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! 🚀
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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!
OBV Divergence Indicator [TradingFinder] On-Balance Vol Reversal🔵 Introduction
The On-Balance Volume (OBV) indicator, introduced by Joe Granville in 1963, is a powerful technical analysis tool used to measure buying and selling pressure based on trading volume and price.
By aggregating trading volume—adding it on positive days and subtracting it on negative days—OBV creates a cumulative line that reflects market volume pressure, making it valuable for confirming trends, identifying entry and exit points, and forecasting potential price movements.
Divergences between price and OBV often provide significant signals. A bearish divergence occurs when the price forms higher highs while the OBV line forms lower highs. This discrepancy indicates that upward momentum is weakening, increasing the likelihood of a downward trend.
In contrast, a bullish divergence happens when the price makes lower lows, but the OBV line forms higher lows. This suggests increasing buying pressure and the potential for an upward trend reversal.
For instance, if the price is rising but the OBV trendline is falling, it may signal a bearish divergence, warning of a possible price decline. Conversely, if the price is falling while the OBV line is rising, this could signal a bullish divergence, indicating a possible price recovery. These signals are particularly useful for identifying market turning points.
OBV often acts as a leading indicator, moving ahead of price changes. For example, a rising OBV alongside stable or declining prices can signal an impending upward breakout.
Conversely, a declining OBV with rising prices may indicate that the current uptrend is losing strength. Traders using this strategy often consider entering positions at breakout levels while setting stop losses near recent swing highs or lows to manage risk effectively.
This integration highlights how OBV divergences can provide actionable insights for predicting price movements and managing trades efficiently.
Bullish Divergence :
Bearish Divergence :
🔵 How to Use
The OBV indicator, as a cumulative tool, assists analysts in comparing volume and price changes to identify new trends and key levels for entering or exiting trades. Beyond confirming existing trends, it is particularly effective in analyzing positive and negative divergences between price and volume, providing valuable signals for trading decisions.
🟣 Bullish Divergence
A bullish divergence occurs when the price continues its downward or stable trend, but the OBV line starts rising, forming a higher low compared to its previous low. This suggests increasing volume on up days relative to down days and often signals a reversal to the upside.
For instance, if an asset's price stabilizes near a support level but the OBV line shows an upward trend, this divergence could present an opportunity to enter a long position.
🟣 Bearish Divergence
A bearish divergence occurs when the price forms higher highs, but the OBV line declines, creating lower highs compared to previous peaks. This indicates decreasing volume on up days relative to down days and often acts as a warning for a reversal to the downside.
For example, if an asset’s price approaches a resistance level while OBV starts declining, this divergence may signal the beginning of a downtrend and could indicate a good time to exit long trades or enter short positions.
🔵 Setting
Period : The "Period" setting allows you to define the number of bars or intervals for "Periodic" and "EMA" modes. A shorter period captures more short-term movements, while a longer period smooths out the fluctuations and provides a broader view of market trends.
You can enable or disable labels to highlight key levels or divergences and tables to show numerical details like values and divergence types. These options allow for a customized chart display.
🔵 Table
The following table breaks down the main features of the oscillator. It covers four critical categories: Exist, Consecutive, Divergence Quality, and Change Phase Indicator.
Exist : If divergence is detected, a "+" will appear in this row.
Consecutive: Shows the number of consecutive divergences that have formed in a short period.
Divergence Quality : Evaluates the quality of the divergence based on the number of occurrences. One is labeled "Normal," two are "Good," and three or more are considered "Strong."
Change Phase Indicator : If a phase change is detected between two oscillation peaks, this is marked in the table.
🔵 Conclusion
The OBV (On Balance Volume) indicator is a simple yet effective tool in technical analysis that combines volume and price changes to provide a comprehensive view of market buying and selling pressure. By identifying positive and negative divergences, OBV enables analysts to detect early signs of trend reversals and refine their trading strategies.
Divergences in OBV often precede price changes, making it a leading indicator for predicting market movements. Using OBV alongside other technical tools can enhance decision-making accuracy and help traders identify better entry and exit points. However, it is essential to consider the limitations of OBV, such as the potential for signal errors and the impact of sudden news events.
Ultimately, OBV serves as a complementary tool in technical analysis, aiding in trend identification, signal confirmation, and risk management. A thoughtful application of this indicator, in combination with other analytical tools, can create valuable opportunities for profiting in financial markets.
Stoch RSI and RSI Buy/Sell Signals with MACD Trend FilterDescription of the Indicator
This Pine Script is designed to provide traders with buy and sell signals based on the combination of Stochastic RSI, RSI, and MACD indicators, enhanced by the confirmation of candle colors. The primary goal is to facilitate informed trading decisions in various market conditions by utilizing different indicators and their interactions. The script allows customization of various parameters, providing flexibility for traders to adapt it to their specific trading styles.
Usefulness
This indicator is not just a mashup of existing indicators; it integrates the functionality of multiple momentum and trend-detection methods into a cohesive trading tool. The combination of Stochastic RSI, RSI, and MACD offers a well-rounded approach to analyzing market conditions, allowing traders to identify entry and exit points effectively. The inclusion of color-coded signals (strong vs. weak) further enhances its utility by providing visual cues about the strength of the signals.
How to Use This Indicator
Input Settings: Adjust the parameters for the Stochastic RSI, RSI, and MACD to fit your trading style. Set the overbought/oversold levels according to your risk tolerance.
Signal Colors:
Strong Buy Signal: Indicated by a green label and confirmed by a green candle (close > open).
Weak Buy Signal: Indicated by a blue label and confirmed by a green candle (close > open).
Strong Sell Signal: Indicated by a red label and confirmed by a red candle (close < open).
Weak Sell Signal: Indicated by an orange label and confirmed by a red candle (close < open).
Example Trading Strategy Using This Indicator
To effectively use this indicator as part of your trading strategy, follow these detailed steps:
Setup:
Timeframe : Select a timeframe that aligns with your trading style (e.g., 15-minute for intraday, 1-hour for swing trading, or daily for longer-term positions).
Indicator Settings : Customize the Stochastic RSI, RSI, and MACD parameters to suit your trading approach. Adjust overbought/oversold levels to match your risk tolerance.
Strategy:
1. Strong Buy Entry Criteria :
Wait for a strong buy signal (green label) when the RSI is at or below the oversold level (e.g., ≤ 35), indicating a deeply oversold market. Confirm that the MACD shows a decreasing trend (bearish momentum weakening) to validate a potential reversal. Ensure the current candle is green (close > open) if candle color confirmation is enabled.
Example Use : On a 1-hour chart, if the RSI drops below 35, MACD shows three consecutive bars of decreasing negative momentum, and a green candle forms, enter a buy position. This setup signals a robust entry with strong momentum backing it.
2. Weak Buy Entry Criteria :
Monitor for weak buy signals (blue label) when RSI is above the oversold level but still below the neutral (e.g., between 36 and 50). This indicates a market recovering from an oversold state but not fully reversing yet. These signals can be used for early entries with additional confirmations, such as support levels or higher timeframe trends.
Example Use : On the same 1-hour chart, if RSI is at 45, the MACD shows momentum stabilizing (not necessarily negative), and a green candle appears, consider a partial or cautious entry. Use this as an early warning for a potential bullish move, especially when higher timeframe indicators align.
3. Strong Sell Entry Criteria :
Look for a strong sell signal (red label) when RSI is at or above the overbought level (e.g., ≥ 65), signaling a strong overbought condition. The MACD should show three consecutive bars of increasing positive momentum to indicate that the bullish trend is weakening. Ensure the current candle is red (close < open) if candle color confirmation is enabled.
