25-Day Momentum IndexDescription:
The 25-Day Momentum Index (25D MI) is a technical indicator designed to measure the strength and direction of price movements over a 25-day period. Inspired by classic momentum analysis, this indicator helps traders identify trends and potential reversal points in the market.
How It Works:
Momentum Calculation: The 25D MI calculates momentum as the difference between the current closing price and the closing price 25 days ago. This difference provides insights into the market's recent strength or weakness.
Plotting: The indicator plots the Momentum Index as a blue line, showing the raw momentum values. A zero line is also plotted in gray to serve as a reference point for positive and negative momentum.
Highlighting Zones:
Positive Momentum: When the Momentum Index is above zero, it is plotted in green, highlighting positive momentum phases.
Negative Momentum: When the Momentum Index is below zero, it is plotted in red, highlighting negative momentum phases.
Usage:
A rising curve means an increase in upward momentum - if it is above the zero line. A rising curve below the zero line signifies a decrease in downward momentum. By the same token, a falling curve means an increase in downward momentum below the zero line, a decrease in upward momentum above the zero line.
This indicator is ideal for traders looking to complement their strategy with a visual tool that captures the essence of market momentum over a significant period. Use it to enhance your technical analysis and refine your trading decisions.
Oszillatoren
RSI K-Means Clustering [UAlgo]The "RSI K-Means Clustering " indicator is a technical analysis tool that combines the Relative Strength Index (RSI) with K-means clustering techniques. This approach aims to provide more nuanced insights into market conditions by categorizing RSI values into overbought, neutral, and oversold clusters.
The indicator adjusts these clusters dynamically based on historical RSI data, allowing for more adaptive and responsive thresholds compared to traditional fixed levels. By leveraging K-means clustering, the indicator identifies patterns in RSI behavior, which can help traders make more informed decisions regarding market trends and potential reversals.
🔶 Key Features
K-means Clustering: The indicator employs K-means clustering, an unsupervised machine learning technique, to dynamically determine overbought, neutral, and oversold levels based on historical RSI data.
User-Defined Inputs: You can customize various aspects of the indicator's behavior, including:
RSI Source: Select the data source used for RSI calculation (e.g., closing price).
RSI Length: Define the period length for RSI calculation.
Training Data Size: Specify the number of historical RSI values used for K-means clustering.
Number of K-means Iterations: Set the number of iterations performed by the K-means algorithm to refine cluster centers.
Overbought/Neutral/Oversold Levels: You can define initial values for these levels, which will be further optimized through K-means clustering.
Alerts: The indicator can generate alerts for various events, including:
Trend Crossovers: Alerts for when the RSI crosses above/below the neutral zone, signaling potential trend changes.
Overbought/Oversold: Alerts when the RSI reaches the dynamically determined overbought or oversold thresholds.
Reversals: Alerts for potential trend reversals based on RSI crossing above/below the calculated overbought/oversold levels.
RSI Classification: Alerts based on the current RSI classification (ranging, uptrend, downtrend).
🔶 Interpreting Indicator
Adjusted RSI Value: The primary plot represents the adjusted RSI value, calculated based on the relative position of the current RSI compared to dynamically adjusted overbought and oversold levels. This value provides an intuitive measure of the market's momentum. The final overbought, neutral, and oversold levels are determined by K-means clustering and are displayed as horizontal lines. These levels serve as dynamic support and resistance points, indicating potential reversal zones.
Classification Symbols : The "RSI K-Means Clustering " indicator uses specific symbols to classify the current market condition based on the position of the RSI value relative to dynamically determined clusters. These symbols provide a quick visual reference to help traders understand the prevailing market sentiment. Here's a detailed explanation of each classification symbol:
Ranging Classification ("R")
This symbol appears when the RSI value is closest to the neutral threshold compared to the overbought or oversold thresholds. It indicates a ranging market, where the price is moving sideways without a clear trend direction. In this state, neither buyers nor sellers are in control, suggesting a period of consolidation or indecision. This is often seen as a time to wait for a breakout or reversal signal before taking a position.
Up-Trend Classification ("↑")
The up-trend symbol, represented by an upward arrow, is displayed when the RSI value is closer to the overbought threshold than to the neutral or oversold thresholds. This classification suggests that the market is in a bullish phase, with buying pressure outweighing selling pressure. Traders may consider this as a signal to enter or hold long positions, as the price is likely to continue rising until the market reaches an overbought condition.
Down-Trend Classification ("↓")
The down-trend symbol, depicted by a downward arrow, appears when the RSI value is nearest to the oversold threshold. This indicates a bearish market condition, where selling pressure dominates. The market is likely experiencing a downward movement, and traders might view this as an opportunity to enter or hold short positions. This symbol serves as a warning of potential further declines, especially if the RSI continues to move toward the oversold level.
Bullish Reversal ("▲")
This signal occurs when the RSI value crosses above the oversold threshold. It indicates a potential shift from a downtrend to an uptrend, suggesting that the market may start to move higher. Traders might use this signal as an opportunity to enter long positions.
Bearish Reversal ("▼")
This signal appears when the RSI value crosses below the overbought threshold. It suggests a possible transition from an uptrend to a downtrend, indicating that the market may begin to decline. This signal can alert traders to consider entering short positions or taking profits on long positions.
These classification symbols are plotted near the adjusted RSI line, with their positions adjusted based on the standard deviation and a distance multiplier. This placement helps in visualizing the classification's strength and ensuring clarity in the indicator's presentation. By monitoring these symbols, traders can quickly assess the market's state and make more informed trading decisions.
🔶 Disclaimer
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Ultimate Bands [BigBeluga]Ultimate Bands
The Ultimate Bands indicator is an advanced technical analysis tool that combines elements of volatility bands, oscillators, and trend analysis. It provides traders with a comprehensive view of market conditions, including trend direction, momentum, and potential reversal points.
🔵 KEY FEATURES
● Ultimate Bands
Consists of an upper band, lower band, and a smooth middle line
Based on John Ehler's SuperSmoother algorithm for reduced lag
Bands are calculated using Root Mean Square Deviation (RMSD) for adaptive volatility measurement
Helps identify potential support and resistance levels
● Ultimate Oscillator
Derived from the price position relative to the Ultimate Bands
Oscillates between overbought and oversold levels
Provides insights into potential reversals and trend strength
● Trend Signal Line
Based on a Hull Moving Average (HMA) of the Ultimate Oscillator
Helps identify the overall trend direction
Color-coded for easy trend interpretation
● Heatmap Visualization
Displays the current state of the oscillator and trend signal
Provides an intuitive visual representation of market conditions
Shows overbought/oversold status and trend direction at a glance
● Breakout Signals
Optional feature to detect and display breakouts beyond the Ultimate Bands
Helps identify potential trend reversals or continuations
Visualized with arrows on the chart and color-coded candles
🔵 HOW TO USE
● Trend Identification
Use the color and position of the Trend Signal Line to determine the overall market trend
Refer to the heatmap for a quick visual confirmation of trend direction
● Entry Signals
Look for price touches or breaks of the Ultimate Bands for potential entry points
Use oscillator extremes in conjunction with band touches for stronger signals
Consider breakout signals (if enabled) for trend-following entries
● Exit Signals
Use opposite band touches or breakouts as potential exit points
Monitor the oscillator for divergences or extreme readings as exit signals
● Overbought/Oversold Analysis
Use the Ultimate Oscillator and heatmap to identify overbought/oversold conditions
Look for potential reversals when the oscillator reaches extreme levels
● Confirmation
Combine Ultimate Bands, Oscillator, and Trend Signal for stronger trade confirmation
Use the heatmap for quick visual confirmation of market conditions
🔵 CUSTOMIZATION
The Ultimate Bands indicator offers several customization options:
Adjust the main calculation length for bands and oscillator
Modify the number of standard deviations for band calculation
Change the signal line length for trend analysis
Toggle the display of breakout signals and candle coloring
By fine-tuning these settings, traders can adapt the Ultimate Bands indicator to various market conditions and personal trading strategies.
The Ultimate Bands indicator provides a multi-faceted approach to market analysis, combining volatility-based bands, oscillator analysis, and trend identification in one comprehensive tool. Its adaptive nature and visual cues make it suitable for both novice and experienced traders across various timeframes and markets. The integration of multiple analytical elements offers traders a rich set of data points to inform their trading decisions.
TP RSITP RSI - Integrated Trend, Momentum, and Volatility Analyzer
The TP RSI indicator is an innovative 3-in-1 technical analysis tool that combines RSI, Bollinger Bands, and an EMA ribbon to provide traders with a comprehensive view of trend, momentum, and volatility in a single, easy-to-interpret visual display.
