Money Flow Index Trend Zone Strength [UAlgo]The "Money Flow Index Trend Zone Strength " indicator is designed to analyze and visualize the strength of market trends and OB/OS zones using the Money Flow Index (MFI). The MFI is a momentum indicator that incorporates both price and volume data, providing insights into the buying and selling pressure in the market. This script enhances the traditional MFI by introducing trend and zone strength analysis, helping traders identify potential trend reversals and continuation points.
🔶 Customizable Settings
Amplitude: Defines the range for the MFI Zone Strength calculation.
Wavelength: Period used for the MFI calculation and Stochastic calculations.
Smoothing Factor: Smoothing period for the Stochastic calculations.
Show Zone Strength: Enables/disables visualization of the MFI Zone Strength line.
Show Trend Strength: Enables/disables visualization of the MFI Trend Strength area.
Trend Strength Signal Length: Period used for the final smoothing of the Trend Strength indicator.
Trend Anchor: Selects the anchor point (0 or 50) for the Trend Strength Stochastic calculation.
Trend Transform MA Length: Moving Average length for the Trend Transform calculation.
🔶 Calculations
Zone Strength (Stochastic MFI):
The highest and lowest MFI values over a specified amplitude are used to normalize the MFI value:
MFI Highest: Highest MFI value over the amplitude period.
MFI Lowest: Lowest MFI value over the amplitude period.
MFI Zone Strength: (MFI Value - MFI Lowest) / (MFI Highest - MFI Lowest)
By normalizing and smoothing the MFI values, we aim to highlight the relative strength of different market zones.
Trend Strength:
The smoothed MFI zone strength values are further processed to calculate the trend strength:
EMA of MFI Zone Strength: Exponential Moving Average of the MFI Zone Strength over the wavelength period.
Stochastic of EMA: Stochastic calculation of the EMA values, smoothed with the same smoothing factor.
Purpose: The trend strength calculation provides insights into the underlying market trends. By using EMA and stochastic functions, we can filter out noise and better understand the overall market direction. This helps traders stay aligned with the prevailing trend and make more informed trading decisions.
🔶 Usage
Interpreting Zone Strength: The zone strength plot helps identify overbought and oversold conditions. A higher zone strength indicates potential overbought conditions, while a lower zone strength suggests oversold conditions, can suggest areas for entry/exit decisions.
Interpreting Trend Strength: The trend strength plot visualizes the underlying market trend, can help signal potential trend continuation or reversal based on the chosen anchor point.
Using the Trend Transform: The trend transform plot provides an additional layer of trend analysis, helping traders identify potential trend reversals and continuation points.
Combine the insights from the zone strength and trend strength plots with other technical analysis tools to make informed trading decisions. Look for confluence between different indicators to increase the reliability of your trades.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Oszillatoren
Power Law Volatility by G. Santostasi Introduction
This TradingView indicator is designed to provide a comprehensive analysis of Bitcoin's price movements using the concept of power laws. The indicator leverages the mathematical properties of power laws to predict returns and highlight significant deviations from expected trends. By applying the power law model to Bitcoin's price data, we aim to capture the diminishing returns over time and provide valuable insights to traders and analysts.
Theoretical Foundation
The foundation of this indicator is based on the power law, which describes a relationship between two quantities where one quantity varies as a power of another. Specifically, in the context of Bitcoin prices, we observe that returns follow a power law relationship with time.
Mathematically, if the power law holds true, the price P at time 𝑡 can be expressed as:
log(𝑃)=𝑚log(𝑡)+c where m is the slope of the power law and c is the y-intercept.
To understand the returns, we consider two points in time,
𝑡1and 𝑡2, with corresponding prices 𝑃 and 𝑃2. The returns can be derived as follows:
log(𝑃2)−log(𝑃1)=𝑚(log(𝑡2)−log(𝑡1))
This simplifies to:
log(𝑃2/𝑃1)=𝑚log(𝑡2/𝑡1)
For daily data, we let 𝑡2=𝑡1+1resulting in:
log(𝑅)=𝑚log(𝑡+1/𝑡)
where 𝑅 represents the returns, 𝑡 is the number of days from the Genesis Block, and
𝑚 is the slope of the power law.
Observations and Data Analysis
Using historical Bitcoin price data, we observe that returns decrease over time, which is indicative of diminishing returns. To validate this observation, we averaged real returns over a two-month period and compared them with the theoretical results derived from the power law:
𝑚log(𝑡2/𝑡1)
The comparison shows that the averaged real returns align closely with the theoretical predictions, reinforcing the validity of the power law model.