Example Use : If RSI reaches 70, MACD shows increasing momentum that starts to level off, and a red candle forms on a 1-hour chart, initiate a short position with a stop loss set above recent resistance. This is a high-confidence signal for potential price reversal or pullback.
4. Weak Sell Entry Criteria :
Use weak sell signals (orange label) when RSI is between the neutral and overbought levels (e.g., between 50 and 64). These can indicate potential short opportunities that might not yet be fully mature but are worth monitoring. Look for other confirmations like resistance levels or trendline touches to strengthen the signal.
Example Use : If RSI reads 60 on a 1-hour chart, and the MACD shows slight positive momentum with signs of slowing down, place a cautious sell position or scale out of existing long positions. This setup allows you to prepare for a possible downtrend.
Trade Management:
Stop Loss : For buy trades, place stop losses below recent swing lows. For sell trades, set stops above recent swing highs to manage risk effectively.
Take Profit : Target nearby resistance or support levels, apply risk-to-reward ratios (e.g., 1:2), or use trailing stops to lock in profits as price moves in your favor.
Confirmation : Align these signals with broader trends on higher timeframes. For example, if you receive a weak buy signal on a 15-minute chart, check the 1-hour or daily chart to ensure the overall trend is not bearish.
Real-World Example: Imagine trading on a 15-minute chart :
For a buy:
A strong buy signal (green) appears when the RSI dips to 32, MACD shows declining bearish momentum, and a green candle forms. Enter a buy position with a stop loss below the most recent support level.
Alternatively, a weak buy signal (blue) appears when RSI is at 47. Use this as a signal to start monitoring the market closely or enter a smaller position if other indicators (like support and volume analysis) align.
For a sell:
A strong sell signal (red) with RSI at 72 and a red candle signals to short with conviction. Place your stop loss just above the last peak.
A weak sell signal (orange) with RSI at 62 might prompt caution but can still be acted on if confirmed by declining volume or touching a resistance level.
These strategies show how to blend both strong and weak signals into your trading for more nuanced decision-making.
Technical Analysis of the Code
1. Stochastic RSI Calculation:
The script calculates the Stochastic RSI (stochRsiK) using the RSI as input and smooths it with a moving average (stochRsiD).
Code Explanation : ta.stoch(rsi, rsi, rsi, stochLength) computes the Stochastic RSI, and ta.sma(stochRsiK, stochSmoothing) applies smoothing.
2. RSI Calculation :
The RSI is computed over a user-defined period and checks for overbought or oversold conditions.
Code Explanation : rsi = ta.rsi(close, rsiLength) calculates RSI values.
3. MACD Trend Filter :
MACD is calculated with fast, slow, and signal lengths, identifying trends via three consecutive bars moving in the same direction.
Code Explanation : = ta.macd(close, macdLengthFast, macdLengthSlow, macdSignalLength) sets MACD values. Conditions like macdLine < macdLine confirm trends.
4. Buy and Sell Conditions :
The script checks Stochastic RSI, RSI, and MACD values to set buy/sell flags. Candle color filters further confirm valid entries.
Code Explanation : buyConditionMet and sellConditionMet logically check all conditions and toggles (enableStochCondition, enableRSICondition, etc.).
5. Signal Flags and Confirmation :
Flags track when conditions are met and ensure signals only appear on appropriate candle colors.
Code Explanation : Conditional blocks (if statements) update buyFlag and sellFlag.
6. Labels and Alerts :
The indicator plots "BUY" or "SELL" labels with the RSI value when signals trigger and sets alerts through alertcondition().
Code Explanation : label.new() displays the signal, color-coded for strength based on RSI.
NOTE : All strategies can be enabled or disabled in the settings, allowing traders to customize the indicator to their preferences and trading styles.
RSI/MFI Divergence Finder [idahodev]Monitoring RSI (Relative Strength Index) and MFI (Money Flow Index) divergences on a stock or index chart offers several benefits to traders and analysts. Let's break down the advantages:
Comprehensive Market View: Combining both indicators provides a more complete picture of market conditions, as they measure different aspects of price movement. RSI focuses on recent gains/losses relative to price change, while MFI incorporates volume data to assess money flow in and out of a security.
Enhanced Signal Accuracy: When divergences occur simultaneously in both RSI and MFI, it may be considered a stronger signal than if only one indicator showed divergence. This can potentially lead to more reliable trading decisions.
Identification of False Breakouts: Divergences between these indicators and price action can help identify false breakouts or misleading price movements that are not supported by underlying market strength or volume.
More Nuanced Market Understanding: By examining divergent behavior between money flow (MFI) and momentum (RSI), traders gain a more detailed comprehension of the interplay between these factors in shaping market trends.
Early Warning Signs: These divergences can act as early warning signs for potential trend reversals or changes in market sentiment, allowing traders to adjust their strategies proactively.
It's important to note that RSI/MFI divergences should be used as part of a broader trading strategy rather than solely relying on them for buy/sell signals. They can serve as valuable tools for confirming trends, identifying potential turning points, or warning against overbought/oversold conditions.
When using these indicators together, traders must be cautious of false signals, especially in choppy markets or during periods of high volatility. It's crucial to combine this analysis with other technical and fundamental factors before making trading decisions.
In summary, monitoring RSI/MFI divergences may offer a way to gain insights into the underlying strengths and weaknesses of market movements.
This utility differs from other in that it allows for a choke/threshold/sensitivity setting to help weed out noisy signals. This needs to be carefully adjusted per chart.
It also allows for tuning of the MFI smoothing length (number of bars on the current chart) as well as how many previous bars it will take into consideration when calculating RSI and MFI divergences. It will signal when it sees alignment forming between RSI and MFI divergences in a direction. You will likely need to tune this script's settings every few days or at least anytime there is a change in overall market behavior or sustained volatility.
Ultimately, the goal with this script is to provide an additional level of confirmation of weakness or strength. It should be combined with other indicators such as exhaustion, pivots, supply/demand, trendline breaks or tests, and structure changes, to name a few complementary tools or strategies. It's not meant to be a standalone buy/sell signal indicator!
Here are some settings for futures that may help you get started:
ES (4m chart)
RSI Length: 26
MFI Length: 8
MFI Smoothing Length: 32
Divergence Sensitivity: 124
Left Bars for Pivot: 10
Right Bars for Pivot: 1
NQ (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 400
Left Bars for Pivot: 21
Right Bars for Pivot: 1
YM (4m chart)
RSI Length: 14
MFI Length: 14
MFI Smoothing Length: 21
Divergence Sensitivity: 810
Left Bars for Pivot: 33
Right Bars for Pivot: 1
Leading Indicator by Parag RautBreakdown of the Leading Indicator:
Linear Regression (LRC):
A linear regression line is used to estimate the current trend direction. When the price is above or below the regression line, it indicates whether the price is deviating from its mean, signaling potential reversals.
Rate of Change (ROC):
ROC measures the momentum of the price over a set period. By using thresholds (positive or negative), we predict that the price will continue in the same direction if momentum is strong enough.
Leading Indicator Calculation:
We calculate the difference between the price and the linear regression line. This is normalized using the standard deviation of price over the same period, giving us a leading signal based on price divergence from the mean trend.
The leading indicator is used to forecast changes in price behavior by identifying when the price is either stretched too far from the mean (indicating a potential reversal) or showing strong momentum in a particular direction (predicting trend continuation).
Buy and Sell Signals:
Buy Signal: Generated when ROC is above a threshold and the leading indicator shows the price is above the regression line.
