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.
Осцилляторы
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.
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！
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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！
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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.
CofG Oscillator w/ Added Normalizations/TransformationsThis indicator is a unique study in normalization/transformation techniques, which are applied to the CG (center of gravity) Oscillator, a popular oscillator made by John Ehlers.
The idea to transform the data from this oscillator originated from observing the original indicator, which exhibited numerous whips. Curious about the potential outcomes, I began experimenting with various normalization/transformation methods and discovered a plethora of interesting results.
The indicator offers 10 different types of normalization/transformation, each with its own set of benefits and drawbacks. My personal favorites are the Quantile Transformation , which converts the dataset into one that is mostly normally distributed, and the Z-Score , which I have found tends to provide better signaling than the original indicator.
I've also included the option of showing the mean, median, and mode of the data over the period specified by the transformation period. Using this will allow you to gather additional insights into how these transformations effect the distribution of the data series.
I've also included some notes on what each transformation does, how it is useful, where it fails, and what I've found to be the best inputs for it (though I'd encourage you to play around with it yourself).
Types of Normalization/Transformation:
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 transformation period.
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 transformation period.
3. Decimal Scaling
Overview: Normalizes data by moving the decimal point of values.
Benefits: Simple and straightforward, useful for data with varying scales.
Disadvantages: Not commonly used, less intuitive, less advantageous.
Notes: Best used with a mid-longer transformation period.
4. 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 transformation period.
5. Log Transformation
Overview: Applies the logarithm function to compress the data range.
Benefits: Reduces skewness, making the data more normally distributed.
Disadvantages: Only applicable to positive data, breaks on zero and negative values.
Notes: Works with varied transformation period.
6. 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 transformation period.
7. 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 transformation period.
8. 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 transformation period.
9. 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 transformation period. This feature is actually two layered, we first put it through the mean normalization to ensure that it's generally centered around 0.
10. 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 transformation period.
Conclusion
Feel free to explore these normalization/transformation techniques to see how they impact the performance of the CG Oscillator. Each method offers unique insights and benefits, making this study a valuable tool for traders, especially those with a passion for data analysis.
Advanced ADX [CryptoSea]The Advanced ADX Analysis is a sophisticated tool designed to enhance market analysis through detailed ADX calculations. This tool is built for traders who seek to identify market trends, strength, and potential reversals with higher accuracy. By leveraging the Average Directional Index (ADX), Directional Indicator Plus (DI+), and Directional Indicator Minus (DI-), this indicator offers a comprehensive view of market dynamics.
New Overlay Feature: This script uses the new 'force overlay' feature which lets you plot on the chart as well as plotting in an oscillator pane at the same time.
force_overlay=true
Key Features
Comprehensive ADX Tracking: Tracks ADX values along with DI+ and DI- to provide a complete view of market trend strength and direction. The ADX measures the strength of the trend, while DI+ and DI- indicate the trend direction. This combined analysis helps traders identify strong and weak trends with precision.
Trend Duration Monitoring: Monitors the duration of strong and weak trends, offering insights into trend persistence and potential reversals. By keeping track of how long the ADX has been above or below a certain threshold, traders can gauge the sustainability of the current trend.
Customizable Alerts: Features multiple alert options for strong trends, weak trends, and DI crossovers, ensuring traders are notified of significant market events. These alerts can be tailored to notify traders when certain conditions are met, such as when the ADX crosses a threshold or when DI+ crosses DI-.
Adaptive Display Options: Includes customizable background color settings and extended statistics display for in-depth market analysis. Users can choose to highlight strong or weak trends on the chart background, making it easier to visualize market conditions at a glance.
In the example below, we have a bullish scenario play out where the DI+ has been above the DI- for 11 candles and our dashboard shows the average is 10.48 candles. With the ADX above its threshold this would be a bullish signal.
This ended up in a 20%+ move to the upside. The dashboard will help point out things to consider when looking to exit the position, the DI+ getting close to the max DI+ duration would be a sign that momentum is weakening and that price may cool off or even reverse.
How it Works
ADX Calculation: Computes the ADX, DI+, and DI- values using a user-defined period. The ADX is derived from the smoothed average of the absolute difference between DI+ and DI-. This calculation helps determine the strength of a trend without considering its direction.
Trend Duration Analysis: Tracks and calculates the duration of strong and weak trends, as well as DI+ and DI- durations. This analysis provides a detailed view of how long a trend has been in place, helping traders assess the reliability of the trend.
Alert System: Provides a robust alert system that triggers notifications for strong trends, weak trends, and DI crossovers. The alerts are based on specific conditions such as the duration of the trend or the crossover of directional indicators, ensuring traders are informed about critical market movements.
Visual Enhancements: Utilizes color gradients and background settings to visually represent trend strength and duration. This feature enhances the visual analysis of trends, making it easier for traders to identify significant market changes at a glance.
In the example below, we see the ADX weakening after we have just had a move up, if you are looking to get into this position you want to see the ADX growing with either the DI+ or DI- breaking their average durations.
