RSI Bands with Volume and EMAThis script is a comprehensive technical analysis tool designed to help traders identify key market signals using RSI bands, volume, and multiple Exponential Moving Averages (EMAs). It overlays the following on the chart:
RSI Bands: The script calculates and plots two bands based on the Relative Strength Index (RSI), indicating overbought and oversold levels. These bands act as dynamic support and resistance zones:
Resistance Band (Upper Band): Plotted when the RSI exceeds the overbought level, typically indicating a potential sell signal.
Support Band (Lower Band): Plotted when the RSI falls below the oversold level, typically indicating a potential buy signal.
Midline: The average of the upper and lower bands, acting as a neutral reference.
Buy/Sell Labels: Labels are dynamically added to the chart when price reaches the overbought or oversold levels.
A "Buy" label appears when the price reaches the oversold (lower) band.
A "Sell" label appears when the price reaches the overbought (upper) band.
Volume Indicator: The script visualizes trading volume as histograms, with red or green bars representing decreasing or increasing volume, respectively. The volume height is visually reduced for better clarity and comparison.
Exponential Moving Averages (EMAs): The script calculates and plots four key EMAs (12, 26, 50, and 200) to highlight short-term, medium-term, and long-term trends:
EMA 12: Blue
EMA 26: Orange
EMA 50: Purple
EMA 200: Green
The combined use of RSI, volume, and EMAs offers traders a multi-faceted view of the market, assisting in making informed decisions about potential price reversals, trends, and volume analysis. The script is particularly useful for identifying entry and exit points on charts like BTC/USDT, although it can be applied to any asset.
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Quartile For Loop [SeerQuant]Quartile For Loop (QFL)
- The Quartile For Loop (QFL) is an advanced trend-following and scoring oscillator designed to detect momentum shifts and trend transitions using a quartile-based analysis. By leveraging quartile calculations and iterative scoring logic, QFL delivers dynamic trend signals which can be tailored to suit various market conditions.
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⚙️ How It Works
1️⃣ Quartile-Based Calculation
The indicator calculates the weighted average of the first quartile (Q1), median (Q2), and third quartile (Q3) over a customizable length, providing a robust adaptive trend value.
2️⃣ For Loop Scoring System
A unique for-loop structure iteratively scores each quartile value against historical data, delivering actionable trend signals. Users can toggle between price-based and quartile-based scoring methods for flexibility.
3️⃣ Threshold Logic
Bullish (Uptrend): Score exceeds the positive threshold.
Bearish (Downtrend): Score falls below the negative threshold.
Neutral: Score remains between thresholds.
4️⃣ Visual Trend Enhancements
Optional candle coloring and a color-coded SMA provide clear visual cues for identifying trend direction. The adaptive quartile is dynamically updated to reflect changing market conditions.
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✨ Customizable Settings
Indicator Inputs
Quartile Length: Define the calculation length for quartile analysis.
Calculation Source: Choose the data source for quartile calculations (e.g., close price).
Alternate Signal: Toggle between price-based and quartile-based scoring.
Loop Settings
Start/End Points: Set the range for the for-loop scoring system.
Thresholds: Customize uptrend and downtrend thresholds.
Style Settings
Candle Coloring: Enable optional trend-based candle coloring.
Color Schemes: Select from five unique palettes for trend visualization.
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🚀 Features and Benefits
Quartile-Driven Analysis: Harnesses the statistical power of quartiles for adaptive trend evaluation.
Dynamic Scoring: Iterative scoring logic adjusts to market fluctuations.
Clear Visual Representation: Color-coded histograms, candles, and trendlines enhance readability.
Fully Customizable: Flexible inputs allow adaptation to diverse trading styles and strategies.
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📜 Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Market analysis is inherently speculative and subject to risk. Users should consult a licensed financial advisor before making trading decisions. Use at your own discretion.
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Volume Standard Deviation Alert GusPurpose
The script detects and alerts traders when the volume of a trading asset significantly exceeds a calculated threshold based on the standard deviation of volume over a specified lookback period. It optionally filters these alerts based on whether the price action is bullish or bearish.
Key Components
Inputs
lookback (default: 20)
The number of bars to consider when calculating the moving average and standard deviation of volume.
stdDevFactor (default: 2.0)
The multiplier for the standard deviation to determine the threshold for a volume spike.
alertOnClose (default: true)
Determines whether alerts should only be triggered after the bar has closed.
checkBullBear (default: false)
Enables filtering of alerts based on the bullishness or bearishness of the bar.
Calculations
volSMA
The simple moving average (SMA) of the volume over the lookback period.
volStd
The standard deviation of the volume over the lookback period.
threshold
The alert threshold is calculated as:
Threshold
=
volSMA
+
(
stdDevFactor
×
volStd
)
Threshold=volSMA+(stdDevFactor×volStd)
isBullish & isBearish
Determines whether the current bar is bullish (close > open) or bearish (close < open).
volumeSpikeCondition
A condition that triggers when the current volume exceeds the calculated threshold.
bullishCondition & bearishCondition
Refines the spike condition by requiring the bar to be bullish or bearish when checkBullBear is enabled.
finalCondition
The ultimate alert condition based on the user’s preference for bullish/bearish filtering.
finalTrigger
Ensures the alert only triggers at bar close if alertOnClose is set to true.
Visualization
Plots the SMA of the volume (volSMA) and the threshold line (threshold), helping traders visually understand the conditions.
Histograms the current volume and colors the bars:
Red: Volume exceeds the threshold.
Blue: Volume is below the threshold.
Alerts
The script generates an alert message when the finalTrigger condition is met:
"Bullish Volume Spike!" if the bar is bullish.
