Power Law & Heavy Tail + Fractal Dimension [Combined]This indicator adept fractral dimension and powerlaw to find state of price in three states, Up, Down, and sideways.
Regressions
INGMorel 1.2.0El indicador INGMorel 1.2.0 ha sido desarrollado por INGMorel para traders que buscan una herramienta avanzada, precisa y eficiente para identificar oportunidades de trading durante la sesión de Nueva York. Este indicador combina análisis técnico en múltiples marcos de tiempo (HMA en H1 y M15) junto con el potente filtro del RSI, para ofrecer señales claras sobre la tendencia del mercado.
Características destacadas:
Tendencia HMA H1 y HMA M15: Evalúa la tendencia en dos marcos de tiempo, HMA de 1 hora (H1) y HMA de 15 minutos (M15), asegurando que el trader esté alineado con la tendencia general del mercado.
Filtro RSI: El uso del RSI permite detectar condiciones de sobrecompra o sobreventa, añadiendo una capa adicional de confiabilidad a las señales de compra o venta.
Zona de Killzone (Sesión de NY): Las señales solo se muestran dentro de la Killzone (9:30 AM - 4:00 PM, hora de Nueva York), enfocándose en la parte más activa del mercado para aprovechar los movimientos clave.
Colores de Velas y Fibonacci: Cambia el color de las velas cuando ocurre una ruptura del HMA y marca los niveles clave de Fibonacci (0%, 50%, 100%) para ofrecer referencias visuales en el análisis de precios.
Beneficios para traders: El INGMorel 1.2.0 es ideal para traders que operan dentro de la Killzone de la sesión de Nueva York. Este indicador proporciona señales claras y visualizaciones útiles, como el cambio de color de las velas y las etiquetas de Fibonacci, que facilitan la toma de decisiones rápidas y bien fundamentadas.
Con la firma de INGMorel, esta herramienta ha sido creada para optimizar el análisis técnico, mejorar las estrategias de trading y maximizar las oportunidades de éxito en el mercado.
BITCOIN BTC Machine Learning Approximation Strategy by NHBPRODHey everyone, here's a new trading strategy script for Bitcoin, and I’m super excited to share it with you. It’s called the "BITCOIN BTC Machine Learning Approximation Strategy by NHBPROD." It employs a simplified machine learning technique referred to as K-means clustering approximation. It plots the mean price, upper cluster, and lower cluster, and prints a green and yellow background to visually cue you when to buy and when to sell. This is the strategy script, but I also have the indicator script which can be used to automate buy and sell signals directly to your phone, email, or your bot.
What It Does
The script calculates a dynamic mean price using linear regression and defines upper and lower zones based on standard deviation. These zones help identify potential support, resistance, or trend reversal areas on a chart. It visualizes the mean and clusters with plots and highlights significant areas where price moves above or below the clusters. Alerts are triggered when the price crosses these critical levels, enabling traders to stay informed about key market movements.
Backtest Results
Some notables:
Seemingly consistent profits on BTC 1 day chart.
I included slippage & I included commission.
100+, and covers the maximum amount of time allowed in tradingview.
The script is ready for BITCOIN and I deploy it on the 1 day timeframe because I feel like 1 day bars get enough data to make solid judgements for this type of indicator.
How to Use It
Look at the background—it’s color-coded and easy to spot.
green background = buy
red background = sell
This strategy (and the pairing indicator script) is able to be used to trade long only.
Bearish Candlestick Patterns (Patrones de velas Bajista)English
Bearish Candlestick Patterns Indicator
The "Bearish Candlestick Patterns" indicator is designed to identify and highlight key bearish candlestick patterns directly on your chart. This tool is highly beneficial for traders looking to spot potential trend reversals or bearish continuations in various markets, including forex, stocks, and cryptocurrencies. The indicator is built using Pine Script version 6 and includes several customizable options to adapt to your trading strategy.
Features:
Detects a variety of bearish candlestick patterns, including:
Evening Star
Bearish Engulfing
Shooting Star
Three Black Crows
Dark Cloud Cover
Hanging Man
Gravestone Doji
Trend Analysis: Automatically identifies uptrends or downtrends using SMA50 or SMA200 as references, or allows manual trend detection.
Alerts: Sends notifications when a new bearish pattern is detected, ensuring you never miss an opportunity.
Customizable Parameters: Fine-tune the detection settings, including shadow percentage, average body length, and trend rules.
Visual Representation: Patterns are labeled on the chart with clear tooltips for detailed explanations.
Who is it for? This indicator is ideal for traders at all levels who want to improve their technical analysis by integrating bearish candlestick patterns into their strategies. Whether you trade manually or use automated systems, this tool provides valuable insights into market trends and potential reversals.
Español
Indicador de Patrones de Velas Bajistas
El indicador "Patrones de Velas Bajistas" está diseñado para identificar y resaltar patrones clave de velas bajistas directamente en tu gráfico. Esta herramienta es altamente útil para traders que buscan detectar posibles reversas de tendencia o continuaciones bajistas en diversos mercados, incluyendo forex, acciones y criptomonedas. El indicador está desarrollado con Pine Script versión 6 y ofrece múltiples opciones personalizables para adaptarse a tu estrategia de trading.
Características:
Detecta una variedad de patrones de velas bajistas, incluyendo:
Estrella Vespertina (Evening Star)
Envuelven Bajista (Bearish Engulfing)
Estrella Fugaz (Shooting Star)
Tres Cuervos Negros (Three Black Crows)
Cubierta de Nube Oscura (Dark Cloud Cover)
Hombre Colgado (Hanging Man)
Doji Lápida (Gravestone Doji)
Análisis de Tendencia: Identifica automáticamente tendencias alcistas o bajistas utilizando las medias SMA50 o SMA200 como referencia, o permite detección manual de tendencia.
Alertas: Envía notificaciones cuando se detecta un nuevo patrón bajista, asegurándote de no perder oportunidades.