As you can see below, although the ADX manages to move above the threshold, there are no DI+/- breaks which is shown by price moving sideways. Not something most traders would be interested in.
Application
Strategic Decision-Making: Assists traders in making informed decisions by providing detailed analysis of ADX movements and trend durations. By understanding the strength and direction of trends, traders can better time their entries and exits.
Trend Confirmation: Reinforces trading strategies by confirming potential reversals and trend strength through ADX and DI analysis. This confirmation helps traders validate their trading signals, reducing the risk of false signals.
Customized Analysis: Adapts to various trading styles with extensive input settings that control the display and sensitivity of trend data. Traders can customize the indicator to suit their specific needs, making it a versatile tool for different trading strategies.
The Advanced ADX Analysis by is an invaluable addition to a trader's toolkit, offering depth and precision in market trend analysis to navigate complex market conditions effectively. With its comprehensive tracking, alert system, and customizable display options, this indicator provides traders with the tools they need to stay ahead of the market.
Auto Fitting GARCH OscillatorOverview
The Auto Fitting GARCH Oscillator is a sophisticated volatility indicator that dynamically fits GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to the price data. It optimizes the parameters of the GARCH model to provide a reliable measure of volatility, which is then normalized to fit within a 0-100 range, making it easy to interpret as an oscillator. This indicator helps traders identify periods of high and low volatility, which can be crucial for making informed trading decisions.
Key Features
Dynamic GARCH(p, q) Fitting: Automatically optimizes the GARCH model parameters for the best fit.
Volatility Oscillator: Normalizes the volatility measure to a 0-100 range, indicating overbought and oversold conditions.
Customizable Timeframes: Adapts to various chart timeframes, from intraday to monthly data.
Projected Volatility: Provides options for projecting future volatility based on the optimized GARCH model.
User-friendly Visualization: Displays the oscillator with clear overbought and oversold levels.
Concepts Underlying the Calculations
The indicator leverages the GARCH model, which is widely used in financial time series analysis to model volatility clustering. The GARCH model considers past variances and returns to predict future volatility. This indicator dynamically adjusts the p and q parameters of the GARCH model within a specified range to find the optimal fit, minimizing the sum of squared errors (SSE).
How It Works
Data Preparation: Calculates the logarithmic returns and lagged variances from the price data.
SSE Optimization: Iterates through different p and q values to find the best GARCH parameters that minimize the SSE.
GARCH Calculation: Uses the optimized parameters to calculate the GARCH-based volatility.
Normalization: Normalizes the calculated volatility to a 0-100 range to form an oscillator.
Visualization: Plots the oscillator with overbought (70) and oversold (30) levels for easy interpretation.
How Traders Can Use It
Volatility Analysis: Identify periods of high and low volatility to adjust trading strategies accordingly.
Overbought/Oversold Conditions: Use the oscillator levels to identify potential reversal points in the market.
Risk Management: Incorporate volatility measures into risk management strategies to avoid trades during highly volatile periods.
Projection: Use the projected volatility feature to anticipate future market conditions.
Example Usage Instructions
Add the Indicator: Apply the "Auto Fitting GARCH Oscillator" to your chart from the Pine Script editor or TradingView library.
Customize Parameters: Adjust the maxP and maxQ values to set the range for GARCH model optimization.
Select Data Type: Choose between "Projected Variance in %" or "Projected Deviation in %" based on your analysis preference.
Set Projection Periods: Use the perForward input to specify how many periods forward you want to project the volatility.
Interpret the Oscillator: Observe the oscillator line and the overbought/oversold levels to make informed trading decisions.
Efficiency Weighted OrderFlow [AlgoAlpha]Introducing the Efficiency Weighted Orderflow Indicator by AlgoAlpha! 📈✨
Elevate your trading game with our cutting-edge Efficiency Weighted Orderflow Indicator, designed to provide clear insights into market trends and potential reversals. This tool is perfect for traders seeking to understand the underlying market dynamics through efficiency-weighted volume calculations.
🌟 Key Features 🌟
✨ Smooth OrderFlow Calculation : Option to smooth order flow data for more consistent signals.
🔧 Customizable Parameters : Adjust the Order Flow Period and HMA Smoothing Length to fit your trading strategy.
🔍 Visual Clarity : Easily distinguish between bullish and bearish trends with customizable colors.
📊 Standard Deviation Normalization : Keeps order flow values normalized for better comparison across different market conditions.
🔔 Trend Reversal Alerts : Stay ahead with built-in alert conditions for significant order flow changes.
🚀 Quick Guide to Using the Efficiency Weighted Orderflow Indicator
🛠 Add the Indicator: Search for "Efficiency Weighted Orderflow " in TradingView's Indicators & Strategies. Customize settings like smoothing and order flow period to fit your trading style.
📊 Market Analysis: Watch for trend reversal alerts to capture trading opportunities by studying the behaviour of the indicator.
