CDZV Enhanced Coppock CurveThis indicator is a part of the CDZV toolkit (backtesting and automation)
The Enhanced Coppock Curve is an upgraded version of the classic Coppock Curve indicator. It incorporates several additional features for greater flexibility and analysis capabilities. This indicator is used to analyze market trends by plotting a weighted moving average (WMA) of the sum of two Rate of Change (ROC) values.
Key Features of the Indicator:
Base Calculation of the Coppock Curve:
The Coppock Curve is calculated as a weighted moving average (WMA) of the sum of two ROC values (long and short periods).
The source for the calculation is customizable (default is close).
Added Custom Moving Average:
The indicator supports three types of moving averages:
EMA (Exponential Moving Average),
SMA (Simple Moving Average),
HMA (Hull Moving Average).
Users can choose the type and length of the moving average via input settings.
The selected moving average values are displayed in the Data Window for easier analysis.
Dynamic Coloring of the Coppock Curve:
The Coppock Curve line changes color based on its value:
Green if the value is positive,
Red if the value is negative.
The line's color is also displayed in the Data Window as a numeric value:
1 for green (positive),
-1 for red (negative).
Data Window Output:
The values of the selected moving average are displayed in the Data Window.
The Coppock Curve line's color state (1 or -1) is also shown in the Data Window.
Visual Representation:
The Coppock Curve is plotted with dynamic color coding.
The selected moving average is overlaid on the Coppock Curve for deeper trend analysis.
Usage Instructions:
Add the indicator to your chart on TradingView.
Configure the inputs:
Smoothing length for the Coppock Curve,
Long and short periods for ROC,
Type and length of the moving average.
Analyze the chart:
A green Coppock Curve line indicates a bullish trend, while a red line signals a bearish trend.
The selected moving average helps further filter and confirm signals.
Use the Data Window to view numeric values for the moving average and the Coppock Curve line color.
Applications:
This indicator is ideal for assessing trend direction and strength. The added customization options and additional data make it a versatile tool for traders, enabling them to tailor the Coppock Curve to their strategies.
Cerca negli script per "backtesting"
RSI-EMA Signal by stock shooter## Strategy Description: 200 EMA Crossover with RSI, Green/Red Candles, Volume, and Exit Conditions
This strategy combines several technical indicators to identify potential long and short entry opportunities in a trading instrument. Here's a breakdown of its components:
1. 200-period Exponential Moving Average (EMA):
* The 200-period EMA acts as a long-term trend indicator.
* The strategy looks for entries when the price is above (long) or below (short) the 200 EMA.
2. Relative Strength Index (RSI):
* The RSI measures the momentum of price movements and helps identify overbought and oversold conditions.
* The strategy looks for entries when the RSI is below 40 (oversold) for long positions and above 60 (overbought) for short positions.
3. Green/Red Candles:
* This indicator filters out potential entries based on the current candle's closing price relative to its opening price.
* The strategy only considers long entries on green candles (closing price higher than opening) and short entries on red candles (closing price lower than opening).
4. Volume:
* This indicator adds a volume filter to the entry conditions.
* The strategy only considers entries when the current candle's volume is higher than the average volume of the previous 20 candles, aiming for stronger signals.
Overall:
This strategy aims to capture long opportunities during potential uptrends and short opportunities during downtrends, based on a combination of price action, momentum, and volume confirmation.
Important Notes:
Backtesting is crucial to evaluate the historical performance of this strategy before deploying it with real capital.
Consider incorporating additional risk management techniques like stop-loss orders.
This strategy is just a starting point and can be further customized based on your trading goals and risk tolerance.
Moving Average Pullback Signals [UAlgo]The "Moving Average Pullback Signals " indicator is designed to identify potential trend continuation or reversal points based on moving average (MA) pullback patterns. This tool combines multiple types of moving averages, customized trend validation parameters, and candlestick wick patterns to provide reliable buy and sell signals. By leveraging several advanced MA methods (such as TEMA, DEMA, ZLSMA, and McGinley-D), this script can adapt to different market conditions, providing traders with flexibility and more precise trend-based entries and exits. The addition of a gradient color-coded moving average line and wick validation logic enables traders to visualize market sentiment and trend strength dynamically.
🔶 Key Features
Multiple Moving Average (MA) Calculation Methods: This indicator offers various MA calculation types, including SMA, EMA, DEMA, TEMA, ZLSMA, and McGinley-D, allowing traders to select the MA that best fits their strategy.
Trend Validation and Pattern Recognition: The indicator includes a customizable trend validation length, ensuring that the trend is consistent before buy/sell signals are generated. The "Trend Pattern Mode" setting provides flexibility between "No Trend in Progress," "Trend Continuation," and "Both," tailoring signals to the trader’s preferred style.
Wick Validation Logic: To enhance the accuracy of entries, this indicator identifies specific wick patterns for bullish or bearish pullbacks, which signal potential trend continuation or reversal. Wick length and validation factor are adjustable to suit various market conditions and timeframes.
Gradient Color-coded MA Line: This feature provides a quick visual cue for trend strength, with color changes reflecting relative highs and lows of the MA, enhancing market sentiment interpretation.
Alerts for Buy and Sell Signals: Alerts are triggered when either a bullish or bearish pullback is detected, allowing traders to receive instant notifications without continuously monitoring the chart.
Visual Labels for Reversal Points: The indicator plots labels ("R") at potential reversal points, with color-coded labels for bullish (green) and bearish (red) pullbacks, highlighting pullback opportunities that align with the trend or reversal potential.
🔶 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.
Machine Learning RSI [BackQuant]Machine Learning RSI
The Machine Learning RSI is a cutting-edge trading indicator that combines the power of Relative Strength Index (RSI) with Machine Learning (ML) clustering techniques to dynamically determine overbought and oversold thresholds. This advanced indicator adapts to market conditions in real-time, offering traders a robust tool for identifying optimal entry and exit points with increased precision.
Core Concept: Relative Strength Index (RSI)
The RSI is a well-known momentum oscillator that measures the speed and change of price movements, oscillating between 0 and 100. Typically, RSI values above 70 are considered overbought, and values below 30 are considered oversold. However, static thresholds may not be effective in all market conditions.
This script enhances the RSI by integrating a dynamic thresholding system powered by Machine Learning clustering, allowing it to adapt thresholds based on historical RSI behavior and market context.
Machine Learning Clustering for Dynamic Thresholds
The Machine Learning (ML) component uses clustering to calculate dynamic thresholds for overbought and oversold levels. Instead of relying on fixed RSI levels, this indicator clusters historical RSI values into three groups using a percentile-based initialization and iterative optimization:
Cluster 1: Represents lower RSI values (typically associated with oversold conditions).
Cluster 2: Represents mid-range RSI values.
Cluster 3: Represents higher RSI values (typically associated with overbought conditions).
Dynamic thresholds are determined as follows:
Long Threshold: The upper centroid value of Cluster 3.
Short Threshold: The lower centroid value of Cluster 1.
This approach ensures that the indicator adapts to the current market regime, providing more accurate signals in volatile or trending conditions.
Smoothing Options for RSI
To further enhance the effectiveness of the RSI, this script allows traders to apply various smoothing methods to the RSI calculation, including:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Hull Moving Average (HMA)
Linear Regression (LINREG)
Double Exponential Moving Average (DEMA)
Triple Exponential Moving Average (TEMA)
Adaptive Linear Moving Average (ALMA)
T3 Moving Average
Traders can select their preferred smoothing method and adjust the smoothing period to suit their trading style and market conditions. The option to smooth the RSI reduces noise and makes the indicator more reliable for detecting trends and reversals.
Long and Short Signals
The indicator generates long and short signals based on the relationship between the RSI value and the dynamic thresholds:
Long Signals: Triggered when the RSI crosses above the long threshold, signaling bullish momentum.
Short Signals: Triggered when the RSI falls below the short threshold, signaling bearish momentum.
These signals are dynamically adjusted to reflect real-time market conditions, making them more robust than static RSI signals.
Visualization and Clustering Insights
The Machine Learning RSI provides an intuitive and visually rich interface, including:
RSI Line: Plotted in real-time, color-coded based on its position relative to the dynamic thresholds (green for long, red for short, gray for neutral).