This alignment indicates that the power law is not merely an arbitrary straight line but a model that accurately captures the decay of returns over time. The scaling property of the power law holds at all scales, providing a robust framework for analyzing Bitcoin's price dynamics.
Indicator Components
The indicator comprises several components to visualize the power law's implications and provide actionable insights:
Theoretical Power Law Trend:
Plots the theoretical result from the power law model.
Shows the expected returns based on the power law relationship.
Real Returns:
Plots the real returns averaged over a user-defined Simple Moving Average (SMA) or Exponential Moving Average (EMA).
Provides a comparison between actual market performance and theoretical predictions.
When the real volatility is above the theoretical one derived from the power law the indicator identifies times when the price is overvalued.
Standard Deviations:
Calculates standard deviations on a moving window basis.
Plots deviations from the theoretical power law trend, highlighting significant discrepancies.
Color-Coded Thresholds:
Highlights points that deviate significantly from the expected trend.
Red indicates returns above the upper threshold (indicating potential overperformance or overvaluation).
Green indicates returns below the lower threshold (indicating potential underperformance or undervaluation).
Practical Usage
Traders and analysts can use this indicator to:
Identify periods where Bitcoin's returns deviate significantly from the expected power law trend.
Make informed trading decisions based on the likelihood of mean reversion to the theoretical trend.
Understand the long-term diminishing returns trend and adjust investment strategies accordingly.
Conclusion
This TradingView indicator leverages the power law to provide a detailed and theoretically grounded analysis of Bitcoin's price movements. By comparing real returns with theoretical predictions, the indicator offers valuable insights into market behavior and highlights significant deviations. The use of color-coded thresholds further enhances the utility of the indicator, making it an essential tool for traders and analysts seeking to understand and capitalize on Bitcoin's price dynamics
Money Flow Index Crossover IndicatorThe "Money Flow Index Crossover Indicator" is a specialized technical analysis tool designed to assist traders by providing a clear visualization of potential buy and sell signals based on the Money Flow Index (MFI) and its smoothed moving average (SMA). This indicator delineates overbought and oversold zones, offering valuable insights into market dynamics. It operates as an oscillator on a separate pane, helping traders identify bullish and bearish market conditions with greater precision. By incorporating k-Nearest Neighbor (KNN) machine learning techniques, this indicator enhances the reliability and accuracy of the signals provided.
Originality and Usefulness:
This script is not just a simple mashup of existing indicators but integrates multiple components to create a unique and comprehensive analysis tool. The combined information from the MFI, its smoothed moving average, and the KNN machine learning techniques influence the form and accuracy of the Money Flow Index Average line and the Smoothed Money Flow Index line giving a visually helpful representation of overbought and oversold conditions. These lines are displayed in an oscillator style crossover, allowing users to visualize potential buy and sell zones for setting up potential signals. The user can adjust various settings of these tools behind the code to fine-tune the behavior and sensitivity of these lines. This integration provides a more robust and insightful trading tool that can adapt to different market conditions and trading styles.
How It Works:
Inputs:
MFI Settings:
Show Signals: Allows users to toggle the display of MFI and SMA crossing signals, which are critical for identifying potential market reversals.
Plot Amount: Determines the number of plots in the heat map, ranging from 2 to 28, enabling customization based on user preference.
Source: Defines the data source for MFI calculations, typically set to OHLC4 for a balanced view of price movements.
Smooth Initial MFI Length: Specifies the smoothing length for the initial MFI calculations to reduce noise and enhance signal clarity.
MFI SMA Length: Sets the length for the SMA used to smooth the MFI average, providing a more stable reference line.
Machine Learning Settings:
Use KInSource: Option to average MFI data by adding a lookback to the source, improving the accuracy of historical comparisons.
KNN Distance Requirement: Defines the distance calculation method for KNN (Max, Min, Both) to refine the data filtering process.
Machine Learning Length: Specifies the amount of machine learning data stored for smoothing results, balancing between responsiveness and stability.
KNN Length: Sets the number of KNN used to calculate the allowable distance range, enhancing the precision of the machine learning model.
Fast and Slow Lengths: Defines the lengths for fast and slow MFI calculations, allowing the indicator to capture different market dynamics.
Smoothing Length: Determines the length at which MFI calculations start for a more smoothed result, reducing false signals.
Variables and Functions:
KNN Function: Filters machine learning data to calculate valid distances based on defined criteria, ensuring more accurate MFI averages.
MFI Calculations: Computes both fast and slow MFI values, applies smoothing, and stores them for KNN processing to refine signal generation.