"Bearish Volume Spike!" if the bar is bearish.
"High Volume Spike!" if no bull/bear filter is applied.
Alerts are sent using alert() with the message and set to trigger once per bar close.
Usage
Traders can use this script to identify unusual volume activity, which often precedes significant price movements.
Customizability allows traders to tune the lookback period, standard deviation multiplier, and whether to filter for bullish/bearish spikes.
Visual and audible cues help in identifying important market events in real time.
This indicator is particularly useful for spotting market breakouts or breakdowns driven by high trading activity.
[blackcat] L1 Abnormal Volume Monitor█ OVERVIEW
The script is an indicator designed to monitor abnormal volume patterns in the market. It calculates and plots moving average volumes, identifies triple volume bars, and detects potential large order entries based on specific conditions.
█ FEATURES
• Input Parameters: The script defines parameters M1, M2, and lbk which control the calculation of moving averages and the lookback period for detecting abnormal volume.
• Calculations: The script calculates two moving averages of volume (MAVOL1 and MAVOL2), a smoothed price level (mm), and identifies conditions for triple volume bars and large order entries.
• Plotting: The script plots volume histograms for up and down bars, moving average volumes, and highlights triple volume bars with and without large order entries.
• Conditional Statements: The script uses conditional statements to determine when to plot certain data points and labels based on the calculated conditions.
█ LOGICAL FRAMEWORK
• xfl(cond, lbk): This function checks if a condition (cond) has been true within a specified lookback period (lbk). It returns true if the condition has been met and false otherwise.
• Parameters: cond (condition to check), lbk (lookback period).
• Return Value: outb (boolean indicating if the condition was met within the lookback period).
• abnormal_vol_monitor(close, open, high, low, volume, M1, M2, lbk): This function calculates moving average volumes, identifies triple volume bars, and detects large order entries.
• Parameters: close, open, high, low, volume (price and volume data), M1, M2 (periods for moving averages), lbk (lookback period).
• Return Value: A tuple containing MAVOL1, MAVOL2, xa (large order entry condition), and tripleVolume (triple volume condition).
█ KEY POINTS AND TECHNIQUES
• Moving Averages: The script uses simple moving averages (sma) and exponential moving averages (ema) to smooth volume data.
• Volume Analysis: The script identifies triple volume bars and large order entries based on specific conditions, such as volume doubling and price increases.
• Lookback Period: The xfl function uses a lookback period to ensure the accuracy of the detected conditions.
• Plotting Techniques: The script uses different plot styles and colors to distinguish between up bars, down bars, moving averages, and abnormal volume patterns.
█ EXTENDED KNOWLEDGE AND APPLICATIONS
• Modifications: The script could be modified to include additional conditions for detecting other types of abnormal volume patterns or to adjust the sensitivity of the detection.
• Extensions: Similar techniques could be applied to other financial instruments or timeframes to identify unusual trading activity.
• Related Concepts: The script utilizes concepts such as moving averages, exponential moving averages, and conditional plotting, which are fundamental in Pine Script and technical analysis.
Advanced MA Difference (and more)This Pine Script indicator calculates the difference between the price and a main moving average (SMA or EMA), allowing you to track deviations in either absolute or relative (percentage) terms. It offers several features to help visualize and smooth this difference:
- Main MA Difference: Shows the price deviation from the moving average, either as an absolute dollar amount or as a percentage.
- Fast and Slow Moving Averages: Optionally smooths the difference using fast and slow moving averages, giving insights into short-term and long-term trends in price deviations.
- Difference Between Fast and Slow MAs : Highlights the gap between these MAs, helping to identify momentum shifts.
- Customizable Visuals: Offers flexibility in displaying the difference and moving averages using lines or histograms, and includes a zero line for reference.
When to Use It:
- Use the absolute difference for tracking raw price deviations if you’re focused on concrete moves in the asset’s price.
- Use the relative difference for normalized, percentage-based deviations, especially useful when comparing different assets or time frames.
This indicator is suitable for traders looking to spot trends, price deviations, or momentum shifts relative to a moving average. Its flexibility makes it a good fit for both short-term and long-term analysis.
Streak-Based Trading StrategyThe strategy outlined in the provided script is a streak-based trading strategy that focuses on analyzing winning and losing streaks. It’s important to emphasize that this strategy is not intended for actual trading but rather for statistical analysis of streak series.
How the Strategy Works
1. Parameter Definition:
• Trade Direction: Users can choose between “Long” (buy) and “Short” (sell).
• Streak Threshold: Defines how many consecutive wins or losses are needed to trigger a trade.
• Hold Duration: Specifies how many periods the position will be held.
• Doji Threshold: Determines the sensitivity for Doji candles, which indicate market uncertainty.
2. Streak Calculation:
• The script identifies Doji candles and counts winning and losing streaks based on the closing price compared to the previous closing price.
• Streak counting occurs only when no position is currently held.
3. Trade Conditions:
• If the loss streak reaches the defined threshold and the trade direction is “Long,” a buy position is opened.
• If the win streak is met and the trade direction is “Short,” a sell position is opened.
• The position is held for the specified duration.
4. Visualization:
• Winning and losing streaks are plotted as histograms to facilitate analysis.
Scientific Basis
The concept of analyzing streaks in financial markets is well-documented in behavioral economics and finance. Studies have shown that markets often exhibit momentum and trend-following behavior, meaning the likelihood of consecutive winning or losing periods can be higher than what random statistics would suggest (see, for example, “The Behavior of Stock-Market Prices” by Eugene Fama).
Additionally, empirical research indicates that investors often make decisions based on psychological factors influenced by streaks. This can lead to irrational behavior, as they may focus on past wins or losses (see “Behavioral Finance: Psychology, Decision-Making, and Markets” by R. M. F. F. Thaler).