Dynamic Threshold Lines: The script plots the long and short thresholds calculated by the ML clustering process, providing a clear visual reference for overbought and oversold levels.
Cluster Plots: Each RSI cluster is displayed with distinct colors (green, orange, and red) to give traders insights into how RSI values are grouped and how the dynamic thresholds are derived.
Customization Options
The Machine Learning RSI is highly customizable, allowing traders to tailor the indicator to their preferences:
RSI Settings : Adjust the RSI length, source price, and smoothing method to match your trading strategy.
Threshold Settings : Define the range and step size for clustering thresholds, allowing you to fine-tune the clustering process.
Optimization Settings : Control the performance memory, maximum clustering steps, and maximum data points for ML calculations to ensure optimal performance.
UI Settings : Customize the appearance of the RSI plot, dynamic thresholds, and cluster plots. Traders can also enable or disable candle coloring based on trend direction.
Alerts and Automation
To assist traders in staying on top of market movements, the script includes alert conditions for key events:
Long Signal: When the RSI crosses above the long threshold.
Short Signal: When the RSI crosses below the short threshold.
These alerts can be configured to notify traders in real-time, enabling timely decisions without constant chart monitoring.
Trading Applications
The Machine Learning RSI is versatile and can be applied to various trading strategies, including:
Trend Following: By dynamically adjusting thresholds, this indicator is effective in identifying and following trends in real-time.
Reversal Trading: The ML clustering process helps identify extreme RSI levels, offering reliable signals for reversals.
Range-Bound Trading: The dynamic thresholds adapt to market conditions, making the indicator suitable for trading in sideways markets where static thresholds often fail.
Final Thoughts
The Machine Learning RSI represents a significant advancement in RSI-based trading indicators. By integrating Machine Learning clustering techniques, this script overcomes the limitations of static thresholds, providing dynamic, adaptive signals that respond to market conditions in real-time. With its robust visualization, customizable settings, and alert capabilities, this indicator is a powerful tool for traders seeking to enhance their momentum analysis and improve decision-making.
As always, thorough backtesting and integration into a broader trading strategy are recommended to maximize the effectiveness!
Composite Oscillation Indicator Based on MACD and OthersThis indicator combines various technical analysis tools to create a composite oscillator that aims to capture multiple aspects of market behavior. Here's a breakdown of its components:
* Individual RSIs (xxoo1-xxoo15): The code calculates the RSI (Relative Strength Index) of numerous indicators, including volume-based indicators (NVI, PVI, OBV, etc.), price-based indicators (CCI, CMO, etc.), and moving averages (WMA, ALMA, etc.). It also includes the RSI of the MACD histogram (xxoo14).
* Composite RSI (xxoojht): The individual RSIs are then averaged to create a composite RSI, aiming to provide a more comprehensive view of market momentum and potential turning points.
* MACD Line RSI (xxoo14): The RSI of the MACD histogram incorporates the momentum aspect of the MACD indicator into the composite measure.
* Double EMA (co, coo): The code employs two Exponential Moving Averages (EMAs) of the composite RSI, with different lengths (9 and 18 periods).
* Difference (jo): The difference between the two EMAs (co and coo) is calculated, aiming to capture the rate of change in the composite RSI.
* Smoothed Difference (xxp): The difference (jo) is further smoothed using another EMA (9 periods) to reduce noise and enhance the signal.
* RSI of Smoothed Difference (cco): Finally, the RSI is applied to the smoothed difference (xxp) to create the core output of the indicator.
Market Applications and Trading Strategies:
* Overbought/Oversold: The indicator's central line (plotted at 50) acts as a reference for overbought/oversold conditions. Values above 50 suggest potential overbought zones, while values below 50 indicate oversold zones.
* Crossovers and Divergences: Crossovers of the cco line above or below its previous bar's value can signal potential trend changes. Divergences between the cco line and price action can also provide insights into potential trend reversals.
* Emoji Markers: The code adds emoji markers ("" for bullish and "" for bearish) based on the crossover direction of the cco line. These can provide a quick visual indication of potential trend shifts.
* Colored Fill: The area between the composite RSI line (xxoojht) and the central line (50) is filled with color to visually represent the prevailing market sentiment (green for above 50, red for below 50).
Trading Strategies (Examples):
* Long Entry: Consider a long entry (buying) signal when the cco line crosses above its previous bar's value and the composite RSI (xxoojht) is below 50, suggesting a potential reversal from oversold conditions.
* Short Entry: Conversely, consider a short entry (selling) signal when the cco line crosses below its previous bar's value and the composite RSI (xxoojht) is above 50, suggesting a potential reversal from overbought conditions.
* Confirmation: Always combine the indicator's signals with other technical analysis tools and price action confirmation for better trade validation.
Additional Notes:
* The indicator offers a complex combination of multiple indicators. Consider testing and optimizing the parameters (EMAs, RSI periods) to suit your trading style and market conditions.
* Backtesting with historical data can help assess the indicator's effectiveness and identify potential strengths and weaknesses in different market environments.
* Remember that no single indicator is perfect, and the cco indicator should be used in conjunction with other forms of analysis to make informed trading decisions.
By understanding the logic behind this composite oscillator and its potential applications, you can incorporate it into your trading strategy to potentially identify trends, gauge market sentiment, and generate trading signals.
RSI Wave Function Ultimate OscillatorEnglish Explanation of the "RSI Wave Function Ultimate Oscillator" Pine Script Code
Understanding the Code
Purpose:
This Pine Script code creates a custom indicator that combines the Relative Strength Index (RSI) with a wave function to potentially provide more nuanced insights into market dynamics.
Key Components:
* Wave Function: This is a custom calculation that introduces a sinusoidal wave component to the price data. The frequency parameter controls the speed of the oscillation, and the decay factor determines how quickly the influence of past prices diminishes.
* Smoothed Signal: The wave function is applied to the closing price to create a smoothed signal, which is essentially a price series modulated by a sine wave.
* RSI: The traditional RSI is then calculated on this smoothed signal, providing a measure of the speed and change of price movements relative to recent price changes.
Calculation Steps:
* Wave Function Calculation:
* A sinusoidal wave is generated based on the bar index and the frequency parameter.
* The wave is combined with the closing price using a weighted average, where the decay factor determines the weight given to previous values.
* RSI Calculation:
* The RSI is calculated on the smoothed signal using a standard RSI formula.
* Plotting:
* The RSI values are plotted on a chart, along with horizontal lines at 70 and 30 to indicate overbought and oversold conditions.
* The area between the RSI line and the overbought/oversold lines is filled with color to visually represent the market condition.
Interpretation and Usage
* Wave Function: The wave function introduces cyclical patterns into the price data, which can help identify potential turning points or momentum shifts.
* RSI: The RSI provides a measure of the speed and change of price movements relative to recent price changes. When applied to the smoothed signal, it can help identify overbought and oversold conditions, as well as potential divergences between price and momentum.
* Combined Indicator: The combination of the wave function and RSI aims to provide a more sensitive and potentially earlier indication of market reversals.
* Signals:
* Crossovers: Crossovers of the RSI line above or below the overbought/oversold lines can be used to generate buy or sell signals.
* Divergences: Divergences between the price and the RSI can indicate a weakening trend.
* Oscillations: The amplitude and frequency of the oscillations in the RSI can provide insights into the strength and duration of market trends.
How it Reflects Market Volatility
* Amplified Volatility: The wave function can amplify the volatility of the price data, making it easier to identify potential turning points.
* Smoothing: The decay factor helps to smooth out short-term fluctuations, allowing the indicator to focus on longer-term trends.
* Sensitivity: The combination of the wave function and RSI can make the indicator more sensitive to changes in market momentum.
In essence, this custom indicator attempts to enhance traditional RSI analysis by incorporating a cyclical component that can potentially provide earlier signals of market reversals.
Note: The effectiveness of this indicator will depend on various factors, including the specific market, time frame, and the chosen values for the frequency and decay parameters. It is recommended to conduct thorough backtesting and optimize the parameters to suit your specific trading strategy.
Period MarkerThis Period Marker Indicator for TradingView is a visual tool that allows you to highlight a specific date range on your chart. It uses a shaded background color to mark the defined period, making it easy to visually separate and focus on specific time intervals. This is especially useful for analyzing historical events, comparing specific timeframes, or marking earnings seasons or other critical periods in price action.