MFI KNN Calculation: Uses the KNN function to calculate the machine learning average of MFI values, enhancing signal reliability.
MFI Average and SMA: Calculates the average and smoothed MFI values, which are crucial for determining crossover signals.
Calculations:
MFI Values: Calculates current fast and slow MFI values and applies smoothing to reduce market noise.
Storage Arrays: Stores MFI data in arrays for KNN processing, enabling historical comparison and pattern recognition.
KNN Processing: Computes the machine learning average of MFI values using the KNN function, improving the robustness of signals.
MFI Average: Scales the MFI average to fit the heat map and calculates the smoothed SMA, providing a clear visual representation of trends.
Crossover Signals: Identifies bullish (MFI crossing above SMA) and bearish (MFI crossing below SMA) signals, which are key for making trading decisions.
Plots and Visuals:
MFI Average and SMA Lines: Plots the MFI average and smoothed SMA on the chart, allowing traders to easily visualize market trends and potential reversals.
Zones: Defines and plots overbought, neutral, and oversold zones for easy visualization. The recommended settings for these zones are:
Overbought Zone: Level set to approximately 24.6, indicating a potential market top.
Neutral Zone: Level set to 14, representing a balanced market condition.
Oversold Zone: Level set to 5.4, signaling a potential market bottom.
Crossover Marks: Plots circles on the chart to indicate bullish and bearish crossover signals, making it easier to spot entry and exit points.
Visual Alerts:
Bullish and Bearish Alerts: one can see overbought and oversold conditions and up alert conditions for bullish and bearish MFI crossover signals, enabling traders to have access to visual cues when these events are on trajectory to occur and, if they occur, act promptly with the visual representation of its zones.
Why It's Helpful:
The "Money Flow Index Crossover Indicator" provides traders with a sophisticated tool to identify potential buy and sell conditions based on the combined information of the MFI and its smoothed moving average. The KNN machine learning techniques enhance the accuracy of this indicator's clear visual representation of overbought, neutral, and oversold zones. This combination of data represented on the chart helps traders make informed decisions about market conditions. This indicator is particularly useful for traders looking to refine their entry and exit points by leveraging advanced data analysis in respect to overbought and oversold conditions.
Disclaimer:
This indicator is intended to assist traders in making informed decisions based on technical analysis. However, it is not a guarantee of future performance and should be used in conjunction with other analysis techniques and risk management practices. Past performance is not indicative of future results, and traders should exercise caution and perform their own due diligence before making any trading decisions.
Chandelier Exit Strategy with 200 EMA FilterStrategy Name and Purpose
Chandelier Exit Strategy with 200EMA Filter
This strategy uses the Chandelier Exit indicator in combination with a 200-period Exponential Moving Average (EMA) to generate trend-based trading signals. The main purpose of this strategy is to help traders identify high-probability entry points by leveraging the Chandelier Exit for stop loss levels and the EMA for trend confirmation. This strategy aims to provide clear rules for entries and exits, improving overall trading discipline and performance.
Originality and Usefulness
This script integrates two powerful indicators to create a cohesive and effective trading strategy:
Chandelier Exit : This indicator is based on the Average True Range (ATR) and identifies potential stop loss levels. The Chandelier Exit helps manage risk by setting stop loss levels at a distance from the highest high or lowest low over a specified period, multiplied by the ATR. This ensures that the stop loss adapts to market volatility.
200-period Exponential Moving Average (EMA) : The EMA acts as a trend filter. By ensuring trades are only taken in the direction of the overall trend, the strategy improves the probability of success. For long entries, the close price must be above the 200 EMA, indicating a bullish trend. For short entries, the close price must be below the 200 EMA, indicating a bearish trend.
Combining these indicators adds layers of confirmation and risk management, enhancing the strategy's effectiveness. The Chandelier Exit provides dynamic stop loss levels based on market volatility, while the EMA ensures trades align with the prevailing trend.
Entry Conditions
Long Entry
A buy signal is generated by the Chandelier Exit.
The close price is above the 200 EMA, indicating a strong bullish trend.
Short Entry
A sell signal is generated by the Chandelier Exit.
The close price is below the 200 EMA, indicating a strong bearish trend.
Exit Conditions
For long positions: The position is closed when a sell signal is generated by the Chandelier Exit.
For short positions: The position is closed when a buy signal is generated by the Chandelier Exit.