Parámetros Personalizables: Ajusta la configuración de detección, incluyendo porcentaje de sombras, longitud promedio del cuerpo, y reglas de tendencia.
Representación Visual: Los patrones son etiquetados en el gráfico con tooltips claros que ofrecen explicaciones detalladas.
¿Para quién es? Este indicador es ideal para traders de todos los niveles que deseen mejorar su análisis técnico integrando patrones de velas bajistas en sus estrategias. Ya sea que operes manualmente o utilices sistemas automatizados, esta herramienta ofrece valiosos insights sobre tendencias de mercado y posibles reversas.
Bullish Candlestick Patterns (Patrones de velas Alcista)English:
The "Bullish Candlestick Patterns" indicator is designed to automatically identify the most relevant bullish Japanese candlestick formations in any market or timeframe. This powerful tool helps traders spot key entry opportunities, increasing the probability of success in their trades.
Key Features:
- Accurate Identification: Recognizes bullish patterns such as Hammer, Morning Star, Engulfing, Three White Soldiers, and Dragonfly Doji.
- Customizable Settings: Detect trends based on SMA50 and SMA200 or disable trend detection to match your strategy.
- Built-in Alerts: Receive real-time notifications when a new pattern is detected.
- Clear Visualization: Patterns are highlighted on the chart with intuitive labels and customizable colors.
- ATR Integration: Labels and highlighted backgrounds adjust dynamically for improved clarity and usability.
Recommended Use:
- Ideal for beginner traders looking to learn how to recognize common bullish patterns.
- Perfect for advanced traders who want quick visual confirmations of reliable patterns integrated into their strategies.
Supported Bullish Patterns:
1. Hammer: Indicates potential bullish reversal following a downtrend.
2. Morning Star: A strong reversal signal after a prolonged decline.
3. Bullish Engulfing: A shift from bearish to bullish control with one candle fully engulfing the previous one.
4. Three White Soldiers: Three consecutive bullish candles signaling strength in the upward movement.
5. Dragonfly Doji: Rejection of lower prices with a close near the high, suggesting a potential reversal.
Instructions:
1. Add this indicator to your TradingView chart.
2. Customize the parameters to suit your needs (trend detection, colors, etc.).
3. Enable alerts to receive real-time notifications of new patterns.
4. Combine this analysis with other indicators such as RSI, MACD, or support and resistance levels for confirmation.
Note: This indicator does not provide automatic buy/sell signals. It is recommended to use it as a supporting tool and perform additional analysis before making trading decisions.
Español:
El indicador "Patrones de Velas Alcistas (Bullish)" está diseñado para identificar automáticamente las formaciones más relevantes de velas japonesas alcistas en cualquier mercado o marco temporal. Este poderoso indicador ayuda a los traders a detectar oportunidades de entrada en zonas clave, aumentando la probabilidad de éxito en sus operaciones.
Características principales:
- Identificación precisa: Reconoce patrones alcistas como Hammer, Morning Star, Engulfing, Three White Soldiers y Dragonfly Doji.
- Configuración personalizable: Detecta tendencias con base en SMA50 y SMA200 o sin detección, según tu estrategia.
- Alertas integradas: Recibe notificaciones en tiempo real cuando se detecta un nuevo patrón.
- Visualización clara: Los patrones se resaltan en el gráfico con etiquetas intuitivas y colores personalizados.
- Integración con ATR: Las etiquetas y fondos resaltados se ajustan dinámicamente para mejorar la claridad y usabilidad.
Uso recomendado:
- Ideal para traders principiantes que buscan aprender a reconocer patrones alcistas comunes.
- Perfecto para traders avanzados que desean incorporar confirmaciones visuales rápidas de patrones confiables a sus estrategias.
Patrones Alcistas Soportados:
1. Martillo (Hammer): Indica posible reversión alcista tras una tendencia bajista.
2. Estrella de la Mañana (Morning Star): Señal de reversión fuerte después de una caída prolongada.
3. Envolvente Alcista (Engulfing Bullish): Cambio de control de bajista a alcista con una vela que envuelve completamente a la anterior.
4. Tres Soldados Blancos (Three White Soldiers): Tres velas alcistas consecutivas que indican fuerza en el movimiento ascendente.
5. Libelula Doji (Dragonfly Doji): Rechazo a precios más bajos con un cierre cerca del máximo, indicando potencial de reversión.
Instrucciones:
1. Agrega este indicador a tu gráfico en TradingView.
2. Configura los parámetros para adaptarlo a tus necesidades (detección de tendencias, colores, etc.).
3. Activa las alertas para recibir notificaciones de nuevos patrones en tiempo real.
4. Usa el análisis en combinación con otros indicadores como RSI, MACD, o niveles de soporte y resistencia para confirmar tus decisiones.
Nota: Este indicador no proporciona señales de compra/venta automáticas. Se recomienda usarlo como una herramienta de apoyo y realizar un análisis adicional antes de tomar decisiones de trading.
Power Law Regression with SDThis idea use power law to determine price and use to predict state of random walk and trend.
Market Conditions 3W\3M [by Oberlunar]This script represents my preliminary reasoning for evaluating market conditions. I wanted to automate a process that I usually apply manually, starting with the analysis of macro trends across multiple timeframes—monthly, weekly, and daily—to gain a clear understanding of the market's dominant direction and use it as a foundation for making operational decisions.
When I analyze the market, my first step is to determine whether there is a clear trend or if the market is in a sideways phase. To do this, I focus on historical data points. For the monthly and weekly timeframes, I look at the highs, lows, and medians of the last three periods. I then calculate linear regression trendlines for each timeframe to quantify the strength and direction of the trend. The slope of the trendline is particularly important to me, as it reveals whether the market is bullish, bearish, or neutral. I’ve set a specific threshold to filter out minor fluctuations, ensuring that only meaningful movements are classified as trends.
