703 BTC
13 - 21-08-2059 - 2730000 block - 0.00610352 BTC
14 - 25-07-2063 - 2940000 block - 0.00305176 BTC
15 - 28-06-2067 - 3150000 block - 0.00152588 BTC
16 - 01-06-2071 - 3360000 block - 0.00076294 BTC
17 - 05-05-2075 - 3570000 block - 0.00038147 BTC
18 - 08-04-2079 - 3780000 block - 0.00019073 BTC
19 - 12-03-2083 - 3990000 block - 0.00009537 BTC
20 - 13-02-2087 - 4200000 block - 0.00004768 BTC
21 - 17-01-2091 - 4410000 block - 0.00002384 BTC
22 - 21-12-2094 - 4620000 block - 0.00001192 BTC
23 - 24-11-2098 - 4830000 block - 0.00000596 BTC
24 - 29-10-2102 - 5040000 block - 0.00000298 BTC
25 - 02-10-2106 - 5250000 block - 0.00000149 BTC
26 - 05-09-2110 - 5460000 block - 0.00000075 BTC
27 - 09-08-2114 - 5670000 block - 0.00000037 BTC
28 - 13-07-2118 - 5880000 block - 0.00000019 BTC
29 - 16-06-2122 - 6090000 block - 0.00000009 BTC
30 - 20-05-2126 - 6300000 block - 0.00000005 BTC
31 - 23-04-2130 - 6510000 block - 0.00000002 BTC
32 - 27-03-2134 - 6720000 block - 0.00000001 BTC
Trend and Reversal ScannerHello Traders!
The TRN Trend and Reversal Scanner highlights in a user-friendly and easy to read table trend and reversal signals from up to 20 assets of your choosing. With it, you can efficiently monitor your preferred instruments simultaneously without jumping from one chart to the next. You will never miss a signal again. The indicator automatically finds swing-based up and down trends, bullish and bearish divergences, detects ranges and range breakouts as well as trend and reversal signals by the built-in trend detection algorithm called TRN Bars. Furthermore, you can conveniently stay updated with real-time alerts, notifying you whenever the scanner finds interesting market situations.
Feature List
Swing-based up and down trend detection
Divergence detection for any given (Custom) Indicator
Price range and breakout detection
Bar trend and reversal detection
Scanner alerts
The value of this indicator is to support traders to easily identify trend-based signals in an automated way and across many different markets at the same time. The trader saves a lot of time scanning the markets for up and down swings, divergences, consolidations and bar pattern-based trends and reversals, since finding and alerting these signals is done automatically for the trader.
For a visualization of the detected signals, you can add the TRN Bars and the Swing Suite indicator to your chart.
How does Trend Scanner work?
On the right side of the chart, you can find a table displaying the symbols monitored by the TRN Trend and Reversal Scanner for signal detection (first column). The table provides information on the status of each symbol. This visual representation allows you to quickly identify evolving signals across different symbols, helping you stay informed and make timely trading decisions.
The scanner operates specifically on the timeframe you are currently viewing, ensuring that the detected signals align precisely with your trading perspective.
In the following, we will describe the different signals displayed in the different columns of the table
Column 1 – Symbols
Column 2 – Bar Trend & Signals
Column 3 – Up & Down Swing Trend
Column 4 – Ranges & Range Breakouts
Column 5 – Bullish Divergences
Column 6 – Bearish Divergences
Bar Trend & Signals
In the second column, you can observe the status of TRN Bars, the built-in trend detection algorithm.
UP – Uptrend
DN – Downtrend
REV (Green) – Bullish Reversal Bar
REV (Red) – Bearish Reversal Bar
CON (Green) – Bullish Continuation Bar
CON (Red) – Bearish Continuation Bar
B/O (Green) – Bullish Range Breakout Bar
B/O (Red) – Bearish Range Breakout Bar
TRN Bars is designed to spot bullish and bearish trends and reversals. The trend analysis is based on a new algorithm that weights several different inputs:
classical and advanced bar patterns and their statistical frequency
probability distributions of price expansions after certain bar patterns
bar information such as wick length in %, overlapping of the previous bar in % and many more
historical trend and consolidation analysis
It provides high-probability trend continuation analysis and reversal detections.
Up and Downtrend
The second column (Trend) indicates whether the price of the asset moves within an uptrend (UP) or a downtrend (DN), as detected by our unique swing detection algorithm, on the selected timeframe.