Risk Management
Account Size: 1,000,00 yen
Commission and Slippage: 17 pips commission and 1 pip slippage per trade
Risk per Trade: 10% of account equity
Stop Loss: For long trades, the stop loss is placed slightly below the candle that generated the buy signal. For short trades, the stop loss is placed slightly above the candle that generated the sell signal. The stop loss levels are dynamically adjusted based on the ATR.
Settings Options
ATR Period: Set the period for calculating the ATR to determine the Chandelier Exit levels.
ATR Multiplier: Set the multiplier for ATR to define the distance of stop loss levels from the highest high or lowest low.
Use Close Price for Extremums: Choose whether to use the close price for calculating the extremums.
EMA Period: Set the period for the EMA to adjust the trend filter sensitivity.
Show Buy/Sell Labels: Choose whether to display buy and sell labels on the chart for visual confirmation.
Highlight State: Choose whether to highlight the bullish or bearish state on the chart.
Sufficient Sample Size
The strategy has been backtested with a sufficient sample size to evaluate its performance accurately. This ensures that the strategy's results are statistically significant and reliable.
Notes
This strategy is based on historical data and does not guarantee future results.
Thoroughly backtest and validate results before using in live trading.
Market volatility and other external factors can affect performance and may not yield expected results.
Acknowledgment
This strategy uses the Chandelier Exit indicator. Special thanks to the original contributors for their work on the Chandelier Exit concept.
Clean Chart Explanation
The script is published with a clean chart to ensure that its output is readily identifiable and easy to understand. No other scripts are included on the chart, and any drawings or images used are specifically to illustrate how the script works.
Chebyshev Filter Divergences [ChartPrime]The Chebyshev Filter Divergences Oscillator
The Chebyshev Filter indicator is a powerful tool designed to identify potential divergences between price and a filtered version of price based on the Chebyshev filter algorithm. It helps to spot mean reversion points by highlighting areas where price and the filtered price exhibit conflicting signals.
Chebyshev Filter Background:
The Chebyshev filter, named after the Russian mathematician Pafnuty Chebyshev , was invented in the mid-19th century. It's a type of filter used in signal processing and digital signal processing for smoothing or removing unwanted frequency components from a signal.
It provides a sharp cutoff between the passband and stopband of a filter while minimizing ripple in the passband or stopband.
Chebyshev filters are widely used in various applications, including audio and image processing, telecommunications, and financial analysis, due to their efficiency and effectiveness in filtering out noise and extracting relevant information from signals.
◆ Indicator Calculation:
The indicator first applies a Chebyshev filter to the price data, producing a filtered price series. It then normalizes this filtered price series to a range, where it can be used as oscillator with divergences.
◆ Visualization:
The filtered price series is plotted on the chart, highlighting areas where it deviates from its smoothed average.
Bullish and bearish divergences are marked on the chart with specific lines and colors, indicating potential shifts in market sentiment.
Signs of change in direction are also marked on the chart, providing additional insights into possible mean reversals of price.
◆ User Inputs:
Ripple (dB): Specifies the desired ripple factor in decibels for the Chebyshev filter.
Normalization Length: Sets the length of the normalization period used in the Chebyshev filter.
Pivots to Right and Left: Determines the number of pivot points to the right and left of the current point to consider when detecting divergences.
Max and Min of Lookback Range: Specifies the maximum and minimum lookback range for identifying divergences.
Show Divergences: Enables or disables the display of bullish and bearish divergences.
Visual Settings: Allows customization of colors for visual clarity.
In conclusion, the Chebyshev Filter Divergences indicator, with its ability to identify potential mean reversion points through divergences between price and a filtered version of price, offers traders a valuable tool for decision-making in the financial markets. By highlighting areas of divergence, traders can potentially capitalize on market inefficiencies and make more informed trading decisions.
S&P Short-Range Oscillator**SHOULD BE USED ON THE S&P 500 ONLY**
The S&P Short-Range Oscillator (SRO), inspired by the principles of Jim Cramer's oscillator, is a technical analysis tool designed to help traders identify potential buy and sell signals in the stock market, specifically for the S&P 500 index. The SRO combines several market indicators to provide a normalized measure of market sentiment, assisting traders in making informed decisions.
The SRO utilizes two simple moving averages (SMAs) of different lengths: a 5-day SMA and a 10-day SMA. It also incorporates the daily price change and market breadth (the net change of closing prices). The 5-day and 10-day SMAs are calculated based on the closing prices. The daily price change is determined by subtracting the opening price from the closing price. Market breadth is calculated as the difference between the current closing price and the previous closing price.
