AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms.
👽 Core Features
Segment-Based Pattern Recognition
At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions.
AI-Powered k-Nearest Neighbors (kNN) Prediction
The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages.
Dynamic Dictionary of Historical Patterns
The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for:
Candle body ranges, High-low ranges, Wick highs and lows.
This dynamic approach ensures the model adapts continuously to evolving market conditions.
Volatility-Normalized Forecasting
Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments.
AI-Driven Trend Detection
The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes.
👽 Deep Dive into the Core Mathematics
👾 Segment-Based Multivariate Pattern Analysis
The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as:
Body Ranges: Differences between the open and close prices.
High-Low Ranges: Capturing the full volatility of a bar.
Wick Extremes: Quantifying deviations beyond the body, both above and below.
Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically.
👾 Volatility Normalization Using ATR
To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations.
👾 k-Nearest Neighbors (kNN) Algorithm
The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns.
Euclidean Distance Calculation:
The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns.
Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings.
👾 Prediction Horizon and Refinement
The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity.
👽 AI-Driven Pattern Recognition
Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions.
Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias.
Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models.
Visual Outputs
Forecast Line (Y_hat_line):
A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns.
Trend Regime Bands:
Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range.
Historical Pattern Matching:
The nearest historical pattern is displayed, allowing traders to visualize similarities
👽 Applications
Trend Identification:
Detect and follow emerging trends early using dynamic trend regime analysis.
Reversal Signals:
Anticipate market reversals with high-confidence predictions based on historically similar scenarios.
Range and Momentum Trading:
Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
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Bayesian Price Projection Model [Pinescriptlabs]📊 Dynamic Price Projection Algorithm 📈
This algorithm combines **statistical calculations**, **technical analysis**, and **Bayesian theory** to forecast a future price while providing **uncertainty ranges** that represent upper and lower bounds. The calculations are designed to adjust projections by considering market **trends**, **volatility**, and the historical probabilities of reaching new highs or lows.
Here’s how it works:
🚀 Future Price Projection
A dynamic calculation estimates the future price based on three key elements:
1. **Trend**: Defines whether the market is predisposed to move up or down.
2. **Volatility**: Quantifies the magnitude of the expected change based on historical fluctuations.
3. **Time Factor**: Uses the logarithm of the projected period (`proyeccion_dias`) to adjust how time impacts the estimate.
🧠 **Bayesian Probabilistic Adjustment**
- Conditional probabilities are calculated using **Bayes' formula**:
\
This models future events using conditional information:
- **Probability of reaching a new all-time high** if the price is trending upward.
- **Probability of reaching a new all-time low** if the price is trending downward.
- These probabilities refine the future price estimate by considering:
- **Higher volatility** increases the likelihood of hitting extreme levels (highs/lows).
- **Market trends** influence the expected price movement direction.
🌟 **Volatility Calculation**
- Volatility is measured using the **ATR (Average True Range)** indicator with a 14-period window. This reflects the average amplitude of price fluctuations.
- To express volatility as a percentage, the ATR is normalized by dividing it by the closing price and multiplying it by 200.
- Volatility is then categorized into descriptive levels (e.g., **Very Low**, **Low**, **Moderate**, etc.) for better interpretation.
---
🎯 **Deviation Limits (Upper and Lower)**
- The upper and lower limits form a **projected range** around the estimated future price, providing a framework for uncertainty.
- These limits are calculated by adjusting the ATR using:
- A user-defined **multiplier** (`factor_desviacion`).
- **Bayesian probabilities** calculated earlier.
- The **square root of the projected period** (`proyeccion_dias`), incorporating the principle that uncertainty grows over time.
🔍 **Interpreting the Model**
This can be seen as a **dynamic probabilistic model** that:
- Combines **technical analysis** (trends and ATR).
- Refines probabilities using **Bayesian theory**.
- Provides a **visual projection range** to help you understand potential future price movements and associated uncertainties.
⚡ Whether you're analyzing **volatile markets** or confirming **bullish/bearish scenarios**, this tool equips you with a robust, data-driven approach! 🚀
Español :
📊 Algoritmo de Proyección de Precio Dinámico 📈
Este algoritmo combina **cálculos estadísticos**, **análisis técnico** y **la teoría de Bayes** para proyectar un precio futuro, junto con rangos de **incertidumbre** que representan los límites superior e inferior. Los cálculos están diseñados para ajustar las proyecciones considerando la **tendencia del mercado**, **volatilidad** y las probabilidades históricas de alcanzar nuevos máximos o mínimos.
Aquí se explica su funcionamiento:
🚀 **Proyección de Precio Futuro**
Se realiza un cálculo dinámico del precio futuro estimado basado en tres elementos clave:
1. **Tendencia**: Define si el mercado tiene predisposición a subir o bajar.
2. **Volatilidad**: Determina la magnitud del cambio esperado en función de las fluctuaciones históricas.
3. **Factor de Tiempo**: Usa el logaritmo del período proyectado (`proyeccion_dias`) para ajustar cómo el tiempo afecta la estimación.
🧠 **Ajuste Probabilístico con la Teoría de Bayes**
- Se calculan probabilidades condicionales mediante la fórmula de **Bayes**:
\
Esto permite modelar eventos futuros considerando información condicional:
- **Probabilidad de alcanzar un nuevo máximo histórico** si el precio sube.
- **Probabilidad de alcanzar un nuevo mínimo histórico** si el precio baja.
- Estas probabilidades ajustan la estimación del precio futuro considerando:
- **Mayor volatilidad** aumenta la probabilidad de alcanzar niveles extremos (máximos/mínimos).
- **La tendencia del mercado** afecta la dirección esperada del movimiento del precio.
🌟 **Cálculo de Volatilidad**
- La volatilidad se mide usando el indicador **ATR (Average True Range)** con un período de 14 velas. Este indicador refleja la amplitud promedio de las fluctuaciones del precio.
- Para obtener un valor porcentual, el ATR se normaliza dividiéndolo por el precio de cierre y multiplicándolo por 200.
- Además, se clasifica esta volatilidad en categorías descriptivas (e.g., **Muy Baja**, **Baja**, **Moderada**, etc.) para facilitar su interpretación.
🎯 **Límites de Desviación (Superior e Inferior)**
- Los límites superior e inferior representan un **rango proyectado** en torno al precio futuro estimado, proporcionando un marco para la incertidumbre.
- Estos límites se calculan ajustando el ATR según:
- Un **multiplicador** definido por el usuario (`factor_desviacion`).
- Las **probabilidades condicionales** calculadas previamente.
- La **raíz cuadrada del período proyectado** (`proyeccion_dias`), lo que incorpora el principio de que la incertidumbre aumenta con el tiempo.
---
🔍 **Interpretación del Modelo**
Este modelo se puede interpretar como un **modelo probabilístico dinámico** que:
- Integra **análisis técnico** (tendencias y ATR).
- Ajusta probabilidades utilizando **la teoría de Bayes**.
- Proporciona un **rango de proyección visual** para ayudarte a entender los posibles movimientos futuros del precio y su incertidumbre.
⚡ Ya sea que estés analizando **mercados volátiles** o confirmando **escenarios alcistas/bajistas**, ¡esta herramienta te ofrece un enfoque robusto y basado en datos! 🚀
Market Trades PinescriptlabsThis algorithm is designed to emulate the true order book of exchanges by showing the quantity of transactions of an asset in real-time, while identifying patterns of high activity and volatility in the market through the analysis of volume and price movements. 📈 Below, I explain how to understand and use the information provided by the chart, along with the trades table:
Identification of High Activity Zones 🚀
The algorithm calculates the average volume and the rate of price change to detect areas with spikes in activity. This is visualized on the chart with labels "Volatility Spike Buy" and "Volatility Spike Sell":
Volatility Spike Buy: Indicates an unusual increase in volatility in the buying market, suggesting a potential surge in buying interest. 🟢
Volatility Spike Sell: Signals an increase in volatility in the selling market, which may indicate selling pressure or a sudden massive sell-off. 🔴
Market Trades Table 📋
The table provides a detailed view of the latest trades:
Price: Displays the price at which each trade was executed. 💵
Quantity (Traded): Indicates the amount of the asset traded. 💰
Type of Trade (Buy/Sell): Differentiates between buy (Buy) and sell (Sell) operations based on volume and strength. 🔄
Date and Time: Refers to the start of the calculated trading candle. ⏰
Recency: Identifies the most recent trade to facilitate tracking of current activity. 🔍
Analysis of Trade Imbalance ⚖️
The imbalance between buys and sells is calculated based on the volume of both. This indicator helps to understand whether the market has a tendency toward buying or selling, showing if there is greater strength on one side of the market.