The swing detection algorithm identifies pivot points (swings) with high accuracy. It works in real-time and does not need a look-a-head to find swings.
Ranges & Range Breakouts
The third column provides insights into the price behavior of a symbol within the selected timeframe, as analyzed by the range feature of the TRN Bars algorithm.
ACTIVE – Price moves within a price range
UP – Breakout detected
DN – Breakdown detected
UP CONF – Breakout confirmed
DN CONF – Breakdown confirmed
The bar range feature automatically finds consolidations where the price range of several consecutives bars is rather small. The detection of the bar ranges includes among other things the overlapping percentage of these bars.
Divergence Detection for any given (Custom) Indicator
The divergence detector finds with unrivaled precision bullish and bearish as well as regular and hidden divergences. The main difference compared to other divergences indicators is that this indicator finds rigorously the extreme peaks of each swing, both in price and in the corresponding indicator. This precision is unmatched and therefore this is one of the best divergences detectors.
The build in divergence detector works with any given indicator, even custom ones. In addition, there are 11 built-in indicators. Most noticeable is the cumulative delta indicator, which works astonishingly well as a divergence indicator. Full list:
External Indicator (see next section for the setup)
Awesome Oscillator (AO)
Commodity Channel Index (CCI)
Cumulative Delta Volume (CDV)
Chaikin Money Flow (CMF)
Moving Average Convergence Divergence (MACD)
Money Flow Index (MFI)
Momentum
On Balance Volume (OBV)
Relative Strength Index (RSI)
Stochastic
Williams Percentage Range (W%R)
Another highlight of the divergence detection is that it works with every indicator, even custom ones. To do this, you must add the (custom) indicator to your chart. Afterwards, simply go to the “Divergence Detection” section in the indicator settings and choose "External Indicator". If the custom indicator has one reference value, then choose this value in the “External Indicator (High)” field. If there are high and low values (e.g. candles), then you also must set the “External Indicator Low” field.
The visualization of the divergence detection is represented in the fifth column (Div Bull) and the sixth and last column (Div Bear).
REG – Regular divergence detected
HID – Hidden divergence detected
Scanner Alerts
You can opt to receive alerts for the following scenarios:
Detected up and down swings
Detected bullish and bearish divergences
Detected bar trend changes
Confirmed Reversal Bars
Confirmed Continuation Bars
Confirmed ange breakouts
The alert function is activated for all symbols listed in the scanner and corresponds to the timeframe of the chart you are currently viewing. This ensures that you receive alerts specifically tailored to the symbols and timeframe you are interested in.
Risk Disclaimer
The content, tools, scripts, articles, and educational resources offered by TRN Trading are intended solely for informational and educational purposes. Remember, past performance does not ensure future outcomes.
Edge AI Forecast [Edge Terminal]This indicator inputs the previous 150 closing prices in a simple two-layer neural network, normalizes the network inputs using a sigmoid function, uses a feedforward calculation to send it to the second layer, shows the MSE loss curve and uses both automatic and manual backpropagation (user input) to find the most likely forecast values and uses the analog forecasting algorithm to adjust and optimize the data furthermore to display potential prices on the chart.
Here's how it works:
The idea behind this script is to train a simple neural network to predict the future x values based on the sample data. For this, we use 2 types of data, Price and Volume.
The thinking behind this is that price alone can’t be used in this case because it doesn’t provide enough meaningful pattern data for the network but price and volume together can change the game. We’re planning to use more different data sets and expand on this in the future.
To avoid a bad mix of results, we technically have two neural networks, each processing a different data type, one for volume data and one for price data.
The actual prediction is decided by the way price and volume of the closing price relate to each other. Basically, the network passes the price and volume and finds the best relation between the two data set outputs and predicts where the price could be based on the upcoming volume of the latest candle.
The network adjusts the weights and biases using optimization algorithms like gradient descent to minimize the difference between the predicted and actual stock prices, typically measured by a loss function, (in this case, mean squared error) which you can see using the error rate bubble.
This is a good measure to see how well the network is performing and the idea is to adjust the settings inputs such as learning rate, epochs and data source to get the lowest possible error rate. That’s when you’re getting the most accurate prediction results.
For each data set, we use a multi-layer network. In a multi-layer neural network, the outputs of neurons in one layer serve as inputs to neurons in the next layer. Initially, the input layer of the neural network receives the historical data. Each input neuron represents a feature, such as previous stock prices and trading volumes over a specific period.