Why This Combination? This mashup addresses three critical aspects of market analysis simultaneously:
Trend identification and strength (EMA ribbon)
Momentum measurement (RSI)
Volatility assessment (Bollinger Bands)
By integrating these components, traders can make more informed decisions based on multiple factors without switching between different indicators.
How Components Work Together:
1. EMA Ribbon (Trend):
10 EMAs form 5 color-coded bands
Blue: Uptrend, Red: Downtrend
Provides a nuanced view of trend strength and potential reversals
2. RSI (Momentum):
Color-coded for quick interpretation
Blue: Upward momentum, Red: Downward momentum, White: Neutral
Position relative to the ribbon offers additional insight
3. Bollinger Bands (Volatility):
Applied to RSI for dynamic overbought/oversold levels
Narrow bands indicate low volatility, suggesting potential breakouts
Unique Aspects and Originality:
Synergistic visual cues: Color coordination between ribbon and RSI
Multi-factor confirmation: Requires alignment of trend, momentum, and volatility for strong signals
Volatility-adjusted momentum: RSI interpreted within the context of Bollinger Bands
How these components work together:
Buy Signal: Blue ribbon with blue RSI outside the ribbon.
Sell Signal: Red ribbon with red RSI outside the ribbon.
Neutral: White RSI or RSI inside the ribbon (not recommended for trading)
Increasing Momentum: RSI crossing above upper Bollinger Band (upward) or below lower Band (downward).
Trend Strength: RSI rejection by the ribbon, while all bands are colored along with the trend direction, identifies a strong trend.
Market Structure Oscillator [LuxAlgo]The Market Structure Oscillator indicator analyzes and synthesizes short-term, intermediate-term, and long-term market structure shifts and breaks, visualizing the output as oscillators and graphical representations of real-time market structures on the main price chart.
The oscillator presentation of the detected market structures helps traders visualize trend momentum and strength, identifying potential trend reversals, and providing different perspectives to enhance the analysis of classic market structures.
🔶 USAGE
A market structure shift signals a potential change in market sentiment or direction, while a break of structure indicates a continuation of the current trend. Detecting these events in real-time helps traders recognize both trend changes and continuations. The market structure oscillator translates these concepts visually, offering deeper insights into market momentum and strength. It aids traders in identifying overbought or oversold conditions, potential trend reversals, and confirming trend direction.
Oscillators often generate signals based on crossing certain thresholds or diverging from price movements, providing cues for traders to enter or exit positions.
The weights determine the influence of each period (short-term, intermediate-term, long-term) on the final oscillator value. By changing the weights, traders can emphasize or de-emphasize the importance of each period. Higher weights increase their respective market structure's influence on the oscillator value. For example, if the weight for the short-term period is set to 0, the final value of the oscillator will be calculated using only the intermediate-term and long-term market structures.
The indicator features a Cycle Oscillator component, which uses the market structure oscillator values to generate a histogram and provide further insights into market cycles and potential signals. The Cycle Oscillator aids in timing by allowing traders to more easily see the median length of an oscillation around the average point, helping them identify both favorable prices and favorable moments for trading.
Users can also display detected market structures on the price chart by enabling the corresponding market structure toggle from the "Market Structures on Chart" settings group.
🔶 DETAILS
The script initiates its analysis by detecting swing levels, which form the fundamental basis for its operations. It begins by identifying short-term swing points, automatically detected solely based on market movements without any reliance on user-defined input. Short-Term Swing Highs (STH) are peaks in price surrounded by lower highs on both sides, while Short-Term Swing Lows (STL) are troughs surrounded by higher lows.
To identify intermediate-term and long-term swing points, the script uses previously detected short-term swing points as reference points. It examines these points to determine intermediate-term swings and further analyzes intermediate-term swings to identify long-term swing points. This method ensures a thorough and unbiased evaluation of market dynamics, providing traders with reliable insights into market structures.
Once swing levels are detected, the process continues with the analysis of Market Structure Shifts (MSS) and Breaks of Structure (BoS). A Market Structure Shift, also known as a Change of Character (CHoCH), is a critical event in price action analysis that suggests a potential shift in market sentiment or direction. It occurs when the price reverses from an established trend, indicating that the current trend may be losing momentum and a reversal could be imminent.
On the other hand, a Break of Structure signifies the continuation of the existing market trend. This event occurs when the price decisively moves beyond a previous swing high or low, confirming the strength and persistence of the prevailing trend.
The indicator analyzes price patterns using a pure price action approach and identifies market structures for short-term, intermediate-term, and long-term periods. The collected data is then normalized and combined using specified weights to calculate the final Market Structure Oscillator value.
🔶 SETTINGS
The indicator incorporates user-defined settings, allowing users to tailor it according to their preferences and trading strategies.
🔹 Market Structure Oscillator
Market Structure Oscillator: Toggles the visibility of the market structures oscillator.
Short Term Weight: Defines the weight for the short-term market structure.
Intermediate Term Weight: Defines the weight for the intermediate-term market structure.
Long Term Weight: Defines the weight for the long-term market structure.
Oscillator Smoothing: Determines the smoothing factor for the oscillator.
Gradient Colors: Allows customization of bullish and bearish gradient colors.
Market Structure Oscillator Crosses: Provides signals based on market structure oscillator equilibrium level crosses.
🔹 Cycle Oscillator
Cycle Oscillator - Histogram: Toggles the visibility of the cycle oscillator.
Cycle Signal Length: Defines the length of the cycle signal.
Cycle Oscillator Crosses: Provides signals based on cycle oscillator crosses.
🔹 Market Structures on Chart
Market Structures: Allows plotting of market structures (short, intermediate, and long term) on the chart.
Line, Label, and Color: Options to display lines and labels for different market structures with customizable colors.
🔹 Oscillator Components
Oscillators: Separately plots short-term, intermediate-term, and long-term oscillators. Provides options to display these oscillators with customizable colors.
🔶 RELATED SCRIPTS
Market-Structures-(Intrabar)
Regression Indicator [BigBeluga]Regression Indicator
Indicator Overview:
The Regression Indicator is designed to help traders identify trends and potential reversals in price movements. By calculating a regression line and a normalized regression indicator, it provides clear visual signals for market direction, aiding in making informed trading decisions. The indicator dynamically updates with the latest market data, ensuring timely and relevant signals.
Key Features:
⦾ Calculations
Regression Indicator: Calculates the linear regression coefficients (slope and intercept) and derives the normalized distance close from the regression line.
// @function regression_indicator is a Normalized Ratio of Regression Lines with close
regression_indicator(src, length) =>
sum_x = 0.0
sum_y = 0.0
sum_xy = 0.0
sum_x_sq = 0.0
distance = 0.0
// Calculate Sum
for i = 0 to length - 1 by 1
sum_x += i + 1
sum_y += src
sum_xy += (i + 1) * src
sum_x_sq += math.pow(i + 1, 2)
// Calculate linear regression coefficients
slope = (length * sum_xy - sum_x * sum_y)
/ (length * sum_x_sq - math.pow(sum_x, 2))
intercept = (sum_y - slope * sum_x) / length
// Calculate Regression Indicator
y1 = intercept + slope
distance := (close - y1)
distance_n = ta.sma((distance - ta.sma(distance, length1))
/ ta.stdev(distance, length1), 10)
⦿ Reversion Signals:
Marks potential trend reversal points.
⦿ Trend Identification:
Highlights when the regression indicator crosses above or below the zero line, signaling potential trend changes.
⦿ Color-Coded Candles:
Changes candle colors based on the regression indicator's value.
⦿ Arrow Markers:
Indicate trend directions on the chart.
⦿ User Inputs
Regression Length: Defines the period for calculating the regression line.
Normalization Length: Period used to normalize the regression indicator.
Signal Line: Length for averaging the regression indicator to generate signals.
Main Color: Color used for plotting the regression line and signals.
The Regression Indicator is a powerful tool for analyzing market trends and identifying potential reversal points. With customizable inputs and clear visual aids, it enhances the trader's ability to make data-driven decisions. The dynamic nature of the indicator ensures it remains relevant with up-to-date market information, making it a valuable addition to any trading strategy."
Oscillator Scatterplot Analysis [Trendoscope®]In this indicator, we demonstrate how to plot oscillator behavior of oversold-overbought against price movements in the form of scatterplots and perform analysis. Scatterplots are drawn on a graph containing x and y-axis, where x represent one measure whereas y represents another. We use the library Graph to collect the data and plot it as scatterplot.
Pictorial explanation of components is defined in the chart below.
🎲 This indicator performs following tasks
Calculate and plot oscillator
Identify oversold and overbought areas based on various methods
Measure the price and bar movement from overbought to oversold and vice versa and plot them on the chart.
In our example,
The x-axis represents price movement. The plots found on the right side of the graph has positive price movements, whereas the plots found on the left side of the graph has negative price movements.
