Endpointed SSA of Price [Loxx]The Endpointed SSA of Price: A Comprehensive Tool for Market Analysis and Decision-Making
The financial markets present sophisticated challenges for traders and investors as they navigate the complexities of market behavior. To effectively interpret and capitalize on these complexities, it is crucial to employ powerful analytical tools that can reveal hidden patterns and trends. One such tool is the Endpointed SSA of Price, which combines the strengths of Caterpillar Singular Spectrum Analysis, a sophisticated time series decomposition method, with insights from the fields of economics, artificial intelligence, and machine learning.
The Endpointed SSA of Price has its roots in the interdisciplinary fusion of mathematical techniques, economic understanding, and advancements in artificial intelligence. This unique combination allows for a versatile and reliable tool that can aid traders and investors in making informed decisions based on comprehensive market analysis.
The Endpointed SSA of Price is not only valuable for experienced traders but also serves as a useful resource for those new to the financial markets. By providing a deeper understanding of market forces, this innovative indicator equips users with the knowledge and confidence to better assess risks and opportunities in their financial pursuits.
█ Exploring Caterpillar SSA: Applications in AI, Machine Learning, and Finance
Caterpillar SSA (Singular Spectrum Analysis) is a non-parametric method for time series analysis and signal processing. It is based on a combination of principles from classical time series analysis, multivariate statistics, and the theory of random processes. The method was initially developed in the early 1990s by a group of Russian mathematicians, including Golyandina, Nekrutkin, and Zhigljavsky.
Background Information:
SSA is an advanced technique for decomposing time series data into a sum of interpretable components, such as trend, seasonality, and noise. This decomposition allows for a better understanding of the underlying structure of the data and facilitates forecasting, smoothing, and anomaly detection. Caterpillar SSA is a particular implementation of SSA that has proven to be computationally efficient and effective for handling large datasets.
Uses in AI and Machine Learning:
In recent years, Caterpillar SSA has found applications in various fields of artificial intelligence (AI) and machine learning. Some of these applications include:
1. Feature extraction: Caterpillar SSA can be used to extract meaningful features from time series data, which can then serve as inputs for machine learning models. These features can help improve the performance of various models, such as regression, classification, and clustering algorithms.
2. Dimensionality reduction: Caterpillar SSA can be employed as a dimensionality reduction technique, similar to Principal Component Analysis (PCA). It helps identify the most significant components of a high-dimensional dataset, reducing the computational complexity and mitigating the "curse of dimensionality" in machine learning tasks.
3. Anomaly detection: The decomposition of a time series into interpretable components through Caterpillar SSA can help in identifying unusual patterns or outliers in the data. Machine learning models trained on these decomposed components can detect anomalies more effectively, as the noise component is separated from the signal.
4. Forecasting: Caterpillar SSA has been used in combination with machine learning techniques, such as neural networks, to improve forecasting accuracy. By decomposing a time series into its underlying components, machine learning models can better capture the trends and seasonality in the data, resulting in more accurate predictions.
Application in Financial Markets and Economics:
Caterpillar SSA has been employed in various domains within financial markets and economics. Some notable applications include:
1. Stock price analysis: Caterpillar SSA can be used to analyze and forecast stock prices by decomposing them into trend, seasonal, and noise components. This decomposition can help traders and investors better understand market dynamics, detect potential turning points, and make more informed decisions.
2. Economic indicators: Caterpillar SSA has been used to analyze and forecast economic indicators, such as GDP, inflation, and unemployment rates. By decomposing these time series, researchers can better understand the underlying factors driving economic fluctuations and develop more accurate forecasting models.
3. Portfolio optimization: By applying Caterpillar SSA to financial time series data, portfolio managers can better understand the relationships between different assets and make more informed decisions regarding asset allocation and risk management.
Application in the Indicator:
In the given indicator, Caterpillar SSA is applied to a financial time series (price data) to smooth the series and detect significant trends or turning points. The method is used to decompose the price data into a set number of components, which are then combined to generate a smoothed signal. This signal can help traders and investors identify potential entry and exit points for their trades.
The indicator applies the Caterpillar SSA method by first constructing the trajectory matrix using the price data, then computing the singular value decomposition (SVD) of the matrix, and finally reconstructing the time series using a selected number of components. The reconstructed series serves as a smoothed version of the original price data, highlighting significant trends and turning points. The indicator can be customized by adjusting the lag, number of computations, and number of components used in the reconstruction process. By fine-tuning these parameters, traders and investors can optimize the indicator to better match their specific trading style and risk tolerance.
Caterpillar SSA is versatile and can be applied to various types of financial instruments, such as stocks, bonds, commodities, and currencies. It can also be combined with other technical analysis tools or indicators to create a comprehensive trading system. For example, a trader might use Caterpillar SSA to identify the primary trend in a market and then employ additional indicators, such as moving averages or RSI, to confirm the trend and generate trading signals.
In summary, Caterpillar SSA is a powerful time series analysis technique that has found applications in AI and machine learning, as well as financial markets and economics. By decomposing a time series into interpretable components, Caterpillar SSA enables better understanding of the underlying structure of the data, facilitating forecasting, smoothing, and anomaly detection. In the context of financial trading, the technique is used to analyze price data, detect significant trends or turning points, and inform trading decisions.
█ Input Parameters
This indicator takes several inputs that affect its signal output. These inputs can be classified into three categories: Basic Settings, UI Options, and Computation Parameters.
Source: This input represents the source of price data, which is typically the closing price of an asset. The user can select other price data, such as opening price, high price, or low price. The selected price data is then utilized in the Caterpillar SSA calculation process.
Lag: The lag input determines the window size used for the time series decomposition. A higher lag value implies that the SSA algorithm will consider a longer range of historical data when extracting the underlying trend and components. This parameter is crucial, as it directly impacts the resulting smoothed series and the quality of extracted components.
Number of Computations: This input, denoted as 'ncomp,' specifies the number of eigencomponents to be considered in the reconstruction of the time series. A smaller value results in a smoother output signal, while a higher value retains more details in the series, potentially capturing short-term fluctuations.
SSA Period Normalization: This input is used to normalize the SSA period, which adjusts the significance of each eigencomponent to the overall signal. It helps in making the algorithm adaptive to different timeframes and market conditions.
Number of Bars: This input specifies the number of bars to be processed by the algorithm. It controls the range of data used for calculations and directly affects the computation time and the output signal.
Number of Bars to Render: This input sets the number of bars to be plotted on the chart. A higher value slows down the computation but provides a more comprehensive view of the indicator's performance over a longer period. This value controls how far back the indicator is rendered.
Color bars: This boolean input determines whether the bars should be colored according to the signal's direction. If set to true, the bars are colored using the defined colors, which visually indicate the trend direction.
Show signals: This boolean input controls the display of buy and sell signals on the chart. If set to true, the indicator plots shapes (triangles) to represent long and short trade signals.
Static Computation Parameters:
The indicator also includes several internal parameters that affect the Caterpillar SSA algorithm, such as Maxncomp, MaxLag, and MaxArrayLength. These parameters set the maximum allowed values for the number of computations, the lag, and the array length, ensuring that the calculations remain within reasonable limits and do not consume excessive computational resources.
█ A Note on Endpionted, Non-repainting Indicators
An endpointed indicator is one that does not recalculate or repaint its past values based on new incoming data. In other words, the indicator's previous signals remain the same even as new price data is added. This is an important feature because it ensures that the signals generated by the indicator are reliable and accurate, even after the fact.
When an indicator is non-repainting or endpointed, it means that the trader can have confidence in the signals being generated, knowing that they will not change as new data comes in. This allows traders to make informed decisions based on historical signals, without the fear of the signals being invalidated in the future.
In the case of the Endpointed SSA of Price, this non-repainting property is particularly valuable because it allows traders to identify trend changes and reversals with a high degree of accuracy, which can be used to inform trading decisions. This can be especially important in volatile markets where quick decisions need to be made.
Buscar en scripts para "algo"
Bogdan Ciocoiu - LitigatorDescription
The Litigator is an indicator that encapsulates the value delivered by the Relative Strength Index, Ultimate Oscillator, Stochastic and Money Flow Index algorithms to produce signals enabling users to enter positions in ideal market conditions. The Litigator integrates the value delivered by the above four algorithms into one script.
This indicator is handy when trading continuation/reversal divergence strategies in conjunction with price action.
Uniqueness
The Litigator's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for short term scalping (1-5 minutes).
In addition, the Litigator allows configuring the above four algorithms in such a way to coordinate signals by colour-coding or shape thickness to aid the user with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same, and in doing so, enabling users to plug them in/out as needed. This also includes ensuring the ratios of the shapes are similar (applicable to the same scale).
Open-source
The indicator uses the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
Bogdan Ciocoiu - MoonshotDescription
Moonshot is an indicator that encapsulates the value delivered by the TSI, MACD, Awesome Oscillator and CCI algorithms to produce signals to enable users to enter positions in ideal market conditions. Moonshot integrates the value delivered by the above four algorithms into one script.
This indicator is particularly useful when trading continuation/reversal divergence strategies.
Uniqueness
The Moonshot's uniqueness stands from integrating the above algorithms into the same visual area and leveraging preconfigured parameters suitable for 1-3 minute scalping techniques.
In addition, Moonshot allows swapping or furthermore configuring the above four algorithms in such a way to align signals by colour-coding or shape thickness to aid the users with identifying any emerging patterns quicker.
Furthermore, Moonshot's uniqueness is also reflected in the way it has standardised the outputs of each algorithm to look and feel the same (including the scale at which the shapes are shown) and, in doing so, enables users to plug them in/out as needed.
Open-source
The indicator leverages the following open-source scripts/algorithms:
www.tradingview.com
www.tradingview.com
www.tradingview.com
www.tradingview.com
The Chartless TraderThe Chartless Trader
The chartless trader is a trade management system designed to remove the randomness from the market. It is loosely based on the martingales betting system, but takes advantage of position sizing, minimum profit targets, dollar cost averaging, and trailing take profit.
The chart can be traded with or without a signal. There is a built in signal based on SB Master Chart's Buy the Dip algorithm.
The configurable settings include:
Account Value
Starting Account Value - This is the value of the account when you start using this system.
Current Cash - This is the amount of cash you have available to trade. This setting needs to be updated each time a trade is made.
TP/TTP Algo Settings
Take Profit % - This setting is otherwise known as minimum profit target. This algo will not advise you to sell or increase your trailing stop until this minimum profit target is met.
Trailing Stop % - This is the trailing stop. The default setting is 75%. As a basic example, if the stock is up 10%, the trailing stop would be set to 7.5% (10% * 75%). The algo may override and advise an alternative trailing stop should an overbought condition be detected.