The hidden layers perform feature extraction and transformation through a series of weighted connections and activation functions. Each neuron in a hidden layer computes a weighted sum of the inputs from the previous layer, applies an activation function to the sum, and passes the result to the next layer using the feedforward (activation) function.
For extraction, we use a normalization function. This function takes a value or data (such as bar price) and divides it up by max scale which is the highest possible value of the bar. The idea is to take a normalized number, which is either below 1 or under 2 for simple use in the neural network layers.
For the activation, after computing the weighted sum, the neuron applies an activation function a(x). To introduce non-linearity into the model to pass it to the next layer. We use sigmoid activation functions in this case. The main reason we use sigmoid function is because the resulting number is between 0 to 1 and is better for models where we have to predict the probability as an output.
The final output of the network is passed as an input to the analog forecasting function. This is an algorithm commonly used in weather prediction systems. In this case, this is used to make predictions by comparing current values and assuming the patterns might repeat in the future.
There are many different ways to build an analog forecasting function but in our case, we’re used similarity measurement model:
X, as the current situation or set of current variables.
Y, as the outcome or variable of interest.
Si as the historical situations or patterns, where i ranges from 1 to n.
Vi as the vector of variables describing historical situation Si.
Oi as the outcome associated with historical situation Si.
First, we define a similarity measure sim(X,Vi) that quantifies the similarity between the current situation X and historical situation Si based on their respective variables Vi.
Then we select the K most similar historical situations (KNN Machine learning) based on the similarity measure sim(X,Vi). We denote the rest of the selected historical situations as {Si1, Si2,...Sik).
Then we examine the outcomes associated with the selected historical situations {Oi1, Oi2,...,Oik}.
Then we use the outcomes of the selected historical situations to forecast the future outcome Y^ using weighted averaging.
Finally, the output value of the analog forecasting is standardized using a standardization function which is the opposite of the normalization function. This function takes a normalized number and turns it back to its original value by multiplying it by the max scale (highest value of the bar). This function is used when the final number is produced by the network output at the end of the analog forecasting to turn the final value back into a price so it can be displayed on the chart with PineScript.
Settings:
Data source: Source of the neural network's input data.
Sample Bars: How many historical bars do you want to input into the neural network
Prediction Bars: How many bars you want the script to forecast
Show Training Rate: This shows the neural network's error rate for the optimization phase
Learning Rate: how many times you want the script to change the model in response to the estimated error (automatic)
Epochs: the network cycle or how many times you want to run the data through the network from the first layer to the last one.
Usage:
The sample bars input determines the number of historical bars to be used as a reference for the network. You need to change the Epochs and Learning Rate inputs for each asset and chart timeframe to get the lowest error rate.
On the surface, the highest possible epoch and learning rate should produce the most effective results but that's not always the case.
If the epochs rate is too high, there is a chance we face overfitting. Essentially, you might be over processing good data which can make it useless.
On the other hand, if the learning rate is too high, the network may overshoot the optimal solution and diverge. This is almost like the same issue I mentioned above with a high epoch rate.
Access:
It took over 4 months to develop this script and we’re constantly improving it so it took a lot of manpower to develop this script. Also when it comes to neural networks, Pine Script isn’t the most optimal language to build a neural network in, so we had to resort to a few proprietary mathematical formulas to ensure this runs smoothly without giving out an error for overprocessing, specially when you have multiple neural networks with many layers.
The optimization done to make this script run on Pine Script is basically state of the art and because of this, we would like to keep the code closed source at the moment.
On the other hand we don’t want to publish the code publicly as we want to keep the trading edge this script gives us in a closed loop, for our own small group of members so we have to keep the code closed. We only accept invites from expert traders who understand how this script and algo trading works and the type of edge it provides.
Additionally, at the moment we don’t want to share the code as some of the parts of this network, specifically the way we hand the data from neural network output into the analog method formula are proprietary code and we’d like to keep it that way.
You can contact us for access and if we believe this works for your trading case, we will provide you with access.
RBS | Profitholders Thanks for source code author , I have modified this for especially Indian market.
RBS Indicator is Rang Breakout System, This is same "Opening Range Breakout" which is a common trading strategy. The indicator can analyze the market trend in the current session and give "Buy / Sell", "Take Profit" and "Stop Loss" signals. For more information about the analyzing process of the indicator, you can read "How Does It Work ?" section of the description.
Features of RBS indicator :
Buy & Sell Signals
Up To 3 Take Profit Signals
Stop-Loss Signals
Alerts for Buy / Sell, Take-Profit and Stop-Loss
Session Dashboard
Back testing Dashboard
HOW DOES IT WORK ?