The y-axis represents the number of bars it took for reaching overbought to oversold and/or oversold to overbought. Positive bars mean we are measuring oversold to overbought, whereas negative bars are a measure of overbought to oversold.
🎲 Graph is divided into 4 equal quadrants
Quadrant 1 is the top right portion of the graph. Plots in this quadrant represent the instances where positive price movement is observed when the oscillator moved from oversold to overbought
Quadrant 2 is the top left portion of the graph. Plots in this quadrant represent the instances where negative price movement is observed when the oscillator moved from oversold to overbought.
Quadrant 3 is the bottom left portion of the chart. Plots in this quadrant represent the instances where negative price movement is observed when the oscillator moved from overbought to oversold.
Quadrant 4 is the bottom right portion of the chart. Plots in this quadrant represent the instances where positive price movement is observed when the oscillator moved from overbought to oversold.
🎲 Indicator components in Detail
Let's dive deep into the indicator.
🎯 Oscillator Selection
Select the Oscillator and define the overbought oversold conditions through input settings
Indicator - Oscillator base used for performing analysis
Length - Loopback length on which the oscillator is calculated
OB/OS Method - We use Bollinger Bands, Keltener Channel and Donchian channel to calculate dynamic overbought and oversold levels instead of static 80-10. This is also useful as other type of indicators may not be within 0-100 range.
Length and Multiplier are used for the bands for calculating Overbought/Oversold boundaries.
🎯 Define Graph Properties
Select different graph properties from the input settings that will instruct how to display the scatterplot.
Type - this can be either scatterplot or heatmap. Scatterplot will display plots with specific transparency to indicate the data, whereas heatmap will display background with different transparencies.
Plot Color - this is the color in which the scatterplot or heatmap is drawn
Plot Size - applicable mainly for scatterplot. Since the character we use for scatterplot is very tiny, the large at present looks optimal. But, based on the user's screen size, we may need to select different sizes so that it will render properly.
Rows and Columns - Number of rows and columns allocated per quadrant. This means, the total size of the chart is 2X rows and 2X columns. Data sets are divided into buckets based on the number of available rows and columns. Hence, changing this can change the appearance of the overall chart, even though they are representing the same data. Also, please note that tables can have max 10000 cells. If we increase the rows and columns by too much, we may get runtime errors.
Outliers - this is used to exclude the extreme data. 20% outlier means, the chart will ignore bottom 20% and top 20% when defining the chart boundaries. However, the extreme data is still added to the boundaries.
Self Optimizing RSI and Self Adaptive TP/SL [Starbots]Self Optimizing RSI and Self Adaptive TP/SL Strategy. (non-repainting)
This script continuously backtests 20 different combinations of RSI Buy conditions across 5 different Take Profit/Stop Loss combinations. In total, it tests 100 variants on every bar close and records the Net Profit gained for each combination. The strategy then selects and uses the best-performing combination of settings currently available for you to trade.
---------------------------------------------------------------------------------------------------------
The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. Signals can be generated by looking for divergences and failure swings. RSI can also be used to identify the general trend.
To improve our results we are calculating Multiple Length RSI - Average RSI based on the multiple periods. You can use just 1 Length or Multiple.
Set Inputs to Min=14, Max=14 if you want to use just 1 period.
= RSI(14)
3 RSI Lengths example (12,13 and 14):
Min=12, Max=14
(12+13+14) / 3 = avg. RSI
-----------------------------------------------------------------------------------------------------------
Backtester - Optimizer Explained:
The backtester runs numerous backtests in the background to optimize trading strategies. Here’s how it works:
Default Inputs (Combinations of TP/SL)
TP 1%, SL4%
TP 2%, SL4%
TP 3%, SL4%
TP 2%, SL5%
TP 4.5%, SL10%
Default Inputs (RSI Crossover Buys) :
18 ,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,45,55, 69
_______________________________________________________
Backtest RSI Crossover 18:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
,...
,...
Backtest RSI Crossover 69:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
Self Optimizing Buy Condition and Self Optimizing Take Profit - Stop Los
This process involves testing various combinations of RSI crossover values with different Take Profit (TP) and Stop Loss (SL) percentages. The net profit for each combination is saved, allowing the optimizer to select the best-performing settings for trading.
It recalculates on every bar close. If one combination starts performing better than others—achieving a higher net profit gain (essentially like running 100 backtests with different settings in the background)—the strategy switches to that combination of TP/SL and Buy condition. It continues trading with the new settings until another parameter starts performing better and the strategy switches to that setting.
________________________________________________________________________
If you wish to use it as INDICATOR - turn on 'Recalculate - On every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Other functions:
Set the %fee for optimizing engine. If you set this % higher, you also punish small average trades and make the strategy prefer larger avg. trades, giving you better chances to make your strategy profitable.
Trade with trend and optimize the strategy only when the market is uptrending with EMA/HMA
Use Moving Average of avg.RSI and smooth the values for indicator even more. (Yes strategy is self optimizing RSI or avg.RSI or RSI-MA, you can select all sorts of this indicator for optimizing)
All trading alerts are working and functional, if you want to automate the strategy
This script is simple to use for any trader as it saves a lot of time for searching good parameters on your own. It's self-optimizing and adjusting to the markets on the go.
Momentum with ATR and Volatility [ST]Momentum with ATR and Volatility
Description in English:
This indicator combines price momentum with market volatility to identify entry and exit points in trades.
It utilizes the difference in closing prices (momentum) and the Average True Range (ATR) to measure volatility. Buy and sell signals are generated based on the combination of these two components.
Detailed Explanation:
Configuration:
Momentum Length: This input defines the period for calculating the momentum, which is the difference between the closing prices. The default value is 10.
ATR Length: This input defines the period for calculating the Average True Range (ATR), which measures market volatility. The default value is 14.
ATR Threshold: This input defines the threshold multiplier for the ATR to generate buy and sell signals. The default value is 3.5.
Momentum Calculation:
Momentum is calculated as the difference between the current closing price and the closing price momentum_length periods ago.
ATR Calculation:
The ATR is calculated based on the specified length and is used to measure market volatility.
Buy and Sell Signals:
Buy Signal: Generated when momentum is positive, the current close is higher than the previous close, and momentum is greater than ATR * threshold.
Sell Signal: Generated when momentum is negative, the current close is lower than the previous close, and momentum is less than -ATR * threshold.
Plotting:
Buy signals are plotted as green triangles below the bars.
Sell signals are plotted as red triangles above the bars.
Momentum and ATR thresholds are plotted in a separate panel below the main chart.
Momentum is plotted as a blue line.
The ATR threshold lines are plotted as solid orange lines.
Indicator Benefits:
Momentum Measurement: Helps traders gauge the momentum of price movements.
Volatility Measurement: Utilizes ATR to measure market volatility, providing a more comprehensive analysis.
Visual Cues: Provides clear visual signals for buy and sell points, aiding in making informed trading decisions.
Justification of Component Combination:
Combining momentum with ATR provides a more robust measure of potential entry and exit points by considering both price movement and market volatility.
How Components Work Together:
The script calculates momentum and ATR for the specified periods.
It generates buy and sell signals based on the conditions of momentum and ATR.
The signals and values are plotted on the chart to provide a visual representation, helping traders identify potential trading opportunities.
Título: Indicador de Momentum com ATR e Volatilidade
Descrição em Português:
Este indicador combina o momentum do preço com a volatilidade do mercado para identificar pontos de entrada e saída em operações.
Utiliza a diferença entre os preços de fechamento (momentum) e o Average True Range (ATR) para medir a volatilidade. Sinais de compra e venda são gerados com base na combinação desses dois componentes.
Explicação Detalhada:
Configuração:
Comprimento do Momentum: Este parâmetro define o período para calcular o momentum, que é a diferença entre os preços de fechamento. O valor padrão é 10.
Comprimento do ATR: Este parâmetro define o período para calcular o Average True Range (ATR), que mede a volatilidade do mercado. O valor padrão é 14.
Limite do ATR: Este parâmetro define o multiplicador de limite para o ATR para gerar sinais de compra e venda. O valor padrão é 3.5.
Cálculo do Momentum:
O momentum é calculado como a diferença entre o preço de fechamento atual e o preço de fechamento momentum_length períodos atrás.
Cálculo do ATR:
O ATR é calculado com base no comprimento especificado e é usado para medir a volatilidade do mercado.
Sinais de Compra e Venda:
Sinal de Compra: Gerado quando o momentum é positivo, o fechamento atual é maior que o fechamento anterior, e o momentum é maior que ATR * threshold.
Sinal de Venda: Gerado quando o momentum é negativo, o fechamento atual é menor que o fechamento anterior, e o momentum é menor que -ATR * threshold.
Plotagem:
Sinais de compra são plotados como triângulos verdes abaixo das barras.
Sinais de venda são plotados como triângulos vermelhos acima das barras.