Sell Signal: Generated when ROC is below a negative threshold and the leading indicator shows the price is below the regression line.
Visual Representation:
The indicator oscillates around zero. Values above zero signal potential upward price movements, while values below zero signal potential downward movements.
Background colors highlight potential buy (green) and sell (red) areas based on our conditions.
How It Works as a Leading Indicator:
This indicator attempts to predict price movements before they happen by combining the trend (via linear regression) and momentum (via ROC).
When the price significantly diverges from the trendline and momentum supports a continuation, it signals a potential entry point (either buy or sell).
It is leading in that it anticipates price movement before it becomes fully apparent in the market.
Next Steps:
You can adjust the length of the linear regression and ROC to fine-tune the indicator’s sensitivity to your trading style.
This can be combined with other indicators or used as part of a larger strategy
BTC Top Indicator - Extension from 20 Week SMA (Normalized)This Indicator calculates the logarithmic deviation of the BTCUSD price from its 20-week SMA and dynamically normalizes it between a lower signal line (-0.57) and an upper trendline defined by two historical points (May 30, 2011, at 1.75 and March 4, 2024, at 0.45).
The indicator line color changes dynamically:
green below 0
blue at 0.5
red above 1
Ideal for analyzing BTCUSD on the Index chart to identify potential overbought or oversold levels. It's better suited for identifying tops, than bottoms.
Retest Confirm Point TibbuCreating a "Retest Confirm Point" indicator that generates buy and sell signals involves defining criteria to confirm that a price retest is valid before issuing a trade signal. This generally requires identifying a key level (such as support, resistance, or a trendline), detecting a retest of this level, and then confirming the validity of the retest.
Here’s a Pine Script example to help you create such an indicator. This script identifies and confirms retests of previous highs and lows, and generates buy and sell signals based on those retests: Explanation:
Recent High and Low:
The script identifies the highest and lowest prices over a specified lookback period.
These levels are plotted on the chart as reference points.
Retest Conditions:
Retest High: The closing price is within a buffer range around the recent high.
Retest Low: The closing price is within a buffer range around the recent low.
Confirmation:
Confirm High: The closing price reaches a new high over a set number of bars after the retest condition.
Confirm Low: The closing price reaches a new low over a set number of bars after the retest condition.
Signals:
Buy Signal: Issued when a confirmed retest of the recent high occurs.
Sell Signal: Issued when a confirmed retest of the recent low occurs.
Customization:
Lookback Period: Adjust to determine the historical range for finding recent highs and lows.
Confirmation Bars: Change the number of bars used to confirm the retest.
Retest Buffer: Adjust the percentage buffer to fine-tune the retest conditions.
Testing and Optimization:
Backtest: Always backtest the strategy on historical data to ensure it behaves as expected.
Adjust Parameters: Modify parameters based on the asset, timeframe, and market conditions.
Feel free to modify this script further based on your specific trading strategy and needs. If you need help with any additional features or further customization, let me know!
ChatGPT can make mistakes. Check important info.
Ripster MTF CloudsDescription:
MTF EMA Cloud By Ripster
EMA Cloud System is a Trading System Invented by Ripster where areas are shaded between two desired EMAs. The concept implies the EMA cloud area serves as support or resistance for Intraday & Swing Trading. This can be utilized effectively on 10 Min for day trading and 1Hr/Daily for Swings. Ripster himself utilizes various combinations of the 5-12, 34-50, 8-9, 20-21 EMA clouds but the possibilities are endless to find what works best for you.
“Ideally, 5-12 or 5-13 EMA cloud acts as a fluid trendline for day trades. 8-9 EMA Clouds can be used as pullback Levels –(optional). Additionally, a high level price over or under 34-50 EMA clouds confirms either bullish or bearish bias on the price action for any timeframe” – Ripster
This indicator is an extension of the Ripster EMA Clouds. It allows you to visualize Exponential Moving Average (EMA) clouds from any time frame on your current chart, regardless of the chart's own time frame. This functionality is especially useful for traders who want to monitor higher time frame trends and support/resistance levels while trading on lower time frames.
What does this code do?
The Ripster MTF Clouds indicator displays two sets of EMA clouds. Each set consists of a short EMA and a long EMA. By default, the indicator uses Daily 20/21 and 50/55 EMAs, but you can customize these settings to fit your trading strategy. The EMAs are plotted on your chart along with their corresponding clouds, colored for easy differentiation:
EMA 1 (default 50/55): Plotted in blue.
EMA 2 (default 20/21): Plotted in teal.
The indicator uses the security function to fetch EMA values from higher time frames and plots them on your current chart, allowing you to see how these higher time frame EMAs interact with your current time frame's price action.
How to use this indicator:
Adjust Resolution:
Set the "Resolution" input to the time frame from which you want to fetch EMA values. For example, set it to "1H" if you want to see 1-hour EMAs on your current chart.
Customize EMAs:
Modify the "EMA 1 Short Length" and "EMA 1 Long Length" inputs to change the default 50/55 EMAs.
Adjust the "EMA 2 Short Length" and "EMA 2 Long Length" inputs to change the default 20/21 EMAs.
Monitor Clouds:
The indicator fills the area between the short and long EMAs, creating a cloud that helps visualize the trend. A blue cloud indicates the area between the EMA 1 pair, while a teal cloud indicates the area between the EMA 2 pair.
Use Multiple Instances:
You can add multiple instances of this indicator to your chart to monitor multiple higher time frames simultaneously. For instance, one instance can show daily clouds while another shows hourly clouds.
Integration with Trading Strategy:
Use this indicator to identify higher time frame trends and support/resistance levels, which can help improve your trading decisions on lower time frames.
For example, you can go long when the stock is above the 50-55 EMA clouds and 20-21 EMA clouds with daily resolution on a 10-minute chart and short when it is below it.
Similarly, you can short a stock under the 1-hour 34/50 EMA clouds while still trading on a 10-minute chart.
Hindsight TrendNon-realtime but highly accurate trend analyzer with only one fundamental parameter ( period aka "minimum trend length")
Basically Hindsight Trend is pivot points on steroids (handles many cases much better). Plus it shows the trend line.
Period
I usually like periods of 10, 20 or 30.
The indicator's delay is identical to the chosen period.
You can actually try a low period like 4 or 5 to get something resembling a realtime indicator.
Uptrends are based on candle lows, downtrends are based on candle highs. So it is possible to have an uptrend and a downtrend at the same time.
Triangles
At trend start, a triangle is drawn. (Trendline isn't always there if the trend didn't last that long.)
Triangle size shows how long the high or low that started the trend remained unbroken. E.g. with period 20: Small triangle = 20+ candles, medium triangle = 40+ candles, big triangle = 80+ candles. So a big triangle marks an important reversal point.
How Hindsight Trend works
Whenever a candle completes, its high and low are saved as potentially "notable" points. A high or low is the more notable the longer it stays unbroken (= not touched again by price).
Now we simply take the notable highs and lows (as in, staying unbroken at least for the user-selected period)... and connect them together - if they are close enough to each other (less than "period" candles away). And decorate the first point in each trend with a triangle.
We only know whether a point is notable after "period" more candles have printed, so that's where the indicator's delay comes from.
Finally we divide the period by 2 and look at highs and lows which are unbroken for that shorter time. While they are not fully "notable" as defined above, we'll call them "semi-notable". Those points are only considered at the end of a trend, and help us extend the trend line a bit further.
Bilson Gann CountGann counting is a method for identifying swing points,trends, and overall market structure. It simplifies price action by drawing short trend lines that summarize moves.
There's essentially 4 types of bar/candle.