Once I’ve identified the trends for the monthly and weekly timeframes, I combine them to assess the overall market condition. If both timeframes indicate a bullish trend, I interpret this as a strong signal for a positive macro environment. Similarly, if both are bearish, it suggests a downtrend. However, if the trends diverge or the slope is too weak, I consider the market to be uncertain or sideways, and I avoid long-term operations.
For shorter-term decisions, like scalping or daily trading, I refine my analysis further. Here, I integrate daily conditions, focusing on specific criteria that align with my strategy. For example, I use the relationship between the 21-period and 200-period moving averages as a key filter. If the 21-period moving average is above the 200-period, and the daily close is higher than both the open and the 21-period moving average, I consider it a bullish confirmation. The opposite applies for bearish conditions. These additional filters ensure that my short-term decisions align with the broader market structure and trend dynamics.
The script then presents all this information in a table. It shows the slope and intercept of the trendlines for each timeframe, the classified market condition (bullish, bearish, or sideways), and the combined signals for both macro trends and short-term strategies. This structured output helps me translate my reasoning into actionable insights.
Previous Candle Sweep IndicatorThis script identifies candlesticks where the current candle's high is higher than the previous candle's high, and the current candle's low is lower than the previous candle's low. If both conditions are met, the candle's body is highlighted in blue on the chart, allowing traders to quickly spot these patterns.
Features:
Highlights candles with both higher highs and lower lows.
Uses clear visual cues (blue body) for easy identification.
Ideal for traders looking to identify specific volatility patterns or reversals.
Adjust Asset for Future Interest (Brazil)Este script foi criado para ajustar o preço de um ativo com base na taxa de juros DI11!, que reflete a expectativa do mercado para os juros futuros. O objetivo é mostrar como o valor do ativo seria influenciado se fosse diretamente ajustado pela variação dessa taxa de juros.
Como funciona?
Preço do Ativo
O script começa capturando o preço de fechamento do ativo que está sendo visualizado no gráfico. Esse é o ponto de partida para o cálculo.
Taxa de Juros DI11!
Em seguida, ele busca os valores diários da taxa DI11! no mercado. Esta taxa é uma referência de juros de curto prazo, usada para ajustes financeiros e projeções econômicas.
Fator de Ajuste
Com a taxa de juros DI11!, o script calcula um fator de ajuste simples:
Fator de Ajuste
=
1
+
DI11
100
Fator de Ajuste=1+
100
DI11
Esse fator traduz a taxa percentual em um multiplicador aplicado ao preço do ativo.
Cálculo do Ativo Ajustado
Multiplica o preço do ativo pelo fator de ajuste para obter o valor ajustado do ativo. Este cálculo mostra como o preço seria se fosse diretamente influenciado pela variação da taxa DI11!.
Exibição no Gráfico
O script plota o preço ajustado do ativo como uma linha azul no gráfico, com maior espessura para facilitar a visualização. O resultado é uma curva que reflete o impacto teórico da taxa de juros DI11! sobre o ativo.
Utilidade
Este indicador é útil para entender como as taxas de juros podem influenciar ativos financeiros de forma hipotética. Ele é especialmente interessante para analistas que desejam avaliar a relação entre o mercado de renda variável e as condições de juros no curto prazo.
This script was created to adjust the price of an asset based on the DI11! interest rate, which reflects the market's expectation for future interest rates. The goal is to show how the asset's value would be influenced if it were directly adjusted by the variation of this interest rate.
How does it work?
Asset Price
The script starts by capturing the closing price of the asset that is being viewed on the chart. This is the starting point for the calculation.
DI11! Interest Rate
The script then searches for the daily values of the DI11! rate in the market. This rate is a short-term interest reference, used for financial adjustments and economic projections.
Adjustment Factor
With the DI11! interest rate, the script calculates a simple adjustment factor:
Adjustment Factor
=
1
+
DI11
100
Adjustment Factor=1+
100
DI11
This factor translates the percentage rate into a multiplier applied to the asset's price.
Adjusted Asset Calculation
Multiplies the asset price by the adjustment factor to obtain the adjusted asset value. This calculation shows how the price would be if it were directly influenced by the variation of the DI11! rate.
Display on the Chart
The script plots the adjusted asset price as a blue line on the chart, with greater thickness for easier visualization. The result is a curve that reflects the theoretical impact of the DI11! interest rate on the asset.
Usefulness
This indicator is useful for understanding how interest rates can hypothetically influence financial assets. It is especially interesting for analysts who want to assess the relationship between the equity market and short-term interest rate conditions.
Moving Average Cross; Linear RegressionThis Pine Script is designed to display smoothed linear regression lines on a chart, with an option to adjust the regression period lengths and smoothing factor. The script calculates short-term and long-term linear regression lines based on the selected timeframe. These regression lines act as a regressed moving average cross , visually representing the interaction between the two smoothed linear regressions.
Short Regression Line: A linear regression line based on a short lookback period, colored blue for an uptrend and orange for a downtrend .
Long Regression Line: A linear regression line based on a longer lookback period, similarly colored blue for an uptrend and orange for a downtrend .
The script provides input options to adjust:
The length of short and long regression periods.
The smoothing length for the regression lines.
The timeframe for the linear regression calculations.
This tool can help traders observe the crossovers between the two smoothed linear regression lines, which are similar to moving average crossovers, but with the added benefit of regression-based smoothing to reduce noise. The color-coding allows for easy trend identification, with blue indicating an uptrend and orange indicating a downtrend.
Log Regression OscillatorThe Log Regression Oscillator transforms the logarithmic regression curves into an easy-to-interpret oscillator that displays potential cycle tops/bottoms.
🔶 USAGE
Calculating the logarithmic regression of long-term swings can help show future tops/bottoms. The relationship between previous swing points is calculated and projected further. The calculated levels are directly associated with swing points, which means every swing point will change the calculation. Importantly, all levels will be updated through all bars when a new swing is detected.
