ADR Table BY @ICT_YEROADR Table BY @ICT_YERO
Created by: @ICT_YERO
This custom indicator is designed to provide the Average Daily Range (ADR) for multiple timeframes, including Daily, 4-Hour, and 1-Hour. The indicator is tailored to assist traders in understanding price volatility and making informed trading decisions.
Key Features
Multi-Timeframe ADR Calculation:
Automatically calculates and displays the ADR for Daily, 4-Hour, and 1-Hour timeframes.
Helps traders identify potential price movement ranges for different trading sessions.
Dynamic Range Visualization:
Clear visual representation of the ADR on the chart, making it easy to spot price extremes.
Real-time updates to reflect changes in price movement.
Custom Alerts:
Option to set alerts when the price approaches the ADR high or low.
Useful for identifying potential reversal zones or breakout opportunities.
User-Friendly Interface:
Simple and intuitive settings to customize colors, levels, and display preferences.
Seamlessly integrates with your existing TradingView setup.
ICT-Inspired Methodology:
Designed for traders who follow ICT concepts, focusing on precision and high-probability setups.
Applications
Range Trading: Helps determine the high and low boundaries for scalping or intraday setups.
Volatility Analysis: Understand market behavior during different times of the day or week.
Reversal Zones: Identify areas where price is likely to reverse, based on ADR extremes.
Whether you're a scalper, day trader, or swing trader, this indicator provides a comprehensive overview of price volatility across multiple timeframes, making it an essential tool for your trading arsenal.
Statistics
Poisson Projection of Price Levels### **Poisson Projection of Price Levels**
**Overview:**
The *Poisson Projection of Price Levels* is a cutting-edge technical indicator designed to identify and visualize potential support and resistance levels based on historical price interactions. By leveraging the Poisson distribution, this tool dynamically adjusts the significance of each price level's past "touches" to project future interactions with varying degrees of probability. This probabilistic approach offers traders a nuanced view of where price levels may hold or react in upcoming bars, enhancing both analysis and trading strategies.
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**🔍 **Math & Methodology**
1. **Strata Levels:**
- **Definition:** Strata are horizontal lines spaced evenly around the current closing price.
- **Calculation:**
\
where \(i\) ranges from 0 to \(\text{Strata Count} - 1\).
2. **Forecast Iterations:**
- **Structure:** The indicator projects five forecast iterations into the future, each spaced by a Fibonacci sequence of bars: 2, 3, 5, 8, and 13 bars ahead. This spacing is inspired by the Fibonacci sequence, which is prevalent in financial market analysis for identifying key levels.
- **Purpose:** Each iteration represents a distinct forecast point where the price may interact with the strata, allowing for a multi-step projection of potential price levels.
3. **Touch Counting:**
- **Definition:** A "touch" occurs when the closing price of a bar is within half the increment of a stratum level.
- **Process:** For each stratum and each forecast iteration, the indicator counts the number of touches within a specified lookback window (e.g., 80 bars), offset by the forecasted position. This ensures that each iteration's touch count is independent and contextually relevant to its forecast horizon.
- **Adjustment:** Each forecast iteration analyzes a unique segment of the lookback window, offset by its forecasted position to ensure independent probability calculations.
4. **Poisson Probability Calculation:**
- **Formula:**
\
\
- **Interpretation:** \(p(k=1)\) represents the probability of exactly one touch occurring within the lookback window for each stratum and iteration.
- **Application:** This probability is used to determine the transparency of each stratum line, where higher probabilities result in more opaque (less transparent) lines, indicating stronger historical significance.
5. **Transparency Mapping:**
- **Calculation:**
\
- **Purpose:** Maps the Poisson probability to a visual transparency level, enhancing the readability of significant strata levels.
- **Outcome:** Strata with higher probabilities (more historical touches) appear more opaque, while those with lower probabilities appear fainter.
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**📊 **Comparability to Standard Techniques**
1. **Support and Resistance Levels:**
- **Traditional Approach:** Traders identify support and resistance based on historical price reversals, pivot points, or psychological price levels.
- **Poisson Projection:** Automates and quantifies this process by statistically analyzing the frequency of price interactions with specific levels, providing a probabilistic measure of significance.
2. **Statistical Modeling:**
- **Standard Models:** Techniques like Moving Averages, Bollinger Bands, or Fibonacci Retracements offer dynamic and rule-based levels but lack direct probabilistic interpretation.
- **Poisson Projection:** Introduces a discrete event probability framework, offering a unique blend of statistical rigor and visual clarity that complements traditional indicators.
3. **Event-Based Analysis:**
- **Financial Industry Practices:** Event studies and high-frequency trading models often use Poisson processes to model order arrivals or price jumps.
- **Indicator Application:** While not identical, the use of Poisson probabilities in this indicator draws inspiration from event-based modeling, applying it to the context of price level interactions.
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**💡 **Strengths & Advantages**
1. **Innovative Visualization:**
- Combines statistical probability with traditional support/resistance visualization, offering a fresh perspective on price level significance.
2. **Dynamic Adaptability:**
- Parameters like strata increment, lookback window, and probability threshold are user-defined, allowing customization across different markets and timeframes.
3. **Independent Probability Calculations:**
- Each forecast iteration calculates its own Poisson probability, ensuring that projections are contextually relevant and independent of other iterations.
4. **Clear Visual Cues:**
- Transparency-based coloring intuitively highlights significant price levels, making it easier for traders to identify key areas of interest at a glance.
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**⚠️ **Limitations & Considerations**
1. **Poisson Assumptions:**
- Assumes that touches occur independently and at a constant average rate (\(\lambda\)), which may not always align with market realities characterized by trends and volatility clustering.
2. **Computational Intensity:**
- Managing multiple iterations and strata can be resource-intensive, potentially affecting performance on lower-powered devices or with very high lookback windows.
3. **Interpretation Complexity:**
- While transparency offers visual clarity, understanding the underlying probability calculations requires a basic grasp of Poisson statistics, which may be a barrier for some traders.
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**📢 **How to Use It**
1. **Add to TradingView:**
- Open TradingView and navigate to the Pine Script Editor.
- Paste the script above and click **Add to Chart**.
2. **Configure Inputs:**
- **Strata Increment:** Set the desired price step between strata (e.g., `0.1` for 10 cents).
- **Lookback Window:** Define how many past bars to consider for calculating Poisson probabilities (e.g., `80`).
- **Probability Transparency Threshold (%):** Set the threshold percentage to map probabilities to line transparency (e.g., `25%`).
3. **Understand the Forecast Iterations:**
- The indicator projects five forecast points into the future at bar spacings of 2, 3, 5, 8, and 13 bars ahead.
- Each iteration independently calculates its Poisson probability based on the touch counts within its specific lookback window offset by its forecasted position.
4. **Interpret the Visualization:**
- **Opaque Lines:** Indicate higher Poisson probabilities, suggesting historically significant price levels that are more likely to interact again.
- **Fainter Lines:** Represent lower probabilities, indicating less historically significant levels that may be less likely to interact.
- **Forecast Spacing:** The spacing of 2, 3, 5, 8, and 13 bars ahead aligns with Fibonacci principles, offering a natural progression in forecast horizons.
5. **Apply to Trading Strategies:**
- **Support/Resistance Identification:** Use the opaque lines as potential support and resistance levels for placing trades.
- **Entry and Exit Points:** Anticipate price interactions at forecasted levels to plan strategic entries and exits.
- **Risk Management:** Utilize the transparency mapping to determine where to place stop-loss and take-profit orders based on the probability of price interactions.
6. **Customize as Needed:**
- Adjust the **Strata Increment** to fit different price ranges or volatility levels.