Up bar - Higher high and higher low than previous bar
Down bar - Lower high and lower low than previous bar
Inside bar - Lower high and higher low than previous bar
Outside bar - Higher high and lower low than previous bar
We use these determinations to decide how the trendline moves through the candles.
Up bars we join to the high, down bars we join to the low, inside bars are ignored.
There are other indicators that already exist which do this, the difference here is how we handle outside bars.
Other gann counting methods skip outside bars, this method determines how to handle the outside bar after the outside bar is broken.
examples
UP -> OUTSIDE -> UP = Outside bar treated as swing low
UP -> OUTSIDE -> DOWN = Outside bar treated as swing high
DOWN -> OUTSIDE -> UP = Outside bar treated as swing low
DOWN -> OUTSIDE -> DOWN = Outside bar treated as swing high
Ghost Tangent Crossings [ChartPrime]Ghost Tangent Crossings (ChartPrime) is a revolutionary way to visualize pivot points and zig-zag patterns that utilizes ellipses. This indicator makes sure that each pivot is plotted from high to low, ensuring a correct zig-zag wave pattern. Before a zig-zag is confirmed Ghost Tangent Crossings (ChartPrime) plots an estimate of the next valid move allowing you to plan well ahead of time. Once it is confirmed, the indicator will fill in the plot with a solid color and print a break label.
Unlike other zig-zag or pivot point indicators, Ghost Tangent Crossings (ChartPrime) only has a pivot lookforward input. This is because the lookback is automatically adjusted based on the last known zig-zag. This allows the indicator to dynamically look for the most recent valid market movement. The equipoint is calculated as the point along the ellipse with an equal change in price on either side. From this point we plot a line with the slope at that location and when the price breaks this level a break label is plotted. Alternatively you can plot this point as a horizontal line. This area works as support and resistance for the market as its the point where the balance in movement is found. We feel that this is a simple and elegant solution to connected zig-zag patterns that utilizes a novel method of visualization that many traders will find useful. With its simple controls and intuitive style, we believe that Ghost Tangent Crossings (ChartPrime) will find a home on most traders charts.
To use Ghost Tangent Crossings (ChartPrime) simply add it to your chart and adjust the lookforward to your taste. From there you can adjust the color of the zig-zags and enable or disable any of the visual features. We have included both wick and body pivot types to accommodate most trading style. From there, you are all done and ready to trade!
Enjoy
Sector ETF macro trendThe Sector ETF Macro Trend indicator is designed for technical analysis of broad economic trends through sector-specific exchange-traded funds (ETFs). It uses logarithmic price transformation, linear regression, and volatility analysis to examine sector trends and stability, providing a technical basis for analytical assessment.
Core Analysis Techniques
Logarithmic Transformation and Regression: Converts ETF closing prices logarithmically to reveal sector growth patterns and dynamics. Linear regression on these prices defines the main trend direction, essential for trend analysis.
Volatility Bands for Market State Assessment: Applies standard deviation on logarithmic prices to create dynamic bands around the trendline, identifying overbought or oversold sector conditions by marking deviations from the central trend.
Sector-Specific Analysis: Selection among different sector ETFs allows for precise examination of sectors like technology, healthcare, and financials, enabling focused insights into specific market segments.
Adaptability and Insight
Customizable Parameters: Offers flexibility in modifying regression length and smoothing factors to accommodate various analysis strategies and risk preferences.
Trend Direction and Momentum: Evaluates the ETF's trajectory against historical data and volatility bands to determine sector trend strength and direction, aiding in the prediction of market shifts.
Strategic Application
Without providing explicit trading signals, the indicator focuses on trend and volatility analysis for a strategic view on sector investments. It supports:
Identifying macroeconomic trends through ETF performance analysis.
Informing portfolio decisions with insights into sector momentum and stability.
Forecasting market movements by analyzing overbought or oversold conditions against the ETF price movement and volatility bands.
The Sector ETF Macro Trend indicator serves as a technical tool for analyzing sector-level market trends, offering detailed insights into the dynamics of economic sectors for thorough market analysis.
Institutional Supply and Demand ZonesThis indicator aims to identify price levels where institutional investors have positioned their buy or sell orders. These buy orders establish "demand zones," while sell orders create "supply zones." Identifying these zones enables us to anticipate potential reversals in price trends, allowing us to profitably engage in these significant market movements alongside major institutions. These zones are formed when price action goes from balanced to imbalanced. These zones are based on orders. Unlike standard support and resistance levels, when price breaks below a demand zone or above a supply zone, these zones disappear from the chart.
Supply is formed by a green candle followed by a major red candle that is at least double the size of previous green candle. The zone is then charted from the open of the green candle to the highest point in the candle. Vice versa for a demand zone (red into green).
These zones are traded by:
1. Look for a volume spike in a zone
2. A trend/trendline break out of the zone
lib_mathLibrary "lib_math"
a collection of functions calculating without history operator to avoid max_bars_back errors
mean(value, reset)
Parameters:
value (float) : series to track
reset (bool) : flag to reset tracking
@return returns average/mean of value since last reset
vwap(value, reset)
Parameters:
value (float) : series to track
reset (bool) : flag to reset tracking
@return returns vwap of value and volume since last reset
variance(value, reset)
Parameters:
value (float) : series to track
reset (bool) : flag to reset tracking
@return returns variance of value since last reset
trend(value, reset)
Parameters:
value (float) : series to track
reset (bool) : flag to reset tracking
@return where slope is the trend direction, correlation is a measurement for how well the values fit to the trendline (positive means ), stddev is how far the values deviate from the trend, x1 would be the time where reset is true and x2 would be the current time
[AIO] Multi Collection Moving Averages 140 MA TypesAll In One Multi Collection Moving Averages.
Since signing up 2 years ago, I have been collecting various Сollections.
I decided to get it into a decent shape and make it one of the biggest collections on TV, and maybe the entire internet.
And now I'm sharing my collection with you.