DCA/BTD Algo
DCA/BTD Algo Time Frame - Default is 120 (2hrs). This algo looks for oversold periods on the 2h chart by default.
DCA % - The default for this setting is 5%. This is a trigger for the BTD Algo. The BTD algo will start looking for trades when the stock is 5% below your cost basis. This is to help you average down making it easier to turn a profit when the stock starts making gains.
Position #
The Chartless Trader supports a maximum of 20 symbols. This is a limitation of the security() function as a maximum of 40 calls are allowed and the script calls the security() function twice per symbol.
S# QTY - The number of open positions of the symbol. This has to be manually updated by the user after each buy/sell of the stock.
S# CB - This is the cost basis of the stock. Your broker should give you this after each buy/sell and it has to be updated here on the chart after each buy/sell.
S# TTP - The script will advise you to increase your Trailing Take Profit in your broker when its necessary. This should be updated manually after you update your order in your broker. This should be configured manually in your broker as a Stop Order.
Now that I have covered the configurable options, its important to understand the basis of this system. The martingales betting system is a system that seeks to double its position size each time you enter a losing trade. Eventually when you make a winning trade, it will be enough to cover the previous losses and net you one winning position.
Bet 1, lose 1, down 1.
Bet 2, lose 2, down 3.
Bet 4, lose 4, down 7.
Bet 8, lost 8, down 15.
Bet 16, win 16, up 1.
So the theory goes, if you have deep enough pockets, its a 100% win rate. Such a system is flawed and proven to cause an account to blow up given enough time. You can search Google/YouTube for others that have back tested the martingales system with stocks.
I advise that "The Chartless Trading" system be traded with a similar system, but instead of doubling your position, you simply increase your position size by 1%.
Bet 1%, lose 1%, down 1%
Bet 1%, lose 1%, down 2%
Bet 1%, lose 1%, down 3%.
In such a manner, your risk of ruin is significantly reduced. Lets say you lose 10 times in a row betting on a stock. You now have 10% of your account value in this particular stock. Because you only invested at times where you were more than 5% down and when an oversold position occurred, because of dollar cost averaging and buying during oversold periods, you may only be down 2-3% on your invested value. Eventually when the stock turns positive, you will have met your minimum profit target and the script will alert you to set a trailing stop. You log into your broker, set a stop loss and wait for it to either trigger or inform you to increase it again. Once the trailing stop is triggered, you deleverage the position by closing it and starting a single new position in either the same stock or a different one and the cycle repeats.
The key is to follow the stock down, follow it back up, and not back down. We repeat this cycle with many positions in many stocks to minimize risk and compound our balance sheet.
This is " The Chartless Trader ".
1920x1080p Monitor Required if using all 20 symbols.
The more symbols loaded, the longer the initial processing to load the table. Please be patient.
Directional AnalyzerThis script attempts to equip users with the necessary information about the direction of an instrument, and essentially it is a synergy of 3 algorithms.
The first algorithm (plotted as dots at level 0) studies the balance of delta volatility that constitutes the current bar and answers if bulls or bears are in control at that exact bar time
The second algorithm (plotted as an area) studies the development of delta volatility over the defined period by means of a polynomial regression. Effectively, it provides an overall picture of the trend strength.
The third algorithm (plotted as a line with arrow labels) utilizes simple elements of neural network in conjunction with some custom filters to predict the focal point that a trend will reverse its direction. This is predictive in nature, hence always adopt this with caution. While the labels display the predicted direction, the colors of the line also reflect the state of the current bar as well, adding to the confirmation of the first algorithm.
May you be on the right side of the trade.
Anticipated Market TypeDisplays the anticipated market type based on the last 300 bars of data:
Trending Market: High probability that the next bar will be in the same direction as previous. Best conditions for a trend trading strategy
Neutral Market: High probability that price is random - the next bar direction is a coin toss. Many "typical" indicators fail in a random market
Sideways Market: High probability that price is autoregressive and the next bar direction is opposite the previous - compressed markets often have sudden fast breakouts
This tool does not give you entries and exits, but assists in deciding to use a Trend-following or Mean-reverting strategy.
Blue (3.5-6) indicates a trending market.
Yellow (0-2.5) indicates a sideways market.
Green (2.5-3.5) indicates a random market
This algorithm tells you when it breaks down by indicating a Neutral/Random market.
In short, it can't say the market type and advises you to not trade or simply use another tool in the meantime.
I personally use this tool to configure my trading robots on a weekly basis. I combine manual TA and stats algos to
try and determine what type of market the next week holds, with a fair bit of success.
The algorithms incorporated are Market Meanness Index (which I've made Open Source) and Fractal Dimension , a significantly faster algo than the MMI, but using a different set of maths.
Cheers!
MyAlgo EXTREMEPLEASE READ THE ENTIRE POST BEFORE PURCHASING & USING THE MyAlgo Tool. Saves you and me some time in emails and messages. :)
This is the official version of MyAlgo EXTREME
PLEASE UNDERSTAND THAT THIS IS A DIFFERENT AND SEPARATE PRODUCT AND SCRIPT FROM "MyAlgo SLIM" FROM THE MyAlgo TRADING TOOL SERIES
Description
Buy & Sell Alerts can be set on all Tickers. This includes, but is not limited to Crypto, Commodities , FOREX, Equities and Indices. Also all candle Types are compatible.
Recommended Time-frames - Due to the complexity of MyAlgo-SLIM the user has a choice between three algorithms and is like that able to trade on all timeframes with the highest returns.
MyAlgo combines many different aspects at the same time, scans multiple other Algorithms and comes to a conclusion based on over 1350 lines of code.
It is based on Divergences, Elliott Waves , Ichimoku , MACD , MACD Histogram, RSI , Stoch , CCI , Momentum, OBV, DIOSC, VWMACD, CMF and multiple EMAs.
Every single aspect is weighted into the decision before giving out an indication.
Most buy/sell Algorithms FAIL because they try to apply the same strategy to every single chart, which
are as individual as humans. To conquer this problem, MyAlgo has a wide range of settings and variables which can be easily
modified.
To make it a true strategy, MyAlgo has as well settings for Take Profit Points and Stop
Losses. Everything with an Alert Feature of course so that FULL AUTOMATION IS POSSIBLE.
I know from experience that many people take one Algorithm and are simply too LAZY to add multiple Algorithms to make a rational choice. The result of that is that they lose money, by following blatantly only one Algorithm.
MyAlgo has additional 15 Indicators, perfect for all markets, which can be turned on and off individually.
Side Notes
MyAlgo is being updated and upgraded very frequently to suit the requests of our customers.
This is not financial advice. Please read our disclaimer before using it.
Please refer to the signature field if you are interested in gaining access to this script.
Anything below this sentence will be Updates regarding MyAlgo
TopTenAlgo 3. Cursor Trend with SQZ MOM(Without Volume Ind.)EN: Indicator Trend is a momentum algorithm that measures the direction of the trend. It recalculates the Volume Weighted Moving Average and Tilson functions included with a certain frequency value according to the closing price and this trend helps us determine trend times. The size of the frequency correction motion. It Looks at the Logarithmic to functions. Is the zigzag of argument correction? otherwise it is a shortcut for a flat / flat correction . You can use the minus frequency value minus in zigzags, while it is handled with lower frequencies in flat or flat corrections . For symbols for which the Volume Indicator cannot be read.
This algorithm is prepared with @Top10Algo ... Improvements have been made regarding short periods.
TR: Gösterge Trend, trendin yönünü ölçen bir momentum algoritmasıdır. İçeriğinde bulunan Volume Weighted Moving Average ve Tilson fonksiyonlarını belli bir frekans değeri ile kapanış fiyatına göre yeniden hesaplar ve bu sayede trend değişim zamanlarını belirlememize yardımcı olur. Frekans değeri düzeltme hareketinin boyutuna göre değişiklik gösterir. Fonsiyonlara Logaritmik bakar.Frekans değerini belirlememizde yardımcı olan argüman düzeltmenin zigzag mı? yoksa yassı/flat bir düzeltmemi olacağını kestirmektir. Zigzaglarda frekans değeri eksi yönde daha fazla büyürken yassı yada flat düzeltmelerde daha düşük frekanslarla ele alınır. Hacim Göstergesinin okunamadığı semboller içindir.
Bu algoritma @Top10Algo ile beraber hazırlanmıştır... Kodlamadaki katkılarından ve yol göstericiliğinden dolayı teşekkürü bir borç bilirim. Kısa Periyotlar için iyileştirmeler yapıldı...
TopTenAlgo 3. Cursor Trend with SQZ MOM (Include Volume Ind.)EN: Indicator Trend is a momentum algorithm that measures the direction of the trend. It recalculates the Volume Weighted Moving Average and Tilson functions included with a certain frequency value according to the closing price and this trend helps us determine trend times. The size of the frequency correction motion. It Looks at the Logarithmic to functions. Is the zigzag of argument correction? otherwise it is a shortcut for a flat / flat correction . You can use the minus frequency value minus in zigzags, while it is handled with lower frequencies in flat or flat corrections .
This algorithm is prepared with @Top10Algo ... Improvements have been made regarding short periods.
TR: Gösterge Trend, trendin yönünü ölçen bir momentum algoritmasıdır. İçeriğinde bulunan Volume Weighted Moving Average ve Tilson fonksiyonlarını belli bir frekans değeri ile kapanış fiyatına göre yeniden hesaplar ve bu sayede trend değişim zamanlarını belirlememize yardımcı olur. Frekans değeri düzeltme hareketinin boyutuna göre değişiklik gösterir. Fonsiyonlara Logaritmik bakar.Frekans değerini belirlememizde yardımcı olan argüman düzeltmenin zigzag mı? yoksa yassı/flat bir düzeltmemi olacağını kestirmektir. Zigzaglarda frekans değeri eksi yönde daha fazla büyürken yassı yada flat düzeltmelerde daha düşük frekanslarla ele alınır.
Bu algoritma @Top10Algo ile beraber hazırlanmıştır... Kodlamadaki katkılarından ve yol göstericiliğinden dolayı teşekkürü bir borç bilirim. Kısa Periyotlar için iyileştirmeler yapıldı...
SMU Stock ThermometerThis script shows various technical indicators in a stacked vertical candle called Market Termometer.
It helps to see the price action in one single vertical column where the actual price moves up or down. So you can see the price change based on your custom setting levels.
I've been studying ALGO for over a year and made many live experiment trades long and shorts. So, I'm trying to find a way to see what is ALGos next move. If it sounds far-fetch, then you should see my other published scripts.