O momentum e os limites do ATR são plotados em um painel separado abaixo do gráfico principal.
O momentum é plotado como uma linha azul.
As linhas de limite do ATR são plotadas como linhas laranjas sólidas.
Benefícios do Indicador:
Medição do Momentum: Ajuda os traders a avaliar o momentum dos movimentos de preços.
Medição da Volatilidade: Utiliza o ATR para medir a volatilidade do mercado, proporcionando uma análise mais abrangente.
Sinais Visuais: Fornece sinais visuais claros para pontos de compra e venda, auxiliando na tomada de decisões informadas.
Justificação da Combinação de Componentes:
Combinar o momentum com o ATR fornece uma medida mais robusta de potenciais pontos de entrada e saída ao considerar tanto o movimento dos preços quanto a volatilidade do mercado.
Como os Componentes Funcionam Juntos:
O script calcula o momentum e o ATR para os períodos especificados.
Gera sinais de compra e venda com base nas condições de momentum e ATR.
Os sinais e valores são plotados no gráfico para fornecer uma representação visual, ajudando os traders a identificar oportunidades de negociação potenciais.
Trend Strength with Volatility and Volume [ST]Trend Strength with Volatility and Volume
Description in English:
This indicator combines market volatility and trading volume to measure the current trend strength. It helps identify when the trend is gaining or losing momentum.
Detailed Explanation:
Configuration:
Length: This input defines the period over which the moving average is calculated. The default value is 14.
MA Type: This input allows you to choose between a Simple Moving Average (SMA) and an Exponential Moving Average (EMA).
Volatility Length: This input defines the period over which the ATR (Average True Range) is calculated. The default value is 14.
Volume Length: This input defines the period over which the moving average of volume is calculated. The default value is 14.
Trend Strength Calculation:
Moving Average (MA): The script calculates the moving average of the closing price based on the selected type (SMA or EMA) and period.
Volatility (ATR): The ATR is used to measure market volatility over the specified period.
Volume MA: The script calculates the moving average of the trading volume based on the selected type (SMA or EMA) and period.
Trend Strength: The trend strength is calculated as the difference between the closing price and the moving average, divided by the volatility, and multiplied by the volume normalized by its moving average.
Plotting:
The trend strength is plotted as a line chart. Positive values indicate a strong upward trend, while negative values indicate a strong downward trend.
A horizontal line is added at the zero level to help identify the neutral point.
Indicator Benefits:
Trend Identification: Helps traders identify the strength of the current trend by combining price, volatility, and volume.
Visual Cues: Provides clear visual signals for trend strength, aiding in making informed trading decisions.
Customizable Parameters: Allows traders to adjust the length of the moving averages, ATR, and volume to suit different trading strategies and market conditions.
Justification of Component Combination:
Combining price, volatility, and volume provides a comprehensive measure of trend strength. This combination enhances the trader's ability to make informed decisions based on multiple market factors.
How Components Work Together:
The script calculates the moving average of the closing price and trading volume.
It measures market volatility using the ATR.
The trend strength is calculated by combining these components, providing a robust measure of the current trend's strength.
Título: Força da Tendência com Volatilidade e Volume
Descrição em Português:
Este indicador combina a volatilidade do mercado, medida pelo ATR (Average True Range), e o volume de negociações para medir a força da tendência atual. Ele ajuda a identificar quando a tendência está ganhando ou perdendo força.
Explicação Detalhada:
Configuração:
Comprimento: Este parâmetro define o período para o cálculo da média móvel. O valor padrão é 14.
Tipo de MA: Este parâmetro permite escolher entre uma Média Móvel Simples (SMA) e uma Média Móvel Exponencial (EMA).
Comprimento da Volatilidade: Este parâmetro define o período para o cálculo do ATR (Average True Range). O valor padrão é 14.
Comprimento do Volume: Este parâmetro define o período para o cálculo da média móvel do volume. O valor padrão é 14.
Cálculo da Força da Tendência:
Média Móvel (MA): O indicador calcula a média móvel do preço de fechamento com base no tipo selecionado (SMA ou EMA) e período.
Volatilidade (ATR): O ATR é usado para medir a volatilidade do mercado ao longo do período especificado.
Média Móvel do Volume: O indicador calcula a média móvel do volume de negociação com base no tipo selecionado (SMA ou EMA) e período.
Força da Tendência: A força da tendência é calculada como a diferença entre o preço de fechamento e a média móvel, dividida pela volatilidade e multiplicada pelo volume normalizado pela sua média móvel.
Plotagem:
A força da tendência é plotada como um gráfico de linhas. Valores positivos indicam uma forte tendência de alta, enquanto valores negativos indicam uma forte tendência de baixa.
Uma linha horizontal é adicionada no nível zero para ajudar a identificar o ponto neutro.
Benefícios do Indicador:
Identificação de Tendências: Este indicador ajuda os traders a identificar a força da tendência atual, combinando preço, volatilidade e volume.
Sinais Visuais Claros: Fornece sinais visuais claros para a força da tendência, facilitando a tomada de decisões informadas.
Parâmetros Personalizáveis: Os traders podem ajustar o comprimento das médias móveis, ATR e volume para se adequar a diferentes estratégias de negociação e condições de mercado.
Justificação da Combinação de Componentes:
A combinação de preço, volatilidade e volume fornece uma medida abrangente da força da tendência.
Isso melhora a capacidade dos traders de tomar decisões informadas com base em múltiplos fatores do mercado.
Como os Componentes Funcionam Juntos:
O indicador calcula a média móvel do preço de fechamento e do volume de negociação.
Mede a volatilidade do mercado usando o ATR.
A força da tendência é calculada combinando esses componentes, fornecendo uma medida robusta da força da tendência atual.
Adaptive RSI BandsThe RSI Band Optimizer is an innovative technical analysis tool designed to identify and display the most effective Relative Strength Index (RSI) band values for any given trading instrument. This powerful indicator dynamically calculates optimal overbought and oversold levels, moving beyond the traditional static 70/30 or 80/20 bands.
Core Functionality:
Dynamic RSI Band Calculation:
The indicator analyzes historical price data to determine the most effective RSI levels for identifying overbought and oversold conditions specific to the current trading instrument and timeframe.
Adaptive Optimization:
Rather than relying on external factors, the tool uses a proprietary algorithm that focuses solely on the relationship between historical RSI values and subsequent price movements. This pure RSI-based approach ensures that the bands are optimized for the indicator's own dynamics.
Continuous Recalibration:
The optimal RSI bands are continuously recalculated as new price data becomes available, ensuring that the indicator adapts to changing market conditions and remains relevant over time.
Key Inputs:
RSI Length:
Allows users to set the period for the RSI calculation. While the default is typically 14, users can adjust this to suit their trading style and the characteristics of the instrument they're trading.
Optimization Lookback:
Defines the historical period the indicator uses to calculate optimal bands. This balance between recent market behavior and longer-term patterns.
Band Sensitivity:
Enables fine-tuning of how aggressively the indicator adjusts the RSI bands. Higher sensitivity results in more frequent band adjustments, while lower sensitivity provides more stable levels.
What Makes It Unique:
Self-Contained Optimization:
Unlike indicators that rely on external data sources or comparisons, this tool focuses purely on optimizing RSI bands based on the indicator's own historical performance.
Instrument-Specific Bands:
By calculating optimal bands for each specific instrument, the indicator acknowledges that different assets may have different typical RSI ranges and behaviors.
Timeframe Adaptability:
The optimization process adapts to the selected timeframe, recognizing that optimal RSI bands may differ between short-term and long-term charts.
Dynamic Band Adjustment:
The continuous recalibration of bands allows the indicator to adapt to changing market volatility and trends, providing more relevant signals over time.
Enhanced RSI Interpretation:
By providing optimized, asset-specific overbought and oversold levels, the indicator offers a more nuanced and potentially more accurate interpretation of RSI values.
The RSI Band Optimizer represents a significant advancement in the application of the Relative Strength Index. By dynamically calculating optimal band values, it addresses one of the main criticisms of traditional RSI usage – the reliance on static, one-size-fits-all overbought and oversold levels. This tool empowers traders to make more informed decisions based on RSI readings that are truly tailored to the specific characteristics of the asset they're trading.
TradeBuilderOverview
TradeBuilder is an ever-growing toolbox that lets you combine and compound any number of bundled indicators and algorithms to create a compound strategy. At launch, we're including two Moving Averages (SMA, EMA), RSI, and Stochastic Oscillator, with many more to come. You can use any combination of indicators, be it just one, two, or all.
Key Concepts
Indicator Integration: Tradebuilder allows the use of Moving Averages, RSI, and Stochastic Oscillators, with customizable parameters for each. More indicators to come.
Mode Selection : Choose between Confirm Trend Mode (using indicators to confirm trends) and Momentum Mode (using indicators to spot reversals).