🔔 Alerts: Enable notifications for significant order flow changes to stay updated on market trends.
🔍 How It Works
The Efficiency Weighted Orderflow Indicator starts by calculating the efficiency of price movements using the absolute difference between the close and open prices, divided by volume. The order flow is then computed by summing these efficiency-weighted volumes over a specified period, with an option to apply Hull Moving Average (HMA) smoothing for enhanced signal stability. To ensure robust comparison, the order flow is normalized using standard deviation. The indicator plots these values as columns, with distinct colors representing bullish and bearish trends. Customizable parameters for period length and smoothing allow traders to tailor the indicator to their strategies. Additionally, visual cues and alert conditions for trend reversals and significant order flow changes keep traders informed and ready to act. This indicator improves on the Orderflow aspect of our Standardized Orderflow indicator. The Efficiency Weighted Orderflow is less susceptible to noise and is also quicker at detecting trend changes.
Uptrick: Bullish/Bearish Signal DetectorDetailed Explanation of the "Uptrick: Bullish/Bearish Signal Detector" Script
The "Uptrick: Bullish/Bearish Signal Detector" script is a sophisticated tool designed for the TradingView platform, leveraging Pine Script version 5. This script is crafted to enhance traders' ability to identify bullish (buy) and bearish (sell) signals directly on their trading charts. By combining the power of the MACD (Moving Average Convergence Divergence) and RSI (Relative Strength Index) indicators, this script provides a unique and efficient method for detecting potential trading opportunities. Below is an in-depth exploration of its purpose, features, and functionality.
Purpose
The primary purpose of this script is to assist traders in identifying potential entry and exit points in the market by signaling bullish and bearish conditions. This automated detection helps traders make more informed decisions without the need to manually analyze complex indicators. By overlaying signals directly on the price chart, the script allows for quick visual identification of market trends and reversals.
Uniqueness
What sets this script apart is its dual use of MACD and RSI indicators. While many trading strategies might rely on a single indicator, combining MACD and RSI enhances the reliability of the signals by filtering out false positives. The script not only identifies trends but also adds a layer of confirmation through the RSI, which measures the speed and change of price movements.
Inputs and Features
Customizable Label Appearance:
The script allows users to customize the appearance of the labels that indicate bullish and bearish signals. Users can set their preferred colors for the labels and the text, ensuring that the signals are easily distinguishable and aesthetically pleasing on their charts.
MACD Calculation:
The script calculates the MACD line and signal line using user-defined input values for the fast length, slow length, and signal length. The MACD histogram, which is the difference between the MACD line and the signal line, is used to determine the momentum of the market.
RSI Calculation:
The RSI is calculated using a user-defined input length. The RSI helps in identifying overbought or oversold conditions, which are crucial for confirming the strength of the trend detected by the MACD.
Bullish and Bearish Conditions:
The script defines bullish conditions as those where the MACD histogram is positive and the RSI is above 50. Bearish conditions are defined where the MACD histogram is negative and the RSI is below 50. This combination of conditions ensures that signals are generated based on both momentum and relative strength, reducing the likelihood of false signals.
Label Plotting:
The script plots labels on the chart to indicate bullish and bearish signals. When a bullish condition is met, and the previous signal was not bullish, a "LONG" label is plotted. Similarly, when a bearish condition is met, and the previous signal was not bearish, a "SHORT" label is plotted. This feature helps in clearly marking the points of interest for traders, making it easier to spot potential trades.
Tracking Previous Signals:
To avoid repetitive signals, the script keeps track of the last signal. If the last signal was bullish, it avoids plotting another bullish signal immediately. The same logic applies to bearish signals. This tracking ensures that signals are spaced out and only significant changes in market conditions are highlighted.
How It Works
The script operates in a loop, processing each bar (or candlestick) on the chart as new data comes in. It calculates the MACD and RSI values for each bar and checks if the current conditions meet the criteria for a bullish or bearish signal. If a signal is detected and it is different from the last signal, a label is plotted on the chart at the current bar's price level. This real-time processing allows traders to see the signals as they form, providing timely insights into market movements.
Practical Application
For practical use, a trader would add this script to their TradingView chart. They can customize the input parameters for the MACD and RSI calculations to fit their trading strategy or preferred settings. Once added, the script will automatically analyze the price data and start plotting "LONG" and "SHORT" labels based on the detected signals. Traders can then use these labels to make decisions on entering or exiting trades, adjusting their strategy as necessary based on the signals provided.
Conclusion
The "Uptrick: Bullish/Bearish Signal Detector" script is a powerful tool for any trader looking to leverage technical indicators for better trading decisions. By combining MACD and RSI, it offers a robust method for detecting market trends and potential reversals. The customizable features and real-time signal plotting make it a versatile and user-friendly addition to any trading toolkit. This script not only simplifies the process of technical analysis but also enhances the accuracy of trading signals, thereby potentially increasing the trader's success rate in the market.