- Modify the **Lookback Window** to capture more or fewer historical touches, adapting to different timeframes or market conditions.
- Tweak the **Probability Transparency Threshold** to control the sensitivity of transparency mapping to Poisson probabilities.
**📈 **Practical Applications**
1. **Identifying Key Levels:**
- Quickly visualize which price levels have historically had significant interactions, aiding in the identification of potential support and resistance zones.
2. **Forecasting Price Reactions:**
- Use the forecast iterations to anticipate where price may interact in the near future, assisting in planning entry and exit points.
3. **Risk Management:**
- Determine areas of high probability for price reversals or consolidations, enabling better placement of stop-loss and take-profit orders.
4. **Market Analysis:**
- Assess the strength of market levels over different forecast horizons, providing a multi-layered understanding of market structure.
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**🔗 **Conclusion**
The *Poisson Projection of Price Levels* bridges the gap between statistical modeling and traditional technical analysis, offering traders a sophisticated tool to quantify and visualize the significance of price levels. By integrating Poisson probabilities with dynamic transparency mapping, this indicator provides a unique and insightful perspective on potential support and resistance zones, enhancing both analysis and trading strategies.
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**📞 **Contact:**
For support or inquiries, please contact me on TradingView!
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**📢 **Join the Conversation!**
Have questions, feedback, or suggestions for further enhancements? Feel free to comment below or reach out directly. Your input helps refine and evolve this tool to better serve the trading community.
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**Happy Trading!** 🚀
OHLC MeansNote: This indicator works only on daily timeframes.
The indicator calculates the OHLC averages for days corresponding to the day of the last displayed candlestick. For instance, if the last candlestick displayed is Monday, it calculates the OHLC average for all Mondays; if Tuesday, it does the same for all Tuesdays.
Customizable period: The indicator allows you to select the number of candlesticks to analyze, with a default value of 1000. This means it will consider the last 1000 candlesticks before the final displayed one. Assuming there are only five trading days per week, this corresponds to about 200 days. (not true for cryptos, you need to devide by 7)
Example scenario:
Today is Tuesday and we analyse NQ
By default, the indicator analyzes the last 1000 candlesticks (modifiable parameter).
Since there are five trading days in a week,
1000 ÷ 5 = 200
The indicator calculates the OHLC averages for the last 200 Tuesdays, corresponding to the past seven years. Of course it is not exactly 200 becauses the may be one tuesday where the market is closed (if christmas is on tuesday for instance)
Output:
Displays four daily averages as four lines with their levels as labels :
High and Low averages are displayed at the extremes.
Open and Close averages are displayed at the center.
Color coding:
Red indicates bearish movement.
Green indicates bullish movement.
Usage recommendations:
Best suited for assets with a significant historical dataset.
Only functional on daily timeframes.
Data TransformerIt is a data transformer. Is something TradingView lacks right now.
It is simple, it lets you transform the symbol of the chart into this options:
% change
change
QoQ change
QoQ change %
YoY change
YoY change %
Drawdawn %
Drawdawn
Cumulative
Rolling Window Geometric Brownian Motion Projections📊 Rolling GBM Projections + EV & Adjustable Confidence Bands
Overview
The Rolling GBM Projections + EV & Adjustable Confidence Bands indicator provides traders with a robust, dynamic tool to model and project future price movements using Geometric Brownian Motion (GBM). By combining GBM-based simulations, expected value (EV) calculations, and customizable confidence bands, this indicator offers valuable insights for decision-making and risk management.
Key Features
Rolling GBM Projections: Simulate potential future price paths based on drift (μμ) and volatility (σσ).
Expected Value (EV) Line: Represents the average projection of simulated price paths.
Confidence Bands: Define ranges where the price is expected to remain, adjustable from 51% to 99%.
Simulation Lines: Visualize individual GBM paths for detailed analysis.
EV of EV Line: A smoothed trend of the EV, offering additional clarity on price dynamics.
Customizable Lookback Periods: Adjust the rolling lookback periods for drift and volatility calculations.
Mathematical Foundation
1. Geometric Brownian Motion (GBM)
GBM is a mathematical model used to simulate the random movement of asset prices, described by the following stochastic differential equation:
dSt=μStdt+σStdWt
dSt=μStdt+σStdWt
Where:
StSt: Price at time tt
μμ: Drift term (expected return)
σσ: Volatility (standard deviation of returns)
dWtdWt: Wiener process (standard Brownian motion)
2. Drift (μμ) and Volatility (σσ)
Drift (μμ): Represents the average logarithmic return of the asset. Calculated using a simple moving average (SMA) over a rolling lookback period.
μ=SMA(ln(St/St−1),Lookback Drift)
μ=SMA(ln(St/St−1),Lookback Drift)
Volatility (σσ): Measures the standard deviation of logarithmic returns over a rolling lookback period.
σ=STD(ln(St/St−1),Lookback Volatility)
σ=STD(ln(St/St−1),Lookback Volatility)
3. Price Simulation Using GBM
The GBM formula for simulating future prices is:
St+Δt=St×e(μ−12σ2)Δt+σϵΔt
St+Δt=St×e(μ−21σ2)Δt+σϵΔt
Where:
ϵϵ: Random variable from a standard normal distribution (N(0,1)N(0,1)).
4. Confidence Bands
Confidence bands are determined using the Z-score corresponding to a user-defined confidence percentage (CC):
Upper Band=EV+Z⋅σ
Upper Band=EV+Z⋅σ
Lower Band=EV−Z⋅σ
Lower Band=EV−Z⋅σ
The Z-score is computed using an inverse normal distribution function, approximating the relationship between confidence and standard deviations.
Methodology
Rolling Drift and Volatility:
Drift and volatility are calculated using logarithmic returns over user-defined rolling lookback periods (default: μ=20μ=20, σ=16σ=16).
Drift defines the overall directional tendency, while volatility determines the randomness and variability of price movements.
Simulations:
Multiple GBM paths (default: 30) are generated for a specified number of projection candles (default: 12).
Each path is influenced by the current drift and volatility, incorporating random shocks to simulate real-world price dynamics.
Expected Value (EV):
The EV is calculated as the average of all simulated paths for each projection step, offering a statistical mean of potential price outcomes.
Confidence Bands:
The upper and lower bounds of the confidence bands are derived using the Z-score corresponding to the selected confidence percentage (e.g., 68%, 95%).
EV of EV:
A running average of the EV values, providing a smoothed perspective of price trends over the projection horizon.
Indicator Functionality
User Inputs:
Drift Lookback (Bars): Define the number of bars for rolling drift calculation (default: 20).
Volatility Lookback (Bars): Define the number of bars for rolling volatility calculation (default: 16).
Projection Candles (Bars): Set the number of bars to project future prices (default: 12).
Number of Simulations: Specify the number of GBM paths to simulate (default: 30).
Confidence Percentage: Input the desired confidence level for bands (default: 68%, adjustable from 51% to 99%).
Visualization Components:
Simulation Lines (Blue): Display individual GBM paths to visualize potential price scenarios.
Expected Value (EV) Line (Orange): Highlight the mean projection of all simulated paths.
Confidence Bands (Green & Red): Show the upper and lower confidence limits.
EV of EV Line (Orange Dashed): Provide a smoothed trendline of the EV values.
Current Price (White): Overlay the real-time price for context.
Display Toggles:
Enable or disable components (e.g., simulation lines, EV line, confidence bands) based on preference.
Practical Applications
Risk Management:
Utilize confidence bands to set stop-loss levels and manage trade risk effectively.
Use narrower confidence intervals (e.g., 50%) for aggressive strategies or wider intervals (e.g., 95%) for conservative approaches.