Overall, this strategy serves as a tool for statistical analysis of streak series, providing deeper insights into market behavior and trends rather than being directly used for trading decisions.
Market DirectionThe "Market Direction" indicator combines four advanced sub-indicators to provide a comprehensive and multi-dimensional analysis of market trends, momentum, and potential reversals. This innovative approach leverages different aspects of price action, volume, and market sentiment, offering traders an in-depth view of market conditions.
1. Fractal Indicator: Multi-Scale Price Action Analysis
The Fractal Indicator identifies significant highs and lows over six different pivot lengths, offering a nuanced view of price action across multiple timeframes. By comparing distances from current closing prices to these key fractal points, the indicator determines potential trend reversals and market direction. This approach enables traders to adapt their strategies to various market conditions, capturing both short-term fluctuations and long-term trends.
2. Volume MACD Indicator: Enhanced Market Momentum
The Volume MACD Indicator goes beyond traditional MACD analysis by incorporating volume-weighted movement and the structural attributes of candlesticks (such as body length and wicks). This hybrid model offers a more comprehensive understanding of market momentum by integrating both price action and trading volume. The use of Smoothed Moving Averages (SMMA) reduces noise and ensures more stable signals, helping traders focus on sustainable trends and longer-term investment opportunities.
3. Cumulative Volume Momentum Indicator: Volume Dynamics Insight
The Cumulative Volume Momentum Indicator evaluates the momentum of cumulative buying and selling volumes, offering a clear picture of market strength and potential reversals. By comparing the relationship between open, close, high, and low prices, and applying a MACD approach to these volume dynamics, this indicator helps traders identify momentum shifts that often precede price movements. The visualization through histograms adds clarity to bullish and bearish volume momentum, enhancing decision-making in volatile markets.
4. POC-Price Momentum Indicator: Market Depth and Sentiment
The POC-Price Momentum Indicator assesses the difference between the Point of Control (POC) and closing prices, providing insights into underlying market sentiment. Positive differences indicate a buildup of upward momentum, while negative differences suggest a bearish tilt. By calculating moving averages of these differences, the indicator highlights the strength and sustainability of ongoing trends, helping traders align their strategies with the broader market direction.
Unified Rating for Confirming Market Direction
The "Market Direction" indicator consolidates the outputs of these four sub-indicators into a single, aggregated sentiment score. This score helps traders confirm the prevailing market trend by weighing the combined insights from fractal analysis, volume momentum, price action, and POC dynamics. A positive score suggests a bullish market, while a negative score indicates bearish conditions.
Super Technical RatingsThis indicator, titled "Super Technical Ratings," is designed to provide a multi-timeframe technical analysis based on Moving Averages (MAs) and Oscillators. It offers a comprehensive view by evaluating the strength of buy and sell signals across multiple timeframes, displaying these evaluations both visually on the chart and in a table format.
I know that Technical Ratings is one of the most excellent indicators, but it’s also true that trends can often be misread due to the influence of other timeframes. Especially on shorter timeframes, there can be sudden price movements influenced by trends in longer timeframes. While it’s important to check other timeframes, switching between charts can be very cumbersome. I created this indicator with the hope of being able to check the Technical Ratings across multiple timeframes on a single screen. It goes without saying, I recommend displaying it as lines rather than histograms.
Key Features:
1. **Multi-Timeframe Analysis:**
- The indicator evaluates technical ratings on five different timeframes: 60 minutes, 240 minutes, 1 day, 1 week, and 1 month.
- Each timeframe is individually analyzed using a combination of Moving Averages and Oscillators, or either one depending on the user’s settings.
2. **Technical Ratings Calculation:**
- The ratings are based on the overall combination of MAs and Oscillators (`All`), MAs only, or Oscillators only, depending on the user's selection.
- The rating results are categorized into five statuses: "Strong Buy," "Buy," "Neutral," "Sell," and "Strong Sell."
3. **Table Display:**
- A table is generated on the chart to show the technical ratings for each timeframe. The table columns display the timeframe and the corresponding ratings for MAs, Oscillators, and their combination.
- The table cells are color-coded based on the rating, making it easy to quickly identify strong buy or sell signals.
4. **Graphical Plotting:**
- The indicator plots the technical rating signals for each timeframe on the chart. Different colors are used for each timeframe to help distinguish between them.
- Horizontal lines are plotted at 0, +0.5, and -0.5 levels to indicate key thresholds, making it easier to interpret the strength of the signals.
5. **Alert Conditions:**
- The indicator can trigger alerts when the technical rating crosses certain thresholds (e.g., moving from a neutral rating to a buy or sell rating).
- This helps users stay informed of significant changes in the market conditions.
Use Case:
This indicator is particularly useful for traders who want to see a consolidated view of technical ratings across multiple timeframes. It allows for a quick assessment of whether a security is generally considered a buy or sell across different time periods, aiding in making more informed trading decisions. The visual representation, combined with the color-coded table, provides an intuitive way to understand the current market sentiment.
Fair Value Gap (FVG) Oscillator [UAlgo]The "Fair Value Gap (FVG) Oscillator " is designed to identify and visualize Fair Value Gaps (FVG) within a given lookback period on a trading chart. This indicator helps traders by highlighting areas where price gaps may signify potential trading opportunities, specifically bullish and bearish patterns. By leveraging volume and Average True Range (ATR) data, the FVG Oscillator aims to enhance the accuracy of pattern recognition and provide more reliable signals for trading decisions.