This indicator works best in 15-minute timeframe. Need to change Chart time frame depends on symbols , The idea is that the trend of the current session can be forecasted by analyzing the market for a while after the session starts. However, each market has it's own dynamics and the algorithm will need fine-tuning to get the best performance possible. So, we've implemented a "Back testing Dashboard" that shows the past performance of the algorithm in the current ticker with your current settings. Always keep in mind that past performance does not guarantee future results. So this is for educational purpose.
Here are the steps of the algorithm explained briefly :
1. The algorithm follows and analyzes the first 15 minutes (can be adjusted) of the session.
2. Then, algorithm checks for breakouts of the opening range's high or low.
3. If a breakout happens in a bullish or a bearish direction, the algorithm will now check for retests of the breakout. Depending on the sensitivity setting, there must be 0 / 1 / 2 / 3 failed retests for the breakout to be considered as reliable.
4. If the breakout is reliable, the algorithm will give an entry signal.
5. After the position entry, algorithm will now wait for Take-Profit or Stop-Loss zones and signal if any of them occur.
If you wonder how does the indicator find Take-Profit & Stop-Loss zones, you can check the "Settings" section of the description.
UNIQUENESS
While there are indicators that show the opening range of the session, they come short with features like indicating breakouts, entries, and Take-Profit & Stop-Loss zones. We are also aware of that different stock markets have different dynamics, and tuning the algorithm for different markets is really important for better results, so we decided to make the algorithm fully customizable. Besides all that, our indicator contains a detailed back testing dashboard, so you can see past performance of the algorithm in the current ticker. While past performance does not yield any guarantee for future results, we believe that a back testing dashboard is necessary for tuning the algorithm. Another strength of this indicator is that there are multiple options for detection of Take-Profit and Stop-Loss zones, which the trader can select one of their liking.
⚙️SETTINGS
Keep in mind that best chart timeframe for this indicator to work is the 15-minute timeframe on Indian Market.
TP = Take-Profit
SL = Stop-Loss
EMA = Exponential Moving Average
OR = Opening Range
ATR = Average True Range
1. Algorithm
RBS Timeframe -> This setting determines the timeframe that the algorithm will analyze the market after a new session begins before giving any signals. It's important to experiment with this setting and find the best option that suits the current ticker for the best performance. More volatile stocks will often require this setting to be larger, while more stabilized stocks may have this setting shorter.
Sensitivity -> This setting determines how much failed retests are needed to take a position entry. Higher sensitivity means that less retests are needed to consider the breakout as reliable. If you think that the current ticker makes strong movements in a bullish & bearish direction after a breakout, you should set this setting higher. If you think the opposite, meaning that the ticker does not decide the trend right after a breakout, this setting show be lower.
(High = 0 Retests, Medium = 1 Retest, Low = 2 Retests, Lowest = 3 Retests)
Breakout Condition -> The condition for the algorithm to detect breakouts.
Close = Bar needs to close higher than the OR High Line in a bullish breakout, or lower than the OR Low Line in a bearish breakout. EMA = The EMA of the bar must be higher / lower than OR Lines instead of the close price.
TP Method -> The method for the algorithm to use when determining TP zones.
Dynamic = This TP method essentially tries to find the bar that price starts declining the current trend and going to the other direction, and puts a TP zone there. To achieve this, it uses an EMA line, and when the close price of a bar crosses the EMA line, It's a TP spot.
ATR = In this TP method, instead of a dynamic approach the TP zones are pre-determined using the ATR of the entry bar. This option is generally for traders who just want to know their TP spots beforehand while trading. Selecting this option will also show TP zones at the ORB Dashboard.
"Dynamic" option generally performs better, while the "ATR" method is safer to use.
EMA Length -> This setting determines the length of the EMA line used in "Dynamic TP method" and "EMA Breakout Condition". This is completely up to the trader's choice, though the default option should generally perform well. You might want to experiment with this setting and find the optimal length for the current ticker.
Stop-Loss -> Algorithm will place the Stop-Loss zone using setting.
Safer = The SL zone will be placed closer to the OR High for a bullish entry, and closer to the OR Low for a bearish entry.
Balanced = The SL zone will be placed in the center of OR High & OR Low
Risky = The SL zone will be placed closer to the OR Low for a bullish entry, and closer to the OR High for a bearish entry.
Adaptive SL -> This option only takes effect if the first TP zone is hit.
