Fibonacci Trend - Aynet1. Inputs
lookbackPeriod: Defines the number of bars to consider for calculating swing highs and lows. Default is 20.
fibLevel1 to fibLevel5: Fibonacci retracement levels to calculate price levels (23.6%, 38.2%, 50%, 61.8%, 78.6%).
useTime: Enables or disables time-based Fibonacci projections.
riskPercent: Defines the percentage of risk for trading purposes (currently not used in calculations).
2. Functions
isSwingHigh(index): Identifies a swing high at the given index, where the high of that candle is higher than both its previous and subsequent candles.
isSwingLow(index): Identifies a swing low at the given index, where the low of that candle is lower than both its previous and subsequent candles.
3. Variables
swingHigh and swingLow: Store the most recent swing high and swing low prices.
swingHighTime and swingLowTime: Store the timestamps of the swing high and swing low.
fib1 to fib5: Fibonacci levels based on the difference between swingHigh and swingLow.
4. Swing Point Detection
The script checks if the last bar is a swing high or swing low using the isSwingHigh() and isSwingLow() functions.
If a swing high is detected:
The high price is stored in swingHigh.
The timestamp of the swing high is stored in swingHighTime.
If a swing low is detected:
The low price is stored in swingLow.
The timestamp of the swing low is stored in swingLowTime.
5. Fibonacci Levels Calculation
If both swingHigh and swingLow are defined, the script calculates the Fibonacci retracement levels (fib1 to fib5) based on the price difference (priceDiff = swingHigh - swingLow).
6. Plotting Fibonacci Levels
Fibonacci levels (fib1 to fib5) are plotted as horizontal lines using the line.new() function.
Labels (e.g., "23.6%") are added near the lines to indicate the level.
Lines and labels are color-coded:
23.6% → Blue
38.2% → Green
50.0% → Yellow
61.8% → Orange
78.6% → Red
7. Filling Between Fibonacci Levels
The plot() function creates lines for each Fibonacci level.
The fill() function is used to fill the space between two levels with semi-transparent colors:
Blue → Between fib1 and fib2
Green → Between fib2 and fib3
Yellow → Between fib3 and fib4
Orange → Between fib4 and fib5
8. Time-Based Fibonacci Projections
If useTime is enabled:
The time difference (timeDiff) between the swing high and swing low is calculated.
Fibonacci time projections are added based on multiples of 23.6%.
If the current time reaches a projected time, a label (e.g., "T1", "T2") is displayed near the high price.
9. Trading Logic
Two placeholder variables are defined for trading logic:
longCondition: Tracks whether a condition for a long trade is met (currently not implemented).
shortCondition: Tracks whether a condition for a short trade is met (currently not implemented).
These variables can be extended to define entry/exit signals based on Fibonacci levels.
How It Works
Detect Swing Points: It identifies recent swing high and swing low points on the chart.
Calculate Fibonacci Levels: Based on the swing points, it computes retracement levels.
Visualize Levels: Plots the levels on the chart with labels and fills between them.
Time Projections: Optionally calculates time-based projections for future price movements.
Trading Opportunities: The framework provides tools for detecting potential reversal or breakout zones using Fibonacci levels.
Komut dosyalarını "track" için ara
OBV TSI IndicatorThe OBV TSI Indicator combines two powerful technical analysis tools: the On-Balance Volume (OBV) and the True Strength Index (TSI). This hybrid approach provides insights into both volume dynamics and momentum, helping traders identify potential trend reversals, breakouts, or continuations with greater accuracy.
The OBV TSI Indicator tracks cumulative volume shifts via OBV and integrates the TSI for momentum analysis. It offers customizable moving average options for further smoothing. Visual trendlines, pivot points, and signal markers enhance clarity.
The OBV tracks volume flow by summing volumes based on price changes. Positive volume is added when prices rise, and negative volume is subtracted when prices fall. The result is smoothed to detect meaningful trends in volume. A volume spread is derived from the difference between the smoothed OBV and cumulative volume. This is then adjusted by the price deviation to generate the shadow spread, which highlights critical volume-driven price levels.
The shadow spread is added to either the high or low price, depending on its sign, producing a refined OBV output. This serves as the main source for the subsequent TSI calculation. The TSI is a momentum oscillator calculated using double-smoothed price changes. It provides an accurate measure of trend strength and direction.
Various moving average options, such as EMA, DEMA, or TEMA, are applied to the smoothed OBV for additional trend filtering. Users can select their preferred type and length to suit their trading strategy. Trendlines are plotted to visualize the overall direction. When a significant change in trend is detected, up or down arrows indicate potential buy or sell signals. The script identifies key pivot points based on the highest and lowest levels within a defined period. These pivots help pinpoint reversal zones.
The indicator offers customization options, allowing users to adjust the OBV length for smoothing, choose from various moving average types, and fine-tune the short, long, and signal periods for TSI. Additionally, users can toggle visibility for trendlines, signals, and pivots to suit their preferences.
This indicator is ideal for practical use cases such as spotting potential trend reversals by observing TSI crossovers and pivot levels, anticipating breakouts from key price levels using the shadow spread, and validating trends by aligning TSI signals with OBV and moving averages.
The OBV TSI Indicator is a versatile tool designed to enhance decision-making in trading by combining volume and momentum analysis. Its flexibility and visual aids make it suitable for traders of all experience levels. By leveraging its insights, you can confidently navigate market trends and improve your trading outcomes.
COIN/BTC Trend OscillatorThe COIN/BTC Trend Oscillator is a versatile tool designed to measure and visualize momentum divergences between Coinbase stock ( NASDAQ:COIN ) and Bitcoin ( CRYPTOCAP:BTC ). It helps identify overbought and oversold conditions, while also highlighting potential trend reversals.
Key Features:
VWAP-Based Divergence Analysis:
• Tracks the difference between NASDAQ:COIN and CRYPTOCAP:BTC relative to their respective VWAPs.
• Highlights shifts in momentum between the two assets.
Normalized Oscillator:
• Uses ATR normalization to adapt to different volatility conditions.
• Displays momentum shifts on a standardized scale for better comparability.
Overbought and Oversold Conditions:
• Identifies extremes using customizable thresholds (default: ±80).
• Dynamic background colors for quick visual identification:
• Blue for overbought zones (potential sell).
• White for oversold zones (potential buy).
Rolling Highs and Lows Detection:
• Tracks turning points in the oscillator to identify possible trend reversals.
• Useful for spotting exhaustion or accumulation phases.
Use Case:
This indicator is ideal for trading Coinbase stock relative to Bitcoin’s momentum. It’s especially useful during strong market trends, helping traders time entries and exits based on extremes in relative performance.
Limitations:
• Performance may degrade in choppy or sideways markets.
• Assumes a strong correlation between NASDAQ:COIN and CRYPTOCAP:BTC , which may not hold during independent events.
Pro Tip: Use this oscillator with broader trend confirmation tools like moving averages or RSI to improve reliability. For macro strategies, consider combining with higher timeframes for alignment.
Master Litecoin Market Cap Network Value ModelMaster Litecoin Market Cap Network Value Model
This indicator visualizes Litecoin's network fundamentals compared to Bitcoin, developed by @masterbtcltc. By analyzing various on-chain metrics and market data, this script helps users evaluate Litecoin’s intrinsic value relative to Bitcoin.
Key Features:
Network Metrics:
NewAddressValueModel: Tracks the ratio of new addresses in Litecoin compared to Bitcoin.