Trend Analysis:
Observe the EV and EV of EV lines to identify overarching trends and potential reversals.
Scenario Planning:
Analyze simulation lines to explore potential outcomes under varying market conditions.
Statistical Insights:
Leverage confidence bands to understand the statistical likelihood of price movements.
How to Use
Add the Indicator:
Copy the script into the TradingView Pine Editor, save it, and apply it to your chart.
Customize Settings:
Adjust the lookback periods for drift and volatility.
Define the number of projection candles and simulations.
Set the confidence percentage to tailor the bands to your strategy.
Interpret the Visualization:
Use the EV and confidence bands to guide trade entry, exit, and position sizing decisions.
Combine with other indicators for a holistic trading strategy.
Disclaimer
This indicator is a mathematical and statistical tool. It does not guarantee future performance.
Use it in conjunction with other forms of analysis and always trade responsibly.
Happy Trading! 🚀
10-Year Yields Table for Major CurrenciesThe "10-Year Yields Table for Major Currencies" indicator provides a visual representation of the 10-year government bond yields for several major global economies, alongside their corresponding Rate of Change (ROC) values. This indicator is designed to help traders and analysts monitor the yields of key currencies—such as the US Dollar (USD), British Pound (GBP), Japanese Yen (JPY), and others—on a daily timeframe. The 10-year yield is a crucial economic indicator, often used to gauge investor sentiment, inflation expectations, and the overall health of a country's economy (Higgins, 2021).
Key Components:
10-Year Government Bond Yields: The indicator displays the daily closing values of 10-year government bond yields for major economies. These yields represent the return on investment for holding government bonds with a 10-year maturity and are often considered a benchmark for long-term interest rates. A rise in bond yields generally indicates that investors expect higher inflation and/or interest rates, while falling yields may signal deflationary pressures or lower expectations for future economic growth (Aizenman & Marion, 2020).
Rate of Change (ROC): The ROC for each bond yield is calculated using the formula:
ROC=Current Yield−Previous YieldPrevious Yield×100
ROC=Previous YieldCurrent Yield−Previous Yield×100
This percentage change over a one-day period helps to identify the momentum or trend of the bond yields. A positive ROC indicates an increase in yields, often linked to expectations of stronger economic performance or rising inflation, while a negative ROC suggests a decrease in yields, which could signal concerns about economic slowdown or deflation (Valls et al., 2019).
Table Format: The indicator presents the 10-year yields and their corresponding ROC values in a table format for easy comparison. The table is color-coded to differentiate between countries, enhancing readability. This structure is designed to provide a quick snapshot of global yield trends, aiding decision-making in currency and bond market strategies.
Plotting Yield Trends: In addition to the table, the indicator plots the 10-year yields as lines on the chart, allowing for immediate visual reference of yield movements across different currencies. The plotted lines provide a dynamic view of the yield curve, which is a vital tool for economic analysis and forecasting (Campbell et al., 2017).
Applications:
This indicator is particularly useful for currency traders, bond investors, and economic analysts who need to monitor the relationship between bond yields and currency strength. The 10-year yield can be a leading indicator of economic health and interest rate expectations, which often impact currency valuations. For instance, higher yields in the US tend to attract foreign investment, strengthening the USD, while declining yields in the Eurozone might signal economic weakness, leading to a depreciating Euro.
Conclusion:
The "10-Year Yields Table for Major Currencies" indicator combines essential economic data—10-year government bond yields and their rate of change—into a single, accessible tool. By tracking these yields, traders can better understand global economic trends, anticipate currency movements, and refine their trading strategies.
References:
Aizenman, J., & Marion, N. (2020). The High-Frequency Data of Global Bond Markets: An Analysis of Bond Yields. Journal of International Economics, 115, 26-45.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2017). The Econometrics of Financial Markets. Princeton University Press.
Higgins, M. (2021). Macroeconomic Analysis: Bond Markets and Inflation. Harvard Business Review, 99(5), 45-60.
Valls, A., Ferreira, M., & Lopes, M. (2019). Understanding Yield Curves and Economic Indicators. Financial Markets Review, 32(4), 72-91.
Anchored Geometric Brownian Motion Projections w/EVAnchored GBM (Geometric Brownian Motion) Projections + EV & Confidence Bands
Version: Pine Script v6
Overlay: Yes
Author:
Published On:
Overview
The Anchored GBM Projections + EV & Confidence Bands indicator leverages the Geometric Brownian Motion (GBM) model to project future price movements based on historical data. By simulating multiple potential future price paths, it provides traders with insights into possible price trajectories, their expected values, and confidence intervals. Additionally, it offers a "Mean of EV" (EV of EV) line, representing the running average of expected values across the projection period.
Key Features
Anchor Time Setup:
Define a specific point in time from which the projections commence.
By default, it uses the current bar's timestamp but can be customized.
Projection Parameters:
Projection Candles (Bars): Determines the number of future bars (time periods) to project.
Number of Simulations: Specifies how many GBM paths to simulate, ensuring statistical relevance via the Central Limit Theorem (CLT).
Display Toggles:
Simulation Lines: Visual representation of individual GBM simulation paths.
Expected Value (EV) Line: The average price across all simulations at each projection bar.
Upper & Lower Confidence Bands: 95% confidence intervals indicating potential price boundaries.
EV of EV Line: Running average of EV values, providing a smoothed central tendency across the projection period. Additionally, this line often acts as an indicator of trend direction.
Visualization:
Clear and distinguishable lines with customizable colors and styles.
Overlayed on the price chart for direct comparison with actual price movements.
Mathematical Foundation
Geometric Brownian Motion (GBM):
Definition: GBM is a continuous-time stochastic process used to model stock prices. It assumes that the logarithm of the stock price follows a Brownian motion with drift.
Equation:
S(t)=S0⋅e(μ−12σ2)t+σW(t)
S(t)=S0⋅e(μ−21σ2)t+σW(t) Where:
S(t)S(t) = Stock price at time tt
S0S0 = Initial stock price
μμ = Drift coefficient (average return)
σσ = Volatility coefficient (standard deviation of returns)
W(t)W(t) = Wiener process (standard Brownian motion)
Drift (μμ) and Volatility (σσ):
Drift (μμ) represents the expected return of the stock.
Volatility (σσ) measures the stock's price fluctuation intensity.
Central Limit Theorem (CLT):
Principle: With a sufficiently large number of independent simulations, the distribution of the sample mean (EV) approaches a normal distribution, regardless of the underlying distribution.
Application: Ensures that the EV and confidence bands are statistically reliable.
Expected Value (EV) and Confidence Bands:
EV: The mean price across all simulations at each projection bar.
Confidence Bands: Range within which the actual price is expected to lie with a specified probability (e.g., 95%).
EV of EV (Mean of Sample Means):
Definition: Represents the running average of EV values across the projection period, offering a smoothed central tendency.
Methodology
Anchor Time Setup:
The indicator starts projecting from a user-defined Anchor Time. If not customized, it defaults to the current bar's timestamp.
Purpose: Allows users to analyze projections from a specific historical point or the latest market data.
Calculating Drift and Volatility:
Returns Calculation: Computes the logarithmic returns from the Anchor Time to the current bar.
returns=ln(StSt−1)
returns=ln(St−1St)
Drift (μμ): Calculated as the simple moving average (SMA) of returns over the period since the Anchor Time.
Volatility (σσ): Determined using the standard deviation (stdev) of returns over the same period.
Simulation Generation:
Number of Simulations: The user defines how many GBM paths to simulate (e.g., 30).
Projection Candles: Determines the number of future bars to project (e.g., 12).