Moving Average Z-Score Suite [BackQuant]Moving Average Z-Score Suite
1. What is this indicator
The Moving Average Z-Score Suite is a versatile indicator designed to help traders identify and capitalize on market trends by utilizing a variety of moving averages. This indicator transforms selected moving averages into a Z-Score oscillator, providing clear signals for potential buy and sell opportunities. The indicator includes options to choose from eleven different moving average types, each offering unique benefits and characteristics. It also provides additional features such as standard deviation levels, extreme levels, and divergence detection, enhancing its utility in various market conditions.
2. What is a Z-Score
A Z-Score is a statistical measurement that describes a value's relationship to the mean of a group of values. It is measured in terms of standard deviations from the mean. For instance, a Z-Score of 1.0 means the value is one standard deviation above the mean, while a Z-Score of -1.0 indicates it is one standard deviation below the mean. In the context of financial markets, Z-Scores can be used to identify overbought or oversold conditions by determining how far a particular value (such as a moving average) deviates from its historical mean.
3. What moving averages can be used
The Moving Average Z-Score Suite allows users to select from the following eleven moving averages:
Simple Moving Average (SMA)
Hull Moving Average (HMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Double Exponential Moving Average (DEMA)
Running Moving Average (RMA)
Linear Regression Curve (LINREG) (This script can be found standalone )
Triple Exponential Moving Average (TEMA)
Arnaud Legoux Moving Average (ALMA)
Kalman Hull Moving Average (KHMA)
T3 Moving Average
Each of these moving averages has distinct properties and reacts differently to price changes, allowing traders to select the one that best fits their trading style and market conditions.
4. Why Turning a Moving Average into a Z-Score is Innovative and Its Benefits
Transforming a moving average into a Z-Score is an innovative approach because it normalizes the moving average values, making them more comparable across different periods and instruments. This normalization process helps in identifying extreme price movements and mean-reversion opportunities more effectively. By converting the moving average into a Z-Score, traders can better gauge the relative strength or weakness of a trend and detect potential reversals. This method enhances the traditional moving average analysis by adding a statistical perspective, providing clearer and more objective trading signals.
5. How It Can Be Used in the Context of a Trading System
In a trading system, it can be used to generate buy and sell signals based on the Z-Score values. When the Z-Score crosses above zero, it indicates a potential buying opportunity, suggesting that the price is above its mean and possibly trending upward. Conversely, a Z-Score crossing below zero signals a potential selling opportunity, indicating that the price is below its mean and might be trending downward. Additionally, the indicator's ability to show standard deviation levels and extreme levels helps traders set profit targets and stop-loss levels, improving risk management and trade planning.
6. How It Can Be Used for Trend Following
For trend-following strategies, it can be particularly useful. The Z-Score oscillator helps traders identify the strength and direction of a trend. By monitoring the Z-Score and its rate of change, traders can confirm the persistence of a trend and make informed decisions to enter or exit trades. The indicator's divergence detection feature further enhances trend-following by identifying potential reversals before they occur, allowing traders to capitalize on trend shifts. By providing a clear and quantifiable measure of trend strength, this indicator supports disciplined and systematic trend-following strategies.
No backtests for this indicator due to the many options and ways it can be used,
Enjoy
FX Index Curve Oscillator (FICO)We can approximate the TVC:DXY with simple multiplication, rather than using geometric weighted averages; the values will be different, but the charts will look almost the same. Because we can make a "good enough" version of DXY, we can also extend this concept to the other major currencies:
AUD - Yellow
CAD - Red
CHF - Orange
EUR - Purple
GBP - Green
JPY - White
NZD - Lime green
USD - Blue
This indicator works by constructing an "index" for each currency, performing a lookback to figure out the rate of change, and then smoothing the values. These values are fed through an oscillator to normalize them between -1.00 and +1.00, before finally being smoothed again. Interestingly, using HMA to smooth them the second time will cause the values to leak past 1.00, which we can also use as a signal.
If you want to change the values, I find that the biggest difference comes from the lookback and oscillator settings; the MA/smoothing is probably good enough. The default settings are for doing forex trades on the daily chart. Other timeframes are possible, but I could not find any settings that work. It might also be possible to use a similar approach on other assets (crypto, metals, indexes, etc) but I have not tried yet.
In my own testing, what I found to be a good approach is to look for a currency to be above +1 and another to be below -1, and then look for color changes; ideally this will happen on the same bar/candle.
You can also consider two line crosses, breaking above or below 1, etc as other entry signals. I find that price will either move immediately, or take a candle or two to retrace and then start moving.
Happy trading!
Unfortunately, the indicator pane can get quite crowded; if you're testing for a single currency pair, you may want to disable some of the plotted lines:
Zig Zag/Consecutive Bars [UkutaLabs]█ OVERVIEW
The Zig Zag/Consecutive Bars indicator is a powerful trading tool that helps to visualise the flow of the market. This indicator allows users to see at a glance when a candle closes at a new high or a new low, which can be incorporated into a variety of trading strategies to better understand points of reversal and consolidation.