TotalAddressValueModel: Compares total addresses across the two networks.
Transaction & Volume Metrics:
TXValueModel: Compares transaction activity.
VolumeValueModel and VolumeUSDValueModel: Analyzes transaction volumes in native units and USD.
Usage & Adoption:
ActiveValueModel: Tracks the ratio of active addresses between Litecoin and Bitcoin.
RetailValueModel: Measures retail adoption strength in the Litecoin network.
Blockchain & Holder Data:
BlockValueModel: Compares block sizes.
NonZeroModel: Evaluates addresses with non-zero balances.
HodlerModel: Compares long-term holders between Litecoin and Bitcoin.
Averaged Insights:
AverageValueModel: Aggregates all metrics for a complete view of network valuation.
Visual Design:
Blue Themed Metrics: Network value models are displayed in a uniform blue color with a line thickness of 4 and 25% transparency for clarity.
Distinct Price Plot: Litecoin’s price is plotted in yellow, with a thin line (width 2) and no transparency, keeping it visually separate.
Use Cases:
Ideal for traders, investors, and enthusiasts aiming to:
Identify Litecoin’s market trends.
Detect periods of undervaluation or overvaluation.
Gain deeper insights into Litecoin’s network fundamentals.
Important Instruction: To ensure accurate results, plot this indicator on VANTAGE:LTCUSD * GLASSNODE:LTC_SUPPLY. This ensures alignment with the data sources and guarantees the script performs as intended.
Feel free to explore, use, and share this open-source script to better understand Litecoin’s value potential!
Infinity Market Grid -AynetConcept
Imagine viewing the market as a dynamic grid where price, time, and momentum intersect to reveal infinite possibilities. This indicator leverages:
Grid-Based Market Flow: Visualizes price action as a grid with zones for:
Accumulation
Distribution
Breakout Expansion
Volatility Compression
Predictive Dynamic Layers:
Forecasts future price zones using historical volatility and momentum.
Tracks event probabilities like breakout, fakeout, and trend reversals.
Data Science Visuals:
Uses heatmap-style layers, moving waveforms, and price trajectory paths.
Interactive Alerts:
Real-time alerts for high-probability market events.
Marks critical zones for "buy," "sell," or "wait."
Key Features
Market Layers Grid:
Creates dynamic "boxes" around price using fractals and ATR-based volatility.
These boxes show potential future price zones and probabilities.
Volatility and Momentum Waves:
Overlay volatility oscillators and momentum bands for directional context.
Dynamic Heatmap Zones:
Colors the chart dynamically based on breakout probabilities and risk.
Price Path Prediction:
Tracks price trajectory as a moving "wave" across the grid.
How It Works
Grid Box Structure:
Upper and lower price levels are based on ATR (volatility) and plotted dynamically.
Dashed green/red lines show the grid for potential price expansion zones.
Heatmap Zones:
Colors the background based on probabilities:
Green: High breakout probability.
Blue: High consolidation probability.
Price Path Prediction:
Forecasts future price movements using momentum.
Plots these as a dynamic "wave" on the chart.
Momentum and Volatility Waves:
Shows the relationship between momentum and volatility as oscillating waves.
Helps identify when momentum exceeds volatility (potential breakouts).
Buy/Sell Signals:
Triggers when price approaches grid edges with strong momentum.
Provides alerts and visual markers.
Why Is It Revolutionary?
Grid and Wave Synergy:
Combines structural price zones (grid boxes) with real-time momentum and volatility waves.
Predictive Analytics:
Uses momentum-based forecasting to visualize what’s next, not just what’s happening.
Dynamic Heatmap:
Creates a living map of breakout/consolidation zones in real-time.
Scalable for Any Market:
Works seamlessly with forex, crypto, and stocks by adjusting the ATR multiplier and box length.
This indicator is not just a tool but a framework for understanding market dynamics at a deeper level. Let me know if you'd like to take it even further — for example, adding machine learning-inspired probability models or multi-timeframe analysis! 🚀
Multi-Timeframe Period Separators█ OVERVIEW
This indicator plots period separators for up to four higher timeframes. The separators are fully customizable and designed to work on any symbols.
█ FEATURES
Reference
You can choose to plot the separators starting from midnight 00:00 or the opening of the exchange trading session.
Timezone
You can specify to localize midnight 00:00 to the region of your liking. The timezone format conveniently requires no manual adjustment during clock changes.
█ NOTES
Scans the bar opening and closing times
The script checks the bar ` time ` and ` time_close ` to pinpoint the separators that can occur intrabar.
Tracks from the last separator
The script tracks the time elapsed since the last separator, which is useful when there is no trading activity or the market is closed. As it can result in missing bars, it plots the separator on the first available bar.
Others
The script automatically hides the separators when navigating to an equal or higher chart timeframe.
Ultimate Machine Learning RSI (Deep Learning Edition)This script represents an advanced implementation of a Machine Learning-based Relative Strength Index (RSI) indicator in Pine Script, incorporating several sophisticated techniques to create a more adaptive, intelligent, and responsive RSI.
Key Components and Features:
Lookback Period: The period over which the indicator "learns" from past data, set to 1000 bars by default.
Momentum and Volatility Weighting: These factors control how much the momentum and volatility of the market influence the learning and signal generation.
RSI Length Range: The minimum and maximum values for the RSI length, allowing the algorithm to adjust the RSI length dynamically.
Learning Rate: Controls how quickly the system adapts to new data. An adaptive learning rate can change based on market volatility.
Memory Factor: Influences how much the system "remembers" previous performance when making adjustments.
Monte Carlo Simulations: Used for probabilistic modeling to create a more robust signal.
\
Price Change: Tracks the difference between the current close and the previous close.
Momentum: A measure of the rate of change in the price over the lookback period.
Volatility: Calculated using the standard deviation of the close prices.
ATR (Average True Range): Tracks the volatility of the market over a short period to influence decisions.
Monte Carlo Simulation:
Probabilistic Signal: This uses multiple random simulations (Monte Carlo) to generate potential future signals. These simulations are weighted by the momentum and volatility of the market. A cluster factor further enhances the simulation based on volatility regimes.
Z-Score for Extreme Conditions:
Z-Score: Measures how extreme current price movements are compared to the historical average, providing context for identifying overbought and oversold conditions.
Dynamic Learning Rate:
The learning rate adjusts based on the volatility of the market, becoming more responsive in high-volatility periods and slower in low-volatility markets. This prevents the system from overreacting to noise but ensures responsiveness to significant shifts.
Recursive Learning and Feedback:
Error Calculation: The system calculates the difference between the true RSI and the predicted RSI, creating an error that is fed back into the system to adjust the RSI length and other parameters dynamically.
RSI Length Adjustment: Based on the error, the RSI length is adjusted, ensuring that the system evolves over time to better reflect market conditions.
Adaptive Smoothing:
In periods of high volatility, the indicator applies a Triple Exponential Moving Average (TEMA) for faster adaptation, while in quieter markets, it uses an Exponential Moving Average (EMA) for smoother adjustments.
Recursive Memory Feedback:
The system maintains a memory of past RSI values, which helps refine the output further. The memory factor influences how much weight is given to past performance versus the current adaptive signal.
Volatility-Based Reinforcement: Higher market volatility increases the impact of this memory feedback, making the model more reactive in volatile conditions.
Multi-Factor Dynamic Thresholds:
Dynamic Overbought/Oversold: Instead of fixed RSI levels (70/30), the thresholds adjust dynamically based on the Z-Score, making the system more sensitive to extreme market conditions.