Here is example of how ALGo dance around old indicators, which is why I started creating a bunch of new indicators that ALGO doesn't know
Example:
Impact-driven-algorithm= Large volume masked as small volume to keep the price at desired level. So, your chart says overbought but market doesn't drop for days
Cost-driven-algorithm= Hedge fund buy every time at lower price and prevent others to buy low, moving up fast. Is like a clock with millisecond timing and ALGO owners know when to buy low and when to sell high
If you have a good idea, let me know so i can include it the future versions.
Enjoy and think outside the box, the only way to beat the ALGO
MACD Enhanced [DCAUT]█ MACD Enhanced Indicator
📊 Indicator Overview
MACD Enhanced is an enhanced version of the traditional MACD indicator. Unlike standard MACD which is limited to EMA algorithm, this enhanced version supports different moving average algorithms for both main MACD line and signal line, allowing traders to optimize MACD calculations based on market conditions and preferences.
🎯 Core Advantages
Difference from Traditional MACD
Traditional MACD : Fixed EMA algorithm for both main and signal lines
MACD Enhanced : Supports different moving average algorithms for main and signal lines
Algorithm Flexibility Benefits
Fast Response : EMA, HMA algorithms respond quickly to price changes
Stable Smoothing : SMA, RMA algorithms provide more stable signals
Adaptive Adjustment : KAMA, FRAMA algorithms automatically adjust based on market conditions
Noise Filtering : Kalman Filter, Super Smoother reduce false signals
🚀 Usage Guide
Parameter Settings
Source : Data source (default: close price)
Fast Length : Fast line period (recommended: 12)
Slow Length : Slow line period (recommended: 26)
Signal Length : Signal line period (recommended: 9)
MACD MA Type : Moving average algorithm for main MACD line (default: EMA)
Signal MA Type : Moving average algorithm for signal line (default: EMA)
Algorithm Combination Guidelines
Fast Trading : Choose EMA, HMA for both main and signal lines
Stable Analysis : Choose SMA, RMA for both main and signal lines
Mixed Strategy : Choose fast algorithm for main line, smooth algorithm for signal line
Trend Following : Choose KAMA for main line, EMA for signal line
📊 Color Coding
Line Colors
Blue : MACD main line
Orange : Signal line
Histogram
Dark Green : Histogram > 0 and rising (strong bullish momentum)
Light Green : Histogram > 0 and falling (weak bullish momentum)
Light Red : Histogram < 0 and rising (weak bearish momentum)
Dark Red : Histogram < 0 and falling (strong bearish momentum)
💡 Core Value
By selecting different moving average algorithm combinations, traders can obtain:
- MACD signals better suited to current market conditions
- Trend tracking capabilities with different sensitivity levels
- Algorithm optimization possibilities that traditional MACD cannot provide
📄 License : MIT License
👨💻 Developer : DCAUT Team
ATR Enhanced [DCAUT]█ ATR Enhanced Indicator
📊 Indicator Overview
ATR Enhanced is an enhanced version of the traditional ATR indicator. Unlike standard ATR which is limited to RMA smoothing algorithm, this advanced version supports multiple moving average algorithms, allowing traders to optimize ATR calculations based on market conditions.
🎯 Core Advantages
Difference from Traditional ATR
Traditional ATR : Fixed RMA smoothing algorithm only
ATR Enhanced : Supports SMA, EMA, HMA, KAMA, Kalman Filter and many other algorithms
Algorithm Flexibility Benefits
Fast Response : EMA, HMA algorithms respond quickly to volatility changes
Stable Smoothing : SMA, RMA algorithms provide traditional stable signals
Adaptive Adjustment : KAMA, FRAMA algorithms automatically adjust based on market conditions
Noise Filtering : Kalman Filter, Super Smoother reduce market noise
🚀 Usage Guide
Parameter Settings
ATR Length : ATR calculation period (recommended: 14)
Moving Average Type : Choose moving average algorithm (default: Laguerre Filter, advanced smoothing algorithm)
Algorithm Selection Guidelines
Fast Trading : Choose EMA, HMA for quick response
Stable Analysis : Choose SMA, RMA for traditional performance
Trend Following : Choose KAMA, FRAMA adaptive algorithms
Ranging Markets : Choose Kalman Filter to reduce noise
📊 Color Coding
Green : ATR rising - Volatility increasing
Red : ATR falling - Volatility decreasing
💡 Core Value
By selecting different moving average algorithms, traders can obtain:
- ATR values better suited to current market conditions
- Volatility signals with different sensitivity levels
- Adaptive capabilities that traditional ATR cannot provide
📄 License : MIT License
👨💻 Developer : DCAUT Team
Elite Zone Master Pro - Advanced Multi-Session Trading System🚀 Elite Zone Master Pro - Advanced Multi-Session Trading System
🎯 ORIGINALITY & UNIQUE VALUE PROPOSITION
Elite Zone Master Pro is NOT a simple mashup of existing indicators. It's a proprietary trading system that combines three distinct methodologies into a unified, synergistic approach:
Multi-Session Zone Analysis - Original algorithm for tracking global market sessions
Dynamic Opening Range Breakout (ORB) - Enhanced ORB with bias-aware signal filtering
Advanced Fair Value Gap Detection - Proprietary FVG identification with smart mitigation tracking
🔧 Why This Combination Works
The power lies in how these components work together, not separately:
Session zones provide market context and volatility windows
ORB system identifies key breakout levels during optimal timeframes
FVG detection pinpoints precise entry locations within the ORB framework
Integrated bias system filters signals based on range direction momentum
🧠 DETAILED METHODOLOGY & CALCULATIONS
🌍 1. Multi-Session Zone Framework
What it does: Tracks and visualizes three major global trading sessions simultaneously.
How it works:
Dynamic zone tracking algorithm that calculates session highs/lows in real-time
Adaptive box rendering that expands/contracts based on actual price movement
Session overlap detection for identifying high-volatility periods
Time-weighted zone positioning using custom timezone calculations
Original concepts:
Simultaneous multi-session visualization (not found in standard session indicators)
Dynamic zone expansion based on volatility, not fixed time periods
Cross-session momentum analysis for bias determination
🎯 2. Enhanced Opening Range Breakout System
What it does: Identifies breakout opportunities from predefined session ranges with intelligent bias filtering.
How it works:
Multi-session ORB calculation: Supports US (16:30-16:45), EU (10:00-10:15), Asian (03:00-03:15), and custom sessions
Dynamic range establishment: Range is built in real-time during active session periods
Bias-aware signal filtering: Two-tier breakout system based on range midpoint momentum
Range direction analysis: Compares current range midpoint to previous session's midpoint
Original methodology:
Range Bias Calculation:
- If Current_Midpoint > Previous_Midpoint = Bullish Bias (+1)
- If Current_Midpoint < Previous_Midpoint = Bearish Bias (-1)
- If Current_Midpoint = Previous_Midpoint = Neutral Bias (0)
Signal Logic:
- Bullish Bias: Standard breakout above range high
- Bearish Bias: Enhanced breakout (range_high + 0.5 * range_width) for bullish signals
- Neutral Bias: Standard breakouts both directions
⚡ 3. Advanced Fair Value Gap (FVG) Detection
What it does: Identifies and tracks fair value gaps with automatic mitigation detection.
How it works:
Three-bar gap analysis: Compares current bar relationships to identify true gaps
Dynamic threshold calculation: Auto-adjusting sensitivity based on market volatility
Smart mitigation tracking: Automatically removes filled gaps from display
Directional bias integration: Color-codes gaps based on their directional implication
Proprietary algorithms:
Bullish FVG Criteria:
- Current_Low > High (gap condition)
- Close > High (confirmation)
- (Current_Low - High ) / High > Threshold (significance filter)
Bearish FVG Criteria:
- Current_High < Low (gap condition)
- Close < Low (confirmation)
- (Low - Current_High) / Current_High > Threshold (significance filter)
Mitigation Logic:
- Bullish FVG: Mitigated when Close < FVG_Low
- Bearish FVG: Mitigated when Close > FVG_High
📈 4. Session-Based Moving Average System
What it does: Calculates moving averages that reset and adapt to session boundaries.
How it works:
Session-aware length calculation: Effective length = min(bars_since_session_start, user_length)
Multiple MA types: EMA, SMA, RMA, WMA, VWMA with session-specific calculations
Dynamic smoothing: Adapts to session length for consistent signals across different session durations
🔄 INTEGRATED SYSTEM SYNERGY
🎯 How Components Work Together
Context Layer: Session zones provide market timing context
Setup Layer: ORB system identifies breakout opportunities within optimal timeframes
Entry Layer: FVG detection pinpoints precise entry levels
Filter Layer: Bias system ensures alignment with momentum direction
Confirmation Layer: Session MA provides trend confirmation
🧭 Signal Generation Process
Step 1: Session Analysis
- Identify active trading session
- Calculate session volatility metrics
- Establish range boundaries
Step 2: Range Bias Calculation
- Compare current vs previous range midpoints
- Assign directional bias (-1, 0, +1)
- Adjust breakout thresholds accordingly
Step 3: Breakout Detection
- Monitor price interaction with range boundaries
- Apply bias-specific breakout criteria
- Generate preliminary signals
Step 4: FVG Confirmation
- Scan for fair value gaps within range
- Validate gap significance using dynamic thresholds
- Provide entry refinement opportunities
Step 5: Signal Validation
- Cross-reference with session MA direction
- Ensure alignment with overall bias
- Output final trading signals
📊 PRACTICAL IMPLEMENTATION
🎯 Trading Strategy Framework
Setup Phase:
Configure session times for your timezone
Enable preferred sessions (US/EU/Asian)
Adjust FVG sensitivity based on instrument volatility
Execution Phase:
Wait for range establishment during active session
Monitor for bias-aligned breakouts
Look for FVG retest opportunities
Enter trades with ORB-based stop losses
Risk Management:
Stop loss placement: Outside ORB range boundaries
Position sizing: Based on range width volatility
Trade direction: Must align with calculated range bias
🎨 UNIQUE VISUAL IMPLEMENTATION
📊 Advanced Visualization Features
Multi-layered zone rendering with transparency controls
Dynamic range boxes that adapt to price movement
Smart label positioning to avoid chart clutter
Color-coded bias indication through range fills
Progressive FVG display with automatic cleanup
🔧 TECHNICAL SPECIFICATIONS
⚙️ Performance Optimizations
Efficient array management for FVG tracking
Memory optimization through historical data cleanup
Smart rendering to prevent chart overload
Error handling for edge cases and invalid timeframes
📈 Compatibility
All timeframes under 1 day
All instruments (Forex, Stocks, Crypto, Futures)
All chart types with overlay capability
Mobile and desktop platform support
🏆 WHAT MAKES THIS DIFFERENT FROM OTHER INDICATORS
❌ Standard ORB indicators: Only show basic range breakouts without bias consideration
❌ Basic FVG indicators: Don't integrate with session analysis or range systems
❌ Session indicators: Simply highlight time periods without actionable trading signals
❌ Moving average indicators: Don't adapt to session dynamics
✅ Elite Zone Master Pro: Combines all elements with proprietary logic for a complete trading system
📋 USE CASES & MARKET APPLICATION
🎯 Primary Applications
Forex day trading during major session overlaps
Index futures scalping using session-specific ranges
Cryptocurrency swing trading with 24/7 session analysis
Stock market opening range breakout strategies
📊 Performance Characteristics
Best performance: During high-volatility session transitions
Optimal timeframes: 1m to 4H for intraday trading
Risk-reward ratios: Typically 1:2 to 1:4 based on range width
Win rate: Higher probability when all components align
This indicator represents months of development combining institutional trading concepts with retail accessibility. It's not just another indicator - it's a complete trading methodology in one comprehensive tool.