This indicator also displays the RSI score of each pivot, as well as a trailing count of how many bars it has been since there was a new high/low.
█ USAGE
As each bar finishes, the script will check if it closed above or below the previous bar’s high or low, depending on the current trend direction. When a new high or low is set, the script will then look for a move in the other direction. This can be a powerful tool that can identify when the market is trending strongly, as well as identifying when the market has a weak or no trend.
At each pivot point, the RSI score is displayed. This serves as additional confirmation to how strong the trend is. The RSI labels can be turned off in the settings.
As each trend develops, the script will count and display the number of bars that have closed since the most recent pivot. These labels can be turned off in the settings.
█ SETTINGS
Configuration
• Show RSI Scores: Determines whether or not labels displaying RSI scores are drawn.
• Show Counter: Determines whether or not labels displaying the number of bars since the most recent pivot are drawn.
• Line Color: Determines the color of the Zig Zag line.
Fisher Transform on RSIOverview
The Fisher Transform on RSI indicator combines the Relative Strength Index (RSI) with the Fisher Transform to offer a refined tool for identifying market turning points and trends. By applying the Fisher Transform to the RSI, this indicator converts RSI values into a Gaussian normal distribution, enhancing the precision of detecting overbought and oversold conditions. This method provides a clearer and more accurate identification of potential market reversals than the standard RSI.
Key/Unique Features
Fisher Transform Applied to RSI : Transforms RSI values into a Gaussian normal distribution, improving the detection of overbought and oversold conditions.
Smoothing : Applies additional smoothing to the Fisher Transform, reducing noise and providing clearer signals.
Signal Line : Includes a signal line to identify crossover points, indicating potential buy or sell signals.
Custom Alerts : Built-in alert conditions for bullish and bearish crossovers, keeping traders informed of significant market movements.
Visual Enhancements : Background color changes based on crossover conditions, offering immediate visual cues for potential trading opportunities.
How It Works
RSI Calculation : The indicator calculates the Relative Strength Index (RSI) based on the selected source and period length.
Normalization : The RSI values are normalized to fit within a range of -1 to 1, which is essential for the Fisher Transform.
Fisher Transform : The normalized RSI values undergo the Fisher Transform, converting them into a Gaussian normal distribution.
Smoothing : The transformed values are smoothed using a simple moving average to reduce noise and provide more reliable signals.
Signal Line : A signal line, which is a simple moving average of the smoothed Fisher Transform, is plotted to identify crossover points.
Alerts and Visuals : Custom alert conditions are set for bullish and bearish crossovers, and the background color changes to indicate these conditions.
Usage Instructions
Trend Identification : Use the Fisher Transform on RSI to identify overbought and oversold conditions with enhanced precision, aiding in spotting potential trend reversals.
Trade Signals : Monitor the crossovers between the smoothed Fisher Transform and the signal line. A bullish crossover suggests a potential buying opportunity, while a bearish crossover indicates a potential selling opportunity.
Alerts : Set custom alerts based on the built-in conditions to receive notifications when important crossover events occur, ensuring you never miss a trading opportunity.
Visual Cues : Utilize the background color changes to quickly identify bullish (green) and bearish (red) conditions, providing immediate visual feedback on market sentiment.
Complementary Analysis : Combine this indicator with other technical analysis tools and indicators to enhance your overall trading strategy and make more informed decisions.
Comprehensive Market Overview1. What is this indicator about?
The "Comprehensive Market Overview" indicator provides a holistic view of the market by incorporating several key metrics:
Close Price: Displays the current close price below each candle.
Percent from All-Time High: Calculates how far the current close price is from the highest high observed over a specified period.
RSI (Relative Strength Index): Measures the momentum of price movements to assess whether a stock is overbought or oversold.
Volume Gain: Computes the current volume relative to its 20-period simple moving average (SMA), indicating volume strength or weakness.
Volatility: Quantifies market volatility by calculating the ratio of the Bollinger Bands' width (difference between upper and lower bands) to the SMA.
2. How it works?
Close Price Label: This label is displayed below each bar, showing the current close price.
Percent from All-Time High: Calculates the percentage difference between the highest high observed (all-time high) and the current close price.
RSI Calculation: Computes the RSI using a 14-period setting, providing insight into whether a stock is potentially overbought or oversold.
Volume Strength: Computes the current volume divided by its 20-period SMA, indicating whether volume is above or below average.
Volatility Calculation: Calculates the width of the Bollinger Bands (based on a 20-period SMA and 2 standard deviations) and expresses it as a percentage of the SMA, providing a measure of market volatility
3.Correct Trend Identification with Indicators
All-Time High (ATH) Levels:
Low Value (Near ATH): When the percent from ATH is low (close to 0%), it indicates that the current price is near the all-time high zone. This suggests strong bullish momentum and potential resistance levels.