Combined Multi-Factor Signal:
The final output signal is the result of combining the true RSI, adaptive RSI, and the probabilistic signal generated from the Monte Carlo simulations. This creates a robust, multi-factor signal that incorporates various market conditions and machine learning techniques.
Visual Representation:
The final combined signal is plotted in blue on the chart, along with reference lines at 55 (overbought), 10 (oversold), and 35 (neutral).
Alerts are set up to trigger when the combined signal crosses above the dynamic overbought level or below the dynamic oversold level.
Conclusion:
This "Ultimate Machine Learning RSI" script leverages multiple machine learning techniques—probabilistic modeling, adaptive learning, recursive feedback, and dynamic thresholds—to create an advanced, highly responsive RSI indicator. The result is an RSI that continuously learns from market conditions, adjusts itself in real-time, and provides a more nuanced and robust signal compared to traditional fixed-length RSI. This indicator pushes the boundaries of what's possible with Pine Script and introduces cutting-edge techniques for technical analysis.
Crossover CounterExplanation:
Crossover Detection: We detect the crossover of the 20-period and 50-period moving averages using ta.crossover().
Tracking Price Movement: After the crossover, we start tracking the price to check if it moves up or down by 2%. If an up movement occurs before a down movement, we increment the positive counter. If a down movement occurs first, we increment the negative counter.
Reset Condition: Once either a 2% up or down move is detected, we stop tracking until the next crossover.
Table Display: A table shows the counts of positive and negative events.
Elite By Ashu4750Inside Bar Detection:
The script identifies inside bars, which are candles where the high is lower and the low is higher than the previous bar. It tracks the high and low of the mother candle (the candle preceding the inside bars) and plots the ranges on the chart using lines and labels.
Exponential Moving Averages (EMA):
Three EMAs are calculated and plotted (with default periods of 9, 21, and 50). This is a classic trend-following technique used to smooth price data and identify the direction of the market.
Bollinger Bands (BB):
The script includes a Bollinger Band calculation using the simple moving average (SMA) with a standard deviation multiplier. The bands help visualize volatility and potential overbought or oversold conditions.
The user can configure settings like the length of the SMA and the multiplier for the upper and lower bands.
Volume Weighted Average Price (VWAP):
The VWAP is plotted on the chart and reset based on user-defined timeframes (e.g., session, week, month). VWAP is a popular indicator for institutional trading, as it shows the average price weighted by volume and can act as support or resistance.
Crossover Signals (Buy/Sell):
A combination of crossovers between VWAP, EMAs, and Bollinger Bands triggers buy and sell signals. Specifically:
Buy signal is generated when VWAP crosses over the 9 EMA, the close crosses over the Bollinger Band line, and VWAP crosses over the Bollinger Band.
Sell signal is triggered when VWAP crosses under the 9 EMA, and similar conditions exist for the other indicators.
These signals are plotted with a green "Buy" or red "Sell" marker below the bars, and alerts are set up for both buying and selling.
Additional Bollinger Band Configuration:
The script provides more flexibility in Bollinger Bands by allowing the user to select between SMA, EMA, or SMMA for the moving average.
The user can also choose the standard deviation multiplier and whether to display the bands.
Alerts:
Buy and sell conditions are linked to alert conditions, allowing the user to be notified when a signal is triggered, based on the defined crossover logic.
Technical Breakdown:
Inside Bar Logic: Tracks inside bars and plots lines representing the high and low of the mother candle. The line and label functions are used to draw these on the chart, which provides a visual representation of the range.
EMA and VWAP Crossovers:
The 9, 21, and 50-period EMAs are calculated and used in crossover logic with VWAP. Crossovers between VWAP and EMAs are a common method for identifying potential trend changes.
Bollinger Bands:
The Bollinger Band component allows for volatility analysis by calculating the upper and lower bands based on the moving average's standard deviation.
Alert System:
Alerts are set for crossover signals, allowing for real-time notifications of potential buy and sell opportunities.
Visualization:
The script plots the EMAs, VWAP, and Bollinger Bands on the price chart. It highlights inside bar patterns and displays buy/sell markers on the chart when the specified conditions are met. These visual cues make it easier to follow the market’s movements and spot trading opportunities.
Customizability:
The script is highly customizable with inputs for:
EMA periods.
VWAP settings.
Bollinger Band parameters (moving average type, length, standard deviation).
Candle color options for inside bars.
In this traders looking for multiple indicators to analyze market trends, volatility, and price action.
SMA, 20%UP, 20% SMA, LTH newFeatures:
Simple Moving Averages (SMAs):
200 SMA (Gray): Long-term trend indicator. A widely used benchmark in many trading strategies.
50 SMA (Red): Mid-term trend indicator.
20 SMA (Green): Short-term trend indicator. These three SMAs allow traders to visualize the general market trend over different time horizons.
20% Gain on Green Candles:
This feature tracks continuous green candles and calculates the percentage gain from the lowest low to the highest high in that series.
If the gain is greater than or equal to 20%, the script highlights it with a purple triangle above the candle.
If the series of green candles starts with a candle where the low is below the 200 SMA, a purple diamond appears under the bar, indicating potential strong buying signals.
Lifetime High (LTH):
The script automatically tracks and displays the Lifetime High (LTH), i.e., the highest price ever recorded on the chart.
This level is important for identifying potential resistance areas and monitoring long-term market tops.
Once a new LTH is reached, it is displayed as a green line across the chart.
Support Levels from LTH:
The script calculates 30%, 50%, and 67% down from the LTH, marking key support levels.
These levels are plotted on the chart as orange lines and labeled to assist in spotting potential buy zones or market reversals.
52-Week Low:
It also calculates and displays the 52-week low for quick reference, plotted as a green line.
This helps traders assess major market bottoms and potential areas of support.
Harish Algo 2The script "Harish Algo 2" is a Pine Script-based TradingView indicator that automatically identifies significant trendlines based on fractal points and tracks price interactions with those trendlines. Key features include:
Fractal Detection: The script identifies fractal highs and lows, using a configurable fractal period, to serve as pivot points for generating trendlines. Fractal highs are marked in blue, and fractal lows are marked in red.
Dynamic Trendlines: It draws trendlines between consecutive fractal points, with a limit on the maximum number of active trendlines. The trendlines can be extended either in both directions or to the right, as per user input. The line width can also be customized.
Support/Resistance Counting: Each trendline tracks how many times the price interacts with it. If the price approaches the line from above and touches or stays near it, the line is considered a support. If the price approaches from below, it is considered a resistance. These counts are used to modify the trendline's color and appearance.
Trendlines with 2 support interactions turn green.
Trendlines with 2 resistance interactions turn red.
Trendlines with 3 or more interactions turn black.
Trendline Styling: Trendlines that extend over a long period (more than 100 bars) change to a dotted style to highlight their persistence.
Break Detection: The script monitors if the price crosses a trendline, signaling a potential breakout or breakdown. Once a trendline is broken, it stops extending further.
Trendline Removal: The script ensures that only a limited number of trendlines are active at a time. If the maximum number of trendlines is reached, the oldest trendline is removed to make space for new ones.
This indicator is designed to help traders visualize important trendlines, spot potential support and resistance levels, and detect breakouts or breakdowns based on price movement.