140 Different Types of Moving Averages are waiting for you.
Specifically :
"
AARMA | Adaptive Autonomous Recursive Moving Average
ADMA | Adjusted Moving Average
ADXMA | Average Directional Moving Average
ADXVMA | Average Directional Volatility Moving Average
AHMA | Ahrens Moving Average
ALF | Ehler Adaptive Laguerre Filter
ALMA | Arnaud Legoux Moving Average
ALSMA | Adaptive Least Squares
ALXMA | Alexander Moving Average
AMA | Adaptive Moving Average
ARI | Unknown
ARSI | Adaptive RSI Moving Average
AUF | Auto Filter
AUTL | Auto-Line
BAMA | Bryant Adaptive Moving Average
BFMA | Blackman Filter Moving Average
CMA | Corrected Moving Average
CORMA | Correlation Moving Average
COVEMA | Coefficient of Variation Weighted Exponential Moving Average
COVNA | Coefficient of Variation Weighted Moving Average
CTI | Coral Trend Indicator
DEC | Ehlers Simple Decycler
DEMA | Double EMA Moving Average
DEVS | Ehlers - Deviation Scaled Moving Average
DONEMA | Donchian Extremum Moving Average
DONMA | Donchian Moving Average
DSEMA | Double Smoothed Exponential Moving Average
DSWF | Damped Sine Wave Weighted Filter
DWMA | Double Weighted Moving Average
E2PBF | Ehlers 2-Pole Butterworth Filter
E2SSF | Ehlers 2-Pole Super Smoother Filter
E3PBF | Ehlers 3-Pole Butterworth Filter
E3SSF | Ehlers 3-Pole Super Smoother Filter
EDMA | Exponentially Deviating Moving Average (MZ EDMA)
EDSMA | Ehlers Dynamic Smoothed Moving Average
EEO | Ehlers Modified Elliptic Filter Optimum
EFRAMA | Ehlers Modified Fractal Adaptive Moving Average
EHMA | Exponential Hull Moving Average
EIT | Ehlers Instantaneous Trendline
ELF | Ehler Laguerre filter
EMA | Exponential Moving Average
EMARSI | EMARSI
EPF | Edge Preserving Filter
EPMA | End Point Moving Average
EREA | Ehlers Reverse Exponential Moving Average
ESSF | Ehlers Super Smoother Filter 2-pole
ETMA | Exponential Triangular Moving Average
EVMA | Elastic Volume Weighted Moving Average
FAMA | Following Adaptive Moving Average
FEMA | Fast Exponential Moving Average
FIBWMA | Fibonacci Weighted Moving Average
FLSMA | Fisher Least Squares Moving Average
FRAMA | Ehlers - Fractal Adaptive Moving Average
FX | Fibonacci X Level
GAUS | Ehlers - Gaussian Filter
GHL | Gann High Low
GMA | Gaussian Moving Average
GMMA | Geometric Mean Moving Average
HCF | Hybrid Convolution Filter
HEMA | Holt Exponential Moving Average
HKAMA | Hilbert based Kaufman Adaptive Moving Average
HMA | Harmonic Moving Average
HSMA | Hirashima Sugita Moving Average
HULL | Hull Moving Average
HULLT | Hull Triple Moving Average
HWMA | Henderson Weighted Moving Average
IE2 | Early T3 by Tim Tilson
IIRF | Infinite Impulse Response Filter
ILRS | Integral of Linear Regression Slope
JMA | Jurik Moving Average
KA | Unknown
KAMA | Kaufman Adaptive Moving Average & Apirine Adaptive MA
KIJUN | KIJUN
KIJUN2 | Kijun v2
LAG | Ehlers - Laguerre Filter
LCLSMA | 1LC-LSMA (1 line code lsma with 3 functions)
LEMA | Leader Exponential Moving Average
LLMA | Low-Lag Moving Average
LMA | Leo Moving Average
LP | Unknown
LRL | Linear Regression Line
LSMA | Least Squares Moving Average / Linear Regression Curve
LTB | Unknown
LWMA | Linear Weighted Moving Average
MAMA | MAMA - MESA Adaptive Moving Average
MAVW | Mavilim Weighted Moving Average
MCGD | McGinley Dynamic Moving Average
MF | Modular Filter
MID | Median Moving Average / Percentile Nearest Rank
MNMA | McNicholl Moving Average
MTMA | Unknown
MVSMA | Minimum Variance SMA
NLMA | Non-lag Moving Average
NWMA | Dürschner 3rd Generation Moving Average (New WMA)
PKF | Parametric Kalman Filter
PWMA | Parabolic Weighted Moving Average
QEMA | Quadruple Exponential Moving Average
QMA | Quick Moving Average
REMA | Regularized Exponential Moving Average
REPMA | Repulsion Moving Average
RGEMA | Range Exponential Moving Average
RMA | Welles Wilders Smoothing Moving Average
RMF | Recursive Median Filter
RMTA | Recursive Moving Trend Average
RSMA | Relative Strength Moving Average - based on RSI
RSRMA | Right Sided Ricker MA
RWMA | Regressively Weighted Moving Average
SAMA | Slope Adaptive Moving Average
SFMA | Smoother Filter Moving Average
SMA | Simple Moving Average
SSB | Senkou Span B
SSF | Ehlers - Super Smoother Filter P2
SSMA | Super Smooth Moving Average
STMA | Unknown
SWMA | Self-Weighted Moving Average
SW_MA | Sine-Weighted Moving Average
TEMA | Triple Exponential Moving Average
THMA | Triple Exponential Hull Moving Average
TL | Unknown
TMA | Triangular Moving Average
TPBF | Three-pole Ehlers Butterworth
TRAMA | Trend Regularity Adaptive Moving Average
TSF | True Strength Force
TT3 | Tilson (3rd Degree) Moving Average
VAMA | Volatility Adjusted Moving Average
VAMAF | Volume Adjusted Moving Average Function
VAR | Vector Autoregression Moving Average
VBMA | Variable Moving Average
VHMA | Vertical Horizontal Moving Average
VIDYA | Variable Index Dynamic Average
VMA | Volume Moving Average
VSO | Unknown
VWMA | Volume Weighted Moving Average
WCD | Unknown
WMA | Weighted Moving Average
XEMA | Optimized Exponential Moving Average
ZEMA | Zero Lag Moving Average
ZLDEMA | Zero-Lag Double Exponential Moving Average
ZLEMA | Ehlers - Zero Lag Exponential Moving Average
ZLTEMA | Zero-Lag Triple Exponential Moving Average
ZSMA | Zero-Lag Simple Moving Average
"
Don't forget that you can use any Moving Average not only for the chart but also for any of your indicators without affecting the code as in my example.
But remember that some MAs are not designed to work with anything other than a chart.
All MA and Code lists are sorted strictly alphabetically by short name (A-Z).
Each MA has its own number (ID) by which you can display the Moving Average you need.
Next to the ID selection there are tooltips with short names and their numbers. Use them.
The panel below will help you to read the Name of the selected MA.
Because of the size of the collection I think this is the optimal and most convenient use. Correct me if this is not the case.
Unknown - Some MAs I collected so long ago that I lost the full real name and couldn't find the authors. If you recognize them, please let me know.
I have deliberately simplified all MAs to input just Source and Length.
Because the collection is so large, it would be quite inconvenient and difficult to customize all MA functions (multipliers, offset, etc.).
If you need or like any MA you will still have to take it from my collection for your code.
I tried to leave the basic MA settings inside function in first strings.
I have tried to list most of the authors, but since the bulk of the collection was created a long time ago and was not intended for public publication I could not find all of them.
Some of the features were created from scratch or may have been slightly modified, so please be careful.
If you would like to improve this collection, please write to me in PM.
Also Credits, Likes, Awards, Loves and Thanks to :
@alexgrover
@allanster
@andre_007
@auroagwei
@blackcat1402
@bsharpe
@cheatcountry
@CrackingCryptocurrency
@Duyck
@ErwinBeckers
@everget
@glaz
@gotbeatz26107
@HPotter
@io72signals
@JacobAmos
@JoshuaMcGowan
@KivancOzbilgic
@LazyBear
@loxx
@LuxAlgo
@MightyZinger
@nemozny
@NGBaltic
@peacefulLizard50262
@RicardoSantos
@StalexBot
@ThiagoSchmitz
@TradingView
— 𝐀𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 !
So just a Big Thank You to everyone who has ever and anywhere shared their codes.
True Range Moving Average Deviation🔶 Overview
The True Range Moving Average Deviation Indicator (TRMAD) is a technical analysis tool that combines elements of price deviation, volatility, and overbought/oversold conditions.
🔶 Key Components
Current price (Close) : most recent closing price of the asset.
Moving Average (MA) : represents a smoothed trendline of the asset's closing prices over a specified period. By default, TRMAD uses the Simple Moving Average (SMA) with a 20-period setting.
Average True Range (ATR) : reflects the average price range between the high and low over a given time frame. By default, TRMAD uses a 14-period ATR setting with a Simple Moving Average (SMA) calculation. ATR quantifies the historical price volatility of the asset, which is crucial for normalizing the price deviation.
🔶 Calculation
(Close - MA) / ATR
🔶 Interpretation
When TRMAD is above +3 ATR , it is often considered an indication that the asset may be overbought, suggesting a potential reversal or correction to the downside.