The "Log Regression Oscillator" transforms the calculated levels, where the top level is regarded as 100 and the bottom level as 0. The price values are displayed in between and calculated as a ratio between the top and bottom, resulting in a clear view of where the price is situated.
The main picture contains the Logarithmic Regression Alternative on the chart to compare with this published script.
Included are the levels 30 and 70. In the example of Bitcoin, previous cycles showed a similar pattern: the bullish parabolic was halfway when the oscillator passed the 30-level, and the top was very near when passing the 70-level.
🔹 Proactive
A "Proactive" option is included, which ensures immediate calculations of tentative unconfirmed swings.
Instead of waiting 300 bars for confirmation, the "Proactive" mode will display a gray-white dot (not confirmed swing) and add the unconfirmed Swing value to the calculation.
The above example shows that the "Calculated Values" of the potential future top and bottom are adjusted, including the provisional swing.
When the swing is confirmed, the calculations are again adjusted, showing a red dot (confirmed top swing) or a green dot (confirmed bottom swing).
🔹 Dashboard
When less than two swings are available (top/bottom), this will be shown in the dashboard.
The user can lower the "Threshold" value or switch to a lower timeframe.
🔹 Notes
Logarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time, meaning some symbols/tickers will fit better than others.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Users have to check the validity of swings; for example, if the direction of swings is downwards, then the dataset is not fitted for logarithmic regression.
In the example above, the "Threshold" is lowered. However, the calculated levels are unreliable due to the swings, which do not fit the model well.
Here, the combination of downward bottom swings and price accelerates slower at first and faster recently, resulting in a non-fit for the logarithmic regression model.
Note the price value (white line) is bound to a limit of 150 (upwards) and -150 (down)
In short, logarithmic regression is best used when there are enough tops/bottoms, and all tops are around 100, and all bottoms around 0.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 DETAILS
In mathematics, the dot product or scalar product is an algebraic operation that takes two equal-length sequences of numbers (arrays) and returns a single number, the sum of the products of the corresponding entries of the two sequences of numbers.
The usual way is to loop through both arrays and sum the products.
In this case, the two arrays are transformed into a matrix, wherein in one matrix, a single column is filled with the first array values, and in the second matrix, a single row is filled with the second array values.
After this, the function matrix.mult() returns a new matrix resulting from the product between the matrices m1 and m2.
Then, the matrix.eigenvalues() function transforms this matrix into an array, where the array.sum() function finally returns the sum of the array's elements, which is the dot product.
dot(x, y)=>
if x.size() > 1 and y.size() > 1
m1 = matrix.new()
m2 = matrix.new()
m1.add_col(m1.columns(), y)
m2.add_row(m2.rows (), x)
m1.mult (m2)
.eigenvalues()
.sum()
🔶 SETTINGS
Threshold: Period used for the swing detection, with higher values returning longer-term Swing Levels.
Proactive: Tentative Swings are included with this setting enabled.
Style: Color Settings
Dashboard: Toggle, "Location" and "Text Size"
Custom Strategy: ETH Martingale 2.0Strategic characteristics
ETH Little Martin 2.0 is a self-developed trading strategy based on the Martingale strategy, mainly used for trading ETH (Ethereum). The core idea of this strategy is to place orders in the same direction at a fixed price interval, and then use Martin's multiple investment principle to reduce losses, but this is also the main source of losses.
Parameter description:
1 Interval： The minimum spacing for taking profit, stop loss, and opening/closing of orders. Different targets have different spacing. Taking ETH as an example, it is generally recommended to have a spacing of 2% for fluctuations in the target.
2 Base Price： This is the price at which you triggered the first order. Similarly, I am using ETH as an example. If you have other targets, I suggest using the initial value of a price that can be backtesting. The Base Price is only an initial order price and has no impact on subsequent orders.
3 Initial Order Amount： Users can set an initial order amount to control the risk of each transaction. If the stop loss is reached, we will double the amount based on this value. This refers to the value of the position held, not the number of positions held.
4 Loss Multiplier： The strategy will increase the next order amount based on the set multiple after the stop loss, in order to make up for the previous losses through a larger position. Note that after taking profit, it will be reset to 1 times the Initial Order Amount.
5. Long Short Operation: The first order of the strategy is a multiple entry, and in subsequent orders, if the stop loss is reached, a reverse order will be opened. The position value of a one-way order is based on the Loss Multiplier multiple investment, so it is generally recommended that the Loss Multiplier default to 2.
Improvement direction
Although this strategy already has a certain trading logic, there are still some improvement directions that can be considered:
1. Dynamic adjustment of spacing: Currently, the spacing is fixed, and it can be considered to dynamically adjust the spacing based on market volatility to improve the adaptability of the strategy. Try using dynamic spacing, which may be more suitable for the actual market situation.
2. Filtering criteria: Orders and no orders can be optimized separately. The biggest problem with this strategy is that it will result in continuous losses during fluctuations, and eventually increase the investment amount. You can consider filtering out some fluctuations or only focusing on trend trends.
3. Risk management: Add more risk management measures, such as setting a maximum loss limit to avoid huge losses caused by continuous stop loss.
4. Optimize the stop loss multiple: Currently, the stop loss multiple is fixed, and it can be considered to dynamically adjust the multiple according to market conditions to reduce risk.
Engulfing Candle IndicatorThis indicator helps identify Bullish and Bearish Engulfing candle patterns on your chart.
Bullish Engulfing: Occurs when a green candle completely engulfs the prior red candle, signaling potential upward momentum.
Bearish Engulfing: Occurs when a red candle completely engulfs the prior green candle, signaling potential downward momentum.
The script highlights these patterns with green triangles below the bars for Bullish Engulfing and red triangles above the bars for Bearish Engulfing.
This tool is helpful for traders who use candlestick patterns as part of their technical analysis strategy.