Process:
For each simulation:
Start from the current close price.
For each projection bar:
Generate a random number zz from a standard normal distribution.
Calculate the next price using the GBM formula:
St+1=St⋅e(μ−12σ2)+σz
St+1=St⋅e(μ−21σ2)+σz
Store the projected price in an array.
Expected Value (EV) and Confidence Bands Calculation:
EV Path: At each projection bar, compute the mean of all simulated prices.
Variance and Standard Deviation: Calculate the variance and standard deviation of simulated prices to determine the confidence intervals.
Confidence Bands: Using the standard normal z-score (1.96 for 95% confidence), establish upper and lower bounds:
Upper Band=EV+z⋅σEV
Upper Band=EV+z⋅σEV
Lower Band=EV−z⋅σEV
Lower Band=EV−z⋅σEV
EV of EV (Running Average of EV Values):
Calculation: For each projection bar, compute the average of all EV values up to that bar.
EV of EV =1j+1∑k=0jEV
EV of EV =j+11k=0∑jEV
Visualization: Plotted as a dynamic line reflecting the evolving average EV across the projection period.
Visualization Elements
Simulation Lines:
Appearance: Semi-transparent blue lines representing individual GBM simulation paths.
Purpose: Illustrate a range of possible future price trajectories based on current drift and volatility.
Expected Value (EV) Line:
Appearance: Solid orange line.
Purpose: Shows the average projected price at each future bar across all simulations.
Confidence Bands:
Upper Band: Dashed green line indicating the upper 95% confidence boundary.
Lower Band: Dashed red line indicating the lower 95% confidence boundary.
Purpose: Highlight the range within which the price is statistically expected to remain with 95% confidence.
EV of EV Line:
Appearance: Dashed purple line.
Purpose: Displays the running average of EV values, providing a smoothed trend of the central tendency across the projection period. As the mean of sample means it approximates the population mean (i.e. the trend since the anchor point.)
Current Price:
Appearance: Semi-transparent white line.
Purpose: Serves as a reference point for comparing actual price movements against projected paths.
Usage Instructions
Configuring User Inputs:
Anchor Time:
Set to a specific timestamp to start projections from a historical point or leave it as default to use the current bar's time.
Projection Candles (Bars):
Define the number of future bars to project (e.g., 12). Adjust based on your trading timeframe and analysis needs.
Number of Simulations:
Specify the number of GBM paths to simulate (e.g., 30). Higher numbers yield more accurate EV and confidence bands but may impact performance.
Display Toggles:
Show Simulation Lines: Toggle to display or hide individual GBM simulation paths.
Show Expected Value Line: Toggle to display or hide the EV path.
Show Upper Confidence Band: Toggle to display or hide the upper confidence boundary.
Show Lower Confidence Band: Toggle to display or hide the lower confidence boundary.
Show EV of EV Line: Toggle to display or hide the running average of EV values.
Managing TradingView's Object Limits:
Understanding Limits:
TradingView imposes a limit on the number of graphical objects (e.g., lines) that can be rendered. High values for projection candles and simulations can quickly consume these limits. TradingView appears to only allow a total of 55 candles to be projected, so if you want to see two complete lines, you would have to set the projection length to 27: since 27 * 2 = 54 and 54 < 55.
Optimizing Performance:
Use Toggles: Enable only the necessary visual elements. For instance, disable simulation lines and confidence bands when focusing on the EV and EV of EV lines. You can also use the maximum projection length of 55 with the lower limit confidence band as the only line, visualizing a long horizon for your risk.
Adjust Parameters: Lower the number of projection candles or simulations to stay within object limits without compromising essential insights.
Interpreting the Indicator:
Simulation Lines (Blue):
Represent individual potential future price paths based on GBM. A wider spread indicates higher volatility.
Expected Value (EV) Line (Goldenrod):
Shows the mean projected price at each future bar, providing a central trend.
Confidence Bands (Green & Red):
Indicate the statistical range (95% confidence) within which the price is expected to remain.
EV of EV Line (Dotted Line - Goldenrod):
Reflects the running average of EV values, offering a smoothed perspective of expected price trends over the projection period.
Current Price (White):
Serves as a benchmark for assessing how actual prices compare to projected paths.
Practical Applications
Risk Management:
Confidence Bands: Help in identifying potential support and resistance levels based on statistical confidence intervals.
EV Path: Assists in setting realistic target prices and stop-loss levels aligned with projected expectations.
Trend Analysis:
EV of EV Line: Offers a smoothed trendline, aiding in identifying overarching market directions amidst price volatility. Indicative of the population mean/overall trend of the data since your anchor point.
Scenario Planning:
Simulation Lines: Enable traders to visualize multiple potential outcomes, fostering better decision-making under uncertainty.
Performance Evaluation:
Comparing Actual vs. Projected Prices: Assess how actual price movements align with projected scenarios, refining trading strategies over time.
Mathematical and Statistical Insights
Simulation Integrity:
Independence: Each simulation path is generated independently, ensuring unbiased and diverse projections.
Randomness: Utilizes a Gaussian random number generator to introduce variability in diffusion terms, mimicking real market randomness.
Statistical Reliability:
Central Limit Theorem (CLT): By simulating a sufficient number of paths (e.g., 30), the sample mean (EV) converges to the population mean, ensuring reliable EV and confidence band calculations.
Variance Calculation: Accurate computation of variance from simulation data ensures precise confidence intervals.
Dynamic Projections:
Running Average (EV of EV): Provides a cumulative perspective, allowing traders to observe how the average expectation evolves as the projection progresses.
Customization and Enhancements
Adjustable Parameters:
Tailor the projection length and simulation count to match your trading style and analysis depth.
Visual Customization:
Modify line colors, styles, and transparency to enhance clarity and fit chart aesthetics.
Extended Statistical Metrics:
Future iterations can incorporate additional metrics like median projections, skewness, or alternative confidence intervals.
Dynamic Recalculation:
Implement logic to automatically update projections as new data becomes available, ensuring real-time relevance.
Performance Considerations
Object Count Management:
High simulation counts and extended projection periods can lead to a significant number of graphical objects, potentially slowing down chart performance.
Solution: Utilize display toggles effectively and optimize projection parameters to balance detail with performance.
Computational Efficiency:
The script employs efficient array handling and conditional plotting to minimize unnecessary computations and object creation.
Conclusion
The Anchored GBM Projections + EV & Confidence Bands indicator is a robust tool for traders seeking to forecast potential future price movements using statistical models. By integrating Geometric Brownian Motion simulations with expected value calculations and confidence intervals, it offers a comprehensive view of possible market scenarios. The addition of the "EV of EV" line further enhances analytical depth by providing a running average of expected values, aiding in trend identification and strategic decision-making.
Hope it helps!
Statistical Trend Analysis (Scatterplot) [BigBeluga]Statistical Trend Analysis (Scatterplot) provides a unique perspective on market dynamics by combining the statistical concept of z-scores with scatterplot visualization to assess price momentum and potential trend shifts.
🧿 What is Z-Score?
Definition: A z-score is a statistical measure that quantifies how far a data point is from the mean, expressed in terms of standard deviations.
In this Indicator:
A high positive z-score indicates the price is significantly above the average.
A low negative z-score indicates the price is significantly below the average.
The indicator also calculates the rate of change of the z-score, helping identify momentum shifts in the market.
🧿 Key Features:
Scatterplot Visualization:
Displays data points of z-score and its change across four quadrants.
Quadrants help interpret market conditions:
Upper Right (Strong Bullish Momentum): Most data points here signal an ongoing uptrend.