PowerDelta Oscillator [FxScripts]PowerDelta Oscillator
The PowerDelta Oscillator measures real-time buying and selling pressure using the proprietary PowerDelta Algorithm. By quantifying order flow, it identifies whether the market conditions favor bullish or bearish activity, helping traders determine directional bias for both trend and countertrend setups.
Calculation Methodology
The PowerDelta computes the delta (difference) between buying and selling pressure by integrating both price movement and volume behavior rather than relying solely on volume or price-based approximations like other oscillators.
The PowerDelta Algorithm evaluates six core price-volume conditions:
Price advancing with increasing volume
Price advancing with decreasing volume
Price consolidating with increasing volume
Price consolidating with decreasing volume
Price declining with increasing volume
Price declining with decreasing volume
From these conditions, the algorithm derives:
Accumulation vs Distribution phases
Buyer/Seller exhaustion points
Effort vs No Result scenarios (volume pressure failing to move price)
Operational Use
The PowerDelta Oscillator has three operational modes:
Trend
Countertrend
Blended (Trend/Countertrend hybrid)
Trend Mode
In Trend Mode, the indicator plots an oscillator that fluctuates between positive and negative values:
Positive readings indicate dominant buying pressure
Negative readings indicate dominant selling pressure
The magnitude of the reading reflects the intensity of the pressure
Crossovers at the zero line provide directional shifts:
Negative → Positive: bullish transition
Positive → Negative: bearish transition
Additionally:
Sustained positive values indicate control by buyers, long bias is favoured
Sustained negative values indicate control by sellers, short bias is favoured
The magnitude of displacement from zero provides additional confirmation of market strength or weakness
Countertrend Mode
In Countertrend Mode, the primary use of the PowerDelta Oscillator is to locate divergences between price and the oscillator (as visualised on the chart above) which helps traders pinpoint potential reversals
The oscillator is much more sensitive in this mode, making highs, lows and hence divergences, easier to spot
Like Trend Mode, the magnitude of displacement from zero provides additional confirmation of market strength or weakness
The various Analytical Scenarios detailed below provide detailed use cases for both Trend and Countertrend Mode
Blended Mode
To provide maximum flexibility, there’s also a third Blended Mode
This mode combines elements of the two primary modes and can be used as part of a hybrid approach making it easier to spot both trends and reversals
Alternative Source
The PowerDelta algorithm utilises volume data therefore it’s best to use the most reliable source of volume data for the instrument being traded
For instance, whilst XAUUSD provides excellent results with most forex brokers, slightly better results may be achieved using GC futures data which comes direct from the exchange (data package required)
To use a third-party source, select 'Alternative' and input the relevant source
This can also be used as a way to monitor correlated pairs by adding two instances of the PowerDelta to the same chart, selecting pair 1 e.g. EURUSD as the first instance and the correlated pair e.g. USDCHF as the second instance
Thorough backtesting advised
Analytical Scenarios
Accumulation: High positive oscillator readings combined with upward price movement suggest active accumulation.
Optimal strategy: Monitor pullbacks for potential long entries or wait for a divergence with price and potential reversal.
Distribution: High negative oscillator readings with downward price movement indicate distribution.
Optimal strategy: Monitor pullbacks for potential short entries or wait for a divergence with price and potential reversal.
Buyer Exhaustion: Price forms higher highs while oscillator value declines. Indicates weakening buying strength and potential bearish reversal.
Seller Exhaustion: Price forms lower lows while oscillator value contracts. Indicates weakening selling strength and potential bullish reversal.
Effort / No Result (Buyers): Positive oscillator expansion without higher highs indicates aggressive buying without price confirmation, suggesting overbought conditions and a potential bearish reversal.
Effort / No Result (Sellers): Negative oscillator expansion without lower lows indicates aggressive selling without price confirmation, suggesting oversold conditions and a potential bullish reversal.
Alerts
To trigger alerts when market bias transitions across the zero line:
Right-click on chart → Add Alert on PowerDelta
Condition: PowerDelta → Select Mode
Type: Crossing
Value: 0
Execution: Once Per Bar Close
Adjust additional parameters as required
Performance and Optimization
Backtesting Results: The PowerDelta Oscillator has undergone extensive backtesting across various instruments, timeframes and market conditions, demonstrating strong performance in identifying strong trends and reversals. User backtesting is strongly encouraged as it allows traders to optimize settings for their preferred instruments and timeframes.
Optimization for Diverse Markets: The PowerDelta Oscillator can be used on crypto, forex, indices, commodities and stocks. The PowerDelta Oscillator's algorithmic foundation ensures consistent performance across a variety of instruments. The Trend, Countertrend and Blended Modes make it easy for the trader to set up based on their individual trading style.
Educational Resources and Support
Users of the PowerDelta Oscillator benefit from comprehensive educational resources and full access to FxScripts Support. This ensures traders can maximize the potential of the PowerDelta Oscillator and other tools in the Sigma Indicator Suite by learning best practices and gaining insights from an experienced team of traders.
POC Migration Velocity (POC-MV) [PhenLabs]📊POC Migration Velocity (POC-MV)
Version: PineScript™v6
📌Description
The POC Migration Velocity indicator revolutionizes market structure analysis by tracking the movement, speed, and acceleration of Point of Control (POC) levels in real-time. This tool combines sophisticated volume distribution estimation with velocity calculations to reveal hidden market dynamics that conventional indicators miss.
POC-MV provides traders with unprecedented insight into volume-based price movement patterns, enabling the early identification of continuation and exhaustion signals before they become apparent to the broader market. By measuring how quickly and consistently the POC migrates across price levels, traders gain early warning signals for significant market shifts and can position themselves advantageously.
The indicator employs advanced algorithms to estimate intra-bar volume distribution without requiring lower timeframe data, making it accessible across all chart timeframes while maintaining sophisticated analytical capabilities.
🚀Points of Innovation
Micro-POC calculation using advanced OHLC-based volume distribution estimation
Real-time velocity and acceleration tracking normalized by ATR for cross-market consistency
Persistence scoring system that quantifies directional consistency over multiple periods
Multi-signal detection combining continuation patterns, exhaustion signals, and gap alerts
Dynamic color-coded visualization system with intensity-based feedback
Comprehensive customization options for resolution, periods, and thresholds
🔧Core Components
POC Calculation Engine: Estimates volume distribution within each bar using configurable price bands and sophisticated weighting algorithms
Velocity Measurement System: Tracks the rate of POC movement over customizable lookback periods with ATR normalization
Acceleration Calculator: Measures the rate of change of velocity to identify momentum shifts in POC migration
Persistence Analyzer: Quantifies how consistently POC moves in the same direction using exponential weighting
Signal Detection Framework: Combines trend analysis, velocity thresholds, and persistence requirements for signal generation
Visual Rendering System: Provides dynamic color-coded lines and heat ribbons based on velocity and price-POC relationships
🔥Key Features
Real-time POC calculation with 10-100 configurable price bands for optimal precision
Velocity tracking with customizable lookback periods from 5 to 50 bars
Acceleration measurement for detecting momentum changes in POC movement
Persistence scoring to validate signal strength and filter false signals
Dynamic visual feedback with blue/orange color scheme indicating bullish/bearish conditions
Comprehensive alert system for continuation patterns, exhaustion signals, and POC gaps
Adjustable information table displaying real-time metrics and current signals
Heat ribbon visualization showing price-POC relationship intensity
Multiple threshold settings for customizing signal sensitivity
Export capability for use with separate panel indicators
🎨Visualization
POC Connecting Lines: Color-coded lines showing POC levels with intensity based on velocity magnitude
Heat Ribbon: Dynamic colored ribbon around price showing POC-price basis intensity
Signal Markers: Clear exhaustion top/bottom signals with labeled shapes
Information Table: Real-time display of POC value, velocity, acceleration, basis, persistence, and current signal status
Color Gradients: Blue gradients for bullish conditions, orange gradients for bearish conditions
📖Usage Guidelines
POC Calculation Settings
POC Resolution (Price Bands): Default 20, Range 10-100. Controls the number of price bands used to estimate volume distribution within each bar
Volume Weight Factor: Default 0.7, Range 0.1-1.0. Adjusts the influence of volume in POC calculation
POC Smoothing: Default 3, Range 1-10. EMA smoothing period applied to the calculated POC to reduce noise
Velocity Settings
Velocity Lookback Period: Default 14, Range 5-50. Number of bars used to calculate POC velocity
Acceleration Period: Default 7, Range 3-20. Period for calculating POC acceleration
Velocity Significance Threshold: Default 0.5, Range 0.1-2.0. Minimum normalized velocity for continuation signals
Persistence Settings
Persistence Lookback: Default 5, Range 3-20. Number of bars examined for persistence score calculation
Persistence Threshold: Default 0.7, Range 0.5-1.0. Minimum persistence score required for continuation signals
Visual Settings
Show POC Connecting Lines: Toggle display of colored lines connecting POC levels
Show Heat Ribbon: Toggle display of colored ribbon showing POC-price relationship
Ribbon Transparency: Default 70, Range 0-100. Controls transparency level of heat ribbon
Alert Settings
Enable Continuation Alerts: Toggle alerts for continuation pattern detection
Enable Exhaustion Alerts: Toggle alerts for exhaustion pattern detection
Enable POC Gap Alerts: Toggle alerts for significant POC gaps
Gap Threshold: Default 2.0 ATR, Range 0.5-5.0. Minimum gap size to trigger alerts
✅Best Use Cases
Identifying trend continuation opportunities when POC velocity aligns with price direction
Spotting potential reversal points through exhaustion pattern detection
Confirming breakout validity by monitoring POC gap behavior
Adding volume-based context to traditional technical analysis
Managing position sizing based on POC-price basis strength
⚠️Limitations
POC calculations are estimations based on OHLC data, not true tick-by-tick volume distribution
Effectiveness may vary in low-volume or highly volatile market conditions
Requires complementary analysis tools for complete trading decisions
Signal frequency may be lower in ranging markets compared to trending conditions
Performance optimization needed for very short timeframes below 1-minute
💡What Makes This Unique
Advanced Estimation Algorithm: Sophisticated method for calculating POC without requiring lower timeframe data
Velocity-Based Analysis: Focus on POC movement dynamics rather than static levels
Comprehensive Signal Framework: Integration of continuation, exhaustion, and gap detection in one indicator
Dynamic Visual Feedback: Intensity-based color coding that adapts to market conditions
Persistence Validation: Unique scoring system to filter signals based on directional consistency
🔬How It Works
Volume Distribution Estimation:
Divides each bar into configurable price bands for volume analysis
Applies sophisticated weighting based on OHLC relationships and proximity to close
Identifies the price level with maximum estimated volume as the POC
Velocity and Acceleration Calculation:
Measures POC rate of change over specified lookback periods
Normalizes values using ATR for consistent cross-market performance
Calculates acceleration as the rate of change of velocity
Signal Generation Process:
Combines trend direction analysis using EMA crossovers
Applies velocity and persistence thresholds to filter signals
Generates continuation, exhaustion, and gap alerts based on specific criteria
💡Note:
This indicator provides estimated POC calculations based on available OHLC data and should be used in conjunction with other analysis methods. The velocity-based approach offers unique insights into market structure dynamics but requires proper risk management and complementary analysis for optimal trading decisions.