The raw value of the oscillator, referred to as SRO Raw, is the sum of the daily price change, the 5-day SMA, the 10-day SMA, and the market breadth. This raw value is then normalized using its mean and standard deviation over a 20-day period, ensuring that the oscillator is centered and maintains a consistent scale. Finally, the normalized value is scaled to fit within the range of -15 to 15.
When interpreting the SRO, a value below -5 indicates that the market is potentially oversold, suggesting it might be a good time to start buying stocks as the market could be poised for a rebound. Conversely, a value above 5 suggests that the market is potentially overbought. In this situation, it may be prudent to hold on to existing positions or consider selling if you have substantial gains.
The SRO is visually represented as a blue line on a chart, making it easy to track its movements. Red and green horizontal lines mark the overbought (5) and oversold (-5) levels, respectively. Additionally, the background color changes to light red when the oscillator is overbought and light green when it is oversold, providing a clear visual cue.
By incorporating the S&P Short-Range Oscillator into your trading strategy, you can gain valuable insights into market conditions and make more informed decisions about when to buy, sell, or hold your stocks. However, always consider other market factors and perform your own analysis before making any trading decisions.
The S&P Short-Range Oscillator is a powerful tool for traders looking to gain insights into market sentiment. It provides clear buy and sell signals through its combination of multiple indicators and normalization process. However, traders should be aware of its lagging nature and potential complexity, and use it in conjunction with other analysis methods for the best results.
Disclaimer
The S&P Short-Range Oscillator is for informational purposes only and should not be considered financial advice. Trading involves risk, and you should conduct your own research or consult a financial advisor before making investment decisions. The author is not responsible for any losses incurred from using this indicator. Use at your own risk.
Trend Follower IndexDescription
The purpose of this index is to give an idea about the possible direction of the trend. The index is overbought between 70 and 100, and oversold between 30 and 0. Unlike a typical RSI calculation, the 6-bar simple moving average of the price is calculated first. Then, the 21-bar RSI value of this moving average is calculated.
Why
The 6-bar average is often one of the best averages to show the direction of prices. Closes below this average give strong indications of a trend reversal. To display this average on the horizontal plane, I used the RSI function and took 21 bar as the reference length. Because in my research, I realized that 21 bar length is the most ideal upper and lower points. That's why I coded an indicator that shows where a trend is going and how far that trend needs to go.
Use
It becomes oversold when the Moving Average falls below 30. Here we encounter 3 types of colors;
Light Blue: Indicates that the average is between 30 and 20. It indicates the stage when small purchases begin and the decline rate of the trend begins to decrease.
Blue: Indicates that the average is between 20 and 10. It indicates the stage when purchases begin to become more frequent and the rate of trend decline begins to decrease slightly.
Green: Indicates that the average has fallen below 10. It is the ideal level for purchasing. This indicates the stage when buying pressure has increased significantly and the trend is ready to reverse upward.
As the level decreases, purchases should increase.
Again, when the average value exceeds 70, it becomes overbought. Here we encounter three types of colors;
Yellow: Indicates that the average is between 70 and 80. It indicates the stage when small sales begin and the rate of increase in the trend begins to decrease.
Orange: Indicates that the average is between 80 and 90. It indicates the stage when sales begin to become more frequent and the upward trend begins to decrease somewhat.
Red: Indicates the average is above 90. It is an ideal level for sales. It now marks the stage where selling pressure has increased significantly and the trend is ready to turn downwards.
As the level increases, sales should increase.
Originality
First of all, this moving average is not an RSI. RSI is only used to establish the average on a flat basis. The RSI is merely a helpful tool in determining how much the moving average will rise or fall.
The 6-bar average of the value obtained by calculating Bar (Opening + Closing + High + Low) / 4 gives information about the main trend. In my research and usage, I have observed that as long as the price remains above this average, the price continues to move upwards, and when it remains below it, it is willing to move downwards.
Disclaimer
This indicator is for informational purposes only and should be used for educational purposes only. You may lose money if you rely on this to trade without additional information. Use at your own risk.
Version
v1.0
Momentum & Squeeze Oscillator [UAlgo]The Momentum & Squeeze Oscillator is a technical analysis tool designed to help traders identify shifts in market momentum and potential squeeze conditions. This oscillator combines multiple timeframes and periods to provide a detailed view of market dynamics. It enhances the decision-making process for both short-term and long-term traders by visualizing momentum with customizable colors and alerts.
🔶 Key Features
Custom Timeframe Selection: Allows users to select a custom timeframe for oscillator calculations, providing flexibility in analyzing different market periods.