Salience Theory Crypto Returns (AiBitcoinTrend)The Salience Theory Crypto Returns Indicator is a sophisticated tool rooted in behavioral finance, designed to identify trading opportunities in the cryptocurrency market. Based on research by Bordalo et al. (2012) and extended by Cai and Zhao (2022), it leverages salience theory—the tendency of investors, particularly retail traders, to overemphasize standout returns.
In the crypto market, dominated by sentiment-driven retail investors, salience effects are amplified. Attention disproportionately focused on certain cryptocurrencies often leads to temporary price surges, followed by reversals as the market stabilizes. This indicator quantifies these effects using a relative return salience measure, enabling traders to capitalize on price reversals and trends, offering a clear edge in navigating the volatile crypto landscape.
👽 How the Indicator Works
Salience Measure Calculation :
👾 The indicator calculates how much each cryptocurrency's return deviates from the average return of all cryptos over the selected ranking period (e.g., 21 days).
👾 This deviation is the salience measure.
👾 The more a return stands out (salient outcome), the higher the salience measure.
Ranking:
👾 Cryptos are ranked in ascending order based on their salience measures.
👾 Rank 1 (lowest salience) means the crypto is closer to the average return and is more predictable.
👾 Higher ranks indicate greater deviation and unpredictability.
Color Interpretation:
👾 Green: Low salience (closer to average) – Trending or Predictable.
👾 Red/Orange: High salience (far from average) – Overpriced/Unpredictable.
👾 Text Gradient (Teal to Light Blue): Helps visualize potential opportunities for mean reversion trades (i.e., cryptos that may return to equilibrium).
👽 Core Features
Salience Measure Calculation
The indicator calculates the salience measure for each cryptocurrency by evaluating how much its return deviates from the average market return over a user-defined ranking period. This measure helps identify which assets are trending predictably and which are likely to experience a reversal.
Dynamic Ranking System
Cryptocurrencies are dynamically ranked based on their salience measures. The ranking helps differentiate between:
Low Salience Cryptos (Green): These are trending or predictable assets.
High Salience Cryptos (Red): These are overpriced or deviating significantly from the average, signaling potential reversals.
👽 Deep Dive into the Core Mathematics
Salience Theory in Action
Salience theory explains how investors, particularly in the crypto market, tend to prefer assets with standout returns (salient outcomes). This behavior often leads to overpricing of assets with high positive returns and underpricing of those with standout negative returns. The indicator captures these deviations to anticipate mean reversions or trend continuations.
Salience Measure Calculation
// Calculate the average return
avgReturn = array.avg(returns)
// Calculate salience measure for each symbol
salienceMeasures = array.new_float()
for i = 0 to array.size(returns) - 1
ret = array.get(returns, i)
salienceMeasure = math.abs(ret - avgReturn) / (math.abs(ret) + math.abs(avgReturn) + 0.1)
array.push(salienceMeasures, salienceMeasure)
Dynamic Ranking
Cryptos are ranked in ascending order based on their salience measures:
Low Ranks: Cryptos with low salience (predictable, trending).
High Ranks: Cryptos with high salience (unpredictable, likely to revert).
👽 Applications
👾 Trend Identification
Identify cryptocurrencies that are currently trending with low salience measures (green). These assets are likely to continue their current direction, making them good candidates for trend-following strategies.
👾 Mean Reversion Trading
Cryptos with high salience measures (red to light blue) may be poised for a mean reversion. These assets are likely to correct back towards the market average.
👾 Reversal Signals
Anticipate potential reversals by focusing on high-ranked cryptos (red). These assets exhibit significant deviation and are prone to price corrections.
👽 Why It Works in Crypto
The cryptocurrency market is dominated by retail investors prone to sentiment-driven behavior. This leads to exaggerated price movements, making the salience effect a powerful predictor of reversals.
👽 Indicator Settings
👾 Ranking Period : Number of bars used to calculate the average return and salience measure.
Higher Values: Smooth out short-term volatility.
Lower Values: Make the ranking more sensitive to recent price movements.
👾 Number of Quantiles : Divide ranked assets into quantile groups (e.g., quintiles).
Higher Values: More detailed segmentation (deciles, percentiles).
Lower Values: Broader grouping (quintiles, quartiles).
👾 Portfolio Percentage : Percentage of the portfolio allocated to each selected asset.
Enter a percentage (e.g., 20 for 20%), automatically converted to a decimal (e.g., 0.20).
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.
Linear Regression Channel [TradingFinder] Existing Trend Line🔵 Introduction
The Linear Regression Channel indicator is one of the technical analysis tool, widely used to identify support, resistance, and analyze upward and downward trends.
The Linear Regression Channel comprises five main components : the midline, representing the linear regression line, and the support and resistance lines, which are calculated based on the distance from the midline using either standard deviation or ATR.
This indicator leverages linear regression to forecast price changes based on historical data and encapsulates price movements within a price channel.
The upper and lower lines of the channel, which define resistance and support levels, assist traders in pinpointing entry and exit points, ultimately aiding better trading decisions.
When prices approach these channel lines, the likelihood of interaction with support or resistance levels increases, and breaking through these lines may signal a price reversal or continuation.
Due to its precision in identifying price trends, analyzing trend reversals, and determining key price levels, the Linear Regression Channel indicator is widely regarded as a reliable tool across financial markets such as Forex, stocks, and cryptocurrencies.
🔵 How to Use
🟣 Identifying Entry Signals
One of the primary uses of this indicator is recognizing buy signals. The lower channel line acts as a support level, and when the price nears this line, the likelihood of an upward reversal increases.
In an uptrend : When the price approaches the lower channel line and signs of upward reversal (e.g., reversal candlesticks or high trading volume) are observed, it is considered a buy signal.
In a downtrend : If the price breaks the lower channel line and subsequently re-enters the channel, it may signal a trend change, offering a buying opportunity.