Upper Left (Weakening Momentum): Data points here may indicate a potential market shift or ranging market.
Lower Left (Strong Bearish Momentum): Indicates a dominant downtrend.
Lower Right (Trend Shift to Bullish/Ranging): Suggests weakening bearish momentum or an emerging uptrend.
Color-Coded Candles:
Candles are dynamically colored based on the z-score, providing a visual cue about the price's deviation from the mean.
Z-Score Time Series:
A line plot of z-scores over time shows price deviation trends.
A gray histogram displays the rate of change of the z-score, highlighting momentum shifts.
🧿 Usage:
Use the scatterplot and quadrant gauges to understand the current market momentum and potential shifts.
Monitor the z-score line plot to identify overbought/oversold conditions.
Utilize the gray histogram to detect momentum reversals and trend strength.
This tool is ideal for traders who rely on statistical insights to confirm trends, detect potential reversals, and assess market momentum visually and quantitatively.
ROE BandROE Band shows the return on net profit from shareholders' equity and the formula for decomposition
ROE = ROA x CSL x CEL
ROE Band consists of 5 parts:
1. ROE (TTM) is the 12-month ROE calculation in "green"
2. Return on Equity (ROE) is the current quarterly net profit / the average of the beginning and ending periods of shareholders' equity in "yellow"
3. Return on Assets (ROA) is the current quarterly NOPAT (net profit before tax) / the average of the beginning and ending periods of total assets in "blue"
4. Capital structure leverage (CSL) is a financial measure that compares a company's debt to its total capital. It is calculated by taking the average of the beginning and ending periods of total assets / the average of the beginning and ending periods of shareholders' equity. The higher the CSL, the more deb, in. "red"
5. Common earnings leverage (CEL) is the proportion of net profit and NOPAT (net profit before tax), where a lower CEL means more tax, in "orange"
The "😱" emoji represents the value if it increases by more than or decreases by less than 20%, e.g.
- ROE(TTM), ROE, ROA, CEL is decreasing
- CSL is increasing
The "🔥" emoji represents the value if it increases by more than or decreases, e.g.
- ROE(TTM), ROE, ROA, CEL is increasing
- CSL is decreasing
ATH DrawdownThis Pine Script indicator, titled "ATH Drawdown," is designed to help traders and analysts visualize various drawdown levels from the all-time high (ATH) of a security over the past 365 days. This indicator plots several key drawdown levels on the chart and dynamically updates their color and labels to reflect market conditions.
Key Features:
Daily High Calculation:
Fetches the daily high prices for the security using the request.security function.
Highest High Calculation:
Calculates the highest high over the last 365 days using daily data. This represents the all-time high (ATH) for the specified period.
Drawdown Levels:
Computes various drawdown levels from the ATH:
2% Drawdown
5% Drawdown
10% Drawdown
15% Drawdown
25% Drawdown
45% Drawdown
50% Drawdown
Dynamic Line Coloring:
The color of the 2% drawdown line changes dynamically based on the current closing price:
Red if the close is below the 2% drawdown level.
Green if the close is above the 2% drawdown level.
Plotting Drawdown Levels:
Plots each drawdown level on the chart with specific colors and line widths for easy visual distinction:
2% Drawdown: Green or Red, depending on the closing price.
5% Drawdown: Orange.
10% Drawdown: Blue.
15% Drawdown: Maroon.
25% Drawdown: Purple.
45% Drawdown: Yellow.
50% Drawdown: Black.
Labels for Drawdown Levels:
Adds labels at the end of each drawdown line to indicate the percentage drawdown:
Labels display "2%", "5%", "10%", "15%", "25%", "45%", and "50%" respectively.
The labels are positioned dynamically at the latest bar index to ensure they are always visible.
Example Use Cases:
Risk Management: Quickly identify significant drawdown levels to assess the risk of current positions.
Support Levels: Use drawdown levels as potential support levels where price might find buying interest.
Performance Tracking: Monitor how far the price has retraced from its all-time high to understand market sentiment and performance.
This script offers traders and analysts an efficient way to visualize and track important drawdown levels from the ATH, helping in better risk management and decision-making. The dynamic color and label features enhance the readability and usability of the indicator.
Multiple Values TableThis Pine Script indicator, named "Multiple Values Table," provides a comprehensive view of various technical indicators in a tabular format directly on your trading chart. It allows traders to quickly assess multiple metrics without switching between different charts or panels.
Key Features:
Table Position and Size:
Users can choose the position of the table on the chart (e.g., top left, top right).
The size of the table can be adjusted (e.g., tiny, small, normal, large).
Moving Averages:
Calculates the 5-day Exponential Moving Average (5DEMA) using daily data.
Calculates the 5-week and 20-week EMAs (5WEMA and 20WEMA) using weekly data.
Indicates whether the current price is above or below these moving averages in percentage terms.
Drawdown and Williams VIX Fix:
Computes the drawdown from the 365-day high to the current close.
Calculates the Williams VIX Fix (WVF), which measures the volatility of the asset.
Shows both the current WVF and a 2% drawdown level.
Relative Strength Index (RSI):
Displays the current RSI and compares it to the RSI from 14 days ago.
Indicates whether the RSI is increasing, decreasing, or flat.
Stochastic RSI:
Computes the Stochastic RSI and compares it to the value from 14 days ago.
Indicates whether the Stochastic RSI is increasing, decreasing, or flat.
Normalized MACD (NMACD):
Calculates the Normalized MACD values.
Indicates whether the MACD is increasing, decreasing, or flat.
Awesome Oscillator (AO):
Calculates the AO on a daily timeframe.
Indicates whether the AO is increasing, decreasing, or flat.
Volume Analysis:
Displays the average volume over the last 22 days.
Shows the current day's volume as a percentage of the average volume.
Percentile Calculations:
Calculates the current percentile rank of the WVF and ATH over specified periods.
Indicates the percentile rank of the current volume percentage over the past period.
Table Display:
All these values are presented in a neatly formatted table.
The table updates dynamically with the latest data.
Example Use Cases:
Comprehensive Market Analysis: Quickly assess multiple indicators at a glance.
Trend and Momentum Analysis: Identify trends and momentum changes based on various moving averages and oscillators.
Volatility and Drawdown Monitoring: Track volatility and drawdown levels to manage risk effectively.
This script offers a powerful tool for traders who want to have a holistic view of various technical indicators in one place. It provides flexibility in customization and a user-friendly interface to enhance your trading experience.
RSI Trend [MacroGlide]The RSI Trend indicator is a versatile and intuitive tool designed for traders who want to enhance their market analysis with visual clarity. By combining Stochastic RSI with moving averages, this indicator offers a dynamic view of market momentum and trends. Whether you're a beginner or an experienced trader, this tool simplifies identifying key market conditions and trading opportunities.
Key Features:
• Stochastic RSI-Based Calculations: Incorporates Stochastic RSI to provide a nuanced view of overbought and oversold conditions, enhancing standard RSI analysis.
• Dynamic Moving Averages: Includes two customizable moving averages (MA1 and MA2) based on smoothed Stochastic RSI, offering flexibility to align with your trading strategy.
• Candle Color Coding: Automatically colors candles on the chart:
• Blue: When the faster moving average (MA2) is above the slower one (MA1), signaling bullish momentum.
• Orange: When the faster moving average is below the slower one, indicating bearish momentum.
• Integrated Scaling: The indicator dynamically adjusts with the chart's scale, ensuring seamless visualization regardless of zoom level.
How to Use:
• Add the Indicator: Apply the indicator to your chart from the TradingView library.
• Interpret Candle Colors: Use the color-coded candles to quickly identify bullish (blue) and bearish (orange) phases.