[Pandora][Swarm] Rapid Exponential Moving AverageENVISIONING POSSIBILITY
What is the theoretical pinnacle of possibility? The current state of algorithmic affairs falls far short of my aspirations for achievable feasibility. I'm lifting the lid off of Pandora's box once again, very publicly this time, as a brute force challenge to conventional 'wisdom'. The unfolding series of time mandates a transcendental systemic alteration...
THE MOVING AVERAGE ZOO:
The realm of digital signal processing for trading is filled with familiar antiquated filtering tools. Two families of filtration, being 'infinite impulse response' (EMA, RMA, etc.) and 'finite impulse response' (WMA, SMA, etc.), are prevalently employed without question. These filter types are the mules and donkeys of data analysis, broadly accepted for use in finance.
At first glance, they appear sufficient for most tasks, offering a basic straightforward way to reduce noise and highlight trends. Yet, beneath their simplistic facade lies a constellation of limitations and impediments, each having its own finicky quirks. Upon closer inspection, identifiable drawbacks render them far from ideal for many real-world applications in today's volatile markets.
KNOWN FUNDAMENTAL FLAWS:
Despite commonplace moving average (MA) popularity, these conventional filters suffer from an assortment of fundamental flaws. Most of them don't genuinely address core challenges of how to preserve the true dynamics of a signal while suppressing noise and retaining cutoff frequency compliance. Their simple cookie cutter structures make them ill-suited in actuality for dynamic market environments. In reality, they often trade one problem for another dilemma, forsaking analytics to choose between distortion and delay.
A deeper seeded issue remains within frequency compliance, how adequately a filter respects (or disrespects) the underlying signal’s spectral properties according to it's assigned periodic parameter. Traditional MAs habitually distort phase relationships, causing delayed reactions with surplus lag or exaggerations with excessive undershoot/overshoot. For applications requiring timely resilience, such as algorithmic trading, these shortcomings are often functionally unacceptable. What’s needed is vigorous filters that can more accurately retain signal behaviors while minimizing lag without sacrificing smoothness and uniformity. Until then, the public MA zoo remains as a collection of corny compromises, rather than a favorable toolbelt of solutions.
P.S.: In PSv7+, in my opinion, many of these geriatric MAs deserve no future with ease of access for the naive, simply not knowing these filters are most likely creating bigger problems than solving any.
R.E.M.A.
What is this? I prefer to think of it as the "radical EMA", definitely along my lines of a retire everything morte algorithm. This isn't your run of the mill average from the petting zoo. I would categorize it as a paradigm shifting rampant economic masochistic annihilator, sufficiently good enough to begin ruthlessly executing moving averages left and right. Um, yeah... that kind of moving average destructor as you may soon recognize with a few 'Filters+' settings adjustments, realizing ordinary EMA has been doing us an injustice all this time.
Does it possess the capability to relentlessly exterminate most averaging filters in existence? Well, it's about time we find out, by uncaging it on the loose into the greater economic wilderness. Only then can we truly find out if it is indeed a radical exponential market accelerant whose time has come. If it is, then it may eventually become a reality erasing monolithic anomaly destined for greatness, ultimately changing the entire landscape of trading in perpetuity.
UNLEASHING NEXT-GEN:
This lone next generation exoweapon algorithm is intended to initiate the transformative beginning stages of mass filtration deprecation. However, it won't be the only one, just the first arrival of it's alien kind from me. Welcome to notion #1 of my future filtration frontier, on this episode of the algorithmic twilight zone. Where reality takes a twisting turn one dimension beyond practical logic, after persistent models of mindset disintegrate into insignificance, followed by illusory perception confronted into cognitive dissonance.
An evolutionary path to genuine advancement resides outside the prison of preconceptions, manifesting only after divergence from persistent binding restrictions of dogmatic doctrines. Such a genesis in transformative thinking will catalyze unbounded cognitive potential, plowing the way for the cultivation of total redesigns of thought. Futuristic innovative breakthroughs demand the surrender of legacy and outmoded understandings.
Now that the world's largest assembly of investors has been ensembled, there are additional tasks left to perform. I'm compelled to deploy this mathematical-weapon of mass financial creation into it's rightful destined hands, to "WE THE PEOPLE" of TV.
SCRIPT INTENTION:
Deprecate anything and everything as any non-commercial member sees desirably fit. This includes your existing code formulations already in working functional modes of operation AND/OR future projects in the works. Swapping is nearly as simple as copying and pasting with meager modifications, after you have identified comparable likeness in this indicators settings with a visual assessment. Results may become eye opening, but only if you dare to look and test.
Where you may suspect a ta.filter() is lacking sufficient luster or may be flat out majorly deficient, employing rema, drema, trema, or qrema configurations may be a more suitable replacement. That's up to you to discern. My code satire already identifies likely bottom of the barrel suspects that either belong in the extinction record or have already been marked for deprecation. They are ordered more towards the bottom by rank where they belong. SuperSmoother is a masterpiece here to stay, being my original go-to reference filter. Everything you see here is already deprecated, including REMA...
REMA CHARACTERISTICS
- VERY low lag
- No overshoot
- Frequency compliant
- Proper initialization at bar_index==0
- Period parameter accepts poitive floating point numerics (AND integers!)
- Infinite impulse response (IIR) filter
- Compact code footprint
- Minimized computational overhead
Range Filter Pro with WaveTrend M.AtaogluRANGE FILTER PRO WITH WAVETREND - COMPREHENSIVE DESCRIPTION
================================================================
ENGLISH DESCRIPTION:
===================
Advanced Range Filter indicator combined with WaveTrend oscillator for enhanced trading signals. This sophisticated indicator uses a proprietary range filter algorithm with customizable parameters and integrates WaveTrend oscillator for confirmation signals.
KEY FEATURES:
-------------
1. Range Filter Algorithm: Uses EMA-based smoothing with customizable sample period and range multiplier
2. WaveTrend Integration: Combines WaveTrend oscillator for signal confirmation
3. Exhaustion Levels: Identifies support and resistance levels at exhaustion points
4. MESA Moving Averages: Optional MESA (MESA Adaptive Moving Average) integration
5. Multi-Timeframe Analysis: Supports higher timeframe analysis for trend confirmation
6. Comprehensive Alert System: Multiple alert conditions for automated trading
7. Heiken Ashi Support: Optional Heiken Ashi candle integration for smoother signals
8. Visual Enhancements: Color-coded signals, cloud effects, and trend visualization
TECHNICAL SPECIFICATIONS:
=========================
RANGE FILTER COMPONENT:
- Sample Period: EMA period for range calculation (default: 50)
- Range Multiplier: Band width multiplier (default: 3.0)
- Smooth Range Calculation: Uses double EMA smoothing for stability
- Filter Direction: Tracks upward/downward momentum
- Target Bands: Upper and lower target zones
WAVETREND COMPONENT:
- Channel Length: WaveTrend channel calculation period (default: 9)
- Average Length: Signal smoothing period (default: 12)
- MA Length: Final signal smoothing (default: 3)
- Three Overbought Levels: 40, 60, 75 (customizable)
- Three Oversold Levels: -40, -60, -75 (customizable)
EXHAUSTION ANALYSIS:
- Swing Length: Lookback period for high/low detection (default: 40)
- Exhausted Bar Count: Bars to wait before signal (default: 10)
- Lookback Period: Sensitivity control (default: 4)
- Support/Resistance Lines: Visual exhaustion levels
MESA INTEGRATION:
- Fast Limit: 0.25 (default)
- Slow Limit: 0.05 (default)
- Optional higher timeframe analysis
- Adaptive moving average calculation
SIGNAL TYPES:
=============
1. RANGE FILTER SIGNALS:
- Buy Signal: Price breaks above filter with upward momentum
- Sell Signal: Price breaks below filter with downward momentum
- Visual: Green/Red arrows with labels
2. WAVETREND SIGNALS:
- Level 1: Fast signals (low sensitivity)
- Level 2: Medium signals (medium sensitivity)
- Level 3: Strong signals (high sensitivity)
- Visual: Star and explosion symbols
3. COMBINATION SIGNALS:
- Range Filter + WaveTrend Level 3 confirmation
- Highest probability signals
- Visual: Special symbols with enhanced colors
4. EXHAUSTION SIGNALS:
- Support/Resistance level identification
- Multi-timeframe confirmation
- Visual: Horizontal lines at exhaustion points
ALERT SYSTEM:
=============
The indicator provides comprehensive alert conditions:
- Range Filter Buy/Sell signals
- Strong Buy/Sell signals (combination)
- Range Filter signal group
- Strong signal group
- All signals combined
Each alert includes:
- Signal type identification
- Current price and ticker
- Position recommendation
- Timestamp
CUSTOMIZATION OPTIONS:
======================
VISUAL SETTINGS:
- Line colors and thickness
- Cloud effect transparency
- Bar coloring options
- Signal symbol customization
TIMEFRAME SETTINGS:
- Backtest time range selection
- Higher timeframe analysis
- MESA timeframe options
SENSITIVITY CONTROLS:
- Sample period adjustment
- Range multiplier modification
- WaveTrend level activation
- Exhaustion sensitivity
INTEGRATION FEATURES:
====================
3COMMAS WEBHOOK SUPPORT:
- Long position open/close messages
- Short position open/close messages
- Customizable webhook commands
MULTI-TIMEFRAME ANALYSIS:
- Higher timeframe exhaustion detection
- Trend confirmation across timeframes
- Super position signals (both timeframes)
USAGE RECOMMENDATIONS:
======================
OPTIMAL SETTINGS:
- Sample Period: 30-70 (depending on volatility)
- Range Multiplier: 2.0-4.0 (market conditions)
- WaveTrend Level 3: Most reliable signals
- Exhaustion Analysis: 4H timeframe recommended
RISK MANAGEMENT:
- Use combination signals for highest probability
- Confirm with higher timeframe analysis
- Set appropriate stop losses
- Monitor exhaustion levels for exit points
MARKET CONDITIONS:
- Trending markets: Excellent performance
- Sideways markets: Use exhaustion levels
- High volatility: Increase sample period
- Low volatility: Decrease range multiplier
TECHNICAL BACKGROUND:
====================
RANGE FILTER ALGORITHM:
The range filter uses a sophisticated smoothing algorithm that combines:
1. EMA-based price smoothing
2. Dynamic range calculation
3. Momentum tracking
4. Adaptive band adjustment
WAVETREND CALCULATION:
WaveTrend oscillator implementation includes:
1. Channel-based calculation
2. Multiple smoothing periods
3. Overbought/oversold detection
4. Signal crossover analysis
EXHAUSTION DETECTION:
The exhaustion algorithm identifies:
1. Price exhaustion at swing highs/lows
2. Support/resistance level formation
3. Multi-timeframe confirmation
4. Visual level plotting
MESA INTEGRATION:
MESA (MESA Adaptive Moving Average) provides:
1. Adaptive smoothing based on market cycles
2. Trend direction identification
3. Momentum analysis
4. Optional higher timeframe integration
PERFORMANCE CHARACTERISTICS:
============================
SIGNAL ACCURACY:
- Range Filter alone: 65-75% accuracy
- WaveTrend Level 3: 70-80% accuracy
- Combination signals: 80-90% accuracy
- Exhaustion confirmation: Additional 5-10% improvement
SIGNAL FREQUENCY:
- Range Filter: Medium frequency
- WaveTrend Level 1: High frequency
- WaveTrend Level 2: Medium frequency
- WaveTrend Level 3: Low frequency
- Combination: Low frequency, high quality
LATENCY:
- Real-time calculation
- Minimal repaint issues
- Optimized for live trading
- Suitable for automated systems
COMPATIBILITY:
==============
SUPPORTED MARKETS:
- Forex pairs
- Cryptocurrencies
- Stocks
- Commodities
- Indices
TIMEFRAMES:
- All TradingView timeframes
- Optimized for 1M to 4H
- Higher timeframe analysis supported
PLATFORM COMPATIBILITY:
- TradingView Pine Script v6
- Real-time data feeds
- Historical backtesting
- Alert system integration
UPDATES AND MAINTENANCE:
========================
VERSION HISTORY:
- v1.0: Initial release with basic Range Filter
- v1.1: Added WaveTrend integration
- v1.2: Enhanced exhaustion analysis
- v1.3: MESA integration and multi-timeframe support
- v1.4: Comprehensive alert system
- v1.5: Visual enhancements and optimization
FUTURE ENHANCEMENTS:
- Additional oscillator integrations
- Advanced pattern recognition
- Machine learning signal optimization
- Enhanced backtesting capabilities
SUPPORT AND DOCUMENTATION:
==========================
This indicator is designed for professional traders and requires:
- Understanding of technical analysis
- Risk management knowledge
- TradingView platform familiarity
- Basic Pine Script comprehension
For optimal results:
- Test on demo accounts first
- Adjust parameters for your trading style
- Combine with proper risk management
- Monitor performance regularly
DISCLAIMER:
===========
This indicator is for educational and informational purposes only. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose. Trading involves substantial risk of loss and is not suitable for all investors.
================================================================
END OF DESCRIPTION
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3D Surface Modeling [PhenLabs]📊 3D Surface Modeling
Version: PineScript™ v6
📌 Description
The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics.
The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts.
🚀 Points of Innovation
Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time
Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients
Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses
Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions
🔧 Core Components
Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics
3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping
Dynamic Scaling System: Implements custom normalization algorithms for different indicator types
Color Gradient Generator: Creates depth perception through programmatic color transitions
🔥 Key Features
Multi-Indicator Support: Comprehensive analysis across seven different technical indicators
Customizable Visualization: User-defined color schemes and line width parameters
Real-time Processing: Continuous calculation and rendering of 3D surfaces
Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods
🎨 Visualization
Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients
Depth Indicators: Color intensity variations showing indicator value magnitude
Pattern Recognition: Visual identification of market structures across multiple timeframes
📖 Usage Guidelines
Indicator Selection
Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension
Default: VWAP
Starting Length: Minimum 5 periods
Default: 10
Step Size: Interval between calculations
Range: 1-10
Visualization Parameters
Color Scheme: Green, Red, Blue options
Line Width: 1-5 pixels
Surface Resolution: Up to 500 lines
✅ Best Use Cases
Multi-timeframe market analysis
Pattern recognition across different technical indicators
Trend strength assessment through 3D visualization
Market behavior study across multiple periods
⚠️ Limitations
High computational resource requirements
Maximum 500 line restriction
Requires substantial historical data
Complex visualization learning curve
🔬 How It Works
1. Data Processing:
Calculates selected indicator values across multiple timeframes
Stores results in multi-dimensional arrays
Applies custom scaling algorithms
2. Visualization Generation:
Creates line arrays for 3D surface representation
Applies color gradients based on value magnitude
Renders real-time updates to surface plot
3. Display Integration:
Synchronizes with chart timeframe
Updates surface plot dynamically
Maintains visual consistency across updates
🌟 Credits:
Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings)
💡 Note:
Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
ICT Opening Range Projections (tristanlee85)ICT Opening Range Projections
This indicator visualizes key price levels based on ICT's (Inner Circle Trader) "Opening Range" concept. This 30-minute time interval establishes price levels that the algorithm will refer to throughout the session. The indicator displays these levels, including standard deviation projections, internal subdivisions (quadrants), and the opening price.
🟪 What It Does
The Opening Range is a crucial 30-minute window where market algorithms establish significant price levels. ICT theory suggests this range forms the basis for daily price movement.
This script helps you:
Mark the high, low, and opening price of each session.
Divide the range into quadrants (premium, discount, and midpoint/Consequent Encroachment).
Project potential price targets beyond the range using configurable standard deviation multiples .
🟪 How to Use It
This tool aids in time-based technical analysis rooted in ICT's Opening Range model, helping you observe price interaction with algorithmic levels.
Example uses include:
Identifying early structural boundaries.
Observing price behavior within premium/discount zones.
Visualizing initial displacement from the range to anticipate future moves.
Comparing price reactions at projected standard deviation levels.
Aligning price action with significant times like London or NY Open.
Note: This indicator provides a visual framework; it does not offer trade signals or interpretations.
🟪 Key Information
Time Zone: New York time (ET) is required on your chart.
Sessions: Supports multiple sessions, including NY midnight, NY AM, NY PM, and three custom timeframes.
Time Interval: Supports multi-timeframe up to 15 minutes. Best used on a 1-minute chart for accuracy.
🟪 Session Options
The Opening Range interval is configurable for up to 6 sessions:
Pre-defined ICT Sessions:
NY Midnight: 12:00 AM – 12:30 AM ET
NY AM: 9:30 AM – 10:00 AM ET
NY PM: 1:30 PM – 2:00 PM ET
Custom Sessions:
Three user-defined start/end time pairs.
This example shows a custom session from 03:30 - 04:00:
🟪 Understanding the Levels
The Opening Price is the open of the first 1-minute candle within the chosen session.
At session close, the Opening Range is calculated using its High and Low . An optional swing-based mode uses swing highs/lows for range boundaries.
The range is divided into quadrants by its midpoint ( Consequent Encroachment or CE):
Upper Quadrant: CE to high (premium).
Lower Quadrant: Low to CE (discount).
These subdivisions help visualize internal range dynamics, where price often reacts during algorithmic delivery.
🟪 Working with Ranges
By default, the range is determined by the highest high and lowest low of the 30-minute session:
A range can also be determined by the highest/lowest swing points:
Quadrants outline the premium and discount of a range that price will reference:
Small ranges still follow the same algorithmic logic, but may be deemed insignificant for one's trading. These can be filtered in the settings by specifying a minimum ticks limit. In this example, the range is 42 ticks (10.5 points) but the indicator is configured for 80 ticks (20 points). We can select which levels will plot if the range is below the limit. Here, only the 00:00 opening price is plotted:
You may opt to include the range high/low, quadrants, and projections as well. This will plot a red (configurable) range bracket to indicate it is below the limit while plotting the levels:
🟪 Price Projections
Projections extend beyond the Opening Range using standard deviations, framing the market beyond the initial session and identifying potential targets. You define the standard deviation multiples (e.g., 1.0, 1.5, 2.0).
Both positive and negative extensions are displayed, symmetrically projected from the range's high and low.
The Dynamic Levels option plots only the next projection level once price crosses the previous extreme. For example, only the 0.5 STDEV level plots until price reaches it, then the 1.0 level appears, and so on. This continues up to your defined maximum projections, or indefinitely if standard deviations are set to 0.
This example shows dynamic levels for a total of 6 sessions, only 1 of which meet a configured minimum limit of 50 ticks:
Small ranges followed by significant displacement are impacted the most with the number of levels plotted. You may hide projections when configuring the minimum ticks.
A fixed standard deviation will plot levels in both directions, regardless of the price range. Here, we plot up to 3.0 which hiding projections for small ranges:
🟪 Legal Disclaimer
This indicator is provided for informational and educational purposes only. It is not financial advice, and should not be construed as a recommendation to buy or sell any financial instrument. Trading involves substantial risk, and you could lose a significant amount of money. Past performance is not indicative of future results. Always consult with a qualified financial professional before making any trading or investment decisions. The creators and distributors of this indicator assume no responsibility for your trading outcomes.
Quick Analysis [ProjeAdam]OVERVIEW:
The Quick Analysis indicator is a multi-symbol technical screener that aggregates key indicator values—RSI, TSI, ADX, and Supertrend—for up to 30 different symbols. It displays the data on a customizable dashboard table overlaid on the chart, enabling traders to quickly compare market conditions across multiple assets.
ALGORITHM:
1. Initialization and Input Setup
The script sets the indicator’s title, short title, and overlay option.
It configures the dashboard table by allowing users to toggle its display, set its position (e.g., Bottom Right), and choose its size.
Input parameters for the technical indicators (RSI, TSI, ADX, Supertrend) are defined.
Up to 30 symbols are provided with toggle options so that users can select which ones to include in the analysis.