Enabled = After the 1st TP zone is hit, the SL zone will be moved to the entry price, essentially making the position risk-free.
Disabled = The SL zone will never change.
2. RBS Dashboard
RBS Dashboard shows the information about the current session.
3. RBS Back testing
RBS Back testing Dashboard allows you to see past performance of the algorithm in the current ticker with current settings.
Total amount of days that can be back tested depends on your TV subscription.
Back testing Exit Ratios -> You can select how much of percent your entry will be closed at any TP zone while back testing. For example, %90, %5, %5 means that %90 of the position will be closed at the first TP zone, %5 of it will be closed at the 2nd TP zone, and %5 of it will be closed at the last TP zone.
Adaptive Fisherized Z-scoreHello Fellas,
It's time for a new adaptive fisherized indicator of me, where I apply adaptive length and more on a classic indicator.
Today, I chose the Z-score, also called standard score, as indicator of interest.
Special Features
Advanced Smoothing: JMA, T3, Hann Window and Super Smoother
Adaptive Length Algorithms: In-Phase Quadrature, Homodyne Discriminator, Median and Hilbert Transform
Inverse Fisher Transform (IFT)
Signals: Enter Long, Enter Short, Exit Long and Exit Short
Bar Coloring: Presents the trade state as bar colors
Band Levels: Changes the band levels
Decision Making
When you create such a mod you need to think about which concepts are the best to conclude. I decided to take Inverse Fisher Transform instead of normalization to make a version which fits to a fixed scale to avoid the usual distortion created by normalization.
Moreover, I chose JMA, T3, Hann Window and Super Smoother, because JMA and T3 are the bleeding-edge MA's at the moment with the best balance of lag and responsiveness. Additionally, I chose Hann Window and Super Smoother because of their extraordinary smoothing capabilities and because Ehlers favours them.
Furthermore, I decided to choose the half length of the dominant cycle instead of the full dominant cycle to make the indicator more responsive which is very important for a signal emitter like Z-score. Signal emitters always need to be faster or have the same speed as the filters they are combined with.
Usage
The Z-score is a low timeframe scalper which works best during choppy/ranging phases. The direction you should trade is determined by the last trend change. E.g. when the last trend change was from bearish market to bullish market and you are now in a choppy/ranging phase confirmed by e.g. Chop Zone or KAMA slope you want to do long trades.
Interpretation
The Z-score indicator is a momentum indicator which shows the number of standard deviations by which the value of a raw score (price/source) is above or below the mean value of what is being observed or measured. Easily explained, it is almost the same as Bollinger Bands with another visual representation form.
Signals
B -> Buy -> Z-score crosses above lower band
S -> Short -> Z-score crosses below upper band
BE -> Buy Exit -> Z-score crosses above 0
SE -> Sell Exit -> Z-score crosses below 0
If you were reading till here, thank you already. Now, follows a bunch of knowledge for people who don't know the concepts I talk about.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Inverse Fisher Transform
The Inverse Fisher Transform is a transform used in DSP to alter the Probability Distribution Function (PDF) of a signal or in our case of indicators.
The result of using the Inverse Fisher Transform is that the output has a very high probability of being either +1 or –1. This bipolar probability distribution makes the Inverse Fisher Transform ideal for generating an indicator that provides clear buy and sell signals.
Hann Window
The Hann function (aka Hann Window) is named after the Austrian meteorologist Julius von Hann. It is a window function used to perform Hann smoothing.
Super Smoother
The Super Smoother uses a special mathematical process for the smoothing of data points.
The Super Smoother is a technical analysis indicator designed to be smoother and with less lag than a traditional moving average.
Adaptive Length
Length based on the dominant cycle length measured by a "dominant cycle measurement" algorithm.
Happy Trading!
Best regards,
simwai
---
Credits to
@cheatcountry
@everget
@loxx
@DasanC
@blackcat1402
Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW
K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach.
Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities.
█ BACKGROUND
The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method.
So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks.
█ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS
1) K-means Approach:
Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns.
Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels.
Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past.
Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster.
Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters.
High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely.
Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and
2) Traditional Approach:
Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels.
Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points.
Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period.
Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance.
Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance.
The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns.
It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update.
█ K MEANS ALGORITHM
The algorithm for K means is as follows:
Initialize cluster centers
assign data to clusters based on minimum distance
calculate cluster center by taking the average or median of the clusters
repeat steps 1-3 until cluster centers stop moving
█ LIMITATIONS OF K MEANS
There are 3 main limitations of this algorithm:
Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results.
Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes.
Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure.
█ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING
Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading:
Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:
Indicator Distortion: Outliers can skew the calculations of indicators, leading to misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.
Risk Management: Outliers can lead to overly aggressive trading decisions if not properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies.
Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:
Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.
Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others.
Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:
Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.
Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account.
█ APPLICATION OF THIS INDICATOR
This indicator can be used in 2 ways:
1) Make a directional trade:
If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio.
Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio.
2) Take a reversal Trade:
We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level.
Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong.
In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk.
Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move.
█ FEATURES & SETTINGS
General Settings:
Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good.
Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred.
Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there.
Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself.
Features:
Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas.
█ WORKS CITED
Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com
Chris Piech, "K means", stanford.edu
█ ACKNOLWEDGMENTS
@jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
Auto Harmonic Pattern - Screener [Trendoscope]At Trendoscope, we take pride in offering a wide range of indicators on Harmonic Patterns, including both free and premium options. While we have successfully developed various advanced tools, we recognize that creating a Harmonic Pattern screener is an audacious endeavor that few have ventured into.
Creating a harmonic pattern screener presents a formidable challenge. The intricate nature of the algorithm, coupled with the limitations of cloud-based processing and platform memory, makes it exceedingly difficult to implement the screener functionality without encountering runtime errors.
Today marks a historic achievement as we overcome numerous challenges to unveil our groundbreaking harmonic pattern-based screener. This significant leap signifies our commitment to innovation in the field.
Without further delay, let's dive right into the new Auto Harmonic Pattern - Screener algorithm
🎲 Features Overview
🎯 Primary Functionality
We prefer not to categorize this as a traditional indicator, as it goes beyond that scope. Instead, it's a unique amalgamation of both a screener and an indicator, designed to achieve primarily two essential functions.
Firstly, it efficiently scans multiple tickers, up to 20, for harmonic pattern formations and presents them on a user-friendly dashboard
Secondly, it provides harmonic pattern drawings on the chart, but only if the current chart ticker is part of the screener and exhibits a harmonic pattern formation.
🎯 Secondary Features
In addition to its primary functionalities, our revolutionary algorithm offers an array of secondary features that cater to traders' diverse needs
Users have the privilege of accessing enhanced settings, providing limitless customization options for the zigzag and pattern detection algorithm
The platform empowers traders to effortlessly customize stop entry target ratios, facilitating automatic calculations and display of suggestions
The freedom to personalize the visualization and display of patterns and dashboard ensures a seamless and intuitive user experience
And finally, the algorithm leaves no stone unturned, keeping traders well-informed through timely alerts on every bar, highlighting tickers exhibiting Harmonic Pattern formations.
🎯 Limitations
Our innovative screener harnesses the power of the recursive zigzag algorithm to deliver efficient and accurate harmonic pattern detections. While the deep search algorithm, present in our other Harmonic Pattern algorithms, offers unparalleled precision, its resource-intensive nature makes it unsuitable for simultaneous scanning of 20 tickers. By focusing on the recursive zigzag approach, we strike the perfect balance between performance and functionality, ensuring seamless scanning across multiple tickers without compromising on accuracy. This strategic decision allows us to deliver a powerful and reliable screener that meets the diverse needs of traders and empowers them with real-time harmonic pattern insights.
🎲 Chart Components
Upon loading the indicator and configuring your tickers, our user-friendly interface offers two key components seamlessly integrated into the chart:
A color-coded screener dashboard : The dashboard presents a clear visualization of tickers with bullish and bearish harmonic patterns. This intuitive display allows you to quickly identify potential trading opportunities based on pattern formations.
Dynamic pattern display : As you interact with the chart, our algorithm dynamically highlights possible harmonic patterns based on the latest zigzag pivots. Please note that patterns may not always be visible on the chart, especially in cases where higher-level zigzags take time to form pivots. However, rest assured that our sophisticated algorithm ensures real-time updates, providing you with accurate and timely harmonic pattern insights.
🎯 Screener Dashboard
In our screener dashboard, you will find a wealth of information at your fingertips:
Bullish patterns : Tickers exhibiting bullish harmonic patterns are prominently highlighted with a refreshing green background
Bearish patterns : Similarly, tickers featuring bearish harmonic patterns stand out with a striking red background
Dual patterns : Tickers displaying both bullish and bearish patterns are cleverly highlighted in a captivating purple background, providing a comprehensive view of the harmonic pattern landscape.