A positive imbalance suggests more buying pressure. 📊
A negative imbalance indicates greater selling pressure. 📉
Volume Presentation
Visualizes the volume of buying and selling in the market, allowing the identification of buying or selling strength through the size of the volume candle. 🔍
Español :
"Este algoritmo está diseñado para emular el verdadero libro de órdenes de los intercambios al mostrar la cantidad de transacciones de un activo en tiempo real, mientras identifica patrones de alta actividad y volatilidad en el mercado a través del análisis de volumen y movimientos de precios. 📈 A continuación, explico cómo entender y usar la información proporcionada por el gráfico, junto con la tabla de operaciones:"
Identificación de Zonas de Alta Actividad 🚀
El algoritmo calcula el volumen promedio y la velocidad de cambio de precio para detectar zonas con picos de actividad. Esto se visualiza en el gráfico con etiquetas de "Volatility Spike Buy" y "Volatility Spike Sell":
Volatility Spike Buy: Indica un incremento inusual de volatilidad en el mercado de compra, sugiriendo un posible interés de compra elevado. 🟢
Volatility Spike Sell: Señala un incremento de volatilidad en el mercado de venta, lo cual puede indicar presión de venta o una venta masiva repentina. 🔴
Tabla de Operaciones en el Mercado (Market Trades) 📋
La tabla proporciona una vista detallada de las últimas operaciones:
Precio: Muestra el precio al cual se realizó cada operación. 💵
Cantidad (Transaccionada): Indica la cantidad del activo transaccionada. 💰
Tipo de operación (Buy/Sell): Diferencia entre operaciones de compra (Buy) y de venta (Sell), dependiendo del volumen y fuerza. 🔄
Fecha y Hora: Refleja el inicio de la vela de negociación calculada. ⏰
Recency: Identifica la operación más reciente para facilitar el seguimiento de la actividad actual. 🔍
Análisis de Desequilibrio de Operaciones (Imbalance) ⚖️
El desequilibrio entre compras y ventas se calcula con base en el volumen de ambas. Este indicador ayuda a entender si el mercado tiene una tendencia hacia la compra o venta, mostrando si hay una mayor fuerza en uno de los lados del mercado.
Un desequilibrio positivo sugiere más presión de compra. 📊
Un desequilibrio negativo indica mayor presión de venta. 📉
Presentación en Volumen
Visualiza el volumen de compra y venta en el mercado, permitiendo identificar mediante el tamaño de la vela de volumen la fuerza, ya sea compradora o vendedora. 🔍
Uptrick: Dynamic AMA RSI Indicator### **Uptrick: Dynamic AMA RSI Indicator**
**Overview:**
The **Uptrick: Dynamic AMA RSI Indicator** is an advanced technical analysis tool designed for traders who seek to optimize their trading strategies by combining adaptive moving averages with the Relative Strength Index (RSI). This indicator dynamically adjusts to market conditions, offering a nuanced approach to trend detection and momentum analysis. By leveraging the Adaptive Moving Average (AMA) and Fast Adaptive Moving Average (FAMA), along with RSI-based overbought and oversold signals, traders can better identify entry and exit points with higher precision and reduced noise.
**Key Components:**
1. **Source Input:**
- The source input is the price data that forms the basis of all calculations. Typically set to the closing price, traders can customize this to other price metrics such as open, high, low, or even the output of another indicator. This flexibility allows the **Uptrick** indicator to be tailored to a wide range of trading strategies.
2. **Adaptive Moving Average (AMA):**
- The AMA is a moving average that adapts its sensitivity based on the dominant market cycle. This adaptation allows the AMA to respond swiftly to significant price movements while smoothing out minor fluctuations, making it particularly effective in trending markets. The AMA adjusts its responsiveness dynamically using a calculated phase adjustment from the dominant cycle, ensuring it remains responsive to the current market environment without being overly reactive to market noise.
3. **Fast Adaptive Moving Average (FAMA):**
- The FAMA is a more sensitive version of the AMA, designed to react faster to price changes. It serves as a signal line in the crossover strategy, highlighting shorter-term trends. The interaction between the AMA and FAMA forms the core of the signal generation, with crossovers between these lines indicating potential buy or sell opportunities.
4. **Relative Strength Index (RSI):**
- The RSI is a momentum oscillator that measures the speed and change of price movements, providing insights into whether an asset is overbought or oversold. In the **Uptrick** indicator, the RSI is used to confirm the validity of crossover signals between the AMA and FAMA, adding an additional layer of reliability to the trading signals.
**Indicator Logic:**
1. **Dominant Cycle Calculation:**
- The indicator starts by calculating the dominant market cycle using a smoothed price series. This involves applying exponential moving averages to a series of price differences, extracting cycle components, and determining the instantaneous phase of the cycle. This phase is then adjusted to provide a phase adjustment factor, which plays a critical role in determining the adaptive alpha.
2. **Adaptive Alpha Calculation:**
- The adaptive alpha, a key feature of the AMA, is computed based on the fast and slow limits set by the trader. This alpha is clamped within these limits to ensure the AMA remains appropriately sensitive to market conditions. The dynamic adjustment of alpha allows the AMA to be highly responsive in volatile markets and more conservative in stable markets.
3. **Crossover Detection:**
- The indicator generates trading signals based on crossovers between the AMA and FAMA:
- **CrossUp:** When the AMA crosses above the FAMA, it indicates a potential bullish trend, suggesting a buy opportunity.
- **CrossDown:** When the AMA crosses below the FAMA, it signals a potential bearish trend, indicating a sell opportunity.
4. **RSI Confirmation:**
- To enhance the reliability of these crossover signals, the indicator uses the RSI to confirm overbought and oversold conditions:
- **Buy Signal:** A buy signal is generated only when the AMA crosses above the FAMA and the RSI confirms an oversold condition, ensuring that the signal aligns with a momentum reversal from a low point.
- **Sell Signal:** A sell signal is triggered when the AMA crosses below the FAMA and the RSI confirms an overbought condition, indicating a momentum reversal from a high point.
5. **Signal Management:**
- To prevent signal redundancy during strong trends, the indicator tracks the last generated signal (buy or sell) and ensures that the next signal is only issued when there is a genuine reversal in trend direction.
6. **Signal Visualization:**
- **Buy Signals:** The indicator plots a "BUY" label below the bar when a buy signal is generated, using a green color to clearly mark the entry point.
- **Sell Signals:** A "SELL" label is plotted above the bar when a sell signal is detected, marked in red to indicate an exit or shorting opportunity.
- **Bar Coloring (Optional):** Traders have the option to enable bar coloring, where green bars indicate a bullish trend (AMA above FAMA) and red bars indicate a bearish trend (AMA below FAMA), providing a visual representation of the market’s direction.
**Customization Options:**
- **Source:** Traders can select the price data input that best suits their strategy (e.g., close, open, high, low, or custom indicators).
- **Fast Limit:** Adjustable sensitivity for the fast response of the AMA, allowing traders to tailor the indicator to different market conditions.
- **Slow Limit:** Sets the slower boundary for the AMA’s sensitivity, providing stability in less volatile markets.
- **RSI Length:** The period for the RSI calculation can be adjusted to fit different trading timeframes.
- **Overbought/Oversold Levels:** These thresholds can be customized to define the RSI levels that trigger buy or sell confirmations.
- **Enable Bar Colors:** Traders can choose whether to enable bar coloring based on the AMA/FAMA relationship, enhancing visual clarity.
**How Different Traders Can Use the Indicator:**
1. **Day Traders:**
- **Uptrick: Dynamic AMA RSI Indicator** is highly effective for day traders who need to make quick decisions in fast-moving markets. The adaptive nature of the AMA and FAMA allows the indicator to respond rapidly to intraday price swings. Day traders can use the buy and sell signals generated by the crossover and RSI confirmation to time their entries and exits with greater precision, minimizing exposure to false signals often prevalent in high-frequency trading environments.
2. **Swing Traders:**
- Swing traders can benefit from the indicator’s ability to identify and confirm trend reversals over several days or weeks. By adjusting the RSI length and sensitivity limits, swing traders can fine-tune the indicator to catch longer-term price movements, helping them to ride trends and maximize profits over medium-term trades. The dual confirmation of crossovers with RSI ensures that swing traders enter trades that have a higher probability of success.
3. **Position Traders:**
- For position traders who hold trades over longer periods, the **Uptrick** indicator offers a reliable method to stay in trades that align with the dominant trend while avoiding premature exits. By adjusting the slow limit and extending the RSI length, position traders can smooth out the indicator’s sensitivity, allowing them to focus on major market shifts rather than short-term volatility. The bar coloring feature also provides a clear visual indication of the overall trend, aiding in trade management decisions.