When TRMAD is below -3 ATR , it is often considered an indication that the asset may be oversold, suggesting a potential reversal or bounce to the upside.
TRMAD values around 0 ATR may indicate a balanced market condition.
🔶 Usage
🔹 Overbought and Oversold Conditions:
TRMAD can help identify overbought and oversold conditions. When TRMAD reaches or exceeds certain user-defined thresholds (e.g., +3 ATR or -3 ATR), it can signal that the asset is in an extreme condition.
Traders can use these extreme conditions to adjust their positions or look for potential reversal opportunities.
🔹 Divergence Analysis:
Traders often analyze divergences between the TRMAD indicator and price movements. For example, if the price is making higher highs while TRMAD is making lower highs (bearish divergence), it could indicate a potential trend reversal.
🔹 Trend Confirmation:
TRMAD can be used in conjunction with other technical indicators to confirm trends. For example, if TRMAD is consistently positive during an uptrend, it can provide confirmation of the trend's strength.
Positive TRMAD : When TRMAD is positive but hasn't reached the overbought threshold (e.g., +3 ATR), it suggests that there is some bullish momentum, but traders may exercise caution and look for other confirming signals before considering a long position.
Negative TRMAD : When TRMAD is negative but hasn't reached the oversold threshold (e.g., -3 ATR), it suggests some bearish sentiment, but traders may want to seek additional confirmation before considering a short position.
🔹 Risk Management:
Traders can use TRMAD as part of their risk management strategy. For instance, if TRMAD suggests that an asset is overbought, a trader might consider tightening their stop-loss orders to manage potential downside risk.
🔶 Credits
The idea about this indicator came from Fabio Figueiredo (Vlad)
High/Low Fibs using Bullish Anchors I do Love me some fibs!!
i used a lot of 30 min Opening Range Fibs for interday trading, but have found that using more bars back can make for stronger levels just like when we use higher time frame to see support & resistant levels.
You can just find high and lows for making an easy auto draw fib retracment, I think you will find these to be fairly accurate or at least just entertaining .
Here are some basics on how to use FIb Retracments
Fibonacci retracement is a popular technical analysis tool used by traders to identify potential levels of support and resistance in financial markets, including stocks. It is based on the Fibonacci sequence, a series of numbers where each number is the sum of the two preceding ones (e.g., 0, 1, 1, 2, 3, 5, 8, 13, 21, ...). The key Fibonacci retracement levels are 23.6%, 38.2%, 50%, 61.8%, and 78.6%. These levels are used to identify potential reversal points or areas of price consolidation. Here's how to use Fibonacci retracement in stock trading:
1. Identify a Significant Price Move:
Start by identifying a significant price move in the stock you are analyzing. This move can be either an uptrend or a downtrend. For uptrends, you'll be measuring from the low point to the high point, and for downtrends, you'll measure from the high point to the low point.
2. Draw Fibonacci Levels: *With this indicator We do this for you
Once you have identified the price move, use a Fibonacci retracement tool available on most trading platforms to draw the retracement levels. Typically, you will draw lines from the low point to the high point for uptrends and vice versa for downtrends.
3. Analyze Key Levels:
Pay attention to the key Fibonacci retracement levels, especially the most commonly used ones, which are 38.2%, 50%, and 61.8%. These levels are considered significant in determining potential support and resistance areas. The 23.6% and 78.6% levels are also used but are considered secondary.
4. Look for Confluence:
Consider other technical analysis tools and indicators to look for confluence at these Fibonacci retracement levels. For example, if a 50% retracement level coincides with a moving average or a trendline, it may strengthen the level's significance.
5. Monitor Price Action:
Watch how the stock's price reacts when it approaches these Fibonacci retracement levels. If the price stalls, reverses direction, or shows signs of consolidation around a particular level, it may act as support or resistance.
6. Set Entry and Exit Points:
Based on your analysis, you can set entry and exit points for your trades. Traders often look for buying opportunities near Fibonacci support levels and selling opportunities near resistance levels. Stop-loss orders can be placed just below support or above resistance levels to manage risk.
7. Practice Risk Management:
Always use proper risk management techniques in your trading. This includes setting stop-loss orders, determining your position size, and not risking more than you can afford to lose on a single trade.
8. Monitor Market Conditions:
Be aware that Fibonacci retracement levels are not foolproof and should be used in conjunction with other analysis methods and market conditions. Market sentiment, news events, and economic factors can also influence stock prices.
9. Continuously Learn and Adapt:
As with any trading strategy, it's essential to continuously learn and adapt. Test the effectiveness of Fibonacci retracement levels on different time frames and with different stocks to refine your trading strategy.
** Special Thanks to @KioseffTrading for doing most all of the HEAVY LIFTING on the code here... he is beyond a Top G!!
CCPD Candle Color Price DetectorThe "CCPD Candle Color Price Detector" is a custom indicator developed for TradingView, a popular platform for technical analysis and trading. This indicator assists traders in identifying potential trend reversals and assessing market sentiment based on candlestick color changes and key price levels.
This indicator operates as follows:
Color Change Detection: It primarily focuses on the color of candlesticks (green for bullish and red for bearish). When a candlestick closes higher than it opens, it is considered green (bullish), and when it closes lower, it is red (bearish).
High and Low Analysis: The indicator calculates the highest high and lowest low over a user-defined number of bars (specified by the 'Bars for High/Low' input parameter). This helps identify recent price extremes.
Midpoint Calculation: It then computes the midpoint between the highest high and lowest low, effectively determining a central reference point within the specified period.
Signal Generation: Buy and sell signals are generated based on the relationship between the current candlestick's close price, the midpoint, and the candlestick color. Buy signals occur when a green candle closes above the midpoint, suggesting potential bullish momentum. Conversely, sell signals trigger when a red candle closes below the midpoint, indicating possible bearish pressure.
Visualization: The indicator visualizes the highest high, lowest low, midpoint, and additional lines to aid in understanding the price action and potential reversal points.
Alerts: It provides alerts for buy and sell signals, allowing traders to receive notifications when potential trading opportunities arise.
Usage:
Traders can utilize the "CCPD Candle Color Price Detector" in the following ways:
Trend Reversal Identification: This indicator can help traders spot potential trend reversals by signaling when candlestick colors change and close near the midpoint. Buy and sell signals offer entry points for trades based on these reversals.
Confirmation Tool: It can be used in conjunction with other technical analysis tools to confirm trading decisions. For example, a buy signal from this indicator, coupled with a bullish trendline break or a bounce from a key support level, may provide a stronger bullish signal.
Risk Management: By understanding potential reversal points and using stop-loss orders, traders can better manage their risk and protect their capital when entering positions based on the indicator's signals.
Customization: The indicator allows users to adjust the number of bars for high/low calculations, making it adaptable to different trading strategies and timeframes.
In summary, the "CCPD Candle Color Price Detector" is a versatile indicator that can aid traders in spotting potential trend changes, enhancing trading decisions, and managing risk effectively. However, like any trading tool, it should be used in conjunction with other analysis methods and risk management strategies for optimal results.
MACD 3D with Signals [Quantigenics]Quantigenics MACD 3D with Buy Sell Signals is a MACD-based trading indicator that aims to identify market trends and potential turning points, for Buy/Sell opportunities, by leveraging price data and volatility.
Unlike the traditional MACD indicator, the average price is calculated from the high, low, and close prices, from which a specialized MACD value is derived. This MACD value, combined with an average and standard deviation, takes into account volatility, and is used to generate an upper and lower boundary.