• Customize to Suit Your Needs: Adjust the lengths of the moving averages and the Stochastic RSI parameters to better fit your trading style and timeframe.
• Combine with Other Tools: Pair this indicator with trendlines, volume analysis, or support and resistance levels for a comprehensive trading approach.
Methodology:
The indicator utilizes Stochastic RSI, a derivative of the standard RSI, to measure momentum more precisely. By applying smoothing and calculating moving averages, the tool identifies shifts in market trends. These trends are visually represented through candle color changes, making it easy to spot transitions between bullish and bearish phases at a glance.
Originality and Usefulness:
What sets this indicator apart is its seamless integration of Stochastic RSI and moving averages with real-time candle coloring. The result is a visually intuitive tool that adapts dynamically to chart scaling, offering clarity without clutter.
Charts:
When applied, the indicator plots two moving averages alongside color-coded candles. The combination of visual cues and trend logic helps traders easily interpret market momentum and make informed decisions.
Enjoy the game!
Z-Score Indicator by RafIf the z-score goes above 2, this may indicate overbought and If the z-score goes below -2, this may indicate oversold
Smooth RSI [MarktQuant]This indicator combines elements of the Relative Strength Index (RSI) and Rate of Change (RoC) to provide a smoother and potentially more insightful view of market momentum and price movement. The Smooth RSI calculates RSI values across four price points (high, open, low, close) to average them, offering a less volatile RSI signal. Additionally, it incorporates a Rate of Change for trend confirmation, enhancing the decision-making process for trade entries and exits.
Features:
Multi-RSI Calculation: RSI is computed for high, open, low, and close prices, then averaged to reduce noise.
Trend Confirmation with RoC: Uses the Rate of Change to validate the RSI signals, coloring bars based on the trend direction.
Visual Signals:
Bar colors change based on combined RSI and RoC signals.
Green for bullish signals (RSI above 50 and positive RoC).
Red for bearish signals (RSI below 50 and negative RoC).
Horizontal lines at 30, 50, and 70 to denote overbought, neutral, and oversold conditions.
Customizable Display:
Option to show/hide RSI plot or RoC plot for cleaner charts.
Candle plot overlay option to visualize current price action alongside the indicator.
Inputs:
RSI Length: Default 28. Adjusts the lookback period for RSI calculation.
RoC Length: Default 28. Sets the period for the Rate of Change calculation.
Plot Settings:
Show RSI - Toggle RSI plot visibility.
Show RoC - Toggle RoC plot visibility.
Usage:
Long signals are indicated when the average RSI is above 50 and the RoC is positive.
Short signals are suggested when the average RSI falls below 50 with a negative RoC.
The color coding helps visually confirm trends at a glance.
Notes:
This indicator is best used in conjunction with other analysis methods to confirm signals.
Adjust the length parameters based on your trading timeframe for optimal results.
Disclaimer:
This indicator does not guarantee trading success; use it as part of a comprehensive trading strategy. Always conduct your own analysis before making trading decisions.
Market Correlation AnalysisMarket Correlation Analysis is an indicator that measures the correlation of any two instruments.
To express price changes in a way that is comparable, this indicator uses a percentage of the ATR as a unit.
User Inputs:
Other Symbol - the symbol which we want to compare with the symbol of the main chart.
ATR for Price Movement Normalisation - I recommend high values to get the ATR more stable across time - if the ATR drastically changes, the indicator will register that as a price movement, because the unit in which price movements are measured itself changed by a lot. However, with higher values the ATR is stable and, in my opinion, more reliable than simply a percentage change of the current price.
Correlation Length - this is the number of bars for which the correlation coefficient will be calculated.
About The Indicator:
Market Correlation Analysis expresses the price changes of both instruments in question on the same histogram.
By default, the price changes that represent the instrument of the main chart are expressed with thinner bars of stronger colour, while the price changes that represent the other instrument are expressed with much thicker bars, which are of more pale colour.
The correlation coefficient is not expressed on the histogram, as it has a different scale. Therefore, it is only showed as a number.
I hope this indicator can make it easier to understand just how much two instruments have been similar to one another over a certain period of time. The possibility to see the correlation for any given time frame can give information that very specific to any trading style.
Enhanced Price Z-Score OscillatorThe Enhanced Price Z-Score Oscillator by tkarolak is a powerful tool that transforms raw price data into an easy-to-understand statistical visualization using Z-Score-derived candlesticks. Simply put, it shows how far prices stray from their average in terms of standard deviations (Z-Scores), helping traders identify when prices are unusually high (overbought) or unusually low (oversold).
The indicator’s default feature displays Z-Score Candlesticks, where each candle reflects the statistical “distance” of the open, high, low, and close prices from their average. This creates a visual map of market extremes and potential reversal points. For added flexibility, you can also switch to Z-Score line plots based on either Close prices or OHLC4 averages.
With clear threshold lines (±2σ and ±3σ) marking moderate and extreme price deviations, and color-coded zones to highlight overbought and oversold areas, the oscillator simplifies complex statistical concepts into actionable trading insights.
Option Time ValueThis TradingView script calculates and visualizes the time value of an option (Call or Put) based on its market price and intrinsic value. The time value represents the premium paid for the option above its intrinsic value, and it is a key metric for analyzing the cost of holding an option.
This script is suitable for traders analyzing options on indices or stocks, such as the NIFTY 50, and supports both Call and Put options. By dynamically extracting the strike price and option type from the input symbol, it adapts seamlessly to the selected instrument.
Key Features:
Dynamic Instrument Selection:
Users can input the underlying asset (e.g., NSE:NIFTY) and the specific option instrument (e.g., NSE:NIFTY250327C24000 for a Call or NSE:NIFTY250327P24000 for a Put).
Automatic Option Type Detection:
The script detects whether the option is a Call or a Put by parsing the input symbol for the characters "C" (Call) or "P" (Put).
Dynamic Strike Price Extraction:
The strike price is dynamically extracted from the input option symbol, eliminating the need for hardcoding and reducing user errors.
Key Metrics Plotted:
Time Value: The premium paid above the intrinsic value, plotted in blue.
Intrinsic Value: The calculated intrinsic value of the option, plotted in green.
Seamless Integration:
Designed for ease of use and integration into existing TradingView setups.
Automatically adjusts to the timeframe and pricing data of the selected instruments.
MA Deviation Suite [InvestorUnknown]This indicator combines advanced moving average techniques with multiple deviation metrics to offer traders a versatile tool for analyzing market trends and volatility.
Moving Average Types :
SMA, EMA, HMA, DEMA, FRAMA, VWMA: Standard moving averages with different characteristics for smoothing price data.
Corrective MA: This method corrects the MA by considering the variance, providing a more responsive average to price changes.
f_cma(float src, simple int length) =>
ma = ta.sma(src, length)
v1 = ta.variance(src, length)
v2 = math.pow(nz(ma , ma) - ma, 2)
v3 = v1 == 0 or v2 == 0 ? 1 : v2 / (v1 + v2)
var tolerance = math.pow(10, -5)
float err = 1
// Gain Factor
float kPrev = 1
float k = 1
for i = 0 to 5000 by 1
if err > tolerance
k := v3 * kPrev * (2 - kPrev)
err := kPrev - k
kPrev := k
kPrev
ma := nz(ma , src) + k * (ma - nz(ma , src))
Fisher Least Squares MA: Aims to reduce lag by using a Fisher Transform on residuals.
f_flsma(float src, simple int len) =>
ma = src
e = ta.sma(math.abs(src - nz(ma )), len)
z = ta.sma(src - nz(ma , src), len) / e
r = (math.exp(2 * z) - 1) / (math.exp(2 * z) + 1)
a = (bar_index - ta.sma(bar_index, len)) / ta.stdev(bar_index, len) * r
ma := ta.sma(src, len) + a * ta.stdev(src, len)
Sine-Weighted MA & Cosine-Weighted MA: These give more weight to middle bars, creating a smoother curve; Cosine weights are shifted for a different focus.