4. **Scalpers:**
- Scalpers, who seek to profit from small price movements, can use the fast responsiveness of the FAMA in conjunction with the RSI to identify micro-trends within larger market moves. The indicator’s ability to adapt quickly to changing conditions makes it a valuable tool for scalpers looking to execute numerous trades in a short period, capturing profits from minor price fluctuations while avoiding prolonged exposure.
5. **Algorithmic Traders:**
- Algorithmic traders can incorporate the **Uptrick** indicator into automated trading systems. The precise crossover signals combined with RSI confirmation provide clear and actionable rules that can be coded into algorithms. The adaptive nature of the indicator ensures that it can be used across different market conditions and timeframes, making it a versatile component of algorithmic strategies.
**Usage:**
The **Uptrick: Dynamic AMA RSI Indicator** is a versatile tool that can be integrated into various trading strategies, from short-term day trading to long-term investing. Its ability to adapt to changing market conditions and provide clear buy and sell signals makes it an invaluable asset for traders seeking to improve their trading performance. Whether used as a standalone indicator or in conjunction with other technical tools, **Uptrick** offers a dynamic approach to market analysis, helping traders to navigate the complexities of financial markets with greater confidence.
**Conclusion:**
The **Uptrick: Dynamic AMA RSI Indicator** offers a comprehensive and adaptable solution for traders across different styles and timeframes. By combining the strengths of adaptive moving averages with RSI confirmation, it delivers robust signals that help traders capitalize on market trends while minimizing the risk of false signals. This indicator is a powerful addition to any trader’s toolkit, enabling them to make informed decisions with greater precision and confidence. Whether you're a day trader, swing trader, or long-term investor, the **Uptrick** indicator can enhance your trading strategy and improve your market outcomes.
KNN OscillatorOverview
The KNN Oscillator is an advanced technical analysis tool designed to help traders identify potential trend reversals and market momentum. Using the K-Nearest Neighbors (KNN) algorithm, this oscillator normalizes KNN values to create a dynamic and responsive indicator. The oscillator line changes color to reflect the market sentiment, providing clear visual cues for trading decisions.
Key Features
Dynamic Color Oscillator: The line changes color based on the oscillator value – green for positive, red for negative, and grey for neutral.
Advanced KNN Algorithm: Utilizes the K-Nearest Neighbors algorithm for precise trend detection.
Normalized Values: Ensures the oscillator values are normalized to align with the stock price range, making it applicable to various assets.
Easy Integration: Can be easily added to any TradingView chart for enhanced analysis.
How It Works
The KNN Oscillator leverages the K-Nearest Neighbors algorithm to calculate the average distance of the nearest neighbors over a specified period. These values are then normalized to match the stock price range, ensuring they are comparable across different assets. The oscillator value is derived by taking the difference between the normalized KNN values and the source price. The line's color changes dynamically to provide an immediate visual indication of the market's state:
Green: Positive values indicate upward momentum.
Red: Negative values indicate downward momentum.
Grey: Neutral values indicate a stable or consolidating market.
Usage Instructions
Trend Reversal Detection: Use the color changes to identify potential trend reversals. A shift from red to green suggests a bullish reversal, while a shift from green to red indicates a bearish reversal.
Momentum Analysis: The oscillator's value and color help gauge market momentum. Strong positive values (green) indicate strong upward momentum, while strong negative values (red) indicate strong downward momentum.
Market Sentiment: The dynamic color changes provide an easy-to-understand visual representation of market sentiment, helping traders make informed decisions quickly.
Confirmation Tool: Use the KNN Oscillator in conjunction with other technical indicators to confirm signals and improve the accuracy of your trades.
Scalability: Applicable to various timeframes and asset classes, making it a versatile tool for all types of traders.
Tri-State SupertrendTri-State Supertrend: Buy, Sell, Range
( Credits: Based on "Pivot Point Supertrend" by LonesomeTheBlue.)
Tri-State Supertrend incorporates a range filter into a supertrend algorithm.
So in addition to the Buy and Sell states, we now also have a Range state.
This avoids the typical "whipsaw" problem: During a range, a standard supertrend algorithm will fire Buy and Sell signals in rapid succession. These signals are all false signals as they lead to losing positions when acted on.
In this case, a tri-state supertrend will go into Range mode and stay in this mode until price exits the range and a new trend begins.
I used Pivot Point Supertrend by LonesomeTheBlue as a starting point for this script because I believe LonesomeTheBlue's version is superior to the classic Supertrend algorithm.
This indicator has two additional parameters over Pivot Point Supertrend:
A flag to turn the range filter on or off
A range size threshold in percent
With that last parameter, you can define what a range is. The best value will depend on the asset you are trading.
Also, there are two new display options.
"Show (non-) trendline for ranges" - determines whether to draw the "trendline" inside of a range. Seeing as there is no trend in a range, this is usually just visual noise.
"Show suppressed signals" - allows you to see the Buy/Sell signals that were skipped by the range filter.
How to use Tri-State Supertrend in a strategy
You can use the Buy and Sell signals to enter positions as you would with a normal supertrend. Adding stop loss, trailing stop etc. is of course encouraged and very helpful. But what to do when the Range signal appears?
I currently run a strategy on LDO based on Tri-State Supertrend which appears to be profitable. (It will quite likely be open sourced at some point, but it is not released yet.)
In that strategy, I experimented with different actions being taken when the Range state is entered:
Continue: Just keep last position open during the range
Close: Close the last position when entering range
Reversal: During the range, execute the OPPOSITE of each signal (sell on "buy", buy on "sell")
In the backtest, it transpired that "Continue" was the most profitable option for this strategy.
How ranges are detected
The mechanism is pretty simple: During each Buy or Sell trend, we record price movement, specifically, the furthest move in the trend direction that was encountered (expressed as a percentage).
When a new signal is issued, the algorithm checks whether this value (for the last trend) is below the range size set by the user. If yes, we enter Range mode.
The same logic is used to exit Range mode. This check is performed on every bar in a range, so we can enter a buy or sell as early as possible.
I found that this simple logic works astonishingly well in practice.
Pros/cons of the range filter
A range filter is an incredibly useful addition to a supertrend and will most likely boost your profits.
You will see at most one false signal at the beginning of each range (because it takes a bit of time to detect the range); after that, no more false signals will appear over the range's entire duration. So this is a huge advantage.
There is essentially only one small price you have to pay:
When a range ends, the first Buy/Sell signal you get will be delayed over the regular supertrend's signal. This is, again, because the algorithm needs some time to detect that the range has ended. If you select a range size of, say, 1%, you will essentially lose 1% of profit in each range because of this delay.
In practice, it is very likely that the benefits of a range filter outweigh its cost. Ranges can last quite some time, equating to many false signals that the range filter will completely eliminate (all except for the first one, as explained above).
You have to do your own tests though :)
Recursive Micro Zigzag🎲 Overview
Zigzag is basic building block for any pattern recognition algorithm. This indicator is a research-oriented tool that combines the concepts of Micro Zigzag and Recursive Zigzag to facilitate a comprehensive analysis of price patterns. This indicator focuses on deriving zigzag on multiple levels in more efficient and enhanced manner in order to support enhanced pattern recognition.
The Recursive Micro Zigzag Indicator utilises the Micro Zigzag as the foundation and applies the Recursive Zigzag technique to derive higher-level zigzags. By integrating these techniques, this indicator enables researchers to analyse price patterns at multiple levels and gain a deeper understanding of market behaviour.
🎲 Concept:
Micro Zigzag Base : The indicator utilises the Micro Zigzag concept to capture detailed price movements within each candle. It allows for the visualisation of the sequential price action within the candle, aiding in pattern recognition at a micro level.
Basic implementation of micro zigzag can be found in this link - Micro-Zigzag
Recursive Zigzag Expansion : Building upon the Micro Zigzag base, the indicator applies the Recursive Zigzag concept to derive higher-level zigzags. Through recursive analysis of the Micro Zigzag's pivots, the indicator uncovers intricate patterns and trends that may not be evident in single-level zigzags.
Earlier implementations of recursive zigzag can be found here:
Recursive Zigzag
Recursive Zigzag - Trendoscope
And the libraries
rZigzag
ZigzagMethods
The major differences in this implementation are
Micro Zigzag Base - Earlier implementation made use of standard zigzag as base whereas this implementation uses Micro Zigzag as base
Not cap on Pivot depth - Earlier implementation was limited by the depth of level 0 zigzag. In this implementation, we are trying to build the recursive algorithm progressively so that there is no cap on the depth of level 0 zigzag. But, if we go for higher levels, there is chance of program timing out due to pine limitations.
These algorithms are useful in automatically spotting patterns on the chart including Harmonic Patterns, Chart Patterns, Elliot Waves and many more.