🟣 Identifying Exit Signals
The Linear Regression Channel is also used to identify sell signals. The upper channel line generally acts as a resistance level, and when the price approaches this line, the likelihood of a price decrease increases.
In an uptrend : Approaching the upper channel line and observing weakness in the uptrend (e.g., declining volume or reversal patterns) indicates a sell signal.
In a downtrend : When the price reaches the upper channel line and reverses downward, this is considered a signal to exit trades.
🟣 Analyzing Channel Breakouts
The Linear Regression Channel allows traders to identify price breakouts as strong signals of potential trend changes.
Breaking the upper channel line : Indicates buyer strength and the likelihood of a continued uptrend, often accompanied by increased trading volume.
Breaking the lower channel line : Suggests seller dominance and the possibility of a continued downtrend, providing a strong sell signal.
🟣 Mean Reversion Analysis
A key concept in using the Linear Regression Channel is the tendency for prices to revert to the midline of the channel, which acts as a dynamic moving average, reflecting the price's equilibrium over time.
In uptrends : Significant deviations from the midline increase the likelihood of a price retracement toward the midline.
In downtrends : When prices deviate considerably from the midline, a return toward the midline can be used to identify potential reversal points.
🔵 Settings
🟣 Time Frame
The time frame setting enables users to view higher time frame data on a lower time frame chart. This feature is especially useful for traders employing multi-time frame analysis.
🟣 Regression Type
Standard : Utilizes classical linear regression to draw the midline and channel lines.
Advanced : Produces similar results to the standard method but may provide slightly different alignment on the chart.
🟣 Scaling Type
Standard Deviation : Suitable for markets with stable volatility.
ATR (Average True Range) : Ideal for markets with higher volatility.
🟣 Scaling Coefficients
Larger coefficients create broader channels for broader trend analysis.
Smaller coefficients produce tighter channels for precision analysis.
🟣 Channel Extension
None : No extension.
Left: Extends lines to the left to analyze historical trends.
Right : Extends lines to the right for future predictions.
Both : Extends lines in both directions.
🔵 Conclusion
The Linear Regression Channel indicator is a versatile and powerful tool in technical analysis, providing traders with support, resistance, and midline insights to better understand price behavior. Its advanced settings, including time frame selection, regression type, scaling options, and customizable coefficients, allow for tailored and precise analysis.
One of its standout advantages is its ability to support multi-time frame analysis, enabling traders to view higher time frame data within a lower time frame context. The option to use scaling methods like ATR or standard deviation further enhances its adaptability to markets with varying volatility.
Designed to identify entry and exit signals, analyze mean reversion, and assess channel breakouts, this indicator is suitable for a wide range of markets, including Forex, stocks, and cryptocurrencies. By incorporating this tool into your trading strategy, you can make more informed decisions and improve the accuracy of your market predictions.
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.
Trend Condition [TradersPro]
OVERVIEW
The Trend Condition Indicator measures the strength of the bullish or bearish trend by using a ribbon pattern of exponential moving averages and scoring system. Trend cycles naturally expand and contract as a normal part of the cycle. It is the rhythm of the market. Perpetual expansion and contraction of trend.
As trend cycles develop the indicator shows a compression of the averages. These compression zones are key locations as trends typically expand from there. The expansion of trend can be up or down.
As the trend advances the ribbon effect of the indicator can be seen as each average expands with the price action. Once they have “fanned” the probability of the current trend slowing is high.
This can be used to recognize a powerful trend may be concluding. Traders can tighten stops, exit positions or utilize other prudent strategies.
CONCEPTS
Each line will display green if it is higher than the prior period and red if it is lower than the prior period. If the average is green it is considered bullish and will score one point in the bullish display. Red lines are considered bearish and will score one point in the bearish display.
The indicator can then be used at a quick glance to see the number of averages that are bullish and the number that are bearish.
A trader may use these on any tradable instrument. They can be helpful in stock portfolio management when used with an index like the S&P 500 to determine the strength of the current market trend. This may affect trade decisions like possession size, stop location and other risk factors.
Scatter PlotThe Price Volume Scatter Plot publication aims to provide intrabar detail as a Scatter Plot .
🔶 USAGE
A dot is drawn at every intrabar close price and its corresponding volume , as can seen in the following example:
Price is placed against the white y-axis, where volume is represented on the orange x-axis.
🔹 More detail
A Scatter Plot can be beneficial because it shows more detail compared with a Volume Profile (seen at the right of the Scatter Plot).
The Scatter Plot is accompanied by a "Line of Best Fit" (linear regression line) to help identify the underlying direction, which can be helpful in interpretation/evaluation.
It can be set as a screener by putting multiple layouts together.
🔹 Easier Interpretation
Instead of analysing the 1-minute chart together with volume, this can be visualised in the Scatter Plot, giving a straightforward and easy-to-interpret image of intrabar volume per price level.
One of the scatter plot's advantages is that volumes at the same price level are added to each other.
A dot on the scatter plot represents the cumulated amount of volume at that particular price level, regardless of whether the price closed one or more times at that price level.
Depending on the setting "Direction" , which sets the direction of the Volume-axis, users can hoover to see the corresponding price/volume.
🔹 Highest Intrabar Volume Values
Users can display up to 5 last maximum intrabar volume values, together with the intrabar timeframe (Res)
🔹 Practical Examples
When we divide the recent bar into three parts, the following can be noticed:
Price spends most of its time in the upper part, with relative medium-low volume, since the intrabar close prices are mostly situated in the upper left quadrant.
Price spends a shorter time in the middle part, with relative medium-low volume.
Price moved rarely below 61800 (the lowest part), but it was associated with high volume. None of the intrabar close prices reached the lowest area, and the price bounced back.
In the following example, the latest weekly candle shows a rejection of the 45.8 - 48.5K area, with the highest volume at the 45.8K level.