Recalculation Option: Enables or disables the recalculation of the indicator, offering more control over real-time data processing.
Squeeze Background Visualization: Highlights potential squeeze conditions with a background color, helping traders quickly spot consolidation periods.
Adjustable Squeeze Sensitivity: Users can modify the sensitivity of the squeeze detection, tailoring the indicator to their specific trading style and market conditions.
Bar Coloring Condition: Option to color the price bars based on momentum conditions, enhancing the visual representation of market trends.
Threshold Bands: Option to fill threshold bands for a clearer visualization of overbought and oversold levels.
Reference Lines: Display reference lines for overbought, oversold, and mid-levels, aiding in quick assessment of momentum extremes.
Multiple Output Modes: Offers different output visualization modes, including:
ALL: Displays all calculated momentum values (fast, medium, slow).
AVG: Shows the average momentum, providing a consolidated view.
STD: Displays the standard deviation of momentum, useful for understanding volatility.
Alerts: Configurable alerts for key momentum events such as crossovers and squeeze conditions, keeping traders informed of important market changes.
🔶 Usage
The Momentum & Squeeze Oscillator can be used for various trading purposes:
Trend Identification: Use the oscillator to determine the direction and strength of market trends. By analyzing the average, fast, medium, and slow momentum lines, traders can gain insights into short-term and long-term market movements.
Squeeze Detection: The indicator highlights periods of low volatility (squeeze conditions) which often precede significant price movements. Traders can use this information to anticipate and prepare for potential breakouts.
Overbought/Oversold Conditions: The oscillator helps identify overbought and oversold conditions, indicating potential reversal points. This is particularly useful for timing entry and exit points in the market.
Momentum Shifts: By monitoring the crossover of momentum lines with key levels (e.g., the 50 level), traders can spot shifts in market momentum, allowing them to adjust their positions accordingly.
🔶 Disclaimer:
Use with Caution: This indicator is provided for educational and informational purposes only and should not be considered as financial advice. Users should exercise caution and perform their own analysis before making trading decisions based on the indicator's signals.
Not Financial Advice: The information provided by this indicator does not constitute financial advice, and the creator (UAlgo) shall not be held responsible for any trading losses incurred as a result of using this indicator.
Backtesting Recommended: Traders are encouraged to backtest the indicator thoroughly on historical data before using it in live trading to assess its performance and suitability for their trading strategies.
Risk Management: Trading involves inherent risks, and users should implement proper risk management strategies, including but not limited to stop-loss orders and position sizing, to mitigate potential losses.
No Guarantees: The accuracy and reliability of the indicator's signals cannot be guaranteed, as they are based on historical price data and past performance may not be indicative of future results.
Comprehensive Technical AnalysisComprehensive Technical Analysis Script
Overview
This Script for TradingView is designed to perform and display a detailed technical analysis using a range of moving averages and oscillators. The script provides a summary of market conditions based on various indicators to help traders make informed decisions.
Key Features - Technical Indicators:
Moving Averages:
Simple Moving Average (SMA): Calculates the average price over a specified period.
Exponential Moving Average (EMA): Reacts faster to recent price changes by giving more weight to recent prices.
Weighted Moving Average (WMA): Weighs prices based on their position, giving more importance to recent prices.
Hull Moving Average (HMA): Reduces lag and provides a smoother trend line.
Triple Exponential Moving Average (TEMA): Combines three EMAs to minimize lag and offer a responsive trend indicator.
Exponential Moving Average of an Exponential Moving Average (EMAX): Applies an EMA twice to smooth out trends further.
Triangular Moving Average (TMA): Provides a smoother moving average by averaging over a triangular window.
Oscillators:
Relative Strength Index (RSI): Measures the speed and change of price movements to identify overbought or oversold conditions.
Stochastic Oscillator (%K): Compares a security’s closing price to its price range over a specific period to spot potential reversal points.
Commodity Channel Index (CCI): Identifies cyclical trends and measures the deviation of the price from its average.
Moving Average Convergence Divergence (MACD): Shows the relationship between two EMAs to identify changes in trend strength, direction, momentum, and duration.
Awesome Oscillator (AO): Measures market momentum by comparing two different moving averages.
Average Directional Index (ADX): Determines the strength of a trend and whether the market is trending or ranging.
Williams %R (WPR): Identifies overbought and oversold levels with a different calculation approach compared to the RSI.
Point System - Indicator Points:
Bullish Signal: Each indicator contributing to a positive market sentiment adds points.
Bearish Signal: Each indicator contributing to a negative market sentiment subtracts points.