🔶 Identification of Fair Value Gap (FVG)
Fair Value Gaps (FVG) are specific price areas where gaps occur, and they are often considered significant in technical analysis. These gaps can indicate potential future price movements as the market may return to fill these gaps. This indicator identifies two types of FVGs:
Bullish FVG: Occurs when the current low price is higher than the high price two periods ago. This condition suggests a potential upward price movement.
Obtains with:
low > high
Bearish FVG: Occurs when the current high price is lower than the low price two periods ago. This condition suggests a potential downward price movement.
Obtains with:
high < low
The FVG Oscillator not only identifies these gaps but also verifies them using volume and ATR conditions to ensure more reliable trading signals.
🔶 Key Features
Lookback Period: Users can set the lookback period to determine how far back the indicator should search for FVG patterns.
ATR Multiplier: The ATR Multiplier is used to adjust the sensitivity of the ATR-based conditions for verifying FVG patterns.
Volume SMA Period: This setting determines the period for the Simple Moving Average (SMA) of the volume, which helps in identifying high volume conditions.
Why ATR and Volume are Used?
ATR (Average True Range) and volume are integrated into the Fair Value Gap (FVG) Oscillator to enhance the accuracy and reliability of the identified patterns. ATR measures market volatility, helping to filter out insignificant price gaps and focus on impactful ones, ensuring that the signals are relevant and strong. Volume, on the other hand, confirms the strength of price movements. High volume often indicates the sustainability of these movements, reducing the likelihood of false signals. Together, ATR and volume ensure that the detected FVGs are both significant and supported by market activity, providing more trustworthy trading signals.
Normalized Values: The FVG counts are normalized to enhance the visual representation and interpretation of the patterns on the chart.
Visual Customization and Plotting: Users can customize the colors for positive (bullish) and negative (bearish) areas, and choose whether to display these areas on the chart, also plots the bullish and bearish FVG counts, a zero line, and the net value of FVG counts. Additionally, it uses histograms to display the width of verified bullish and bearish patterns.
🔶 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 Analysis with Statistical Summary Scientific Analysis of the Script "RSI Analysis with Statistical Summary"
Introduction
I observed that there are outliers in the price movement liquidity, and I wanted to understand the RSI value at those points and whether there are any notable patterns. I aimed to analyze this statistically, and this script is the result.
Explanation of Key Terms
1. Outliers in Price Movement Liquidity: An outlier is a data point that significantly deviates from other values. In this context, an outlier refers to an unusually high or low liquidity of price movement, which is the ratio of trading volume to the price difference between the open and close prices. These outliers can signal important market changes or unusual trading activities.
2. RSI (Relative Strength Index): The RSI is a technical indicator that measures the speed and change of price movements. It ranges from 0 to 100 and helps identify overbought or oversold conditions of a trading instrument. An RSI value above 70 indicates an overbought condition, while a value below 30 suggests an oversold condition.
3. Mean: The mean is a measure of the average of a dataset. It is calculated by dividing the sum of all values by the number of values. In this script, the mean of the RSI values is calculated to provide a central tendency of the RSI distribution.
4. Standard Deviation (stdev): The standard deviation is a measure of the dispersion or variation of a dataset. It shows how much the values deviate from the mean. A high standard deviation indicates that the values are widely spread, while a low standard deviation indicates that the values are close to the mean.
5. 68% Confidence Interval: A confidence interval indicates the range within which a certain percentage of values of a dataset lies. The 68% confidence interval corresponds to a range of plus/minus one standard deviation around the mean. It indicates that about 68% of the data points lie within this range, providing insight into the distribution of values.
Overview
This Pine Script™, written in Pine version 5, is designed to analyze the Relative Strength Index (RSI) of a stock or other trading instrument and create statistical summaries of the distribution of RSI values. The script identifies outliers in price movement liquidity and uses this information to calculate the frequency of RSI values. At the end, it displays a statistical summary in the form of a table.
Structure and Functionality of the Script
1. Input Parameters
- `rsi_len`: An integer input parameter that defines the length of the RSI (default: 14).
- `outlierThreshold`: An integer input parameter that defines the length of the outlier threshold (default: 10).
2. Calculating Price Movement Liquidity
- `priceMovementLiquidity`: The volume is divided by the absolute difference between the close and open prices to calculate the liquidity of the price movement.
3. Determining the Boundary for Liquidity and Identifying Outliers
- `liquidityBoundary`: The boundary is calculated using the Exponential Moving Average (EMA) of the price movement liquidity and its standard deviation.
- `outlier`: A boolean value that indicates whether the price movement liquidity exceeds the set boundary.
4. Calculating the RSI
- `rsi`: The RSI is calculated with a period length of 14, using various moving averages (e.g., SMA, EMA) depending on the settings.
5. Storing and Limiting RSI Values
- An array `rsiFrequency` stores the frequency of RSI values from 0 to 100.
- The function `f_limit_rsi` limits the RSI values between 0 and 100.
6. Updating RSI Frequency on Outlier Occurrence
- On an outlier occurrence, the limited and rounded RSI value is updated in the `rsiFrequency` array.
7. Statistical Summary
- Various variables (`mostFrequentRsi`, `leastFrequentRsi`, `maxCount`, `minCount`, `sum`, `sumSq`, `count`, `upper_interval`, `lower_interval`) are initialized to perform statistical analysis.
- At the last bar (`bar_index == last_bar_index`), a loop is run to determine the most and least frequent RSI values and their frequencies. Sum and sum of squares of RSI values are also updated for calculating mean and standard deviation.
- The mean (`mean`) and standard deviation (`stddev`) are calculated. Additionally, a 68% confidence interval is determined.
8. Creating a Table for Result Display
- A table `resultsTable` is created and filled with the results of the statistical analysis. The table includes the most and least frequent RSI values, the standard deviation, and the 68% confidence interval.