Lsma ATR | viResearchLsma ATR | viResearch
Conceptual Foundation and Innovation
The "Lsma ATR" indicator from viResearch combines the power of the Least Squares Moving Average (LSMA) with the Average True Range (ATR) to offer traders a dynamic approach to trend analysis and volatility management. The LSMA is highly regarded for its ability to fit a linear regression line to price data, providing a smooth and precise trend line with minimal lag. When paired with the ATR, which measures market volatility, this indicator not only tracks trend direction but also adapts to changes in volatility. The integration of both elements allows traders to identify potential trend reversals and assess the strength of trends in the context of market volatility. This combination makes the "Lsma ATR" a versatile tool for following trends while managing risk, as it responds quickly to changes in price direction while accounting for shifts in market volatility.
Technical Composition and Calculation
The "Lsma ATR" script consists of two primary components: the Least Squares Moving Average (LSMA) and the Average True Range (ATR). The LSMA is calculated over a user-defined length, providing a smoothed representation of the market trend based on linear regression. The ATR, also user-defined, is used to measure market volatility by calculating the average range between high and low prices over a specified period. By adding and subtracting the ATR from the LSMA, the indicator creates upper and lower boundaries that help define the market's current volatility-adjusted range. The script monitors for price crossovers with these boundaries to generate trend signals. When the price crosses above the upper boundary, it signals a potential upward trend. Conversely, when the price crosses below the lower boundary, it signals a possible downward trend. These boundaries dynamically adjust based on volatility, providing more accurate signals as market conditions change.
Features and User Inputs
The "Lsma ATR" script offers several customizable inputs, allowing traders to fine-tune the indicator to their trading preferences. The LSMA Length controls the lookback period for the LSMA, determining how smooth or responsive the trend line is. The ATR Length defines the period used for calculating the average volatility, affecting the width of the volatility-adjusted range. Additionally, the indicator includes alert conditions that notify traders when a trend shift occurs, either to the upside or downside.
Practical Applications
The "Lsma ATR" indicator is designed for traders who want to follow market trends while accounting for changes in volatility. The LSMA provides a clear, smoothed trend line that helps identify the direction of the market, while the ATR adjusts the boundaries based on the current volatility level. This combination makes the indicator particularly effective for detecting trend reversals, as the LSMA tracks the overall trend direction and price crossovers with the ATR boundaries provide early signals of potential trend changes. It also helps manage risk by understanding market volatility, allowing traders to adjust their strategies based on the strength of price movements. The indicator improves trend-following strategies by combining LSMA’s trend detection with ATR’s volatility adjustment, offering a nuanced approach in various market conditions.
Advantages and Strategic Value
The "Lsma ATR" script offers significant value by integrating the precision of the LSMA with the adaptability of the ATR. This dual approach allows traders to reduce noise in price data while responding to changes in volatility, leading to more accurate trend signals. The volatility-adjusted boundaries provide a dynamic range that helps traders avoid false signals and stay aligned with stronger trends. This makes the "Lsma ATR" an ideal tool for traders seeking to enhance their trend-following strategies while accounting for market volatility.
Alerts and Visual Cues
The script includes alert conditions that notify traders when the price crosses the ATR boundaries, signaling a potential trend change. The "Lsma ATR Long" alert is triggered when the price crosses above the upper boundary, indicating a potential upward trend, while the "Lsma ATR Short" alert signals a possible downward trend when the price crosses below the lower boundary. Visual cues, such as changes in the color of the LSMA line and shaded areas between the ATR boundaries, help traders quickly identify these trend shifts.
Summary and Usage Tips
The "Lsma ATR | viResearch" indicator combines the smoothing benefits of the LSMA with the volatility sensitivity of the ATR, providing traders with a robust tool for trend detection and volatility management. By incorporating this script into your trading strategy, you can improve your ability to detect trend reversals, confirm trend direction, and manage risk by adjusting to market volatility. The "Lsma ATR" offers a reliable and customizable solution for traders looking to enhance their technical analysis in both trending and volatile market environments.
Note: Backtests are based on past results and are not indicative of future performance.
Low Volatility Range Breaks [BigBeluga]Low Volatility Range Breaks
The Low Volatility Range Breaks indicator is an advanced technical analysis tool designed to identify periods of low volatility and potential breakout opportunities. By visualizing low volatility ranges as ranges and tracking subsequent price movements, this indicator helps traders spot potential high-probability trade setups.
🔵 KEY FEATURES
● Low Volatility Detection
Identifies periods of low volatility based on highest and lowest periods and user-defined sensitivity
Uses a combination of highest/lowest price calculations and ATR for dynamic adaptation
● Volatility Box Visualization
Creates a box to represent the low volatility range
Box height is adjustable based on ATR multiplier
Includes a mid-line for reference within the box
● Breakout Detection
Identifies when price breaks above or below the volatility box
Labels breakouts as "Break Up" or "Break Dn" on the chart
Changes box appearance to indicate a completed breakout
● Probability Tracking
Counts the number of closes above and below the box's mid-line
Displays probability counters for potential upward and downward moves
Resets counters after a confirmed breakout
🔵 HOW TO USE
● Identifying Low Volatility Periods
Watch for the formation of volatility boxes on the chart
These boxes represent periods where price movement has been confined
● Anticipating Breakouts
Monitor price action as it approaches the edges of the volatility box
Use the probability counters to gauge the likely direction of the breakout
● Trading Breakouts
Consider posible entering trades when price breaks above or below the volatility box
Use the breakout labels ("Break Up" or "Break Dn") as a trading opportunity
● Managing Risk
Use the opposite side of the volatility box as a potential invalidation level
Consider the box height for position sizing and risk management
● Trend Analysis
Multiple upward breakouts may indicate a developing uptrend
Multiple downward breakouts may suggest a forming downtrend
Use in conjunction with other trend indicators for confirmation
🔵 CUSTOMIZATION
The Low Volatility Box Breaks indicator offers several customization options:
Adjust the volatility length to change the period for highest/lowest price calculations
Modify the volatility level to fine-tune the sensitivity of low volatility detection
Adjust the box height multiplier to change the size of volatility boxes
By fine-tuning these settings, traders can adapt the indicator to various market conditions and personal trading strategies.
The Low Volatility Range Breaks indicator provides a unique approach to identifying potential breakout opportunities following periods of consolidation. By visually representing low volatility periods and tracking subsequent price movements, it offers traders a powerful tool for spotting high-probability trade setups.
This indicator can be particularly useful for traders focusing on breakout strategies, mean reversion tactics, or those looking to enter trades at the beginning of new trends. The combination of visual cues (boxes and breakout labels) and quantitative data (probability counters) provides a comprehensive view of market dynamics during and after low volatility periods.
As with all technical indicators, it's recommended to use the Low Volatility Range Breaks indicator in conjunction with other forms of analysis and within the context of a well-defined trading strategy. While this indicator can provide valuable insights into potential breakouts, it should be considered alongside other factors such as overall market trends, volume, and fundamental analysis when making trading decisions.
Bitcoin Macro Trend Map [Ox_kali]
## Introduction
__________________________________________________________________________________
The “Bitcoin Macro Trend Map” script is designed to provide a comprehensive analysis of Bitcoin’s macroeconomic trends. By leveraging a unique combination of Bitcoin-specific macroeconomic indicators, this script helps traders identify potential market peaks and troughs with greater accuracy. It synthesizes data from multiple sources to offer a probabilistic view of market excesses, whether overbought or oversold conditions.
This script offers significant value for the following reasons:
1. Holistic Market Analysis : It integrates a diverse set of indicators that cover various aspects of the Bitcoin market, from investor sentiment and market liquidity to mining profitability and network health. This multi-faceted approach provides a more complete picture of the market than relying on a single indicator.