High Value (Below ATH): A high percentage from ATH indicates how much the current price is below the all-time high. This could signal potential support levels or opportunities for price recovery towards previous highs.
RSI (Relative Strength Index):
Overbought (High RSI): RSI values above 70 typically indicate that the asset is overbought, suggesting a potential reversal or correction in price.
Oversold (Low RSI): RSI values below 30 indicate oversold conditions, suggesting a potential rebound or price increase.
Swing Trading Strategies
Confirmation with Visual Analysis: Visualizing the chart to confirm ATH levels and RSI readings can provide strong indications of market sentiment and potential trading opportunities:
Bullish Signals: Look for prices near ATH with RSI confirming strength (not yet overbought), indicating potential continuation or breakout.
Bearish Signals: Prices significantly below ATH with RSI showing weakness (not yet oversold), indicating potential for a bounce or reversal.
Volume Confirmation: Comparing current volume to its SMA helps confirm the strength of price movements. Higher current volume relative to the SMA suggests strong price action.
Volatility Assessment: Monitoring volatility through the Bollinger Bands' width ratio helps assess potential price swings. Narrow bands suggest low volatility, while wide bands indicate higher volatility and potential trading opportunities.
4.Entry and Exit Points:
Entry: Consider entering long positions near support levels when prices are below ATH and RSI is oversold. Conversely, enter short positions near resistance levels when prices are near ATH and RSI is overbought.
Exit: Exit long positions near resistance or ATH levels when prices show signs of resistance or RSI becomes overbought. Exit short positions near support levels or when prices rebound from oversold conditions.
Risk Management: Always incorporate risk management techniques such as setting stop-loss orders based on support and resistance levels identified through ATH and RSI analysis.
Implementation Example
Biquad MACDThis indicator reimagines the traditional MACD by incorporating a biquad band pass filter, offering a refined approach to identifying momentum and trend changes in price data. The standard MACD is essentially a band pass filter, but often it lacks precision. The biquad band pass filter addresses this limitation by providing a more focused frequency range, enhancing the quality of signals.
The MACD Length parameter determines the length of the band pass filter, influencing the frequency range that is isolated. Adjusting this length allows you to focus on different parts of the price movement spectrum.
The Bandwidth (BW) setting controls the width of the frequency band in octaves. It affects the smoothness of the MACD line. A larger bandwidth results in less smooth output, capturing a broader range of frequencies, while a smaller bandwidth focuses on a narrower range, providing a smoother signal.
The Signal Length parameter sets the period for the exponential moving average of the MACD line, which acts as a signal line to identify potential buy and sell points.
Key Features of the Biquad MACD
The MACD is a well-known momentum indicator used to identify changes in the strength, direction, momentum, and duration of a trend in a stock's price. By applying a biquad band pass filter, this version of the MACD provides a more refined and accurate representation of price movements.
The biquad filter offers smooth response and minimal phase distortion, making it ideal for technical analysis. The customizable MACD length and bandwidth allow for flexible adaptation to different trading strategies and market conditions. The signal line smooths the MACD values, providing clear crossover points to indicate potential market entry and exit signals.
The histogram visually represents the difference between the MACD and the signal line, changing colors to indicate rising or falling momentum, which helps in quickly identifying trend changes.
By incorporating the Biquad MACD into your trading toolkit, you can enhance your chart analysis with clearer insights into momentum and trend changes, leading to more informed trading decisions.
Stochastic Biquad Band Pass FilterThis indicator combines the power of a biquad band pass filter with the popular stochastic oscillator to provide a unique tool for analyzing price movements.
The Filter Length parameter determines the center frequency of the biquad band pass filter, affecting which frequency band is isolated. Adjusting this parameter allows you to focus on different parts of the price movement spectrum.
The Bandwidth (BW) controls the width of the frequency band in octaves. It represents the bandwidth between -3 dB frequencies for the band pass filter. A narrower bandwidth results in a more focused filtering effect, isolating a tighter range of frequencies.
The %K Length parameter sets the period for the stochastic calculation, determining the range over which the stochastic values are calculated.
The %K Smoothing parameter applies a simple moving average to the %K values to smooth out the oscillator line.
The %D Length parameter sets the period for the %D line, which is a simple moving average of the %K line, providing a signal line for the oscillator.
Key Features of the Stochastic Biquad Band Pass Filter
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. In this implementation, the biquad filter is configured as a band pass filter, which allows frequencies within a specified band to pass while attenuating frequencies outside this band. This is particularly useful in trading to isolate specific price movements, making it easier to detect patterns and trends within a targeted frequency range.
The stochastic oscillator is a popular momentum indicator that shows the location of the close relative to the high-low range over a set number of periods. Combining it with a biquad band pass filter enhances its effectiveness by focusing on specific frequency bands of price movements.
By incorporating this stochastic biquad band pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into specific frequency bands of price movements, leading to more informed trading decisions.