The next three successive candles show a declining maximum intrabar volume, after which the price broke through the 45.8K area.
🔹 Visual Options
There are many visual options available.
🔹 Change Direction
The Scatter Plot can be set in 4 different directions.
🔶 NOTES
🔹 Notes
The script uses the maximum available resources to draw the price/volume dots, which are 500 boxes and 500 labels. When the population size exceeds 1000, a warning is provided ( Not all data is shown ); otherwise, only the population size is displayed.
The Scatter Plot ideally needs a chart which contains at least 100 bars. When it contains less, a warning will be shown: bars < 100, not all data is shown
🔹 LTF Settings
When 'Auto' is enabled ( Settings , LTF ), the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error.
Examples with current Daily TF (when Premium is enabled):
500 : 3 minute LTF
1500 (default): 1 minute LTF
5000: 30 seconds LTF (1 minute if Premium is disabled)
🔶 SETTINGS
Direction: Direction of Volume-axis; Left, Right, Up or Down
🔹 LTF
LTF: LTF setting
Auto + multiple: Adjusts the initial set LTF
Premium: Enable when your TradingView plan is Premium or higher
🔹 Character
Character: Style of Price/Volume dot
Fade: Increasing this number fades dots at lower price/volume
Color
🔹 Linear Regression
Toggle (enable/disable), color, linestyle
Center Cross: Toggle, color
🔹 Background Color
Fade: Increasing this number fades the background color near lower values
Volume: Background color that intensifies as the volume value on the volume-axis increases
Price: Background color that intensifies as the price value on the price-axis increases
🔹 Labels
Size: Size of price/volume labels
Volume: Color for volume labels/axis
Price: Color for price labels/axis
Display Population Size: Show the population size + warning if it exceeds 1000
🔹 Dashboard
Location: Location of dashboard
Size: Text size
Display LTF: Display the intrabar Lower Timeframe used
Highest IB volume: Display up to 5 previous highest Intrabar Volume values
Logarithmic Regression AlternativeLogarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time. Bitcoin is a good example.
𝑦 = 𝑎 + 𝑏 * ln(𝑥)
With this logarithmic regression (log reg) formula 𝑦 (price) is calculated with constants 𝑎 and 𝑏, where 𝑥 is the bar_index .
Instead of using the sum of log x/y values, together with the dot product of log x/y and the sum of the square of log x-values, to calculate a and b, I wanted to see if it was possible to calculate a and b differently.
In this script, the log reg is calculated with several different assumed a & b values, after which the log reg level is compared to each Swing. The log reg, where all swings on average are closest to the level, produces the final 𝑎 & 𝑏 values used to display the levels.
🔶 USAGE
The script shows the calculated logarithmic regression value from historical swings, provided there are enough swings, the price pattern fits the log reg model, and previous swings are close to the calculated Top/Bottom levels.
When the price approaches one of the calculated Top or Bottom levels, these levels could act as potential cycle Top or Bottom.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Swings are based on Weekly bars. A Top Swing, for example, with Swing setting 30, is the highest value in 60 weeks. Thirty bars at the left and right of the Swing will be lower than the Top Swing. This means that a confirmation is triggered 30 weeks after the Swing. The period will be automatically multiplied by 7 on the daily chart, where 30 becomes 210 bars.
Please note that the goal of this script is not to show swings rapidly; it is meant to show the potential next cycle's Top/Bottom levels.
🔹 Multiple Levels
The script includes the option to display 3 Top/Bottom levels, which uses different values for the swing calculations.
Top: 'high', 'maximum open/close' or 'close'
Bottom: 'low', 'minimum open/close' or 'close'
These levels can be adjusted up/down with a percentage.
Lastly, an "Average" is included for each set, which will only be visible when "AVG" is enabled, together with both Top and Bottom levels.
🔹 Notes
Users have to check the validity of swings; the above example only uses 1 Top Swing for its calculations, making the Top level unreliable.
Here, 1 of the Bottom Swings is pretty far from the bottom level, changing the swing settings can give a more reliable bottom level where all swings are close to that level.
Note the display was set at "Logarithmic", it can just as well be shown as "Regular"
In the example below, the price evolution does not fit the logarithmic regression model, where growth should accelerate rapidly at first and then slows over time.
Please note that this script can only be used on a daily timeframe or higher; using it at a lower timeframe will show a warning. Also, it doesn't work with bar-replay.
🔶 DETAILS
The code gathers data from historical swings. At the last bar, all swings are calculated with different a and b values. The a and b values which results in the smallest difference between all swings and Top/Bottom levels become the final a and b values.
The ranges of a and b are between -20.000 to +20.000, which means a and b will have the values -20.000, -19.999, -19.998, -19.997, -19.996, ... -> +20.000.
As you can imagine, the number of calculations is enormous. Therefore, the calculation is split into parts, first very roughly and then very fine.
The first calculations are done between -20 and +20 (-20, -19, -18, ...), resulting in, for example, 4.
The next set of calculations is performed only around the previous result, in this case between 3 (4-1) and 5 (4+1), resulting in, for example, 3.9. The next set goes even more in detail, for example, between 3.8 (3.9-0.1) and 4.0 (3.9 + 0.1), and so on.
1) -20 -> +20 , then loop with step 1 (result (example): 4 )
2) 4 - 1 -> 4 +1 , then loop with step 0.1 (result (example): 3.9 )
3) 3.9 - 0.1 -> 3.9 +0.1 , then loop with step 0.01 (result (example): 3.93 )
4) 3.93 - 0.01 -> 3.93 +0.01, then loop with step 0.001 (result (example): 3.928)
This ensures complicated calculations with less effort.