Trade Flexibility : Offers options for both long and short trades, enabling diverse trading strategies.
Customizable Inputs : Easily toggle indicators on or off and adjust specific settings like periods and thresholds.
Signal Generation : Combines multiple conditions to generate entry and exit signals.
Input Parameters:
Moving Average (MA):
use_ma : Enable this to include the Moving Average in your strategy.
ma_cross_type : Choose between "Close/MA" (price crossing the MA) or "MA/MA" (one MA crossing another).
ma_length : Set the period for the primary MA.
ma_type : Choose between "SMA" (Simple Moving Average) or "EMA" (Exponential Moving Average).
ma_length2 : Set the period for the secondary MA if using the "MA/MA" cross type.
ma_type2 : Set the type for the secondary MA.
Relative Strength Index (RSI):
use_rsi : Enable this to include RSI in your strategy.
rsi_length : Set the period for RSI calculation.
rsi_overbought : Define the overbought level.
rsi_oversold : Define the oversold level.
Stochastic Oscillator:
use_stoch : Enable this to include the Stochastic Oscillator in your strategy.
stoch_k : Set the %K period.
stoch_d : Set the %D period.
stoch_smooth : Define the smoothing factor.
stoch_overbought : Set the overbought level.
stoch_oversold : Set the oversold level.
Confirmation or Momentum Mode:
confirm_trend : Set this to true to use RSI and Stochastic Oscillator to confirm trends (long when above overbought, short when below oversold). Set to false to trade on momentum (short when above overbought, long when below oversold).
Tip: When set to false and used with just momentum oscillators like Stochastic or RSI, it's geared toward scalping as it essentially becomes momentum trading.
Trade Directions:
trade_long : Enable to allow long trades.
trade_short : Enable to allow short trades.
Example Strategy on E-mini S&P 500 Index Futures ( CME_MINI:ES1! ), 1-minute Chart
Let’s say you want to create a strategy to go long when:
A 5-period SMA crosses above a 100-period EMA.
RSI is above 20.
The Stochastic Oscillator is above 95.
Trend Confirmation Mode is on.
For short:
A 5-period SMA crosses below a 100-period EMA.
RSI is below 45.
The Stochastic Oscillator is below 5.
Trend Confirmation Mode is on.
Here’s how you would set it up in Tradebuilder:
use_ma = true
ma_cross_type = "MA/MA"
ma_length = 5
ma_type = "SMA"
ma_length2 = 100
ma_type2 = "EMA"
use_rsi = true
rsi_length = 14
rsi_overbought = 20
rsi_oversold = 45
use_stoch = true
stoch_k = 8
stoch_d = 1
stoch_smooth = 1
stoch_overbought = 95
stoch_oversold = 5
confirm_trend = true
trade_long = true
trade_short = false
Alerts
Here is how to set TradeBuilder alerts: open a TradingView chart, attach TradeBuilder, right-click on chart -> Add Alert. Condition: Symbol (e.g. NQ) >> TradeBuilder >> Open-Ended Alert >> Once Per Bar Close.
Development Roadmap
We plan to add many more compoundable indicators to TradeBuilder over the coming months from all walks of technical analysis, including Volume, Volatility, Trend Detection/Validation, Momentum, Divergences, Chart Patterns, Support/Resistance Analysis. etc.
Money Flow Index Trend Zone Strength [UAlgo]The "Money Flow Index Trend Zone Strength " indicator is designed to analyze and visualize the strength of market trends and OB/OS zones using the Money Flow Index (MFI). The MFI is a momentum indicator that incorporates both price and volume data, providing insights into the buying and selling pressure in the market. This script enhances the traditional MFI by introducing trend and zone strength analysis, helping traders identify potential trend reversals and continuation points.
🔶 Customizable Settings
Amplitude: Defines the range for the MFI Zone Strength calculation.
Wavelength: Period used for the MFI calculation and Stochastic calculations.
Smoothing Factor: Smoothing period for the Stochastic calculations.
Show Zone Strength: Enables/disables visualization of the MFI Zone Strength line.
Show Trend Strength: Enables/disables visualization of the MFI Trend Strength area.
Trend Strength Signal Length: Period used for the final smoothing of the Trend Strength indicator.
Trend Anchor: Selects the anchor point (0 or 50) for the Trend Strength Stochastic calculation.
Trend Transform MA Length: Moving Average length for the Trend Transform calculation.
🔶 Calculations
Zone Strength (Stochastic MFI):
The highest and lowest MFI values over a specified amplitude are used to normalize the MFI value:
MFI Highest: Highest MFI value over the amplitude period.
MFI Lowest: Lowest MFI value over the amplitude period.
MFI Zone Strength: (MFI Value - MFI Lowest) / (MFI Highest - MFI Lowest)
By normalizing and smoothing the MFI values, we aim to highlight the relative strength of different market zones.
Trend Strength:
The smoothed MFI zone strength values are further processed to calculate the trend strength:
EMA of MFI Zone Strength: Exponential Moving Average of the MFI Zone Strength over the wavelength period.
Stochastic of EMA: Stochastic calculation of the EMA values, smoothed with the same smoothing factor.
Purpose: The trend strength calculation provides insights into the underlying market trends. By using EMA and stochastic functions, we can filter out noise and better understand the overall market direction. This helps traders stay aligned with the prevailing trend and make more informed trading decisions.
🔶 Usage
Interpreting Zone Strength: The zone strength plot helps identify overbought and oversold conditions. A higher zone strength indicates potential overbought conditions, while a lower zone strength suggests oversold conditions, can suggest areas for entry/exit decisions.
Interpreting Trend Strength: The trend strength plot visualizes the underlying market trend, can help signal potential trend continuation or reversal based on the chosen anchor point.
Using the Trend Transform: The trend transform plot provides an additional layer of trend analysis, helping traders identify potential trend reversals and continuation points.
Combine the insights from the zone strength and trend strength plots with other technical analysis tools to make informed trading decisions. Look for confluence between different indicators to increase the reliability of your trades.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Power Law Volatility by G. Santostasi Introduction
This TradingView indicator is designed to provide a comprehensive analysis of Bitcoin's price movements using the concept of power laws. The indicator leverages the mathematical properties of power laws to predict returns and highlight significant deviations from expected trends. By applying the power law model to Bitcoin's price data, we aim to capture the diminishing returns over time and provide valuable insights to traders and analysts.
Theoretical Foundation
The foundation of this indicator is based on the power law, which describes a relationship between two quantities where one quantity varies as a power of another. Specifically, in the context of Bitcoin prices, we observe that returns follow a power law relationship with time.
Mathematically, if the power law holds true, the price P at time 𝑡 can be expressed as:
log(𝑃)=𝑚log(𝑡)+c where m is the slope of the power law and c is the y-intercept.
To understand the returns, we consider two points in time,
𝑡1and 𝑡2, with corresponding prices 𝑃 and 𝑃2. The returns can be derived as follows:
log(𝑃2)−log(𝑃1)=𝑚(log(𝑡2)−log(𝑡1))
This simplifies to:
log(𝑃2/𝑃1)=𝑚log(𝑡2/𝑡1)
For daily data, we let 𝑡2=𝑡1+1resulting in:
log(𝑅)=𝑚log(𝑡+1/𝑡)
where 𝑅 represents the returns, 𝑡 is the number of days from the Genesis Block, and
𝑚 is the slope of the power law.
Observations and Data Analysis
Using historical Bitcoin price data, we observe that returns decrease over time, which is indicative of diminishing returns. To validate this observation, we averaged real returns over a two-month period and compared them with the theoretical results derived from the power law:
𝑚log(𝑡2/𝑡1)
The comparison shows that the averaged real returns align closely with the theoretical predictions, reinforcing the validity of the power law model.
This alignment indicates that the power law is not merely an arbitrary straight line but a model that accurately captures the decay of returns over time. The scaling property of the power law holds at all scales, providing a robust framework for analyzing Bitcoin's price dynamics.
Indicator Components
The indicator comprises several components to visualize the power law's implications and provide actionable insights:
Theoretical Power Law Trend:
Plots the theoretical result from the power law model.
Shows the expected returns based on the power law relationship.
Real Returns:
Plots the real returns averaged over a user-defined Simple Moving Average (SMA) or Exponential Moving Average (EMA).
Provides a comparison between actual market performance and theoretical predictions.
When the real volatility is above the theoretical one derived from the power law the indicator identifies times when the price is overvalued.
Standard Deviations:
Calculates standard deviations on a moving window basis.
Plots deviations from the theoretical power law trend, highlighting significant discrepancies.
Color-Coded Thresholds:
Highlights points that deviate significantly from the expected trend.
Red indicates returns above the upper threshold (indicating potential overperformance or overvaluation).