Biquad Band Pass FilterThis indicator utilizes a biquad band pass filter to isolate and highlight a specific frequency band in price data, helping traders focus on price movements within a targeted frequency range.
The Length parameter determines the center frequency of the filter, affecting which frequency band is isolated. Adjusting this parameter allows you to focus on different parts of the price movement spectrum.
The Bandwidth (BW) controls the width of the frequency band in octaves. It represents the bandwidth between -3 dB frequencies for the band pass filter. A narrower bandwidth results in a more focused filtering effect, isolating a tighter range of frequencies.
Key Features of Biquad Filters
Biquad filters are a type of digital filter that provides a combination of low-pass, high-pass, band-pass, and notch filtering capabilities. In this implementation, the biquad filter is configured as a band pass filter, which allows frequencies within a specified band to pass while attenuating frequencies outside this band. This is particularly useful in trading to isolate specific price movements, making it easier to detect patterns and trends within a targeted frequency range.
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. The customizable length and bandwidth allow for flexible adaptation to different trading strategies and market conditions. Designed for real-time charting, the biquad filter operates efficiently without significant lag, ensuring timely analysis.
By incorporating this biquad band pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into specific frequency bands of price movements, leading to more informed trading decisions.
Biquad High Pass FilterThis indicator utilizes a biquad high pass filter to filter out low-frequency components from price data, helping traders focus on high-frequency movements and detect rapid changes in trends.
The Length parameter determines the cutoff frequency of the filter, affecting how quickly the filter responds to changes in price. A shorter length allows the filter to react more quickly to high-frequency movements.
The Q Factor controls the sharpness of the filter. A higher Q value results in a more precise filtering effect by narrowing the frequency band. However, be cautious when setting the Q factor too high, as it can induce resonance, amplifying certain frequencies and potentially making the filter less effective by introducing unwanted noise.
Key Features of Biquad Filters
Biquad filters are a type of digital filter that provides a combination of low-pass, high-pass, band-pass, and notch filtering capabilities. In this implementation, the biquad filter is configured as a high pass filter, which allows high-frequency signals to pass while attenuating lower-frequency components. This is particularly useful in trading to highlight rapid price movements, making it easier to spot short-term trends and patterns.
Biquad filters are known for their smooth response and minimal phase distortion, making them ideal for technical analysis. The customizable length and Q factor allow for flexible adaptation to different trading strategies and market conditions. Designed for real-time charting, the biquad filter operates efficiently without significant lag, ensuring timely analysis.
By incorporating this biquad high pass filter into your trading toolkit, you can enhance your chart analysis with clearer insights into rapid price movements, leading to more informed trading decisions.
Strategy SEMA SDI WebhookPurpose of the Code:
The strategy utilizes Exponential Moving Averages (EMA) and Smoothed Directional Indicators (SDI) to generate buy and sell signals. It includes features like leverage, take profit, stop loss, and trailing stops. The strategy is intended for backtesting and automating trades based on the specified indicators and conditions.
Key Components and Functionalities:
1.Strategy Settings:
Overlay: The strategy will overlay on the price chart.
Slippage: Set to 1.
Commission Value: Set to 0.035.
Default Quantity Type: Percent of equity.
Default Quantity Value: 50% of equity.
Initial Capital: Set to 1000 units.
Calculation on Order Fills: Enabled.
Process Orders on Close: Enabled.
2.Date and Time Filters:
Inputs for enabling/disabling start and end dates.
Filters to execute strategy only within specified date range.
3.Leverage and Quantity:
Leverage: Adjustable leverage input (default 3).
USD Percentage: Adjustable percentage of equity to use for trades (default 50%).
Initial Capital: Calculated based on leverage and percentage of equity.
4.Take Profit, Stop Loss, and Trailing Stop:
Inputs for enabling/disabling take profit, stop loss, and trailing stop.
Adjustable parameters for take profit percentage (default 25%), stop loss percentage (default 4.8%), and trailing stop percentage (default 1.9%).
Calculations for take profit, stop loss, trailing price, and maximum profit tracking.
5.EMA Calculations:
Fast and slow EMAs.
Smoothed versions of the fast and slow EMAs.
6.SDI Calculations:
Directional movement calculation for positive and negative directional indicators.
Difference between the positive and negative directional indicators, smoothed.
7.Buy/Sell Conditions:
Long (Buy) Condition: Positive DI is greater than negative DI, and fast EMA is greater than slow EMA.
Short (Sell) Condition: Negative DI is greater than positive DI, and fast EMA is less than slow EMA.
8.Strategy Execution:
If buy conditions are met, close any short positions and enter a long position.
If sell conditions are met, close any long positions and enter a short position.
Exit conditions for long and short positions based on take profit, stop loss, and trailing stop levels.
Close all positions if outside the specified date range.
Usage:
This strategy is used to automate trading based on the specified conditions involving EMAs and SDI. It allows backtesting to evaluate performance based on historical data. The strategy includes risk management through take profit, stop loss, and trailing stops to protect gains and limit losses. Traders can customize the parameters to fit their specific trading preferences and risk tolerance. Differently, it can perform leverage analysis and use it as a template.