9. Graphical Representation
- The script draws horizontal lines and fills to indicate overbought and oversold regions of the RSI.
Interpretation of the Results
The script provides a detailed analysis of RSI values based on specific liquidity outliers. By calculating the most and least frequent RSI values, standard deviation, and confidence interval, it offers a comprehensive statistical summary that can help traders identify patterns and anomalies in the RSI. This can be particularly useful for identifying overbought or oversold conditions of a trading instrument and making informed trading decisions.
Critical Evaluation
1. Robustness of Outlier Identification: The method of identifying outliers is solely based on the liquidity of price movement. It would be interesting to examine whether other methods or additional criteria for outlier identification would lead to similar or improved results.
2. Flexibility of RSI Settings: The ability to select various moving averages and period lengths for the RSI enhances the adaptability of the script, allowing users to tailor it to their specific trading strategies.
3. Visualization of Results: While the tabular representation is useful, additional graphical visualizations, such as histograms of RSI distribution, could further facilitate the interpretation of the results.
In conclusion, this script provides a solid foundation for analyzing RSI values by considering liquidity outliers and enables detailed statistical evaluation that can be beneficial for various trading strategies.
VIX Statistical Sentiment Index [Nasan]** THIS IS ONLY FOR US STOCK MARKET**
The indicator analyzes market sentiment by computing the Rate of Change (ROC) for the VIX and S&P 500, visualizing the data as histograms with conditional coloring. It measures the correlation between the VIX, the specific stock, and the S&P 500, displaying the results on the chart. The reliability measure combines these correlations, offering an overall assessment of data robustness. One can use this information to gauge the inverse relationship between VIX and S&P 500, the alignment of the specific stock with the market, and the overall reliability of the correlations for informed decision-making based on the inverse relationship of VIX and price movement.
**WHEN THE VIX ROC IS ABOVE ZERO (RED COLOR) AND RASING ONE CAN EXPECT THE PRICE TO MOVE DOWNWARDS, WHEN THE VIX ROC IS BELOW ZERO (GREEN)AND DECREASING ONE CAN EXPECT THE PRICE TO MOVE UPWARDS"
Understanding the VIX Concept:
The VIX, or Volatility Index, is a widely used indicator in finance that measures the market's expectation of volatility over the next 30 days. Here are key points about the VIX:
Fear Gauge:
Often referred to as the "fear gauge," the VIX tends to rise during periods of market uncertainty or fear and fall during calmer market conditions.
Inverse Relationship with Market:
The VIX typically has an inverse relationship with the stock market. When the stock market experiences a sell-off, the VIX tends to rise, indicating increased expected volatility.
Implied Volatility:
The VIX is derived from the prices of options on the S&P 500. It represents the market's expectations for future volatility and is often referred to as "implied volatility."
Contrarian Indicator:
Extremely high VIX levels may indicate oversold conditions, suggesting a potential market rebound. Conversely, very low VIX levels may signal complacency and a potential reversal.
VIX vs. SPX Correlation:
This correlation measures the strength and direction of the relationship between the VIX (Volatility Index) and the S&P 500 (SPX).
A negative correlation indicates an inverse relationship. When the VIX goes up, the SPX tends to go down, and vice versa.
The correlation value closer to -1 suggests a stronger inverse relationship between VIX and SPX.
Stock vs. SPX Correlation:
This correlation measures the strength and direction of the relationship between the closing price of the stock (retrieved using src1) and the S&P 500 (SPX).
This correlation helps assess how closely the stock's price movements align with the broader market represented by the S&P 500.
A positive correlation suggests that the stock tends to move in the same direction as the S&P 500, while a negative correlation indicates an opposite movement.
Reliability Measure:
Combines the squared values of the VIX vs. SPX and Stock vs. SPX correlations and takes the square root to create a reliability measure.
This measure provides an overall assessment of how reliable the correlation information is in guiding decision-making.
Interpretation:
A higher reliability measure implies that the correlations between VIX and SPX, as well as between the stock and SPX, are more robust and consistent.
One can use this reliability measure to gauge the confidence they can place in the correlations when making decisions about the specific stock based on VIX data and its correlation with the broader market.
Harmonic Trend Fusion [kikfraben]📈 Harmonic Trend Fusion - Your Personal Trading Assistant
This versatile tool combines multiple indicators to provide a holistic view of market trends and potential signals.
🚀 Key Features:
Multi-Indicator Synergy: Benefit from the combined insights of Aroon, DMI, MACD, Parabolic SAR, RSI, Supertrend, and SMI Ergodic Oscillator, all in one powerful indicator.
Customizable Plot Options: Tailor your chart by choosing which signals to visualize. Whether you're interested in trendlines, histograms, or specific indicators, the choice is yours.
Color-Coded Trends: Quickly identify bullish and bearish trends with the color-coded visualizations. Stay ahead of market movements with clear and intuitive signals.
Table Display: Stay informed at a glance with the interactive table. It dynamically updates to reflect the current market sentiment, providing you with key information and trend direction.
Precision Control: Fine-tune your analysis with precision control over indicator parameters. Adjust lengths, colors, and other settings to align with your unique trading strategy.
🛠️ How to Use:
Customize Your View: Select which indicators to display and adjust plot options to suit your preferences.
Table Insights: Monitor the dynamic table for real-time updates on market sentiment and trend direction.
Indicator Parameters: Experiment with different lengths and settings to find the combination that aligns with your trading style.
Whether you're a seasoned trader or just starting, Harmonic Trend Fusion equips you with the tools you need to navigate the markets confidently. Take control of your trading journey and enhance your decision-making process with this comprehensive trading assistant.