Green indicates returns below the lower threshold (indicating potential underperformance or undervaluation).
Practical Usage
Traders and analysts can use this indicator to:
Identify periods where Bitcoin's returns deviate significantly from the expected power law trend.
Make informed trading decisions based on the likelihood of mean reversion to the theoretical trend.
Understand the long-term diminishing returns trend and adjust investment strategies accordingly.
Conclusion
This TradingView indicator leverages the power law to provide a detailed and theoretically grounded analysis of Bitcoin's price movements. By comparing real returns with theoretical predictions, the indicator offers valuable insights into market behavior and highlights significant deviations. The use of color-coded thresholds further enhances the utility of the indicator, making it an essential tool for traders and analysts seeking to understand and capitalize on Bitcoin's price dynamics
Money Flow Index Crossover IndicatorThe "Money Flow Index Crossover Indicator" is a specialized technical analysis tool designed to assist traders by providing a clear visualization of potential buy and sell signals based on the Money Flow Index (MFI) and its smoothed moving average (SMA). This indicator delineates overbought and oversold zones, offering valuable insights into market dynamics. It operates as an oscillator on a separate pane, helping traders identify bullish and bearish market conditions with greater precision. By incorporating k-Nearest Neighbor (KNN) machine learning techniques, this indicator enhances the reliability and accuracy of the signals provided.
Originality and Usefulness:
This script is not just a simple mashup of existing indicators but integrates multiple components to create a unique and comprehensive analysis tool. The combined information from the MFI, its smoothed moving average, and the KNN machine learning techniques influence the form and accuracy of the Money Flow Index Average line and the Smoothed Money Flow Index line giving a visually helpful representation of overbought and oversold conditions. These lines are displayed in an oscillator style crossover, allowing users to visualize potential buy and sell zones for setting up potential signals. The user can adjust various settings of these tools behind the code to fine-tune the behavior and sensitivity of these lines. This integration provides a more robust and insightful trading tool that can adapt to different market conditions and trading styles.
How It Works:
Inputs:
MFI Settings:
Show Signals: Allows users to toggle the display of MFI and SMA crossing signals, which are critical for identifying potential market reversals.
Plot Amount: Determines the number of plots in the heat map, ranging from 2 to 28, enabling customization based on user preference.
Source: Defines the data source for MFI calculations, typically set to OHLC4 for a balanced view of price movements.
Smooth Initial MFI Length: Specifies the smoothing length for the initial MFI calculations to reduce noise and enhance signal clarity.
MFI SMA Length: Sets the length for the SMA used to smooth the MFI average, providing a more stable reference line.
Machine Learning Settings:
Use KInSource: Option to average MFI data by adding a lookback to the source, improving the accuracy of historical comparisons.
KNN Distance Requirement: Defines the distance calculation method for KNN (Max, Min, Both) to refine the data filtering process.
Machine Learning Length: Specifies the amount of machine learning data stored for smoothing results, balancing between responsiveness and stability.
KNN Length: Sets the number of KNN used to calculate the allowable distance range, enhancing the precision of the machine learning model.
Fast and Slow Lengths: Defines the lengths for fast and slow MFI calculations, allowing the indicator to capture different market dynamics.
Smoothing Length: Determines the length at which MFI calculations start for a more smoothed result, reducing false signals.
Variables and Functions:
KNN Function: Filters machine learning data to calculate valid distances based on defined criteria, ensuring more accurate MFI averages.
MFI Calculations: Computes both fast and slow MFI values, applies smoothing, and stores them for KNN processing to refine signal generation.
MFI KNN Calculation: Uses the KNN function to calculate the machine learning average of MFI values, enhancing signal reliability.
MFI Average and SMA: Calculates the average and smoothed MFI values, which are crucial for determining crossover signals.
Calculations:
MFI Values: Calculates current fast and slow MFI values and applies smoothing to reduce market noise.
Storage Arrays: Stores MFI data in arrays for KNN processing, enabling historical comparison and pattern recognition.
KNN Processing: Computes the machine learning average of MFI values using the KNN function, improving the robustness of signals.
MFI Average: Scales the MFI average to fit the heat map and calculates the smoothed SMA, providing a clear visual representation of trends.
Crossover Signals: Identifies bullish (MFI crossing above SMA) and bearish (MFI crossing below SMA) signals, which are key for making trading decisions.
Plots and Visuals:
MFI Average and SMA Lines: Plots the MFI average and smoothed SMA on the chart, allowing traders to easily visualize market trends and potential reversals.
Zones: Defines and plots overbought, neutral, and oversold zones for easy visualization. The recommended settings for these zones are:
Overbought Zone: Level set to approximately 24.6, indicating a potential market top.
Neutral Zone: Level set to 14, representing a balanced market condition.
Oversold Zone: Level set to 5.4, signaling a potential market bottom.
Crossover Marks: Plots circles on the chart to indicate bullish and bearish crossover signals, making it easier to spot entry and exit points.
Visual Alerts:
Bullish and Bearish Alerts: one can see overbought and oversold conditions and up alert conditions for bullish and bearish MFI crossover signals, enabling traders to have access to visual cues when these events are on trajectory to occur and, if they occur, act promptly with the visual representation of its zones.
Why It's Helpful:
The "Money Flow Index Crossover Indicator" provides traders with a sophisticated tool to identify potential buy and sell conditions based on the combined information of the MFI and its smoothed moving average. The KNN machine learning techniques enhance the accuracy of this indicator's clear visual representation of overbought, neutral, and oversold zones. This combination of data represented on the chart helps traders make informed decisions about market conditions. This indicator is particularly useful for traders looking to refine their entry and exit points by leveraging advanced data analysis in respect to overbought and oversold conditions.
Disclaimer:
This indicator is intended to assist traders in making informed decisions based on technical analysis. However, it is not a guarantee of future performance and should be used in conjunction with other analysis techniques and risk management practices. Past performance is not indicative of future results, and traders should exercise caution and perform their own due diligence before making any trading decisions.
Chandelier Exit Strategy with 200 EMA FilterStrategy Name and Purpose
Chandelier Exit Strategy with 200EMA Filter
This strategy uses the Chandelier Exit indicator in combination with a 200-period Exponential Moving Average (EMA) to generate trend-based trading signals. The main purpose of this strategy is to help traders identify high-probability entry points by leveraging the Chandelier Exit for stop loss levels and the EMA for trend confirmation. This strategy aims to provide clear rules for entries and exits, improving overall trading discipline and performance.
Originality and Usefulness
This script integrates two powerful indicators to create a cohesive and effective trading strategy:
Chandelier Exit : This indicator is based on the Average True Range (ATR) and identifies potential stop loss levels. The Chandelier Exit helps manage risk by setting stop loss levels at a distance from the highest high or lowest low over a specified period, multiplied by the ATR. This ensures that the stop loss adapts to market volatility.
200-period Exponential Moving Average (EMA) : The EMA acts as a trend filter. By ensuring trades are only taken in the direction of the overall trend, the strategy improves the probability of success. For long entries, the close price must be above the 200 EMA, indicating a bullish trend. For short entries, the close price must be below the 200 EMA, indicating a bearish trend.
Combining these indicators adds layers of confirmation and risk management, enhancing the strategy's effectiveness. The Chandelier Exit provides dynamic stop loss levels based on market volatility, while the EMA ensures trades align with the prevailing trend.
Entry Conditions
Long Entry
A buy signal is generated by the Chandelier Exit.
The close price is above the 200 EMA, indicating a strong bullish trend.
Short Entry
A sell signal is generated by the Chandelier Exit.
The close price is below the 200 EMA, indicating a strong bearish trend.
Exit Conditions
For long positions: The position is closed when a sell signal is generated by the Chandelier Exit.
For short positions: The position is closed when a buy signal is generated by the Chandelier Exit.
Risk Management
Account Size: 1,000,00 yen
Commission and Slippage: 17 pips commission and 1 pip slippage per trade
Risk per Trade: 10% of account equity
Stop Loss: For long trades, the stop loss is placed slightly below the candle that generated the buy signal. For short trades, the stop loss is placed slightly above the candle that generated the sell signal. The stop loss levels are dynamically adjusted based on the ATR.
Settings Options
ATR Period: Set the period for calculating the ATR to determine the Chandelier Exit levels.
ATR Multiplier: Set the multiplier for ATR to define the distance of stop loss levels from the highest high or lowest low.
Use Close Price for Extremums: Choose whether to use the close price for calculating the extremums.
EMA Period: Set the period for the EMA to adjust the trend filter sensitivity.
Show Buy/Sell Labels: Choose whether to display buy and sell labels on the chart for visual confirmation.
Highlight State: Choose whether to highlight the bullish or bearish state on the chart.
Sufficient Sample Size
The strategy has been backtested with a sufficient sample size to evaluate its performance accurately. This ensures that the strategy's results are statistically significant and reliable.