Smart Money Concepts Probability (Expo)█ Overview
The Smart Money Concept Probability (Expo) is an indicator developed to track the actions of institutional investors, commonly known as "smart money." This tool calculates the likelihood of smart money being actively engaged in buying or selling within the market, referred to as the "smart money order flow."
The indicator measures the probability of three key events: Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS ). These probabilities are displayed as percentages alongside their respective levels, providing a straightforward and immediate understanding of the likelihood of smart money order flow.
Finally, the backtested results are shown in a table, which gives traders an understanding of the historical performance of the current order flow direction.
█ Calculations
The algorithm individually computes the likelihood of the events ( CHoCH , SMS , and BMS ). A positive score is assigned for events where the price successfully breaks through the level with the highest probability, and a negative score when the price fails to do so. By doing so, the algorithm determines the probability of each event occurring and calculates the total profitability derived from all the events.
█ Example
In this case, we have an 85% probability that the price will break above the upper range and make a new Break Of Structure and only a 16.36% probability that the price will break below the lower range and make a Change Of Character.
█ Settings
The Structure Period sets the pivot period to use when calculating the market structure.
The Structure Response sets how responsive the market structure should be. A low value returns a more responsive structure. A high value returns a less responsive structure.
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a Change of Character ( CHoCH ), Shift in Market Structure ( SMS ), and Break of Structure ( BMS )
The insights provided by this tool help traders gain an understanding of the smart money order flow direction, which can be used to determine the market trend.
█ Any Alert function call
An alert is sent when the price breaks the upper or lower range, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Timeframe
Probability percentage
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Breakout Probability (Expo)█ Overview
Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels.
The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias.
█ Calculations
The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line.
█ Example
In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made.
█ Settings
Percentage Step
The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one.
Disable 0.00% values
If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values.
Number of Lines
Set the number of levels you want to display.
Show Statistic Panel
Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not)
█ Any Alert function call
An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Bias
Probability percentage
The first level high and low price
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit.
The indicator can be used for setting a stop loss based on where the price is most likely not to reach.
The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
FATL, SATL, RFTL, & RSTL Digital Signal Filter Smoother [Loxx]FATL, SATL, RFTL, & RSTL Digital Signal Filter (DSP) Smoother is is a baseline indicator with DSP processed source inputs
What are digital indicators: distinctions from standard tools, types of filters.
To date, dozens of technical analysis indicators have been developed: trend instruments, oscillators, etc. Most of them use the method of averaging historical data, which is considered crude. But there is another group of tools - digital indicators developed on the basis of mathematical methods of spectral analysis. Their formula allows the trader to filter price noise accurately and exclude occasional surges, making the forecast more effective in comparison with conventional indicators. In this review, you will learn about their distinctions, advantages, types of digital indicators and examples of strategies based on them.
Two non-standard strategies based on digital indicators
Basic technical analysis indicators built into most platforms are based on mathematical formulas. These formulas are a reflection of market behavior in past periods. In other words, these indicators are built based on patterns that were discovered as a result of statistical analysis, which allows one to predict further trend movement to some extent. But there is also a group of indicators called digital indicators. They are developed using mathematical analysis and are an algorithmic spectral system called ATCF (Adaptive Trend & Cycles Following). In this article, I will tell you more about the components of this system, describe the differences between digital and regular indicators, and give examples of 2 strategies with indicator templates.
ATCF - Market Spectrum Analysis Method
There is a theory according to which the market is chaotic and unpredictable, i.e. it cannot be accurately analyzed. After all, no one can tell how traders will react to certain news, or whether some large investor will want to play against the market like George Soros did with the Bank of England. But there is another theory: many general market trends are logical, and have a rationale, causes and effects. The economy is undulating, which means it can be described by mathematical methods.
Digital indicators are defined as a group of algorithms for assessing the market situation, which are based exclusively on mathematical methods. They differ from standard indicators by the form of analysis display. They display certain values: price, smoothed price, volumes. Many standard indicators are built on the basis of filtering the minute significant price fluctuations with the help of moving averages and their variations. But we can hardly call the MA a good filter, because digital indicators that use spectral filters make it possible to do a more accurate calculation.
Simply put, digital indicators are technical analysis tools in which spectral filters are used to filter out price noise instead of moving averages.
The display of traditional indicators is lines, areas, and channels. Digital indicators can be displayed both in the form of lines and in digital form (a set of numbers in columns, any data in a text field, etc.). The digital display of the data is more like an additional source of statistics; for trading, a standard visual linear chart view is used.
All digital models belong to the category of spectral analysis of the market situation. In conventional technical indicators, price indications are averaged over a fixed period of time, which gives a rather rough result. The use of spectral analysis allows us to increase trading efficiency due to the fact that digital indicators use a statistical data set of past periods, which is converted into a “frequency” of the market (period of fluctuations).
Fourier theory provides the following spectral ranging of the trend duration:
low frequency range (0-4) - a reflection of a long trend of 2 months or more
medium frequency range (5-40) - the trend lasts 10-60 days, thus it is referred to as a correction
high frequency range (41-130) - price noise that lasts for several days
The ATCF algorithm is built on the basis of spectral analysis and includes a set of indicators created using digital filters. Its consists of indicators and filters:
FATL: Built on the basis of a low-frequency digital trend filter
SATL: Built on the basis of a low-frequency digital trend filter of a different order
RFTL: High frequency trend line
RSTL: Low frequency trend line
Inclucded:
4 DSP filters
Bar coloring
Keltner channels with variety ranges and smoothing functions
Bollinger bands
40 Smoothing filters
33 souce types
Variable channels
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs.
Versions:
- 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers
- 2016, TASC September, "Measuring Market Cycles"
Description
The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
How to use this indicator
The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators.
Usage Example 1
Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period.
Usage Example 2
Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto)
Description:
This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms.
To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest
neighbours of the current indicator(s) values.
The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours,
sees their state and thus classifies the current point.
The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours+++/Days
Relative Strength(RSMK) + Perks - Markos KatsanosIf you are desperately looking for a novel RSI, this isn't that. This is another lesser known novel species of indicator. Hot off the press, in multiple stunning color schemes, I present my version of "Relative Strength (RSMK)" employing PSv4.0, originally formulated by Markos Katsanos for TASC - March 2020 Traders Tips. This indicator is used to compare performance of an asset to a market index of your choosing. I included the S&P 500 index along side the Dow Jones and the NASDAQ indices selectively by an input() in "Settings". You may comparatively analyze other global market indices by adapting the code, if you are skilled enough in Pine to do so.
With this contribution to the Tradingview community, also included is MY twin algorithmic formulation of "Comparative Relative Strength" as a supplementary companion indicator. They are eerily similar, so I decided to include it. You may easily disable my algorithm within the indicator "Settings". I do hope you may find both of them useful. Configurations are displayed above in multiple scenarios that should be suitable for most traders.
As always, I have included advanced Pine programming techniques that conform to proper "Pine Etiquette". For those of you who are newcomers to Pine Script, this script may also help you understand advanced programming techniques in Pine and how they may be utilized in a most effective manner. Utilizing the "Power of Pine", I included the maximum amount of features I could surmise in an ultra small yet powerful package, being less than a 60 line implementation at initial release.
Unfortunately, there are so many Pine mastery techniques included, I don't have time to write about all of them. I will have to let you discover them for yourself, excluding the following Pine "Tricks and Tips" described next. Of notable mention with this release, I have "overwritten" the Pine built-in function ema(). You may overwrite other built-in functions too. If you weren't aware of this Pine capability, you now know! Just heed caution when doing so to ensure your replacement algorithms are 100% sound. My ema() will also accept a floating point number for the period having ultimate adjustability. Yep, you heard all of that properly. Pine is becoming more impressive than `impressive` was originally thought of...
Features List Includes:
Dark Background - Easily disabled in indicator Settings->Style for "Light" charts or with Pine commenting
AND much, much more... You have the source!
The comments section below is solely just for commenting and other remarks, ideas, compliments, etc... regarding only this indicator, not others. When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members, I may implement more ideas when they present themselves as worthy additions. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
the "fasle" hull moving averageThere is a little different between my "fasle hull moving average" the "correct one".
the correct algorithm:
hma = wma((2*wma(close,n/2) - wma(close,n),sqrt(n))
the "fasle" algorithm:
=wma((2*wma(close,n/4) - wma(close,n),sqrt(n))
Amazing! Why the "fasle" describe the trend so accurate!?
MACD, backtest 2015+ only, cut in half and doubledThis is only a slight modification to the existing "MACD Strategy" strategy plugin!
found the default MACD strategy to be lacking, although impressive for its simplicity. I added "year>2014" to the IF buy/sell conditions so it will only backtest from 2015 and beyond ** .