2. Customization and Flexibility : Users can customize the script to suit their specific trading strategies and preferences. The script offers configurable parameters for each indicator, allowing traders to adjust settings based on their analysis needs.
3. Visual Clarity : The script plots all indicators on a single chart with clear visual cues. This includes color-coded indicators and background changes based on market conditions, making it easy for traders to quickly interpret complex data.
4. Proven Indicators : The script utilizes well-established indicators like the EMA, NUPL, PUELL Multiple, and Hash Ribbons, which are widely recognized in the trading community for their effectiveness in predicting market movements.
5. A New Comprehensive Indicator : By integrating background color changes based on the aggregate signals of various indicators, this script essentially creates a new, comprehensive indicator tailored specifically for Bitcoin. This visual representation provides an immediate overview of market conditions, enhancing the ability to spot potential market reversals.
Optimal for use on timeframes ranging from 1 day to 1 week , the “Bitcoin Macro Trend Map” provides traders with actionable insights, enhancing their ability to make informed decisions in the highly volatile Bitcoin market. By combining these indicators, the script delivers a robust tool for identifying market extremes and potential reversal points.
## Key Indicators
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Macroeconomic Data: The script combines several relevant macroeconomic indicators for Bitcoin, such as the 10-month EMA, M2 money supply, CVDD, Pi Cycle, NUPL, PUELL, MRVR Z-Scores, and Hash Ribbons (Full description bellow).
Open Source Sources: Most of the scripts used are sourced from open-source projects that I have modified to meet the specific needs of this script.
Recommended Timeframes: For optimal performance, it is recommended to use this script on timeframes ranging from 1 day to 1 week.
Objective: The primary goal is to provide a probabilistic solution to identify market excesses, whether overbought or oversold points.
## Originality and Purpose
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This script stands out by integrating multiple macroeconomic indicators into a single comprehensive tool. Each indicator is carefully selected and customized to provide insights into different aspects of the Bitcoin market. By combining these indicators, the script offers a holistic view of market conditions, helping traders identify potential tops and bottoms with greater accuracy. This is the first version of the script, and additional macroeconomic indicators will be added in the future based on user feedback and other inputs.
## How It Works
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The script works by plotting each macroeconomic indicator on a single chart, allowing users to visualize and interpret the data easily. Here’s a detailed look at how each indicator contributes to the analysis:
EMA 10 Monthly: Uses an exponential moving average over 10 monthly periods to signal bullish and bearish trends. This indicator helps identify long-term trends in the Bitcoin market by smoothing out price fluctuations to reveal the underlying trend direction.Moving Averages w/ 18 day/week/month.
Credit to @ryanman0
M2 Money Supply: Analyzes the evolution of global money supply, indicating market liquidity conditions. This indicator tracks the changes in the total amount of money available in the economy, which can impact Bitcoin’s value as a hedge against inflation or economic instability.
Credit to @dylanleclair
CVDD (Cumulative Value Days Destroyed): An indicator based on the cumulative value of days destroyed, useful for identifying market turning points. This metric helps assess the Bitcoin market’s health by evaluating the age and value of coins that are moved, indicating potential shifts in market sentiment.
Credit to @Da_Prof
Pi Cycle: Uses simple and exponential moving averages to detect potential sell points. This indicator aims to identify cyclical peaks in Bitcoin’s price, providing signals for potential market tops.
Credit to @NoCreditsLeft
NUPL (Net Unrealized Profit/Loss): Measures investors’ unrealized profit or loss to signal extreme market levels. This indicator shows the net profit or loss of Bitcoin holders as a percentage of the market cap, helping to identify periods of significant market optimism or pessimism.
Credit to @Da_Prof
PUELL Multiple: Assesses mining profitability relative to historical averages to indicate buying or selling opportunities. This indicator compares the daily issuance value of Bitcoin to its yearly average, providing insights into when the market is overbought or oversold based on miner behavior.
Credit to @Da_Prof
MRVR Z-Scores: Compares market value to realized value to identify overbought or oversold conditions. This metric helps gauge the overall market sentiment by comparing Bitcoin’s market value to its realized value, identifying potential reversal points.
Credit to @Pinnacle_Investor
Hash Ribbons: Uses hash rate variations to signal buying opportunities based on miner capitulation and recovery. This indicator tracks the health of the Bitcoin network by analyzing hash rate trends, helping to identify periods of miner capitulation and subsequent recoveries as potential buying opportunities.
Credit to @ROBO_Trading
## Indicator Visualization and Interpretation
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For each horizontal line representing an indicator, a legend is displayed on the right side of the chart. If the conditions are positive for an indicator, it will turn green, indicating the end of a bearish trend. Conversely, if the conditions are negative, the indicator will turn red, signaling the end of a bullish trend.
The background color of the chart changes based on the average of green or red indicators. This parameter is configurable, allowing adjustment of the threshold at which the background color changes, providing a clear visual indication of overall market conditions.
## Script Parameters
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The script includes several configurable parameters to customize the display and behavior of the indicators:
Color Style:
Normal: Default colors.
Modern: Modern color style.
Monochrome: Monochrome style.
User: User-customized colors.
Custom color settings for up trends (Up Trend Color), down trends (Down Trend Color), and NaN (NaN Color)
Background Color Thresholds:
Thresholds: Settings to define the thresholds for background color change.
Low/High Red Threshold: Low and high thresholds for bearish trends.
Low/High Green Threshold: Low and high thresholds for bullish trends.
Indicator Display:
Options to show or hide specific indicators such as EMA 10 Monthly, CVDD, Pi Cycle, M2 Money, NUPL, PUELL, MRVR Z-Scores, and Hash Ribbons.
Specific Indicator Settings:
EMA 10 Monthly: Options to customize the period for the exponential moving average calculation.
M2 Money: Aggregation of global money supply data.
CVDD: Adjustments for value normalization.
Pi Cycle: Settings for simple and exponential moving averages.
NUPL: Thresholds for unrealized profit/loss values.
PUELL: Adjustments for mining profitability multiples.
MRVR Z-Scores: Settings for overbought/oversold values.
Hash Ribbons: Options for hash rate moving averages and capitulation/recovery signals.
## Conclusion
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The “Bitcoin Macro Trend Map” by Ox_kali is a tool designed to analyze the Bitcoin market. By combining several macroeconomic indicators, this script helps identify market peaks and troughs. It is recommended to use it on timeframes from 1 day to 1 week for optimal trend analysis. The scripts used are sourced from open-source projects, modified to suit the specific needs of this analysis.
## Notes
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This is the first version of the script and it is still in development. More indicators will likely be added in the future. Feedback and comments are welcome to improve this tool.
## Disclaimer:
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Please note that the Open Interest liquidation map is not a guarantee of future market performance and should be used in conjunction with proper risk management. Always ensure that you have a thorough understanding of the indicator’s methodology and its limitations before making any investment decisions. Additionally, past performance is not indicative of future results.
Persistent Homology Based Trend Strength OscillatorPersistent Homology Based Trend Strength Oscillator
The Persistent Homology Based Trend Strength Oscillator is a unique and powerful tool designed to measure the persistence of market trends over a specified rolling window. By applying the principles of persistent homology, this indicator provides traders with valuable insights into the strength and stability of uptrends and downtrends, helping to inform better trading decisions.
What Makes This Indicator Original?