These calculations are done at the last bar, where the levels are displayed, which means you can see different results when a new swing is found.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 SETTINGS
Three sets
High/Low
• color setting
• Swing Length settings for 'High' & 'Low'
• % adjustment for 'High' & 'Low'
• AVG: shows average (when both 'High' and 'Low' are enabled)
Max/Min (maximum open/close, minimum open/close)
• color setting
• Swing Length settings for 'Max' & 'Min'
• % adjustment for 'Max' & 'Min'
• AVG: shows average (when both 'Max' and 'Min' are enabled)
Close H/Close L (close Top/Bottom level)
• color setting
• Swing Length settings for 'Close H' & 'Close L'
• % adjustment for 'Close H' & 'Close L'
• AVG: shows average (when both 'Close H' and 'Close L' are enabled)
Show Dashboard, including Top/Bottom levels of the desired source and calculated a and b values.
Show Swings + Dot size
N-Degree Moment-Based Adaptive Detection🙏🏻 N-Degree Moment-Based Adaptive Detection (NDMBAD) method is a generalization of MBAD since the horizontal line fit passing through the data's mean can be simply treated as zero-degree polynomial regression. We can extend the MBAD logic to higher-degree polynomial regression.
I don't think I need to talk a lot about the thing there; the logic is really the same as in MBAD, just hit the link above and read if you want. The only difference is now we can gather cumulants not only from the horizontal mean fit (degree = 0) but also from higher-order polynomial regression fit, including linear regression (degree = 1).
Why?
Simply because residuals from the 0-degree model don't contain trend information, and while in some cases that's exactly what you need, in other cases, you want to model your trend explicitly. Imagine your underlying process trends in a steady manner, and you want to control the extreme deviations from the process's core. If you're going to use 0-degree, you'll be treating this beautiful steady trend as a residual itself, which "constantly deviates from the process mean." It doesn't make much sense.
How?
First, if you set the length to 0, you will end up with the function incrementally applied to all your data starting from bar_index 0. This can be called the expanding window mode. That's the functionality I include in all my scripts lately (where it makes sense). As I said in the MBAD description, choosing length is a matter of doing business & applied use of my work, but I think I'm open to talk about it.
I don't see much sense in using degree > 1 though (still in research on it). If you have dem curves, you can use Fourier transform -> spectral filtering / harmonic regression (regression with Fourier terms). The job of a degree > 0 is to model the direction in data, and degree 1 gets it done. In mean reversion strategies, it means that you don't wanna put 0-degree polynomial regression (i.e., the mean) on non-stationary trending data in moving window mode because, this way, your residuals will be contaminated with the trend component.
By the way, you can send thanks to @aaron294c , he said like mane MBAD is dope, and it's gonna really complement his work, so I decided to drop NDMBAD now, gonna be more useful since it covers more types of data.
I wanned to call it N-Order Moment Adaptive Detection because it abbreviates to NOMAD, which sounds cool and suits me well, because when I perform as a fire dancer, nomad style is one of my outfits. Burning Man stuff vibe, you know. But the problem is degree and order really mean two different things in the polynomial context, so gotta stay right & precise—that's the priority.
∞
Automatic Fibonacci Levels with EMAAutomatic Fibonacci Levels with EMA
Description:
This script automatically calculates and displays Fibonacci retracement levels based on the highest and lowest prices over a dynamic lookback period. The Fibonacci levels are recalculated on every bar to adapt to price changes, providing an ongoing analysis of key support and resistance areas.
The Fibonacci levels are dynamically colored to reflect the trend direction, determined by the position of the price relative to the Exponential Moving Average (EMA). When the market is in an uptrend (price above EMA), Fibonacci levels are displayed in green, and in a downtrend (price below EMA), they are shown in red. This color coding helps traders quickly identify the current market direction.
Key Features:
Dynamic Fibonacci Levels: Automatically adjusts Fibonacci retracement levels based on recent price action, recalculated with each new bar.
EMA Trend Confirmation: The trend is confirmed by the position of the price relative to the 20-period EMA. Fibonacci levels are color-coded to reflect this trend.
Customizable Lookback Period: Adjust the base lookback period (default 50) and scale it according to your preferred timeframe for more or less sensitivity to recent price action.
Flexible Fibonacci Duration: The Fibonacci levels remain on the chart for a customizable duration (default 2 bars), allowing for visual clarity while adapting to new price action.
Timeframe Scaling: The script automatically adjusts the lookback period based on a scaling factor, making it suitable for different timeframes.
How to Use:
Use the Fibonacci levels to identify potential support and resistance zones based on the market's current price range.
Combine the trend color coding with your own strategy to enhance decision-making, whether for long or short entries.
Adjust the Lookback Period and Fibonacci Duration based on your trading style and timeframe preferences.
This script provides an automatic and customizable way to visualize Fibonacci retracements in a dynamic manner, helping traders make informed decisions based on trend direction and key price levels.
Note: As with any trading tool, always use proper risk management and test the script before using it in live trading.
SynthesisDeFi - Anchored TWAPA simple Anchored TWAP created by Oliver Fujimori
Key Concept
TWAP is calculated by taking the average of multiple asset prices at regular time intervals across a set period. By averaging out these prices, TWAP helps smooth out short-term fluctuations, providing a more stable price representation over time.
Advantages of TWAP
Simplicity: The TWAP calculation is straightforward and computationally light, making it practical for on-chain calculations in DeFi.
Protection Against Flash Loan Attacks: By averaging prices over time, TWAP offers some protection against temporary price manipulations commonly seen with flash loans.
Uses and Benefits of TWAP
Reducing Market Impact for Large Orders: TWAP is used as a strategy for executing large orders by breaking them into smaller parts over a period, ensuring that the average execution price is close to the TWAP value, reducing the risk of price manipulation.
Minimizing Slippage: In DeFi, TWAP provides a stable price reference by averaging prices over time, making it less susceptible to sudden price changes (slippage) that can occur in highly volatile markets.
Protection Against Manipulation: TWAP prices are less vulnerable to flash loan attacks and sudden price spikes since they rely on multiple price points over a period rather than a single spot price.