Key Features
Easy Date Range Selection:
The indicator has a calendar-style date input for both the start and end dates. This allows for quick and precise selection of date ranges without manually entering each date component (year, month, day).
Customizable Period Highlight:
When active, the indicator shades the background of the chart over the specified period. The default highlight color is a semi-transparent green, but this can be customized within the script to any color and opacity you prefer.
The shaded background helps you easily identify and focus on the defined date range.
Dynamic Adjustment:
You can adjust the start and end dates in real-time, and the background shading will automatically update to reflect the new period, allowing flexibility in testing and viewing multiple periods quickly.
Practical Uses
Event Marking: Track significant historical events (e.g., economic data releases, geopolitical events) to see their effects on price action.
Seasonal Analysis: Highlight and compare seasonal trends, such as quarterly earnings or year-end rallies, across multiple years.
Backtesting Specific Periods: When analyzing strategies, you can visually isolate specific date ranges to review performance or behavior in defined intervals.
The Period Marker Indicator is a simple yet effective way to enhance time-based analysis on TradingView, helping you gain insights by focusing on relevant periods with ease.
Custom AO with Open Difference**Custom AO with Open Difference Indicator**
This indicator, *Custom AO with Open Difference*, is designed to help confirm trend direction based on the relationship between the daily open price and recent 4-hour open prices. It calculates the Awesome Oscillator (AO) based on the difference between the daily open price and the average of the previous six 4-hour open prices. This approach provides insight into whether the current open price is significantly diverging from recent short-term opens, which can indicate a trend shift or continuation.
### Technical Analysis and Features
1. **Trend Confirmation**: By comparing the daily open with the mean of six previous 4-hour open prices, this indicator helps identify trends. When the current daily open is below the average of recent opens, the AO value will plot as green, signaling potential upward momentum. Conversely, if the daily open is above the recent average, the histogram will plot red, suggesting possible downward momentum.
2. **Non-Repainting**: Since it relies on completed 4-hour and daily open prices, this indicator does not repaint, ensuring that all values remain fixed after the close of each period. This non-repainting feature makes it suitable for backtesting and reliable for trend confirmation without fear of historical changes.
3. **AO Mean Calculation**: The indicator calculates the average of six previous 4-hour open prices, providing a smoothed value to reduce short-term noise. This helps in identifying meaningful deviations, making the AO values a more stable basis for trend determination than using just the latest 4-hour or daily open.
4. **Histogram for Visual Clarity**: The indicator is displayed as a histogram, making it easy to identify trend changes visually. If the AO bar turns green, it’s a signal that the 4-hour average is below the daily open, suggesting an uptrend or bullish momentum. Red bars indicate that the daily open is above the recent 4-hour averages, potentially signaling a downtrend or bearish momentum.
### Practical Application
The *Custom AO with Open Difference* is a versatile tool for confirming the open price trend without needing complex oscillators or lagging indicators. Traders can use this tool to gauge the market sentiment by observing open price variations and use it as a foundation for decision-making in both short-term and daily timeframes. Its non-repainting nature adds reliability for traders using this indicator as part of a broader trading strategy.
Volumetric Rejection Blocks [UAlgo]The Volumetric Rejection Blocks is designed to help traders identify and visualize key price levels where volumetric rejections occur, which may indicate a shift in market sentiment. These rejections can signal potential trend reversals or areas where price action is likely to face support or resistance. By drawing rejection blocks based on volumetric strength, the indicator allows users to observe where significant buying or selling pressure has been exerted, which can be used as a reference point for future price action.
Also indicator dynamically calculates swing highs and lows, analyzes bullish and bearish strengths based on volume-weighted price movements, and displays rejection blocks on the chart. Each rejection block represents an area where the price attempted to move beyond a certain level but faced rejection, either on a close or wick basis. This can be particularly useful for traders who rely on market structure and order flow to make informed decisions about entering or exiting trades.
🔶 Key Features
Swing Length Customization: Allows users to define the swing length, helping tailor the sensitivity of the swing high and low detection to the specific market conditions.
Rejection Block Visualization: Displays up to the last 10 rejection blocks based on user settings, clearly marking areas of significant bullish or bearish rejections.
Volumetric Strength Analysis: The indicator calculates bullish and bearish strength for each rejection block, based on volume-weighted price movements over the last few bars, giving insight into the intensity of the rejection.
Violation Check Type: Offers two options for violation detection—"Close" and "Wick". This allows traders to specify whether a price level is considered broken only if it closes beyond the level or if any wick breaches it.
Bullish and Bearish Block Coloring: Rejection blocks are colored to represent bullish (green) and bearish (red) rejection areas. The color transparency can be adjusted for clear visibility overlaid on the price chart.
Market Structure Labels: Labels and lines marking "Market Structure Shift" (MSS) and "Break of Structure" (BOS) are displayed, giving traders context about significant market structure changes.
🔶 Interpreting the Indicator
Rejection Blocks: These colored blocks on the chart indicate areas where the price faced significant buying or selling pressure. A green block suggests a bullish rejection (support zone), where buyers absorbed the sell-off, potentially pushing the price upward. Conversely, a red block indicates a bearish rejection (resistance zone), where sellers overpowered buyers, potentially driving the price lower.
Strength Analysis: The width of the green and red sections within a rejection block represents the relative bullish and bearish strengths. A wider green section indicates stronger bullish support, while a wider red section suggests more robust bearish resistance. This helps traders gauge the likelihood of price holding or breaching these levels.
Market Structure Shift (MSS) and Break of Structure (BOS): The indicator automatically detects and labels significant changes in market structure. An "MSS" label indicates the first break, suggesting a potential shift in trend direction. A "BOS" label indicates a subsequent confirmation in trend direction, allowing traders to recognize potential trend continuations.
Violation Check: Traders can choose how to interpret breaks of these rejection blocks. Using the "Close" option provides a more conservative approach, requiring a close beyond the level for confirmation. The "Wick" option is more aggressive, treating any wick beyond the level as a break.
🔶 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.
VWAP Stdev Bands Strategy (Long Only)The VWAP Stdev Bands Strategy (Long Only) is designed to identify potential long entry points in trending markets by utilizing the Volume Weighted Average Price (VWAP) and standard deviation bands. This strategy focuses on capturing upward price movements, leveraging statistical measures to determine optimal buy conditions.
Key Features:
VWAP Calculation: The strategy calculates the VWAP, which represents the average price a security has traded at throughout the day, weighted by volume. This is an essential indicator for determining the overall market trend.
Standard Deviation Bands: Two bands are created above and below the VWAP, calculated using specified standard deviations. These bands act as dynamic support and resistance levels, providing insight into price volatility and potential reversal points.
Trading Logic:
Long Entry Condition: A long position is triggered when the price crosses below the lower standard deviation band and then closes above it, signaling a potential price reversal to the upside.
Profit Target: The strategy allows users to set a predefined profit target, closing the long position once the specified target is reached.
Time Gap Between Orders: A customizable time gap can be specified to prevent multiple orders from being placed in quick succession, allowing for a more controlled trading approach.
Visualization: The VWAP and standard deviation bands are plotted on the chart with distinct colors, enabling traders to visually assess market conditions. The strategy also provides optional plotting of the previous day's VWAP for added context.
Use Cases:
Ideal for traders looking to engage in long-only positions within trending markets.
Suitable for intraday trading strategies or longer-term approaches based on market volatility.
Customization Options:
Users can adjust the standard deviation values, profit target, and time gap to tailor the strategy to their specific trading style and market conditions.
Note: As with any trading strategy, it is important to conduct thorough backtesting and analysis before live trading. Market conditions can change, and past performance does not guarantee future results.
FMS Suite [KFB Quant]FMS Suite
Overview
The FMS Suite is a powerful and adaptive trend and momentum analysis tool that leverages multiple technical indicators to deliver a comprehensive signal for market direction. This suite combines the strengths of the Aroon, DMI, RSI, Supertrend, and Trix indicators, offering traders a well-rounded perspective on market trends.