The indicator color-codes market trends: aqua indicates upward trends (signifying increased buying pressure), red suggests downward trends (increased selling pressure). When the MACD value crosses above the upper boundary or falls below the lower boundary, the color changes to yellow indicating a possible reversal point and "Momentum Crossover Signals" can be plotted at this point. "Standard Signal" arrows can also plotted when the MACD 3D changes from auqa to red and vice-versa.
A trendline is drawn at the median value, providing a baseline for comparison. A differential value, which measures the distance between the MACD value and the median line, provides additional insight into the price's deviation from this baseline (divergences from the underlying price can be spotted using this data as well). The differential is color-coded: green when MACD is above the median, and red when it's below, with darker shades representing a decreasing gap.
Alerts can be set to trigger with the "Standard Signal" arrows appearing after MACD 3D changes from auqa to red and vice-versa and when the "Momentum Crossover Signal" arrows appear when the MACD value crosses above the upper boundary or falls below the lower boundary indicating a potential reversal. Providing immediate notifications which can be especially helpful in larger time frames where it may take time for a trade setup to develop.
CME_MINI:NQ1!
OANDA:XAUUSD
Enjoy the MACD 3D indicator. Happy Trading!
Gradient Money Flow Divergence DetectorThe "Gradient Money Flow Divergence Detector" indicator has several use cases for traders. Let's explore the main use cases:
1. Money Flow Analysis : The primary purpose of this indicator is to analyze money flow in a particular asset. The Money Flow Index (MFI) is a momentum indicator that uses price and volume data to assess the buying and selling pressure in a market. Traders can use the MFI to identify overbought and oversold conditions, potential trend reversals, and divergences between the MFI and price movement.
2. Divergence Detection : The indicator incorporates a divergence detection mechanism for multiple timeframes (micro, sub-mid, mid, and macro). Divergence occurs when the price movement and an indicator (MFI in this case) move in opposite directions, signaling a potential shift in the price trend. Traders can use divergences to anticipate trend reversals or trend continuation.
3. Multiple Lookback Analysis : The indicator allows traders to assess divergences and money flow trends across various time horizons by providing divergence detection for different lengths. This can help traders identify confluence areas where divergences align on multiple timeframes, strengthening the potential signal.
4. Overbought and Oversold Conditions : The indicator plots horizontal lines at MFI levels of 20, 50, and 80. These levels can be used to identify overbought (MFI above 80) and oversold (MFI below 20) conditions. Traders may look for potential reversal signals when the MFI reaches extreme levels.
5. Confirmation of Price Trends : The indicator's color gradient visually represents the MFI value, which can help traders confirm the strength of a prevailing price trend. For example, an uptrend with a consistently high MFI might suggest strong buying pressure, reinforcing the bullish bias.
6. Fine-Tuning Divergence Signals : Traders can adjust the parameters of divergence detection (e.g., pivot points, rangeUpper, rangeLower) to fine-tune the sensitivity of the divergence signals. This allows for greater customization based on individual trading preferences.
7. Combining with Other Indicators : The indicator can be used in combination with other technical indicators or price action analysis to strengthen trading decisions. For example, traders may look for divergences in conjunction with support and resistance levels or chart patterns to increase the probability of successful trades.
8. Trend Reversal Confirmation : When a divergence is detected, it may indicate a potential trend reversal. Traders can use other confirmation signals (e.g., candlestick patterns, trendline breaks) to validate the reversal before making trading decisions.
Remember that no single indicator should be used in isolation, and it's essential to use the indicator in combination with other confirmations such as support and resistance, and analysis methods for more robust trading strategies. Additionally, thorough backtesting and practice in a demo environment are recommended before using the indicator in live trading.
90 Minute Cycles + MTFCredit goes to LuxAlgo for the inspiration from 'Sessions' which allowed users to analyse specific price movements within a user defined period with tools such as trendline, mean and vwap.
Settings
Sessions
Enable Session: Allows to enable or disable all associated elements with a specific user set session.
Session Time: Opening and closing times of the user set session in the hh:mm format.
Range: Highlights the associated session range on the chart.
Ranges Settings
Range Area colour: Set each range to a specific colour.
Range Label: Shows the session label at the mid-point of the session interval.
Usage
By breaking 24hrs in quarters, starting with an Asian range of 18:00 NY time you can visualise the principles of Accumulation, Manipulation, Distribution and Rebalance. Know as AMD or PO3 (Power of Three), the principle is that the Manipulation phase will break above or below the Accumulation, before moving in an apposing direction and then rebalancing. This only works when there is a higher timeframe PD array or liquidity to support an apposing move.
Further to the daily quarters, each one can then be broken down again into 90min cycles. Again, each represents AMD, allowing the user an opportunity to watch for reversals during the 90min manipulation phase.
Note: Ensure the Asian Cycle always begins at 18:00 NY time.
The example shows that the 90min cycle occurs, followed by an apposing move away in price action
Here is the Daily cycle, highlighting the Manipulation phase.
Enjoy!
ICT Donchian Smart Money Structure (Expo)█ Concept Overview
The Inner Circle Trader (ICT) methodology is focused on understanding the actions and implications of the so-called "smart money" - large institutions and professional traders who often influence market movements. Key to this is the concept of market structure and how it can provide insights into potential price moves.
Over time, however, there has been a notable shift in how some traders interpret and apply this methodology. Initially, it was designed with a focus on the fractal nature of markets. Fractals are recurring patterns in price action that are self-similar across different time scales, providing a nuanced and dynamic understanding of market structure.
However, as the ICT methodology has grown in popularity, there has been a drift away from this fractal-based perspective. Instead, many traders have started to focus more on pivot points as their primary tool for understanding market structure.
Pivot points provide static levels of potential support and resistance. While they can be useful in some contexts, relying heavily on them could provide a skewed perspective of market structure. They offer a static, backward-looking view that may not accurately reflect real-time changes in market sentiment or the dynamic nature of markets.
This shift from a fractal-based perspective to a pivot point perspective has significant implications. It can lead traders to misinterpret market structure and potentially make incorrect trading decisions.
To highlight this issue, you've developed a Donchian Structure indicator that mirrors the use of pivot points. The Donchian Channels are formed by the highest high and the lowest low over a certain period, providing another representation of potential market extremes. The fact that the Donchian Structure indicator produces the same results as pivot points underscores the inherent limitations of relying too heavily on these tools.
While the Donchian Structure indicator or pivot points can be useful tools, they should not replace the original, fractal-based perspective of the ICT methodology. These tools can provide a broad overview of market structure but may not capture the intricate dynamics and real-time changes that a fractal-based approach can offer.
It's essential for traders to understand these differences and to apply these tools correctly within the broader context of the ICT methodology and the Smart Money Concept Structure. A well-rounded approach that incorporates fractals, along with other tools and forms of analysis, is likely to provide a more accurate and comprehensive understanding of market structure.
█ Smart Money Concept - Misunderstandings
The Smart Money Concept is a popular concept among traders, and it's based on the idea that the "smart money" - typically large institutional investors, market makers, and professional traders - have superior knowledge or information, and their actions can provide valuable insight for other traders.
One of the biggest misunderstandings with this concept is the belief that tracking smart money activity can guarantee profitable trading.
█ Here are a few common misconceptions:
Following Smart Money Equals Guaranteed Success: Many traders believe that if they can follow the smart money, they will be successful. However, tracking the activity of large institutional investors and other professionals isn't easy, as they use complex strategies, have access to information not available to the public, and often intentionally hide their moves to prevent others from detecting their strategies.