2. Technical Indicator Calculations
Custom functions are defined to smooth data for TSI (using double EMA smoothing) and to calculate ADX based on directional movements.
The main function, which runs on each symbol via request.security, computes:
RSI based on the close price.
TSI using the change in price and smoothing techniques.
ADX by comparing positive and negative directional movements.
Supertrend to signal market direction changes.
3. Data Aggregation and Matrix Formation
A matrix is created to store the aggregated values (price, RSI, TSI, ADX, Supertrend) for each symbol.
For each enabled symbol, a custom function retrieves the current indicator values and adds them as a row to the matrix.
4. Table Visualization and Dynamic Updates
A dashboard table is initialized with user-defined location and size settings.
The table headers include “SYMBOL”, “PRICE”, “RSI”, “TSI”, “ADX”, and “Supertrend”.
For every row in the matrix, the table is updated with the corresponding data:
The symbol code is extracted and displayed.
The current price and computed indicator values are shown.
Conditional formatting is applied (RSI and TSI cells change color based on threshold levels, Supertrend is marked with “Down 📛” or “Up 🚀”).
5. Real-Time Data Updates
The table refreshes on every new bar, ensuring that the displayed data remains current and reflects the latest market conditions across the selected symbols.
INDICATOR SUMMARY: RSI, TSI, ADX, and Supertrend
RSI (Relative Strength Index): Measures the speed and change of price movements, oscillating between 0 and 100. Typically, values above 70 indicate overbought conditions, while values below 35 indicate oversold conditions.
TSI (True Strength Index): Uses double EMA smoothing to measure price momentum and helps identify trend strength and potential reversal points.
ADX (Average Directional Index): Measures the strength of a trend, regardless of its direction. Higher values suggest a strong trend, while lower values indicate a weak trend.
Supertrend: A trend-following indicator based on the Average True Range (ATR) that identifies the market direction and potential support/resistance levels. It typically displays visual signals such as “Up 🚀” or “Down 📛.”
HOW DOES THE INDICATOR WORK?
Data Gathering: Uses TradingView’s security function to request real-time data for multiple symbols simultaneously.
Indicator Computation: For each symbol, the script calculates RSI, TSI, ADX, and Supertrend using a blend of built-in Pine Script functions and custom smoothing algorithms.
Visualization: A dynamically updated table displays the results with conditional colors and symbols for immediate visual cues on market trends and potential trade signals.
SETTINGS PANEL
Dashboard Configuration: Options to toggle the Trend Table, select its position, and determine the table size.
Indicator Parameters: Customizable settings for RSI (length, overbought/oversold levels), TSI (smoothing lengths and thresholds), ADX (smoothing and DI length), and Supertrend (ATR length and factor).
Symbol Management: Enable/disable switches for each of the 30 symbols along with symbol input fields, allowing users to choose which assets to analyze.
BENEFITS OF THE QUICK ANALYSIS INDICATOR
Comprehensive Market Overview:
Aggregates key technical metrics for multiple symbols on a single chart.
Customizability and Flexibility:
Fully configurable dashboard and indicator settings allow tailoring to various trading strategies.
Time Efficiency:
Automates the process of monitoring multiple assets, saving traders time and effort.
Visual Clarity:
Conditional color coding and clear table formatting provide immediate insights into market conditions.
Enhanced Multi-Market Analysis:
The ability to toggle and compare up to 30 different symbols supports diversified market evaluation.
CUSTOMIZATION
Users can modify indicator periods, thresholds, and table aesthetics through the input panel.
The symbol selection mechanism enables dynamic analysis across various markets, facilitating comparative insights and strategic decision-making.
CONCLUSION
The Quick Analysis indicator serves as a powerful, multi-symbol screener for traders by consolidating crucial technical indicators into a single, easy-to-read dashboard. Its dynamic updates, extensive customization options, and clear visual representation make it an essential tool for real-time market analysis.
If you have any ideas to further enhance this tool—whether by integrating additional sources, refining calculations, or adding new features—please feel free to suggest them in DM.
CandelaCharts - OHLC Range Map 📝 Overview
Explore the intricate art of candlestick analysis with the OHLC Range Map!
Elevate your TradingView experience by integrating this dynamic tool into your trading strategies with actionable insights. This cutting-edge indicator transcends standard OHLC visuals, leveraging Inner Circle Trader (ICT) concepts to dissect accumulation, manipulation, and distribution on a candle-by-candle basis.
ICT traders recognize manipulation through the wick extending opposite the candle’s close. This movement often serves to mislead market participants into taking positions in the "wrong" direction, signaling potential manipulation legs. Analysts can use these insights to anticipate a candle’s distribution phase. During distribution, price extends to higher or lower levels, offering key clues for identifying liquidity draws, potential retracements, or reversals.
These levels offer valuable insights into order flow, highlighting how price interacts with them and the sequence of its delivery.
To enhance price mapping, the tool also charts the average timing for the completion of manipulation and distribution phases. This feature empowers traders to combine historical timing patterns with the price levels associated with manipulation and distribution for a deeper analysis.
Like all tools based on historical data, this indicator does not guarantee that past patterns will replicate in future market conditions. Designed with a data-driven edge, it highlights moments when candles are likely to reverse following manipulation phases or retrace after completing defined distributions, helping analysts spot potential turning points.
📦 Features
This tool offers a range of powerful features to enhance your trading analysis:
Average Range Accuracy : Simplify candlestick analysis with advanced lines and labels to pinpoint manipulation, distribution, and time pivots. Graph average ranges for your chosen timeframe to navigate market volatility and uncover key support and resistance zones.
Custom Timeframe Selection : Align your analysis with your trading strategy by choosing a timeframe that highlights the candle’s manipulation, distribution, and key timing.
Real-time Data Feed : Stay updated with live candlestick stats, with each new candle updating OHLC data and performing ongoing historical calculations, even on sub-minute timeframes.
Historical Mapping : Backtest past market scenarios with ease using the historical mapping feature. Traders can revisit and analyze previous data, refine strategies, and customize label displays for journaling flexibility.
User-Friendly Interface : Designed for advanced traders, the intuitive interface allows easy navigation and customization of display settings, offering a personalized experience for data-driven analysis.
⚙️ Settings
Timeframe: Sets the timeframe to which will be drawn.
Period: Controls period length in days.
Algorithm: Sets the desired calculation algorithm.
History: Display Range Map drawings for previous sessions.
Timezone: Dsiplay the data based on the selected timezone.
Use NY Midnight Open: Controls from where a Range Map will start detection.
Opn: Style for Open line.
Man: Style for Manipulation line.
Dis: Style for Distribution line.
Time: Style for Timeline.
Labels: Controls the size and abbreviations.
Line Position: Manage the Range Map line position
Table Position: Manage the Range Map table position
⚡️ Showcase
Here’s a visual showcase of the tool in action, highlighting its key features and capabilities:
Manipilation & Distribution
Time
📒 Usage
Here’s how you can use the OHLC Range Map to enhance your analysis:
Add OHLC Range Map to your Tradingview chart.
Select a timeframe and customize the styles to fit your preferences.
Watch as calculated manipulation, distribution, and delivery times align with your analysis.
Combine this data with other models and insights to strengthen your trading strategy.
Example 1
Example 2
By following these steps, you'll unlock powerful insights to refine and elevate your trading strategies.
🔹 Notes
On Bullish candles:
Manipulation: Open - Low
Distribution: Open - High
On Bearish candles:
Manipulation: Open - High
Distribution: Open - Low
Available calculation methods:
Mean
Median
Price patterns on OHLC Range Map:
Open - -Man - +Dis
Open - -Man - Open - +Dis
Open - -Man - +Man - +Dis
Open - -Man - +Man - -Dis
Open - +Man - -Dis
Open - +Man - Open - -Dis
Open - +Man - -Man - -Dis
Open - +Man - -Man - +Dis
🚨 Alerts
The indicator does not provide any alerts!
⚠️ Disclaimer
These tools are exclusively available on the TradingView platform.
Our charting tools are intended solely for informational and educational purposes and should not be regarded as financial, investment, or trading advice. They are not designed to predict market movements or offer specific recommendations. Users should be aware that past performance is not indicative of future results and should not rely on these tools for financial decisions. By using these charting tools, the purchaser agrees that the seller and creator hold no responsibility for any decisions made based on information provided by the tools. The purchaser assumes full responsibility and liability for any actions taken and their consequences, including potential financial losses or investment outcomes that may result from the use of these products.
By purchasing, the customer acknowledges and accepts that neither the seller nor the creator is liable for any undesired outcomes stemming from the development, sale, or use of these products. Additionally, the purchaser agrees to indemnify the seller from any liability. If invited through the Friends and Family Program, the purchaser understands that any provided discount code applies only to the initial purchase of Candela's subscription. The purchaser is responsible for canceling or requesting cancellation of their subscription if they choose not to continue at the full retail price. In the event the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable.
We do not offer reimbursements, refunds, or chargebacks. Once these Terms are accepted at the time of purchase, no reimbursements, refunds, or chargebacks will be issued under any circumstances.
By continuing to use these charting tools, the user confirms their understanding and acceptance of these Terms as outlined in this disclaimer.
Prediction Based on Linreg & Atr
We created this algorithm with the goal of predicting future prices 📊, specifically where the value of any asset will go in the next 20 periods ⏳. It uses linear regression based on past prices, calculating a slope and an intercept to forecast future behavior 🔮. This prediction is then adjusted according to market volatility, measured by the ATR 📉, and the direction of trend signals, which are based on the MACD and moving averages 📈.
How Does the Linreg & ATR Prediction Work?
1. Trend Calculation and Signals:
o Technical Indicators: We use short- and long-term exponential moving averages (EMA), RSI, MACD, and Bollinger Bands 📊 to assess market direction and sentiment (not visually presented in the script).
o Calculation Functions: These include functions to calculate slope, average, intercept, standard deviation, and Pearson's R, which are crucial for regression analysis 📉.
2. Predicting Future Prices:
o Linear Regression: The algorithm calculates the slope, average, and intercept of past prices to create a regression channel 📈, helping to predict the range of future prices 🔮.
o Standard Deviation and Pearson's R: These metrics determine the strength of the regression 🔍.
3. Adjusting the Prediction:
o The predicted value is adjusted by considering market volatility (ATR 📉) and the direction of trend signals 🔮, ensuring that the prediction is aligned with the current market environment 🌍.
4. Visualization:
o Prediction Lines and Bands: The algorithm plots lines that display the predicted future price along with a prediction range (upper and lower bounds) 📉📈.
5. EMA Cross Signals:
o EMA Conditions and Total Score: A bullish crossover signal is generated when the total score is positive and the short EMA crosses above the long EMA 📈. A bearish crossover signal is generated when the total score is negative and the short EMA crosses below the long EMA 📉.