How It Works
The FMS Suite integrates five essential components to assess market behavior:
Aroon Indicator : Detects trend strength and direction by analyzing the frequency of recent highs and lows over multiple timeframes. Directional Movement Index (DMI) : Measures the direction and strength of trends, with an ADX component for better trend assessment. Relative Strength Index (RSI) : Evaluates market momentum by indicating overbought or oversold conditions, with signals derived from the 50-line. Supertrend : Utilizes ATR-based volatility measures to establish dynamic support and resistance levels, signaling potential trend changes. Trix : A triple-smoothed EMA oscillator that highlights trend reversals using rate-of-change dynamics.
Each component is calculated across three separate timeframes (fast, medium, and slow), which are then averaged to produce a final FMS Signal . Users can also apply signal smoothing to reduce noise and enhance clarity.
Key Features
Customizable Parameters : Adjust the lengths for each component (fast, medium, slow) to optimize the indicator's responsiveness to different markets. Signal Smoothing Options : Select from various smoothing methods, including SMA, EMA, DEMA, and WMA, to fine-tune the FMS signal. Visual Representation : The FMS Suite plots a histogram representing the raw signal and a smoother line for clearer trend visualization. The background color shifts dynamically to indicate long, short, or neutral conditions. Threshold-Based Alerts : Set your own long and short thresholds, tailoring the indicator to your trading strategy and market outlook. Informative Table Display : An integrated table provides an at-a-glance summary of the current FMS and smoothed FMS signals, along with their respective scores and market state.
How to Use
Trend Confirmation : Utilize the FMS histogram and smoothed signal to validate or challenge existing trend assumptions. Trade Entries and Exits : Identify potential buy (long) or sell (short) signals based on the relationship between the FMS signal and predefined thresholds. Strategy Customization : Fine-tune the indicator settings to align with your trading style, whether it’s short-term scalping or long-term trend following.
Important Considerations
Not Predictive : The FMS Suite does not predict future price movements and should be used in conjunction with other analysis methods. It is based on historical price data, and past performance is not indicative of future results. Settings and Backtesting : Experiment with different lengths and smoothing techniques to optimize performance for specific instruments and market conditions. Always backtest thoroughly.
Disclaimer: This tool is provided for informational and educational purposes only and should not be considered as financial advice. Always conduct your own research and consult with a licensed financial advisor before making any investment decisions.
RBF Kijun Trend System [InvestorUnknown]The RBF Kijun Trend System utilizes advanced mathematical techniques, including the Radial Basis Function (RBF) kernel and Kijun-Sen calculations, to provide traders with a smoother trend-following experience and reduce the impact of noise in price data. This indicator also incorporates ATR to dynamically adjust smoothing and further minimize false signals.
Radial Basis Function (RBF) Kernel Smoothing
The RBF kernel is a mathematical method used to smooth the price series. By calculating weights based on the distance between data points, the RBF kernel ensures smoother transitions and a more refined representation of the price trend.
The RBF Kernel Weighted Moving Average is computed using the formula:
f_rbf_kernel(x, xi, sigma) =>
math.exp(-(math.pow(x - xi, 2)) / (2 * math.pow(sigma, 2)))
The smoothed price is then calculated as a weighted sum of past prices, using the RBF kernel weights:
f_rbf_weighted_average(src, kernel_len, sigma) =>
float total_weight = 0.0
float weighted_sum = 0.0
// Compute weights and sum for the weighted average
for i = 0 to kernel_len - 1
weight = f_rbf_kernel(kernel_len - 1, i, sigma)
total_weight := total_weight + weight
weighted_sum := weighted_sum + (src * weight)
// Check to avoid division by zero
total_weight != 0 ? weighted_sum / total_weight : na
Kijun-Sen Calculation
The Kijun-Sen, a component of Ichimoku analysis, is used here to further establish trends. The Kijun-Sen is computed as the average of the highest high and the lowest low over a specified period (default: 14 periods).
This Kijun-Sen calculation is based on the RBF-smoothed price to ensure smoother and more accurate trend detection.
f_kijun_sen(len, source) =>
math.avg(ta.lowest(source, len), ta.highest(source, len))
ATR-Adjusted RBF and Kijun-Sen
To mitigate false signals caused by price volatility, the indicator features ATR-adjusted versions of both the RBF smoothed price and Kijun-Sen.
The ATR multiplier is used to create upper and lower bounds around these lines, providing dynamic thresholds that account for market volatility.
Neutral State and Trend Continuation
This indicator can interpret a neutral state, where the signal is neither bullish nor bearish. By default, the indicator is set to interpret a neutral state as a continuation of the previous trend, though this can be adjusted to treat it as a truly neutral state.
Users can configure this setting using the signal_str input:
simple string signal_str = input.string("Continuation of Previous Trend", "Treat 0 State As", options = , group = G1)
Visual difference between "Neutral" (Bottom) and "Continuation of Previous Trend" (Top). Click on the picture to see it in full size.
Customizable Inputs and Settings:
Source Selection: Choose the input source for calculations (open, high, low, close, etc.).
Kernel Length and Sigma: Adjust the RBF kernel parameters to change the smoothing effect.
Kijun Length: Customize the lookback period for Kijun-Sen.
ATR Length and Multiplier: Modify these settings to adapt to market volatility.
Backtesting and Performance Metrics
The indicator includes a Backtest Mode, allowing users to evaluate the performance of the strategy using historical data. In Backtest Mode, a performance metrics table is generated, comparing the strategy's results to a simple buy-and-hold approach. Key metrics include mean returns, standard deviation, Sharpe ratio, and more.
Equity Calculation: The indicator calculates equity performance based on signals, comparing it against the buy-and-hold strategy.
Performance Metrics Table: Detailed performance analysis, including probabilities of positive, neutral, and negative returns.
Alerts
To keep traders informed, the indicator supports alerts for significant trend shifts:
// - - - - - ALERTS - - - - - //{
alert_source = sig
bool long_alert = ta.crossover (intrabar ? alert_source : alert_source , 0)
bool short_alert = ta.crossunder(intrabar ? alert_source : alert_source , 0)
alertcondition(long_alert, "LONG (RBF Kijun Trend System)", "RBF Kijun Trend System flipped ⬆LONG⬆")
alertcondition(short_alert, "SHORT (RBF Kijun Trend System)", "RBF Kijun Trend System flipped ⬇Short⬇")
//}
Important Notes
Calibration Needed: The default settings provided are not optimized and are intended for demonstration purposes only. Traders should adjust parameters to fit their trading style and market conditions.
Neutral State Interpretation: Users should carefully choose whether to treat the neutral state as a continuation or a separate signal.
Backtest Results: Historical performance is not indicative of future results. Market conditions change, and past trends may not recur.
Adaptive MA Crossover with ATR-Based Risk MarkersDescription:
The Cross MA Entry Indicator with ATR-Based Stop-Loss and Take-Profit Markers is a powerful tool designed to help traders identify trend-following opportunities while managing risk effectively. By combining customizable moving average (MA) crossovers with ATR-based stop-loss (SL) and take-profit (TP) markers, this indicator provides a complete entry and risk management framework in a single script.
Unique Features:
1. Versatile Moving Average Combinations: The indicator allows users to select from four types of moving averages—SMA, EMA, DEMA, and TEMA—for both fast and slow lines, enabling a variety of crossover configurations. This flexibility helps traders tailor entry signals to specific trading strategies, asset types, or market conditions, enhancing the adaptability of the indicator across different styles and preferences.
2. ATR-Based Dynamic Risk Management: Leveraging the Average True Range (ATR), the indicator dynamically calculates stop-loss and take-profit levels based on market volatility. This approach adjusts to changing market conditions, making it more responsive and reliable for setting realistic, volatility-based risk parameters.
3. Customizable Risk/Reward Ratio: Users can define their preferred risk/reward ratio (e.g., 2:1, 3:1) to tailor take-profit levels relative to stop-loss distances. This feature empowers traders to align trades with their individual risk management strategies and objectives, while maintaining consistency and discipline in execution.
4. Streamlined Visualization of Entry and Risk Levels: Upon a crossover, the indicator places discrete markers at the calculated SL and TP levels, avoiding clutter while providing traders with an immediate view of potential risk and reward. Small dots represent SL and TP levels, offering a clean, clear display of critical decision points.
How to Use:
1. Entry Signals from MA Crossovers: This indicator generates entry signals when the selected moving averages cross, with green markers indicating long entries and red markers indicating short entries. The customizable MA selection enables traders to optimize crossover signals for various timeframes and asset classes.