Point Calculation:
Moving Averages: Points are assigned based on whether the current price is above or below each moving average.
Oscillators: Points are assigned based on whether the oscillator values are in bullish or bearish zones.
Summary Text:
Categorization: Based on the total points calculated from all indicators, the market condition is categorized into:
Strong Bullish: More than 8 points
Bullish: Between 3 and 8 points
Neutral: Between -2 and 2 points
Bearish: Between -3 and -8 points
Strong Bearish: Less than -8 points
Text Display: The summary text reflects the overall market sentiment and is color-coded for easy interpretation.
Table Display - The Position of the table can be customized by the user:
Vertical: Options include Top, Center, Bottom
Horizontal: Options include Left, Center, Right
Table Content:
Summary Text and Points: Displays the summary of technical indicators along with the calculated points.
RSI Buy-30d Cooldown-AHR999亲爱的数字资产投资者，您是否在寻找一种智能、可靠的方式来积累您的投资组合？我们为您带来了一个革命性的交易策略！
🚀 引入"智慧积累者"策略 🚀
这是一个为长期数字资产投资者量身定制的智能买入策略。它能帮您在最佳时机买入，让您的投资组合稳步增长！
✨ 主要特点：
智能时机选择：结合RSI和创新的AHR999指标，精准捕捉买入机会。
自动防御机制：设有冷却期，避免过度交易，保护您的资金。
底部猎手：专注于市场低迷期，为您寻找最佳入场点。
灵活可定制：根据您的风险偏好，轻松调整各项参数。
可视化决策：直观的图表标记，让您清晰了解每次交易背后的逻辑。
💡 它是如何工作的？
当市场情绪低落（低RSI）且资产被低估（低AHR999）时，策略会自动为您买入。
每次买入固定金额，帮您实现美元成本平均。
智能冷却期确保您不会在短期内过度买入。
📊 实时跟踪您的投资：
随时查看您的总投资额、持有的资产数量和平均买入成本。
清晰记录每次交易，助您分析和优化策略。
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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。
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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.
Multiple Divergences [UAlgo]🔶 Description:
"Multiple Divergences " is providing insights into potential divergences across multiple indicators. Divergence, a concept in technical analysis, occurs when the price of an asset diverges from the direction of an accompanying indicator, suggesting a possible reversal or continuation in the price trend.
🔶 Key Features:
Customizable Divergence Settings: Users can adjust parameters such as the minimum number of divergences required to display labels, pivot lookback periods, and plot options for various types of divergences (regular or hidden) and bullish/bearish labels.
Multiple Technical Indicators: The script supports a wide range of popular indicators, including MACD, RSI, Stochastic, CCI, Momentum, OBV, DMI Oscillator, VWmacd, Chaikin Money Flow, Money Flow Index, and Awesome Oscillator. You can choose any of the above-mentioned technical indicators for which you want to capture divergences.
🔶 Purpose of Using Multiple Technical Indicators
In the complex and volatile world of trading, relying on a single indicator can provide an incomplete or misleading picture of market conditions. Different technical indicators analyze various aspects of price movement, volume, and momentum, offering unique insights that can complement each other. By utilizing multiple indicators, traders can cross-verify signals, reduce false positives, and increase the reliability of their trading decisions.
Identifying divergences across multiple indicators further enhances this reliability, as a divergence spotted in several indicators simultaneously is a stronger signal than one found in isolation. This comprehensive approach helps traders to anticipate potential market turning points with greater confidence and precision.
By integrating multiple technical indicators and meticulously tracking their divergences, this script aims traders with a robust tool for navigating the complexities of financial markets.
🔶 How to Obtain Divergences
Regular Bullish Divergence:
This occurs when the price makes a new lower low compared to a previous pivot low, indicating a downward trend. Simultaneously, the selected oscillator makes a higher low compared to its previous pivot low, indicating a potential upward momentum. This divergence suggests that, despite the falling price, the underlying momentum is strengthening, potentially signaling a reversal to an upward trend.
Regular Bearish Divergence:
This happens when the price makes a new higher high compared to a previous pivot high, indicating an upward trend. Concurrently, the selected oscillator makes a lower high compared to its previous pivot high, indicating weakening momentum. This divergence suggests that, despite the rising price, the underlying momentum is weakening, potentially signaling a reversal to a downward trend.