This indicator's originality lies in its application of persistent homology , a method from topological data analysis, to financial markets. Persistent homology examines the shape and features of data across multiple scales, identifying patterns that persist as the scale changes. By adapting this concept, the oscillator tracks the persistence of uptrends and downtrends in price data, offering a novel approach to trend analysis.
Concepts Underlying the Calculations:
Persistent Homology: This method identifies features such as clusters, holes, and voids that persist as the scale changes. In the context of this indicator, it tracks the duration and stability of price trends.
Rolling Window Analysis: The oscillator uses a specified window size to calculate the average length of uptrends and downtrends, providing a dynamic view of trend persistence over time.
Threshold-Based Trend Identification: It differentiates between uptrends and downtrends based on specified thresholds for price changes, ensuring precision in trend detection.
How It Works:
The oscillator monitors consecutive changes in closing prices to identify uptrends and downtrends.
An uptrend is detected when the closing price increase exceeds a specified positive threshold.
A downtrend is detected when the closing price decrease exceeds a specified negative threshold.
The lengths of these trends are recorded and averaged over the chosen window size.
The Trend Persistence Index is calculated as the difference between the average uptrend length and the average downtrend length, providing a measure of trend persistence.
How Traders Can Use It:
Identify Trend Strength: The Trend Persistence Index offers a clear measure of the strength and stability of uptrends and downtrends. A higher value indicates stronger and more persistent uptrends, while a lower value suggests stronger and more persistent downtrends.
Spot Trend Reversals: Significant shifts in the Trend Persistence Index can signal potential trend reversals. For instance, a transition from positive to negative values might indicate a shift from an uptrend to a downtrend.
Confirm Trends: Use the Trend Persistence Index alongside other technical indicators to confirm the strength and duration of trends, enhancing the accuracy of your trading signals.
Manage Risk: Understanding trend persistence can help traders manage risk by identifying periods of high trend stability versus periods of potential volatility. This can be crucial for timing entries and exits.
Example Usage:
Default Settings: Start with the default settings to get a feel for the oscillator’s behavior. Observe how the Trend Persistence Index reacts to different market conditions.
Adjust Thresholds: Fine-tune the positive and negative thresholds based on the asset's volatility to improve trend detection accuracy.
Combine with Other Indicators: Use the Persistent Homology Based Trend Strength Oscillator in conjunction with other technical indicators such as moving averages, RSI, or MACD for a comprehensive analysis.
Backtesting: Conduct backtesting to see how the oscillator would have performed in past market conditions, helping you to refine your trading strategy.
Market Sentiment Technicals [LuxAlgo]The Market Sentiment Technicals indicator synthesizes insights from diverse technical analysis techniques, including price action market structures, trend indicators, volatility indicators, momentum oscillators, and more.
The indicator consolidates the evaluated outputs from these techniques into a singular value and presents the combined data through an oscillator format, technical rating, and a histogram panel featuring the sentiment of each component alongside the overall sentiment.
🔶 USAGE
The Market Sentiment Technicals indicator is a tool able to swiftly and easily gauge market sentiment by consolidating the individual sentiment from multiple technical analysis techniques applied to market data into a single value, allowing users to asses if the market is uptrending, consolidating, or downtrending.
The tool includes various components and presentation formats, each described in the sub-sections below.
🔹Indicators Sentiment Panel
The indicators sentiment panel provides normalized sentiment scores for each supported indicator, along with a synthesized representation derived from the average of all individual normalized sentiments.
🔹Market Sentiment Meter
The market sentiment meter is obtained from the synthesized representation derived from the average of all individual normalized sentiments. It allows users to quickly and easily gauge the overall market sentiment.
🔹Market Sentiment Oscillator
The market sentiment oscillator provides a visual means to monitor the current and historical strength of the market. It assists in identifying the trend direction, trend momentum, and overbought and oversold conditions, aiding in the anticipation of potential trend reversals.
Divergence occurs when there is a difference between what the price action is indicating and what the market sentiment oscillator is indicating, helping traders assess changes in the price trend.
🔶 DETAILS
The indicator employs a range of technical analysis techniques to interpret market data. Each group of indicators provides valuable insights into different aspects of market behavior.
🔹Momentum Indicators
Momentum indicators assess the speed and change of price movements, often indicating whether a trend is strengthening or weakening.
Relative Strength Index (RSI): Measures the magnitude of recent price changes to evaluate overbought or oversold conditions.
Stochastic %K: Compares the closing price to the range over a specified period to identify potential reversal points.
Stochastic RSI Fast: Combines features of Stochastic oscillators and RSI to gauge both momentum and overbought/oversold levels efficiently.
Commodity Channel Index (CCI): Measures the deviation of an asset's price from its statistical average to determine trend strength and overbought and oversold conditions.
Bull Bear Power: Evaluates the strength of buying and selling pressure in the market.
🔹Trend Indicators
Trend indicators help traders identify the direction of a market trend.
Moving Averages: Provides a smoothed representation of the underlying price data, aiding in trend identification and analysis.
Bollinger Bands: Consists of a middle band (typically a simple moving average) and upper and lower bands, which represent volatility levels of the market.
Supertrend: A trailing stop able to identify the current direction of the trend.
Linear Regression: Fits a straight line to past data points to predict future price movements and identify trend direction.
🔹Market Structures
Market Structures: Analyzes the overall pattern of price movements, including Break of Structure (BOS), Market Structure Shifts (MSS), also referred to as Change of Character (CHoCH), aiding in identifying potential market turning and continuation points.
🔹The Normalization Technique
The normalization technique employed for trend indicators relies on buy-sell signals. The script tracks price movements and normalizes them based on these signals.
normalize(buy, sell, smooth)=>
var os = 0
var float max = na
var float min = na
os := buy ? 1 : sell ? -1 : os
max := os > os ? close : os < os ? max : math.max(close, max)
min := os < os ? close : os > os ? min : math.min(close, min)
ta.sma((close - min)/(max - min), smooth) * 100
In this Pine Script snippet:
The variable os tracks market sentiment, taking a value of 1 for buy signals and -1 for sell signals, indicating bullish and bearish sentiments, respectively.
max and min are used to identify extremes in sentiment and are updated based on changes in os . When market sentiment shifts from buying to selling (or vice versa), max and min adjust accordingly.
Normalization is achieved by comparing current price levels to historical extremes in sentiment. The result is smoothed by default using a 3-period simple moving average. Users have the option to customize the smoothing period via the script settings input menu.
🔶 SETTINGS
🔹Generic Settings
Timeframe: This option selects the timeframe for calculating sentiment. If a timeframe lower than the chart's is chosen, calculations will be based on the chart's timeframe.
Horizontal Offset: Determines the distance at which the visual components of the indicator will be displayed from the primary chart.
Gradient Colors: Allows customization of gradient colors.
🔹Indicators Sentiment Panel
Indicators Sentiment Panel: Toggle the visibility of the indicators sentiment panel.
Panel Height: Determines the height of the panel.
🔹Market Sentiment Meter
Market Sentiment Meter: Toggle the visibility of the market sentiment meter (technical ratings in the shape of a speedometer).
🔹Market Sentiment Oscillator
Market Sentiment Oscillator: Toggle the visibility of the market sentiment oscillator.
Show Divergence: Enables detection of divergences based on the selected option.
Oscillator Line Width: Customization option for the line width.
Oscillator Height: Determines the height of the oscillator.
🔹Settings for Individual Components
In general,
Source: Determines the data source for calculations.
Length: The period to be used in calculations.
Smoothing: Degree of smoothness of the evaluated values.