2. Integrated Risk Markers: SL and TP levels are shown as small dots at the crossover point, based on the ATR multiplier and risk/reward ratio settings. These markers allow traders to quickly visualize the defined risk and potential reward for each entry.
This indicator offers a comprehensive solution for trend-following strategies by combining entry signals with adaptive risk management. Suitable for multiple timeframes, it allows for backtesting and adjustments to ATR and risk/reward parameters for improved alignment with individual trading goals. As with all strategies, thorough testing is recommended to ensure compatibility with your trading approach.
Demo GPT - Day Trading Scalping StrategyOverview:
This strategy is designed for day trading and scalping, utilizing a combination of technical indicators, candlestick patterns, and volume analysis to determine entry and exit points. It focuses on capturing short-term price movements while ensuring that trades are executed under specific market conditions.
Key Components:
Technical Indicators Used:
Exponential Moving Average (EMA): The strategy uses the 20-period EMA to identify the trend direction. The EMA smooths out price data, helping traders make more informed decisions about potential buy or sell signals.
Volume Weighted Average Price (VWAP): VWAP is used to measure the average price a security has traded at throughout the day, based on both volume and price. This indicator helps assess whether the current price is above or below the average trading price.
Camarilla Pivot Points: The strategy calculates four levels of Camarilla pivots (S2, S3, R2, R3) based on the highest and lowest prices over the last 14 daily candles. These levels act as potential support and resistance zones, guiding entry and exit decisions.
Candlestick Analysis:
Buy Condition: A buy signal is triggered when:
The first candle (previous candle) is green (close > open).
The second candle (current candle) is also green and opens above the first candle.
The volume of the current candle exceeds the 20-period moving average of volume, indicating strong buying interest.
Sell Condition: A sell signal is triggered when:
The first candle is red (close < open).
The second candle opens below the first red candle.
The volume of the current candle also exceeds the 20-period moving average of volume, indicating strong selling pressure.
Position Management:
The strategy enters a long position (buy) when the buy condition is met and closes the long position when the sell condition is met. This approach aims to capture upward momentum while avoiding extended exposure to downside risks.
Trading Settings:
Capital Management: The strategy uses 100% of available capital for each trade, allowing for maximum exposure to potential gains.
Commission and Slippage: The script includes settings for a commission rate of 0.1% and slippage of 3, accounting for trading costs and potential price changes during order execution.
Date Filtering: The strategy allows users to set a start date (January 1, 2018) and an end date (December 31, 2069) for trade execution, providing flexibility in backtesting and live trading.
Visualization:
The script plots the 20 EMA, VWAP, and the Camarilla pivot levels on the chart for visual reference.
Buy and sell signals are visually represented with shapes on the chart, making it easy to identify potential trade opportunities at a glance.
Volume is plotted in a separate pane to assess trading activity, and a horizontal line at zero provides a reference point.
Summary:
This Day Trading Scalping Strategy is designed to exploit short-term price movements by using a combination of EMAs, VWAP, and Camarilla pivot levels, alongside candlestick patterns and volume analysis. It is well-suited for traders looking to make quick trades based on real-time market conditions while maintaining a disciplined approach to entry and exit points. The strategy is highly visual, allowing traders to quickly assess market conditions and make informed trading decisions.
Feel free to modify or adjust any aspects of the strategy according to your specific trading goals or preferences!
Engulfing Pattern & Impulse [UAlgo]The Engulfing Pattern & Impulse is a tool designed for technical traders who utilize price action and volume analysis to assess market trends and potential reversals. This indicator identifies two powerful trading signals: Engulfing Patterns and Volume Impulses, which are essential components for evaluating potential bullish or bearish market momentum.
Engulfing Patterns are classic candlestick formations often associated with reversals or trend continuations, depending on the overall trend context. This indicator highlights both bullish and bearish engulfing patterns based on configurable criteria such as trend detection settings, comparison with average body size, and a customizable body multiplier for validation. The Volume Impulse feature signals moments of significant volume compared to historical levels, which often precede substantial price movements. Together, these features provide traders with a versatile tool for better timing entry and exit points.
The indicator also offers an adaptive trend detection system, allowing traders to choose from multiple methods (e.g., SMA50 or SMA50/SMA200 combinations) to assess the trend context, making it ideal for various market conditions.
🔶Key Features
Engulfing Pattern Detection: Identifies bullish and bearish engulfing patterns with customizable parameters, including body length and average size comparison.
Configurable trend basis: Choose between SMA50 or SMA50 with SMA200 to define trend direction.
Body size multiplier: Adjust the size threshold for valid engulfing patterns, providing flexibility based on market conditions.
Volume Impulse Signal: Highlights volume spikes that meet or exceed a specified multiplier, which can indicate increased buying or selling interest.
Customizable volume period and multiplier: Allows you to tailor the volume impulse detection based on the instrument’s average volume behavior.
Trend Detection Options: Select different trend detection methods to suit various trading styles and instruments.
SMA50-based detection: Classifies the trend based on the position of price relative to the 50-period SMA.
SMA50 and SMA200 combination: Incorporates a dual-moving average approach, classifying trends based on the relationship between price, SMA50, and SMA200.
Enhanced Visualization: Distinguishes bullish and bearish signals with customizable colors, providing clear and immediate visual cues for easy interpretation.
Custom label colors: Allows you to set distinct colors for bullish, bearish, and neutral signals for quick identification.
Pattern filtering: Enable or disable specific patterns (Bullish, Bearish, or Both) based on your trading preferences.
🔶 Interpreting Indicator
Bullish Engulfing Pattern: Indicates a potential bullish reversal in a downtrend. This signal occurs when a white candlestick with a body size exceeding a specified multiplier completely engulfs the previous black candlestick. The pattern will display a “BE” label below the candle if it meets the criteria, signaling potential upward momentum.
Bearish Engulfing Pattern: Indicates a potential bearish reversal in an uptrend. A black candlestick with a body size exceeding the specified multiplier fully engulfs the previous white candlestick, signaling possible downward movement. The “BE” label appears above the candle to denote this pattern.
Volume Impulse Up: Displays a “VI” label below the candle when the volume surpasses the defined multiplier, and the price closes higher than it opened, indicating strong upward buying interest.
Volume Impulse Down: Displays a “VI” label above the candle when the volume meets or exceeds the specified threshold, and the price closes lower than it opened, signaling strong selling pressure.
Indicator uses the SMA50 and SMA200 to determine trend direction due to their popularity in technical analysis as indicators of medium- and long-term trends. The SMA50 reflects the average price over the past 50 periods, providing insight into intermediate trends, while the SMA200 is often used to identify the broader trend direction. These SMAs help traders quickly assess whether the market is in an uptrend, downtrend, or consolidation phase, enhancing decision-making for both short-term and long-term strategies.
🔶 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.
Z-Score RSI StrategyOverview
The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals.
Theory and Interpretation
The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions.
By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself.
While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies.
Recommendations
Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.
SuperATR 7-Step Profit - Strategy [presentTrading] Long time no see!
█ Introduction and How It Is Different
The SuperATR 7-Step Profit Strategy is a multi-layered trading approach that integrates adaptive Average True Range (ATR) calculations with momentum-based trend detection. What sets this strategy apart is its sophisticated 7-step take-profit mechanism, which combines four ATR-based exit levels and three fixed percentage levels. This hybrid approach allows traders to dynamically adjust to market volatility while systematically capturing profits in both long and short market positions.
Traditional trading strategies often rely on static indicators or single-layered exit strategies, which may not adapt well to changing market conditions. The SuperATR 7-Step Profit Strategy addresses this limitation by:
- Using Adaptive ATR: Enhances the standard ATR by making it responsive to current market momentum.
- Incorporating Momentum-Based Trend Detection: Identifies stronger trends with higher probability of continuation.
- Employing a Multi-Step Take-Profit System: Allows for gradual profit-taking at predetermined levels, optimizing returns while minimizing risk.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The strategy revolves around detecting strong market trends and capitalizing on them using an adaptive ATR and momentum indicators. Below is a detailed breakdown of each component of the strategy.