Example for Regular Bullish and Regular Bearish Divergences (Minimum Divergenes Count to Display = 3, All Selected):
Hidden Bullish Divergence:
Hidden bullish divergence is observed when the price makes a higher low compared to a previous pivot low, indicating an upward trend. At the same time, the oscillator makes a lower low compared to its previous pivot low, indicating a potential strengthening momentum. This condition suggests that the underlying strength of the upward trend is intact, despite the oscillator indicating otherwise.
Hidden Bearish Divergence:
This occurs when the price makes a lower high compared to a previous pivot high, indicating a downward trend. Simultaneously, the oscillator makes a higher high compared to its previous pivot high, indicating a potential weakening momentum. This divergence suggests that the underlying weakness of the downward trend is intact, despite the oscillator indicating otherwise.
Divergence Labeling: The script dynamically generates labels on the chart to visually highlight detected divergences based on user-defined criteria. (E.g. "5 Regular Bullish Divs." , "1 Hidden Bearish Div")
🔶 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.
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.
Bitcoin Power Law Global Liqudity Model by G. SantostasiIn recent studies, we've observed a notable correlation between Bitcoin's price and global liquidity metrics. This relationship reveals significant insights into Bitcoin's price movements and offers a new perspective on using macroeconomic indicators to understand and predict Bitcoin's market trends.
Our analysis shows that Bitcoin's price exhibits periodic bubbles, which seem closely associated with oscillations in global liquidity. Notably, the overall price path of Bitcoin appears to be a complex function of global liquidity. This relationship is not as simple as the Bitcoin Power Law in time that can be described with a simple equation, Price ∼ time⁶.
Instead, we have developed a polynomial model to describe this complex relationship between liquidity and Bitcoin price. With a 4-degree polynomial (with 5 different parameters needed to fit the data), we can get a decent fit to the data.
The fit is obtained using 500 data points by polynomial regression. The vector coefficients of the polynomial are obtained such that the sum of squared error between the observations and theoretical polynomial model is minimized.
This model needs to be taken with a grain of salt given the warning by famous mathematician Von Neumann: "With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." discussing a model created by Italian Physicist Fermi. By this he meant that the Fermi simulations relied on too many input parameters, presupposing an overfitting phenomenon.
We can still gain some insights into the relationship between Global Liquidity and the price evolution of Bitcoin using this complex model.
When the price of Bitcoin is plotted against our global liquidity index, we observe a polynomial relationship. This model allows us to see when Bitcoin's price deviates significantly from the predicted value based on global liquidity:
Above the Model: When Bitcoin's price is above the polynomial fit, it indicates a potential lack of sufficient liquidity to support the current price level, suggesting a likely correction.
Below the Model: Conversely, when the price is below the fit, it implies that liquidity might be higher than what is reflected in the price, indicating potential upward movement.
Our global liquidity index comprises several key macroeconomic metrics from major financial institutions worldwide. Here are some of the major components:
RRP (Reverse Repurchase Agreements): This metric indicates the level of liquidity in the financial system through temporary sales of securities with an agreement to repurchase them.
FED (Federal Reserve System): Represents the balance sheet of the US central bank, reflecting its monetary policy actions.
TGA (Treasury General Account): Reflects the US Treasury’s cash balance, impacting the liquidity in the banking system.
PBC (People's Bank of China): Shows the monetary policy actions and liquidity management by China’s central bank.
ECB (European Central Bank): Represents the balance sheet and liquidity management actions of the Eurozone's central bank.
BOJ (Bank of Japan): Reflects Japan's central bank's monetary policy and liquidity measures.
Other Central Banks: Includes metrics from various other central banks like the Bank of England, Bank of Canada, Reserve Bank of Australia, etc.
M2 Money Supply: This includes money supply metrics from various countries like the USA, Europe, China, Japan, and other significant economies.
These components collectively provide a comprehensive view of global liquidity, which is crucial for understanding its impact on Bitcoin's price.
Using the polynomial model and the author's Bitcoin power law model we can create 2 oscillators, one that shows deviations from the trend (normalized to the price to make the peaks more uniform) and the other showing deviations of the polynomial liquidity model from the power law trend.
The oscillators show the difference between the price and the power law model relative to the price, Orange Line. The Blue Line is instead the difference between the Global Liquidity Model of the price and the power law model relative to the model itself. The two oscillators can be overlayed to show their differences and similarities.
Analysis: In addition to similar observations from the discussion above we can see that most Bitcoin bottoms are not directly associated with bottoms in the liquidity model indicating a different mechanism at play that determines Bitcoin bottoms (probably due to miners' capitulation).
Using the new force_overlay function we plot the polynomial liquidity model directly over the Bitcoin price chart while we display the 2 oscillators in a separate panel.
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