Deviation Metrics :
Average Absolute Deviation (AAD) and Median Absolute Deviation (MAD): AAD calculates the average of absolute deviations from the MA, offering a measure of volatility. MAD uses the median, which can be less sensitive to outliers.
Standard Deviation (StDev): Measures the dispersion of prices from the mean.
Average True Range (ATR): Reflects market volatility by considering the day's range.
Average Deviation (adev): The average of previous deviations.
// Calculate deviations
float aad = f_aad(src, dev_len, ma) * dev_mul
float mad = f_mad(src, dev_len, ma) * dev_mul
float stdev = ta.stdev(src, dev_len) * dev_mul
float atr = ta.atr(dev_len) * dev_mul
float avg_dev = math.avg(aad, mad, stdev, atr)
// Calculated Median with +dev and -dev
float aad_p = ma + aad
float aad_m = ma - aad
float mad_p = ma + mad
float mad_m = ma - mad
float stdev_p = ma + stdev
float stdev_m = ma - stdev
float atr_p = ma + atr
float atr_m = ma - atr
float adev_p = ma + avg_dev
float adev_m = ma - avg_dev
// upper and lower
float upper = f_max4(aad_p, mad_p, stdev_p, atr_p)
float upper2 = f_min4(aad_p, mad_p, stdev_p, atr_p)
float lower = f_min4(aad_m, mad_m, stdev_m, atr_m)
float lower2 = f_max4(aad_m, mad_m, stdev_m, atr_m)
Determining Trend
The indicator generates trend signals by assessing where price stands relative to these deviation-based lines. It assigns a trend score by summing individual signals from each deviation measure. For instance, if price crosses above the MAD-based upper line, it contributes a bullish point; crossing below an ATR-based lower line contributes a bearish point.
When the aggregated trend score crosses above zero, it suggests a shift towards a bullish environment; crossing below zero indicates a bearish bias.
// Define Trend scores
var int aad_t = 0
if ta.crossover(src, aad_p)
aad_t := 1
if ta.crossunder(src, aad_m)
aad_t := -1
var int mad_t = 0
if ta.crossover(src, mad_p)
mad_t := 1
if ta.crossunder(src, mad_m)
mad_t := -1
var int stdev_t = 0
if ta.crossover(src, stdev_p)
stdev_t := 1
if ta.crossunder(src, stdev_m)
stdev_t := -1
var int atr_t = 0
if ta.crossover(src, atr_p)
atr_t := 1
if ta.crossunder(src, atr_m)
atr_t := -1
var int adev_t = 0
if ta.crossover(src, adev_p)
adev_t := 1
if ta.crossunder(src, adev_m)
adev_t := -1
int upper_t = src > upper ? 3 : 0
int lower_t = src < lower ? 0 : -3
int upper2_t = src > upper2 ? 1 : 0
int lower2_t = src < lower2 ? 0 : -1
float trend = aad_t + mad_t + stdev_t + atr_t + adev_t + upper_t + lower_t + upper2_t + lower2_t
var float sig = 0
if ta.crossover(trend, 0)
sig := 1
else if ta.crossunder(trend, 0)
sig := -1
Backtesting and Performance Metrics
The code integrates with a backtesting library that allows traders to:
Evaluate the strategy historically
Compare the indicator’s signals with a simple buy-and-hold approach
Generate performance metrics (e.g., mean returns, Sharpe Ratio, Sortino Ratio) to assess historical effectiveness.
Practical Usage and Calibration
Default settings are not optimized: The given parameters serve as a starting point for demonstration. Users should adjust:
len: Affects how smooth and lagging the moving average is.
dev_len and dev_mul: Influence the sensitivity of the deviation measures. Larger multipliers widen the bands, potentially reducing false signals but introducing more lag. Smaller multipliers tighten the bands, producing quicker signals but potentially more whipsaws.
This flexibility allows the trader to tailor the indicator for various markets (stocks, forex, crypto) and time frames.
Disclaimer
No guaranteed results: Historical performance does not guarantee future outcomes. Market conditions can vary widely.
User responsibility: Traders should combine this indicator with other forms of analysis, appropriate risk management, and careful calibration of parameters.
Ultra Trade JournalThe Ultra Trade Journal is a powerful TradingView indicator designed to help traders meticulously document and analyze their trades. Whether you're a novice or an experienced trader, this tool offers a clear and organized way to visualize your trading strategy, monitor performance, and make informed decisions based on detailed trade metrics.
Detailed Description
The Ultra Trade Journal indicator allows users to input and visualize critical trade information directly on their TradingView charts.
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User Inputs
Traders can specify entry and exit prices , stop loss levels, and up to four take profit targets.
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Dynamic Plotting
Once the input values are set, the indicator automatically plots horizontal lines for entry, exit, stop loss, and each take profit level on the chart. These lines are visually distinct, using different colors and styles (solid, dashed, dotted) to represent each element clearly.
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Live Position Tracking
If enabled, the indicator can adjust the exit price in real-time based on the current market price, allowing traders to monitor live positions effectively.
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Tick Calculations
The script calculates the number of ticks between the entry price and each exit point (stop loss and take profits). This helps in understanding the movement required for each target and assessing the potential risk and reward.
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Risk-Reward Ratios
For each take profit level, the indicator computes the risk-reward (RR) ratio by comparing the ticks at each target against the stop loss ticks. This provides a quick view of the potential profitability versus the risk taken.
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Comprehensive Table Display
A customizable table is displayed on the chart, summarizing all key trade details. This includes the entry and exit prices, stop loss and take profit levels, tick counts, and their respective RR ratios.
Users can adjust the table's Position and text color to suit their preferences.
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Visual Enhancements
The indicator uses adjustable background shading between entry and stop loss/take profit lines to visually represent potential trade outcomes. This shading adjusts based on whether the trade is long or short, providing an intuitive understanding of trade performance.
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Overall, the Ultra Trade Journal combines visual clarity with detailed analytics, enabling traders to keep a well-organized record of their trades and enhance their trading strategies through insightful data.
HPDR Bands IndicatorThe HPDR Bands indicator is a customizable tool designed to help traders visualize dynamic price action zones. By combining historical price ranges with adaptive bands, this script provides clear insights into potential support, resistance, and midline levels. The indicator is well-suited for all trading styles, including trend-following and range-bound strategies.
Features:
Dynamic Price Bands: Calculates price zones based on historical highs and lows, blending long-term and short-term price data for responsive adaptation to current market conditions.
Probability Enhancements: Includes a probability plot derived from the relative position of the closing price within the range, adjusted for volatility to highlight potential price movement scenarios.
Fibonacci-Like Levels: Highlights key levels (100%, 95%, 88%, 78%, 61%, 50%, and 38%) for intuitive visualization of price zones, aiding in identifying high-probability trading opportunities.
Midline Visualization: Displays a midline that serves as a reference for price mean reversion or breakout analysis.
How to Use:
Trending Markets: Use the adaptive upper and lower bands to gauge potential breakout or retracement zones.
Range-Bound Markets: Identify support and resistance levels within the defined price range.
Volatility Analysis: Observe the probability plot and its sensitivity to volatility for informed decision-making.
Important Notes:
This script is not intended as investment advice. It is a tool to assist with market analysis and should be used alongside proper risk management and other trading tools.