🔶 1. True Range Calculation with Enhanced Volatility Detection
The True Range (TR) measures market volatility by considering the most significant price movements. The enhanced TR is calculated as:
TR = Max
Where:
High and Low are the current bar's high and low prices.
Previous Close is the closing price of the previous bar.
Abs denotes the absolute value.
Max selects the maximum value among the three calculations.
🔶 2. Momentum Factor Calculation
To make the ATR adaptive, the strategy incorporates a Momentum Factor (MF), which adjusts the ATR based on recent price movements.
Momentum = Close - Close
Stdev_Close = Standard Deviation of Close over n periods
Normalized_Momentum = Momentum / Stdev_Close (if Stdev_Close ≠ 0)
Momentum_Factor = Abs(Normalized_Momentum)
Where:
Close is the current closing price.
n is the momentum_period, a user-defined input (default is 7).
Standard Deviation measures the dispersion of closing prices over n periods.
Abs ensures the momentum factor is always positive.
🔶 3. Adaptive ATR Calculation
The Adaptive ATR (AATR) adjusts the traditional ATR based on the Momentum Factor, making it more responsive during volatile periods and smoother during consolidation.
Short_ATR = SMA(True Range, short_period)
Long_ATR = SMA(True Range, long_period)
Adaptive_ATR = /
Where:
SMA is the Simple Moving Average.
short_period and long_period are user-defined inputs (defaults are 3 and 7, respectively).
🔶 4. Trend Strength Calculation
The strategy quantifies the strength of the trend to filter out weak signals.
Price_Change = Close - Close
ATR_Multiple = Price_Change / Adaptive_ATR (if Adaptive_ATR ≠ 0)
Trend_Strength = SMA(ATR_Multiple, n)
🔶 5. Trend Signal Determination
If (Short_MA > Long_MA) AND (Trend_Strength > Trend_Strength_Threshold):
Trend_Signal = 1 (Strong Uptrend)
Elif (Short_MA < Long_MA) AND (Trend_Strength < -Trend_Strength_Threshold):
Trend_Signal = -1 (Strong Downtrend)
Else:
Trend_Signal = 0 (No Clear Trend)
🔶 6. Trend Confirmation with Price Action
Adaptive_ATR_SMA = SMA(Adaptive_ATR, atr_sma_period)
If (Trend_Signal == 1) AND (Close > Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Elif (Trend_Signal == -1) AND (Close < Short_MA) AND (Adaptive_ATR > Adaptive_ATR_SMA):
Trend_Confirmed = True
Else:
Trend_Confirmed = False
Local Performance
🔶 7. Multi-Step Take-Profit Mechanism
The strategy employs a 7-step take-profit system
█ Trade Direction
The SuperATR 7-Step Profit Strategy is designed to work in both long and short market conditions. By identifying strong uptrends and downtrends, it allows traders to capitalize on price movements in either direction.
Long Trades: Initiated when the market shows strong upward momentum and the trend is confirmed.
Short Trades: Initiated when the market exhibits strong downward momentum and the trend is confirmed.
█ Usage
To implement the SuperATR 7-Step Profit Strategy:
1. Configure the Strategy Parameters:
- Adjust the short_period, long_period, and momentum_period to match the desired sensitivity.
- Set the trend_strength_threshold to control how strong a trend must be before acting.
2. Set Up the Multi-Step Take-Profit Levels:
- Define ATR multipliers and fixed percentage levels according to risk tolerance and profit goals.
- Specify the percentage of the position to close at each level.
3. Apply the Strategy to a Chart:
- Use the strategy on instruments and timeframes where it has been tested and optimized.
- Monitor the positions and adjust parameters as needed based on performance.
4. Backtest and Optimize:
- Utilize TradingView's backtesting features to evaluate historical performance.
- Adjust the default settings to optimize for different market conditions.
█ Default Settings
Understanding default settings is crucial for optimal performance.
Short Period (3): Affects the responsiveness of the short-term MA.
Effect: Lower values increase sensitivity but may produce more false signals.
Long Period (7): Determines the trend baseline.
Effect: Higher values reduce noise but may delay signals.
Momentum Period (7): Influences adaptive ATR and trend strength.
Effect: Shorter periods react quicker to price changes.
Trend Strength Threshold (0.5): Filters out weaker trends.
Effect: Higher thresholds yield fewer but stronger signals.
ATR Multipliers: Set distances for ATR-based exits.
Effect: Larger multipliers aim for bigger moves but may reduce hit rate.
Fixed TP Levels (%): Control profit-taking on smaller moves.
Effect: Adjusting these levels affects how quickly profits are realized.
Exit Percentages: Determine how much of the position is closed at each TP level.
Effect: Higher percentages reduce exposure faster, affecting risk and reward.
Adjusting these variables allows you to tailor the strategy to different market conditions and personal risk preferences.
By integrating adaptive indicators and a multi-tiered exit strategy, the SuperATR 7-Step Profit Strategy offers a versatile tool for traders seeking to navigate varying market conditions effectively. Understanding and adjusting the key parameters enables traders to harness the full potential of this strategy.
Range Detection [No Repaint]DETECTS RANGE EARLY
Using Confirmed Data:
All calculations now use to reference the previous completed candle
Signals are only generated based on completed candles
Range state is stored and confirmed before displaying
Key Changes to Prevent Repainting:
ATR calculations use previous candle data
Bollinger Bands calculate from previous closes
Price range checks use previous highs and lows
Range state is confirmed before displaying
How to Verify No Repainting:
Signals will only appear after a candle closes
Historical signals will remain unchanged
Alerts will only trigger on confirmed changes
This means:
The indicator will be slightly delayed (one candle)
But signals will be more reliable
Historical analysis will be accurate
Backtesting results will match real-time performance
Usage Tips with No-Repaint Version:
Wait for candle close before acting on signals
Use the confirmed range state for decision making
Consider the one-candle delay in your strategy timing
Alerts will only trigger on confirmed condition changes
Would you like me to:
Add a parameter to choose between real-time and no-repaint modes?
Add visual indicators for pending vs confirmed signals?
Modify the sensitivity of the range detection?
Supertrend StrategyThe Supertrend Strategy was created based on the Supertrend and Relative Strength Index (RSI) indicators, widely respected tools in technical analysis. This strategy combines these two indicators to capture market trends with precision and reliability, looking for optimizing exit levels at oversold or overbought price levels.
The Supertrend indicator identifies trend direction based on price and volatility by using the Average True Range (ATR). The ATR measures market volatility by calculating the average range between an asset’s high and low prices over a set period. It provides insight into price fluctuations, with higher ATR values indicating increased volatility and lower values suggesting stability. The Supertrend Indicator plots a line above or below the price, signaling potential buy or sell opportunities: when the price closes above the Supertrend line, an uptrend is indicated, while a close below the line suggests a downtrend. This line shifts as price movements and volatility levels change, acting as both a trailing stop loss and trend confirmation.
To enhance the Supertrend strategy, the Relative Strength Index (RSI) has been added as an exit criterion. As a momentum oscillator, the RSI indicates overbought (usually above 70) or oversold (usually below 30) conditions. This integration allows trades to close when the asset is overbought or oversold, capturing gains before a possible reversal, even if the percentage take profit level has not been reached. This mechanism aims to prevent losses due to market reversals before the Supertrend signal changes.
### Key Features
1. **Entry criteria**:
- The strategy uses the Supertrend indicator calculated by adding or subtracting a multiple of the ATR from the closing price, depending on the trend direction.
- When the price crosses above the Supertrend line, the strategy signals a long (buy) entry. Conversely, when the price crosses below, it signals a short (sell) entry.
- The strategy performs a reversal if there is an open position and a change in the direction of the supertrend occurs
2. **Exit criteria**:
- Take profit of 30% (default) on the average position price.
- Oversold (≤ 5) or overbought (≥ 95) RSI
- Reversal when there is a change in direction of the Supertrend
3. **No Repainting**:
- This strategy is not subject to repainting, as long as the timeframe configured on your chart is the same as the supertrend timeframe .
4. **Position Sizing by Equity and risk management**:
- This strategy has a default configuration to operate with 35% of the equity. At the time of opening the position, the supertrend line is typically positioned at about 12 to 16% of the entry price. This way, the strategy is putting at risk about 16% of 35% of equity, that is, around 5.6% of equity for each trade. The percentage of equity can be adjusted by the user according to their risk management.