I also had a problem with the standard MACD trading late, per se. To that end I modified the inputs for fast/slow/signal to double. Example: my defaults are 10, 21, 10 so I put 20, 42, 20 in. This has the effect of making a 30min interval the same as 1 hour at 10,21,10. So if you want to backtest at 4hr, you would set your time interval to 2hr on the main chart. This is a handy way to make shorter time periods more useful even regardless of strategy/testing, since you can view 15min with alot less noise but a better response.
Used on BTCCNY OKcoin, with the chart set at 45 min (so really 90min in the strategy) this gave me a percent profitable of 42% and a profit factor of 1.998 on 189 trades.
Personally, I like to set the length/signals to 30,63,30. Meaning you need to triple the time, it allows for much better use of shorter time periods and the backtests are remarkably profitable. (i.e. 15min chart view = 45min on script, 30min= 1.5hr on script)
** If you want more specific time periods you need to try plugging in different bar values: replace "year" with "n" and "2014" with "5500". The bars are based on unix time I believe so you will need to play around with the number for n, with n being the numbers of bars.
RSI Fibonacci Flow [JOAT]RSI Fibonacci Flow - Advanced Fibonacci Retracement with RSI Confluence
Introduction
RSI Fibonacci Flow is an open-source overlay indicator that combines automatic Fibonacci retracement levels with RSI momentum analysis to identify high-probability trading zones. The indicator automatically detects swing highs and lows, draws Fibonacci levels, and generates confluence signals when RSI conditions align with key Fibonacci zones.
This indicator is designed for traders who use Fibonacci retracements but want additional confirmation from momentum analysis before entering trades.
Originality and Purpose
This indicator is NOT a simple mashup of RSI and Fibonacci tools. It is an original implementation that creates a synergistic relationship between two complementary analysis methods:
Why Combine RSI with Fibonacci? Fibonacci retracements identify WHERE price might reverse, but they don't tell you WHEN. RSI provides the timing component by showing momentum exhaustion. When price reaches the Golden Zone (50%-61.8%) AND RSI shows oversold conditions, the probability of a successful bounce increases significantly.
Original Confluence Scoring System: The indicator calculates a 0-5 confluence score that weights multiple factors: Golden Zone presence (+2), entry zone presence (+1), RSI extreme alignment (+1), RSI divergence (+1), and strong RSI momentum (+1). This scoring system is original to this indicator.
Automatic Pivot Detection: Unlike manual Fibonacci tools, this indicator automatically detects swing highs and lows using a configurable pivot algorithm, then draws Fibonacci levels accordingly. The pivot detection uses a center-bar comparison method that checks if a bar's high/low is the highest/lowest within the specified depth on both sides.
Dynamic Trend Awareness: The indicator determines trend direction based on pivot sequence (last pivot was high or low) and adjusts Fibonacci orientation accordingly. In uptrends, 0% is at swing low; in downtrends, 0% is at swing high.
Each component serves a specific purpose:
Fibonacci levels identify potential reversal zones based on natural price ratios
RSI provides momentum context to filter out low-probability setups
Confluence scoring quantifies setup quality for position sizing decisions
Automatic pivot detection removes subjectivity from level placement
Core Concept: RSI-Fibonacci Confluence
The most powerful trading setups occur when multiple factors align. RSI Fibonacci Flow identifies these moments by:
Automatically detecting price pivots and drawing Fibonacci levels
Tracking which Fibonacci zone the current price occupies
Monitoring RSI for overbought/oversold conditions
Generating signals when RSI extremes coincide with key Fibonacci levels
Scoring confluence strength on a 0-5 scale
When price reaches the Golden Zone (50%-61.8%) while RSI shows oversold conditions in an uptrend, the probability of a bounce increases significantly.
Fibonacci Levels Explained
The indicator draws nine Fibonacci levels based on the most recent swing:
0% (Swing Low/High): The starting point of the move
23.6%: Shallow retracement - often seen in strong trends
38.2%: First significant support/resistance level
50%: Psychological midpoint of the move
61.8% (Golden Ratio): The most important Fibonacci level
78.6%: Deep retracement - last defense before trend failure
100% (Swing High/Low): The end point of the move
127.2% (TP1): First extension target for take profit
161.8% (TP2): Second extension target for take profit
The Golden Zone
The area between 50% and 61.8% is highlighted as the "Golden Zone" because:
It represents the optimal retracement depth for trend continuation
Institutional traders often place orders in this zone
It offers favorable risk-to-reward ratios
Price frequently bounces from this area in healthy trends
When price enters the Golden Zone, the indicator highlights it with a semi-transparent box and optional background coloring.
Pivot Detection System
The indicator uses a configurable pivot detection algorithm:
pivotDetect(float src, int len, bool isHigh) =>
int halfLen = len / 2
float centerVal = nz(src , src)
bool isPivot = true
for i = 0 to len - 1
if isHigh
if nz(src , src) > centerVal
isPivot := false
break
else
if nz(src , src) < centerVal
isPivot := false
break
isPivot ? centerVal : float(na)
This identifies swing highs and lows by checking if a bar's high/low is the highest/lowest within the specified depth on both sides.
Visual Components
1. Fibonacci Lines
Horizontal lines at each Fibonacci level:
Solid lines for major levels (0%, 50%, 61.8%, 100%)
Dashed lines for secondary levels (23.6%, 38.2%, 78.6%)
Dotted lines for extension levels (127.2%, 161.8%)
Color-coded for easy identification
Configurable line width
2. Fibonacci Labels
Price labels at each level showing:
Fibonacci percentage
Actual price at that level
Golden Zone label highlighted
TP1 and TP2 labels for targets
3. Golden Zone Box
A semi-transparent box highlighting the 50%-61.8% zone:
Gold colored border and fill
Extends from swing start to current bar (or beyond if extended)
Provides clear visual of the optimal entry zone
4. ZigZag Lines
Connecting lines between detected pivots:
Cyan for moves from low to high
Orange for moves from high to low
Helps visualize market structure
Configurable line width
5. Pivot Markers
Small labels at detected swing points:
"HH" (Higher High) at swing highs
"LL" (Lower Low) at swing lows
Helps track market structure
6. Entry Signals
BUY and SELL labels when confluence conditions are met:
BUY: RSI oversold + price in entry zone + uptrend + positive momentum
SELL: RSI overbought + price in entry zone + downtrend + negative momentum
Labels include "RSI+FIB" to indicate confluence
Confluence Scoring System
The indicator calculates a confluence score from 0 to 5:
+2 points: Price is in the Golden Zone (50%-61.8%)
+1 point: Price is in the entry zone (38.2%-61.8%)
+1 point: RSI is oversold in uptrend OR overbought in downtrend
+1 point: RSI divergence detected (bullish or bearish)
+1 point: Strong RSI momentum (change > 2 points)
Confluence ratings:
STRONG (4-5): Multiple factors align - high probability setup
MODERATE (2-3): Some factors align - proceed with caution
WEAK (0-1): Few factors align - wait for better setup
Dashboard Panel
The 10-row dashboard provides comprehensive analysis:
RSI Value: Current RSI reading (large text)
RSI State: OVERBOUGHT, OVERSOLD, BULLISH, BEARISH, or NEUTRAL
Fib Trend: UPTREND or DOWNTREND based on last pivot sequence
Price Zone: Current Fibonacci zone (e.g., "GOLDEN ZONE", "38.2% - 50%")
Price: Current close price (large text)
Confluence: Score rating with numeric value (e.g., "STRONG (4/5)")
Nearest Fib: Closest key Fibonacci level with price
TP1 (127.2%): First take profit target price
TP2 (161.8%): Second take profit target price
Input Parameters
Pivot Detection:
Pivot Depth: Bars to look back for swing detection (default: 10)
Min Deviation %: Minimum price move to confirm pivot (default: 1.0)
RSI Settings:
RSI Length: Period for RSI calculation (default: 14)
Source: Price source (default: close)
Overbought: Upper threshold (default: 70)
Oversold: Lower threshold (default: 30)
Fibonacci Display:
Show Fib Lines: Toggle Fibonacci lines (default: enabled)
Show Fib Labels: Toggle price labels (default: enabled)
Show Golden Zone Box: Toggle zone highlight (default: enabled)
Line Width: Thickness of Fibonacci lines (default: 2)
Extend Fib Lines: Extend lines into future (default: enabled)
ZigZag:
Show ZigZag: Toggle connecting lines (default: enabled)
ZigZag Width: Line thickness (default: 2)
Signals:
Show Entry Signals: Toggle BUY/SELL labels (default: enabled)
Show TP Levels: Toggle take profit in dashboard (default: enabled)
Show RSI-Fib Confluence: Toggle confluence analysis (default: enabled)
Dashboard:
Show Dashboard: Toggle information panel (default: enabled)
Position: Choose corner placement
Colors:
Bullish: Color for bullish elements (default: cyan)
Bearish: Color for bearish elements (default: orange)
Neutral: Color for neutral elements (default: gray)
Golden Zone: Color for Golden Zone highlight (default: gold)
How to Use RSI Fibonacci Flow
Identifying Entry Zones:
Wait for price to retrace to the 38.2%-61.8% zone
Check if RSI is approaching oversold (for longs) or overbought (for shorts)
Look for STRONG confluence rating in the dashboard
Enter when BUY or SELL signal appears
Setting Take Profit Targets:
TP1 at 127.2% extension for conservative target
TP2 at 161.8% extension for aggressive target
Consider scaling out at each level
Using the Price Zone:
"BELOW 23.6%" - Price hasn't retraced much; wait for deeper pullback
"23.6% - 38.2%" - Shallow retracement; strong trend continuation possible
"38.2% - 50%" - Good entry zone for trend trades
"GOLDEN ZONE" - Optimal entry zone; highest probability
"61.8% - 78.6%" - Deep retracement; trend may be weakening
"78.6% - 100%" - Very deep; trend reversal possible
"ABOVE/BELOW 100%" - Trend has likely reversed
Confluence Trading Strategy:
Only take trades with confluence score of 3 or higher
STRONG confluence (4-5) warrants larger position size
MODERATE confluence (2-3) warrants smaller position size
WEAK confluence (0-1) - wait for better setup
Alert Conditions
Ten alert conditions are available:
RSI-Fib BUY Signal: Strong bullish confluence detected
RSI-Fib SELL Signal: Strong bearish confluence detected
Price in Golden Zone: Price enters 50%-61.8% zone
New Pivot High: Swing high detected
New Pivot Low: Swing low detected
RSI Overbought: RSI crosses above overbought threshold
RSI Oversold: RSI crosses below oversold threshold
Bullish Divergence: Potential bullish RSI divergence
Bearish Divergence: Potential bearish RSI divergence
Strong Confluence: Confluence score reaches 4 or higher
Understanding Trend Direction
The indicator determines trend based on pivot sequence:
UPTREND: Last pivot was a low after a high (expecting move up)
DOWNTREND: Last pivot was a high after a low (expecting move down)
Fibonacci levels are drawn accordingly:
In uptrend: 0% at swing low, 100% at swing high
In downtrend: 0% at swing high, 100% at swing low
Bar Coloring
When confluence features are enabled:
Cyan bars on strong bullish signals
Orange bars on strong bearish signals
Gold-tinted bars when price is in Golden Zone
Best Practices
Use on 1H timeframe or higher for more reliable pivots
Adjust Pivot Depth based on timeframe (higher for longer timeframes)
Wait for price to enter Golden Zone before considering entries
Confirm RSI is in favorable territory before trading
Use extension levels (127.2%, 161.8%) for realistic profit targets
Combine with support/resistance and candlestick patterns
Higher confluence scores indicate higher probability setups
Limitations
Pivot detection has inherent lag (must wait for confirmation)
Fibonacci levels are subjective - different swings produce different levels
Works best in trending markets with clear swings
RSI can remain overbought/oversold in strong trends
Not all Golden Zone entries will be successful
The source code is open and available for review and modification.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trading involves substantial risk of loss. Past performance does not guarantee future results. Fibonacci levels are not guaranteed support/resistance - they are probability zones based on historical price behavior. Always conduct your own analysis and use proper risk management.
- Made with passion by officialjackofalltrades :D
Rolling Volume Structure: HVN & SentimentTitle:
Rolling Volume Structure: HVN & Sentiment
Description:
This indicator visualizes the distribution of volume over price levels for a user-defined rolling period. It is designed to identify structural market nodes (HVN/LVN) and correlate them with Pivot Points to filter out market noise.
NOTE: This script utilizes a mathematical array binning algorithm to calculate the profile efficiently on the chart timeframe, avoiding the runtime timeouts often associated with standard iterative volume profiles.
How it works (Technical Methodology)
Binning Algorithm: The script calculates the price range (Highest High - Lowest Low) of the lookback period and divides it into a fixed number of vertical bins defined by the Resolution input.
Volume Allocation: It iterates through historical bars once. The volume of each bar is assigned to the corresponding price bin based on the bar's closing price.
Sentiment Approximation: Since tick-level Bid/Ask data is not available for historical bars in standard Pine Script strategies, this indicator estimates directional volume based on candle polarity:
If Close > Open: Volume is categorized as "Up Volume" (Buying Sentiment).
If Close < Open: Volume is categorized as "Down Volume" (Selling Sentiment).
Disclaimer: This is a standard approximation for structural analysis and does not represent true tick-data delta.
Why this Combination? (Originality & Synergy)
This script addresses the problem of validating structural levels. Traders often use Pivots and Volume Profiles separately. This script combines them programmatically to provide context:
Pivot Confluence: A Pivot Point is only plotted if it aligns with significant volume structure.
HVN Validation: A pivot occurring within a High Volume Node (HVN) suggests a high-liquidity reversal zone, whereas a pivot in a Low Volume Node (LVN) may indicate a liquidity void or a "weak" high/low.
The Dashboard summarizes these metrics (Position relative to Value Area, Net Sentiment, and Trend), removing the need for multiple separate indicators.
Educational Use for Beginners
If you are new to Volume Profile, think of the market structure in these simple terms:
Value Area (VA): This is the "Fair Price" zone where 70% of trading happened. If price is inside here, the market is balanced. If price breaks out, it may be starting a trend.
HVN (High Volume Nodes - Colored Boxes): Think of these as "Traffic Jams". Price often slows down, bounces, or gets stuck here because there are many orders. They act as Support or Resistance.
LVN (Low Volume Nodes - Gray Strips): Think of these as "Empty Highways". Because there is little volume here, price tends to move through these zones very quickly to get to the next HVN.
Features
HVN (High Volume Nodes): Colored boxes highlighting areas of high accumulation.
LVN (Low Volume Nodes): Gray strips highlighting gaps or acceleration zones.
Value Area (VA): Displays the VAH, VAL, and PoC (Point of Control).
Volume-Filtered Pivots: Plots pivots only when supported by the profile structure.
Sentiment Coloring: The profile bins are colored based on the net bullish/bearish candle volume.
Settings
Rolling Period: The lookback window size (default 150 bars).
Resolution: Precision of the profile bins (higher = more detail, lower = smoother).
HVN Thresholds: Percentage of PoC volume required to identify a node.
Global Text Size: Adjusts labels and dashboard for 4K or standard screens.
Credits: The core binning logic is adapted from generic open-source array management concepts for custom volume profiles.
Quarter Point Autopilot v2.0.0Hello traders,
I am pleased to release the Quarter Point Autopilot . This is a specialized structural framework designed to impose mathematical order on price action by synthesizing major market cycles with fractal geometric subdivisions.
Defining accurate Support and Resistance often presents a dilemma: rely on subjective, manually drawn lines that vary from trader to trader, or clutter charts with lagging moving averages. The Quarter Point Autopilot solves this by quantifying "Algorithmic Geometry." It eliminates subjectivity by projecting a universal grid based on the mathematical quarter-points that institutional algorithms utilize to execute orders.
📐 The Concept: Algorithmic Geometry
To the untrained eye, price movement can appear chaotic or random. However, professional analysis reveals that markets move in measured "Steps." Large institutions do not place orders at random numbers; they utilize specific mathematical fractions of a Major Cycle to manage liquidity.
This indicator is specifically engineered to visualize this hidden framework. By defining a "Major Cycle" (Point A and Point B), the script calculates the entire universe of the chart. It mathematically subdivides the range into Halves, Quarters, Eighths, and Sixteenths, highlighting the precise levels where price creates "Structure" and where algorithmic reactions are most likely to occur.
⚙️ The Autopilot Logic: Infinite Scroll
In previous iterations of quarter-theory tools, traders were forced to manually redraw grids as price expanded into new territories.
This version introduces the "Autopilot" engine ( current_base logic ). The script dynamically detects which "Block" price is currently trading within and automatically projects the grid forward and backward in real-time. Whether price rallies 1,000 points or drops 500, the mathematical subdivisions snap to the correct integer block immediately, ensuring you never trade without context.
📊 Fractal Hierarchy
Not all levels are created equal. The indicator uses a visual hierarchy to help you distinguish the strength of a level at a glance:
Major Cycle: The "Hard Deck" boundaries of the range (0% / 100%).
Half-Major: The Equilibrium of the cycle (50%).
Large Quarters: The standard deviation points (25% / 75%).
Mid & Small Quarters: The granular detail (Eighths and Sixteenths) for precision entries.
User Guide:
Simply input two "Major Cycle Points" (a significant High and Low) in the settings. The script calculates the "Step Size" and handles the rest, projecting the grid relative to current price action.