Volume and Price Z-Score [Multi-Asset] - By LeviathanThis script offers in-depth Z-Score analytics on price and volume for 200 symbols. Utilizing visualizations such as scatter plots, histograms, and heatmaps, it enables traders to uncover potential trade opportunities, discern market dynamics, pinpoint outliers, delve into the relationship between price and volume, and much more.
A Z-Score is a statistical measurement indicating the number of standard deviations a data point deviates from the dataset's mean. Essentially, it provides insight into a value's relative position within a group of values (mean).
- A Z-Score of zero means the data point is exactly at the mean.
- A positive Z-Score indicates the data point is above the mean.
- A negative Z-Score indicates the data point is below the mean.
For instance, a Z-Score of 1 indicates that the data point is 1 standard deviation above the mean, while a Z-Score of -1 indicates that the data point is 1 standard deviation below the mean. In simple terms, the more extreme the Z-Score of a data point, the more “unusual” it is within a larger context.
If data is normally distributed, the following properties can be observed:
- About 68% of the data will lie within ±1 standard deviation (z-score between -1 and 1).
- About 95% will lie within ±2 standard deviations (z-score between -2 and 2).
- About 99.7% will lie within ±3 standard deviations (z-score between -3 and 3).
Datasets like price and volume (in this context) are most often not normally distributed. While the interpretation in terms of percentage of data lying within certain ranges of z-scores (like the ones mentioned above) won't hold, the z-score can still be a useful measure of how "unusual" a data point is relative to the mean.
The aim of this indicator is to offer a unique way of screening the market for trading opportunities by conveniently visualizing where current volume and price activity stands in relation to the average. It also offers features to observe the convergent/divergent relationships between asset’s price movement and volume, observe a single symbol’s activity compared to the wider market activity and much more.
Here is an overview of a few important settings.
Z-SCORE TYPE
◽️ Z-Score Type: Current Z-Score
Calculates the z-score by comparing current bar’s price and volume data to the mean (moving average with any custom length, default is 20 bars). This indicates how much the current bar’s price and volume data deviates from the average over the specified period. A positive z-score suggests that the current bar's price or volume is above the mean of the last 20 bars (or the custom length set by the user), while a negative z-score means it's below that mean.
Example: Consider an asset whose current price and volume both show deviations from their 20-bar averages. If the price's Z-Score is +1.5 and the volume's Z-Score is +2.0, it means the asset's price is 1.5 standard deviations above its average, and its trading volume is 2 standard deviations above its average. This might suggest a significant upward move with strong trading activity.
◽️ Z-Score Type: Average Z-Score
Calculates the custom-length average of symbol's z-score. Think of it as a smoothed version of the Current Z-Score. Instead of just looking at the z-score calculated on the latest bar, it considers the average behavior over the last few bars. By doing this, it helps reduce sudden jumps and gives a clearer, steadier view of the market.
Example: Instead of a single bar, imagine the average price and volume of an asset over the last 5 bars. If the price's 5-bar average Z-Score is +1.0 and the volume's is +1.5, it tells us that, over these recent bars, both the price and volume have been consistently above their longer-term averages, indicating sustained increase.
◽️ Z-Score Type: Relative Z-Score
Calculates a relative z-score by comparing symbol’s current bar z-score to the mean (average z-score of all symbols in the group). This is essentially a z-score of a z-score, and it helps in understanding how a particular symbol's activity stands out not just in its own historical context, but also in relation to the broader set of symbols being analyzed. In other words, while the primary z-score tells you how unusual a bar's activity is for that specific symbol, the relative z-score informs you how that "unusualness" ranks when compared to the entire group's deviations. This can be particularly useful in identifying symbols that are outliers even among outliers, indicating exceptionally unique behaviors or opportunities.
Example: If one asset's price Z-Score is +2.5 and volume Z-Score is +3.0, but the group's average Z-Scores are +0.5 for price and +1.0 for volume, this asset’s Relative Z-Score would be high and therefore stand out. This means that asset's price and volume activities are notably high, not just by its own standards, but also when compared to other symbols in the group.
DISPLAY TYPE
◽️ Display Type: Scatter Plot
The Scatter Plot is a visual tool designed to represent values for two variables, in this case the Z-Scores of price and volume for multiple symbols. Each symbol has it's own dot with x and y coordinates:
X-Axis: Represents the Z-Score of price. A symbol further to the right indicates a higher positive deviation in its price from its average, while a symbol to the left indicates a negative deviation.
Y-Axis: Represents the Z-Score of volume. A symbol positioned higher up on the plot suggests a higher positive deviation in its trading volume from its average, while one lower down indicates a negative deviation.
Here are some guideline insights of plot positioning:
- Top-Right Quadrant (High Volume-High Price): Symbols in this quadrant indicate a scenario where both the trading volume and price are higher than their respective mean.
- Top-Left Quadrant (High Volume-Low Price): Symbols here reflect high trading volumes but prices lower than the mean.
- Bottom-Left Quadrant (Low Volume-Low Price): Assets in this quadrant have both low trading volume and price compared to their mean.
- Bottom-Right Quadrant (Low Volume-High Price): Symbols positioned here have prices that are higher than their mean, but the trading volume is low compared to the mean.
The plot also integrates a set of concentric squares which serve as visual guides:
- 1st Square (1SD): Encapsulates symbols that have Z-Scores within ±1 standard deviation for both price and volume. Symbols within this square are typically considered to be displaying normal behavior or within expected range.
- 2nd Square (2SD): Encapsulates those with Z-Scores within ±2 standard deviations. Symbols within this boundary, but outside the 1 SD square, indicate a moderate deviation from the norm.