5. **Backtest results**:
- This strategy was subjected to deep backtesting and operations in replay mode, including transaction fees of 0.12%, and slippage of 5 ticks.
- The past results in deep backtest and replay mode were compatible and profitable (Variable results depending on the take profit used, supertrend and RSI parameters). However, it should be noted that few operations were evaluated, since the currency in question has been created for a short time and the frequency of operations is relatively small.
- Past results are no guarantee of future results. The strategy's backtest results may even be due to overfitting with past data.
Default Settings
Chart timeframe: 2h
Supertrend Factor: 3.42
ATR period: 14
Supertrend timeframe: 2 h
RSI timeframe: 15 min
RSI Lenght: 5 min
RSI Upper limit: 95
RSI Lower Limit: 5
Take Profit: 30%
BYBIT:1000000MOGUSDT.P
Advanced Multi-Seasonality StrategyThe Multi-Seasonality Strategy is a trading system based on seasonal market patterns. Seasonality refers to recurring market trends driven by predictable calendar-based events. These patterns emerge due to economic cycles, corporate activities (e.g., earnings reports), and investor behavior around specific times of the year. Studies have shown that such effects can influence asset prices over defined periods, leading to opportunities for traders who exploit these patterns (Hirshleifer, 2001; Bouman & Jacobsen, 2002).
How the Strategy Works:
The strategy allows the user to define four distinct periods within a calendar year. For each period, the trader selects:
Entry Date (Month and Day): The date to enter the trade.
Holding Period: The number of trading days to remain in the trade after the entry.
Trade Direction: Whether to take a long or short position during that period.
The system is designed with flexibility, enabling the user to activate or deactivate each of the four periods. The idea is to take advantage of seasonal patterns, such as buying during historically strong periods and selling during weaker ones. A well-known example is the "Sell in May and Go Away" phenomenon, which suggests that stock returns are higher from November to April and weaker from May to October (Bouman & Jacobsen, 2002).
Seasonality in Financial Markets:
Seasonal effects have been documented across different asset classes and markets:
Equities: Stock markets tend to exhibit higher returns during certain months, such as the "January effect," where prices rise after year-end tax-loss selling (Haugen & Lakonishok, 1987).
Commodities: Agricultural commodities often follow seasonal planting and harvesting cycles, which impact supply and demand patterns (Fama & French, 1987).
Forex: Currency pairs may show strength or weakness during specific quarters based on macroeconomic factors, such as fiscal year-end flows or central bank policy decisions.
Scientific Basis:
Research shows that market anomalies like seasonality are linked to behavioral biases and institutional practices. For example, investors may respond to tax incentives at the end of the year, and companies may engage in window dressing (Haugen & Lakonishok, 1987). Additionally, macroeconomic factors, such as monetary policy shifts and holiday trading volumes, can also contribute to predictable seasonal trends (Bouman & Jacobsen, 2002).
Risks of Seasonal Trading:
While the strategy seeks to exploit predictable patterns, there are inherent risks:
Market Changes: Seasonal effects observed in the past may weaken or disappear as market conditions evolve. Increased algorithmic trading, globalization, and policy changes can reduce the reliability of historical patterns (Lo, 2004).
Overfitting: One of the risks in seasonal trading is overfitting the strategy to historical data. A pattern that worked in the past may not necessarily work in the future, especially if it was based on random chance or external factors that no longer apply (Sullivan, Timmermann, & White, 1999).
Liquidity and Volatility: Trading during specific periods may expose the trader to low liquidity, especially around holidays or earnings seasons, leading to slippage and larger-than-expected price swings.
Economic and Geopolitical Shocks: External events such as pandemics, wars, or political instability can disrupt seasonal patterns, leading to unexpected market behavior.
Conclusion:
The Multi-Seasonality Strategy capitalizes on the predictable nature of certain calendar-based patterns in financial markets. By entering and exiting trades based on well-established seasonal effects, traders can potentially capture short-term profits. However, caution is necessary, as market dynamics can change, and seasonal patterns are not guaranteed to persist. Rigorous backtesting, combined with risk management practices, is essential to successfully implementing this strategy.
References:
Bouman, S., & Jacobsen, B. (2002). The Halloween Indicator, "Sell in May and Go Away": Another Puzzle. American Economic Review, 92(5), 1618-1635.
Fama, E. F., & French, K. R. (1987). Commodity Futures Prices: Some Evidence on Forecast Power, Premiums, and the Theory of Storage. Journal of Business, 60(1), 55-73.
Haugen, R. A., & Lakonishok, J. (1987). The Incredible January Effect: The Stock Market's Unsolved Mystery. Dow Jones-Irwin.
Hirshleifer, D. (2001). Investor Psychology and Asset Pricing. Journal of Finance, 56(4), 1533-1597.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Performance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.
This strategy harnesses the power of seasonality but requires careful consideration of the risks and potential changes in market behavior over time.
CPR by NKDCentral Pivot Range (CPR) Trading Strategy:
The Central Pivot Range (CPR) is a widely-used tool in technical analysis, helping traders pinpoint potential support and resistance levels in the market. By using the CPR effectively, traders can better gauge market trends and determine favorable entry and exit points. This guide explores how the CPR works, outlines its calculation, and describes how traders can enhance their strategies using an extended 10-line version of CPR.
What Really Central Pivot Range (CPR) is?
At its core, the CPR consists of three key lines:
Pivot Point (PP) – The central line, calculated as the average of the previous day’s high, low, and closing prices.
Upper Range (R1) – Positioned above the Pivot Point, acting as a potential ceiling where price may face resistance.
Lower Range (S1) – Found below the Pivot Point, serving as a potential floor where price might find support.
Advanced traders often expand on the traditional three-line CPR by adding extra levels above and below the pivot, creating up to a 10-line system. This extended CPR allows for a more nuanced understanding of the market and helps identify more detailed trading opportunities.
Applying CPR for Trading Success
1. How CPR is Calculation
The CPR relies on the previous day's high (H), low (L), and close (C) prices to create its structure:
Pivot Point (PP) = (H + L + C) / 3
First Resistance (R1) = (2 * PP) - L
First Support (S1) = (2 * PP) - H
Additional resistance levels (R2, R3) and support levels (S2, S3) are calculated by adding or subtracting multiples of the previous day’s price range (H - L) from the Pivot Point.
2. Recognizing the Market Trend
To effectively trade using CPR, it’s essential to first determine whether the market is trending up (bullish) or down (bearish). In an upward-trending market, traders focus on buying at support levels, while in a downward market, they look to sell near resistance.
3. Finding Ideal Entry Points
Traders often look to enter trades when price approaches key levels within the CPR range. Support levels (S1, S2) offer buying opportunities, while resistance levels (R1, R2) provide selling opportunities. These points are considered potential reversal zones, where price may bounce or reverse direction.
4. Managing Risk with Stop-Loss Orders
Proper risk management is crucial in any trading strategy. A stop-loss should be set slightly beyond the support level for buy positions and above the resistance level for sell positions, ensuring that losses are contained if the market moves against the trader’s position.
5. Determining Profit Targets
Profit targets are typically set based on the distance between entry points and the next support or resistance level. Many traders apply a risk-reward ratio, aiming for larger potential profits compared to the potential losses. However, if the next resistance and support level is far then middle levels are used for targets (i.e. 50% of R1 and R2)
6. Confirmation Through Other Indicators
While CPR provides strong support and resistance levels, traders often use additional indicators to confirm potential trade setups. Indicators such as moving averages can
help validate the signals provided by the CPR.
7. Monitoring Price Action At CPR Levels
Constantly monitoring price movement near CPR levels is essential. If the price fails to break through a resistance level (R1) or holds firm at support (S1), it can offer cues on when to exit or adjust a trade. However, a strong price break past these levels often signals a continued trend.
8. Trading Breakouts with CPR
When the price breaks above resistance or below support with strong momentum, it may signal a potential breakout. Traders can capitalize on these movements by entering positions in the direction of the breakout, ideally confirmed by volume or other technical indicators.
9. Adapting to Changing Market Conditions
CPR should be used in the context of broader market influences, such as economic reports, news events, or geopolitical shifts. These factors can dramatically affect market direction and how price reacts to CPR levels, making it important to stay informed about external market conditions.