Notes
This strategy is based on historical data and does not guarantee future results.
Thoroughly backtest and validate results before using in live trading.
Market volatility and other external factors can affect performance and may not yield expected results.
Acknowledgment
This strategy uses the Chandelier Exit indicator. Special thanks to the original contributors for their work on the Chandelier Exit concept.
Clean Chart Explanation
The script is published with a clean chart to ensure that its output is readily identifiable and easy to understand. No other scripts are included on the chart, and any drawings or images used are specifically to illustrate how the script works.
Chebyshev Filter Divergences [ChartPrime]The Chebyshev Filter Divergences Oscillator
The Chebyshev Filter indicator is a powerful tool designed to identify potential divergences between price and a filtered version of price based on the Chebyshev filter algorithm. It helps to spot mean reversion points by highlighting areas where price and the filtered price exhibit conflicting signals.
Chebyshev Filter Background:
The Chebyshev filter, named after the Russian mathematician Pafnuty Chebyshev , was invented in the mid-19th century. It's a type of filter used in signal processing and digital signal processing for smoothing or removing unwanted frequency components from a signal.
It provides a sharp cutoff between the passband and stopband of a filter while minimizing ripple in the passband or stopband.
Chebyshev filters are widely used in various applications, including audio and image processing, telecommunications, and financial analysis, due to their efficiency and effectiveness in filtering out noise and extracting relevant information from signals.
◆ Indicator Calculation:
The indicator first applies a Chebyshev filter to the price data, producing a filtered price series. It then normalizes this filtered price series to a range, where it can be used as oscillator with divergences.
◆ Visualization:
The filtered price series is plotted on the chart, highlighting areas where it deviates from its smoothed average.
Bullish and bearish divergences are marked on the chart with specific lines and colors, indicating potential shifts in market sentiment.
Signs of change in direction are also marked on the chart, providing additional insights into possible mean reversals of price.
◆ User Inputs:
Ripple (dB): Specifies the desired ripple factor in decibels for the Chebyshev filter.
Normalization Length: Sets the length of the normalization period used in the Chebyshev filter.
Pivots to Right and Left: Determines the number of pivot points to the right and left of the current point to consider when detecting divergences.
Max and Min of Lookback Range: Specifies the maximum and minimum lookback range for identifying divergences.
Show Divergences: Enables or disables the display of bullish and bearish divergences.
Visual Settings: Allows customization of colors for visual clarity.
In conclusion, the Chebyshev Filter Divergences indicator, with its ability to identify potential mean reversion points through divergences between price and a filtered version of price, offers traders a valuable tool for decision-making in the financial markets. By highlighting areas of divergence, traders can potentially capitalize on market inefficiencies and make more informed trading decisions.
S&P Short-Range Oscillator**SHOULD BE USED ON THE S&P 500 ONLY**
The S&P Short-Range Oscillator (SRO), inspired by the principles of Jim Cramer's oscillator, is a technical analysis tool designed to help traders identify potential buy and sell signals in the stock market, specifically for the S&P 500 index. The SRO combines several market indicators to provide a normalized measure of market sentiment, assisting traders in making informed decisions.
The SRO utilizes two simple moving averages (SMAs) of different lengths: a 5-day SMA and a 10-day SMA. It also incorporates the daily price change and market breadth (the net change of closing prices). The 5-day and 10-day SMAs are calculated based on the closing prices. The daily price change is determined by subtracting the opening price from the closing price. Market breadth is calculated as the difference between the current closing price and the previous closing price.
The raw value of the oscillator, referred to as SRO Raw, is the sum of the daily price change, the 5-day SMA, the 10-day SMA, and the market breadth. This raw value is then normalized using its mean and standard deviation over a 20-day period, ensuring that the oscillator is centered and maintains a consistent scale. Finally, the normalized value is scaled to fit within the range of -15 to 15.
When interpreting the SRO, a value below -5 indicates that the market is potentially oversold, suggesting it might be a good time to start buying stocks as the market could be poised for a rebound. Conversely, a value above 5 suggests that the market is potentially overbought. In this situation, it may be prudent to hold on to existing positions or consider selling if you have substantial gains.
The SRO is visually represented as a blue line on a chart, making it easy to track its movements. Red and green horizontal lines mark the overbought (5) and oversold (-5) levels, respectively. Additionally, the background color changes to light red when the oscillator is overbought and light green when it is oversold, providing a clear visual cue.
By incorporating the S&P Short-Range Oscillator into your trading strategy, you can gain valuable insights into market conditions and make more informed decisions about when to buy, sell, or hold your stocks. However, always consider other market factors and perform your own analysis before making any trading decisions.
The S&P Short-Range Oscillator is a powerful tool for traders looking to gain insights into market sentiment. It provides clear buy and sell signals through its combination of multiple indicators and normalization process. However, traders should be aware of its lagging nature and potential complexity, and use it in conjunction with other analysis methods for the best results.
Disclaimer
The S&P Short-Range Oscillator is for informational purposes only and should not be considered financial advice. Trading involves risk, and you should conduct your own research or consult a financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Use at your own risk.
Trend Follower IndexDescription
The purpose of this index is to give an idea about the possible direction of the trend. The index is overbought between 70 and 100, and oversold between 30 and 0. Unlike a typical RSI calculation, the 6-bar simple moving average of the price is calculated first. Then, the 21-bar RSI value of this moving average is calculated.
Why
The 6-bar average is often one of the best averages to show the direction of prices. Closes below this average give strong indications of a trend reversal. To display this average on the horizontal plane, I used the RSI function and took 21 bar as the reference length. Because in my research, I realized that 21 bar length is the most ideal upper and lower points. That's why I coded an indicator that shows where a trend is going and how far that trend needs to go.
Use
It becomes oversold when the Moving Average falls below 30. Here we encounter 3 types of colors;
Light Blue: Indicates that the average is between 30 and 20. It indicates the stage when small purchases begin and the decline rate of the trend begins to decrease.
Blue: Indicates that the average is between 20 and 10. It indicates the stage when purchases begin to become more frequent and the rate of trend decline begins to decrease slightly.
Green: Indicates that the average has fallen below 10. It is the ideal level for purchasing. This indicates the stage when buying pressure has increased significantly and the trend is ready to reverse upward.
As the level decreases, purchases should increase.
Again, when the average value exceeds 70, it becomes overbought. Here we encounter three types of colors;
Yellow: Indicates that the average is between 70 and 80. It indicates the stage when small sales begin and the rate of increase in the trend begins to decrease.
Orange: Indicates that the average is between 80 and 90. It indicates the stage when sales begin to become more frequent and the upward trend begins to decrease somewhat.
Red: Indicates the average is above 90. It is an ideal level for sales. It now marks the stage where selling pressure has increased significantly and the trend is ready to turn downwards.
As the level increases, sales should increase.
Originality
First of all, this moving average is not an RSI. RSI is only used to establish the average on a flat basis. The RSI is merely a helpful tool in determining how much the moving average will rise or fall.
The 6-bar average of the value obtained by calculating Bar (Opening + Closing + High + Low) / 4 gives information about the main trend. In my research and usage, I have observed that as long as the price remains above this average, the price continues to move upwards, and when it remains below it, it is willing to move downwards.
Disclaimer
This indicator is for informational purposes only and should be used for educational purposes only. You may lose money if you rely on this to trade without additional information. Use at your own risk.
Version
v1.0
Momentum & Squeeze Oscillator [UAlgo]The Momentum & Squeeze Oscillator is a technical analysis tool designed to help traders identify shifts in market momentum and potential squeeze conditions. This oscillator combines multiple timeframes and periods to provide a detailed view of market dynamics. It enhances the decision-making process for both short-term and long-term traders by visualizing momentum with customizable colors and alerts.
🔶 Key Features
Custom Timeframe Selection: Allows users to select a custom timeframe for oscillator calculations, providing flexibility in analyzing different market periods.
Recalculation Option: Enables or disables the recalculation of the indicator, offering more control over real-time data processing.
Squeeze Background Visualization: Highlights potential squeeze conditions with a background color, helping traders quickly spot consolidation periods.
Adjustable Squeeze Sensitivity: Users can modify the sensitivity of the squeeze detection, tailoring the indicator to their specific trading style and market conditions.
Bar Coloring Condition: Option to color the price bars based on momentum conditions, enhancing the visual representation of market trends.
Threshold Bands: Option to fill threshold bands for a clearer visualization of overbought and oversold levels.
Reference Lines: Display reference lines for overbought, oversold, and mid-levels, aiding in quick assessment of momentum extremes.
Multiple Output Modes: Offers different output visualization modes, including:
ALL: Displays all calculated momentum values (fast, medium, slow).
AVG: Shows the average momentum, providing a consolidated view.