Settings Include:
Calculation Group: Set your Point A and Point B to define the grid size.
Visual Group: Toggle the upper/lower buffers and customize the lookback/lookforward lengths to keep your chart clean.
Label Group: Choose to see Level Names, Prices, or both.
Moon Phases & Declinations - Chronos Capital [BETA]High-Precision Lunar Cycles: Moon Phases & Declinations (Swiss Ephemeris)
Overview
This indicator provides institutional-grade astronomical data directly on your chart. Unlike standard scripts that use basic sine-wave approximations, this tool implements the **Swiss Ephemeris algorithm**, the gold standard for high-precision celestial calculations.
By tracking the Moon’s phases and its **Maximum/Minimum Declinations**, traders can identify potential "turning points" or "energy shifts" in market volatility often associated with lunar cycles.
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Key Features
Ultra-High Precision: Calculations are accurate to within *seconds* of time, ensuring that the visual plot aligns perfectly with astronomical reality.
Moon Phase Tracking: Distinct markers for New Moon, Full Moon, and Quarters.
Lunar Declination Peaks: Automatically identifies when the moon reaches its *Maximum North* and *Maximum South* points (Lunar Extremes).
Customizable Visuals: Toggle between background highlights, vertical lines, or plot signals to suit your trading style.
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Technical Accuracy
This script is built using a ported version of the Swiss Ephemeris
Positional Accuracy: Within 0.1 arcseconds.
Time Accuracy: Within **~1-2 seconds** of official JPL data.
Algorithm: Integration of the *ELP2000-85* lunar theory for maximum reliability over decades of historical data.
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### **How to Use**
1. **Reversal Zones:** Watch for the Moon’s *Max/Min Declination* points, which often coincide with local tops or bottoms in trending markets.
2. **Volatility Shifts:** Use the *New Moon* and *Full Moon* markers to anticipate periods of increased or decreased market liquidity and volume.
3. **Confluence:** Best used in combination with your existing price action or momentum indicators to add a "time-based" filter to your entries.
*Disclaimer: This tool is for educational and analytical purposes only. Lunar cycles are a study of time-based correlation, not a guaranteed financial signal.*
Neosha Concept V4 (NY Time)
Imagine the financial market as a huge ocean. Millions of traders throw orders into it every second. But beneath all the noise, there is a powerful current that quietly controls where the waves move. That current is not a person, not a trader, and not random—it is an algorithm.
This algorithm is called the Interbank Price Delivery Algorithm (IPDA).
Think of it as the “navigation system” that guides price through the market.
IPDA has one job:
to move prices in a way that keeps the market efficient and liquid.
To do this, it constantly looks for two things:
1. Where liquidity is hiding
Liquidity is usually found above highs and below lows—where traders place stop losses. The algorithm moves price there first to collect that liquidity.
2. Where price became unbalanced
Sometimes price moves too fast and creates gaps or imbalances. IPDA returns to those areas later to “fix” the missing orders.
Once you start looking at the charts with this idea in mind, everything makes more sense:
Why price suddenly spikes above a high and crashes down
Why big moves leave gaps that price later fills
Why the market reverses right after taking stops
Why trends begin only after certain levels are hit
These are not accidents.
They are the algorithm doing its job.
Price moves in a repeating cycle:
Gather liquidity
Make a strong move (displacement)
Return to fix inefficiency
Deliver to the next target
Most beginners only see the candles.
But once you understand IPDA, you see the intention behind the candles.
Instead of guessing where price might go, you begin to understand why it moves there.
And once you understand the “why,” your trading becomes clearer, calmer, and far more accurate.
Gold AI RSI Monitor [Stacked + KNN]Here is a comprehensive description and user guide for the Gold AI RSI Monitor. You can copy and paste this into the "Description" field if you publish the script on TradingView, or save it for your own reference.
Gold AI RSI Monitor
🚀 Overview
The Gold AI RSI Monitor is a next-generation dashboard designed specifically for trading volatile assets like Gold (XAUUSD). It completely reimagines the traditional RSI by "stacking" 10 different timeframes (from 1-minute to Monthly) into a single, vertical view.
Integrated into this dashboard is a K-Nearest Neighbors (KNN) Machine Learning algorithm. This AI analyzes historical price action to find patterns similar to the current market and predicts the next likely move with a confidence score.
📊 Visual Guide: How to Read the Chart
1. The "Stacked" Lanes Instead of switching timeframes constantly, this indicator displays them all at once using vertical offsets.
Bottom Lane (0-100): 1-Minute RSI
Middle Lanes: 5m, 15m, 30m, 1H, 2H, 4H, Daily
Top Lane (900-1000): Monthly RSI
2. Gradient Color System The RSI lines change color based on momentum strength:
🔴 Red: Oversold / Bearish (Approaching 30 or lower)
🟡 Yellow: Neutral (Around 50)
🟢 Green: Overbought / Bullish (Approaching 70 or higher)
3. Tracker Lines Each timeframe has a dotted horizontal line extending to the right. This allows you to instantly see the exact RSI value for every timeframe without squinting.
🤖 The AI Engine (KNN)
The "AI" component uses a K-Nearest Neighbors algorithm.
Learning: It scans the last 1,000 bars of history.
Matching: It finds the 5 historical moments that look mathematically identical to the current market conditions (based on RSI and Volatility).
Predicting: It checks if price went UP or DOWN after those historical matches.
The Signals:
Buying Signal: If the majority of historical matches resulted in a price increase, the AI triggers a BUY.
Selling Signal: If the majority resulted in a drop, the AI triggers a SELL.
🎯 How to Trade with This Indicator
1. The "Crosshair" Signal
When the AI detects a high-probability setup, a massive Crosshair appears on your chart:
Green Crosshair: Strong BUY signal.
Red Crosshair: Strong SELL signal.
Note: The crosshair consists of a thick vertical line and a dashed horizontal line intersecting at the signal candle.
2. Timeframe Alignment (Confluence)
Do not rely on the AI alone. Look at the stacked RSIs:
Strong Long: The AI shows a Green Crosshair AND the lower timeframes (1m, 5m, 15m) are all turning Green/upward.
Strong Short: The AI shows a Red Crosshair AND the lower timeframes are turning Red/downward.
3. Support & Resistance Zones
Bottom Dotted Line (30): Support. If RSI hits this and turns up, it's a buying opportunity.
Top Dotted Line (70): Resistance. If RSI hits this and turns down, it's a selling opportunity.
⚙️ Settings Guide
RSI Length: Default is 14. Lower (e.g., 7) makes it faster/choppier; higher (e.g., 21) makes it smoother.
Enable AI Signals: Toggles the KNN calculation on/off.
Neighbors (K): How many historical matches to check. Default is 5.
Increase to 9-10 for fewer, more conservative signals.
Decrease to 3 for faster, more aggressive signals.
AI Timeframe: CRITICAL SETTING.
If left empty, the AI calculates based on your current chart.
Recommendation: For Gold scalping, set this to 15m or 1h. This ensures the AI looks at the bigger trend even if you are zooming in on the 1-minute chart.
⚠️ Disclaimer
This tool is for educational and analytical purposes. The "AI" is a statistical probability algorithm based on past performance, which is not indicative of future results. Always manage your risk.
RCV Essentials════════════════════════════════════════════
RCV ESSENTIALS - MULTI-TIMEFRAME & SESSION ANALYSIS TOOL
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📊 WHAT THIS INDICATOR DOES
This professional-grade indicator combines two powerful analysis modules:
1. TRADING SESSION TRACKER - Visualizes high/low ranges for major global market sessions (NY Open, London Open, Asian Session, etc.)