🔹Normalization Settings - Trend Indicators
Smoothing: The period used in smoothing normalized values, where normalization is applied to moving averages, Bollinger Bands, Supertrend, VWAP bands, and market structures.
🔶 LIMITATIONS
Like any technical analysis tool, the Market Sentiment Technicals indicator has limitations. It's based on historical data and patterns, which may not always accurately predict future market movements. Additionally, market sentiment can be influenced by various factors, including economic news, geopolitical events, and market psychology, which may not be fully captured by technical analysis alone.
[Spinn] Average True RangeThe "Average True Range" indicator is a popular tool that measures price volatility. In this modified indicator, I present two methods of calculating ATR: the outdated classical one based on RMA (EMA, SMA, WMA), and the modernized one using the Super Smoother filter.
Why has exponential smoothing become outdated?
Exponential smoothing (EMA) has drawbacks, especially when it comes to identifying cyclical components in the data (and RMA is a variant of EMA). EMA creates phase shifts and distortions, making it less predictable and accurate in tracking real price movements. Modern filters, such as Super Smoother, offer a higher degree of adaptability and precision while ensuring significantly less lag, better smoothness, and superior cycle detection.
Why use more contemporary filters like Super Smoother?
The Super Smoother filter combines exponential smoothing and trigonometric functions for more accurate and smooth tracking of price movements. This filter enhances cycle tracking and reduces the lag often found when using EMA. As a result, signals based on Super Smoother are often more precise and representative of real price movements.
Drawbacks of other smoothing filters commonly used with ATR:
SMA. The lag is (N-1)/2, where N = period. This is terrible.
WMA. According to John F. Ehlers, "It appears that the WMA was invented by a trader who did not have a firm grasp of filter theory in hopes of reducing lag". It has been proven that WMA has worse suppression than the equivalent SMA, and WMA has more delay in the passband than the equivalent EMA. In short, WMA has drawbacks but no advantages compared to other popular moving averages.
It is also a good idea to use the median to average the results.
Test, experiment, use!
Down30%FromATHThis indicator tracks the latest ATH of any stock and tracks when the price is down by 30% from the ATH value.
Strat Dashboard [TFO]The Strat Dashboard tracks up to 10 signals while highlighting common strat reversal patterns, the SSS 50% rule, timeframe continuity, and some additional criteria with VWAP and moving averages.
With the strat, all price action bars/candles are simplified into 3 total possibilities: 1 (inside bar), 2 (a bar that takes the previous bar's high OR low), and 3 (outside bar). The first table column for Last X Candles shows the most recent candles according to this notation, for example, 1 - 2D - 2U. This would mean we had an inside bar, followed by a bar that took the previous bar's low, followed then by a bar that took the previous bar's high. Note that the colors in this column are set according to whether the current bar's close exceeds the previous bar's high/low. By default, these colors are green if above the previous bar's highs, or red if below the previous bar's lows. If the current close is in between the previous candle's high and low (even after already taking the prior high or low), no color will be applied.
The SSS 50% column shows a yes or no value for whether the current bar aligns with the SSS 50% rule, where a bar has taken either the previous high or low, and has since reversed to at least the midway point of the previous bar's height - essentially anticipating a 2 that may become a 3 (outside bar).
Timeframe continuity (TFC) shows a yes or no value for when the current candle on multiple timeframes are all green or red (above the open price or below the open price, respectively). For example, if you were looking at the current 15m, 1h, and 1D bars, and they were all above the open price, you could say there's TFC between all three timeframes. As of the initial release, you can select up to 3 different timeframes. The table values will only be true when all selected timeframes are in alignment. When setting alerts, first deselect the timeframes if you don't want TFC logic to impact alerts.
The "Last" column shows the last strat reversal pattern that was confirmed (after the last bar closes). Waiting for a candle close is the safer option since a 2 can turn into a 3; however for higher timeframes, it may be beneficial to make an update to this indicator in which you can have live alerts as well (not waiting for a candle close). You can select which strat reversals you want to be shown from the settings. Various strat reversals may be selected for alerts of type "Any"; for example, if setting up an alert for "Any" strat reversal on Symbol 1, then this alert will go off when any of the *selected* strat reversals occur for that specific symbol. Deselect any strat reversals that you don't want to be included in these alerts.
Lastly, the EMA and VWAP columns simply show whether price is above or below said value. This tracks the current candle close, and may repaint/change several times if the current bar is oscillating above and below these values.
Range Bound - Rev NR - 12-25-22RangeBound - Code tracks price action within a user specified range (lookback), and tracks/charts overall high/lows, open high/lows, and close high/lows. Code resets certain parameters based on break of range to assist with determine price action - Can be useful to determine resistances to movement regardless of S&R levels. Code also uses the previous 5X Close High/Lows ranges as will chart as support and resistance to assist with determine resistance to price action
Note if using "redraw" shorter lookback periods will take additional time to compile due to multiple "redraws/deletes of previous lines" Uncheck redraw to reduce compile time
//The first code I have decided to publish :)
Bitcoin Miner Sell PressureBitcoin miners are in pain and now (November 2022) selling more than they have in almost 5 years!
Introducing: Bitcoin Miner Sell Pressure.
A free, open-source indicator which tracks on-chain data to highlight when Bitcoin miners are selling more of their reserves than usual.
The indicator tracks the ratio of on-chain miner Bitcoin outflows to miner Bitcoin reserves.
- Higher = more selling than usual
- Lower = less selling than usual
- Red = extraordinary sell pressure
Today , it's red.
What can we see now ?
Miners are not great at treasury management. They tend to sell most when they are losing money (like today). But there have been times when they sold well into high profit, such as into the 2017 $20K top and in early 2021 when Bitcoin breached $40K.
Bitcoin Miner Sell Pressure identifies industry stress, excess and miner capitulation.
Unsurprisingly, there is a high correlation with Bitcoin Production Cost; giving strong confluence to both.
In some instances, BMSP spots capitulation before Hash Ribbons. Such as today!
Price Correction to fix data manipulation and mispricingPrice Correction corrects for index and security mispricing to the extent possible in TradingView on both daily and intraday charts. Price correction addresses mispricing issues for specific securities with known issues, or the user can build daily candles from intraday data instead of relying on exchange reported daily OHLC prices, which can include both legitimate special auction and off-exchange trades or illegitimate mispricing. The user can also detect daily OHLC prices that don’t reflect the intraday price action within a specified percent deviation. Price Correction functions as normal candles or bars for any time frame when correction is not needed.
On the 4th of October 2022, the AMEX exchange, owned by the New York Stock Exchange, decided to misprice the daily OHLC data for the SPY, the world’s largest ETF fund. The exchange eliminated the overnight gap that should have occurred in the daily chart that represents regular trading hours by showing a wick connecting near the close of the previous day. Neither the SPX, the SP500 cash index that the SPY ETF tracks, nor other SPX ETFs such as VOO or IVV show such a wick because significant price action at that level never occurred. The intraday SPY chart never shows the price drop below 372.31 that day, but there is a wick that extends to 366.57. On the 6th of October, they continued this practice of using a wick that connects with the close of the previous day to eliminate gaps in daily price action. The objective of this indicator is to fix such inconsistent mispricing practices in the SPY, NYA, and other indices or securities.
Price Correction corrects for the daily mispricing in the SPY to agree with the price action that actually occurred in the SPX index it tracks, as well as the other SPX ETFs, by using intraday data. The chart below compares the Price Correction of the SPY (top) to the SPX (middle) and the original mispriced SPY (bottom) with incorrect wicks. Price correction (top) removes those incorrect wicks (bottom) to match the SPX (middle).