STD: Displays the standard deviation of momentum, useful for understanding volatility.
Alerts: Configurable alerts for key momentum events such as crossovers and squeeze conditions, keeping traders informed of important market changes.
🔶 Usage
The Momentum & Squeeze Oscillator can be used for various trading purposes:
Trend Identification: Use the oscillator to determine the direction and strength of market trends. By analyzing the average, fast, medium, and slow momentum lines, traders can gain insights into short-term and long-term market movements.
Squeeze Detection: The indicator highlights periods of low volatility (squeeze conditions) which often precede significant price movements. Traders can use this information to anticipate and prepare for potential breakouts.
Overbought/Oversold Conditions: The oscillator helps identify overbought and oversold conditions, indicating potential reversal points. This is particularly useful for timing entry and exit points in the market.
Momentum Shifts: By monitoring the crossover of momentum lines with key levels (e.g., the 50 level), traders can spot shifts in market momentum, allowing them to adjust their positions accordingly.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Comprehensive Technical AnalysisComprehensive Technical Analysis Script
Overview
This Script for TradingView is designed to perform and display a detailed technical analysis using a range of moving averages and oscillators. The script provides a summary of market conditions based on various indicators to help traders make informed decisions.
Key Features - Technical Indicators:
Moving Averages:
Simple Moving Average (SMA): Calculates the average price over a specified period.
Exponential Moving Average (EMA): Reacts faster to recent price changes by giving more weight to recent prices.
Weighted Moving Average (WMA): Weighs prices based on their position, giving more importance to recent prices.
Hull Moving Average (HMA): Reduces lag and provides a smoother trend line.
Triple Exponential Moving Average (TEMA): Combines three EMAs to minimize lag and offer a responsive trend indicator.
Exponential Moving Average of an Exponential Moving Average (EMAX): Applies an EMA twice to smooth out trends further.
Triangular Moving Average (TMA): Provides a smoother moving average by averaging over a triangular window.
Oscillators:
Relative Strength Index (RSI): Measures the speed and change of price movements to identify overbought or oversold conditions.
Stochastic Oscillator (%K): Compares a security’s closing price to its price range over a specific period to spot potential reversal points.
Commodity Channel Index (CCI): Identifies cyclical trends and measures the deviation of the price from its average.
Moving Average Convergence Divergence (MACD): Shows the relationship between two EMAs to identify changes in trend strength, direction, momentum, and duration.
Awesome Oscillator (AO): Measures market momentum by comparing two different moving averages.
Average Directional Index (ADX): Determines the strength of a trend and whether the market is trending or ranging.
Williams %R (WPR): Identifies overbought and oversold levels with a different calculation approach compared to the RSI.
Point System - Indicator Points:
Bullish Signal: Each indicator contributing to a positive market sentiment adds points.
Bearish Signal: Each indicator contributing to a negative market sentiment subtracts points.
Point Calculation:
Moving Averages: Points are assigned based on whether the current price is above or below each moving average.
Oscillators: Points are assigned based on whether the oscillator values are in bullish or bearish zones.
Summary Text:
Categorization: Based on the total points calculated from all indicators, the market condition is categorized into:
Strong Bullish: More than 8 points
Bullish: Between 3 and 8 points
Neutral: Between -2 and 2 points
Bearish: Between -3 and -8 points
Strong Bearish: Less than -8 points
Text Display: The summary text reflects the overall market sentiment and is color-coded for easy interpretation.
Table Display - The Position of the table can be customized by the user:
Vertical: Options include Top, Center, Bottom
Horizontal: Options include Left, Center, Right
Table Content:
Summary Text and Points: Displays the summary of technical indicators along with the calculated points.
RSI Buy-30d Cooldown-AHR999亲爱的数字资产投资者，您是否在寻找一种智能、可靠的方式来积累您的投资组合？我们为您带来了一个革命性的交易策略！
🚀 引入"智慧积累者"策略 🚀
这是一个为长期数字资产投资者量身定制的智能买入策略。它能帮您在最佳时机买入，让您的投资组合稳步增长！
✨ 主要特点：
智能时机选择：结合RSI和创新的AHR999指标，精准捕捉买入机会。
自动防御机制：设有冷却期，避免过度交易，保护您的资金。
底部猎手：专注于市场低迷期，为您寻找最佳入场点。
灵活可定制：根据您的风险偏好，轻松调整各项参数。
可视化决策：直观的图表标记，让您清晰了解每次交易背后的逻辑。
💡 它是如何工作的？
当市场情绪低落（低RSI）且资产被低估（低AHR999）时，策略会自动为您买入。
每次买入固定金额，帮您实现美元成本平均。
智能冷却期确保您不会在短期内过度买入。
📊 实时跟踪您的投资：
随时查看您的总投资额、持有的资产数量和平均买入成本。
清晰记录每次交易，助您分析和优化策略。
🌟 为什么选择"智慧积累者"？
无需盯盘：策略自动为您捕捉最佳买点。
情绪管理：避免人为判断带来的偏差。
长期价值：专注于积累，为未来做准备。
市场洞察：通过AHR999指标，深入了解市场周期。
无论您是经验丰富的投资者，还是刚开始接触数字资产，"智慧积累者"策略都能为您提供一种智能、低风险的方式来增加您的持仓。
准备好开始您的智能积累之旅了吗？立即尝试"智慧积累者"策略，让您的投资更上一层楼！
🚀 智能、安全、高效 - 您的数字资产积累好帮手！🚀
Normalized Hull Moving Average Oscillator w/ ConfigurationsThis indicator uniquely uses normalization techniques applied to the Hull Moving Average (HMA) and allows the user to choose between a number of different types of normalization, each with their own advantages. This indicator is one in a series of experiments I've been working on in looking at different methods of transforming data. In particular, this is a more usable example of the power of data transformation, as it takes the Hull Moving Average of Alan Hull and turns it into a powerful oscillating indicator.
The indicator offers multiple types of normalization, each with its own set of benefits and drawbacks. My personal favorites are the Mean Normalization , which turns the data series into one centered around 0, and the Quantile Transformation , which converts the data into a data set that is normally distributed.
I've also included the option of showing the mean, median, and mode of the data over the period specified by the length of normalization. Using this will allow you to gather additional insights into how these transformations affect the distribution of the data series.
Types of Normalization:
1. Z-Score
Overview: Standardizes the data by subtracting the mean and dividing by the standard deviation.
Benefits: Centers the data around 0 with a standard deviation of 1, reducing the impact of outliers.
Disadvantages: Works best on data that is normally distributed
Notes: Best used with a mid-longer length of transformation.
2. Min-Max
Overview: Scales the data to fit within a specified range, typically 0 to 1.
Benefits: Simple and fast to compute, preserves the relationships among data points.
Disadvantages: Sensitive to outliers, which can skew the normalization.
Notes: Best used with mid-longer length of transformation.
3. Mean Normalization
Overview: Subtracts the mean and divides by the range (max - min).
Benefits: Centers data around 0, making it easier to compare different datasets.
Disadvantages: Can be affected by outliers, which influence the range.
Notes: Best used with a mid-longer length of transformation.
4. Max Abs Scaler
Overview: Scales each feature by its maximum absolute value.
Benefits: Retains sparsity and is robust to large outliers.
Disadvantages: Only shifts data to the range , which might not always be desirable.
Notes: Best used with a mid-longer length of transformation.
5. Robust Scaler
Overview: Uses the median and the interquartile range for scaling.
Benefits: Robust to outliers, does not shift data as much as other methods.
Disadvantages: May not perform well with small datasets.
Notes: Best used with a longer length of transformation.
6. Feature Scaling to Unit Norm
Overview: Scales data such that the norm (magnitude) of each feature is 1.
Benefits: Useful for models that rely on the magnitude of feature vectors.
Disadvantages: Sensitive to outliers, which can disproportionately affect the norm. Not normally used in this context, though it provides some interesting transformations.
Notes: Best used with a shorter length of transformation.
7. Logistic Function
Overview: Applies the logistic function to squash data into the range .
Benefits: Smoothly compresses extreme values, handling skewed distributions well.
Disadvantages: May not preserve the relative distances between data points as effectively.
Notes: Best used with a shorter length of transformation. This feature is actually two layered, we first put it through the mean normalization to ensure that it's generally centered around 0.
8. Quantile Transformation
Overview: Maps data to a uniform or normal distribution using quantiles.
Benefits: Makes data follow a specified distribution, useful for non-linear scaling.
Disadvantages: Can distort relationships between features, computationally expensive.
Notes: Best used with a very long length of transformation.
Conclusion
This indicator is a powerful example into how normalization can alter and improve the usability of a data series. Each method offers unique insights and benefits, making this indicator a useful tool for any trader. Try it out, and don't hesitate to reach out if you notice any glaring flaws in the script, room for improvement, or if you just have questions.