Tickers without current patterns : Tickers lacking any current patterns are elegantly displayed with a silver background. These tickers do not trigger tooltips, streamlining your focus on actionable pattern-related data.
🎲 Settings in Detail
🎯 Tickers
Our platform currently allows users to select up to 20 tickers for the harmonic pattern screener. We understand the importance of flexibility and scalability, and while we are excited to accommodate more tickers in the future, our present focus is to ensure optimal performance within the CPU and memory limitations. Rest assured, we are continuously working on enhancing our capabilities to provide you with an even more comprehensive experience. Stay tuned for updates as we strive to meet your evolving needs.
🎯 Zigzag and Harmonic Pattern
In this section, we present a range of essential settings that play a pivotal role in the calculation of the zigzag and the scanning of patterns. These parameters share similarities with other premium indicators associated with Harmonic patterns. These settings serve as building blocks for our advanced algorithms' suite.
This include
Zigzag length and depth settings for calculation of the multi level recursive zigzag
Pattern scanning settings to filter patterns based on preferences of category, pattern name, accuracy of calculation, and other considerations.
User preference of pattern trading ratios that are used for calculating entry, stop and target prices.
🎯 Screener Dashboard and Alerts
In this section, we introduce the parameters that define the format and content of alerts and the screener dashboard, offering you maximum flexibility in customizing their display. These settings encompass the following key aspects:
Screener dashboard position, layout and size that influence the display of screener dashboard.
List of parameters that can be shown on dashboard tooltips as well as on alerts.
Format of alert and tooltip data
🎯 Pattern Display
These are the settings related to pattern display on the chart and to limit calculation to last n bars
Will soon make video tutorials on this soon.
Recursive Micro Zigzag🎲 Overview
Zigzag is basic building block for any pattern recognition algorithm. This indicator is a research-oriented tool that combines the concepts of Micro Zigzag and Recursive Zigzag to facilitate a comprehensive analysis of price patterns. This indicator focuses on deriving zigzag on multiple levels in more efficient and enhanced manner in order to support enhanced pattern recognition.
The Recursive Micro Zigzag Indicator utilises the Micro Zigzag as the foundation and applies the Recursive Zigzag technique to derive higher-level zigzags. By integrating these techniques, this indicator enables researchers to analyse price patterns at multiple levels and gain a deeper understanding of market behaviour.
🎲 Concept:
Micro Zigzag Base : The indicator utilises the Micro Zigzag concept to capture detailed price movements within each candle. It allows for the visualisation of the sequential price action within the candle, aiding in pattern recognition at a micro level.
Basic implementation of micro zigzag can be found in this link - Micro-Zigzag
Recursive Zigzag Expansion : Building upon the Micro Zigzag base, the indicator applies the Recursive Zigzag concept to derive higher-level zigzags. Through recursive analysis of the Micro Zigzag's pivots, the indicator uncovers intricate patterns and trends that may not be evident in single-level zigzags.
Earlier implementations of recursive zigzag can be found here:
Recursive Zigzag
Recursive Zigzag - Trendoscope
And the libraries
rZigzag
ZigzagMethods
The major differences in this implementation are
Micro Zigzag Base - Earlier implementation made use of standard zigzag as base whereas this implementation uses Micro Zigzag as base
Not cap on Pivot depth - Earlier implementation was limited by the depth of level 0 zigzag. In this implementation, we are trying to build the recursive algorithm progressively so that there is no cap on the depth of level 0 zigzag. But, if we go for higher levels, there is chance of program timing out due to pine limitations.
These algorithms are useful in automatically spotting patterns on the chart including Harmonic Patterns, Chart Patterns, Elliot Waves and many more.
Adaptive Predictive Stops and Targets The indicator is an experiment to Predict Stops and 1st target for any liquid security and for any timeframe,
Intro
The indicator is made using Predictive Differential Filter of 2nd Degree
and an Adaptive Filter to generate Signals and define Targets and Stops
An adaptive filter is a system with a linear filter that has a transfer function controlled by variable parameters and a means to adjust those parameters according to an optimisation algorithm. Because of the complexity of the optimisation algorithms, almost all adaptive filters are digital filters. Thus Helping us classify our intent either long side or short side
The indicator use Adaptive Least mean square algorithm, for convergence of the filtered signals into a category of intents, (either buy or sell)
The Other Category of Filter used in the indicator is Predictive Differential Filter, which helps us estimate the acceleration of the prices and levels of significance for targeting and Stops
The Predictive Differential Filters are capable of predicting the next state of the input based on the interaction with a pre-specified number of filters, The prediction helps in minimising the quantisation error and in removing the granular noise which are caused by PCM systems.