2. MULTI-TIMEFRAME CANDLE DISPLAY - Shows up to 8 higher timeframes simultaneously on your chart (15m, 30m, 1H, 4H, 1D, 1W, 1M, 3M)
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🎯 KEY FEATURES
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TRADING SESSIONS MODULE:
✓ Track up to 6 custom trading sessions simultaneously
✓ Real-time high/low range detection during active sessions
✓ Pre-configured for NYO (7-9am), LNO (2-3am), Asian Session (4:30pm-12am)
✓ 60+ global timezone options
✓ Customizable colors, labels, and transparency
✓ Daily divider lines (optional Sunday skip for traditional markets)
✓ Only displays on ≤30m timeframes for optimal clarity
MULTI-TIMEFRAME CANDLES MODULE:
✓ Display 1-8 higher timeframes with up to 10 candles each
✓ Real-time candle updates (non-repainting)
✓ Fully customizable colors (separate bullish/bearish for body/border/wick)
✓ Adjustable candle width, spacing, and positioning
✓ Smart label system (top/bottom/both, aligned or follow candles)
✓ Automatic timeframe validation (only shows TFs higher than chart)
✓ Memory-optimized with automatic cleanup
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🔧 HOW IT WORKS
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TECHNICAL IMPLEMENTATION:
Session Tracking Algorithm:
• Detects session start/end using time() function with timezone support
• Continuously monitors and updates high/low during active session
• Finalizes range when session ends using var persistence
• Draws boxes using real-time bar_index positioning
• Maintains session ranges across multiple days for reference
Multi-Timeframe System:
• Uses ta.change(time()) detection to identify new MTF candle formation
• Constructs candles using custom Type definitions (Candle, CandleSet, Config)
• Stores OHLC data in arrays with automatic size management
• Renders using box objects (bodies) and line objects (wicks)
• Updates current candle every tick; historical candles remain static
• Calculates dynamic positioning based on user settings (offset, spacing, width)
Object-Oriented Architecture:
• Custom Type "Candle" - Stores OHLC values, timestamps, visual elements
• Custom Type "CandleSet" - Manages arrays of candles + settings per timeframe
• Custom Type "Config" - Centralizes all display configuration
• Efficient memory management via unshift() for new candles, pop() for old
Performance Optimizations:
• var declarations minimize recalculation overhead
• Conditional execution (sessions only on short timeframes)
• Maximum display limits prevent excessive object creation
• Timeframe validation at barstate.isfirst reduces redundant checks
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📈 HOW TO USE
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SETUP:
1. Add indicator to chart (works best on 1m-30m timeframes)
2. Open Settings → "Trading Sessions" group
- Enable desired sessions (NYO, LNO, AS, or custom)
- Select your timezone from 60+ options
- Adjust colors and transparency
3. Open Settings → "Multi-TF Candles" group
- Enable timeframes (TF1-TF8)
- Configure each timeframe and display count
- Customize colors and layout
READING THE CHART:
• Session boxes show high/low ranges during active sessions
• MTF candles display to the right of current price
• Labels identify each timeframe (15m, 1H, 4H, etc.)
• Real-time updates on the most recent MTF candle
TRADING APPLICATIONS:
Session Breakout Strategy:
→ Identify session high/low (e.g., Asian session 16:30-00:00)
→ Wait for break above/below range
→ Confirm with higher timeframe candle close
→ Enter in breakout direction, stop at opposite side of range
Multi-Timeframe Confirmation:
→ Spot setup on primary chart (e.g., 5m)
→ Verify 15m, 1H, 4H candles align with trade direction
→ Only take trades where higher TFs confirm
→ Exit when higher TF candles show reversal
Combined Session + MTF:
→ Asian session establishes range overnight
→ London Open breaks Asian high
→ Confirm with bullish 15m + 1H candles
→ Enter long with stop below Asian high
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🎨 ORIGINALITY & INNOVATION
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What makes this indicator original:
1. INTEGRATED DUAL-MODULE DESIGN
Unlike separate session or MTF indicators, this combines both in a single performance-optimized script, enabling powerful correlation analysis between session behavior and timeframe structure.
2. ADVANCED RENDERING SYSTEM
Uses custom Pine Script v5 Types with dynamic box/line object management instead of basic plot functions. This enables:
• Precise visual control over positioning and spacing
• Real-time updates without repainting
• Efficient memory handling via automatic cleanup
• Support for 8 simultaneous timeframes with independent settings
3. INTELLIGENT SESSION TRACKING
The algorithm continuously recalculates ranges bar-by-bar during active sessions, then preserves the final range. This differs from static zone indicators that simply draw fixed boxes at predefined levels.
4. MODULAR ARCHITECTURE
Custom Type definitions (Candle, CandleSet, Config) create extensible, maintainable code structure while supporting complex multi-timeframe operations with minimal performance impact.
5. PROFESSIONAL FLEXIBILITY
Extensive customization: 6 configurable sessions, 8 timeframe slots, 60+ timezones, granular color/sizing/spacing controls, multiple label positioning modes—adaptable to any market or trading style.
6. SMART VISUAL DESIGN
Automatic timeframe validation, dynamic label alignment options, and intelligent spacing calculations ensure clarity even with multiple timeframes displayed simultaneously.
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⚙️ CONFIGURATION OPTIONS
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TRADING SESSIONS:
• Session 1-6: On/Off toggles
• Time Ranges: Custom start-end times
• Labels: Custom text for each session
• Colors: Individual color per session
• Timezone: 60+ options (Americas, Europe, Asia, Pacific, Africa)
• Range Transparency: 0-100%
• Outline: Optional border
• Label Display: Show/hide session names
• Daily Divider: Dotted lines at day changes
• Skip Sunday: For traditional markets vs 24/7 crypto
MULTI-TF CANDLES:
• Timeframes 1-8: Enable/disable individually
• Timeframe Selection: Any TF (seconds to months)
• Display Count: 1-10 candles per timeframe
• Bullish Colors: Body/Border/Wick (independent)
• Bearish Colors: Body/Border/Wick (independent)
• Candle Width: 1-10+ bars
• Right Margin: 0-200+ bars from edge
• TF Spacing: Gap between timeframe groups
• Label Color: Any color
• Label Size: Tiny/Small/Normal/Large/Huge
• Label Position: Top/Bottom/Both
• Label Alignment: Follow Candles or Align
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📋 TECHNICAL SPECIFICATIONS
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• Pine Script Version: v5
• Chart Overlay: True
• Max Boxes: 500
• Max Lines: 500
• Max Labels: 500
• Max Bars Back: 5000
• Update Frequency: Real-time (every tick)
• Timeframe Compatibility: Chart TF must be lower than selected MTFs
• Session Display: Activates only on ≤30 minute timeframes
• Memory Management: Automatic cleanup via array operations
Pattern Match & Forward Projection – Weekly (EN)
Overview
This indicator searches for recurring price patterns in weekly data and projects their average forward performance.
The logic is based on historical pattern repetition: it scans past price sequences similar to the most recent one, then aggregates their forward returns to estimate potential outcomes.
⚠️ Important: The indicator is designed for weekly timeframe only. Using it on daily or intraday charts will trigger an error message.
Settings (Inputs)
Pattern Settings
Pattern length (weeks): Number of weeks used to define the reference pattern.
Forward length (weeks): Number of weeks into the future to evaluate after each pattern match.
Lookback (weeks): Historical window to scan for past pattern matches.
Normalize by shape (z-score): If enabled, patterns are normalized by z-score, focusing on shape similarity rather than absolute values.
Distance threshold (Euclidean): Maximum allowed Euclidean distance between the reference pattern and historical candidates. Smaller values = stricter matching.
Min. required matches: Minimum number of valid matches needed for analysis.
Quality Filters
Min required Hit%: Minimum percentage of positive outcomes (upside forward returns) required for the pattern to be considered valid.
Return filter mode:
Either: absolute average return ≥ threshold
Long only: average return ≥ threshold
Short only: average return ≤ -threshold
Min avg return (%): Minimum average forward return threshold for validation.
Visual Options
Highlight historical matches (labels): Marks where in history similar patterns occurred.
Max match labels to draw: Caps the number of match markers shown to avoid clutter.
Draw average projection: Displays the average projected forward curve if conditions are met.
Show summary panel: Enables/disables the information panel.
Show weekly avg curve in panel: Adds a breakdown of average returns week by week.
Projection color: Choose the color of the projected forward curve.
What the Screen Shows
Summary Panel (top-left by default)
Total matches found in history
Matches with valid forward data
Average, minimum, and maximum distance (similarity measure)
Average forward return and Hit%
Distance threshold and normalization setting
Weekly average forward curve (if enabled)
Quality filter results (pass/fail)
Projection Curve (dotted line on price chart)
Drawn only if enough valid matches are found and filters are satisfied
Represents the average forward performance of historical matches, anchored at the current bar
Historical Match Labels (▲ markers)
Small arrows below past bars where similar patterns occurred
Tooltip: “Historical match”
Forecast Logic
The indicator does not predict the future in a deterministic way.
Instead, it relies on a pattern-matching algorithm:
The most recent N weeks (defined by Pattern length) are taken as the reference.
The algorithm scans the last Lookback (weeks) for segments with similar shape and magnitude.
Similarity is measured using Euclidean distance (optionally z-score normalized).
For each valid match, the subsequent Forward length weeks are collected.
These forward paths are averaged to generate a composite forward projection.
The summary panel reports whether the current setup passes the quality filters (Hit% and minimum average return).
Usage Notes
Best used as a contextual tool, not a standalone trading system.
Works only on weekly timeframe.
Quality filters help distinguish between noisy and statistically meaningful patterns.
A higher number of matches usually improves reliability, but very strict thresholds may reduce sample size.
📊 This tool is useful for traders who want to evaluate how similar historical setups have behaved and to visualize potential forward paths in a statistically aggregated way.