10. Practice and Backtesting for Improvements
Like any trading tool, the CPR requires practice. Traders are encouraged to backtest their strategies on historical price data to get a better sense of how CPR works in different market environments. Continuous analysis and practice help improve decision-making and strategy refinement.
The Advantages of Using a 10-Line CPR System
An extended 10-line CPR system—comprising up to five resistance and five support levels—provides more granular control and insight into market movements. This expanded view helps traders better gauge trends and identify more opportunities for entry and exit. Key benefits include:
R2, S2 Levels: These act as secondary resistance or support zones, giving traders additional opportunities to refine their trade entries and exits.
R3, S3 Levels: Provide an even wider range for identifying reversals or trend continuations in more volatile markets.
Flexibility: The broader range of levels allows traders to adapt to changing market conditions and make more precise decisions based on market momentum.
So in Essential:
The Central Pivot Range is a valuable tool for traders looking to identify critical price levels in the market. By providing a clear framework for identifying potential support and resistance zones, it helps traders make informed decisions about entering and exiting trades. However, it’s important to combine CPR with sound risk management and additional confirmation through other technical indicators for the best results.
Although no trading tool guarantees success, the CPR, when used effectively and combined with practice, can significantly enhance a trader’s ability to navigate market fluctuations.
Parent Session Sweeps + Alert Killzone Ranges with Parent Session Sweep
Key Features:
1. Multiple Session Support: The script tracks three major trading sessions - Asia, London, and New York. Users can customize the timing of these sessions.
2. Killzone Visualization: The strategy visually represents each session's range, either as filled boxes or lines, allowing traders to easily identify key price levels.
3. Parent Session Logic: The core of the strategy revolves around identifying a "parent" session - a session that encompasses the range of the following session. This parent session becomes the basis for potential trade setups.
4. Sweep and Reclaim Setups: The strategy looks for price movements that sweep (break above or below) the parent session's high or low, followed by a reclaim of that level. This price action often indicates a potential reversal.
5. Risk-Reward Filtering: Each potential setup is evaluated based on a user-defined minimum risk-reward ratio, ensuring that only high-quality trade opportunities are considered.
6. Candle Close Filter: An optional filter that checks the characteristics of the candle that reclaims the parent session level, adding an extra layer of confirmation to the setup.
7. Performance Tracking: The strategy keeps track of bullish and bearish setup success rates, providing valuable feedback on its performance over time.
8. Visual Aids: The script draws lines to mark the parent session's high and low, making it easy for traders to identify key levels.
How It Works:
1. The script continuously monitors price action across the defined sessions.
2. When a session fully contains the range of the next session, it's identified as a potential parent session.
3. The strategy then waits for price to sweep either the high or low of this parent session.
4. If a sweep occurs, it looks for a reclaim of the swept level within the parameters set by the user.
5. If a valid setup is identified, the script generates an alert and places a trade (if backtesting or running live).
6. The strategy continues to monitor the trade for either reaching the target (opposite level of the parent session) or hitting the stop loss.
Considerations for Signals:
- Sweep: A break of the parent session's high or low.
- Reclaim: A close back inside the parent session range after a sweep.
- Candle Characteristics: Optional filter for the reclaim candle (e.g., bullish candle for long setups).
- Risk-Reward: Each setup must meet or exceed the user-defined minimum risk-reward ratio.
- Session Timing: The strategy is sensitive to the defined session times, which should be set according to the trader's preferred time zone.
This strategy aims to capitalize on institutional order flow and liquidity patterns in the forex market, providing traders with a systematic approach to identifying potential reversal points with favorable risk-reward profiles.
Momentum Nexus Oscillator [UAlgo]The "Momentum Nexus Oscillator " indicator is a comprehensive momentum-based tool designed to provide traders with visual cues on market conditions using multiple oscillators. By combining four popular technical indicators—RSI (Relative Strength Index), VZO (Volume Zone Oscillator), MFI (Money Flow Index), and CCI (Commodity Channel Index)—this heatmap offers a holistic view of the market's momentum.
The indicator plots two lines: one representing the current chart’s combined momentum score and the other representing a higher timeframe’s (HTF) score, if enabled. Through smooth gradient color transitions and easy-to-read signals, the Momentum Nexus Heatmap allows traders to easily identify potential trend reversals or continuation patterns.
Traders can use this tool to detect overbought or oversold conditions, helping them anticipate possible long or short trade opportunities. The option to use a higher timeframe enhances the flexibility of the indicator for longer-term trend analysis.
🔶 Key Features
Multi-Oscillator Approach: Combines four popular momentum oscillators (RSI, VZO, MFI, and CCI) to generate a weighted score, providing a comprehensive picture of market momentum.
Dynamic Color Heatmap: Utilizes a smooth gradient transition between bullish and bearish colors, reflecting market momentum across different thresholds.
Higher Timeframe (HTF) Compatibility: Includes an optional higher timeframe input that displays a separate score line based on the same momentum metrics, allowing for multi-timeframe analysis.
Customizable Parameters: Adjustable RSI, VZO, MFI, and CCI lengths, as well as overbought and oversold levels, to match the trader’s strategy or preference.
Signal Alerts: Built-in alert conditions for both the current chart and higher timeframe scores, notifying traders when long or short entry signals are triggered.
Buy/Sell Signals: Displays visual signals (▲ and ▼) on the chart when combined scores reach overbought or oversold levels, providing clear entry cues.
User-Friendly Visualization: The heatmap is separated into four sections representing each indicator, providing a transparent view of how each contributes to the overall momentum score.
🔶 Interpreting Indicator:
Combined Score
The indicator generates a combined score by weighing the individual contributions of RSI, VZO, MFI, and CCI. This score ranges from 0 to 100 and is plotted as a line on the chart. Lower values suggest potential oversold conditions, while higher values indicate overbought conditions.
Color Heatmap
The indicator divides the combined score into four distinct sections, each representing one of the underlying momentum oscillators (RSI, VZO, MFI, and CCI). Bullish (greenish) colors indicate upward momentum, while bearish (grayish) colors suggest downward momentum.
Long/Short Signals
When the combined score drops below the oversold threshold (default is 26), a long signal (▲) is displayed on the chart, indicating a potential buying opportunity.
When the combined score exceeds the overbought threshold (default is 74), a short signal (▼) is shown, signaling a potential sell or short opportunity.
Higher Timeframe Analysis
If enabled, the indicator also plots a line representing the combined score for a higher timeframe. This can be used to align lower timeframe trades with the broader trend of a higher timeframe, providing added confirmation.
Signals for long and short entries are also plotted for the higher timeframe when its combined score reaches overbought or oversold levels.
🔶Purpose of Using Multiple Technical Indicators
The combination of RSI, VZO, MFI, and CCI in the Momentum Nexus Heatmap provides a comprehensive approach to analyzing market momentum by leveraging the unique strengths of each indicator. This multi-indicator method minimizes the limitations of using just one tool, resulting in more reliable signals and a clearer understanding of market conditions.
RSI (Relative Strength Index)
RSI contributes by measuring the strength and speed of recent price movements. It helps identify overbought or oversold levels, signaling potential trend reversals or corrections. Its simplicity and effectiveness make it one of the most widely used indicators in technical analysis, contributing to momentum assessment in a straightforward manner.
VZO (Volume Zone Oscillator)
VZO adds the critical element of volume to the analysis. By assessing whether price movements are supported by significant volume, VZO distinguishes between price changes that are driven by real market conviction and those that might be short-lived. It helps validate the strength of a trend or alert the trader to potential weakness when price moves are unsupported by volume.
MFI (Money Flow Index)
MFI enhances the analysis by combining price and volume to gauge money flow into and out of an asset. This indicator provides insight into the participation of large players in the market, showing if money is pouring into or exiting the asset. MFI acts as a volume-weighted version of RSI, giving more weight to volume shifts and helping traders understand the sustainability of price trends.
CCI (Commodity Channel Index)
CCI contributes by measuring how far the price deviates from its statistical average. This helps in identifying extreme conditions where the market might be overextended in either direction. CCI is especially useful for spotting trend reversals or continuations, particularly during market extremes, and for identifying divergence signals.
🔶 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.