- 3rd Square (3SD): Represents symbols with Z-Scores within ±3 standard deviations. Any symbol outside this square is deemed to be a significant outlier, exhibiting extreme behavior in terms of either its price, its volume, or both.
By assessing the position of symbols relative to these squares, traders can swiftly identify which assets are behaving typically and which are showing unusual activity. This visualization simplifies the process of spotting potential outliers or unique trading opportunities within the market. The farther a symbol is from the center, the more it deviates from its typical behavior.
◽️ Display Type: Columns
In this visualization, z-scores are represented using columns, where each symbol is presented horizontally. Each symbol has two distinct nodes:
- Left Node: Represents the z-score of volume.
- Right Node: Represents the z-score of price.
The height of these nodes can vary along the y-axis between -4 and 4, based on the z-score value:
- Large Positive Columns: Signify a high or positive z-score, indicating that the price or volume is significantly above its average.
- Large Negative Columns: Represent a low or negative z-score, suggesting that the price or volume is considerably below its average.
- Short Columns Near 0: Indicate that the price or volume is close to its mean, showcasing minimal deviation.
This columnar representation provides a clear, intuitive view of how each symbol's price and volume deviate from their respective averages.
◽️ Display Type: Circles
In this visualization style, z-scores are depicted using circles. Each symbol is horizontally aligned and represented by:
- Solid Circle: Represents the z-score of price.
- Transparent Circle: Represents the z-score of volume.
The vertical position of these circles on the y-axis ranges between -4 and 4, reflecting the z-score value:
- Circles Near the Top: Indicate a high or positive z-score, suggesting the price or volume is well above its average.
- Circles Near the Bottom: Represent a low or negative z-score, pointing to the price or volume being notably below its average.
- Circles Around the Midline (0): Highlight that the price or volume is close to its mean, with minimal deviation.
◽️ Display Type: Delta Columns
There's also an option to utilize Z-Score Delta Columns. For each symbol, a single column is presented, depicting the difference between the z-score of price and the z-score of volume.
The z-score delta essentially captures the disparity between how much the price and volume deviate from their respective mean:
- Positive Delta: Indicates that the z-score of price is greater than the z-score of volume. This suggests that the price has deviated more from its average than the volume has from its own average. Such a scenario could point to price movements being more significant or pronounced compared to the changes in volume.
- Negative Delta: Represents that the z-score of volume is higher than the z-score of price. This might mean that there are substantial volume changes, yet the price hasn't moved as dramatically. This can be indicative of potential build-up in trading interest without an equivalent impact on price.
- Delta Close to 0: Means that the z-scores for price and volume are almost equal, indicating their deviations from the average are in sync.
◽️ Display Type: Z-Volume/Z-Price Heatmap
This visualization offers a heatmap either for volume z-scores or price z-scores across all symbols. Here's how it's presented:
Each symbol is allocated its own horizontal row. Within this row, bar-by-bar data is displayed using a color gradient to represent the z-score values. The heatmap employs a user-defined gradient scale, where a chosen "cold" color represents low z-scores and a chosen "hot" color signifies high z-scores. As the z-score increases or decreases, the colors transition smoothly along this gradient, providing an intuitive visual indication of the z-score's magnitude.
- Cold Colors: Indicate values significantly below the mean (negative z-score)
- Mild Colors: Represent values close to the mean, suggesting minimal deviation.
- Hot Colors: Indicate values significantly above the mean (positive z-score)
This heatmap format provides a rapid, visually impactful means to discern how each symbol's price or volume is behaving relative to its average. The color-coded rows allow you to quickly spot outliers.
VOLUME TYPE
The "Volume Type" input allows you to choose the nature of volume data that will be factored into the volume z-score calculation. The interpretation of indicator’s data changes based on this input. You can opt between:
- Volume (Regular Volume): This is the classic measure of trading volume, which represents the volume traded in a given time period - bar.
- OBV (On-Balance Volume): OBV is a momentum indicator that accumulates volume on up bars and subtracts it on down bars, making it a cumulative indicator that sort of measures buying and selling pressure.
Interpretation Implications:
- For Volume Type: Regular Volume:
Positive Z-Score: Indicates that the trading volume is above its average, meaning there's unusually high trading activity .
Negative Z-Score: Suggests that the trading volume is below its average, signifying unusually low trading activity.
- For Volume Type: OBV:
Positive Z-Score: Signifies that “buying pressure” is above its average.
Negative Z-Score: Signifies that “selling pressure” is above its average.
When comparing Z-Score of OBV to Z-Score of price, we can observe several scenarios. If Z-Price and Z-Volume are convergent (have similar z-scores), we can say that the directional price movement is supported by volume. If Z-Price and Z-Volume are divergent (have very different z-scores or one of them being zero), it suggests a potential misalignment between price movement and volume support, which might hint at possible reversals or weakness.
SimilarityMeasuresLibrary "SimilarityMeasures"
Similarity measures are statistical methods used to quantify the distance between different data sets
or strings. There are various types of similarity measures, including those that compare:
- data points (SSD, Euclidean, Manhattan, Minkowski, Chebyshev, Correlation, Cosine, Camberra, MAE, MSE, Lorentzian, Intersection, Penrose Shape, Meehl),
- strings (Edit(Levenshtein), Lee, Hamming, Jaro),
- probability distributions (Mahalanobis, Fidelity, Bhattacharyya, Hellinger),
- sets (Kumar Hassebrook, Jaccard, Sorensen, Chi Square).
---
These measures are used in various fields such as data analysis, machine learning, and pattern recognition. They
help to compare and analyze similarities and differences between different data sets or strings, which
can be useful for making predictions, classifications, and decisions.