By using this strategy, traders can systematically execute trades based on technical indicators, helping to remove emotional bias and improve consistency in trading decisions.
Important Note:
This script is provided for educational and template purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
Internal Bar Strength IBS [Anan]This indicator calculates and displays the Internal Bar Strength (IBS) along with its moving average. The IBS is a measure that represents where the closing price is relative to the high-low range of a given period.
█ Main Formula
The core of this indicator is the Internal Bar Strength (IBS) calculation. The basic IBS formula is:
ibs = (close - low) / (high - low)
I enhanced the original formula by incorporating a user-defined length parameter. This modification allows for greater flexibility in analysis and interpretation. The extended version enables users to adjust the indicator's length according to their specific needs or market conditions. Notably, setting the length parameter to 1 reproduces the behavior of the original formula, maintaining backward compatibility while offering expanded functionality:
ibs = (close - ta.lowest(low, ibs_length)) / (ta.highest(high, ibs_length) - ta.lowest(low, ibs_length))
Where:
- `close` is the closing price of the current bar
- `lowest low` is the lowest low price over the specified IBS length
- `highest high` is the highest high price over the specified IBS length
█ Key Features
- Calculates IBS using a user-defined length
- Applies a moving average to the IBS values
- Offers multiple moving average types
- Includes optional Bollinger Bands or Donchian Channel overlays
- Visualizes bull and bear areas
█ Inputs
- IBS Length: The period used for IBS calculation
- MA Type: The type of moving average applied to IBS (options: SMA, EMA, SMMA, WMA, VWMA, Bollinger Bands, Donchian)
- MA Length: The period used for the moving average calculation
- BB StdDev: Standard deviation multiplier for Bollinger Bands
█ How to Use and Interpret
1. IBS Line Interpretation:
- IBS values range from 0 to 1
- Values close to 1 indicate the close was near the high, suggesting a bullish sentiment
- Values close to 0 indicate the close was near the low, suggesting a bearish sentiment
- Values around 0.5 suggest the close was near the middle of the range
2. Overbought/Oversold Conditions:
- IBS values above 0.8 (teal zone) may indicate overbought conditions
- IBS values below 0.2 (red zone) may indicate oversold conditions
- These zones can be used to identify potential reversal points
3. Trend Identification:
- Consistent IBS values above 0.5 may indicate an uptrend
- Consistent IBS values below 0.5 may indicate a downtrend
4. Using Moving Averages:
- The yellow MA line can help smooth out IBS fluctuations
- Crossovers between the IBS and its MA can signal potential trend changes
5. Bollinger Bands/Donchian Channel:
- When enabled, these can provide additional context for overbought/oversold conditions
- IBS touching or exceeding the upper band may indicate overbought conditions
- IBS touching or falling below the lower band may indicate oversold conditions
Remember that no single indicator should be used in isolation. Always combine IBS analysis with other technical indicators, price action analysis, and broader market context for more reliable trading decisions.
MTF-Colored EMA Difference and Stochastic indicatorThis indicator combines two popular technical analysis tools: the Exponential Moving Average (EMA) and the Stochastic Oscillator, with the added flexibility of analyzing them across multiple time frames. It visually represents the difference between two EMAs and the crossover signals from the Stochastic Oscillator, providing a comprehensive view of the market conditions.
Components:
EMA Difference Histogram :
EMA Calculation : The indicator calculates two EMAs (EMA1 and EMA2) for the selected time frame.
EMA Difference : The difference between EMA1 and EMA2 is plotted as a 4 coloured histogram.
Stochastic Oscillato r:
Calculation : The %K and %D lines of the Stochastic Oscillator are calculated for the selected time frame.
Additional Confirmation via Colors :
Green: %K is above %D, indicating a bullish signal.
Red: %K is below %D, indicating a bearish signal.
Entry and Exit Strategies
Entry Strategy :
Bullish Entry :
Condition 1: The histogram is Dark green (indicating a strong upward trend).
Condition 2: The Stochastic colour is green (%K is above %D).
Bearish Entry :
Condition 1: The histogram is Dark Red (indicating a strong downward trend).
Condition 2: The Stochastic colour is red (%K is below %D).
Exit Strategy:
Bullish Exit:
Condition: The Stochastic colour turns red (%K crosses below %D).
Bearish Exit:
Condition: The Stochastic colour turns green (%K crosses above %D).
Additional Considerations:
Time Frame Selection : The chosen time frame for both the EMA and Stochastic calculations should align with the trader’s strategy (e.g., daily for swing trading, hourly for intraday trading).
Risk Management : Implement stop-loss orders to manage risk effectively. The stop-loss can be placed below the recent swing low for long positions and above the recent swing high for short positions.
Confirmation : Consider using this indicator in conjunction with other technical analysis tools to confirm signals and reduce the likelihood of false entries and exits.