Instantaneous Reaction and Results: Another misconception is that market movements will reflect smart money actions immediately. However, large institutions often slowly accumulate or distribute positions over time to avoid moving the market drastically. As a result, their actions might not produce an immediate noticeable effect on the market.
Smart Money Always Wins: It's not accurate to assume that smart money always makes the right decisions. Even the most experienced institutional investors and professional traders make mistakes, misjudge market conditions, or are affected by unpredictable events.
Smart Money Activity is Transparent: Understanding what constitutes smart money activity can be quite challenging. There are many indicators and metrics that traders use to try and track smart money, such as the COT (Commitments of Traders) reports, Level II market data, block trades, etc. However, these can be difficult to interpret correctly and are often misleading.
Assuming Uniformity Among Smart Money: 'Smart Money' is not a monolithic entity. Different institutional investors and professional traders have different strategies, risk tolerances, and investment horizons. What might be a good trade for a long-term institutional investor might not be a good trade for a short-term professional trader, and vice versa.
█ Market Structure
The Smart Money Concept Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts or changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market.
█ Three common concepts in this regard are Change of Character (CHoCH), and Shift in Market Structure (SMS), Break of Structure (BMS/BoS).
Change of Character (CHoCH): This refers to a noticeable change in the behavior of price movement, which could suggest that a shift in the market might be about to occur. This might be signaled by a sudden increase in volatility, a break of a trendline, or a change in volume, among other things.
Shift in Market Structure (SMS): This is when the overall structure of the market changes, suggesting a potential new trend. It usually involves a sequence of lower highs and lower lows for a downtrend, or higher highs and higher lows for an uptrend.
Break of Structure (BMS/BoS): This is when a previously defined trend or pattern in the price structure is broken, which may suggest a trend continuation.
A key component of this approach is the use of fractals, which are repeating patterns in price action that can give insights into potential market reversals. They appear at all scales of a price chart, reflecting the self-similar nature of markets.
█ Market Structure - Misunderstandings
One of the biggest misunderstandings about the ICT approach is the over-reliance or incorrect application of pivot points. Pivot points are a popular tool among traders due to their simplicity and easy-to-understand nature. However, when it comes to the Smart Money Concept and trying to follow the steps of professional traders or large institutions, relying heavily on pivot points can create misconceptions and lead to confusion. Here's why:
Delayed and Static Information: Pivot points are inherently backward-looking because they're calculated based on the previous period's data. As such, they may not reflect real-time market dynamics or sudden changes in market sentiment. Furthermore, they present a static view of market structure, delineating pre-defined levels of support and resistance. This static nature can be misleading because markets are fundamentally dynamic and constantly changing due to countless variables.
Inadequate Representation of Market Complexity: Markets are influenced by a myriad of factors, including economic indicators, geopolitical events, institutional actions, and market sentiment, among others. Relying on pivot points alone for reading market structure oversimplifies this complexity and can lead to a myopic understanding of market dynamics.
False Signals and Misinterpretations: Pivot points can often give false signals, especially in volatile markets. Prices might react to these levels temporarily but then continue in the original direction, leading to potential misinterpretation of market structure and sentiment. Also, a trader might wrongly perceive a break of a pivot point as a significant market event, when in fact, it could be due to random price fluctuations or temporary volatility.
Over-simplification: Viewing market structure only through the lens of pivot points simplifies the market to static levels of support and resistance, which can lead to misinterpretation of market dynamics. For instance, a trader might view a break of a pivot point as a definite sign of a trend, when it could just be a temporary price spike.
Ignoring the Fractal Nature of Markets: In the context of the Smart Money Concept Structure, understanding the fractal nature of markets is crucial. Fractals are self-similar patterns that repeat at all scales and provide a more dynamic and nuanced understanding of market structure. They can help traders identify shifts in market sentiment or direction in real-time, providing more relevant and timely information compared to pivot points.
The key takeaway here is not that pivot points should be entirely avoided or that they're useless. They can provide valuable insights and serve as a useful tool in a trader's toolbox when used correctly. However, they should not be the sole or primary method for understanding the market structure, especially in the context of the Smart Money Concept Structure.
█ Fractals
Instead, traders should aim for a comprehensive understanding of markets that incorporates a range of tools and concepts, including but not limited to fractals, order flow, volume analysis, fundamental analysis, and, yes, even pivot points. Fractals offer a more dynamic and nuanced view of the market. They reflect the recursive nature of markets and can provide valuable insights into potential market reversals. Because they appear at all scales of a price chart, they can provide a more holistic and real-time understanding of market structure.
In contrast, the Smart Money Concept Structure, focusing on fractals and comprehensive market analysis, aims to capture a more holistic and real-time view of the market. Fractals, being self-similar patterns that repeat at different scales, offer a dynamic understanding of market structure. As a result, they can help to identify shifts in market sentiment or direction as they happen, providing a more detailed and timely perspective.
Furthermore, a comprehensive market analysis would consider a broader set of factors, including order flow, volume analysis, and fundamental analysis, which could provide additional insights into 'smart money' actions.
█ Donchian Structure
Donchian Channels are a type of indicator used in technical analysis to identify potential price breakouts and trends, and they may also serve as a tool for understanding market structure. The channels are formed by taking the highest high and the lowest low over a certain number of periods, creating an envelope of price action.
Donchian Channels (or pivot points) can be useful tools for providing a general view of market structure, and they may not capture the intricate dynamics associated with the Smart Money Concept Structure. A more nuanced approach, centered on real-time fractals and a comprehensive analysis of various market factors, offers a more accurate understanding of 'smart money' actions and market structure.
█ Here is why Donchian Structure may be misleading:
Lack of Nuance: Donchian Channels, like pivot points, provide a simplified view of market structure. They don't take into account the nuanced behaviors of price action or the complex dynamics between buyers and sellers that can be critical in the Smart Money Concept Structure.
Limited Insights into 'Smart Money' Actions: While Donchian Channels can highlight potential breakout points and trends, they don't necessarily provide insights into the actions of 'smart money'. These large institutional traders often use sophisticated strategies that can't be easily inferred from price action alone.
█ Indicator Overview
We have built this Donchian Structure indicator to show that it returns the same results as using pivot points. The Donchian Structure indicator can be a useful tool for market analysis. However, it should not be seen as a direct replacement or equivalent to the original Smart Money concept, nor should any indicator based on pivot points. The indicator highlights the importance of understanding what kind of trading tools we use and how they can affect our decisions.
The Donchian Structure Indicator displays CHoCH, SMS, BoS/BMS, as well as premium and discount areas. This indicator plots everything in real-time and allows for easy backtesting on any market and timeframe. A unique candle coloring has been added to make it more engaging and visually appealing when identifying new trading setups and strategies. This candle coloring is "leading," meaning it can signal a structural change before it actually happens, giving traders ample time to plan their next trade accordingly.
█ How to use
The indicator is great for traders who want to simplify their view on the market structure and easily backtest Smart Money Concept Strategies. The added candle coloring function serves as a heads-up for structure change or can be used as trend confirmation. This new candle coloring feature can generate many new Smart Money Concepts strategies.
█ Features
Market Structure
The market structure is based on the Donchian channel, to which we have added what we call 'Structure Response'. This addition makes the indicator more useful, especially in trending markets. The core concept involves traders buying at a discount and selling or shorting at a premium, depending on the order flow. Structure response enables traders to determine the order flow more clearly. Consequently, more trading opportunities will appear in trending markets.
Structure Candles
Structure Candles highlight the current order flow and are significantly more responsive to structural changes. They can provide traders with a heads-up before a break in structure occurs
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