---
References:
en.wikipedia.org
cran.r-project.org
numerics.mathdotnet.com
github.com
github.com
github.com
Encyclopedia of Distances, doi.org
ssd(p, q)
Sum of squared difference for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the squared euclidean distance.
euclidean(p, q)
Euclidean distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of distance that calculates the straight-line (or Euclidean).
manhattan(p, q)
Manhattan distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of absolute differences between both points.
minkowski(p, q, p_value)
Minkowsky Distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
p_value (float) : `float` P value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.
Returns: Measure of similarity in the normed vector space.
chebyshev(p, q)
Chebyshev distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
correlation(p, q)
Correlation distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Measure of maximum absolute difference.
cosine(p, q)
Cosine distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Cosine distance between vectors `p` and `q`.
---
angiogenesis.dkfz.de
camberra(p, q)
Camberra distance for N dimensions.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Weighted measure of absolute differences between both points.
mae(p, q)
Mean absolute error is a normalized version of the sum of absolute difference (manhattan).
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean absolute error of vectors `p` and `q`.
mse(p, q)
Mean squared error is a normalized version of the sum of squared difference.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Mean squared error of vectors `p` and `q`.
lorentzian(p, q)
Lorentzian distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Lorentzian distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
intersection(p, q)
Intersection distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Intersection distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
penrose(p, q)
Penrose Shape distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Penrose shape distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
meehl(p, q)
Meehl distance between provided vectors.
Parameters:
p (float ) : `array` Vector with first numeric distribution.
q (float ) : `array` Vector with second numeric distribution.
Returns: Meehl distance of vectors `p` and `q`.
---
angiogenesis.dkfz.de
edit(x, y)
Edit (aka Levenshtein) distance for indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Number of deletions, insertions, or substitutions required to transform source string into target string.
---
generated description:
The Edit distance is a measure of similarity used to compare two strings. It is defined as the minimum number of
operations (insertions, deletions, or substitutions) required to transform one string into another. The operations
are performed on the characters of the strings, and the cost of each operation depends on the specific algorithm
used.
The Edit distance is widely used in various applications such as spell checking, text similarity, and machine
translation. It can also be used for other purposes like finding the closest match between two strings or
identifying the common prefixes or suffixes between them.
---
github.com
www.red-gate.com
planetcalc.com
lee(x, y, dsize)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
dsize (int) : `int` Dictionary size.
Returns: Distance between two strings by accounting for dictionary size.
---
www.johndcook.com
hamming(x, y)
Distance between two indexed strings of equal length.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Length of different components on both sequences.
---
en.wikipedia.org
jaro(x, y)
Distance between two indexed strings.
Parameters:
x (int ) : `array` Indexed array.
y (int ) : `array` Indexed array.
Returns: Measure of two strings' similarity: the higher the value, the more similar the strings are.
The score is normalized such that `0` equates to no similarities and `1` is an exact match.
---
rosettacode.org
mahalanobis(p, q, VI)
Mahalanobis distance between two vectors with population inverse covariance matrix.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
VI (matrix) : `matrix` Inverse of the covariance matrix.
Returns: The mahalanobis distance between vectors `p` and `q`.
---
people.revoledu.com
stat.ethz.ch
docs.scipy.org
fidelity(p, q)
Fidelity distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya Coefficient between vectors `p` and `q`.
---
en.wikipedia.org
bhattacharyya(p, q)
Bhattacharyya distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Bhattacharyya distance between vectors `p` and `q`.
---
en.wikipedia.org
hellinger(p, q)
Hellinger distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The hellinger distance between vectors `p` and `q`.
---
en.wikipedia.org
jamesmccaffrey.wordpress.com
kumar_hassebrook(p, q)
Kumar Hassebrook distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Kumar Hassebrook distance between vectors `p` and `q`.
---
github.com
jaccard(p, q)
Jaccard distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Jaccard distance between vectors `p` and `q`.
---
github.com
sorensen(p, q)
Sorensen distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
Returns: The Sorensen distance between vectors `p` and `q`.
---
people.revoledu.com
chi_square(p, q, eps)
Chi Square distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Chi Square distance between vectors `p` and `q`.
---
uw.pressbooks.pub
stats.stackexchange.com
www.itl.nist.gov
kulczynsky(p, q, eps)
Kulczynsky distance between provided vectors.
Parameters:
p (float ) : `array` 1D Vector.
q (float ) : `array` 1D Vector.
eps (float)
Returns: The Kulczynsky distance between vectors `p` and `q`.
---
github.com
Linear RegressionThis indicator can be used to determine the direction of the current trend.
The indicator plots two different histograms based on the linear regression formula:
- The colored ones represent the direction of the short-term trend
- The gray one represents the direction of the long-term trend
In the settings, you can change the length of the short-term value, which also influences the long-term as a basis that will be multiplied
Ratio To Average - The Quant ScienceRatio To Average - The Quant Science is a quantitative indicator that calculates the percentage ratio of the market price in relation to a reference average. The indicator allows the calculation of the ratio using four different types of averages: SMA, EMA, WMA, and HMA. The ratio is represented by a series of histograms that highlight periods when the ratio is positive (in green) and periods when the ratio is negative (in red).
What is the Ratio to Average?
The Ratio to Average is a measure that tracks the price movements with one of its averages, calculating how much the price is above or below its own average, in percentage terms.
USER INTERFACE
Lenght: it adjusts the number of bars to include in the calculation of the average.
Moving Average: it allows you to choose the type of average to use.
Color Up/Color Down : it allows you to choose the color of the indicator for positive and negative ratios.