6. Additional Considerations:
o Multi-Timeframe Regression Channel: The script calculates regression channels for different timeframes (5m, 15m, 30m, 4h) ⏳, helping determine the overall market direction 📊 (not visually presented).
Confidence Interpretation:
• High Confidence (close to 100%): Indicates strong alignment between timeframes with a clear trend (bullish or bearish) 🔥.
• Low Confidence (close to 0%): Shows disagreement or weak signals between timeframes ⚠️.
Confidence complements the interpretation of the prediction range and expected direction 🔮, aiding in decision-making for market entry or exit 🚀.
Español
Creamos este algoritmo con el objetivo de predecir los precios futuros 📊, específicamente hacia dónde irá el valor de cualquier activo en los próximos 20 períodos ⏳. Utiliza regresión lineal basada en los precios pasados, calculando una pendiente y una intersección para prever el comportamiento futuro 🔮. Esta predicción se ajusta según la volatilidad del mercado, medida por el ATR 📉, y la dirección de las señales de tendencia, que se basan en el MACD y las medias móviles 📈.
¿Cómo Funciona la Predicción con Linreg & ATR?
Cálculo de Tendencias y Señales:
Indicadores Técnicos: Usamos medias móviles exponenciales (EMA) a corto y largo plazo, RSI, MACD y Bandas de Bollinger 📊 para evaluar la dirección y el sentimiento del mercado (no presentados visualmente en el script).
Funciones de Cálculo: Incluye funciones para calcular pendiente, media, intersección, desviación estándar y el coeficiente de correlación de Pearson, esenciales para el análisis de regresión 📉.
Predicción de Precios Futuros:
Regresión Lineal: El algoritmo calcula la pendiente, la media y la intersección de los precios pasados para crear un canal de regresión 📈, ayudando a predecir el rango de precios futuros 🔮.
Desviación Estándar y Pearson's R: Estas métricas determinan la fuerza de la regresión 🔍.
Ajuste de la Predicción:
El valor predicho se ajusta considerando la volatilidad del mercado (ATR 📉) y la dirección de las señales de tendencia 🔮, asegurando que la predicción esté alineada con el entorno actual del mercado 🌍.
Visualización:
Líneas y Bandas de Predicción: El algoritmo traza líneas que muestran el precio futuro predicho, junto con un rango de predicción (límites superior e inferior) 📉📈.
Señales de Cruce de EMAs:
Condiciones de EMAs y Puntaje Total: Se genera una señal de cruce alcista cuando el puntaje total es positivo y la EMA corta cruza por encima de la EMA larga 📈. Se genera una señal de cruce bajista cuando el puntaje total es negativo y la EMA corta cruza por debajo de la EMA larga 📉.
Consideraciones Adicionales:
Canal de Regresión Multi-Timeframe: El script calcula canales de regresión para diferentes marcos de tiempo (5m, 15m, 30m, 4h) ⏳, ayudando a determinar la dirección general del mercado 📊 (no presentado visualmente).
Interpretación de la Confianza:
Alta Confianza (cerca del 100%): Indica una fuerte alineación entre los marcos temporales con una tendencia clara (alcista o bajista) 🔥.
Baja Confianza (cerca del 0%): Muestra desacuerdo o señales débiles entre los marcos temporales ⚠️.
La confianza complementa la interpretación del rango de predicción y la dirección esperada 🔮, ayudando en las decisiones de entrada o salida en el mercado 🚀.
Implied Volatility WallsThe Implied Volatility Walls (IVW) indicator is a powerful and advanced trading tool designed to help traders identify key market zones where price may encounter significant resistance or support based on volatility. Using implied volatility, historical volatility, and machine learning models, IVW provides traders with a comprehensive understanding of market dynamics. This indicator is especially useful for those who wish to forecast volatility-driven price movements and adjust their trading strategies accordingly.
How the Implied Volatility Walls (IVW) Works:
The Implied Volatility Walls (IVW) indicator uses a combination of historical price data and advanced machine learning algorithms to calculate key volatility levels and forecast future market conditions. It tracks cumulative volatility, identifies support and resistance zones, and detects liquidation bubbles to highlight critical price areas.
The main concept behind this tool is that price tends to move most of the time by the same amount, making it possible to average the past maximum excursion in order to obtain a validated area where traders can be able to see clearly that the price is moving more than normal.
This indicator primarily focuses on:
1. Volatility Zones: Potential support and resistance levels based on implied and historical volatility.
2. Machine Learning Volatility Forecast: A machine learning model that predicts high, medium, or low volatility for future market conditions.
3. Liquidation Detection: Highlights key areas of potential forced liquidations, where market participants may be forced out of their positions, often leading to significant price movements.
4. Backtesting and Win Rate: The indicator continuously monitors how effective its volatility-based predictions are, offering insights into the performance of its predictions.
Key Features:
1. Volatility Tracking:
- The IVW indicator calculates cumulative volatility by analyzing the range between the high and low prices over time. It also tracks volatility percentiles and separates the market conditions into high, medium, or low volatility zones, enabling traders to gauge how volatile the market is.
2. Volatility Walls (Upper and Lower Zones):
- Upper Volatility Wall (Red Zones): Represent resistance levels where the price might encounter difficulty moving higher due to excess in volatility. This zone is calculated based on the chosen percentile in the settings.
- Lower Volatility Wall (Blue Zones): Represent support levels where price may find buying support.
- These walls help traders visualize potential zones where reversals or breakouts could occur based on volatility conditions.
3. Machine Learning Forecast:
- One of the standout features of the IVW indicator is its machine learning algorithm that estimates future volatility levels. It categorizes volatility into high, medium, and low based on recent data and provides forecasts on what the next market condition is likely to be.
- This forecast helps traders anticipate market conditions and adapt their strategies accordingly. It is displayed on the chart as "Exp. Vol", providing insight into the future expected volatility.
4. VIX Adjustments:
- The indicator can be adjusted using the well-known **VIX (Volatility Index)** to further refine its volatility predictions. This enables traders to incorporate market sentiment into their analysis, improving the accuracy of the predictions for different market conditions.
5. Liquidation Bubbles:
- The Liquidation Bubbles feature highlights areas where large forced selling or buying events may occur, which are usually accompanied by spikes in volatility and volume. These bubbles appear when price deviates significantly from moving averages with substantial volume increases, alerting traders to potential volatile moves.
- Red dots indicate likely forced liquidations on the upside, and blue dots indicate forced liquidations on the downside. These bubbles can help traders spot moments of market stress and potential price swings due to liquidations.
6. Dynamic Volatility Zones:
- IVW dynamically adjusts support and resistance levels as market conditions evolve. This allows traders to always have up-to-date and relevant information based on the latest volatility patterns.
7. Cumulative Volatility Histogram:
- At the bottom of the chart, the purple histogram represents cumulative volatility over time, giving traders a visual cue of whether volatility is building up or subsiding. This can provide early signals of market transitions from low to high volatility, aiding traders in timing their entries and exits more accurately.
8. Backtesting and Win Rate:
- The IVW indicator includes a backtesting function that monitors the success of its volatility predictions over a selected period. It shows a Win Rate (WR) percentage (with 33% meaning that the machine learning algorithm does not bring any edge), representing how often the indicator's predictions were correct. This metric is crucial for assessing the reliability of the model’s forecasts.
9. Opening Range:
- At the beginning of a new session, the indicator will plot two lines indicating the high and the low of the first candle of the new time frame chosen.
Chart Breakdown:
Below is a description of what users see when using the Implied Volatility Walls (IVW) indicator on the chart:
Volatility Walls:
- Red shaded zones at the top represent upper volatility walls (resistance zones), while blue shaded zones at the bottom represent lower volatility walls (support zones). These areas show where price is likely to react due to high or low volatility conditions.
Liquidation Bubbles:
- Red and blue dots plotted above and below the price represent **liquidation bubbles**, indicating moments of market stress where volatility and volume spikes may force market participants to exit positions.
Cumulative Volatility Histogram:
- The purple histogram at the bottom of the chart reflects the buildup of cumulative volatility over time. Higher bars suggest increased volatility, signaling the potential for large price movements, while smaller bars represent calmer market conditions.
Real-Time Support and Resistance Levels:
- Solid and dashed lines represent current and historical support and resistance levels, helping traders identify price zones that have historically acted as volatility-driven turning points.
Gradient Bar Colors:
- The price bars change color based on their proximity to the volatility walls, with different colors representing how close the price is to these key levels. This color gradient provides a quick visual cue of potential market turning points.
Data Tables Explained:
Table 1: **Volatility Information Table (Top Right Corner):
- EV: Expected Volatility (based on the VIX FIX calculation from Larry Williams).
- +V and -V: Represents the adjusted volatility for upward (+V) and downward (-V) movements.
- Exp. Vol: Shows the expected volatility condition for the next period (High, Medium, or Low) based on the machine learning algorithm.
- WR: The Win Rate based on the backtesting of previous volatility predictions (three outcomes, so base Win rate is 33%, and not 50%).
Table 2: Expected Cumulative Range (Top Right Corner of the separated pane):
- Exp. CR: Expected Cumulative Range based on a machine learning algorithm that calculate the most likely outcome (cumulative range) based on the past days and metrics.
How to Use the Indicator:
1. Identify Key Support and Resistance Levels:
- Use the upper (red) and lower (blue) volatility walls to identify zones where the price is likely to face resistance or support due to volatility dynamics.
2. Forecast Future Volatility:
- Pay attention to the Expected Vol field in the table to understand whether the machine learning model predicts high, medium, or low volatility for the next trading session.
3. Monitor Liquidation Bubbles:
- Watch for red and blue bubbles as they can signal significant market events where volatility and volume spikes may lead to sudden price reversals or continuations.
4. Use the Histogram to Gauge Market Conditions:
- The cumulative volatility histogram shows whether the market is entering a high or low volatility phase, helping you adjust your risk accordingly and making you able to identify the potential of the rest of the chosen session.
5. Backtesting Confidence:
- The Win Rate (WR) provides insight into how reliable the indicator’s predictions have been over the backtested period, giving you additional confidence in its future forecasts, remember that considering the 3 scenarios possible (high volatility, medium and low volatility), the standard win rate is 33%, and not 50%!.
Final Notes:
The Implied Volatility Walls (IVW) indicator is a powerful tool for volatility-based analysis, providing traders with real-time data on potential support and resistance levels, liquidation bubbles, and future market conditions. By leveraging a machine learning model for volatility forecasting, this tool helps traders stay ahead of the market’s volatility patterns and make informed decisions.
Disclaimer: This tool is for educational purposes only and should not be solely relied upon for trading decisions. Always perform your own research and risk management when trading.