How to Use
The logic to use is simple Buy at the High of the Signal Candle and Sell at the Low of the Signal Candle
Book your 50% position on the first target shown (respectively in green and red lines) and Trail the rest of the positions till you reach stop or breakeven!
vice versa for Sell,
Just Sell on the Low of the Signal Candle
What securities and timeframes will it work upon
The system is designed to work over any liquid security over any timeframe,
The Indicator has provisions for Alert
How to request Access
Just Private message me, do not use comment box for requesting access, use it only for constructive comments
STD-Stepped Fast Cosine Transform Moving Average [Loxx]STD-Stepped Fast Cosine Transform Moving Average is an experimental moving average that uses Fast Cosine Transform to calculate a moving average. This indicator has standard deviation stepping in order to smooth the trend by weeding out low volatility movements.
What is the Discrete Cosine Transform?
A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.
The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the type-II DCT, which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT, is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs (MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT. A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT. One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT, : ix, xiii, 1, 141–304 used in several ISO/IEC and ITU-T international standards.
Notable settings
windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm
smthper = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods
Included
Alerts
Signals
Loxx's Expanded Source Types
Additional reading
A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al.
Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al.
Cooley–Tukey FFT algorithm
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]Weighted Burg AR Spectral Estimate Extrapolation of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm. This method is commonly used in speech modeling and speech prediction engines. This method also includes Levinson–Durbin algorithm. As was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Levinson recursion or Levinson–Durbin recursion?
In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes.
The Burg algorithm estimates the AR parameters by determining reflection coefficients that minimize the sum of for-ward and backward residuals. The extension of the algorithm to segments is that the reflection coefficients are estimated by minimizing the sum of forward and backward residuals of all segments taken together. This means a single model is fitted to all segments in one time. This concept is also used for prediction error methods in system identification, where the input to the system is known, like in ARX modeling
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
BurgWin - weighing function index, rectangular, hamming, or parabolic
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Included
Bar color muting
Further reading
Performance of the weighted burg methods of ar spectral estimation for pitch-synchronous analysis of voiced speech
The Burg algorithm for segments
Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions
Related Indicators
Auto Fibonacci Retracement - Real-Time (Expo)█ Fibonacci retracement is a popular technical analysis method to draw support and resistance levels. The Fibonacci levels are calculated between 2 swing points (high/low) and divided by the key Fibonacci coefficients equal to 23.6%, 38.2%, 50%, 61.8%, and 100%. The percentage represents how much of a prior move the price has retraced.
█ Our Auto Fibonacci Retracement indicator analyzes the market in real-time and draws Fibonacci levels automatically for you on the chart. Real-time fib levels use the current swing points, which gives you a huge advantage when using them in your trading. You can always be sure that the levels are calculated from the correct swing high and low, regardless of the current trend. The algorithm has a trend filter and shifts the swing points if there is a trend change.
The user can set the preferred swing move to scalping, trend trading, or swing trading. This way, you can use our automatic fib indicator to do any trading. The auto fib works on any market and timeframe and displays the most important levels in real-time for you.
█ This Auto Fib Retracement indicator for TradingView is powerful since it does the job for you in real-time. Apply it to the chart, set the swing move to fit your trading style, and leave it on the chart. The indicator does the rest for you. The auto Fibonacci indicator calculates and plots the levels for you in any market and timeframe. In addition, it even changes the swing points based on the current trend direction, allowing traders to get the correct Fibonacci levels in every trend.
█ How does the Auto Fib Draw the levels?
The algorithm analyzes the recent price action and examines the current trend; based on the trend direction, two significant swings (high and low) are identified, and Fibonacci levels will then be plotted automatically on the chart. If the algorithm has identified an uptrend, it will calculate the Fibonacci levels from the swing low and up to the swing high. Similarly, if the algorithm has identified a downtrend, it will calculate the Fibonacci levels from the swing high and down to the swing low.
█ HOW TO USE
The levels allow for a quick and easy understanding of the current Fibonacci levels and help traders anticipate and react when the price levels are tested. In addition, the levels are often used for entries to determine stop-loss levels and to set profit targets. It's also common for traders to use Fibonacci levels to identify resistance and support levels.
Traders can set alerts when the levels are tested.
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Disclaimer
Copyright by Zeiierman.
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