The daily mispricing of the SPY follows after the successful deployment of the NYSE Composite Index mispricing, NYA, an index that represents all common stocks within the New York Stock Exchange, the largest exchange in the world. The importance of the NYA should not be understated. It is the price counterpart to NYSE’s market internals or statistics. Beginning in 2021, the New York Stock Exchange eliminated gaps in daily OHLC data for the NYA by using the close of the previous day as the open for the following day, in violation of their own NYSE Index Series Methodology. The Methodology states for the opening price that “The first index level is calculated and published around 09:30 ET, when the U.S. equity markets open for their regular trading session. The calculation of that level utilizes the most updated prices available at that moment.” You can verify for yourself that this is simply not the case. The first update of the NYA price for each day matches the close of the previous day, not the “most updated prices available at that moment”, causing data providers to often represent the first intraday bar with a huge sudden price change when an overnight price change occurred instead. For example, on 13 Jun 2022, TradingView shows a one-minute bar drop 2.3%. With a market capitalization of roughly 23 trillion dollars, the NYSE composite capitalization did not suddenly drop a half-trillion dollars in just one minute as the intraday chart data would have you believe. All major US indices, index ETFs, and even foreign indices like the Toronto TAX, the Australian ASXAL, the Bombay SENSEX, and German DAX had down gaps that day, except for the mispriced NYSE index. Price Correction corrects for this mispricing in daily OHLC data, as shown in the main chart at the top of this page comparing the original NYA (top) to the Price Corrected NYA (bottom).
Price Correction also corrects for the intraday mispricing in the NYA. The chart below shows how the Price Correction (top) replaces the incorrect first one-minute candles with gaps (bottom) from 22 Sep 2022 to 29 Sep 2022. TradingView is inconsistent in how intraday data is reported for overnight gaps by sometimes connecting the first intraday bar of the day to the close of the previous day, and other times not. This inconsistency may be due to manually changing the intraday data based on user support tickets. For example, after reporting the lack of a major gap in the NYA daily OHLC prices that existed intraday for 13 Jun 2022, TradingView opted to remove the true gap in intraday prices by creating a 2.3% half-a-trillion-dollar one-minute bar that connected the close of the previous day to show a sudden drop in price that didn’t occur, instead of adding the gap in the daily OHLC data that actually took place from overnight price action.
Price Correction allows users to detect daily OHLC data that does not reflect the intraday price action within a certain percent difference by changing the color of those candles or bars that deviate. The chart below clearly shows the start of the NYSE disinformation campaign for NYA that started in 2021 by painting blue those candles with daily OHLC values that deviated from the intraday values by 0.1%. Before 2021, the number of deviating candles is relatively sparse, but beginning in 2021, the chart is littered with deviating candles.
If there are other index or security mispricing or data issues you are aware of that can be incorporated into Price Correction, please let me know. Accurate financial data is indispensable in making accurate financial decisions. Assert your right to accurate financial data by reporting incorrect data and mispricing issues.
How to use the Price Correction
Simply add this “indicator” to your chart and remove the mispriced default candles or bars by right clicking on the chart, selecting Settings, and de-selecting Body, Wick, and Border under the Symbol tab. The Presets settings automatically takes care of mispricing in the NYA and SPY to the extent possible in TradingView. The user can also build their own daily candles based off of intraday data to address other securities that may have mispricing issues.
R:R Trading System FrameworkFirst off, huge thanks to @fikira! He was able to adapt what I built to work much more efficiently, allowing for more strategies to be used simultaneously. Simply put, I could not have gotten to this point without you. Thanks for what you do for the TV community. Second, I am fairly new to pinescript writing, so I welcome criticism, thoughtful input and improvement suggestions. I would love to grow this concept into something even better, if possible. So please let me know if you have any ideas for improvement. However I do juggle a lot of different things outside of TV, so implementations may be delayed.
I have decided, at this time, not to add alerts. First, because I feel most people looking to adapt this framework can add their own pretty easily. Also, given how customized the framework is currently, while also attempting to account for all the possible ways in which people may want alerts to function after they customize it, it seems best to leave them out as it doesn't exactly fit the idea of a framework.
For best viewing, I recommend hovering over the script's name > ... > Visual order > Bring to front. Also I found hollow candles with mono-toned colors (like pictured) are more visually appealing for me personally. I HIGHLY RECOMMEND USING WITH BAR REPLAY TO BETTER UNDERSTAND THE FRAMEWORK'S FUNCTIONALITY.
▶️ WHAT THIS FRAMEWORK IS
- A huge collection of concepts and capabilities for those trying to better understand, learn, or teach pinescript.
- A system designed to showcase Risk:Reward concepts more holistically by providing all of the most popular components of retail trading to include backtesting, trade visual plotting, position tracking, market condition shifts, and useful info while positioned to help highlight changes in your risk:reward based decision-making processes.
- A system that can showcase individual strategies regardless of trade direction, allowing you to develop hedging strategies without having multiple indicators that do not correlate with each other.
- Designed around the idea that you trade less numbers of assets but manage your positions and risk based on multiple concurrently running strategies to manage your risk exposure and reward potential.
- An attempt to combine all the things you need to execute with an active trading management style.
- A framework that uses backtested results (in this case the number of averaged bars it takes to hit key levels) in real-time to inform your risk:reward decision-making while in-trade (in this case in your Trade Tracking Table using dynamic color to show how you might be early, on-time, or late compared to the average amount of backtested time it normally takes to hit that specific key level).
▶️ WHAT THIS FRAMEWORK IS NOT
- A complete trading product. DO NOT USE as-is. It is a FRAMEWORK for you to generate ideas of your own and fairly easily implement your own triggering conditions in the appropriate sections of the script.
▶️ USE CASES
- If you decide you like the Stop, Target, Trailing Stop, and Risk:Reward components as-is, then just understanding how to plug in your Entry and Bullish / Bearish conditions (Triangles) and adjust the input texts to match your custom naming will be all you need to make it your own!
- If you want to adapt certain components, then this system gives you a great starting point to adapt your different concepts and ideas from.
▶️ SYSTEM COMPONENTS
- Each of the system's components are described via tooltips both in the input menu and in the tables' cells.
- Each label on the chart displays the corresponding price at those triggered conditions on hover with tooltips.
- The Trailing Stop only becomes active once it is above the Entry Price for that trade, and brightens to show it is active. The STOP line (right of price) moves once it takes over for the Entry Stop representing the level of the Trailing Stop at that time for that trade.
- The Lines / Labels to the right of price will brighten once price is above for Longs or below for Shorts. The Trade Tracking Table cells will add ☑️ once price is above for Longs or below for Shorts.
- The brighter boxes on the chart show the trades that occurred based on your criteria and are color coded for all components of each trade type to ensure your references are consistent. (Defaults are TV built-in strategies)
- The lighter boxes on the chart show the highest and lowest price levels reached during those trades, to highlight areas where improvements can be made or additional considerations can be accounted for by either adjusting Entry triggers or Bullish / Bearish triggers.
- Default Green and Red Triangles (Bullish / Bearish) default to having the same triggering condition as the Entry it corresponds to. This is to highlight either a pyramiding concept, early exit, or you can change to account for other things occurring during your trades which could help you with Stop and Target management/considerations.
TradingView and many of its community members have done a lot for me, so this is my attempt to give back.