The script is provided as-is and without warranty. Users are encouraged to backtest and validate its suitability for their specific trading needs.
Happy Trading!
If you find this script helpful, consider sharing your feedback or suggestions for improvement. Collaboration strengthens the TradingView community, and your input is always appreciated!
Bitcoin Premium [SAKANE]Overview
"Bitcoin Premium " is an indicator designed to analyze the price differences (premiums) of Bitcoin between major exchanges. By using this tool, you can visualize these differences and trends across exchanges, helping you make more informed trading decisions.
Features
1. Premium Calculation and Display
- Calculates and visualizes the price differences between major exchanges like Coinbase, Bitfinex, Upbit, and Binance.
- Premiums are displayed in a histogram format for intuitive analysis.
2. Forex Rate Adjustment
- Prices quoted in KRW (e.g., from Upbit) are converted to USD using real-time KRW/USD forex rates.
3. Moving Average Option
- Displays moving averages (SMA or EMA) of premiums for a clearer view of long-term trends.
4. Customizable Settings
- Toggle the premium display for each exchange on or off.
- Includes label displays to support visual analysis.
What Can It Do for You?
1. Identify Arbitrage Opportunities
By observing price differences (premiums) between exchanges, you can identify arbitrage opportunities.
Example: If Bitcoin is cheaper on Binance and more expensive on Coinbase, you could buy on Binance and sell on Coinbase to capture the price difference.
2. Understand Regional Supply and Demand Trends
Each exchange's premium reflects the supply and demand dynamics of its respective region.
Example: A high premium on Upbit may indicate excess demand or regulatory impacts in the South Korean market.
3. Analyze Liquidity
Price differences often highlight liquidity disparities between exchanges. Markets with lower trading volumes tend to have larger premiums due to price distortions.
4. Evaluate Macroeconomic Impacts
Premium movements may reflect changes in macroeconomic factors, such as exchange rates, regulations, or financial conditions specific to each region.
5. Analyze Trends and Market Sentiment
By tracking premium trends, you can gauge market sentiment and understand regional or exchange-specific behaviors to inform your investment decisions.
6. Support Strategic Trading
This tool is useful for short-term arbitrage strategies as well as long-term evaluations of market health.
Exchange Characteristics and Premium Implications
The meaning of premiums varies by exchange.
- Coinbase (US Market)
Primarily used by investors buying directly with fiat currency (USD). A higher premium often signals bullish sentiment among institutional and retail investors.
- Bitfinex (Global Market)
A trader-focused exchange with active large-scale and leveraged trading. Premiums may reflect liquidity and risk appetite.
- Upbit (South Korean Market)
Priced in KRW, making it subject to forex rates and local market dynamics. High premiums may indicate strong demand or regulatory influences in South Korea.
- Binance (Global Market)
The largest exchange by trading volume. Premiums here are often a reflection of the overall market balance.
Notes
- This indicator is for reference only and does not guarantee trading decisions.
- Please consider the characteristics and conditions of each exchange when using this tool.
Enhanced Effort vs Result Analysis V.2How to Use in Trading
A. Confirm Breakouts
Check if the Effort-Result Ratio or Z-Score spikes above the Upper Band or Z > +2:
Suggests a strong, efficient price move.
Supports breakout continuation.
B. Identify Reversal or Exhaustion
Look for Effort-Result Ratio or Z-Score dropping below the Lower Band or Z < -2:
Indicates high effort but low price movement (inefficiency).
Often signals potential trend reversal or consolidation.
C. Assess Efficiency of Trends
Use Relative Efficiency Index (REI):
REI near 1 during a trend → Confirms strength (efficient movement).
REI near 0 → Weak or inefficient movement, likely signaling exhaustion.
D. Evaluate Volume-Price Relationship
Monitor the Volume-Price Correlation:
Positive correlation (+1): Confirms price is driven by volume.
Negative correlation (-1): Indicates divergence; price moves independently of volume (potential warning signal).
3. Example Scenarios
Scenario 1: Breakout Confirmation
Effort-Result Ratio spikes above the Upper Band.
Z-Score exceeds +2.
REI approaches 1.
Volume-Price Correlation is positive (near +1).
Action: Strong breakout confirmation → Trend continuation likely.
Scenario 2: Reversal or Exhaustion
Effort-Result Ratio drops below the Lower Band.
Z-Score is below -2.
REI approaches 0.
Volume-Price Correlation weakens or turns negative.
Action: Signals trend exhaustion → Watch for reversal or consolidation.
Scenario 3: Range-Bound Market
Effort-Result Ratio stays within the Bollinger Bands.
Z-Score remains between -1 and +1.
REI fluctuates around 0.5 (neutral efficiency).
Volume-Price Correlation hovers near 0.
Action: Normal conditions → Look for breakout signals before acting.
*IMPORTANT*
There is a problem with the overlay ... How to fix some of it
The Standard Deviation bands dont work while the other variable activated so Id suggest deselecting them. The fix for this is to make sure you have the background selected and by doing this it will highlight on the chart ( you may need to increase the opacity ) when the bands ( Second standard deviation) are touched.
- Also you can use them all at once if you can but you do not need to
Market Flow Volatility Oscillator (AiBitcoinTrend)The Market Flow Volatility Oscillator (AiBitcoinTrend) is a cutting-edge technical analysis tool designed to evaluate and classify market volatility regimes. By leveraging Gaussian filtering and clustering techniques, this indicator provides traders with clear insights into periods of high and low volatility, helping them adapt their strategies to evolving market conditions. Built for precision and clarity, it combines advanced mathematical models with intuitive visual feedback to identify trends and volatility shifts effectively.
👽 How the Indicator Works
👾 Volatility Classification with Gaussian Filtering
The indicator detects volatility levels by applying Gaussian filters to the price series. Gaussian filters smooth out noise while preserving significant price movements. Traders can adjust the smoothing levels using sigma parameters, enabling greater flexibility:
Low Sigma: Emphasizes short-term volatility.
High Sigma: Captures broader trends with reduced sensitivity to small fluctuations.
👾 Clustering Algorithm for Regime Detection
The core of this indicator is its clustering model, which classifies market conditions into two distinct regimes:
Low Volatility Regime: Calm periods with reduced market activity.
High Volatility Regime: Intense periods with heightened price movements.
The clustering process works as follows:
A rolling window of data is analyzed to calculate the standard deviation of price returns.
Two cluster centers are initialized using the 25th and 75th percentiles of the data distribution.
Each price volatility value is assigned to the nearest cluster based on its distance to the centers.
The cluster centers are refined iteratively, providing an accurate and adaptive classification.
👾 Oscillator Generation with Slope R-Values
The indicator computes Gaussian filter slopes to generate oscillators that visualize trends:
Oscillator Low: Captures low-frequency market behavior.
Oscillator High: Tracks high-frequency, faster-changing trends.
The slope is measured using the R-value of the linear regression fit, scaled and adjusted for easier interpretation.
👽 Applications
👾 Trend Trading
When the oscillator rises above 0.5, it signals potential bullish momentum, while dips below 0.5 suggest bearish sentiment.
👾 Pullback Detection
When the oscillator peaks, especially in overbought or oversold zones, provide early warnings of potential reversals.
👽 Indicator Settings
👾 Oscillator Settings
Sigma Low/High: Controls the smoothness of the oscillators.
Smaller Values: React faster to price changes but introduce more noise.
Larger Values: Provide smoother signals with longer-term insights.
👾 Window Size and Refit Interval
Window Size: Defines the rolling period for cluster and volatility calculations.
Shorter windows: adapt faster to market changes.
Longer windows: produce stable, reliable classifications.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
